notebooks/EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this Dataset\n",
    "\n",
    "## Problem Statement:\n",
    "Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban, and rural areas. Customer-first applies for a home loan after that company validates the customer eligibility for a loan.\n",
    "\n",
    "The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and others. To automate this process, they have given a problem to identify the customer's segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load data to describe \n",
    "df_train = pd.read_csv('/workspaces/loan-elegibility-prediction/data/raw/loan-train.csv')\n",
    "df_test = pd.read_csv('/workspaces/loan-elegibility-prediction/data/raw/loan-test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows in train dataset 614\n",
      "Total rows in train dataset 367\n",
      "Total rows of the dataset 981\n",
      "The proportion of traind dataset is 0.6258919469928644\n",
      "The proportion of test dataset is 0.37410805300713557\n"
     ]
    }
   ],
   "source": [
    "#Porportion of Train and Test\n",
    "print(f'Total rows in train dataset {len(df_train)}')\n",
    "print(f'Total rows in train dataset {len(df_test)}')\n",
    "print(f'Total rows of the dataset {len(df_test)+len(df_train)}')\n",
    "print(f'The proportion of traind dataset is {len(df_train)/(len(df_train)+len(df_test))}')\n",
    "print(f'The proportion of test dataset is {len(df_test)/(len(df_train)+len(df_test))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Dataset Key Information.\n",
    "\n",
    "        Loan_ID--------------> Unique Loan ID.\n",
    "        Gender --------------> Male/ Female\n",
    "        Married --------------> Applicant married (Y/N)\n",
    "        Dependents ------------> Number of dependents\n",
    "        Education -------------> Applicant Education (Graduate/ Under Graduate)\n",
    "        Self_Employed ---------> Self-employed (Y/N)\n",
    "        ApplicantIncome -------> Applicant income\n",
    "        CoapplicantIncome -----> Coapplicant income\n",
    "        LoanAmount -----------> Loan amount in thousands\n",
    "        Loan_Amount_Term ------> Term of a loan in months\n",
    "        Credit_History --------> Credit history meets guidelines\n",
    "        Property_Area ---------> Urban/ Semi-Urban/ Rural\n",
    "        Loan_Status -----------> Loan approved (Y/N)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 367 entries, 0 to 366\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            367 non-null    object \n",
      " 1   Gender             356 non-null    object \n",
      " 2   Married            367 non-null    object \n",
      " 3   Dependents         357 non-null    object \n",
      " 4   Education          367 non-null    object \n",
      " 5   Self_Employed      344 non-null    object \n",
      " 6   ApplicantIncome    367 non-null    int64  \n",
      " 7   CoapplicantIncome  367 non-null    int64  \n",
      " 8   LoanAmount         362 non-null    float64\n",
      " 9   Loan_Amount_Term   361 non-null    float64\n",
      " 10  Credit_History     338 non-null    float64\n",
      " 11  Property_Area      367 non-null    object \n",
      "dtypes: float64(3), int64(2), object(7)\n",
      "memory usage: 34.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the data set we have:\n",
    "-  12 independent variables\n",
    "- 1 target variable **Loan_Status** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Missing Values**\n",
    "\n",
    "We are going to check the percentage of NAN values in each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender has total 13 missing values\n",
      "Married has total 3 missing values\n",
      "Dependents has total 15 missing values\n",
      "Self_Employed has total 32 missing values\n",
      "LoanAmount has total 22 missing values\n",
      "Loan_Amount_Term has total 14 missing values\n",
      "Credit_History has total 50 missing values\n"
     ]
    }
   ],
   "source": [
    "for col in df_train.columns:\n",
    "    miss = df_train[col].isna().sum() \n",
    "    if miss > 0:\n",
    "        print(f\"{col} has total {miss} missing values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender 0.0  % missing values\n",
      "Married 0.0  % missing values\n",
      "Dependents 0.0  % missing values\n",
      "Self_Employed 10.0  % missing values\n",
      "LoanAmount 0.0  % missing values\n",
      "Loan_Amount_Term 0.0  % missing values\n",
      "Credit_History 10.0  % missing values\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "## 1 -step make the list of features which has missing values\n",
    "features_with_nan=[features for features in df_train.columns if df_train[features].isnull().sum()>1]\n",
    "## 2- step print the feature name and the percentage of missing values\n",
    "\n",
    "for feature in features_with_nan:\n",
    "    print(feature, 100*np.round(df_train[feature].isnull().mean(), 1),  ' % missing values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Understanding Data Types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('O'), dtype('int64'), dtype('float64')], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of numerical variables:  5\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
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       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.0  \n",
       "4             1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of numerical variables\n",
    "numerical_features = [feature for feature in df_train.columns if df_train[feature].dtypes != 'O']\n",
    "\n",
    "print('Number of numerical variables: ', len(numerical_features))\n",
    "\n",
    "# visualise the numerical variables\n",
    "df_train[numerical_features].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check data with datatime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of variables that contain year information\n",
    "year_feature = [feature for feature in numerical_features if 'Yr' in feature or 'Year' in feature]\n",
    "len(year_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical variables are usually of 2 type, continous variable and discrete Variables\n",
    "\n",
    "Discrete variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Amount_Term  Credit_History\n",
       "0             360.0             1.0\n",
       "1             360.0             1.0\n",
       "2             360.0             1.0\n",
       "3             360.0             1.0\n",
       "4             360.0             1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrete_feature=[feature for feature in numerical_features if len(df_train[feature].unique())<25 and feature not in year_feature+['Id']]\n",
    "print(\"Discrete Variables Count: {}\".format(len(discrete_feature)))\n",
    "discrete_feature\n",
    "# visualise the numerical variables\n",
    "df_train[discrete_feature].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[360. 120. 240.  nan 180.  60. 300. 480.  36.  84.  12.]\n",
      "[ 1.  0. nan]\n"
     ]
    }
   ],
   "source": [
    "print(df_train.Loan_Amount_Term.unique())\n",
    "print(df_train.Credit_History.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous feature Count 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount\n",
       "0             5849                0.0         NaN\n",
       "1             4583             1508.0       128.0\n",
       "2             3000                0.0        66.0\n",
       "3             2583             2358.0       120.0\n",
       "4             6000                0.0       141.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_feature=[feature for feature in numerical_features if feature not in discrete_feature+year_feature+['Id']]\n",
    "print(\"Continuous feature Count {}\".format(len(continuous_feature)))\n",
    "continuous_feature\n",
    "# visualise the numerical variables\n",
    "df_train[continuous_feature].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there are three formats of data types:\n",
    "\n",
    "    - object: Object format means variables are categorical. Categorical variables in our dataset are Loan_ID, Gender, Married, Dependents, Education, Self_Employed, Property_Area, Loan_Status.\n",
    "    - int64: It represents the integer variables. ApplicantIncome is of this format.\n",
    "    - float64: It represents the variable that has some decimal values involved. They are also numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Univariate Analysis**\n",
    "\n",
    "Dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status\n",
      "Y    422\n",
      "N    192\n",
      "Name: count, dtype: int64\n",
      "Loan_Status\n",
      "Y    0.687296\n",
      "N    0.312704\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Loan_Status'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o6NGj13wer9crj8fjtwAAgM4roCso39Xa2qo5c+bogQce0MCBA631P/3pT9W7d28lJyfr8OHDWrhwoWpqarRt2zZJktvt9osTSdZjt9t9zecqLi7WsmXL2jpVAADQwbQ5UPLz83XkyBH94Q9/8Fs/Y8YM6+tBgwYpKSlJY8aM0YkTJ3TPPfe06bkKCwtVUFBgPfZ4PEpJSWnbxAEAgPHa9BLPrFmzVFpaqg8++EA9e/b83rEjR46UJB0/flyS5HQ6VV9f7zfmyuPr3bdis9lkt9v9FgAA0HkFFCg+n0+zZs3S9u3b9f7776tv37433KeqqkqSlJSUJElyuVyqrq5WQ0ODNaasrEx2u11paWmBTAcAAHRSAb3Ek5+fr5KSEr3zzjuKiYmx7hlxOByKjIzUiRMnVFJSovHjxys+Pl6HDx/W3Llz9dBDDyk9PV2SNHbsWKWlpWnKlClauXKl3G63ioqKlJ+fL5vN1v5nCAAAOpyArqCsX79ejY2NGj16tJKSkqzlzTfflCRFRERoz549Gjt2rPr3769f/vKXysnJ0Y4dO6xjhIWFqbS0VGFhYXK5XPqHf/gHTZ061e9zUwAAwJ0toCsoPp/ve7enpKSovLz8hsfp3bu3/uM//iOQpwYAAHcQfhcPAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwTUKAUFxdrxIgRiomJUUJCgh577DHV1NT4jbl06ZLy8/MVHx+v6Oho5eTkqL6+3m9MbW2tsrOzFRUVpYSEBM2fP1/Nzc03fzYAAKBTCChQysvLlZ+frwMHDqisrEyXL1/W2LFjdfHiRWvM3LlztWPHDr311lsqLy9XXV2dJk2aZG1vaWlRdna2mpqatH//fm3evFmbNm3S4sWL2++sAABAhxbi8/l8bd35m2++UUJCgsrLy/XQQw+psbFRPXr0UElJiR5//HFJ0ueff67U1FRVVFRo1KhR2rlzpx599FHV1dUpMTFRkrRhwwYtXLhQ33zzjSIiIm74vB6PRw6HQ42NjbLb7W2dfofUZ9G7wZ4CbqMvV2QHewoA0G4C+fl9U/egNDY2SpLi4uIkSZWVlbp8+bIyMjKsMf3791evXr1UUVEhSaqoqNCgQYOsOJGkzMxMeTweHT169GamAwAAOonwtu7Y2tqqOXPm6IEHHtDAgQMlSW63WxEREYqNjfUbm5iYKLfbbY35bpxc2X5l27V4vV55vV7rscfjaeu0AQBAB9DmKyj5+fk6cuSItm7d2p7zuabi4mI5HA5rSUlJueXPCQAAgqdNgTJr1iyVlpbqgw8+UM+ePa31TqdTTU1NOnfunN/4+vp6OZ1Oa8xfvqvnyuMrY/5SYWGhGhsbreXUqVNtmTYAAOggAgoUn8+nWbNmafv27Xr//ffVt29fv+3Dhg1Tly5dtHfvXmtdTU2Namtr5XK5JEkul0vV1dVqaGiwxpSVlclutystLe2az2uz2WS32/0WAADQeQV0D0p+fr5KSkr0zjvvKCYmxrpnxOFwKDIyUg6HQ9OmTVNBQYHi4uJkt9s1e/ZsuVwujRo1SpI0duxYpaWlacqUKVq5cqXcbreKioqUn58vm83W/mcIAAA6nIACZf369ZKk0aNH+63fuHGjnnrqKUnSqlWrFBoaqpycHHm9XmVmZmrdunXW2LCwMJWWlmrmzJlyuVzq1q2b8vLytHz58ps7EwAA0Gnc1OegBAufg4I7BZ+DAqAzCeTnd5vfZgwAaF/8B+TOwn9Avh+/LBAAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxgk4UD788ENNmDBBycnJCgkJ0dtvv+23/amnnlJISIjfMm7cOL8xZ8+eVW5urux2u2JjYzVt2jRduHDhpk4EAAB0HgEHysWLFzV48GCtXbv2umPGjRun06dPW8u//uu/+m3Pzc3V0aNHVVZWptLSUn344YeaMWNG4LMHAACdUnigO2RlZSkrK+t7x9hsNjmdzmtu++yzz7Rr1y4dOnRIw4cPlyS99NJLGj9+vF588UUlJycHOiUAANDJ3JJ7UPbt26eEhATdf//9mjlzps6cOWNtq6ioUGxsrBUnkpSRkaHQ0FAdPHjwmsfzer3yeDx+CwAA6LzaPVDGjRun119/XXv37tWvf/1rlZeXKysrSy0tLZIkt9uthIQEv33Cw8MVFxcnt9t9zWMWFxfL4XBYS0pKSntPGwAAGCTgl3hu5Mknn7S+HjRokNLT03XPPfdo3759GjNmTJuOWVhYqIKCAuuxx+MhUgAA6MRu+duM7777bnXv3l3Hjx+XJDmdTjU0NPiNaW5u1tmzZ69734rNZpPdbvdbAABA53XLA+Xrr7/WmTNnlJSUJElyuVw6d+6cKisrrTHvv/++WltbNXLkyFs9HQAA0AEE/BLPhQsXrKshknTy5ElVVVUpLi5OcXFxWrZsmXJycuR0OnXixAktWLBA9957rzIzMyVJqampGjdunKZPn64NGzbo8uXLmjVrlp588knewQMAACS14QrKxx9/rKFDh2ro0KGSpIKCAg0dOlSLFy9WWFiYDh8+rL/5m7/Rfffdp2nTpmnYsGH6z//8T9lsNusYW7ZsUf/+/TVmzBiNHz9eDz74oF555ZX2OysAANChBXwFZfTo0fL5fNfdvnv37hseIy4uTiUlJYE+NQAAuEPwu3gAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCfgQPnwww81YcIEJScnKyQkRG+//bbfdp/Pp8WLFyspKUmRkZHKyMjQF1984Tfm7Nmzys3Nld1uV2xsrKZNm6YLFy7c1IkAAIDOI+BAuXjxogYPHqy1a9dec/vKlSu1Zs0abdiwQQcPHlS3bt2UmZmpS5cuWWNyc3N19OhRlZWVqbS0VB9++KFmzJjR9rMAAACdSnigO2RlZSkrK+ua23w+n1avXq2ioiJNnDhRkvT6668rMTFRb7/9tp588kl99tln2rVrlw4dOqThw4dLkl566SWNHz9eL774opKTk2/idAAAQGfQrvegnDx5Um63WxkZGdY6h8OhkSNHqqKiQpJUUVGh2NhYK04kKSMjQ6GhoTp48OA1j+v1euXxePwWAADQebVroLjdbklSYmKi3/rExERrm9vtVkJCgt/28PBwxcXFWWP+UnFxsRwOh7WkpKS057QBAIBhOsS7eAoLC9XY2Ggtp06dCvaUAADALdSugeJ0OiVJ9fX1fuvr6+utbU6nUw0NDX7bm5ubdfbsWWvMX7LZbLLb7X4LAADovNo1UPr27Sun06m9e/da6zwejw4ePCiXyyVJcrlcOnfunCorK60x77//vlpbWzVy5Mj2nA4AAOigAn4Xz4ULF3T8+HHr8cmTJ1VVVaW4uDj16tVLc+bM0a9+9Sv169dPffv21bPPPqvk5GQ99thjkqTU1FSNGzdO06dP14YNG3T58mXNmjVLTz75JO/gAQAAktoQKB9//LF+8pOfWI8LCgokSXl5edq0aZMWLFigixcvasaMGTp37pwefPBB7dq1S127drX22bJli2bNmqUxY8YoNDRUOTk5WrNmTTucDgAA6AxCfD6fL9iTCJTH45HD4VBjY+Mddz9Kn0XvBnsKuI2+XJEd7CngNuL7+85yJ35/B/Lzu0O8iwcAANxZCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJx2D5SlS5cqJCTEb+nfv7+1/dKlS8rPz1d8fLyio6OVk5Oj+vr69p4GAADowG7JFZQBAwbo9OnT1vKHP/zB2jZ37lzt2LFDb731lsrLy1VXV6dJkybdimkAAIAOKvyWHDQ8XE6n86r1jY2N+t3vfqeSkhI9/PDDkqSNGzcqNTVVBw4c0KhRo27FdAAAQAdzS66gfPHFF0pOTtbdd9+t3Nxc1dbWSpIqKyt1+fJlZWRkWGP79++vXr16qaKi4lZMBQAAdEDtfgVl5MiR2rRpk+6//36dPn1ay5Yt049//GMdOXJEbrdbERERio2N9dsnMTFRbrf7usf0er3yer3WY4/H097TBgAABmn3QMnKyrK+Tk9P18iRI9W7d2/927/9myIjI9t0zOLiYi1btqy9pggAAAx3y99mHBsbq/vuu0/Hjx+X0+lUU1OTzp075zemvr7+mvesXFFYWKjGxkZrOXXq1C2eNQAACKZbHigXLlzQiRMnlJSUpGHDhqlLly7au3evtb2mpka1tbVyuVzXPYbNZpPdbvdbAABA59XuL/HMmzdPEyZMUO/evVVXV6clS5YoLCxMkydPlsPh0LRp01RQUKC4uDjZ7XbNnj1bLpeLd/AAAABLuwfK119/rcmTJ+vMmTPq0aOHHnzwQR04cEA9evSQJK1atUqhoaHKycmR1+tVZmam1q1b197TAAAAHVi7B8rWrVu/d3vXrl21du1arV27tr2fGgAAdBL8Lh4AAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxglqoKxdu1Z9+vRR165dNXLkSH300UfBnA4AADBE0ALlzTffVEFBgZYsWaJPPvlEgwcPVmZmphoaGoI1JQAAYIigBcpvfvMbTZ8+XT/72c+UlpamDRs2KCoqSq+99lqwpgQAAAwRHownbWpqUmVlpQoLC611oaGhysjIUEVFxVXjvV6vvF6v9bixsVGS5PF4bv1kDdPq/VOwp4Db6E78O34n4/v7znInfn9fOWefz3fDsUEJlP/93/9VS0uLEhMT/dYnJibq888/v2p8cXGxli1bdtX6lJSUWzZHwASO1cGeAYBb5U7+/j5//rwcDsf3jglKoASqsLBQBQUF1uPW1ladPXtW8fHxCgkJCeLMcDt4PB6lpKTo1KlTstvtwZ4OgHbE9/edxefz6fz580pOTr7h2KAESvfu3RUWFqb6+nq/9fX19XI6nVeNt9lsstlsfutiY2Nv5RRhILvdzj9gQCfF9/ed40ZXTq4Iyk2yERERGjZsmPbu3Wuta21t1d69e+VyuYIxJQAAYJCgvcRTUFCgvLw8DR8+XD/60Y+0evVqXbx4UT/72c+CNSUAAGCIoAXKE088oW+++UaLFy+W2+3WkCFDtGvXrqtunAVsNpuWLFly1ct8ADo+vr9xPSG+H/JeHwAAgNuI38UDAACMQ6AAAADjECgAAMA4BAqMUVdXF+wpAAAMQaDAGAMGDFBJSUmwpwEAMACBAmM8//zz+sd//Ef93d/9nc6ePRvs6QAAgohAgTF+/vOf6/Dhwzpz5ozS0tK0Y8eOYE8JABAkfA4KjPTyyy9r7ty5Sk1NVXi4/+cJfvLJJ0GaFYCbERoaesNf8BoSEqLm5ubbNCOYrEP8NmPcWb766itt27ZNd911lyZOnHhVoADomLZv337dbRUVFVqzZo1aW1tv44xgMv7lh1FeffVV/fKXv1RGRoaOHj2qHj16BHtKANrJxIkTr1pXU1OjRYsWaceOHcrNzdXy5cuDMDOYiECBMcaNG6ePPvpIL7/8sqZOnRrs6QC4herq6rRkyRJt3rxZmZmZqqqq0sCBA4M9LRiEQIExWlpadPjwYfXs2TPYUwFwizQ2NuqFF17QSy+9pCFDhmjv3r368Y9/HOxpwUDcJAsAuC1WrlypX//613I6nXrhhReu+ZIPcAWBAgC4LUJDQxUZGamMjAyFhYVdd9y2bdtu46xgKl7iAQDcFlOnTr3h24yBK7iCAgAAjMMnyQIAAOMQKAAAwDgECgAAMA6BAgAAjEOgAHeYp556So899liwp3Fdr776qgYPHqzo6GjFxsZq6NChKi4utra3df5Lly7VkCFD2m+iAG4p3mYMwBivvfaa5syZozVr1uiv//qv5fV6dfjwYR05ciTYUwNwm3EFBYClvLxcP/rRj2Sz2ZSUlKRFixapubnZ2r5r1y49+OCDio2NVXx8vB599FGdOHHC2v7ll18qJCRE27Zt009+8hNFRUVp8ODBqqio+EHP/+///u/6+7//e02bNk333nuvBgwYoMmTJ+v555+X9OerIJs3b9Y777yjkJAQhYSEaN++fZKkhQsX6r777lNUVJTuvvtuPfvss7p8+bIkadOmTVq2bJn++7//29pv06ZN1nyrqqqsOZw7d87vuN9++61yc3PVo0cPRUZGql+/ftq4ceNN/CkD+CG4ggJAkvTHP/5R48eP11NPPaXXX39dn3/+uaZPn66uXbtq6dKlkqSLFy+qoKBA6enpunDhghYvXqy//du/VVVVlUJD////d5555hm9+OKL6tevn5555hlNnjxZx48fV3j49/+T43Q6VV5erq+++kq9e/e+avu8efP02WefyePxWJEQFxcnSYqJidGmTZuUnJys6upqTZ8+XTExMVqwYIGeeOIJHTlyRLt27dKePXskSQ6HQ/X19Tf8c3n22Wd17Ngx7dy5U927d9fx48f1f//3fz/ozxRA2xEoACRJ69atU0pKil5++WWFhISof//+qqur08KFC7V48WKFhoYqJyfHb5/XXntNPXr00LFjx/x+E+28efOUnZ0tSVq2bJkGDBig48ePq3///t87hyVLlmjSpEnq06eP7rvvPrlcLo0fP16PP/64QkNDFR0drcjISHm9XjmdTr99i4qKrK/79OmjefPmaevWrVqwYIEiIyMVHR2t8PDwq/a7kdraWg0dOlTDhw+3jg3g1uMlHgCSpM8++0wul8vvo8gfeOABXbhwQV9//bUk6YsvvtDkyZN19913y263Wz+sa2tr/Y6Vnp5ufZ2UlCRJamhouOEckpKSVFFRoerqav3iF79Qc3Oz8vLyNG7cOLW2tn7vvm+++aYeeOABOZ1ORUdHq6io6Kp5tcXMmTO1detWDRkyRAsWLND+/ftv+pgAboxAAfCDTZgwQWfPntWrr76qgwcP6uDBg5KkpqYmv3FdunSxvr4SPDcKjO8aOHCgfv7zn+uNN95QWVmZysrKVF5eft3xFRUVys3N1fjx41VaWqpPP/1UzzzzzFXz+ktXXpb67m/8uHLfyhVZWVn66quvNHfuXNXV1WnMmDGaN2/eDz4XAG1DoACQJKWmpqqiosLvh/V//dd/KSYmRj179tSZM2dUU1OjoqIijRkzRqmpqfr2229v+bzS0tIk/fn+F0mKiIhQS0uL35j9+/erd+/eeuaZZzR8+HD169dPX331ld+Ya+3Xo0cPSdLp06etdd+9Yfa74/Ly8vTGG29o9erVeuWVV276vAB8P+5BAe5AjY2NV/0gnjFjhlavXq3Zs2dr1qxZqqmp0ZIlS1RQUKDQ0FDdddddio+P1yuvvKKkpCTV1tZq0aJF7TqvmTNnKjk5WQ8//LB69uyp06dP61e/+pV69Oghl8sl6c/3gOzevVs1NTWKj4+Xw+FQv379VFtbq61bt2rEiBF69913tX37dr9j9+nTRydPnlRVVZV69uypmJgYRUZGatSoUVqxYoX69u2rhoYGv3tZJGnx4sUaNmyYBgwYIK/Xq9LSUqWmprbreQO4Bh+AO0peXp5P0lXLtGnTfPv27fONGDHCFxER4XM6nb6FCxf6Ll++bO1bVlbmS01N9dlsNl96erpv3759Pkm+7du3+3w+n+/kyZM+Sb5PP/3U2ufbb7/1SfJ98MEHN5zb73//e9/48eN9SUlJvoiICF9ycrIvJyfHd/jwYWtMQ0OD75FHHvFFR0f7HXf+/Pm++Ph4X3R0tO+JJ57wrVq1yudwOKz9Ll265MvJyfHFxsb6JPk2btzo8/l8vmPHjvlcLpcvMjLSN2TIEN97773nd9znnnvOl5qa6ouMjPTFxcX5Jk6c6Puf//mftvzRAwhAiM/3neu5AAAABuAeFAAAYBwCBcBtk5WVpejo6GsuL7zwQrCnB8AgvMQD4Lb54x//eN1PYY2Li7M+FRYACBQAAGAcXuIBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGOf/AZ+C1FO4YEyQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df_train['Loan_Status'].value_counts())\n",
    "print(df_train['Loan_Status'].value_counts(normalize=True))\n",
    "df_train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent Variables - Categorical and Numerical Discrete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "Male      489\n",
      "Female    112\n",
      "Name: count, dtype: int64\n",
      "Gender\n",
      "Male      0.813644\n",
      "Female    0.186356\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender\n",
    "print(df_train['Gender'].value_counts())\n",
    "print(df_train['Gender'].value_counts(normalize=True))\n",
    "df_train['Gender'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Married\n",
      "Yes    398\n",
      "No     213\n",
      "Name: count, dtype: int64\n",
      "Married\n",
      "Yes    0.651391\n",
      "No     0.348609\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Married'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Married, Dependents, Education, Self_Employed, Property_Area\n",
    "print(df_train['Married'].value_counts())\n",
    "print(df_train['Married'].value_counts(normalize=True))\n",
    "df_train['Married'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependents\n",
      "0     345\n",
      "1     102\n",
      "2     101\n",
      "3+     51\n",
      "Name: count, dtype: int64\n",
      "Dependents\n",
      "0     0.575960\n",
      "1     0.170284\n",
      "2     0.168614\n",
      "3+    0.085142\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Dependents'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dependents\n",
    "print(df_train['Dependents'].value_counts())\n",
    "print(df_train['Dependents'].value_counts(normalize=True))\n",
    "df_train['Dependents'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education\n",
      "Graduate        480\n",
      "Not Graduate    134\n",
      "Name: count, dtype: int64\n",
      "Education\n",
      "Graduate        0.781759\n",
      "Not Graduate    0.218241\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Education'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Education\n",
    "print(df_train['Education'].value_counts())\n",
    "print(df_train['Education'].value_counts(normalize=True))\n",
    "df_train['Education'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Self_Employed\n",
      "No     500\n",
      "Yes     82\n",
      "Name: count, dtype: int64\n",
      "Self_Employed\n",
      "No     0.859107\n",
      "Yes    0.140893\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Self_Employed'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Self_Employed\n",
    "print(df_train['Self_Employed'].value_counts())\n",
    "print(df_train['Self_Employed'].value_counts(normalize=True))\n",
    "df_train['Self_Employed'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Property_Area\n",
      "Semiurban    233\n",
      "Urban        202\n",
      "Rural        179\n",
      "Name: count, dtype: int64\n",
      "Property_Area\n",
      "Semiurban    0.379479\n",
      "Urban        0.328990\n",
      "Rural        0.291531\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Property_Area'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAADshUABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABinxgNl2rRpcjgcfrcOHTr41l+4cEEpKSkKDw9X48aNlZSUpIKCgpoeAwAA3MJqZQ/Kj370I508edJ3+/TTT33rxo0bpzVr1ui9997T1q1bdeLECQ0bNqw2xgAAALeooFp50KAgRUVFVVpeXFysRYsWadmyZXrggQckSYsXL1bHjh21Y8cO9ezZszbGAQAAt5ha2YNy+PBhRUdHq3Xr1hoxYoTy8vIkSXv27FF5ebkSEhJ823bo0EEtWrRQTk7OVR+vrKxMJSUlfjcAAFB31Xig9OjRQ0uWLNGGDRs0f/585ebm6t5779XZs2eVn5+v4OBghYaG+v1MZGSk8vPzr/qYGRkZcrvdvltMTExNjw0AAAxS44d4EhMTfX/u0qWLevTooZYtW2rFihVq0KDBDT1menq60tLSfPdLSkqIFAAA6rBa/5hxaGio7rjjDh05ckRRUVG6ePGiioqK/LYpKCio8pyVK5xOp1wul98NAADUXbUeKKWlpTp69KiaNWumbt26qV69esrKyvKtP3jwoPLy8hQfH1/bowAAgFtEjR/imTBhggYPHqyWLVvqxIkTmjp1qgIDAzV8+HC53W6NHj1aaWlpCgsLk8vl0tixYxUfH88neAAAgE+NB8q3336r4cOH68yZM4qIiFCvXr20Y8cORURESJJee+01BQQEKCkpSWVlZerXr5/mzZtX02MAAIBbWI0HyvLly6+5vn79+srMzFRmZmZNPzUAAKgjuBYPAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4lgZKZmamWrVqpfr166tHjx7atWuXleMAAABDWBYo7777rtLS0jR16lTt3btXcXFx6tevn06dOmXVSAAAwBCWBcqcOXP05JNPatSoUerUqZMWLFighg0b6q233rJqJAAAYIggK5704sWL2rNnj9LT033LAgIClJCQoJycnErbl5WVqayszHe/uLhYklRSUlL7w/4AnrLzVo9QZ5j+d30r4X1ZM3hP1hzekzXH9Pfllfm8Xu/3bmtJoPz973/XpUuXFBkZ6bc8MjJSf/vb3yptn5GRoenTp1daHhMTU2szwizuuVZPAPjjPQkT3Srvy7Nnz8rtdl9zG0sC5Xqlp6crLS3Nd9/j8aiwsFDh4eFyOBwWTnbrKykpUUxMjI4fPy6Xy2X1OADvSRiH92TN8Xq9Onv2rKKjo793W0sC5bbbblNgYKAKCgr8lhcUFCgqKqrS9k6nU06n029ZaGhobY5oOy6Xi//xYBTekzAN78ma8X17Tq6w5CTZ4OBgdevWTVlZWb5lHo9HWVlZio+Pt2IkAABgEMsO8aSlpWnkyJHq3r27/vM//1Nz587VuXPnNGrUKKtGAgAAhrAsUB599FGdPn1aU6ZMUX5+vu666y5t2LCh0omzqF1Op1NTp06tdAgNsArvSZiG96Q1HN7qfNYHAADgJuJaPAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwzi1xLR7UPI/HoyNHjujUqVPyeDx+63r37m3RVAAAXEag2NCOHTv005/+VP/3f/9X6ZLXDodDly5dsmgy2FVBQYEmTJigrKwsnTp1qtL7kvckbpZhw4ZVe9tVq1bV4iQgUGzo6aefVvfu3bVu3To1a9aMK0LDcsnJycrLy9OLL77IexKWqu6F7FD7+CZZG2rUqJH27duntm3bWj0KIEkKCQnRJ598orvuusvqUQAYgpNkbahHjx46cuSI1WMAPjExMZUO6wCwNw7x2NDYsWM1fvx45efnq3PnzqpXr57f+i5dulg0Gexq7ty5mjRpkt544w21atXK6nEAn5UrV2rFihXKy8vTxYsX/dbt3bvXoqnsgUM8NhQQUHnHmcPhkNfr5SRZWKJJkyY6f/68Kioq1LBhw0rRXFhYaNFksLPXX39dv/rVr5ScnKyFCxdq1KhROnr0qHbv3q2UlBTNnDnT6hHrNPag2FBubq7VIwB+5s6da/UIQCXz5s3TwoULNXz4cC1ZskTPP/+8WrdurSlTphDNNwF7UAAAqELDhg114MABtWzZUk2bNtXGjRsVFxenw4cPq2fPnjpz5ozVI9Zp7EGxsa+//rrK46pDhgyxaCJAunDhQqX3pMvlsmga2FlUVJQKCwvVsmVLtWjRQjt27FBcXJxyc3M5qfsmIFBs6JtvvtHDDz+sL7/80nfuiSTfd09wDgputnPnzmnixIlasWJFlf8q5T0JKzzwwAP64IMP1LVrV40aNUrjxo3TypUr9dlnn13XF7rhxnCIx4YGDx6swMBAvfnmm4qNjdWuXbt05swZjR8/Xq+88oruvfdeq0eEzaSkpGjz5s166aWX9PjjjyszM1Pfffed3njjDf3mN7/RiBEjrB4RNuTxeOTxeBQUdPnf8suXL9f27dvVrl07PfXUUwoODrZ4wrqNQLGh2267TZs2bVKXLl3kdru1a9cutW/fXps2bdL48eP1+eefWz0ibKZFixZaunSp+vTpI5fLpb1796pt27Z6++239c477+jDDz+0ekTYTEVFhWbNmqVf/OIXat68udXj2BJf1GZDly5dUkhIiKTLsXLixAlJUsuWLXXw4EErR4NNFRYWqnXr1pIun29y5RMSvXr1UnZ2tpWjwaaCgoI0e/ZsVVRUWD2KbREoNnTnnXdq3759ki5/q+zs2bO1bds2zZgxw/dLAriZWrdu7fv4e4cOHbRixQpJ0po1axQaGmrhZLCzvn37auvWrVaPYVucJGtDkydP1rlz5yRJM2bM0KBBg3TvvfcqPDxc7777rsXTwY5GjRqlffv26b777tOkSZM0ePBg/eEPf1B5ebnmzJlj9XiwqcTERE2aNElffvmlunXrpkaNGvmt5xOPtYtzUCDp8i72Jk2acBVZGOHYsWO+81C49AKsUtW3bl/Bt27XPgLF5o4fPy7p8sXaAAAwBeeg2FBFRYVefPFFud1utWrVSq1atZLb7dbkyZNVXl5u9XiwqaysLA0aNEht2rRRmzZtNGjQIH388cdWjwXAIpyDYkNjx47VqlWrNHv2bMXHx0uScnJyNG3aNJ05c0bz58+3eELYzbx58/Tss8/qkUce0bPPPitJ2rFjhwYMGKDXXntNKSkpFk8IO5oxY8Y110+ZMuUmTWJPHOKxIbfbreXLlysxMdFv+Ycffqjhw4eruLjYoslgV82bN9ekSZOUmprqtzwzM1OzZs3Sd999Z9FksLOuXbv63S8vL1dubq6CgoLUpk0b7d2716LJ7IE9KDbkdDrVqlWrSstjY2P5ZkRYoqioSP3796+0/KGHHtLEiRMtmAhQlV9aWVJSouTkZD388MMWTGQvnINiQ6mpqXrppZdUVlbmW1ZWVqaZM2dW+hcscDMMGTJE77//fqXlf/nLXzRo0CALJgKq5nK5NH36dL344otWj1LnsQfFJv79wlYff/yxmjdvrri4OEnSvn37dPHiRfXt29eK8WBDr7/+uu/PnTp10syZM7VlyxbfeVE7duzQtm3bNH78eKtGBKpUXFzMofCbgHNQbGLUqFHV3nbx4sW1OAlwWWxsbLW2czgc+uabb2p5GqCyf41oSfJ6vTp58qTefvtt9e7dW++8845Fk9kDgWIzXq9Xx48fV0REhBo0aGD1OABgrH+P6ICAAEVEROiBBx5Qenq675pmqB2cg2IzXq9Xbdu21bfffmv1KICky5+MaNOmjQ4cOGD1KICf3Nxcv9vRo0e1ZcsWhYeHq127dlaPV+cRKDYTEBCgdu3a6cyZM1aPAkiS6tWrpwsXLlg9BuBTVlam9PR0de/eXffcc49Wr14t6fLh7zZt2uh3v/udxo0bZ+2QNsAhHhtas2aNZs+erfnz5+vOO++0ehxAs2bN0qFDh/Tmm28qKIhz92GtiRMn6o033lBCQoK2b9+u06dPa9SoUdqxY4deeOEF/dd//ZcCAwOtHrPOI1BsqEmTJjp//rwqKioUHBxc6VyUwsJCiyaDXT388MPKyspS48aN1blz50pXjV21apVFk8GOWrdurblz52rIkCH661//qi5duig5OVmLFi3igqo3Ef9UsaG5c+daPQLgJzQ0VElJSVaPAUiSvv32W3Xr1k2SdOedd8rpdGrcuHHEyU3GHhQAAP5FYGCg8vPzFRERIUkKCQnR/v37q/3ReNQM9qDYUF5e3jXXt2jR4iZNArtr0qRJlf8qdbvduuOOOzRhwgQ9+OCDFkwGO/N6vUpOTpbT6ZQkXbhwQU8//TSHHm8y9qDYUEBAwDV3VV66dOkmTgM7++Mf/1jl8qKiIu3Zs0fvvvuuVq5cqcGDB9/kyWBn1f1iS77UsnYRKDa0b98+v/vl5eX6/PPPNWfOHM2cObPS1+IDVpkzZ45Wrlyp7du3Wz0KgJuMQIHPunXr9PLLL2vLli1WjwJIkg4dOqSePXvyyTLAhviiNvi0b99eu3fvtnoMwKesrEzBwcFWjwHAApwka0MlJSV+969cAGvatGl8fTOMsmjRIt11111WjwHAAgSKDYWGhlY6Sdbr9SomJkbLly+3aCrYUVpaWpXLi4uLtXfvXh06dEjZ2dk3eSoAJiBQbGjz5s1+969cobNt27Z8zThuqs8//7zK5S6XSw8++KBWrVrFd08ANsVJsgAAwDj8c9kmPvjgAyUmJqpevXr64IMPrrntkCFDbtJUAABUjT0oNhEQEKD8/Hw1bdpUAQFX//CWw+Hgi9oAAJYjUAAAgHH4HhQAAGAczkGxqd27d2vz5s06deqUPB6P37o5c+ZYNBUAAJcRKDY0a9YsTZ48We3bt1dkZKTfd6Jc6yKCAADcLJyDYkORkZH67W9/q+TkZKtHAQCgSpyDYkMBAQG65557rB4DAICrIlBsaNy4ccrMzLR6DAAAropDPDbk8Xg0cOBAHTp0SJ06dVK9evX81q9atcqiyQAAuIyTZG3of/7nf7R582bdf//9Cg8P58RYAIBx2INiQyEhIVq+fLkGDhxo9SgAAFSJc1BsKCwsTG3atLF6DAAAropAsaFp06Zp6tSpOn/+vNWjAABQJQ7x2FDXrl119OhReb1etWrVqtJJsnv37rVoMgAALuMkWRsaOnSo1SMAAHBN7EEBAADG4RwUmyoqKtKbb76p9PR0FRYWSrp8aOe7776zeDIAANiDYkv79+9XQkKC3G63jh07poMHD6p169aaPHmy8vLytHTpUqtHBADYHHtQbCgtLU3Jyck6fPiw6tev71s+YMAAZWdnWzgZAACXESg2tHv3bj311FOVlt9+++3Kz8+3YCIAAPwRKDbkdDpVUlJSafmhQ4cUERFhwUQAAPgjUGxoyJAhmjFjhsrLyyVJDodDeXl5mjhxopKSkiyeDgAATpK1peLiYj3yyCP67LPPdPbsWUVHR+vkyZOKj4/X+vXr1ahRI6tHBADYHIFiY59++qn279+v0tJSdevWTX379rV6JAAAJHGIx1ZycnK0du1a3/1evXqpUaNGmjdvnoYPH64xY8aorKzMwgkBALiMQLGRGTNm6KuvvvLd//LLL/Xkk0/qwQcf1KRJk7RmzRplZGRYOCEAAJdxiMdGmjVrpjVr1qh79+6SpF/96lfaunWrPv30U0nSe++9p6lTp+rrr7+2ckwAANiDYif/+Mc/FBkZ6bu/detWJSYm+u7ffffdOn78uBWjAQDgh0CxkcjISOXm5kqSLl68qL1796pnz56+9WfPnlW9evWsGg8AAB8CxUYGDBigSZMm6ZNPPlF6eroaNmyoe++917d+//79atOmjYUTAgBwWZDVA+DmeemllzRs2DDdd999aty4sf74xz8qODjYt/6tt97SQw89ZOGEAABcxkmyNlRcXKzGjRsrMDDQb3lhYaEaN27sFy0AAFiBQAEAAMbhHBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFKAOSk5OlsPhkMPhUHBwsNq2basZM2aooqLC6tGqNG3aNN1111218tg5OTkKDAzUwIEDa+XxAdQOAgWoo/r376+TJ0/q8OHDGj9+vKZNm6aXX3650nYXL160YLrLvF5vrUfTokWLNHbsWGVnZ+vEiROWzwOgeggUoI5yOp2KiopSy5Yt9cwzzyghIUEffPCBkpOTNXToUM2cOVPR0dFq3769pMtXt37ggQfUoEEDhYeHa8yYMSotLfU93pWfmz59uiIiIuRyufT000/7BY7H41FGRoZiY2PVoEEDxcXFaeXKlb71W7ZskcPh0Pr169WtWzc5nU796U9/0vTp07Vv3z7fXp8lS5boF7/4hQYNGuT3msrLy9W0aVMtWrSoWv8NSktL9e677+qZZ57RwIEDtWTJEr/1Vc3z6aeffu/ruHTpkkaPHu1b3759e/3ud7+r9t8NgO/HN8kCNtGgQQOdOXNGkpSVlSWXy6WNGzdKks6dO6d+/fopPj5eu3fv1qlTp/TEE08oNTXV75d6VlaW6tevry1btujYsWMaNWqUwsPDNXPmTElSRkaG/vSnP2nBggVq166dsrOz9bOf/UwRERG67777fI8zadIkvfLKK2rdurXq16+v8ePHa8OGDfr4448lSW63W3fccYd69+6tkydPqlmzZpKktWvX6vz583r00Uer9ZpXrFihDh06qH379vrZz36m5557Tunp6XI4HH7b/es8TZo0+d7X4fF41Lx5c7333nsKDw/X9u3bNWbMGDVr1kz//d//fWN/QQD8eQHUOSNHjvT+5Cc/8Xq9Xq/H4/Fu3LjR63Q6vRMmTPCOHDnSGxkZ6S0rK/Ntv3DhQm+TJk28paWlvmXr1q3zBgQEePPz832PGRYW5j137pxvm/nz53sbN27svXTpkvfChQvehg0berdv3+43y+jRo73Dhw/3er1e7+bNm72SvKtXr/bbZurUqd64uLhKr6NTp07e3/72t777gwcP9iYnJ1f7v8OPf/xj79y5c71er9dbXl7uve2227ybN2/2ra9qnuq8jqqkpKR4k5KSqj0bgGtjDwpQR61du1aNGzdWeXm5PB6PfvrTn2ratGlKSUlR586d/S5pcODAAcXFxalRo0a+Zffcc488Ho8OHjyoyMhISVJcXJwaNmzo2yY+Pl6lpaU6fvy4SktLdf78eT344IN+c1y8eFFdu3b1W9a9e/dqvYYnnnhCCxcu1PPPP6+CggKtX79emzZtqtbPHjx4ULt27dL7778vSQoKCtKjjz6qRYsWqU+fPled58iRI9V6HZmZmXrrrbeUl5enf/7zn7p48WKtnegL2BGBAtRR999/v+bPn6/g4GBFR0crKOj//+/+ryFSU66cr7Ju3TrdfvvtfuucTqff/eo+/89//nNNmjRJOTk52r59u2JjY/2uwH0tixYtUkVFhaKjo33LvF6vnE6n/vCHP8jtdlc5T3Vex/LlyzVhwgS9+uqrio+PV0hIiF5++WXt3LmzWrMB+H4EClBHNWrUSG3btq3Wth07dtSSJUt07tw53y/rbdu2KSAgwHcSrSTt27dP//znP9WgQQNJ0o4dO9S4cWPFxMQoLCxMTqdTeXl5fuebVEdwcLAuXbpUaXl4eLiGDh2qxYsXKycnR6NGjarW41VUVGjp0qV69dVXK12he+jQoXrnnXf09NNPV/mznTp1+t7XsW3bNv34xz/WL3/5S9+yo0ePVms2ANVDoADQiBEjNHXqVI0cOVLTpk3T6dOnNXbsWD3++OO+wzvS5cMco0eP1uTJk3Xs2DFNnTpVqampCggIUEhIiCZMmKBx48bJ4/GoV69eKi4u1rZt2+RyuTRy5MirPn+rVq2Um5urL774Qs2bN1dISIhvb8UTTzyhQYMG6dKlS9d8jH+1du1a/eMf/9Do0aP99pRIUlJSkhYtWnTVQKnO62jXrp2WLl2qjz76SLGxsXr77be1e/duxcbGVms+AN+PQAGghg0b6qOPPtKzzz6ru+++Ww0bNlRSUpLmzJnjt13fvn3Vrl079e7dW2VlZRo+fLimTZvmW//SSy8pIiJCGRkZ+uabbxQaGqr/+I//0AsvvHDN509KStKqVat0//33q6ioSIsXL1ZycrIkKSEhQc2aNdOPfvQjv8M117Jo0SIlJCRUipMrzzV79mzt37//qj//fa/jqaee0ueff65HH31UDodDw4cP1y9/+UutX7++WvMB+H4Or9frtXoIAOZLTk5WUVGRVq9efVOft7S0VLfffrsWL16sYcOG3dTnBmAd9qAAMJLH49Hf//53vfrqqwoNDdWQIUOsHgnATUSgADBSXl6eYmNj1bx5cy1ZssTvU0h5eXnq1KnTVX/266+/VosWLW7GmABqCYd4ANxyKioqdOzYsauub9WqlV/QALj1ECgAAMA4XCwQAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJz/BwQQMcyR9rw/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Property_Area\n",
    "print(df_train['Property_Area'].value_counts())\n",
    "print(df_train['Property_Area'].value_counts(normalize=True))\n",
    "df_train['Property_Area'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit_History\n",
      "1.0    475\n",
      "0.0     89\n",
      "Name: count, dtype: int64\n",
      "Credit_History\n",
      "1.0    0.842199\n",
      "0.0    0.157801\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Credit_History'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAG5CAYAAACk38/+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhT0lEQVR4nO3de1DVBf7/8dcBBBU9BzEFTUzdMiUvKZae2q+7JkmKLSZONrmKpjYaOCpbW+66pG67mjVZ7nhpu4i1ObZtWabrbWm1i3ijMNcL2ZYLrR7UTFBKQPz8/tjx/ELJRJHzBp+Pmc9MfC7nvD8zAU8/53MOLsdxHAEAABgSFOgBAAAAzkWgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwJyQQA9wKc6cOaODBw+qadOmcrlcgR4HAABcBMdxdOLECbVu3VpBQRe+RlInA+XgwYOKiYkJ9BgAAOASFBQUqE2bNhfcp04GStOmTSX97wTdbneApwEAABejuLhYMTEx/t/jF1InA+Xsyzput5tAAQCgjrmY2zO4SRYAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmhAR6AFRPu8dWB3oE1KIDcxIDPQIABARXUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMOeyAmXOnDlyuVyaMmWKf92pU6eUmpqq5s2bq0mTJkpOTlZhYWGl4/Lz85WYmKjGjRurZcuWeuSRR3T69OnLGQUAANQjlxwo27dv1/PPP69u3bpVWj916lS9++67euONN7Rp0yYdPHhQQ4cO9W+vqKhQYmKiysrKtHnzZi1dulSZmZnKyMi49LMAAAD1yiUFysmTJzVixAi98MILatasmX99UVGRXnrpJT3zzDO64447FBcXpyVLlmjz5s3asmWLJGn9+vXas2eP/vKXv+jmm2/WwIED9fvf/14LFixQWVlZzZwVAACo0y4pUFJTU5WYmKj4+PhK63NyclReXl5pfadOndS2bVtlZ2dLkrKzs9W1a1dFRUX590lISFBxcbF2795d5fOVlpaquLi40gIAAOqvkOoesHz5cn388cfavn37edt8Pp9CQ0MVERFRaX1UVJR8Pp9/n+/HydntZ7dVZfbs2Zo5c2Z1RwUAAHVUta6gFBQUaPLkyXrttdfUsGHDKzXTeaZNm6aioiL/UlBQUGvPDQAAal+1AiUnJ0eHDx9Wz549FRISopCQEG3atEnz589XSEiIoqKiVFZWpuPHj1c6rrCwUNHR0ZKk6Ojo897Vc/brs/ucKywsTG63u9ICAADqr2oFSv/+/bVr1y7l5ub6l169emnEiBH+/27QoIGysrL8x+Tl5Sk/P19er1eS5PV6tWvXLh0+fNi/z4YNG+R2uxUbG1tDpwUAAOqyat2D0rRpU3Xp0qXSuvDwcDVv3ty/fuzYsUpPT1dkZKTcbrcmTZokr9erPn36SJIGDBig2NhYjRw5UnPnzpXP59P06dOVmpqqsLCwGjotAABQl1X7JtkfM2/ePAUFBSk5OVmlpaVKSEjQwoUL/duDg4O1atUqTZw4UV6vV+Hh4UpJSdGsWbNqehQAAFBHuRzHcQI9RHUVFxfL4/GoqKjoqrsfpd1jqwM9AmrRgTmJgR4BAGpMdX5/87d4AACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYE61AmXRokXq1q2b3G633G63vF6v1qxZ499+6tQppaamqnnz5mrSpImSk5NVWFhY6THy8/OVmJioxo0bq2XLlnrkkUd0+vTpmjkbAABQL1QrUNq0aaM5c+YoJydHO3bs0B133KGkpCTt3r1bkjR16lS9++67euONN7Rp0yYdPHhQQ4cO9R9fUVGhxMRElZWVafPmzVq6dKkyMzOVkZFRs2cFAADqNJfjOM7lPEBkZKSeeuopDRs2TC1atNCyZcs0bNgwSdK+ffvUuXNnZWdnq0+fPlqzZo0GDx6sgwcPKioqSpK0ePFiPfroozpy5IhCQ0Mv6jmLi4vl8XhUVFQkt9t9OePXOe0eWx3oEVCLDsxJDPQIAFBjqvP7+5LvQamoqNDy5ctVUlIir9ernJwclZeXKz4+3r9Pp06d1LZtW2VnZ0uSsrOz1bVrV3+cSFJCQoKKi4v9V2GqUlpaquLi4koLAACov6odKLt27VKTJk0UFhamCRMmaMWKFYqNjZXP51NoaKgiIiIq7R8VFSWfzydJ8vl8leLk7Paz237I7Nmz5fF4/EtMTEx1xwYAAHVItQPlxhtvVG5urrZu3aqJEycqJSVFe/bsuRKz+U2bNk1FRUX+paCg4Io+HwAACKyQ6h4QGhqq66+/XpIUFxen7du367nnntPw4cNVVlam48ePV7qKUlhYqOjoaElSdHS0tm3bVunxzr7L5+w+VQkLC1NYWFh1RwUAAHXUZX8OypkzZ1RaWqq4uDg1aNBAWVlZ/m15eXnKz8+X1+uVJHm9Xu3atUuHDx/277Nhwwa53W7FxsZe7igAAKCeqNYVlGnTpmngwIFq27atTpw4oWXLlmnjxo1at26dPB6Pxo4dq/T0dEVGRsrtdmvSpEnyer3q06ePJGnAgAGKjY3VyJEjNXfuXPl8Pk2fPl2pqalcIQEAAH7VCpTDhw9r1KhROnTokDwej7p166Z169bpzjvvlCTNmzdPQUFBSk5OVmlpqRISErRw4UL/8cHBwVq1apUmTpwor9er8PBwpaSkaNasWTV7VgAAoE677M9BCQQ+BwVXCz4HBUB9UiufgwIAAHClECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOdUKlNmzZ+uWW25R06ZN1bJlSw0ZMkR5eXmV9jl16pRSU1PVvHlzNWnSRMnJySosLKy0T35+vhITE9W4cWO1bNlSjzzyiE6fPn35ZwMAAOqFagXKpk2blJqaqi1btmjDhg0qLy/XgAEDVFJS4t9n6tSpevfdd/XGG29o06ZNOnjwoIYOHerfXlFRocTERJWVlWnz5s1aunSpMjMzlZGRUXNnBQAA6jSX4zjOpR585MgRtWzZUps2bVLfvn1VVFSkFi1aaNmyZRo2bJgkad++fercubOys7PVp08frVmzRoMHD9bBgwcVFRUlSVq8eLEeffRRHTlyRKGhoT/6vMXFxfJ4PCoqKpLb7b7U8eukdo+tDvQIqEUH5iQGegQAqDHV+f19WfegFBUVSZIiIyMlSTk5OSovL1d8fLx/n06dOqlt27bKzs6WJGVnZ6tr167+OJGkhIQEFRcXa/fu3VU+T2lpqYqLiystAACg/rrkQDlz5oymTJmi22+/XV26dJEk+Xw+hYaGKiIiotK+UVFR8vl8/n2+Hydnt5/dVpXZs2fL4/H4l5iYmEsdGwAA1AGXHCipqan617/+peXLl9fkPFWaNm2aioqK/EtBQcEVf04AABA4IZdyUFpamlatWqX3339fbdq08a+Pjo5WWVmZjh8/XukqSmFhoaKjo/37bNu2rdLjnX2Xz9l9zhUWFqawsLBLGRUAANRB1bqC4jiO0tLStGLFCr333ntq3759pe1xcXFq0KCBsrKy/Ovy8vKUn58vr9crSfJ6vdq1a5cOHz7s32fDhg1yu92KjY29nHMBAAD1RLWuoKSmpmrZsmV655131LRpU/89Ix6PR40aNZLH49HYsWOVnp6uyMhIud1uTZo0SV6vV3369JEkDRgwQLGxsRo5cqTmzp0rn8+n6dOnKzU1laskAABAUjUDZdGiRZKkn//855XWL1myRKNHj5YkzZs3T0FBQUpOTlZpaakSEhK0cOFC/77BwcFatWqVJk6cKK/Xq/DwcKWkpGjWrFmXdyYAAKDeuKzPQQkUPgcFVws+BwVAfVJrn4MCAABwJRAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDnVDpT3339fd999t1q3bi2Xy6W333670nbHcZSRkaFWrVqpUaNGio+P1/79+yvtc+zYMY0YMUJut1sREREaO3asTp48eVknAgAA6o9qB0pJSYm6d++uBQsWVLl97ty5mj9/vhYvXqytW7cqPDxcCQkJOnXqlH+fESNGaPfu3dqwYYNWrVql999/Xw8++OClnwUAAKhXQqp7wMCBAzVw4MAqtzmOo2effVbTp09XUlKSJOmVV15RVFSU3n77bd13333au3ev1q5dq+3bt6tXr16SpD/96U8aNGiQnn76abVu3foyTgcAANQHNXoPypdffimfz6f4+Hj/Oo/Ho969eys7O1uSlJ2drYiICH+cSFJ8fLyCgoK0devWKh+3tLRUxcXFlRYAAFB/1Wig+Hw+SVJUVFSl9VFRUf5tPp9PLVu2rLQ9JCREkZGR/n3ONXv2bHk8Hv8SExNTk2MDAABj6sS7eKZNm6aioiL/UlBQEOiRAADAFVSjgRIdHS1JKiwsrLS+sLDQvy06OlqHDx+utP306dM6duyYf59zhYWFye12V1oAAED9VaOB0r59e0VHRysrK8u/rri4WFu3bpXX65Ukeb1eHT9+XDk5Of593nvvPZ05c0a9e/euyXEAAEAdVe138Zw8eVKff/65/+svv/xSubm5ioyMVNu2bTVlyhQ98cQTuuGGG9S+fXv97ne/U+vWrTVkyBBJUufOnXXXXXdp/PjxWrx4scrLy5WWlqb77ruPd/AAAABJlxAoO3bsUL9+/fxfp6enS5JSUlKUmZmpX//61yopKdGDDz6o48eP66c//anWrl2rhg0b+o957bXXlJaWpv79+ysoKEjJycmaP39+DZwOAACoD1yO4ziBHqK6iouL5fF4VFRUdNXdj9LusdWBHgG16MCcxECPAAA1pjq/v+vEu3gAAMDVhUABAADmVPseFADAlcFLuFcXXsK9MK6gAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5AQ2UBQsWqF27dmrYsKF69+6tbdu2BXIcAABgRMAC5fXXX1d6eroef/xxffzxx+revbsSEhJ0+PDhQI0EAACMCFigPPPMMxo/frzGjBmj2NhYLV68WI0bN9bLL78cqJEAAIARAQmUsrIy5eTkKD4+/v8PEhSk+Ph4ZWdnB2IkAABgSEggnvTo0aOqqKhQVFRUpfVRUVHat2/fefuXlpaqtLTU/3VRUZEkqbi4+MoOatCZ0m8DPQJq0dX4//jVjO/vq8vV+P199pwdx/nRfQMSKNU1e/ZszZw587z1MTExAZgGqD2eZwM9AYAr5Wr+/j5x4oQ8Hs8F9wlIoFxzzTUKDg5WYWFhpfWFhYWKjo4+b/9p06YpPT3d//WZM2d07NgxNW/eXC6X64rPi8AqLi5WTEyMCgoK5Ha7Az0OgBrE9/fVxXEcnThxQq1bt/7RfQMSKKGhoYqLi1NWVpaGDBki6X/RkZWVpbS0tPP2DwsLU1hYWKV1ERERtTApLHG73fwAA+opvr+vHj925eSsgL3Ek56erpSUFPXq1Uu33nqrnn32WZWUlGjMmDGBGgkAABgRsEAZPny4jhw5ooyMDPl8Pt18881au3bteTfOAgCAq09Ab5JNS0ur8iUd4PvCwsL0+OOPn/cyH4C6j+9v/BCXczHv9QEAAKhF/LFAAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKDDv3D8WCaDu2rNnjx566CH16NFDrVq1UqtWrdSjRw899NBD2rNnT6DHgyEECkzasGGDBg0apGbNmqlx48Zq3LixmjVrpkGDBukf//hHoMcDcAnWrFmjHj166JNPPlFSUpIyMjKUkZGhpKQk7dy5Uz179tS6desCPSaM4HNQYM7SpUs1btw4DRs2TAkJCf5PFy4sLNT69ev1t7/9TS+99JJGjhwZ4EkBVEf37t2VlJSkWbNmVbl9xowZeuutt/Tpp5/W8mSwiECBOR07dtTkyZOVmppa5faFCxdq3rx52r9/fy1PBuByNGrUSLm5ubrxxhur3J6Xl6ebb75Z3333XS1PBot4iQfm5OfnKz4+/ge39+/fX1999VUtTgSgJrRr106rV6/+we2rV6/WddddV4sTwbKA/i0eoCo33XSTXnrpJc2dO7fK7S+//LJiY2NreSoAl2vWrFm6//77tXHjRsXHx1d6+TYrK0tr167VsmXLAjwlrOAlHpizceNGDR48WB06dKjyh9gXX3yh1atXq2/fvgGeFEB1bd68WfPnz1d2drZ8Pp8kKTo6Wl6vV5MnT5bX6w3whLCCQIFJBw4c0KJFi7Rly5bzfohNmDBB7dq1C+yAAIArikABAADmcJMsAMCE3/zmN3rggQcCPQaMIFBQ56SkpOiOO+4I9BgAathXX32lAwcOBHoMGMG7eFDntG7dWkFBtDVQ37zyyiuBHgGGcA8KAKDWHD16VC+//PJ57+K57bbbNHr0aLVo0SLAE8IK/hmKOqegoIDXqYE6aPv27erYsaPmz58vj8ejvn37qm/fvvJ4PJo/f746deqkHTt2BHpMGMEVFNQ5Z/+oWEVFRaBHAVANffr0Uffu3bV48WK5XK5K2xzH0YQJE/Tpp58qOzs7QBPCEu5BgTkrV6684PYvvviiliYBUJN27typzMzM8+JEklwul6ZOnaoePXoEYDJYRKDAnCFDhsjlculCF/eq+gEHwLbo6Ght27ZNnTp1qnL7tm3b/J8cDRAoMKdVq1ZauHChkpKSqtyem5uruLi4Wp4KwOV6+OGH9eCDDyonJ0f9+/c/789YvPDCC3r66acDPCWsIFBgTlxcnHJycn4wUH7s6goAm1JTU3XNNddo3rx5Wrhwof8+suDgYMXFxSkzM1P33ntvgKeEFdwkC3M++OADlZSU6K677qpye0lJiXbs2KGf/exntTwZgJpSXl6uo0ePSpKuueYaNWjQIMATwRoCBQAAmMPnoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKgBrhcrn09ttvS5IOHDggl8ul3NzcK/68mZmZioiIuOLPA6B2EShAPebz+TRp0iR16NBBYWFhiomJ0d13362srKwr+rwxMTE6dOiQunTpIknauHGjXC6Xjh8/ftGPMXr0aA0ZMuS89ec+1vDhw/XZZ59d1GMSM0DdwQe1AfXUgQMHdPvttysiIkJPPfWUunbtqvLycq1bt06pqanat2/feceUl5fXyOdRBAcHKzo6+rIf52I0atRIjRo1qpXnOquiokIul0tBQfwbD7hS+O4C6qmHHnpILpdL27ZtU3Jysjp27KibbrpJ6enp2rJli6T/vSyzaNEi/eIXv1B4eLj+8Ic/SJLeeecd9ezZUw0bNlSHDh00c+ZMnT592v/Y+/fvV9++fdWwYUPFxsZqw4YNlZ77+y/xHDhwQP369ZMkNWvWTC6XS6NHj66x8zz3qsjOnTvVr18/NW3aVG63W3FxcdqxY4c2btyoMWPGqKioSC6XSy6XSzNmzJAkffPNNxo1apSaNWumxo0ba+DAgdq/f/95z7Fy5UrFxsYqLCxMH374oRo0aCCfz1dpnilTpuj//u//auz8gKsVgQLUQ8eOHdPatWuVmpqq8PDw87Z//xf6jBkzdM8992jXrl164IEH9MEHH2jUqFGaPHmy9uzZo+eff16ZmZn+eDlz5oyGDh2q0NBQbd26VYsXL9ajjz76g7PExMTozTfflCTl5eXp0KFDeu6552r2hL9nxIgRatOmjbZv366cnBw99thjatCggW677TY9++yzcrvdOnTokA4dOqSHH35Y0v9eTtqxY4dWrlyp7OxsOY6jQYMGqby83P+43377rZ588km9+OKL2r17t3r16qUOHTro1Vdf9e9TXl6u1157TQ888MAVOz/gquEAqHe2bt3qSHLeeuutC+4nyZkyZUqldf3793f++Mc/Vlr36quvOq1atXIcx3HWrVvnhISEOP/973/929esWeNIclasWOE4juN8+eWXjiTnk08+cRzHcf75z386kpxvvvnmos8hJSXFCQ4OdsLDwystDRs2rPRYS5YscTwej/+4pk2bOpmZmVU+5rn7Oo7jfPbZZ44k56OPPvKvO3r0qNOoUSPnr3/9q/84SU5ubm6lY5988kmnc+fO/q/ffPNNp0mTJs7Jkycv+jwBVI0rKEA95FTjL1j06tWr0tc7d+7UrFmz1KRJE/8yfvx4HTp0SN9++6327t2rmJgYtW7d2n+M1+utsdm/r1+/fsrNza20vPjiixc8Jj09XePGjVN8fLzmzJmjf//73xfcf+/evQoJCVHv3r3965o3b64bb7xRe/fu9a8LDQ1Vt27dKh07evRoff755/6XzM7+sbuqrloBqB5ukgXqoRtuuEEul6vKG2HPde4v05MnT2rmzJkaOnToefs2bNiwxma8GOHh4br++usrrfvqq68ueMyMGTN0//33a/Xq1VqzZo0ef/xxLV++XPfcc89lzdKoUSO5XK5K61q2bKm7775bS5YsUfv27bVmzRpt3Ljxsp4HwP9wBQWohyIjI5WQkKAFCxaopKTkvO0Xertvz549lZeXp+uvv/68JSgoSJ07d1ZBQYEOHTrkP+bsFYQfEhoaKul/736pDR07dtTUqVO1fv16DR06VEuWLPHPce4MnTt31unTp7V161b/uq+//lp5eXmKjY390ecaN26cXn/9df35z3/WT37yE91+++01ezLAVYpAAeqpBQsWqKKiQrfeeqvefPNN7d+/X3v37tX8+fMv+JJMRkaGXnnlFc2cOVO7d+/W3r17tXz5ck2fPl2SFB8fr44dOyolJUU7d+7UBx98oN/+9rcXnOW6666Ty+XSqlWrdOTIEZ08ebJGz/Ws7777Tmlpadq4caP+85//6KOPPtL27dvVuXNnSVK7du108uRJZWVl6ejRo/r22291ww03KCkpSePHj9eHH36onTt36pe//KWuvfZaJSUl/ehzJiQkyO1264knntCYMWOuyHkBVyMCBainOnTooI8//lj9+vXTr371K3Xp0kV33nmnsrKytGjRoh88LiEhQatWrdL69et1yy23qE+fPpo3b56uu+46SVJQUJBWrFih7777TrfeeqvGjRvnf4fPD7n22ms1c+ZMPfbYY4qKilJaWlqNnutZwcHB+vrrrzVq1Ch17NhR9957rwYOHKiZM2dKkm677TZNmDBBw4cPV4sWLTR37lxJ0pIlSxQXF6fBgwfL6/XKcRz9/e9/v6jPhAkKCtLo0aNVUVGhUaNGXZHzAq5GLqc6d9MBAM4zduxYHTlyRCtXrgz0KEC9wU2yAHCJioqKtGvXLi1btow4AWoYgQKg1uXn51/wBtQ9e/aobdu2tTjRpUlKStK2bds0YcIE3XnnnYEeB6hXeIkHQK07ffq0Dhw48IPb27Vrp5AQ/v0EXM0IFAAAYA7v4gEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADDn/wHV77tayrWpMAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Property_Area\n",
    "print(df_train['Credit_History'].value_counts())\n",
    "print(df_train['Credit_History'].value_counts(normalize=True))\n",
    "df_train['Credit_History'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Amount_Term\n",
      "360.0    512\n",
      "180.0     44\n",
      "480.0     15\n",
      "300.0     13\n",
      "84.0       4\n",
      "240.0      4\n",
      "120.0      3\n",
      "60.0       2\n",
      "36.0       2\n",
      "12.0       1\n",
      "Name: count, dtype: int64\n",
      "Loan_Amount_Term\n",
      "360.0    0.853333\n",
      "180.0    0.073333\n",
      "480.0    0.025000\n",
      "300.0    0.021667\n",
      "84.0     0.006667\n",
      "240.0    0.006667\n",
      "120.0    0.005000\n",
      "60.0     0.003333\n",
      "36.0     0.003333\n",
      "12.0     0.001667\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Loan_Amount_Term'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Loan Amount Term\n",
    "print(df_train['Loan_Amount_Term'].value_counts())\n",
    "print(df_train['Loan_Amount_Term'].value_counts(normalize=True))\n",
    "df_train['Loan_Amount_Term'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent Variables - Numerical Continous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ApplicantIncome \tCoapplicantIncome \tLoanAmount \t \t\n",
    "sns.histplot(df_train['ApplicantIncome'])\n",
    "plt.show()\n",
    "sns.catplot(df_train['ApplicantIncome'])\n",
    "plt.show()\n",
    "df_train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applicant income leans left, suggesting non-normal distribution. We'll address this later as algorithms prefer normal data. The boxplot shows income outliers, likely due to social inequality and varying education levels. Let's analyze by education group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.boxplot(column='ApplicantIncome', by = 'Education') \n",
    "plt.suptitle('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_train['CoapplicantIncome'])\n",
    "plt.show()\n",
    "sns.catplot(df_train['CoapplicantIncome'])\n",
    "plt.show()\n",
    "df_train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bivariate Analysis**\n",
    "\n",
    "Analysis of the independet variables related to dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical Independent Variable vs Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender = pd.crosstab(df_train[\"Gender\"],df_train[\"Loan_Status\"])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Married=pd.crosstab(df_train[\"Married\"],df_train[\"Loan_Status\"])\n",
    "Dependents=pd.crosstab(df_train[\"Dependents\"],df_train[\"Loan_Status\"])\n",
    "Education=pd.crosstab(df_train['Education'],df_train['Loan_Status'])\n",
    "Self_Employed=pd.crosstab(df_train['Self_Employed'],df_train['Loan_Status'])\n",
    "Credit_History=pd.crosstab(df_train['Credit_History'],df_train['Loan_Status'])\n",
    "Property_Area=pd.crosstab(df_train['Property_Area'],df_train['Loan_Status'])\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - The proportion of married applicants is higher for approved loans.\n",
    "    - Distribution of applicants with 1 or 3+ dependents is similar across both the categories of Loan_Status.\n",
    "    - There is nothing significant we can infer from Self_Employed vs Loan_Status plot.\n",
    "    - It seems people with a credit history as 1 are more likely to get their loans approved.\n",
    "    - The proportion of loans getting approved in the semi-urban area is higher as compared to that in rural or urban areas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical Independent Variable vs Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Loan_Status'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uDTpe47uUjh3z63cuVVdXy+l0Kioq6qTzcjgccjgcTT0dAABgoLP+HJiGhgbV1dWpX79+at++vTZs2GBvq6ioUGVlpTwejyTJ4/GovLxcNTU19pji4mI5nU6lp6fbY449RuOYxmMAAAA06Q7M9OnTNWTIEHXt2lX79u3TihUrtGnTJq1fv14ul0tjx45Vfn6+4uLi5HQ69dBDD8nj8WjgwIGSpMGDBys9PV1333235s2bJ5/PpxkzZig3N9e+ezJu3DgtXrxYU6ZM0X333aeNGzdq1apVKiriyXsAAPCLJgVMTU2NxowZox9++EEul0u9evXS+vXrdcstt0iS5s+fr/DwcGVnZ6uurk6ZmZlasmSJvX+7du20du1ajR8/Xh6PR506dVJOTo5mz55tj0lNTVVRUZHy8vK0cOFCJScn64UXXlBmZuY5OmUAAGC6s/4cmJaKz4FBW9EWPyeiLeP6blva4vV93j8HBgAAIFQIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgnCYFzJw5c3TNNdcoJiZGnTt31u23366KioqgMYcOHVJubq7i4+MVHR2t7OxsVVdXB42prKxUVlaWOnbsqM6dO2vy5Mk6cuRI0JhNmzapb9++cjgc6t69uwoLC5t3hgAAoNVpUsCUlJQoNzdXn3zyiYqLi3X48GENHjxYBw4csMfk5eVpzZo1euONN1RSUqKqqiqNGDHC3n706FFlZWWpvr5emzdv1vLly1VYWKiCggJ7zO7du5WVlaVBgwaprKxMEydO1P3336/169efg1MGAACmC7Msy2ruzj/++KM6d+6skpIS3XjjjfL7/UpISNCKFSs0cuRISdKuXbuUlpYmr9ergQMH6t1339WwYcNUVVWlxMRESdLSpUs1depU/fjjj4qMjNTUqVNVVFSk7du329/rrrvuUm1trdatW3dGcwsEAnK5XPL7/XI6nc09RSN1m1YU6ingAvpmblaop4ALiOu7bWmL1/eZ/v4+q2dg/H6/JCkuLk6SVFpaqsOHDysjI8Me06NHD3Xt2lVer1eS5PV6ddVVV9nxIkmZmZkKBALasWOHPebYYzSOaTzGidTV1SkQCAQtAACgdWp2wDQ0NGjixIm67rrrdOWVV0qSfD6fIiMjFRsbGzQ2MTFRPp/PHnNsvDRub9x2qjGBQEAHDx484XzmzJkjl8tlLykpKc09NQAA0MI1O2Byc3O1fft2rVy58lzOp9mmT58uv99vL3v27An1lAAAwHkS0ZydJkyYoLVr1+rDDz9UcnKyvd7tdqu+vl61tbVBd2Gqq6vldrvtMVu3bg06XuO7lI4d8+t3LlVXV8vpdCoqKuqEc3I4HHI4HM05HQAAYJgm3YGxLEsTJkzQ6tWrtXHjRqWmpgZt79evn9q3b68NGzbY6yoqKlRZWSmPxyNJ8ng8Ki8vV01NjT2muLhYTqdT6enp9phjj9E4pvEYAACgbWvSHZjc3FytWLFCb7/9tmJiYuxnVlwul6KiouRyuTR27Fjl5+crLi5OTqdTDz30kDwejwYOHChJGjx4sNLT03X33Xdr3rx58vl8mjFjhnJzc+07KOPGjdPixYs1ZcoU3Xfffdq4caNWrVqloiKevgcAAE28A/Pcc8/J7/frt7/9rbp06WIvr7/+uj1m/vz5GjZsmLKzs3XjjTfK7XbrzTfftLe3a9dOa9euVbt27eTxePTHP/5RY8aM0ezZs+0xqampKioqUnFxsXr37q2nnnpKL7zwgjIzM8/BKQMAANOd1efAtGR8Dgzairb4ORFtGdd329IWr+8L8jkwAAAAoUDAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACM0+SA+fDDD3XbbbcpKSlJYWFheuutt4K2W5algoICdenSRVFRUcrIyNCXX34ZNGbv3r0aPXq0nE6nYmNjNXbsWO3fvz9ozLZt23TDDTeoQ4cOSklJ0bx585p+dgAAoFVqcsAcOHBAvXv31rPPPnvC7fPmzdOiRYu0dOlSbdmyRZ06dVJmZqYOHTpkjxk9erR27Nih4uJirV27Vh9++KEefPBBe3sgENDgwYN1ySWXqLS0VP/4xz80a9YsPf/88804RQAA0NpENHWHIUOGaMiQISfcZlmWFixYoBkzZmj48OGSpFdeeUWJiYl66623dNddd+mLL77QunXr9Omnn6p///6SpGeeeUZDhw7Vk08+qaSkJL322muqr6/XSy+9pMjISPXs2VNlZWV6+umng0IHAAC0Tef0GZjdu3fL5/MpIyPDXudyuTRgwAB5vV5JktfrVWxsrB0vkpSRkaHw8HBt2bLFHnPjjTcqMjLSHpOZmamKigr9/PPP53LKAADAQE2+A3MqPp9PkpSYmBi0PjEx0d7m8/nUuXPn4ElERCguLi5oTGpq6nHHaNx20UUXHfe96+rqVFdXZ38dCATO8mwAAEBL1WrehTRnzhy5XC57SUlJCfWUAADAeXJOA8btdkuSqqurg9ZXV1fb29xut2pqaoK2HzlyRHv37g0ac6JjHPs9fm369Ony+/32smfPnrM/IQAA0CKd04BJTU2V2+3Whg0b7HWBQEBbtmyRx+ORJHk8HtXW1qq0tNQes3HjRjU0NGjAgAH2mA8//FCHDx+2xxQXF+uKK6444ctHkuRwOOR0OoMWAADQOjU5YPbv36+ysjKVlZVJ+uXB3bKyMlVWViosLEwTJ07UX//6V73zzjsqLy/XmDFjlJSUpNtvv12SlJaWpltvvVUPPPCAtm7dqo8//lgTJkzQXXfdpaSkJEnSH/7wB0VGRmrs2LHasWOHXn/9dS1cuFD5+fnn7MQBAIC5mvwQ72effaZBgwbZXzdGRU5OjgoLCzVlyhQdOHBADz74oGpra3X99ddr3bp16tChg73Pa6+9pgkTJujmm29WeHi4srOztWjRInu7y+XSe++9p9zcXPXr108XX3yxCgoKeAs1AACQJIVZlmWFehLnQyAQkMvlkt/vb3MvJ3WbVhTqKeAC+mZuVqingAuI67ttaYvX95n+/m4170ICAABtBwEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIzTogPm2WefVbdu3dShQwcNGDBAW7duDfWUAABAC9BiA+b1119Xfn6+Zs6cqc8//1y9e/dWZmamampqQj01AAAQYi02YJ5++mk98MADuvfee5Wenq6lS5eqY8eOeumll0I9NQAAEGIRoZ7AidTX16u0tFTTp0+314WHhysjI0Ner/eE+9TV1amurs7+2u/3S5ICgcD5nWwL1FD3f6GeAi6gtvi/8baM67ttaYvXd+M5W5Z1ynEtMmD+97//6ejRo0pMTAxan5iYqF27dp1wnzlz5uixxx47bn1KSsp5mSPQUrgWhHoGAM6Xtnx979u3Ty6X66TbW2TANMf06dOVn59vf93Q0KC9e/cqPj5eYWFhIZwZLoRAIKCUlBTt2bNHTqcz1NMBcA5xfbctlmVp3759SkpKOuW4FhkwF198sdq1a6fq6uqg9dXV1XK73Sfcx+FwyOFwBK2LjY09X1NEC+V0OvkPHNBKcX23Hae689KoRT7EGxkZqX79+mnDhg32uoaGBm3YsEEejyeEMwMAAC1Bi7wDI0n5+fnKyclR//79de2112rBggU6cOCA7r333lBPDQAAhFiLDZg777xTP/74owoKCuTz+dSnTx+tW7fuuAd7AemXlxBnzpx53MuIAMzH9Y0TCbNO9z4lAACAFqZFPgMDAABwKgQMAAAwDgEDAACMQ8AAAFqEqqqqUE8BBiFgAAAtQs+ePbVixYpQTwOGIGAAAC3C3/72N/3pT3/S7373O+3duzfU00ELR8AAAFqEP//5z9q2bZt++uknpaena82aNaGeElowPgcGxgkPDz/tH+gMCwvTkSNHLtCMAJxrixcvVl5entLS0hQREfyZq59//nmIZoWWpMV+Ei9wMqtXrz7pNq/Xq0WLFqmhoeECzgjAufTtt9/qzTff1EUXXaThw4cfFzCARMDAQMOHDz9uXUVFhaZNm6Y1a9Zo9OjRmj17dghmBuBsLVu2TH/5y1+UkZGhHTt2KCEhIdRTQgtFwMBoVVVVmjlzppYvX67MzEyVlZXpyiuvDPW0ADTDrbfeqq1bt2rx4sUaM2ZMqKeDFo6AgZH8fr+eeOIJPfPMM+rTp482bNigG264IdTTAnAWjh49qm3btik5OTnUU4EBeIgXxpk3b57+/ve/y+1264knnjjhS0oAgNaNgIFxwsPDFRUVpYyMDLVr1+6k4958880LOCsAwIXES0gwzpgxY077NmoAQOvGHRgAAGAcPokXAAAYh4ABAADGIWAAAIBxCBgAAGAcAgbAce655x7dfvvtoZ7GSS1btky9e/dWdHS0YmNjdfXVV2vOnDn29ubOf9asWerTp8+5myiA84a3UQMwyksvvaSJEydq0aJF+s1vfqO6ujpt27ZN27dvD/XUAFxA3IEB0CQlJSW69tpr5XA41KVLF02bNk1Hjhyxt69bt07XX3+9YmNjFR8fr2HDhum///2vvf2bb75RWFiY3nzzTQ0aNEgdO3ZU79695fV6z+j7v/POO/r973+vsWPHqnv37urZs6dGjRqlv/3tb5J+uYuyfPlyvf322woLC1NYWJg2bdokSZo6daouv/xydezYUZdeeqkeffRRHT58WJJUWFioxx57TP/5z3/s/QoLC+35lpWV2XOora0NOu7PP/+s0aNHKyEhQVFRUbrsssv08ssvn8VPGcDpcAcGwBn7/vvvNXToUN1zzz165ZVXtGvXLj3wwAPq0KGDZs2aJUk6cOCA8vPz1atXL+3fv18FBQW64447VFZWpvDw////mR555BE9+eSTuuyyy/TII49o1KhR+uqrrxQRcer/LLndbpWUlOjbb7/VJZdcctz2SZMm6YsvvlAgELAjIi4uTpIUExOjwsJCJSUlqby8XA888IBiYmI0ZcoU3Xnnndq+fbvWrVun999/X5LkcrlUXV192p/Lo48+qp07d+rdd9/VxRdfrK+++koHDx48o58pgOYhYACcsSVLliglJUWLFy9WWFiYevTooaqqKk2dOlUFBQUKDw9XdnZ20D4vvfSSEhIStHPnzqC/FD5p0iRlZWVJkh577DH17NlTX331lXr06HHKOcycOVMjRoxQt27ddPnll8vj8Wjo0KEaOXKkwsPDFR0draioKNXV1cntdgftO2PGDPvf3bp106RJk7Ry5UpNmTJFUVFRio6OVkRExHH7nU5lZaWuvvpq9e/f3z42gPOLl5AAnLEvvvhCHo8n6E85XHfdddq/f7++++47SdKXX36pUaNG6dJLL5XT6bR/mVdWVgYdq1evXva/u3TpIkmqqak57Ry6dOkir9er8vJyPfzwwzpy5IhycnJ06623qqGh4ZT7vv7667ruuuvkdrsVHR2tGTNmHDev5hg/frxWrlypPn36aMqUKdq8efNZHxPAqREwAM6p2267TXv37tWyZcu0ZcsWbdmyRZJUX18fNK59+/b2vxuD6HQBcqwrr7xSf/7zn/Xqq6+quLhYxcXFKikpOel4r9er0aNHa+jQoVq7dq3+/e9/65FHHjluXr/W+LLXsX91pfG5mUZDhgzRt99+q7y8PFVVVenmm2/WpEmTzvhcADQdAQPgjKWlpcnr9Qb9Mv/4448VExOj5ORk/fTTT6qoqNCMGTN08803Ky0tTT///PN5n1d6erqkX56/kaTIyEgdPXo0aMzmzZt1ySWX6JFHHlH//v112WWX6dtvvw0ac6L9EhISJEk//PCDve7YB3qPHZeTk6NXX31VCxYs0PPPP3/W5wXg5HgGBsAJ+f3+435RP/jgg1qwYIEeeughTZgwQRUVFZo5c6by8/MVHh6uiy66SPHx8Xr++efVpUsXVVZWatq0aed0XuPHj1dSUpJuuukmJScn64cfftBf//pXJSQkyOPxSPrlGZT169eroqJC8fHxcrlcuuyyy1RZWamVK1fqmmuuUVFRkVavXh107G7dumn37t0qKytTcnKyYmJiFBUVpYEDB2ru3LlKTU1VTU1N0LM0klRQUKB+/fqpZ8+eqqur09q1a5WWlnZOzxvAr1gA8Cs5OTmWpOOWsWPHWps2bbKuueYaKzIy0nK73dbUqVOtw4cP2/sWFxdbaWlplsPhsHr16mVt2rTJkmStXr3asizL2r17tyXJ+ve//23v8/PPP1uSrA8++OC0c/vXv/5lDR061OrSpYsVGRlpJSUlWdnZ2da2bdvsMTU1NdYtt9xiRUdHBx138uTJVnx8vBUdHW3deeed1vz58y2Xy2Xvd+jQISs7O9uKjY21JFkvv/yyZVmWtXPnTsvj8VhRUVFWnz59rPfeey/ouI8//riVlpZmRUVFWXFxcdbw4cOtr7/+ujk/egBnKMyyjrkXDAAAYACegQEAAMYhYAC0KEOGDFF0dPQJlyeeeCLU0wPQQvASEoAW5fvvvz/pp9jGxcXZn6oLoG0jYAAAgHF4CQkAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgnP8HORkw1UKgcDoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Low','Average','High','Very high']\n",
    "df_train['Income_bin']=pd.cut(df_train['ApplicantIncome'],bins,labels=group)\n",
    "Income_bin=pd.crosstab(df_train['Income_bin'],df_train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('ApplicantIncome')\n",
    "P=plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,1000,3000,42000]\n",
    "group=['Low','Average','High']\n",
    "df_train['Coapplicant_Income_bin']=pd.cut(df_train['CoapplicantIncome'],bins,labels=group)\n",
    "Coapplicant_Income_bin=pd.crosstab(df_train['Coapplicant_Income_bin'],df_train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "P=plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Total_Income'] = df_train['ApplicantIncome']+df_train['CoapplicantIncome']\n",
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Low','Average','High','Very high']\n",
    "df_train['Total_Income_bin']=pd.cut(df_train['Total_Income'],bins,labels=group)\n",
    "Total_Income_bin=pd.crosstab(df_train['Total_Income_bin'],df_train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('Total_Income')\n",
    "P=plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Proportion of loans getting approved for applicants having low Total_Income is very less compared to that of applicants with Average, High & Very High Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "plt.title(\"Relation Between Applicatoin Income vs Loan Amount \")\n",
    "\n",
    "plt.grid()\n",
    "plt.scatter(df_train['ApplicantIncome'] , df_train['LoanAmount'], c='k', marker='x')\n",
    "plt.xlabel(\"Applicant Income\")\n",
    "plt.ylabel(\"Loan Amount\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df_train['Loan_Status'], df_train['LoanAmount'])\n",
    "plt.title(\"Loan Application Amount \")\n",
    "plt.xlabel('Loan Status')\n",
    "plt.ylabel('Loan Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Income_bin</th>\n",
       "      <th>Coapplicant_Income_bin</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Total_Income_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>High</td>\n",
       "      <td>Average</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>Very high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status Income_bin Coapplicant_Income_bin  \\\n",
       "0             1.0         Urban           Y       High                    NaN   \n",
       "1             1.0         Rural           N       High                Average   \n",
       "2             1.0         Urban           Y    Average                    NaN   \n",
       "3             1.0         Urban           Y    Average                Average   \n",
       "4             1.0         Urban           Y       High                    NaN   \n",
       "\n",
       "   Total_Income Total_Income_bin  \n",
       "0        5849.0             High  \n",
       "1        6091.0        Very high  \n",
       "2        3000.0          Average  \n",
       "3        4941.0             High  \n",
       "4        6000.0             High  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Urban' 'Rural' 'Semiurban']\n"
     ]
    }
   ],
   "source": [
    "print(df_train.Property_Area.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_39581/1137912016.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Dependents'].replace('3+', 3,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Loan_Status'].replace('N', 0,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Loan_Status'].replace('Y', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Loan_Status'].replace('Y', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Gender'].replace('Male', 0,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Gender'].replace('Female', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Gender'].replace('Female', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Married'].replace('No', 0,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:8: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Married'].replace('Yes', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:8: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Married'].replace('Yes', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Education'].replace('Not Graduate', 0,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:10: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Education'].replace('Graduate', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:10: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Education'].replace('Graduate', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Self_Employed'].replace('No', 0,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:12: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Self_Employed'].replace('Yes', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Self_Employed'].replace('Yes', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:13: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Property_Area'].replace('Rural', 1,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:14: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Property_Area'].replace('Semiurban', 2,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:15: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_train['Property_Area'].replace('Urban', 3,inplace=True)\n",
      "/tmp/ipykernel_39581/1137912016.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_train['Property_Area'].replace('Urban', 3,inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_train=df_train.drop(['Loan_ID','Income_bin', 'Coapplicant_Income_bin', 'Total_Income_bin', 'Total_Income'], axis=1)\n",
    "df_train['Dependents'].replace('3+', 3,inplace=True)\n",
    "df_train['Loan_Status'].replace('N', 0,inplace=True)\n",
    "df_train['Loan_Status'].replace('Y', 1,inplace=True)\n",
    "df_train['Gender'].replace('Male', 0,inplace=True)\n",
    "df_train['Gender'].replace('Female', 1,inplace=True)\n",
    "df_train['Married'].replace('No', 0,inplace=True)\n",
    "df_train['Married'].replace('Yes', 1,inplace=True)\n",
    "df_train['Education'].replace('Not Graduate', 0,inplace=True)\n",
    "df_train['Education'].replace('Graduate', 1,inplace=True)\n",
    "df_train['Self_Employed'].replace('No', 0,inplace=True)\n",
    "df_train['Self_Employed'].replace('Yes', 1,inplace=True)\n",
    "df_train['Property_Area'].replace('Rural', 1,inplace=True)\n",
    "df_train['Property_Area'].replace('Semiurban', 2,inplace=True)\n",
    "df_train['Property_Area'].replace('Urban', 3,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vm5ycjFWrViE5ORlOTk4AgClTpmDv3r1YtWoVvv76awBAVVUVlixZgmbNmtWZW0VFBSoq5LtEPY5d3QzsbVCRmSM3ryIzB3oWZhAaGkBcXndXroakiZU+ACC/oEpufn5Bpex/inTrYAtfb1O8N+lCncs0ZOam0g/IohL5L/NFpRKYm7xYO6cAQFQ3I9xOqUZaTsNvFCgtzodEXAMTc2u5+SZm1shNv6twnWJRDkzM5b89mphbo6QwR+HyDVFZiTRvYzP5vI3NrJGXpTjvkqIcGJvZPLN8qUhz8gYAE0Ppz5Jy+fkl5RLZ/xojUyPp+V38VN7F5bX/a4zMHuddJt/TobhM0mjzLi3Kh1hcA9On6jVTCxvkZNxTU1SqUVqUDQAweqpuMzK1QWmR4rqq/HF9aPrUOmY2yM9q+Pur7PH7mNmz72N5GXXU56IcGD/1PmZsbo0SDavP6ypv4xcobyPTZ9cp0IDyVhVhI+zGry5sFGiAzpw5A7FYjOHDh6OiogKXLl1CcXExrK3lK4aysjLcuVM7OJyHh4esQQAAHB0dkZWVBUDai6CyshJt2rSR/b9JkyZyDRJXrlxBTU0NfH195V6noqJC7rX19fURGvr8QU7mzZuHuXPl7/GfPXs2Wv1d8lSnHp3sMHVcbdlM++LKP96GnY0BPn7PB//+/DIqqzSjm23rQH283bN2sM0fN776GAhDXzOGs60O/rv2OSOXEalQkLsAr7eo/XCz/njDb6xShhBPAfpF1N40v/aQ5o4J8E808xZiQLvaj2Br9lc9Z2nSdDcv7MCRjbNlf/cZs1SN0VB9u3lhB45ueqK832V5U8PHRgE18vHxgUAgQFJSktx8Ly8vAICRkREAoLi4GI6OjnL39j9maWkp+11PT/7+OoFA8I8GASwuLoaOjg7Onz8PHR35kY1MTU1lvxsZGclua6jLzJkzMWnSJLl5BgYG+OurP144nvpSkZkDA3v5lmcDextUFRY16F4CJ87k4vrNc7K/9fWkV8WtLPWQm18pm29lqY/bdxV/cfbzMUUTK32sWNBCNk9XR4BmQRYY1NcZXQcdg5rHjXzGpduVuJdWe6uA7qNay9xECFFJ7RcIM2MBUrL+/gvF0O5GCPHWw/e/F6GgSDMaRoxNrSAQ6jwzqGBJUS5MLBT3JTc1t3nmakqJqO7lGyIjE2neTw+iVVqUCxMzxXmYmD175aW0KPeZq00Nza1UCdJya4/Hx4MJmhjK9xYwMRQgs0AzjtsXkfRQgtSc2vP7cd6mhkDxEw9RMTWUDiTYWCQmi/Ewq7be1n10tcvUSICiJ3oLmBoJkK5BT0j5J4zNrCAU6jwzqGBxYQ5MG/j5+k95BHbBW5NqL6bUVEvLvqwoFybmtbdglhXnwNopQOE2DB/Xh08NMldW9OzV9IbI6PH7WJGC97E64jcxt3mml1epqO7lGwqPwC5yTwioq7xLi3Ng8zflXfZUeZcWP9sbTpsJhOwpoCwcU0CNrK2t0aNHD/zvf/9DSUlJncuFh4cjIyMDurq68PHxkZtsbF6sYvD29oaenh5Onz4tm5efn4+bN2tHcG3evDlqamqQlZX1zOs4ODj8o9wMDAxgbm4uNzWU2wcK4hNg3TVCbp5Nt7bIj09QT0AvqKysBqnp5bLpXnIpcvIq0LKZlWwZYyMdBPqa4+oNkcJtnLtUgBHjzmL0hHOyKfGWCPuPZmH0hHMNrkEAACoqgewCsWxKzxGjsFgMf/faNk1DfcDTSRd3054/aOLQ7kYI89XHgnVFyC1sgMnWQUdXH45uQbh/o3YsDIlYjPuJcXDxaq5wHWfvMNy7ES837971U3D2CqvPUJVKR1cfdq5BeHhTPu+HN+Pg6KE4bwfPMDy8KZ93ctIpOHqE1Weor6yyGsgvrp1yRNKu4x72tR949HUBJ2sgVbN6zj5XZTWQV1Q7ZRdKbwXycqz9eGKgBzjbCvAwu/E0ClRWyeedVSCBqFQCbyf5vF1sBUjOajx5P0lXVx+OHkG4e732/BaLxbiXGA9XnzD1BVYP9A1NYWHjLpus7H1gbGaLlFu1uVeWFyMz+TIc3MMUbkNHVx+2zkFIvSVfH6bcjq9znYZER1cf9q5BSE6Sjz85KQ5OdbyPOXmG4UGSfH3+4MYpOHmG1Weor6zO8r4tX95ZyZdh/zfl/eQ6ErEYqbfj61yH6FWwUUDNlixZgurqarRs2RJ//vknEhMTkZSUhN9++w03btyAjo4OunfvjsjISAwYMAD79+/H/fv3cerUKcyaNQvnzp37+xeB9Er/mDFjMHXqVBw6dAhXr17FqFGjIBTWHgK+vr4YPnw4oqOjsXnzZty7dw9nzpzBvHnzsGvXrvraBa9Mx8QY5s38Yd7MHwBg7OkC82b+MHR1BAD4fTkJzVZ9K1v+wS/rYOzpCv95U2Hi5wX3sW/DMep13FsYq47wX8mG7akY+ZYb2rW2hpe7CT6d5I/cvAocj6/91rDgy1AM6iMdI6KsrAb3kkvlpvJyMUSiKtxLLlVXGv/YwXPleL2tIUJ99OBkI8SoPiYoKBYj4WZtF9yJb5mic3htQ9SwHkZoHaSPFTtKUF4pgbmJAOYmAuhpSH+pNj1G4+Lx9bh8agty0u9gz9o5qKosQ2i7QQCA7Sun4fDm2ieKtO4WjbtXjyN+/0rkpN/Bse2Lkf7gKlp2eUddKbyU8M6jcTVuPa6f2YK8jDs4uEGad2Abad77fpuGEztq827eKRoPEo/j/KGVyMu8g7g9i5H58CqaddCsvAHgzE0J2gUK0NQJsLUA+rURoqgMSEqt/ZL4dmchWvjUNhzo6QJ2ltIJACxMBLCzBMyNoTHiE8XoGCKEn4s09oHtdFBUCtxIrs17ZA8dtParff/S1wUcrKQTAFiZCuBgBVgoHsO3QTp1rQZdwnTg7yaEvZUAUZ10UVQKuScLjHldDxEB8nk7NhHAsYn0GGhiKv1dU/Ju+9ooXDi6AQkntiA77Q52rpmDyooyNG8vPb83L5uOAxtqz+/q6kqkJyciPTkRNTVVEOVnIj05EbmZD9SUwcsRCAQI7RCN8weX4t61Q8hNT8LBP6bDxNwOnsHdZcttWzoKV07UjrTfrNMoXD+9ATfObkFe5h0c3TwH1ZVl8G81SB1p/GMtu43G5ZPrcTV+C3Iz7uDAujmoqihDcIQ0/t2rp+HYttryDu8SjfvXj+PsXyuRm3EHJ3ctRkbyVYR10qz6XGF5r5sOY3M7eAbVlvf2n0fhysknyrvjKCSe3oAb57YgP/MOjm2Wvv9pSnmTZtGQj8ONl7e3Ny5evIivv/4aM2fOREpKCgwMDBAYGIgpU6bgo48+gkAgwO7duzFr1iyMHj0a2dnZcHBwQMeOHWFvb//Cr/Xf//4XxcXF6NevH8zMzDB58mQUFhbKLbNq1Sp8+eWXmDx5MlJTU2FjY4OIiAj07dtX2akrjUWLYEQe/FX2d+D8TwAAD9dsxuUxM2HgaAujRw0EAFB2PwVn3/gAgd/PhMe/olGekoErH3yKnAMnVB77q1q76SEMDXUwbbwvTE10ceV6ISbPviI3XoCzgxEszTX30U2K7D9dAQM9AYb3NIaxoQC3U6qxeH0xqp+4e8DWSig3QFencOnobJPfNpPb1updJYi7WomGLrBVb5QU5eHo9kUoEWXD3iUAQycsl3WzLcxLh0BQ+2XBxTscA2Lm48i2BTiy9Qc0sfNA1Ec/ws7Zt66XaJD8wnujrDgPcbsXoVSUDRuXAAwYu1zWfVSUnw48kbeTZzh6Rc9H3O4FOLXzB1jaeqDfmB9h46RZeQNA/A0J9HWB11sKYagPPMwG/jwqRs0TnVwsTQHjJzphOVoB73Stvf2rR3Ppvrl8T4ydZzTjivOJa2Lo6QL9InVgqA8kZ0nw21/VqH4ibyszAYwNa/NxshZgdM/ajzS9WukA0MHF22JsPaUZ4xQcu1wDfV1gYDtdGOoDDzIlWLWvSq5ea2ImgIlhbb3mbCPAe31qB5btEyHdB+dv1mDT8Yb/uNngNtJ67dDWxSguzIaDWwBGTFoG00e3ORXmpsndrlhUkIWlswfK/j61dyVO7V0JD79WGD3j12e235A17xKD6soyHNn4OSrLRHD0bIG+7y2Drl7tCS3KTUZZSe0ThpqG9UZ5cR7O7FuM0qJs2DgFoG/MMo3pTu7fojdKi/JwcucilBZlw9Y5AG+Ok6/Pn3wfc/YKR5/R83FixwKc2CGtzwe8/yNsNbA+D+scg6rKMhzd+Dkqy0Vw8GiBvjHPlnf5E+XtE9YbZSV5OKuh5a0KAh1e31YWgUQi0YxPCdQo7NLz+/uFGpE+VdLxItr3O6rmSFTrxI5OAICx3+b/zZKNy9Lp0suUa7SruBEtLW78tFe9cajah72kP7/+UzO+dCrLJ29JGx1mr9GuwfHmRksbVz9Z0XDHnqkPX4+RfmlZd0q7Pi4ObSttjFi4Q7vy/rifNO9lf6k5EBV779EF+wXbtau8J76h2ffkn2qpviHM2547q7bXrg/sKUBEREREREQahY8kVB72uSAiIiIiIiLSUuwpQERERERERBqFjyRUHvYUICIiIiIiItJSbBQgIiIiIiIi0lK8fYCIiIiIiIg0CgcaVB72FCAiIiIiIiLSUuwpQERERERERBpFwJ4CSsOeAkRERERERERaio0CRERERERERFqKtw8QERERERGRRhEIeX1bWbgniYiIiIiIiLQUewoQERERERGRRhEIOdCgsrCnABEREREREZGWYk8BIiIiIiIi0ihCPpJQadhTgIiIiIiIiEhLsVGAiIiIiIiISEvx9gEiIiIiIiLSKBxoUHnYU4CIiIiIiIhISwkkEolE3UEQERERERERvajLvTur7bVDdx9R22vXB/YUICIiIiIiItJSHFOAVKp9v6PqDkGlTuzoBADYpeen5khUq09VEgDg4a3rao5EtVybBgIAFu7Qrg5YH/eT3tP34x41B6Ji416X/lyjXdUaoqXVGtad0q7jfGhb6XF+4FKFmiNRrR7NDAAAn6zQrry/HiPNW1vrNW3Ne0O8WL2BqFhUBK8PkxQbBYiIiIiIiEijcKBB5WHzEBEREREREZGWYk8BIiIiIiIi0ihCHfYUUBb2FCAiIiIiIiLSUmwUICIiIiIiItJSvH2AiIiIiIiINAoHGlQe9hQgIiIiIiIi0lLsKUBEREREREQaRSDk9W1l4Z4kIiIiIiIi0lLsKUBEREREREQahWMKKA97ChARERERERFpKTYKEBEREREREWkp3j5AREREREREGoW3DygPewoQERERERERaSn2FCAiIiIiIiKNwp4CysOeAkRERERERERaio0CRERERERERFqKjQJayMPDAwsWLHilbcyZMwdhYWFKiYeIiIiIiOifEAiFapsaG44poCajRo3C6tWr8cEHH2Dp0qVy/xs3bhyWLFmCkSNHIjY2VumvffbsWZiYmCh9u+o2ZrgH+r3mADMTXVxJFGH+kltISS97oXXfedMVY0d6Yf22FCxafqeeI1WOJu1bwmvyGFiEB8PQyQ7nBn+EzO0Hn79Ox9YInD8DpoFNUf4wHbfn/YSUNVtUFLHybNu5G+s3b0VefgG8PT0w/oMY+Pv5Klz2+Kk4/LF+E1LT01FTXQNnJ0e8ObA/enTtrNqglUAikeDsvsW4fnoDKspEcPQMR8dBs2Fp6/Hc9a6cXIuEIytQWpQDa0d/dBj4KezdQlUTtBJcOr4WFw6tQGlRNmyc/NFp8GdwcK87/lsJexC/eyFEeamwtPVAu35T4BHYSYURK8e5w2sRv38FiguzYe/ij9eGfQZnT8V5Z6fdwtFti5CRfA2FuanoMWQmWncfpdqAleT0wbU4tWcFigtzYO/mj97DP4WLl+K8s1Jv4dCWRUi/fw0FuWnoNWwmIl8bqeKIlePo3nU4uCMWooIcOLv7IurdmfDwCVG4bPrD29j55494eC8RedlpGDxyKrr0GaHiiJWne7gOWvrpwEgfeJApwbZT1cgVSepc3sNBgA4hOnC2FsLcRIBf/6pC4gOxCiN+ddpar2lr3vF/rcWJPStRXJgDB1d/9H1nFly8FeedmXILB7csRtr9ayjISUPvt2egbU/NrNdIczS+Zg4N4urqinXr1qGsrPaLa3l5OX7//Xe4ubm90rarqqqemVdZWQkAsLW1hbGx8Sttv6EZPtgVb/Z1xvwlt/D+lIsoK6/BD1+EQF/v7wcg8W9qhjd6OeL2vWIVRKo8OibGEF1OwtUJc19oeSMPF7Ta/jNyj5zGiZb9cW/xaoT8/CVserSv50iV6/CxE1i6fBVGDHsLSxd+Dy9PD8z4/AvkFxQoXN7M1AxvD3kTi+Z/g1/+93/o2b0r/rtgMc6ev6jawJXg4uHluHziV3QaPAeDJ6yHrr4Rdi6LQXVVRZ3r3ErYjZPbv0HLHuMQNXEzbJz8sHNZDEqLclUY+cu7eWE3jm+dhza9xmHolC2wcfbHtqVj6ow//d4F7F0zGYERb2LYlK3wCumGnSvGITf9poojfzXXz+7GXxvmoUPfcRjz6RbYufpj3cIxKBEpzruqsgxWti7oMnAyTMxtVRyt8lw9vRv71n2Dzv3H4YM5m+Hg6odfv49BcV15V5TDytYV3aMmw9RCc/M+f2ovtqz5L15/cyymf/snnN398ONXY1FUqDjvyopy2Ni74I23P4a5pY2Ko1WujqE6iAzUwbaT1fhpexUqqyUY3VMPujp1r6OvK0BGngTb46pVF6gSaWu9pq15Xzm9G3v++BZd+o/DR3M3wcHVD7Hz36u7XqssRxNbV7wWNQmmFpp9ftc3oY5AbVNjw0YBNQoPD4erqys2b94sm7d582a4ubmhefPmsnl79+5F+/btYWlpCWtra/Tt2xd37tRezb5//z4EAgH+/PNPdOrUCYaGhli7di1GjRqFAQMG4KuvvoKTkxP8/PwAPHv7QEFBAWJiYmBrawtzc3N07doVly5dkov1m2++gb29PczMzDBmzBiUl5fX0155OVFvOGPN+gc4cToXd+6X4Mv/uwHrJgboEPH8ytTIUIjZk/3x3eKbKCrWrA8X2fuO4ebsBcjc9tcLLe/+/lCU3UtB4rRvUXzjLh4sWYuMTfvg+fGo+g1UyTZt3Y7ePXugV49ucHdzxcRxY2FgYIC9BxT3kggLDUb7thFwd3WFk6MjBvXvBy9PD1y9nqjiyF+NRCLB5eNr0KL7WHgGd4ONkx+6Df0WJaIs3Lta9zFw6WgsAttEIaD1YDRx8EGnwXOhq2eIG2c3qTD6l3fxyCoERw5BYJvBsHbwQdeoudDVN8T104rjTzi6Bu7+HdCiawyaOHgjsvdE2LoE4tLx31Qc+as5fWAVwtoPQbN2g2Hr5IPew6V5XzqpOG8nj1B0e3M6glr3ga6evoqjVZ5T+2PRomMUmncYDDtnH/SNngs9fUNcPK44b2evEPR8axpC2vSBrq6eiqNVnkM716Btt8GI7DIAji7eGPreZ9DXN0Lc4a0Kl3f3CcbAEZPRst3rGl3eANA2SAeHE2qQmCxGRr4EG45Ww8wYCHSv+yPqzRQxDpyvwXUN6x3wmLbWa9qa98m9q9GyUxRadBwEO2cfvDFqDvT0DXH+2GaFy7t4haDX0KkIjdDs+pw0CxsF1Ozdd9/FqlWrZH+vXLkSo0ePllumpKQEkyZNwrlz53Dw4EEIhUIMHDgQYrH8m+GMGTPw8ccfIzExET179gQAHDx4EElJSThw4AB27typMIaoqChkZWVhz549OH/+PMLDw9GtWzfk5eUBANavX485c+bg66+/xrlz5+Do6IglS5Yocze8Eid7Q9g0McDZhHzZvJLSGly/KUKwv/lz1500tilOncvDuUsF9Ryl+llGhCHnUJzcvOwDJ2AVEaaegF5CVVUVbt6+g/CwZrJ5QqEQ4WGhuH4j6W/Xl0gkuJBwGSkpqQgNDqzPUJVOlJeC0qJsuDZtK5tnYGQGe7dQZDxIULhOTXUlslOvwcW3dh2BUAiXppF1rtOQ1FRXIivlGlyfit/Vty3S7yvu6ZF+PwGuvpFy89z92yPjfkJ9hqpUNdWVSE++Bs8A+bw9A9oi5a7m9XB5UdXVlUi/fw1eQbV5C4VCeAVG4uHtBPUFVs+qq6vw8G4i/EIiZPOEQiH8Qtrg3s1Lz1lT81mZAebGAtxJq/08U1EFpGRL4GbX+K7EAdpdr2lj3tXVlUi7fw3eQbV5CIVCeAc17npNVQRCgdqmxoZjCqjZO++8g5kzZ+LBgwcAgJMnT2LdunU4cuSIbJnBgwfLrbNy5UrY2tri+vXrCA4Ols2fOHEiBg0aJLesiYkJli9fDn19xS2NJ06cwJkzZ5CVlQUDAwMAwPz587F161Zs3LgR77//PhYsWIAxY8ZgzJgxAIAvv/wSf/31V4PpLdDESppbfoH8LRP5BZWy/ynSrYMtfL1N8d6kC/UaX0NhYG+DiswcuXkVmTnQszCD0NAA4vK6u6A3FIWiIojFYlhZWsjNt7K0xMOU1DrXKy4pwdCRMaiqqoJQKMSED99Hi+Zh9RytcpUWZQMAjMys5eYbmdqgtChH0SooL8mHRFwDY9On1jGzQX7WvfoJVInKHsf/VM7GZtbIz7yrcJ3SohwYm9k8s3yJSPE+aohKi6V5m5jL521iZo3cdMV5NwalRfkQi2tg+lTephY2yMlo+MfryyoWSfM2s5TP29zSGplpjTdvADAzkn6wLi6THz+guEwCU6PG96Eb0N56TVvzLi0qkNZrFk/Xa9bISW/c5zdpFjYKqJmtrS369OmD2NhYSCQS9OnTBzY28hXgrVu38Pnnn+P06dPIycmR9RBITk6WaxRo2bLlM9sPCQmps0EAAC5duoTi4mJYW8tXVmVlZbJbFBITEzF27Fi5/0dGRuLw4cN1breiogIVFfJfMh83OryqHp3sMHVc7aBy07648o+3YWdjgI/f88G/P7+Myqq6BzOixsHYyAg/L/oBZeXluJhwGUtXrIKjgwPCQoP/fmU1uXlhB45snC37u8+Ypc9Zmoio4WvmLcSAdrUfPdfsf3b8IyIiUj02CjQA7777LsaPHw8A+PHHH5/5f79+/eDu7o5ly5bByckJYrEYwcHBsoEDH1P0RIG/e8pAcXExHB0d5XomPGZpafniSTxl3rx5mDtXfgC82bNnA+jy0tt87MSZXFy/eU72t76e9C4YK0s95ObX7hMrS33cvqt48EA/H1M0sdLHigUtZPN0dQRoFmSBQX2d0XXQMYg181bFOlVk5sDAXr7BycDeBlWFRRrRSwAALMzNIBQKkV9QKDc/v6AAVlaWda4nFArh7OQIAPDx8kRySgr+2LCpQTcKeAR2wVuTakcmrqmWHttlRbkwMbeTzS8rzoG1U4DCbRiaWEEg1EFpsfxgRmVFOTA2b/iDFxk9jv+pQahKi3LrjN/Y7NmeE6VFuTDRgHwfMzaV5v30oIIlRbkwacSDThmbWUEo1Hlm8K3iwhyYalD5/VOm5tK8iwrk8xYV5Gr8IIJPS0wW42FW7fu07qPBukyNBCh6oreAqZEA6XmN7E34EW2t17Q1b2MzS2m9Vvh0vZbLQQSVoDE+GlBduCcbgF69eqGyshJVVVWysQAey83NRVJSEj799FN069YNAQEByM/Pr2NL/1x4eDgyMjKgq6sLHx8fuelxj4WAgACcPn1abr34+PjnbnfmzJkoLCyUm2bOnKmUmMvKapCaXi6b7iWXIievAi2bWcmWMTbSQaCvOa7eECncxrlLBRgx7ixGTzgnmxJvibD/aBZGTzjX6BoEAKAgPgHWXSPk5tl0a4v8+AT1BPQS9PT04OvjjQuXLsvmicViXLx0BYH+fi+8HbFYrPAJHQ2JvqEpLGzcZZOVvQ+MzWyRcqt2XIjK8mJkJl+Gg3uYwm3o6OrD1jkIqU+sIxGLkXI7vs51GhIdXX3YuQTh4VPxP7wZB0eP5grXcfQIw8Nb8vVTctIpOHiE1WeoSqWjqw9HtyDcvyGf9/3EOLh4Kc67MdDV1YejRxDuXq/NWywW415iPFx9wtQXWD3T1dWDq1cAkq7Wvs+KxWLcvHoanr7NnrOm5qmsAvKKaqesAglEpRJ4O9V+HDXQA1xsBUjOapy9+LS5XtPGvHV19eHkEYS712vzEIvFuHu9cddrpHnYU6AB0NHRQWJiouz3J1lZWcHa2hq//PILHB0dkZycjBkzZijttbt3747IyEgMGDAA3333HXx9fZGWloZdu3Zh4MCBaNmyJT7++GOMGjUKLVu2RLt27bB27Vpcu3YNXl5edW7XwMBAabcLvIgN21Mx8i03PEwrQ3pmOWLe8UBuXgWOx9e2MC/4MhTH4nKweVcayspqcC+5VG4b5eViiERVz8xvqHRMjGHiU/voSmNPF5g380dlXiHKH6bD78tJMHS2x6XR0wEAD35ZB/ePhsN/3lQ8jN0Emy4RcIx6HWff+EBdKbyUwQPewHf/twh+Tb3h59sUm7ftRHl5OXp17wYA+Ob7hbCxboKYUdJndv++fhP8mnrD0dEBVVVVOHP2Av46fBQff6RZeQsEAoR2iMb5g0thYesB8ybOOLN3EUzM7eAZ3F223Lalo+AV3B0h7d8BADTrNAqH1s2ArUsw7NxCcfn4alRXlsG/1aC6XqpBad55NA78Ph32rsGwdwtFwlFp/IFtpPHv/20aTCzs0a7fZABAWKdobFo8AhcOr4RHYCfcvLAbWQ+vottbX6gzjX+sTY/R2L5qOhzdg+HkGYozf61GVWUZQttJ896+chrMLO3RZZA075rqSmSn35H9XlSQiYyHidA3MEYTO3e15fFPtX1tFLYsnwFnj2A4e4Uibv9qVFaUoXl7ad6bl02HmaUdekRJ866urkR22qO8a6ogys9EerI0b2t7zcm7a99o/Prjp3DzCoSHTwgO7/4NFRVliOg8AACw5n+fwKKJPfq//TEA6eCEGSl3ZL8X5GUh5f4NGBgaw9bh1R5prGqnrtWgS5gOckQS5BdJ0KOFDopKIfdkgTGv6+Ha/RrEJ0rn6esC1ua1Yw40MRXAsYkApRUSFJaoPIV/TFvrNW3Nu12vkdi0bCacPIPh4hWCU/vWoLKiDC06DAQAbPx5Osyt7PHakEkAHtVrqY/r8yqI8rOQ/iAR+oaaVa+pQmMc8E9d2CjQQJibKx4lXygUYt26dZgwYQKCg4Ph5+eHRYsWoXPnzkp5XYFAgN27d2PWrFkYPXo0srOz4eDggI4dO8Le3h4A8NZbb+HOnTuYNm0aysvLMXjwYHz44YfYt2+fUmJQhrWbHsLQUAfTxvvC1EQXV64XYvLsK3LjBTg7GMHSXHMfWfU0ixbBiDz4q+zvwPmfAAAertmMy2NmwsDRFkaujrL/l91Pwdk3PkDg9zPh8a9olKdk4MoHnyLnwAmVx/4qunRsj8JCEWJ/W4f8/Hx4e3li3hefy24fyMrOhvCJN4nyinIsWvILsnNzYaCvD1cXZ8yYPBFdOrZXUwYvr3mXGFRXluHIxs9RWSaCo2cL9H1vGXT1ahvgRLnJKCup7U3UNKw3yovzcGbfYpQWZcPGKQB9Y5Y9M3hTQ+Ub3htlJXmI37MIJaJs2DoHoP8Hy2XxF+WnQyCovcro6BmOntHzEbdrAU7t/AGWth7oO+ZHWDv61vUSDVJgq94oKcrD0e3SvO1dAjB0wnJZN/rCPPm8iwqysOI/A2R/x+9fifj9K+Hm2xojpvz69OYbrOA20rwPbV2M4sJsOLgFYMSkZbJutoW5aRAIas/vooIsLJ09UPb3qb0rcWrvSnj4tcLoGZqTd4u2vVAsyseu9UtQVJADZw8/jPvkJ5g/GnwwLydDrrwL87LwzbQhsr8P7liNgztWwyewJSbOWany+F/Fscs10NcFBrbThaE+8CBTglX7qlBdU7tMEzMBTAxry93ZRoD3+tSOl9QnQvpx9vzNGmw63vAfL6yt9Zq25h3SpjdKRPk4uHkRigtz4OgWgJFTfpHVawV56XLd4Ivys/Hj57UN9yf2rMSJPSvh4d8KMTPXqDx+0g4CiUTSOPtnUYPUvt9RdYegUid2dAIA7NJ78a7tjUGfKunjAR/euq7mSFTLtan0MYcLd2hXtfpxP+mH9R/3qDkQFRv3uvTnGu2q1hAtrdaw7pR2HedD20qP8wOXNGMMFmXp0Uza6PjJCu3K++sx0ry1tV7T1rw3xDfC+0efIypCs+8kvx/TX22v7bF8m9peuz6wpwARERERERFpFN4+oDya3TxERERERERERC+NPQWIiIiIiIhIo/CRhMrDPUlERERERESkpdgoQERERERERBpFIBSobfqnfvzxR3h4eMDQ0BBt2rTBmTNnnrv8ggUL4OfnByMjI7i6uuLf//43ysvLX3ZX/S02ChARERERERHVgz///BOTJk3C7NmzceHCBTRr1gw9e/ZEVlaWwuV///13zJgxA7Nnz0ZiYiJWrFiBP//8E5988km9xchGASIiIiIiIqJ68MMPP+C9997D6NGjERgYiKVLl8LY2BgrV65UuPypU6fQrl07vP322/Dw8MBrr72GYcOG/W3vglfBRgEiIiIiIiLSKAKhUG1TRUUFRCKR3FRRUfFMjJWVlTh//jy6d+8umycUCtG9e3fExcUpzKtt27Y4f/68rBHg7t272L17N3r37l0/OxJsFCAiIiIiIiJ6YfPmzYOFhYXcNG/evGeWy8nJQU1NDezt7eXm29vbIyMjQ+G23377bXzxxRdo37499PT04O3tjc6dO/P2ASIiIiIiIiIZgUBt08yZM1FYWCg3zZw5UylpHTlyBF9//TWWLFmCCxcuYPPmzdi1axf+85//KGX7iujW25aJiIiIiIiIGhkDAwMYGBj87XI2NjbQ0dFBZmam3PzMzEw4ODgoXOezzz7DiBEjEBMTAwAICQlBSUkJ3n//fcyaNQtCofKv67OnABEREREREZGS6evro0WLFjh48KBsnlgsxsGDBxEZGalwndLS0me++Ovo6AAAJBJJvcTJngJERERERESkUQRCgbpDeCGTJk3CyJEj0bJlS7Ru3RoLFixASUkJRo8eDQCIjo6Gs7OzbEyCfv364YcffkDz5s3Rpk0b3L59G5999hn69esnaxxQNjYKEBEREREREdWDt956C9nZ2fj888+RkZGBsLAw7N27Vzb4YHJyslzPgE8//RQCgQCffvopUlNTYWtri379+uGrr76qtxjZKEBEREREREQaRVAP99bXl/Hjx2P8+PEK/3fkyBG5v3V1dTF79mzMnj1bBZFJac6eJCIiIiIiIiKlYk8BIiIiIiIi0iiaMqaAJmBPASIiIiIiIiItxUYBIiIiIiIiIi0lkNTXww6JiIiIiIiI6kHG1HfU9toO//1Nba9dH9hTgIiIiIiIiEhLcaBBUqmx3+arOwSVWjrdCgDw8NZ1NUeiWq5NAwEAu/T81ByJavWpSgIAfP1njZojUa1P3tIBAPx6TM2BqNiIjtKfO85XqzcQFevXQvrRYflBNQeiYjHdpD8PXilXbyAq1i3EEAAwa2WFmiNRra/eNQAAzFuvXfX5zCHS+vynvWoORMU+7CX9qa15ayoONKg87ClAREREREREpKXYKEBERERERESkpXj7ABEREREREWkU3j6gPOwpQERERERERKSl2FOAiIiIiIiINIuQ17eVhXuSiIiIiIiISEuxpwARERERERFpFIGAYwooC3sKEBEREREREWkpNgoQERERERERaSnePkBEREREREQaRcCBBpWGe5KIiIiIiIhIS7GnABEREREREWkUgZADDSoLewoQERERERERaSk2ChARERERERFpKd4+QERERERERJqFAw0qDfckERERERERkZZiTwEiIiIiIiLSKBxoUHnYU4CIiIiIiIhIS7FRQMPFxsbC0tJS3WEQERERERGpjEAgVNvU2Gj17QOjRo3C6tWrAQC6urpo0qQJQkNDMWzYMIwaNQpCLR284siRI+jSpQvy8/M1qsGhX3tDtG9mACMDAe6kVuOP/aXIyhfXuXzPCEM099WDQxMdVFZLcDe1GluOliEzr+51GpptO3dj/eatyMsvgLenB8Z/EAN/P1+Fyx4/FYc/1m9Cano6aqpr4OzkiDcH9kePrp1VG/QratK+Jbwmj4FFeDAMnexwbvBHyNx+8PnrdGyNwPkzYBrYFOUP03F73k9IWbNFRRErV8dgAcK8BDDQA1JygL3nxcgvrnt5V1sgwk8IhyaAmZEAG0/U4Gaq6uJVhnOH1yJu3woUF2bD3tUfPYd9BmfPUIXLZqfewtHti5D+4BoKc1PR462ZaNN9lGoDVpKT+3/HkZ2rUFSYA0c3Pwwc+QncfBTnnZFyG/s2LEbKvevIz0nDGyOmo+Pr0SqOWDkuHF2LswdWoESUDTsXf3Qb8hkcPRTnDQBJF/bgxI6FKMxNhZWdBzoNmAKv4E4qjFg5ju5ZhwPbV0NUkAMXd18MGTMDHk1DFC574sAmnD66A2kPbwMA3LwC0f/tf9W5fEPXrbkOWvnpwFAfeJAlwfZT1cgVSepc3sNegA4hOnCyEcLcWIDf/qpCYrLmvHc/1iHoifo8F9j3d/W5DdDGXwgHq9r6/Faa6uJVhkvH1+LcoRUoFWXDxtkfXQZ/Bgf3us/vmxf3IG73QojyUmFp64H2/abAM0jzzm9tzZs0h3Z+631Cr169kJ6ejvv372PPnj3o0qULPv74Y/Tt2xfV1dXqDo9e0GttDNClhQF+31eKb38tQmWVBP8aYgpdnbrX8XXVxdELFfj2NxEW/lkMHR0BJgwxhb6e6uJ+FYePncDS5aswYthbWLrwe3h5emDG518gv6BA4fJmpmZ4e8ibWDT/G/zyv/9Dz+5d8d8Fi3H2/EXVBv6KdEyMIbqchKsT5r7Q8kYeLmi1/WfkHjmNEy37497i1Qj5+UvY9Ghfz5EqX4S/AC2bCrDnnBixf4lRVQMM7SSEznNqcj0dIKtAgn3nNe8DMwBcO7sbB9bPQ4d+4xDz2RbYu/jjjwVjUCLKVbh8VWUZLG1c0HXQZJha2Ko4WuVJiNuD7b99hx6DPsLErzbAyc0Py775AEWFivOurChDEztX9B76b5hZ2qg4WuW5cW43jmyah7Z9xiF65hbYOvtjw+IxKClSnHfqnQvYsXIyQtq+iZEzt6Jps27Y8vM4ZKfdVHHkr+bcyb3YtHo++kR9gJnfrYOzhx8Wf/lhneV969o5tGz/OibOWY6pX/8KKxt7LP7PhyjIzVRx5K+uQ4gOIgN1sO1UNX7aUYWqKglG9dR77vu3vp4A6XkS7IjT3M9pj+vzvefFWH1QjKpq4K2Of1Of60rr8/0XNLM+T7qwG8e2zENEz3F4e+oW2Dr5Y8tPY1Bax/mddu8C9qyZjKCINzF86lZ4h3TDjhXjkKNh57e25k2aResbBQwMDODg4ABnZ2eEh4fjk08+wbZt27Bnzx7ExsYCAAoKChATEwNbW1uYm5uja9euuHTpkmwbc+bMQVhYGH7++We4urrC2NgYQ4YMQWFhodxrLV++HAEBATA0NIS/vz+WLFki+9/9+/chEAiwefNmdOnSBcbGxmjWrBni4uLkthEbGws3NzcYGxtj4MCByM19tkLZtm0bwsPDYWhoCC8vL8ydO1eugUMgEGD58uUYOHAgjI2N0bRpU2zfvl0WR5cuXQAAVlZWEAgEGDVqFABg48aNCAkJgZGREaytrdG9e3eUlJS8/M5Xom4tDbEnrhyXblchNbsGq3aWwNJUiDDfur/hL95QjLirlUjPESM1uward5XA2kIHbvaa0YFm09bt6N2zB3r16AZ3N1dMHDcWBgYG2HtA8VXzsNBgtG8bAXdXVzg5OmJQ/37w8vTA1euJKo781WTvO4absxcgc9tfL7S8+/tDUXYvBYnTvkXxjbt4sGQtMjbtg+fHo+o30HrQ2leAk9cluJUGZBcCO06LYWYE+DnXPdDO3Qzg6FWJxvUOeOz0gVVo3mEIwtoNhq2TD3q/Mxd6+oZIOLlJ4fJOnqHoHjUdQa37QEdXX8XRKs/R3avRpsubaN15IBxcfDB4zGzoGRji7NHNCpd38w5Bv+FT0Lxtb+hqcN7nDq1CaLshCIkcDBtHH7w2TFreV08pLu/zh9fAM7ADWveIgbWjN9r3mwh710BcPPKbiiN/NYd2/Ip23QchsusAOLp6Y9j7n0LfwBCnDm1VuPzoifPQqddbcPX0h4OzJ94ZOwcSiRg3rpxRbeBK0C5IB0cu1SAxWYzMfAk2HKuGmREQ4Fb3R9SbKWL8daEG1x9o5pdjAGjVVICTibX1+c4z0vrc92/q82MaXJ9fOLIKwW2HIChiMKwdfNBtyFzo6hviWrzi8/vi0TXw8O+Alt1i0MTBG237TISdSyAuHdes81tb81YJoUB9UyOj9Y0CinTt2hXNmjXD5s3SD19RUVHIysrCnj17cP78eYSHh6Nbt27Iy8uTrXP79m2sX78eO3bswN69e3Hx4kV89NFHsv+vXbsWn3/+Ob766iskJibi66+/xmeffSa7feGxWbNmYcqUKUhISICvry+GDRsm+0J/+vRpjBkzBuPHj0dCQgK6dOmCL7/8Um7948ePIzo6Gh9//DGuX7+On3/+GbGxsfjqq6/klps7dy6GDBmCy5cvo3fv3hg+fDjy8vLg6uqKTZuklVRSUhLS09OxcOFCpKenY9iwYXj33XeRmJiII0eOYNCgQZBI6u7epyo2FkJYmAqReL+24aO8EriXVg0vpxf/gm9kID3BS8sb/oeMqqoq3Lx9B+FhzWTzhEIhwsNCcf1G0t+uL5FIcCHhMlJSUhEaHFifoaqdZUQYcg7JN65lHzgBq4gw9QT0kixNAFMjAe5l1p5zFVVAWi7grLkXhp+rproS6Q+uwTOgrWyeQCiER0BbpN7RrB4u/0R1dSVS712Hb3CkbJ5QKETT4Ag8uHXpOWtqtprqSmQkX4O7n3x5u/u3Rdo9xeWddi8B7v6RcvM8Atsj7V5CfYaqVNVVVUi+mwi/0AjZPKFQCP+QCNxLuvxC26isLEdNTTVMTM3rK8x6YWUGmBkLcCet9n23ogpIyZbAza7xfeh+7HF9fl9RfW6txsDqUU11JbIeXoOrr/z57ebbFun3FZ/fGfcS4Oonf367+7dH+v2E+gxVqbQ1b9I8mnFJVA38/f1x+fJlnDhxAmfOnEFWVhYMDAwAAPPnz8fWrVuxceNGvP/++wCA8vJyrFmzBs7OzgCAxYsXo0+fPvj+++/h4OCA2bNn4/vvv8egQYMAAJ6enrIv7SNHjpS97pQpU9CnTx8A0i/uQUFBuH37Nvz9/bFw4UL06tUL06ZNAwD4+vri1KlT2Lt3r2z9uXPnYsaMGbJtenl54T//+Q+mTZuG2bNny5YbNWoUhg0bBgD4+uuvsWjRIpw5cwa9evVCkyZNAAB2dnayMQXu3LmD6upqDBo0CO7u7gCAkJC6712sqKhARUWF3LzH+0/ZzE2lHxxEJfJf5otKJTA3ebF2LwGAqG5GuJ1SjbScht8oUCgqglgshpWlhdx8K0tLPEyp+xJCcUkJho6MQVVVFYRCISZ8+D5aNA+r52jVy8DeBhWZOXLzKjJzoGdhBqGhAcTlFXWs2bCYGEp/lpTLzy8pl8j+19iUFudDIq6Bibn8p2RTc2vkZtxVU1T1r6SoAGJxDUwt5PM2s7BGVto9NUVV/8oelbfxU+VtbGaNvEzF5V0iyoGJmXyrmImZNUpEOQqXb4iKi/IhFtfA/OnytrRGZuqLlfeW3xbAwsoW/k80LGgCMyPp+3dxmfwFhuJyCUyNGm+jQJ31eUXjrc/LSh6d32YKzu+sOs7vohwYP3V+G5tZo1SDzm9tzVtVBFo6/lt9YKNAHSQSCQQCAS5duoTi4mJYW8ufzGVlZbhz547sbzc3N1mDAABERkZCLBYjKSkJZmZmuHPnDsaMGYP33ntPtkx1dTUsLOS/1IWG1g464ujoCADIysqCv78/EhMTMXDgQLnlIyMj5RoFLl26hJMnT8r1DKipqUF5eTlKS0thbGz8zOuYmJjA3NwcWVlZde6PZs2aoVu3bggJCUHPnj3x2muv4c0334SVlZXC5efNm4e5c+Xv+Z49ezZg9HGdr/GiWgfq4+2exrK/f9z4nFF5XtDQ14zhbKuD/64teuVtNWTGRkb4edEPKCsvx8WEy1i6YhUcHRwQFhqs7tDoKUHuArzeovZD8frjDb+xiohUb9+WFTh/ci8mzlkBPf36aXxXlmZeQvRvV/vRc82BKjVGozpBbgL0erI+P8H6nIgaFjYK1CExMRGenp4oLi6Go6Mjjhw58swyLzoyf3Gx9EvrsmXL0KZNG7n/6ejIj6Sjp1d7D7xAIH0DEYtf/M2juLgYc+fOlfVIeJKhYW3z85Ov8/i1nvc6Ojo6OHDgAE6dOoX9+/dj8eLFmDVrFk6fPg1PT89nlp85cyYmTZokN8/AwAAfLyh94Vzqcul2Je6l1d4qoPvoKDY3EUJUUiObb2YsQEpWzdOrP2NodyOEeOvh+9+LUFCk/tshXoSFuRmEQiHyC+THrcgvKICVlWWd6wmFQjg7SRubfLw8kZySgj82bGrUjQIVmTkwsJdvcTewt0FVYVGD7iVwK1WCtNza4/Hx4FMmhvJXl0wMBcgs0Izj9p8yNrWCQKjzzKCCxaJcmJo30nsmAJiYWUIo1EHxU4PMFRXmwlyDBxH8O0aPyrv0qfIuLcqFSR3lbWJug5Ii+atnJc9ZviEyNbOCUKgD0dPlXfD35X1g22rs37IKEz7/GS4eip8805AkJovxMLtS9reujvRzjqmRAEVP9BYwNRQgXYOeBPR3bqVJkJb3AvW5QeOtz41MHp3fRQrOb7M6zm8zG5Q+dX6XFuXCWIPOb23NmzQP+1wocOjQIVy5cgWDBw9GeHg4MjIyoKurCx8fH7nJxqb25ExOTkZaWu1zYeLj4yEUCuHn5wd7e3s4OTnh7t27z2xD0RfqugQEBOD06dNy8+Lj4+X+Dg8PR1JS0jOv4+Pj88KPWNTXlw5SVVMj/4VaIBCgXbt2mDt3Li5evAh9fX1s2aL4sW4GBgYwNzeXm5R1+0BFJZBdIJZN6TliFBaL4e9e28ZlqA94OunibtrzRyYe2t0IYb76WLCuCLmFmvMBRE9PD74+3rhwqfZ+U7FYjIuXriDQ3++FtyMWi1FV1biv1BTEJ8C6q3yXWptubZEfn6CegF5QZTWQX1w75YikXWw97GuvNunrAk7WQGoj7VGoo6sPR/cg3EusHRNCIhbjfmIcnL2bqzGy+qWrqw9nz0DculZbv4vFYty+dhruTZs9Z03NpqOrDwe3IDxIki/vB0lxcPJUXN5OnmFIviH/Pvgg8RScPMPqM1Sl0tXTg5tXAJKu1L6/i8ViJF05DU+/uh9Ztn/rKuzZ9AvGf7oE7j5Bqgj1lVVWA3lFtVNWgQRFpRJ4OdV+PjHQA1xsBUjOajxfjuusz+0U1OeKB6TXeDq6+rBzDcLDm/Ln98ObcXD0UHx+O3iG4eFN+fM7OekUHD3C6jNUpdLWvFVFIBSobWpstL6nQEVFBTIyMlBTU4PMzEzs3bsX8+bNQ9++fREdHQ2hUIjIyEgMGDAA3333HXx9fZGWloZdu3Zh4MCBaNmyJQDpVfiRI0di/vz5EIlEmDBhAoYMGQIHBwcA0nv9J0yYAAsLC/Tq1QsVFRU4d+4c8vPzn7miXpcJEyagXbt2mD9/Pvr37499+/bJ3ToAAJ9//jn69u0LNzc3vPnmmxAKhbh06RKuXr36zKCEdXF3d4dAIMDOnTvRu3dvGBkZ4dq1azh48CBee+012NnZ4fTp08jOzkZAQMA/2Nv15+C5crze1hBZ+WLkFNTgjQ5GKCgWI+Fm7RfeiW+ZIuFWFY5ckF4dHtbDCK0C9fHT5hKUV0pgbiI9wcsqJKjSgKccDR7wBr77v0Xwa+oNP9+m2LxtJ8rLy9GrezcAwDffL4SNdRPEjBoBAPh9/Sb4NfWGo6MDqqqqcObsBfx1+Cg+/ugDdabxj+mYGMPEx032t7GnC8yb+aMyrxDlD9Ph9+UkGDrb49Lo6QCAB7+sg/tHw+E/byoexm6CTZcIOEa9jrNvaFbeAHDmpgTtAgXIL5KgoAToGCxEURmQlFr74fntzkIkpUhw/rZ0np4uYGVauw0LEwHsLCUorwREr95xp9616TEa21dOh6NHMJw9Q3H6r9WoqixDs3bS3lDbVkyDmZU9ug6aDEA6qFN22h3Z70X5mchIToS+oTGa2LmrLY9/qlPvkVi39BO4eAXBzTsEx/f8isryMrTqJL2F7I8lM2HRxA69h/4bgHRwwsyUx3lXoTAvC6n3E2FgaAwbB83Ju2XX0di9Zjoc3IPh6B6Kc4dXo6qiDMGR0vLeFTsNZpb26DhAWt4tukRj3f+NwNm/VsIruBNunNuNjOSreG34F+pM4x/r2m8E1vzvM7h7B8HdJxiHd/2GiooyRHYZAACIXTQLltZ2GDBcegve/i0rsfPPJRg98Rs0sXVCYb60ZdDA0BiGRsZ1vUyDdPJaDbo000FuoQT5xRJ0D9dBUZm0V8Fj7/bSw/UHNYhPlM7T1wWszWs/lFuZCeDYRIDSCgkKG8ZDkf7W2VsStA0UIK9YGvPj+vzmE/X5sE5C3Eytuz63NNWs+jy882jsXzsd9m7BcHALxYWj0vo8sI30/N732zSYWNijfT/p+d28UzQ2LhqB84dWwjOoE5Iu7Ebmw6vo9pZmnd/amjdpFq1vFNi7dy8cHR2hq6sLKysrNGvWDIsWLcLIkSNlV9Z3796NWbNmYfTo0cjOzoaDgwM6duwIe3t72XZ8fHwwaNAg9O7dG3l5eejbt6/cIwdjYmJgbGyM//73v5g6dSpMTEwQEhKCiRMnvnCsERERWLZsGWbPno3PP/8c3bt3x6effor//Oc/smV69uyJnTt34osvvsC3334LPT09+Pv7IyYm5oVfx9nZWTZg4ejRoxEdHY3p06fj2LFjWLBgAUQiEdzd3fH999/j9ddff+Ht1qf9pytgoCfA8J7GMDYU4HZKNRavL0b1E50dbK2EcgMXdQqX3k4x+W0zuW2t3lWCuKuVaOi6dGyPwkIRYn9bh/z8fHh7eWLeF5/Lbh/Iys6G8ImWzPKKcixa8guyc3NhoK8PVxdnzJg8EV06tldTBi/HokUwIg/+Kvs7cP4nAICHazbj8piZMHC0hZGro+z/ZfdTcPaNDxD4/Ux4/Csa5SkZuPLBp8g5cELlsb+q+BsS6OsCr7cUwlAfeJgN/HlUjJonOrlYmgLGT3TKcbQC3ulae5tSj+bSeu3yPTF2nmn4V+KCWvVGaVEejm5bhBJRNuxdAzDs4+Wy2wcK89IhENReZSwqyMLy/wyQ/R2/fyXi96+Em29rRE/99enNN1hhka+jWJSHfRv/h6KCHDi5+yNmxs8ws5DmnZ+bLnelQpSfjf/75E3Z30d3rcLRXavgFdAKH30Wq+rwX5p/y94oLc7DyZ3S8rZzCcCb45fLbgcoyk+XG1jK2Tscfd+dj+PbF+D49h9gZeuBgR/8CFunht+V/kkt2/VCsSgfO9ctgaggBy4efhg/awnMLaXjGeXnZMj19ju2fwOqq6uwbP5kue30jhqLvm99qNLYX9XxKzXQ1wUGtNOFoT7wIEuC2H1Vcu/fTcwEMDasPd6dbQSI6V376M0+baQfZy/cqsGm4xrQqg9pfa6nA7ze4lF9ngOsP/ZsfW70VH0+vEttfd49rLY+33W24dfnfuG9UVach7jdi1AqyoaNSwAGjK09v0X56cAT9bmTZzh6Rc9H3O4FOLXzB1jaeqDfmB9ho2Hnt7bmrRICdnpXFoGkITxTTsPNmTMHW7duRUJCgrpDafDGfpuv7hBUaul06UCMD29dV3MkquXaVPqYw116L34rQ2PQp0r6OMiv//z7sSwak0/ekn5I/fWYmgNRsREdpT93nNeMLyHK0q+F9AvY8oNqDkTFYqSdsHDwSvnzF2xkuoVIG9BnrWy4Y7DUh6/elX4bn7deu+rzmUOk9flPe/9mwUbmw17Sn9qat6YqnP/qA5i/LIspC9X22vWBzStEREREREREWkrrbx8gIiIiIiIizdIYB/xTF/YUUII5c+bw1gEiIiIiIiLSOOwpQERERERERJrlBR+3Tn+Pe5KIiIiIiIhIS7GnABEREREREWkUgYBjCigLewoQERERERERaSk2ChARERERERFpKd4+QERERERERJqFAw0qDfckERERERERkZZiTwEiIiIiIiLSKAIhBxpUFvYUICIiIiIiItJSbBQgIiIiIiIi0lK8fYCIiIiIiIg0i4DXt5WFe5KIiIiIiIhIS7GnABEREREREWkWDjSoNOwpQERERERERKSl2FOAiIiIiIiINIqAYwooDfckERERERERkZYSSCQSibqDICIiIiIiInpRJT/PUttrm3zwldpeuz7w9gEiIiIiIiLSLBxoUGnYKEAqteaouiNQrehO0p8Ld2hXh5yP+0kr6a//rFFzJKr1yVs6AIBden5qjkS1+lQlAQCW7lNzICo2tqf0596ESvUGomK9wvQBAL8d16567Z0O0nrtyNUyNUeiWp2DjQAA605pV3kPbSst7/VxYjVHolpDIqV3Fv9+QrvK++320vLeEK9d5R0VwTvJSYqNAkRERERERKRRBEI2aigL9yQRERERERGRlmKjABEREREREZGW4u0DREREREREpFkEHGhQWdhTgIiIiIiIiEhLsacAERERERERaRYONKg03JNEREREREREWoo9BYiIiIiIiEizcEwBpWFPASIiIiIiIiItxUYBIiIiIiIiIi3F2weIiIiIiIhIowg40KDScE8SERERERERaSn2FCAiIiIiIiLNIuD1bWXhniQiIiIiIiLSUmwUICIiIiIiItJSvH2AiIiIiIiINItQoO4IGg32FCAiIiIiIiLSUmwUUCGBQICtW7eqOwyMGjUKAwYMUHcYREREREREL0UgEKptamx4+8BLGjVqFFavXv3M/J49e2Lv3r1qiOhZ9+/fh6enJy5evIiwsDDZ/IULF0IikagvsHpw7vBaxO9fgeLCbNi7+OO1YZ/B2TO0zuUTz+3B0W0LUZCbiiZ2Hug6eAp8QjqpMGLlkEgkOLtvMa6f3oCKMhEcPcPRcdBsWNp6PHe9KyfXIuHICpQW5cDa0R8dBn4Ke7e691dD1DFYgDAvAQz0gJQcYO95MfKL617e1RaI8BPCoQlgZiTAxhM1uJmqunhfVZP2LeE1eQwswoNh6GSHc4M/Qub2g89fp2NrBM6fAdPApih/mI7b835CypotKopYeRKOrcX5QytQIsqGrbM/urz5GRzc6z5eb17cg1O7FkKUlwpLWw90eGMKPIM07/w+vu8PHNoRC1FBDpzd/TB49Ey4+4QoXDb94W3sXv8jUu5dR152GgZGT0PnPiNUHLFynD20FnH7VqC4MAf2rv7oNexTOHspLu+s1Fs4um0R0h9cQ2FuGl57ayba9Bip4oiV4/CedTiwbTUKC3Lh4uGLoWOmw7Op4vI+fmAT4o/uRFrybQCAm1cgBgwfX+fyDdnpg2txas+j8nbzR+/hn8LlOeV9aMsipN+/hoLcNPQaNhORr2lmeZ/+ay1O7FmJ4sIcOLj5o887s+rMOzP1Fg5tXoy0R3m/PmwG2vbUzLzPHFqLU3ul5e3g6o/X337++X1k6yKkPTq/ew6diQgNPb/jnyxvV3/0fWcWXLzrKO+UWzi45VF556Sh99uaW96kORpfM4cK9erVC+np6XLTH3/8oe6w/paFhQUsLS3VHYbSXD+7G39tmIcOfcdhzKdbYOfqj3ULx6BElKtw+ZQ7F7Bl+WQ0a/8mYj7bCt/m3bBhyThkpd5UceSv7uLh5bh84ld0GjwHgyesh66+EXYui0F1VUWd69xK2I2T279Byx7jEDVxM2yc/LBzWQxKixTvr4Yowl+Alk0F2HNOjNi/xKiqAYZ2EkLnOTWang6QVSDBvvNi1QWqRDomxhBdTsLVCXNfaHkjDxe02v4zco+cxomW/XFv8WqE/PwlbHq0r+dIlSvpwm4c2zIPEb3GYfjULbBx9sfmJWPqPF7T7l7A7tWTERz5JoZP2wqf0G7YvnwcctI06/y+cGovtqz5L3oOHoup36yHk7svfvr6AxQVKs67sqIcNvYu6DdsIswtbVQcrfJcO7MbB9Z/g479xuG9zzfD3tUPvy+IqbM+r64sh5WtK7oOngxTC1sVR6s8Z0/uw8bY79FnyAeY9d8/4OLui0X/+QiiwjyFy9+8dg6t2vfCpLnLMP3rNbCyscfCLz5Efm6miiN/NVdP78a+dd+gc/9x+GDOZji4+uHX72NQXEd5V1VIy7t7lGaX95XTu7Fn3bfoMmAcPpy7CQ6uflg9/72/zbtH1CSYWmju+X31zG7s//MbdHpjHD6YLT2/f/u/us/vqspyWNq6oruGn99XTu/Gnj++RZf+4/DRo/KOfV55V5ajia0rXtPw8lYJoUB9UyPDRoFXYGBgAAcHB7nJysoKAHDr1i107NgRhoaGCAwMxIEDB+TWPXLkCAQCAQoKCmTzEhISIBAIcP/+fdm8kydPonPnzjA2NoaVlRV69uyJ/Px8AMDevXvRvn17WFpawtraGn379sWdO3dk63p6egIAmjdvDoFAgM6dOwN49vaBiooKTJgwAXZ2djA0NET79u1x9uzZZ2I9ePAgWrZsCWNjY7Rt2xZJSUnK2I2v7PSBVQhrPwTN2g2GrZMPeg+fC119Q1w6uUnh8mcOroF3UAdE9oyBjaM3OvefCAe3QJw7/JuKI381EokEl4+vQYvuY+EZ3A02Tn7oNvRblIiycO/qX3Wud+loLALbRCGg9WA0cfBBp8FzoatniBtnFe+vhqi1rwAnr0twKw3ILgR2nBbDzAjwc667kr6bARy9KtGo3gFPyt53DDdnL0DmtrrL9knu7w9F2b0UJE77FsU37uLBkrXI2LQPnh+Pqt9AlezC4VUIbjsEQRGDYe3og+5DpOf31XjFx+vFo2vgEdABLbvFwNrBG237TISdSyASjmvW+X1k1xq07TYYEV0GwsHFG0NiPoe+vhHiDyvu6eHuE4z+70xGeLvXoaunr+JolSf+QCyad4hCWHtpfd7nnbnQ0zdEwgnF5e3kGYLuUdMQ3LoPdHT1VByt8vy141e07z4I7boOgJOrN4Z/8Cn0DQxx6uBWhcuPmTgPnXu9BVdPfzi4eCL6w9mQSCS4ceWMagN/Raf2x6JFxyg07zAYds4+6BstLe+LxxWXt7NXCHq+NQ0hbfpAV4PL+9S+1WjZKQrhHQbBztkH/UbOgZ6+IS4c26xweRevEPQaOhWhEX2gq6vB5/f+WIR3jELzR+d33xGPyruO89vZMwSvDZmG4DaafX6f3Cst7xYdpeX9xihpeZ9/kfLW4Pqc5P3444/w8PCAoaEh2rRpgzNnnl9fFxQUYNy4cXB0dISBgQF8fX2xe/fueouPjQL1QCwWY9CgQdDX18fp06exdOlSTJ8+/R9vJyEhAd26dUNgYCDi4uJw4sQJ9OvXDzU1NQCAkpISTJo0CefOncPBgwchFAoxcOBAiMXSK6GPD7a//voL6enp2LxZceUzbdo0bNq0CatXr8aFCxfg4+ODnj17Ii9P/grFrFmz8P333+PcuXPQ1dXFu++++49zUraa6kqkJ1+DZ0Bb2TyBUAjPgLZIuXtR4TqpdxLgGRApN88rqD1S7ybUZ6hKJ8pLQWlRNlyb1uZuYGQGe7dQZDxIULhOTXUlslOvwcVXfn+5NI2sc52GxtIEMDUS4F5m7S0wFVVAWi7gzAZ1GcuIMOQcipObl33gBKwiwtQT0Euoqa5E5sNrcPOTP17d/Noi/Z7i8zv9fgLcfOXPb/eA9ki/l1CfoSpVdXUVHt69Dt+QCNk8oVAI35AI3L91SY2R1a+a6kqkP7gGz8Cn6/NIpGhY/fxPVFdVIflOIgJC28jmCYVC+Ie2wd2bl19oG5WV5aipqYaJqUV9hal01dWVSL9/DV5BteUtFArhFRiJh7cT1BdYPauurkTa/WvwCqytp4RCIbyDIvHwToL6AqtnNdWVSHtwDV5PfV7zCoxESiPO+3F5ewcpKO9GfJyTvD///BOTJk3C7NmzceHCBTRr1gw9e/ZEVlaWwuUrKyvRo0cP3L9/Hxs3bkRSUhKWLVsGZ2fneouRYwq8gp07d8LU1FRu3ieffIKWLVvixo0b2LdvH5ycnAAAX3/9NV5//fV/tP3vvvsOLVu2xJIlS2TzgoKCZL8PHjxYbvmVK1fC1tYW169fR3BwMGxtpV2trK2t4eDgoPA1SkpK8NNPPyE2NlYW37Jly3DgwAGsWLECU6dOlS371VdfoVMn6X25M2bMQJ8+fVBeXg5DQ8NntltRUYGKCvku7AYGBgAM/sEe+HulxfmQiGtgYm4tN9/EzBq56XcVrlMsyoGJufy3RxNza5QU5ig1tvpWWpQNADAyk8/dyNQGpUWKcykvke4vY9On1jGzQX7WvfoJVMlMHh1uJeXy80vKJbL/EWBgb4OKTPnjoCIzB3oWZhAaGkBcXvctJg1F2ePj9alj3NjMGvmZis/vElEOjJ8+v82s6zwnGqISUT7E4hqYWcjnbWZhjaw0zThPX8bj+tz06frc3AY5GY037+KiR+VtKZ+3uYU1MlLvv9A2Nv+6ABZWtnINCw1d6aO8ny5vU4vGXd6lRQXSvJ86v03NrZGT3pjzruPzmrlNI8+7jvK2aNzlrTIaMuDfDz/8gPfeew+jR48GACxduhS7du3CypUrMWPGjGeWX7lyJfLy8nDq1Cno6Ul7yXh4eNRrjJqxJxuoLl26ICEhQW4aO3YsEhMT4erqKmsQAIDIyMjnbEmxxz0F6nLr1i0MGzYMXl5eMDc3lx0sycnJL/wad+7cQVVVFdq1ayebp6enh9atWyMxMVFu2dDQ2gFRHB0dAaDOFq558+bBwsJCbpo3b94Lx0XPunlhB375JFw2iWuq1R2SSgS5CzBlkFA2PW/cACIibbN380qcPbkPH077AXr6ym14JyIixSoqKiASieSmpy+IAtKr/ufPn0f37t1l84RCIbp37464uLhnlgeA7du3IzIyEuPGjYO9vT2Cg4Px9ddfy3qL1wf2FHgFJiYm8PHxeal1hULpN5snnwJQVVUlt4yRkdFzt9GvXz+4u7tj2bJlcHJyglgsRnBwMCorK18qpr/zuKUKkD5eEYDsVoWnzZw5E5MmTZKbZ2BggD/jlRuTsakVBEKdZwapKSnKhUkdg7OYmtugRCR/1bBEVPfyDYVHYBe8Nam2YaamWlrOZUW5MDG3k80vK86BtVOAwm0Ymkj3V2mx/P4qK3r26mpDcStVgrTc2vPkcaOAiaF8bwETQwEyCxrXUzVeRUVmDgzs5cvUwN4GVYVFGtFLAACMHh+vTw0qWFqUC2MzxceribkNSp8+v5+zfENkYm4FoVDnmUEFiwpzn7ma3Jg8rs+fHnyrRJTTqAfbMjV7VN4F8nmLCnNh8TeDRu7fthp7t6zExNk/w8XDtz7DVDrjR3k/Xd7FhTkwbaDvR8pgbGYpzfup87tYlNuoj3Njszo+rzXy87vO8i5s3OWtMgL1Dfg3b948zJ0rP/jz7NmzMWfOHLl5OTk5qKmpgb29vdx8e3t73LhxQ+G27969i0OHDmH48OHYvXs3bt++jY8++ghVVVWYPXu2UvN4jNfc6kFAQAAePnyI9PR02bz4ePlvw4+79j+5TEJCgtwyoaGhOHhQ8SPHcnNzkZSUhE8//RTdunVDQECAbADCx/T1pYOTPK9VydvbG/r6+jh58qRsXlVVFc6ePYvAwMDnZPl8BgYGMDc3l5uktw8ol46uPhzdgnD/Rm1Lm0Qsxv3EOLh4NVe4jrN3GO7dkC+Pe9dPwdkrTOnxKZO+oSksbNxlk5W9D4zNbJFyqzb3yvJiZCZfhoN7mMJt6Ojqw9Y5CKm35PdXyu34OtdRt8pqIL+4dsoRAcVlEnjY174R6OsCTtZAqub0EK93BfEJsO4aITfPpltb5McnqCegl6Cjqw971yA8vCl/vD5MioOjp+Lz29EjDMk35c/v5Bun4OgZVp+hKpWurh5cvQJx88pp2TyxWIybV+Ph0bSZGiOrXzq6+nB0D8L9RPnyvncjHi4NvH5+Fbp6enDzDkDiE4MEisVi3Lh8Bl6+dT96c9/WVdi1cRkmfLYEHj5BdS7XUOnq6sPRIwh3r9eWt1gsxr3EeLj6hKkvsHqmq6sPJ48g3L1eW0+JxWLcvR4PV+8w9QVWz3R09eHkHoS7T53fdxPj4dKI835ueTfi41wbzJw5E4WFhXLTzJkzlbJtsVgMOzs7/PLLL2jRogXeeustzJo1C0uXLlXK9hVhT4FXUFFRgYyMDLl5urq66N69O3x9fTFy5Ej897//hUgkwqxZs+SW8/HxgaurK+bMmYOvvvoKN2/exPfffy+3zMyZMxESEoKPPvoIY8eOhb6+Pg4fPoyoqCg0adIE1tbW+OWXX+Do6Ijk5ORn7kmxs7ODkZER9u7dCxcXFxgaGsLCQn4QIhMTE3z44YeYOnUqmjRpAjc3N3z33XcoLS3FmDFjlLi36k+bHqOxfdV0OLoHw8kzFGf+Wo2qyjKEthsEANi+chrMLO3RZdBkAEDrbtH49b8jEL9/JXxCOuH62d1If3AVvUd8oc40/jGBQIDQDtE4f3ApLGw9YN7EGWf2LoKJuR08g2u7KG1bOgpewd0R0v4dAECzTqNwaN0M2LoEw84tFJePr0Z1ZRn8Ww1SVyr/2JmbErQLFCC/SIKCEqBjsBBFZUBSam1Pgbc7C5GUIsH529J5erqA1RNDgFiYCGBnKUF5JSAqVXUG/5yOiTFMfNxkfxt7usC8mT8q8wpR/jAdfl9OgqGzPS6Nlg5q+uCXdXD/aDj8503Fw9hNsOkSAceo13H2jQ/UlcJLCe8yGvt+mw4712A4uIfi4hHp+R3URnq87v11Gkwt7NH+Den53bxTNDYsGoHzh1bCM6gTks7vRubDq+g+VLPO7859orF2ySy4eQfBzTsER3f/isqKMrTpPAAA8Nv/PoFFEzv0e3siAOnghBkpd2S/F+ZnIeX+DRgYGsPWwa2OV2l4InqMwraVM+Tr84oyNHtUn29dMR1mlnboNlha3jXVlchOu/Po9yoUFWQiIzkR+gbGaGLvrrY8/qnu/UYgdvFn8PAOhEfTYBzcuRaVFWVo27U/AGDVok9h2cQOA9+ZAADYu2UVdqxbgjET58Ha1gmF+dIWUQNDYxgaGastj3+q7WujsGX5DDh7BMPZKxRx+1ejsqIMzdtLy3vzMml594iSlnf1k+VdUwVRfibSH5W3tQaVd9ueI7F52Uw4ewbD2SsEcfvXoLKiDOEdBgIANv4yHeZW9ngtStrjsrq6EtmpT+adhfQHidA31Ky8I14bha0rZsDJIxjOnqGIf3R+hz06v7csnw4zKzt0r+P8FuVr5vndrtdIbFo2E06ewXDxCsGpfdLybvG4vH9+VN5DFJR3teaWd2NnYGDwQhc8bWxsoKOjg8xM+UfGZmZm1jnmm6OjI/T09KCjoyObFxAQgIyMDFRWVsou/CoTGwVewd69e2X31j/m5+eHGzduYMuWLRgzZgxat24NDw8PLFq0CL169ZItp6enhz/++AMffvghQkND0apVK3z55ZeIioqSLePr64v9+/fjk08+QevWrWFkZIQ2bdpg2LBhEAqFWLduHSZMmIDg4GD4+flh0aJFsscOAtIGikWLFuGLL77A559/jg4dOuDIkSPP5PHNN99ALBZjxIgRKCoqQsuWLbFv3z7Z4xUbusBWvVFSlIej2xehRJQNe5cADJ2wXNb9sDAvHYInBiJx8Q7HgJj5OLJtAY5s/QFN7DwQ9dGPsHPWrK6XANC8SwyqK8twZOPnqCwTwdGzBfq+twy6erWVlCg3GWUltb1Imob1RnlxHs7sW4zSomzYOAWgb8wyjepeHX9DAn1d4PWWQhjqAw+zgT+PilHzxN0slqaA8RN1taMV8E7X2sq1R3PpMXH5nhg7zzT82w4sWgQj8uCvsr8D538CAHi4ZjMuj5kJA0dbGLnW1kdl91Nw9o0PEPj9THj8KxrlKRm48sGnyDlwQuWxvwq/8N4oK85D3O5FKBVlw9YlAAM/XC4bLLQoX/78dvIKx+sj5+PUrgU4ueMHWNp54I2YH2HjpFnnd3jbXigW5WH3+h8hKsiBi4c/xs5cCvNH3cnzc9MheOI5yYV5Wfjv9Nr3j0M7YnFoRyx8AlviX7NXqTz+lxXUujdKi/NwdNtiFIuyYe8agLcnLpN1sxXlpsluXwOAooIsLPtioOzvuH0rEbdvJdx9WyF62q/PbL+hatWuJ4oL87F93U/S8vb0w4RPl8D80e0ieTnpcnkf27ce1dVV+Hn+FLnt9B3yAfq99aFKY38VwW2k79+Hti5GcWE2HNwCMGJSbXkXKijvpbNry/vU3pU4tXclPPxaYfQMzSnvkDa9UVKUj4NbFqG4MAeObgGInvzLE3mnQ/hEvVaUn40ls2sb7k/uXYmTj/IeM3ONyuN/WcGte6O0KA9HtkrPbwfXAAz/9xPlnfdsef88V8H57dcKozTo/A5p0xslonwc3Fxb3iOn1JZ3QV46BEL58v7x89ryPrFnJU7sWQkP/1aI0aDyVglhw+/0rq+vjxYtWuDgwYOyR8KLxWIcPHgQ48ePV7hOu3bt8Pvvv0MsFstuOb958yYcHR3rpUEAAASSJ29qJ6pna46qOwLVipY+rAELd2jXafZxP+mb+td/1t+AKA3RJ29JGx126fmpORLV6lOVBABYuk/NgajY2J7Sn3sT6mccl4aqV5j0A8lvx7WrXnung7ReO3K1TM2RqFbnYOn4RutOaVd5D20rLe/1cYrHTmqshkRKv4D8fkK7yvvt9tLy3hCvXeUdFdHwv1Q/T/mm/1PbaxsO/vcLL/vnn39i5MiR+Pnnn9G6dWssWLAA69evx40bN2Bvb4/o6Gg4OzvLBmV/+PAhgoKCMHLkSPzrX//CrVu38O6772LChAnP9D5XFvYUICIiIiIiIs2iIY8kfOutt5CdnY3PP/8cGRkZCAsLw969e2WDDyYnJ8t6BACAq6sr9u3bh3//+98IDQ2Fs7MzPv74Y0yfPr3eYmSjABEREREREVE9GT9+fJ23Cyi6vTsyMvKZgerrExsFiIiIiIiISLMI1fdIwsZGM/pcEBEREREREZHSsVGAiIiIiIiISEvx9gEiIiIiIiLSLBoy0KAm4J4kIiIiIiIi0lLsKUBERERERESaRcCBBpWFPQWIiIiIiIiItBQbBYiIiIiIiIi0FG8fICIiIiIiIs0i5PVtZeGeJCIiIiIiItJS7ClAREREREREmoUDDSoNewoQERERERERaSn2FCAiIiIiIiLNIuD1bWXhniQiIiIiIiLSUmwUICIiIiIiItJSvH2AiIiIiIiINAsfSag03JNEREREREREWkogkUgk6g6CiIiIiIiI6EWV71uhttc27DlGba9dH9hTgIiIiIiIiEhLcUwBUqmf9qo7AtX6sJf054971BuHqo17Xfrz12PqjUPVRnSU/ly6T71xqNrYntKfu/T81BuIivWpSgIA7E2oVHMkqtUrTB8AsHiXdnU0/FcfAQBg54VqNUeiWn3DpR8V528WqzkS1ZoySHrdTFvfv7/fql3n9+QB0vP7f7u1K+/xvQXqDoEaCDYKEBERERERkWYRsNO7snBPEhEREREREWkp9hQgIiIiIiIizSLg7Q/Kwp4CRERERERERFqKjQJEREREREREWoq3DxAREREREZFmEfL6trJwTxIRERERERFpKfYUICIiIiIiIo0i4UCDSsOeAkRERERERERaij0FiIiIiIiISLMIeH1bWbgniYiIiIiIiLQUGwWIiIiIiIiItBRvHyAiIiIiIiLNwtsHlIZ7koiIiIiIiEhLsacAERERERERaRQ+klB52FOAiIiIiIiISEuxUYCIiIiIiIhIS/H2ASIiIiIiItIsHGhQabSyUWDOnDnYunUrEhIS5Ob99NNPyMrKwpYtWzBgwAC1xfc8imJXh9jYWEycOBEFBQVqjeOxS8fX4tyhFSgVZcPG2R9dBn8GB/fQOpe/eXEP4nYvhCgvFZa2Hmjfbwo8gzqpMGLluHR8LS4cWoHSomzYOPmj09/kfSthD+KfyLtdvynwCNS8vM8dXou4fStQXJgNe1d/9Bz2GZw9FeednXoLR7cvQvqDayjMTUWPt2aiTfdRqg1YSRKOrcX5QytQIsqGrbM/urz598f5qV215d3hDc07zpu0bwmvyWNgER4MQyc7nBv8ETK3H3z+Oh1bI3D+DJgGNkX5w3TcnvcTUtZsUVHEynN83x84tCMWooIcOLv7YfDomXD3CVG4bPrD29i9/kek3LuOvOw0DIyehs59Rqg4YuWQSCQ4s3cxrsVvQEWZCI6e4ej85mxY2no8d73LJ9bi4uEVKC3KgY2TPzoO/BT2zzk/GpoT+3/HkR2rUFSYAyc3Pwwc9QncfBTHn/HwNvZuXIyUu9eRn5OG/iOmo2PvaBVHrBwSiQTn/1qMG2c3oLKsCPbuzdF+wGxY2Hg8d71rcWtx+dhKlBXnoImDP9q+MQt2rppT3tr6/i2RSHD+wGIkntmAyjIRHDzC0X7gC5T3qbW4dGwFyopy0MTRH+36f6pR5S2RSHB672Jci9uAinIRHD3C0SXqxeo16XHyqF4b9OlzjxOil6WRzSvZ2dn48MMP4ebmBgMDAzg4OKBnz544efLkS20vMTERc+fOxc8//4z09HS8/vrrz11+zpw5EAgEz0z+/v4v9fr0apIu7MaxLfMQ0XMc3p66BbZO/tjy0xiUFuUqXD7t3gXsWTMZQRFvYvjUrfAO6YYdK8YhJ+2miiN/NTcv7MbxrfPQptc4DJ2yBTbO/ti2tO680+9dwN41kxEY8SaGTdkKr5Bu2LliHHLTNSvva2d348D6eejQbxxiPtsCexd//LFgDEpEivOuqiyDpY0Lug6aDFMLWxVHqzyy47zXOAyfKi3vzUuec5zfvYDdqycjOPJNDJ+2FT6h3bB9ueYd5zomxhBdTsLVCXNfaHkjDxe02v4zco+cxomW/XFv8WqE/PwlbHq0r+dIlevCqb3Ysua/6Dl4LKZ+sx5O7r746esPUFSouLwrK8phY++CfsMmwtzSRsXRKteFQ8tx6fiv6Bw1B1ET10NP3wjbf45BdVVFnevcurgbJ7Z9g1Y9x+GtSZth7eSH7b/E1Hl+NDQX4/Zg+6/f4bXBH+HfX2+Ak7sffvnmOeVdWQZrO1f0GfZvmGl4eV86thzXTv2G9gPmoP9Hf0JP3xh7Vr733PK+c3k34nd9i/Bu4zBw/CZYO/phz8r3UFasGeWtre/fAHDp6HJcPfkrOgycgwHj10NX3wi7Vzz//L5zaTfidn6DFt3GYdCEzbB29MPuFTEaU97Ao3rt2K/oEjUHQyauh56BEbYtfX7eNy/uxvGt36B1z3EYOnkzbJz8sP1nzanXVEIgUN/UyGhko8DgwYNx8eJFrF69Gjdv3sT27dvRuXNn5Oa+3Ely584dAED//v3h4OAAAwODv10nKCgI6enpctOJEyde6vXp1Vw4sgrBbYcgKGIwrB180G3IXOjqG+Ja/CaFy188ugYe/h3QslsMmjh4o22fibBzCcSl47+pOPJXc/HIKgRHDkFgG2neXaOkeV8/rTjvhKNr4O7fAS26SvOO7D0RthqY9+kDq9C8wxCEtRsMWycf9H5nLvT0DZFwUnHeTp6h6B41HUGt+0BHV1/F0SrPhcNPHOeOPuj+6Di/+rzjPEB6nFs/cZwnaFh5Z+87hpuzFyBz218vtLz7+0NRdi8FidO+RfGNu3iwZC0yNu2D58ej6jdQJTuyaw3adhuMiC4D4eDijSExn0Nf3wjxhxX3eHD3CUb/dyYjvN3r0NXT3ONcIpHg0rE1aNljLLyCu8HGyQ/d3/4WJaIs3L1a9zGQcDQWQRFRCGw9GE0cfNDlzbnQ1TNE4hnF50dDc2zXakR0fROtOw+Eg4sPBo+ZDT19Q5w5slnh8m7eIeg3fAqat+0NXQ2u1yQSCa6eXIPmXcbCI7AbrB390HnINygtysKD63WX95Xjq+HfKgp+LQfByt4H7QfMga6+IZLOKd5fDY22vn9LJBJcObEGzbuOhUeQtLy7DPkWpaIs3L9Wd3lfPh4L/9ZR8Gs1GFb2PugwUHp+J53VjPNbIpEg4egatHptLLxCpPVaj8f12pXn1GtHYhEUGYXANo/qtb85TohehcY1ChQUFOD48eP49ttv0aVLF7i7u6N169aYOXMm3njjDdkyMTExsLW1hbm5Obp27YpLly4p3N6cOXPQr18/AIBQKITgBVt+dHV14eDgIDfZ2NS21nt4eODLL79EdHQ0TE1N4e7uju3btyM7Oxv9+/eHqakpQkNDce7cOdk6sbGxsLS0xNatW9G0aVMYGhqiZ8+eePjwYZ1xiMVifPHFF3BxcYGBgQHCwsKwd+9e2f+7du2K8ePHy62TnZ0NfX19HDwo7YJbUVGBKVOmwNnZGSYmJmjTpg2OHDkit05sbCzc3NxgbGyMgQMHvnQDjLLVVFci6+E1uPq2lc0TCIVw822L9PsXFa6TcS8Brn6RcvPc/dsj/X5CfYaqVDXVlchKeTZv1+fknX4/Aa6+z+adoWF5pz+4Bs8A+bw9Atoi9Y7ivBuDmupKZD68Bje/p45zv7ZIv1d3ebs9Xd4B7ZF+L6E+Q1U7y4gw5ByKk5uXfeAErCLC1BPQS6iursLDu9fhGxIhmycUCuEbEoH7txS/lzUWorwUlBZly9VtBkZmsHcLrbOuqqs+dPGN1Ij6rbq6Ein3rqNpcO35KhQK4RscgQeNvLyL8lNQVpQDZ5/a3PUNzWDrGorMZMW511RXIiftmtw6AqEQzt6RyEpOqO+QX5m2vn8DQFFeCsqKsuHctDZ3fSMz2LmG1ll2NdWVyEm9Bpem8vvL2ScSmRpQ3gAgyq2jXnP/5/Waa9NIZDxQvI5WEgrVNzUyGpeRqakpTE1NsXXrVlRUKO5yExUVhaysLOzZswfnz59HeHg4unXrhry8vGeWnTJlClatWgUAsiv+yvJ///d/aNeuHS5evIg+ffpgxIgRiI6OxjvvvIMLFy7A29sb0dHRkEgksnVKS0vx1VdfYc2aNTh58iQKCgowdOjQOl9j4cKF+P777zF//nxcvnwZPXv2xBtvvIFbt24BAGJiYvD777/L7avffvsNzs7O6Nq1KwBg/PjxiIuLw7p163D58mVERUWhV69esm2cPn0aY8aMwfjx45GQkIAuXbrgyy+/VNp+ehVlJfmQiGtgbGYtN9/YzBolRTkK1ykpyoGxmc0zy5eKFC/fED0v77ryKK0j7xINyru0WJq3ibl83qbm1ijWoDz+qeeWd13HuSgHxuby5W3ynOUbCwN7G1RkyudYkZkDPQszCA3/vhdYQ1AiyodYXAMzC/nyNrOwRlFBw2iQrS+lomwAUHCs29R57D4+P4z+wToNSYmoQGF5m1pYo6ig4cf/KsoelY+RqXzuRqY2KCvKVrhOeWmBtLyfXkdD6jdtff8GgNJHZWqsoLzrKrvy0vw6ylszzm+g7ryNTW3q/Kxa93Fio1GfV0lzaFyjgK6uLmJjY7F69WpYWlqiXbt2+OSTT3D58mUAwIkTJ3DmzBls2LABLVu2RNOmTTF//nxYWlpi48aNz2zP1NQUlpaWACC74v8irly5ImugeDyNHTtWbpnevXvjgw8+QNOmTfH5559DJBKhVatWiIqKgq+vL6ZPn47ExERkZmbK1qmqqsL//vc/REZGokWLFli9ejVOnTqFM2fOKIxj/vz5mD59OoYOHQo/Pz98++23CAsLw4IFCwAAgwYNAgBs27ZNtk5sbCxGjRoFgUCA5ORkrFq1Chs2bECHDh3g7e2NKVOmoH379rLGkoULF6JXr16YNm0afH19MWHCBPTs2fO5+6eiogIikUhuqqsRh4iItFPS+R34eUa4bBLXVKs7JKpHty/uwKrZLWSTuKZK3SFRPbp1cQdWfhYum8Ri7Ti/k87vwNLp4bKJ9RppAo18+sDgwYPRp08fHD9+HPHx8dizZw++++47LF++HCUlJSguLoa1tXzLWllZmWzsAGXw8/PD9u3b5eaZm5vL/R0aWjs6qL29PQAgJCTkmXlZWVmyxghdXV20atVKtoy/vz8sLS2RmJiI1q1by21fJBIhLS0N7dq1k5vfrl072e0ShoaGGDFiBFauXIkhQ4bgwoULuHr1qiz2K1euoKamBr6+vnLbqKiokO3DxMREDBw4UO7/kZGRcrcpPG3evHmYO1d+YLDZs2fDPmJOneu8DCMTKwiEOs8MulJalAsTM8WDL5koaF0uLcp95qpqQ/a8vOvKQ9FVs9KiXJhoUN7GptK8nx5UsFiUC1MNyuOfem5513Wcmz97NaHkOcs3FhWZOTCwl8/RwN4GVYVFEJdrRsOkibkVhEKdZwaZKyrMhZmldR1raSbPoC6wd6t9r6ypqQTwuG6yk80vLcqBjXOAwm08Pj/Knjk/nr262hCZmFsqLO/iwlyNH0TwaW6BXTHI9dnyLivOhfET5V1WnANrR8XlbWhsKS3vpwaZK9OQ+k2b3r/dA7vIPSGgpvrR+a2ovJ3qKm+rOsq74Z7fnkFdYD9Fcd4mFk/Ua8U5sK0j77qPk2d7AWozSSMc8E9dNK6nwGOGhobo0aMHPvvsM5w6dQqjRo3C7NmzUVxcDEdHRyQkJMhNSUlJmDp1qtJeX19fHz4+PnKTnZ2d3DJ6enqy3x+PVaBonlgsVlpcisTExODAgQNISUnBqlWr0LVrV7i7uwMAiouLoaOjg/Pnz8vtr8TERCxcuPClX3PmzJkoLCyUm2bOnKmslGR0dPVh5xqEhzdr7yGWiMV4eDMOjh7NFa7j4BmGhzfj5eYlJ52Co0eY0uOrLzq6+rBzCcLDWy+et6NHGB7eejZvBw3L29E9CPcS5fO+nxgHZ2/FeTcGOrr6sFd0nCfFwdGz7vJOfvo4v3EKjp5h9Rmq2hXEJ8C6a4TcPJtubZEfn6CegF6Crq4eXL0CcfPKadk8sViMm1fj4dG0mRojUz59Q1NY2rrLpib2PjA2s0XKE3VbZXkxMpMv11lX1VUfptyK14j6TVdXHy6egbh1tfZ8FYvFuHXtNNwbW3kbmMDCxl02Wdn5wMjMBql3anOvLC9G9sPLsHdTnLuOrj5snILk1pGIxUi7Ew87t7D6TuGVadP7t76BqXx52/vAyMwWabflz++sh5frLDsdXX3YOAch9bb8/kq7HQ/7Blrez9RrDtJ67cn38MryYmQ++Pt6LeXp9/1b8XBwV7wO0avQ2EaBpwUGBqKkpATh4eHIyMiArq7uM1/anxwIsKGqrq6WG3wwKSkJBQUFCAh4tiXR3NwcTk5OzzyK8eTJkwgMDJT9HRISgpYtW2LZsmX4/fff8e6778r+17x5c9TU1CArK+uZ/fW490JAQABOnz4t9xrx8fJvTk8zMDCAubm53PQiT3V4GeGdR+Nq3HpcP7MFeRl3cHDDHFRVliGwjfTWiX2/TcOJHd/Llm/eKRoPEo/j/KGVyMu8g7g9i5H58CqadXinXuKrL807j8a1uPVIfJT34Q1zUP1E3vt/m4aTT+Qd1ikayYnHceGwNO/4PYuRpYF5t+kxGhePr8elU1uQk34Hu9dKy7tZu0e3yqyYhkOba/Ouqa5ERnIiMpITUVNdiaL8TGQkJyIv64G6Ungp4V1G48qp9bh2egtyM+7g4Hpp3kGPynvvr9NwYvtzjvPd0uM8TMPKW8fEGObN/GHeTPrIV2NPF5g384ehqyMAwO/LSWi26lvZ8g9+WQdjT1f4z5sKEz8vuI99G45Rr+Pewlh1hP/SOveJRtyhTThzdBsyUu5iw/L/oLKiDG06DwAA/Pa/T7Dj9wWy5aurq5By/wZS7t9AdXUVCvOzkHL/BrIzktWTwEsSCARo1jEa5w4sxb2rh5CTloQDv0+HibkdvIK7y5bb+tMoXH5i5PWwTqNwPX4DEs9uQV7mHRzZKK0PA1oPUkca/1jHPiNx+vBGnD26FZmpd7Bp5ReorChD607SXnq/L5mJXX/8n2z56upKpN5PROr9RNQ8Ku/U+4nIydCsek0gECC4XTQuHlqKB9cPIS/jJo5smAFjMzu4B9aW967lo3Ht1FrZ3yEdRiLp7AbcPL8V+Vl3cGLbXFRVlsG3xUBFL9PgaOv7t0AgQEj7aFw4tBT3rx9CXnoSDv85HcbmdvAIqi3vnb+MwtVTted3aIdRuHFmA26e34L8zDs4vmUOqqrK4NtSM85vgUCAsE7Seu3uo3pt/9pH9VpIbd5bloySe6JEWOdRuBa/QXqcZN7B4Y3yxwkBEAjVNzUyGnf7QG5uLqKiovDuu+8iNDQUZmZmOHfuHL777jv0798f3bt3R2RkJAYMGIDvvvsOvr6+SEtLw65duzBw4EC0bNlSKXFUV1cjIyNDbp5AIJDdEvCy9PT08K9//QuLFi2Crq4uxo8fj4iIiGduHXhs6tSpmD17Nry9vREWFoZVq1YhISEBa9eulVsuJiYG48ePh4mJidytAL6+vhg+fDiio6Px/fffo3nz5sjOzsbBgwcRGhqKPn36YMKECWjXrh3mz5+P/v37Y9++fc+9dUDV/MJ7o6w4D3G7F6FUlA0blwAMGLtc1q1OlJ8ud/I6eYajV/R8xO1egFM7f4ClrQf6jfkRNk6+db1Eg+Qb3htlJXmI37MIJaJs2DoHoP8Hy2Xd6Yry0yF4Im9Hz3D0jJ6PuF21efcd8yOsHTUr76BWvVFalIej26R527sGYNjHy2W3DxTmyeddVJCF5f8ZIPs7fv9KxO9fCTff1oie+quqw39pTx/nti4BGPhh7XH+dHk7eYXj9ZHzcWrXApzc8QMs7TzwRozmHecWLYIRebC2nALnfwIAeLhmMy6PmQkDR1sYPWogAICy+yk4+8YHCPx+Jjz+FY3ylAxc+eBT5BzQrEfGhrfthWJRHnav/xGighy4ePhj7MylMH/UnTw/Nx0CYW23ycK8LPx3epTs70M7YnFoRyx8AlviX7NXqTz+VxHeNQbVlWU4vOFzVJSJ4OjZAv3eXwZdvdqG5cKcZJSV5Mv+btpcen6c2btYVh/2e39Zg+1e/LTmka+jRJSHfRv/B1FBDpzd/fHejJ9ltw8U5KTLPR1JlJ+NH2a+Kfv7yM5VOLJzFbwDWuGjz2NVHf4radZRWt7Ht8xGZbkI9u7h6DX6F7nyFuUmo7y0try9Q3ujvDgf5/9ahNIi6a0Gr4/+RWPKW1vfvwGgWadH5b3pc1SWi+Dg0QKvvyt/fovyklH+xPnt3Uy6v87tX4zSomxYOwWg97uac34D0nqtqrIMh9fX1mtvfPBsvfZk3r6P6rXTT9Rrb3ygWXmT5hBInhz6XgNUVFRgzpw52L9/P+7cuYOqqiq4uroiKioKn3zyCYyMjFBUVIRZs2Zh06ZNyM7OhoODAzp27Ih58+bB1dUVc+bMwdatW5GQkAAA2Lp1KwYOHIgX3RVz5sx55n55QHp1vLy8HID0kYQTJ07ExIkTZf8XCATYsmULBgwYAAC4f/8+PD09cfHiRYSFhSE2NhYTJ07EypUrMXXqVKSmpqJDhw5YsWIF3NzcZK/9ZOxisRj/+c9/sGzZMmRlZSEwMBDffPMNevXqJRdbcXEx7O3tMWrUKPz4449y/6uqqsKXX36JNWvWIDU1FTY2NoiIiMDcuXNlYyCsXLkSs2fPRm5uLrp3745OnTrhP//5DwoKCl5onz32U8NpS1CJDx8Vw4971BuHqo17Xfrz12PqjUPVRnSU/ly6T71xqNrYR+OO7tLzU28gKtanKgkAsDehUs2RqFavMH0AwOJdGvXx4ZX9q4/0S/nOC9o1aFjfcOn1o/mb6/dWx4ZmyiDpl3Jtff/+fqt2nd+TB0jP7//t1q68x/fW7HvyS+K2qu21TSIHqO2164PGNQo0Zo8bBf7pF+0Xcf/+fXh7e+Ps2bMIDw9X+vZfFBsFtAMbBdQbh6qxUYCNAtqAjQJsFNAGbBTQrrw1vVGgOH773y9UT0wj3lDba9cHjbt9gP6Zqqoq5Obm4tNPP0VERIRaGwSIiIiIiIioYWGjgAKmpqZ1/m/Pnj3o0KGDCqN5NSdPnkSXLl3g6+uLjRs3qjscIiIiIiKiV8dHEioNGwUUeHy/viLOzs719rqjRo3CqFGjlLrNzp07v/BYCURERERERKRd2CiggI+Pj7pDICIiIiIiojpIGuGjAdWFe5KIiIiIiIhIS7FRgIiIiIiIiEhL8fYBIiIiIiIi0iwcaFBp2FOAiIiIiIiISEuxpwARERERERFpFg40qDTck0RERERERERaio0CRERERERERFqKtw8QERERERGRRpFwoEGlYU8BIiIiIiIiIi3FngJERERERESkWTjQoNJwTxIRERERERFpKfYUICIiIiIiIo0iAccUUBb2FCAiIiIiIiLSUmwUICIiIiIiItJSAolEIlF3EEREREREREQvquDiIbW9tmXzrmp77frAngJEREREREREWooDDZJKff1njbpDUKlP3tIBAKw5quZAVCy6k/TnjvPV6g1Exfq1kFapexMq1RyJavUK0wegvXnv0vNTcySq1acqCQAw/ZcyNUeiWt++bwQAKN/+o5ojUS3DN8YBAL7dKFZzJKo1/U3pdbOFO7SrQ+3H/aQDty3epV15/6uPNO//7dauvMf31vCB+vhIQqXhniQiIiIiIiLSUmwUICIiIiIiItJSvH2AiIiIiIiINIpEoOG3PzQg7ClAREREREREpKXYU4CIiIiIiIg0ioQDDSoN9yQRERERERGRlmJPASIiIiIiItIsHFPg/9m77/Coqq2Bw7+Z9N57QkII6UDohF5FQToIKiIIlit45SIKKFJs4LVcBBQbXf1QpHcxEEroJdQQOqGENNJ7MvP9MZBhSIKUtCHrfZ55IGf2PrPWnDazZ+99Koz0FBBCCCGEEEIIIWopaRQQQgghhBBCCCEqybfffouPjw+mpqa0bNmSAwcOPFC9ZcuWoVAo6Nu3b6XGJ40CQgghhBBCCCH0ilqhrLbHw/j9998ZN24cU6dO5ciRIzRq1Iju3buTmJh433qXL19m/PjxtGvX7nHepgcijQJCCCGEEEIIIUQl+Prrr3n11VcZMWIEwcHBfP/995ibm7NgwYJy6xQXF/Piiy8yffp0fH19Kz1GaRQQQgghhBBCCKFX1Ciq7ZGfn09GRobOIz8/v1SMBQUFHD58mK5du5YsUyqVdO3alb1795ab20cffYSzszMjR46slPfuXtIoIIQQQgghhBBCPKAZM2ZgY2Oj85gxY0apcsnJyRQXF+Pi4qKz3MXFhZs3b5a57t27dzN//nx++umnSom9LHJLQiGEEEIIIYQQ4gFNmjSJcePG6SwzMTF57PVmZmby0ksv8dNPP+Ho6PjY63tQ0igghBBCCCGEEEKvPOyEfxXJxMTkgRoBHB0dMTAwICEhQWd5QkICrq6upcpfuHCBy5cv06tXr5JlKpUKAENDQ2JjY6lXr95jRl+aDB8QQgghhBBCCCEqmLGxMU2bNiUiIqJkmUqlIiIigvDw8FLlAwMDOXHiBNHR0SWP3r1706lTJ6Kjo/Hy8qqUOKWngBBCCCGEEEII/aJQVHcED2TcuHG8/PLLNGvWjBYtWjBr1iyys7MZMWIEAMOGDcPDw4MZM2ZgampKaGioTn1bW1uAUssrkjQKCCGEEEIIIYQQlWDw4MEkJSUxZcoUbt68SVhYGJs3by6ZfDAuLg6lsno78D/xjQLTpk1j9erVREdHAzB8+HDS0tJYvXp1tcYlhBBCCCGEEOLRqPVoJPyYMWMYM2ZMmc9FRkbet+6iRYsqPqB7VHujwN69e2nbti1PP/00GzZsqPTX++abb1Cr1ZX+Onfr2LEjYWFhzJo1q2TZ5cuXqVu3LkePHiUsLKxK43lStQ9VEOarwMQIriXD5sMqUrPKL+/lBK0ClLjag5WZgj93F3P2etXFWxEObf+VfX/NJys9CRfPQJ56/kM86jYss2zSjXPsWDObm3GnSE+5TrfnJtGi6/CqDbiCRP31G5HrF5KZnoxbnQD6vfw+dfzKzvvmtfNsWT6Ha5dOk5p8g94vTaD9M8OqOOKKsWvL/7Ft3SIy0pLx8A5gwIhJePs1KLNs/NXzbPzjW65dOs2tpBv0G/YeHXu+VMURV4zamLd922b4vjMSmyahmLo7c2jAmySsjbh/nfYtCP5yIpbB9cm7Gs/5GfO4tmRVFUVcsbo1NaRFkCFmxnD5popVuwtJySj/2l3XVUn7RoZ4OiqxtlCweEs+p6+oqjDix7cs6hiLdxwhOTMHfzdHJvbtQIM6pSehuiMjN5+5m/YQcfIC6Tl5uNlZ817v9rQL8qm6oCuAWq3maMQcYg8upyAvE2fvxrTuPRUbR5/71ju971dO7lpAblYydq6BhD/7AU5eZV8HaiK1Ws3BLXM4vX85+bkZuNVtQvv+U7F18rlvvRNRvxIdOZ+czGQc3AJp128yLnX0K+8Dm+dwap82744D/znv47t/5eh2Td6O7oG07zcZF2/9ynv/5jmc2ruc/LwM3Hya0GnQg+V9ZNtdefefjKse5S30R7U3r8yfP5+33nqLnTt3cuPGjUp/PRsbm5JxGeLJ0SpQQbP6CjYdUrHobxWFxTCkgxKD++zhRgaQmKZmy2H9+uB4x+mDG/l7+QzaPTuakZNX4ewVyLJvRpKdkVJm+cKCXOycPOnU7x0srJ2qONqKE713E2t/+S/d+r/J2E+X414ngJ9mvk5metl5F+TnYu/sRY8h/8HKtupu7VLRjuzZzKolX9B9wBu8O/MP3L39mffZ/fLOw9HFk17Pj8Va8tY7BhbmZByP5eS/pz9QeTMfT5qv/YGUyP3sbtaHS3MW0+CHT3Ds1raSI614HRoZ0ibUkFW7Cpi7Op+CIhjZwxhDg/LrGBtBfIqK1VEFVRdoBdocfZYv1+3i9W4tWTZ2CAHujvzr5zWkZOWUWb6wqJg3flzFjdRMvnypB2veG8bUgZ1xtrGo4sgf34ldP3N67y+07jONXv/6HSMjc7YsepWiwvxy61w8vpEDGz8nrPNoeo9egb1rAFsWvUpuVtnnhZro6PafOb57KR0GTGPAv//A0NiM9T+Num/e56I3ErV2Js26jWbQ2JU4ugew/qdR5GTqT95Htv3MsV1L6ThoGoPG/oGRsRlrf/iHvI9uZPeamTTvPprB41bi4B7A2h/1MO+dS+k0aBrPjf0DIxMz1nx//7zPHt3IrtUzadF9NEPe0WzvtT/oV95Cf1Rro0BWVha///47//rXv+jZs6dO14jIyEgUCgUbNmygYcOGmJqa0qpVK06ePFlSZtGiRdja2rJ69Wrq16+Pqakp3bt35+rVq+W+5vDhw+nbt2/J3yqViv/+97/4+flhYmJCnTp1+PTTT0uenzBhAv7+/pibm+Pr68uHH35IYWFhyfPTpk0jLCyMpUuX4uPjg42NDUOGDCEzM7Pk9Xbs2ME333yDQqFAoVBw+fLlUnHdyTciIoJmzZphbm5O69atiY2N1Sm3bt06mjdvjqmpKY6OjvTr16/kudTUVIYNG4adnR3m5uY888wznDt3rtT7tX79egICAjA3N2fgwIHk5OSwePFifHx8sLOz49///jfFxcUl9fLz8xk/fjweHh5YWFjQsmXLf+zmUtVa+CuIOq3m3A1ISod1+1VYmUGAR/kTkFy8CTtOqvWud8Ad+7cuJKztczRqMwAndz96vDgdQ2NTjkWtKLO8u09DugycQEiLnhgaGVdxtBVnx8bFtOw0kBYd++Hq6ceAkVMxMjHl4I6VZZavU68BvV4cT+PWPTA01N+8IzcsoXWXAbTq1A9Xz3o8N2oKxsZm7Nte9i/B3n6h9Bn6Dk3aPKPX27u25p20ZSdnp84iYc3fD1Te+7Uh5F66Rsx7n5N15iJXvvuVmyu2UPft4ZUbaCVo28CQbUeLOH1Fxc1bav7YXoC1uYIQn/JbBWKvqvjrUBGnLutnI+/SnUfp3zKUvs2DqefiwOT+nTE1MmT1gdNlll918DTpOXn8b3hPGtd1x8Pemmb1PAlw168GX7VazamoJTTq+AbewV2wdw2g/aCZ5GYmEhdT/r5/MmoxAc0G4d+0P3bOfrTpMw1DI1POHi77OlDTqNVqju9aQtOub1A3tAuO7gF0GfI52RmJXDpZft7HdiwiuOUggloMwN7Vjw4DpmNoZMqZg2Vf92satVrNsZ1LaNbtDXxv5931BU3eF++Td/SORYS0GkTw7bw7DdTkHXNAf/KO3rGE5k+9gW8DTd7d7uR94j55Ry4iJHwQwS1v5z1I8znv9H79yLsqqBWKans8aaq1UeCPP/4gMDCQgIAAhg4dyoIFC0p17X/33Xf56quvOHjwIE5OTvTq1UvnS3lOTg6ffvopS5YsISoqirS0NIYMGfLAMUyaNImZM2fy4Ycfcvr0aX777beSSR8ArKysWLRoEadPn+abb77hp59+4n//+5/OOi5cuMDq1atZv34969evZ8eOHcycORPQDFcIDw/n1VdfJT4+nvj4+PveSuKDDz7gq6++4tChQxgaGvLKK6+UPLdhwwb69etHjx49OHr0KBEREbRo0aLk+eHDh3Po0CHWrl3L3r17UavV9OjRo9T7NXv2bJYtW8bmzZuJjIykX79+bNy4kY0bN7J06VJ++OEH/vzzz5I6Y8aMYe/evSxbtozjx48zaNAgnn76aZ0Gh+pkawGWZgouJWj3nfxCuJECHvr7Q+F9FRcVEB93irpBrUuWKZRK6ga15trFo9UYWeUqKirg+qXT+Idqb+GiVCqpH9qKK+eOVWNklauoqJCrF0/j36BVyTKlUol/g1ZclrwFYNsqjORte3WWJW3djV2rsOoJ6BHZWymwNldw7rq2YTqvEK4mqqjjXO2dGytFYVExMdcTaVVf+9lAqVTQqr4Xx6/El1lnx+mLNPR2Y8aqSDpN/4n+X/7CzxEHKVbpV6NIZuo1crOSca+nPacbm1rh5NmQxLiyj/HiogJSbpzC3U9bR6FU4u4XTlJcdGWHXCEybl0jJzMJr/raa7iJmRUudRpy80p0mXWKiwpIun4KT3/d675n/fBy69Q0JXn7l5H35egy6xQXFZB47ZROHYVSiad/eLl1apqMlHLy9n74vL30aHsL/VKtcwrMnz+foUOHAvD000+Tnp7Ojh076NixY0mZqVOn0q1bNwAWL16Mp6cnq1at4rnnngOgsLCQuXPn0rJly5IyQUFBHDhwQOcLc1kyMzP55ptvmDt3Li+//DIA9erVo21bbXfLyZMnl/zfx8eH8ePHs2zZMt57772S5SqVikWLFmFlZQXASy+9REREBJ9++ik2NjYYGxtjbm6Oq2v5YwPv+PTTT+nQoQMAEydOpGfPnuTl5WFqasqnn37KkCFDmD5d2520UaNGAJw7d461a9cSFRVF69aaE8ivv/6Kl5cXq1evZtCgQSXv17x586hXrx4AAwcOZOnSpSQkJGBpaUlwcDCdOnVi+/btDB48mLi4OBYuXEhcXBzu7u4AjB8/ns2bN7Nw4UI+++yzMvPIz88nP1+3S5SJiQmVsctZmGr+zc7TXZ6dpy557kmTk5WKWlWMhbWDznILKwdS4i9WU1SVLzszDZWqGEsb3bytbBxIvHGpmqKqfNkZqahUxVhJ3sCTn/ejMHFxJD8hWWdZfkIyRjZWKE1NUOWV30W1JrEy1/z6kpWj+wNBVq4aK/PqiKjypWbnUqxS42Cpm6CDpTmXElPLrHMtJYMbqdfo0TiAb0f2IS45jc9WRVJUrOKNp1pWRdgVIjdTs8+aWeoe46aWjuRmJZVZJz8nDbWquFQdM0sH0pL047yQk6nJzczq3hwcyclMLqsKedma6775vXlbOZKaqCd5Z2jyNr8nb3Or8vPOvZ33ve+VuZUjafqS9+3tfe+2M7d0JPsf8i7rvdKX7V0V1Ions7G4OlRbo0BsbCwHDhxg1SpNF1BDQ0MGDx7M/PnzdRoFwsO1LcH29vYEBAQQExNTsszQ0JDmzZuX/B0YGIitrS0xMTH/2CgQExNDfn4+Xbp0KbfM77//zuzZs7lw4QJZWVkUFRVhbW2tU8bHx6ekQQDAzc2NxMTE+78B5WjYUDt5iJubGwCJiYnUqVOH6OhoXn311XJzMTQ0LGkcAXBwcCj1fpmbm5c0CAC4uLjg4+ODpaWlzrI78Z84cYLi4mL8/f11Xi8/Px8HB90T1d1mzJih03gBmgYe46APy63zoEK8FTzTVNtt549d+vXLiBBCCI0wPwP6tzMq+XvhZv2cE6CqqdRq7C3NmDKwMwZKJcGeziSmZ7F4x5Ea3ShwIXodUWumlfzdbdi86gumCp09so7IP6eW/N1z5PfVGE3ViT28jsjl2ryfHVV78t7+hzbvXq/WjryFfqu2RoH58+dTVFRU8uszaMbcmJiYMHfu3CqJwczM7L7P7927lxdffJHp06fTvXt3bGxsWLZsGV999ZVOOSMjI52/FQoFqkfswnf3uhS3x6vcWdc/xfuw67/zGveLPysrCwMDAw4fPoyBge54zrsbEu41adIkxo0bp7PMxMSEr1Y/RvC3nbuu5kaK9lekO5MJWpjq9hawMFWQkFa1d5qoKuaWdiiUBqUmFczOTMHC5gkdMwFYWNmiVBqQdc8kc5npKXo9qdw/sbC2Q6k0KDW5XmZ6Cla25TfO6bvamvejyE9IxsRF9xgwcXGkMD2zRvcSOH2lmKuJ2uvlnckELc0VZOZqz9+WZgqd8/6TxM7CDAOlotSkgilZOTiW0z3CydocQ6UBBnfd19rXxZ7kzBwKi4oxut+sjNWoTlBnnTsEFBdpGoFys1Iwt3YuWZ6XlYy9W1CZ6zAxt0WhNCg1qWBuVgrmljXzOuAT3InB48rIOzMFi7vyzs1KxsG97LxNLTTX/Zx7885Mxty6ZuZdN6STzp0Rios1eefck3dOZjKOHmXnbXY779x7JtfLyUzG3KoG5z2+9PbOyUrBwuauvLOScSpne9/J+95JBXNq8PYW+q1a+lwUFRWxZMkSvvrqK6Kjo0sex44dw93dnf/7v/8rKbtv376S/6empnL27FmCgoJ01nXo0KGSv2NjY0lLS9MpU5769etjZmZGRETZt3nas2cP3t7efPDBBzRr1oz69etz5cqVh87X2NhYZ+K+R9WwYcNyYw0KCqKoqIj9+/eXLEtJSSE2Npbg4OBHfs3GjRtTXFxMYmIifn5+Oo/7DYcwMTHB2tpa56EZPvD4CoogNUv7SM7QdC31cdH2HjA2BHcHuF52ryy9Z2BojFudEC6f0Y4hVqtUXI7Zi6dv42qMrHIZGhrjUTeYc6e05wWVSsX5U/vxrt+oGiOrXIaGRnj5BnP2hPb4VqlUnD25Dx/JWwBp+6Jx6NxKZ5ljl9ak7ouunoAeUEEhpGSoSx4JqWoyctT4uWu/1JoYgZezkrjEJ7NXmJGhAUEezuw/r50kWaVSs//8VRp6u5VZJ8zHnaspaahU2oaSK0lpOFlb1NgGAQAjEwusHbxLHrbOfphZOnLjovacXpCXRdK14zjXKfsYNzA0xsE9hBsXtHXUKhU3LuzDqU5YZafwSIxNLbFx9C552Ln4YW7lxLVz2mt4QV4WCXHHcfUOK3MdBobGOHmEcP2c7nX/2vl95dapbsamltg6eZc87O+Xt09YmeswMDTG2TOEq/fmfW5fuXWqW6m8XTV5Xz17T95X/jnva2d18756ruZu7+qgRlFtjydNtfQUWL9+PampqYwcORIbGxud5wYMGMD8+fP54osvAPjoo49wcHDAxcWFDz74AEdHR527BxgZGfHWW28xe/ZsDA0NGTNmDK1atfrHoQMApqamTJgwgffeew9jY2PatGlDUlISp06dYuTIkdSvX5+4uDiWLVtG8+bN2bBhQ8lwh4fh4+PD/v37uXz5MpaWltjb2z/0OkDT/b5Lly7Uq1ePIUOGUFRUxMaNG5kwYQL169enT58+vPrqq/zwww9YWVkxceJEPDw86NOnzyO9HoC/vz8vvvgiw4YN46uvvqJx48YkJSURERFBw4YN6dmz5yOvuyIdOKumTbCC1Ew1adnQPlRJZi7EXtd+YHqho5LYa2oOn9csMzIEu7s6O9hYKHC2VZNXABll3wWqRmnZbQRrF07AzTsU97oNOfD3YgoLcmnYpj8Aaxe8h5WtC536vwPcnqQo/kLJ/zPTErh5NQZjE3Psnb2rLY+H1aHHyyz7/n08fUOoU68BuzYtpSAvl+YdNHfi+L/vJmFj70yPIf8BNJMTJly7k3ch6bcSuX45BhNTcxxd9Sfvjj2H8et3H1CnnibvHRuXUpCfS8uOfQH4Ze772Ng70+uFsYBmkr6bt/MuKiokPTWRa5fPYGJqjpNrnWrK4uHV1rwNLMyx8NPGa17XE+tGgRTcSifvajwBn4zD1MOFYyMmAHDlx2V4v/kigTPe5eqiFTh2aoXboGc42Pv16krhke0+UUTnJoYkZ6hIzVDzVHMjMnLUnLqsbVx/tacxJy8Xs/eUZpmxITjYaD+k2VsrcHNQkJsHadk1v4fBS+0b8+HvWwnxdCHUy4VfdkWTW1BE3+aaRv0P/u8vnG0seLtHGwCeC2/AsqhjfL52B8+3aURccho/bzvIC23DqjGLh6dQKAhpM4xj27/HxsEbSztPjvw9GzMrZ+oEdS0pt2n+CLyDuxIc/iIAoW1eZteKSTh6hOLk2YBTe5ZQVJCLf9N+5b1UjaJQKGjYbhiHI77HxskHa3sPDmyejYW1M3VDtXmv+X44vqFdadBWM/9Wow7D2bZsIk6eoTjXacjxXYspKsglsHn/6krloSgUChq1H8ahrd9j6+iDlb0H+2/n7XtX3qvnafJu2E6Td1iH4fz9fxNx9grFpU5Dju3Q5B3UQn/yDutwO+/b23vfptt5N9Dmveq74fg26EqjO3l3HM7fv93O27sh0bfzDm6pH3kL/VItjQLz58+na9eupRoEQNMo8N///pfjx48DMHPmTN5++23OnTtHWFgY69atw9hYe5spc3NzJkyYwAsvvMD169dp164d8+fPf+BYPvzwQwwNDZkyZQo3btzAzc2NN954A4DevXvzn//8hzFjxpCfn0/Pnj358MMPmTZt2kPlO378eF5++WWCg4PJzc3l0qVHmyCkY8eOLF++nI8//piZM2dibW1N+/btS55fuHAhb7/9Ns8++ywFBQW0b9+ejRs3lhoe8LAWLlzIJ598wjvvvMP169dxdHSkVatWPPvss4+13oq074waY0N4ppkSU2O4mgS/71BRfNcPS7aWYH5XZwU3OxjaWfuLSrfGmo4zxy+pWH+g5n+IDG7eg+zMW+xYO5vsjCRcPIMY8u+fsbzdrSz9VjyKuyZgyUxLZP7HfUv+3vfXAvb9tYA6/i14afzSqg7/kYWFP0NWxi22/DmXzLRk3L0DGTXxB6xuD5tITYlHodR+OchITeJ/7w8s+XvHhoXs2LAQ36DmvPnhoqoO/5E1af00WRm32PjHt2SkJePpE8gbk74vGTZxb97ptxL5YsKgkr+3rVvEtnWL8AtuxltTF1Z5/I+qtuZt0zSU8AjtcRn85fsAXF2ykuMjJ2Hi5oSZl/ZX5NzL1zjY+3WCv5qEz1vDyLt2kxOvTyZ56+4qj/1x7ThWhLEhDGhnjKkxXL6pYsGmAoru6nBnb63AwlS73T2dlLzeS3uC7xWu+ZxwKLaI5Tu0d+CpqZ4O8yc1O5fvtuwjOTObAHcnvhvVB4fbwwdupmWivOsWWK62Vswb1Zcv1u1k0Ne/4WxtwYttwxjRqWl1pfDIGrQbRVFBLlGrp1KQl4GzdxO6D/8RQyPt9sy8FUdejnbSRd+GPcjLTuVIxGxyMzVDDZ4a/iNmNXT4QFkad9LkHfnnFApyM3Cr25RnX/1JJ++MlDhys7V51w/rQV7WLQ5smUNOZhKO7kE8O+qnGtuNvixNOmvy3r58Cvm38+71mm7e6cn35N24B7lZtziweQ7ZGUk4eQTR6zX9y7uwIJftf2jz7v166bzz7srb/3be++/Ku/fr+pV3ZZOJBiuOQn3vPQBriMjISDp16kRqaiq2trZlllm0aBFjx44lLS2tSmMTj+6z3x9/GIU+eX+wptFhyY5qDqSKDdPcQIN1h4uqN5Aq1quppp11c3Ttmizt6TDNF7DamvcGo4BqjqRq9SyMBWDCj7nVHEnV+vw1zbw+eWu/reZIqpZp79EAfP7nkzl8ozwTBmq+bHyzrkZ+TK40b/fSND7N2VC78n6rpybvuRtrV95jeuh3N/gbscer7bXdAxr+cyE9Is0rQgghhBBCCCFELVVtdx8QQgghhBBCCCEehVqh3z0dapIa21OgY8eOqNXqcocOAAwfPlyGDgghhBBCCCGEEI9IegoIIYQQQgghhNArT+KtAatLje0pIIQQQgghhBBCiMolPQWEEEIIIYQQQugVuSVhxZF3UgghhBBCCCGEqKWkUUAIIYQQQgghhKilZPiAEEIIIYQQQgi9IhMNVhzpKSCEEEIIIYQQQtRS0lNACCGEEEIIIYRekYkGK468k0IIIYQQQgghRC0ljQJCCCGEEEIIIUQtJcMHhBBCCCGEEELoFZlosOJITwEhhBBCCCGEEKKWkp4CQgghhBBCCCH0ikw0WHHknRRCCCGEEEIIIWop6SkghBBCCCGEEEKvyJwCFUd6CgghhBBCCCGEELWUQq1Wq6s7CCGEEEIIIYQQ4kFdvHCh2l7bt169anvtyiDDB4QQQgghhBBC6BW1QoYPVBRpFBBVauqSwuoOoUpNH2YEwLI9tatDzpDWmpP0zxHVHEgVG9VF8+8vu2rX9h7aTrO952yoXXm/1VOT94Qfc6s5kqr1+WtmAGwwCqjmSKpWz8JYAHafzq7mSKpW22ALAD5dVlzNkVStD4YYADB3Y+06r43poTmvfb+lmgOpYm901/z72e+1az9/f7BBdYcgaghpFBBCCCGEEEIIoVfUaukpUFFkokEhhBBCCCGEEKKWkkYBIYQQQgghhBCilpLhA0IIIYQQQggh9Ipaft+uMPJOCiGEEEIIIYQQtZT0FBBCCCGEEEIIoVfUyESDFUV6CgghhBBCCCGEELWU9BQQQgghhBBCCKFXpKdAxZGeAkIIIYQQQgghRC0ljQJCCCGEEEIIIUQtJcMHhBBCCCGEEELoFRk+UHGkp4AQQgghhBBCCFFLSU8BIYQQQgghhBB6RXoKVBzpKSCEEEIIIYQQQtRS0igghBBCCCGEEELUUjJ8QAghhBBCCCGEXlGrZfhARZGeAkIIIYQQQgghRC31xDcKXL58GYVCQXR0NACRkZEoFArS0tKqNS4hhBBCCCGEEI9GjaLaHk+aRx4+cPPmTT799FM2bNjA9evXcXZ2JiwsjLFjx9KlS5eKjLFCtW7dmvj4eGxsbKrsNRctWsTYsWNLNUR07NiRsLAwZs2aVWWxPMk6NVLStL4SU2OIS1Kzfl8xtzLLL+/trKBNiBI3BwXW5gr+b3sRZ66qqy7gCrA/4lf2bJpPVnoyLnUC6fHiZDx9G5ZZNvH6Obatmk385VOkpdzg6ecnEf7Uy1UcccU4suNXDm6dT3ZGEs6egXR57kPcfMrOGyD2yCZ2r/uG9JTr2Dn70KHveHxDO1RhxBXj4LZf2bvl9vb2CuTp5yfjcZ/tvWPNbOKvnCI95QZPDZ5Ey276ub3VajUHNs/h1L7l5Odm4Fa3CR0HTsXWyee+9Y7v/pWj2+eTk5mMo3sg7ftNxsW7/P2kJurW1JAWQYaYGcPlmypW7S4kJaP881RdVyXtGxni6ajE2kLB4i35nL6iqsKIH49922b4vjMSmyahmLo7c2jAmySsjbh/nfYtCP5yIpbB9cm7Gs/5GfO4tmRVFUVccbZt/J3Nq5eQnpaCl48/L4x6D1//0DLL7vhrJXsj13M97gIA3vWC6P/imHLL13TtQxU0rqfAxAiuJcOmQypSs8ov7+UE4YFKXO3BykzB8l3FnL1edfFWBLVazf7Nczi1dzn5eRm4+TSh06AHO68d2XbXea3/ZFz16LwWvfNXDm/TXL+dPALpNPDD+8Z/9ugm9mz4hoxb17F18qFd7/HUDdG/6zdo9vMwX+1+vvnwP+/nrQK0+/mfu/VvPxf645F6Cly+fJmmTZuybds2vvjiC06cOMHmzZvp1KkTo0ePrugYK5SxsTGurq4oFE9eC09t1jZEScsgJev2F/PTxiIKi+ClroYY3mcPNzKEm6lqNuwvrrpAK9DJ/RvZsmwmHfuM5vVpK3H1CmDpV6PIykgps3xhfh52Tl50HfQOljZOVRxtxTlzaCORK2bQuudohk1ahZNHIMvnjCQ7s+y8r184wroF79Cg9UBenrSa+o26sOqH0STdOFvFkT+eUwc2svWPmbTvNZpXp6zExSuA32aNIruc7V1UoNnenQfo9/YGOLLtZ47tWkrHQdMYNPYPjIzNWPvDKIoK88utc+7oRnavmUnz7qMZPG4lDu4BrP1xFDnl7Cc1UYdGhrQJNWTVrgLmrs6noAhG9jDG0KD8OsZGEJ+iYnVUQdUFWoEMLMzJOB7LyX9Pf6DyZj6eNF/7AymR+9ndrA+X5iymwQ+f4NitbSVHWrEO7N7C7wu/pvfg15j61W94+dTnfx+NJiPtVpnlY08dpkW7p3n34x95f+Yi7B1d+Hr6m6SmJFZx5I8vPFBBc38Fmw6pWLRVRWERPN9RicF9rt/GhpCQpmbLIf1p8LrXkW0/c2znUjoNmsZzY//AyMSMNd/f/7x29uhGdq2eSYvuoxnyzkoc3QNY+4P+nNdij2xk56oZtHp6NC++uwpHj0BWfjey3PhvXDzCxsXvEBo+kBffW41fwy6s/Xk0yXp2/QZoFaigWf3b+/nfKgqLYUiH++/nRgaQmKZmy2H93c8rm/QUqDiP1Cjw5ptvolAoOHDgAAMGDMDf35+QkBDGjRvHvn37AIiLi6NPnz5YWlpibW3Nc889R0JCQsk6Lly4QJ8+fXBxccHS0pLmzZvz999/67yOj48PH3/8Mc8//zwWFhZ4eHjw7bff6pRRKBTMmzePZ555BjMzM3x9ffnzzz/Ljb2s4QNRUVF07NgRc3Nz7Ozs6N69O6mpqQBs3ryZtm3bYmtri4ODA88++ywXLlwoqXtneMLKlSvp1KkT5ubmNGrUiL1795a83ogRI0hPT0ehUKBQKJg2bVqZsfn4+PDZZ5/xyiuvYGVlRZ06dfjxxx91yly7do3nn38ee3t7LCwsaNasGfv37y95ft68edSrVw9jY2MCAgJYunRpqffrhx9+4Nlnn8Xc3JygoCD27t3L+fPn6dixIxYWFrRu3VonR4A1a9bQpEkTTE1N8fX1Zfr06RQVFZX7Ple1VkFKdh5XEXtVTUIarNxdjJU5BNYp/6A9f0PNtmiV3vUOuGPPX4to2n4QjdsNwNnDj2eHTcfI2JSju1aUWd7DtwHdB79Hg5Y9MTQ0quJoK86hbQtp2OY5GoQPwNHNj6ee1+R9ck/ZeR/evoS6we1o0W0UDm71aNtrLC5ewRyN/KWKI388+7YuonG7QYS1HYCTux89h2ryjt5ddt7udRvQddB7hLboiYEeb2+1Ws2xnUto1u0NfEO74OgeQNcXPic7I5GLJ/8ut170jkWEtBpEcIsB2Lv60WngdAyNTIk5UPb7VRO1bWDItqNFnL6i4uYtNX9sL8DaXEGIT/mtArFXVfx1qIhTl/XzQ2TSlp2cnTqLhDXlb9u7eb82hNxL14h573Oyzlzkyne/cnPFFuq+PbxyA61gf639lfbd+tG2Sx/cvXx56Y0PMDYxZXfEmjLLv/afT+n8zHPUqRuAm2ddhr85BbVaTczxA1Uc+eNrEaBg9yk1Z69DYjqs3a/CygwCPMu/fl+Ihx0n1MTq6a+marWa6B1LaP7UG/g20JzXut05r524z3ktchEh4YMIbnn7vDZoOobGppzerx/ntSPbFxLa+jlCWg3Awc2Prs9p4j+5r+z4j+5Ygk9QO5p1GYWDaz1a9xyLs2cw0bv06/oN0MJfQdRpNeduQFI6rLuzn3uUv59fvAk7Tqqld4CoEg/dKHDr1i02b97M6NGjsbCwKPW8ra0tKpWKPn36cOvWLXbs2MHWrVu5ePEigwcPLimXlZVFjx49iIiI4OjRozz99NP06tWLuLg4nfV98cUXNGrUiKNHjzJx4kTefvtttm7dqlPmww8/ZMCAARw7dowXX3yRIUOGEBMT80D5REdH06VLF4KDg9m7dy+7d++mV69eFBdrfj3Ozs5m3LhxHDp0iIiICJRKJf369UOl0v3A9cEHHzB+/Hiio6Px9/fn+eefp6ioiNatWzNr1iysra2Jj48nPj6e8ePHlxvPV199RbNmzTh69Chvvvkm//rXv4iNjS15zzp06MD169dZu3Ytx44d47333iuJZdWqVbz99tu88847nDx5ktdff50RI0awfft2ndf4+OOPGTZsGNHR0QQGBvLCCy/w+uuvM2nSJA4dOoRarWbMmDEl5Xft2sWwYcN4++23OX36ND/88AOLFi3i008/faD3uLLZWYKVuYKL8dptkl8I15PUeDk9eS15AEVFBcRfPoVvSOuSZUqlEt/gcK6ej66+wCpZcVEBN+NO4R2gzVuhVOId2Jobl46WWefGpWi8A8N1lvkEt+XGpejKDLVCFRcVEH/lFHWDdfOuGxTOtYvR1RdYFci4dY2czCS8/LW5m5hZ4VKnITcvR5dZp7iogMRrp3TqKJRKPP3Dy61T09hbaYY1nbuu7cmUVwhXE1XUcX7ipwN6YLatwkjetldnWdLW3di1CquegB5BUWEhVy7EENSoZckypVJJcMOWXIg9/kDryC/Io7i4CAtL68oKs1LYWoClmYLLCdrG+fxCuJ4CHg7VGFgly0gp57zm/fDnNa/64dy8UnadmqS4qICEq6eoc8/1u05Aa+LLuX7HX46mjr/u9ds7qC3xenT9Bu1+fume/fxGCng4VmNgQtzloecUOH/+PGq1msDAwHLLREREcOLECS5duoSXlxcAS5YsISQkhIMHD9K8eXMaNWpEo0aNSup8/PHHrFq1irVr1+p8IW3Tpg0TJ04EwN/fn6ioKP73v//RrVu3kjKDBg1i1KhRJevZunUrc+bM4bvvvvvHfP773//SrFkznbIhISEl/x8wYIBO+QULFuDk5MTp06cJDdWO3Rs/fjw9e/YEYPr06YSEhHD+/HkCAwOxsbFBoVDg6ur6j/H06NGDN998E4AJEybwv//9j+3btxMQEMBvv/1GUlISBw8exN7eHgA/P7+Sul9++SXDhw8vqX+n58aXX35Jp06dSsqNGDGC5557ruQ1wsPD+fDDD+nevTsAb7/9NiNGjCgpP336dCZOnMjLL2vGI/v6+vLxxx/z3nvvMXXq1DLzyM/PJz9ftwuciYkJlTG3paWZ5ot/Vp7u8qw87XNPmpzMVFSqYiytdT81Wdo4knzzUjVFVflys1JRq4oxvydvcysHbiVcLLNOdkYyFla6V10LKweyM5IrLc6KlnM773u3t4X1k729AXIykgDNNr6buZUjOZllb8PcbM37ZVZGnbRE/Xi/rMxvn9dydHsyZeWqsTKvjohqJhMXR/ITdPeD/IRkjGysUJqaoMorvyt2TZGZmYZKVYy1jb3Ocmtbe+KvX36gdfy5ZDa2dk4E39WwoA8sTDX/Zt9z/c7OU2NpVvXxVJWczNvnNct7zlGWjmT/w3mtrHNhqh6c18qP34HU+1y/za1LX7/LO/fXVPfbz+88Jx7Nk9iNv7o8dKOAWv3PXa1jYmLw8vIqaRAACA4OxtbWlpiYGJo3b05WVhbTpk1jw4YNxMfHU1RURG5ubqmeAuHh4aX+vndivrLK3LnbwD+Jjo5m0KBB5T5/7tw5pkyZwv79+0lOTi75VT4uLk6nUaBhQ+0kKW5ubgAkJibet/GkLHev505DQmJiYkmsjRs3LmkQuFdMTAyvvfaazrI2bdrwzTfflPsaLi4uADRo0EBnWV5eHhkZGVhbW3Ps2DGioqJ0egYUFxeTl5dHTk4O5ualP6HOmDGD6dN1x4ROnToVfD+4b/4PokFdBb1aabvP/rpNP+cEEEKUFnt4HZHLtY2Nz476vhqjqTphfgb0b6cd5rFws37OCSCq3sYVCzmwewvvffwjRsYm1R3OfYV4K+jRTPsh/ved+jnM5WHFHl7H9j+057Ver9aO81ptFeKt4Jmm2v38j121Yz8X+u2hGwXq16+PQqHgzJkzj/XC48ePZ+vWrXz55Zf4+flhZmbGwIEDKSio2g9CZmb3b4ru1asX3t7e/PTTT7i7u6NSqQgNDS0Vp5GR9sPcnUkM7x1i8CDuXs+ddd1Zzz/F+iivcSfW+8WflZXF9OnT6d+/f6l1mZqW3cQ5adIkxo0bp7PMxMSEz35/vNgBYq+quZ6snc/gziQtlqaQlastZ2mqmUjwSWRuZYdSaVBqUsGs9GQsrZ/cvmhmlnYolAbk3JN3TmYKFuXkbWFd+peX7PuUr4nMb+d97/bOzkjG0kZ/8ngQdUM64VJH23BZXKw512q2sXPJ8pzMZBw9gspch5mF5v3Kzbx3P0nG3Kpmvl+nrxRzNVF7zbgzmaCluYLMXO15zNJMwY2UJ/O89ijyE5IxcdHdpiYujhSmZ+pFLwEAKytblEoDMtJ1JxXMSLuFje39+9BvXr2EjSsXMn7693j5+FdmmBXi3HU1P9+1/965fluY6vb2szBVkPAEXb/rhnTCZfxd57Wi2+e1rBQsbO46r2Ul4+R+//PavZPy5WSW/jW9Jio//pRyz8sW1o7kZJS+ftfU8/gd566rdc7Td+/n2ffu52lPzn5eHdRq6SlQUR66L7e9vT3du3fn22+/JTs7u9TzaWlpBAUFcfXqVa5evVqy/PTp06SlpREcHAxoJvcbPnw4/fr1o0GDBri6unL58uVS67szceHdfwcFBT10mfI0bNiQiIiyb3eUkpJCbGwskydPpkuXLgQFBZVMQPgwjI2NS+YoeBwNGzYkOjqaW7fKno04KCiIqKgonWVRUVEl7/mjatKkCbGxsfj5+ZV6KJVl70ImJiZYW1vrPDTDBx5fQRHcytQ+ktIhM0eNr5s2FhMj8HBScDXpyTzZGhoa4+YTwsXT2rG0KpWKSzH78PILq77AKpmBoTGudUK4EqvNW61ScSV2L+51G5dZx71uGHFndM8RV2L24F43rDJDrVAGhsa4eYdwOUY370tn9uHpG1Z9gVUCY1NLbJ28Sx72Ln6YWzlx7Zw294K8LBLijuPqE1bmOgwMjXH2DOHqOd3369q5feXWqW4FhZCSoS55JKSqychR4+eu7RVlYgRezkriEuVXpzvS9kXj0LmVzjLHLq1J3RddPQE9AkMjI7zrBelMEqhSqYg5cYB6AeXfqm3TqkWsX/4z/5kyFx+/x7vOV5WCIkjN0j6SMzRDYnxctB/sjQ018wlc148J9R9IqfOaq+a8dvXsPee1K/98Xrt2Vve8dvXcPly9y65TkxgYGuPiFaKTs1ql4mrsXtzKuX67+YQRd1b3+h13Zg9uNfz6/aD7ubsDXNevkRDiCfZIA7y//fZbiouLadGiBStWrODcuXPExMQwe/ZswsPD6dq1Kw0aNODFF1/kyJEjHDhwgGHDhtGhQweaNWsGaHocrFy5kujoaI4dO8YLL7xQ5i/rUVFR/Pe//+Xs2bN8++23LF++nLffflunzPLly1mwYAFnz55l6tSpHDhwQGdegvuZNGkSBw8e5M033+T48eOcOXOGefPmkZycjJ2dHQ4ODvz444+cP3+ebdu2lfr1+0H4+PiQlZVFREQEycnJ5OTkPPQ6AJ5//nlcXV3p27cvUVFRXLx4kRUrVpTc6eDdd99l0aJFzJs3j3PnzvH111+zcuXK+05s+CCmTJnCkiVLmD59OqdOnSImJoZly5YxefLkx1pvRdoXo6J9AyUBngqcbaFfGwMyc+BMnLZR4OVuBrQI0O7yxobgaqd5ANhZKnC1A5vS82fWSK2fGs6RHcuJ3r2KpBsXWL9kGgX5uTRuq+nRsfKnCWxd/lVJ+aKiAuLjYoiPi6G4uJCM1ATi42JISbhSTRk8mmadR3A86g9O7ltFSvwF/lo2jcL8XELDNXlvWPQeO1dr827aaRiXTu/i4N8LSLl5gaj1c7gZd5LGHYdWVwqPpFW34RzZuZxjUZrtvfEXTd6N2mjyXj1/AhErtHlrJmWM4WZcDMVFhWSmJXAzLoZbera9FQoFjdoP49DW77l0chvJN2LZ+tsELKyd8Q3tWlJu9bzhHL9rRuqwDsM5vW85MQdXcSvhApF/TqOoIJegFqV7PNVUu08U0bmJIUHeSlztFAzuZExGjppTl7WNzK/2NCY8RNtwYGwIbg4K3Bw0Hz7trTX/t7XQj19TDCzMsW4UiHUjzdA787qeWDcKxNRLMywv4JNxNFr4eUn5Kz8uw7yuF4Ez3sUiwBfvN17AbdAzXPpmUXWE/8ie6v0iO7euImrbOm5cvcgvP3xGfl4ubbr0BuDnbz5kxdI5JeU3rlzE6t/mMXzMVByd3UlPTSY9NZm83Ef7fFGdDsSqaROioL47ONlA71ZKMnMh9pr2+v1CJyXN6mv3YSNDcLHVPABsLRS42IK1nsy3oVAoCOugOa9dvH1e++vX2+e1Btrz2qrvhnPs7vNax+Gc2recmAOa89r22+e14Jb6cV5r0mkEJ/b8wan9q0i5eYGIP6ZRWJBLyO34Ny99j91rtdexxh2GcSVmF4e3LeBWwgX2bpxDwtWThLXTr+s3wIGzatoEa/fzXi1v7+fX79rPOypp6qe7nzvbah4ANhaaz7j6sp8L/fLQwwdAM9HckSNH+PTTT3nnnXeIj4/HycmJpk2bMm/ePBQKBWvWrOGtt96iffv2KJVKnn76aebM0V7Qvv76a1555RVat26No6MjEyZMICMjo9RrvfPOOxw6dIjp06djbW3N119/XTIh3h3Tp09n2bJlvPnmm7i5ufF///d/D/zruL+/P3/99Rfvv/8+LVq0wMzMjJYtW/L888+jVCpZtmwZ//73vwkNDSUgIIDZs2fTsWPHh3q/WrduzRtvvMHgwYNJSUlh6tSp5d6W8H6MjY3566+/eOedd+jRowdFRUUEBweX3Kaxb9++fPPNN3z55Ze8/fbb1K1bl4ULFz50vPfq3r0769ev56OPPuLzzz/HyMiIwMDAkskda4Ldp1QYGUKvcANMjSEuUc0vfxdRdFc7k52VAnNT7cnX3UHBiO7aQ+Dp5gaAAUfPq1i9p+bPUxDasgfZmbfYtnoOWelJuNYJ4qVxP5V0J09PuVEyFAQgMy2R76f2K/l7z+YF7Nm8AJ+A5oyYuLTU+muqwGY9yMm6RdT62WRnJOHsGcTAMT+XDAfITI1HcVcPFo96TXj2lS/ZtXYWu9Z+jZ2TD/1e/xYn95rf1fZuIS00ee9YM4esjCRcvIJ4Yax2e2eUsb1/+ki7vfduWcDeLQvw9m/OsPf0Z3sDNOk8iqKCXLYvn0J+bgZudZvS67WfMDTS9j5KT44jN1vbk6t+4x7kZt3iwOY5ZGck4eQRRK/Xfqrx3U7vtuNYEcaGMKCdMabGcPmmigWbCii66/Rkb63AwlS73T2dlLzeS/u+9Ao3BuBQbBHLdxRWWeyPyqZpKOER2v0z+Mv3Abi6ZCXHR07CxM0Js9sNBAC5l69xsPfrBH81CZ+3hpF37SYnXp9M8tbdVR7742jRtjuZGamsXjaPjNQUvOoG8J8pc0uGD9xKuolCoT2vRW5eTlFRIfP++67OenoPfo0+Q96o0tgf194zaowMoUdzJabGcDUJlu1QUXz39dsSzO7qbOhmDy911jaGdWuieW+OXVKxfr9+9BBs0nkUhQW5bP9De17r/Xrp81reXec1/9vntf13ndd6v64/57WAJpr4926cTU5GEk6eQfT71z3X77v2c3ffJjzz8pfs2TCLqHVfY+vsQ+9R3+KoZ9dvgH1n1BgbwjPNtPv57/fs57aWYH73fm4HQ+/ezxtr3pvjl1SsP6Af+3llU8lEgxVGoX6QmQOriY+PD2PHjmXs2LHlllEoFKxatYq+fftWWVzi0U1dUvM/lFak6cM0czUs21NjD7NKMaS15iT9c9kjc55Yo7po/v1lV+3a3kPbabb3nA21K++3emrynvBj7j+UfLJ8/ppmfpsNRgHVHEnV6lmouT3w7tOlh04+ydoGa7rPfbqs5jeWV6QPhmi+jM3dWLvOa2N6aM5r32+p5kCq2Bu3f2/87PfatZ+/P9jgnwvVYNHnkqrttcPqO1Xba1eGR+opIIQQQgghhBBCVBe5JWHFqfibxgshhBBCCCGEEEIv1OieAmXdjeBeNXj0gxBCCCGEEEKISiC3JKw40lNACCGEEEIIIYSopaRRQAghhBBCCCGEqKVq9PABIYQQQgghhBDiXjLRYMWRngJCCCGEEEIIIUQtJT0FhBBCCCGEEELoFZlosOJITwEhhBBCCCGEEKKWkkYBIYQQQgghhBCilpLhA0IIIYQQQggh9IpMNFhxpKeAEEIIIYQQQghRS0lPASGEEEIIIYQQekUmGqw40lNACCGEEEIIIYSopaSngBBCCCGEEEIIvaKq7gCeINJTQAghhBBCCCGEqKWkUUAIIYQQQgghhKilpFFACCGEEEIIIYReUasV1fZ4WN9++y0+Pj6YmprSsmVLDhw4UG7Zn376iXbt2mFnZ4ednR1du3a9b/mKII0CQgghhBBCCCFEJfj9998ZN24cU6dO5ciRIzRq1Iju3buTmJhYZvnIyEief/55tm/fzt69e/Hy8uKpp57i+vXrlRajQq1Wqytt7UIIIYQQQgghRAXbE5NZba/dOsjqgcu2bNmS5s2bM3fuXABUKhVeXl689dZbTJw48R/rFxcXY2dnx9y5cxk2bNgjx3w/0lNACCGEEEIIIYR4QPn5+WRkZOg88vPzS5UrKCjg8OHDdO3atWSZUqmka9eu7N2794FeKycnh8LCQuzt7Sss/nvJLQlFlXp/fumD5Un22UgTALYeq115d2ukyTviRF41R1K1ujQwBSDyZG41R1K1OoaaAbD+SFE1R1K1nm2iuYTmrf22miOpWqa9RwOw+3R2NUdStdoGWwCwwSigmiOpWj0LYwFYuL2aA6liIzpp/p2zoXZ1qH2rp2as9Od/1q6bvU0YqPmd9MuVtSvv8f3l9+FHNWPGDKZPn66zbOrUqUybNk1nWXJyMsXFxbi4uOgsd3Fx4cyZMw/0WhMmTMDd3V2nYaGiSaOAEEIIIYQQQgi98igT/lWUSZMmMW7cOJ1lJiYmFf46M2fOZNmyZURGRmJqalrh679DGgWEEEIIIYQQQogHZGJi8kCNAI6OjhgYGJCQkKCzPCEhAVdX1/vW/fLLL5k5cyZ///03DRs2fKx4/4n0GRFCCCGEEEIIoVfUKKrt8aCMjY1p2rQpERERJctUKhURERGEh4eXW++///0vH3/8MZs3b6ZZs2aP9T49COkpIIQQQgghhBBCVIJx48bx8ssv06xZM1q0aMGsWbPIzs5mxIgRAAwbNgwPDw9mzJgBwOeff86UKVP47bff8PHx4ebNmwBYWlpiaWlZKTFKo4AQQgghhBBCCFEJBg8eTFJSElOmTOHmzZuEhYWxefPmkskH4+LiUCq1HfjnzZtHQUEBAwcO1FlPWRMZVhRpFBBCCCGEEEIIoVdUenRzkDFjxjBmzJgyn4uMjNT5+/Lly5Uf0D1kTgEhhBBCCCGEEKKWkp4CQgghhBBCCCH0ysNM+CfuT3oKCCGEEEIIIYQQtZT0FBBCCCGEEEIIoVfUaukpUFGkp4AQQgghhBBCCFFLSaOAEEIIIYQQQghRS8nwASGEEEIIIYQQekWtR7ckrOmkp4AQQgghhBBCCFFLSU8BIYQQQgghhBB6RSW3JKww0lNACCGEEEIIIYSopaRRQAghhBBCCCGEqKVk+IAQQgghhBBCCL2iVsvwgYpSqxoFhg8fTlpaGqtXr67uUAgMDOTSpUtcuXIFV1fX6g7ngS1atIixY8eSlpZW3aGU0rWJAc0CDDAzhisJatbsKSIlo/xpSX1cFbRrYICHgxJrCwVL/y4k5oqqCiN+fDs2LyNi3SIy0pLx8PZn0CuT8PFrUGbZ+KvnWf/7t1y9FMOtpBsMePldOvV8qYojrhg7Ni1j69rFZKQl4+ntz3MjJ+JTv+y8d29dwf4d67hx9TwAdXyD6fPCW+WWr8m2b1rG1jWLSU9LwdPHnyEjJ1C3nDx2bV3Bvh3ruRGnzbvvi2PKLV+T7f7rNyLXLSQzPRn3OgH0G/4+dfwalln25tXzbP5zDtcuniY1+QZ9XppA+x7DqjjiirEs6hiLdxwhOTMHfzdHJvbtQIM65V8vMnLzmbtpDxEnL5Cek4ebnTXv9W5PuyCfqgu6Amzb+DubVy8hPS0FLx9/Xhj1Hr7+oWWW3fHXSvZGrud63AUAvOsF0f/FMeWWr6ns2zbD952R2DQJxdTdmUMD3iRhbcT967RvQfCXE7EMrk/e1XjOz5jHtSWrqijiinM48lf2/zWf7IwknD0D6Tb4Q9zrln18A5w5vImda78hPeU69s4+dOw3nnoNOlRhxBVDrVZzYPMcTu1bTn5uBm51m9Bx4FRsnXzuW+/47l85un0+OZnJOLoH0r7fZFy8y3+/ahq1Ws3RiDnEHlxOQV4mzt6Nad17KjaOPvetd3rfr5zctYDcrGTsXAMJf/YDnLz0K+/Df8/hzMHlFORm4uLdmLZ9/znvU3t/5fhOTd72roG07v0BznqUt9AfMnygGuzevZvc3FwGDhzI4sWLqzucJ0L7hgaEBxuwJqqIeWsLKShSM6K7EYYG5dcxNlRw85aatXuLqi7QCnR4z2ZWLfmCZwa+wYTPf8fDO4BvP32DzPSUMssX5Ofh6OJJ7xfextrWsYqjrTiHojazYvGX9Bz0OpP+uwwPnwDmfPKvcvM+d+oQzdo+w9hpP/PuZ0uxc3Rhzsf/Ii0loYojfzwHo7bw56Kv6Pnc63zwxf/h6e3P7I/fJCP9Vpnlz546RPO2TzNu+k9M+GwJdo4ufPPRv0jVs7yP7t3E2qX/5akBb/Kfz5bj7h3AjzNfL38/L8jFwdmLns//Bys93s83R5/ly3W7eL1bS5aNHUKAuyP/+nkNKVk5ZZYvLCrmjR9XcSM1ky9f6sGa94YxdWBnnG0sqjjyx3Ng9xZ+X/g1vQe/xtSvfsPLpz7/+2g0GWll7+expw7Tot3TvPvxj7w/cxH2ji58Pf1NUlMSqzjyx2NgYU7G8VhO/nv6A5U38/Gk+dofSIncz+5mfbg0ZzENfvgEx25tKznSihVzaCPb/pxB22dHM+L9VTh7BvL7nJFkZ5R9fF+7cIQ189+hUZuBjPhgNfXDurDi+9EkXT9bxZE/viPbfubYrqV0HDSNQWP/wMjYjLU/jKKoML/cOueObmT3mpk07z6aweNW4uAewNofR5GTWfb7VROd2PUzp/f+Qus+0+j1r98xMjJny6JX75v3xeMbObDxc8I6j6b36BXYuwawZdGr5GbpT97Hdv7MqT2/0LbvNPq8+TtGxuZsWnD/vC8c38i+DZ/TpMto+o1ZgYNbAJsW6FfelU2trr7Hk0YaBW7bsWMHLVq0wMTEBDc3NyZOnEhRkfbL4ubNm2nbti22trY4ODjw7LPPcuHChZLnL1++jEKhYOXKlXTq1Alzc3MaNWrE3r17S73W/PnzeeGFF3jppZdYsGBBqed9fHz45JNPGDZsGJaWlnh7e7N27VqSkpLo06cPlpaWNGzYkEOHDunUW7FiBSEhIZiYmODj48NXX32l87xCoSjVS8LW1pZFixY9UA6RkZGMGDGC9PR0FAoFCoWCadOmPczbXGlahxiwPbqYmDgVN1PVLN9RhJU5BHuXv4ufvaZi6+FiTutZ74A7tq1fQusuAwjv1Bc3z3oMefVDjI3N2Lt9dZnlvf1C6ffSOzRr8wyGRsZVG2wF2rZuKW269ie8c1/cvOrx/GuTMTYxZc+21WWWHzF2Bh2eHoxX3UBcPeoy9I1pqNUqzpw4ULWBP6a/1y2lbdf+tOncF3everz4+u28I1aXWX7k2Bl0vJO3Z12G/WsqarVa7/LeuWExrToPpEXHfrh6+jFg5FSMjE05ELmyzPJ16jWg14vjady6B4aG+rufL915lP4tQ+nbPJh6Lg5M7t8ZUyNDVh84XWb5VQdPk56Tx/+G96RxXXc87K1pVs+TAHenKo788fy19lfad+tH2y59cPfy5aU3PsDYxJTdEWvKLP/afz6l8zPPUaduAG6edRn+5hTUajUxx/VrP0/aspOzU2eRsObvByrv/doQci9dI+a9z8k6c5Er3/3KzRVbqPv28MoNtIId+Hshjdo8R8PWA3B09+PpF6ZjZGTK8T0ryix/aNsSfEPa0fKpUTi61aN977G41gnmcOQvVRz541Gr1RzbuYRm3d7AN7QLju4BdH3hc7IzErl4svx9IHrHIkJaDSK4xQDsXf3oNHA6hkamxBwo+/2qadRqNaeiltCo4xt4B3fB3jWA9oNmkpuZSFxM+XmfjFpMQLNB+Dftj52zH236TMPQyJSzh8u+DtQ0arWak1FLaNzpDXyCu+DgFkDH52aSk5nIldPl531i12ICmw8ioFl/7Fz8aNt3GobGpsQe0o+8hX6RRgHg+vXr9OjRg+bNm3Ps2DHmzZvH/Pnz+eSTT0rKZGdnM27cOA4dOkRERARKpZJ+/fqhUul+ofzggw8YP3480dHR+Pv78/zzz+s0LmRmZrJ8+XKGDh1Kt27dSE9PZ9euXaVi+t///kebNm04evQoPXv25KWXXmLYsGEMHTqUI0eOUK9ePYYNG4b6dlPV4cOHee655xgyZAgnTpxg2rRpfPjhhyVf+B9GeTm0bt2aWbNmYW1tTXx8PPHx8YwfP/6h11/R7KzA2lzBhRvabZFfCNeS1NRxfjLHGhUVFXL1YgwBDVqVLFMqlQQ0aMmls8eqMbLKVVRYSNzFGAIa6uYd2KAVl2KPP9A6CgryKC4uwsLSurLCrHBFhYXEXYghqGHLkmVKpZLAhi25ePZh87aprDArXFFRAdcunaZ+aHjJMqVSiX9oK66ce3L388KiYmKuJ9KqvlfJMqVSQav6Xhy/El9mnR2nL9LQ240ZqyLpNP0n+n/5Cz9HHKRYpT+NnkWFhVy5EENQI939PLhhSy484PGdr4fH96OwbRVG8jbdHx2Stu7GrlVY9QT0CIqLCrgZdwqfoNYlyxRKJT5Brbl+8WiZdW5cjMYnMFxnWd3gtly/GF2ZoVa4jFvXyMlMwstfm7uJmRUudRpy83J0mXWKiwpIvHZKp45CqcTTP7zcOjVNZuo1crOSca+n3YbGplY4eTYkMa7sc3pxUQEpN07h7qeto1AqcfcLJykuurJDrhCZqdfIzUzGw++evL0aknCfvJNvnNKpo1Aq8agXTqKe5F0V1Ciq7fGkqVVzCpTnu+++w8vLi7lz56JQKAgMDOTGjRtMmDCBKVOmoFQqGTBggE6dBQsW4OTkxOnTpwkN1Y5dHD9+PD179gRg+vTphISEcP78eQIDAwFYtmwZ9evXJyQkBIAhQ4Ywf/582rVrp7P+Hj168PrrrwMwZcoU5s2bR/PmzRk0aBAAEyZMIDw8nISEBFxdXfn666/p0qULH374IQD+/v6cPn2aL774guHDhz/U+3G/HGxsbFAoFDVqHgQrM82BmZWr25cnK1eNpdmTd9ACZGWkolIVY2XroLPc2taBhBuXqimqypeVqcnb2kY3bytbBxKuP1jeq36ZhY2dE4F3NSzUdHfyLrW9bRy4ef3yA61j5VJN3nc3LNR02Rlpmrzv2d6WNg4kPsH7eWp2LsUqNQ6W5jrLHSzNuZSYWmadaykZ3Ei9Ro/GAXw7sg9xyWl8tiqSomIVbzylH9s8MzPt9vFtr7Pc2tae+Afcz/9cMhtbOyeCG+lHzo/KxMWR/IRknWX5CckY2VihNDVBlVd+l+SaIicrFbWqGAtr3ePbwsqBlJsXy6yTlZGMhbVjqfLZGclllq+pcjKSADC30s3d3MqRnMyyc8nN1rxfZmXUSUvUj/Nh7u3czCx1czC1dCQ3K6nMOvk5aZq876ljZulAWpJ+521m6UhuZtl555WXt5X+5C30izQKADExMYSHh6NQaL9AtmnThqysLK5du0adOnU4d+4cU6ZMYf/+/SQnJ5f0EIiLi9NpFGjYUDv5h5ubGwCJiYkljQILFixg6NChJWWGDh1Khw4dmDNnDlZWVmWux8XFBYAGDRqUWpaYmIirqysxMTH06dNHJ682bdowa9YsiouLMTC4z+D6e/xTDg8iPz+f/HzdDyUmJiYPXP9+GtVT0reNdtdd8ldhhaxXPPm2rJrP4ajNjJ02HyPjitkf9cHmlQs4GLWFd6b/XKvyrk1UajX2lmZMGdgZA6WSYE9nEtOzWLzjiN40CjyujSsWcmD3Ft77+EfZz0WNEnt4HZHLp5b8/eyo76sxmqpzIXodUWumlfzdbdi86gumCp0/uo5dq6eV/P30y7Ujb6HfpFHgAfXq1Qtvb29++ukn3N3dUalUhIaGUlBQoFPOyMio5P93GhnuNCCcPn2affv2ceDAASZMmFBSrri4mGXLlvHqq6/edz33W/eDUCgUJcMN7igsLP2F+nFfB2DGjBlMn647YdLUqVPBa9JDracsMXEqriZq33dDA02MlmYKMu/qLWBppiD+lv50nX0YltZ2KJUGZKbpTjaTkZai15MI/hNLK03eGfdMMpf5AHlvXbOYv1Yt5N9TfsDTx78yw6xwd/Iutb3TU7D5h7z/WrOYzasWMHaq/uVtYW2ryfue7Z2VnqLXkwj+EzsLMwyUilKTCqZk5eBoZV5mHSdrcwyVBhgotaMCfV3sSc7MobCoGKP7zbpaQ1hZ2d4+vnUnFcxIu4XNPb1k7rV59RI2rlzI+Onf46Vn+/mjyE9IxsRF9xgwcXGkMD1TL3oJAJhb2qFQGpSaVDA7M6VUb4A7LK0dS/UKuF/5mqJuSCdc6mh/cCku1nyGyclMwcLauWR5TmYyjh5BZa7DzELzfuXeM6lgTmYy5lY1M/86QZ117hBQXKTJOzcrBfO78s7LSsberey8TcxtNXnfM7leblYK5pY1NO/gzvT3Kr297807NysZh3LyNi0v78yUGru9q4PqCZzwr7rInAJAUFAQe/fu1fnCHBUVhZWVFZ6enqSkpBAbG8vkyZPp0qULQUFBpKaW3YXzfubPn0/79u05duwY0dHRJY9x48Yxf/78x84hKipKZ1lUVBT+/v4lvQScnJyIj9eORz137hw5OWXPZF0eY2NjiouL/7HcpEmTSE9P13lMmvT4DQIABYVwK1P7SExTk5Gjpp67dnc2MQJPJwVxiU/m2cLQ0Agv3yBiT+4vWaZSqTh7cj91/RtVY2SVy9DIiDq+QcSe0M079sR+6gaUf4uev1YvZNOKHxkz+Tu8/UKqItQKZWhkRJ16QcTcNUmgSqXizPED+PqXn/eW1QvZ8OdP/PvD7/DRx7wNjfGsG8y5k/tKlqlUKs6d2o93/Sd3PzcyNCDIw5n956+WLFOp1Ow/f5WG3m5l1gnzcedqShqquz4hXUlKw8naQi8aBECzn3vXC9KZJFClUhFz4gD17nN8b1q1iPXLf+Y/U+bi4xdcFaFWu7R90Th01h0C5dilNan7oqsnoEdgYGiMa50QLp/Rzo2gVqm4cmYvHr6Ny6zj7hvG5TP7dJZdjtmDh29YZYb62IxNLbF18i552Lv4YW7lxLVz2twL8rJIiDuOq09YmeswMDTG2TOEq+d0369r5/aVW6e6GZlYYO3gXfKwdfbDzNKRGxe127AgL4uka8dxrlP2Od3A0BgH9xBuXNDWUatU3LiwD6c6YZWdwiMxNrHAxtG75GHn7IeZlSPXL9yT99XjuNwnb0f3EJ06d/J2rqF5C/1W63oKpKenEx0drbPstddeY9asWbz11luMGTOG2NhYpk6dyrhx41AqldjZ2eHg4MCPP/6Im5sbcXFxTJw48aFet7CwkKVLl/LRRx/pDDcAGDVqFF9//TWnTp0qmWvgYb3zzjs0b96cjz/+mMGDB7N3717mzp3Ld999V1Kmc+fOzJ07l/DwcIqLi5kwYYJOr4AH4ePjQ1ZWFhERETRq1Ahzc3PMzUv/cmViYlLOcIHK+QVjz6liOoUZkJyhJjVTTbemBmTmoHNngZHPGHHqcjH7YjTLjA3BwVo7ZMTeUoGbvYKcfDXp2ZUSZoXq/Owwln47mTq+wfj4NWD7xl/Iz8+lVce+ACyZ+z429i70eeFtQDM54c1rF0r+n3YrkWuXz2Biao6Ta53qSuOhde71Ekvmfoh3vRC8/ULZvkGTd3invgAsmv0Btg7O9H1Rk/dfqxaw/vfvGDF2JvZO7qSnan5lMjE1x9Ss7F9da6KuvV5i0ZwP8akXjE/9UCLW/0pBfi6tO2uGDS2cPRlbe2f6Df03AJtXLWTdsu8YOXYGDnqcd/ueL7Ns3vt4+YZQx68BOzctpSA/lxYd+gHw23eTsLFzpufz/wE0kxMm3N7Pi4sKSU9N5PrlGExMzXF09a62PB7WS+0b8+HvWwnxdCHUy4VfdkWTW1BE3+aaL70f/N9fONtY8HaPNgA8F96AZVHH+HztDp5v04i45DR+3naQF9qGVWMWD++p3i8yf/ZUfOoFU7d+CH+v/438vFzadOkNwM/ffIidvTMDXnoLgI0rF7Hm/+bx6rjPcHTW3/3cwMIcCz/tedi8rifWjQIpuJVO3tV4Aj4Zh6mHC8dGaHoZXvlxGd5vvkjgjHe5umgFjp1a4TboGQ72fr26UngkLbqOYP2iCbh5h+Lm05BD2xZTUJBLw9b9AVi38D2sbF3o2O8dAJp1HsZvX73E/q0L8GvQgdMHNxJ/5SRPv/hRdabx0BQKBY3aD+PQ1u+xdfTByt6D/ZtnY2HtjG9o15Jyq+cNxze0Kw3baYadhnUYzt//NxFnr1Bc6jTk2I7FFBXkEtSif3Wl8lAUCgUhbYZxbPv32Dh4Y2nnyZG/Z2Nm5UydIG3em+aPwDu4K8HhLwIQ2uZldq2YhKNHKE6eDTi1ZwlFBbn4N+1XXak8FIVCQWibYRzdpsnbyt6TQ1tnY27ljHewNu8NP4/AJ7grIa01eTdo9zI7lk/CySMUJ68GnIxaQqEe5V0V1Oonc+6w6lDrGgUiIyNp3Fi3BXrkyJFs3LiRd999l0aNGmFvb8/IkSOZPHkyoJn9eNmyZfz73/8mNDSUgIAAZs+eTceOHR/4ddeuXUtKSgr9+pU+kIOCgggKCmL+/Pl8/fXXj5RXkyZN+OOPP5gyZQoff/wxbm5ufPTRRzqTDH711VeMGDGCdu3a4e7uzjfffMPhw4cf6nVat27NG2+8weDBg0lJSWHq1Kk14raEO48XY2wI/doYYmoMVxLULNxSSNFdnRrsrRRYmGpPHh6OCl7tqb1lWc9WmsPh8NliVuzS3jGipmra+mmyMlLZ8Md3ZKYl4+ETwOj352F9u5vtreSbKBTa3hPptxKZ+d5zJX9HrFtMxLrF+AU3Y+y00rfGrKmatdHkvX7Zd2SkJePpE8CYD74ryTs1+SbKu7pQ7/xrOUVFhfz05Ts66+kx6A2eHfyvKo39cTRv052s9FTWLpunybtuAP+e/N1d2zteZ16UnVv+oKiokB++1L1DyLPPvU4vPcq7cfgzZGfcYsufc8lIS8bDO5BXJ/5QMnwg7Z68M1KT+HrSwJK/I9cvJHL9QuoFNefNKYuqOvxH9nSYP6nZuXy3ZR/JmdkEuDvx3ag+ONwePnAzLRPlXXm72loxb1Rfvli3k0Ff/4aztQUvtg1jRKem1ZXCI2nRtjuZGamsXjaPjNQUvOoG8J8pc0uGD9xK0j2vRW7WHN/z/vuuznp6D36NPkPeqNLYH4dN01DCI5aW/B385fsAXF2ykuMjJ2Hi5oSZl7aXSO7laxzs/TrBX03C561h5F27yYnXJ5O8dXeVx/44gpr1ICfzFrvWzSY7IwlnzyAGv/VzyXCAjFvxOtvbs14Teo/8kp1rZ7FzzdfYOfsw4I1vcfLQvyEjTTqPoqggl+3Lp5Cfm4Fb3ab0eu0nDI20P6ikJ8eRm63tmVq/cQ9ys25xYPMcsjOScPIIotdrP+lVd/IG7TR5R62eSkFeBs7eTeg+/EedvDNvxZGXo83bt2EP8rJTORIxm9xMzVCDp4b/iFkNHT5QlkbtNXnvWqXJ28W7CU+P0M07I0U373oNe5CXlcrhv2eTk6kZavDMiB/1ansL/aFQ3zvIXIhK9P58/RjrWFE+G6k52W89Vrvy7tZIk3fEibxqjqRqdWlgCkDkydxqjqRqdQw1A2D9kZrfmFaRnm2iaUjMW/ttNUdStUx7jwZg92k96FJVgdoGWwCwwSigmiOpWj0LYwFYuL2aA6liIzpp/p2zoXZ9TH6rp6ax8fM/n8w5mcozYaCm8enLlbUr7/H99Xsk+aaj1TfZ+DONH663dU1X63oKCCGEEEIIIYTQb/LTdsXR7+YhIYQQQgghhBBCPDLpKSCEEEIIIYQQQq+okIkGK4r0FBBCCCGEEEIIIWop6SkghBBCCCGEEEKvyJwCFUd6CgghhBBCCCGEELWUNAoIIYQQQgghhBC1lAwfEEIIIYQQQgihV9RqmWiwokhPASGEEEIIIYQQopaSngJCCCGEEEIIIfSKSiYarDDSU0AIIYQQQgghhKilpFFACCGEEEIIIYSopWT4gBBCCCGEEEIIvaKW4QMVRnoKCCGEEEIIIYQQtZT0FBBCCCGEEEIIoVfUyC0JK4r0FBBCCCGEEEIIIWop6SkghBBCCCGEEEKvyC0JK470FBBCCCGEEEIIIWopaRQQQgghhBBCCCFqKYVaLTdzEEIIIYQQQgihP5bvU1Xbaw9q9WT9tv5kZSOEEEIIIYQQQogHJhMNiiq1bE/t6pgypLXmVinvz8+v5kiq1mcjTQD4YEHtyvvTVzR519b9/MuV1ddiXx3G99e0q3/+Z+3Ke8JATd6fLiuu5kiq1gdDDABYuL2aA6liIzpp/t1gFFC9gVSxnoWxAHy7qZoDqWKjn9H8O/2XwuoNpIpNHWoEwKy1tev6Pba3ft/ST/q7VxzpKSCEEEIIIYQQQtRS0igghBBCCCGEEELUUjJ8QAghhBBCCCGEXlGp9Xv4Q00iPQWEEEIIIYQQQohaSnoKCCGEEEIIIYTQKzLRYMWRngJCCCGEEEIIIUQtJT0FhBBCCCGEEELoFekpUHGkp4AQQgghhBBCCFFLSaOAEEIIIYQQQghRS8nwASGEEEIIIYQQekUlwwcqjPQUEEIIIYQQQgghainpKSCEEEIIIYQQQq+o1YrqDuGJIT0FhBBCCCGEEEKIWkoaBYQQQgghhBBCiFpKhg8IIYQQQgghhNAraplosMJITwEhhBBCCCGEEKKWkp4CQgghhBBCCCH0itySsOJITwEhhBBCCCGEEKKWkp4CQgghhBBCCCH0iswpUHEeqlFg+PDhpKWlsXr16koKp2IEBgZy6dIlrly5gqura3WH88AWLVrE2LFjSUtL+8eyly9fpm7duvcts3DhQoYPH14xwdVw+yN+Zc+m+WSlJ+NSJ5AeL07G07dhmWUTr59j26rZxF8+RVrKDZ5+fhLhT71cxRFXnK5NDGgWYICZMVxJULNmTxEpGeWfJX1cFbRrYICHgxJrCwVL/y4k5oqqCiOuGF0aG9A8wABTY7iSqGbtP+Xtosnb3VGJtbmCX/4uJCZOv/Kurfu5Wq3m8N9zOHNwOQW5mbh4N6Zt36nYOPrct96pvb9yfOcCcrOSsXcNpHXvD3D2Kvv9qonUajVHI+YQe3A5BXmZOHs3pnXvf8779L5fOblLk7edayDhz36Akx7lDdA+VEHjegpMjOBaMmw6pCI1q/zyXk4QHqjE1R6szBQs31XM2etVF29FOBz5K/v/mk92RhLOnoF0G/wh7nXL325nDm9i59pvSE+5jr2zDx37jadegw5VGPHjs2/bDN93RmLTJBRTd2cODXiThLUR96/TvgXBX07EMrg+eVfjOT9jHteWrKqiiCvOsV2/cmTbfHIyk3B0D6TDgA9x9S5/e5+L3sS+jd+Qces6tk4+tOk1Hp9g/dred3RsqKRJfSWmRnA1Sc2GA8Xcyiy/fB1nBa2DlbjbK7AyV7AssojYa/r1bVCtVnPwrznE7F9Ofm4Grj5NaN9/KrZOPvetdzLqV6J3zCcnMxkHt0Da9p2MSx39Op8L/fDEDR/YvXs3ubm5DBw4kMWLF1d3OJXGy8uL+Pj4ksc777xDSEiIzrLBgwc/8PrUajVFRUWVGHHlObl/I1uWzaRjn9G8Pm0lrl4BLP1qFFkZKWWWL8zPw87Ji66D3sHSxqmKo61Y7RsaEB5swJqoIuatLaSgSM2I7kYYGpRfx9hQwc1batbu1c/tDdCuwe289xQxb10hhYVqhv9T3kYK4m+pWaenedfm/fzYzp85tecX2vadRp83f8fI2JxNC16lqDC/3DoXjm9k34bPadJlNP3GrMDBLYBNC14lN6vs96smOrHrZ07v/YXWfabR61+/Y2RkzpZF98/74vGNHNj4OWGdR9N79ArsXQPYski/8g4PVNDcX8GmQyoWbVVRWATPd1RicJ9PLMaGkJCmZssh/WrouyPm0Ea2/TmDts+OZsT7q3D2DOT3OSPJLuf4vnbhCGvmv0OjNgMZ8cFq6od1YcX3o0m6fraKI388BhbmZByP5eS/pz9QeTMfT5qv/YGUyP3sbtaHS3MW0+CHT3Ds1raSI61YZ49sZNfqGbR8ejRDxq/C0SOQNd+PJCez7O0df+kIm5e8Q3CrgTw/fjW+Dbqwfv5oUuL1a3sDtAlW0jJQyYb9xfy8uYiCIhja2fCfj+9UNRsPFlddoBUsOvJnTuxeSvv+0xjw1h8YGZux/udR9z2fn4/eSNS6mTTrNpqBY1fi4B7A+p9HkaNH53OhPyqsUWDHjh20aNECExMT3NzcmDhxos6XzM2bN9O2bVtsbW1xcHDg2Wef5cKFCyXPX758GYVCwcqVK+nUqRPm5uY0atSIvXv3PlQc8+fP54UXXuCll15iwYIFpZ738fHhk08+YdiwYVhaWuLt7c3atWtJSkqiT58+WFpa0rBhQw4dOqRTb8WKFYSEhGBiYoKPjw9fffWVzvMKhaJUDwpbW1sWLVr0QPlFRkYyYsQI0tPTUSgUKBQKpk2bVm6eBgYGuLq6ljwsLS0xNDQs+dvZ2ZlZs2ZRt25dzMzMaNSoEX/++WdJ/cjISBQKBZs2baJp06aYmJiwe/duOnbsyFtvvcXYsWOxs7PDxcWFn376iezsbEaMGIGVlRV+fn5s2rTpIbZK5drz1yKath9E43YDcPbw49lh0zEyNuXorhVllvfwbUD3we/RoGVPDA2NqjjaitU6xIDt0cXExKm4mapm+Y4irMwh2Lv8Q/vsNRVbDxdzWg97B9zRJsSAyGOavBNS1SzfWYSVGQTVuX/efx/R37xr636uVqs5GbWExp3ewCe4Cw5uAXR8biY5mYlcOf13ufVO7FpMYPNBBDTrj52LH237TsPQ2JTYQyurMPpHp1arORW1hEYd38A7uAv2rgG0HzST3MxE4mLKz/tk1GICmg3Cv2l/7Jz9aNNnGoZGppw9rB95A7QIULD7lJqz1yExHdbuV2FlBgGeinLrXIiHHSfUxOpZ74A7Dvy9kEZtnqNh6wE4uvvx9AvTMTIy5fieso/vQ9uW4BvSjpZPjcLRrR7te4/FtU4whyN/qeLIH0/Slp2cnTqLhDXl79N3835tCLmXrhHz3udknbnIle9+5eaKLdR9e3jlBlrBjkYuJDT8OYJbDsDB1Y/Og6ZjaGzK6f1lb+/oHUvwDmxH086jsHetR3iPsTh5BnNsl35tb4CWQUp2nlARe01NYhqs3lOMlTkEepV/fJ+/oWb7MRVnrupX74A71Go1x3ctoWmXN6gb2gUH9wA6D/mcnIxELp0qf98/tnMRwS0HEdh8APYufnTorzkvnDlQ9n5SG6nV1fd40lRIo8D169fp0aMHzZs359ixY8ybN4/58+fzySeflJTJzs5m3LhxHDp0iIiICJRKJf369UOl0v2A/sEHHzB+/Hiio6Px9/fn+eeff+BfsDMzM1m+fDlDhw6lW7dupKens2vXrlLl/ve//9GmTRuOHj1Kz549eemllxg2bBhDhw7lyJEj1KtXj2HDhqG+vcUPHz7Mc889x5AhQzhx4gTTpk3jww8/LPnC/zDKy69169bMmjULa2vrkl/6x48f/9Drv2PGjBksWbKE77//nlOnTvGf//yHoUOHsmPHDp1yEydOZObMmcTExNCwoaY70uLFi3F0dOTAgQO89dZb/Otf/2LQoEG0bt2aI0eO8NRTT/HSSy+Rk5PzyPFVlKKiAuIvn8I3pHXJMqVSiW9wOFfPR1dfYFXAzgqszRVcuKE9hvIL4VqSmjrO5V9c9Z2dFVjVsrxr836emXqN3MxkPPzCS5YZm1rh5NWQhLhjZdYpLiog+cYpnToKpRKPeuEkxkVXdsgVIjP1GrlZybjXuydvz4Yk3ifvlBuncL8nb3e/cJL0JG9bC7A0U3A5QfuJK78QrqeAh0M1BlaJiosKuBl3Cp8g7fGtUCrxCWrN9YtHy6xz42I0PoHhOsvqBrfl+sXoygy12tm2CiN5m+6PRUlbd2PXKqx6AnoExUUFJF47hZe/7vb28m9N/OWyt3f85Wi8/HW3t3dgW25ejq7MUCucraVmeM/Fm/dcv5PVeDk9mddvgMxb18jJTMKzvnabm5hZ4VynIQlXosusU1xUQNL1Uzp1FEolHvXDy60jxOOokIkGv/vuO7y8vJg7dy4KhYLAwEBu3LjBhAkTmDJlCkqlkgEDBujUWbBgAU5OTpw+fZrQ0NCS5ePHj6dnz54ATJ8+nZCQEM6fP09gYOA/xrFs2TLq169PSEgIAEOGDGH+/Pm0a9dOp1yPHj14/fXXAZgyZQrz5s2jefPmDBo0CIAJEyYQHh5OQkICrq6ufP3113Tp0oUPP/wQAH9/f06fPs0XX3zx0GP275efjY0NCoXisedByM/P57PPPuPvv/8mPFxzEfH19WX37t388MMPdOigHYP20Ucf0a1bN536jRo1YvLkyQBMmjSJmTNn4ujoyKuvvgpo37Pjx4/TqlWrcmPIz9ftEmViYgIYP1Zu98rJTEWlKsbSWvfToqWNI8k3L1Xoa9U0VmaaC2hWrm5zZVauGkuzJ/fiWm7eeU9u3rV5P8/NTAbAzFI3dzNLR3Izk8qsk5eThlpVXLqOlQNpSfrxfpWXt6mlI7lZZeedX17elvqTt4Wp5t/sPN3l2XlqLM2qPp6qkJOVilpVjMU9x7eFlQMpNy+WWScrIxkLa8dS5bMzkistzprAxMWR/ATdHPMTkjGysUJpaoIqr/yu2DVFbrZme5tb6W5vcysHUhPK3t45mcmYWzmWKq9v29vSVHONLn18g4Xpk3n9Bsi5fa0yu3ebWzqSk1n2Nsy7vZ/cez43t3QkLVE/zudVQW5JWHEqpFEgJiaG8PBwFArtAd2mTRuysrK4du0aderU4dy5c0yZMoX9+/eTnJxc0kMgLi5Op1Hgzq/VAG5ubgAkJiY+UKPAggULGDp0aMnfQ4cOpUOHDsyZMwcrK6syX8PFxQWABg0alFqWmJiIq6srMTEx9OnTR+e12rRpw6xZsyguLsbA4D4Dme/xOPk9qPPnz5OTk1Pqy35BQQGNGzfWWdasWbP7xmhgYICDg0O57095ZsyYwfTpumMEp06dSuBTUx88EaGjUT0lfdtoD9klfxVWYzRVp5Gvkj535721duRdW50/uo5dq6eV/P30y/OqL5gqdCF6HVFrppX83W1Y7cg7xFtBj2bazw6/79TP4T1CiNIa+Ch4tqX2M/Jv2/V3ToCHcfbIOnas0H7e7fnK99UYjRAPpspuSdirVy+8vb356aefcHd3R6VSERoaSkFBgU45IyPt2Nc7jQz3DjEoy+nTp9m3bx8HDhxgwoQJJcuLi4tZtmxZya/c5b3Go77u3XXU9wwwKSws/eXlcV/nQWRlaaZo3rBhAx4eHjrPaX6t17KwsLhvjKCJ82HjnjRpEuPGjSv12qsOP0ACD8Hcyg6l0qDUZGtZ6clY3vMrir6LiVNxNVF7vBgaaLaDpZmCzLt+Nbc0UxB/68n5YB0Tp+Jq0gPkbfpk5X232rSf1wnuTP+7ZsovLtZs+9ysFMytnUuW52Yl4+AWVOY6TM1tUSgNSk2ul5uZUurXtpqiTlBnnTsEFBeVnXdeVjL25eRtUl7eWSmYW9bMvM9dV/NzivY4vjPZmIUpZN31a6KFqYKE1CfzJyFzSzsUSoNSkwpmZ6aU6g1wh6W1Y6lfie9X/kmRn5CMiYtujiYujhSmZ+pFLwEAMwvN9r53UsGczBTMy9l+5lalf1HO0YPtHXtNzbVk7RDgO5MBW5hCVq62nIUpT9Tx7RPcSecOASXn88wULO46n+dkJePoXs517PZ+cu/5PCerdK8RISpChcwpEBQUxN69e3W+FEdFRWFlZYWnpycpKSnExsYyefJkunTpQlBQEKmpqRXx0iXmz59P+/btOXbsGNHR0SWPcePGMX/+/Mdad1BQEFFRUTrLoqKi8Pf3L+kl4OTkRHx8fMnz586de+gx98bGxhQXP34ranBwMCYmJsTFxeHn56fz8PLyeuz1PwgTExOsra11Hvc2SFQEQ0Nj3HxCuHhaO8ZQpVJxKWYfXn5hFf561amgEG5lah+JaWoyctTUc9cexiZG4OmkIC7xybm4FhSVzjszR43vE5733WrTfm5sYoGNo3fJw87ZDzMrR65f2FdSpiAvi6Srx3Gp06jMdRgYGuPoHqJTR61ScePCPpzrhFV2Co/EyMQCawfvkoetsx9mlo7cuHhP3teO43yfvB3cQ7hRRt5ONTTvgiJIzdI+kjM0Q4N8XLS9B4wNNfMJXH9CJ9w2MDTGtU4Il89oj2+1SsWVM3vx8G1cZh133zAun9mns+xyzB48fMMqM9Rql7YvGofOusMWHbu0JnVfdPUE9AgMDI1x9gzh6jnd7X317F7cfMre3m4+YVw9p7u942L34OoTVpmhPrZ7j++kdMjMVePrqr1+GxuBp6OCq0lPzvXb2NRS9zrm4oe5lRPXzmu3eUFeFolxx3HxDitzHQaGxjh5hOjUUatUXD+/r9w6tZFMNFhxHrqnQHp6OtHR0TrLXnvtNWbNmsVbb73FmDFjiI2NZerUqYwbNw6lUomdnR0ODg78+OOPuLm5ERcXx8SJEysqBwoLC1m6dCkfffSRzlAEgFGjRvH1119z6tSpkrkGHtY777xD8+bN+fjjjxk8eDB79+5l7ty5fPfddyVlOnfuzNy5cwkPD6e4uJgJEyaU+sX9n/j4+JCVlUVERASNGjXC3Nwcc3Pzh47XysqK8ePH85///AeVSkXbtm1JT08nKioKa2trXn5ZP+9VXp7WTw1n1c8T8fAJxcO3IXv/WkxBfi6N2/YHYOVPE7CydabboHcAzaRtSTc0d74oLi4kIzWB+LgYjE3McXDxrrY8HsWeU8V0CjMgOUNNaqaabk0NyMxBZ4b9kc8YcepyMftiNMuMDcHBWvuB295SgZu9gpx8NenZVZ7CI4k6VUynRgakpKtJzVLTtYkBmbmaXgV3vPK0EaevlJ+3nZV+5V1b93OFQkFom2Ec3fY9Ng7eWNl7cmjrbMytnPEO7lpSbsPPI/AJ7kpI6xcBaNDuZXYsn4STRyhOXg04GbWEwoJc/Jv2q65UHopCoSCkzTCObdfkbWnnyZG/Z2Nm5UydIG3em+aPwDu4K8HhmrxD27zMrhWTcPQIxcmzAaf2LKFIj/IGOBCrpk2IgluZatKyoUMDJZm56NyX/IVOSs5eU3PonGaZkSHYW2rXYWuhwMVWTW4BZFT/nLj/qEXXEaxfNAE371DcfBpyaNtiCgpyadhac3yvW/geVrYudOynOb6bdR7Gb1+9xP6tC/Br0IHTBzcSf+UkT7/4UXWm8dAMLMyx8KtT8rd5XU+sGwVScCudvKvxBHwyDlMPF46N0PQAvfLjMrzffJHAGe9yddEKHDu1wm3QMxzs/Xp1pfBIGnccwdbfJuDiFYpLnYZE71hMUUEuwS012/uvX97DwsaFNr002zuswzBWzHmJI9sX4BPcgbNHNpJ49SRdBuvX9gbYH6OiXaiSlEw1aVlqOjXSfG65+84CL3Ux4MxVNQfPaq7fRoZgrx0FjJ2lAhc7Nbn5+nF8KxQKGrYbxuGI77Fx9MHa3oMDW2Zjbu1M3RDt+XztD8OpG9qVBm00Q6EbtR/Ott8n4uQZiotXQ47vWkxhQS6BzftXVyriCfbQjQKRkZGlxqWPHDmSjRs38u6779KoUSPs7e0ZOXJkyWR1SqWSZcuW8e9//5vQ0FACAgKYPXs2HTt2rJAk1q5dS0pKCv36lf7QExQURFBQEPPnz+frr79+pPU3adKEP/74gylTpvDxxx/j5ubGRx99pDPJ4FdffcWIESNo164d7u7ufPPNNxw+/HB95Vu3bs0bb7zB4MGDSUlJYerUqfe9LeH9fPzxxzg5OTFjxgwuXryIra0tTZo04f3333+k9dVkoS17kJ15i22r55CVnoRrnSBeGvcTljaa7lXpKTd05rvITEvk+6nafWXP5gXs2bwAn4DmjJi4tMrjfxw7jxdjbAj92hhiagxXEtQs3FJI0V0dTuytFDoT+Hg4Kni1p3bCx56tNKeBw2eLWbHrwe70Ud12ndDk3fdO3olqFpWRt/k9eY/qcVfeLTV5HzmnH3nX5v28UftRFBXksmvVVAryMnDxbsLTI37E0Ejb+ygjJY68HG0PtHoNe5CXlcrhv2eTk6kZavDMiB/1qttlg3aavKNWa/J29m5C9+G6eWfe0s3bt2EP8rJTORIxm9xMzVCDp4b/iFkNHT5Qlr1n1BgZQo/mSkyN4WoSLNuhoviu0UF2lmB2V+czN3t4qbN27HK3JppfIo9dUrF+f83/SSeoWQ9yMm+xa91ssjOScPYMYvBbP5d0D8+4FY9Cof111bNeE3qP/JKda2exc83X2Dn7MOCNb3Hy8K+uFB6JTdNQwiO056PgLzWfUa4uWcnxkZMwcXPCzMut5Pncy9c42Pt1gr+ahM9bw8i7dpMTr08meevuKo/9cfg36UFu9i32bdJsbyePIPq8/nPJ+SkzVXd7u9VtQvdhX7J3wyz2rP8aWycfnh35LQ5u+rW9AaJOqzAyhF4tDTA1hrhENb9sK9I5vjXXb+1x6+6gYHg37VeW7s0MAAOiL6hYs1c/5ikI6ziKwoJcdvw5hYK8DFx9mvLsqJ9KX8eytedzvzDNfnJwyxxyMpNwdA/i2VE/6dV1rLJV8AjsWk2hvncgvBCVaNme2rW7DWmt+ZL2/nz9GOtYUT4bqbnIfbCgduX96SuavGvrfv7lytp1dR7fX/Oh/fM/a1feEwZq8v50mX58GK8oHwzRNDos3F7NgVSxEZ00/24wCqjeQKpYz8JYAL7dVM2BVLHRz2j+nf5L7ZrUd+pQTe/eWWtr1/V7bG/9vuvDD39V32u//lT1vXZlqJA5BYQQQgghhBBCCKF/9KZR4JlnnsHS0rLMx2effVbd4VWKXbt2lZuzpaXlP69ACCGEEEIIIZ5AMtFgxamyWxI+rp9//pnc3Nwyn7O3t6/iaKpGs2bNSk3qKIQQQgghhBBCVBS9aRTw8PCo7hCqnJmZGX5+ftUdhhBCCCGEEELUKE/iL/bVRW+GDwghhBBCCCGEEPrm22+/xcfHB1NTU1q2bMmBAwfuW3758uUEBgZiampKgwYN2LhxY6XGJ40CQgghhBBCCCH0ikpdfY+H8fvvvzNu3DimTp3KkSNHaNSoEd27dycxMbHM8nv27OH5559n5MiRHD16lL59+9K3b19OnjxZAe9a2aRRQAghhBBCCCGEqARff/01r776KiNGjCA4OJjvv/8ec3NzFixYUGb5b775hqeffpp3332XoKAgPv74Y5o0acLcuXMrLUZpFBBCCCGEEEIIIR5Qfn4+GRkZOo/8/PxS5QoKCjh8+DBdu3YtWaZUKunatSt79+4tc9179+7VKQ/QvXv3cstXBGkUEEIIIYQQQgihV9RqdbU9ZsyYgY2Njc5jxowZpWJMTk6muLgYFxcXneUuLi7cvHmzzLxu3rz5UOUrgt7cfUAIIYQQQgghhKhukyZNYty4cTrLTExMqimaxyeNAkIIIYQQQggh9Ep13pLQxMTkgRoBHB0dMTAwICEhQWd5QkICrq6uZdZxdXV9qPIVQYYPCCGEEEIIIYQQFczY2JimTZsSERFRskylUhEREUF4eHiZdcLDw3XKA2zdurXc8hVBegoIIYQQQgghhBCVYNy4cbz88ss0a9aMFi1aMGvWLLKzsxkxYgQAw4YNw8PDo2ROgrfffpsOHTrw1Vdf0bNnT5YtW8ahQ4f48ccfKy1GaRQQQgghhBBCCKFXVKrqjuDBDB48mKSkJKZMmcLNmzcJCwtj8+bNJZMJxsXFoVRqO/C3bt2a3377jcmTJ/P+++9Tv359Vq9eTWhoaKXFKI0CQgghhBBCCCFEJRkzZgxjxowp87nIyMhSywYNGsSgQYMqOSotaRQQQgghhBBCCKFXqnOiwSeNTDQohBBCCCGEEELUUtJTQAghhBBCCCGEXlFJT4EKIz0FhBBCCCGEEEKIWkqhVstoDCGEEEIIIYQQ+uPrNdX3NXZcH0W1vXZlkOEDQgghhBBCCCH0ivy0XXGkUUBUqW/W1a6j9+1emlbEbzdVcyBVbPQzmn9n/FFcvYFUsUnPGQDwx149uXFuBXkuXDMSrbbu57X1vDZ3Y+3Ke0wPTd5zNtSuvN/qWbuvYxuMAqo3kCrWszAWqL3Hd976edUcSdUyffZf1R2CqCGkUUAIIYQQQgghhF5RV+tMg0/W8AGZaFAIIYQQQgghhKilpFFACCGEEEIIIYSopWT4gBBCCCGEEEIIvVKtoweeMNJTQAghhBBCCCGEqKWkp4AQQgghhBBCCL0itySsONJTQAghhBBCCCGEqKWkp4AQQgghhBBCCL2ikkkFKoz0FBBCCCGEEEIIIWopaRQQQgghhBBCCCFqKRk+IIQQQgghhBBCr8hEgxVHegoIIYQQQgghhBC1lPQUEEIIIYQQQgihV6SnQMWRngJCCCGEEEIIIUQtJY0CQgghhBBCCCFELSXDB4QQQgghhBBC6BWVjB+oMNJTQAghhBBCCCGEqKWkUeARKBQKVq9eDcDly5dRKBRER0dX+usuWrQIW1vbSn8dIYQQQgghhKjJ1KrqezxpnpjhAzdv3uTTTz9lw4YNXL9+HWdnZ8LCwhg7dixdunSptNf18vIiPj4eR0dHACIjI+nUqROpqakP/AV++PDhpKWllTQ03HHvugYPHkyPHj0eaJ2LFi1i7NixpKWlPUQ2+kutVnNwyxxO719Ofm4GbnWb0L7/VGydfO5b70TUr0RHzicnMxkHt0Da9ZuMS52GVRN0BTi261eObJtPTmYSju6BdBjwIa7e5cd/LnoT+zZ+Q8at69g6+dCm13h8gjtUYcQVp12IgjBfBSZGcC0FthxWkZpVfnkvR2gZqMTVDqzMFPy5u5hzN6ou3oqw/+9f2b1pAVnpybjWCaTn0A/w9C17eydcP8e2lXO4cfkUaSk3eOb5ibTu/nIVR1wxaut+XlvPa2q1mv2b53Bq73Ly8zJw82lCp0H/nPfx3Xf2k2Qc3QNp33/yffeTmkatVnNg8xxO7dNu744DHyzvo9vvyrvfZFz0KO/aeHzbt22G7zsjsWkSiqm7M4cGvEnC2oj712nfguAvJ2IZXJ+8q/GcnzGPa0tWVVHEFae2Ht/Ldh9jceQhkjNz8Hd3ZGK/TjSo41pu+YzcPOZu3EPEifOk5+TjZmfFe3070C6obhVGLWqTJ6KnwOXLl2natCnbtm3jiy++4MSJE2zevJlOnToxevToMusUFhZWyGsbGBjg6uqKoWHlt6+YmZnh7Oxc6a9zt+LiYlSqmt8cdnT7zxzfvZQOA6Yx4N9/YGhsxvqfRlFUmF9unXPRG4laO5Nm3UYzaOxKHN0DWP/TKHIyU6ow8kd39shGdq2eQcunRzNk/CocPQJZ8/3IcuOPv3SEzUveIbjVQJ4fvxrfBl1YP380KfFnoT0VQgAAn+JJREFUqzjyx9cqUEGz+go2H1axOEJFYREMbq/E4D5nNCNDSExT89eRmr8/l+XE/o1sWvY5nfqO5l/TV+DqFcDiL18lK6Ps7V2Yn4edkxfdBo3D0saxiqOtOLV5P6+N5zWAI9t+5tjOpXQaNI3nxv6BkYkZa76/f95nj25k1+qZtOg+miHvaPJe+4Me5r1rKR0HTWPQ2D8wMjZj7Q//sL2PbmT3mpk07z6aweNW4uAewNof9Sfv2np8G1iYk3E8lpP/nv5A5c18PGm+9gdSIvezu1kfLs1ZTIMfPsGxW9tKjrTi1cbje/PRWL5cu5PXn2rFsv+8QIC7E//6cRUpmTllli8sKuaNH1ZxIzWDL19+ljUThzH1ua4421hWceQ1n1qtrrbHk+aJaBR48803USgUHDhwgAEDBuDv709ISAjjxo1j3759gKbL/7x58+jduzcWFhZ8+umnAKxZs4YmTZpgamqKr68v06dPp6ioqGTd586do3379piamhIcHMzWrVt1Xvvu4QOXL1+mU6dOANjZ2aFQKBg+fHiF5Xnv8IFjx47RqVMnrKyssLa2pmnTphw6dIjIyEhGjBhBeno6CoUChULBtGnTAEhNTWXYsGHY2dlhbm7OM888w7lz50q9xtq1awkODsbExITdu3djZGTEzZs3deIZO3Ys7dq1q7D8HpVareb4riU07foGdUO74OgeQJchn5Odkcilk3+XW+/YjkUEtxxEUIsB2Lv60WHAdAyNTDlzcEUVRv/ojkYuJDT8OYJbDsDB1Y/Og6ZjaGzK6f1lxx+9Ywnege1o2nkU9q71CO8xFifPYI7t+qWKI398zesriIpRc+4GJKXD+gMqrMzA30NRbp2LN2HnSTVnr1dhoBVoz5bFNOswiCbt+uPs4Uevl6dhZGzKkZ0ryyzv6duAp4e8S8NWPTE0NK7iaCtObd3Pa+t5Ta1WE71jCc2fegPfBpq8u72gyfviifLzjo5cREj4IIJbavLu9A/7SU2jVqs5tnMJzbq9ge/t7d31Tt732d7ROxYR0moQwbe3d6eBmu0dc0A/8q6tx3fSlp2cnTqLhDXlb9u7eb82hNxL14h573Oyzlzkyne/cnPFFuq+PbxyA61gtfX4XrrzCP1bhdK3RQj1XB2YPKALpkaGrD5wqszyqw6cIj0nj/+N6EXjuu542NvQrJ4nAe5OVRy5qE30vlHg1q1bbN68mdGjR2NhYVHq+bu/RE+bNo1+/fpx4sQJXnnlFXbt2sWwYcN4++23OX36ND/88AOLFi0qaTBQqVT0798fY2Nj9u/fz/fff8+ECRPKjcXLy4sVKzQnqNjYWOLj4/nmm28qNuG7vPjii3h6enLw4EEOHz7MxIkTMTIyonXr1syaNQtra2vi4+OJj49n/PjxgGaowqFDh1i7di179+5FrVbTo0cPnZ4TOTk5fP755/z888+cOnWKZs2a4evry9KlS0vKFBYW8uuvv/LKK69UWn4PKuPWNXIyk/Cq37pkmYmZFS51GnLzSnSZdYqLCki6fgpPf20dhVKJZ/3wcuvUJMVFBSReO4XXPfF7+bcm/vLRMuvEX47Gyz9cZ5l3YFtuXo6uzFArnK0FWJopuJygbaXNL4QbKeDhUI2BVaKiogJuXD6Fb7B2+ymVSuqFhHP1QnT1BVbJavN+XhvPawAZKbfz9r8nb++G5W7DcvcTfcr7Vjl513n4vD39w/Vif6/Nx/fDsm0VRvK2vTrLkrbuxq5VWPUE9Ihq4/FdWFRMzLVEWtX3KlmmVCpo5V+H41fiy6yz49RFGnq7MWPldjpN/ZH+Xyzl578PUKwHPXeF/tL7OQXOnz+PWq0mMDDwH8u+8MILjBgxouTvV155hYkTJ/Lyy5pxtr6+vnz88ce89957TJ06lb///pszZ86wZcsW3N3dAfjss8945plnyly/gYEB9vb2ADg7Oz/UpIDr16/H0lK3W1BxcfF968TFxfHuu++W5F6/fv2S52xsbFAoFLi6ascrnTt3jrVr1xIVFUXr1pqT66+//oqXlxerV69m0KBBgOYL/3fffUejRo1K6o4cOZKFCxfy7rvvArBu3Try8vJ47rnnyowtPz+f/HzdrmAmJiZAxf9imZOZBICZle43QjNLR3Iyk8usk5edilpVjLnlPXWsHElNvFThMVa03Dvx35OzuZUDqQkXy6yTk5mMuZVjqfLZGWW/RzWVhanm3+w83eXZ+eqS5540OZlpqFTFWNrobm9LaweS42v+/vqoavN+XhvPa6DN+94czC0dyS4n7/L3Ez3KO+N23mXkUN72vpP3vfuIuZUjaXqQd20+vh+WiYsj+Qm6OeYnJGNkY4XS1ARVXvld72uS2nh8p2bnUqxS42BlrrPcwdKcS4m3yqxzLSWdG+ev0qNJIN+O6kNcchqfrdxOUbGKN7q3qoqw9Ya0k1QcvW8UeJgxHc2aNdP5+9ixY0RFRZX0DADNF/G8vDxycnKIiYnBy8urpEEAIDxct4W6onTq1Il58+bpLNu/fz9Dhw4tt864ceMYNWoUS5cupWvXrgwaNIh69eqVWz4mJgZDQ0NatmxZsszBwYGAgABiYmJKlhkbG9Owoe7kLcOHD2fy5Mns27ePVq1asWjRIp577rkye2cAzJgxg+nTdcfKTZ06FbumU8uN70GdPbKOyD+16+k58vvHXqeouULqKHi6qXZYwB+75Qognjy19bwWe3gd2//Q5t3r1dqTd+Rybd7PjqodeYvapbYe349LpVZjb2nOlEFdMFAqCfZyITEjm8XbD0mjgKg0et8oUL9+fRQKBWfOnPnHsvd+gc3KymL69On079+/VFlT06r9ydHCwgI/Pz+dZdeuXbtvnWnTpvHCCy+wYcMGNm3axNSpU1m2bBn9+vV7rFjMzMxQKHTHZjs7O9OrVy8WLlxI3bp12bRpE5GRkeWuY9KkSYwbN05nmYmJCd//9VihAeAT3InB47SNFsVFBQDkZqZgYa2diDE3KxkH96Ay12FqYYdCaUBOlu4kNbmZyZhb1/xJ2czuxH/PJDs5mSnlxl/WL045mSlY1PB8z91Qc+OWtvHvzmSCFqa6vQUsTBQkpD15E78AmFvZolQakJWuu72zMlL0ehLBf1Kb9vPael6rG9IJl/Gl887JSsHCRpt3TlYyTuXkXf5+UsPzvuuOEMXFt/O+Z3vnZCbj6HH/vHPLytuqZuZ9t9p0fD+u/IRkTFx0czRxcaQwPbNG9xKorcf33ewszDBQKkpNKpiSlYOjVdk/rDlZW2BooMRAqR3l7etsR3JmDoVFxRgZGlRqzPrkSZzwr7ro/ZwC9vb2dO/enW+//Zbs7OxSz9/vlnxNmjQhNjYWPz+/Ug+lUklQUBBXr14lPl475ufOxIXlMTbWdI//p67/FcXf35///Oc//PXXX/Tv35+FCxeWxHFvDEFBQRQVFbF///6SZSkpKcTGxhIcHPyPrzVq1Ch+//13fvzxR+rVq0ebNm3KLWtiYoK1tbXOQzN84PEZm1pi4+hd8rBz8cPcyolr57Tj7QryskiIO46rd1iZ6zAwNMbJI4Trd9VRq1RcO7+v3Do1iYGhMc6eIVy9J/6rZ/fi5tO4zDpuPmFcPae7/8bF7sHVJ6wyQ31sBUWQmqV9JGdAVq4aH2dtw5WxIbg7wHX9mIj4oRkaGuPuE8LF09rtp1KpuHh6H171wqovsEpWm/bz2npeMza1xNbJu+Rh76rJ++rZe/K+crzcbXhnP7l29p795Jwe5X2/7f0Ped97fFw7t6/G7+9Qu47vx5W2LxqHzrq/EDt2aU3qvujqCegB1dbj+25GhgYEeTqz/9zVkmUqlZr9567S0NutzDphdd25mpyGSqX9wnslKQ0nawtpEBCVRu8bBQC+/fZbiouLadGiBStWrODcuXPExMQwe/bs+3b3nzJlCkuWLGH69OmcOnWKmJgYli1bxuTJkwHo2rUr/v7+vPzyyxw7doxdu3bxwQcf3DcWb29vFAoF69evJykpiays+9w4/THk5uYyZswYIiMjuXLlClFRURw8eJCgIE1Lq4+PD1lZWURERJCcnExOTg7169enT58+vPrqq+zevZtjx44xdOhQPDw86NOnzz++Zvfu3bG2tuaTTz7RmZuhuikUChq2G8bhiO+5dGobKfGxRPzfBCysnakb2rWk3Jrvh3Nit3aG4kYdhnN6/3LOHFzFrYQL7Fg5jaKCXAKbl+45UhM17jiCU3v/IObAKm7dvMD25Zr4g1tq4v/rl/eIWvdVSfmwDsOIi9nFke0LuJVwgX2b5pB49SSN2pU/RKWmOnhOTetgBX7u4GQDvVoqycyFs9e1F9DnOyhp6qdtODAyBGdbzeP/2bvvsKauPg7g3wCy9x6yQRBQcIuzintQi1XrAhRtcdfirBPrbNU6X60KImprcS8cFRUFwYWAA0FAxQGyZG+S94/USAyobUkuufl9nodHuUnge7g59+aeewYAaKtzYKgNaAoP82uyuvT3wd3Iw7gXdQLZr9NwOjQQVZXlaNud3zPoyK75uHh4o+D5NTVVyHyehMznSaitrUbR22xkPk9C3pvnTBXhX5HV97msHtc4HA7cenrjzl87kf7gMnJfJ+PiQX65bVq9L/fx//kKzTjv9oUvHsYe5r9P3qThyhHh90lTx+Fw4NqDX+6nf5f7r9//Lned/X1ihy8S65a7py8exR5G0t/7++rf5W7ZUTrKLav1W15NFZqujtB05c8JpWrdHJqujlA2518kOqz8Aa571wme/3zXIaham8NxzVyoOdjA0n8MTEYMxNPNIUzE/9dktX6P79EWx24+wKnbj5D+Jh8rj0agvKoawzryb8gt+v0CNp+NEjx/pHtrFJZVYt2Jq3iW8xbXHj3FnojbGNXVtaFfQch/JvXDBwD+BIFxcXFYtWoVAgICkJmZCQMDA7Rr105knH5d/fv3x5kzZ7BixQqsW7cOzZo1g6OjIyZNmgSAP7v38ePH4efnh44dO8LKygpbtmzBgAEDGvyZZmZmCAwMxIIFCzBhwgR4e3sjJCSksYsMeXl55OXlwdvbG2/evIG+vj68vLwE4/i7dOkCf39/jBo1Cnl5eVi2bBmWL1+OvXv3YtasWRgyZAiqqqrQo0cPhIeHo1mzZp/8nXJycvD19cXq1avh7e3d6GX6L9r0moSaqnJcPbIUVeVFMLFuhyGTd0Oh2fveCUV5GSgvfSv43t5tECpK8nHrwlaUFedA37QlhkzaLRXdLgGgRdtBKC/NR+y5LSgtyoGBWUt8+d0eQf7it5ngcN63+5lYt0V/7/WIObsJN85shLaBFYb4bYeeSQumivCvxT7moZk8MLCdHJQVgRe5QNg1LmrrTDegrQ6o1OmcYqIDjO31voW9jxv/b5P4lIuzt5t+97NWnQahtPgtIo5vQUlhLkwsWsI7YJdg+EBhXibk6uzv4rc5+N+y9x+Yos8HI/p8MKwcOsBvYajE8/9bsvw+l8XjGgC07T0J1VXluBK2FJV/l9vzO+FyF+ZmoKJOuVu0GYTyknzcPL9V8D7x/E76yl1TVY4rh9+Xe+i3ouUW2t9/l/tWnXIP/VZ6yi2r9VurnQvcI96v6OS0/kcAwIvQY0j0WwglEwOomL+/i1z+7CVue34Hpw0LYTXDGxUvs3D/u8XI/StK5Gc3dbJYvwe0ccDb0nL870IMcovK4GCmj/9NHga9v4cPZBUUQa7OqF1jHQ3s+HYYfjl5DSPWH4ChljrGdnfDhN7tG/gNsovb9D++SQ0OjwZjkH/Az88POTk5OHXq1L96/ebTsvV2mzWUf5Tffo7hIBI27e8FOtaESWYYTVOxcCS/0SEsRrYmQxzpzv/QLqvvc1k9rm0Ll61yTx/EL/fWs7JV7hmDZfs8draZA7NBJGxwdTIA2a3fFWcavpnIRspDpjAd4T9ZHFLF2O9e6dv4K6oxiRU9BYj4FRYW4v79+/j999//dYMAIYQQQgghhDQGHnUVaDTUKCBmGRkZH53E79GjR7CwsJBgon/nyy+/xK1bt+Dv74++ffsyHYcQQgghhBBCSCOgRgExMzU1RXx8/EcflwYfW36QEEIIIYQQQiSJBsE3HmoUEDMFBQXY2dkxHYMQQgghhBBCCBHBiiUJCSGEEEIIIYQQ8s9RTwFCCCGEEEIIIVKFSxMNNhrqKUAIIYQQQgghhMgo6ilACCGEEEIIIUSq8GimwUZDPQUIIYQQQgghhBAZRY0ChBBCCCGEEEKIjKLhA4QQQgghhBBCpAqPy3QC9qCeAoQQQgghhBBCiIyingKEEEIIIYQQQqQKlyYabDTUU4AQQgghhBBCCJFR1FOAEEIIIYQQQohUoSUJGw/1FCCEEEIIIYQQQmQUNQoQQgghhBBCCCEyioYPEEIIIYQQQgiRKlwuDR9oLNRTgBBCCCGEEEIIkVEcHs3QQAghhBBCCCFEiny/tYSx371phjpjv1scqKcAIYQQQgghhBAio2hOASJRuy8xnUCyJvfh/7v9HLM5JG3aQP6/O84zm0PSpgzg//t7lGx1wBrTjQMA2HBCtsodMIxf7q1nZavcMwbzy73zAsNBJMy/P//fdUe4zAaRsPlf8+8fBR6oZjiJZC0b1wwAsC1ctur39EH8+n22mQPDSSRrcHUyACD4MsNBJGxib6YTkKaCGgUIIYQQQgghhEgVHk002Gho+AAhhBBCCCGEECKjqKcAIYQQQgghhBCpwqX58hsN9RQghBBCCCGEEEJkFDUKEEIIIYQQQgghMoqGDxBCCCGEEEIIkSo00WDjoZ4ChBBCCCGEEEKIjKKeAoQQQgghhBBCpAr1FGg81FOAEEIIIYQQQgiRUdRTgBBCCCGEEEKIVKGOAo2HegoQQgghhBBCCCEyihoFCCGEEEIIIYQQGUXDBwghhBBCCCGESBWaaLDxUE8BQgghhBBCCCFERlFPAUIIIYQQQgghUoXHo54CjYV6ChBCCCGEEEIIITKKGgUIIYQQQgghhBAZRcMHCCGEEEIIIYRIFS5NNNhoqFGAsMK9yIO4fSkIpUU5MDBzhMfIJTCxat3g85PjziH6zGYU5r2CjqEVenw5BzYuPSWYuHEkXD+IuMtBKCvOgb6pI3oOXwJjy4bL/ST+HGLDN6Mo/xW0DazQdegcWDlJZ7nvXA5CWVEO9M0c0esT5U65dw4xdcrdbegcWDtLX7lvXT6IG+eDUFKYC2NzRwwcsxhmNvWXO/vVE1w9sQWvnz9EYd5r9P9mITr39ZFw4sbB4/Fw96+tSLp1GFXlRTC2aotuXy2Dlr7VR1/38MZBJFwLQnlxLnRNHNH1y8UwNG/4fdLU8Hg83Dq/FQ9jD6OyvAgm1m3xxdfLoG1g9dHXJUYdxL0rQSgrzoW+qSN6fLUYRh+pH01N/LWDuHv5/fG819efrt83zr6v3909pbN+83g83IvYiuTbh1FVUQxDyzbo4vnp9/mj2IN4cD0Y5SW50DF2hPuQRTCQovc5AHzRWg5t7eWg3Ax4kcPD2Vu1yC9u+PkWhhx0cZKDqS4HGqocHLpag+SX0nVxwOPxcPP8VjyMOYzKiiKYWLVFrxGfV7/55/2/67fX4o/Wj6ZEt1t72AT4QautC5RNDXFn+FS8ORXx8df06Ain9Qug7mSPiheZSF2zAy9Dj0soceOJu3oQN//iH9cMmzuiz6glMP3I59THd8/h+un3n1O/+GoObKXwcyqRHqwYPuDr6wsOhwMOhwNFRUXY2dlhxYoVqKmpYTpavZYvXw43Nzex/OyYmBjIy8tj8ODBYvn5TdHju+G4emwN3AdNw/gFx2HY3BFHtvmhtDiv3ue/So/Dmb0BcHH/Gt4LT8CutQdO7JqGnNcpEk7+36TEheP6iTXoNGAavplzHPpmjji50w9lDZQ782kczocGwKnz1xg95wRsWnngTNA05GVKV7mT48Jx7fgadO4/DWPmHoeBqSOO72i43K+fxuFcaACcO3+NsXNPwLaVB04HTUOulO3vB7fCcfHPtejpOQ3fLTsGI3MHHPh1EkqL6i93dVUFtA3M0Wd4ANS1DCSctnElRO7Bg+j96P7VcgybHgYFRRWEB01CTXVlg69JSwhHzJm1aOcxDV4zj0HPxAHhQZNQXlL/36spiru8BwnX9+OLEcsx4vswNFNUwanfPl7uJ/fCEXVyLTr0n4ZRPxyDnqkDTu2a1GD9aGoE9XvANIydyz+uHfvfR+p3ehzC9/GP52Pn8Y/np/ZIX/0GgPvX9+BRzAF0+XI5hk75E82aqeJCyOSP7u/0xHDcCl8Ht97T4DntKHSNHXAhZLJUvc+7Osmhk6Mczt6sxZ7zNaiqAcb1VoD8Rz6hKioAb97yEH67VnJBG1nc5T1IuLYfvUYsx8jvw9BMSQUnd368fqfcC8f1E2vRsf80fBNwDPqmDjj1m/TUb3k1VRQlJuPBzMDPer6KVXN0OPUb8q7eRFT7L/F06z60+m0l9Pt2E3PSxpV0JxyXj65B18HT4Psj/3Nq2Ba/Bs/fL9PicCo4AK27fA3fH0/A3tUDx3ZOQ84r6TuuiRuPx2Psi21Y0SgAAAMGDEBmZiaePHmCgIAALF++HL/88ovI86qqqhhIx8fj8cTeUBEUFIQZM2bg2rVreP36NeN5JOFOxF606jISrdyHQ9/EDn2/CUQzRWU8iDla7/PjroTC2qk7OvadBD1jW3Qb+j2MzJ0QH3lAwsn/m3tX98LFfSScOg2HnrEdeo8IhIKiMh7drL/c8ZGhsHTsjna9J0HX2Bbug76HQXMnJFyXrnLHXd0Lly4j4dyZX26PkfxyP4ytv9z3IkNh5dgd7T345e4y+HsYSmG5Yy+GoG2PEWjTbTgMTO0wZDz/fX4vqv5ym1m3Qr+R8+DSaTDkFZpJOG3j4fF4uB8Vija9/WHl7AE9Ewf0GrkOZUXZePbwUoOvS7weAseOI+DQYTh0jOzQ/atAKDRTRvLt+v9eTQ2Px0PCtVC07+sPGxcP6Js6oM+YdSgtykb6g4bLHR8ZAufOI+DUcTh0je3Q62t+uZNuSUe5467Uqd8mdujzd/1+8LH63ZJfv/Xq1O94KavfPB4PD6ND4fqFPyydPKBr7IAeI9aivDgbGUkN7+8H0fvg0H4EWrTzgo6hHbp+uRwKzZSRcveYBNP/N51ayuHafS6SX/KQXQCcuFELDVXA0ZzT4GtSX/NwJYGLxy+k84M5j8dDfGQoOvTzh00rfv3u+65+3/9I/b4aAmf3EXDq9Hf9/sR5v6nJuXANKcs24c3JhstYl+W336D86UskzVuHksfpeP6/g8g6egHWs3zFG7SR3Y7YC9euI9G6C/9zav/R/PP3/QY+p969Egobp+7o1G8S9E1s0cOT/zk1Tso+pxLpwppGASUlJRgbG8PS0hJTpkxBnz59cOrUKfj6+mLYsGFYtWoVTE1N4eDgAAC4f/8+evfuDRUVFejp6eHbb79FSUmJ4Oe9e11gYCAMDAygqakJf39/oUYFLpeLNWvWwNraGioqKnB1dcWRI0cEj1+9ehUcDgfnzp1Du3btoKSkhAMHDiAwMBAJCQmC3g0hISGYOHEihgwZIlSm6upqGBoaIigo6LP+BiUlJfjzzz8xZcoUDB48GCEhIUKP15cnKirqk+Wora2Fn5+f4HEHBwds3rz5s/eNONXWVOHNi4ewdOwi2MaRk4OFYxe8Tr9X72teP42HpYO70Darlt3w+mm8OKM2qtqaKmS/fAjzFsLlNm/RBZnP6i935rN4mLcQLrelYzdkPYsXZ9RGVVtThewXouW2+Ei5s57Gw9xBtNyZUlbu188fwqalcLltnNzxMi2euWASUJz/EuXFOTCzf192RRUNGJq3RnZGfL2vqa2pQu6rh2huL/z3MrNzx5sGXtPUFOW/RFlxjtB7XUlFA0YWrRussw0dF5q3cJeKev7ueG7h8EH9duiCzKcNH9csPjyuteyGTCk6ngNA8duXKC/Jhant+7IoKmvAoHlrZGck1Pua2poq5L1+CFO796/hyMnB1M4dOVLyPtdWBzRUOEjP4gq2VVYDL3N5MDdouFFA2hXlNVC/Lf95/Ta3d0fW8/pfI+20O7sh93KM0Lacv6Kg09mNmUD/Qm1NFbIyRD+nWjl2wasGPqe+So+HpaPwcc3aqRtepceLM6pU4nF5jH2JS35+PsaOHQtNTU1oa2vDz89P6Bq1vufPmDEDDg4OUFFRgYWFBWbOnInCwsJ/9HtZO6eAiooK8vL43XIiIiKgqamJv/76CwBQWlqK/v37w93dHbdv30Z2djYmTZqE6dOnC11IR0REQFlZGVevXsWzZ88wYcIE6OnpYdWqVQCANWvW4MCBA9i5cyfs7e1x7do1jBs3DgYGBujZ8/24nwULFmD9+vWwsbGBsrIyAgICcP78eVy6xG8p1dLSQosWLdCjRw9kZmbCxMQEAHDmzBmUlZVh1KhRn1XmsLAwODo6wsHBAePGjcP333+PhQsXgsMRPrHWzaOjo/PJcnC5XDRv3hyHDx+Gnp4ebty4gW+//RYmJiYYOXLkv9tBjaS85C143FqoaegJbVfT0EN+Vnq9ryktyoWqpr7QNlVNPZQW5YotZ2MrL+WXW/WDcqtq6OHtm/rLXVacC1UNfZHns6Xc+dkN7O8Gyl0mReUuK/77fa75wftcUx+5mU8ZSiUZZcU5AABVdeGyq6jro6y4/n1YUcb/e6l8+BoNfRTkSMffq6zo73KLvNcbLve7+qFSz2sKspt+uf/Nca2+47mahl6Df6OmqvzvvB++Z5XV9VFeklPvayrLCup/n6vrSc37XF2Z//mktEJ4e2kFoKbM3kaBho5rqur6KP1E/a7vmPBWCur3v6FkpI/KN8J/j8o3uWimpQE5ZSVwKxoeatFUlJXUf/5W1dRD3keOa2ofHtek7HMq+ffGjh2LzMxM/PXXX6iursaECRPw7bff4vfff6/3+a9fv8br16+xfv16ODk54fnz5/D398fr16+FbvJ+CusaBXg8HiIiInDhwgXMmDEDOTk5UFNTw549e6CoqAgA2L17NyoqKhAaGgo1NTUAwLZt2zB06FCsW7cORkZGAABFRUUEBwdDVVUVzs7OWLFiBebOnYuffvoJ1dXVWL16NS5dugR3d35rno2NDaKiovDbb78JNQqsWLECffv2FXyvrq4OBQUFGBsbC7Z16dIFDg4O2L9/P+bNmwcA2Lt3L0aMGAF1dfXPKntQUBDGjRsHgD+corCwEJGRkfjiiy+Enlc3T2Vl5SfL0axZMwQGvh//ZW1tjZiYGISFhTXYKFBZWYnKSuGDtZKSEgClzyoLIUQ2Pbl3GtePLRN8P2DCTgbTSE7y3dO4evh9uYdMko1yy6q0+NOIPrlc8H1f7x3MhZGgVlYcDOkkL/j+9yvSOyfAP5F89zSuhL2v30MnU/0mRNo1dK3Dv975d5KSknD+/Hncvn0b7du3BwBs3boVgwYNwvr162FqairyGhcXFxw9+n4oiq2tLVatWoVx48ahpqYGCgqfd7nPmkaBM2fOQF1dHdXV1eByuRgzZgyWL1+OadOmoVWrVoIGAYD/B3d1dRU0CABA165dweVykZycLGgUcHV1haqqquA57u7uKCkpwYsXL1BSUoKysjKhi32AP2dBmzZthLa926mfMmnSJOzatQvz5s3DmzdvcO7cOVy+fPmzXpucnIxbt27h+HH+jKwKCgoYNWoUgoKCRBoF6uZJTU39rHJs374dwcHByMjIQHl5Oaqqqj46WeKaNWuEGhIAYNmyZTDrtvyzyvO5VNR1wJGTF5lUsLQ4T6SV9R01TX2Ru8RlRQ0/vylSUeOX+8PJhcqK80Tumr1T3x3Gso/8nZqij5VbTaOB/d1AuRv6OzVFqhp/v88/mJSotCgX6lrSU47PYenUS2iFgNoa/pCtspI8qGoaCraXl+RCz7RlvT9DWZX/9/pwsrXyenqNNBXWzr1gZFGn3LV/l7s4D2p1yl1WnAt9s/rL/a5+lIvUj6Zb7ro+elxrqH7Xczwv/cjzmwqLlr2FVgh49z4v/+B9XlGSC12T+ve3kqp2/e/zkjyoqjfN8ie/5OFl7vu5jBT+bh9QUwZKyt8/T02ZP5EgW1g794LRnPqPa2padep3SS4MGjiuNVw/RHvLsEXlm1woGQmXTclIH9WFxVLRSwAAVNXrP39/7HOnmqa+SK+AUin7nCop4uzG/ykNXessX778X//MmJgYaGtrC12r9enTB3Jycrh58ya++uqrz/o5hYWF0NTU/OwGAYBFcwr06tUL8fHxePLkCcrLy7Fv3z7BRX/di//G8m5sx9mzZxEfHy/4evTokUhXjc/9/d7e3khPT0dMTAwOHDgAa2trdO/e/bNeGxQUhJqaGpiamkJBQQEKCgrYsWMHjh49KjKmpG6ezynHoUOHMGfOHPj5+eHixYuIj4/HhAkTPjpp48KFC1FYWCj0tXDhws8qyz8hr6AII3NnZCS/H3PG43KRkRwDU5s29b7G1NoNz5NjhbY9f3wDptZujZ5PXOQVFGHY3BkvngiX+0VKDEys6i+3iZUbXjwRLndG8g0YW7mJM2qjkldQhKG5M16kfH65ja3d8CJFtNwmUlZuU0tnpCcJlzs9KRbNbd2YCyYGikrq0NK3FHzpGNlBRcMAr1Pfl72qogTZLxJhaOFW78+QV1CEvpkzXqUK/71ep8bCqIHXME1RWR3aBpaCL10jO6hqGODlE+Fyv8lIbLDONnRcePkkVirq+bvjuUj9To6BiXXDx7WMD+v34xswaeLH82ZKatDUsxR8aRvaQUVdH6/T35elqqIEOS8TYWjhWu/PkFdQhJ6pM16nvX8Nj8vF67RYGDTR93lVDfC25P1XTiFQXM6DjfH7j6OKzYDm+hy8yGFPo4BI/Tbm1++67/WqihK8ef7p+v3yw/rxJBbGlvW/RtoVxMZDr3dnoW36Hl3wNjaemUD/gryCIowtnPH8g8+pz5JjYNbA51QzG9HPqc8e34CZjZs4o5J/SBzXOllZWTA0NBTapqCgAF1dXWRlZX3Wz8jNzcVPP/2Eb7/99h/9btY0CqipqcHOzg4WFhafbBVp2bIlEhISUFpaKtgWHR0NOTk5wUSEAJCQkIDy8vdN17GxsVBXV4e5uTmcnJygpKSEjIwM2NnZCX2Zm5t/9PcrKiqitla0y5yenh6GDRuGvXv3IiQkBBMmTPisstfU1CA0NBQbNmwQurBPSEiAqakp/vjjjwZf+znliI6ORpcuXTB16lS0adMGdnZ2SEtL+2gmJSUlaGpqCn39l+40H9PeYwISo8PwIPY48rLS8Neh5aiuLIdLZy8AQPi+ebh2coPg+W17eePZo+u4fSkYeVlpiD67FVkZD+DWc5xY8olLmy8m4GFMGJJuHUd+VhquHF6OmqpyOHXil/vigXmIPv2+3G49vZGRdB1xV4KR/yYNsee2IvvFA7h2l65yt/1iAh7EhOHR3+WOOLwc1XXKfeHAPETVKXebnt54nnQddy/zyx1zbiveSGG5O/fzRdy1w4iPPo6c12k4c4D/Pnfryi/38T3zceno+3LzJzdKQlZGEmprqlH09g2yMpKQ/+Y5U0X4VzgcDlp180bc5Z149ugy8jOTceXP+VDVNISVcx/B887s8sWDG+9nZm7d3RePbx1Gyt3jePsmDdePL0d1dTlatPdiohj/GIfDgWsPb9z5ayeePriM3NfJ+Ov3+VDTNISNy/tyn9jhi8Q6M+279fTFo9jDSLp9HPlv0nD1CP+40LKjdJS7ba8JuH8jDA9v8o/nEWH8+u38d/0+v38eok59pH6H8+u3m5TVbw6HA+eu3ki4shMZSZeRn5WCa0cWQEXDEBYt3+/vc0ET8CjmoOB7l64+SLlzGE/iTqAgOw03TgWipqocLdp93t2kpuBmEhfdXeTQojkHhtrAV13kUVwGoZUFxnvIo0OL9x9ZmykARjr8LwDQUefASAfQVIVU4HA4cOvJr9/pf9fviwf/rt+t3u/v4//zFVopx+0LXzyMPcw/779Jw5Ujwuf9pk5eTRWaro7QdHUEAKhaN4emqyOUzflzaTms/AGue9cJnv981yGoWpvDcc1cqDnYwNJ/DExGDMTTzSFMxP/XOnhMQEJUGO7HHEduZhou/ME/f7dy5++3MyHzEHni/XGtXS9vPH14Hbf+/pwadWYrsp4/QFsp+5wqCVwej7Gvf3Kts2DBAsEE8w19PX78+D//PYqKijB48GA4OTn94x4LrBk+8E+MHTsWy5Ytg4+PD5YvX46cnBzMmDED48ePFwwdAPhd6P38/LB48WI8e/YMy5Ytw/Tp0yEnJwcNDQ3MmTMHs2fPBpfLRbdu3VBYWIjo6GhoamrCx8enwd9vZWWFp0+fIj4+Hs2bN4eGhobgTTRp0iQMGTIEtbW1H/0ZdZ05cwZv376Fn58ftLS0hB4bPnw4goKC4O/vX+9rP6cc9vb2CA0NxYULF2BtbY39+/fj9u3bsLa2/qx84ubYbhDKivMRfWYLyopzYGDWEl9P2yPoZlX0NhMczvsPE2Y2bTF4wnpEnd6EqNMboW1ghWHfboeBaQumivCvtGg7COWl+Yg9twWlRfxyf/ndHkG32eIPym1i3Rb9vdcj5uwm3DjDL/cQv+3QM5Gucju0HYTyknzEhG9BWVEO9Ju3xDB/4f2NOuU2tW6LAd7rERP+vtxD/bZDX8r2t0tH/vv86omtKCnKgbF5S4ydvVswfKAw/7XQpKLFBdn4LfD9hUHMhWDEXAiGpUMH+M7bL/H8/4Vrz0moqSrH9aNLUVVRBGOrdhg4cTcUmr0/+RblZ6Ci9K3ge1tXfv24c3EryopzoGfaEoMm7m7y3crratubX+4rh5eisrwIJtbtMPRb4XIX5magvE657dvw68et81sFx4Wh30pPuT+s3wbNW+KrKe/r94fHNVObthjosx43zm5C9OmN0Da0guck6avfANCqO39/R59YhqqKIhhatkV/311C+7s4PwMVZe/3t03rQagofYu4iC0oL+YPNejnuwsqTXT4QH2iH3HRTAEY2kkeyopARjYPBy7XoPb9ggTQ1eBAVfl9I4GpHge+fd9/hO3fXh6APOLTuDgZIx3zFLTtPQnVVeW4Eva+fnt+J1q/6x7XWvxdv2/Wqd+e30lP/dZq5wL3iPfnH6f1PwIAXoQeQ6LfQiiZGEDl7wYCACh/9hK3Pb+D04aFsJrhjYqXWbj/3WLk/hUl8ez/Rcv2g1BWko+oM/zPa4bNW2LkjDqfW/KFj2vNbdti6MT1uH5qE66d3AgdAyt4+W+HgZn0HdcIX0BAAHx9fT/6HBsbGxgbGyM7O1toe01NDfLz84XmoqtPcXExBgwYAA0NDRw/fhzNmv2z5ag5PB5P6vtn+fr6oqCgACdOnPjsx+7fv49Zs2YhJiYGqqqqGD58ODZu3CiY1O/d61xdXbF9+3ZUVlZi9OjR2Lp1q+ACnsfjYcuWLdixYwfS09Ohra2Ntm3b4scff0SPHj1w9epV9OrVC2/fvoW2trbgd1dWVmLs2LGIiIhAQUEB9u7dK3ij8Hg8WFtbw9nZGWfPnv2s8g8dOhRcLrfe59+6dQudOnVCQkIC8vPz683zqXJUVlbC398fx48fB4fDwejRo6GlpYVz584hPj7+szK+s/vzlqZljcl/N/hvP8dsDkmbNpD/747zzOaQtCkD+P/+HiX1h9V/ZEw3fmPEhhOyVe6AYfxybz0rW+WeMZhf7p0XGA4iYf79+f+uO8L9+BNZZv7X/IuVwAPVDCeRrGXj+B+ot4XLVv2ePohfv882c/jEM9llcHUyACD486byYo2JvZlO8N/4Ln/D2O8OWW706Sf9Q0lJSXBycsKdO3fQrl07AMDFixcxYMAAvHz5st6JBgF+D4H+/ftDSUkJ4eHhQnPifS5W9BSou4zg5z7WqlWrz5rELzAwUGQSiXc4HA5mzZqFWbNm1fv4F198gfraXJSUlBpcIqK0tFRw1/9znT59usHHOnbsKJShvjyfKoeSkhL27t2LvXv3Cm1fs2bNZ2ckhBBCCCGEkMbC5ESD4tCyZUsMGDAAkydPxs6dO1FdXY3p06fjm2++ETQIvHr1Ch4eHggNDUXHjh1RVFSEfv36oaysDAcOHEBRURGKiooAAAYGBpCXl//YrxRgRaMAG3C5XOTm5mLDhg3Q1taGp6cn05EIIYQQQgghhEjIwYMHMX36dHh4eEBOTg7Dhw/Hli1bBI9XV1cjOTkZZWVlAIC4uDjcvHkTAGBnZyf0s54+fQorK6vP+r3UKNBEZGRkwNraGs2bN0dISIjQZIkZGRlwcnJq8LWPHj2ChYWFJGISQgghhBBCCONYMApehK6uLn7//fcGH7eyshIqd0M90/8pahRowMeGJIjDhzu4LlNT04+O3W9ofAkhhBBCCCGEEPIx1CggBRQUFES6gxBCCCGEEEKIrOKybE4BJsl9+imEEEIIIYQQQghhI2oUIIQQQgghhBBCZBQNHyCEEEIIIYQQIlXYtiQhk6inACGEEEIIIYQQIqOopwAhhBBCCCGEEKnCxiUJmUI9BQghhBBCCCGEEBlFjQKEEEIIIYQQQoiMouEDhBBCCCGEEEKkCo/LZToCa1BPAUIIIYQQQgghREZRTwFCCCGEEEIIIVKFS0sSNhrqKUAIIYQQQgghhMgo6ilACCGEEEIIIUSq0JKEjYd6ChBCCCGEEEIIITKKGgUIIYQQQgghhBAZxeFRvwtCCCGEEEIIIVJkxOynjP3uw79aM/a7xYF6ChBCCCGEEEIIITKKJhokErXplGx1TPnekwMAOBzLZTiJZI3ozG9v3HGe4SASNmUA/19Z3d/bwmWrfk8fxK/fslru1X/WMpxEsn4cJQ8AWH9Mtur3HC9+/ZbV83fFmR0MJ5Es5SFTAADBlxkOImETe/P/PdvMgdkgEja4OpnpCP8Jj5YkbDTUU4AQQgghhBBCCJFR1ChACCGEEEIIIYTIKBo+QAghhBBCCCFEqnB5sjWcS5yopwAhhBBCCCGEECKjqKcAIYQQQgghhBCpQhMNNh7qKUAIIYQQQgghhMgo6ilACCGEEEIIIUSqUE+BxkM9BQghhBBCCCGEEBlFjQKEEEIIIYQQQoiMouEDhBBCCCGEEEKkCo9HwwcaC/UUIIQQQgghhBBCZBT1FCCEEEIIIYQQIlW4XC7TEViDegoQQgghhBBCCCEyihoFCCGEEEIIIYQQGUXDBwghhBBCCCGESBUelyYabCzUU4AQQgghhBBCCJFR1FOAEEIIIYQQQohU4fFoosHGQj0FCCGEEEIIIYQQGUU9BQghhBBCCCGESBWaU6DxyGyjgK+vLwoKCnDixAmmo9Rr9+7d2LZtG9LS0qCgoABra2uMHDkSCxcuBPDv8y9fvhwnTpxAfHx844dmEI/Hw+2LW5F08zAqy4tgbNUWPbyWQdvA6qOvexB9EPGRQSgrzoWeiSO6DVsMI4vWkgndCGIvHUTUuWCUFObC2NwRQ8YtQnPb+vO/efkEEce34vWzhyjIfY1BYxagS38fCSduHAnXD+LO5SCUFeVA38wRvYYvgbFlw/st5d45xIRvRlH+K2gbWKHb0Dmwdu4pwcSNQ1b3N4/Hw83zW/Ew5jAqK4pgYtUWvUZ8un4nRh1E3GV+/dY3dUQPr8UffZ80NbJabgDo4cKBmw0HSs2Al7nA+btcvC1p+PnmBkBnBzkY6wIaKhwciapFyivJ5W0MPB4Pdy9txePbh1FVXgwjyzboNmwZtPStPvq6hzEHkXgtGOUludA1dkQXz0UwNJee/S2r5+9DUQnYd/UOcovL0MJUHwu+6oVWFsYNPr+ovALbwm8g4n4qCssqYaKjgXnDeqJ7S2sJpv7v4q4exM2/glBalAPD5o7oM2oJTK0a3m+P757D9dObUZj3CjqGVvjiqzmwdZGu87dut/awCfCDVlsXKJsa4s7wqXhzKuLjr+nREU7rF0DdyR4VLzKRumYHXoYel1BiIoto+EATFBwcjO+//x4zZ85EfHw8oqOjMW/ePJSUfOQTkYyLv7oH96P2o4fXcgyfEYZmiio4s2cSaqorG3xNanw4ok+vRfu+0/D198egZ+qAM3smoawkT3LB/4P7N8Nx7o916PXlNEwNPApjcweErJ+MkqL681dXVUDXwBz9RvwAdS19CadtPMlx4bh2fA0695+GMXOPw8DUEcd3+KGsuP5yv34ah3OhAXDu/DXGzj0B21YeOB00DbmvUySc/L+R1f0NAHGX9yDh2n70GrEcI78PQzMlFZzc+fH6nXIvHNdPrEXH/tPwTcAx6Js64NRvkxp8nzRFslruzo4ctLfn4NwdLkIucVFdC3zTUw7yH/nE0kweyC7g4cJd6R1fmnBtDx7eOIBuw5bjy6l/opmiKs4FT/7o/k5LDEfs2XVo6zENX00/Cj0TB5wLnoxyKTmPAbJ5/j5/LxnrT13Dd/0649DsMXAwNcCUXceRV1xW7/Ora2rh/9txvH5bhPU+Q3BygTeWjewDQy11CSf/b5LuhOPy0TXoOngafH88DsPmjgjb4ofSBs5jL9PicCo4AK27fA3fH0/A3tUDx3ZOQ84r6Tp/y6upoigxGQ9mBn7W81WsmqPDqd+Qd/Umotp/iadb96HVbyuh37ebmJMSWUaNAvWIjIxEx44doaSkBBMTEyxYsAA1NTWCx8+fP49u3bpBW1sbenp6GDJkCNLS0gSPP3v2DBwOB8eOHUOvXr2gqqoKV1dXxMTEfNbvP3XqFEaOHAk/Pz/Y2dnB2dkZo0ePxqpVqwDw7/bv27cPJ0+eBIfDAYfDwdWrVwEA8+fPR4sWLaCqqgobGxssWbIE1dXVAICQkBAEBgYiISFB8LqQkBBB3rq9BwoKCoR+7tu3bzF27FgYGBhARUUF9vb22Lt373/4KzceHo+HxOuhaOfhD2sXD+iZOqD3N+tQVpSNpw8vNfi6hGshcOo0Ao4dhkPXyA49vQLRrJkyHt86KsH0/170+X1o33ME2vXwgqGZHTx9l6OZojLuXjtW7/Ob27TCgG/monXnwVBopijhtI0n7upeuHQZCefOw6FnbAePkYFQUFTGw9j699u9yFBYOXZHe49J0DW2RZfB38OwuRMSrh+QcPL/Rlb3N4/HQ3xkKDr084dNKw/omzqg75h1KC3KRvr9hut3/NUQOLuPgFOn4dA1tkOvEfz3yaOb0lG/ZbXcANCxBQfRj3h48hrIKQRO3+RCQwVwMOM0+Jr0LCDyAU/qege8w+Px8CA6FG16+cPKyQN6Jg74YuRalBVn4/mjhvf3/ev74NhhBBzae0HHyA7dhi2HgqIyku/Uf1xoamT1/L3/Why8OrtgWEdn2BrrYfFwDyg3U8CJWw/rff7xWw9RWFaBXycMRRtrU5jpaqG9bXM4mBpIOPl/cztiL1y7jkTrLsOhb2KH/qMD0UxRGfdj6t9vd6+EwsapOzr1mwR9E1v08PweRuZOiIuUrvN3zoVrSFm2CW9ONvyersvy229Q/vQlkuatQ8njdDz/30FkHb0A61m+4g0qhXhcHmNfbEONAh949eoVBg0ahA4dOiAhIQE7duxAUFAQVq5cKXhOaWkpfvjhB9y5cwcRERGQk5PDV199BS5X+A7FokWLMGfOHMTHx6NFixYYPXq0UONCQ4yNjREbG4vnz5/X+/icOXMwcuRIDBgwAJmZmcjMzESXLl0AABoaGggJCcGjR4+wefNm7N69G7/++isAYNSoUQgICICzs7PgdaNGjfqsv8uSJUvw6NEjnDt3DklJSdixYwf09ZvG3cfi/JcoK85Bc/sugm1KKhowtGiNN8/j631NbU0Vcl49FHoNR04OZvbuDb6mKampqcLrZw9h6+wu2CYnJwdbZ3e8SI1nLpiY1dZUIfvFQ5i3EN5vFi26IPPZvXpfk/U0HuYO7kLbLB27IfNZvDijNipZ3d8AUJTHr99197mSigaMLFsjq4F9WFtTheyXou8Tc3t3ZElB/QZkt9zaaoC6CgdP37z/wFVZDbzOA8yaxilHLIrfvkR5cS7M7N7XcUVlDRiYt8abjIR6X1NbU4Xc1w+FXsORk4OZrTuyM+LFHblRyOL5u7qmFkkvs9HZ3lywTU6Og84tLJD4PLPe10Q+TEdrSxOsOXYFvZbtgtcv+7Hn0i3UcqWnZ0xtTRWyMh7C0lF4v1k5dsGr9PrP36/S42HpKHz+tnbqhlfp8eKMyjjtzm7IvSx8IzHnryjodHZjJhCRCTI7p0BD/ve//8Hc3Bzbtm0Dh8OBo6MjXr9+jfnz52Pp0qWQk5PD8OHDhV4THBwMAwMDPHr0CC4uLoLtc+bMweDBgwEAgYGBcHZ2RmpqKhwdHT+aYdmyZfDy8oKVlRVatGgBd3d3DBo0CF9//TXk5OSgrq4OFRUVVFZWwthYePzZ4sWLBf+3srLCnDlzcOjQIcybNw8qKipQV1eHgoKCyOs+JSMjA23atEH79u0FP/tjKisrUVkp3PVPSUkJQOPfsSwrzgEAqGjoCW1XVddHWXFuva+pKH0LHrcWKuqirynIftroGRtbWXEBuNxaqGsJ51fX0kNuZtPP/2+V/73fVD/c1xp6yM9Or/c1pcW5UNXQF3l+WVH9742mSFb3N/C+fqvWU1dLG6jfDb9P9PFWCuo3ILvlVlPm/1taIby9tIIneIyNyv/epx+ek1TU9VH+93vhQxVlBfWex1Q09FCQIx37WxbP329Ly1HL5UFPQ1Vou566Kp5m59f7mpd5hXid+gKD2jpi+6QvkZFbgNXHrqCmlgv//p0lEfs/Kyvh7zc1zQ/2m6Ye8t40cP4uyoWapvD5W01TD6VSdP7+N5SM9FH5RriMlW9y0UxLA3LKSuBWNDy0RtZwaUnCRkONAh9ISkqCu7s7OJz33RS7du2KkpISvHz5EhYWFnjy5AmWLl2KmzdvIjc3V9BDICMjQ6hRoHXr9xOnmJiYAACys7M/2ShgYmKCmJgYPHjwANeuXcONGzfg4+ODPXv24Pz585CTa7iDx59//oktW7YgLS0NJSUlqKmpgaam5r/6W9Q1ZcoUDB8+HHFxcejXrx+GDRsm6J1QnzVr1iAwUHjs1LJly6Dddtl/zpISdxqRR9//nMETd/7nn0kIaRqS757GlbD39XvoZNmo37JabmdLDga2e3++DbsuGx/wUu+dxvUTywXfD/DZwVwYCaLz97/D5fGgq66KpSM8IC8nBydzI2QXlWLflTtS0yhACGnaqFHgXxg6dCgsLS2xe/dumJqagsvlwsXFBVVVVULPa9asmeD/7xoZPhxi8DEuLi5wcXHB1KlT4e/vj+7duyMyMhK9evWq9/kxMTEYO3YsAgMD0b9/f2hpaeHQoUPYsGHDR3/Pu0YGHu99d8138xC8M3DgQDx//hzh4eH466+/4OHhgWnTpmH9+vX1/syFCxfihx9+ENqmpKSEHRc+WexPsnLqJTTDcG0N/+9eXpwHNU1Dwfayklzom7as92coq+mAIycvMhlTWYnoXeWmSFVDG3Jy8igpFM5fUpgn9ZPKfYzK3/vtw0nTyorzoNbAflPTEL3jVFacB1VN6fk7ydL+tnbuBaM5ovW7rCQPalrC9duggfrd8Pskt8nud1kt95NXPLzOe3/ueTeZoJqycG8BNWUO3hSwZwynhVNveNVZIaC29u/zWEkeVOucx8pLcqFn0sB5TFW73vNYeXFekz2P0fkb0FFTgbwcR2RSwbySMuhrqNX7GgNNNSjIy0G+zk0hG0Md5BaXobqmFs0U5MWauTGoqvP324eTCpYV5Yn0BnhHTVNfpFdA6UeezxaVb3KhZCRcRiUjfVQXFlMvASI2NKfAB1q2bImYmBihC+To6GhoaGigefPmyMvLQ3JyMhYvXgwPDw+0bNkSb9++FXsuJycnAPz5DABAUVERtbW1Qs+5ceMGLC0tsWjRIrRv3x729vYi8xLU9zoDA/5ENZmZ78ey1bdkoYGBAXx8fHDgwAFs2rQJu3btajCvkpISNDU1hb74wwf+O0VldWjpWwq+dIzsoKphgJep78dfVVWUIDsjEUaWbvX+DHkFRRiYOQu9hsfl4lVqbIOvaUoUFBRhauWM9Eexgm1cLhfpj2JhbufGXDAxk1dQhKG5M16kCO+3FykxMLFqU+9rjK3d8CIlVmhbRvINmFi5iTNqo5Kl/a2orA5tA0vBl64xv37X3edVFSV48zwRxg3sQ3kFRRg2d8bLD98nT2Jh3ETrt6yWu6oGeFvy/iu3CCgp58HK6H3vAUUFwFQPeMWiHsOKSmrC5zFDO6ho6ONV2vs6XlVRgpwXiTCycK33Z8grKELf1FnoNTwuF6/TYmFo4SbuIvwrdP4GminIo2VzQ9x88kKwjcvl4eaTF2htaVLva9ysTfEitwDcOpObPc8pgIGmmlQ0CAD8/WZs4YznycL77VlyDMxs6j9/m9m44Xmy8Pn72eMbMLNxE2dUxhXExkOvt3APEH2PLngbG89MoCaMJhpsPDLdU6CwsFDk4vfbb7/Fpk2bMGPGDEyfPh3JyclYtmwZfvjhB8jJyUFHRwd6enrYtWsXTExMkJGRgQULFjRqrilTpsDU1BS9e/dG8+bNkZmZiZUrV8LAwADu7vwJV6ysrHDhwgUkJydDT08PWlpasLe3R0ZGBg4dOoQOHTrg7NmzOH5ceE1TKysrPH36FPHx8WjevDk0NDSgoqKCzp07Y+3atbC2tkZ2drbQ3AQAsHTpUrRr1w7Ozs6orKzEmTNn0LJl/a34ksbhcNC6uzfuRuyElr4VNHXNcOvCFqhqGsLauY/gead+84W1Sx+06joOAODawxeX/1wAg+YuMDJvjcTr+1BdVQ7HDl5MFeUf6TrAB0d3L4SptQua27TCjQuhqKosR7vuXwEAjvw2H5o6Rug3kt9jo6amCjmv+Ktk1NZUo+htNjKfJ0FRWRV6RpaMleOfavvFBFw8OB9GFi4wtmiNuEj+fnPqxN9vFw7Mg5qWEboNDQAAtOnpjSNbxuPu5WBYO/dEclw43rx4AI9RK5gsxj8mq/ubw+HArac37vy1E9oG/Pode24L1DQNYdPqff0+/j9f2LTqA9fu/Prt9oUvLv2+AIbmLjCybI34yH2oqfM+aepktdwAcCuFh65OHLwt5qGgFOjhIoficiD51fsPYWO+kEPySx7upvK3NVMAdOqszqalxoGhNg8VVUBR/au8NSkcDgcuXb1x7/JOaOlZQkO3Oe78tQWqGoawdHq/v8/umQArpz5w7jIWANCquw8iDy+EgZkLDMxb4UF0KKqrytGi3VdMFeUfkdXz9/gebbHk0EU4mxvBxcIYB67FobyqGsM68m8ALfr9Agy11DBrMH8JupHurXEoKgHrTlzF6O5uyMgpwJ6I2xjT3Y3BUvxzHTwm4Oy++TC2cIGJVWvcubwP1ZXlaOXO329nQuZBQ9sIPYfxz9/tennjj43jcetSMGxdeiLpTjiynj/AgDHSdf6WV1OFmp2F4HtV6+bQdHVEVX4hKl5kwmHlD1A2M0LChPkAgOe7DsFy6lg4rpmLFyFHod+rM0xGDMRtz++YKgKRATLdKHD16lW0aSPcOunn54fw8HDMnTsXrq6u0NXVhZ+fn+AiWU5ODocOHcLMmTPh4uICBwcHbNmyBV988UWj5erTpw+Cg4OxY8cO5OXlQV9fH+7u7oiIiICeHn+ClsmTJ+Pq1ato3749SkpKcOXKFXh6emL27NmYPn06KisrMXjwYCxZsgTLly8X/Ozhw4cLlkosKCjA3r174evri+DgYPj5+aFdu3ZwcHDAzz//jH79+glep6ioiIULF+LZs2dQUVFB9+7dcejQoUYr83/l9sUkVFeVI/LIUlRVFMHYqh2GTNoNhWbveycU5WWgovR9rw47t0EoL83H7QtbUVacA33TlhgyabdUdD8EgFadBqG06C0ijm1BSWEuTCxawmfOLkF38oL8THDqdDUsfpuD7Uvff2CKOheMqHPBsHLsgEkLQyWe/99yaDsI5SX5iAnfgrKiHOg3b4lh/nsE3QmL3mYCnPflNrVuiwHe6xETvgk3zmyEtoEVhvpth75pC6aK8K/I6v4GgLa9+fX7SthSVJYXwcS6HTy/E67fhbnC9btFG/775Ob5rSgtyoGBWUt4fic99RuQ3XLHPuZBUQEY2F4OyorAixzgz0guauuMvtNWB1TrdD4z0QHG9X5/x7RvG35dSHzKxZlb0nFHx7XHJNRUleP68WWoqiiCkWVbDJiwS/Q8VvZ+f9u2HoSKkre4e2kLyor5Qw0GTtglVftbFs/fA9o44G1pOf53IQa5RWVwMNPH/yYPg97fwweyCoogV2cFTmMdDez4dhh+OXkNI9YfgKGWOsZ2d8OE3u0ZKsG/07L9IJSV5CPqzBaUFuXAsHlLjJxR5/ydnwlOnfN3c9u2GDpxPa6f2oRrJzdCx8AKXv7bYWAmXedvrXYucI/YL/jeaf2PAIAXoceQ6LcQSiYGUDF/30uk/NlL3Pb8Dk4bFsJqhjcqXmbh/neLkftXlMSzN3U8KVqBo6nj8Or2kydEzDadkq232/ee/LP64VjZOmiN6Mw/qe84z3AQCZsygP+vrO7vbeGyVb+nD+LXb1kt9+o/az/xTHb5cRS/0WH9Mdmq33O8+PVbVs/fFWdkYxLId5SHTAEABF9mOIiETezN//dsMwdmg0jY4OpkpiP8J33H3mXsd/91sB1jv1scaE4BQgghhBBCCCFERlGjAAMGDhwIdXX1er9Wr17NdDxCCCGEEEIIadJoosHGI9NzCjBlz549KC8vr/cxXV1dCachhBBCCCGEECKrqFGAAWZmZkxHIIQQQgghhBCpxePJ1hwv4kTDBwghhBBCCCGEEBlFPQUIIYQQQgghhEgVLgvH9jOFegoQQgghhBBCCCEyihoFCCGEEEIIIYQQGUXDBwghhBBCCCGESBUelyYabCzUU4AQQgghhBBCCJFR1FOAEEIIIYQQQohU4dFEg42GegoQQgghhBBCCCEyihoFCCGEEEIIIYQQGUXDBwghhBBCCCGESBUejyYabCzUU4AQQgghhBBCCJFR1FOAEEIIIYQQQohUoYkGGw/1FCCEEEIIIYQQQmQU9RQghBBCCCGEECJVeFyaU6CxUE8BQgghhBBCCCFERnF4PB4NxiCEEEIIIYQQIjW6DY1k7HdHne7J2O8WCx4hLFdRUcFbtmwZr6KigukoEkXlpnLLAio3lVsWULmp3LKAyi1b5SZNC/UUIKxXVFQELS0tFBYWQlNTk+k4EkPlpnLLAio3lVsWULmp3LKAyi1b5SZNC80pQAghhBBCCCGEyChqFCCEEEIIIYQQQmQUNQoQQgghhBBCCCEyihoFCOspKSlh2bJlUFJSYjqKRFG5qdyygMpN5ZYFVG4qtyygcstWuUnTQhMNEkIIIYQQQgghMop6ChBCCCGEEEIIITKKGgUIIYQQQgghhBAZRY0ChBBCCCGEEEKIjKJGAUIIIYQQQgghREZRowBhHR6Ph4yMDFRUVDAdhRBCCCGEEEKaNGoUIKzD4/FgZ2eHFy9eMB2FEEJII6LGXkIIIaTxUaMAYR05OTnY29sjLy+P6ShEgsrLy1FWVib4/vnz59i0aRMuXrzIYCrJqaqqwsuXL5GRkSH0xRY6OjrQ1dX9rC+2SktLw+LFizF69GhkZ2cDAM6dO4eHDx8ynEy8uFwufvrpJ5iZmUFdXR3p6ekAgCVLliAoKIjhdOK1YsUKoePaO+Xl5VixYgUDiYi49OzZE6GhoSgvL2c6CiFEBnF4PB6P6RCENLbTp0/j559/xo4dO+Di4sJ0HLFr06YNOBzOZz03Li5OzGmY0a9fP3h5ecHf3x8FBQVwdHREs2bNkJubi40bN2LKlClMRxSLJ0+eYOLEibhx44bQdh6PBw6Hg9raWoaSNa59+/YJ/p+Xl4eVK1eif//+cHd3BwDExMTgwoULWLJkCWbPns1UTLGJjIzEwIED0bVrV1y7dg1JSUmwsbHB2rVrcefOHRw5coTpiGKzYsUK7Nu3DytWrMDkyZPx4MED2NjY4M8//8SmTZsQExPDdESxkZeXR2ZmJgwNDYW25+XlwdDQkDX1uz4VFRVITExEdnY2uFyu0GOenp4MpRKf77//Hr///jsqKysxcuRI+Pn5oXPnzkzHkpgjR44gLCwMGRkZqKqqEnqMrZ9b9u3bB319fQwePBgAMG/ePOzatQtOTk74448/YGlpyXBCIkuoUYCwko6ODsrKylBTUwNFRUWoqKgIPZ6fn89QMvEIDAwU/L+iogL/+9//4OTkJLhgio2NxcOHDzF16lSsWbOGqZhipa+vj8jISDg7O2PPnj3YunUr7t27h6NHj2Lp0qVISkpiOqJYdO3aFQoKCliwYAFMTExEGodcXV0ZSiY+w4cPR69evTB9+nSh7du2bcOlS5dw4sQJZoKJkbu7O0aMGIEffvgBGhoaSEhIgI2NDW7dugUvLy+8fPmS6YhiY2dnh99++w0eHh5CZX/8+DHc3d3x9u1bpiOKjZycHN68eQMDAwOh7ZcvX8aoUaOQk5PDUDLxOn/+PLy9vZGbmyvyGJsaOz9UU1ODU6dOYd++fTh37hzs7OwwceJEjB8/HkZGRkzHE5stW7Zg0aJF8PX1xa5duzBhwgSkpaXh9u3bmDZtGlatWsV0RLFwcHDAjh070Lt3b8TExKBPnz749ddfcebMGSgoKODYsWNMRyQyhBoFCCvVvatYHx8fHwklkbxJkybBxMQEP/30k9D2ZcuW4cWLFwgODmYomXipqqri8ePHsLCwwMiRI+Hs7Cwos4ODQ71dcNlATU0Nd+/ehaOjI9NRJEZdXR3x8fGws7MT2p6amgo3NzeUlJQwlEx81NXVcf/+fVhbWwtdGD979gyOjo6sHmuvoqKCx48fw9LSUqjsjx49QseOHVm5v3V0dMDhcFBYWAhNTU2hxr7a2lqUlJTA398f27dvZzCl+Njb26Nfv35YunQpqy+GPyY7Oxu7du3CqlWrUFtbi0GDBmHmzJno3bs309EanaOjI5YtW4bRo0cL1fGlS5ciPz8f27ZtYzqiWNT93DJ//nxkZmYiNDQUDx8+xBdffMHaRj/SNCkwHYAQcWDzRf+nHD58GHfu3BHZPm7cOLRv3561jQJ2dnY4ceIEvvrqK1y4cEHQhTw7OxuampoMpxMfJyeneu+msZmenh5OnjyJgIAAoe0nT56Enp4eQ6nES1tbG5mZmbC2thbafu/ePZiZmTGUSjKcnJxw/fp1ka60R44cQZs2bRhKJV6bNm0Cj8fDxIkTERgYCC0tLcFjioqKsLKyEvQEY6M3b97ghx9+kNkGgVu3bmHv3r04dOgQDA0N4evri1evXmHIkCGYOnUq1q9fz3TERpWRkYEuXboA4DcCFhcXAwDGjx+Pzp07s7ZRQF1dHXl5ebCwsMDFixfxww8/AACUlZVpbgkicdQoQFgrLS0Ne/fuRVpaGjZv3gxDQ0OcO3cOFhYWcHZ2Zjqe2KioqCA6Ohr29vZC26Ojo6GsrMxQKvFbunQpxowZg9mzZ8PDw0PwgfnixYusvXAAgHXr1mHevHlYvXo1WrVqhWbNmgk9zsYGkcDAQEyaNAlXr15Fp06dAAA3b97E+fPnsXv3bobTicc333yD+fPn4/Dhw+BwOOByuYiOjsacOXPg7e3NdDyxWrp0KXx8fPDq1StwuVwcO3YMycnJCA0NxZkzZ5iOJxbvGratra3RpUsXkXrNdl9//TWuXr0KW1tbpqNITHZ2Nvbv34+9e/fiyZMnGDp0KP744w/0799f0FPE19cXAwYMYF2jgLGxMfLz82FpaQkLCwvExsbC1dUVT58+BZs7NPft2xeTJk1CmzZtkJKSgkGDBgEAHj58CCsrK2bDEZlDwwcIK8nypFxr165FYGAgJk+ejI4dOwLgXzAFBwdjyZIlWLBgAcMJxScrKwuZmZlwdXWFnBx/cZVbt25BS0sLDg4ODKcTj3fl/HAuAbZNNPihmzdvYsuWLYK5Ilq2bImZM2cKGgnYpqqqCtOmTUNISAhqa2uhoKCA2tpajBkzBiEhIZCXl2c6olhdv34dK1asQEJCAkpKStC2bVssXboU/fr1Yzqa2HG5XKSmptY74V6PHj0YSiVeZWVlGDFiBAwMDOpt7Jw5cyZDycRHUVERtra2mDhxInx9fUXmkQCAoqIifPnll7hy5QoDCcVn0qRJMDc3x7Jly7B9+3bMnTsXXbt2xZ07d+Dl5cXaVUYKCgqwePFivHjxAlOmTMGAAQMA8Id7KioqYtGiRQwnJLKEGgUIK8nypFwAEBYWhs2bNwtdMM2aNQsjR45kOJn4TJw4EZs3b4aGhobQ9tLSUsyYMYO1wyYiIyM/+njPnj0llIRIQkZGBh48eICSkhK0adNGpEcQYZfY2FiMGTMGz58/F7ljyuZGv6CgIPj7+0NZWRl6enpCjZ4cDkewLCVb8Hg8REVFoX379iITI8sCLpcLLpcLBQV+B+ZDhw7hxo0bsLe3x3fffQdFRUWGExLCftQoQFhJliflklUNLd2Vm5sLY2Nj1NTUMJSMiMO74UHp6enYtGmTzAwPknUlJSUid8vZOETmHTc3N7Ro0QKBgYH1ri5Sd64BNjE2NsbMmTOxYMECQW8oNuNyuVBWVsbDhw+poU+GXLt27aOPs7UnEGmaaE4BwkqyPCkXwO+SduTIEaSnp2POnDnQ1dVFXFwcjIyMWFf+oqIi8Hg88Hg8FBcXC82bUFtbi/DwcJGGArYpKChAUFCQoGeIs7MzJk6cyNoLhg+HB61cuRKGhoZISEhAUFAQK4cH8Xg8HDlyBFeuXKm3Gzmbl656+vQppk+fjqtXrwo16LJ9iAwAPHnyBEeOHBFZaYPtqqqqMGrUKJloEAD4w8Ds7e2Rl5cns40C169fx2+//Ya0tDQcOXIEZmZm2L9/P6ytrdGtWzem44nFF198IbLtw5VGCJEU2TjaEpnzblKurKwsmZuUKzExES1atMC6devwyy+/oKCgAAD/omHhwoXMhhMDbW1t6OrqgsPhoEWLFtDR0RF86evrY+LEiZg2bRrTMcXmzp07sLW1xa+//or8/Hzk5+dj48aNsLW1RVxcHNPxxGLBggVYuXIl/vrrL6Fupb1790ZsbCyDycTn+++/x/jx4/H06VOoq6tDS0tL6IvNxo0bh7dv3yI4OBgRERG4fPkyLl++jCtXruDy5ctMxxOrTp06ITU1lekYEufj44M///yT6RgStXbtWsydOxcPHjxgOorEHT16FP3794eKigru3buHyspKAEBhYSFWr17NcDrxefv2rdBXdnY2zp8/jw4dOuDixYtMxyMyhoYPEFaS5Um5+vTpg7Zt2+Lnn38WGjpx48YNjBkzBs+ePWM6YqOKjIwEj8dD7969cfToUejq6goeU1RUhKWlJUxNTRlMKF7du3eHnZ0ddu/eLRiPWVNTg0mTJiE9Pf2T3ROlkSwOD9LV1cWBAwcEs1PLEnV1ddy9e5e1k4V+zPHjx7F48WLMnTu33gn3WrduzVAy8Zo5cyZCQ0Ph6uqK1q1bi5R748aNDCUTHx0dHZSVlaGmpgaKiooicwvk5+czlEz82rRpg9mzZ8Pb21vomH7v3j0MHDgQWVlZTEeUqMjISPzwww+4e/cu01GIDKHhA4SVFBUVsXv3bixZskTmJuW6ffs2fvvtN5HtZmZmrDyxvptI7+nTpzA3N5eZ7qbv3LlzR6hBAAAUFBQwb948tG/fnsFk4iOLw4O0tLRgY2PDdAxGdOjQAS9evJDJRoHhw4cD4E+k+g6Hw2H90In79+8LlpL98M75h/MqsMWmTZuYjsCY5OTkesfPa2lpCXo7yhIjIyMkJyczHYPIGGoUIKxmYWEBCwsLpmNIlJKSEoqKikS2p6Sk1LvEEVtYWlqioKAAt27dqnfMNVuHjWhqaiIjIwOOjo5C21+8eCGyEgNbvBsedPjwYZkZHrR8+XIEBgYiODhY5mYn37NnD/z9/fHq1Su4uLjIzN1ygN/YKWtqa2sRGBiIVq1aQUdHh+k4EuPj48N0BMYYGxsjNTUVVlZWQtujoqJY3RiamJgo9D2Px0NmZibWrl0LNzc3ZkIRmUWNAoQ1fvjhh89+Lhu7Hr7j6emJFStWICwsDAD/rkpGRgbmz58vuOvERqdPn8bYsWNRUlICTU1NkSWs2HqxOGrUKPj5+WH9+vXo0qULACA6Ohpz587F6NGjGU4nHqtXr8a0adNgbm6O2tpaODk5CYYHLV68mOl4YjFy5Ej88ccfMDQ0hJWVlciFMVvnjwCAnJwcpKWlYcKECYJtsnC3HOA3dsoaeXl59OvXD0lJSTLVKADwG0ROnDghNGmsp6cnq4c8AsDkyZMxa9YsBAcHg8Ph4PXr14iJicGcOXOwZMkSpuOJjZubm+BYVlfnzp1Zu4wyabpoTgHCGr169RL6Pi4uDjU1NYIupykpKZCXl0e7du1YPTlVYWEhvv76a9y5cwfFxcUwNTVFVlYW3N3dER4eDjU1NaYjikWLFi0waNAgrF69GqqqqkzHkZiqqirMnTsXO3fuFCy72KxZM0yZMgVr166FkpISwwnFJyMjQ2aGB40cORJXrlzB119/DSMjI5Eu1MuWLWMomfg5OTmhZcuWmDdvXr1lZ/OFc2ho6EcfZ2tjZ/v27bFu3Tp4eHgwHUViUlNTMWjQILx69UrwuSU5ORnm5uY4e/YsbG1tGU4oPjweD6tXr8aaNWtQVlYGgN/rcc6cOfjpp58YTic+z58/F/peTk4OBgYGQqsoESIp1ChAWGnjxo24evUq9u3bJ7jT8PbtW0yYMAHdu3dHQEAAwwnFLyoqComJiSgpKUHbtm3Rp08fpiOJlZqaGu7fv8/qroYfU1ZWhrS0NACAra0tqxtGrly5ItIIyHZqamq4cOECa5fm+hg1NTUkJCTI3LJ8AETulFdXV6OsrAyKiopQVVVl7eRz58+fx8KFC/HTTz+hXbt2Io3ZmpqaDCUTn0GDBoHH4+HgwYOCCXPz8vIwbtw4yMnJ4ezZswwnFI/a2lpER0ejdevWUFVVRWpqKkpKSuDk5AR1dXWm44lVaGgoRo0aJdJ4X1VVhUOHDrG20Y80TdQoQFjJzMwMFy9ehLOzs9D2Bw8eoF+/fnj9+jVDyYi4eHl54ZtvvsHIkSOZjkLETElJCc2bN8eECRPg4+MDc3NzpiOJnaOjI8LCwlg9fr4hQ4cOha+vL6uHP/0TT548wZQpUzB37lz079+f6ThiUXfC2Lo9Q9g8ZERNTQ2xsbFo1aqV0PaEhAR07doVJSUlDCUTP2VlZSQlJYlMHst28vLyyMzMhKGhodD2vLw8GBoasvJ9TpoumlOAsFJRURFycnJEtufk5KC4uJiBROK1ZcsWfPvtt1BWVsaWLVs++tyZM2dKKJVkDR48GHPnzsWjR4/qXbrL09OToWSNz8vLCyEhIdDU1ISXl9dHn3vs2DEJpZKcV69eYf/+/di3bx8CAwPRu3dv+Pn5YdiwYVBUVGQ6nlhs2LAB8+bNw86dO0Um42K7oUOHYvbs2bh//z7r6/bnsLe3x9q1azFu3Dg8fvyY6ThiceXKFaYjSJySklK9n09KSkpYe1x7x8XFBenp6TLXKPCuketDL1++hJaWFgOJiCyjngKElby9vXH9+nVs2LABHTt2BADcvHkTc+fORffu3bFv3z6GEzYua2tr3LlzB3p6eh89qXI4HKSnp0swmeR8bClCtt1ZmjBhArZs2QINDQ34+vp+dImuvXv3SjCZ5MXFxWHv3r34448/AABjxoyBn58fXF1dGU7WuOquYa6qqipyYczWbuSAbNXtzxUfH48ePXrUu9IMkU7e3t6Ii4tDUFCQ0OeWyZMno127dggJCWE2oBjJ2nCRNm3agMPhICEhAc7OzkJLCtfW1uLp06cYMGCAYMJoQiSBGgUIK5WVlWHOnDkIDg5GdXU1AP7a7X5+fvjll19YO9keIbLo9evX2LVrF9auXQsFBQVUVFTA3d0dO3fuFBlCJK0+1ZApy8uZsdmpU6eEvn+3ZNm2bdtgbm6Oc+fOMZRM/K5fv47ffvsN6enpOHz4MMzMzLB//35YW1uzcm6NgoIC+Pj44PTp04JGv5qaGnh6emLv3r3Q1tZmNqAYydpwkcDAQMG/AQEBQnMnKCoqwsrKCsOHD2d9DxHStFCjAGG10tJSocnX2N4YUF1dDUdHR5w5cwYtW7ZkOg5jKioqZGb23t69e+PYsWMiHxiLioowbNgw1q60UV1djZMnTyI4OBh//fUX2rdvDz8/P4wePRo5OTlYvHgx4uLi8OjRI6ajEvKvfdhLgsPhwMDAAL1798aGDRtgYmLCUDLxOnr0KMaPH4+xY8di//79ePToEWxsbLBt2zaEh4cjPDyc6Yhik5qaKliSsGXLljIxwWZkZORHH+/Zs6eEkkjWvn37MGrUKJn5vEKaNmoUIIRlzMzMcOnSJZlrFKitrcXq1auxc+dOvHnzBikpKbCxscGSJUtgZWUFPz8/piOKhZycHLKyskQmKsrOzoaZmZmgpwybzJgxA3/88Qd4PB7Gjx+PSZMmwcXFReg5WVlZMDU1BZfLZShl45PVNcwB/kXD+vXrBWV3cnISDAcj7NOmTRvMnj0b3t7e0NDQQEJCAmxsbHDv3j0MHDgQWVlZTEdsdCtWrMCcOXNEVo4pLy/HL7/8gqVLlzKUjFkPHjwQOb4TQhpfwwP1CJFipaWlWLJkCbp06QI7OzvY2NgIfbHZtGnTsG7dOsGa9bJi1apVCAkJwc8//yzU5c7FxQV79uxhMJl4JCYmIjExEQDw6NEjwfeJiYm4d+8egoKCYGZmxnBK8Xj06BG2bt2K169fY9OmTfV+YNTX12fVZGWpqalo2bIlvL29cezYMRw7dgzjxo2Ds7OzoDcUWx04cAB9+vSBqqoqZs6ciZkzZ0JFRQUeHh74/fffmY4nMTweD7JyHyc5ORk9evQQ2a6lpYWCggLJB5KAwMDAelcYKCsrE3Q3lxXFxcXYtWsXOnbsyLr5Yeqqra3F+vXr0bFjRxgbG0NXV1foixBJotUHCCtNmjQJkZGRGD9+PExMTD46ERvb3L59GxEREbh48SJatWolMmSCjbPRA/z1fnft2gUPDw/4+/sLtru6urJyhm43NzdwOBxwOBz07t1b5HEVFRVs3bqVgWTiFxER8cnnKCgosKrL6cyZM2Fra4vY2FiRNcxnzpzJ2jXMAX6D388//4zZs2cLts2cORMbN27ETz/9hDFjxjCYTvxCQ0Pxyy+/4MmTJwCAFi1aYO7cuRg/fjzDycTH2NgYqampIittREVFsbZhv6GZ6BMSEmTmAvHatWsICgrC0aNHYWpqCi8vL2zfvp3pWGITGBiIPXv2ICAgAIsXL8aiRYvw7NkznDhxQmZ7hhDmUKMAYaVz587h7Nmz6Nq1K9NRJE5bW1sm1/N+9epVvWMvuVwuK7vQP336FDweDzY2Nrh16xYMDAwEjykqKsLQ0JDV3crT0tKwadMmoe7ks2bNgq2tLcPJxCMyMlKoQQAA9PT0sHbtWtYf59LT0zF06FCR7Z6envjxxx8ZSCQ5GzduxJIlSzB9+nTBfo6KioK/vz9yc3OFGkrYIDQ0FKNGjcLkyZMxa9YsBAcHg8Ph4PXr14iJicGcOXOwZMkSpmM2Kh0dHUEDb4sWLYQaBmpra1FSUiLU0M02WVlZCAkJQVBQEIqKijBy5EhUVlbixIkTcHJyYjqeWB08eBC7d+/G4MGDsXz5cowePRq2trZo3bo1YmNjWbuENGmaqFGAsJKOjo7MtKzXVVNTg169eqFfv34wNjZmOo5EOTk54fr167C0tBTafuTIEbRp04ahVOLzrpxsGjP/uS5cuABPT0+4ubkJLpSio6Ph7OyM06dPo2/fvgwnbHyyvIa5ubk5IiIiRBr9Ll26BHNzc4ZSScbWrVuxY8cOeHt7C7Z5enrC2dkZy5cvZ12jwIQJEzBgwAAsWLAAXC4XHh4eKCsrQ48ePaCkpIQ5c+ZgxowZTMdsVJs2bQKPx8PEiRMRGBgotD79u5no3d3dGUwoPkOHDsW1a9cwePBgbNq0CQMGDIC8vDx27tzJdDSJyMrKQqtWrQAA6urqKCwsBAAMGTKEdY1fpOmjRgHCSj/99BOWLl2Kffv2iUzaw2YKCgrw9/cX3D2VJUuXLoWPjw9evXoFLpeLY8eOITk5GaGhoThz5gzT8cTu0aNHyMjIQFVVldB2T09PhhKJz4IFCzB79mysXbtWZPv8+fNZ2SgwZMgQfPvttyJrmPv7+7NyH9cVEBCAmTNnIj4+Hl26dAHAbwQKCQnB5s2bGU4nXpmZmYIy19WlSxdkZmYykEi83s2ZwOFwsGjRIsydOxepqakoKSmBk5OT0NJtbPFuOVFra2t07dpVaM16tjt37hxmzpyJKVOmwN7enuk4Ete8eXNkZmbCwsICtra2uHjxItq2bYvbt29DSUmJ6XhE1vAIYSE3NzeehoYGT11dnefi4sJr06aN0Beb9ezZk3f8+HGmYzDi2rVrvD59+vAMDAx4KioqvK5du/IuXLjAdCyxSktL47Vu3ZrH4XB4cnJyPA6HI/i/nJwc0/HEQklJiZeSkiKyPTk5maekpMRAIvF7+/Ytz9PTk8fhcHiKioo8RUVFnpycHG/YsGG8goICpuOJ3bFjx3hdu3bl6erq8nR1dXldu3blnThxgulYYufs7MxbtWqVyPaffvqJ5+LiwkAi8eJwOLzs7GymYzDi7t27vMTERMH3J06c4H355Ze8hQsX8iorKxlMJj4xMTG8SZMm8TQ0NHgdO3bkbd26lZeTk8NTUFDgPXz4kOl4Yjd//nxB/T506BBPQUGBZ2dnx1NUVOTNnz+f4XRE1tCShISVPjVT77JlyySURPLCwsKwcOFCzJ49G+3atROZaLB169YMJSPiMHToUMjLy2PPnj2wtrbGrVu3kJeXh4CAAKxfv56VS7aZm5tj48aNGDFihND2sLAwzJkzBxkZGQwlEz9ZXMNclh09ehSjRo1Cnz59hIbKREREICwsDF999RXDCRuXnJwcXFxcPnm3PC4uTkKJJKdDhw5YsGABhg8fjvT0dDg5OcHLywu3b98WdK9nq9LSUvz5558IDg7GrVu3UFtbi40bN2LixInQ0NBgOp7ExMbG4saNG7C3t693HhVCxIkaBQhhGTk50ZVGORyOYGbj2tpaBlIRcdHX18fly5fRunVraGlp4datW3BwcMDly5cREBCAe/fuMR2x0a1YsQK//vorFixYINSdfN26dfjhhx9oLCbL3L59G1wuF506dRLafvPmTcjLy6N9+/YMJZOMu3fv4tdffxVqDAoICGDlXClycnIICAj45DABNjbsa2lpIS4uDra2tli3bh0uX76MCxcuIDo6Gt988w1evHjBdESJSE5ORlBQEPbv34+CggL07dsXp06dYjqWWFy7dg1dunQRaQSrqanBjRs36l2WkxBxoUYBwloFBQU4cuQI0tLSMHfuXOjq6iIuLg5GRkasXb8dAJ4/f/7Rxz+ciE+avZu1+XPk5+eLOQ0zdHR0EBcXB2tra9ja2mLPnj3o1asX0tLS0KpVK5SVlTEdsdHxeDxs2rQJGzZswOvXrwEApqammDt3LmbOnMnKJUiHDx+Ojh07Yv78+ULbf/75Z9y+fRuHDx9mKJn4dezYEfPmzcPXX38ttP3YsWNYt24dbt68yVAy0tjk5OSQlZUFQ0NDpqNInKamJu7evQt7e3v07dsXQ4YMwaxZs5CRkQEHBweUl5czHVGiamtrcfr0aQQHBwsaBV6+fAlTU9N6b35II3l5eWRmZoq83/Py8mBoaEg3cYhEyc5sJkSmJCYmok+fPtDS0sKzZ88wefJk6Orq4tixY8jIyEBoaCjTEcWGTRf9n1K3O2VeXh5WrlyJ/v37C2ZqjomJwYULF1h959jFxQUJCQmwtrZGp06d8PPPP0NRURG7du1i7XreHA4Hs2fPxuzZswUz8rO9i+m1a9ewfPlyke0DBw7Ehg0bJB9Igh49eoS2bduKbG/Tpg0ePXrEQCLJy87ORnZ2tshqI2wbDsbGBr3P1b59e6xcuRJ9+vRBZGQkduzYAYC//KyRkRHD6SRPXl4ew4YNw7BhwwTbnJycEB8fz5pz27senB/Ky8sTGfpJiLhRowBhpR9++AG+vr74+eefhS4WBg0ahDFjxjCYTHJkYTb6d7M2A/w7qStWrMD06dMF22bOnIlt27bh0qVLrFu6653FixejtLQUAL9b/ZAhQ9C9e3fo6enhzz//ZDid+LG9MeCdhpYebNasGYqKihhIJDlKSkp48+aNyIVAZmYm62dqv3v3Lnx8fJCUlIQPO3aycTiYLHde3bRpE8aOHYsTJ05g0aJFgvlCjhw5Uu8KFLKILe8PLy8vAPw67OvrK7TSQG1tLRITE2mfE4mj4QOEleqOzdPQ0EBCQgJsbGzw/PlzODg4oKKigumIYpOeno6vvvoK9+/fF8wlALy/A8O2D5HvqKurIz4+XmTitdTUVLi5uaGkpIShZJKXn5//j4ZWSIM2bdp8dnnYOAlZx44dMWTIECxdulRo+/Lly3H69GncvXuXoWTiN3r0aGRmZuLkyZOCNdwLCgowbNgwGBoaIiwsjOGE4uPq6gpbW1vMnz8fRkZGInWAbT3Dnj9/DgsLi8+u65qamqy6c1yfiooKyMvLo1mzZkxHYVzdz3PSbMKECQCAffv2YeTIkVBRURE8pqioCCsrK0yePBn6+vpMRSQyiN1N7ERmKSkp1Xv3LCUlBQYGBgwkkpxZs2bB2toaERER9c5Gz1Z6eno4efIkAgIChLafPHkSenp6DKUSv8LCQtTW1kJXV1ewTVdXF/n5+VBQUICmpiaD6RpP3S6ksmjJkiXw8vJCWloaevfuDQCIiIjAH3/8wer5BABg/fr16NGjBywtLQWT68XHx8PIyAj79+9nOJ14paen4+jRozKzysQ/beSQhftaysrKTEcgjWzv3r0AACsrK8yZM4eGCpAmgXoKEFaaNGkS8vLyEBYWBl1dXSQmJgrGp/Xo0YPVS/vI4mz0ABASEoJJkyZh4MCBglnKb968ifPnz2P37t3w9fVlNqCYDBw4EEOHDsXUqVOFtu/cuROnTp1CeHg4Q8lIYzt79ixWr16N+Ph4qKiooHXr1li2bBl69uzJdDSxKy0txcGDB5GQkCAo++jRo1l/93TYsGEYP348hg8fznSUJkna7xzr6uoiJSUF+vr6n+zdxdbJcv8Jad/fnxIZGYnS0lK4u7tDR0eH6ThExlCjAGGlwsJCfP3117hz5w6Ki4thamqKrKwsdO7cGefOnWN1q6wszkb/zs2bN7FlyxahpbtmzpwpspQZm+jq6iI6OhotW7YU2v748WN07doVeXl5DCUTvzt37gj2tZOTE9q1a8dwIkIaV25uLnx8fNCxY0e4uLiINIKwaY6Yf0PaLxL37duHb775BkpKSti3b99Hn1t3Dh1ZxZbhIuvWrUNJSQl++uknAPweLwMHDsTFixcBAIaGhoiIiICzszOTMYmMoeEDhJW0tLTw119/ITo6GgkJCSgpKUHbtm3Rp08fpqOJnSzORv9Op06dcPDgQaZjSFRlZSVqampEtldXV7N2CauXL19i9OjRiI6Ohra2NgD+GPMuXbrg0KFDaN68ObMBxaiqqqreWegtLCwYSiQZT548wZUrV+ot+4fzLLBJTEwMoqOjce7cOZHH2DjRoKype6FPF/2fxpb7mH/++afQ8rJHjhzBtWvXcP36dbRs2RLe3t4IDAxk9XwppOmhngKEVcrLyxEREYEhQ4YAABYuXIjKykrB4woKClixYgWrx+hduHABpaWl8PLywpMnTzB06FCkpKRAT08Phw4dgoeHB9MRxYbL5SI1NbXeC4cePXowlEq8evXqBRcXF2zdulVo+7Rp05CYmIjr168zlEx8BgwYgIKCAuzbtw8ODg4AgOTkZEyYMAGampo4f/48wwkb35MnTzBx4kTcuHFDaPu7Ja3YfHG4e/duTJkyBfr6+jA2NhbqYs3hcFg5seQ7VlZWGDJkCJYsWSKTy9J9ChvuHH/u6iFsmR+mPnv37sWoUaOgqqr60ee9ePECpqamkJeXl1Ay8dDR0cGNGzcEPfwmTJiA2tpawXLZsbGxGDFiBF68eMFkTCJjqFGAsMrOnTtx9uxZnD59GgC/a6Gzs7NgZtfHjx9j3rx5rF2eriFsnI3+Q7GxsRgzZgyeP38uE0t3vRMdHY0+ffqgQ4cOggafiIgI3L59GxcvXkT37t0ZTtj4VFRUcOPGDcGkc+/cvXsX3bt3Z+UQma5du0JBQQELFiyAiYmJSF12dXVlKJn4WVpaYurUqUJ31mSFhoYG4uPjYWtry3SUJknahw8AgJyc3EfPzbLQ8GdkZITy8nKMGDECfn5+rF+O78P3raOjI77//nv4+/sDADIyMuDg4MDa3n6kaaLhA4RVDh48iHnz5glt+/333wUH3gMHDmD79u2sbBSYOHHiZz0vODhYzEmY4e/vj/bt2+Ps2bP1XjSxVdeuXRETE4NffvkFYWFhgknYgoKCYG9vz3Q8sTA3N0d1dbXI9traWpiamjKQSPzi4+Nx9+5dODo6Mh1F4t6+fYsRI0YwHYMRXl5euHLlisw1CqxYsQJz5swRuXNcXl6OX375RTBk5Ny5czAzM2MiYqO5cuWK4P88Hg+DBg3Cnj17pL5c/8SrV69w+vRphISE4IsvvoCNjQ0mTJgAHx8fGBsbMx2v0dna2uLatWuwsbFBRkYGUlJShHozvnz5ktWrJpGmiXoKEFYxMTFBTEwMrKysAAAGBga4ffu24PuUlBR06NABhYWFzIUUEzk5OcGSXR+r1sePH5dgKslRU1NDQkKCzCzdJctOnjyJ1atXY/v27Wjfvj0A/qSDM2bMwPz581m5fGGHDh3w66+/olu3bkxHkTg/Pz906NBBcBdNlqxatQqbNm3C4MGD0apVK5GJBmfOnMlQMvGSl5dHZmYmDA0Nhbbn5eXB0NCQ1XfN2dD74b948+YNDhw4gH379uHx48cYMGAA/Pz8MHToUMjJyTEdr1Hs3r0bs2fPxqhRoxAbGwttbW1ER0cLHl+5ciVu3rwp6PVKiCRQTwHCKgUFBUJzCOTk5Ag9zuVyhR5nkylTpuCPP/7A06dPMWHCBIwbN05o7Xq269SpE1JTU2WuUSAjI+Ojj7NxAjpfX1+UlZWhU6dOUFDgn8ZqamqgoKCAiRMnCvWaYcsyXuvWrcO8efOwevXqei8O2Tze2M7ODkuWLEFsbKxMXRgDwJ49e6Curo7IyEhERkYKPcbhcFhb9ndd5j+UkJAgU+c1WWRkZIRu3bohJSUFKSkpuH//Pnx8fKCjo4O9e/fiiy++YDrifzZ58mTIy8vj9OnT6NGjB5YtWyb0+OvXrz+79ychjYV6ChBWsbe3x9q1axtc0zksLAw//vgjUlNTJZxMMiorK3Hs2DEEBwfjxo0bGDx4MPz8/NCvXz/Wd6c/fvw4Fi9ejLlz59Z74dC6dWuGkonXp8ajsvGO2qeW7qqLLTN6v7tD9uG+loXxxtbW1g0+xuFwkJ6eLsE0RJzezX1TWFgITU1Nofd7bW0tSkpK4O/vj+3btzOYUrxktafAmzdvsH//fuzduxfp6ekYNmwY/Pz80KdPH5SWlmLFihU4dOgQnj9/znRUiVu7di38/f0Fq+0QIg7UKEBYZdasWbh06RLu3r0rssJAeXk52rdvjz59+mDz5s0MJZSc58+fIyQkBKGhoaipqcHDhw+hrq7OdCyxqa9bIYfDYf1FU0JCgtD31dXVuHfvHjZu3IhVq1bBy8uLoWSkMX14l/hDPXv2lFAS0hQkJSUhKCgI69evZzpKo9q3bx94PB4mTpyITZs2QUtLS/CYoqIirKys4O7uzmBC8dPQ0EBiYuJHG8PYZujQobhw4QJatGiBSZMmwdvbW6RHSHZ2NoyNjUVWFpIFbFhlgzR9NHyAsMqPP/6IsLAwODg4YPr06WjRogUA/nJl27ZtQ01NDX788UeGU0rGuzvIPB6PtRfEdT19+pTpCIyob9b59u3bw9TUFL/88gurGwWys7PrXX6Sjb1C6KKflJaW4tChQwgKCkJsbCycnJxY1yjwrmePtbU1unTpItLji40+PEZXVFTA398fampqQtuPHTsmyVgSZWhoiMjIyI82+BgYGMjseZ7u3xJJoEYBwipGRka4ceMGpkyZggULFggOpBwOB3379sX//vc/Vq/1XHf4QFRUFIYMGYJt27ZhwIABrJmgpyGWlpZMR2hSHBwccPv2baZjiMXdu3fh4+ODpKQk1i8/mZiY+FnPY2NDyA8//PBZz9u4caOYkzArOjoaQUFBCAsLQ3l5OWbPno3g4GBWr0TRs2dPcLlcpKSk1NvwV3emdmlXtzcEAIwbN46hJMzp2bMn2rZtK7K9qqoKhw4dgre3NzgcDp3nCREjGj5AWCs/P18wd4CdnR3rJyeaOnUqDh06BHNzc0ycOBFjx46Fvr4+07Ekav/+/di5cyeePn2KmJgYWFpaYtOmTbC2tsaXX37JdDyxKCoqEvqex+MhMzMTy5cvx+PHjxEfH89MMDFydXWFra0t5s+fDyMjI5Fx9mz64Fi3x09D2NYQ8k6vXr0+63l1l3Rji+zsbISEhCA4OBiFhYUYPXo0xowZA3d3dyQkJMDJyYnpiGIVGxuLMWPG4Pnz56xv+PunXr58CVNTU1Y19MvyahOfQ1bnmSCSRT0FCGvp6uqiY8eOTMeQmJ07d8LCwgI2Njb1zlT9Dlu7IO7YsQNLly7F999/j1WrVgk+RGhra2PTpk2sbRTQ1taud/I5c3NzHDp0iKFU4pWeno6jR4/KxEoTstpdFmDnxf7nsrS0xNdff43Nmzejb9++rLoA/Bz+/v5o3749zp49CxMTE9ZPlPtPODk5sW58eUOrTbx8+VKkJwUhRDyoUYAQlnjXvU5Wbd26Fbt378awYcOwdu1awfb27dtjzpw5DCYTrw8vnOTk5GBgYAA7OzvBcn1s4+HhgYSEBJloFHjX6yEjIwPm5ub11vFPLUsp7VasWIE5c+ZAVVVVaHt5eTl++eUXLF26lKFk4mNpaYmoqChYWFjA0tKS1UMF6vPkyRMcOXJEJur4P8WmDr5t2rQBh8MBh8OBh4eH0DmrtrYWT58+xYABAxhMSIjsYOcnRkJkUEhICNMRGPX06VO0adNGZLuSkhJKS0sZSCQZsjgB3Z49e+Dj44MHDx7AxcVFZDIyT09PhpKJj7W1dYPda62trVndvTYwMBD+/v4ijQJlZWUIDAxkZaPA48ePBXMJdOjQAS1atBCMNZeFxt9OnTohNTWVGgVYbtiwYQCA+Ph49O/fX2iFpHerTTS0xLQs6d69O1RUVJiOQViOGgUIIaxgbW2N+Ph4kfHk58+fR8uWLRlKJR6nTp367Oey8QI5JiYG0dHROHfunMhjbB1v3FD32pKSEpHlV9mmobInJCSweq6Yrl27omvXrtiyZQv++OMP7N27F7W1tZg6dSrGjBmDYcOGwcDAgOmYYjFjxgwEBAQgKysLrVq1Emn4Y+PEmrJo2bJlqK2thZWVFfr16wcTExOmI0kcl8tFamrqRyfUDA8PZyIakTE00SAhhBX27NmD5cuXY8OGDfDz88OePXuQlpaGNWvWYM+ePfjmm2+YjthoPhxf/OFEdHUvoNh4gWxlZYUhQ4ZgyZIlrF5NBHg/A//mzZsxefJkobvltbW1uHnzJuTl5REdHc1URLHR0dEBh8NBYWEhNDU1Rd7XJSUl8Pf3x/bt2xlMKVlJSUnYs2cPDhw4gPz8fFRXVzMdSSzqm0Ph3XGOrQ1/n4uNk84pKysjKSkJ1tbWTEeRKJpQkzQl1FOAEMIKkyZNgoqKChYvXoyysjKMGTMGpqam2Lx5M6saBAAI3U24dOkS5s+fj9WrVwvWeI6JicHixYuxevVqpiKKVV5eHmbPns36BgEAuHfvHgD+3fL79+9DUVFR8JiioiJcXV1ZO2fGpk2bwOPxMHHiRAQGBgpNOPaua/HH1jVno5YtW2LDhg1Yt27dP+oxJG1keYLNT2Hj8BEXFxekp6fLXKMATahJmhLqKUAIYZ2ysjKUlJSIjL9mIxcXF+zcuRPdunUT2n79+nV8++23SEpKYiiZ+Pj4+KB79+6YNGkS01EkZsKECdi8eTM0NTWZjiJxkZGR6NKli0gXclnxOd2LiexgY0+B8+fPY+HChfjpp5/Qrl07qKmpCT3O1uOempqazEyaS5o+6ilACGGV7OxsJCcnA+DfUWHrmNt30tLSoK2tLbJdS0sLz549k3geSWjRogUWLlyIqKioescbz5w5k6Fk4rN3716mIzCmZ8+e4HK5SElJkbkLY1ntXhwaGvrRx729vSWURHImTpyIzZs3Q0NDQ2h7aWkpZsyYgeDgYADAo0ePYGpqykREsRk0aBAA/hw4de+Ws324CE2oSZoS6ilACGGF4uJiTJ06FX/88YfgokFeXh6jRo3C9u3bWbvWcY8ePaCsrIz9+/cLutO/efMG3t7eqKioQGRkJMMJG9/HuphyOBykp6dLMI1klJaWYu3atYiIiKj3wpiNZX5HVi+MAcDNzQ0tWrRAYGBgvd2L2Xpc09HREfq+uroaZWVlUFRUhKqqKvLz8xlKJj7y8vL1rjCSm5sLY2Nj1NTUMJRM/D51nmLrKjvHjx/H4sWLMXfuXJpQkzCOGgUIIawwatQo3Lt3D1u3bhUaWz9r1iy4ubnh0KFDDCcUj9TUVHz11VdISUmBubk5AODFixewt7fH8ePHYW9vz3BC0hhGjx6NyMhIjB8/vt6Lw1mzZjGUTPxk9cIYoO7FdT158gRTpkzB3Llz0b9/f6bjNJqioiLweDzo6OjgyZMnQr3bamtrcfr0aSxYsACvX79mMCURB5pQkzQl1ChACGEFNTU1XLhwod6x9QMGDEBpaSlDycSPx+Ph0qVLgvkDWrZsiT59+tCkRSyira2Ns2fPomvXrkxHkThZvjDu3bs35s2bhwEDBjAdpUm4c+cOxo0bh8ePHzMdpdHIycl99FjN4XAQGBiIRYsWSTCV5F2/fh2//fYb0tPTcfjwYZiZmWH//v2wtrYWOa+zxfPnzz/6+IdLLBMiTjSnACGEFfT09Oq9Y6ilpSXSFZUNBg0ahD/++ANaWlrgcDi4e/cu/P39BfML5OXloXv37nj06BGzQRuRk5MToqKiBGvTT506FStWrIC+vj4A/nwSVlZWKCsrYzKmWOjo6AjKLWtkedztjBkzEBAQgKysLOpeDEBBQYF1d8yvXLkCHo+H3r174+jRo0L1XFFREZaWlqybQ+BDR48exfjx4zF27FjExcWhsrISAFBYWIjVq1cjPDyc4YTiQRf9pCmhngKEEFbYtWsXDh8+jP3798PY2BgAkJWVBR8fH3h5eeG7775jOGHj+nD8qaamJuLj4wUzUr958wampqas6n4oJyeHrKysj5bZxMREZLw9Gxw4cAAnT57Evn37oKqqynQciZLlcbey2r34w+UWeTweMjMzsW3bNpibm+PcuXMMJROf58+fw8LCQiZ7eLVp0wazZ8+Gt7e30OoK9+7dw8CBA5GVlcV0RLF69OgRMjIyUFVVJbTd09OToUREFlFPAUIIK+zYsQOpqamwsLCAhYUFACAjIwNKSkrIycnBb7/9JnhuXFwcUzEbzYftubLYvltfmdn6gXrDhg1IS0uDkZERrKysRC6M2fCebsjw4cMB8Gdnf0cWLowB4OnTp0xHYMSwYcOEvn+3kkzv3r2xYcMGZkKJQWJiIlxcXCAnJ4fCwkLcv3+/weeyufErOTm53lVEtLS0UFBQIPlAEpKeno6vvvoK9+/fFxzTgPfnMTYf20jTQ40ChBBW+PBDJCFsIsvvb1m9MAZkt3sxG3v71MfNzU3Q+8nNzU3owrAutjd+GRsbIzU1FVZWVkLbo6KiBD3B2GjWrFmwtrZGREQErK2tcevWLeTl5SEgIADr169nOh6RMdQoQAhhhWXLljEdQaI4HI7IXXG23iV/RxbL/I6svb/rktUL47pkuXvxh3dP2eTp06eC1QZkufFr8uTJmDVrFoKDg8HhcPD69WvExMRgzpw5WLJkCdPxxCYmJgaXL1+Gvr4+5OTkICcnh27dumHNmjWYOXMm7t27x3REIkOoUYAQwhoFBQU4cuQI0tLSMHfuXOjq6iIuLg5GRkYwMzNjOl6j4vF48PX1hZKSEgCgoqIC/v7+UFNTAwDBRE1swuPx4OHhAQUF/qmrvLwcQ4cOhaKiIgCweh1vIpsXxrLcvTg0NBS//PILnjx5AgBo0aIF5s6di/HjxzOcrPHUbfCS5cavBQsWgMvlwsPDA2VlZejRoweUlJQwZ84czJgxg+l4YlNbWwsNDQ0AgL6+Pl6/fg0HBwdYWloiOTmZ4XRE1lCjACGEFRITE9GnTx9oaWnh2bNnmDx5MnR1dXHs2DFkZGQgNDSU6YiNysfHR+j7cePGiTzH29tbUnEk4sO75V9++aXIc96NP2eb2tpa/PrrrwgLC6v3wjg/P5+hZOInyxfGstq9eOPGjViyZAmmT58uWIYzKioK/v7+yM3NxezZsxlO2Dg+nFDxY9jc+MXhcLBo0SLMnTsXqampKCkpgZOTE9TV1ZmOJlYuLi5ISEiAtbU1OnXqhJ9//hmKiorYtWsXq4dNkCaKRwghLODh4cGbO3cuj8fj8dTV1XlpaWk8Ho/Hi46O5llaWjKYjDAlKiqKV1FRwXSMRrFkyRKeiYkJb/369TxlZWXeTz/9xPPz8+Pp6enxNm/ezHQ8sRoyZAjvyy+/5OXk5PDU1dV5jx494l2/fp3XsWNH3rVr15iOJ1Z6enq8hIQEHo/H42lqavIeP37M4/F4vIiICJ6bmxuT0cTKysqKt2/fPpHtISEhPCsrKwYSiQeHwxH6kpOTE/n+3ZesyMjI4GVkZDAdQyLOnz/PO3r0KI/H4/GePHnCc3Bw4HE4HJ6+vj4vIiKC4XRE1oiudUMIIVLo9u3b9S47aGZmxvrljEj9Bg4ciFevXjEdo1EcPHgQu3fvRkBAABQUFDB69Gjs2bMHS5cuRWxsLNPxxComJgYrVqxocNwtm9XXvRgA67sXZ2ZmokuXLiLbu3TpgszMTAYSiQeXyxV8Xbx4EW5ubjh37hwKCgpQUFCA8PBwtG3bFufPn2c6qljV1NRgyZIl0NLSgpWVFaysrKClpYXFixejurqa6Xhi079/f3h5eQEA7Ozs8PjxY+Tm5iI7Oxu9e/dmOB2RNTR8gBDCCkpKSigqKhLZnpKSIpjIicgWHouWaczKykKrVq0AAOrq6igsLAQADBkyhNUTcQGyPe5WVrsX29nZISwsDD/++KPQ9j///BP29vYMpRKv77//Hjt37kS3bt0E2/r37w9VVVV8++23SEpKYjCdeM2YMQPHjh3Dzz//DHd3dwD8xsDly5cjLy8PO3bsYDih+L18+RIA0Lx5c4aTEFlFjQKEEFbw9PTEihUrEBYWBoA/RjEjIwPz589n7ThzIjuaN2+OzMxMWFhYwNbWFhcvXkTbtm1x+/ZtwWSTbCWrF8YAsHjxYpSWlgIAVqxYgSFDhqB79+7Q09PDn3/+yXA68QkMDMSoUaNw7do1wZwC0dHRiIiIEBzj2SYtLQ3a2toi29/Nk8Nmv//+Ow4dOoSBAwcKtrVu3Rrm5uYYPXo0axsFuFwuVq5ciQ0bNqCkpAQAoKGhgYCAACxatAhyctShm0gOh8emWymEEJlVWFiIr7/+Grdv30ZJSQlMTU2RlZUFd3d3hIeHC2blJ7JDQ0MDCQkJrLhwXLBgATQ1NfHjjz/izz//xLhx42BlZYWMjAzMnj0ba9euZTqi2Fy4cAGlpaXw8vJCamoqhgwZgpSUFMGFsax1s83Pz4eOjg4rl+ir6+7du/j1118Fd8hbtmyJgIAAtGnThuFk4tGjRw8oKytj//79MDIyAgC8efMG3t7eqKioQGRkJMMJxcfQ0BCRkZFo2bKl0PakpCT06NEDOTk5DCUTr4ULFyIoKAiBgYFCE2ouX74ckydPxqpVqxhOSGQJNQoQQlglOjoaCQkJKCkpQdu2bdGnTx+mIxGGsKlR4EOxsbG4ceMG7O3tMXToUKbjSJysXBjXRd2L2S01NRVfffUVUlJSYG5uDgB48eIF7O3tceLECdjZ2TGcUHxWrFiBx48fY+/evYKeT5WVlfDz84O9vb3IyjNsYWpqip07d4qsLHHy5ElMnTqVNXPiEOlAwwcIIVKPy+UiJCQEx44dw7Nnz8DhcGBtbQ1jY2PweDyZunAg77F5v3fu3BmdO3dmOoZEFBYWora2Frq6uoJturq6yM/Ph4KCAjQ1NRlMJ16y3r04Ozsb2dnZ4HK5Qttbt27NUCLxsbOzQ2JiIv766y88fvwYAL93RJ8+fVh9LAOAe/fuISIiAs2bN4erqysAICEhAVVVVfDw8BBMxgcAx44dYypmo8vPz4ejo6PIdkdHR1YvM0uaJmoUIIRINR6PB09PT4SHh8PV1RWtWrUCj8dDUlISfH19cezYMZw4cYLpmOQ/OnXqFAYOHIhmzZp99mvY1BFuzZo1MDIywsSJE4W2BwcHIycnB/Pnz2comfh98803GDp0KKZOnSq0PSwsDKdOnUJ4eDhDycRv0aJFCAoKwtq1a0W6F1dUVLC2e/Hdu3fh4+ODpKQkkXrM4XBQW1vLUDLx4nA46NevH3r06AElJSXWNwa8o62tLTL3z7veEmzm6uqKbdu2YcuWLULbt23bxsqGL9K00fABQohU27t3L2bNmoWTJ0+iV69eQo9dvnwZw4YNw7Zt2+Dt7c1QQtIY5OXlkZWVBQMDA8jLyyMzMxOGhoZMx5IYKysr/P777yLLtN28eRPffPMNnj59ylAy8dPV1UV0dLTIeOPHjx+ja9euyMvLYyiZ+Mlq92JXV1fY2tpi/vz5MDIyErk4trS0ZCiZ+HC5XKxatQo7d+7EmzdvkJKSAhsbGyxZsgRWVlbw8/NjOiJpZJGRkRg8eDAsLCyEVl148eIFwsPD0b17d4YTElnC7n5nhBDW++OPP/Djjz+KNAgAQO/evbFgwQIcPHiQgWSkMRkYGCA2NhYAZHJISFZWFkxMTES2GxgYsGrd9vpUVlaipqZGZHt1dTXKy8sZSCQ5stq9OD09HT///DM6deoEKysrWFpaCn2x0cqVKxESEiJYXeMdFxcX7Nmzh8FkkpOTk4OoqChERUWxdnLBunr27ImUlBR89dVXKCgoQEFBAby8vPDw4UPs37+f6XhExlCjACFEqiUmJmLAgAENPj5w4EAkJCRIMBERB39/f3z55ZeQl5cHh8OBsbEx5OXl6/1iI3Nzc0RHR4tsj46OhqmpKQOJJKdjx47YtWuXyPadO3eiXbt2DCSSnHfdiz/E9u7FHh4eMnfcDg0Nxa5duzB27Fih45irq6tgjgG2Ki0txcSJE2FiYoIePXqgR48eMDU1hZ+fH8rKypiOJ1ampqZYtWoVjh49iqNHj2LlypV4+/YtgoKCmI5GZAzNKUAIkWr5+fmC5ZvqY2RkhLdv30owERGH5cuX45tvvkFqaio8PT2xd+/eetf0ZqvJkyfj+++/R3V1tWAJvoiICMybNw8BAQEMpxOvlStXok+fPkhISICHhwcAftlv376NixcvMpxOvH7++WcMHjwYly5dqrd7MVvt2bMHPj4+ePDgAVxcXETmEvlwOAUbvHr1qt4VBrhcLqqrqxlIJDk//PADIiMjcfr0aaG5M2bOnImAgADs2LGD4YSEsB81ChBCpFptbS0UFBo+lMnLy9fb9ZhIH0dHRzg6OmLZsmUYMWIEVFVVmY4kMXPnzkVeXh6mTp2KqqoqAICysjLmz5+PhQsXMpxOvLp27YqYmBj88ssvCAsLg4qKClq3bo2goCDY29szHU+s3nUv3r59u+BusZeXF7799lusXLmStWOOY2JiEB0djXPnzok8xtaJBp2cnHD9+nWR4RFHjhxBmzZtGEolGUePHsWRI0fwxRdfCLYNGjQIKioqGDlyJDUKECIBNNEgIUSqycnJYeDAgYK1jT9UWVmJ8+fPs/JDJJE9JSUlSEpKgoqKCuzt7Rt83xN2S0hIQNu2bVl7XLOyssKQIUOwZMmSj/YEY5OTJ0/Cx8cHCxcuxIoVKxAYGIjk5GSEhobizJkz6Nu3L9MRxUZVVRV3794VmUz04cOH6NixI0pLSxlKxgy212/SNFGjACFEqk2YMOGznrd3714xJyHi1KZNm8+eXDAuLk7MaYi4FRUVQVNTU/D/j3n3PFnC9osGDQ0NxMfHw9bWlukoEnX9+nWsWLECCQkJKCkpQdu2bbF06VL069eP6Whi5eHhAT09PYSGhkJZWRkAUF5eDh8fH+Tn5+PSpUsMJ2xcXl5eH328oKAAkZGRrK3fpGmi4QOEEKlGF/uyYdiwYUxHkDgvLy+EhIRAU1Pzkx8ijx07JqFUkqGjoyNYdlJbW7veBqF3q1DQB2f28fLywpUrV2SmUaCmpgarV6/GxIkT8ddffzEdR+I2bdqEAQMGoHnz5nB1dQXAb/hSVlbGhQsXGE7X+LS0tD75OC2jTCSNGgUIIYQ0ecuWLWM6gsRpaWkJLoY/9SGSbS5fvgxdXV0AwJUrVxhOQyStRYsWWLhwIaKiotCqVSuRiQZnzpzJUDLxUFBQwM8//yyzF4KtWrXCkydPcPDgQcHcGaNHj8bYsWOhoqLCcLrGRzczSFNEwwcIIYRInYKCAhw5cgRpaWmYO3cudHV1ERcXByMjI5iZmTEdj5D/RNa7F1tbWzf4GIfDQXp6ugTTSMaXX34JLy8v+Pj4MB1Foqqrq+Ho6IgzZ86IzClACJEc6ilACCFEqiQmJqJPnz7Q0tLCs2fPMHnyZOjq6uLYsWPIyMhAaGgo0xHJf5SYmPjZz23durUYkzBD1rsXP336lOkIEjdw4EAsWLAA9+/fR7t27aCmpib0OBuXYQSAZs2aoaKigukYhMg86ilACCFEqvTp0wdt27bFzz//DA0NDSQkJMDGxgY3btzAmDFj8OzZM6YjNgpZnlxRTk4OHA4Hn/qIQnMKyJakpCQEBQVh/fr1TEdpdHJycg0+xvb3+erVq5GSkoI9e/Z8dIlhQoj4UM0jhBAiVW7fvo3ffvtNZLuZmRmysrIYSCQesji54juyeKeY1K+0tBSHDh1CUFAQYmNj4eTkxMpGAS6Xy3QExty+fRsRERG4ePEiWrVqJdJLgm0TqRLSFFGjACGEEKmipKRU7zJ1KSkpMDAwYCCReMji5IrvWFpaMh2BMCw6OhpBQUEICwtDeXk5Zs+ejeDgYDg6OjIdrVFdvnwZ06dPR2xsrMjymoWFhejSpQt27tyJ7t27M5RQ/LS1tTF8+HCmYxAi06hRgBBCiFTx9PTEihUrEBYWBoDftTYjIwPz589n/QfLO3fuICkpCQDg5OSEdu3aMZxIMpKTk7F161ZB2Vu2bIkZM2bAwcGB4WSkMWVnZyMkJATBwcEoLCzE6NGjcfXqVbi7u2PixImsaxAA+MvxTZ48WaRBAODPHfHdd99h48aNrGwU4HK5+OWXX5CSkoKqqir07t0by5cvZ+WKA4Q0dQ0PYCKEEEKaoA0bNqCkpASGhoYoLy9Hz549YWtrC3V1daxatYrpeGLx8uVLdO/eHR07dsSsWbMwa9YsdOjQAd26dcPLly+ZjidWR48ehYuLC+7evQtXV1e4uroiLi4OLi4uOHr0KNPxSCOytLTE/fv3sXnzZrx69QobN25E+/btmY4lVgkJCRgwYECDj/fr1w93796VYCLJWbVqFX788Ueoq6vDzMwMW7ZswbRp05iORYhMookGCSGESKWoqCgkJiaipKQE7dq1g4eHB9ORxGbAgAEoKCjAvn37BHfHk5OTMWHCBGhqauL8+fMMJxQfW1tbjB07FitWrBDavmzZMhw4cABpaWkMJSONzdHREZWVlRgzZgzGjx8v6BnQrFkzJCQkwMnJieGEjU9ZWRkPHjyAnZ1dvY+npqaiVatWKC8vl3Ay8bO3t8ecOXPw3XffAQAuXbqEwYMHo7y8/KMTLxJCGh/VOEIIIVIhJiYGZ86cEXzfrVs3qKmp4X//+x9Gjx6Nb7/9FpWVlQwmFJ/IyEjs2LFDqLu8g4MDtm7dimvXrjGYTPwyMzPrXX5v3LhxyMzMZCAREZfHjx/jwIEDyMzMRIcOHdCuXTv8+uuvAPDZK3FIGzMzMzx48KDBxxMTE2FiYiLBRJKTkZGBQYMGCb7v06cPOBwOXr9+zWAqQmQTNQoQQgiRCitWrMDDhw8F39+/fx+TJ09G3759sWDBApw+fRpr1qxhMKH4mJubo7q6WmR7bW0tTE1NGUgkOV988QWuX78usj0qKoqV46xlXdeuXREcHIzMzEz4+/vj8OHDqK2txdSpU7F7927k5OQwHbFRDRo0CEuWLEFFRYXIY+Xl5Vi2bBmGDBnCQDLxq6mpgbKystC2Zs2a1XusI4SIFw0fIIQQIhVMTExw+vRpwRjjRYsWITIyElFRUQCAw4cPY9myZXj06BGTMcXi5MmTWL16NbZv3y4o/507dzBjxgzMnz+f1csX7ty5E0uXLsXIkSPRuXNnAEBsbCwOHz6MwMBAoUYRT09PpmISMUpKSkJQUBD279+P/Px8Vl00vnnzBm3btoW8vDymT58u6A30+PFjbN++HbW1tYiLi4ORkRHDSRufnJwcBg4cCCUlJcG206dPo3fv3kLLEtKShISIHzUKEEIIkQrKysp48uQJzM3NAfCHDwwcOBCLFi0CADx79gytWrVCcXExkzHFQkdHB2VlZaipqYGCAn/hoHf//3BN7/z8fCYiis3nji3mcDiora0VcxrCpJqaGpw6dQpeXl4AgLVr18Lf3x/a2trMBvuPnj9/jilTpuDChQt497Gcw+Ggf//+2L59O6ytrRlOKB4TJkz4rOft3btXzEkIIdQoQAghRCpYWlpi//796NGjB6qqqqCtrY3Tp08LJhi8f/8+evbsybqLYgDYt2/fZz/Xx8dHjEkIaTo0NTURHx8PGxsbpqM0irdv3yI1NRU8Hg/29vbQ0dFhOhIhREYoMB2AEEII+RyDBg3CggULsG7dOpw4cQKqqqpCY8oTExNha2vLYELxoQt9QkSx7b6Wjo4OOnTowHQMQogMokYBQgghUuGnn36Cl5cXevbsCXV1dezbtw+KioqCx4ODg9GvXz8GE4pXbW0tjh8/jqSkJACAk5MTvvzyS8FwAjaLiIjAr7/+Kih7y5Yt8f3336NPnz4MJyOEEEKkHw0fIIQQIlUKCwuhrq4OeXl5oe35+flQV1cXaihgi4cPH8LT0xNZWVmCichSUlJgYGCA06dPw8XFheGE4vO///0Ps2bNwtdffw13d3cA/IkGjxw5gl9//RXTpk1jOCFhioaGBhISElgzfIAQQphCjQKEEEJIE+fu7g4DAwPs27dPMM747du38PX1RU5ODm7cuMFwQvFp3rw5FixYgOnTpwtt3759O1avXo1Xr14xlIwwjRoFCCGkcVCjACGEENLEqaio4M6dO3B2dhba/uDBA3To0AHl5eUMJRM/dXV1xMfHw87OTmj7kydP0KZNG5SUlDCUjDCNGgUIIaRxfN46P4QQQghhTIsWLfDmzRuR7dnZ2SIXy2zj6emJ48ePi2w/efIkhgwZwkAi0lR0794dKioqTMcghBCpRz0FCCGEkCYuPDwc8+bNw/Lly9G5c2cA/HH1K1aswNq1a9GtWzfBczU1NZmKKRYrV67E+vXr0bVrV6E5BaKjoxEQECBU3pkzZzIVkzQyLpeL1NRUZGdng8vlCj3Wo0cPhlIRQgg7UaMAIYQQ0sTJyb3v2MfhcAC8X46t7vccDge1tbWSDyhG1tbWn/U8DoeD9PR0MachkhAbG4sxY8bg+fPnIssOsvE9TgghTGP/OkaEEEKIlLty5UqDjyUmJqJ169YSTCNZT58+ZToCkTB/f3+0b98eZ8+ehYmJiaDhixBCiHhQTwFCCCFEyhQXF+OPP/7Anj17cPfuXbpzSlhFTU0NCQkJrJ8vgxBCmgrqKUAIIYRIiWvXriEoKAhHjx6FqakpvLy8sH37dqZjid3Lly9x6tQpZGRkoKqqSuixjRs3MpSKiEunTp2QmppKjQKEECIh1ChACCGENGFZWVkICQlBUFAQioqKMHLkSFRWVuLEiRNwcnJiOp7YRUREwNPTEzY2Nnj8+DFcXFzw7Nkz8Hi8/7d3PyFRb30cxz8+OnAhbByyREsmM1GxjIxsKiSMUstUdCVI7SIMddGsok0ZQUiIlIKLdOOi3BQVBJGIxWRhaE1qqZgUZWR/pl+W1GamZ3FJnuje+2ycOb87836thnM2H2Y3nznne1RQUGA6HsKgsbFRXq9Xb9++1caNG+VwOH7Zj+brMgBgAtcHAACwqYqKCt29e1fl5eWqq6tTWVmZ4uPj5XA45Pf7Y6IUKCws1L59+3Tq1KnFd+lXrVq1+H3U19ebjogl9r+DNX+Ki4uL2mGaAGAapQAAADaVkJCgpqYm1dfXKysra3E9lkqBxMREPX78WJmZmXK5XPL5fMrLy5Pf71dVVZVevHhhOiKW2MuXL/9x3+12RygJAMQGrg8AAGBTPp9PXV1d2rJli3Jzc3Xw4EHV1taajhVRy5YtW5wjkJqaqufPnysvL0+S9OHDB5PRECb86AeAyKIUAADApjwejzwej9ra2tTb26vu7m4dO3ZMoVBIt2/fVnp6uhITE03HDCuPxyOfz6fc3Fzt379fXq9Xo6OjunLlijwej+l4CKOnT5/+5XDJyspKQ4kAIDpxfQAAgH+RyclJdXV1qaenR5Zlae/evbp+/brpWGEzMzOjr1+/Kj8/XwsLC/J6vRocHFRWVpZaW1v5VzkKzczMqLq6WqOjo4uzBKQ/5wpIYqYAACwxSgEAAP6FgsGgbty4oe7u7qguBRB7KioqFB8fr4sXLyojI0NDQ0P6+PGjvF6vzp07p6KiItMRASCqUAoAAADbGx4e1rNnzyRJeXl52rx5s+FECJfk5GT19/crPz9fTqdTQ0NDys7OVn9/v7xerx49emQ6IgBEFWYKAAAA23r37p1qa2s1MDCgpKQkSZJlWSouLtbly5e1cuVKswGx5ILB4OKsjOTkZL1580bZ2dlyu92anJw0nA4Aos/vD8ECAADYRGNjo758+aLx8XEFAgEFAgGNjY1pfn5eTU1NpuMhDDZs2CC/3y9J2rZtm1paWnTv3j01Nzdr3bp1htMBQPTh+gAAALAtp9Opvr4+bd269Zf1oaEhlZSUyLIsM8EQNrdu3dLCwoJqamo0PT2tAwcOaGpqSitWrFBvb692795tOiIARBWuDwAAANsKhUJyOBy/rTscDoVCIQOJEG6lpaWLn9evX6+JiQkFAgG5XK7FFwgAAEuHkwIAAMC2qqqqZFmWLl26pLS0NEnS7Oys6urq5HK5dPXqVcMJEU6vX7+WJK1Zs8ZwEgCIXswUAAAAttXe3q75+XmtXbtWmZmZyszMVEZGhubn53XhwgXT8RAGoVBIzc3NcjqdcrvdcrvdSkpK0unTpzkdAgBhwPUBAABgW+np6RoZGVFfX58mJiYkSbm5udqzZ4/hZAiXEydOqKurS2fPntXOnTslST6fTydPntT379915swZwwkBILpwfQAAANhOf3+/Ghoa9ODBAy1fvvyXvc+fP2vHjh3q7OxUUVGRoYQIl7S0NHV2dqqysvKX9WvXruno0aOanZ01lAwAohPXBwAAgO20tbXp8OHDvxUC0p8vEhw5ckStra0GkiHcAoGAcnJyflvPyclRIBAwkAgAohulAAAAsB2/36+ysrK/3S8pKdHw8HAEEyFSNm3apPb29t/W29vblZ+fbyARAEQ3ZgoAAADbmZub+8unCH9KSEjQ+/fvI5gIkdLS0qLy8nL19fVp+/btkqT79+/r1atXunnzpuF0ABB9OCkAAABsZ/Xq1RobG/vb/SdPnig1NTWCiRApu3bt0tTUlKqrq2VZlizLUk1NjcbHx9XT02M6HgBEHQYNAgAA22lsbNTAwIAePnyoP/7445e9b9++qbCwUMXFxTp//ryhhIg0v9+vgoICBYNB01EAIKpQCgAAANuZm5tTQUGB4uPj1dDQoOzsbEnSxMSEOjo6FAwGNTIyopSUFMNJESmUAgAQHswUAAAAtpOSkqLBwUHV19fr+PHj+vkfRlxcnEpLS9XR0UEhAADAEuCkAAAAsLVPnz5penpaP378UFZWllwul+lIMICTAgAQHpQCAAAAMK6mpuYf9y3L0p07dygFAGCJcX0AAAAAxjmdzv+7f+jQoQilAYDYwUkBAAAAAABi1H9MBwAAAAAAAGZQCgAAAAAAEKMoBQAAAAAAiFGUAgAAAAAAxChKAQAAAAAAYhSlAAAAAAAAMYpSAAAAAACAGPVfKWprbkaOZGIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(df_train.corr(), cmap='coolwarm', annot=True, fmt='.1f', linewidths=.1)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}