Project 16 Predict the Automobile Insurance claim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "\n",
    "\n",
    "# Algorithms\n",
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim Amount</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Effective To Date</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>Months Since Policy Inception</th>\n",
       "      <th>Number of Open Complaints</th>\n",
       "      <th>Number of Policies</th>\n",
       "      <th>Policy Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Claim Reason</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>Total Claim Amount</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Vehicle Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>BU79786</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>276.351928</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/24/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>QZ44356</td>\n",
       "      <td>US</td>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>697.953590</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/31/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Scratch/Dent</td>\n",
       "      <td>Agent</td>\n",
       "      <td>1131.464935</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AI49188</td>\n",
       "      <td>US</td>\n",
       "      <td>OK</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1288.743165</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/19/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>WW63253</td>\n",
       "      <td>US</td>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>764.586183</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/20/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HB64268</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>281.369258</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/3/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer Country State Code     State  Claim Amount Response  Coverage  \\\n",
       "0  BU79786      US         KS    Kansas    276.351928       No     Basic   \n",
       "1  QZ44356      US         NE  Nebraska    697.953590       No  Extended   \n",
       "2  AI49188      US         OK  Oklahoma   1288.743165       No   Premium   \n",
       "3  WW63253      US         MO  Missouri    764.586183       No     Basic   \n",
       "4  HB64268      US         KS    Kansas    281.369258       No     Basic   \n",
       "\n",
       "  Education Effective To Date EmploymentStatus  ...  \\\n",
       "0  Bachelor           2/24/11         Employed  ...   \n",
       "1  Bachelor           1/31/11       Unemployed  ...   \n",
       "2  Bachelor           2/19/11         Employed  ...   \n",
       "3  Bachelor           1/20/11       Unemployed  ...   \n",
       "4  Bachelor            2/3/11         Employed  ...   \n",
       "\n",
       "  Months Since Policy Inception  Number of Open Complaints Number of Policies  \\\n",
       "0                             5                          0                  1   \n",
       "1                            42                          0                  8   \n",
       "2                            38                          0                  2   \n",
       "3                            65                          0                  7   \n",
       "4                            44                          0                  1   \n",
       "\n",
       "      Policy Type        Policy  Claim Reason  Sales Channel  \\\n",
       "0  Corporate Auto  Corporate L3     Collision          Agent   \n",
       "1   Personal Auto   Personal L3  Scratch/Dent          Agent   \n",
       "2   Personal Auto   Personal L3     Collision          Agent   \n",
       "3  Corporate Auto  Corporate L2     Collision    Call Center   \n",
       "4   Personal Auto   Personal L1     Collision          Agent   \n",
       "\n",
       "   Total Claim Amount  Vehicle Class Vehicle Size  \n",
       "0          384.811147   Two-Door Car      Medsize  \n",
       "1         1131.464935  Four-Door Car      Medsize  \n",
       "2          566.472247   Two-Door Car      Medsize  \n",
       "3          529.881344            SUV      Medsize  \n",
       "4          138.130879  Four-Door Car      Medsize  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile=pd.read_csv('Automobile_insurance_claim.csv')\n",
    "automobile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9134, 26)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here we can observe that 9134 rows and 26 columns present in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9134 entries, 0 to 9133\n",
      "Data columns (total 26 columns):\n",
      "Customer                         9134 non-null object\n",
      "Country                          9134 non-null object\n",
      "State Code                       9134 non-null object\n",
      "State                            9134 non-null object\n",
      "Claim Amount                     9134 non-null float64\n",
      "Response                         9134 non-null object\n",
      "Coverage                         9134 non-null object\n",
      "Education                        9134 non-null object\n",
      "Effective To Date                9134 non-null object\n",
      "EmploymentStatus                 9134 non-null object\n",
      "Gender                           9134 non-null object\n",
      "Income                           9134 non-null int64\n",
      "Location Code                    9134 non-null object\n",
      "Marital Status                   9134 non-null object\n",
      "Monthly Premium Auto             9134 non-null int64\n",
      "Months Since Last Claim          9134 non-null int64\n",
      "Months Since Policy Inception    9134 non-null int64\n",
      "Number of Open Complaints        9134 non-null int64\n",
      "Number of Policies               9134 non-null int64\n",
      "Policy Type                      9134 non-null object\n",
      "Policy                           9134 non-null object\n",
      "Claim Reason                     9134 non-null object\n",
      "Sales Channel                    9134 non-null object\n",
      "Total Claim Amount               9134 non-null float64\n",
      "Vehicle Class                    9134 non-null object\n",
      "Vehicle Size                     9134 non-null object\n",
      "dtypes: float64(2), int64(6), object(18)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "automobile.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Here we can observe that Total_claim amount and claim amount both attributes are in floating format  \n",
    "2. Income,Monthly_premium_auto,months since last claim,months since policy inception,numer of open complaints and policies all these attributes are in interger format.\n",
    "3. OTHER than these two datatypes and all other attributes are in object format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here Effective_to_date is in object format not in date format need to change it to date format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburban    5779\n",
       "Rural       1773\n",
       "Urban       1582\n",
       "Name: Location Code, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile['Location Code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "automobile['Effective To Date']=pd.to_datetime(automobile['Effective To Date'])\n",
    "automobile['Year']=automobile['Effective To Date'].apply(lambda x:x.year)\n",
    "automobile['Month']=automobile['Effective To Date'].apply(lambda x:x.month)\n",
    "automobile['Day']=automobile['Effective To Date'].apply(lambda x:x.day)"
   ]
  },
  {
   "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>Claim Amount</th>\n",
       "      <th>Income</th>\n",
       "      <th>Monthly Premium Auto</th>\n",
       "      <th>Months Since Last Claim</th>\n",
       "      <th>Months Since Policy Inception</th>\n",
       "      <th>Number of Open Complaints</th>\n",
       "      <th>Number of Policies</th>\n",
       "      <th>Total Claim Amount</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.0</td>\n",
       "      <td>9134.000000</td>\n",
       "      <td>9134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>800.494047</td>\n",
       "      <td>37657.380009</td>\n",
       "      <td>93.219291</td>\n",
       "      <td>15.097000</td>\n",
       "      <td>48.064594</td>\n",
       "      <td>0.384388</td>\n",
       "      <td>2.966170</td>\n",
       "      <td>434.088794</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>1.463762</td>\n",
       "      <td>15.461134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>687.096761</td>\n",
       "      <td>30379.904734</td>\n",
       "      <td>34.407967</td>\n",
       "      <td>10.073257</td>\n",
       "      <td>27.905991</td>\n",
       "      <td>0.910384</td>\n",
       "      <td>2.390182</td>\n",
       "      <td>290.500092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.498712</td>\n",
       "      <td>8.653770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>189.800768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099007</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>399.425179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>272.258244</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>578.018220</td>\n",
       "      <td>33889.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>383.945434</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>896.216704</td>\n",
       "      <td>62320.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>547.514839</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>8332.538119</td>\n",
       "      <td>99981.000000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2893.239678</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Claim Amount        Income  Monthly Premium Auto  \\\n",
       "count   9134.000000   9134.000000           9134.000000   \n",
       "mean     800.494047  37657.380009             93.219291   \n",
       "std      687.096761  30379.904734             34.407967   \n",
       "min      189.800768      0.000000             61.000000   \n",
       "25%      399.425179      0.000000             68.000000   \n",
       "50%      578.018220  33889.500000             83.000000   \n",
       "75%      896.216704  62320.000000            109.000000   \n",
       "max     8332.538119  99981.000000            298.000000   \n",
       "\n",
       "       Months Since Last Claim  Months Since Policy Inception  \\\n",
       "count              9134.000000                    9134.000000   \n",
       "mean                 15.097000                      48.064594   \n",
       "std                  10.073257                      27.905991   \n",
       "min                   0.000000                       0.000000   \n",
       "25%                   6.000000                      24.000000   \n",
       "50%                  14.000000                      48.000000   \n",
       "75%                  23.000000                      71.000000   \n",
       "max                  35.000000                      99.000000   \n",
       "\n",
       "       Number of Open Complaints  Number of Policies  Total Claim Amount  \\\n",
       "count                9134.000000         9134.000000         9134.000000   \n",
       "mean                    0.384388            2.966170          434.088794   \n",
       "std                     0.910384            2.390182          290.500092   \n",
       "min                     0.000000            1.000000            0.099007   \n",
       "25%                     0.000000            1.000000          272.258244   \n",
       "50%                     0.000000            2.000000          383.945434   \n",
       "75%                     0.000000            4.000000          547.514839   \n",
       "max                     5.000000            9.000000         2893.239678   \n",
       "\n",
       "         Year        Month          Day  \n",
       "count  9134.0  9134.000000  9134.000000  \n",
       "mean   2011.0     1.463762    15.461134  \n",
       "std       0.0     0.498712     8.653770  \n",
       "min    2011.0     1.000000     1.000000  \n",
       "25%    2011.0     1.000000     8.000000  \n",
       "50%    2011.0     1.000000    15.000000  \n",
       "75%    2011.0     2.000000    23.000000  \n",
       "max    2011.0     2.000000    31.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here we can observe that Minimum claim amount is 189 and maximum is 8332.\n",
    "* And also we can see in Claim amount there is alot of difference between 75% and maximum in the sense it contains outliers.\n",
    "* Minimum income here is 0 and maximum income is 99981.\n",
    "* And we can observe another one in income is the difference between 75% and maximum is alot so it contains outliers .\n",
    "* minimum number of policies is 1 and maximum number of policies is 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From above we seeing complete numerical data for categorical i'm going to handle with grouping all together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer  Country  State Code  State     Response  Coverage  Education             Effective To Date  EmploymentStatus  Gender  Location Code  Marital Status  Policy Type     Policy        Claim Reason  Sales Channel  Vehicle Class  Vehicle Size\n",
       "AA10041   US       MO          Missouri  No        Basic     High School or Below  2011-02-02         Unemployed        M       Suburban       Single          Personal Auto   Personal L3   Collision     Call Center    SUV            Medsize         1\n",
       "AA11235   US       OK          Oklahoma  No        Basic     Bachelor              2011-01-28         Medical Leave     F       Suburban       Married         Personal Auto   Personal L3   Hail          Branch         Two-Door Car   Medsize         1\n",
       "AA16582   US       KS          Kansas    Yes       Basic     Bachelor              2011-01-26         Medical Leave     M       Suburban       Divorced        Personal Auto   Personal L2   Collision     Agent          Four-Door Car  Medsize         1\n",
       "AA30683   US       MO          Missouri  No        Premium   Bachelor              2011-01-15         Unemployed        M       Suburban       Single          Personal Auto   Personal L3   Collision     Web            Four-Door Car  Medsize         1\n",
       "AA34092   US       MO          Missouri  No        Extended  College               2011-02-11         Employed          M       Suburban       Married         Personal Auto   Personal L3   Collision     Web            Luxury SUV     Medsize         1\n",
       "                                                                                                                                                                                                                                                        ..\n",
       "ZZ77357   US       OK          Oklahoma  No        Basic     High School or Below  2011-01-09         Unemployed        M       Suburban       Single          Corporate Auto  Corporate L2  Collision     Agent          Two-Door Car   Medsize         1\n",
       "ZZ83340   US       IA          Iowa      No        Basic     Doctor                2011-02-04         Employed          F       Urban          Married         Personal Auto   Personal L1   Scratch/Dent  Branch         Two-Door Car   Medsize         1\n",
       "ZZ89380   US       IA          Iowa      No        Extended  Bachelor              2011-01-10         Employed          F       Rural          Married         Special Auto    Special L1    Hail          Call Center    Two-Door Car   Large           1\n",
       "ZZ91716   US       MO          Missouri  No        Extended  Bachelor              2011-01-22         Unemployed        F       Suburban       Married         Personal Auto   Personal L3   Hail          Web            Four-Door Car  Small           1\n",
       "ZZ97035   US       MO          Missouri  No        Extended  College               2011-02-01         Unemployed        M       Suburban       Single          Corporate Auto  Corporate L1  Collision     Branch         Luxury Car     Small           1\n",
       "Length: 9134, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.groupby(by =['Customer', 'Country', 'State Code', 'State',\n",
    "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
    "       'EmploymentStatus', 'Gender',  'Location Code',\n",
    "       'Marital Status', \n",
    "\n",
    "       'Policy Type', 'Policy', 'Claim Reason',\n",
    "       'Sales Channel',  'Vehicle Class', 'Vehicle Size']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing With Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer                         0\n",
       "Country                          0\n",
       "State Code                       0\n",
       "State                            0\n",
       "Claim Amount                     0\n",
       "Response                         0\n",
       "Coverage                         0\n",
       "Education                        0\n",
       "Effective To Date                0\n",
       "EmploymentStatus                 0\n",
       "Gender                           0\n",
       "Income                           0\n",
       "Location Code                    0\n",
       "Marital Status                   0\n",
       "Monthly Premium Auto             0\n",
       "Months Since Last Claim          0\n",
       "Months Since Policy Inception    0\n",
       "Number of Open Complaints        0\n",
       "Number of Policies               0\n",
       "Policy Type                      0\n",
       "Policy                           0\n",
       "Claim Reason                     0\n",
       "Sales Channel                    0\n",
       "Total Claim Amount               0\n",
       "Vehicle Class                    0\n",
       "Vehicle Size                     0\n",
       "Year                             0\n",
       "Month                            0\n",
       "Day                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* from here it is clear that there is no missing data present in this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2090945b088>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(automobile.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here you may see that there is no null data present in this dataset and both two format are collect to know weathr missing values are present or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer                         9134\n",
       "Country                             1\n",
       "State Code                          5\n",
       "State                               5\n",
       "Claim Amount                     8041\n",
       "Response                            2\n",
       "Coverage                            3\n",
       "Education                           5\n",
       "Effective To Date                  59\n",
       "EmploymentStatus                    5\n",
       "Gender                              2\n",
       "Income                           5694\n",
       "Location Code                       3\n",
       "Marital Status                      3\n",
       "Monthly Premium Auto              202\n",
       "Months Since Last Claim            36\n",
       "Months Since Policy Inception     100\n",
       "Number of Open Complaints           6\n",
       "Number of Policies                  9\n",
       "Policy Type                         3\n",
       "Policy                              9\n",
       "Claim Reason                        4\n",
       "Sales Channel                       4\n",
       "Total Claim Amount               5106\n",
       "Vehicle Class                       6\n",
       "Vehicle Size                        3\n",
       "Year                                1\n",
       "Month                               2\n",
       "Day                                31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here you can see that one state shows up in 5 times "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer', 'Country', 'State Code', 'State', 'Claim Amount',\n",
       "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
       "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
       "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
       "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
       "       'Number of Policies', 'Policy Type', 'Policy', 'Claim Reason',\n",
       "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
       "       'Year', 'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning columns\n",
    "automobile.columns=['Customer', 'Country', 'State_Code', 'State', 'Claim_Amount',\n",
    "       'Response', 'Coverage', 'Education', 'Effective_To_Date',\n",
    "       'Employment_Status', 'Gender', 'Income', 'Location_Code',\n",
    "       'Marital_Status', 'Monthly_Premium Auto', 'Months_Since_Last_Claim',\n",
    "       'Months_Since_Policy_Inception', 'Number_of_Open_Complaints',\n",
    "       'Number_of_Policies', 'Policy_Type', 'Policy', 'Claim_Reason',\n",
    "       'Sales_Channel', 'Total_Claim_Amount', 'Vehicle_Class', 'Vehicle_Size','Year','Month','Day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2317\n",
       "95697      12\n",
       "40864       7\n",
       "24910       7\n",
       "25965       7\n",
       "         ... \n",
       "26952       1\n",
       "84300       1\n",
       "22862       1\n",
       "20815       1\n",
       "57240       1\n",
       "Name: Income, Length: 5694, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile['Income'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Here you  can see columns had been cleaned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And it's better to drop Effective_to_Date column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "automobile.drop('Effective_To_Date',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's check data once "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Claim_Reason</th>\n",
       "      <th>Sales_Channel</th>\n",
       "      <th>Total_Claim_Amount</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>BU79786</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>276.351928</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>QZ44356</td>\n",
       "      <td>US</td>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>697.953590</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Scratch/Dent</td>\n",
       "      <td>Agent</td>\n",
       "      <td>1131.464935</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AI49188</td>\n",
       "      <td>US</td>\n",
       "      <td>OK</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1288.743165</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>WW63253</td>\n",
       "      <td>US</td>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>764.586183</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HB64268</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>281.369258</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer Country State_Code     State  Claim_Amount Response  Coverage  \\\n",
       "0  BU79786      US         KS    Kansas    276.351928       No     Basic   \n",
       "1  QZ44356      US         NE  Nebraska    697.953590       No  Extended   \n",
       "2  AI49188      US         OK  Oklahoma   1288.743165       No   Premium   \n",
       "3  WW63253      US         MO  Missouri    764.586183       No     Basic   \n",
       "4  HB64268      US         KS    Kansas    281.369258       No     Basic   \n",
       "\n",
       "  Education Employment_Status Gender  ...     Policy_Type        Policy  \\\n",
       "0  Bachelor          Employed      F  ...  Corporate Auto  Corporate L3   \n",
       "1  Bachelor        Unemployed      F  ...   Personal Auto   Personal L3   \n",
       "2  Bachelor          Employed      F  ...   Personal Auto   Personal L3   \n",
       "3  Bachelor        Unemployed      M  ...  Corporate Auto  Corporate L2   \n",
       "4  Bachelor          Employed      M  ...   Personal Auto   Personal L1   \n",
       "\n",
       "   Claim_Reason  Sales_Channel  Total_Claim_Amount  Vehicle_Class  \\\n",
       "0     Collision          Agent          384.811147   Two-Door Car   \n",
       "1  Scratch/Dent          Agent         1131.464935  Four-Door Car   \n",
       "2     Collision          Agent          566.472247   Two-Door Car   \n",
       "3     Collision    Call Center          529.881344            SUV   \n",
       "4     Collision          Agent          138.130879  Four-Door Car   \n",
       "\n",
       "   Vehicle_Size  Year Month Day  \n",
       "0       Medsize  2011     2  24  \n",
       "1       Medsize  2011     1  31  \n",
       "2       Medsize  2011     2  19  \n",
       "3       Medsize  2011     1  20  \n",
       "4       Medsize  2011     2   3  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see Effective_to_date column had been droped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "automobile['Claim_Amount']=automobile['Claim_Amount'].astype('int')\n",
    "automobile['Total_Claim_Amount']=automobile['Total_Claim_Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8332"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile['Claim_Amount'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Claim_Reason</th>\n",
       "      <th>Sales_Channel</th>\n",
       "      <th>Total_Claim_Amount</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>BU79786</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>276</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>QZ44356</td>\n",
       "      <td>US</td>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>697</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Scratch/Dent</td>\n",
       "      <td>Agent</td>\n",
       "      <td>1131</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AI49188</td>\n",
       "      <td>US</td>\n",
       "      <td>OK</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1288</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>WW63253</td>\n",
       "      <td>US</td>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>764</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HB64268</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>281</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer Country State_Code     State  Claim_Amount Response  Coverage  \\\n",
       "0  BU79786      US         KS    Kansas           276       No     Basic   \n",
       "1  QZ44356      US         NE  Nebraska           697       No  Extended   \n",
       "2  AI49188      US         OK  Oklahoma          1288       No   Premium   \n",
       "3  WW63253      US         MO  Missouri           764       No     Basic   \n",
       "4  HB64268      US         KS    Kansas           281       No     Basic   \n",
       "\n",
       "  Education Employment_Status Gender  ...     Policy_Type        Policy  \\\n",
       "0  Bachelor          Employed      F  ...  Corporate Auto  Corporate L3   \n",
       "1  Bachelor        Unemployed      F  ...   Personal Auto   Personal L3   \n",
       "2  Bachelor          Employed      F  ...   Personal Auto   Personal L3   \n",
       "3  Bachelor        Unemployed      M  ...  Corporate Auto  Corporate L2   \n",
       "4  Bachelor          Employed      M  ...   Personal Auto   Personal L1   \n",
       "\n",
       "   Claim_Reason  Sales_Channel  Total_Claim_Amount  Vehicle_Class  \\\n",
       "0     Collision          Agent                 384   Two-Door Car   \n",
       "1  Scratch/Dent          Agent                1131  Four-Door Car   \n",
       "2     Collision          Agent                 566   Two-Door Car   \n",
       "3     Collision    Call Center                 529            SUV   \n",
       "4     Collision          Agent                 138  Four-Door Car   \n",
       "\n",
       "   Vehicle_Size  Year Month Day  \n",
       "0       Medsize  2011     2  24  \n",
       "1       Medsize  2011     1  31  \n",
       "2       Medsize  2011     2  19  \n",
       "3       Medsize  2011     1  20  \n",
       "4       Medsize  2011     2   3  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9134 entries, 0 to 9133\n",
      "Data columns (total 28 columns):\n",
      "Customer                         9134 non-null object\n",
      "Country                          9134 non-null object\n",
      "State_Code                       9134 non-null object\n",
      "State                            9134 non-null object\n",
      "Claim_Amount                     9134 non-null int32\n",
      "Response                         9134 non-null object\n",
      "Coverage                         9134 non-null object\n",
      "Education                        9134 non-null object\n",
      "Employment_Status                9134 non-null object\n",
      "Gender                           9134 non-null object\n",
      "Income                           9134 non-null int64\n",
      "Location_Code                    9134 non-null object\n",
      "Marital_Status                   9134 non-null object\n",
      "Monthly_Premium Auto             9134 non-null int64\n",
      "Months_Since_Last_Claim          9134 non-null int64\n",
      "Months_Since_Policy_Inception    9134 non-null int64\n",
      "Number_of_Open_Complaints        9134 non-null int64\n",
      "Number_of_Policies               9134 non-null int64\n",
      "Policy_Type                      9134 non-null object\n",
      "Policy                           9134 non-null object\n",
      "Claim_Reason                     9134 non-null object\n",
      "Sales_Channel                    9134 non-null object\n",
      "Total_Claim_Amount               9134 non-null int32\n",
      "Vehicle_Class                    9134 non-null object\n",
      "Vehicle_Size                     9134 non-null object\n",
      "Year                             9134 non-null int64\n",
      "Month                            9134 non-null int64\n",
      "Day                              9134 non-null int64\n",
      "dtypes: int32(2), int64(9), object(17)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "automobile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mostly whose claim amount is high What their education qualification?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2090a09a088>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Claim_Amount',y='Education',data=automobile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here we can observe that Mostly whose education qualification is Master and High school or below their total number of persons who claim amount total number is more than 800.\n",
    "* Whose education qualification is Bachelor and College their total number of persons in in between 700-800.\n",
    "* who have doctor qualification total number of claim amount is in 700 -800. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What are the different vehicle classes present?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209092c7d08>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(automobile['Vehicle_Class'],palette='hsv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Here you can see four door car vehicle's are more compare to all other cars.\n",
    "2. Luxury cars are least number of vehicle's compare to all other cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In which country people's are mostly employed ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2090932fb88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='State',y='Employment_Status',hue='Claim_Amount',data=automobile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2090935c488>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "automobile['Gender'].value_counts().plot.pie(autopct='%1.2f%% ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here female percentage is 51% and male percentage is 49%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20909991248>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l8hinRyda2TNqKU2+HImyOHHyy2CuHjzDQ288TfSeY5xbG0rtsS/gXNSNRz8xsvOxZyLZ1nsmxauXp/H4HmitUUpxeN7PXPn3dAZ7vLuFbQyldnNfpm6aS3xsHEuCPk5eN3bNdCYGBAHw+ZhPeGnGIFzcXNkfvJuw4N0A/DLvO175aDhNu7Uk6uxF5g80EoTnjp5h/6bdjP91Jtqq+XPl75w9ZNTlyx++Ts2G/0exUsV5b9sCfpy9ks2r7B97oz8v4jWzP28x+3OHoc9yMuwo+9aHsHnVBl6aNZhJwXO4Fh3Dp4NnGzGY/Xn8utkkJlj5cuynaKsVgL4fvpYcw7Rt81k9exVbHMQgbsuPQKBSagXGN9uXtdbnlFK/AVNtTuBrjXHpviyRqRe5wDxxbjvwqdb6E3NZfYy5Sh+Z0ypmA+Fa65lKqReBxVprZWfqhZ/WOtDcxgnz/sXU61LtvwTGNI37tNZx5rIXgaZa677pbSdpHcZJRgMwTjDyAEIwGqgL8CfGFAw3jOtZTrCZepG0XT9ghtbaXyk1HCihtR6XUd3l5tdBdyKjqRf5wZ1MvchNGU29yA/ydSM02Zt6kZ+kN/Uiv8js1Iu8dLdMvcjPUk+9yI9yferF+cO59jLnUrZ6us9NKfUlxkn/pTESiOMwxhqYU00VxnXx22Kc0P6i1jrEfOxLGBdLAJiitU5/Xl0mSEY5F2hjxPsU8L5SaiRGtvgEKX9Q4GPgG6XUMxiXFcq+VBJ0ATYkDZJNPwDvKaUKOXhMat8BjTBOMNLAG1rrCACl1CqMM7gPY5xxnpHVwNdKqU7AYK31n5mMQQghhBD3MK11unMutZHhHeRg3WKM6zBnG8koi3xNMspZJxnlrMvXjdAkGeWsk4xy1klGOXvkekb5v4O5l1EuVzP/HwAbcjKfEEIIIYQQdsjUi3uMUuojjEu72fogO+bpCCGEEOIeZJ6AKNKSgfI9Rmttd96OEEIIIYS4PTJQFkIIIYQowLSWjLIjMkdZCCGEEEIIOySjLIQQQghRkMkcZYckoyyEEEIIIYQdklEWQgghhCjIZI6yQ5JRFkIIIYQQwg4ZKAshhBBCCGGHTL0QQgghhCjIrIl5HUG+JRllIYQQQggh7JCMshBCCCFEQSYn8zkkGWUhhBBCCCHskIyyEEIIIURBJj844pBklIUQQgghhLBDMspCCCGEEAWYljnKDklGWQghhBBCCDskoyyEEEIIUZDJHGWHJKMshBBCCCGEHZJRFkIIIYQoyGSOskMyUBb52nC/t/I6hHTNDHknr0PIUH+/oLwOIV1W8v9Pp7reBV++FVf5++X8+l1wnLtFbc7rEDKUkNcBZMCCyusQMnRN5/+2KPKP/P3KKoQQQgghcpZVPjw4kv/TJEIIIYQQQuQBySgLIYQQQhRkMkfZIckoCyGEEEIIYYcMlIUQQgghhLBDpl4IIYQQQhRk8oMjDklGWQghhBBCCDskoyyEEEIIUZDJyXwOSUZZCCGEEEIIOySjLIQQQghRkMkcZYckoyyEEEIIIYQdklEWQgghhCjAtJafsHZEMspCCCGEEELYIRllIYQQQoiCTK564ZBklIUQQgghhLBDMspCCCGEEAWZXPXCIckoCyGEEEIIYYdklIUQQgghCjKZo+yQZJSFEEIIIYSwQzLKQgghhBAFmVWuo+yIZJSFEEIIIYSwQwbKQgghhBBC2CFTL8Rd7aFmdekytg9OFie2rdzA+nk/pFjv7OpMj1mDuK9WVa5FX2Vp4AdEhV+gZtPadHzzeSwuziTeTOD7qZ9zeNuBFI99+ZMgPO8vx7Q2I3LluYyZOos/tuzAo5Q7338+P1f2meT5cS9Rp7kv8bHxLBoxh5MHjqcpU6lWVfrNCMTFzZV9G0P534TFAPgFNKLz68/iXa0CkzqN5ETYUQAadnqMdgM6JT++4oOVGN8+iNN/n7ijGF8Y9xJ1zRg/cRBjZTNGVzdX9m4M5Qszxmff6oXPE34kxidw/lQEnwbN5fqV6zRKFeN9D1ZiXPsgTqUT43PjXqR2c1/iY+NYMuIjTtmJ4/5aVXlxxiBc3VwJ2xjKiglLAChSshgD5g7Fs2IZIsMvsGDQLK5fuZbudruO7EGdFr4oJ8Xff+5L3laDjk1oN7ALaE30+UssfX0O1y5dTRHHw83q0m3siyiLE1tW/s5aO/2j96xA7jf7x6eB7xMVfgGANgM707hbC3SilZUTlvDPH3sBKFyiCD2mvUL5mvehtWb5G/M4Hno4eZtPvNyBrqN7MqJe3zTx2NN93EvUbl6P+Nh4Fo+Ya7c+K6Woz918aR7Xosn1WZbI8PPMN+uzcPEi9Js9BI8KpXGyWFj7yY9s+WojNRv9H8++3Sd5u94PVGDB4NnsWbszwzjtmT1rIu3atuB6bCx9+w5l9579acr8vPpzvLzL4exsYfPmHQweMgqreSmuQQNfZODAF0lISOCXX35n5FtT7iiO/Oj/mvnQbeyLOFmc2Lzyd36b932K9c6uzrw4a3By2/skcDaRZttrO7AzTbq1xJpoZeWExfxttj1H2/Tv1ZaWLz1J2cpeDKv3UnK7a92/Iw06PwaAk8UJ72oVGe7bl2vRlzOMv+f4vvg09yUuNo6FI+ZyYv+xNGUq16rKgJmDcXVzZc/GUJaPXwTA08O749uqPtqquRJ5mQXD5xB9/hKNOz9O+1c6A3Dj+g2Wjl7IqX9O3EHt5gA5mc8hySgXUEqpmLyOIauUk+KZiS8xv887TG01jEc6NsGrWoUUZRp2a8H1y9eY5P8awYvW0HHk8wBcu3SVBX3fY1rbID4f/jE9ZwemeFydNg2Iu34j154LQOeAVsyfNTlX9wlQx9+XclW8GekfyNJR8+g5pb/dcr0m92fpqPmM9A+kXBVvavvXA+DMwVPMfeU9Du34O0X57T/8ybiAEYwLGMEnQz/kYviFOx4k1/H3xauKN2/4B7Jk1Dx6O4ix9+T+LBk1nzf8A/Gq4k0dM8YDm/cyuvXrjGk3jIjjZ2k/sAsA2374k7EBIxgbMIKFZozpDZJr+dejbBVvRvsPZvmoBbww5WW75XpMfpnloxYw2n8wZat4U8vfB4B2r3bmn61hjGk+hH+2htFuYOd0t/uAbw2q+dVkfNsRjGs9nMp1q1Gj4cM4WZx4duyLzOw+ngntRhD+z0n8e7dNEYNyUjw3sS9z+0xlYquh1LfTPxqb/WOc/xA2LPqZp0a+AIBXtQr4dWjMpNbDmNN7Ct0n9UU5KQC6jXuRvzftYULLoUxpF0TEkTPJ2yvl7clDj9VOHvBkpLb5vEf5D2bZqPn0cHBce0x+mWWjFjAquT7rpajP0c0Hm/X5FADNe7bl7JFwJrQbwfTnxtFtdC8sLs4c3HaAiQFBTAwIYmb3CcTHxiUPwm5Xu7YtqF6tCg8+3JRXX32Tj+a+Y7fcc8+/wiN+rajr04IyZTx4+un2APg3a0zHDm2o5/sEdX1aMHNW7n44zknKyYnuE/syp88Uxpttz7taxRRlmnRrwbXLMbztP5j1i36iy8geAHhXq4hfhyZMaD2UD3tP4flJ/VBOTulu8+iuf3m/x0Quhp9PsY+1C39kckAQkwOC+P69/3Hor7+5fjnjt766zY3Xm+HNBrHorfn0mWy/Xb44ZQCL3prH8GaDUrze/Lzge0a1HcbogOHs/j2Ep17rBsCF0/8xudvbjGo7jO8//IqX3nnl9ipW5AkZKIu7ViWfalw4+R+Rp8+TeDOR0NVbqd26fooytVv7seObTQDsWbOdGo1rARB+4ARXzl8C4Nyh07gUcsHZ1fiCxbVIIZr3e5K1c77NxWcDfj61KVmieK7uE6Be6/ps/daoo2O7D1OkeFFKlnFPUaZkGXcKFy/C0dBDAGz9dhO+rRsAcO7oGSKOnU13H492bMpfP26+4xh9W9dnixnj0XRidLOJcYtNjPv/3Is10Wo+/hClvDzT7KNhx6ZszyBGn9b12Z6JunIrXphjZhzbv92EjxmHT6v6bPs6GIBtXwfj06pButvVgEshV5xdnHFxdcbibOHKhcsopVBK4VqkEACFixfm8n9RKeKo7FONCycjuGj2j5DVW6mbqn/Ube3H9m+MeELXbOdBs3/UbV2fkNVbSYhPIDL8AhdORlDZpxpuxQpTrcFDbFm5AYDEm4nEXrmevL2n3+7Nt+98Aeh069G2Prd9G2zzvIs4PK5J9bnt22Dqmc/Dp1V9tpr1ufXrYOq1MpZrNG5F3QBwK+LGtegYrAkpT1Z6JKAhYcF7iL8Rn6lYU+vQoQ3Lv/gagL92hFLSvSReXmXTlLt61RiYOTs74+rqijarZsCAXrw3/SPi4439X7gQeUdx5EdVfKpxPrntJRCyegt1W/ulKFO3dX22m6/NKdueHyGrt5ht7zznT0ZQxadauts8feBEhh/O6ndsys5MvgY90qoBm81+cXT3IYqWKIp72VIpyriXLUXhYoU5YrbLzd8E49f6UQBiY2KTyxUq4oY2D/rhXQeTv0E6EnoID++0r0N5xmrNvb+7jAyUCzillL9SKlgp9bVS6l+l1BdKKWWuq6+U2qqU2quU2qGUKq6UclNKLVFKhSmldiulmptl+yilvldKrVZKHVdKBSqlhplltiulPMxyDyilflVK7VJK/amUevBOY3cv50H02VtvLtHnIilZLuWLWUmbMtZEKzeuXqdoqZSDUZ92jxJ+4AQJ8QkAPDn8WTZ++tMdv4HebdzLeRB19mLy/UsRkWkGkqW8PIk6d6uuo85F4l7OI9P7aNC+CX/9+Ocdx1iqnAeRNjFGOYjxUqoYS9mJ8bFnWhIWvDvN8kfbN2F7BjGWKudBlE2buxQRibtXyn24e3mkiOOSTRwlypTk8oVoAC5fiKZ46RLpbvdY6CH+3bafGTsXMn3HJxz4Yy8RR8+QmJDI52M+YfyvM5m+YyHe1SomD16T4yjnwaWzKeNIfcxsy1gTrcSa/SPtY6NwL+dB6fvLEhN5hV4zBjLq53fpMW0AroWNwXqdJx4h+r8ozvxzMt06TLl/z1TPOwr3VMfVPdVxNWLxNOvTPVV9lgRgw2e/4F2tIjN2fML432by5YQlyYOVJPU7NGFHFj68VSjvRfjpWx8Qz4Sfo0J5L7tl1/z0BefO7OXq1Ri++eYnAKpXr0rTpg3Yunk1G9Z/jd8jde84lvzGfvvxTFMm6XUnZdvztNv2MrNNR1zcXPm/Zj6E/vJXpsqX8rLzepOq75Qq50FURKrXG5vXgmeCnueDbQtp3Plxvpm1Is0+/J97gn12XodE/iMDZQFQD3gdeBioCjRRSrkCK4HXtNZ1gSeAWGAQgNa6NtAd+Ewp5WZupxbwPNAAmAJc11rXA7YBvcwyC4HBWutHgBHAx3cctTGeTyHVe6G9IineML2qV6TjyOdZOeoTACo8XIkylbzY99udzVm8Gym79ahTlbHzwMwlDanqU5342DjOHDp9B9GlE0AmYkzdHjoM6oo1MZGt3/+RJsa4zMSYqTaXcX1mdrtlKnnhXa0ibzR8hTcaDuDBxrWo3uAhLM4W/Hu0ZtKTbxDUoD/h/56irTnt4LbicFBp9h6LBieLhftqVeGPz9cy9ck3iYuNo82rnXFxc6VtYBdWz1qZ/vPM+Gln6rimqfRUaj3uw+m/TzCiwctMDAji+Yl9cStWOHl9yTLuVKx5Pwf+2HNb8aaMK/PHOaD9C1S835dChVxp0bwJAM7OFtzdS9K4aQfeHDmZL/9370y9IFPH1VHbs7dYZ2qbjtR9wo+jIf9matqFo9jSviamX+ar6f/jtUb92fr9H7Tq3S5FuYca1aLZsy1Z8c6yTMWTK7Q19/7uMnIynwDYobUOB1BK7QEqA5eBc1rrnQBa6yvm+qbAHHPZv0qpk0ANczsbtdZXgatKqcvAanN5GFBHKVUMaAx8ZfMiUyh1MEqp/kB/gOYej1Cr+AN2g46OiMS9/K2Mgru3Z/J0iltlonAv70l0RBROFifcihfherTxYunu5UG/BcNZPuxjLp76D4AqvjW4r3YVxm2eg8VioZhnSQavGMuc5yZmohrvHi16tqVZ9ycAOL73CB7lSyevK+XlSXSqr/GjzkWm+JrQw9uT6PMpyzjSoEOTDIC3LuIAACAASURBVKc02NMyVYye5UuTdMqYh5cnl+zEWCqdGJt09cen5SO8+/z4NPtqmE6MLXu2pXn3VslxeNi0uVJenmmmPFxKFUcpb0+izXZ55cJlSppZ0JJl3Ll68YrxmIhIu9t99KnHObb7UPJ8+f3Bu6larzo3zW87LpjtNuTnrQS82jllHBGRlCqfMo7LafqHUSapfxQuXoRr0TF2HutB9PkooiMiiY6I5MSeIwDsXrOd1q92pkylcpSuWJYxv0wHjCzwqJ/e5d3Ob3HlQsoTp5r3bMtj3VsCcGLv0VTP2yNN20tbnx7Jx/WKWY+36tPYV5NnmvOLeaJX0tf13g9U4PheI26/9o0J/W0HiQm3d+3YV1/pTd++xjzukJA9VLyvfPK6ChW9OXvuP4ePjYuLY/VP6+jQoQ3rf/+TM+Hn+P77XwDYGbIHq9VK6dIeXLyYuX6Vn0VHRNltP7aMNl86U20vqd1mtE1H/Do0YcePW9It80SvtjR/zujnx/YZrzdJPLxu9eEkURGReHiler35L2UZgK0//MmIJaP5drbxIfK+ByvR792BTO89iZjou/5UoQJBMsoCIM7mdiLGByiF/Zyhvc/19rZjtblvNbfpBERrrX1s/h5KvRGt9UKttZ/W2s/RIBng1N6jlKnshUfFMlhcLPh2aEzYupAUZfavC6FB12YA+AQ05PBW48oWhUsUYcCSkax+70uO7zqYXH7z5+t4+9FXmdB0MO8/M47zx8/dc4NkgA3Lf00+0S507Q4adzHqqGq96sRevZ78dXaSyxeiuRETS9V61QFo3KUZuzNxpQClFPUDGrNjdfpvUvb8vvzX5BPtQtfuoIkZ4wMZxPiAGWOTLs0INWOs3cyHJ1/pzPv9pqWZUpMU418OYvx9+a/JJ4DtWbuThpmoqzibumrYpVnyVRX2rg+h0dP+ADR62p8968zl60Lsbjfq7EVqPGqcvGdxtlDj0Yc5d+QMlyKi8K5ekWIextSNh5vWSXFSHcDJvUcpW9kbT7N/+HVozL5U/WPful007GrE4xvQkINm/9i3LgS/Do1xdnXGs2IZylb25sSeI1y5cJlLZyMpV9UbgJpNahNxOJyzB0/zht/LjGkayJimgURHRDK1/ZtpBskAG23qc/faHTTq4p9hfdq2vUZd/JPrc8/6EBqb9dnYpj6jzl7koSa1AShRuiReVcsnf6gAaNCxKTtW3/6Ht3nzP8Ovfmv86rfmxx9/o+cLTwPwaANfrly+QkREypPJihYtkjxv2WKx0K5tCw4eNAbrP/z4G83N7HL16lVxdXW9JwbJACf2HjHbXlksLs74dWjC3jRtL4SG5muzb0BD/t1qXDFk77oQ/Do0MdteWcpW9ub4niOZ2qY9bsWLUOPRh9m7Lv3Xq/XLfmV0wHBGBwxn19odNDX7xQP1anD96vU0A+Xo85e4ce0GD9Qz8kRNu/qza90OAMpV9k4u59uqPueOGn3Ts3xpXl/wBvOHfkDE8XMZxp6rZI6yQ5JRFo78C5RXStXXWu9UShXHmHrxB/ACsEEpVQO4HzgI+Ga0Qa31FXP+8jNa66/MudB1tNZ3dNq5NdHK12MXM3DZKJwsTmxfFUzE4XAChj7DqbBj7F+/i22rNtJzViBvB3/A9egYlg7+AIDHerWldKVytBnSlTZDugLwcc8pxEReuZNQskXQuGns3L2P6OgrtOzcg4F9e9K1Q5sc3+++jaHUae7Lu5s+Ij42jkVBHyWvm7BmBuMCjMvjLRuzkL7mpdfCgnezLzgUAN82DXhhfD+Ke5Tg9cWjOP3PCWb2mgRAjUcf5lJEJBdOO860ZcZeM8bpmz4iLjaOT21inLhmBmPNGD8bs5CXzRj32cTYc0I/nF1dCPp8LGCcoPPZ6IUA1Hz0YaIyGWPYxlBqN6/HlE1ziI+NZ6lNHGPXTGdiQBAAn4/5hBdnDMLFzZX9wXvYb85F/GXedwz4aBhNu7Ug6uxF5g+cle52d5knOY3/bSZaw4FNe9j3+y4AfvrgK95YNYHEm4lEnrnA8hEpZzFZE62sGLuYwctG42RxYuuqjZw7HE77od04FXaUfet3sWXVBvrMCmRC8Idcj45h0eD3ATh3OJxdP21j7LpZWBOsrBi7CG01PjevHL+YF98fgsXFmYunz6fZ7+0wnrcvUzfNNS6LF3RrW6nr86Xk+tydPMf8l3nf8cpHw2naraVZnzMBWP3h17w0I5Dxv85EKcU30z4nxrxkmGfFMnh4e3Jo+99kxZpffqdt2xYc/GcL12Nj6ddvWPK6kJ1r8avfmqJFi/Ddt0soVMgVi8XCxo1bWLBwOQBLlq7g009msmf378TH3+Slvq9nKZ78xGh7i3jNbHtbzLbXYeiznAw7yr71IWxetYGXZg1mUvAcrkXH8Ong2cCttjd+3WwSE6x8OfZTtNWKBrvbBGjepx1tBnSiRBl3xv46g/0bd7N8pDGVpV6bBvz9517iY+MchZvGng27qNvcl5l/fEy8eXm4JFPWzGR0wHAAloxeQH/z8nB7g0PZu9F4vXl2ZA+8q1ZAW61cPHOBJaMWAPDUa90oVqo4fSYZV9FITExkbIc3slbZIsepDOfOiXuSUipGa11MKeUPjNBatzeXzwVCtNZLlVL1MaZZFMYYJD8BJADzgUfM28O01huVUn0AP611oLmdE+b9i7brlFJVgHmAN+ACrNBaO0zZDqn8bL5uoDND7F8SKj/p7xeU1yGk627IL7jeBV++Oaf7ZU/eu3kXHOmlZ7fldQgZSog/k3GhPDSg8jN5HUKGrun8/3PNn5/8Nlc79I0/l+fae63bYz3z94tVKpJRLqC01sXM/8FAsM3yQJvbO4GGdh7ex872lgJLbe5XtrdOa30cSHnBVyGEEEKIfEgGykIIIYQQBZi+C7LseSX/f58ohBBCCCFEHpCMshBCCCFEQXYXXo0it0hGWQghhBBCCDskoyyEEEIIUZDdhb+Yl1skoyyEEEIIIYQdMlAWQgghhBDCDpl6IYQQQghRkMnJfA5JRlkIIYQQQgg7JKMshBBCCFGQycl8DklGWQghhBBCCDskoyyEEEIIUZDJHGWHJKMshBBCCCGEHZJRFkIIIYQoyGSOskOSURZCCCGEEMIOySgLIYQQQhRkMkfZIckoCyGEEEIIYYdklIUQQgghCjLJKDskGWUhhBBCCCHskIyyEEIIIURBJle9cEgyykIIIYQQQtghGWWRr71WLDqvQ0hXf7+gvA4hQwtDpud1COka4jcyr0PIkAWV1yFk6DqJeR3CXe/yyMfyOoS73t3QV9yU5AjTkDnKDklrEUIIIYQQwg4ZKAshhBBCCGGHTL0QQgghhCjI5GQ+hySjLIQQQgghhB2SURZCCCGEKMjkZD6HJKMshBBCCCGEHZJRFkIIIYQoyGSOskOSURZCCCGEEMIOySgLIYQQQhRkMkfZIckoCyGEEEIIYYdklIUQQgghCjLJKDskGWUhhBBCCCHskIyyEEIIIURBpnVeR5BvSUZZCCGEEEIIOySjLIQQQghRkOWjOcpKqbbAB4AF+FRrPS3V+tlAc/NuEaCs1trdXJcIhJnrTmmtO2Y1HhkoCyGEEEKIPKeUsgAfAa2AcGCnUupHrfXfSWW01kNtyg8G6tlsIlZr7ZOdMclAWQghhBCiIMs/GeUGwBGt9TEApdQKoBPwt4Py3YFxORmQzFEWQgghhBD5QQXgtM39cHNZGkqpSkAVYIPNYjelVIhSartSqnN2BCQZZSGEEEKIgkznXkZZKdUf6G+zaKHWemHSajsPcXRJjueAr7XWiTbL7tdan1VKVQU2KKXCtNZHsxKvDJSFEEIIIUSuMAfFCx2sDgfus7lfETjroOxzwKBU2z5r/j+mlArGmL+cpYGyTL0QQgghhBD5wU6gulKqilLKFWMw/GPqQkqpmkApYJvNslJKqULm7dJAExzPbc40ySgLIYQQQhRk+eRkPq11glIqEPgN4/Jwi7XWB5RSE4EQrXXSoLk7sELrFL+U8hCwQCllxUgET7O9WsadkoGyuCcVbuJH6ZGvoCwWrnzzC9GLVtktV7RVU7xmv034s4HEHTicI7E8P+4l6jT3JT42nkUj5nDywPE0ZSrVqkq/GYG4uLmyb2Mo/5uwGAC/gEZ0fv1ZvKtVYFKnkZwIM75BatjpMdoN6JT8+IoPVmJ8+yBO/30iR55DkjFTZ/HHlh14lHLn+8/n5+i+bD3crC7dxr6IsjixZeXvrJ33Q4r1zq7O9J4VyP21qnIt+iqfBr5PVPgFANoM7Ezjbi3QiVZWTljCP3/sBWDy5rnciLmB1WrFmpDItI5vZUusDzWry9Nj++BkcWLryg2ssxNrz1mDkmNdHPgBUeEXeLBpbTq++TzOLs4k3Ezg+6mfc2jbgWyJKcnz416idvN6ZlucyykHbbHvjEG4uLkStnF3irbY6fVueFerwORObyW3xSp1q9H7nQEAKKX44f1VhP62I9/EV9S9GAPnjaBKnQfY8nUwX4xbdEexpWapVhfXgN6gnEgI3cDNP9MkvbD8X0Ncmz8NaKwRp4j7eg6qZGkKdR8GygllsXBz+28khKzPlpjuBg83q8szZl/emk5fvs/sH4vMvlzUvRgvzxvG/XWqsf3rYFaNW5z8mI4jnuPRLo9TuGQxhv1fr2yJs7tNW1ycTlt8ccYgXM22+KXZFh8JaERHsy1O6fQWJ23a4qvzRlC5zgNs/TqY/2VTW7zXaK3XAGtSLRub6v54O4/bCtTO7nhk6oW49zg5UWbMIM69OoZTHV+mWEBzXKren6aYKlKYki905sbef3IslDr+vpSr4s1I/0CWjppHzyn97ZbrNbk/S0fNZ6R/IOWqeFPb37gs5JmDp5j7ynsc2pHyQ/H2H/5kXMAIxgWM4JOhH3Ix/EKOD5IBOge0Yv6syTm+H1vKSfHcxL7M7TOVia2GUr9jE7yqpTwJunG3Fly/fI1x/kPYsOhnnhr5AgBe1Srg16Exk1oPY07vKXSf1BfldOtckdndJzA14I1sGyQrJ0W3iS/xcZ93mNxqGI/YibVRtxbEXr7GBP/X2LhoDZ1GPg9AzKWrLOj7HlPbBrF8+Mf0mh2YLTElqe1fj3JVvHnLfzCfjZpPLwdtsefkl/ls1ALe8h+cpi1+9Mp0Du1I2V/OHDzFxA5vMj4giFm9JtNrygCcLLf/1pJT8d2Mu8n3M1ewaury247JIaVwbf8SN5ZPI3bucCy1m6DKpDzOysMLl8c7EfvpOGLnBhH3y2cA6JhL3PhkLDfmjSR24RhcHuuEKl4q+2LLx5ST4lmzL09qNRS/dPry+FR9+WbcTVbPXMl3do7jvt938W6nUdkWZ23/epSt4s0o/8EsGzWfHg7aYo/JL7Ns1AJG+Q+mbBVvaplt8ezBU3z8ynQOO2iLX2VnW8wuWufe313mrh0oK6W0Umq5zX1npdQFpdRPd7g9d6XUQJv7/o62pZQKVkr53cE+EpVSe5RS+5VSXymlitxJrBnso7xS6uvs3q7N9ocqpW4opUpmsnz2vXplUqHaNbl56iwJ4RGQkEDML8EUbdEoTTmPwb2JXvIVOj4+x2Kp17o+W7/dBMCx3YcpUrwoJcu4pyhTsow7hYsX4WjoIQC2frsJ39YNADh39AwRxxydx2B4tGNT/vpxcw5En5afT21KliieK/tKUtmnGhdORnDx9HkSbyYSsnordVvXT1Gmbms/tn8TDEDomu082LiWubw+Iau3khCfQGT4BS6cjKCyT7UcjfXiyf+INGMNXb2VOqlirdPaj7++MdrE7jXbqWnGGn7gBJfPXwLg3KHTuBRywdk1+770M9piMJDUFotkoi0GU8+M31FbjL8RjzXR+NrWpZAr+g7fCHMsvtg4Dof8y8247OvnThWrYY2KQF86D4mJJIZtxfnBlG8Jzn4tSPhrLdy4Ziy4dsX4n5gIiQnGbYsLKHsn+d+bkvpyUv/YZacv17Hpy7b9Iz42jqMhB+0exxO7D3PlQnS2xenTuj7bMtEW3YoX4ZjZFrelaov/OWiLR7K5LYqcd9cOlIFrQC2lVGHzfivgTBa25w4MzLBU1sRqrX201rWAeOAV25XKkKVjorU+q7V+OivbyEB3jMn2T2WyfK4PlJ3LepIQcSH5fsJ/F3EuWzpFGdcHH8DZqwzXN/2Vo7G4l/Mg6uzF5PuXIiIp5eWZokwpL0+izkUm3486F4l7OY9M76NB+yb89eOfWQ82n3Iv58Gls7fq55Kd+rEtY020Env1OkVLFbfz2Kjkx2oNQ5aP5q3V02javWW2xFrSTqwly5VyWMY2Vls+7R7l9IETJMQnZEtcAKXKeRJlE1tURJTdtngpRVuMolS5lGXsqepTnUlrZzPxt5ksH7MweeCcX+LLbqq4B/ryrTj0lShUiZRt0snTG1XaG7d+E3B7eRKWanVvPb6EJ4UHvkuR4R9xc/OP6KuXci32vGSvL5fMZF/O3ThTtsVLEVG4p2qL7qnaovHakvttMdtYrbn3d5e5mwfKAL8AT5q3uwNfJq1QSnkopb5XSu0zLzxdx1w+Xim12MwKH1NKDTEfMg14wMz4TjeXFVNKfa2U+lcp9YVSKT/6K6X6mr85nnT/ZaXUrEzG/idQTSlVWSn1j1LqYyAUuE8p1VoptU0pFWpmnouZ2z+hlJpqrgtRSvkqpX5TSh1VSr1ilqmslNpv3u6jlJprE99PSil/83aMUupdpdQupdR6pVQDmzqx+9voSqkHgGLAGLO+k5bb3Y9SahpQ2KzTL8x1w8yM+n6l1OuZrKvbYy9DY5vlUorSbw4gcrqjq9NkZyhpY0mdcbObUMpkUq6qT3XiY+M4c+h0xoXvUpmpQ0fH3N5jk+p2Rte3eaf9SOb2mUqzXm2o1uChHIk19bG0f7xvFfKqXpFOI59nxahPshxPyh3b223q4DJRxo5jew7zduuhTOo4koBXn8K5kEu+ii/bZXAMAXCy4OThxY3FE4n76kNcO/UHN+NLRH0lktiP3yT2g9dx9nkcimbqC7q7X0avzZktk8My6qOZLSPuDXf7yXwrgLHmFIk6wGLgMXPdBGC31rqzUqoFsAxI+v3vB4HmQHHgoFJqHjASqJX0G+HmgLIe8H8Y1/DbgnGpEdvvuFcA+5RSb2itbwIvAgMyClop5Qy0A341F9UEXtRaDzQvaTIGeEJrfU0p9SYwDJholj2ttW5kDtCXmjG5AQeA2zm7qigQrLV+Uyn1HTAZIyv/MPAZdi7Hwq0PI38CNZVSZbXW5x3tQGs9UikVaFOnj2DU0aMYbzV/KaU2aa13p6qf5IuRT/Z+mOc8Kt7G0zIzyF5lku87lytNwoVbn/ydihbGtVplyi95DwBLaQ+85kwgYvC4bDmhr0XPtjTr/gQAx/cewaP8rWx2KS9Pov+LSlE+6lwkHt63MhEe3p5En09ZxpEGHZqwPZemXeSVSxGRlCp/q35KeXsmT1FIEm2WiY6IwsniROHiRbgWHWPnsR7JdZu0jauRV9jz204q163GkR1Zm68enalYo+zGCuDu5UH/BcNZPuxjLp76L0uxgNEWHzez5cf3HsXDJjYPL480bfHSuUhKpWiLHplui2B85RwXG0fFGvcnn0yXn+LLLvpKFKrkrThUCY80WWF9JRLr6SNgTURHX0BHnsPJwwvr2WO3yly9hPV8OJZKD5L4d85+u5UfZK5/2O/LOa15z7Y8ZrbFE6naYqlMtMVSedQWs81dmOnNLXd1RllrvQ+ojDGAW5NqdVNguVluA+BpM6/2Z611nNb6InAeKOdgFzu01uFaayuwx9yX7f6vYfx0Ynul1IOAi9Y6LJ2QCyul9gAhwCkg6ZTXk1rr7ebthhiD1S1m2d5AJZttJA1gw4C/tNZXtdYXgBtKqZSTqNIXz62BehiwyRzsh6V+njaew7gcixX4FnjmNvYHxjH5Tmt9TWsdY27jsdSFtNYLtdZ+Wmu/2x0kA8TtP4jL/RVwrlAOnJ0p1s6faxu3J6+3xlznxGPdONWmN6fa9CZu3z/ZNkgG2LD81+QT7ULX7qBxl2YAVK1Xndir17mcai7d5QvR3IiJpWq96gA07tKM3Wt3ZrgfpRT1AxqzY/WWbIk7vzq59yhlK3vjWbEMFhcLfh0as29dSIoy+9btomFXfwB8AxpycOsBc3kIfh0a4+zqjGfFMpSt7M2JPUdwLVyIQkXdAHAtXIiHHqvD2UOnsiXWMpW9kmP1tRNr2LoQHu1qtIl6AQ05ZMZauEQRXlkykh/f+5Jjuw5mORYw2uL4gCDGBwSxe+0OGnfxB4y2eD1TbdE/w7ZYumLZ5JP3PCuUxrtqeS6GO/z8nOvx5QTrmaM4eXih3MuAxYKldmMS/t2VokziPyE4VXnYuFOkOMrTG+ul88YUDWcz4+5WFMv9NbFeTP88hHtF6r78SAZ9uZ5NX85pG5f/ysSAICaabbGRTVvMzOt2oy7+7MmDtihy3t2eUQZj4DgD8AdsJwil94V2nM2yRBzXQ2bKfYoxD/dfYEkGscYmZVeTgzS+v7lmuwhYp7Xujn1JMVlTxWe1E18CKT8Mudncvmlz/cHkbWmtrWbGOwVz6kp1YJ0ZsytwDPgog/2k2IyD5dkr0crFqR/hvWAqyuLEle/WcvPoSUoN6kXcgUNcD96e8Tayyb6NodRp7su7mz4iPjaORUEfJa+bsGYG4wJGALBszEL6zgg0LjMUvJt9waEA+LZpwAvj+1HcowSvLx7F6X9OMLPXJABqPPowlyIiuXA665nHzAoaN42du/cRHX2Flp17MLBvT7p2aJOj+7QmWlkxdjGDl402Lrm2aiPnDofTfmg3ToUdZd/6XWxZtYE+swKZEPwh16NjWDT4fQDOHQ5n10/bGLtuFtYEKyvGLkJbNSVKl2TAQqPunSwWdv6wmb837c2WWFeNXcygZaNQFie2rwom4nA4Tw59hlNhxwhbv4utqzbSa1Yg44I/4Fp0DEsGfwDA473aUqZSOdoO6UrbIV0BmNtzCjGRV7IcF9xqi9M2zSU+No7FQR8nrxu/ZjrjA4IAWD7mE15KuuRV8G7Cgo0vfHzbNOD58X0p7lGC1xa/xel/TjCr12Sq13+QgFefIjEhAW3VLH/7E2IuXc038QG8t/lj3IoVxtnFmXqtGzCr5yTOHgm/s4oEsFqJ/3kJbr1GgZMTCaEb0RfCcWnxDNYzx0g8uIvEI3uxVKtD4cAZoK3E//Y5xMagytfGrU0PNMYL4s0tP6HP37tTp2xZE62sHLuYQLMvb7PpyyfDjpr9w+jL41P1ZYBJm+fiVqwIFhdn6rauz5yek4k4coanRr6AX6emuBZ2Zcq2eWxduYGf3//qjuMM2xhK7ea+TDXb4hKbtjh2zXQmmm3xc7Mturi5st+mLdZr04DuNm3x1D8neN9si9M2f0zhYoWxuDjj07oBs3tO4lxW2mJ2ycWfsL7bqDyZ35UNlFIxWutiSqmKQFet9QfmdIkRWuv2SqkPgQta60nm8tla63pKqfFAjNZ6hrmd/UB74CoQqrWuZC5P3pZ5fy7Gxa6XKuNnEUdorUPMdaFAGaCO1trhWRlJMadaVhn4yTzBD6VUGWAX0EJrfcS8MkZFrfUhpdQJwE9rfVEp1ce8HWg+7gTghzGH+CetdS2lVFPgPYxMbgWM6RkdtdbBtrHYqRN7cb4DXNFav2Oz7DjGB5T70tnPJaCs1vqmUsoXY7pIQ8ypF0DP1FMvbB2t1SZfN9DJMcUyLpTHFoZMz7hQHhriNzKvQ8iQJZc+42XFdRLzOoS73pxe+T93VHTiirwOIV0DK3fL6xAyFE/+HxR+euLrXH3Rif10WK691xbuNyv/v6DayP+vChnQWocDH9hZNR5YopTaB1zHmMKQ3nYilVJbzIHzL8DPtxHGKsAnvUFyZmmtL5iD4C+V+VOMGHOWD93B5rYAxzGmU+zHOFnwTj2HMa/a1nfm8vfS2c9CjHncoVrrF5RSS4GkXyP4NL1BshBCCCFynrbm65xUnrprB8qpM57msmAg2LwdBXSyU2Z8qvu1bG4/n6p4sM26QJvb/qnKNQVmkwEHMZ8AaqVatgGob6dsZZvbSzGys6nXXUzanjm14oWMYrFTJ/birGJn2TCbu4728ybwps39WUBmrwwihBBCCJFn7tqBcn5gnjy3A9irtf49r+MRQgghhLhtctULh2SgnAVa62ighu0ypZQnYG/Q3FJrHWlnuRBCCCGEyIdkoJzNzMGwT4YFhRBCCCHyA7nqhUN39XWUhRBCCCGEyCkyUBZCCCGEEMIOmXohhBBCCFGQyeXhHJKMshBCCCGEEHZIRlkIIYQQoiCTy8M5JBllIYQQQggh7JCMshBCCCFEQSYZZYckoyyEEEIIIYQdklEWQgghhCjItFz1whHJKAshhBBCCGGHZJSFEEIIIQoymaPskGSUhRBCCCGEsEMyykIIIYQQBZn8Mp9DklEWQgghhBDCDskoCyGEEEIUZFrmKDsiGWUhhBBCCCHskIyyEEIIIURBJnOUHZKMshBCCCGEEHZIRlnkazNjSuZ1COmykpjXIWRoiN/IvA4hXR+GTMvrEDLU65FheR1ChtyVa16HcNcbsSz/z9OcNzGvI0hf7F3wmlhchj7iNkhrEUIIIYQowLT84IhDMvVCCCGEEEIIOySjLIQQQghRkMnJfA5JRlkIIYQQQgg7JKMshBBCCFGQyQ+OOCQZZSGEEEIIIeyQjLIQQgghREEmc5QdkoyyEEIIIYQQdkhGWQghhBCiIJPrKDskGWUhhBBCCCHskIyyEEIIIURBJnOUHZKMshBCCCGEEHZIRlkIIYQQoiCT6yg7JBllIYQQQggh7JCMshBCCCFEQSZzlB2SjLIQQgghhBB2yEBZCCGEEEIIO2TqhRBCCCFEAablB0cckoyyEEIIIYQQdkhGWQghhBCiIJOT2o6gaAAAIABJREFU+RySjLIQQgghhBB2SEZZ3NUeblaXZ8a+iLI4sXXl76yd90OK9c6uzvSeFch9tapyLfoqiwLfJyr8AkXdi/HyvGHcX6ca278OZtW4xQC4uLny8sfDKF2pHNZEK2G/7+KHd/+XpRhfGPcSdZv7Eh8bzycj5nDywPE0ZSrXqkq/GYG4urmyd2MoX0ww4nn2rV74POFHYnwC509F8GnQXK5fuU6jTo/RbkCn5Mff92AlxrUP4tTfJ247voeb1aWbWYdb0qnD+806/NSsQ4A2AzvTuFsLdKKVlROW8M8fewGYvHkuN2JuYLVasSYkMq3jW7cd150YM3UWf2zZgUcpd77/fH6u7DNJ7/H98Gn+CPGxccwb8SEn9h9LU6ZKrQd4ZeYQXN1c2bNxF5+N/zTF+if7d6LH6Bfp79OTq5eu8kirBnQb/jxWq8aamMiyCYs4GPJPpmPKi2P7xMsd6Dq6JyPq9eXapau5Hl/zF9vR9LmWoBRbVvzOhsVrAPANaMiTrz+DV7UKvNtpFKfC0h6f/2fvzuOiqt4Hjn8eNndlSYGyXNJWF1A0U0usRKU007Lym1uWpaJlSbnlvpZpi6VWamnfSlu+lWWlplhpZrhn5r6hkAjiiijM+f0xl3GAGRhcEH8+79eLl86dc8997rn3Hs4898ylKGIMrh5Kj6n9Hetfc30lvpsyn6WzFnL/84/Q9LF7OZ56DIBvXv2UzXHrPIrzStJp+JPUsfrEmW76xCpWn+hb0o+Ny9byidUndrT6xEyrT5wZO5X0Y6co41+WPtNiqVbnRlZ8EcfHwz/IU6enbm1Wl/bDuuHl7cXv85ayxMUxf2JyH8fvlQ9j3iQ1IZmbm9am7cud8Pb1IetsJl+P+5jtv28GoNdHgyhfKQAvby92/vkPn78yE1OcsrjFKZZiRjPK6oolXsKjo3owtds4RrfoT0TbJoTUuC5HmcYd7+HU0ZOMiOzH0pnf89DA/wBwNuMsC16fx//Gzc1T75L3FzDq3v6Mv/8lbqx/M7dFhp13jHUi6xFSLZSXImOYPXgaXcf2dFmu65iezB48nZciYwipFkqdyHAANv+2gSFRzzO09Qsk7T7IA73bA/D7N78yLHoAw6IH8F7/tzickHxeg2TxEh6z2nBUi/40yKcNh+dqw5Aa1xHRpjGjo17g7a5jeXx0D8RLHOtNeXwk46JfKrJBMkC76BZMnzymyLaXLax5fUKqhdK/WS/eH/QuPcY867Lck2Of4YNB79K/WS9CqoVSN7Ke473A0Guo3TSM5IRDjmV/rdjIy62eZ1B0f2bEvs3TE/t4HNPlOLYBoUHceldtUqyBYlHHd+1N19P0sXuZ8OBgxraOpfY99ahYNQSAg1v3896zk9ix2vMPGpcixn93JTIu+iXGRb/E+Ade5szpM6z/abWjvp9nfu94///jILlOZD2Cq4UyMDKGDwdPo7ObPrHLmJ58OHg6AyNjCK4WSm2nPnFo1PMMa/0C/zr1iWczzvK/1z9l3rg5FxSfeAmPjHqS6d3GM67FC9R3ccwbWcd8dORzxM1cSNuBnQA4eeQ4M3q8yoRWsXz84rt0nhLjWGd2nzeY2PolxkcNoGxgecLvv/OC4lRFp9gMlEXEiMhcp9c+IpIsIt+dZ33+ItLb6XXk+dblVMfNIhInIutFZIuIvGctjxCRty6kbg+3301Epl5gHe1E5DYPyg0QkX9E5C8R2SAiXazlcSISUcC6H3iyjQtVNawGyXuTSNl/iKyzWaxZsJK6UQ1ylKkTFcGqL+MAWLdwFTc3rgXAmfQMdsZv5WzGmRzlz54+wzYrA5B1Nov9m3cTEBJ03jHWi2rAiq+WA7Bz3XZKlytDhYr+OcpUqOhPyXKl2bl2GwArvlpOvaiGAPz16wZsWTZr/W0uY2nUtimrvv3tvOLLbsPDVhvGu2jDuk5tuHbhKm6x2rBuVAPiF6wk80wmKQnJJO9NompYjfOK42KJCKtNhfLliny79Vs05FerjXas20bp8mXwrxSQo4x/pQBKlS3N9rVbAfj1yzgiou5wvN9l2JN8Mv4jcErsZJw67fh/idIlyfFmAS7HsX34la58Nf6/HsV5KeILqXEdu9dt5+zpM9iybGz7YwthLe3XUtLOA/y7K7HAuC51jM5uaVKbw3uTSD1wuFBxXcnCoxqw0uoTd+XTJ5Zy6hNXOvWJm930iWfSM9ge/w9nM85eUHxVwmqQvPdfx++VtQtWUjvXMa8dFcHqL+37sH7hKm6yjnnC5j0cO3QEgMRt+/Et4YuPn/3G/ekT6QB4+Xjj4+uDMcUsg2tsRfdzhSk2A2XgJFBLREpZr1sABy6gPn+gd4GlCuctYIoxJswYcyvwNoAxJt4Y0+8ib+tSaQfkO4gVkWext39DY0wt4G5A8lvHmTHmKWPM3xcUpQf8gwM5cjDF8fpIYgoVggPdlrFl2Ug/fooyAZ4NpEqVL03te+vzz4pN5x1jQHAgKQfP/RJMTUrJM9gNCAniSOK5/UhNTCEg134A3PXIvWxykWG644EmrPr21/OKz1Ub+nvYhnnXTXWsawz0mzuEQQsm0PTxe88rtitJYEje4xyYqx0DgwNJTTrXXimJKQSG2MvUv68BqUkp7NuyJ0/dES3vYNLPU3lp9lBmxHr+Obmoj22d++qT9m8qB7bsvWzxHdy6nxoNb6WMf1l8S/pRq3k4AaHn/0H3UrVhtog2Tfjz2xU5lkV2bcmQH16j86u9KF2+zHnHXlz5BweS6nStHHHTJ6bm6hNztx247xMvNL40p+OWlphCheCcH3orOJWxZdk47eL3SljrO0jYvIfMM5mOZb3mDGbcmvc4fTKd9QtXXdS41aVTnAbKAD8A91v/fxz4NPsNEQkUka9FZKOIrBKROtbyESIyy8p07hKR7AHrBOBGK/v7mrWsrIh8YWVK/ysiYtUxQUT+tuqelE98oUBC9gtjzCZrfUe2Op94EJEu1jY2ZGfPRaSiiHwpIn9aP00K22giMk1E4kVks4iMdFqeY79EpDHQFnjNapcb3VQ5GOhtjDlm7edRY8xHhdiuI+ssIidEZKKIrBGRJSLS0Klt2hZ2X3MFkHdZ7k/pnpRxwcvbiyffeo5lH/5Ayv5DBZZ3y4PtexJimz4dsGVlsfLrX3Isrx5Wk4z0DA5s23+e4eXdeJ5Mh5sAXa2bnUic1OEVxj8wkKndxtGsS0tqNLz1vOK7UrhuxzyFXJbxK+lHu5hH+Hzyp3neB4j/6Q8G3BvD60+P55EXO11gTJfm2PqW9KNVTHsWTJ53WeNL2nmARdO/od/HQ+n70WAStux1ZB/Px6VqQwBvX2/q3FeftU4Dpl8+XsQrd/dlXPRLHD10hA5Du5xv6MWWJ23qquly36R4oE8HsrKy+D1Xn3jBPLiWXR/yc4VCalam7cBOzBv8fo4y07qMY2jDZ/Hx83VkoYsNmym6nytMcfsy32fAMGvQWQeYBdxlvTcSWGeMaSci9wBzgOzJo7cAzYFywFYRmQYMBGoZY8LAPpgFwoHbgYPACqCJiPwNPATcYowxIpLzHlBOU4ClIrISWATMNsakuSjnKp6bgCFAE2PMYRHJ/nj8JvYs9W8icgPwE1DYUcUQY0yqiHgDP1sfIhJy75cxJk1EvgW+M8Z84aoiESkHlDPG7Dyf7RpjNuYqUwaIM8a8LCL/A8Zgz1bfBnwEfOsihp5AT4BmgfW5rVx1lxtPS0oh4NpzmYiA0CCOWre9cpdJS0rFy9uLUuVKczLtRIE71mn8MxzancQy64tAhXFv51Y0e/w+AHZv2EHQtdew3XovMCSII/+m5iifmpiSI+sVGBpE2qFzZZp0iCTs3vpM7DQiz7YatWly3tMuwMrmnGcb5l030BF3dh3HU46x/qc/qVq3RqHmhl4JWnRpzT2PRQGwa+N2gq69xvFeYEgQRw7lOs5JKQQ6Zc6CQu3nQnCVUCpeX4mJP7xhXzc0iHHfT2bog7EcTT7Xvfyz+m+Cq4RQLqAcxwv4khwU7bE9dfQE11SuxNAf7DkJ/5AgBn83kYntBnEs+WiRxrdy/jJWzl8GwIOxj+e4W1NYlypGgNsjw9n3126OHz7XPs7//+2zn+kz8+Xzjr04uSdXnxjodK0EhASR5qJPDCygT6x7b31ec9EnXqi0pBT8nY6bf2iQYzrFuTKp+Dsd85LlSnPK+r3iHxLIUzNeZO4L73J437956s/MOMtfS+Kp3SKCrb+d/91KVXSKVUbZGmRVxZ5Nzj1CaQrMtcotBYJEpIL13vfGmAxjzGHgEBDsZhOrjTEJxhgbsN7a1jHgNPCBiLQHTuUT32zsg9jPgUhglYiUcFHUVTz3AF9YyzDGZF/19wFTRWQ99kFjeWuwWhgdRWQtsA77B4HbCrNfuQieT4R0td3czgA/Wv/fBCw3xpy1/l/VVaXGmPeMMRHGmAh3g2SAvRt2UqlqKEGVK+Lt6039No3ZuDg+R5mNi9fQqEMkAOHRjdi6cnOBO9XmxUcpVa40X4z6sMCyrvw890fHF+3WLlpNk/bNALgxvCbpx0/lGPwAHE1O4/SJdG4MrwlAk/bNWLvoTwBqNwvj/mfb8cZTEzhzOud8ahGhQXRj/liQ89ZtYeRuw4gC2rCeUxtuXBxPRJvG+Pj5EFS5IpWqhrJn/Q78SpWgRJmSAPiVKsGtd9Xh4LZ95x1jcbV4zg8Miu7PoOj+xC/6g7usNqoRfhOnjp8kLfcv10NHOH0ynRrhNwFwV4dI1ixezf6te3m2fjf6Ne1Jv6Y9SU1MYfD9L3A0OY3gKiGO9avWqo6Pr49Hg2Qo2mN7cOt+Xop4mqFNYxjaNIa0pBTGPfCy20HypYoPoFxQeQACrg0irFVD4r8tXtdHtgZtmxCf69ot7zRXN6xlQw6e552i4mbp3B8ZHj2A4Vaf2NjqE6sX0CdWt/rExu2bsc7qE2s1C6P1s+14y0WfeDHs27CTilVDCLSOeb02jdmU65j/tTiehh3s+xAW3Yjt1jEvVb40z8weyIJXP2X3mq2O8n6lSziOrZe3F7c1D+ffnQcveuwXwthMkf1caYpbRhnsg8VJ2AeizhOX8rsZk+G0LAv3+5WnnDEmU0QaAvcCjwEx2Ae1LhljDmLPdM8Skb8AV/dPXMXjbgDqBdxpjEl3t838iEg1YADQwBhzREQ+BEoWdr+yGWOOichJEalujHH7/CR323VR9Kw5d0/KhtU2xhibiFzQ+WfLsjFv2Cxi5gyxP8Zn/jIStyfwQP+O7N20k01L1rBy/lK6TY5hRNxbnEo7wcy+bzjWH/3bVEqWLY23rw91oxrwducxnD6RTuu+HUjakcDA7ycCsPyjH1k5b+l5xbhh2VrqNK/Ha8vfISM9gw9i33G8N2rhJIZFDwDgo6Hv8bT1eLiNcevYGLcWgM4jn8LHz5fYj4cB9i+vfDTkPQBuvuM2UpNSSN6fN2vhKVuWjc+GzaKv1YYrndpw36adbFyyhhVWG47M1YaJ2xNY893vDFs8GVumjc+G2R93VP6aCjzznn2/vLy9+fOb3/h7+YbzjrEwYodP4M91G0lLO8a97Z6gd4/OdGjT8pJvd93SNYQ1r88bv0wnIz2DGQPOfbd3/MIpDIq2Pw5s1pDp1uPhSrA+bg3rl63Jt96Gre/k7g7NyTybxZmMDN7qk9/MsJyK+7G9FPEB9Jz2ImUCypGVmclnr8zk1LGTANRt2YBHRzxJ2cDy9Jk1kIQte3i7y7jLEqNvST9uaVqH/w5+L8f22g96gsq3VcUYQ2pCcp73/z/YaPWJE5e/w5n0DGY69YkjF05iuNUnzhn6Hj2sPnGTU5/4xMin8PXzZYBTnzjH6hNf+20aJcuWwsfXh/CohrzeeRQHdyRQGLYsG18Mm0XvOYPx8vZi1fw4krYnEN3/EfZt2sVfS9bw+/xldJ4cwytxb3Iq7QQf9n0TgLu6tOKaKsG07NeBlv06APBu57GICE9/8BI+fj54eXuxbeVmVvx38YU1pCoyUly+eSkiJ4wxZUWkMtDBGPOmNV1igDHmAbE/VSLZGDPaWj7FGBMuIiOAE8aYSVY9fwEPAMeBtcaYKtZyR13W66lAPPAFUNoYc8iaDrHDGJP3WwP2dVoBPxtjzopICPZMajj2qRbZcbqLpwzwP+yD4hQRCbSmLXyCfUrJa1b5MGPMejfb7wZEGGNinJbVxT4NJRyoCGwEXna3XyLyttUus/M5Fr2BNsCj1sC5PPCYMeY9EYnDPkA+62q7xpgPs8sYY+Kzj6tVb+62cbznTu+qHYvHCerGSbIudwgFKo335Q4hX2/FT7jcIRSoS/0XLncIBfIXv8sdgioC0/bMv9wh5Kt71Q6XO4QClSuWOcKc3tozz+Mv0F8Mx/s9UGS/a8u99V2R7tuFKnZnizEmAfu83dxGALNFZCP2aQRdC6gnRURWWAPVH4Dv3RQtB3wjIiWxZ337uykHEAW8KSLZz2yKNcYkicgt+cVixbNZRMYCy0UkC/sguxvQD3jH2i8f4BfA9UNY7bqJSDun142sujYDu7DPvc5vvz4D3re+ZPiwm7nI04CywJ8ichb7oPj1XPuzQURcbVcppZRS6v+FYpNRVsoVzShfOM0oXzjNKKviQjPKF04zynkdj4kuuozy1IVXVEa5WH2ZTymllFJKqeKi+H+sugxEZAjwSK7FnxtjxhbR9rsDz+VavMIY4/nfr/VsO+8AuZ/b/GZ+85eVUkoppa4WOlB2wRoQF8mg2M32ZwOXfLB6sQfeSimllLoCXYGPbSsqOvVCKaWUUkopFzSjrJRSSil1NdOMsluaUVZKKaWUUsoFzSgrpZRSSl3F9FHB7mlGWSmllFJKKRc0o6yUUkopdTXTOcpuaUZZKaWUUkopFzSjrJRSSil1NdOMsluaUVZKKaWUUsoFzSgrpZRSSl3FjGaU3dKMslJKKaWUUi5oRlkppZRS6mqmGWW3NKOslFJKKaWUC5pRVkoppZS6mtkudwDFl2aUlVJKKaWUckEHykoppZRSSrmgUy+UUkoppa5i+ng49zSjrJRSSimllAuaUVbFWnH/jOt3BXzW9EYudwj56lL/hcsdQoHmrJl8uUMoUO+Ily93CPnKKvZXM/gW82vlSlAK78sdQoGOk3m5Qyh+NKPsVvH/La+UUkoppdRloBllpZRSSqmrmT4ezi3NKCullFJKKeWCZpSVUkoppa5i+tQL9zSjrJRSSimllAuaUVZKKaWUuprpHGW3NKOslFJKKaWKBRFpJSJbRWSHiAx08X43EUkWkfXWz1NO73UVke3WT9eLEY9mlJVSSimlrmLFZY6yiHgD7wAtgATgTxH51hjzd66i84wxMbnWDQSGAxHY/wzDGmvdIxcSk2aUlVJKKaVUcdAQ2GGM2WWMOQN8Bjzo4botgcXGmFRrcLwYaHWhAelAWSmllFLqamYrwp/8XQfsd3qdYC3LrYOIbBSRL0Tk+kKuWyg6UFZKKaWUUkVCRHqKSLzTT0/nt12sknteyAKgqjGmDrAE+KgQ6xaazlFWSimllLqKmSJ86oUx5j3gPTdvJwDXO72uDBzMtX6K08v3gYlO60bmWjfuAkIFNKOslFJKKaWKhz+BmiJSTUT8gMeAb50LiEio08u2wBbr/z8BUSISICIBQJS17IJoRlkppZRSSl12xphMEYnBPsD1BmYZYzaLyCgg3hjzLdBPRNoCmUAq0M1aN1VERmMfbAOMMsakXmhMOlBWSimllLqaFaM/OGKMWQgszLVsmNP/BwGD3Kw7C5h1MePRqRdKKaWUUkq5oBllpZRSSqmrWFF+me9KoxllpZRSSimlXNCMslJKKaXU1Uwzym5pRlkppZRSSikXNKOslFJKKXUV0znK7mlGWSmllFJKKRc0o6yuOLc1q0vHYd0Rby9WzPuZRdO+yfG+j58PXSfHcEOt6pxMO84HMW+QmpAMQMve7Wjc8R5Mlo15I2ez5ZcNADTv3pqmj90LIqz47GeWzsrxCEfue7oNHYZ0ZkB4D04eOe4yrseGd6d283qcSc9g9oB32Ld5d54yN9SqTvdJffAr6cemZWv5bORsAEpXKMszU/sTVLkiKQnJzOgzmVPHTuZbb4eBT1DnnnqIl/D3rxsddTVs24TWvduDMaQdOsLc56e6jTm3W5vV5eFh3fDy9mLlvKUsdtG2nSf3cbTtrJg3SU1I5pamtWn7cid8fH3IPJvJ1+M+Ztvvmz3apqe6jniKsOb1OZOewbQBb7Hnr115ylSrdSPPvt4Pv5J+rF+2ho9GfJDj/ft7PsgTQ7rTM6wzx48cp36LhnR8sRM2m8GWlcWckTPZGr8lT70X09Bxk/llxWoCA/z5+uPpl3RbcGnOy5Abr6Xba3244fZqfD3pUxa9vwCAgNAgnpwcQ4WK/hib4ZdPl/Dz7IV5tudOp+FPUrt5OGfSzzBzwFSXsVapVZ0ek/rgW9KPTcvW8clI+yNTI6Lv5MHnOxJa4zrGPDiIPZt2AlCtbg26jn8GABHhmzfms/an1R7HdHuzMDoO646Xtxe/zfuZn6Z9neN9Hz8fuk/u67gm3o+ZQorV37Tq3Y4mHe/FlmVj3shZ/P3LBnxK+DJg3ih8Svjg7e3N2h9WsWDKfAA6T+xFlTrVEYR/dyfy0YB3yDh12uNYrwS3NavLI1b/vTKf/vt6qz1nWv13Gf+yPD3tBW6oU4NVX8Qxf/i5R+W2HfAYd7S/m1IVyvLC7V0uSpydhj9Jneb1rHPxbfa6ORefmhSDb0k/Ni5bm+NcbPf8o4TWuI7RDw50nIuNHryL1s886Fi/8i1VGPFALPv/3nNRYr4QmlF2TzPK6ooiXsJjo3owtds4RrXoT4O2TQipcV2OMo073sOpoycZHtmPpTO/56GB/wEgpMZ1RLRpzOioF3i761geH90D8RKuvel6mj52LxMeHMzY1rHUvqceFauGOOoLCA3i1rtqO375uVIrMpxK1UIZEtmXuYNn8J+xT7ss98SYp5k7eAZDIvtSqVootSLDAGjdqx1bVm5iaPN+bFm5ida92+Vb7431bqJGxM2MaDWA4VEvUrVuDW5qdBte3l48Oqw7rz8+gpGtB5CwZS/Nurb0uG07jnqSd7uNZ0yLF6jvom3v7HgP6UdPMjLyOZbNXMiDAzsBcOLIcWb0eJVxrWKZ++K7dJkS49E2PRXWvD4h1ULp36wX7w96lx5jnnVZ7smxz/DBoHfp36wXIdVCqRtZz/FeYOg11G4aRnLCIceyv1Zs5OVWzzMouj8zYt/m6Yl9LmrcrrSLbsH0yWMu+Xbg0p2XJ9NO8NmIWY4BcjZbZhafj5nDsPv6M+6hwTTv3JLQGpU9irV2ZDjB1UIZFNmXjwZPp8vYni7LdR7zNB8NnsGgyL4EVwuldmQ4AAe27uOdZ19j2+qcH3QObN3HqDYvMyI6lsldxtBl7DN4eXv2q0+8vHh8VA/e7jaWEVZ/k3t/mnS8h5NHT/BKZF+WzPyO9gOfACC0RmUi2jRhZFR/3uo6lk6jn0K8vMjMOMuUTiMZ0zqW0dGx3N4sjGrhNQH4fPSH9uWtB3Dk4GEiu7byKM4rhXgJj1r99+gW/YnIp/8ekav/PptxlgWvz+N/4+bmqXfjz2uY+ODgixZnnch6BFcLZWBkDB8OnkZnN+dilzE9+XDwdAZGxuQ5F6c++yrbVv+do/yqb35lePQAhkcP4P3+b3E4IblYDJJV/grsLUTEiMhcp9c+IpIsIt+dzwZFxF9Eeju9jjzfupzquFlE4kRkvYhsEZH3rOURIvLWhdTt4fa7WW2yXkT+FhHXv41ylp9q/f9ZEbkoH4EvRlsWYltVRaST0+siaeuqYTVI3pvE4f2HyDqbRfyCldSNapCjTN2oCFZ9GQfA2oWruKVxLWt5A+IXrCTzTCYpCckk702ialgNQmpcx+512zl7+gy2LBvb/thCWMuGjvoefqUrX43/L2DcxhUW1YBVXy0HYNe67ZQuV4YKFf1zlKlQ0Z+S5Uqxa+02AFZ9tZywKPt2wlo04Pcv7DH//kUcYS0a5luvAXxL+OHj64Ovnw/ePt4cSz6KiCAi+JUuAUCpcqU4+u8Rj9v28N5/SbHadu2CldTJ1bZ1oiL440t7POsWruJmq20TNu/h6CH7dhK37ce3hC8+fhfvhlX9Fg351TqmO9Zto3T5MvhXCshRxr9SAKXKlmb72q0A/PplHBFRdzje7zLsST4Z/1GOw+icrStRuiT5HeOLJSKsNhXKl7vk24FLd14eTznGno07ycrMzFHX0eQ0RxY44+RpEncewD8k0KNYw6MasPKrOKdYS7uMtVS50uy0Yl35VRzh1jmauPMASbsO5qn3jHVdg/2aMcbzY1wtrAaHHP1NJvELVlA3KiJHmbpRDVhlXRM5+5sI4hessPqbQxzam0S1sBrAufPO28cbbx9vR0ynT6Q76vUt6QeFiPVKkN1/Z/cxa1z033Wc+m/nPuZMegY747dyNuNMnnr3rNvOseS0ixan/Vws+LrJeS4up5513bg7F53d0bYpf3z720WL+UIZW9H9XGk8+U12EqglIqWMMelAC+DABWzTH+gNvHsBdeT2FjDFGPMNgIjUBjDGxAPxF3E7+ZlnjIkRkUrAZhH51hjzb0ErGWMu/b3XS6Mq0An4BIqurf2DAzlyMMXx+khiCtXCarotY8uykX78FGUCyuEfHMjuddud1k3FPziQg1v303bAY5TxL8uZ02eo1TycvRvtt8rq3FeftH9TObBlb75xBQQHkuocV1IK/iGBHHXqvP1DAjmSmDP2gGD7IKJ8xQqOskeT0yh3Tfl86921dhv//P4Xk/58DxCWzfmRpJ32y/Ljoe8z4sfXyUjP4NDuRL54xbO/5lnBRdtWtX6xuyrj3LbOUzvCWt/B/s17yDyTcxB1IQJDAkk5eNjxOjUphcDgQNIOnfsQEBhARcy4AAAgAElEQVQcSGrSufhTElMItAZp9e9rQGpSCvu27MlTd0TLO3jspc5UuKYCr3YvmkxvUblU56UngipX5PrbqrF7/faCCwMBwUE5Yk1NSiUgJChHrAEhQTliTU1MJSA4qMC6q4fVpPurvQm67ho+eOFtx8C5IHn7m1SX/U2qdW7m7G+C2L1uW451/a12FS8vhnw3kYpVQlg+90f2rN/hKNf1td7UigwncUcCn4/5yKM4rxSu+u+qHvbfnk4fu1hxpjr1N0eSUlyei6k5zsUUx/H1RMMHmvDW0xMuTsDqkvJ06sUPwP3W/x8HPs1+Q0QCReRrEdkoIqtEpI61fISIzLIyvbtEpJ+1ygTgRiv7+pq1rKyIfCEi/4jIf0VErDomWBnajSIyKZ/4QoGE7BfGmE3W+o4Maz7xICJdrG1syM6ei0hFEflSRP60fpp40lDGmEPATqCKu7ZxZsU1wPp/DRFZYsWxVkRuFJG5IvKgU/n/ikjbguK4WPtr1TNXRJaKyHanbPkE4C7rOPbP1daFPSc8Zp0aOeTJELkogzEu18VA0s4DLJr+Df0+HkrfjwaTsGUvtiwbviX9aBXTngWT53kSmKtNFj52D+utWCWE0BqVeanRs7zU6BluaVyLmg1vxdvHm8gnohh9/0vENuxJwj/7iOr9UMHxu4kvd4LVVRHnHQ2pWZkHB3bis8Hve7RNT7luuzyFXJbxK+lHu5hH+Hzyp3neB4j/6Q8G3BvD60+P55EXO7ksc8W6VOdlAUqULkmvaQOYN2p2jixpvlxetrmD9aCMC7vWb+eVqP6MbjuQ6F4P4VPC97xjyt2Arq8b46Ybsq9rbDbGRMcy8M5nqFq3BtfedL2jzEex7/LSHc+QuOMADdo09izOK4Wbtip0mUvMk2vCdV/oWf3Vw2pyJj2DA9v2n0d0l4iRovu5wnh6b/QzYJg1EKoDzALust4bCawzxrQTkXuAOUCY9d4tQHOgHLBVRKYBA4FaxpgwsA9mgXDgduAgsAJoIiJ/Aw8BtxhjjIjkvO+R0xRgqYisBBYBs40xru7DuIrnJmAI0MQYc1hEsj8Svok9S/2biNwA/ATcWlBDiUh1oDqwo4C2ceW/wARjzP9EpCT2DzIfAP2Bb0SkAtAY6FpQHBd5f+sAjYAywDoR+R77cRxgjHnA2u9Ip+0W6pwwxpx1DlpEegI9Ae4OrM9t5ao73juSlELAtecySAGhQY5b/tnSrDJpSal4eXtRqlxpTqadcLFuIGmHUgFYOX8ZK+cvA+DB2Mc5kphCxSrBXFO5EkN/sH+e8w8JYvB3E5nYbhDHko/SrHNLmjx+LwC7N+wg0LnukCCO/puaI64jiSkEhOaMPTsjeiz5KBUq+nM0OY0KFf05fviYY39d1XvHQ3eza902xy3cv+LWUT28JmdP229LJu+z38yI/34l9/fybKCc5lHbprpsW3v7BNJzxovMfeFdDu8r8GZKgVp0ac09j0UBsGvjdoKuvcbxXmBIEEcO5Wzf1KQUAkPOxR8UGsSRf1MJrhJKxesrMfGHN+zrhgYx7vvJDH0wNkeG6J/VfxNcJYRyAeU4XoTZq4stsnNL7n78PuDSnZf58fbxptf0F/nj619ZV8CX5u7p3Iq7HdfQzhyxBoYEklZArIFO17AnEnceICM9g8o33eD4glV+ss/3bAEutme/Rq/xqL/JfT2lHzvFtlWbub1ZGAedBk3GZiP+u5VE9WzLys/jPN6/4s6zPsZ1/32p3dO5Fc1yXDfn+puAkKA852JqYgqBOc7FII/PxYZtmrCqGE27UPnzKKNsjNmI/Vb740DurzA3BeZa5ZYCQdaADuB7Y0yGMeYwcAgIdrOJ1caYBGOMDVhvbesYcBr4QETaA6fyiW829kHd50AksEpESrgo6iqee4AvrGUYY7LP9PuAqSKyHvgWKC8i+U0sfNQq+ynwjFVPfm2Tg1X3dcaY/1nlTxtjThljlgM1rCkdjwNfGmM8vad9sfb3G2NMurXOMqAh+bugc8IY854xJsIYE+E8SAbYu2EnlaqGElS5It6+3kS0aczGxTlnfGxcvIZGHSIBqBfdiK0rN1vL44lo0xgfPx+CKlekUtVQxy3PckHWVIdrgwhr1ZD4b1dwcOt+Xop4mqFNYxjaNIa0pBTGPfAyx5KPArB87k+Mio5lVHQs6xf9SaP2zQCoHl6T9OOncgzCwH7rOuNEOtWtL+40at+M9Yv+BGDDknjufNge850PR7J+sbV8cbzLelMPHuamO+xf3vP28eamO24jcccBjiSlElqzMmUD7ftzW9M6JO3wbKbU3g07qVg1xNG29Vy07abF8dzRwR5PeHQjtlltW6p8aZ6dPZBvX/2UXWu2erS9giye8wODovszKLo/8Yv+4C7rmNYIv4lTx0/mmHYBkHboCKdPplMj/CYA7uoQyZrFq9m/dS/P1u9Gv6Y96de0J6mJKQy+/wWOJqcRXOXclzar1qqOj6/PFT1IBogrgvMyP10n9iJxxwEWzyz46xJL5/7IiOhYRkTHsm7Rahq3j3TEespNrKedYm3cPpJ1i/KP6ZrKlRxf3gu67hpCq1/LYacvdOZnz4YdVn9TCW9fHyLaNGFDnv4mnkbWNVEvuhH/rPwLsF+7EW2aWP1NJSpVDWX3+h2UDSxPqfKlAfuc6Vua1HFMm6rodD7Wube+Y/n/F7n77/oF9N/hTv33pbZ07o+OL9qtXbSaxh5cNznPxWYFnotgz1Y3iG7M6gUrLv5OXACdo+xeYb5t8y0wCftA1HlSWH43IDKclmXls7085YwxmSLSELgXeAyIwT7Ic8kYcxB7pnuWiPwF1PJkO1b8rm6YeAF3WvOyPTHPGJP7q/6FuTmT3/2IucB/sLfDkx7GAxdhf61bULnLF3SD6WKcEy7Zsmx8NmwWfecMsT/CbP4yErcn8ED/juzbtJONS9awYv5Suk2OYWTcW5xKO8HMvvZMYuL2BNZ89zvDFk/Glmnjs2EzMTZ7WD2nvUiZgHJkZWby2SszHY9m89SmZWup3Tycscvf5kz6GT6Mfcfx3rCFrzEqOhawzx/ubj3a6q+49fwVtw6AH6b9j2feeYGmHe8h9eBhpveenG+9a6wvDY346XWMgc3L17Px5zUAfPfm57w0fyRZZ7NIOZDMJwOmedy284fNos+cwYi3F6vmx5G0PYH7+z/Cvk272LRkDSvnL6PL5BiGx73JybQTzO77JgB3d2lFxSrBtOrXgVb9OgAwtfNYTqQUnIH0xLqlawhrXp83fplORnoGMwac+97o+IVTGBTdH4BZQ6Zbj4crwfq4Naxftibfehu2vpO7OzQn82wWZzIyeKtPfjO8Lo7Y4RP4c91G0tKOcW+7J+jdozMd2nj2ZJLCulTnZfmK/gz9dgIly5bCGMN9T97PsBb9qXxLFe7s0IyELXsZttB+J+arVz9x1JefjcvWUqd5PSYsn8qZ9AxmxZ77GsuIha8xwop17tD3eTL7UXZx69hk1V2vZUM6jehBucDyPDdrEPu37GFylzHUbHAL0b0eIiszE2MzzH3lfU54+GHI3t/M5Dmrv1lh9Tdt+j/K3k072bgknt/mL+XJyX0ZHfc2J9NO8EHfKcC5/mbE4ilkZdr4dNgHGJuNCpX86fZ6DF5eXoiXsOb739m0dC0iQrfX+1CqbGkQSNiyl0+GXtwpTJebLcvGvGGziLHa83en/nvvpp1WH2Pvv0fk6r8BRv82lZJlS+Pt60PdqAa83XkMSTsO8NDA/xDxYFP8Svkx9vdprJy3lO/f+Py848w+Fycuf4cz6RnMdLpuRi6cxPDoAQDMGfoePSbFOM7FjXFrAfu5+J8RT1EusDzPzxrM/i17eL3LaABuuuM2jiSlkLz/wu+6qaIhBc3vEpETxpiyIlIZ6GCMedO6zT7AGPOA2J90kGyMGW0tn2KMCReREcAJY8wkq56/gAeA48BaY0wVa7mjLuv1VOxfCvsCKG2MOWRND9hhjHE5U15EWgE/G2POikgIsA77dI5bnOJ0F08Z4H/YB4kpIhJojEkVkU+wTx94zSofZoxZ72b73YCI3APlfNrGUd45LhFZhX3qxddWRtzbGHNKRIKB1UCSMeYO3Mh1XC7K/lr1tMNp6oX1/1BgsjGmmYttF+qcMMbscbdPvap2LNZf+84sgqckXKgSxfwpkCkmo+BCl9mcNZMvdwgF6h3x8uUOIV9ZV8C14ptvvqJ4mLHn/AeARaF31Y6XO4QCpZN1uUMo0Ow9XxbpyZh0d2SRXaAhv8QV/wvNicfZPGNMAvZ5rLmNAGaLyEbs0yPynT9rDc5WWIOkH4Dv3RQth31ebknsGcr++VQbBbwpItnPeoo1xiSJyC35xWLFs1lExgLLRSQL+0CwG9APeMfaLx/gF8D1w1vdG0Eh2gboDMwQkVHAWeARYJcx5l8R2QJ8ne/aHjjP/V2N/TjdAIw2xhwUkWQgU0Q2AB9a9WQbQeH2WymllFKXibFdUWPXIlVgRlldfiJSGtgE1DPGHC3ibY/AKQtc1DSjfOE0o3zhNKN84TSjfHFoRvnCaUY5r8SmzYvsAg39bVnxv9Cc6J+wLuZE5D7sc68nF/UgWSmllFL//12JX7IrKlfUQFlEhmCfjuDsc2PM2CLafnfguVyLVxhjLtnfvTXGLME+5cE5jpbAxFxFdxtjPHsOWOG2P+Ji16mUUkopdSW4ogbK1oC4SAbFbrY/G5h9ubbvFMdP2J9zrJRSSil1QcwV+IdAikrxnryolFJKKaXUZXJFZZSVUkoppdTFpXOU3dOMslJKKaWUUi5oRlkppZRS6iqmz1F2TzPKSimllFJKuaAZZaWUUkqpq5j+7Tn3NKOslFJKKaWUC5pRVkoppZS6iukcZfc0o6yUUkoppZQLmlFWSimllLqKaUbZPc0oK6WUUkop5YIOlJVSSimllHJBp14opZRSSl3F9PFw7mlGWSmllFJKKRc0o6yUUkopdRXTL/O5pxllpZRSSimlXNCMslJKKaXUVcwYzSi7owNlVawdN5mXO4R8lZPifwmdIutyh5Avf/G73CEUqHfEy5c7hAK9Gz/xcoeQr5groA3Pot9oulBnsF3uEApUToc+qhD0bFFKKaWUuoqZ4v/55rLROcpKKaWUUkq5oBllpZRSSqmrmE3nKLulGWWllFJKKaVc0IyyUkoppdRVTJ964Z5mlJVSSimllHJBM8pKKaWUUlcx/ct87mlGWSmllFJKKRc0o6yUUkopdRUz+rd23NKMslJKKaWUUi7oQFkppZRSSikXdOqFUkoppdRVTL/M555mlJVSSimllHJBM8pKKaWUUlcx/RPW7mlGWSmllFJKKRc0o6yUUkopdRXTP2HtnmaUlVJKKaWUckEzykoppZRSVzH9gyPuaUZZKaWUUkopFzSjrJRSSil1FdOnXrinGWWllFJKKaVc0IyyUkoppdRVTJ964Z4OlNUVr/OIHoQ1r0dGegbvDZjKnr925SlTtVZ1nnm9L34l/Vi/bC1zR8wE4OEXH6deiwYYm+FYylFmvPg2aYeOAHBro9t5YtiTePt6czz1OGMffcWjeG5rVpeOw7oj3l6smPczi6Z9k+N9Hz8fuk6O4YZa1TmZdpwPYt4gNSEZgJa929G44z2YLBvzRs5myy8bAChVvjRPTHiWa2++HmMMc1+axu612x113vd0GzoM6cyA8B6cPHK80G3YafiT1G4ezpn0M8wcMJV9m3fnKVOlVnV6TOqDb0k/Ni1bxycjZwEQEX0nDz7fkdAa1zHmwUHs2bQTgGp1a9B1/DMAiAjfvDGftT+t9jimS9GOY36byukTp7HZbNgys5jQdlCOOgvbjo8N707t5vU4k57B7AHvuGy3G2pVp/ukPviV9GPTsrV8NnI2AKUrlOWZqf0JqlyRlIRkZvSZzKljJwm58Vq6vdaHG26vxteTPmXR+wsACAgN4snJMVSo6I+xGX75dAk/z17ocXt6aui4yfyyYjWBAf58/fH0i16/s9ubhdFxWHe8vL34bd7P/DTt6xzv+/j50H1yX8cxfj9mCinWMW7Vux1NOt6LLcvGvJGz+Ns6xl1e7UXte+pzPOUoo1q+6Kjrgecfoelj93Ei9RgAX7/6CX/FrSt0zJfiWskWeO01jFk8hW/e+Jyf3v+20LEVZ487tdusfNrt3LWyjk+tdivjuFYqkZJwiOnWtQJwc6PbeXRYN7x9fDhx5BivPTocnxK+vDxvFD4lfPHy9mbND7/z7ZT5Hsd6a7O6tB/WDS9vL36ft5QlLvqeJyb34XrrvPww5k1SE5K5uWlt2r7cCW9fH7LOZvL1uI/Z/vtmAPp+NozyFQM4m3EGgHc7j+VEyrHzaktVtHTqhbqi1W1ej5BqobzYrA8zB02n25ieLst1H/sMMwdN48VmfQipFkqdyHAAvp/xNYNbvcCQ6BdZ93M8Dz3XEYDS5UvTbUxPJj81noEtnuft3pM8ike8hMdG9WBqt3GMatGfBm2bEFLjuhxlGne8h1NHTzI8sh9LZ37PQwP/A0BIjeuIaNOY0VEv8HbXsTw+ugfiZf+U33F4d/5evp6R9/ZnbOtYknYccNQXEBrErXfVdgwgCqt2ZDjB1UIZFNmXjwZPp8tY123YeczTfDR4BoMi+xJcLZTaVhse2LqPd559jW2rt+Qof2DrPka1eZkR0bFM7jKGLmOfwcvbsy7nUrUjwJTHRzIu+qU8g+TCtmOtyHAqVQtlSGRf5g6ewX/GPu2y3BNjnmbu4BkMiexLpWqh1IoMA6B1r3ZsWbmJoc37sWXlJlr3bgfAybQTfDZilmOAnM2WmcXnY+Yw7L7+jHtoMM07tyS0RmWPYi2MdtEtmD55zEWvNzfx8uLxUT14u9tYRljHOPf+NOl4DyePnuCVyL4smfkd7Qc+AUBojcpEtGnCyKj+vNV1LJ1GP4V42c+t37+I462uY11u8+eZ3zEmOpYx0bHnNUi+VNdKtsde6camuPWFjqu4q21dK4Mj+zJn8HSecNNuT4x5mjmDZzDYca3Y2y37WhnSvK91rTwE2BMI/xn9FFOfmsjwqP5M7/06AJkZZ5nUaSQjWw9gVPQAajULp3p4TY9iFS/hkVFPMr3beMa1eIH6LvqeRlbfMzryOeJmLqTtwE4AnDxynBk9XmVCq1g+fvFdOk+JybHenOff5tXol3k1+uViN0g2puh+rjQXNFAWESMirzu9HiAiIy44KntdH4rIwxejLg+3109EtojIf/Mp005ENorIPyKySUTaFWF8ZUVkhojsFJHNIvKLiNxxCbdXVUT+KqDMtSLyhQd1Db54keVUv0VDfvsyDoCd67ZRpnwZ/CsF5CjjXymAUmVLsWPtNgB++zKOiCh706WfSHeUK1G6JMa6ihs/eDd//riKlIOHATiWctSjeKqG1SB5bxKH9x8i62wW8QtWUjeqQY4ydaMiWGXFvHbhKm5pXMta3oD4BSvJPJNJSkIyyXuTqBpWg5JlS1Gj4a2smLcUgKyzWaQfO+Wo7+FXuvLV+P8C59cDhUc1YOVX9nh2rdtO6XKlqVDRP0eZChX9KVWuNDutNlz5VRzh1n4l7jxA0q6Deeo9c/oMtiwbAL4l/Bxt64lL0Y4FKWw7hkU1YNVXy4Hsdivjst1KlivFLqvdVn21nLCohvb1WzTg9y/s8f/+RRxhLezLj6ccY8/GnWRlZuao62hymiMLl3HyNIk7D+AfEuhRrIUREVabCuXLXfR6c6sWVoNDjmOcSfyCFdSNishRpm5UA1Z9aW/jnMc4gvgFK6xjfIhDe5OoZh3j7au3cOroiUsS86W6VrLrTt73Lwe3778ksV9OYVEN+N2DditZrrTjWvndqd3CWjRgpXWtrPwijvAW9uV3tL2LtT/+QarVTx93GnxmnDoNgLePN94+3h4P0KqE1SB577+kWH3P2gUrqZ2r76kdFcFq67xcv3AVN1nnZcLmPRyz7kgmbtuPbwlffPz0xv2V7kKPYAbQXkTGG2MOX4yALgYR8TbGZBVytd5Aa2NM3vtB9jrrApOAFsaY3SJSDVgsIruMMRsvMGRPfADsBmoaY2wiUh24tQi265Yx5iDgyYeZwcC4SxFDQEigYzALkJqUQkBwoGP6BEBAcCCpSSnnyiSmEOA0wHgkthNN20dy6vgpxj02DICQatfi7evNkM9GUbJsKX6a9T2/WR19fvyDAzly8Ny2jiSmUC2sptsytiwb6cdPUSagHP7Bgexet91p3VT8gwM5e/oMJ1KO0WVSbyrfWoV9m3Yxf+SHnEnPoM599Un7N5UDW/Z62GJ5BQQHkeoUc2pSKgEhQRxNTjtXJiSII4nObZhKQHBQgXVXD6tJ91d7E3TdNXzwwtuOgXNBLkU7gj2b0W/uEDDw6yeL+e3TnwHOqx0DggNztNuRpBT8QwJztJt/SGCOdjuSaD8/AcpXrOAoezQ5jXLXlPd420GVK3L9bdXYvX57wYWLqbzHONXlMc4eBOU8xkHsXrctx7rZxzg/kV1b0ah9M/Zu2skXY+Y4bt976lJdK36lStD62Xa8/sRoWvVsW6iYrgT+udrtSFIq/rnazT9Xu9mPqb3dylf0z3WtVAAguHoo3j4+xH42kpJlSrJk9kJ+tz68ipcXr3w3kUpVQlg29yePrxX/4EDSnGJNS0yhSq4P2hWcytiybJy2zkvn6Vphre8gYfMeMs+c+8D7n9d6YbPZ2PDDH/z09lcexVNU9KkX7l3o1ItM4D2gf+43cmeEReSE9W+kiCwXkfkisk1EJojIf0RktZWlvdGpmvtE5Fer3APW+t4i8pqI/Glld59xqneZiHwCbHIXsIi8ICJ/WT/PW8umA9WBb0Ukz75YBgDjsgfS1r/jgVirjjgReUNEVlp1N7SWlxGRWVa860TkQWt5NxH5SkR+FJHtIvJqPjHfCNwBDDXG2Kzt7zLGfJ/PPlW1Mt8fWMv/KyL3icgKa3vZ8Y0QkbkistRanuf+sVXXryKy1vpp7LT8r/z2R0QmAKVEZL0VQxkR+V5ENlhxPepuvz0hkvfizp25LKjM5699wnN39mTl17/QomtrALx8vKhW60YmdR/LxM6jaNfvYUKqhV6UeHBRBmNcrosBL29vrq9VjV8+XsS4+18mIz2Dlr3a4VvSj1Yx7VkweV6BceUftKtwcsfsQRkXdq3fzitR/RnddiDRvR7Cp4SvZyFdgnYEmNThFcY/MJCp3cbRrEtLajS89fzb0WWMuYt4sB+FVKJ0SXpNG8C8UbM57XRH5Irj6veyB9eu/Ri7Wpx/uy7/eBFD7+7LmOhYjh5K4+GhXQoRbHZAHmz3PGJr1/9RFs/8zpEF/f/G1fHKe6wLLpObt7c3VWpX583u45jSZQwP9H2YYKufNjYbo6Jjib3zGarVrcG1N11/3sF61vWcKxRSszJtB3Zi3uD3HcvmPPc2E1rF8uYjw7mxwS00aH+3Z/Goy+5i3BN4B9iY30DPhbrYs6GpwC7gA2NMQxF5DugLPG+Vqwo0A24ElolIDaALcNQY00BESgArRGSRVb4hUCufrHB9oDv2QacAf4jIcmPMsyLSCmieT2b8duwZZWfxQB+n12WMMY1F5G5gFlALGAIsNcY8KSL+wGoRWWKVDwPCsWfmt4rI28YYV/fdbgfWu8qSu9sn4AhQA3gE6An8CXQCmgJtsWd5s6eO1AEaAWWAdSLyfa7NHMKeST8tIjWBT4EI8nK1PwNFJMYYE2bF2wE4aIy533pdwcU+9bRipmFgGDXLVsvx/n1dWtH8sRYA7Nq4g6Brr3G8FxgSlCObDPYsc2DIuYxOYGgQaf/mLAOw8ptfGTB7CF9NmceRxBQ2ph4nIz2DjPQM/ln9NzfcWpWk3YkudvucI0kpBFx7blsBoUEczRVPmlUmLSkVL28vSpUrzcm0Ey7WDSTtUCppSSmkJaWwZ/0OANYtXEVUr3ZUrBLMNZUrMfSH1wB7RmbwdxOZ2G4Qx5LznypyT+dW3P34vQDs3rCTQKftBoYEkvZvas79SkwhINS5De2xeSpx5wEy0jOofNMNeb7A5MqlaEfAUcfxlGOs/+lPqtatwamjJzxux8jOLbn78fsA2L1hR452CwgJ4mgB7RYQeu78PJZ8lApWpqxCRX+OHy54zqK3jze9pr/IH1//yrpCfDGyOEpLSnV7nLIdSUoh8NprPDrGuc+P3I4fPncsf/tsCX1mDvQozqK4VqqH1SQiuhGPDOpM6fJlsNlsnM04w9I5P3oUY3HUvHMr7rLabU+udgvwoN2cz4dj1jVy7lqxH8sjSSmcOHKcM+kZnEnPYNvqv6l8a1X+deqn04+dYuuqzdRqFs7BbQVPa0lLSsHfKVb/0CDHdIpzZVLxd+p7SpYrzak0+3Qf/5BAnprxInNfeJfD+/51rHPU+p2TcfI08d+uoErdG/nzq18KjKeo6FMv3LvgL/MZY44Bc4B+hVjtT2NMojEmA9gJZA90N2EfHGebb4yxGWO2Yx9Q3wJEAV1EZD3wBxAEZN+vW+1ukGxpCvzPGHPSGHMC+Aq4y8OYhbyTF3Mv+xTAGPMLUN4aGEcBA61444CSwA1W+Z+NMUeNMaeBv4EqHsbiLL992m2M2WRloTdb2zPkbedvjDHp1oeEZdg/cDjzBd4XkU3A58BtbmLxZH82Yb9TMFFE7jLG5BnRGWPeM8ZEGGMicg+SAZbM+ZEh0S8yJPpF1ixaTdMOkQDcGH4Tp46fyjNQTjt0hNMnT3Nj+E32BusQyZrF9kFGcNVzWeJ6LRqQuNP+Jbk1i1dzc8Nb8fL2wq+kHzeG3cRBpy/QubN3w04qVQ0lqHJFvH29iWjTmI2L43OU2bh4DY2smOtFN2Lrys3W8ngi2jTGx8+HoMoVqVQ1lD3rd3As+ShHDqYQXN0e681NapO0PYGDW/fzUsTTDG0aw9CmMaQlpTDugZcLHF549gYAACAASURBVCQDLJ37IyOiYxkRHcu6Ratp3N4eT/Xwmpw6firHLVGw3+48fSLd8YWYxu0jWbfoz3y3cU3lSo4v7wVddw2h1a/lcMKhAmODS9OOfqVKUKJMScB+q/vWu+pwcNu+QrVj3NyfGBUdy6joWNYv+pNG7Zs52i3dTbtlOLVbo/bNWG+124Yl8dz5sD3+Ox+OZP3i/NsToOvEXiTuOMDimd951I7F2Z4NO6xjXAlvXx8i2jRhQ55jHE+jDvY2rhfdiH9W2r82sWFxPBFtmljHuBKVqoay2/og6U55pzmxYS0bejRogqK5ViZ0fIWXmvbmpaa9WTzre75/539X9CAZYNncHx3XyrpFq7nTqd3cXSvO7XZn+0jHtbJ+STyNrWulsdO1sn7Rn9RscK6frh5Wk8QdCZQNLE+p8qUB+/cjbm1Sh6SdBfffAPs27KRi1RACrb6nXpvGbMp1Xv61OJ6G1nkZFt2I7VbfU6p8aZ6ZPZAFr37K7jVbHeW9vL0oE2Cf9+/l402te+qR6OH5py6/izXL/A1gLTDbaVkm1kBc7PfP/Jzey3D6v83ptS1XTLkHpgb74LSvMeYn5zdEJBIoaMLZhXxk2ow9i+o8H7ke9gGhc3zkei1AB2PMVuc3xP5FPOd2yML98dgM1BURr+ypF85V5RPzhbSzs/7Av9jvBHgB7u4PFrg/xphtVhY8GhgvIouMMaPy2Yd8rV+6hrrN6/H6L+9yxno8XLaxC19nSLT98VCzh8ygp/V4uA1xa9mwbC0Ajw58gtDq12FsNg4fSGb24BkAHNxxgI3L1zH+pynYbIa4z5aQsG1fgfHYsmx8NmwWfecMwcvbi5Xzl5G4PYEH+ndk36adbFyyhhXzl9Jtcgwj497iVNoJZvZ9A4DE7Qms+e53hi2ejC3TxmfDZmJs9kMxb8Qsur/RD29fHw7vP8TcAe+eb5PlsXHZWuo0r8eE5VM5k57BrNhzdY9Y+BojomMBmDv0fZ7MfnRT3Do2WU8NqNeyIZ1G9KBcYHmemzXo/9i77/ioqvSP458nCVVqqEFUQFhdpQQMiMBqAKVkRREVUSkiFqTo4oIiKh1FUXCtCIIoWEBsoKioFH80IXSURYooVUoIvSVzfn/MJaTMJKGksPm+X6+8krn33Hufe+6cyZlnzj3DlrWbGdlxKNXqXknMI7eRmJCA8zkmPjuWQ5mcui4r6rFY6eI8PKY34B/OsuTLefw6d+VZ19vq2cuo0bg2w+a+xomjJ5jQ542kdf1njGCwV2+TnhlLZ2+qsDVzViTNtvDNW5/z8BuP06htE+K272F0t5GAv0P3zLThFCxSCOccN97/T/rf1IuKV17GdbffwNa1f9B/hj/7/dlZTnGWnj4DhrNk+Sri4w/QtHV7unXpwO2tmp/XY8CpazyOx7xrPN+7xq163cUfqzey6odY5k2Zxf0jezJkzmscjj/EOz1HAaev8cDvR5GY4OOj/u/gfP6Xxi6vPsYV9a+mSMmiDF84mumjpjB/yixuf6oDl1xVCecce7fuZpLX1s9EVrWV/3X+tlKH57x6ezdZvaVuK/cntZXT9fbNW5/T9Y1/06htU6+t+OcQ2LFxG2vmLmfgty/jfI7/m/wj23/bQsUrL+P+l3sQEhKChRhLvl7AqllLMxWrL9HH1P7j6fZ+P0JCQ1g0ZQ47128lpted/Ll6E2t+WMrCKbPpMLIHz875D0fiDzGh538A+EfHFpS+rBzNH72d5o/eDvingTtx5Lh/f2GhhISGsG7+ahZ490dI7mfnMl7OzA4554p4f78ItAPGO+cGmtkzQFHn3JPmnx3ic+eceR3a3s65U2OO53iPY5OvM7MJQFngZqAyMBf/UIKO+DtZdzrnTprZ34BtQN3k+w0Sbx1gAv5hBoY/I93BObfczDYDUcGGXphZJP5s6k3Ouc1mVgn4AbjDObfCO4//esM4GgFvOedqmNlzQDH8nXtnZrW9493nHa+Ht/+vgJecc3OCHH8KsA7o7+2nGv7M7pZA54R/6MVXzrnq3vYTvMdTvdi/cs5VN/8sJa1JNvTC+zt/sjKjgK3OuZfNrDP+a2yp9hP0fMxsH1DWu14VgDhvGEdr4D7nXNDZQ9pf1iZXTyZT1HL/Hc3HydwNdDmlwAUwS2XCWc4okp3ejH0hp0NIV4+oJ3M6hAydvACu8/jNGU40lKMeqJRtk1WdtcKE5nQIGXp18+RsHQvxc4Xs+1977fbPLqhxHufzv/zLQPJJA8cCX5rZYuBHMs72BrIOfwe5HNDV61y9g3/YwDIvU72b02Nt0+WcW+Z1GE8N7nvHOZepdIzXGX4SmG5m+YCTwBPOueSTXu4zswX4O8b3e8uG4M+4r/Li3Yy/83+mHsBfxxvM7AiwF+gT7Jy8TmxmLQa+xj8kZIhzbnuq7d8EPjWzO/EPzTjTazkG//kvwz9MZ4SZ+fDX4SNnuC8RERGRbHFOGWU5LXlmPKdjORNeRvmQcy5z36iRzZRRPnfKKJ87ZZTPnTLK54cyyudOGeW0FmVjRrn+BZZRzv3/oUREREREckDuT4edBTMrhX+4R2pNnXN7AyxPvm1n4LFUi+c757oHKn+Kcy76jIIMfvyfgQKpFndwzgWdG/pcOOcGZsV+RURE5MKgLxwJ7n+yo+x1hiPPctt3STl7R7ZyzmXZ11KLiIiISOb9T3aURURERCRz9IUjwWmMsoiIiIhIAMooi4iIiORhuXtupJyljLKIiIiISADKKIuIiIjkYQ6NUQ5GGWURERERkQCUURYRERHJw3y5/0spc4wyyiIiIiIiASijLCIiIpKH+TRGOShllEVEREREAlBHWUREREQkAA29EBEREcnDND1ccMooi4iIiIgEoIyyiIiISB6mr7AOThllEREREZEAlFEWERERycM0Rjk4ZZRFREREJFcwsxZmts7MNphZ3wDrHzezX81slZn9aGaXJVuXaGYrvJ9p5yMeZZRFRERE8rDcMkbZzEKBN4CbgK3AEjOb5pz7NVmx5UCUc+6ImT0CvAjc5a076pyLPJ8xqaMsuVq45cvpENJ1hMScDkGyQSIup0PIUI+oJ3M6hHS9HvtCToeQoW65vA4vBPkugA+qj+eabqEEUA/Y4JzbBGBmHwO3AkkdZefc7GTlFwHtszKg3P+MFhEREZEs48vGnwxcDGxJ9nirtyyYLsA3yR4XNLNYM1tkZq0zPlzGlFEWERERkWxhZg8BDyVbNMY5N+bU6gCbBPxIz8zaA1HADckWX+qc225mVYBZZrbaObfxXOJVR1lEREQkD8vOWS+8TvGYIKu3Apcke1wR2J66kJndCDwN3OCcO55s39u935vMbA5QGzinjrKGXoiIiIhIbrAEqGZmlc0sP9AOSDF7hZnVBt4GbnHO7Uq2vKSZFfD+Lg00JNnY5rOljLKIiIhIHubLJdMoO+cSzKwH8B0QCox3zv1iZoOBWOfcNGAEUAT4xMwA/nTO3QL8HXjbzHz4E8HDU82WcVbUURYRERGRXME5NwOYkWpZ/2R/3xhkuwVAjfMdjzrKIiIiInmYT9/MF5TGKIuIiIiIBKCOsoiIiIhIABp6ISIiIpKH5f7vHs05yiiLiIiIiASgjLKIiIhIHpaJr5bOs5RRFhEREREJQBllERERkTzMZ5oeLhhllEVEREREAlBGWURERCQP06wXwSmjLCIiIiISgDLKIiIiInmYZr0IThllEREREZEAlFEWERERycN8mvQiKGWURUREREQCUEZZLmh/v6EWbfrfR0hoCAsnz+KHt75MsT4sfxjtR3bnkupVOBx/kAk9/kPc1t1c0agGtzx5D6H5wkg8mcAXz01i/cJfyFcwP/e/2YvSl5XDl+hjzY9Lmf7CR2cc190D7qdG49qcOHqC8b1f589ffk9T5rLqVej8UnfyF8zP6tnL+WjQeAAuKl6Eh1/vRamKZdm7dReju4/kyIHDFCpamAdGPUr4xaUJCQ1l5thpzP9kNldcdzV3PXtf0n4jLr+Yt3uOYsXMJZmO955k8Y5LJ94uL3Unnxfvh168UTHXceu/2hJR9WKG3voUm1dv9J9HiSJ0e6s3lWtezvypc/hgwLhMx3PVDbVo278zFhrC/Mk/MjPAde00sgeXetf1nR6vELd1NwDNu7WmQdsmuEQfkwe9y9qfVgLQuHNLGrVrCmbM//hHZo2fAUCdmPr88193Ur7qxbxwaz/+XL0p03EmlxV1WLlWVTo9/zAAZsaXr0xh2XeLMx3T1TdE0rZ/Z0JCQ5g3+Ue+e+uLFOvD8ofReWTPpHoc22MUe716bNGtNQ3bNsWX6GPyoPH86tVjxxcfoUaTazi4dz+Dm/87aV83/+tOGrW7kUNxBwD44sUPWTNneaZjPRPPPDeSn+YvJrxkCb6YNDpLjpFcuwGdqdG4DieOHufd3m8EvLaXpmjPy/h40LsAFE5qz2XYu3U3b3vt+dpbG9Gia2sAjh05xgfPjGXr2j+S9mchITwzfTjxO+N4rcvwLD/HrJSd7bnV43dR86YonHMc3LOf93u/yf5d+zKMMSvaCvivYz/vOr7hXcf7X3mUy2pcTmJCAptXbmBSvzH4EhLPsnbPDx9KKQejjLJcsCzEuHPw/Yy+73meu+lxrrmlIeWrXpyiTP22TTiy/zBDoh9jzrgZ3NL3HgAO7zvI211eZHiLPkz695t0GNUjaZtZY79iWNPHefGfT1Llmiv4e3TkGcVVI7o2ZStH0C+6J+/3G037YQ8FLNd+6IO83+9t+kX3pGzlCKpH1wag5SOtWbtgNU837snaBatp2e02ABp3aMH2DVsZ1LI3I9oNoO3THQnNF8a6hb8wOKYPg2P68PLdgzhx9HiKF+rMxFuucgRPRffkvX6j6Rgk3g5DH+S9fm/zVHRPylWOoIYX77Z1f/JG1xH8tnhtivInj5/ki5c/ZspzEzMdC/iva7vBXXj9vucYfFMv6ga4rg286zog+lFmjfua2/reC0D5qhcT1aoBQ5o9zmudhnH3kC5YiFHhb5fQqF1Tht/aj2Et+1CjSR3KVCoPwPZ1WxjT9SU2pIr/TGRVHW5b9yeDWz3JwJg+jOw4lI7DHiYkNHMv2xYSwt2Du/DafcMY6NVjRNWKKco0bNuEw/sP8Wx0T34Y9xVt+rYHIKJqRaJaNWRQs1682mkY9wx5AAvxH3fh1Dm82mlYwGP+OO4rhsb0YWhMnyzrJAO0jrmJ0SOHZtn+k6vuteeno3sysd/b3DvswYDl2g99kIn93ubppPbsf9041Z6fafyo1579neM9W3Yx4q4BDGrZm69fm0oH7w3RKTd2jmHHhm1Ze3LZILvb8/djpjGsZR+ei3mCNbOWEfPYHZmIMWvaCkDTzjHsTHUdF3/xfwxo+hiDm/+bfAXz+zv8kmupowyYmTOzl5M97m1mA8/TvieYWcYt9Twxs0fNbK2ZfRBk/X1mttvMVpjZr2YW+FU/ZfnXvb+7mlnHdMpWMLOp53YGmXdZZFV2//EXe7fsIvFkIsumL6BGs7opytRoFsXiT+cCsGLGIv7WoDoAW3/ZzAEvy7Djty3kK5CPsPxhnDx2gvULfwEg8WQiW375nRLlw88orshmdVn42RwANi1fT+GihSlepkSKMsXLlKBg0cJsWvYbAAs/m0NtL/bIm+qyYKp/+wVT51D7Jv9yh6PgRQUBKFi4IIfjD6XJQlwTU5/Vc1Zw4tiJTMdbu1ldFmQi3kJFC7PRi3dBsnh3bNzGzk3b0+z3xNHjrI/9LyePZz4WgEqRVdn9x072eNc1dvoCaqW6rrWaRbHoU3/My2Ys4krvutZqVpfY6QtIOJHA3q272f3HTipFVqV81Yv5ffl6Th47gS/Rx28/ryWyeT0Adm7cxl+bdpxRjKllWR168QLkK5Af5zI/22nlyKrsSqrHBGKnz6dWs6gUZWo1q8sir32krMcoYqfP9+pxF7v+2EnlyKoArF+8liP7D2U6jqwQFVmD4sWKZsuxIpvVZdFn/jryX9uLgrTnQkntedFnc4ls5n9+Rd5Ul4Vee144dQ6RN/mXb1z2G0cOHPbvd9l6SpYvlbS/kuXDqdGkDvM+/jFLzy07ZHd7PnboaNJ+8xcuAJloM1nVVkoEuY7J30RuXrkhxbXPKS4bfy406ij7HQfamFnpnA4kOTMLPYvNugExzrl70ykz2TkXCUQDz5lZuczs2Dk32jn3fjrrtzvnsu1NQYly4cRv35v0OH7HXoqXK5miTPFkZXyJPo4dPMJFJVP+g41seS1bf9lMwomEFMsLFStM9abX8Nv8NWcYVyniksW1b2ccJVK9EJYoX4p9O5KV2RFHiXL+MsXKlGD/7ngA9u+Op2jp4gDMeu8bIqpW5KXFYxn43ct8NOjdNB2nuq0asnjavDOKt2SqeON2xqV54S6ZKt64HXGULJc1L+4lyoWzL3n97dhLiXLhQcv4En0c9a5r2m3jKFEunO3rtlC13t+5qEQR8hXMT/XGtSkZcf7iz8o6rBJZjSEzRzH4u5eZ+MyYpI5zRgLXRak0ZeK27wFS12OpgPWYkehOLXj2m5fo+OIjFC52UabizO1KlgtP1Z73pnnzXKJ8eKr2vJeSXn0VK1M8VXsuluYYje5qkqLzdFf/zkx9fhI+d+FP2pUT7fmW3u0YtuBN6t3aiOkjJ59FjOenrbTt35lPn5+EC3IdQ8JCqX/b9fwyN+s+fZFzp46yXwIwBuiVekXqjLCZHfJ+R5vZXDObYma/mdlwM7vXzBab2WozuzzZbm40s//zyt3sbR9qZiPMbImZrTKzh5Ptd7aZfQisDhawmT1uZmu8n395y0YDVYBpZpbmXFJzzu0CNgKXmVm4mX3hxbLIzGoGOOZAM+vt/V3VzH4ws5VmtszMLjezSma2JoPzizCzn7yM9hoz+0dGcQYV4LvpUycPAn19ffLOZflqFbml7z1M7jc2RZmQ0BA6vfooP034lr1bdp1rWGkCy0yZ1KpfH8mWXzfTu96DDI7pwz2Du1CwSKGk9cXLlKDiFZfyy08rzijeQEPT0mQuM1PmPLGA1zVTFzbgtjh/1njm6C95dNIz9HyvH1vX/pHpDmemZGEdblqxnmeb9WLILX2JeeQ2wgrkO+uY0j4Pg9Xjmcc6d9JMnrm+J0Nj+rB/Vzx3PBP0w6cLS6ZeZzLxnA3iiuuuptFdTfh0+CQAajapw4G9+/lzzdmNlc9tcqI9T3vpY55u0I3FX84julOLTAQZYNk5tpUaTepwMIPreM+QB1i/eC0blvw34xglx+hmvtPeAFaZ2YtnsE0t4O9AHLAJeMc5V8/MHgN6Av/yylUCbgAuB2abWVWgI7DfOVfXzAoA881sple+HlDdOZf2jhHAzK4BOgPX4m/iP5vZXOdcVzNrATR2zu3JKHgzq4K/Y70BGAQsd861NrMmwPtAeoNzPwCGO+c+N7OC+N90lU22vkuQ82sDfOecG+ZlzAsHiOsh4CGAxuHXUL3o5amLABC/cy8lKpx+118iolTScIrTZeIoUaEU8TvjCAkNoWDRwhyJ939sXKJ8OA+8/W8mPv4me/78K8V27Z5/iN2/72SOd4NIRhp3aME/7vaPM9u8ciPhFVJ+jBr/V1yK8vt27E2RASkZEU78Ln+ZA7vjKe5llYuXKcHBPfsBaHhnY77xbjA59TFhxOUX8/vKDQBE3dyAZd8tJjETN4U06dCC6714f08Vb3gm4g1PFu/5tm/nXkomr7+IUmluxon3ypy6roWKFuZw/KEA256Oc8GU2SyYMhuAW/vcnSIDeDayuw53bNzG8aPHqfi3S5Nu9ktP/M64oHWRFNPOvYRXKJ2peszohqhTz1OAeR//QPdxfTN7arlOdIfmXH/3jQD8vnJDqvZciv0ZtudSxHv1dWD3/lTt+UBSuYuvvJSOw7vy6n3Pcdh7Xbo86koib4yiRuPa5CuQn4JFCtFlVE/G9Xoty843K+Vke17y5Ty6j+/LV6M+STfGrGgrNW+MotaNUVT3rmOhIoW4f1RPxnvX8ebH7qBoqWKMfvildGPLLpoeLjhllD3OuQP4O4ePnsFmS5xzO5xzx/FnZk91dFfj7xyfMsU553POrcffob4SaAZ0NLMVwM9AKaCaV35xsE6ypxHwuXPusHPuEPAZcCaZ2bu8434EPOyci/P2ORHAOTcLKGVmxQNtbGZFgYudc5975Y85546kKhbs/JYAnb0x4DWccwdT7985N8Y5F+WciwrWSQb4c+VGylQqT3jFMoTmC6VOqwas/j42RZk138dS7/YbAIiMqc/6Bf7xx4WKFebhd/sy/cWP+H3puhTb/PPfd1GwaGE+G/xe0GOnNnvit0k31C2fuZjr2kQDUKV2NY4ePJL00esp+3fHc+zQUarU9l/y69pEJ81SseKHWBrc4d++wR3RrPjevzxu+x7+3rAGAMVKF6d8lQrsTtbBr3dLIxZPz9ywi1kTv2VgTB8GevE2SBbvkUzE26BNNMvPYFaNM/HHyo2UrRRBKe+6RrVqwKpU13XV90upf7s/5jox9VnnXddV38cS1aoBYfnDKFWxDGUrRbB5hf+NRNFS/o+8S1YoRWSLesROm39OcWZHHZauWDbp5r1SF5cmokoF9mzN3Cccm1du8OqxLKH5wohq1ZCVaeoxlvpe+6gTU5//LvAPM1r5fSxRrRp69ViWspUi+N2rx2CKJRu3G9m8Htt/25KpOHOjORO/S2rPK2YuoX4bfx2l156PJ7u29dvckNSeV/4Qy3Vee74uWXsOr1CabqP7ML7Xa/z1++kx8p+/+CFPXNeVpxp1Z0zPUaxbsOaC7SRD9rfnUzf1AdS8MYqdG9OO/U8tK9rKFy9+SN/ruvJ0o+6803MU/12wJqmT3PCuJlx1fSTv9PxPln0yJ+ePMsopvQIsA95NtiwB7w2F+T97yZ9s3fFkf/uSPfaRsm5TtwSHPxPc0zn3XfIVZhYNHM4gznN97zfZOdcj1bKAHz6dw/EDnh+AmV0P/BOYaGYj0hv3nB5foo+p/cfT7f1+hISGsGjKHHau30pMrzv5c/Um1vywlIVTZtNhZA+enfMfjsQfYkLP/wDwj44tKH1ZOZo/ejvNH70dgDc7DCMsXxjNe7Zh54Zt9PnaP5XP/733HQsnz8p0XKtnL6NG4zo8N/d1/3RSfd5MWtd/xggGx/QBYNIzY7nfmypszZzlrPbGKH7z1ud0fePfNGrblLjtexjdzX+f6fRXp3L/Sz0Y+O3LmBmfDp/EoX3+9xmlKpYhPKIUvy369YzrcdXsZdRsXIfhXrzjk8U7cMYIBnrxTvTizV8wP6uTxVuneT3uGdiFouHFeGz8U2xZu5mRHf0zErw4700KFilEWL4wajerx8gOQ9i+YWu68fgSfXzcfzw933+akNAQFkyZzY71W7m5V1v+XL2RVT8sZf6UWdw3sgeD5rzKkfhDjOv5CgA71m9l6VcL6f/9SHwJPj7uPw7n8z+NH3rr31xUsiiJCQl8/Oy4pBupajWvy10D76dIeDG6j+/L1rWbea3jc7miDqvVvZKYR24jMSEB53NMfHZs0jXPiL8ex/GYV4/zvXps1esu/li9kVU/xDJvyizuH9mTIXNe43D8Id7pOSpFPQ78fhSJCT4+6v8Ozuf/aLvLq49xRf2rKVKyKMMXjmb6qCnMnzKL25/qwCVXVcI5x96tu5nU7+0zqsMz0WfAcJYsX0V8/AGatm5Pty4duL1V8yw5lr8912bY3Nc4cfQEE/q8kbQudXvunNSeVySNOf7mrc95+I3HadS2ideeRwJw86N3cFHJItw71H8/dWJCIsNuuXCz8MFkd3u+7cl7KVclAp/PEbdtDx8+PSaTMZ7/thLMvcMeIm7bbp783D97zPJvf+brV7PtPviALvzR8FnH9G7GP+7YOVfE+/tFoB0w3jk30MyeAYo65540s9b4M7nmdWh7O+dOjTme4z2OTb7OzCbgH5JwM1AZmAucGnoRA9zpnDtpZn8DtgF1k+83SLx1gAlAfbyhF0AH59xyM9sMRAUbemFm93nre6Ra/iqw2zk3xIt/lHOudvLyXhb4kHPuJTNbhH/oxRfe0IpQ7zy/cs5V94ZPBDq/0sA251yCN7a6knPuXwTxaKW7cvUT9Ag5O/dlZuT2F8ACF8AHW8dzfS1Cvlw+D+rrsS/kdAgZ6hb1ZE6HkKGxm9MfRpDTHqnUNqdDyJDvAph74e3Nn2Rrg55wcftsq5T7tk3K3S9WqSijnNbLQPJO5FjgSzNbDPxIxtneQNbh7yCXA7o6546Z2Tv4h2cs8zLVu4HWmdmZc26Z1wE/9c0D7zjnzvW22YHAu2a2CjgCdMqgfAfgbTMbDJwE7iRlnyzY+UUDfczsJHAI/xsGERERySG5/61DzlFGWXI1ZZTPXW7PhSqjfH4oo3zulFE+d8oonx/ZnVF+Nxszyp2VURYRERGRC4VmvQhOHeVczMxK4R/ukVpT51y6c1uZWWfgsVSL5zvnup+v+ERERET+l6mjnIt5neH05jJOb9t3STl7h4iIiEgauX9wWc7J/YMDRURERERygDLKIiIiInmYMsrBKaMsIiIiIhKAMsoiIiIieZjTrBdBKaMsIiIiIhKAMsoiIiIieZjGKAenjLKIiIiISADqKIuIiIiIBKChFyIiIiJ5mIZeBKeMsoiIiIhIAMooi4iIiORhLqcDyMWUURYRERERCUAZZREREZE8zKcvHAlKGWURERERkQCUURYRERHJwzTrRXDKKIuIiIiIBKCMsoiIiEgepoxycMooi4iIiIgEoIyy5GrRx0JzOoR0tY2bl9MhZGh/33/kdAjp6v1+7s9l5CP33xJ+MpfPhNot6smcDiFDb8a+kNMhSDbI/a842S93v3rkLGWURUREREQCUEZZREREJA/TPMrBKaMsIiIiIhKAMsoiIiIieZjGbQenjLKIiIiIqHiFEwAAIABJREFUSADqKIuIiIiIBKChFyIiIiJ5mKaHC04ZZRERERGRAJRRFhEREcnDfMopB6WMsoiIiIhIAMooi4iIiORhmh4uOGWURUREREQCUEZZREREJA/TCOXglFEWEREREQlAGWURERGRPExjlINTRllEREREJABllEVERETyMJ/ldAS5lzLKIiIiIiIBKKMsIiIikofpm/mCU0ZZRERERCQAZZRFRERE8jDlk4NTRllEREREJABllOV/RrnGNak5pCMWGsLmD2bz2+vTU6yv+nAMle6NxiX4OL73AEt7jeHo1j0Uqlia+uN6YaFGSL4wNo77jt/f/zHL4hw1cjAtWzThyNGjdOnSi+Ur1qQp8/X0SZSPKEdYWCjz5i2m56P98Pn8M11279aZbt06k5CQwDff/Ejfp4adt9hCq9Yif0wnsBASls3i5P9NS1vm6vrkb3wH4PDt/JPjU1/DipemwN2Pg4VgoaGcXPQdCbE/nHUcV91Qi7b9O2OhIcyf/CMz3/oyxfqw/GF0GtmDS6tX4XD8Qd7p8QpxW3cD0Lxbaxq0bYJL9DF50Lus/Wkl5apE0OX1Xknbl76kLF+NmsKs8TP457/upFG7phyMOwDAly9+xC9zlmcY49U3RNK2f2dCQkOYN/lHvnvrizQxdh7ZMynGsT1GsdeLsUW31jRs2xRfoo/Jg8bz608rCSuQj96TBxNWIIzQ0FCWfbOI6aOmANDhhUe4rGYVDOOv33fwXu83OH7k2BnX6z0D7qdG49qcOHqCcb1f589ffk9T5rLqVejyUnfyFczP6tnL+XDQeACiYq7j1n+1JaLqxQy99Sk2r96YYrvwCqUZ+v0ovnzlE74bm/Z5E0y7AZ2p0bgOJ44e593ebwSM6dLqVej8UnfyF8zP6tnL+HjQuwAULl6Eh1/vRamKZdi7dTdvdx/JkQOHufbWRrTo2hqAY0eO8cEzY9m69o+k/VlICM9MH078zjhe6zI807GeiWeeG8lP8xcTXrIEX0wanSXHuFCc7/YMUKhYYdoP70qFKy7BOcfEJ97i92XrqXjVZdwz7EHCCuTHl5DIR8++wx8rN6aJKZCseC7+rf5VdB/zJHu37gJg2bc/89WrUylXpQIPp3pN+nLUZH4cP+PMK1iylDrK8r8hxKj1fGfmtX2eozv20vjboeyYuYyDv21LKhK/ZjOzmz9D4tETVO50IzWevZvFD7/Gsb/2MbfVAHwnEggtXIAb577Iju+Wcuyv+PMeZssWTahWtTJXXtWIa+vV4Y3Xn6dBo1ZpyrW7pysHDx4CYMrkMdxxx81MmTKN6BsacEur5tSucyMnTpygTJlS5y84M/LffD/H3huGO7CXgg8/R8J/l+J2n65DCy9Pvutv5eg7A+DYYbioGADu0D6Oje0PiQmQvwCFur9E4rqluIP7zjyMEKPd4C682n4o+3bupe+051n1fSw7N5yOo0HbJhzZf5gB0Y8S1aoBt/W9l3E9XqF81YuJatWAIc0ep3jZkjz2wbMMaPwYf23awXMxTyTt//mf32bFd4uT9vfjuK/5Yez0NLEEjzGEuwd34ZX2Q9i3M46nvBh3bNiaVKZh2yYc3n+IZ6N7EtWqAW36tmdsj1FEVK1IVKuGDGrWi+Jlw+n1wbM82/gxEo6fZNQ9gzh+5BghYaE8MXUIa+Ys5/fl6/lkyASOHToKwJ3PdCK6U4s0HfOM1IiuTbnKETwV3ZMqtavRcdhDDG39VJpyHYY+yHv93mbjst/oNeFpakTXZvWc5Wxb9ydvdB1Bx+ceDrj/ds/ex+o5K84opurRtSlbOYKnvZjuHfYgz7ful6Zc+6EPMrHf22xa9huPTuhH9ehI1sxZQctHWrN2wWq+fesLWjzSmpbdWvPp8A/Ys2UXI+4awJEDh6keHUmH5x9Osd8bO8ewY8M2ChUpdEbxnonWMTdxz+230G/IS1l2jAtBVrRn53O0HdCZX+euYGy3kYTmCyV/oQIA3Na3PV//Zyq/zFnB1dG1afNUe0a1G5RhnFn1XATYsGRtmjdkf23azuCYPl4dhTDi57dZnuw1KbvpC0eCy1NDL8yslJmt8H52mtm2ZI/zBygfbmZdM7HfMDNLt1dlZlea2Tdmtt7M1prZx2ZW1sxuNLN0/+OZ2bVmNirjM8yYmZUzswQz63I+9ncOcbQxsyvP1/7Ca1fl8O9/ceTPXbiTiWz9YiERza9JUWbP/F9JPHoCgLil6ykUEQ6AO5mI70QCAKEF8mGWdRNKtmrVnIkfTAXg58XLKF6iOOXLl01T7lQnOSwsjPz58+O8AWQPP9yRF0e8wYkT/vPYvXvveYstpGJVfHE7cft2QWIiiasXEHZlVIoyYVFNSPh5pr+TDHDYn4UlMdHfSQYIzQfnUIeVIquy+4+d7Nmyi8STicROX0CtZnVTlKnVLIpFn84BYNmMRVzZoLq3vC6x0xeQcCKBvVt3s/uPnVSKrJpi2ysb1mDPHzuJ27bnrGOsHFmVXUkxJhA7fT61mqWsq1rN6rLo07kBYowidvp8L8Zd7PpjJ5W9GE9liUPDQgkNC8V5F/5UJxkgX8H8JD0hzkDtZnVZ8NkcADYtX0/hooUpXqZEijLFy5SgUNHCbFz2GwALPptDba/ud2zcxs5N24Pue/eff7F9/ZYziimyWV0WfTY3WUwXBYypYNFCbPJiWvTZXCKb1fNvf1NdFk71n9PCqXOIvMm/fOOy3zhywP8c3bRsPSXLn35DWbJ8ODWa1GHex1n3qRFAVGQNihcrmqXHuBBkRXsuWKQQVev9nfmTZwGQeDKRoweOeHtzFPTeABUqVpj9f2XuzXpWPRcz4+8Nq7P7HF+TJOvkqY6yc26vcy7SORcJjAZGnXrsnDsRYJNwIMOOckbMrBDwFfCac66ac+7vwFggU+lA59zPzrleGZfMlLuAhcDd52l/Z6sNcN46ygUjSnJ0++lO49EdcUkd4UAq3dOYnbNWJj0uVCGcprOG02Lpa/z2xvQsySYDXFyhPFu3nO5sbNu6g4srlA9YdsZXH7Bj20oOHjzEp59+BUC1alVo1KgeC+ZNZ9YPU4m6ptZ5i82KhuP2n65DdyAOK5ayDkNKRWClIyj4wCAKPjiE0Kqnj2/FSlGo2wsU/vcbnJw37ayyyQAlyoWzL9m13LdjLyXKhQct40v0cfTgES4qWTTAtnFpto1q1ZAl0+anWBbdqTlPfzOCDi8+QuFiF51FjHGUKFcqTZm47XsCxFgqaIwWEsIzM0bw0tJxrJ23is0rNiSV6zSiGyOWjKX85RWYNeGbDGNMrWS5UsQlO27czrgUHUiAkuVLsW9HsjI74ihZLv2XqfyFCtCya2um/eeTs4gpPEVM+3bupUT5VNe6fHiKmPbt2EtJr76KlSnO/t3+trp/dzxFSxdLc4xGdzVhTbKhNHf178zU5yfhc8qhZYesaM+lLy3Lob0H6PhSN/p9/QLthz+clFH+ZNB7tHmqA8MWvMnt/TrwxYsfZirOrHwuVqnzN/p/M4JHJ/SjQrWKaY5dt1VDFqd6TcpuPly2/Vxo8lRHOT1m9oSZrfF+enqLhwNXeBnn4WZWzMxmmdkyM1tlZjdncvcdgJ+cc0mDj5xzPzrn1qaKob6ZLTSz5WY238yqecuTss5mNtTMJpjZTDPbbGatzexlL+6vzSyj4TR3A/8CqphZeW+fYWYWb2YjvHP7zstizzWzTWYW45UrZGbvmdlqr9z13vIHzOyVZOfxrZk1Srbf4Wa20ju3smb2DyAGGOXVbaVM1mNQAbPAQbJul9zekJK1KrP+za+Slh3dHsePTfoy87peXNr2egoE+Id7PgSK0wWJM+bme6l4aR0KFMhPk8YNAQgLC6VEieI0aNSKJ/sO5aMPz+PYx0BJ4NSxhYQSEl6eY+MHc/yTV8l/60NQsLC/6IG9HH3zSY7+51+ERV4PFxU/uzAyU0dBrnfg58HpP0PzhVLzxmtYNmNR0rKfJs3k2et78lzME+zftY/bn+mYiSADLEsVY7DnZODF/m2dz8fQmD70ve5hKtWqSoW/XZJU5r0+b/LEtQ+zY8M26rZqkHGMmYg5bb1mokwqrXvdxffjvjqrMdOBKiPtpc58m0ntiuuuptFdTfh0+CQAajapw4G9+/lzzaYzj1XOSla055DQUC6pXpmfJs3kuX8+yfGjx2n+iH9M+vXtmzF1yHs83aAbnwx5jw4vZDLXlUXPxT/X/E7fht0Y3LIPsyZ8Q7cxT6RYH5ovjFo3RhE7Y2Hm4pRsp44yYGb1gHuBesB1QDczqwn0BdZ5Gee+wFHgVudcHeBGILPDIaoDSzNRbi3QyDlXGxgCDA1SrjL+jubtwIfAt8656viHGbUItnOvQ1rSObcUmAq0Tba6ODDTO7cTwECgKXAnMNgr8yhwwjlXA3/nf2KgISupFAfmOudq4c9k3++c+z9gBtDLq9vNqeJ8yMxizSx25pENafcYwNHtcRSqcDrzVSginKM702Y0y/yjOlc81pqFnV5OGm6R3LG/4jm4biul6p+3ZDePdO1E7JKZxC6ZyfYdO6l4SYWkdRdXjGD7jr+Cbnv8+HGmf/U9rVo1B/wZ6C++8GcTl8SuwOfzUbp08Mz5mXAH4rDip+vQioWnyQq7A3tJ/O9S8CXi4nfj9u4gJDxlRtwd3Idv11ZCLzu7Oty3cy8lk13LkhGl2L8rZRzxycqEhIZQqGhhDscfCrBtOPG74pIeXx1dmz/X/M7BPfuTlh3csx/nczjnmPfxj1SqdXmGMcbvjEv3OKfOI7xC6UzFmPr8jh44wm+LfuHqGyJTLHc+H7FfLaB2i/oZxgjQpEMLBs4YwcAZI4j/ax/hyY4bXj6c+L9SxbxjLyUjkpUJcF6pVYmsxp1PdeDFeW9y0/3/5J/db6NJx6AvQ0R3aE7/GSPoP2ME8X/FpYipZPlS7M8gppIRpYj36uvA7v1JH48XL1OCg3sOJJW7+MpL6Ti8K288+CKH4/1DmS6PupLIG6N4ft4bPPRaL65oUJ0uo3oiWScr2nP8zr3E79yb9InL8hmLuKR6ZQDq334Dy7/9GYBlXy/ksloph14llx3PxWOHjia9iVwzZzmh+UIpUvL0kJzq0ZFpXpNygsvGnwuNOsp+/wA+dc4dcc4dBL4AGgUoZ8ALZrYKmAlcYmalz2McJYDPzGwN8BJwdZByM5xzCcBqAOfc997y1UCldPZ/NzDZ+/tjUg6/OJpqP3OSHePUPhsBE71j/gJsB4K/Cp3e76nPiZdmEB/evsc456Kcc1HNCme0e799KzZSpEp5Cl9aBssXSsXW17FjZsr3JsWrX0btEV1Y2Olljif7h1ooIpyQgvkAyFf8IsLr/o1DG3Zk6riZ8dbo94iq24yous2YNu07Otx7BwDX1qvDgf0H2LlzV4ryF11UOGnccmhoKC1bNGHdOv8/hC+nfUdjL7tcrVoV8ufPz5496XdkMsu3bSMh4eWxEmUgNJTQGg1I+G/KOkxcG0tI5av8DwoXxUpF4Nu3yz9EI8xfhxS8iNBLr8C3J/B41oz8sXIjZStFUKpiGULzhRLVqgGrvo9NUWbV90upf3s0AHVi6rNuwS/e8liiWjUgLH8YpSqWoWyliBTDF+re0pDY6Sk/4iyWbBxiZPN6bP8t43G2m1du8GIsS2i+MKJaNWRlmhhjqX/7DUkx/neBf3aTld/HEtWqoRdjWcpWiuD3FRsoEl6MQsX82fl8BfJzZcOa7Nzov+GpzGWn34zUbHpN0vKMzJr4LQNj+jAwpg/LZy6mQRt/nVWpXY0jB48kfVR8yv7d8Rw7dJQqtasB0KBNNMtnLkn3GMPbPssTjbrxRKNufD/+a75+43Nmvf9t0PJzJn7H4Jg+DI7pw4qZS6jf5oakmI4Giel4spjqt7mBFV5MK3+I5bo7/Od03R3RrPjevzy8Qmm6je7D+F6v8dfvp9vy5y9+yBPXdeWpRt0Z03MU6xasYVyv19I9Pzk3WdGeD+zez77teylXJQKAKxrWYOd6/4208bviqFbf/xp1RYPq7N68M2hs2fFcTP76UqlWVcxCOLTvYNKyerc0YvH0eZmsTckJmvXCL7N3HnXEnyGt45xLMLOtQMFMbPcLcG0myg0DvnPOvWlmVYFg/22Oe799+LO/JHuc3jW9GyhlZp28xxXMrDKwJcB+kh/j1D6D1VMCKd90Ja+T5PtNzCC+s+YSfazoN4GGH/XFQkP446M5HFy3jb8/cQfxKzaxY+YyavS/l7CLCnLt2EcBOLptLws7vUzRahVoMLA9zvuob/1bX3Pgv2d2U1JmzfjmR1q0aMK6tfM5cvQoDzzweNK62CUziarbjIsuKsznn71LgQL5CQ0NZfbs+bw9ZiIA7074mHfGvsyK5T9y4sRJ7u/yr/MXnM/Hia/fpWDHfhASQsKy2bjdW8nX5E582zaRuG4piRtWElq1JoV6vATOx4nvJsHRQ1iFGhRs3h6H/0lycv5XuF1nV4e+RB8f9x9Pz/efJiQ0hAVTZrNj/VZu7tWWP1dvZNUPS5k/ZRb3jezBoDmvciT+EON6+kf+7Fi/laVfLaT/9yPxJfj4uP84nM+fw8hXMD9XNqrJB/3GpDhem6faU/GqSjjniNu6O8364DGO4zEvxvlejK163cUfqzey6odY5k2Zxf0jezJkzmscjj/EOz1HpYhx4PejSEzw8VH/d3A+H8XLluC+l3sQEhKChRhLv17I6lnLMDPue7k7hYoUBoOta//gw2fGnnG9rpq9jJqN6zB87uucOHqc8X3eTFo3cMYIBnp34E98Ziz3n5r+as5yVnvje+s0r8c9A7tQNLwYj41/ii1rNzOyY7APvTJn9exl1Ghcm2FzX+PE0RNM6PNG0rr+M0YkzQow6ZmxdPamrFszZ0XSmONv3vqch994nEZtmxC3fQ+ju40E4OZH7+CikkW4d+iDACQmJDLslr7nFOuZ6jNgOEuWryI+/gBNW7enW5cO3O59MpSXZFV7njxwPJ1feZTQfGHs2bKLib39z+cP+r5N2wGdCQkL4eTxk3zw1NuZijOrnovXtKxPdPtmJCYmcvLYCcb2PP1BdP6C+bmqUU0mZeI1J6tpxH5wltmxXv9rzGwgcMg595I39OJtoAEQCizGf9PbLmCBc+5yb5t/AxWdc73M7Ca8rDKwE9jjnCuR9khgZoXxZ2a7O+e+9ZbFAJuBCkAP51xrM5sOvOOc+9LMhgLtnHNVzezGZGWGesd6xRuPnHTc5OsCxHAV8Ilz7upky4YBh4ARwfaT/Bhm9gRwuXPuYTP7O/AN8Dev3oYA1wOXeefaEliUar/tgBudcw+Y2Vte3U5M7zp9Vv6eXP0EbRs3N6dDyND+vv/I6RDS1fv93P8SfSHcgHIyl8cYmul8RM55M/aFnA4hQ/lKV8npENL1SKW2GRfKYQm5vK0AjN38SbY2mN6V7s62Snlp80e5/8UgGQ29AJxzi4GPgCX4O3dvOedWO+f+AmK9m9eG4x920MDMYvGP3V2fyf0fAVoBvbzp4X4F2gO7UxV9ARhhZllx++s9wOepln3qLc+s14BCZrYa+ADo6M0WMhfYhr+DPBzIzGSqHwH9ztfNfCIiInJ2NOtFcHk2oywXBmWUz50yyufuQnhxV0b53CmjfO6UUT4/sjuj/HildtlWKSM3f5z7XwyS0RhlERERkTws9791yDnqKJ9HZhYJTEi1+Ihz7iwmPT2nOKYBl6Za3Ns590N2xiEiIiJyIVNH+Txyzq0AIjMsmPVx3JLTMYiIiMiFIfcPgMs5uplPRERERCQAZZRFRERE8jCnUcpBKaMsIiIiIhKAOsoiIiIiIgFo6IWIiIhIHqab+YJTRllEREREJABllEVERETysAvh20dzijLKIiIiIpIrmFkLM1tnZhvMrG+A9QXMbLK3/mczq5Rs3VPe8nVm1vx8xKOOsoiIiEge5rLxJz1mFgq8AbQErgLuNrOrUhXrAuxzzlUFRgEveNteBbQDrgZaAG96+zsn6iiLiIiISG5QD9jgnNvknDsBfAzcmqrMrcB73t9TgaZmZt7yj51zx51zvwMbvP2dE41RFhEREcnDctEY5YuBLckebwWuDVbGOZdgZvuBUt7yRam2vfhcA1JGWURERESyhZk9ZGaxyX4eSr46wCape/HBymRm2zOmjLKIiIhIHpad8yg758YAY4Ks3gpckuxxRWB7kDJbzSwMKA7EZXLbM6aMsoiIiIjkBkuAamZW2czy4785b1qqMtOATt7fdwCznHPOW97OmxWjMlANWHyuASmjLCIiIpKHuVwyRtkbc9wD+A4IBcY7534xs8FArHNuGjAOmGhmG/Bnktt52/5iZlOAX4EEoLtzLvFcY1JHWURERERyBefcDGBGqmX9k/19DLgzyLbDgGHnMx51lEVERETysOwco3yh0RhlEREREZEAzD/+WSTX0hNURETymkBTnWWZzpVuz7b/te9u/jRbz+1cKaMsIiIiIhKAOsoiIiIiIgHoZj4RERGRPEw38wWnjLKIiIiISADKKIuIiIjkYT5N7BCUMsoiIiIiIgEooywiIiKShymfHJwyyiIiIiIiASijLCIiIpKH+ZRTDkoZZRERERGRAJRRFhEREcnDnDLKQSmjLCIiIiISgDLKIiIiInmYvpkvOGWURUREREQCUEZZREREJA/TrBfBKaMsIiIiIhKAMsoiIiIieZhmvQhOGWURERERkQDUURYRERERCUBDL0RERETyME0PF5wyyiIiIiIiASijLCIiIpKHOaeb+YJRRlkyzfzmmVnLZMvamtm3ORmXiIiISFZQRlkyzTnnzKwr8ImZzQZCgWFAi3PZr5mFOecSzkeMIiIicmb0hSPBKaMsZ8Q5twaYDjwJDADed85tNLNOZrbYzFaY2ZtmFgJgZmPMLNbMfjGz/qf2Y2ZbzexZM5sP3JYjJyMiIiKSDmWU5WwMApYBJ4AoM6uOv7PbwDmXYGZjgHbAh0Bf51ycmYUBs81sqnPuV28/h51zDXPiBERERMRPs14Ep4yynDHn3GFgMjDROXccuBGoC8Sa2QrgBuByr/jdZrYMf8f678BVyXY1OdD+zewhLwsdO2bMmKw6DREREZF0KaMsZ8vH6TehBox3zj2bvICZVQMeA+o55+LNbBJQMFmRw4F27JwbA5zqIWvglIiISBbSV1gHp4yynA8/AG3NrDSAmZUys0uBYsBB4ICZRQDNczBGERERkTOijLKcM+fcajMbBPzg3cR3EugKxAK/AmuATcD8nItSREREAtGsF8GZJpmWXE5PUBERyWssOw8Wc2lMtv2vnfHnjGw9t3OljLKIiIhIHqakaXAaoywiIiIiEoAyyiIiIiJ5mOZRDk4ZZRERERGRAJRRFhEREcnDNI9ycMooi4iIiIgEoI6yiIiIiEgAGnohIiIikofpC0eCU0ZZRERERCQAZZRFRERE8jB94UhwyiiLiIiIiASgjLKIiIhIHqYxysEpoywiIiIiEoAyyiIiIiJ5mL5wJDhllEVEREREAlBGWURERCQP82nWi6CUURYRERERCUAZZREREZE8TPnk4JRRFhEREREJQBllERERkTxM8ygHp4yyiIiIiEgAyiiLiIiI5GHKKAenjrLkag9UuiOnQ0hXKJbTIWQot8d4lMScDiFDhQjN6RAydAJfToeQrnz6APO8eGvzlJwOIV0n92zK6RAydEvt7jkdQoa+2fJNTocgHr1yiYiIiIgEoIyyiIiISB7m9IUjQSmjLCIiIiISgDLKIiIiInmYbuYLThllEREREZEAlFEWERERycOcMspBKaMsIiIiIhKAMsoiIiIieZhmvQhOGWURERERkQCUURYRERHJwzTrRXDKKIuIiIiIBKCMsoiIiEgepjHKwSmjLCIiIiISgDLKIiIiInmYxigHp4yyiIiIiEgAyiiLiIiI5GH6Zr7glFEWEREREQlAHWURERERkQA09EJEREQkD/NperiglFEWEREREQlAGWURERGRPEw38wWnjrJckO4ecD81GtfmxNETjO/9On/+8nuaMpdVr0Lnl7qTv2B+Vs9ezkeDxgNwUfEiPPx6L0pVLMverbsY3X0kRw4cBuCK+ldzV//7CA0L49C+A4y4awAA973YjZpNruHg3v0MaP54mmNdfUMkbft3JiQ0hHmTf+S7t75IsT4sfxidR/bk0upVOBx/kLE9RrF3624AWnRrTcO2TfEl+pg8aDy//rQy3X1Gd2xB0/v/SdlK5Xm89v0c3ncQgGYP3UK91v8AICQ0hIiqFfl3nS4c33843bq86oZa3Nm/MxYawoLJPzLzrS/TxN5pZA8u8WIf1+MV4rbu5qISRXjwrce5tGZVFk2dw5QB45O2uaV3O65tcz2Fihfh8as7pnv8zLpnwP3UbFyHE0dPMK73a/wR5Jo/8FIP8hXMz6rZy/jQu+Ztn+pI5I1RJJxIYNefOxnX53WOHjjCRSWK0P2tPlSueTnzp85h0oB3ziq23FyH2dlWwgrk48nJgwkrkI+Q0FCWfrOQaaOmpFtvbb16m59OvZ1qN+949QbQvFtrGrRtgkv0MXnQu6z12k3jzi1p1K4pmDH/4x+ZNX4GAK0ev4uaN0XhnOPgnv283/tN9u/al2H9ZUWMhYoVpv3wrlS44hKcc0x84i1+X7aeilddxj3DHiSsQH58CYl89Ow7/LFyY4Yx/q965rmR/DR/MeElS/DFpNE5HQ4AXQd1pW6Tuhw/epyXH3+ZjWvSXp9OT3Si6e1NKVK8CG2ubJMDUcr5pKEXksTMnJlNTPY4zMx2m9lXZ7m/EmbWLdnj6LPdV3I1omtTtnIE/aJ78n6/0bQf9lDAcu2HPsj7/d6mX3RPylaOoHp0bQBaPtKatQtW83TjnqxdsJqW3W4D/P+87h3yAK8/8AIDmvVidLeXk/Y1f+psXuk0NPAHfz6FAAAgAElEQVR5hoRw9+AuvHbfMAbe1Iu6tzQkomrFFGUatm3C4f2HeDa6Jz+M+4o2fdsDEFG1IlGtGjKoWS9e7TSMe4Y8gIWEpLvPjUv/yyvtB7Nn664Ux5g5ZhpDY/owNKYPX7z4Ib/9/CtH9h9Kty4txLhrcBdev+85htzUi6hbGlK+6sUpyjRo24Qj+w8zMPpRZo37mtv63gvAyeMnmf7yZD5/bmKa/a76cSkv3Nov3WOfiZrRdShXOYK+0T2Y0O8tOgS55h2HPsSEfqPpG92DcpUjqOFd81/mreSZZv+if8vH+ev37dzcrU3SOXz+8kdMfu79s44tN9dhdreVhOMneemeQQxq2ZvBMb2pfkNtqtSuFvCYFmK08+ptsPccD1ZvA1LVW/mqFxPVqgFDmj3Oa52GcfeQLliIUeFvl9CoXVOG39qPYS37UKNJHcpUKg/A92OmMaxlH56LeYI1s5YR89gdGdZfVsQI0HZAZ36du4JBTXsxrGUfdm7YBsBtfdvz9X+m8lzME0wfOYU2T7XPMMb/Za1jbmL0yMCvuzmhbuO6VKhcgS7/6MKrT75Kj+d6BCz38/c/81irx7I5unPjcy7bfi406ihLcoeB6mZWyHt8E7DtHPZXAuiWYakzFNmsLgs/mwPApuXrKVy0MMXLlEhRpniZEhQsWphNy34DYOFnc6jdrK5/+5vqsmCqf/sFU+dQ+yb/8mtv+QfLvv2ZuO17ADi490DS/tYvXsvhIJ3OypFV2fXHTvZs2UXiyQRip8+nVrOoFGVqNavLok/nArBsxiKubFDdWx5F7PT5JJxIYO/WXez6YyeVI6umu88tv2xOykYHU/eWRiyZNi/dMgCVIquy+4+d7N2yi8STiSydvoBaXj2dUrNZFIs+9dfX8hmLuMKL/cTR42yMXcfJ4yfS7Hfz8vUc2B2f4fEzq3azuiz4zF9//mt+UcBrXqhoYTZ613zBZ3Op06weAL/830p8iT4ANi7/jZLlS/0/e/cdL0V5tnH8d9FEpaMCahRRsaKg2NBErFFib1Fj11jBEjWx5BVLLLHGFkvsaOyJXWNixBJURKkqFhQrCNJEQBC43j9m9rDnsKfQzsw55/762Q+7s7O7lzNndu999pnnKft/+HjIaH6a/dNiZ8vzNsziWJk980cAGjdpTOMmjansc7Gw3b5Lt9uQEttt06LtVv642YIhTw9Kj5uJTPx8PJ27r0PHdVbjs6Ef89OPc5g/bz4fvfUB3X+Z/A38+MOssudttsJyVBpsGWds3mJ51tlyA/738H8BmPfTPGZ9PzN9NtO8RfL2u3yrFZj2bfUt3vVZz+7daN2qZdYxymy969a89PhLAIweOpoWrVrQdpW2C603euhoptTg14pQN0ShHCp6HvhVev0Q4MHCHZLaSXpC0ghJb0raJF1+oaS7JA2U9KmkU9OHXAGsLWmYpKvSZS0kPSZptKQHJGlRA7bp0J7J30wquz1l/GTapIVP2Tod2zNlXNE64ybTpkOyTquV2zAtLUCmTZxKy5VaA9ChSydWaN2Csx+6iP97+s9ss9/2NczTjinflH6t4nUKRcX8efOZNX0mK7ZtSZsO7Us8tl2NnrMyTZs3Y6Ptu/Pu828tRvZJtO7QrtJ1irPXpuLtBzBl/KSyYregbcf2TC7a55PHTaJNhf8XgJ8fuBMjBw5dqtnyug2zOFbUqBEXPHcV175zJ++/PoLPhn1cSbaFt1vF/VXZdit9fLTjmw+/ZJ0tN2DFNi1o2rwZG+/Qg7adFvz/7nXWwVw66K9sufd2PH3twzXYfks/40prrMIPk77niKtP5rxn/8xhV5xAs+WXA+DRi+5lv3MP59JBf2X/8w7niSv/Xm3GUHvad2zPd0XvQ9+N+46VOq6UYaKlx7X4X10ThXKo6CHgYEnNgU2A4mrrImCo7U2A84Di36vXB34JbAn0l9QUOAcYY7u77bPT9XoApwMbAl2AbRc1YMnSukLrUE3Wqahx48as2a0L1x99Gdcd8Sf26HcAHdbqVINANclTYiW7ZE7bNXrOymy6c0/GDBldbbeLNFj1r1OTdZaxUtvPNdrn5W/uccr+zJs3jzeeeHVphivxuvnYhlkcK54/n4v7nM3Z25zAWpuuw6pdf1ZJtur3aWXbrfTxBOPHfM2Ltz7Jqff/kX73nsdXH3xe9ksCwFNXP8T5vU5m8JOv0/vI3ar8f1xWGRs1bszPNl6LV+9/kct+9Qdmz5rNL0/aB4BfHLYrj11yL+f3OplHL7mXw/98YrUZQ+2p0d9DqHfiZL5Qju0RkjqTtCY/V+Hu7YD90/X+K6m9pNbpfc/ang3MljQB6FDJSwy2/RWApGFAZ6BcHwFJxwPHA2zbrgfrt+zCDofvxs8P2QmAscPH0G7VBa1EbTu2Y+q3k8u9yJRxk8q1JLXt1I6pE5J1vp84ldZpS1nrldsw/btpyWPGT+KHKdOZM2s2c2bN5qPB77P6Bp359rNxVW6zqeMn03bV0q9Vlmf8JNqtuhJTx0+mUeNGLN9yBWZM/SFpGa3w2MIJRtU9Z2V67rktg5/6X43WnbrQ67df6ASnwjoVsy9rOx6+G9sfsjMAnw3/hHarLmi5adux/UL7fPK4SbQr2uftOrUvt8223b83m+60OVcdeuFSzZm3bZiXY2XW9zP58M332Hj7Hnzz0ZcL5Vz4b7/m263UcVPIPOiRlxn0yMsA7H32IeVaywvefvJ1TrnrHJ657tGqNuUyyTh1/CSmjp/E2GGfAElXnF3TQnnr/bfnkYvuBuDdZ9/gsCtOqDJfWPb2OHIPdjsk+VL10fCPWKnofWilTisx6duF/77qorrYd7i2RItyKOUp4GqKul2kqmqzm120bB6Vfwmrdj3bt9vuabvn+i27APDygBe4uM/ZXNznbIa+OJht9usNQJce6zJr+syyn4cLpk2cyo8/zCo7kWib/Xoz7MW3ARj2nyH0OiB5fK8DejPs3+nyF99m3S02oFHjRjRr3owu3ddl3CdfVfK/scDY4Z+wSudOtF99FRo3bULPPbdl+L+HlFtnxL+HsPX+yc/Tm/XZmtGDRgEw/N9D6LnntjRp1oT2q6/CKp078dmwT2r0nKU0b7kCXbfakOHp/1N1Ph8+Jn2dlWnctDGb79mLEQtlf4et9+8NQI8+W/PhoPdq9NxL6r8DXqB/n7Po3+cs3n1xML3Sn/drus977bc9Q9N9vvH23dn9xH244bgrmPPjwv2Bl0TetmGWx0qLdq1YvtUKADRdrhkbbLsJ48eUPs2h4nbrWc1226xou4349xB67tkrPW5WZpXOncoKz5btWwHJF83uu23JkPRLY+GkPoBNdu7J+DHfVLstl0XG7ydOY8o3k+jQJWmBX2/bboz/OHmfmTphMutuvWGyvNfGTBw7vtqMYdl65t5n6LtbX/ru1pc3/vUGO+2ffAldv8f6zJg+I/oiNwCKnw1CgaQfbLeQtDqwv+3rJfUGzrK9h6QbgIm2L0mXX2e7h6QLgR9sX50+zyhgD2A68K7tNdPlZc+V3r4JGGL7nsoyHdf5gJJ/oIdefBwbb9+dObNmc/fZf+XzkckQPRc8dxUX90l6eazZbW2OufoUmjZvxqiBQ/l7/zsBWLFNC068+UzarboSk7/5jltPvqbsRL1fHr8X2x64A55vXnv4Jf5z17MA/PaG01lv641o0bYl3383jaeue5jXH/kvjdPvDhv37sFBFxxFo8aN+N8jL/P8zf9gzzN+zecjxzDiP0NoslxTjrm2Hz/baC1mTP2BO/pdx3dfJqNW7H7Kfmx70A7MmzufRy65m/cGDqv0OQF2OGp3fnnC3rRauQ3TJ01j1MtDGXBOMnTSNgf0ZqPtu3NHv7+UbavGJb/fLLBR7x4ccMGRNGrciDceeZkXbv4ne5xxEJ+PHMPI/7xDk+WactS1fVl9o7WYOfUH7uz3Fyal2S95/Saat1iBxk2bMOv7Gdx4+J8Y/8nX7HvOb+i593a07tCWad9OYdDD/+XZv5RuvZvFvCrzFRx28XF0274Hc2bN5s6zb2Zsus8veu5q+vc5C4DO3dbm2Kv7JsOcDRxaNtzbFQNvommzpvwwNRlKb8zQj7jv/NsBuOr1W2jeYnmaNG3CzO9ncs3hF/NNhS9Iy9M419sQYA7zSy6vzWNl9fXX5Jhr+tKoUSPUSLz97CCeueExAJqWaJfZqHcPDky326Ci7fbFyDGMKNpuPyvaboXjZrdT9qXXQTswf+58Hr3knrLj5sxHLmLFti2ZN3cuj11yHx+mX0qPv+VMOnTpxPz5ZvLX3/H382+v0clyyyLj6huuyWFXnEjjpk347ssJDDjrr8z8fgZr91yPg/ofTaMmjfhp9k889Mc7+GJU+eH8bhlb+XB7efDTd58utec6u/8VvD10BFOnfk/7dm04+djD2X/PXy7x8+7V45TFfuzJfzqZnr178uOsH7nuzOv4eETSB/+mF26i727JKBjHnHcMO+yzA+06tGPyt5N54cEXeOC6BxbpdZ7/8vlFPn9nSay78ua1Vgx+PPGdWv1/W1JRKIcyhUK5wrLeLCiU2wF3A2sBM4Hj064aF1KiULY9VtLfSfo6Pw88y1IqlPOiuiI0D/KesaaFcpaqK5TzoLJCOS9KFcph0TWkQnlZWZJCubZEoZwf0Uc5lKlYJKfLBgID0+uTgb1LrHNhhdsbF10/tMLqA4vuKz0IZQghhBBqTfRRrlx8xQ8hhBBCCKGEaFEOIYQQQmjA6uL4xrUlWpRDCCGEEEIoIQrlEEIIIYQQSohCOYQQQgihAbPn19plSUhqJ+nfkj5O/21bYp3ukt6Q9J6kEZJ+XXTfPZI+kzQsvXSv7jWjUA4hhBBCCHXBOcBLttcFXkpvVzQTOML2RsBuwF8ktSm6/2zb3dPLsOpeME7mCyGEEEJowObXnZP59gZ6p9fvJRly9g/FK9j+qOj6N5ImACsD5aclraFoUQ4hhBBCCHVBB9vjANJ/V6lqZUlbAs2AMUWLL027ZFwnabnqXjBalEMIIYQQGrDanKVZ0vHA8UWLbrd9e9H9/wE6lnjo+Yv4Op2AAcCRXtA5+lxgPEnxfDtJa/TFVT1PFMohhBBCCKFWpEXx7VXcv3Nl90n6VlIn2+PSQnhCJeu1Ap4F/mj7zaLnHpdenS3pbuCs6vJG14sQQgghhAZsPq61yxJ6CjgyvX4k8GTFFSQ1A/4J3Gf70Qr3dUr/FbAPMKq6F4xCOYQQQggh1AVXALtI+hjYJb2NpJ6S7kjXOQj4BXBUiWHgHpA0EhgJrAT8qboXjK4XIYQQQggNWG32UV4SticBO5VYPgQ4Lr1+P3B/JY/fcVFfM1qUQwghhBBCKCFalEMIIYQQGrD5daRFOQvRohxCCCGEEEIJ0aIcQgghhNCAue7MzFfrokU5hBBCCCGEEqJFOYQQQgihAasro15kIVqUQwghhBBCKCEK5RBCCCGEEEqIrhchhBBCCA3YUphaut6KQjnkWmOUdYQqzfC8rCNUq7ny/cNRyzrwNjSduVlHqFbet+Ns5mcdoVr5T5h/e/U4JesI1Xpq6M1ZRwh1SL7fWUMIIYQQwjIVJ/NVLt9NTSGEEEIIIWQkWpRDCCGEEBqwmMK6ctGiHEIIIYQQQgnRohxCCCGE0IBFH+XKRYtyCCGEEEIIJUSLcgghhBBCAxbjKFcuWpRDCCGEEEIoIVqUQwghhBAasOijXLloUQ4hhBBCCKGEaFEOIYQQQmjAYhzlykWLcgghhBBCCCVEi3IIIYQQQgPmGPWiUtGiHEIIIYQQQglRKIcQQgghhFBCdL0IIYQQQmjA4mS+ykWLcgghhBBCCCVEi3IIIYQQQgMWE45ULlqUQwghhBBCKCFalEMIIYQQGrAYHq5y0aIcQgghhBBCCdGiHEIIIYTQgEUf5cpFoRzqnI22785BFxxNo8aNeP3hl/jXLU+Uu79JsyYcfW0/1ti4CzOmTudvfa9j0lcTAdjt5H3Y9qCdmD9vPg9fdBfvvzocgCOuPIluO27O9EnTuPiXZy70mrv8dk8OOP8IftfjGGZMmb7ImQ+/8Fi677AZs2fN5vazbmLsqE8XWqfzxl044Zp+NGvejGEvv8uAC+8E4IAzD2GzXbbA8833k6Zx25k3MnXCFHrt8wv2OHEfAH6c+SP3nH87X3wwdpGzARzS/xi67dCDObPmcNdZN/HFe58ttM6aG3fh6KtPoVnzZox8eSgPXnQXAJv32Ya9Tj+ITuusxqV7n8vnI8cAsGKbFpx0y1l03mRtBj02kL/3v3OxsgFssP2m7HfBUTRq3Ig3Hv4v/7nlyXL3N2nWhMOuPYWfpfv8nr7XM/mriay3XTf2+sOhNG7ahHk/zeWJy+7n4zfeA+Cke8+l1SptadS4EWPeHs2j/3cnnr/4HxaH9j+GTXbYjDmz5nDnWTfyeSXb8Lir+9K0eTNGvPwuf0+3Yc8+27DP6b+m0zqrccne5zA23YZb7/1zdj9h77LHr77+mly4x9l8+f7YRc63LLZhv4cuoNXKbflp9hwA/nr4pfww6fsaZ1oWxzKAGjXivKevYOr4ydx87BUAHPOXU1mz29rMmzuXscM/4f7zbmf+3Hk1ynlw/6PptsNmzJk1m7vPurnk8bFGuePjXR666G4AVmjdghNuOoP2q6/MpK8mctsp1zLz+xl03XpDTrn9D0z6agIA777wFs/c8BgduqzKCTedUfa8K/1sFZ687mFeuuu5Gm/XuurEi05kix23YPas2Vzzu2sYM2rMQusc+fsj2Wn/nWjRugX7rb9fBikX+ONl1/Lq/wbTrm0bnrj/1kyzhGUnul6ExSJpnqRhkt6TNFzS7yQt878nNWrEIRcfy41HXcqFu5zBFnttS6d1Vi+3zrYH7ciMaT/wf7378Z87n2G/cw4DoNM6q9Nzz225aNczuOHISzn0kuNQoyTyG48N5IYjLy35mm07tWeDn29S9gG9qDbdYTM6rtWJM7c/hTvPvZWj/nR8yfWOvvQE7jz3Fs7c/hQ6rtWJTXr3AODZ257gvN1+x/l9zmToS0PY97SDAJj45bf86aD/47zdfscTNzzKMZefuFj5uvXuwSprdeK83v2477xbOezS0vkO+9Nvue+82zivdz9WWasTG6f5vvnwC/564lV8PPiDcuv/NPsnnrjmIR69bMBi5SpQI3Hgxcdw61GXc9kuv2Pzvbal4zqrlVtn64N2ZOa0GVzS+zQG3vkce51zKAAzpkzntmOv5Irdzub+M//K4df1LXvM3af8hT/v/nsu3/UsWrRrRY9fbbPYGTfpvRkd1urEOb37cs95t3B4JdvwiD8dzz3n3co5vfvSYa1OdEu34dcffsFNJ17JR4PfL7f+m0++Rv8+Z9G/z1n87Ywb+O6riYtVJC+rbQhw3+k3cmWfP3Blnz8sUpG8rI5lgJ2O7sP4T74u91yDn3iN/judxsW/PJOmzZux3cE71SjnxunxcX7vfgw47zZ+c+lvS6532J9+y4DzbuP8suOjOwC7n7QPHwwayR93OJUPBo1k95P3KXvMJ29/wMV9zubiPmfzzA2PAfDtp9+ULbtkjz8w58c5DP3X4Bplrcu22GELVl1rVY79+bHc8Icb6HtZ35LrvfXvtzhtz9NqOV1p+/TZhVuv/VPWMZYK27V2qWuiUA6La5bt7rY3AnYB+gD9l/WLrtV9HSZ8Pp7vvpzAvJ/mMuTp/7Hprj3LrbPprlvw5uOvAPDuc2+yfq+N0+U9GfL0/5g7Zy6TvprAhM/Hs1b3dQD4ePAHzJz2Q8nXPPD/juIfl9+/2Cc7bL7Llrz++EAAxgz9iBVbrUibVdqWW6fNKm1ZvsXyfPLuRwC8/vhAeu66FQCzfphVtt5yKzQve6P5+J0Pmfn9DAA+efcj2nVqv1j5uu+6BW/8I8n36dCPWaHlCrReuU25dVqv3IbmLVfg0zTfG/8YSI9dtwBg3Jiv+fbTbxZ63jmzZvPJkNFlrY2La83u6zDx82+Z9OUE5v00j3efHkS39LULuu3ak8HpPh/23Jt0Tff5V++N5fsJU5KcH31J0+Wa0qRZ8kPaj+l2bdSkMU2aNlmiN/Aeu27BoH8kr59swxVLbsPlW67AmHQbDvrHK2y265ZJtjFfM77ENiy21V7b8dZTry9WvmW1DZfEsjqW23RsR7cdN+P1h14q91yjBg4tuz52+Ce07Viz46X7rlvwZg32bfOWy5cdH2/+4xW6p/u2+y5b8MZjA4HkC3n3Xbas0esCbLDtxkz8fDyTv/6uxo+pq7bedWteejzZZ6OHjqZFqxa0rfA+WbhvSvr3mLWe3bvRulXLrGOEZSwK5bDEbE8Ajgf6KtFZ0muS3k0vvQAkDZBU9juypAck7bUor9WmQzumfDOp7PaUcZNp06H9QutM/ib5YJk/bz6zps9kxbYtadOhfYnHtqvy9TbZuSdTv53MVx98vigxy2nbsR2TvlnwQTd5/CTaVnjdth3aMXn8gmyTx02ibccF6xx49qFc/8bt9NrnFzx+7UMLvUbvg3dmRFEhsCjadGjP5OLtMn4ybSoUEW06tmfKuKq3+7LSpkM7phblmzpuEq07lP8AbV20zvx58/kx3efFuu++FV+9N5a5c+aWLTvpvvO47J3b+XHGLIY99+YSZZxctI+njJ+0UCHWtmN7Jo8rv4+r+/srtuUe2/LWU68tdr5ltQ1/c9VJ/P65P/PLfov2M/iyOpYPuuBoHr/8fuz5JV+3UZPGbL3vL3jvlZodL207tKtwfEyiTcfy+61Nx3YVjo8Fx3irlVszbeJUAKZNnErLlVqVrddls65c8PxVnHrPeay6bvnWdIAt9tyWwU/9r0Y567r2HdvzXdEx9N2471ip40oZJmpYXIuXuiYK5bBU2P6U5O9pFWACsIvtzYBfAzekq90BHA0gqTXQC1i0jncq+eLlV1GJlWxKL678sG3avBl9+u7HU9c+vEgRKyqVp+LrVrfOo1f9ndO2OZ5BT7zKLkfuXm69DbbZmO1/vRMPXX7fYuYrsXChfNWvs8yU3DbVrlJu+3Vcd3X2OudQHj7vb+XWueWIy/jjlifSpFnTshbUxYtYk31c4oE13IRduq/LnFmz+fqjLxcjXekXXxrb8L7TbuSK3c7m+gP7s/YW67PFfr9YhEwlli3hsdxtx82YPmkaX5Q4B6Dg0EuO4+PBH/DJ26NrmLMm2676/V/RF6M+45xtT+bi3c/mv/c8z8m3/77c/Y2bNmHTnXsy5Lk3apazjlucbRhCrajNfilxqT8X4IcSy6YCHYDWwABgJDAMmFm0ziiSYvpE4OpKnvt4YEh6Ob7C/dvY/lfR7XPTS/E6/0rXw3YT29/Zlu1zn3jiiX9Ush62O9seVXS7m+0Jtseml7m2v7DdsQbb6BTbw9LL32wfUnTfh7Y7VVi/k+3Rhf//dP3bSjzvmhUybmJ7jO2ui7gPFzufba6//vq/lcg30HbPEq91lO2bFjHfEu3zmTNnTk/3ObZXt/2R7W2reI0jFyNjbW7D62yfl/NtuKj7eYmOZdvnFr0/FNa73PZXTo7X8bZn2r6/6Pn6237CdqNluW9ddPxOmjRpfNH6ndLHl3rNsbZXKrq9t+0Xl2Cf1+hS4j22Ni812s6VZFzoMyiLS9euXTuvvfbaX2edIy7L7pJ5gLjUzUvFQhnoAkwiaSe6ELiapIW5CTC3aL0/AGcAbwEbLcZrN7H9qe21bDezPdx2xec5xfat6fWDbT+SXt9o9OjRM20vlz7+U9uNix7X2eWL0IqXsS7/QVbTy69sP+/kA35r24MrWe9t21tLGpKu3yddvm7ROv1sP5ZeX8P2J7Z7LeH+XKR8tvXKK69MK8pXuAz0simUF3mfP/vss5PT623S9fevsH4LLyhemth+2HbfnG7DRk6Kvy4524ZNvOB4aOrk7/LEZZnJRcey7eHNmzd/x6WPZWz3tv1M0e3jbA+yvfyy3rcuOn5vvfXW8bbPSdc5x/aV6fWOXvBFZEsnX8JV9HwP2T56CfZ5jS7AkGX9Gku6nSvJmJtCuXPnzrOyzhGXZXfJPEBc6ualuFAGVgZeBC5Kb18HnJlePzr54aJs3Q7A58BbS/D6fZy0bo2xfX667GLbe6XXm9t+1EkROdhFBcYVV1zxVfq4D23vXvScD9oeZ/snJ0XJsSVed6wXr1CW7ZvT1x3p8oXQsKLrPW2P+uKLL350UlgWPjQfd1LAj7D9tO3V0uV32J7iBS0yi/uBt0j5bI+59957JxTl2zfdZrNtf+vyrYRjbU928qH2le0Na2Ofb7DBBiPS5X+0PaNoGw2zvYrtDk4KmxG237N9o5PCbXH/JpflNuxt+80lyLastuGKtt8p2obXe+FidZkdy7bPT4+Visdy8XYrLpTnpq9T+H+4YFntWxcdvx06dBhq+yXbH6f/tkvX75tut+Hp/i3+wruC7Um2Wy+F/V7lJUeFcqXb+YMPPphZtN6VTo6V+em/F2aVuWvXrg927dp13Lrrrju/a9euX3Xt2rXU50Zc6vhFdvQBCotO0jySrhVNgbkkXS2utT1f0rrA48BM4GWgn+0WRY99AXjCdq0PPJm21vasfs3sRMYll/d8kP+Mec8HkXFpyHs+yH/GvOcLSyYmHAmLxXbjKu77GNikaNG5hSuSVgDWBR5cdumqdHtGr7soIuOSy3s+yH/GvOeDyLg05D0f5D9j3vOFJRAtyqHWSNoZuIuk5fkvWecJIYQQQqhKFMohhBBCCCGUEOMohxBCCEuJpDVqsiwLkhpL2j/rHCHUJVEoh3pP0rY1WRaqJmlTSX3Ty6ZZ5wkhp56o4bJaZ3secHrWOUKoS6JQDg3BjTVclhlJXSW9JGlUensTSX/MOleBpNOAB0gmi1kFuF9Sv2xT1U1pq96qktYoXLLOVEzSgZJaptf/KOkfkjbLOleBpLofZzIAACAASURBVG0lrZheP0zStZLWzEGurpL2BlpL2qvochjQPOt8Rf4l6XRJnSS1KlyyDlWKpF6SDpV0ROGSdaYCSXtIihqqAYg+yqHekrQNyTTZp5OM7VzQCtjXdm5aRSW9ApwN3Ga7R7pslO3Fn1d5KZI0AtjG9oz09orAG7Y3qfqRtUvSHsAlwJoko/qIZBzvXBQC6ZeL/sC3wPx0sfO0HSWNsL2JpO2Ay0kmDzrP9lYZRwPK/hY3JRlZZwBwJ7Cf7e0zzrUvsB/QB3iu6K7pwIO2X8skWAWSSs2Dbtt5+8I2AFibZHbXeeli2z41u1QLSLof2IZkKNS7bX+QcaSwjMTwcKE+awa0IPk7b1m0/HvggEwSVW4F24MlFS+bm1WYEsSCDyvS66pk3Sz9haRYGel8tgKcBqxne1LWQapQ2M+/Am6x/aSkCzPMU9Fc205bb6+3faekI7MOZfufwD8lbWf79azzVMb2z7LOUEM9gQ1zehxj+7C0Jf4Q4G5JBu4m+VI0Pdt0YWmKQjnUW7ZfAV6RdI/tz7POU43vJK0NGEDSAcC4bCOVczfwlqR/prf3IRnqL2++BEbl9cOVJN+0rENU42tJtwE7A3+WtBz56qY3XdK5wOHAzyU1Jpn4KC9GS/o90Jmiz1jbx2eWqAJJ6wMbUtQlxPbfs0tU0iigI/l6HyzH9veSHgeWJ/nlcl/gbEk32M5V976w+KLrRaj3JHUFzmLhD64ds8pUkaQuJIPW9wKmAJ8Bh9kem2WuYmk/1e1IWpJftT0040gLkbQFSdeLV4DZheW2r80sVBFJdwLrAc+Sw3xQNinQbiSt8h9L6gR0s/1ixtEAkNQROBR42/ZraR/v3rbvyzgaAJL+B7wJvEPRrzC2H84sVJH03IddgfWBfwG/BF63vV+mwVKSniZpMGgJdAcGU/5Y2SujaOVI2hM4hqR7yADgXtsT0uPnA9uZ95sPS0e0KIeG4FHgVuAOyncfyA3bnwI7p31/G+XtpztJA2wfDrxbYlmeXAr8QNJS1izjLKV8kV6akc98kPSTL9uvtsdJuhLIRaFse3zairduuug74J9VPKS2rWj7zKxDVOHXJAXou7YPT78I3ZZxpmJXZx2ghg4ErrP9avFC2zMlHZNRprAMRKEcGoK5tm/JOkRVJLUBjiBt9S70Vc7LiSvARsU30p+7N88oS1Xa2d416xCVsX0RQDqqhG3/kHGkUnK9ryX9FjgeaEfSmrcayRfhnbLMVeR5SbvmpQW+hFm250mam/4djge6ZB2qIO0yh6Q/2/5D8X2S/kzya1HmbFc6Aoftl2ozS1i28tTvLIRl5WlJJ6fDIbUrXLIOVcFzJEXySJKfbAuXTEk6V9J0YBNJ30uant6eADyZcbxS/iMpt4WypI0lDSXpf/mepHckbVTd42pDiX39fU739SnAtiQn5WL7Y5IhC/PiROAFST9ImixpiqTJWYcqMjT9Yn4XMISka8O7VT8kE7uUWLZ7raeohKStJb2d7uc5kuZJ+j7rXGHpiz7Kod6T9FmJxbadm1YUSe/azs1YtRVJutz2uVnnqE5a2K1I0qfxJ/I3PNwg4HzbL6e3ewOX2e6VabAied/Xkt6yvZWkobZ7SGpC0o0gF0PspS3wC0kn+8gVSesArWznplCWdBJwMkkr95iiu1oCg2z/JpNgFUgaAhxM0rWvJ8kvguvYPj/TYGGpi0I5hByQdAZJ39pnKH/iSi5aoiT9otTyiv3zQtUkDa84fnepZVmTtBoLxqIG8rOv0/7SU0kKk34kRdX7eSlQJJX80mN7UG1nqYykg4G1bV8q6WfAKrYz/wULQFJroC3JGN7nFN01PS/vh5AUyrZ7FsYdT5cNytOX3rB0RKEc6r3KZnPKy1nyAJJOITkRbSrpEHHkqNU7PRO9oDmwJfBOXkYOkbS+7dGqZAa5vLSYpcPrvUtyljzAYUBP2/tkl6o8SVeQtJS9T/mJHvIy2kAj4FiSkRtEMnLDHXkZElDS80U3m5P07x6a9YQoBZJuIhlO7xe2N0i7of3L9hYZR1tI2jrfgfJf2L7ILtECkl4lGULxDpJ+3uOAo/L2pTcsuSiUQ70nqXg8y+YkJ/28azs3k45IGgNsZfu7rLPURNoKdaXtQ7LOAiDpdtvHS3q5xN3OUUHfFriIomH2gAttT8k0WBFJHwKb2J5d7cqhWpI6k3SvOTTjKMCCbl6Frivpsjz+qtEXuJCczmKpZNr0b0lGrzkDaA381fYnmQYLS12MehHqPdv9im+nP+0NqGT1rLwHzMw6xCL4CsjF9NqwYDIH2ztknaUqaUGcl5FMKvMpSYtjrgplSY/YPkjSSBb86lImLwVURbbHSsrNsQL8lLbKFyY3as+CQjRPTifHs1ja/lzSyun1i7LOE5adKJRDQzSTBWOw5sU8YFjaIlrcRzkXRVXaKl8oThqRjMM6PLtElUuLkoqzjmXazUbSX2yfXjSZQjl56daQmknyt/gS+fpbPC39d49MU1RD0nWUP1Z6kHwRzoubgceBlSVdBBxE8itH3uRyFkslY3f2B/qS/CrUSNJc4EbbF2caLiwTUSiHeq9CcdIY2AB4JLtEJT2RXvJqSNH1ucCDtv+XVZjKSOoP9CYplJ8jGU7qdSDr/uiFXzDqwmQKT6WXXLFdmMq4ETDO9o8AkpYn6ceaF6OKrs8F/lkYGzhLkp4DTrZ9n6R3SPrXCjjQ9qiqH52JT4GBkvI2i+XpJMMTbmH7MyibWfUWSWfYvi7TdGGpiz7Kod6TVHwSzVzgc9tfZZWnMpKaAV3Tmx/a/inLPFVJ+ygfbPuqrLMUS3+W35Tk5KlNJXUgOdFrz4yjASDpNNvXV7csVC4dlquX7Tnp7WbA//J0Mlo6ZN066c1PbM/NMg+ApIOAPwH3kpxfkNv3Fyj70ruQrLs5pOOg71LxfJK0G8aLhX7fof6IFuVQ79l+JS2YCh+kH2eZp5R0PN17gbEkrTw/k3RkXobkApC0Esm0rYeQzIaWp2mDC2bZnq9k1rFWJJNl5GLkkNSRQMWi+KgSy2pdHeoD3KRQJAPYnpMWy7kg6eckvyB8TXIsd5R0eNa/wNh+JG2dvQAYImkARX2Tc9BSW06OZ7FsWuqka9sTJTXNIlBYtqJQDvVe2pJyFTCQ5IPrRkln234s02DlXQPsavtDAEldgQfJeOrg9ENqX+BQktbufwJdbK+eZa4qDElnHfsbycyGP5DMPJYpSYeQbMO1JBV3a2gJ5OVkpTrRBxiYKGkv208BSNobyNNoMdcBfWy/DyBpA5LCuWemqRI/ATOA5Uj+9vJ4Eh9Qdq7BAJKpypH0HXCE7az7e89ZzPtCHRVdL0K9J2k4yU9lE9LbKwP/ydNwSMWD1le1rLZJmkVSaP4ReN22JX2al/Gdq5IOy9XK9oiMoxSGklqLEpMoACPy8NN8XSFpbeABYFWSL75fkhRQuRiWK8fH8m7AtST9zy+2netRdpTTWSwlzSP5srHQXUBz29GqXM9EoRzqPUkjbXcrut0IGF68LGuS7iL5ubtw0tdvSH5iPjq7VGUzBh5MMi3034GHgX/nrVCubKKRgrxMOAIgqSPJhC0G3rY9PuNI5UjaGriR5KTXZiQnwM5wTqYBL5DUguQzbHrWWYpJuofk5LPiY3kF20dmFgqQ9BpwYg5aZGuk1NjOeRzvOdR/USiHek/SVcAmJF0ZAH4NjLT9++xSlSdpOeAUyk9E8de8TPqQntV9CEnRvC7J8Ej/tP1RpsFSlUw0UpCnCUeOJdl2/yXZz9uTtO7dlWmwIunJcgcDj5J0FzgCWMcZTxEt6TDb90v6Xan789LHVlJzkrGyi4/lGwujdISaqQuzWIaGIQrl0CBI2p9kSB8Br9rO1YloklYEfrQ9L73dGFgujz+PSupGUjT/2vbaWeepS9JZ73oVJlFIJ3sYZHu9bJMtIGmI7Z7F3QUkDcrBT94n2L4tr6MhhKWrLsxiGRqGKJRDg5GOglB2AqvtyRnGKUfSm8DOhTO705+VX8y6OKkrJB1G8n42oMLy35J0G/h7NsnKSyfx2L3C0GbP2d4522QLSHqVZIzdO4DxwDjgqPjJu2bSvsCXAGuSvN+I5FeNdpkGCyEsliiUQ70n6QTgYmAWyVnehQ+u3PSzlTTMdvfqloXS0rFNf1Gxv2r65ehl25mOHlIg6T6gG/AkSR/lvUlOlvwI8tF9ID3xcALJNNZnAK1JugFlerKcpBuquj8HMwcCIOkTktnuRlJ++LV5mYWqQyqMCrOQnM1iGRqAGB4uNARnARuVGvsyR2ZI2qxw0pmkzUkK+1AzjUud1GX7+5yNbTomvRQ8mf7bMoMsJdn+PL06i3xNbfxO1gFq6CtgmO3cDr2Wc9uQjGTyIPAWScNGCJmJFuVQ70l6Adgvj/19CyRtATwEfJMu6kTSB7iuFAeZkvQByYk+Myosb0kyssT62SQrTdKKFbNmrbKJRgqyHt6sohxORAGApC1JTtgcSPmpl6tsEQ+J9PyMXUjOg9gEeBZ4sK6M1hHqnyiUQ70nqQdwN0nrRPEHVy5+qi1IWz7XI2lBGZ2nKWYl7UHpfpe5GDJM0lnATsBJtsemyzoDNwMDnZOptiVtA9wJtLC9hqRNgRNsn5xxtEKXi0oVtTRnqsJEFAImko+JKACQ9DzJxB4Vu178X2ah6qh0NKBDSCaMutj2jRlHCg1QFMqh3pM0GHidhT+47s0sVAmSegGdKX/C4X2ZBSqS9rvcj2RYvVy+aUg6ETgXaEHSMjoDuML2LZkGKyLpLeAA4CnbPdJlo2xvnG2y8vI81nNeJ6IokPROXvrE11VpgfwrkiK5M8kkKXfZ/jrLXKFhij7KoSGYa7vk2Kt5IWkAsDYwDCic9GMgF4UySZ/BUXktkgFs3wrcWtVEFJKOzPoLku0vpXLdLnN1kpek44ALWDDW842S8jTW84qFIhnA9sB0eMW8eEnSjrb/m3WQukjSvcDGwPPARbZHZRwpNHDRohzqPUmXAp8DT1O+60Wehof7ANgwr4Vo2of6EuAVym/DzEdpWBSS3rVd5Sx+y/j1HyOZRvgmYGuSiSl62j44q0wV5X2s57xPRCFpCslIITOBOcTwcItE0nwWTBFd/H6Yq+5eoeGIFuXQEBya/ntu0TIDuRkeDhgFdCQZszaPLgV+AJqTTGtcV2V9Bv2JwPXAaiSjI7xIMiNjnnwFFLfGTyf5RSEvjiEZjeMf6e1XgUyneq9gpawD1GW2G2WdIYRi0aIcGiRJzQqTPuRBOgVzd5IxdYtbbHMxZmhhtrascyyprFuU64I8j/UsaWWSE0o/sT01qxw1lZ4geTBwaEzYEkLdFC3KocFQ0jF0B5IW5j2BDtkmKufCrANU4z+SdrX9YtZBllCmLcpp/8vTCkVeOk3vNbaPyTJXBbkc6zntO30ZSba1JB1vu8rJKbIgaRWSCUcOBTYjGbHhqCwzhRAWX7Qoh3pP0lYkH1r7kgwpdQrJqANTMg1Wh0iaDqxI0tr9EzntLyhpLdufVbZM0k22+2aTLplBsDDaRVXLwsIkjQJ2sD1RUhfgAdvbZJ2rQNLRJKM0dAEeAx4BHre9VqbBQghLJFqUQ72VnsR3EPAFySxPFwNDsh71oFhagJb6tpqrQtR2bmaOq8bjJK14xR4DNgfIskhONZLUtvAlTVI7cvY+LKkncD4LxswGcjHhyBzbE9Msn6ZDiOXJ7cAg4EDbQwEkRUtUCHVcrt6gQ1jKjgc+BG4BnrH9Y94+uOpKASrpF6WW2361trOUIml9YCOgtaT9iu5qRXICYl5cAwxKR78AOJDkRMk8eQA4mwrjjufA6pJuqOx2DiYQWg34NXCzpDbAw0Cepk8PISyG6HoR6q10KtRdSX4O3RF4GdgZ+JntuVlmq2skPV10sznJZBTv2N4xo0jlSNob2AfYi2RygoLpwEO2B2USrARJG5H0lRfwku33M45UjqTXbW+XdY6KJB1Z1f05+6VoTZL3nUOARsA/bV+QbaoQwuKIQjk0CJKaA3uQfHBtR1KgHFr1o0JlJP0MuNL2IVlnKSZpG9tvZJ2jKukXuA6U79bwRXaJypO0E8lx8hLlR2D5R6UPyhFJN9rul3WOAkkbAocUprCOyUhCqFuiUA4NjqRWwL6FFqg8zNZW16QjiIyw3S3rLMUkXQn8CZgFvABsCpxu+/5Mg6Uk9QP6A9+SzMhX6Iuedf/fMpLuB9YH3mNB1wvnbGSOSuV9CMC85wshlBeFcmjw4oOrepJuZMFJh41Ixnwea/uw7FItTNIw290l7UvSFeMM4OW8jGEr6RNgq8Ksd3kkaWTevgAtirwfzzHKSQh1S5zMF0L2s7XVBUOKrs8FHrT9v6zCVKFw8lQfkoyTk8bv3PgSmJZ1iGq8KWnDvPWdrkeidSqEOiQK5RDig6tadahrytOSRpN0vTg5ncntx4wzFfsUGCjpWcr3/81strsStgOOlPQZScbcdQ+pRq6+GYUQ6rYolEOID9ZKSXrE9kGSRlL+C0Uuiyfb50j6M/C97XmSZpBMwZwXX6SXZuklj3bLOkApkgbYPlzSabavr2LVqu7Lgy+zDhBCqLnooxwavKxna8szSZ1sj0uHu1qI7c9rO1N1JG0MbEjR+Mm278suUd0jaVPg5+nN12wPzzIPgKT3gd1Jhv/rTYUvuLYnZxBrIZIakXzZ6Ez5kU1uqOwxIYT8ikI51Hvp4P9HsPAHV9YTFNQ56YghxdswF8VJgaT+JEXUhsBzJIXV67YPyDjX01TRxcf2XrUYp0qSTgN+CxSGg9sXuN32jdmlAkmnAieRTBH9NeULZdvukkmwCor2dbkJWwrDw4UQ6pYolEO9J2kQ8CYLf3DVlX63mZN0AskU4LNYUPDlpjgpSLuIbAoMtb2ppA7AHbb3zDjX9lXdb/uV2spSHUkjgG1sz0hvrwi8kZduNpJusX1S1jkqU9dHDQkhlBd9lEND0Nz277IOUcedBWxk+7usg1Rjlu35kuamrd8TSFogM1XTQljS47b3X9Z5qotBMsZzQWG851ywfVKFriGv2h6RZaYK/hWTioRQf0ShHBqCAZJ+CzxD+ZEGctVtIOfGADOzDlEDQ9KuNn8D3gF+AAZnG2mRZF7UA3cDb0n6Z3p7H+DODPOUk3bBOJ4FXUMekJR515Air5GMvmJgDgtOfG2XbawQwuKIrheh3pN0CnApMJUcdxvIM0k9SAsoyn/ZyG0/b0mdgVbAd7a/yTZNzeRlsgxJm5EMEyeSFtuhGUcqUwe6hnwKHMDCXb3mVfqgEEJuRYtyaAh+B6xTB7oN5NltwH+p8OGfZ7bHAkj6Algj2zT5J2kLYCXbz9t+F3g3Xb6XpEa238k2YZlcdw0BPibpIx+tUCHUA1Eoh4bgPepGt4E8m1uH+3nnqYiqTpZZrwKOKrH8feB2YMdaTVO5XHcNAb4B/ivpOcr/+hLDw4VQB0WhHBqCecAwSS9TR7oN5NDLko4Hnqbu9fPOTcuepD2A52xX1ir/h9rMU0H7Qit8MdufSGqfQZ6SbF8raSALuoYcXdw1RFJb21Oyygd8lV5aZZghhLCURB/lUO9JOrLU8hgerubS6Ywryk0/b0k3UrogFnCk7VwULZLuB7YBHgfutv1BxpHKSPrE9jqLel/e5KWfdwihfohCOYRQ51X2ZaggT1+K0mHrDgGOJinu7wYetD0941y3ApOAPxb3r5V0EdDJ9vGZhVsEkoba7pHB615j+8y0S8hCH6y296vtTCGEJReFcqi3JD1i+6B0EopSH1y5OEu+LpC0AslJkWvYPl7SusB6tp/JONoikXSj7X45yLEScBhwOvABsA5wQ5ZDnKWjR9wBbAkMSxdvCgwBjrP9Q1bZFkVWLcqStrQ9WNJOpe63/VJtZwohLLkolEO9JamT7XGS1ix1v+3PaztTXSXpYZJxiY+wvbGk5UmG5OqecbRFkvXP8pL2BI4B1gYGAPfanpB+EfnAdsm/1dokqQuwUXrzPdufVrh/I9vv1X6ymsl6H4cQ6pc4mS/UW7bHpf9GQbzk1rb9a0mHANieJakujSaRFwcC19l+tXih7ZmSjskoUzlpYfxpFasMAPJciGb6dylpbZJx2zcEmheW2+6aWagQwmJrlHWAEJY1SVtLelvSD5LmSJon6fusc9Uxc9JWZENZMTC76oeEEvpTNFOgpOXTiVHq0k/zmRWikhpJGlXNaiW7PtSie0j6nQvYHXgEeCjLQCGExReFcmgIbiI5eepjYHngOCAv093WFf2BF4CfSXoAeAn4fbaRFkvWreCPUn7Clnnpsroks/566bB6wyVVOoFMDoYsXMH2v9IsY2z/Edgh40whhMUUXS9Cg5COBds4nUb2bkmDss5UV6RdLEYD+wFbkxSbp+V5pkNJKxamOK7g+loPU14T23MKN2zPkdQsy0B1UCfgPUmDgbJ9bHuv7CKVMzs9ZsZIOhH4Glgl40whhMUUhXJoCGamxcgwSVcC44AVM85UZ9i2pCdsbw48m3WeqkjqRTJyQwtgDUmbAifYPhnA9j0ZxgOYKGkv208BSNobyO0XjkrMqX6VZeqijF+/OmeQ/P2dStJXuTXJCZwhhDooRr0I9V466sUEoCnJh1hr4K+2P8k0WB0i6WbgHttvZ52lKpLeAg4AniqMpStplO2Ns02WSPt2PwCsStIy/yXJSCK5+luUtBqwJkWNKRVPQMxSekyva/s/6YghjbMehzqEUD9FoRxCqJak94H1gLEkP3eLpLE5V2NRS3rL9lbFk05IGm5706yzFZPUguT9N3fFnaQ/A78G3ifpQw3Jvs5F1wZJvwWOB9rZXjsd0/tW25mexFfZRCMFMeFICHVTdL0I9VZlE40U5K3Iy7ndsw5QQ1+m3S+cdrc5lWRCj0xJOsz2/ZJ+V2E5ALavzSRYafuQTCaT11FNTiGZFOUtANsfS8pDH+Cbsg4QQlj6olAO9dl+QAeSn7eLrQl8U/tx6h5JzYETSWaOGwncaXtutqmqdCLJCXurAV8BL5IUVlkr9IlvmWmKmvmUpJtSXgvl2elJkABIakKGI3EUGQ60t/1h8UJJ6wMTs4kUQlhS0fUi1FuSngHOsz2iwvKeQH/be2aTrO5IZ+T7CXiNpFX5c9unZZsqLEuSHieZuvoliopl26dmFqpIekLuVOAIoB9wMvC+7fMzzvV34G+2X66wfHfgN7YPyyZZCGFJRKEc6q2qTuKSNNJ2t9rOVNcUb6e05W5wnqcHlnQvydB1U9PbbYFrbGc66oCkG6q6Py9FKICkI0stt31vbWcpRVIj4FhgV5K+8v8C7nDGH2aS3rO9USX35eaE0hDCoomuF6E+a17FfcvXWoq67afCFdtz68Cs1ZsUimQA21Mk9cgyUOqdrAPUlO1701kY16jYjSAPbM9PvxC9RdLl4sOsi+RUVZ+nTWstRQhhqYpCOdRnb0v6re2/FS+UdCx1qHDJ2KZF030LWD69XRj1olV20UpqJKmt7SkAktqRg/e5iq2xklomi/1DRpEqJWlP4GqgGbCWpO7AxTka9eJXwK3AGJK/w7UknWD7+WyTMUbSLwuz8hVI2hX4LKNMIYQlFF0vQr0lqQPwT5IJEgqFcU+SAmBf2+OzylbfFBenGec4AjgXeCxddCBwqe0B2aVaQNLGwACgHUmRN5FkHOX3Mg1WRNI7wI7AwKIh9nLTVUnSaGCPwtjT6djUz9peP+Nc6wNPA69Q/v3mF8CetkdnlS2EsPgyb2kJYVmx/S3QS9IOQKF/4LO2/5thrPrqJSDzvsu270sLvR1ICtH9bL+fcaxitwO/K5zwJak38DegV5ahKphre1qFbjZ5alGZUGGClk9JJhTKlO3RkroBh7Pg/eYtoK/tWdklCyEsiSiUQ72XFiUvV7tiWBJ56rw8GphC+v4maQ3bX2QbqcyKxaMi2B4oKW/TqY+SdCjQOJ3M41RgUMaZkFSYsOM9Sc8Bj5AU8AcCuZgx0vaPJF98KiXpddvb1VKkEMISikI5hLA05KLFUVI/oD/wLcmsciLJlpfJZT6V9H8k3S8ADiN//Vf7AeeTDA33IMmoEpdkmihRPJzjt8D26fWJQNvaj7PY8vbFKIRQheijHEJYYpLezcOwcZI+AbayPSnrLKWkw9VdBBRaFF8FLspD/+6KJLUiOeEwd9Ns12V5OVZCCDUTLcohhKUhL10vvgSmZR2iFEkrk8wKeUHxEHZ5I2kL4C7SWQQlTQOOsZ2LkWIkrUXS6t2Zos+wvIzKEUKoX6JQDiFUS9LVwN1VjM6wU23mqcKnwEBJz1J+Vrlrs4sEko4DLiMZ0mwtScfbfirLTFW4EzjZ9msAkrYD7iY/3VeeIMn4NDA/4yyLIy9fKkMINRCFcgihJkYDt6ez890NPGi7rOXW9uTMkpX3RXppll7y4nRgI9sTJXUBHgDyWihPLxTJALZfl5Sn7hc/2q5ypsOcOyrrACGEmos+yiGEGpO0HnA0cAjwP+BvxaM4hNIq9kvNcz9VSdcBK5CcyGfg1ySjiDwOYPvd7NJBOiLHusCLlP/VIOtcUyh9Umthcp52tRwphLAURKEcQqgRSY2BPUgK5Z+RDM+1HTDD9sEZZ/uL7dMlPU2JYiXr/quSJgAPFS06uPi27VNrPVQlJFX1xce2d6y1MCVIupxkrOIxLOh6kYdcjau63/a82soSQlh6olAOIVRL0rXAXiQTi9xpe3DRfR/aXi+zcEmGzW2/I2n7UvfbfqW2MxWTdGRV91ec4jpULp2ZbxPbc7LOUpV0+vTmhdu2v8kwTghhMUWhHEKolqRjgIdszyxxX+vi/sp5IKkpyexoX9vOfNa2mpJ0o+1+Gb7+xsDZwEYkLfPvA1fbHplVpookPQz0y+t+lfQr4DpgdWASsBrwUdZTbIcQFk+czBdCqJSkQj/aYcD6FaY1xva79XPuqQAADnJJREFUeSiSJd0K3Gj7PUmtgTdIJhxpJ+ks2w9mm7DGts3qhSXtDVwNXA5cQ9K3dnPgH+k2fDKrbBV0AEZLepvyfZTzMjzcpST78UXbPSTtAuyfcaYQwmKKFuUQQqXy3l+1QNJ7tjdKr58O9La9j6SOwPO2e2SbsGayPMlP0nBgb9tjKyzvDDxpe9MMYi0kr91rCiQNsd0z3Z7dbVvSYNtbZp0thLDookU5hFAp2ztknaGGivur7gI8CmB7fMVW8FCpphWLZADbY9OuLLmQl4K4CtMkrQi8DtyXnshZF8d7DiEQhXIIoYYk9WLh2dDuyyxQeVMl7QF8TfKz97EA6bjPy2cZbBFlWdX/JGkN218UL5S0JjA3o0wLScd0LvwU2gxoSjLySqvsUpWzD/AjydjZRwCtSUaLCSHUQVEohxCqJWkAsDZJX+XCMFcG8lIonwDcAHQETrc9Pl2+E/BsZqkqIWlF2zNK3HV9rYdZoD/wH0mXAe+Q7N8tgHOAP2SYqxzbLYtvS9oHyFO3hnNtn0dynNwJkG7T8zJNFUJYLNFHOYRQLUkfABu6jr9hSDrX9uUZvn4v4A6ghe01JG0KnGD75KwyFUvznEky6oWAUcA1todnGqwakt60vXXWOaB0P3NJw/PSxzuEsGiiRTmEUBOjSFprx2UdZAkdSDKqQ1auA35JOn217eGSfpFhnnLSgviIqtbJwRB2+xXdbAT0pPSMeLVK0gnAiUBXScWzBLYEhmSTKoSwpKJQDiFUqmimu5bA+5IGk88huWoq8zP7bH9Z4QTDujZjW2ZD2KX2LLo+FxgL7J1NlHIeIZmQ53KS7ioF0/M65nMIoXpRKIcQqnJ11gGWsqxbHr9Mu19YUjPgVOCDjDPVKbaPzjpDKbanAFOAA9OJW7ZL73oNiEI5hDoqCuUQQqUKQ3FJ+rPtcid0SfozkPehuirKukX5RJIT9lYDvgJeBE7JNFEdIemCKu627UtqLUwVJJ1Csk+fSBc9Iulm23/NMFYIYTHFyXwhhGpVcoLSCNubZJVpcUg6z/ZlWeeoyyQNzWICF0lnlli8IslQgO1tt6jlSCVJGgH0sv1DersFMKiuHSshhESjrAOEEPJL0kmSRgLrSRpRdPkMGJl1vookXSmplaSmkl6S9J2kwwr3Z10kS7pXUpui220l3ZVlpoJ0CEAknVbNqpkMYWf7msIFuJ1kfOyjgYeALllkqoSAn4pu/0T2v2SEEBZTdL0IIVTl78DzlD5BaXI2kaq0q+3fS9qXpGvDgcDLwP3Zxiqzie2phRu2p0jKy/Tam6eTixwj6T4qFHeF/W37ngyyASCpHfA74DfAvcBmad/gzElqYnsuMAB4U9Lj6V37kmQNIdRBUSiHECplexowDThEUmOgA8n7RgtJLSrO4pYDhamW+wAP2p6csymsG0lqWyju0sIvL+/DtwIvkLTOvkP5Qtlk3Gor6SpgP5LW5G6Frg05MpikcL9S0svAz0m24Ym23842WghhcUUf5RBCtST1BS4EvgXmp4udt36Xkq4gmUJ4FslsbW2AZ2xvlWmwlKQjgHOBx9JFBwKX2h6QXaryJN1i+6Ssc1QkaT7J0IRzKT96iUj+FjOdwjqrvtshhGUrCuUQQrUkfQJsZXtS1lmqI6kt8L3teZJWAFoVTWmdOUkbATuQFHgv2X4/40gLSWfo+3l689X/b+/uQi2ryziOf39OKjaN5oRoNzklYsloIlFpRZHMhWllQReZGSpZSr6kgfZiWUFib0R2EYqERYwoaDj4QiTOeKGMgqk5ZGFjWJGZ2YsOlTTzdLHWafYZ9z4zA539X/uc7wc2s9daXvw4F+Mz//Os56mqR1vmmQVJfg98e9Lzqpr4TNJwDeVXfpKG7Xd0LRiz4A3AmiSjf7/9sFWYMR6nm7f7MoAkrxlSC0uSC4FzgVv6Wz9Ocm1VXdMw1ixYAbwCX9yTlhRPlCXtVpLrgaOA25m/mW9Qp2T95IYjgIfZufGuqurCdql2SnIB8CW6Fpbt7GwbGEwLSz/e7ISq2tZfrwTuH1LGIRo3QlHS7PNEWdKeeKr/7Nd/hupNwNE13BOAi4CjBt7CEuav1Z4r6LUwf0bSEmShLGm3qurLAElWdZeDmzgw5zHgMOCPrYNMMAstLD8ANie5tb8+Dbi+YZ5ZcVLrAJL+/2y9kLRbSdbSzYdd3d96Fjizqra0S7VTkg10kxBWAcfRjeoabRF5X6No88xQC8vxwNvpTknvraqfjzz733g7SVrqPFGWtCeuBS6pqnsAkrwLuA44sWWoEd9sHWAPzUQLS1U9BDw04fHdgL24kpYFC2VJe2LlXJEMUFUb+5e8BqGqNgEkubqqLht9luRqYFOTYLuYa2GZcfbiSlo29mkdQNJM2JrkiiRr+s8XgCdbhxpj3Zh7J089xS6SfKf/c0OS23b9tM63l+zXk7RseKIsaU+cDXyZbrZugHuBs5omGpHkPOB84HX9eLM5q4D72qSaZ27z3qy0iEiS8GU+SUtAkoOAg4GrgMtHHj1fVc+1STVZkn2BtcAfquqZ1nn2hquaJS0nFsqSJtpdW8BQpkmMSrICOJSR35i13nyX5PvANVW1pS/q76ebT7wa+ExVrW+Zb06SfYBHq2rtAv/N6iH+40OSFoOtF5IWcgLd7N/1wGYG/iJXkk8BV9JtvtvR3y6g9Va5d1TVJ/vvZwG/rqrTkhwG3En3822uqnYkeWShtdoWyZKWEwtlSQs5jO4FuQ8Dp9PN/10/lPnJY1zMMDffvTjyfR1wM0BVPZ0M7t8erwa2JHkA2DZ3c4i/PZCkxWahLGmiqtoO3AXclWR/uoJ5Y5KvVNU1bdONNdTNd39LcirwB+BtwDkASV4GHNAy2BhLYYSdJP1fWChLWlBfIJ9CVySvAb5LN/1iiLbSFfJD23z3Cbqf22HAxVX1dH//JLpT+sGoqk1JDgeOrKqfJXk5sKJ1LklqwZf5JE2U5Aa66Qx3AjdW1WONIy0oyZfG3Z+VRR9JPltVVzXO8HHgXGB1VR2R5Ejg+1V1UstcktSChbKkiZLsYGef6uhfFgGqqg6cfqrdS7KKLt8LrbPsjSQPVVXT9dBJHgbeDGyeGwOX5BdVdUzLXJLUgq0Xkiaqqpna3plkLd1yj9X99bPAmQN++XBXQ3iz799V9eLcS4Z9H7UnKpKWpZn6n6Ak7ca1wCVVdXhVHQ5cClzXONPeGEJBuinJ54ADksxN6NjQOJMkNWGhLGkpWVlV98xdVNVGYGW7OHttCCfKlwN/Bn5B9xLiHcAXmiaSpEZsvZC0lGxNcgVd+wXAGcCTDfPsrZtbB+iXjtxAt2CmgF+VL7NIWqY8UZa0lJwNHEI3vu7W/vtZTRONSPL1JAcm2TfJ3UmeTXLG3POq+lrLfABJTgF+QzfO7nvAE0lObptKktpw6oUkTUmSh6vquCQfAE4DPg3cU1VvbBztf5I8DpxaVU/010cAt1fV69smk6Tps/VC0sxLcttCzwe0fnnf/s/30K0Cf26AK6yfmSuSe1uBZ1qFkaSWLJQlLQUn0K2vXk/XWzu46rO3oT+x/SdwfpJDgH81zgRAkg/2X7ckuQO4ia5H+UPAg82CSVJDtl5ImnlJVgDr6NZsH0u3Fnr9EOcnJzkY+EdVbe/XQx84stK6Za4fLPC4qursqYWRpIGwUJa0pCTZn65g/gbwlaq6pnGkeZKcCKxh5Dd6VfXDZoEkSRPZeiFpSegL5FPoiuQ1dFMbbmmZaVdJfgQcATwMbO9vFzCYQjnJa4ELeGkxP5Q+b0maGk+UJc28fu7vWuBO4MaqeqxxpLGS/BI4eshziZM8AlxPt3Bkx9z9qtrULJQkNWKhLGnmJdkBbOsvR/9SC11/7YHTT/VSSW4GLqyqP7bOMkmSzVX1ltY5JGkILJQlaZEl2UBXwK8CjgMeAP4993xIbQ1JTgeOBH7K/IwPNQslSY3YoyxJi++brQPshWOAjwLvZmfrRfXXkrSseKIsSVOS5Oqqumx391rq5zwfW1Uvts4iSa3t0zqAJC0j68bcO3nqKRb2CPDK1iEkaQhsvZCkRZbkPOB84HVJHh15tAq4r02qiQ4FHk/yIAPto5akabH1QpIWWZKDgIOBq4DLRx49X1XPtUk1XpJ3jrvveDhJy5GFsiRNUb9u+1DmL/N4ql0iSdIktl5I0pQk+RRwJfAn5k+UOLZVpl0leZ6ds6j3A/YFtg1lFrUkTZOFsiRNz8XAUVX1l9ZBJqmqVaPXSU4D3twojiQ15dQLSZqe3wF/bx1ib1TVT3CGsqRlyhNlSZqercDGJLczf6LEt9tFmi/JB0cu9wHexPy14JK0bFgoS9L0PNV/9us/Q/Teke//AX4LvL9NFElqy6kXkjRlSVYBVVUvtM4iSZrMQlmSpiTJWuBHwOr+1rPAmVW1pV2qTpIvLvC4quqrUwsjSQNhoSxJU5LkPuDzVXVPf/0u4GtVdWLTYF2WS8fcXgmcA7yqql4x5UiS1JyFsiRNSZJHquqNu7vXWt8achFdkXwT8K2qeqZtKkmaPl/mk6Tp2ZrkCrr2C4AzgCcb5pknyWrgEuAjwA3A8VX117apJKkd5yhL0vScDRwC3ALc2n8/q2miXpJvAA8CzwPHVNWVFsmSljtbLyRJJNlBN9v5P8yfmxy6l/lcYS1p2bFQlqRFluS2hZ5X1fumlUWStOfsUZakxXcC3frq9cBmulNaSdLAeaIsSYssyQpgHfBh4FjgdmD9EOYnS5Im82U+SVpkVbW9qu6qqo8BbwWeADYmuaBxNEnSAmy9kKQpSLI/cArdqfIa4Lt00y8kSQNl64UkLbIkNwBrgTuBG6vqscaRJEl7wEJZkhZZP3ptW3/p6DVJmhEWypIkSdIYvswnSZIkjWGhLEmSJI1hoSxJkiSNYaEsSZIkjWGhLEmSJI3xX1X32Rqy4kIdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check cor-relation\n",
    "corr_hmap=automobile.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr_hmap,annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here you can see that year is complete Negatively cor-relate with all other "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer', 'Country', 'State_Code', 'State', 'Claim_Amount',\n",
       "       'Response', 'Coverage', 'Education', 'Employment_Status', 'Gender',\n",
       "       'Income', 'Location_Code', 'Marital_Status', 'Monthly_Premium Auto',\n",
       "       'Months_Since_Last_Claim', 'Months_Since_Policy_Inception',\n",
       "       'Number_of_Open_Complaints', 'Number_of_Policies', 'Policy_Type',\n",
       "       'Policy', 'Claim_Reason', 'Sales_Channel', 'Total_Claim_Amount',\n",
       "       'Vehicle_Class', 'Vehicle_Size', 'Year', 'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "list1=['Customer', 'Country',  'State', 'State_Code',\n",
    "       'Response', 'Coverage', 'Education', 'Employment_Status', 'Gender',\n",
    "       'Income', 'Location_Code', 'Marital_Status', \n",
    "       'Policy_Type',\n",
    "       'Policy', 'Claim_Reason', 'Sales_Channel', \n",
    "       'Vehicle_Class', 'Vehicle_Size']\n",
    "for val in list1:\n",
    "    automobile[val]=le.fit_transform(automobile[val].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "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>Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Claim_Reason</th>\n",
       "      <th>Sales_Channel</th>\n",
       "      <th>Total_Claim_Amount</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>384</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5946</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>697</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1288</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>566</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8016</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>764</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>529</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2488</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer  Country  State_Code  State  Claim_Amount  Response  Coverage  \\\n",
       "0       600        0           1      1           276         0         0   \n",
       "1      5946        0           3      3           697         0         1   \n",
       "2        96        0           4      4          1288         0         2   \n",
       "3      8016        0           2      2           764         0         0   \n",
       "4      2488        0           1      1           281         0         0   \n",
       "\n",
       "   Education  Employment_Status  Gender  ...  Policy_Type  Policy  \\\n",
       "0          0                  1       0  ...            0       2   \n",
       "1          0                  4       0  ...            1       5   \n",
       "2          0                  1       0  ...            1       5   \n",
       "3          0                  4       1  ...            0       1   \n",
       "4          0                  1       1  ...            1       3   \n",
       "\n",
       "   Claim_Reason  Sales_Channel  Total_Claim_Amount  Vehicle_Class  \\\n",
       "0             0              0                 384              5   \n",
       "1             3              0                1131              0   \n",
       "2             0              0                 566              5   \n",
       "3             0              2                 529              3   \n",
       "4             0              0                 138              0   \n",
       "\n",
       "   Vehicle_Size  Year  Month  Day  \n",
       "0             1  2011      2   24  \n",
       "1             1  2011      1   31  \n",
       "2             1  2011      2   19  \n",
       "3             1  2011      1   20  \n",
       "4             1  2011      2    3  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer                         0.000000\n",
       "Country                          0.000000\n",
       "State_Code                       0.021065\n",
       "State                            0.021065\n",
       "Claim_Amount                     3.032259\n",
       "Response                         2.037567\n",
       "Coverage                         1.031310\n",
       "Education                        0.333148\n",
       "Employment_Status                0.832147\n",
       "Gender                           0.039866\n",
       "Income                           0.371418\n",
       "Location_Code                    0.009511\n",
       "Marital_Status                  -0.107553\n",
       "Monthly_Premium Auto             2.123546\n",
       "Months_Since_Last_Claim          0.278586\n",
       "Months_Since_Policy_Inception    0.040165\n",
       "Number_of_Open_Complaints        2.783263\n",
       "Number_of_Policies               1.253333\n",
       "Policy_Type                     -0.468383\n",
       "Policy                          -0.427639\n",
       "Claim_Reason                     0.733526\n",
       "Sales_Channel                    0.506774\n",
       "Total_Claim_Amount               1.714602\n",
       "Vehicle_Class                    0.395146\n",
       "Vehicle_Size                     0.072742\n",
       "Year                             0.000000\n",
       "Month                            0.145359\n",
       "Day                              0.006796\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see there is skewness present in this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>...</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Claim_Reason</th>\n",
       "      <th>Sales_Channel</th>\n",
       "      <th>Total_Claim_Amount</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3322</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>384</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5946</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2820</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>566</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8016</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>529</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2488</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2510</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer  Country  State_Code  State  Response  Coverage  Education  \\\n",
       "0       600        0           1      1         0         0          0   \n",
       "1      5946        0           3      3         0         1          0   \n",
       "2        96        0           4      4         0         2          0   \n",
       "3      8016        0           2      2         0         0          0   \n",
       "4      2488        0           1      1         0         0          0   \n",
       "\n",
       "   Employment_Status  Gender  Income  ...  Policy_Type  Policy  Claim_Reason  \\\n",
       "0                  1       0    3322  ...            0       2             0   \n",
       "1                  4       0       0  ...            1       5             3   \n",
       "2                  1       0    2820  ...            1       5             0   \n",
       "3                  4       1       0  ...            0       1             0   \n",
       "4                  1       1    2510  ...            1       3             0   \n",
       "\n",
       "   Sales_Channel  Total_Claim_Amount  Vehicle_Class  Vehicle_Size  Year  \\\n",
       "0              0                 384              5             1  2011   \n",
       "1              0                1131              0             1  2011   \n",
       "2              0                 566              5             1  2011   \n",
       "3              2                 529              3             1  2011   \n",
       "4              0                 138              0             1  2011   \n",
       "\n",
       "   Month  Day  \n",
       "0      2   24  \n",
       "1      1   31  \n",
       "2      2   19  \n",
       "3      1   20  \n",
       "4      2    3  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=automobile.drop('Claim_Amount',axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     276\n",
       "1     697\n",
       "2    1288\n",
       "3     764\n",
       "4     281\n",
       "Name: Claim_Amount, dtype: int32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=automobile['Claim_Amount']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in x.columns[4:]:\n",
    "    if x.skew().loc[col]>0.55:\n",
    "        x[col]=np.log1p(x[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that it contains alot of outleirs and it's better to drop those outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer                         0.000000\n",
       "Country                          0.000000\n",
       "State_Code                       0.021065\n",
       "State                            0.021065\n",
       "Claim_Amount                     3.032259\n",
       "Response                         2.037567\n",
       "Coverage                         1.031310\n",
       "Education                        0.333148\n",
       "Employment_Status                0.832147\n",
       "Gender                           0.039866\n",
       "Income                           0.371418\n",
       "Location_Code                    0.009511\n",
       "Marital_Status                  -0.107553\n",
       "Monthly_Premium Auto             2.123546\n",
       "Months_Since_Last_Claim          0.278586\n",
       "Months_Since_Policy_Inception    0.040165\n",
       "Number_of_Open_Complaints        2.783263\n",
       "Number_of_Policies               1.253333\n",
       "Policy_Type                     -0.468383\n",
       "Policy                          -0.427639\n",
       "Claim_Reason                     0.733526\n",
       "Sales_Channel                    0.506774\n",
       "Total_Claim_Amount               1.714602\n",
       "Vehicle_Class                    0.395146\n",
       "Vehicle_Size                     0.072742\n",
       "Year                             0.000000\n",
       "Month                            0.145359\n",
       "Day                              0.006796\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automobile.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Standard Scaling to get optimized result\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()\n",
    "rfr=RandomForestRegressor()\n",
    "lg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********************** LinearRegression ************\n",
      "\n",
      "\n",
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
      "\n",
      "\n",
      "score 0.18238378129117505\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 403.96872684696683\n",
      "MSE 413077.1825782042\n",
      "RMSE 642.7108078896792\n",
      "R-square 0.15815788689220045\n",
      "*********************** RandomForestRegressor ************\n",
      "\n",
      "\n",
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "                      max_features='auto', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                      n_jobs=None, oob_score=False, random_state=None,\n",
      "                      verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "score 0.9425888954580233\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 146.35968624589566\n",
      "MSE 154020.5145384896\n",
      "RMSE 392.45447447887454\n",
      "R-square 0.6861096160970229\n",
      "*********************** LogisticRegression ************\n",
      "\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "\n",
      "\n",
      "score 0.21633036133270764\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 330.07624954396204\n",
      "MSE 500322.1820503466\n",
      "RMSE 707.334561611651\n",
      "R-square -0.019645482093962885\n"
     ]
    }
   ],
   "source": [
    "#prepare models\n",
    "models=[]\n",
    "models.append(('LinearRegression',lr))\n",
    "models.append(('RandomForestRegressor',rfr))\n",
    "models.append(('LogisticRegression',lg))\n",
    "#evaluate each model\n",
    "Model=[]\n",
    "Score=[]\n",
    "MAE=[]\n",
    "MSE=[]\n",
    "RMSE=[]\n",
    "RSquare=[]\n",
    "for name,model in models:\n",
    "    print('***********************',name,'************')\n",
    "    Model.append(name)\n",
    "    print('\\n')\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pred=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    sr=model.score(x_train,y_train)\n",
    "    print('score',sr)\n",
    "    Score.append(sr*100)\n",
    "    print('\\n')\n",
    "    print('Performance Measures:')\n",
    "    mae=metrics.mean_absolute_error(y_test, pred)\n",
    "    print('MAE',mae)\n",
    "    MAE.append(mae)\n",
    "    mse=metrics.mean_squared_error(y_test, pred)\n",
    "    print('MSE',mse)\n",
    "    MSE.append(mse)\n",
    "    rmse=np.sqrt(metrics.mean_squared_error(y_test, pred))\n",
    "    print('RMSE',rmse)\n",
    "    RMSE.append(rmse)\n",
    "    rsquare=metrics.r2_score(y_test,pred)\n",
    "    print('R-square',rsquare)\n",
    "    RSquare.append(rsquare)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>models</th>\n",
       "      <th>score</th>\n",
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       "      <th>RMSE</th>\n",
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       "      <td>0</td>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>18.238378</td>\n",
       "      <td>403.968727</td>\n",
       "      <td>413077.182578</td>\n",
       "      <td>642.710808</td>\n",
       "      <td>0.158158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>94.258890</td>\n",
       "      <td>146.359686</td>\n",
       "      <td>154020.514538</td>\n",
       "      <td>392.454474</td>\n",
       "      <td>0.686110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>21.633036</td>\n",
       "      <td>330.076250</td>\n",
       "      <td>500322.182050</td>\n",
       "      <td>707.334562</td>\n",
       "      <td>-0.019645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  models      score         MAE            MSE        RMSE  \\\n",
       "0       LinearRegression  18.238378  403.968727  413077.182578  642.710808   \n",
       "1  RandomForestRegressor  94.258890  146.359686  154020.514538  392.454474   \n",
       "2     LogisticRegression  21.633036  330.076250  500322.182050  707.334562   \n",
       "\n",
       "    RSquare  \n",
       "0  0.158158  \n",
       "1  0.686110  \n",
       "2 -0.019645  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'models':Model,'score':Score,'MAE':MAE,'MSE':MSE,'RMSE':RMSE,'RSquare':RSquare},columns=['models','score','MAE','MSE','RMSE','RSquare'])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above you can see that DecisionTreeRegressor is best model compare to all other models .In terms of Rsquare is also good and error is also too low compare to all other algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted claim_amount [ 410.7  721.1 1041.7 ...  304.3 1185.2  343.6]\n",
      "actual claim_amount 3893     411\n",
      "9        738\n",
      "8946     720\n",
      "7669     517\n",
      "2009    1223\n",
      "        ... \n",
      "1411     261\n",
      "7026     324\n",
      "7171     314\n",
      "3988    1595\n",
      "951      350\n",
      "Name: Claim_Amount, Length: 2741, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "pred=rfr.predict(x_test)\n",
    "print(\"predicted claim_amount\",pred)\n",
    "print(\"actual claim_amount\",y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(pred)\n",
    "x.to_csv('decision_automobile.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 410.7,  721.1, 1041.7, ...,  304.3, 1185.2,  343.6])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#save the model as a pickle in a file\n",
    "joblib.dump(rfr,'automobile.obj')\n",
    "#load the model from a file\n",
    "rfr_from_joblib=joblib.load('automobile.obj')\n",
    "#use the loaded model to make prediction\n",
    "rfr_from_joblib.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "upto now it's done"
   ]
  }
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