1. FINAL_PROJECT_DATA MINING_Hyperparamater Optimization-Fixed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FINAL PROJECT DATA MINING\n",
    "#custom data transform for removing columns with few unique values\n",
    "from numpy import unique, mean, std, where, logical_or\n",
    "from pandas import read_csv\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
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       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000</td>\n",
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       "      <td>2</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
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       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
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       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       X1  X2  X3  X4  X5  X6  X7  X8  X9  X10  ...    X15    X16    X17  \\\n",
       "0   20000   2   2   1  24   2   2  -1  -1   -2  ...      0      0      0   \n",
       "1  120000   2   2   2  26  -1   2   0   0    0  ...   3272   3455   3261   \n",
       "2   90000   2   2   2  34   0   0   0   0    0  ...  14331  14948  15549   \n",
       "3   50000   2   2   1  37   0   0   0   0    0  ...  28314  28959  29547   \n",
       "4   50000   1   2   1  57  -1   0  -1   0    0  ...  20940  19146  19131   \n",
       "\n",
       "    X18    X19    X20   X21   X22   X23  Y  \n",
       "0     0    689      0     0     0     0  1  \n",
       "1     0   1000   1000  1000     0  2000  1  \n",
       "2  1518   1500   1000  1000  1000  5000  0  \n",
       "3  2000   2019   1200  1100  1069  1000  0  \n",
       "4  2000  36681  10000  9000   689   679  0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel ('CREDIT.xls')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X1     int64\n",
       "X2     int64\n",
       "X3     int64\n",
       "X4     int64\n",
       "X5     int64\n",
       "X6     int64\n",
       "X7     int64\n",
       "X8     int64\n",
       "X9     int64\n",
       "X10    int64\n",
       "X11    int64\n",
       "X12    int64\n",
       "X13    int64\n",
       "X14    int64\n",
       "X15    int64\n",
       "X16    int64\n",
       "X17    int64\n",
       "X18    int64\n",
       "X19    int64\n",
       "X20    int64\n",
       "X21    int64\n",
       "X22    int64\n",
       "X23    int64\n",
       "Y      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Y'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11',\n",
       "       'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18', 'X19', 'X20', 'X21',\n",
       "       'X22', 'X23', 'Y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show all names of features \n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:\t\t\n",
    "\n",
    "\n",
    "X1 = Amount of the given credit (NT dollar).\t\n",
    "X2 = Gender (1 = male; 2 = female).\t\n",
    "X3 = Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\t\n",
    "X4 = Marital status (1 = married; 2 = single; 3 = others).\t\n",
    "X5 = Age(year)\t\n",
    "X6 = History of past payment the repayment status in September, 2005; \t\n",
    "X7 = the repayment status in Agustus  2005\t\n",
    "X8 = the repayment status in July, 2005\t\n",
    "X9 = the repayment status in June, 2005\t\n",
    "X10 = the repayment status in Mei, 2005\t\n",
    "X11 = the repayment status in April, 2005. \t\n",
    "X12 = amount of bill statement in September, 2005.\tAmount of bill statement (NT dollar).\t \n",
    "X13 = amount of bill statement in August, 2005.\t\n",
    "X14 = amount of bill statement in July, 2005.\t\n",
    "X15\tamount of bill statement in June, 2005.\t\n",
    "X16\tamount of bill statement in Mei, 2005.\t\n",
    "X17\tamount of bill statement in April, 2005.\t\n",
    "X18\tamount paid in September, 2005.\tX18-X23:Amount of previous payment (NT dollar).\t\n",
    "X19\tamount paid in August, 2005.\t\n",
    "X20\tamount paid in July, 2005.\t\n",
    "X21\tamount paid in June, 2005.\t\n",
    "X22\tamount paid in Mei, 2005.\t\n",
    "X23\tamount paid in April, 2005.\t\n",
    "<b>Y default payment next month\tTarget Variable,  default payment (Yes = 1, No = 0) </b>\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
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       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   X1            X2            X3            X4            X5  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "                 X6            X7            X8            X9           X10  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...            X15            X16            X17            X18  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "                X19           X20            X21            X22  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "                 X23             Y  \n",
       "count   30000.000000  30000.000000  \n",
       "mean     5215.502567      0.221200  \n",
       "std     17777.465775      0.415062  \n",
       "min         0.000000      0.000000  \n",
       "25%       117.750000      0.000000  \n",
       "50%      1500.000000      0.000000  \n",
       "75%      4000.000000      0.000000  \n",
       "max    528666.000000      1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### We consider summary statistics result towards numeric features such as X1-X23 (int64). The details of the atributes/features are discribed above\n",
    "\n",
    "We can interpret feature X1 (Amount of the given credit (dollar)) has <b> mean / average 167484.322667  greater than standar deviation 129747.661567. It's mean that the data has lower volatility .<b>\n",
    "\n",
    "A lower volatility means that the value does not fluctuate dramatically, but changes in value at a steady pace over a period of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#get correlation of each features in dataset\n",
    "corrmat = df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "#plot heat map\n",
    "g = sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking correlation \n",
    "From the result above we can see positive and negative correlation between each independent features &dependen feature. We check this whether each and every independent featues is useful for dependent feature.\n",
    "\n",
    "Negative values mean tht features not that important features\n",
    "we can see from diagram above important features are (give positive values) : X3,X5,X6,X7,X8,X9,X10,X11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spliting dataset into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INDEPENDENCE & DEPENDENT FEATURES\n",
    "X = df.drop('Y', axis=1).values\n",
    "y = df['Y'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "#secara default train test data di bagi train 70% test 30%    \n",
    "X_train,X_test, y_train,y_test= train_test_split(X,y,test_size=0.3, #test 30%\n",
    "                stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Models\n",
    "\n",
    "* Linear Regression\n",
    "* Logistic Regression\n",
    "* kNN\n",
    "* SVM\n",
    "* Naive Bayes \n",
    "* Linear Discriminan\n",
    "* Gradient Boosting Classifier\n",
    "* Decision Tree\n",
    "* Xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean X and mean Y\n",
    "X_lin = df['X1'].values\n",
    "y_lin = df['X12'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16199038569777965 24092.48087289529\n"
     ]
    }
   ],
   "source": [
    "mean_x = np.mean(X_lin)\n",
    "mean_y = np.mean(y_lin)\n",
    "\n",
    "#Total\n",
    "m = len(X_lin)\n",
    "\n",
    "#calculate b1 and b0\n",
    "number = 0\n",
    "denom = 0\n",
    "for i in range(m):\n",
    "    number += (X_lin[i] - mean_x)* (y_lin[i]-mean_y)\n",
    "    denom += (X_lin[i] - mean_x)** 2\n",
    "b1 = number / denom\n",
    "b0 = mean_y - (b1 * mean_x)\n",
    "\n",
    "#print coef\n",
    "print(b1,b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting value & Regression Line\n",
    "max_x = np.max(X_lin) + 100\n",
    "min_x = np.min(X_lin) - 100\n",
    "\n",
    "#calculate line value x & y\n",
    "x1 = np.linspace(min_x, max_x, 1000)\n",
    "y1 = b0 + b1 * x1\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (7, 7)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "#Ploting line\n",
    "plt.plot(x1,y1,color='red', label='Regression Line')\n",
    "\n",
    "\n",
    "#Plot Scatter points\n",
    "plt.scatter(X_lin, y_lin, c='blue', label='Scatter Plot')\n",
    "\n",
    "plt.xlabel('X1 - Amount of the given credit')\n",
    "plt.ylabel('X12 - Amount of bill statement')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph above we can see x:X1 (Amount of the given credit) , y : X12 (amount of bill statement in September, 2005).\n",
    "        the amount of bill increases in september 2005 respect to the number of credits extended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08147020781391412\n"
     ]
    }
   ],
   "source": [
    "ss_t = 0\n",
    "ss_r = 0\n",
    "for i in range(m):\n",
    "    y_predReg = b0 + b1 * X_lin[i]\n",
    "    ss_t += (y_lin[i]- mean_y)** 2\n",
    "    ss_r += (y_lin[i]- y_predReg)** 2\n",
    "r2 = 1 - (ss_r/ss_t)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.12\n",
      "Accuracy of Logistic regression classifier on test set: 0.13\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "plt.style.use('classic')\n",
    "sns.set()\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,classification_report, confusion_matrix\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "      .format(reg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "      .format(reg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient =  [-9.88089539e-08 -1.63534954e-02 -1.61037950e-02 -2.07116740e-02\n",
      "  1.49500665e-03  9.65762803e-02  2.11892549e-02  9.47863309e-03\n",
      "  1.90892147e-03  7.61970280e-03 -2.33792876e-03 -5.68885342e-07\n",
      " -2.64014092e-08  1.37689021e-07 -1.14448507e-07 -5.39666983e-09\n",
      "  1.98762327e-07 -7.26814849e-07 -3.86525490e-07  1.65347119e-08\n",
      " -4.86395889e-07 -2.67191568e-07 -2.33329852e-08]\n",
      "Intercept = 0.3123178617287227\n"
     ]
    }
   ],
   "source": [
    "# Show Coefficent and Intercept\n",
    "print('Coefficient = ', reg.coef_)\n",
    "print('Intercept =', reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12742396552820068"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictReg = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.23142096, 0.2033401 , 0.19385311, ..., 0.17906777, 0.07306502,\n",
       "       0.23560139])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predictReg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.78\n",
      "Accuracy of Logistic regression classifier on test set: 0.78\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', random_state=0)\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "      .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "      .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7786666666666666"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "[[7008    1]\n",
      " [1991    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      1.00      0.88      7009\n",
      "           1       0.00      0.00      0.00      1991\n",
      "\n",
      "    accuracy                           0.78      9000\n",
      "   macro avg       0.39      0.50      0.44      9000\n",
      "weighted avg       0.61      0.78      0.68      9000\n",
      "\n",
      "Accuracy of Logistic Regression classifier on training set: 0.78\n",
      "Accuracy of Logistic Regression classifier on test set: 0.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      1.00      0.88      7009\n",
      "           1       0.00      0.00      0.00      1991\n",
      "\n",
      "    accuracy                           0.78      9000\n",
      "   macro avg       0.39      0.50      0.44      9000\n",
      "weighted avg       0.61      0.78      0.68      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "pred = logreg.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, pred)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "print('Accuracy of Logistic Regression classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic Regression classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. kNN ( K-Nearest Neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Scalling - Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.86832980505137"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.84\n",
      "Accuracy of K-NN classifier on test set: 0.79\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=93)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import KNeighborsClassifier from sklearn.neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))\n",
    "\n",
    "# Create a k-NN classifier with 93 neighbors\n",
    "#Define model:Init K-NN (94-1)\n",
    "knn = KNeighborsClassifier(n_neighbors=93,p=2,metric='euclidean')\n",
    "knn.fit(X_train, y_train)\n",
    "# Fit the classifier to the data\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2212222222222222"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "[[   0 7009]\n",
      " [   0 1991]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      7009\n",
      "           1       0.22      1.00      0.36      1991\n",
      "\n",
      "    accuracy                           0.22      9000\n",
      "   macro avg       0.11      0.50      0.18      9000\n",
      "weighted avg       0.05      0.22      0.08      9000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bqdesy/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      7009\n",
      "           1       0.22      1.00      0.36      1991\n",
      "\n",
      "    accuracy                           0.22      9000\n",
      "   macro avg       0.11      0.50      0.18      9000\n",
      "weighted avg       0.05      0.22      0.08      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#Evaluate Model\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "pred = knn.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, pred)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3622964243471931\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2212222222222222\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. SVM ( Support Vector Machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, gamma=1, random_state=50)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(C=10,gamma=1,degree=3, random_state=50, shrinking=True,verbose=False)\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 0.97\n",
      "Accuracy of SVM classifier on test set: 0.77\n",
      "Confusion matrix\n",
      "[[6486  523]\n",
      " [1568  423]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.86      7009\n",
      "           1       0.45      0.21      0.29      1991\n",
      "\n",
      "    accuracy                           0.77      9000\n",
      "   macro avg       0.63      0.57      0.57      9000\n",
      "weighted avg       0.73      0.77      0.73      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.86      7009\n",
      "           1       0.45      0.21      0.29      1991\n",
      "\n",
      "    accuracy                           0.77      9000\n",
      "   macro avg       0.63      0.57      0.57      9000\n",
      "weighted avg       0.73      0.77      0.73      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Model Score\n",
    "print('Accuracy of SVM classifier on training set: {:.2f}'\n",
    "     .format(svm.score(X_train, y_train)))\n",
    "print('Accuracy of SVM classifier on test set: {:.2f}'\n",
    "     .format(svm.score(X_test, y_test)))\n",
    "\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "predSVM = svm.predict(X_test)\n",
    "print(confusion_matrix(y_test, predSVM))\n",
    "print(classification_report(y_test, predSVM))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, predSVM)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='d')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, predSVM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2880490296220633\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test,predSVM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7676666666666667\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,predSVM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GNB classifier on training set: 0.74\n",
      "Accuracy of GNB classifier on test set: 0.74\n",
      "Confusion matrix\n",
      "[[5445 1564]\n",
      " [ 767 1224]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.78      0.82      7009\n",
      "           1       0.44      0.61      0.51      1991\n",
      "\n",
      "    accuracy                           0.74      9000\n",
      "   macro avg       0.66      0.70      0.67      9000\n",
      "weighted avg       0.78      0.74      0.75      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.78      0.82      7009\n",
      "           1       0.44      0.61      0.51      1991\n",
      "\n",
      "    accuracy                           0.74      9000\n",
      "   macro avg       0.66      0.70      0.67      9000\n",
      "weighted avg       0.78      0.74      0.75      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of GNB classifier on training set: {:.2f}'\n",
    "     .format(gnb.score(X_train, y_train)))\n",
    "print('Accuracy of GNB classifier on test set: {:.2f}'\n",
    "     .format(gnb.score(X_test, y_test)))\n",
    "\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "predgnb = gnb.predict(X_test)\n",
    "print(confusion_matrix(y_test, predgnb))\n",
    "print(classification_report(y_test, predgnb))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, predgnb)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, predgnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.512241054613936\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test,predgnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.741\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,predgnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LDA classifier on training set: 0.81\n",
      "Accuracy of LDA classifier on test set: 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train, y_train)\n",
    "print('Accuracy of LDA classifier on training set: {:.2f}'\n",
    "     .format(lda.score(X_train, y_train)))\n",
    "print('Accuracy of LDA classifier on test set: {:.2f}'\n",
    "     .format(lda.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6779  230]\n",
      " [1469  522]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.97      0.89      7009\n",
      "           1       0.69      0.26      0.38      1991\n",
      "\n",
      "    accuracy                           0.81      9000\n",
      "   macro avg       0.76      0.61      0.63      9000\n",
      "weighted avg       0.79      0.81      0.78      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "pred = lda.predict(X_test)\n",
    "plt.figure(figsize=(5, 4))\n",
    "print(confusion_matrix(y_test, pred))\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g',cmap=\"Blues\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Gradient boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Gradient boosting from sklearn\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB_classifier = GradientBoostingClassifier()\n",
    "GB_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Gradeint Boosting classifier on training set: 0.81\n",
      "Accuracy of Gradeint Boosting classifier on test set: 0.81\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Gradeint Boosting classifier on training set: {:.2f}'\n",
    "     .format(lda.score(X_train, y_train)))\n",
    "print('Accuracy of Gradeint Boosting classifier on test set: {:.2f}'\n",
    "     .format(lda.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "[[6647  362]\n",
      " [1231  760]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89      7009\n",
      "           1       0.68      0.38      0.49      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.76      0.67      0.69      9000\n",
      "weighted avg       0.81      0.82      0.80      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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3Xb3g5bKdOHGKocPG8uJf/szTf36cnNwjRD7yRz5Jz+D/Pv2c4mIrW9NSuO661qSn7+bBUVEAPPF4NM7Ozry96dUqx3sqdhErEl6t7lRNUxOqmdfZB51LS0vZtWsX2dnZWK1W3NzcsFgs9O7dG1dX18s6tj7o3HTog85Nz+V80Pnc02NqvY/HvDccPl99qrNHE11dXbnrrrvq6nQiIk3qOXO9zi8ixlWHT7N8/vnn+Pn52ddfe+01+vXrR69evZg5cybn/vMwQ0VFBXFxcQQGBtK3b19SUv77zkBBQQETJkzA39+fIUOGsG/fvhqfX8lcRIzLVln7xQElJSXExsbyU9X622+/Ze3atWzcuJEdO3Zw+vRp1q5dC0BycjKHDh1i+/btrF69mqVLl5KVdeEN7tjYWPz8/Ni9ezdRUVHMnDnTPk/TpSiZi4hx1dHIfNmyZfTr99/7Obm5uVRWVlL5nzKPk5MTJtOFF/e2bNlCZGQkZrOZrl27EhYWxubNmykqKiI9PZ0pU6bg4uLC8OHDMZvNZGRk1CgGJXMRMay6eM78yy+/ZO/evYwfP97eFhwczPXXX8+AAQMICAjg7Nmz9v6cnBx8fX3t2/r6+pKZmUlubi6tW7fGbDZf1FcTSuYiYlxXeWReVlbGvHnzePrpp+0vdcGFp/e6d+/ORx99xK5du/D09GTJkiUAWK1W+ygdwGQyYbVaKS4uvujJPpPJRElJSY1i0ayJImJcDpRN8vPzKSgoqLbPy8sLb29v+/ry5csJCQnBz8+PY8eOVWm/6aab7CPwGTNmMHHiRObOnWt/z+YnJSUleHh44ObmVqX9pz53d/caxa1kLiLG5cANzZSUFFasWFFtX3R0NFOnTrWvb9u2jYKCAtavX2+/+dmzZ0+6detW5c32Fi1a0OI/E+JZLBZycnKwWCwAZGdnY7FY8PHxobCwkKKiIjw9PQHIyspi1KhRNYpbyVxEjMuBkXl4eDghISHV9nl5eVVZT0tLs//+2LFj9O/fnz179pCcnExSUhL33HMPrVq1YsWKFQwcOBCAwYMHk5iYiL+/P3l5eaSmppKUlISnpyfBwcHEx8cza9Ys0tLSKCwspGfPmn2HQMlcRAzL5kAy9/b2rlJKccSYMWM4ceIEo0aNory8nAEDBthnh42IiCAvL4/Q0FCcnZ2ZPXu2/fn0BQsWEBMTQ1BQEB06dCAhIaHG33yos9f5rya9zt906HX+pudyXuc/Oy2s1vuY41MdPl990shcRIyrCb3Or2QuIsalKXBFRAxAyVxEpPEzwC3BGlMyFxHj0shcRMQAlMxFRBo/R54zb6yUzEXEuJTMRUQMoOk8Zq5kLiLGpTKLiIgRKJmLiBiAyiwiIo2fyiwiIkagkbmISOOnkbmIiBFoZC4i0vg58AnQRkvJXESMS8lcRKTx08hcRMQIlMxFRBo/jcxFRAxAyVxExACaUjJvVt8BiIhcNTan2i8O+vzzz/Hz87Ovp6Sk0K9fPwICAoiLi6OiogKAiooK4uLiCAwMpG/fvqSkpNj3KSgoYMKECfj7+zNkyBD27dtX4/MrmYuIYdkqa784oqSkhNjYWPsHpPfv3098fDxJSUls27aNb775hrfffhuA5ORkDh06xPbt21m9ejVLly4lKysLgNjYWPz8/Ni9ezdRUVHMnDnT/kPgUpTMRcSwbJVOtV4csWzZMvr162dff//99xk6dCidO3fm2muvJSoqik2bNgGwZcsWIiMjMZvNdO3albCwMDZv3kxRURHp6elMmTIFFxcXhg8fjtlsJiMjo0YxKJmLiGHVxcj8yy+/ZO/evYwfP97elp2dja+vr33dx8eHw4cPA5CTk1Olz9fXl8zMTHJzc2ndujVms/mivprQDVARMSybAzXw/Px8CgoKqu3z8vLC29vbvl5WVsa8efNYvHgxzZs3t7dbrVZMJpN93c3NDavVWm2fyWTCarVSXFyMq6trlfOZTCZKSkpqFLeSuYgYliMj7ZSUFFasWFFtX3R0NFOnTrWvL1++nJCQEPz8/Dh27Ji93WQyUVpaal+3Wq24u7tX21dSUoKHhwdubm5V2n/q+2m/S1EyFxHDcqQGHh4eTkhISLV9Xl5eVda3bdtGQUEB69evt9/87NmzJwMHDiQ7O9u+XXZ2Np06dQLAYrGQk5ODxWKx91ksFnx8fCgsLKSoqAhPT08AsrKyGDVqVI3iVjIXEcOyOTCdube3d5VSyq9JS0uz//7YsWP079+fPXv28PXXXzN58mRGjBiBl5cXq1evZvjw4QAMHjyYxMRE/P39ycvLIzU1laSkJDw9PQkODiY+Pp5Zs2aRlpZGYWEhPXv2rFEsSuYiYliOPp1yuW6//XZmzJjBpEmTOHfuHMOGDWP06NEAREREkJeXR2hoKM7OzsyePdv+fPqCBQuIiYkhKCiIDh06kJCQgIuLS43O6WSz/fLPrq1bt17yAL///e9rdKKr6fyJw/UdgtQRt/b9Lr2RGEp52b8d3jf7joG13sf3y20On68+/erIfNGiRb+6s5OTU4NI5iIi1XGkzNJY/Woy3759e13FISJyxdVXmaU+1Lhm/sMPP3D8+HH7Hdvz589z8ODBKg/Ki4g0JI48Z95Y1SiZJyQksHz5cpydnXFycqK8vBybzUaPHj2UzEWkwWpKsybWKJlv2LCBdevWUVlZyQcffMD8+fNZsGBBje+yiojUh8omNDKv0dws586dIygoiJtuuon9+/fj4uLC448/zrZtjfOur4g0DTabU62XxqpGI/N27dqRn5+Pt7c3x44do6ysDFdXV86ePXu14xMRcZhugP7M4MGDefjhh/nrX/9KcHAw06dPx8XFhZtvvvlqxyci4rCm9Ghijcosjz32GDNmzMDd3Z358+dz/fXX07x5c55//vmrHZ+IiMPqaj7zhuBX3wBtLPQGaNOhN0Cbnst5A3R/p7Ba73Pr4VSHz1efalRmGTt2LE5O1f/ESk5OvqIBiYhcKY35hmZt1SiZDxgwoMp6YWEhqamp3H///VclKBGRK6Hx1x1qrkbJPDIy8qK24cOH8+STT/LYY49d8aBERK6EpvScucNT4P7vN+1ERBoilVl+5udT4Z4/f54PPviALl26XJWgRESuBJVZfubnU+E2b94cX19f4uLirkZMtdbn9vH1HYLUES/3lvUdgjQiKrP8zMcff0yzZhc/kv7jjz9e8YBERK6UplRmqdFLQ9V9g85mszFwYO2/4iEiUlcqbU61XhqrXxyZ5+bmEh0djc1mo6SkhKFDh1bpLy4urvFHT0VE6kMTKpn/cjLv2LEjM2bMoLCwkLi4uIseT3R1dSUwMPCqBygi4qjGPNKurV+tmYeEhADQqVMnLBYLbm5uuLi4cPjwYVq1asW1115bJ0GKiDhCNfOfOXv2LHfddRcHDhwA4KOPPiI0NJQvvvjiqgYnInI5Kh1YGqsaJfPFixezePFibr/9dgAmT57Ms88+y8KFC69qcCIil8OGU60XR7z99tuEhITg7+/P2LFjycrKwmaz8eKLL9K3b1969erFn/70J/s3ICoqKoiLiyMwMJC+ffuSkpJiP1ZBQQETJkzA39+fIUOGsG/fvhrFUKNkfuTIEX73u99Vabv77rvJzc2t6bWKiNS5Slvtl9rKzMxk0aJFrFq1ii+++IKePXsyb9483nrrLdLT03nnnXfYvn07JSUlvPDCC8CFCQoPHTrE9u3bWb16NUuXLiUrKwuA2NhY/Pz82L17N1FRUcycOZOKiopLxlGjZN6xY0e2b99epW3nzp3ceOONtb1uEZE6U4lTrZfa6ty5Mzt27ODmm2+mpKSEoqIiWrduzenTp3n00Ufx8vLCw8ODkSNH8vXXXwOwZcsWIiMjMZvNdO3albCwMDZv3kxRURHp6elMmTIFFxcXhg8fjtlsJiMj45Jx1OiloenTpzNjxgx69epFu3btyMvLY8+ePSxfvrzWFy4iUlccLZvUloeHB7t372b8+PF4eHjw+uuv07Vr1yrb7Ny5k1tuuQWAnJwcfH197X2+vr5kZGSQm5tL69atMZvNVfoyMzMJDg7+1RhqNDIPCQnhnXfeoUePHjg5OREQEMDcuXNZv359Ta9VRKTO1eUNUH9/f7766ismTZrEo48+SllZmb1v06ZNfPjhh0RHRwNgtVoxmUz2fpPJhNVqpbi4GFdX1yrHNZlMlJSUXPL8NZ41sXPnzkRFRfHuu++SnJxMZmYmffr0qenuIiJ1zpGReX5+PgUFBdX2eXl5/eLLki4uLgA88sgjrFmzhoMHD3LrrbeyevVq1q5dy5o1a+ylaZPJRGlpqX3fkpISPDw8cHNzq9L+U5+7u/sl465RMs/Pz+eNN94gJSWF06dPExUVRWJiIu3atavJ7iIi9cKRkXZKSgorVqyoti86OpqpU6dWadu5cydvvfWWvexcWVnJ+fPnMZvNLFq0iK1bt/LGG2/QuXNn+z4Wi4WcnBwsFgsA2dnZWCwWfHx8KCwspKioCE9PTwCysrIYNWrUJeP+1WT+9ddf89prr7Ft2zaCg4N54YUXmD17NqNHj6ZNmzaXPLiISH1yJJmHh4fbX5j8OS8vr4vaunXrRkZGBp988glBQUGsXLmSm266iX379vHhhx/y5ptvXpQvBw8eTGJiIv7+/uTl5ZGamkpSUhKenp4EBwcTHx/PrFmzSEtLo7CwsNr5sX7uV5P5gw8+yAMPPMBHH32kUbiINDqOlFm8vb1rNe+Ul5cX8fHxLFy4kOPHjxMQEEB8fDzjxo3j5MmThIaG2re98cYbee+994iIiCAvL4/Q0FCcnZ2ZPXs2fn5+ACxYsICYmBiCgoLo0KEDCQkJ9hLOr3Gy2X55+vYFCxawZcsWfHx8GDVqFEOGDOGuu+5i8+bNDWpkHtj+t/UdgtSRI8Un6jsEqWN5hd86vO+Wtg/Vep+hx/7m8Pnq068+zfLUU0/xySefEB4ezoYNG+jXrx9nz561v9YvItKQ1cVz5g3FJW+Aurq6cv/993P//ffzr3/9i7/97W/88Y9/5Prrr2fYsGH2R21ERBqapjQFbo2eM/9J165defrpp0lPT2f8+PF8/PHHVysuEZHL1pQm2qrxc+b/y8PDg9GjRzN69OgrHY+IyBVT6dR4yya15VAyFxFpDJpSmUXJXEQMqzGXTWpLyVxEDKuy6VRZlMxFxLga86OGtaVkLiKGpZq5iIgBqMwiImIAugEqImIAKrOIiBiAyiwiIgagMouIiAEomYuIGIBNZRYRkcZPI3MREQNQMhcRMQA9migiYgB6NFFExABUZhERMQAlcxERA2hKNfNafdBZRKQxqXSq/VJb77//PoMGDSIgIIAxY8Zw6NChKv0JCQmMHTvWvl5RUUFcXByBgYH07duXlJQUe19BQQETJkzA39+fIUOGsG/fvhrHoWQuIoZV6cBSG5mZmfz5z39myZIlfPbZZ/Tv35/o6Gh7//fff09iYmKVfZKTkzl06BDbt29n9erVLF26lKysLABiY2Px8/Nj9+7dREVFMXPmTCoqKmoUi5K5iBiWzYGlNo4ePcrDDz/MbbfdRvPmzRkzZgxZWVmcPXuWiooKYmNjGTlyZJV9tmzZQmRkJGazma5duxIWFsbmzZspKioiPT2dKVOm4OLiwvDhwzGbzWRkZNQoFiVzETGsSmy1XmqjX79+TJs2zb6+c+dO2rdvj9ls5tVXX6VHjx7cfvvtVfbJycnB19fXvu7r60tmZia5ubm0bt0as9l8UV9N6AaoiBiWI0+z5OfnU1BQUG2fl5cX3t7e1fb961//Ii4ujoULF5KVlcXmzZvZuHEj27Ztq7Kd1WrFZDLZ100mE1arleLiYlxdXatsazKZKCkpqVHcSuYiYliOPM2SkpLCihUrqu2Ljo5m6tSpF7Xv2rWL6dOnM3v2bO6++24efvhhYmJicHd3v2hbk8lEaWmpfb2kpAQPDw/c3NyqtP/UV90xqqNkLiKG5cjIPDw8nJCQkGr7vLy8Lmr76KOPmDt3LosWLWLgwIEcPXqU/fv328sv5eXllJWVMXToULZs2YLFYiEnJweLxQJAdnY2FosFHx8fCgsLKSoqwtPTE4CsrCxGjRpVo7iVzEXEsBx51NDb2/sXSyk/l5OTw5w5c1i5ciVBQUEAtG/fnq+//tq+zbvvvstbb73F66+/DsDgwYNJTEzE39+fvLw8UlNTSUpKwtPTk+DgYOLj45k1axZpaWkUFhbSs2fPGsWiZC4ihlXbG5q1tXbtWkpKSpgyZUqV9rS0NNq0aVPtPhEREeTl5REaGoqzszOzZ8/Gz88PgAULFhATE0NQUBAdOnQgISEBFxeXGsXiZLPZGv1LUoHtf1vfIUgdOVJ8or5DkDqWV/itw/vG+I6u9T4Ls//q8Pnqk0bmImJYmptFRMQArnaZpSFRMhcRw2o6qVzJXEQMTGUWEREDUJlFRMQAmk4qVzIXEQNTmUVExABsTWhsrmQuIobVlEbmms+8Abn1zm58/E2qfd3zGk/mLZ1D2pfvsG3/Fl54dSFtOvx3zoiJMyJI3fMWO79PY13qKu7s3eOiY3bwac/fv32flte2rJNrkNq574EwDh3ZU2U5cvKfbHh7NQBTpkWyZ//fOZj7GZtSk+jcxde+75BhA/nHng/I/PceNqUm0eGGdvV0FQ3X1Z7PvCFRMm8AnJycGPHwMJb/dQnOLv/9x9ITz/6Rlq1b8kD/sYQFjqTwZCGLXnkagCEPhDI0fDBRI6bS/6ZQtqR8wJJ1z9K8RXP7/iFD+rP6nRVc08p80TmlYXh7YypdbuhpX+4dPJbTp8/yzPy/EDEhnIjIcMLvnUi3Tn3451ff8vLaJQDcfEtnXlr5HLOnz8PPN4jPdu0lOWVlPV9Nw3O1vzTUkCiZNwDRMZMY8fBQ1ix9rUp7s+bNeGXJq5w9XUSptZQNazdx653dcHZx5v2NaYwKGc/RH/Jo2foarmlp5szpImyVF/5zHDnuXh57chKJS16tj0sSB7Ro0YKExMUse+FlvvnnASZOepgF818k81A25eXlPL8gnplTYwEYGT6M7R+ns+v/9nD+/HleeG4FHW5oR0Dgxf86a8qa0shcNfMGYMOat1i+YBV3Bt1RpT1m8p+rrN8V2pfDB7M5X3YegBJrCf1D+7J4zQLKz5fz5KT5VFZeqBLu+OATNiW/S9sO1c/cJg3PhD88RHlFOWtWrcfN3Y0uN1vw8HQnbceb3HBjez7fvY+YxxcCcNMtndj/zwP2fSsrK8k+nItf15v44vOv6usSGhzVzKVOFRy79EyA99w3kIjHRvP8nL9Uaf90+276+N7Ns48vYeHLcfh06QjAyYJTGGBCzCbD2dmZyVMjefH5ldhsNlq1uoZmzZoRMSGcR8bNIPD2gZw6Vcja118CwN3DHWuxtcoxrNYS3D3c6iP8BsvmwK/GSsm8gXNycuLRxx/hT89M50/j57I3o+qo63zZeSrKK3h/YxqH/pVJcEjveopULseA3/XFycmJD1P/DkBZaRkAK+PXciT3KNZiKwvn/4U77ryNG25sT3GxFZObqcox3NxMnCsqrvPYG7JKB5bGqs7KLE899RROTr/+2Y9nnnmmjqJpHFxcXXjulT9zo+UGJg6bTE7mD/a+P/xpAh6e7iz7c8L/bO/M2TNF9RGqXKZBg0NIffcje5ns5MkfOXXyR8zX/PfmdbPm/7m57QTfH8iky02W//Y1a4Zvp44c/K5mX3JvKhrzSLu26mxkbrFY2LRpE87OzrRp06baRaqat3QOXm2vJ3Jo1UQO8OXurxjx8FBu73krzZs358HI+7jO+zo+2fp/9RStXI6Anj34LGNflba/vr6JGbMm0dHnBkwmV2LiZrLnsy85knuUt99K5fehA/jtXUE4Ozsz68nHKDh+gr17vv6FMzRNGplfBRMnTuTcuXN8++23zJs3r65O22h16NiOQff+jtKSUt7/4q0qfcN+8yCf/2Mvzz+5lPnL5tL6upYc+Of3PPbgHzl96nQ9RSyX48aO7Tl+LL9K26JnXsJqLWHje6/SunUrMj7dwx/GzQDgwLffM/XRJ3hm0Vzad2jL/n/+i3EPPWYf2csFlU3ovlGdfjauvLycBQsWMHXqVK677rordlx9Nq7p0Gfjmp7L+Wzcwz731Xqf9TlvO3y++lSnjya2aNGCuLi4ujyliDRhjfm58drSc+YiYlhN6QaokrmIGFZTuoOgZC4ihtWUyix6aUhEDKsu3wBdu3YtMTEx9vVdu3YxfPhwAgICmDhxIidPngSgoqKCuLg4AgMD6du3LykpKfZ9CgoKmDBhAv7+/gwZMoR9+/ZddJ5fomQuIoZVF8+Znz9/nvj4eJYsWWJv++GHH5g2bRpz584lIyODtm3bsnjxYgCSk5M5dOgQ27dvZ/Xq1SxdupSsrCwAYmNj8fPzY/fu3URFRTFz5kwqKipqFIeSuYgYls1mq/VSWwsWLGD//v2Eh4fb29577z0GDx5Mr169cHZ2Zs6cOUyaNAmALVu2EBkZidlspmvXroSFhbF582aKiopIT09nypQpuLi4MHz4cMxmMxkZGTWKQ8lcRAyrLqbAjY6OJjExscq7M9999x1ms5lx48bRu3dvYmNj7f05OTn4+vrat/X19SUzM5Pc3Fxat26N2Wy+qK8mdANURAzLkbJJfn4+BQUF1fZ5eXnh7e19UdvPnTlzhk2bNrFmzRo6depETEwMCxcuZPHixVitVkym/06SZjKZsFqtFBcX4+rqWuU4JpOJkpKSGsWtZC4ihuXIDc2UlBRWrFhRbV90dDRTp0695DFcXFwIDQ2le/fuAEyaNImIiAjgQoIuLS21b1tSUoKHhwdubm5V2n/qc3d3r1HcSuYiYliOlE3Cw8MJCQmptq+6UXh1fHx8+PHHH+3rFRUV9nq8xWIhJycHi+XCrJfZ2dlYLBZ8fHwoLCykqKgIT09PALKyshg1alSNzqlkLiKG5cgNTW9v74tKKbUVFhbG+PHj2bt3L926dWPVqlUMHDgQgMGDB5OYmIi/vz95eXmkpqaSlJSEp6cnwcHBxMfHM2vWLNLS0igsLKRnz541OqeSuYgYVn29AdqjRw8WLVpETEwMx48fJzg4mCeeeAKAiIgI8vLyCA0NxdnZmdmzZ+Pn5wdceDImJiaGoKAgOnToQEJCAi4uLjU6Z53Omni1aNbEpkOzJjY9lzNr4u9vDK31Plt/SHP4fPVJI3MRMaym9Dq/krmIGJYBCg81pmQuIoalkbmIiAFoPnMREQNoSt8AVTIXEcNqOqlcyVxEDEw1cxERA1AyFxExAD2aKCJiABqZi4gYgB5NFBExAJVZREQMQGUWERED0MhcRMQANDIXETEA3QAVETEAzc0iImIAGpmLiBiARuYiIgagkbmIiAFoZC4iYgBNaWTerL4DEBG5Wipttlovjti1axdDhgwhICCA0aNHk5mZCUBKSgr9+vUjICCAuLg4KioqAKioqCAuLo7AwED69u1LSkrKZV+rkrmIGJbNgV+1VVFRwYwZM4iNjeXzzz+nV69ezJ8/n/379xMfH09SUhLbtm3jm2++4e233wYgOTmZQ4cOsX37dlavXs3SpUvJysq6rGtVMhcRw7LZKmu91Nbp06cpLCyksvLCvs2aNcNkMvH+++8zdOhQOnfuzLXXXktUVBSbNm0CYMuWLURGRmI2m+natSthYWFs3rz5sq5VNXMRMay6eJ3/2muv5b777mPChAk0b96cli1bsmHDBhYtWkT//v3t2/n4+HD48GEAcnJy8PX1tff5+vqSkZFxWXEomYuIYTky0VZ+fj4FBQXV9nl5eeHt7V2lrby8HE9PT5KSkrjzzjtZuXIlM2bM4JprrsFkMtm3c3Nzw2q1AmC1Wqv0mUwme5+jlMxFxLAcGZmnpKSwYsWKavuio6OZOnVqlbatW7dy5MgRgoKCAJg2bRrr16+ne/fulJaW2rezWq24u7sDF5L3//aVlJTg4eFR61j/l5K5iBiWIyPz8PBwQkJCqu3z8vK6qO348eP2p1TgQs28efPmtGrViuzsbHt7dnY2nTp1AsBisZCTk4PFYrH3/fR7RymZi4hhOfKoobe390WllF/Tu3dvli1bxj/+8Q+CgoJYu3Yt3t7eTJw4kSlTpjBixAi8vLxYvXo1w4cPB2Dw4MEkJibi7+9PXl4eqampJCUl1TrW/6VkLiKGVRcvDXXt2pWFCxfyzDPPcPLkSbp3705CQgI+Pj7MmDGDSZMmce7cOYYNG8bo0aMBiIiIIC8vj9DQUJydnZk9ezZ+fn6XFYeTzQCf4ghs/9v6DkHqyJHiE/UdgtSxvMJvHd63TcvaJ8jjpw84fL76pJG5iBiWvjQkImIABig81JiSuYgYlmZNFBExAI3MRUQMQDVzERED0MhcRMQAVDMXETGApvSlISVzETEsjcxFRAxANXMREQNQmUVExAA0MhcRMYCmlMwNMWuiiEhT16y+AxARkcunZC4iYgBK5iIiBqBkLiJiAErmIiIGoGQuImIASuYiIgagZC4iYgBK5iIiBqBkLiJiAErmIiIGoGTeCH3xxRcMHTqUO+64gwkTJnDixIn6DknqwNq1a4mJianvMKSBUjJvZEpKSpg2bRrTpk3js88+w8fHh0WLFtV3WHIVnT9/nvj4eJYsWVLfoUgDpmTeyOzatYs2bdowcOBAXFxcmDFjBh999BHFxcX1HZpcJQsWLGD//v2Eh4fXdyjSgCmZNzI5OTn4+vra11u1aoW7uzu5ubn1F5RcVdHR0SQmJnLdddfVdyjSgCmZNzLFxcW4urpWaXNzc6OkpKSeIpKrzcvLq75DkEZAybyRcXNzo6ysrEqb1WrF3d29niISkYZAybyRsVgsZGdn29cLCws5d+4cHTt2rL+gRKTeKZk3Mr179yYvL48PP/yQsrIyli1bRkhICCaTqb5DE5F6pGTeyJhMJl5++WVWrVpFr169+OGHH4iLi6vvsESknumDziIiBqCRuYiIASiZi4gYgJK5iIgBKJmLiBiAkrmIiAEomYuIGICSuYiIASiZi2GdPn2a06dP13cYInVCyVwu2y233EKPHj3w9/fH39+fO++8kzFjxrBnz54rdg5/f392794NwJAhQ/j4448vuc/vf/97h6cGnjRpEsuXL3doX5H60KK+AxBjWL9+PbfddhsAlZWVJCcnExUVxY4dO2jZsuUVPdf7779fo+0KCwuv6HlFGjKNzOWKa9asGQ8++CDnzp0jNzeX3bt3M2jQICZPnkzPnj358MMPKS0t5fnnn+euu+6iT58+zJkzhzNnztiP8cYbb9C/f38CAwN56aWXqhw/JCSEtLQ0APLz85k6dSoBAQH06dOH559/nsrKSoYNGwbAww8/zDvvvAPAxo0bCQ0NJTAwkHHjxpGZmWk/5u7duxk2bBh33HEHU6dOpaio6Gr/MYlcUUrmcsUVFRWxevVqvLy86NKlCwDZ2dkEBQXx6aefMmDAAF544QW++uorNm7cyNatWzl//jxPPfUUAOnp6bz44ossX76c//u//+PMmTO/+Fm8adOm4erqys6dO9m8eTM7duzgb3/7G++99x5w4V8MI0aMYOvWrSxbtoy//OUvfPrpp9x9991MnDgRq9XKqVOnmDx5MmPHjmXPnj0MGjToipaIROqCyixyRURERNC8eXMAWrRoQbdu3Xj55Zdxc3Ozb3Pvvffi4uKCzWZj48aNrFu3zv4VnTlz5tC3b19OnTpFamoqYWFh3H777QDMmjWLlJSUi8555MgR9u3bx/Lly/H09MTT05NXXnnloi8xAbz55puMHTuW7t272+P961//ys6dOzl37hxt2rThgQceACAsLIw33njjyv4BiVxlSuZyRSQnJ9tr5tVxc3PjmmuuAeDUqVOUlJTwhz/8AScnJ/s2rq6uHDlyhIKCAgIDA6vsW933L0+cOEGLFi2qfFbNx8en2vMfPXqUVatWsWbNGntbeXk5R48epaysjLZt21bZ/oYbbrjEFYs0LErmUif+N2m3bt0aFxcXNmzYwE033QRcSKw5OTn4+PjQpk0bjh49at++rKyMH3/88aJjtm3blvLyck6cOMH1118PXCjRnDx5knvvvbfKtm3atGHMmDGMGTPG3padnY2Xlxdbt26tcj6A48eP6+tN0qioZi51rlmzZowYMYIXXniBU6dO2b+YFBERQXl5Offeey+pqans2bPH3ldaWnrRcdq2bctvfvMblixZQnFxMcePH+f555/HarUC4OLiYr+Ref/997Nu3ToOHjyIzWbj448/JiwsjKysLAYMGEBhYSGvvfYa5eXlfPzxx3z22Wd1+mcicrmUzKVePPnkk/j4+DBixAiCgoL46quvWLNmDSaTiV69ehEbG8vjjz9O7969sVqttG/fvtrjvPjii1itVkJCQrj//vsZNGgQDz30EAAjR47k0Ucf5fXXXycsLIyJEycybdo07rzzTpYuXcrixYu59dZbadWqFYmJibz77rsEBASwfv16+vfvX5d/HCKXTV8aEhExAI3MRUQMQMlcRMQAlMxFRAxAyVxExACUzEVEDEDJXETEAJTMRUQMQMlcRMQAlMxFRAzg/wMcTOU0yDuO6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89      7009\n",
      "           1       0.68      0.38      0.49      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.76      0.67      0.69      9000\n",
      "weighted avg       0.81      0.82      0.80      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "pred = GB_classifier.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, pred)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictionGB =GB_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predictionGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Model: 0.823\n"
     ]
    }
   ],
   "source": [
    "#Accuracy Score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(\"Accuracy of Model:\", accuracy_score(y_test,y_predictionGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see from this data above we get accuracy 82,3 %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dTree_clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dTree_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 1.00\n",
      "Accuracy of Decision Tree classifier on test set: 0.73\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(dTree_clf.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(dTree_clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "[[5722 1287]\n",
      " [1176  815]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.82      0.82      7009\n",
      "           1       0.39      0.41      0.40      1991\n",
      "\n",
      "    accuracy                           0.73      9000\n",
      "   macro avg       0.61      0.61      0.61      9000\n",
      "weighted avg       0.73      0.73      0.73      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.82      0.82      7009\n",
      "           1       0.39      0.41      0.40      1991\n",
      "\n",
      "    accuracy                           0.73      9000\n",
      "   macro avg       0.61      0.61      0.61      9000\n",
      "weighted avg       0.73      0.73      0.73      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "pred = dTree_clf.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, pred)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction\n",
    "y_predictTree = dTree_clf.predict(X_test)\n",
    "y_predictTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Model: 0.7263333333333334\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Model:\", accuracy_score(y_test,y_predictTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Decision Tree with max_depth=6, Random state 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 0.82\n",
      "Accuracy of Decision Tree classifier on test set: 0.82\n",
      "Confusion matrix\n",
      "[[6603  406]\n",
      " [1219  772]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.94      0.89      7009\n",
      "           1       0.66      0.39      0.49      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.75      0.66      0.69      9000\n",
      "weighted avg       0.80      0.82      0.80      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.94      0.89      7009\n",
      "           1       0.66      0.39      0.49      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.75      0.66      0.69      9000\n",
      "weighted avg       0.80      0.82      0.80      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to perform training with max_depth=6, Random state 50\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "dTree_clf1 = DecisionTreeClassifier(max_depth=6, max_leaf_nodes=18, random_state=50)\n",
    "dTree_clf1.fit(X_train,y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(dTree_clf1.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(dTree_clf1.score(X_test, y_test)))\n",
    "\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "y_predictTree1 = dTree_clf1.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_predictTree1))\n",
    "print(classification_report(y_test, y_predictTree1))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, y_predictTree1)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, y_predictTree1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Decision Tree with entropy,max_depth=6, Random state 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 0.74\n",
      "Accuracy of Decision Tree classifier on test set: 0.74\n",
      "Confusion matrix\n",
      "[[6667  342]\n",
      " [1304  687]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89      7009\n",
      "           1       0.67      0.35      0.45      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.75      0.65      0.67      9000\n",
      "weighted avg       0.80      0.82      0.79      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89      7009\n",
      "           1       0.67      0.35      0.45      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.75      0.65      0.67      9000\n",
      "weighted avg       0.80      0.82      0.79      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to perform training with entropy\n",
    "clf_entropy = DecisionTreeClassifier(criterion =\"entropy\",random_state=100,\n",
    "    max_depth=6,min_samples_leaf=18)  \n",
    "# Number of leaf : 3000 * 0.6 = 1800 so 1% of that will be at least our tree create 18 leafs\n",
    "clf_entropy.fit(X_train, y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(gnb.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(gnb.score(X_test, y_test)))\n",
    "\n",
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "pred = clf_entropy.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, pred)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. XGboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xgb_classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGboost classifier on training set: 0.89\n",
      "Accuracy of XGboost classifier on test set: 0.81\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of XGboost classifier on training set: {:.2f}'\n",
    "     .format(xgb_classifier.score(X_train, y_train)))\n",
    "print('Accuracy of XGboost classifier on test set: {:.2f}'\n",
    "     .format(xgb_classifier.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "[[6589  420]\n",
      " [1254  737]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.94      0.89      7009\n",
      "           1       0.64      0.37      0.47      1991\n",
      "\n",
      "    accuracy                           0.81      9000\n",
      "   macro avg       0.74      0.66      0.68      9000\n",
      "weighted avg       0.80      0.81      0.79      9000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89      7009\n",
      "           1       0.67      0.35      0.45      1991\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.75      0.65      0.67      9000\n",
      "weighted avg       0.80      0.82      0.79      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "print('Confusion matrix')\n",
    "#Prediction\n",
    "y_predictXGB = xgb_classifier.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_predictXGB))\n",
    "print(classification_report(y_test, y_predictXGB))\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "cf_mat = confusion_matrix(y_test, y_predictXGB)\n",
    "sns.heatmap(cf_mat, annot=True,fmt='g')\n",
    "\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Model XGBoost Classifier: 0.814\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Model XGBoost Classifier:\", accuracy_score(y_test,y_predictXGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We see from data above we get accuracy 81,4% for XGboost with depth 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUC ( Area Under the Curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n8vOknzye7bj93JlsgY1S51Yq1quLR6XKarVRFNS2w2jP/4HXGIc7vFMON9NwJOYL5i+qxcw5NeG/ydv79lkID3dkK37LLXVp0KAJJlP+u3yklGRMb8xC//FicIO3SyXszR/Abh+LxZXBU2sf5OcjP7C1+mbe7/pRvq9bFMQYG6FIibEEpYd4FqWHeBYlw2az0bFjK2w2G2azmSNHDgMwd+5cBgy4J1/PQlEUPBlZXSxep5OEjes5v3YVjV57C/V/065DjxxD43Rx+I/fcWRkoGg0RLdoDeFhuMqVQxuYy5pIioLasgdtyh/oUv5Am/oHKlcCAI6o/mTU+xQAtxtUKrh8bLDNBna7RHh45guxWi2sX7+G5s1bERYWUcDf1n9cLoyffIhp9kxUqam46zfAOn0G7pZtADiYfIBhK+7jYMoBelfpy9ud5hCoC7q2eyHG2AiCIAhCvk2ZMoFjx44C4HaHUavWLezfv4/evXvn63zJ40GVnsGq/rfleDxt727CGmUu+GgLD0NSFMLvHYrHoAeVyje5W5vj2f9RvIRs7Y7Km4GChCewHo7yA3GHtOa83IpZz+tZuFCL1SqxerWVunWz/njX673s2bOFtWuPkZ6eisOR2Vpz7NhhRo0am6/XCKDVbsBkehW16yBSGyeqg6l4YyuQPmMWzv53cmnAzk+HvmfM2sdxeh1MbfUyI+s/7jcYuqSIwEYQBEG4oTkcDrp370D5yxZSfPTRUXTq1NWv3PPPT2bRoq9Yt+4vKleuAvi3nuVIUQg6dRaNw4Ha40H2eqjWpgMAXq0WlzkAd1oaka3aENqgke80Z0hwztfzOtCmb0NR6fEEN85+XKXBWvMVZF0U7pDmKNoQAMqVy97Cs22b2i+wSUtLZf/+PWRc1qIUGBjEnXfem3NdchAc3AudbkPmhg68PSKw3DkB+yNP4htxDOxN2sMjKx+knCmKr7t9R8uY1vm+R1ETXVFCkRJN7qWHeBalh3gWhUNRFB544F6WLVvi22c2ZwYAM2a8xuDBeX+hq2Qv4Xo1Z4+ewuFykH5gP5JKBYrCyR+/pd6Ul4hxeQHwGPR49QY8Bj0egz7HHDHZeK1oUzejTfkDbcqfaNP/RpKdOMv1Jb3+5/l6nR4PxMWZ8Xgk7rjDzYABbjp1suNyuQgIMPuV/fffbYSEhBAXV7nArSdq9SHCwjKDLbv9YSyWlwBjruU/3/sp3eJ7EBWQPaHgtRJ5bPIgApuSJz7ASw/xLEoP8SwKh9vt5s8/N/L227NJTU3l229/Jjz8Kun+ZRmdxYrG4UTjdKJxOFG73dhSk1n/yVySDx3I8bRWn31NQHzlgldQkQlfVwWVOznz9moznpDmuENb4wrrgCe4ia9oYqLEuHF6Dh5U8eeftmyXWrlSRYMGHrZs+YWkpAQslgxq1qxN5849Cl6vK0gZ6Sh6A2rjfozGz1Cr95GW9ut1X/daiDE2giAIQpl35sxpHnnkQdq0acvzz0/x7ddqtVedau2xWX2rFmQcOcTfo0cQHB1Ll7ET0AWY8ep1OIIDMVWOpWHlVzi+cjleh4OAinFog0OQ1CpC6zVEbcy91UJyJSB5MpBNVXI4qMIeNxJFZcId2gpPYH1Q+X/tpqdDtWr+3UwnTkjEx2dFvA6Hg5MnP+bQoawZThqNBrs9ewCUXxrNFlRyEqqPzxLw2gxsT43D/vBIrNYJKMq1D/4tDURgIwiCIJRKf/yxgX79evm2L1684BfYXE7yeDCkpWNITccWHsqJf7Zw/JsvyDjo3wqTdu4MqfEVUUVEgEqFJIEhIhC1yUyVocPyrJPKcSarWyl1IxrrQZzl+pBe/8scy9uqjL/q9S4FNSqVzBNPfEfbtqHEx7fwK6PX64mNrUhqajLBwSFUrBhPnToN8qxrTtTqPRgN72Fc9yXy0yZU+2x4y0Uhh2W2dCmKf4tXmjOVXYk7aRPb7pruVxJEYCMIgiCUOt988xVPPDHCt92v3wCmTHnRv5CioLXZMaSkos+wICkKslqFpCiU79wVlV5Pwp8bfMVlp5Nbn5+CKh/rHuUk5K/2aDO2+7a9hgo4ogfhCu921fPGj9cTHa0wZox/Zl+LJYPHH1+L13uGqKiLAOzeDU2b1sd4WSuRJEn06NHnmuqcdY0LRERUh53AOGAlSEY71rHPYnt8DJjN2c7ZnbiLYcvv44LtPBvv3krFwLjrqkNxEYGNIAiCUKwURaFx4zqcPn2KBg0a8ttv67KVqV+/IQD33/8Qr732RraBsElbN5Py848kHNiLJeEildp1os7Tz+EMNPumI5dr045ybQrQ0qAoSJ50FG3OM5o8wY3xBNbFHdoKd2gbZGPuX/SKAiNGGPjxx6zJ3VcGNqtW/UpExBm/fb169fMLagqL0fhx5g9fgbIK3Hd3ImP8HOSY2BzLL9z/Jc+uewqv4mV661eoYC5YhuKSJAIbQRAEodikp6dRrVrWl2R4eM6J42rVuoWLF3Nea+iP++7EdvqU37609FScwQUcG6J4UWfshsS/CTy9Bm3Kn7hDWpLe4Ksci1tueSN/l1UgKiqQ+Pjj9Oq1h6pVj1C9+p2Af6tIo0bNMJv307BhE8LCIoo0B4zN9jxO5wDkhyNQdz2Jp16DHMs5PA4mbhzP53s/ISYglo+6f0aT8s2KrF5FQQQ2giAIQrG5PKhZs+YP6tSp63fc63SCLJP092YUWSaqffYlBCSNFo05kHLtOlBp0L2Y4uILHBQE7n4YXcIyVJ7M4EmHCk9QfTyB9a7hVflLSpLo3v0oLVtmTeeuWXM/0MSvXFxcZeLirmG2VZ4caDT/4vE05fJVtb3eGhAEnnq5LwvxyMoHWX5sKe0qdGRu1/lEGK8xY3EJEoGNIAiCUKgsFgsajQbDf8sLXK5hw0aYTAHMn7+AsLCsgarHvviUwx/N9SsrabVUrXEr1shwlMvGxbT6NOcWlWyuWBHbj+zCa74VR2hrTPFdSFbVQVYXzmyg8+d30LLlaiBz9tKQIcPR67P/LoqCWr2bsH9bIR/SY793DDbbxAKd/1iDJ6kdVptnmk5ArcpHnp5SSAQ2giAIQqFQFIX33nubF1+cQqtWbfjii0WYrxiUumLF79nPk2WOfDbftx3Vpj06RSEoMBhjSioevQ5HWGie95c8GWhSN6NN/RNdyh949eXJqPdZjmUz6n4KkoQkgSkiECUxwzc1PD8uXJBYu1bN8eMq1qzR8OuvNi7FXpeCmOjoWPr2vROVSnWVKxUOleo8gWdGopu0GhaDKtSJq2cr0BfsOs2jW9A8ukXeBUsxEdgIgiAI183r9dK7dzf++WcrAEeOHMZkMmUr50xOAkVBf9nYGkmlos5zU3CdPUOdDl3RZViQAJfJRGpkGO4crnM509HX0CX8iibjXyQlM0uwrAnCa6iQ+0nXOJ6lXTsTBw6oaNlyE926reTvvzuwY0c7Nm5U06FD5r0rVozn3nsfJDg472CsMEiJiYS80wn1h6fBC8pdepKf+QNZX6NY7l/aiMBGEARBuC4ul4sKFbIClVdeeZ2hQx/0tVQ4k5LYPOJBnAmZU5oj27SnwUsz/a5RvlMXwg4dRZ1hwWkOwBYRjseUv9lBmvQdqO3HcUX2xB3SCndoazyBdUG6tq4UiwWOH1dRvbqM/ooWjw4dLAwe/NZl21t48cV61KiR9XVqMBhy7IYrCprt/xA8sC+qjHSU5noyps3F2WhAnucdTjlE1ZBqpWLRysImAhtBEAQhTxkZ6Rw4sB+NRkODyxZ7BLBaLYwc+QTvv/8uX3/9HZ07Z+Z1kd1u/ho+BOuJ476yKp2ekCsGDAMgSViio/BqNXj/CwpU9pP/JcP7A1S6XGclZdz6PxRNEEjX1+UzbJiBJUuypmdv3WrxywB8+PBBypfPWpeqU6fu1Kp163Xd83p5atfB06ARjqEP4Ly9X54tUYqisGDvJ0zc8CzPNZ/M4w2fLKaaFh8R2AiCIAg5euSRB9i1aydqtZqD/2XwDQsLY//+474yXqcDs0bLg3ffx4TnJqG/IgdLSN36WE8cJ+iW2jR5Yw5qgyHXL19XoBn9+e8wJa5Am/InakfWlG53YP1cBwNfWgH7Wi1ZAvfdZyYtLevaTZp4CQnxH3QTGBhI5cpVOXbsCMOHP4ZOV8ABLIXGRWDg49jtI/DQiLTvf8nXWTa3jWfXP8WiA18TFxhPuwrti7ieJUMENoIgCDcpu93OqVMnCQoKonz5aL9jNpuNpUsX43a7qVChIjVr1sLtdvPII6OAzO6l9QN6+Z0T9MZ76BtlTWlWabXUenIctcc9nzmuJj0D47kLpFeIQdbpcqyT/sJP6C/+giegBvbYB3GHtsYd2gr5auNlrlPNmtC6tYdff9Xy5JNO7rprJytX/opON5LLV7eOioqmS5eeaLXa3C9WRKTUFJSQzDE7QUEPoNcvQZJspKd/ka/zj6YeZtiKoexN2k3X+O7M6fwBIYbiGQNU3MTq3kKREqsYlx7iWZQeJfUs3n33Lf78cwOrV68kLCyM5OTMVaf/978PGThwULbyCxd+yS231PZlAb7EmZzE+v5ZQU1AfCVC6tQj/q57CIiv5H8RRcGQmo4xMQmN242CF6f2HzKqD8mxjirbURRNIIou8vpebC7S0kCrhUvjkS89i82bd7Fu3XIcDjuXvhZbtmxLw4ZNi6Qe+SWlpmB683WMn80nZcVKAps/jlabuaxDcvJGvN688+78c2Erdy3uh9Vt4blmkxjdaCyq6+y2KypidW9BEAQh344dO8Lq1SvR6/UkJyfTuHFTFEVGf+UI2f8MHnxvjvtllwuVwYDWHEjT9z7AeEVrDwCKQsDZXRjSvagUE4psAcs3SNav0anVUO3eHMfE5LhKdiF46SUd77yT+TrbtvUwZ46D8uUzA5hTp06xbt1y32rZERHlMJlMJRvUuN0YP/0I0+uvokpJwV23PiGGXqi0qQA4nT3yFdQA1Ay7hXoR9RnTeBztK+a8EnpZIlpshCIlWglKD/EsSo+ifhZut5vk5CSiosr77d+/fx9xcfE5TsO+RFEUkrf9zbannwBAYw6k45KVBa+EohC+dyMqWYNiXYRbfRx3aKPMrqXgZqDJvuhiUZg8Wc+8eVndXmq1h0GDbLz5ZmYOm0vP4ttvv8dkMlOzZm2Cg0OKpW45UhR0y5YSMH0ymqNH8EbHYJ0wBeedgzEHPYlev5TU1F/weusU8LLKDTEDSrTYCIIgCABcuHCerl3bc/78OSCztWXmzDf8FlSsVeuWq17DlZrCujtu89sX1rBRjmUldxra1L9QuS7giB2aQwGJjKgAFJUdd/AroC6e6c9XatbMy7x5MGjQam65ZSMArVu3R1EaIl3WYtSxY7fSEfArCqbXX0V9/jzW8ROxjXzC129msbyLxfLuNV32RghqCosIbARBEG5ww4ffzy+//OjbVqvVBAeHFDiXiuO/PDMA5dp1pO6UF1H9l05XciWhTf0TbcpGtCl/osnYhYSMrKuNo/zdoM4+oNYVkXNQVBQOHlTx7bcannnGxeXjktu1c/Pyy+/gdjt8+2w2W7FkA74mKhUZ781DF7MKohWwXz054eUURcHqtmDWBRZhBUs/EdgIgiDc4Pbs2QWAyRTAwYMn0OUy4+iS86tX4k5PI7bPHb7ABSCgQhxN3/uAwOo1UV8x7sa8/2kMF34AQNZXwVN+OiptS9RyEBqnG4+peGcKWa2QmCixY4eahx/OapUaMMBDrVpZQxDOn9/nC2ratOlAvXrFF2xdG4Ww9i0BkOVyuFy98Hqr53mWxW1h7NrHOZ1xmp/u+BWd+ur/BsoyEdgIgiDc4DZs2MK//26nceOcB7sqioL1+DGOLpjPhd/XcKnPxXbmNDUfHwOKgspxAq31MOo6XXK8hiPmfuTAfmikWmhtoAVkVNhDAlHUxf9Vsnq1huHD/XPm9Ohho1Il/3GVNWrcwurVyxk8+H6/RTdLlMeD4avP8dS+FU+TZr7dRuN7BAS85Nu224fmK6g5mHyAYSvu42DKAXpVuR2X7BKBjSAIglD6bdr0BytWLOOhhx6hYsU4336NRkO1gAAu/L6GqA6dsp2XsHE9/04e77cvpmMr6t8RhHbXQ5nJ8JxnUNQBJHY4BarsXw1yYCtMF4+jAO4AE46QYJyBZiihLp0qVWRGjnSRkQG33roZp3MNACrV40DWl7okSYwaNbZE6pgT7ZpVmKdNQrNvL85uPUj/YtF/R+yYzRN85VJTF+N2551A76dD3zNm7eM4vQ6mtnqZkfUfv6nG0+REBDaCIAil2MWLF/ntt2WMHfuEb1/fvv2oWDEOxetl/cA+uFIy89GotDoiW7dFdUUCuZB6DTBXrUZw7TpU6H0HMZ73MJ79CPZlHveYa2OPvA13aGsg55mkXr2ejOgoXOYA5GJKUHfunMTWrWoWLNAydqyLVq28vmN6/T4qVFiJ2+3C6cw6Jz09jYiIosmBcz3U+/ZinjoR3drVKAYD1qfGYX/iqctK6EhJWYdKdQ6Xq2ee11MUhcl/PMcHO9+nnCmKr7t9R8uY1kX3Am4gIrARBEEopb755ivGjn0Ct9vt29ewYSPq1KnHkU8+5Ohn8337VVodFe7onxnUyG7UtqN4zTUB0AUH03J+VoZa14VuKJrAzKnXIS1RdOGZ04wtVnQWJ66gnLsxHKEhRfNCrzBvnpbJk/0HPjdr5vULbAwGA26367+fjbRs2ZZbbinYFOjiYvhoLuZJzyHJMo47B2OdMAVV/EUkORFFvjTQV43H0xBoeLVL+UiShEalpUV0Kz7s9ilRAeXzPukmIfLYCEVK5E4pPcSzKD1yehYOhyPbLCa3201sbDhDhw6jdu1bufvu+3zTt1N27mDfGzOxHj9Gi3kfEhaZhjY1c8FIbeoWABI7ngLV1VtX1A4HhtR0DGnpqLxePDodKVUr5bmYYlE5c0aiYcOsHDctWpyiZ881DB3aD5Mpq04ulwu324XJFHBdXS/F8b5Q79qJecrzWF94EW/DikREVAXAan0em20cmSOWCs4je1AUBW0OM9JuVCKPjSAIwg3G7XZz7NgRxox5m7NnzwOwbt1amjRpxgsvvEjTps19ZTUqFWueGse5lcvpMutNvy/wkLr1aTn/CwKOv4bpWEekk5mtF4rKgDu4Ge7QVkiyAyWXwEafmoYxOQWtI7Mfx6PTYg8LxRESVKRBza+/ati0SU1CgoTJpPDGG06/47GxCo8+6iI1VaF58/dJT09DpdJy+vR+atTIysOj0+nynP1VWnjr1iPj57kEB/dBoznq26/VbuVagxoATQ5joQQR2AiCIBSr8+fP0aZN1kyYgAAzBoOBLVv+Yu/ePTRt2hyv3c75NSvZO2tG1nkrlxPdLSt5niRJoFbjNcbjDmuHK7Q17tA2eIIagCrvVafVLhdqlxt7SDCOkCA8RmORBjROJ1Ss6J9fpUEDL4cOqahe3b9l/cUXnWze/Af//JMGQNWq1alatUaR1a2wSOlpKEHBOR7T63/wBTVebzwpKRtQlJB8XVdW5FK7tlNpJAIbQRCEIrB06WKSk5MYMuQBv/2BgYGMHj2WBg3q0LNnP1Qqtd9xZ1Ii6wf09tvXakACAV0653gfZ8w9OGPuKXD97OFh2CLCi21W06xZWa0rd9zh5rnnnAQEQFRU9v4fh8POP/9sBjIzApfWsTOXqI8cImDaFNSHD5Ky7q/MVTavYLc/ik63irS0HyhIK83uxF08vvpR3u/yEbeE1y7EWpddIrARBEEoJGvWrGLevDmsXbsagGrVqtO6dVuqVKnqKxMSEsrkyVMJCzVxdMNmgmrX8XUxadL+IWjvY8RUy+Ds4UBa9ztNZA0Tqti2WDxp+V7xWvJ4MKRloE9LI71CLLIu+xepolbncGbRGTfOxTvv6Nm61UJ8/NUHsyiKQmBgENHRsaU6qJGSkzDNnonxk4+QPB6cfe5AsmSghIahUh1HlitdVtpAWtriAl1/4f4veXbdU3gVL3uSdonAJp9EYCMIglAIJk0azwcfvO+3r1q1GlSqVNlvX+runfw9ZhSKxwNAux+Wov8vcZwmYyc61zGaPdEXb0QbXKGtsRqr5K+L6L9ZTYbUNHQZFiRAVqnQOJ24cghsipLHA3v2qKhfP6uLyWCAc+cyyCmeslgyMJuzuqmMRhNDhgwvjqpeG6cT4/wPML05C1VaKu7GTbBMnYGneQskKYngoH7odKtJSjqALOew8nkeHB4HEzeO5/O9nxATEMtH3T+jSflmeZ8oACKwEQRBuCqn08nRo0c4dOgA3333DVarla+//j7bwNUWLVrz1Vdf0KdPX6ZOfYnQ0DC/47LHw/qBfXCnpmTtlCSky7L2Oio8iCuiB7KhYF+GKpeL0GMnUXm9JZpAz2KBxx4zsGyZlrp1vXz3nY3Q0KzjVwY1LpeTjz6aQ2xsRXr27ItWe2MMBlZdOE/AjGnIUeVJf+0NnHcMAMlJYOAIDIavfOV0uqU4HAUL0E6mn2D4iqHsSNhOuwodmdt1PhHGiMJ+CWWaCGwEQRBy8eKLL/Duu29m25+enk5EhP+XTe/et9O79+25Xst68jgBFeNITU3BEBVFv9WrSUqyZJtiXNCgBkDWavEY9LgCTDiDg4otgd4lp09LvPGGji++yApM9uxRYTRmL6soCseOHeGffzaTkHABgDNnTmGz2QgOvjECGzkunrRvfsTduGlmUxQQGVnOr0xy8ia83lsLfO1z1nPsSdrN2MbP8EzTCahVxdtlWBaIwEYQhJve8ePHKF8+OlsemctXgL733qG0adOOGjVqZQtq8iOwchXi28fTpu9BjCGugudeURRUHk/OQYskkRZfscB1KihZBpsNzGb//adOqfyCmiVLrDRrlnMOsR07/mHTpvV++4YNG4nBkEMUVArpdL/h9VbG3bqt3/7U1OUEBj6AxfIOLlePa75+8+gWbL53BxUCi/55llUiQZ9QpERSuNJDPIssiqLwwgsTWbZsCSdOHAdg2bLV2RaRTE9Pw2AwFihfSvrB/RyaN4fQBo2ofO/9SLgxnF2I8cTbaGyHUVR6HDH3Ymz9PokpzjyfxeUJ9Dx6HWmV4q5+QhH58EMtEycaePFFB0OHuv1aYywWWL9eQ926XipUUK46JEiWZc6ePU1CwgWqV6/lN7amJOT2vpDSUjHO+x+2UU+AWY0kuQgO7oNW+y822+NYrTNyv6hwzUSCPkEQhALyer1ER2cN/DCZArDZrGg02T8Og3LJSZKT04t/Yt/sV33byf9sJWP7Gjr2/Qu16wKyJgRr5XHYKz4KhiiMah3gzPFal89qujyBnssckLkydzFlBd63T8XAgUYSErJariZPNvDww26/cmYz9Ozp8dt38eJ5jh07gslkom7drGUCVCoVFSrEUaFCyQRoeXK7MSz4mIBZr6BKTiYg7lV4wr+IohhyPlcoFURgIwjCTcXhcFCnTj12797JK6/M4qGHHr3uazqTk/yCmsjWbakzYSp6117Y9TeWGjNwxN6PoslsncgrLDFfSMCQlo6sUmEPDcYRHIzHaCj2ZQ4+/1zrF9Q8/7yTMWNcuVYjJSWZf/7ZzMGD+/z2Xx7YlFqKgm75MgKmT0Zz+BDeqPJkvDML9QNnMfEmDsc9gAerdRqyHHudt1JYsPcTHB47j9Z/rHDqL/iIwEYQhJtKQEAAL774CjVq1CIysnBWgdaHhVOuXUeCat5C3MC7UOsz/6L3BjQluc0uKGDqe3tYKC5zQLHOajp7ViI62r8baeRIF8nJEu+84yA/vXE7dvztF9TUqnUrtWvXLYLaFr7AB+9Dv3QxismE9ZnnsY0aDQEB4FCwOqYV2n1sbhvPrn+KRQe+pnJwFe6/9SEMGtECVJjEGBuhSIlxHaXHzfgsvvjiM1q3bkvlylWu+1rOpERSd+8kZecOIlu2JrxJ5ppOmtTNSF477vAO+b6Wyush3OMiSWtALsbp2FdasULN888bOH1aRZ06Xp56ykWfPp68T8zFuXNnOXLkAJUrVyU2tpR2NV3h0vvCOu1lVAcPYHtuEnL5gs9My4+jaUcYtnwIe5N20zW+O3M6f0CIITTvE28iYoyNIAjCZWRZxuPxcOHCeXr16sr58+cAWLFiLQ0bNi7w9ZL+2crZZYs5v+o3v/2nf/qeRk8PpErkcrSpm/AE3EJKy7+u3lWkKOgyLBjS0tFlWADQxZTHEZL/cTyFZc8eFR07+n957N5dsGnFNpuVpKREKlaM9+2Ljo4hOjqmUOpY3OyPjSYw8G7C9TVJSfkdj6dRoV5/2bGlPL76USyuDJ5vNpknGz8t1n8qIiKwEQShTFAUhe7dO/Lvv9v99r/zzvvXFNQA6EJDkS7LIxJc+1ai6wZRIfwPymlfQEnTYo8Zgr3Sk7kGNZLHgykxGUNaelYCPbMJXcXyOCmZHCWRkVlNdo895mL0aKdfIr2rSUtLZd26VZw+fZKKFeMJDQ0r8ZlN+eb1gsOR2cXk5xgREVmtejrdikINbNxeNzP+moZOpeWbPj/SoWKnQru2kJ3oihKK1M3Y/VFalZVn4fF4mDfvfzz22Ohsxy5evMiUKc+hKAqSpOLFF1+97nE0bosFSaVCYzIRcGgKpuNvIasDcVQYhj1uVN4J9WSZ8IOHkTVaHCFBOIODUHTaInsWsgxz52pZtUrD/v0q+vf3MHy4i0qVsm6kKJnf7zkl0MtNRkYG3333BXa73W//o4+ORq0u/X8ja39fg3nqJNzNW2CZ+YZvv0qVTHh4Jd+21foMNtvkQr//kdRDGNRGYgMrFPq1yxLRFSUIwk1h7949fPrpR5w+fYpV/3ULtW/fkTp1/AemlitXjrlzP76me5xfvZKkf7ZQ8/Gn0JhMvv3ay7LR2WMfRNaE4qgwDEWbzy4klYqUKpUyE+v916pTVHOb0tKgenX/1pMPPtDRoIGXSpWyxs5IUsGCGoAjRw76grDAwCAGD74fbTFnOL4W6gP7CZg2Cf2q31D0epw9bvM7LklWIBRIISEhAdAXST2qhlQvkusK2YnARhCEUu3s2TN06NDStx0UFIxer8dkur5MtemHDnBswcdc3LDOb7/1+DGazfkwx64l2VQZe+Wn/PZdSqAHCtbyUTneSy5Agr/rMXVq1pfySy9lJtIzFHDCjd1u4/jxo1StWh2dLut69es3IiDATPny0QQGBhVWlYuMlJBAwGszMHzxKZLXi6P/nVgnvoBc0X9QsyxXBH4kMbEOcGMs6SBcnQhsBEEo1ZKTk30/X7iQVuClCGSPB6/djjbQvyXDdvKkX1ATWL0G5ZvWoF7zA7hPvIu9UvaurksyE+ilY0hNR+PMTKDnNuiLNXkeZL/dm2866dnTQ5s23gK1yJw9e5qfflrkty8oKJjY2Ky0/pIkUb16zeutcrHRrVqB8bP5uJu3xDJtGvq2iwnR90KS0klKOgp+45vaAxnXfU+LK4P/7XiXpxo/g1Zd+luzyioR2AiCUKrVrFmLXbsOERWVc2vI1XidTtZ0b0+tMeOI7X0HqsuyC0e2bkvbRT+jNhgwubZgOv4WupTX4CLImtxbJEwJiZgSkpCgRBLoyTIcOybRsqWZJ5908uyzLi7vEera1Vug610Z1ERGRqHRaLKtTn6jcd51N5boMxj7LSBU0823X5ZD0Gi24fE0vcrZBXcw+QAPLr+XQ6kHqRBYkXtuGVKo1xfyTwQ2giCUCh6PhzffnEXz5i1p0aKVb30mrVZb4KDm2FcLOLP4J+znzgKw/63XqXjHQL8yaoMBY/pKAva8gsayG0XS4Ii+G1v8aLyBua/K7NXpcAcEZA4ELsYEei4XvPaajnfeyeoemj9fx8MPuylX7tpHIEdFRaPRaAgPj+S2227HZLq+gZulhlqN0qM8as0p3660tG/+W6CycAPQnw59z5i1j+P0Opja6mXurnVfoV5fKBgR2AiCUKKOHz/GX3/9yejRI337fvhhCW3atLum6+2dPZMzi3/029dm4Y85ltVk7EZtP4Yt7rHMGU7GzK4XyeNBUatzbIFxBmfObCpuTZsGcO5cVhA1fLiLiROd2Wcu50KWZXbu3EatWrf6raStVqt5+OEnCr7aeCmgOnoE/crl2B/NeVkCp7M3bndTvN4aFMXXncvrYvqmyXyw830ijeX4qte3tIptU+j3EQpGBDaCIJSYZcuWcv/9d/vte/PN9645qAGI7XU7lqOHie3dl5geva76hW2PG4U97lEUbVhmAr30DAypaegsVtLiKuA2l57Wi/XrrYwbZ2DIEDdt2ngL3FD03XdfkZh4kT//XM/QoQ/75Z650YIaKSUZ0xuvYfz4QyS3G1enrnir1wDcgAfIDNwUJRyvN7zI6vHZnvl8sPN9WkS34sNunxIVUL7I7iXknwhsBEEoMTVq1KBOnXpUqlSZnj1706/fQNTq/CWtUxSFhD82EFK3PrrgrKnXwbVuyZzVBKDIaJN+x2uq5muN8buGJgiNw4k+6QKGtAxfAj2XOQClBJY6OH9eonNnEwkJKo4cyeDy8c7BwfDhh45ruu7//peVt6Vbt943TkK9K7lcGD/+ANMbr6FKTcXdsBHWaTNQaqoJDuyJTreRtLRF/3U3Fb0Hbh2OSlIxtPYwMVi4FBGBjSAIxeL48WPodDpiYrJWRq5atTpr1mws8LVkj4fVXTKb/NUGI60WLMRQLmscjuRKwHDmC4xnPs3saqo0Bmv16dmuI3m9hBw7gQR4dDps4aE4g4ORtcX30agoEBdnxun0bzXZvl1Nu3YFGwh8pbNnT7N69XLfds2atalWrcZ1XbOkSAkJhPTuiubYUbyxFUifMQtn/ztB5SEyPMJXzmicV2yBjVat5aG61786vFC4RGAjCEKRO3r0CC1aNOTdd+cyaNA913Wt/e/M5tQP3/q2az8zITOoURS0KesxnP4E/cXFSIobWRuBrdIY7LEP5HgtRaPBGlUOt8mAx1A8s5quNHeu1hfU1K/vpXp1menTnUREXH9K4piYCnTr1ott27ZQr14jv+nbNxolIgJvzVtw3DME+yOj/ssw6PTLLJ2cvBWv98aZki4UDbGkglCkykoa/7KgpJ6F1WqlcuXMZQfq1q3P6tUbrut6Kzu08P3c4qPPCaz2X0ZXRSH0z8ZobIdxhbbDUeFBnJG90bgUVB43rsDi737ZsUPFL79oeO89PRUqyDRr5mXuXEe2Z/HjjxpCQhQ6dix4C43b7Wb79q2cOHGUKlVqULPmLTduV1NerkjcI0lphIU1QqVKIClpL7Jc8OUK8vO+2JO4G41KQ82wWtdacyGfxJIKgiCUaunpaVSrltVK8Ouvq/I8x5mUyMX1vyNptVTo3Tfb8WoPjyKkbj1C6zXwPyBJWGq/i6wrh6yvjCEtndDjZ9E4XXg1apLN5mJrkbFY4NlnDXz3Xda4i9OnVXTp4smxfL9+Oe/Py6ZN69m+/W/fdkLCRSpXrnpN1yo1rFYwmXJ+VlfsU5RgUlNX4PVWpagWqvhm/1c8u/4pKgVVYe2gP8SK3DeAYg1s/vnnH6ZOncqpU6do2LAhs2bNIiIiwq/MmTNnmDBhArt37yYyMpJnnnmGzp07F2c1BUEoBG63m8GDB/i2N2zYgl6f+zo8afv3smXEML99lqOHqTX6ad+25E6mVicDzugGOV5DUtfDnJCK1nbksgR6IThCimZ6tssF69apsyXFkyS4eDHzi7ZpUy+vv+6gRg2ZfI6LzpeMjHS/oKZnzzsoXz7abyr3DcXjwfD5pwTMmkHGW3Nwdbst73MAr7dakVTH4XEwaeNzLNj7MTEBsczu8LYIam4QxRbYOBwORo8ezdSpU2nfvj0zZszg1Vdf5fXXX/cr9/LLL9O0aVM+/fRTNm3axIgRI9iyZQuGgi54IghCidJqtfzwwxLmzHmb1q3bUrNm7s34Kf9u5+8ns/LYRHfvSUBcJeIH3QOKgiZtM8bTH6O/8BOS7CDZXBtvYJ1s11G7XGjtdlyBZpxBgUWSQM/rhT17VJw4oeKhh4zExsosXmyjQoWsfoyAAPj4YztBRZjuJjAwiODgEOLiKtG2baeiu1FRUxR0q1YQMG0ymoMH8JaLArd/C5ZKdR6j8Q1MprnY7fdjtz9epGNpTqafYPiKoexI2E67Ch2Z23U+EcaIvE8USoViC2w2bdpEVFQUXbt2BWDMmDG0bduW6dOnY7psJd2TJ09Sq1YtZFlGkiSMBV2CVhCEEuHxePB4PH5/hBgMBp5+enye5+pCQont3ZekrZtp+81PAEjuNPRn52M88wkay14A3CEtsEcPx2uskuN1HKEhOEJDMpPrFbILFySGDzewebP/x+aZMyqCg7MPziiMoMbtduPxeFi5cikNGzalYsV4v+P33jsslzNvDOrduzC/MBHdht9RjEasT4/H9tiTcNmK6jrdcoKD7/JtG42fYbM9V2R1srgt9Pi+E4n2BMY2foZnmk5ArSr8f09C0Sm2wObEiRNUqlTJtx0SEoLJZPIFMpfcf//9TJs2jblz5wLw1ltvidYaQSil3G43//67nWXLlvLuu28ycuQTPPzwCCpUyHn2jezxsKZ7e+pNe4VylyXhC4ivRO1xz/uVVdsOEXjgGWRNMLYKo5FD70XnNBOYbsMTocKbw6dXUQQ0l0REKLz9toNRo4xs26Zm0iQngYEKQ4a40RTiJ2laWirr1q3i9OmTfvttNisDBtyNVlt2VqA2vT0b7cZ1OAbfi/X5ycjRMX7Htdrf/YKa1NTvcbu7FmmdzFozzzR9ngrmCnStVDzTxoXCVWyBjc1my9a/bjQacTj8E07Jssz48eMZNGgQGzdu5LnnnqNu3bpER0cXV1UFQciHF16YyPvvv+u37/3332XkyMdzLJ9+cD+bH3kAgH8nPUvX3/+66vW9AQ2w1fgFjVIZo82BlAwKNlzmAKQimtW1cqWahQu1LF6spV8/N/PmZX0+qdVQpYrC8uW2orn5f9LT00hJyVrRvFq1mjgcdlq0aFOmghoA65Tp2Ec/hadu/RyPu90tyMh4D4Phc1JTl1FcX1kP1hleLPcRikaxBTZGoxGXy+W3z263+3VDXbhwgTfeeINNmzahUqno1KkTDRs2ZOXKlQwdOrTA95SkEklLIVzm0u9fPIeSV9jPIiYm84+N6tVr0KFDJzp37kqnTl2ypee3HDvKnw/4565p+82PSBKo07ej8lhwh7XNdn2114PJGo2CA7c5AGdwIK5As69VpjD/Se3apaJTJ/8ppqtWaYrs360kQUJCAhs3bqJRo6YEBGR1vcTFxdO6dXuqVq2OqgSyHxcnJS4Ob1zcf8/Sg063FJfr8plwBpzOoTidQ4v0WVz+f6FkFcZzKLbApnLlyixZssS3nZqaitVqJS4uzrcvMTERt9vtd55arUZzje28ISGlZ52Xm114eBnNq3EDKqxnMWzY/TzyyEOUK1fuquX0tlDfzxW7daP1jBdQnfkW/p4Hyf9ASD24bUf2TzTFDDo1UmgQOq2Gomyr+OOPrJ8HDID33oPy5SWg8P7d/vLLLyQkJJCUlITdbvftb9iwHhER/i3SERFNCu2+JW7Nmswp3H36XKVQKhD2388ngaJPJKgoCrsu7qJeVD1AfEaVJcUW2LRo0YIJEyawbNkyOnfuzFtvvUWnTp38xs9Uq1aNgIAA/ve//zFq1Ci2bNnCli1bmDBhwjXdMzVVJOgraZKU+YGRlCQS9JW0a30WZ86cpk+fHrz88kxuu62Xb79Ol/lFkJiYAUDG0SMk/72FsCbNCKxyWS4VUygtPvyUkLhAjMffhV/iQJaRTV1RYsfjDInFlmTJ+eYqLaTZcz5WiLp1k7BatTz2mItLH0mJiYV7D49H4fTp0wQEBKBSqShXrhw6nQGjMcT3OyxL1IcOEjB1ErrfluONr0RKs7Zkn+/uAVxERGQtHpmcnI4sF+3vw+a28cy6p/j+4CJ+7vcrvep2E59RpYRGo7ruRolizTz877//MmXKFE6ePEmjRo2YNWsWDoeDXr16sXTpUmJiYtizZw8vvfQSBw8eJDo6mvHjx9O2bfZm6vwQmYdLnsg8XHoU9FkoisJ3333DY489AmRmDf7ll+UEBGR96CiyzPbnnyZ157947ZljT2o99QwV+w7Idj3z3mcxJh9HDuyPpK2PhAoFsIeFYC0fla18UVAUuO8+I8OGuejUyVuo3Q+KorB27W+cOnWcgQPv9etegsyB1hqNBkmSyvT7QkpMJGDWDAwLPkHyenHc0R/rxKnI8ZWuKGklMHAMBsM3vj2JicdQlKJbjRvgaOphhq0Yyt6k3XSN787/unxAtQpxZfJZ3IgKI/OwWFJBKFJl+QP8RlPQZ1G3bg0uXDjv2z558qJfC6vX6WDdHT19AQ2AJsBMqwUL0YdfkfNDUQg7dAi1R0EB3AEmX54ZpTCnFOUiIwPmztUxa1bmBIboaJktW6xcJV9gviUkXGTJkh+wX/Z7kCSJhx4ahU6X8w3K6vtCu3Y1QcPvR5WRjrtJMyzTZ+Bp0izX8nr9d5hMs/B4GuB09sHl6l2k9fv16BKeWDMCiyuD55pN4snGT6NWqcrks7hRiSUVBEEoEk6n0xfUjBz5BNOmvZytjEqjJapDJ84uW0KzuZ8QXOuWzAM5fTtIEraISJCkYgtmLnnlFR1vvukfYMyY4SyUoAZg8eLv/GZ39u175w292OT18NSphze+EhljnsbV5448R4I6nQNxOgcWS90++Pd/TPrjOcIN4XzT50c6VLyBkxoKVyVabIQiVVb/Mr0RXf4sDhw4wJIlP3Ps2FHcbjdz587PVn7evDm0bNmaevUakPDnRuznzxHX/86cLy7LmM5vQ5+wGUewF3ul0UX8avJv1So199xjonFjL/ff76J/fw+6QhyJfObMKf74Yx133nlvthlhuSnT74srFqq87AB6/fc4nQMoqnWdruZA8n4mbHyWdzr+j9jArMUyy/SzuAGJrqg8iMCm5IkPjdJE4eLFUyxevIznn3/Gt7dChYps27YnW2nb2TMc/vB/aEwBnFn6C4B/7hlZRpdhwZh4BK1DgyTpUBQ3dtXXWG/J3sJT6K9GgTNnJD74QMfcuTpUKoXXXnPSo4eHcuUK7x+boijs2bOTs2dPI0kSXbrclu8AJjc3/PvCZstcqDKfzOZRGI1fAOB2NyQ1dV1R1azAbvhnUcaIrihBEPLt8OFD3HffXRw9ehTIzD+zcOEPREWVz1Y2dde/bH3iUb99qsv6bgzJKZjPn0dCBYoBxfUvLl0q1gqd8AQXfVBzqRXmcrIssWmTms6dr22l7Mt5PB42bdrAvn278Hj8r2ezWenbN5eWqzJOSk3B9MYsDN99Q/KGLSjheQ/0VasP+IIagIyMOUVZRUEQgY0g3CyqV6/Bxo0bmTNnHtWr16RHj55XTQBnqhiPSquh7gsvoQ+LQHPZ+j2yVossJaNK/RxHSCS2Wg8jG+Nyvdb1cLky/6rWarP2NWrkZdYsB9On6+na1UPLll7uv9+d+0UKSJZlYmMrsm/fLt++li3bcuut9XIdEFymuVwYP/0I0+yZqFJScNdrgCopEW8+ApuwsKb/XaIjaWk/F3VNsbgt6FV6tGpt3oWFMkl0RQlFSjTzFj+Hw0H37h1p374jL7zwIupLmXqveBau1FS2jRtNeNPmVHt4JNLlQY6ioLXZcRsNua6OLbnTQHag6ItuqvbChRpGjzbyyy82WrTwFuq1ZVlm06YNHDiwh6pVa9KuXSe/LiZZllEUxff7K0w3zPtCUdAtW0rA9Mlojh7BGxOLdcIUnAMH5XvV9MDARzEYviYh4TyQ/+6ra3Ew+QDDVtxHx7guvNj6lXydc8M8i5uE6IoSBMHH4/EwfvzTfP75JwCcOnWS6dNnZCuXvGMbSVs3c+yLzwDIOHwQt8VC7afHo3K70aelY0hNQ+Nyk14+HFegFkUbnO06mfuy779eK1equfde/y/A9evVhRbYJCUl8s03C/z2nTlzivT0VIKDs7Ikl/XlDPLFbsf87FNIVivW5ydje/SxAo2tAbDZHsNuf5SiDmp+OvQ9Y9Y+jtPr4J5bCr4Ej1B2iMBGEMqIAQP6sGlT1toAP/64JFuZne++y+65c/32tVn4I8EmM4aTp9FZrEiArFbh0hzFvHc4jsi2WGtmD5Cux8WLEi4XVKiQ/U/kp5/OypVTu7aXYcPcDB1aeN1Mu3Zt9/1sMgXQrl1nqlSpVmjXL1NMJjI++gxP5aooUVdvmQsOvgNJSsRieROPp6lvv9dbr0ir6PK6mL5pMh/sfJ9ypii+7vYdLWNaF+k9hdJNBDaCUAZYLBm+jMALF/5Ap05dcixX74knILQcqXt3U65te8IaN0MlywQdPAKKgsukQ3GtRXfmJXSeRLyGCngDal53/caO1eNySSQnS6xalfmx07+/m7lzHdnKvvSSk6NHVYwe7cpvb4cfp9PBn3+uR6VS0bJlO3RXzO1u2rQlSUmJ9O8/+LpnN90M3C1aXfW4wfA5gYGP+bb1+l/9ApuilOxIYsivg9l6fjMtolvxYbdPiQrIPhheuLmIMTZCkRL918Xn99/XoNfradagEQfnvkviX38SN3Aw8XcOBq7+LHQZFjSJX2I6NhXJa8VjrIK98lgc0YNBdX1JXxISJG691Zxt/9ixTp57znVd177E6/Vy7twZDh3az759u337o6LKc/vtd6LVlq6BpKXqfeHxYPjqc1wdOiHHxRfo1KCgQej1y3zbaWlfFXn24Mu5vW76/9KbxlFNmdj8hWsaMFyqnoUgxtgIws1o166djBz5EAsWfO3XhdKhQyfW9unGmox03z7Z5cwcCGy1oXa7ISLnFYxdgWZUqRq8xnhslZ7GGdUPVIXz8RAZqTBggJsuXTz06OFBo6HQsv5ekpKSzJo1K7BYshZPHDRoKCEhoUUy+Les0K5ZiXnqJDT792Ef8iCW2W8X6HybbRx6/TJstrFYrVOA4h2XpFVr+f72xejURbn2u3CjEYGNIJRyu3fvYvz4sVy4cIGTJ4/79s+e/Rpz5nzg2z6+8As8/wU1AfGVqDJ4CFUaN8dw+ChqtwdZJUHV2Fzv44i9H0eFB0G69i+nM2ck9u1T0aWL/0Df99/P3uV0LRITE0hPT6Vy5Wp+3UgREZFUqVKN0NBw4uIqERgYVCj3K6vUe/dgnjoR3e9rUIxGrGOfwf74mFzLa7VrUamSsmUN9njqkZq6HLf76t1VRUkENcKVRGAjCKXchQvn2Lp1s29bq9XSu/ftvPii/3TW6K49ODT3PZpMnUGlytXRWW2QmIRXo8EaEYYzJJiw9N2obOA1Vs5+o2tsofF64ddfNbzzjo5//1VjMik8+6yTUaMKZ8Dvpcy/69evBkCtVlOvXkNatmznV65Nm46Fcr+yzjR7JqZZryDJMo677sY6YQpyTPaAV5LS0eu/IzBwjG9fQsKVq7briy2okRUZ1XUE3cLNQwQ2glDKyLLsN9W4c+duPPPM84wY8dhVWyL04RF0/f0v9KlpaM+exxloxhESjMscgCZtK+Y9r0PickzRd5FR56PrrqfdDgMHmti61b+rR6eDESMKJ6hJTLzIokVf+O2rVKkqtWsX7Uybssxzy624W7TCOu1lPPUb5lhGki4QEVHdb5/NNqI4qpejb/Z/xYe75vJT36WYdTl3pwrCJWLwsFCkxMC8/Dl79gybNv3ByJHDefXV2fTu3Zdy5crlWj7j6GG2jBpOp2Vrs8/skWUkWUZRq9GmrMd07HV0yetQkJAq9icl9kk8gQ0Kpd4WC0yerGf/fjW9ernp29dDxYqF96CTkhL5999/2L9/Dx06dKVmzVtQq2/8v8dK9H1x6YZXmRGmVh8kKOg+NJr9pKUtxO1ug6IUf/eew+Ng0sbnWLD3Y2ICYvmmz4/UDKtVqPcQn1Gli1gEMw8isCl54kPj6nbt2knnzm2y7T9w4DihoWF++xwXL3Dqp++xHD1C4l+Z+WpqPv4UcQMH5Xht897RGM98iiKpcZYfiL3y04RWanrNz+Ljj7UMHepGU4hxhdPpIDU1FbvdBkBMTGy2JQu8Xk+ZCGYuVyzvC7sdjMY8i6nVe1EUE7JcyW+/Vrv+v26mkvndn0w/wfAVQ9mRsJ12FToyt+t8IowRhX4f8RlVuohZUYJwg1u6NGvtnNtv70erVm24//5hOc7k2XBXX79tTYCZ2N59s5W7xBXRA5CwVRqDbKp8tT/Qc3XunMTnn2t5/fXMYOO55wxcvJiRx1l5O3ToACtXLs22v0qV6rRv3wXjZV/IZS2oKWrqw4cImD4ZyWYn7dufcm2ZkaREQkPbo1afwmp9Hpvteb/jbne7HM8rDmtOrmTkyuGkOFN4qvE4nm06EbVKzG4T8kd8YghCCbr33vtJT09n0qRpmPJIVV9xwF24kpOJ6dGTgPjKGMtHX7W8q1xPXOV6XnPd7rnH6Eumd8ncufZrvt7lbDYrer0ep9NJ5cpVMZsDiYyMwmAw+gU1Qv5JSUkEvP4Khs8+RvJ4cNzeDxyObK02ev236HRLMRh+yDpXuv5gtTDtT96PjMIXPb+hW6XbSro6wg1GdEUJRUo082Z67bUZpKQkM27c84TnY0VkZ1IS+rzKeR2YTyzHa4jDHtMoz2sW9FksWqTh8ceNNGni5amnnHTu7M13JmCHw05aWhrp6amcO3eGdu06+x2XZZn09DSCgoJvyjWZCvV94XRi/HAuprdeR5WehrtxUyzTZuBp1jzH4mbz0xiNH/q2k5K2I8tVr7MShUtRFC7YzlM+4OrBe2EQn1GlixhjkwcR2JS8m/1D46+//uT223v4tiMjy7Fq1Xqio2P8ynlsNi6uX8ueV1/07as2fASV73sg+0U9FoynFmBKSkVl7IuCnaRajVDUVw8QcnsWycmwaJE220wmhyMzkV5+u7DOnDnF1q1/cvbsmWzHBg++n7CwvAO6m0Vhvi80WzYT2rsr3rh4rJOm4uzbP4+HJqNSnUNRjChK2FXK3Rxu9s+o0kaMsRGEUmzz5r/8gppHHhnJxIlTs3W17H9nNqd++Dbb+cG31vXbltypGE/Ow3hxJ6qAR8EUhVeVTlrFankGNVdSFHj7bR1vv63Das38Ely7VsM332R1NRkMuZ2ds/LlYzh79gwhIaGkp6dRsWIlYmJiURQl20BoofB4mjUn7ZMvcXXuetlDUzCbH8Ng+IaMjLdxOu+77AwVspx7okZBuNGJwEYQisjevbupUKEiyclJHDt2LtcFF6s/PIpTP3xLVMfOmCrGU23YIzmWC9z7LHpvCwiegoIHS1QYjrAa+W9S+Y/VCpUq+ecCCQ+XWbDg6uNn7HY7druNZct+xmg00bp1B6KishYcVKvVjBz5lFhYsgS4evXx/WwyzSQg4GXftk639orApnSwuW1M2PAMg2vdS4uYkstcLJQ9IrARhCLy4IPD6dq1O7GxFXxf9vYL53ElJxF8y62+cmqjka6//5Xn9WzxI9Ccl3CbzVjKR6Fc47xrSYImTbzs2qXijTcc9O/vIbfllGRZZunSHzl16oTf/rS0VI4cOeAX2GReWwQ1RUFKS8Xw5efYRzzG1QY66fVf+oIaRZHIyPgfTue9xVXNfDuadoRhy4ewNylzwVIR2AiFSQQ2glBItm37m+rVa/hlB65QoSKyx8OGwf1wJiYAENmmPbXGjMMQEVmg63tCGpEcJF/1i+0Sux3q1zeTkQFLltho3DhrrJnJBL/+asvXPSVJomnTliQmJuDxuKlUqQpqtYYGDZqIMTPFwe3GsOBjAma9gio5GW/FOFx9cp/i73T2BUbidjcmNXVt8dWzAJYdW8rjqx/F4srg+WaTebLx0yVdJaGMEYGNIFwjRVHYtu1vbrsta8bPbbf1Zt68jzH8N9Zhx8RnSPhjg++4SqsjIL4SGlP2wXFqyz5Mx2bjLNcLV1S/nG+aR1CzY4eKbt38r2025+/1JCcnceLEMerWbYDmv9YgSZIoXz6GBx8suXT6NyVFQbdiGQHTJ6M5fAhv+WjS33kfV8/eVxT0Apc3t5lJTDxdIlmC8+KRPbyy+UXe3f4m4YZwvunzIx0qdirpagllkAhsBOEanDx5giZN/Af33nprXapXr+ELagAM/+WaUen0tFn4A/ocWjk06dsxHXsd/cXFgBq1pi1ykAOPsWCjd48elfyCmvr1vUyd6qRmzavPDDx79jQ//bTItx0TE0tUVNFPsxVy4fUSPKg/uvVrUUwBWMdPxDbicQjwD1jDw6vicnXCZnsCrzdr7azSGNQAnMw4wfxdH9A4qgkfdVtAbGCFkq6SUEaJ6d5CkSqrUylPnz7Fd999w4wZ0xky5AFefvk1v4DmEsXrxZWammNOGk3KJgKOvYYuKXPVale5kaiN96P2qHEEBZJRISbbOXk5d07igw90jB/vzDar6cpnceHCOb7//mu/MrVq3UqLFm3zTBYoXJ+83hfm8WPB7cY2fiLyFeOYAIKCBqLX/wZAcvLfeL01irrKhWL7hX+4NaIuOrWupKviU1Y/o25UIo9NHkRgU/LKwoeGx+Nhz55d1M9lJeRrZd4zCsPZr3BG3YsU9Dh6mwpFkrBFhGELD8u120lRYPZsHZs3q/n22/xnAr7yWVitFrZv38rOnduJiCjHnXfeKwb/FpM83xeKkstsN4WQkK5otVsAsFonYbM9W6R1LevKwmdUWSICmzyIwKbk3cgfGm63mzVrVjFkyCCaNGnG0KEPMnjw1WeYJG/7G6/LRWSLvGd5qByn0WXYCUgClSzjCgggI7ocsi7nv2a//17DuHEGX94ZgG+/tdG+vTfPe9lsNmTZQ+XKsTfksyhrJAkiQk0kXkxD0WjzfZ5Gs4nQ0O4A2O0PYLG8U1RVvGncyJ9RZVFhBDY3Xy5zQcjDvn17efTRB4mNDWfIkMyVs//+ewu1at2S6zmOxARWdmjBP2MfZ8dzYzm64OPMA7IHZFeO58iGCnjMFVHUKtIqxJAWF5trULNggZaRI42+oEarVZg920HbtlcPatLT0/joozl8+ulctm79i8TExLxevlAMtGtXQ8OGGD6aV6DzPJ6WuN1NSU39udQGNQeTD/D+jvdKuhrCTUwENoJwhV27/uWff/72bU+YMIXjx8/ToEHO6zFd+H0NGwZmJUiL6tCZyncPxnD6U8L+bIzx9Pxc7+UxGkiuVgVXUOBVE+117erhySedDBrk5uLFDM6csTBkiPuqk6T27dvNF1/Mx+VyAuB2u3yznYSSod6/j6C7BxB8Vz84dAjJlXPQC17Cwuqg13+V7Uhq6mrc7o5FW9Fr9NOh7+n2XQde/GsKR9OOlHR1hJuU+JQThCvcddfdNG/ekpCQEIKDQ/Isf2bpz76fOy1bjjnha4ybGqN2nkHWhKDk9ffDZQGN3Q7x8YH88ouNFi2yWmOioxUmTsztSzA7WZZZu/Y33/YDDzxKQEAAISGZTe5C8ZIuXiTgtRkYvvgUSZZxDLwLw+xZ2E2hcEX3h9H4FmbzFACCgkaQkHAHULoHc7u8LqZvmswHO9+nnCmKD7t9R5Xg0rWwpnDzEC02wk1LURTWr/+doUMHZzsWH18pX0ENQM3RTxPRohV9FjxAuc2NMB8Yj6S4sVSfTnLbPbjLPYD57Hly68C3WODQIRUjRhiIj89c6mDwYGOOZfNLpVLRokUbypePYdSosZhyyJsjFB/jJx9iXPAx7mYtSFmxFsv7H0Fc3BWlFEJC2vuCGoCUlPWU9qDmnOUs/X7uxQc736dFdCtW37mBljGtS7pawk1MtNgINx2Hw8HQoYP5/fc1vn3//LOVxo2bXtP1AirG0fDVN1AfmoqiNpBRZRaO2KFI6DElJGJMPguAMygQt9k/wJBlGDvWwE8/+Q8g3brVek11uVyjRs1o1KjZdV9HuH72x0bjqVsf1229QJLIqdNRki4iSWkAOJ29SU/P3g1VGj23YRxbz29mVIPRTGz+Alp1/gdDC0JREIGNcFNJTU2hRo1437bRaGTChCnUqFEzz3PP/raM5K2bObdyOV1W/4F0xQJL1srjsFadAJIWXYYF8/mzqD0e3AYDluioHBPuqVTQp4+HAwdU1K0rM3Kki1tvLdhMPq/XyyefvE+HDl2pWDEevb6Ay3ILRU4xB+aQNdi/BU9RorDbn8LjuQWP58YJSF9t+zp31bybXlX65F1YEIqBmO4tFKnSNpXy/PlzTJr0HL/88iM//riU1q3b5nlO+oH9bH70Ab99NR4bQ/yd2buwUBSCTp1Bb7Eiq1RYy0XiCA0GScLrheXLNXTv7uF6x/DKsozX6wUUPvwwawbKsGEjMRhy7sYqbc+irFEfPYxm+zacA+7Ks6zZ/BRGYzmSkwfh9VYphtoJuRHvi9JF5LHJgwhsSl5p/NA4ffoUMTGxqPKxmKQ7I4Pf+3T1bcdUs1Dvsb546r+IlMv5ARcSUHk8WKIifStwv/yyjrff1gPQsqWHn3/Of2K9nJw6dYLFi7/323fXXfcREVEu13NK47MoC6SUZEyzZ2L8+EPQaEjavg8lh0zTlxgMHxMYOAaAtLSluFx5B9dC0RHvi9KlMAIb0RUllGnTpk2mQYOG9O3b37evQoWK+T7fwHFuuS2C1APHadHnDOqKLbHFd881qAGwlosASSIjA+66y8SuXSpcrqxRFQ895L62F3MZkymAmjVrA5kLVTZs2JTQ0LDrvq5QAC4Xxo8/wDT7NVRpqbgbNcYydcZVg5rQ0MZoNIf+23oMt/vGCWoWH/mJjnFdMGvzuaqqIJQQEdgIZdZvvy1jzpy3MZlMdOt2G0ZjwWYamfc+ifHMJ4Q1AmfXrtiqfIQnpEXeJ/43fVuS4JZbvPzzj5rq1b3Uqyfz/vuOfN3b5XKxY8dWduz4B0VReOCBEej1et/x8PAIOnfuUaDXIxQe9dHDBA8egPr4MbwVKpI+czbOOwZcdfV1nW6FL6hxu5uj1b4HlP6p9w6Pg0kbn2PB3o95qO4jvNL29ZKukiBclQhshDLH6XTSpk1TTpw4DkDLlq0LHNQAeM21cJbri63y03iCGmQdUBSMScmAhD0i91YSsxmmT3fyxhvOfN/z4MF9bN26ibS0VL/9brfLL7ARSpa3QhxyUDD2SVOxPzwS8vHvy+XqisdTDbv9EZzOEUREFENFr9PJ9BMMXzGUHQnbaVehI083ea6kqyQIeRKBjVDmDB8+1BfUDBp0D++8836+z1VkmaOffwKyTNUHRmCPG+l3XGu1YT53AY3LhUevwx4e6muhGTzYyL33uunTx+Mrby5gq33WoOBM3bv3Ji6uElpt6VkNWQB0OlJ/+/2qLTTZqUhJ2QZcNcl0qbHm5EpGrhxOijOFsY2f4ZmmE1Cr1HmfKAglTAQ2QplTpUo1AFasWEvDho1zL6h40V/4GXdQA2RTFRwJF9lw5+0AaENCierQGXPlzBkrkseD+cJFDGkZKJKENTIcW3gYChJHDku0apUZwfzxh5o+fSzXXPeaNWv7AhmtVuQDKXEuF+SyftfVghq9/hsCAiZjsbyLy9W9iCpXdLac28zdSwYSpA/my56L6FpJdHsKNw6ReVgoc6ZNe5l//92fe1Aju9Gf/ZLQP5sRtOsBjMfeYmWHFr6gBqDWE2MJqFQZAI3dTtjhYxjSMnCaA0iuWglbZARffq0nKirQF9QAzJyZ9xgaWZaxWDL45JP3OXnyGJdPTJQkCZMpQAQ1Jc3jwfDxh4Q3rI16z+4CnmwnKOhh1OrzqFQniqR6Ra1p+WaMbjSWVXeuF0GNcMMRgY1ww9u1a2e2fdHRMdkLeh0YTn1E2B8NCdozEpXrAtbK40iPHUdg9RoARHXsTIclqyjfuSvSf/0FHr0ej8FAWoUY0itmrcAdFyfTqFFmt1H//m6OH8/gnns82e97meTkRObOfYsFCz7EbrezZMmPJCeLFbdLDUVB99syQtu3IPC5p1FUKlQXzhfoEkFB9/t+djgeKewaFgtJkpjY4gXigyqVdFUEocBEV5RwQ7JarYwePZLFi39Cq9UyZsw4nnnm+aueE7xjELrktcjaMKxVJ2Ov+DCKNgQ1UPne+wmsVgNTTlPBVSq+P1yJZs28BF02NqJtWy/Ll9vyXefU1BQWLlzg277lljrUr9+YsLDcpwcLxUe9ayfmqRPRbViHYjJhHfcctlGjCzRQymR6Db1+OQCpqb8WVVUFQbgKEdgINxxZlqlcOdq37Xa787Ukgq3Co+z7OxaLpyrVWo9GdVl3T1SHzlfcA06ckFizRsPzz2cuURAXJ7NqlZWQkGurt8FgoEKFeOx2G3feeW++EgQKxURRCHzmSTTbt+EYfC/W5ycj59TqlweH404CAl7Can0Wt7tNEVS0cNncNk5lnKRmWK2SroogFBoR2Ag3nGefHev7+a+/tlOlStU8z/HYbKwcNP2/rW1YTpyi0Wtv+ZVRuT2gKBw4oadbNxNWq//UlYcecuUrqLHZrJw+fZLo6AoEBgb69hsMRtq27UhwcIgIakobScLy2psokgpv3XrXfBlZrkxy8ia83lsLsXJF42jaEYYtH0KSPZHfB20i3ChaDoWyQQQ2wg1n2LCHWbr0Z3788dd8BTUnvv2ag3Pe9m3XfnYiMT16+ZXR2OwEnT6DrNEQHxdPv35uvvhCx8CBbrp189CihZfy5a+eb91ms/Lpp/N820ajkTvuGOSXEVhkBy69PPUaFPgcleo4ihKEomQ91xshqFl2bCmPr34UiyuD55pNItQQWtJVEoRCIwIbodRSFIU77ujJW2/NoXLlrIUCa9e+lX37juV6njpjF15zbZAyc27oIyIz9xtNtJj/OaaYWL/yhpRUzOcugCRhjQpDp5eYPbtgifU2b/6Df/7Z7NuuVKkKVarUEIFMKaNd/zuK2YynUZPrvJIHs3kcRuPHpKfPw+m8u1DqV9Q8sodXNr/Iu9vfJNwQzjd9fqRDxU4lXS1BKFQisBFKrblz57Bp0x+MHz+WRYt+ytc5uoTlBO0cgr3CQ1hrvgpAeOOmNJr1NuFNm/sXVhTM5y9iTEnFrdaSEReD15g5nqagCdQMBgNRUdFYrRbuuedBNNe7fLdQqNQHDxAwbRL6lStwN2tB6pLfrvlaOt0SgoPv8W2bTLNuiMBGURQeWHYPv51YTuOoJnzUbQGxgRVKulqCUOjEp69Q6gwZMogVK5b5tvv0uSNf5+kuLiZo5wMomkAcMVlfPNqg4ByDmuATp9DZ7Kz6J5BB06oy9SU3gwfnPV17z56dtG3r/1du/fqNqV//KskAhRIhJSQQMGsGhs8/RfJ6cfQbgHXi1Ou4ouIX1GRkvI3D8eB117M4SJJEv+oDqRBYkemtX0GnFtmshbJJBDZCqXLkyCG/oObuu+/jnnuG5Hme/ty3BO55hJ0bKrJnnYlGs52ENVJ8uWiu5PFKLPglmLSkIMZ/UAGvV+L0aW+OZWVZZvfuf9m4ca1vX82atSlXrnwBX51QnHSLfyZwzGOoMtJxN22OZfoMPI2bXvd1vd5oFCWElJS/gBtgbYTLDKhxFwNq3FXS1RCEIiUCG6FUqVq1Ov37D6RLl+4MHDgoX+doTy7g0JwXObA5a8r3kY/mEva/j3I95957jaxdawYkunXz8Pnn9hy7n7xeD/PmveO3r27dhgQHh+SrbkLJ8VarjhwZScZb7+Hq3fcaFmhSkKR0FCX4sn0SyckHCrOagiAUMhHYCCVqx45t1K/f0K9lZe7cj/N9vuxysvzB/yF7s6aq1ps6g3LtO/quabOByeR/3rBhLtauNfHTTzZatcq5pQbA4XAQF1eZkyeP0bVrTypVqiqWO7hBeG+pTcqf/xRwoUoAJxrNDkJDu+J03oHNNhKPp2WR1LGoJNoTiTDeAMuHC0IRkJTLF6opY1JSrHg8cklX46YmSRAREUhiYgaX/0s7evQILVo0BGDz5h1+s54KQvZ4cJw+xJ7XXiVu8ANEtevoO7ZiqcSZTalUrSbTbFAoRuNl5/33z+JmSieT27O44bndUAjBpkp1jvDw7IkeU1JWFHpgU5TP4qdD3/PU70/wbqe59K56e94n3OTK7PviBqXRqAgNDbiua9xEH+tCafHYY4/4ghrIHFdzrVQaDaZKt9D0f58R1a4jigJPP6Vj1mgLvSIOMf6e81QNt+Cw+39iqVS5BzUOR94LWQolT0pNIWDy84R2ap25Cvd1s/t+crvrYbc/RGLi6RumtcbldTFp43geWfkgJo1JJNwTblqiK0ooVmvWrOLbbxcC0LBhI5YvX5vrAN8rKbLMqk6tKNeuA/Wnv5pDAYW9qy3MuCOBCpFujp3T8fH6WIaOM+RrfIXX62HZssWcPHmMhx4ahV5vKNBrE4qJy4Xxkw8xzZ6JKjUVd/2GqC5eQM5pna8CkOUqZGS8jsNxL3B9fzEWt3OWswz/7X62nt9Mi+hWfNjtU6ICxOB24eYkAhuhWLlcLipWjGPgwLt4/vkpeZZXFIWMwwfZNX0ytlMnAbi4/vccy+rT0ukQc56ENA3/WqOI6RjE0M55N0oeOLCX1auX++07ceIYNWrckvcLEoqPoqD7dQkB0yejOXYUb2wF0l9+DeeAu66hT1HGYPgQh+NhLm+4vhFX4/7nwlaG/DqYRHsCoxqMZmLzF9CqxTgw4eYlAhuhWPXo0ZMePXrmu/zhD+Zw/Osv/Pa1nJ85uPjoUYktW9S+3DPO4CAsXi9SzWBi1Op8Xf/kyeN+QU1oaDjt23cmJkYkLittVBcvEDTyIRS1BuuEKdgefQy/gVP5IElphIS0R6M5CoDXWwe3u3VRVLfYRAfEYNaamdnuDfpU7VvS1RGEEicGDwtFatu2rXTr1pGkJMs1D8w7u2wx9r8+pGGjDUixrfhL9y3vzQvj22+1xMbKLFtmy3MdJ4CEhItERERm6/o6c+YUR48epk2bDvnuFrsRlYVBkrplS3E3bopSrtw1nK0QGZk1dVuWQ0lNXYzXe+2LXl6rwn4WHtmDRiX+Tr0WZeF9UZYUxuBh8U4QioTb7aZVq8acOHGcgQMH8v77+Z/CfTmV/SS1yi9A3+Z3MgI689B73xCm8fLtL5lN7WfOqIiMzP3TyOv18Pffm33rOJUvH0P//oP9ysTGViQ29vrGZwjFw3Vbr7wL5SIwMKubKTHxCIoSWRhVKhVEUCMIWfLdMb1u3TpGjBhB//79SUhIYMaMGbjd7qKsm3ADSk5OonbtqsTGhnPixHEAWrfOX1O/oiis7d0V63/nGY+/S9ifTdEnrsAR/SD7re/zxVPH+d+Yk9zVx8quXRYuXswgt16ngwf3MW/eO36LU1arVuN6Xp5Q1DweDF8uQMpIL/RL22zP4nbXIz193g0b1Hjkqy/5IQhCPgObb7/9lqlTp9KoUSNOnjyJWq1m8+bNzJw5s6jrJ9xg2rdvSWJigm97zZqNjBkzJs/zFEXh99u747FksGPSswCo3El4DVWx11iNTvMYTWPTcOn0JMRU5L35MlFRV283PnLkoO/nHj36MGrUWOrVa3RtL0woWoqCbvVvhHZqTeBTj2P84P3rvqQkJftte73VSU3deEMsWHklh8fBuN/H8PjqRyjDowcEoVDka4xNjx49ePvtt6lZsyZNmzZl69atnD9/noEDB7Jx48biqOc1EWNsip+iKMyc+TKPP/4kZnNgvvqvN947EPuZ077tRrPfJbRhU/b/Y6FN0DnUXhmPTou1XCSuQHO+U+MrisLFixcoVy6qTI+dya/SOpZAvWc35qkT0a1bi2I0Yhv5BLbHx4DZfB1XtRAa2o7U1N9QlNKXgbcgz+JUxkkeWj6EHQnbaVehIwtu+xqT1nT1k4R8K63vi5tVsY2xSU5Opnr16gC+L4jIyEjRFSVkI0kSzz03Kd/lFUXxBTUqvZ66k6ajxDejfHkzYOaPDyzUbW/AERJ81YDm9OmTyLJMXFwlv7pERYlcHqVZwAsTMc6bgyTLOO66G+uEKcgxsdd0LY1mG3r9YsCGyZTZ4mM2P0dGRu5rhpV2a06uZOTK4aQ4Uxjb+BmeaToBtSp/M/4E4WaVr8CmTp06fPrppwwbNsy37/vvv+eWW0Sej5vdV199TvXqNWjatPk1na9N20qDEZ0J7DIGVVAkFSoEXnZUYoe7AtVDs48r8Ho9bNmyiSNHDpKenubbP2rU2Guqh1Ay5PLRuFu2xjrtZTz1GlzzdSTpAsHBA1GpEv322+0jr7OGJUNWZGb/PZPXt75KkD6YL3suomulHiVdLUG4IeQrsJkwYQLDhg1j0aJF2Gw27rzzTs6dO8fHH1/bTBfhxuf1elm5cgVjxjxG167d+fLLb/M8R3a52PfGTG4Z9zxqOY2Aw1MxnvmM4Og6JAc/y9ffZ/1zjIuTWbHCRnh4zm3DX3zxMVarxW9fy5btru9FCcXO/shI7CMeu4aVt/0pShR2+yi02j+x2Ubj9VZClisVTiVLgITE7sRd1Imox/zuC6gUXLmkqyQIN4x857Gx2Wz8/vvvnD17lqioKDp06EBgYGDeJ17mn3/+YerUqZw6dYqGDRsya9YsIiL8+78dDgczZsxg9erVqNVqRo8ezcCBAwt0n0vEGJuiU65ckO/nsLAw9u8/nmO5S/3XF04nsLp7BwAaPNKRWjFfoVKCkCOmoFLXIzWuAjZ9AJ07m3j/fQe33nr153bhwjm+//5reva8g7i4SqhuptUsr1GJjiXweEBTGFOSXUhSBkFBw0hL+7kQrlcy8vMsMlzpaFRajJqCJSEUCkaMsSldim0RzIceegiTyUTPnj0ZPnw4ffr0ITAwkEGDBuX7Rg6Hg9GjRzN69Gi2bNlCfHw8r76afb2fl19+mdTUVFatWsWHH37IK6+8wokTJ/L/ioQilZKS7BfUzJz5Bnv3Hr3qOV6XyxfUAMTpvoPAsShR36JS18MZaEbWatFqYeVKW7ag5ujRw9lmgkRFRTNq1FgqVaoigppSTH3oIEFDBmEe//R1XUev/5LIyCAiIyOIiKiMTrcWnW5xIdWydArUBYmgRhCuQa5/Qp06dYrPPvsMgC1btvDSSy/5Hc/IyOD48eP5vtGmTZuIioqia9euAIwZM4a2bdsyffp0TKbMEf4ul4vFixezcuVKjEYjNWvWZNGiRYSHi1VqSwtFUZgz5wMee+wR3njjXe677/48y3/TMGsl786PtyfglqFIkobdpwJwxkYQVzFrsUm9PutcWZb5+utPSUtLBcT4mRuJlJhIwOuvYPjsYySvF0f/gSDL17CmE2g0mwkKyhor43DciUp1Hre7bWFWWRCEMiLXT5mKFSui1WqxWq0oioLVavX7z2Aw8Pbbb+f7RidOnKBSpUq+7ZCQEEwmEydPnvTtO378OGazmSVLltC+fXu6d+/OwYMHMV/XtE+hMIWFhTNw4CAuXkzPM6gBsJ89Q+R/gU2br38gsNFj7DoaSK/nqlF3SC0OXci5yTExMYG5c9/yBTUdOnQttNcgFCGHA+N7bxPWvAHGjz/E06ARKUtWkjH342sKagBkOQar9Rk8npokJKSTkTGftLSlKEpI4da9BBxNPcLgJf25aLtY0lURhDLjqp3e48ePB6Bq1aoMHz78um5ks9nQX/7nOGA0GnE4HL7t9PR0kpOTOXbsGCtWrGDv3r08/PDD1KxZkypVqlzX/YXCU5CcMKbYCrR77z2Sky38sjKc55/RkmZVI8sSR45kkNMwLa/Xw6JFn/u2+/cfTPnyMYVRdaGI6ZctwTx9Mt64eNJnv42zb//rHhgsyxWx2SZjs00upFqWDj/v/5khPw7F4spgw+nfGVDjrpKukiCUCfkazTd8+HBOnTrFhQsXfGMd3G43Bw8e5IEHHsjXjYxGIy6Xy2+f3W73dUMB6HQ6vF4vY8aMwWAw0KhRI1q1asUff/xxTYGNJF33Z+pN7/3332PKlAm88ca7DBmSdwvNHw/cQ7MXHkNfviKyMQ5JAn1ICDqvmj17VKRkZP6T++svC0FBOV/D6XTSpEkL9u3bzR133ElISGhhvqSb1qX3QlG+J1x39CfDZsM58C4wGMj/rTyo1bvQaHYDCk7nXYAhr5NuSB7Zwyt/vcg7294kzBDGott/pEPFTiVdrZtWcbwvhPwrjOeQr1lRc+bM4d1330Wr1SJJEh6PB0VRqF+/PgsXLszXjX7//Xfeffddvv/+ewBSU1Np06YNf//9NwaDwbevRYsWrFixgvj4eABGjhxJy5YtGTp06LW+RuEaXbhwgfLlMxPc1alTh127dl21fOLOnfx2991EVXbQ+fFy0P0PkPy7H9LTyTWguZzH40GtVouMwTcFGbgy6VwN4G+gYDMvS7sLlgsM/n4wvx//nWaxzfj2zm+JC44r6WoJQpmSrxabhQsX8sknnyDLMr/++isvvPACL730EjqdLt83atGiBRMmTGDZsmV07tyZt956i06dOvmCGsgcd9OuXTveeustXn31VXbv3s1ff/3FhAkTCv7KgNRUMd37WiUmJnLLLZmtZNHRMaxd+yeJiRm5lndcOM/6uzPX4JGVAOyat5n5VBpPTNYQHh5IUlLWVMrEy3KoWa0WVq9eQevW7QkPL32p78sSSSLbs7im66Slolu2FOfgewulXmbzKC59DFitU1GUINzu1ni9ALn/m7sR/XJoGb8f/52H6j7MnNvfxZLmuur7Sih6hfW+EAqHRqMiJOT6pnvnq8WmUaNGbNu2jYsXL/Lwww/z888/Y7FY6NWrF+vWrcv3zf7991+mTJnCyZMnadSoEbNmzcLhcNCrVy+WLl1KTEwMaWlpTJs2jT/++IOAgADGjRtHz549r+nFiTw218ZqtdK/fy+2b98GwObNO6hcOfeuwL2vz+DMkl9827fPmo+k0tH92ep88QtUqpQ9R4QsyyxZ8gOnT2cNHhdjaYrWdefrcLsxfDafgNdfRZWcTMryNXgaNbnuemm1awgJuYOUlDV4PNd/vdJu24W/aVy+icidUkqIPDalS7GtFRUdHc3FixcpV64c58+fx+Vyodfrycgo2F8a9evX5+efsyfV2r59u+/n4OBg3njjjQJdVyhcAQEBjB8/kRdemMjHH39x1aAGxYshdRVqrUx03TBq3jEbjVZL7+er4dCZMJtt2U6RZZm5c9/ybQcFBdOrVz9CQ8OK4NUI101R0C3/lYDpk9EcOYw3Oob0d+fiaVA4K6W73Z1IS1twUwQ1AI2ibo7XKQglJV+BTc+ePbnvvvv46quvaN26NU8++SQ6nY4aNWoUdf2EEtKpU1c6dcrHFGtJTZ2HelL/nnTcrqEYVDL9JlflyRc0tGiRPaiBzMDmkgED7hELVZZikiWDoPsGoftzI4opAOtzk7CNeBxMhbu6tMt1R6FeTxCEm1e+l1T49ddf6dChA263m9mzZ2OxWHjyySd9g3xLI9EVVTCyLOcri68iyyiyjOpSinxZJvTYCdROF6/+Ek//RwyE/df4klszr8WSgdlctgaGlnbX1OSuKATfPQBvTCy28RORrzMI1WrXERj4CBkZn+B2NwTKZmbdnw//gE6t57bKvXI8Lro/Sg/xLEqXwuiKyldgM3r0aGbMmHHDJcoTgU3+eL1eoqNDeeGFl3jkkZFotdpcy257dgxJW/6i2piJpFfuS/36mb9frcWKyuPBGRLsVz5zyr0Li8WFyXRj/fspa675A7wQ1nkKCHgGrXYbWu1W377k5E14vbde13VLG5fXxfRNk/lg5/tUC6nO+sGb0aiy/+7El2npIZ5F6VJsa0Vt3boVTaEsYCeURm3bNgNg2rRJVw1qDs2bQ9KWvwCYPekiH3yg4/z5zOnYbnNAtqAGYOXKX3nvvff4999tZGSkF0HthULh9ZLrp/p1vvcNhnmYTPN8QY3b3YKEhKQyF9Scs5yl38+9+GDn+7SIbsWPfZfmGNQIglC08vWu69atG8OHD6d79+6UK1fOL7dIt27diqxyQtFSFIWxY5/g8OFDACxfvibXshd+X8PxrzOzAf+hDGPRxTHwLcyY4cixvMWSwe7d/3Lw4H4AMjLSMZmuLwoXioZ27WrMUydhHT8RV8/ehX59j6cebnczHI67cTgeIHvOmhvfhtPreHTlMBLtCYxqMJqJzV9Aq879jwRBEIpOvgKbDRs2APDJJ5/47ZckSQQ2N7Bu3Trw77+ZM9LGjn2WRrlM3XVnpHPw/XcAiK7u4O/dbQDYuNFKcPZGGhITE/yWRKhXrx5t23YRzbyljHr/PsxTJ6JbswrFYEB17kyR3MfjaUlq6qoiuXZpYPfYGblqOA6Pg/ndP6dP1b4lXSVBuKnlK7BZsyb3v+SFG9d33/3MHXf04pFHRnL33fflWk4bGEStu9uSsf5javd/nmafR3HeLVOjRs7jlyIiIgkLiyA2tgJBQcF06dJBJCErRaSLFwl49WUMX36GJMs4Bg7COvEF5NgKhXN9KQlJsiHLFQvleqWdUWPko+4LiDBEUC20eklXRxBuevmeFXUjEoOHC4c6Yzch2+5GCpmBpK3BR0sj6PVUOBoNHDlyEEWBatVynvovBuaVHpeehePu+zAs/BJXy9ZYp71caPloAHS63wgOHojNNgKr9bVCu25ZI94XpYd4FqVLsSXoE8oGRVHweDxXHSB8Jf25RQQenA2h7yJpojmvD6fHE2GkpFzk55+/xeVyUq5c+VwDG6H0sT3zHM4evXDd1qtQVpxTqw9gMCxEpTqOwZC5FpzB8IMIbARBKBH5mhUl3NgsFgt33z2AqKhg5s+fh8OR84Dfyx374lNOLfwQ3b8fQPj/QFOe9JjyqKtGcPrMIb799gtcLicAVauK5vcbiRwXnzlIuJAWGNXrv8Nkmu0LatzuhiQlHS6Ua5cmTq+Tr/d9QRlu5BaEMkG02JRxiqIweHB/tvw3Tfubb77m0Ucfu+o57ox0Dn80F4DYyTMJJRBbXCxucwCpqSmsWLEEAJVKzYgRTxbtCxCuifrIIVSnTuHu0KnI7+V03oUsR+HxNEKWI5Dlsrda9amMkzy0fAg7ErYTqAuid9XbS7pKgiDkIt+BzZdffsnChQu5ePEiP/74Iy+//DKvvvoqgYEie2xp5XQ6qVgx0re9a9fBPJcvcCYlsX5AVrbUTs93pmplL6/PVQgFQkJCqV+/MU6ng06duhdV1YVrJCUnYZo9E+MnHyGHhZP89y58S2cXCjs63Upcrqwvdq+3Ol5v2W21W3NyJSNXDifFmcJTjcflmk1YEITSIV9dUfPmzWPRokWMHDkSWZYJDAzEZrMxffr0oq6fcB3mzZvj+3n9+s15BjWpu3f6BTUP7PuHdE8Sp1K0hIZmlWvdur0IakobpxPj/94lrHlDTB/OxVOvPukfLSjEoMaCVvsHkZFRBAY+jkp1qpCuW3rJisysra9w95KByCh80fMbnm8+BbWq7OXhEYSyJF8tNosWLeLzzz8nJiaGqVOnEhgYyJtvvkmPHj2Kun7CdRg9eix16tSlZs1biL3KVF6V7Ri6pJUE1XwQgNOu6rx47GOGPfIZ0dHnufPO+4ByxVRroaA0O3cQ9NBQ1CeO460YR/rM2TjvGAD5WPcrL1rtn4SE+L/PVapUZDniuq9d2r39z2xmbX2FuhH1md99AZWCK5d0lQRById8BTY2m42IiMwPsksD50wmk18GYqF0ymuFbt3FxQTuGYUkq3GHdqDL6j/45lsd45Jn+cqcOHGMyEgR2JRW3tiK4PVimTQN+yMjC7XrSZaDfD+73fVwOIbgdN5FWV288nLD6j6MzWNjbJNnMWrK/usVhLIiX4FN06ZNef311xk/frwvmJk7dy6NGhVe/guhmMku9Lsnouz5BCrUQYmcg8EagtWsxuH4n6/YgAF3ExUVXYIVFfKihIeTvHkHFGAaf355vbeSkJDKzTiBMlgfwsQWL5R0NQRBKKB8fVpNnDiRrVu30qRJEywWC61bt2blypVMmjSpqOsnFIDD4eCll6bi8XiuWk5lP4n2t24sGb2B/btaooTPRZI1ePR6HA47MTGZ3VY9e/YVQU1p4vXmfqxQghor4eFxwOVJLSVuxqBGEIQbV74zD8uyzK5duzh79ixRUVHUq1ev1K/4fTNlHt627W969eqK978vv4sXc1lJW1EI2NSLnyYkA6BSq+k+41M+3lyJoaMzvxzPnj2NJElER8ded71EVs9C4HZjWPAJprnvkfLrapTIyLzPycHVnoVO9xPBwUMBsNvvw2L5Xw5XKJuOph0hUBtEpOnafq/XQrwvSg/xLEqXYss83KdPHwYMGMDtt99O/fr1r+uGQuFxu920bNkYnU7rW6EbMmdA5UqSWP1xKJAZ2Nz11of0e6EiQZXMDCUz4d6lFhuhhCkKupXLCZg2Gc2hg3ijyqM+dhTPNQY2uXP6ghoAq3VGIV+/9Fp2bCmPr36UhuUa822fn8S4QUEoA/LVxjxs2DDWr19Px44dGTlyJCtXrsyzu0MoesnJSTRv3oLDhw9x2229+eKLb9i//xi1at2S6zkZhw6ScfgIAN0nz+Lp7w+hjtzI88+X/em7NxL1rp0ED7yd4PsGoT5zGuszz5P813Y8zZoX8p28RF4WKCUkpKEoIYV8j9LHI3t4cdML3L/sbnQqLU80HCOCGkEoIwq0COaFCxdYunQpS5Ys4dy5c/Tu3ZuJEycWZf2uy83QFbV5819UrlyFcuXyN2vJfu4sJ7/7hjNHLLyx+zF69v8UgD59BlCxYnyh1080814Dj4ewpvVQnT2D4+77sD03Cbn89Y91yvlZ2DCbx2M0fkZS0r/Ictmf0nzRdpERK4ex8cx6Gkc14aNuC4gNLN5WSvG+KD3EsyhdCqMrqsCre586dYqlS5eyaNEiANasWXNdFShKN0Ngc6327j3C77//DIBer+ehh66+zMK1Eh8a10b7+xrkiEi8deoW2jUzn4VEYqITRdFdtj8JRQkjc6Bw2ZZkT6Ljolact57jobqPMK3VDHRqXd4nFjLxvig9xLMoXYptjE1SUhK//vorixcv5tChQ3Tp0oWXXnqJli1bXtfNhYLzeDxkZKQTGhqWZ1ndxSXoL/5MRu33QZX1qN1uty+oARg2bFSR1FW4dkWzxpMdiEKt3ojHU8+3V1HCi+BepVO4MZw7qg2gXmR9BtYYVNLVEQShCORrjE27du1YtmwZd955Jxs2bGDWrFm0atVK9EmXgNq1q7Bo0dd5jnHSn/+BoJ0PoLdHk7Dye7xOp++Yx+OmffsumM2BjBz5lHiOJUS7bi2q48eK5V5q9U4iIqIACAgovd3HxWF66xkiqBGEMixfXVEnTpwgPr7wx18UtbLWFTVq1MN89903AJw7l4JanfOaNfpzCwncOwHCZ/H1mMwMwqaKcTjuX0TjJhASAl6vN9fzC5No5s1OfWA/AdMmoV/1G45+A8iY90mR3k+rXUlIyADfdlrad7hc3Yr0nsLVifdF6SGeRelS5F1RM2fOZPz48Xz++ee5lhFJ+orHlCkTfEHNBx98kmtQYjjzOebDH6JEfs7C0U/79p+Mupsn7wnks8/s3Habp1iCGsGflJBAwGszMHzxKZLXi6P/nVgnFnVmW7tfUANe3G5rEd+zdLhgu0CUKaqkqyEIQjG7aleUzWYDwGq15vqfUPSWL/+VuXPfA+Dpp8dzxx0DciynS1hG4PHVEPE+3z2b1d0Q+eQ7zP69A3FxJ9m4UQQ0JUH/9ReENW+A8bP5eJo0I2X5GjLmzkeuGFfEd9aSlLQdt7shiYlp3AxZhF1eF5M2jqf1V004mnakpKsjCEIxu2qLzbRp0wDo378/TZs2zXZ89erVRVMrwU/btu0BGDToHsaPz318hGxqBaFVkDUSFW/vz7FFX9Fi/udMe78Ow4a9gixL9OrVF6hSTDUXLlHCw1EiIkh7531cvfpktn8XCw2yXJXU1HXFd8sSdM5yluG/3c/W85tpEd2KAM31NWkLgnDjyTWwcbvdvhaZRx99lLVr13L5cJyMjAzGjRvH9u3bi76WN7mAgACOHTtHQMDVP6Q9piAyohVcAQFE97mD0KbNMcZX5+efbTz1FKhUCpUri6CmJLi69iC5Y5ciWajycirVWQICppGRMYd8TnosMzacXsejK4eRaE9gVIPRTGz+Alp10f6+BUEofXL95EtOTqZHjx44HA4URaFFixbZynTo0KEo6yZcJq+gRvZ4UGk0OEJDMstXjCOgYhwXLkg89dTbAFSvXquoqynIMqhy6O6RpCIPaoKChqLX/wSAzTYOr7d6kd6vNJm/ax4TN47HpAlgfvfP6VO1b0lXSRCEEpJrYBMVFcWqVauw2+3ccccd/Pzzz37H9Xo9ERERRV5BIW/nV6/k+MLPqfHYGMIaNPI7lpS00/dz+/adi7tqNw0pJRnTG7PQHNhH2jc/FmNXUyaT6WVfUONytcXrLfsZhC9XNaQ6tcJq81G3z6gWevMEdIIgZHfVturw8MzEXX///XexVEbIMmfOOzRu3ITmzVtmzzOjKBhOfYAz5h7cLjW7XpwMQPLfm7MFNl5vZr6bxo2bo9Ppi6XuNxWXC+MnH2KaPRNVairuBg2RUlNQ8pFAsbCo1XsICJgJgNvdnLS0pcV279KiQ8VOrL5zA2qVGBwvCDe7qwY2d911F4sWLaJPnz65llm8eHGhV+pmd/bsGaZNy5xGf+FCmv9BRcZ84FmMSedQW9P4aeQPvkNVHxoBQEYG6PWg00Hdug2pUaM2er0IagqVoqBb8gvmF6egPn4Mb2wF0mfMwtn/zpy7ooqQLFfE643H661MWtovxXrv0kQENYIgQB6BzZAhQ4DM1b2F4uF2u2nQIHN17ho1avq31igy5n1jMCafQwmdzv7FP/oOtft+CZIksWCBlnHjDMTEyGzZYkWnQwQ1RUC9dw/BDw1BNgdimfgC9kdGgdFYInVRlCBSUv5EUQJL5P7FyeV1oVVpRbZsQRByddXA5lJLTb9+/fz2nzlzhoCAAEJCQoqsYjcjt9tNbGzWuj0rV67POqh4CdzzGIaU0yhhryFrtRiaNIdff6Hm40+hD49g1CgDP/ygpnHjv8nIaICu+Nf2u2l4b61Dxhvv4ux2G0o+V1YvLBrNFlSqJFyu23z7boag5mT6CYavGMrdtwzhwTrDS7o6giCUUvlqM9+5cyeDBw8G4Ntvv6Vz5860a9euVK/sfSNasWKZ7+eNG7divNQCIHsI3DUcQ/IhlLCZKBotafFxBDdqzK0TXsDdcBD33Wdk9eoMpkx5kT59lvLDD8WzBtHNzHHf/cUa1JhMMwkPr0RoaBcCAx8vtvuWBmtOrqTrt+3YkbCdRHtCSVdHEIRSLF+JLl599VVat26Noij873//47XXXiMkJISZM2fSqVNRrEJ8c+rd+3ZWr95IYGAglSpdNqtFUiGpq6KEj0VR60iNr4hXr0On1xHT7TY8HtDpbDz55Lu+UzSamyuHSZHweND/9D3OfgOhhJegCAnphlb7l29blsuXYG2Kj6zIzP57Jq9vfZUgfTBf9PyGbpVuy/tEQRBuWvn69jt69ChfffUVBw4cICUlhR49eqDT6XjyySeLun43nbp162XfKak4crQue98fQ+0xzxBZy38667//bqFJk42+7REjxqAq5gGsZYqioFv9GwFTJ6E5eIB0jwfn4HtLsEJe1Op9AFit47DZppRgXYpPiiOZkauGs+bkKupG1Gd+9wVUCr65prELglBw+fr2MxgMJCUlsWrVKho3boxOp+PAgQOEhoYWdf0EwOt0smP2K7hsVs5v+TPb8fj4ysTEVMBsDuTuux8QQc11UO/eRfCddxB8z52oT53EOvZZnL1LOtmbGovlTazWCTdNUAMgIXEk9TD33jKUJf1/E0GNIAj5kq8Wm8GDB9O3b18sFgtz5sxh165dDB8+nJEjRxZ1/QTg7PIlvp/rTpoOwObNasqXl4mLU4iIiKR3735oNCJ9/DVTFMzPPIXh808AcAy6B+vzk5FjYku4YpmczoElXYViF2II5beBvxNqKL6cQIIg3PjyFdiMGDGCtm3bYjabiY+P5+LFi7z55pu0atWqqOt3U9izZzcAt95aB8mTgeTJQDbEAGA5eoT9b84CoNVnCwH4+msNEyd6sFgC2b7dQmysIoKa6yVJoFHjbtUG67T/s3fW0VFcXQD/rcXdcZfibgUCIYFAobi7FLcixd3dneBQpNAPjeK0QHEoLS4JEg8JyWZ3s/L9sbCQRoGEJGR+5+w5O2+e3JnZnblz3333zkFdoVIWChOHlVUPYmIOZqEM2QNBqREQEPhU0u1ham1tzYkTJ3j16hUODg40a9YsM+XKNaxevYKZM6dgbm7Bjs3raGG5ElFCJG9qnEYnsyHqtj7JqHPDRpgXKszlyxJevFhNly7WbN3ai7x5dWmMIJBeYmfM1Uc1zMIYKWLxS2xt6yIWR2BishWFoneWySIgICCQE0mXM8bVq1dp0aIFf/75J2q1mr/++ovWrVtz6dKltBsLpIhWq2XmTH06hO+/r0vTPCeRxdxFZLcSWbx+FU6BVu2wr16LsuOncPashKNHd2FuLidv3tfcvBmUlc/gnItWm3y5sXGWKTWmpquxtByIvf13iMURACQk1M4SWb423k+P08enOxqtJqtFERAQ+AZIl8Vm8eLFTJ06NVGgvkOHDrFkyRIOHDiQacJ96+zf/6vh+26v1ZhcqIzWaSMSnRNShQKVlT7oWpVFywHQ6c6QN+9rAH74oRWOjl83MFxOR/LwAeYzJpNQozbxw3/OanESYWKyC6n0HwDU6qLExi5Do/m2s7GrtWrmX57NyhtLsTex51nME4rZCAksBQQEvox0KTaPHz+mZcvEK0NatmzJ7NmzM0Wo3MLw4Xrn6/XrvTAN3ITIZgYiaSnibW2QOybOnB4SEsw//1wB9AktCxUq+tXlzamIwsMxXzQXkx1bEWk0aJ3zZLVISYiO/g1QAaZotdlPvowmVB7KQP8+XHh5jqrO1djceAf5LPNntVgCAgLfAOmairKzs+Off/5JVHb37l0cHR0zRajcQvv2+mjObZp7YPbWHkzrobC2JNbFKcmUiLm5OXXrNqB48VLUrPl9Voib81AoMF21HLualTDduhl15apEnQggdsmKLBFHKr2IvX1+LC37IRK9SbRPq82PVls0Vyg1l19fwv1APS68PEefcj9xuJWPoNQICAhkGOmy2PTo0YOBAwfSrVs38uXLx4sXL9i9ezeDBw/ObPm+aVav3sC8eYuwfP0ckUlDEqSRvM1bEkQiwi//iV3VGojfRRC2sLCkQoUqVEgmfp9A8phu3oDFrKloChYmetkqVC1aZaljsK1tEwBMTPYDEt6+3ZBlsmQlBx/sI0YZzVr3TbQr2TGrxREQEPjGEOl0unQtqzlw4ACHDx8mPDycvHnz0qZNG5o3b57Z8n0RUVFxqNUpOIpmI2RxckyiwnmbJy9IpJxq5oZGLgegzuGzhESYULRozlz9JBKBg4Ml4eFvSd8vLQOJi8Pk150ouvfWOwZnIY6OVobvYWHRwNdXsLL0WnyEQq0g6G0gJWxLZp0QWUx2uRYCwrXIbkilYmxtzb+sj/RWbN++Pe3bt/+iwQSSJ8HcjATzggC89D5mUGp0XbrRubuaS5csmDZNwZAhCVkpZs7D3BxFv4FZLQVi8WPD9+jo3WSFUpOdMJGa5GqlRkBAIHNJ1ccmNDSULl26UKVKFYYMGUJkZOTXkuub5MKFc/Tr1xP5O8Xlv0Tf+5d/FugdsnXdevBIrcLVdTsAXbsKSk1yiN5EYeTnnXbFLESrLUZ8fDdiYrahUrXIanEEBAQEvmlSVWzmzp2Li4sLy5YtQ6PRsGDBgq8l1zfHv//+Q5s2zTly5HcWLJiTbJ2Xx/5HcM2aPOzQgUcqBQCnTzdAJNJhY/MVhc0JJCRgumkddjUrYdWnO+KgwCwWSItUeh1r65aYmS1Osjc2di1KZZsskCtrUGlUzLw4lSfRj9OuLCAgIJCBpDoVdfHiRc6ePYuJiQmlS5emY0fB0e9zcXWtZfg+Y0byik2hQcM4s32jYVsiKc5ff9WgYsXs7yf01dDpMPI+jvnMKUifPEaTNx+xs+ajzZeVq2qUWFt3wMjoNABGRqeRy8dkoTxZy+vYV/Tz68mV4MvEKGNY3GB5hvT7LQWjfH8s39Ix5VSEa5F1ZJZPU6qKjVqtxsTEBABnZ2fi4uIyR4pcxMuXEUjlcRi9XIKiQB+0Jh8eyObmFtSs+T1isZjKlavTrJkZIKJTJ2EaCkAUEoLVgN4Y/XkBnZk5cROmIB8wBMzMslQuW9taSKV6y4RcPpD4+Ny7WvD8i7MM8O9DeHwYgysNZ1LNaRnSr52dORLJt5e13t7eMqtFEHiHcC2+PhqNlsjIjNcrUlVs/rtgSiSotJ/Fnj07AahQoRKmag02z4IQxUlBtAV58amJ6latWtPwvX9/FQUKSOnTR1BsAHR2dogjwonv3ou4Xyahc3bOapGAeMTiKADevDlCQkKDrBUni9DqtKy+sZy5l2diJjXHq8lOWhRrmXbDdCASgUQiJiIiNsk9SUBAIGciEomwt7dAJMp4y026V0UJfD6lS3+Hu3tjBg4YgsWrV6DTolWeIr5Q6tmbW7VS06qV+itJmQOQyYjyPwfvrIjZA2NiYxchlf6Va5UagDtht5hzaQal7b5ji+fOTEmNoNPphOW4AgLfDJn3Z041jk3p0qUx+8jML5fLE20DXL9+PdOE+1KyWxwbs7AIzMPCIcaLt/mKoyjQn5CQYMLv/0vRCpUx/QY9hD8rRoROl20nvE1NVxMf3w/ITspV+sjseB3eT49TP38DzGVfFoPivwhxRgQEvj1S+l9nehybHTt2fFHnAh+QKJWYhYWjUz9DozmDIt8ctFotBw/uAeDhmuW02LYXialpFkuatchO+WMxYypvV69HXb5iVouTCHPz6ZiZLUWjKYpK1Syrxcl2NC3yQ1aLkKuIj48nIUGFlZV1VosiIJCtSNUbr0aNGml+BNKBToflq2BAhyhyBnElpoBYxu5dXoYqZqGhuVqpkfxzF+sOrbDp1BbJ08dI7v6d1SIlwsjoBGZmS7NaDIFsyOvXr3B1rZl2xU+gW7cO/P337VTrDB3an6dPnwIwevRwTp70S3f/J04cxdW1Jh4e9fDwqIe7e11++KERCxbMRq3O3Onv/fv34O19LFPH+FI0Gg2LF8/H07MhLVs24fDhQynWvXbtCr16daFJE1dGjRpGVFRkmn2MHTuC8PDwTD+O3Mq3t8wgmxETEw06HWoTE1QmkSgcyqFybM6T+//yNvYtAIWPHcPj1J+GNm/ewJ07YhSKLBL6KyIKCcFi1DBs3b7H6MwpFO07EXnxOspOXbNaNAOmpsuxttYnLNXpZLnaWhP0NpCjjw9ntRjfPLt27adcudQTw0VHvzF8X7JkJY0aNf6kMSpVqoq//3n8/c8TEHCBFSvWc/78WY4c+f1zRE4XERHh+Pp606RJ9v4PHTjwK8+ePeG3346yePFKNm1aS2Dg8yT1Xr16yaRJvzB8+CiOHz+Jk5MTa9asSLOPTp26sWbN8q95SLkKQbHJRFxcbBg1ajhyhYLYPM7EFKnN23Ib0ep0+JzUR8s1iYjAfc/BRCvOliwxplEjc3btkmWV6F8NqyH9Md21nYRadYjyP8vbNRuzOCZNUsTilwAoFO0ID4/IYmmyjlOB/rjvr8fggH6ExAVntTg5hu3bvWjduhnNm3swb95M5HL98tbo6Df88stImjRxZdCgvsybNxMvL31i1HbtWnDr1k1UKhUzZkymaVM32rVrwdq1KwGYM2c6ISHBjBo1hD/+OM/Qof3x9T0BwP379+jfvxceHvXp168HDx8+SJecxYuXoHz5ijx9+gSAhIQEVq1aSsuWnrRr14LffttrqPvy5QsGD+5Hkyau/PLLSCZMGMOJE0fTHOPgwf3Ur98AsVj/6Ll48QJ9+nTD07MBLVt6cvDgPgCuX79Knz5dGTiwDy1aNCYmJpq7d/+mX78eeHo24OefhxAS8uE3uHPnNrp0aYuHRz26dWvPjRvXkox969YNg4Xq48+OHVuS1PX396VTp25YWFhQokQp3N09k7Uy+fl54+7emCpVqiGVShk69Gd69OidZh9Vq1bn7t07BAe/TvOcCXw6gmKTSfzvfwfRarUcOfI7Mtk7BeWd8iIWixkwYDi1vqtA5wHDMbKxNbTT6WDDBiMA6tXTfHW5vzZxk6YSvW0P0b8fR12xclaLkyxy+Tji43vz9m3SG2BuQKvTsujKPDofa4cWHVua7MTZ3CWrxcoReHsfw8/Pm7VrN7N///+IiYlmxYolACxZsgArK2uOHPGjf//B+Pv7JGnv43Oc6Og3HD7sw6ZN2zl9OoCHDx8wadJ0nJ1dWLp0Dd9/X89QX6VSMW7czzRt2hwfn9N4ejZj1qwp6ZL19u2b3LhxjapVqwGwc+dW7t+/x86d+1i9ehO///4bly7pLcvTp0+iUqUqHDsWQJMmzTh//ky6xvD1PUG9eq4AyOVxzJgxhVGjfsHH5wyTJ89g9erlBsXvwYP79OzZl717DyESifnllxF069aTY8cCqFWrDlOnTgD0StDRo7+zatUGfH3P0qBBI9avX51k7IoVKxssVB9/evTok6TuixeBFCxY0LBdoEABnj9/lqTeo0cPMDe3YMSIQTRv7s7ChXOwsbFLVx916tTDL5ung8mppHu5t1Kp5OzZs7x69YqOHTvy9OlTypQpk5my5WgGD/4JgGnTZn9QbD5CIpFSpaF7kvJGjT6sOitZMvus6Mos1JWrQrbSZ7SAAvhwHXQ6B2JjV2SZRFlJpCKCwQE/cSowgPIOFfFqsoPC1kWyWqxksbVN3udPpXIjLm5+svvMzcdjZHTKsB0V9VeGyuTv70OXLj3IkycvAIMGDadXry6MHj2eCxfO8uuvhzA2NqZy5aq4urolI58Fz5495cyZk9Sq9T379x9ONZ7YnTu3kMmMaN26HQCtWrXju+/KJVv35s1reHo2QKPRoFKpKF68JMOG/UyDBo0AvTVi3LjJWFlZY2VlTdu2HfH2PkbhwkV48uQRa9duRiaT0ahR40TWnJQIDQ0hNDSEAgUKAWBsbMLWrbvJkyevYVpNo9EQExMDgKmpGbVrf2+QpWjR4gbZOnTowq5d2wkMfEaZMuVYu9YLOzt7QkNDMDU1JTLyyyyrCoUCI6MPKx+NjU1QJOMb8PZtLMePH2HJklUUKlSYefNmsmLFYqZMmZlmH4UKFeaPP859kZwCyZMuxebx48f069cPExMTgoODadiwIR06dGDJkiU0adIks2XMkby/+QwZMjzdbUJCRPz9twSALVvis+uK50/nwQMkQSGoK1TKakmSQYtY/BwTk13IZFcxMjpNQkJ13rw5mdWCZQv6+fbkwstzdP2uB3PrLcJUmnsd3D+HkJBgnJ0/WLecnV1QqZRERUWiUqlwcHBMtO+/NGrkQWhoCDt2bGXOnOnUrv09EyZMxdraJtnxIiMjcHJyMmxLpVLKlk1esalUqSorVqxFrVazdesmzp49Td269Q37Q0NDGTduFGKx/kak1eooWbIU4eFhWFvbJHphc3JK24IXHh6GjY2toZ1EIiEgwI/fftuLubk5ZcuWfxerSL/219b2gyU7NDSEO3du4enZwFCWkJBASEgwTk4urFu3gkuX/sTFJS9OTs7JBnK8desm48aNTFLetWsvunfvlajM2NgElUpp2FYqFZiZJf3tGxnJaNjQnVKlSgPQo0cfhg0bkK4+HB0dCQsLS+l0CXwB6VJsZs+eTd++fenWrRvVq1enUKFCLF++nBUrVgiKTTK8evWShIQESpUsmaj8zGk/SjvlwaVs+WTbyWT6P2OVKhqaN8/5gflEERGYL5kP27ywLF2GqJPns1V8Gqn0Bra2rknK3/vUCMCMOnO4HXaLrmV6ZLUoafI51pa4uPlkZqYYBwfHRL4gISGvkclk2NraIZVKCQsLxcUlDwBhYSHkzZvYvywoKJD69RvQuXM3Xr16ybx5M9m9eweDByf/wmRv70B4+IeHpVarZf361fTtOwBjY+Nk20ilUn76aRD37//LtGkTWbZszbuosPbMmbOQkiX1D+2oqCg0GjVqtYY3b6JQqVQYGRm9kz00zXMhEomQSj88cm7dusmhQ/vx8tqFg4MDcrk8kR/Lx5Ype3sHatWqw/z5H1YmPn/+jLx587F793aio6M5dOgExsbG/PHHeZYtW5hk/IoVK+HjcyZNOQEKFizEixdBFCyoty4FBQVSsGDhJPXy5y+YyIlbo1EblKq0+pBIJAalUSBjSZePzT///EPnzp2BDz82d3d3Xr16lXmS5WAiIiLw8GjC/B75sbreB5EyBJVKxT///s3JLet59M5B8L/Y2UFo6Ft8fORfWeIMRqnEdM1K7GpWwnTzBqhaldj5i7OVUgOgVpcmOno/Gk0+FIo2vHnzO2Fhr4mMvJfVomUbyjtWzBFKTXbg/VTL+49KpcLDowl79uzg9etXyOVxrFu3ivr1G2JkZISbmwdeXhtQKpX8/fcdzp49naTP8+fPMnfuDGJjY7Gzs0cmk2FlZQWATCYz+KO8p2zZ8iQkJHD8+BE0Gg2///4bf/11KUWl5mPGjJnI3bt3OHr0fwA0atSYrVs3ERsby9u3b5k0aSwHD+7HxcWF0qXLsG3bZtRqNefPn+HOnVtp9u/o6Ex09Bs0Gr3voFwei1gsRiaTEh8fz/r1qwCSXW5eq9b3/P33ba5cuYROp+P06QD69etBfLwcuTwOmUyGRCIhPDyMXbu2fvGSdTc3D3bt2kZMTAwPHz4gIMAXNzePJPXc3ZsYjl+pVLBjxxZcXRumq4+IiAgcHZ2S9Cnw5aTLYmNnZ8fDhw8pXbq0oezhw4eJTJ4CHyhfvgJb168kz935iE0GE6804sXbEAASzM2xLFEyjR5yLrKLf2A5bBCSwGdoChQkZvEyrPr1Qh0Rm5kRtD8TU1QqTyIjPbNaEIEcjkajoU2bxAEKFy9eSbNmPxIeHs6QIT8hl8fx/ff1+fnnXwAYNmwUs2ZNpXlzd0qV+o5Klaok8cdr164jT58+pkOHlmi1GurWdaV9e/1LpoeHJ1OmjGPs2ImG+sbGxsybt4QlS+azcuUSihYtzowZc9N1DC4uLvTq1Y+1a1dSt259evfWf+/atS0JCWpcXd3o3VvvOzhhwlRmz57GgQN7qVq1GqVLl0EqTX0Vp4ODA05Ozjx+/JCSJUtTs2YdqlatTvv2LTE1NaF+fTeKFStBYOBzTP8T08vW1pbZsxeyatUygoLGkSdPXubPX4KVlTUdOnRl2rQJNG3qho2NDS1btmHLlo3ExMQYlMBPpUOHzoSGhtClS1tkMhmDBg2neHF9mpBFi/Tnc+zYiZQtW45Jk6Yzf/4swsLCqF69BkOGjEyzD4B79/6lcuVqnyWfQOqkmlLhPQcOHGDFihX06NGDdevWMXbsWLZt20bfvn3p2LHj15Dzs8jKlArm9ydgFlccnUkdwkuX4o8jB7n1KgjH69dpt2knIpGIkBARK1YYMWKECmfnbPfU/ywkD+5j82MT5ENGEv/TQESmJkI4/GxCaqkJLr76gxoutZCIJVkjXCp8qykVbt68Tpky5QzTOdOmTaBy5aq0atUuiyVLHZ1Ox/XrV6lSpZrBgt+/fy/69OlPrVp1Um27ceNajIyM6NWr39cQNdui0+no1Kk1y5evNTiW5zYyM6VCuqai2rdvz9SpU7ly5Qp58+YlICCAESNGfLJSc+3aNVq0aEGlSpXo3bt3qpEXo6KiqFOnDpcvX/6kMbIDYvlTTIM2ojMqh9rElASNmluvggBwqVgFkUiETgfly1uwebMRY8bkvLxDKaEpWYqIG/8SP2xkNkpWqcTUdAkODi5YW3silV7KaoGyDWqtmlkXp9Hyf01ZcX1JVouTq/Dy2sDBg/vR6XQ8fHifv/66TJUq2f8NXiQSMX/+LM6c0TvYX7lyicDA55RNwXfwY9q378Tp0wGG6ajcypUrlylTplyuVWoym3RZbDIChUJBo0aNmD59Oq6ursydO5fY2FgWL16cbP3Ro0dz4sQJtm3bRs2anxeuPKssNpa3e2ESdhLyBKCwtmLzBX8iIvRKXK82nTFzycOVK2J++EGvlb548ZZ3L23fHFn5ti2R3MbGpg1icWLHxri4qcjlY76uMNmA/16LUHkoA/x688er81RxqsrmJjvIb1kgq8VMwrdqsXn27CkLFszm8eNH2Nra0rfvABo3bprVYqWL27dvsnTpQl6+fEGePHkYNuxnqlevla62e/fuwtLSih9++DGTpcy+jB49nPHjJ+dqH5ssS4L5ngkTJqS4b968eeka6OLFizg7O+PhoXeeGjlyJPXq1WPmzJlJMoafOnWK2NhY8ufPXhFo02LmzKmUyS9iUIFDJDhNRwaozM1o27IDlwO8sQsLw+zdCoihQ/VzyPPmKb5ZpSarkUrvAQkAaLWOvH27EpWqIR/HqMmtXH59iX6+PQiRB9On3E/M+H4uxpK0HUwFMo7ChYuwbp1X2hWzIRUqVGLbtj2f1bZTp24ZLE3OY8mSlVktwjdNuhSb/yoeb9684dy5c7Rt2zbdAz1//pzChQsbtm1sbDAzMyMwMDCRU3JMTAyLFi1i69atdO/ePd39ZwdWr14OQLkDc6lj5oFWI0ZpZYlULOb75q0T1X36VD8L2LFjwtcWM0MQRURgPnsa8jHjs10KhPcolR1QKjtktRjZjnNBZ+h4rA0ysYy17ptoVzL7+skJCAgIfCrpUmymTEkakvvmzZusWrUq3QPJ5fIkSw5NTU2TRHOcN28e3bt3x8UlZ4VsDwjwNXwv7TqUaIUCiSoBxEndmFQqKFNGQ2ysCAuLryllxmG+YDamu3eQUL8BytbZxdlRjUgUi05nk9WCZGtq5KlFi6ItGVF1DGXsy2a1OAICAgIZSrpTKvyXihUrcvPmzXTXNzU1RaVSJSqLj49PZA06f/48gYGBzJ2bvuWJaSESfb3QKf376/ONDB48DJEINCbGaE1NSG54Y2MICJATF5ftQrukC8ndvzHZsZWEWnVQtW6b6jG835f5x6nFwcGO2NjFKBQ9AWFa5b+8vwamMhM2NtmatcJ8AjnxPyIgIJA+/vv/zoj/e7oUm7t37ybaTkhI4OjRoxQokH5HwyJFinDs2Ieokm/evCEuLi5RkjA/Pz/++ecfqlevDkBcXBwDBw5k5syZtGjRIt1jvcfG5ssckNJLfHw8sbFvAZg+fQoODpbMmDEDK3NzWpUsSd769TG2sfkqsmQ6Oh1Mnwg6HbK1q3FwTF+cCHt7y0wQRg3cAxYA+nw/FhZjsLAYBcmqlAKQWddCQEBA4NPJjPtRulZFfewDA/rs1IULF2bq1KnUqpU+T3iFQoGbmxtTpkyhUaNGzJ07l8jISFauTNmJysPDg9mzZ3/2qqg3b77Oqqjg4GAmTx7PjRvXuHbtDidOHObp08cAlNi/n9LDR1Gw7bfh62F09DBWfbqj6N6L2KVpO8CJRPofbkRExq9osbTsgbHx/xKVvX27BqUyZ/lmZRYJmgQiFZE4mzsDmXstMpP3cn9rq6IEBHIz71dF/fd+JJWKv9gokS6LzenTp8mTJ88XDWRiYsK6deuYOnUqEydOpEqVKixatIhXr17xww8/cPz4cfLmzdg1/TodX+VG6OzswuDBwyhRoiSvXr00KDXOf+lz1+Rv2ZZDh6ScPi3ll1+U5M+fQ+/OCgXm0yahtbImdvyUTzq3mXEtYmK2Y2q6Fqn0FnL5UDSaChk7QA7mdewr+vn1RJ4g50TbgETJK7/W/0JAQEAgLf57P8qIe1O6AvS1bduW2NjYLx6sYsWKHD58mBs3buDl5YWdnR158+blxo0bySo1/v7+n22t+ZpI4h7SUDkKW+UVfv99HwCmISFYPXuGbaUqPH0uZcAAU/bulbFsWQ5e221sTOzMecTOno/O0THt+hmIWPwcmezUf0pFxMcP4e3bjYJS8xEXXp6j0YF6XAm+jGuBhkhFn+1KJ5AOXr9+hatr4vvUP//8jadnA0Pepbp1q7F58/pEdW7dukm7dvop9uvXr1K3bjWuXk2cyHPbts3MmTM9xbH379+TKHFkdkSj0bB48Xw8PRvSsmUTDh8+lGLdLl3a4u5eFw+Penh41GPVqmVp9jF27IhUg70K5D7Spdg4ODjw/PnzzJYlx/H+z2QcvB/Z2xvotFC+qD4PVL5z5wCoOHshtWrplz5ZWelYtEiZfGc5AZEI1Q8tUHbq+lWHNTefgr19eczNZ37VcXMaOp2OldeX0e7IjyjUCrya7GR6ndnIJKnn8BHIWB48uMeYMSMYOvRnWrRoZSjftWsbjx8/SrXtwoVzkqwUTYmIiHB8fb1p0qTZl4ib6Rw48CvPnj3ht9+OsnjxSjZtWktgYNLniVKpJDj4NSdOnMLf/zz+/ucZNuznNPvo1Kkba9Ys/5qHJJDNSderXJ48eejUqRMlSpRIkvhy/fr1KbT6tnn69Ak1a1bizu37lA75Ha2RI5jXpGX+QCpZ2RJuZUf+lm259/SDc+2lS3HJrf4WSAVT09WYma0AQK1OO2R7bkWr09LXtwfHnxzhO7sybPHcSTGbEmk3FMhQHj9+xKhRwxg6dCTNmiVe8ODu3oR582ayYcNWJJKkObny5s2Ho6MTmzatZdiwUWmOdfDgfurXb4D43U3l4sULbNq0nlevXmBsbEKPHr1p27Yj169fZfXqZRgZGfPy5Qt27z5AUFAQy5Yt5MWLQL77rizjx0/B2VkfYmPnzm14ex8lLCwUZ2cXRo8eT+XKVRONfevWDcaMGZ5Epu7de9OjR59EZf7+vvTu/RMWFhaUKFEKd3dPvL2PMWDAkET1nj59TN68+Q25s9LbR9Wq1VmwYDbBwa9xcfkylwmBb4N0KTbly5enfHnhofIxNWtWAmDN3L6sa/GA+Pz9MI6JQyIWY1upCpZ1vgdg2DB9vqQOHRJwcBAcGz4FE5MNWFjoMxdrtdbExqY/blJuQywSU8a+LMYSY5Y0WIm57OusCMxO1Pu1RrLlDQq4Mavu/GT3TbkwnjNBH6Y4z3f+K9l66eHZs6eMHDmYjh27JlFqAHr3/olffhnJvn176NIlqYO7SCRi3LhJ9OnTDTe3xpQtWy7V8Xx9T7Bo0XIA5PI4ZsyYwuLFKyhXrgJXrlzml19G0rRpcwAePLjPokUrqFChIlqtjl9+GcHYsROpW9eVgwf3MXXqBDZs2Mr161c5evR31q3zwtbWji1bNrJ+/Wo2bEgcHqBixcr4+59P13l58SIw0erXAgUKcO3a1ST1Hj16iFqtpk+froSHh1OzZm1GjhyDublFmn3UqVMPPz/vJEqVQO4kVcWmRYsWHD16lKFDh34teXIEHy8km/VTJQi+gNKxDVYR0aiNjFCbfkj+uGNHPDt2yBg7VpVMTwKpodEUA0Clqkd09PEslib7M7raOESIDBmXBb4eWq2WUaOGUrhwEU6f9qdTp67IZImnAGUyGePGTWb06GG4ujZMtp+CBQvTs2df5s+fyZYtu1McLzQ0hNDQEAoUKASAsbEJW7fuJk+evERHvwH0fikxMTEAmJqaUbu2/mXLz8+bokWL06BBIwA6dOjCrl3bCQx8Rpky5Vi71gs7O3tCQ0MwNTUlMjLii86NQqHAyOjDPdHY2CTF6bYyZcoyePAIjIxkzJ49nRUrljBx4rQ0+yhUqDB//HHui+QU+HZIVbF58eLF15IjR3HgwF4AXFxcsInxRmPkgkT0HWJNOHGOtokiDBUsqGPy5Jyr1IiDX6N1dsmSKGkJCXUID3+KTmf/1cfOiYhFuXue83OsLSlZcj4VnU5H//6DadjQnb59u7N+/WqDf8jHVKhQiaZNmzN//iz69RuYbF+dO3fn1Cl/duzYkuyUFUB4eBg2NrYG5UkikRAQ4Mdvv+3F3NycsmXLo9PpDC9htra2hrahoSHcuXMLT88GhrKEhARCQoJxcnJh3boVXLr0Jy4ueXFycia5iCC3bt1k3LiRScq7du1F9+69EpUZG5ugUn3wLVQqFZiZmfJfmjdvSfPmLQ3bffsOYPToYenqw9HRkbCwsOROlUAuJFXFRnjzS56DB/cD0LNbV1T2sfwd6ojf/h0UefEKj5nzv5nQcKK3Mdi61UXVsBFv12zM1LGk0lsYG/9OXNz0j0rN0OmEhJX/RaFWYCI1SbuiwFdDIpHg6fkDAJMnz2DQoD7UrFmbGjWSxvkaOHAY3bt34MiR35PtSyqVMn78VIYM6cf339fDyChpFG2RSIRU+uH2fevWTQ4d2o+X1y4cHByQy+WJVkt9fC+3t3egVq06zJ+/1FD2/Pkz8ubNx+7d24mOjubQoRMYGxvzxx/nWbZsYZLxK1ashI/PmbRPDFCwYCFevAiiYEG9dSkoKJCCBQsnqXfixFHy5Mlr8OdJSFAZ/G3S6kMikSAWfyt3XoEvJdVXPJVKxYQJE1L95Ebev/0MGT6OuNKL8L9ngkan4+2rFwSf9Mti6TIOs6WLEIeHkfDOhJ1ZiMVPsLWth5nZUqysumTqWDmdU4H+1NhdkZuh17NaFIEUKF36O7p3782cOdOIiopKst/MzIwxYybg63sixT5KlSpNmzYdOHnSP9n9jo7OREe/QaPRACCXxyIWi5HJpMTHx7N+vd4fTa1WJ2lbq9b3/P33ba5cuYROp+P06QD69etBfLwcuTwOmUyGRCIhPDyMXbu2JtvHp+Dm5sGuXduIiYnh4cMHBAT44ubmkaReREQEq1YtIyoqkpiYaDZtWkfjxk3T1UdERASOjk5J+hTInaRpuzYzM0v1kxtZv34LoaExmJiYoNGoDX98q6dPsSxVmvv3xUyaZMz16zl3akDy5BGmG9eSUKESis7dMnUse/tKhu8xMTszdaycilanZdGVeXQ+1o54dTxRiqQPTIHsQ48efXB0dGbevORDFNSu/T1NmjRNtY++fQeQP3/BZPc5ODjg5OTM48cPAahZsw5Vq1anffuWdOrUCp0OihUrkeyyaltbW2bPXsj69Wto0qQB27Z5MX/+EqysrOnQoSsxMTE0berGoEF9+f77+sTERBt8dT6HDh06U7Jkabp0acsvv4xk0KDhFC+uX7G3aNFcFi3S5wbs3LkbFSpUpHv3jnTo0IoCBQrSp0//NPsAuHfvXypXrvbZMgp8W6SaUqFKlSpcv55z3wyjojI/pcL27RuIi4tDolBQ5vJf1Nv3P7y8ZEyYYMKaNfG0b/9lbztZhVW3Dhj7+RB1xBd1rdqf3c/7sNkphcO3tm6GkdEFAMLCgoHcqSynRqQigsEBP3EqMIDyDhXxarKDwtZFPrmftK5FdiWnyp3ZbNy4FiMjI3r16pfVomQpOp2OTp1as3z5WvLkydjo9QKZR0r/a6lUjK3tl63qTNWkkI40UgLvPGoKBARQZbE+d9KOHXqHPkvLnHn+ZKf8MfbzQdG67RcpNelBJNInD42J8UJQapJyK/QG7vvrcyowgC6lu3Osjd9nKTUC3x7t23fi9OkAw3RUbuXKlcuUKVNOUGoEDKSq2FSrJpj20qJ6xarY376NXf4CmBfQm42fPtWf1saNc+YNx+j8OXSmpsRNnZXpY715c56wsBiUyvaZPlZOJUYVw7IGq1nutiZRzieB3I2trR1NmzbHxyd3h0LYt28PgwcnDRYokHtJV3bvnEpmTUWJlCEgkqAzckhk1RKJRMTFQZEilpiZ6Xj27Mvza2UV4pcv0ObL/8X9fDA3vsbcfDoKRVvU6sy1An1rxCijsTK2/uJ+cuqUTk6VW0BAIGWybCpKIDFPnz5hwoQxKG7MwP5CDWRR9xCJRIYPwIED+mkoF5ecfQfOCKXmY8zNJ2FquhFLy9EZ2m9uICOUGgEBAYHcgqDYfALHjx/Fy2sjJqH/Q2f9EzavRUjl8YnqNGqkZsECBYcPy7NIyuyGDniKqak+JPvbtyuzVpxszDdsPBUQEBD4aqQrV5SAnqdPH1O9KDiYadGZNk+SPgGgQAEdvXsnZJGE2Q9r6wbADcO2Wi34bf0XtVbNvMuzMJIYMa7GpKwWR0BAQCBHI1hsPoEnTx7TsTZg1pJdJ46y48D2LEk1kJNISNDnxImP70NExL9ZLE32I1QeSoejrVh1Yxlngk6RoBGUYgEBAYEvQVBsPoE//zxP+xpiNOZdCdRpCNWo0Sbk/AeRkZ83xvt/BW3GO1rL5dOAQOLilqPV5svw/nMyl19fwv1APS68PEff8v053MobmUSWdkMBAQEBgRQRpqLSyd9/36FmMSiYtxIPrtxDbW6ORKFALMvhDyK5HIvxYxC9fYvKvTE6u89POCkWP0UieUlCQmXgvVe7CCgAvM0AYb8NdDodm26vY/rFyRiJjVjrvol2JTtmtVgCX0DdutUwMTFBJBKh1Wqxt3egVau2dOnS47P79PPzJiDAj4ULl6VYZ9GiueTNm4+uXXt+9jjv6datAyEhrwGIj483HA+Av//5L+4/JVasWIKra0MqVaqSaWN8KfHx8cydO4PLly9ibW3NiBGjqVvXNdm6AQG+bNmykcjICGrWrM2ECdMwMfngsvDy5Qv69euBt/cpQJ+AdPToYSxcuDxRPYHPR1Bs0olMJqN+q9E8EH3Hg1d3wMEWtYODYf9vv0m5eFHCsGEqChfOOU6gZmtXInkRROzs+V+k1IAOe/uKAERFBaBW18gYAb9BVFoV+x/spZBVYbY02cV39mWyWiSBDGDPnoM4OTkDcPfu30ycOAYQ0aVL98/qr3HjpoZcSSkxduzEz+o7OXbt2m/47upak50792d60LtHjx7y/PmzbK3UAGzYsBqxWMTRo37cuXOLKVPGc+DAESwsLBLVu337JitWLGHFirXkzZufyZN/Yffu7fTtOwCAa9euMGfOdOTyOEMbmUxG48ZN2b7diwEDhnzV4/pWEaai0kmpUqX5acAIbOq2IyosBIBqFT78GVesMGLnTiOuXpVklYifjPjlC8xWLUNdoiTxvX/6or5MTdcYvgsOwqljLDFmR9Nf8Wt3RlBqvlHKli3HkCEj2bVrmyEy8J9/XqB79w40berGlCnjE+Vf2rdvN61bN6NpUzfmzp2BWq3mxImjjBgxGICLF/+gS5e2hhxODx/eB2DOnOls27YZ0IejGD58IE2auNKzZycuXfoTgOvXrzJoUB9mz56Gh0d9evfuYmifXry8NjBhwmjatm3O8OEDATh27DAdO7aieXMPFi2ai1KpBPQWyZ07t9K2bXNatvRk06Z1aFOY5t6zZweens0M2ydOHKV79w54eNSnffsfOX06wFA+YsQgunXrQKdObdBoNCmeT41Gw6pVy+jYsRXu7nXp168HT548TjK2n583Hh71knz8/LyT1A0I8KVHj74YGxtTrVoNKlasxKlTSROUHj9+hI4du1C0aHFMTEyYNGk6zZq1APRW/zlzptOzZ98k7dzdm3DixFHkcmE1bUYgKDafgLW1DSKxmBArSwAsP8ome/++XqFp0ybn5IYynzkFUXw8sbPmw2dPqcVhb58fCwv9m2NMzHqEn1Xa5LXIh6WRVVaL8U1hW69Gsh/zKeNTbGM+ZXyiuhlJ9eo1iYmJ5vnzZ7x4EcTMmZMZM2Yihw/74OTkxJIl8wC4cOEc+/btYfnytRw8eIyXL19w4MDeRH0tWDCbMWMm4O19ijp16rF9u1ei/SqVikmTxlK7dl2OHz/J0KE/M3XqBF68CALgzp3blC9fEW/vU1SqVJU1a1Z88vHcunWTdeu8mDdvMTdvXmfTprXMm7eEAweOEBMTw5YtGwHw8TmOr683a9duZseOvdy8eZ0jRw4l6U+pVHDu3Gnq1KkH6Kdo1qxZzpw5i/DzO0vPnn1YvnzRR+PfYNq02Xh57eD161cpnk8fn+PcvXsHL69deHufpmjRYknOF+gtYv7+55N8/msli4mJ4c2bNxQo8CEhaf78BXn+/GmSPh89eohOp+Onn3rw449N2Lx5PXbvLOGFChXm118PUaNGrSTtTExM+O67Mly4cC7N6yCQNsIT6DPw6DuIQrb2hsy7715UkMl0iHPIGZVd+hOT3w+ibNKUBDf3z+5HKn1AQkJNAFSq+iiVXTJKRAGBHI2lpf4FSC6Xc/KkH/XrN6RixUoYGRnRt+9Azpw5hVKp4PTpAFq0aEWhQoUxMzNj6tRZNGyY+D9pYWGBv78P9+79S9euPZg9e2Gi/Q8e3EOlUtG5czekUinVq9ekTp26BouHhYUFLVu2QSqVUq+eK69evfzk4ylXrgJOTs6Ym1vg63uCli3bUrRoMUxNTenTpz8+PscA8PU9QdeuPXB2dsHa2oZu3Xri7Z007cO9e/ewtLQyTOc4OjqxZctuChYsRGRkBDKZEREREYb6BQsWokSJkpibW6R6Pl1d3Zg3bwlmZmaEhoZgYWFBZGREkvHTi0IRj0gkwsjIyFBmYmKCQqFIUjc29i0nThxl2rQ57Nq1n8DA5+zcqY/hZWlpibGxcYrjFCxYmNu3b362nAIfEHxsPoO8RYuTt2hxw/bdu3ptpn79nJMbSl2sBPHdexM/ZNiX9aOuTGzsQmJjxWi1QnLGj3kd+4rFVxcwu+58IcfTVyDq/F+f3CZu1nzi0q72WcTERAPg5OREWFgoAQG+nDt32rBfIpESEhJCVFQklStXNZQ7O7sk6Wvu3EVs2LCGYcMGYGFhwcCBQ2nS5MMUTkhIsMG/5+N+wsJCAb21+eNxU5oaSg1bW1vD99DQUPz8vNm/f4+hTKVKQKlUEhYWytKlC1ixYjGgn5qytExqnQwPD8PBwfEjuSTs27cbPz9v7O0dKFKk2H/GtzN8T+18WlpaMm/eLO7evU2BAoUwMzNLNviln58PS5fOT1I+atR4Gjf2NGybmJig0+lISEhA9s6yrVAoMDVNmrRXJjOiWbPm5M9fAICOHbuyZctG+vUbmKTuf3F0dOLatU//DQskRVBs0uDZs6fs2bODJk2aUbVq9WTr+PjoT2OxYhm/XDqz0Dk6ErvkU8zRSsTiYExNtyGXj0CnszHs0WqLpdwsl3L+xVkG+PchPD4M1/wN+LF466wWSeArc/XqX9ja2uHg4IidnT0//tiGkSPHAPqH/bNnT8mXLz/29g6Eh4cZ2t29+zdBQc8N2yqVirCwMObMWYRKpeLMmVPMmTONevU+rMpxcHAkNDQk0fjBwa8pXDjjXjZEH8Xssre3p3//wXTs2BUApVJJSMhrjI2NsbOzp3//Ibi66mNYxcbG8vZtTLL9SaUfHkEBAb7cuHGNvXv/h4WFBU+ePCIgwPfjFoZvqZ3PRYvmYm/vwOHDvkilUg4e3Mfp0yeTjN+4sWciBSYlrKyssbGxISgokKJF9fe6oKDnya6KKlCgALGxH3IE6v2r0reYRCKRIBLlEJN/Nkc4i2nw/NFt+hc6iNGra4g0ySsuv/yiwsZGx5Ahqq8s3ddBIvkbR0dH7O3LY2a2BDu7aohEwvLt5NDqtKy8vpT2R1uiUCvwarJTUGpyGTqdjhs3rrFu3Sp69uyDWCymQYNGnDzpx/3799Bqtezbt5vRo4eh0+lo2NCdY8cO8/LlC+TyODZsWG2w9oBeAZg8eRxnz57GyMgIOzs7TE1Nkck+TI2UKVMOsVjMr7/uQq1Wc+XKZf7887xBuchoGjVqzO+//8aLF0Go1Wo2bFjN3LkzDfv27NlBREQ4CoWCBQtms2HDmiR9ODk5JZoiiouLQyqVIpFIiImJYfPmDQCo1Un9FlM7n3FxcRgZyZBIJAQGPufgwf1oNF/m++jm5sGWLRtRKBRcvfoXt27dpG7d+knqeXg05ejR/xEY+Iy3b9+yd+8u6tdP3zWIjIzA0dEx7YoCaSJYbNLg7/PbadfUk++sGhIaL0f0n+V9AFIpPHiQczN5p4aZ2UzMzRcbtpXKFsTFTUans8xCqbIn0co3DDs5EJ9nJyht9x1bmuyiuG2JrBZL4CvRpUtbRCIRYrEYZ2cXevbsS6tWbQEoWrQYY8aMZ9asKYSGhlKkSFEWLFiKVCqlTp26BAY+Y/jwgSgU8TRq1Ji2bTvi63sC0C8HnjFjLitXLmX27Kk4OjoxY8Y8w7TI+zoLFixjyZL5bN26CUdHR6ZMmUXRosW5fv1qhh9rzZq16dixK6NGDSU6+g1lypRj+vQ5ALRo0YqIiHB++qkncrmcatWqM2pU0mXppUp9R2RkJDEx0VhZWdO0aXMuXfqTli09MTc3p3nzlty+fYPAwGdJ2qZ2Pvv06c/MmZNp3NgVR0dHPDw8OXToABqNBonk81atDhw4lMWL59OqVVOsra2ZNm2WYWps9OjhVKxYiR49+uDm5k5UVCRjxowgJiaahg090h3L6N69f2jevOVnySeQGJHuG868FxUVh1r9ZdNDv82pyaBmI9EaVWLheV/sdNC0XRdMPloR9e2iw9KyNyYm+hUN+vn6TwsglVJq+m+R9bdWM/WPibQp0Z4lDVZiLjNPu9FXJKdei5wqt0DazJw5hZo1ayfyF8qNyOVyunRpy549BzEzS+q78y2S0v9aKhVja/tl905hKioNHMVRYFyFNzIdSq2WiKhI1HGZ5W6YuYg+Mm+nswVv324iKuoUYWExfKpSk9v4qfwgtnnuYZ375myn1AgIZEe6dOnB8eNHslqMLMff34emTZvnGqUmsxEUm1RQqVTUKqePbXHCzwcAaXw8Fu8c8iIjs0y0T8YowBe7quWRnQpIsY7eOfi/c+EyIeBeOpGIJTQr2jyRk6WAgEDKFC9egqJFi2XKdFlOISEhAX9/H7p3753VonwzCD42qZAQ/4ZCzqUBeBkZDg4OVKn5PQAaDZQubUmTJmp27ozPSjHTRqXCfMoERAkqNKW/S7aKlVVbjI31kTSVyh/Ragt8TQkFBARyKSNHjs1qEbIUmUzG6tUbs1qMbwrBYpMKVtJodEZleRUVjOJdXqjv3s0Fv49d888/2f8UmnptRPr4EfIRo9HmTZph29JyoEGpAdBqnZPUEfjAqUB/boXeyGoxBAQEBASSIfs/lbMQjXkJIsu14BlgqUrASa01LLGcOlUfQbJnz4SsEzAdiEJDMVs8H03BQsgHDk2y38amASYm+iBbsbFz3vnSGCWpJ6Bfyr3oyjw6H2vHz2eGJRv0S0BAQEAgaxGmotJAJ5VS4ntXXEqXw9zGRl+mgz//1J+6Hj2yd+wa8/mzEL+NIXr5GjBNGv02JmYv1tatUasrEh//ZVGIv2UiFREMDviJU4EBlHOowJYmOwVfGgEBAYFsiKDYpBNLe3vD999+0582qVTHO10nWyJ6G4ORvy+q7+uhav5jsnW0Wheioi5+ZclyFjdCrtHXtwcvYoPo+l0P5tZbJKRIEBAQEMimCIpNCvz55wVMTEyoUiXpiiATE6hYUcPQodnbWqOztCLqz6uIoqMRiSMxMdmJTHaemJgdgLAcOT1EK9/Q7mhLVBolyxqspmuZ9AXbEhAQEBDIGoQAfckgl8spXNiFggULcfXqnRT6ho9ywmVz5Dg6fkisp9VaExHxBJCl3CSD+BaCq/3+8DeK25SgvGPFrBbli8ip1yK7y/369Svat/+R8eOnJIoc6+t7gqNH/5fmipdFi+aSN28+unbt+cWyeHltYMeOLRgZGaHT6TA2NqZhQw9+/nnsZ0fdzSjOnz/Do0cP6d37pyyVIy22bt3EwYP70Gp1tGvXkT59+ieps2PHFkPWbgCNRotKpeT330/g+FHw1nnzZqLVapk0aToAc+ZMp2PHrhQvLkQkFwL0fWXeJ5OLjX1LzNMnKN+8SVInpyg1IlFMIqUmKuokERHP+RpKzbdC6xLtcrxSI5D5rFmzgvDw8E9uN3bsxAxRat7TtGlz/P3PExBwgZ0793Pz5nWOHv09w/r/HJRKJZs3b6Bjxy5ZKkdanD17ioAAX7Zt+xUvr534+BznypXLSer16NEHf//zhk/Dhm60b985kVJz5colfHyOJ2rXs2dfli1bmOnHkdsRFJtkUCgUjGpujd/M1hzY5cWhzauzWqTPRix+afgeEfEPanV1hMsuIJCxSCQSatWqw9KlC5Ldr9FoWLVqGR07tsLdvS79+vXgyZPHgP4tftu2zfz2217Gjh1haBMd/QZ397rExMQQFxfLnDnTad7cnU6d2nD6dMqBNj/Gzs6e2rW/59Gjh6nKER8fj7t7XV68CDK0XbBgDnv27ATg2LHDdOzYiubNPVi0aC5KpRLQ5zfq06crnp4N6NOnK1ev/pWsHH5+3pQpUxYzM/2b+N27fzNoUF+aNWvEDz80MiTJfP36Fa1bN2P8+FE0a9aIJ08eG/JoeXo2ZMCA3jx58sjQ74kTR+nevQMeHvVp3/7HZM9LcHAwHh71knwWLZqbpK6/vw+tW7fDwcGRPHny0rZtB06cOJrqOb58+SK3b99i4MAhhjK5XM7KlUtp2rR5orr58xdAJBJx69bNVPsU+DKEJ1wy3L17h/6elclv3AClvT1vjXNuKgGNpjRy+RAiI6+g1ebPanGyLWqtmrNBp7NaDIEvoF49s2Q/U6YYp9hmyhTjRHW/hBEjxnD79s1kH64+Pse5e/cOXl678PY+TdGixdi+3StRHTc3D65fv0psrD6h7vnzZ6hSpTpWVlasWLEElUrJb78dY86chaxYsYSnT5+kKVNYWCh//XWRmjVrpyqHqakptWrV4dw5/X9Ao9Fw4cJZGjZsxM2b19m0aS3z5i3hwIEjxMTEsGWLfnptxYrFtG3bER+fM3Tp0oN161YlK4ePz3Hq1nUF9NnPp0+fSPv2nTlx4iQrV25g3749hmSXYWGh1KxZh99+O0LBgoUYN24Udeu6cuyYPx07dmXcuFGoVCpevnzBmjXLmTNnEX5+Z+nZsw/Lly9KMraLi0si68r7z9ixSRNzBgUFUaBAIcN2/vwFef78aarneP36VfTvPxjjj54Ta9eu5Mcf2+DikidJ/Xr1XJNYcgQyFkGxSQZ/v2MULVCF8NevAJCJ9ct6dTrw8sreUzgmm9cj/uitC0TExc1DoymVZTJld0LloXQ42ooOR1tx8dUfWS2OQA7FxsaGESNGs2zZImJiYhLtc3V1Y968JZiZmREaGoKFhQWRkRGJ6tjZ2VOuXAX+/PM8AKdPn8LdvTFarZaAAF8GDRqOiYkJxYoVp3FjT0P27//i63sCT88GNG7sSuvWzRCJxJQrVyFNORo29ODcuTMA3Lx5HRcXF/LkyYuv7wlatmxL0aLFMDU1pU+f/vj4HAPA3NyCCxfOcevWDRo0aMTmzTuSyKPRaPj337sUKlTYULZ8+Vrc3NyJi4slNjYWc3NzIiI+nA83N3fMzMz555+/UavVdOjQGalUipubOzY2Nty4cQ1HRye2bNlNwYKFiIyMQCYzStTH56BQxGNs/EERNjExQaFQpFj/+vWrxMXF4ebmYSi7ceMajx8/oG3bDsm2KViwMHfu3PwiOQVSR1gVlQzVC1sis2pLvOQkaMHG0hqAW7fETJhgwvLlRty5k/0SYcou/oHlxF9QXDrP2807EfTWtLn8+hI/+fUkOO41fcr9RBVnIS9WTuX8efknt5k1S5mhMnh4eOLv78uqVUupVq2GoTwhQcW8ebO4e/c2BQoUwszMLNkAj40aNebMmVPUqvU9f/99i1mz5vPmTRQqlYpevTob6mk0WurXb5CsDE2aNGP8+CmAfkpkxYrFjBw5mG3bfk1Vju+/r8f8+bMIDw/nzJlTNGyof1iHhobi5+fN/v17DGOoVAkolUomTJjK2rUrGT9+NGKxiK5de9GlS/dE8sTERKNSqXB0dARAJBJx8+Z1RowYhEgk5rvvyqDRaAxySCQSrK1tAL31JjQ0BE/PD8eqVqsJDQ1BIpGwb99u/Py8sbd3oEiRYsmej+DgYHr16pSk3N3dkzFjxicqMzExQaX6sNpVoVBgapqyJc/PzxtPzx8MjtkKhYIlSxYwa9Z8xOLk77+Ojk6EhYWm2KfAlyMoNskwqn0XUBnxPDoMzOyxtbICoHFj/fxw167ZMNqw5i2Wk3uCEZgsPUqsKBydzintdrkUnU7HptvrmH5xMkZiI9a6b6JdyY5ZLZbAN8CYMePp3r0DWu2HFZkbNqzB3t6Bw4d9kUqlHDy4j9OnTyZp26CBG2vXruDMmZNUr14LMzMzjI2NkUql7N9/BKt396Lw8DCk0rStx2ZmZrRr15HevbsSFRXJxo1rU5TDxMSEWrXq8Mcf5/jjj3OsWbMJAHt7e/r3H0zHjl0BvSNwSMhrjI2NuXv3DhMmTEUkEnHt2hXGjx9NnTp1KfwuUbAevcVbItE/bkJCglm2bCFbtuyiYMHCADRv7p6s/HZ29hQuXJTt2381lL14EYSDgyMBAb7cuHGNvXv/h4WFBU+ePCIgwDdJHy4uLvj4nEnzXAEUKFCIoKBAatSoBUBQ0PNElqb/cunSnyxevNKwff/+v7x+/ZJBg/oAegVQq9UQHf2GhQuXvzsPEkQi4aUzMxHObjJIVSJQvyJEoX+DsLF3TLS/V6/spdhIpVdxPJIPyZ1QGAUUA6n0UZrtcjMLrsxh8h/jKWRVGO+2pwSlRiDDcHJyZuDAYYmmiuLi4jAykiGRSAgMfM7Bg/vRaNRJ2lpZWVO+fEW2bt2Eu3tjQP8gdHV1Y+PGNSiVSsLCQhk+fCBnz55KUxaVSoWPz3Hy5s2HjY1tmnK4ublz8OA+7O3tyZMnL6C3Iv3++2+8eBGEWq1mw4bVzJ07E4BlyxZy6NB+xGIxDg6OSCRiLCwsEslgY2ODkZGRYcpLLpcjEomQyYxQq9Xs3LmVN2/eJHs+ypYtj1weh7f3MbRaLbdu3aR37y68fv2KuLg4pFIpEomEmJgYNm/eAOgtOp9Lo0YeHDy4j5CQYF6/fsWhQwdo1KhxsnXDw8OJiYmhSJGihrKKFStz8uQf+PicwcfnDD169MbDw9Og1ABERkYYrFcCmYNgsfkvOh0inQk6zRNc+w/n1sULlKxak/f/FWdnLS4u2SmYhgZbkRtMAlwgdvg04sNGZ7VQ2Z5OpbryKvYlc+ouwNLIKqvFEfjGaNWqLSdP+hm2+/Tpz8yZk2nc2BVHR0c8PDw5dOgAGo0mSVt39ybcunWT2rXrGspGjx7H8uWLadv2B0QiMc2bt+THH1snO7a39zHD2CKRmFKlSjN37mLEYnGqckgkEurUqcvcuTPo3ftD7JaaNWvTsWNXRo0aSnT0G8qUKcf06XMAmDRpBkuWzGPTpvVYWVkxevR4HBwSP7RFIhEVKlTi33//wcnJmSJFitKuXSd69+6KVCqlatXqVKtWg8DA5+TPXzBRWyMjI+bPX8qyZQtZsWIxNja2jB8/hSJFiuLikodLl/6kZUtPzM3Nad68Jbdv3yAw8BlFixb/xCump2FDd54/f0b//r3QaNR06NCF77+vB+hj19y6dZMlS/QWmtDQYGxsbD45PtC9e/8kG/hVIOMQAvQlh06HWBmF1sTOUHThgoQ2bcxwd1ezZ098Bkr5pWixnDoAk/X7iFm5CmWnjIuHkRFk9+BquYmcei1yqtw5EZ1OR9u2zVm7dnOyK3o+l6NH/8fdu3cMvj+5mSFDfqJ//yFUrFgpq0XJUjIzQJ9gsUkOkSiRUgNgZaWjcWM1rVtnr2koEKOs3QqCE1B26J5mbQEBAYHkCA5+zYULZ8mXL3+GKjWgd2jet283cXGxmJtbpN3gGyUw8DlarTbXKzWZjeBj8x9OnDjG3bt/JzERV6igZdeueNq2/fz52y9DjqOjFfb2BbGwGJxoj6rpD7zduA1S8MLPzQTHvUahTnm5poCAgJ7t27ewa9d2hg37OcP7NjIy4qefBrF37+4M7zsnsX27Fz//PDarxfjmEaai/oOTkxVlHBw4dvpPLJxd0m6QyYhEEZiarsXc/EPgqbi4McjlU7NQqvSTldMI51+cZYB/HzwLN2Npw+QDh+UmcuqUTk6VW0BAIGWEXFFfifc6XochQ9jrtZb44NdZLBE4OBRJpNRERNzNMUpNVqHT6Vh5fRntj7ZEoVbQsGCjrBZJQEBAQOArIfjYfMTr16+oVbkyiEToJBJMM3ie+XMIC3uDpWU/FIqeJCTU531MCIHkiVa+YdipQfg8PU5pu+/Y0mQXxW2FTLoCAgICuQVBsfmIv04dpHnzpqgBa/mHKKYaDYSHi3B2zgo7uJi3b7ckLlIqwchIb8sTMPA69hUt/9eUZzFPaVOiPUsarMRc9mUmTQEBAQGBnIUwFfURqud/YBqjV2jquX6IhLl5s4xZs4w5e/bT4hVkFhbjR2PdsTWi2LdZLUq2wsnMmVJ2pZlffwnr3DcLSo2AgIBALkRQbD6iYfnveGtthVilwrHW9wA8eSJiyhQT9u+XkUwsrUzByMgbsfhxsvukt25gsmcnaLTocvGyyeSQiCXsaLqXPuV+QiRYswQEkkWn0xESEpzVYgD6PE4CAhmNoNh8RFHn/LiIJUhlMqRm+sRnXbt+SIBWt27maTYmJuuxty+Eo6MV1tYdsbIaiERyN3ElnQ6Lib+AWEzs7PnCVFQyCAqNQFZx8eIfDBs2AE/PBnh41GfIkJ/4669LGdJ3x46tuH79KsHBwYkSQn4Oa9aswNv7WLL76tathrt7XTw86uHuXpdevbpw+/bNLxovJSIjIxIlp/TwqEd4eFiGjrFixRJu3ryeoX1mNPHx8UyZMp7GjV1p3/5HLlw4m2y90aOH4+FRz/Bp0KAWnTq1ASAmJoaJE8fi6dmQtm2bc/To/wBISEhg+PCBqWYo/xYRfGzeo9NhbGJP//qVCC9bFQCFAh4/1ut+f/0Vi5FR5gwtlV7G0vKXRGVKpScaTclEZcaHDiC7cpn4vv3RfFcmc4TJIcgT5JjJUs66KyDwNfH1PcGKFUsYOXIMderUQyaT8scfF5g+fRKLF6+gTJlyGTLOpyR0TIno6DdJ8jl9zJ49B3Fyckan0/H7778xdeoEDh489smpA9JCqVQSH/8hiru///kM7f/Ro4c8f/6MSpWqZGi/Gc2GDasRi0UcPerHnTu3mDJlPAcOHElyjd6ncgB9FvE+fboyYMBQALZu3YSVlRXHjvnz/PkzBgzoRZUq1ciXLz+NGzdl+3YvBgwY8lWPKysRFJt3iBWvEEkKoRFHGSwhQUF6pUYq1VG4cOY5DkskL9HppKjVVXjzJiD5SnFxmM+citbWlrhfJmaaLNkdrU7LkqsLOHB/L77tTmP7nwjRAgJfG7VazcqVSxg5ciyNG3sayt3c9H5679+Whw7tj4tLHi5evECbNh1o06Y9ixbN499/7/L2bQzVq9diypSZmJmZ8ezZU+bMmc6zZ09wc/MwBAx9/foVnTq15uzZywAcO3aYnTu3EhcXh6trQ4YPH42xsTFz5kzHysqKq1evEBISTN269Rk7diL+/t74+XkjFouRy+UMHjw8xeMSiUR4ev7A0qULiI5+g52dPU+fPmHZsoXcv/8vLi55GDRoOLVq1QHgwoWzrF+/hvDwUEqVKsOYMeMpUKAgL1++YM6c6Tx58ggHByd69uyDh4cnw4cPQqPR4OFRj99/98bTswGHDh3n6tW/OH/+LBqNmhs3rlOyZCkmT55Bnjx5USoVLFw4lz/+OIeLS16qVq1GTEwMkyZNTyL/nj078PRsZtg+ceIov/66k+BgfY6nwYOH07ChOydOHMXX9wQRERGo1Wp27z7A5csXWbduJeHh4VSrVoOxYydiZWWFRqNh7dqVXLhwloiIcAoXLsrEidMoWrRYorH9/LxZtGhuEpnGjp1I48ZNE5UFBPiyYsV6jI2NqVatBhUrVuLUKf8Uc4EBbNu2mRIlSuLq2hCAly+DsLW1Q6fTIRKBVCozZFN3d29Cx46t6N69N2ZmueNlUJiKeocs5j6IzdEYfTgldnY6pk1TcOhQ5uaGUirbIJeP5s2bIynWMfbzRvL6FXG/TEJnmzsf5pGKCLocb8eiK/OwNLIiLiEuq0USEODOnVvEx8fTsGHSeElubu6JEh4GB7/m0KHjdOrUlTVrVuDk5MyBA0c4ePAYr169xM/PG4Bp0ybw/ff18PY+TbFixQlOJqbWzZvX2bRpLfPmLeHAgSPExMSwZctGw/6TJ/2ZO3cRv/56iFu3bnL27CmaN29F48ZN6dmzb6pKDYBWq8XH5zglS5bCzs4elUrFpEljqV27LsePn2To0J+ZOnUCL14EERj4jNmzpzNq1C8cOxZAtWrVGT9+FGq1ms2b11O1anV8fM4wfvxkVq9e9k4ZXIdEIsHf/3wS68Qff5yjdev2HDvmj62tLTt26FeGenltJDIygoMHjzFlykx8fb2TlV2pVHDu3Gnq1NEnsHz58gVr1ixnzpxF+PmdpWfPPixf/iE+2K1bN5g2bTZeXjt4/foVM2dOZsyYiRw+7IOTkxNLlswDwMfnOHfv3sHLaxfe3qcpWrQY27d7JRm/ceOm+PufT/L5r1ITExPDmzdvKFDgQ/LP/PkL8vz50xSvS0REOIcOHWDQoBGGstat23HqlD/u7nXp0aMTffr0x8VFH2DWxMSE774rw4UL51Ls81tDsNigd6aLsa6D2jwSxBUM5fb2OoYM+Tq5oeTySanuV7ZuR1S+AqirVP0q8mQ3boRco69vD17EBtH1ux7MrbcIU6lpVoslkI2oV69GsuUNGrgxa9b8ZPdNmTKeM2dOGbbPn//rk8cNDw/DysoamUxmKGvbtjlxcbFoNPq8QIsX66cRvv++PsbGJhgbw8CBwzA3N0er1RIWFoaVlRWRkRG8fPmCFy+C6NatF1KplPbtO7N79/Yk4/r6nqBly7YGa0GfPv0ZOXIQgwYNA/SZqvPlyw9ApUqVefXqZbqOp3v3DohEIpRKJQkJCYwZMwGABw/uoVKp6Ny5GwDVq9ekTp26nD6ttzLXrVvfoMR169aLQ4cOcO/eP5ibW3D9+lXKlatAlSrV+N//fNL0hStatDi1a+sXcNSpUw9f3xMAnD4dwOTJMzA3t6BYseK0aNEqWb+ce/fuYWlpZVCYHB2d2LJlN87OLkREhCOTGREREWGoX7BgIUqU0E/9//bbPurXb2jI59S370CaNm2IUqnA1dWNOnXqYWZmxuvXr7CwsOD161fpOq/JoVDEIxKJMPrIz8HExISoqMgU2/zvfwdxdW1oUFxAbzXs1Kkb3br15MGD+4wfP4oKFSpSunSZd8dXmNu3byayKH7LCIoNMGfODJTHDtN86kxqNmuR6ePJZKcxNj5GbOyST2qnrlEzkyTK3uz+Zwfjzo1CJBKxrMFqupbpkdUiCQgYsLGxJSYmBrVajVSqv6UePKh3ztVPc3ywKtja2hq+Bwe/ZtmyhYSHh1KsWEliYmLQ6XRERIRjbW1j6EskEuHo6Jxk3NDQUPz8vNm/f4+hTKVKQKlUAmBtbW0ol0gkSfLfpcTOnftxctKPd+fOLX755Wesra3RarWG8vc4O7sQFhaKVqvF+aMUNHqZnQgLC2Xw4GGsW7eauXOnI5fH07p1W4NvSEpYW9skK3t4eBgODo6Jxk9OsflvPYlEwr59u/Hz88be3oEiRRJPHdl+ZAUPCwslIMCXc+dOf9ReSkhICJaWlsybN4u7d29ToEAhzMzMSC4rkZ+fD0uXJlWmR40an0i5MDExQafTkZCQYFCMFQoFpqYpTxn5+/swbtxkw3ZCQgKzZ0/jwIEjGBubUL58RRo1aoy/v49BsXF0dOLatU9X2nMqgmIDiMViKpUqyb27NylQqTJ58+bPlHFEohBsbJohlT4EQKHojFpdLY1WAmqdGheLvGxpsoMKjpWyWhyBbMrnWFtSsuR8ChUqVMTISMbZs6dp1Mgj1bofWypmzZpCr179aNq0OQCTJ+sXENjbO/DmzRtUKpXhTT4yMiJJX/b29vTvP5iOHbsCemfckJDXGBsbf/Exvad8+YpUqlSZa9eu4OHhSWhoSKL9wcGvKVy4CCKRiBcvggzlWq2W0NAQbG3tePLkCf37D2bUqF/455+7TJgwmsqVq1G4cJFPlsfBwYnQ0BCDJSosLDTZeiKRyKAYgt6P5caNa+zd+z8sLCx48uQRAQG+H7cwfLOzs+fHH9swcuQYQG/Rf/bsKfny5WfRornY2ztw+LAvUqmUgwf3cfr0ySTjN27smS7riJWVNTY2NgQFBRosb0FBz6lb1zXZ+kFBgcTGvqVixcqGsvh4OXFxcWg0H/IiSiRSg4+NfluCSJR7PE9yz5GmgvhNFBoTE+LMzQ3xHUJCMnbZsEj0BgeHEh8pNR3QaD79j50b6VGmN2c6/ikoNQLZEmNjE0aMGMOSJfPx9T2BUqlErVZz4cJZtm7djJ1d8j5xcXFxGBubAHDp0p9cvPgHarWafPnyU6xYMbZt24xarebw4UNJFAqARo0a8/vvv/HiRRBqtZoNG1Yzd+7MNOWVyWTIP4qsnhpPnjzmzp3blC1bnjJlyiEWi/n1112o1WquXLnMn3+ex9W1IQ0bunPu3BmuX7+KWq1m165tSCQSypYtz44dXmzbthmNRoOTkxMiEe+m7ozQarWftBTZw6MJO3ZsIS4ulsDAZxw7djjZek5OTomUwbi4OKRSKRKJhJiYGDZv3gDop3D+S4MGjTh50o/79++h1WrZt283o0cPQ6fTERcXh5GRDIlEQmDgcw4e3I9Gk7SPT8HNzYMtWzaiUCi4evUvbt26Sd269ZOte+/eP5QoUQqx+MOj28rKmpIlS7Fx41oSEhJ49Ogh/v7e1K/fwFAnMjICR0fHZHr8NhEsNsCT0ycpUk3vu1KgQEEiI2HQIBM2b44nhXvSJ2Ns/OEPGBb2ChCC66UXkUiEhUw4XwLZl6ZNm+Pg4Mivv+5kxYolJCSoKFiwMJ06daVlyzbJthkzZjwrVy5l4cLZFClSjKZNmxMU9ByAGTPmMXv2NDw9G1CjRm2D/8fH1KxZm44duzJq1FCio99Qpkw5pk+fk6as9eo1YMaMScjlcQb/mY/p0qXtO8uSCAsLC1q3bmewKi1YsIwlS+azdesmHB0dmTJlFkWLFgdg8uQZLF26kJCQYEqVKs3ixSuRyWSMHDmWefNm8sMPjTAxMaFjx66ULVsOnU5HtWo1aNHCgx079qXrPPfo0Yd582bSqlUz8ufPT+XKVZFKZUnqlSr1HZGRkcTERGNlZU3Tps25dOlPWrb0xNzcnObNW3L79g0CA58laVu0aDHGjBnPrFlTCA0NpUiRoixYsBSpVEqfPv2ZOXMyjRu74ujoiIeHJ4cOHUCj0Xz2cviBA4eyePF8WrVqirW1NdOmzTJMjY0ePZyKFSvRo0cfAEJDQ5JVlGfNWsCSJQto0aIx1tbWDB36M+XKffAXvXfvH5o3b/lZ8uVERLrkJgi/EaKi4lCrtWnW829QiyA3NxQODnTq1JNbt5zo2NGMMWOU/PKLKkNkkclOYWx8BKXyBxISUjdXA4jeRGHk74uybQcQ51zDWkqp6ZNDv1RRCLCXWXzKtchO5FS5BTKeu3f/pnDhwpi/i7q+bt0qAIPD9MfMnDmFmjVr06RJsyT7chNyuZwuXdqyZ8/BbLXcO6X/tVQqxtb2y9Lh5NwnZgYR+i6kt8LBAdA73P37r/60ZORNNCHBjbi4KelSagDMFs3Dakh/jPx8Mk6IbEyoPJR2R37kVKB/VosiICCQTTl8+CCbNq1Ho9Hw+vUrAgJ8qVYt+dVwXbr04PjxlENo5Bb8/X1o2rR5tlJqMptcr9iYGBlRo3stw7be6Ur/vUyZtK09KSGVXkIiuZ+oTKezT1dbyf17mG7ZRELV6qhywfK8y68v0Wh/Xc6/PMulVxezWhwBAYFsSv/+QwgKCuSHHxoxeHA/2rfvRPXqya8WLV68BEWLFuP69atfWcrsQ0JCAv7+PnTv3jurRfmq5PqpKFFCFDY3j+D/0hRJwcJUrlIDJydLAAIC4qhQ4dOVG7H4Cfb2lYiP70ls7KpPa6zTYd2hFUZnTxPlcwp1lZy9aiq1aQSdTsem2+uYfnEyMrGMJQ1W0q5kx6wRNBeQU6d0cqrcAgICKZOZU1G53nlYGnMHqWlpGpbQ8LZsjUQZvMuX/zyLjb19JQBMTHZ+smJj5OuN0dnTKDp1zfFKTWrEqt7y8+lhHH58iKLWxdjiuYsy9mWzWiwBAQEBgRzOV52KunbtGi1atKBSpUr07t2b8PDwJHX++ecfOnXqRNWqVWnatCkBASnkTsogZNH3QGyD6KP5x40b46lTR/2ZybOVhm8REY8/sakSi6kT0JpbEDdp2ucMnmMIjw/ndNBJfij6I/7tzwpKjYCAgIBAhvDVFBuFQsHw4cMZPnw4f/31F4UKFWL+/MTBsTQaDUOGDKFt27ZcuXKFadOmMW7cOF6+TF8o8M9BFqcP8JRgoY+oKZGAu7uaAwc+Lz+URBKo7y+hWrp9at4jUilR1XNFPnoc2o+ieH6LFLYugl/7M2xpshNLI6usFkdAQEBA4Bvhqyk2Fy9exNnZGQ8PD4yMjBg5ciS+vr6JAkWFh4dTrlw52rdvj1gsplatWhQqVIh///030+SSqPTBlRLMP4Qft7AAWdLQCOlCKr2NTmeOWl3+k9vqLK2IXbKS+KEj0q78DVDUupiwvFtAQEBAIEP5aorN8+fPKVy4sGHbxsYGMzMzAgMDDWXOzs6sWvXBJ+XVq1c8fvyYUqVKZYpMZ7duYuPU8xz13Y3axCRD+lQqf0SlqotC0SdD+hMQEBAQEBBIP1/NeVgulyfJYWJqappiOO3o6GgGDx5Mx44dKVCgQKbItGD2dLr/8APXjawp+SYKW1t7dLovjYcnIyZmD/CZJp9vjAsvznH55gVGV5qY1aIICGQadetWw8TEBJFIhFarxd7egVat2tKly+cnbPXz8yYgwI+FC5elWGfRornkzZuPrl17fvY47+nWrQMhIa8BiI+PNxwPgL//+S/u/2NiYqLp3bsr69Z5JUms+R6NRsPo0cOYNWsBlpaWGTp+RvL8+TPmzJnOkyePKFKkGFOmzKBgwcJJ6nl41Eu0rVQq+eGHlowbN4no6DcsWjSPGzeuYmFhyfDho/n++3o8f/6MrVs3pSuitMAHvppiY2pqikqVOIpvfHx8skGDXr16Rb9+/ahYsSLjx4//7DFFIlJ0AI6IiCCvsTExhQqBSISlpSU//mjKihUKihX70jWlglKj0+lYdWM5cy7NwFxmTqfi3clnkTkKqkD6eP9fyGmzfzlF3j17Dhoe0nfv/s3EiWMAEV26dP+s/ho3bkrjxk1TrTN2bMa9MOzatd/w3dW1Jjt37idPnrwZ1v97Hjy4x+zZ0wx5+VLif/87SKVKVbK1UqPT6Zg6dQI//PAjrVtvYu/eXcybN4t167yS1P1YOXzy5DE//zyY7t17ATB9+iQKFSrC4cO+3Lx5nQkTxnD4sA+FChVGKpVy5colqlevlaTPb4H//r8z4v/+1RSbIkWKcOzYMcP2mzdviIuLo2DBgonqPXnyhJ49e9KyZUvGjBnzRWPa2KS8Fv7GjUs4feRIkyePPZcvw8iRFlz8pBhxkYAXUBpo8elCarU5OmVCcrxRvKHX/3px+P5hyjqW5WCHg5RyyJzpRIFPx94++z4ovhXKli3HkCEjWb58ER07dkEikfDnnxdYt24l4eHhVKtWg7FjJ2JlpXec37dvN3v37kahUFCvniu//DIJPz9vfH29WbFiLRcv/sGqVUuJioqicOEijBr1CyVKlGLOnOnky5efXr368fTpE5YtW8j9+//i4pKHQYOGU6tWHa5fv8qmTWvJl68AZ8+eJn/+/EycOI0SJdL/n/Ty2sCjRw948OA++fLlZ+XK9Rw7dpidO7cSFxeHq2tDhg8fjbGxMTqdjl27tvG//x1ErVbTvHlL+vYdgFgsJjQ0hFGjhjFs2M/MmjU1xfE0Gg179+5izZpNhu21a1dy4cJZIiLCKVy4KBMnTqNo0WLMmTMdlUrFjRvXqFWrDhMmTE1x/KioSBYtmse//97l7dsYqlevxZQpM5O8YC9aNBc/P+8kcu3ceQAXlw8LO54+fUx4eCjt23dCJBLRtWtP9uzZSVBQIAUKFEzS/j0LF86hd++fyJs3H8HBwfz7710WLlyOVCqlWrUarFvnZcg91axZC7Zv3/LNKjaZcT/6aopNrVq1mDhxIt7e3jRq1Ijly5fj5uaGyUe+LSqViqFDh9KxY0eGDh36xWO+eZNygD5//1N8b23Nc1NTzE3NuH49FrBApdIQHp6+zLcSyV1sbWu/k92DmJg6gFH6BYyNxcbTjfiBQ1F2+3yTdXbibvjf9PLuyrOYp7Qt0Z6lbisp6OBCRIQQXC2rEYn0N5Gcdi3ey53TqF69JjEx0Tx//gwjIyNmzpzMggXL+e67MmzYsJolS+YxY8Y8Llw4x759e1i+fC2Ojk6MHTuCAwf2Ym39YUHDggWzmTp1FlWqVGPnzm1s3+7F7NkLDftVKhWTJo2lRYvWLF26mhs3rjFp0i9s2bILgDt3buPp2Zzx46ewZs0K1qxZwfLlaz/peG7dusm2bXswNzfn5s3rbNq0lmXL1pInT17mzp3Bli0bGTRoGD4+x/H19Wbt2s2YmJgwceJYjhw5RKtW7bCysmbv3t+xsLBIVbG5efM6VlbWBguYj89x7t69g5fXLoyNjVm0aC7bt3sxY8ZcQG8F2rPnICIRqY6/Zs0KnJycmTlzHnFxsQwfPgg/P29atWqbaPyxYyemyxoWFBRE/vwFDVN2YrGYvHnz8fz50xQVm/PnzxAb+5Yff9QnR338+CEFChRi06Z1eHsfw87OnhEjRhtcNypWrMzDh/cJCwvF0dEpTZlyGv+9H0ml4lSNEunhq5kKTExMWLduHevXr6dmzZoEBQUxffp0Xr16ReXKlXn16hX+/v48fvwYLy8vKleubPicOHHis8bU6VL+aDRaYt5ZSkxMzZg0Sa9g1aypSbXdx5/3Sg2AXP4zOp1RutvqdGC6YinS+/cgIeGT2mXnz/zLs3kZ+4L59Zew1n0zZlLzNK+F8Pl6n5x6LdKD7Z81kv2Y3095Otv8/vhEdTOS91Mocrmckyf9qF+/IRUrVsLIyIi+fQdy5swplEoFp08H0KJFKwoVKoyZmRlTp86iYUP3RH1ZWFjg7+/DvXv/0rVrj0RKDegf7CqVis6duyGVSqlevSZ16tTl9OkAQ/uWLdsglUqpV8+VV68+PYRGuXIVcHJyxtzcAl/fE7Rs2ZaiRYthampKnz798fHRW+R9fU/QtWsPnJ1dsLa2oVu3nnh7Hwf0zwELC4s0x7pz5xaFChU2bLu6ujFv3hLMzMwIDQ3BwsKCyMgIw/4aNWphYWFhkC2l8QcOHMaAAUPQarWEhYVhZWWVqJ9PRaGIT+I7amJikqLvKMC+fXvo0qUH4nfPn9jYtzx4cA8zMzMOHTpOnz79mTTpF6Kj3wAgkUjIly8/t2/f+mw5szOf+39Pja8aebhixYocPnw4SfmNGzcAyJs3Lz/88MNXkWXiiJ742EuISgBLKyt8ffWnYvDg9GXzlslOG76Hhb3hU3VE8bOnmK1bhbpMORTv5lm/BZY2XM3zmKdUda6e1aIICGQpMTHRADg5OREWFkpAgC/nzn24b0gkUkJCQoiKiqRy5aqGcudkYljNnbuIDRvWMGzYACwsLBg4cGiirNUhIcFJnHCdnV0IC9PH6bK2tkk0rlb76VHVbW1tDd9DQ0Px8/Nm//49hjKVKgGlUklYWChLly5gxYrFAOh0OiwtPy1WVXh4OA4OjobthAQV8+bN4u7d2xQoUAgzMzM+zgZka2tn+J7a+MHBr1m2bCHh4aEUK1aSmJgYkssqtHjxfAICkiYg3rZtb6KpKBMTkyS+owqFAlPT5BNOhoaGcP/+PRYvXmkok8mMMDIypmfPvohEIlxdG7J9uxd37tymbt36ADg6OhEeHpryCRNIRK5NqWAU7kfHGnW4G60k0OjDQ9jZOX3qokTyAJ1OSnz8CD7H8GUxfTIipZLY2fMxZN38BnAwdcDB1CGrxRDIhUTV+euT28SVmk9cJsgCcPXqX9ja2uHg4IidnT0//tiGkSP1foM6nY5nz56SL19+7O0dCA8PM7S7e/dvgoKeG7ZVKhVhYWHMmbMIlUrFmTOnmDNnGvXquRrqODg4Ehoakmj84ODXFC5cJMOO5+OYU/b29vTvP5iOHbsC+hU+ISGvMTY2xs7Onv79h+Dq2hCA2NhY3r6N+eSxpNIPj6cNG9Zgb+/A4cO+SKVSDh7cx+nTJ5Ntm9r4s2ZNoVevfjRt2hyAyZN/SbaPMWPGM2ZM2gtXChQoxIsXQeh0OsOKuJcvXySyNn3MpUt/Ur16zUQuGAUKFEClUqJSKTE21pdrNBrgw7NIIpEgEn1bvpiZSa49U9K395HKXChVpDjly9uyfHk8y5enP9qwQjGA8PBg4uKmffLYsnNnMD5xFGXzliS808gFBAS+DXQ6HTduXGPdulX07NkHsVhMgwaNOHnSj/v376HVatm3bzejRw9Dp9PRsKE7x44d5uXLF8jlcWzYsNpg7QH9Q37y5HGcPXsaIyMj7OzsMDU1RSb74M9Xpkw5xGIxv/66C7VazZUrl/nzz/OGh3tG06hRY37//TdevAhCrVazYcNq5s6dadi3Z88OIiLCUSgULFgwmw0b1nxS/05OTommiOLi4jAykiGRSAgMfM7Bg/vRaNQpypbS+HFxcQbl4dKlP7l48Q/U6uT7SQ9FixbDzs6Offt2k5CQwO7d28mXLz/58ye/AvTevX8oXfq7RGXFi5ekYMFCbNy4DrVazenTAYSFhVLlo1yBkZERODo6/rc7gRTItRYbWVwUmIPazAZzc+jS5XN+3J/gKPwRZiuXoTM2Jnb67M9qn9VodVo23FpL59JdsTGxTbuBgEAuoEuXtohEIsRiMc7OLvTs2dfglFq0aDHGjBnPrFlTCA0NpUiRoixYsBSpVEqdOnUJDHzG8OEDUSjiadSoMW3bdsTXV+9bKJPJmDFjLitXLmX27Kk4OjoxY8Y8ZB+t6pTJZCxYsIwlS+azdesmHB0dmTJlFkWLFuf69asZfqw1a9amY8eujBo1lOjoN5QpU84Qa6VFi1ZERITz0089kcvlVKtWnVGjPm1ZesWKlQkI8DNs9+nTn5kzJ9O4sSuOjo54eHhy6NCBd5aNxKQ2/pgx41m5cikLF86mSJFiNG3aPJF17HOYPXsh8+bNxMtrI8WLFzc4NIM+ds3ixSupWLEyoJ+KKlMmcV48kUjE4sUrWbRoLs2bu+Pg4Mjs2QswM9P7J2q1Wh4/fkilSlW+SM7chEiX3ATjN0JUVAqrorQqHC6PQGQ9kjcF85NgkbYHtqVlL9TqSsTHj/xiuURvY5BevUJCw0Zf3NfXJlIRweCAnzgVGMDAikOZ+f3cVOunlJpe4OuTU69FTpVb4PPRaDR06tSaVas2JvJpyY1cu3aFHTu2smLFp61iy+6k9L+WSsXY2uaQVVHZCVXYNSJeWQOgNjFOozYYG+/GxOQQFhZTEYnefvH4OkurHKnU3Ai5hvv++pwKDKBL6e5MqDklq0USEBD4BpFIJHTq1I3jx5MuNsltHD36P7p2/TbCgXwtcqViM32IJ36rzhAa9AidNK3ZOB1WVoMAUKlc0elyXjyNL0Wn07H97hZa/N6EsPhQljVYzXK3NZhKTbNaNAEBgW+Uli3bcOvWDWJiPs3x+Fvi+fNnaDQaatT4NoPzZRa5cipqeKWi1K9ck8iKlQgM70r79gWpWDH55Y8iUSgODsUBCAuLBnJIfPcM5NyLM7Q78iMFrQqzpckOKjhWSndbYRoh+5BTr0VOlVtAQCBlMnMqKtc5D9+5cwtrjFDa2KA2knHuHIhEMipWVCZb39RUH9JbLh9EblRqAOrlc2X29/PpUKqz4CwsICAgIJCtyXVTUTt3biO/sTGidypieLgDTZumvCJKLh8BgFLZ/rPHlF67gjgo8LPbZzUikYj+FQcLSo2AgICAQLYn11lsjIyMKGpqSpCdPlJlXJw5pUqlFoXT/F30TpNU6qSCQoHVwL6I5HIirt8F47SdlQUEBAQEBAQ+j1xnsSlXrgJaQPtOwVAoTLGwSG3iXsRnKzWA6YY1SJ4/Qz5oWLZXakLloVwN/vTorQICAgICAtmFXGex6dSpKxHFivP4yh8A6HSiZPSNBEAFfOFa+uDXmC9bjLpoMeL7D/qivjKby68v8ZNfT1QaJZe63BCmnQQEBAQEciS5zmIji/wDpzJ5KV++HgkJlejVK3ECMxOT7Tg62mNmtu6LxzKfNQ2RPI64mXPB6POiFGc2Op2OjbfW0vpwM2KU0cyuu0BQagQEBHIcwcHBWS2CQDYhd1lsdFqs7s5AbL8Kj++KUbu2FVLpx6uhdFhaDgP0y7y/BOnVvzA5sBeVmzsqD88v6iuziFW95efTwzj8+BDFbIqzpckuvrMvk9ViCQjkOOrWrYaJiYkhEaK9vQOtWrWlS5fPD6zm5+dNQIAfCxcuS7HOokVzyZs3H1279vzscd7TrVsHQkJeAxAfH284HgB///Nf3H9KrFixBFfXhlSqVCXRefyYBQuWJcqd9F8iIyPo1asTPj5nMk3O9zx//oytWzcZUkgkx717/3L48EHGjZuc6fJ8CYcPH2Lr1k3Ex8vx8GjKzz+PRfKfpMx+ft4sWvQhwrxOp0OhULBunRfly1fk2rUrrFq1jNevX1K2bAWmTJmBra0dGzeupXz5CtSuXfdrH1buUmzE8U8Ri/MCoDU2SpQ9FsDOrhIAOp2EuLiFXzSWSKlEXbwEsbPm6xfsZzN0Oh0djrbmashfNC/akhVua7A0sspqsQQEcix79hzEyckZ0GfonjhxDCCiS5fun9Vf48ZNady4aap1xo79tBxMqbFr137Dd1fXmuzcuZ88efJmWP/J8ejRQ54/f5YoD9LH5zG9KJVK4uPTn8T4SyhUqDBSqZQrVy5RvXrygfOWL1/EtGnZOxfgvXv/4uW1gVWrNmBtbcPYsSM4ceIoLVq0SlTvv7/DTZvW8eTJI8qXr8irVy+ZNOkX5s5dRIUKlVi8eB5r1qxg8uQZdO7cnaFD+1OtWs1Eec2+BrlqKkoS/wxk+mB7SVMpaJBIngIQHX3si8dK+L4eUReuoClR8ov7ygxEIhHDq4xiep05eDXZISg1AgIZSNmy5RgyZCS7dm0zJGr8888LdO/egaZN3ZgyZXyiiLr79u2mdetmNG3qxty5M1Cr1Zw4cZQRIwYDcPHiH3Tp0pamTd0YNKgvDx/eB2DOnOls27YZgKdPnzB8+ECaNHGlZ89OXLr0JwDXr19l0KA+zJ49DQ+P+vTu3cXQPr14eW1gwoTRtG3bnOHDBwJw7NhhOnZsRfPmHixaNBelUm/91ul07Ny5lbZtm9OypSebNq1Dq01+5emePTvw9GyWLhlev35Fu3YtWLt2BZ6eDejcuQ1XrlwCYPjwQWg0Gjw86hEbG0vdutVYuHAOnp4NuXz5Yqrnpn//XsyYMRkPj3oMGNCbwMBnALRr14IrVy4bxt+xYwtLly4AoFmzFuzatSNZOa9cuYyJiYlBKQwKCuTnn4fQokVjmjRxZd68mYbz8V85w8PDmTBhNE2butGzZydu3Lhm6PfixQv06dMNT88GtGzpycGD+5Id38OjXpLP6NHDk9QLCPDFw8OTQoUKY2NjQ7duvThx4kiq1+Dp0yf8/vtvBoXaz88bd/fGVKlSDalUytChP9OjR28ALC0tKVGiJKdO+afaZ2aQqxQb7z2reBNug1akQfsfa41IJEertUOlakhCwvcZM6A4e59ezyLNGFxpWBKzr4BATsT28dNkP+bBKU8rmweHJqqbkVSvXpOYmGieP3/GixdBzJw5mTFjJnL4sA9OTk4sWTIPgAsXzrFv3x6WL1/LwYPHePnyBQcO7E3U14IFsxkzZgLe3qeoU6ce27d7JdqvUqmYNGkstWvX5fjxkwwd+jNTp07gxYsgAO7cuU358hXx9j5FpUpVWbNmxScfz61bN1m3zot58xZz8+Z1Nm1ay7x5Szhw4AgxMTFs2bIRAB+f4/j6erN27WZ27NjLzZvXOXLkUJL+lEoF586dpk6deumWITj4NVKpjGPHAmjZsg1LlugVjZUr1yGRSPD3P4+FhQUAVlbWHD7sTeXKVVM9N//88zclSpTkxIlTVKlSjSlTJgDQoEEjzp07Yxj7zJmTNGzoDuizjz98eP9dKJDE+Pgcp149V8P2ggWzqV69FkeO+LJjxz4uXfojkcL0sZwzZ04mb978HDniy8iRY5k6dQJRUVHI5XHMmDGFUaN+wcfnDJMnz2D16uXI5XFJxvf3P5/ks2TJyiT1XrwIpECBgobtAgUK8OzZs1TP/8aNa+ncuRt2dvYAPHr0AHNzC0aMGETz5u4sXDgHGxs7Q/169Rrg43M81T4zg+z95M1gwi/c4vCqg/ju80GrS/ww1+ksiYh4RnS0kHRNQEDgy7G01OeVk8vlnDzpR/36DalYsRJGRkb07TuQM2dOoVQqOH06gBYtWlGoUGHMzMyYOnWW4QH6HgsLC/z9fbh371+6du3B7NmJp8ofPLiHSqWic+duSKVSqlevSZ06dTl9OsDQvmXLNkilUurVc+XVq5effDzlylXAyckZc3MLfH1P0LJlW4oWLYapqSl9+vTHx0dv6fb1PUHXrj1wdnbB2tqGbt164u2d9OF27949LC2tDIrIe7p374CnZwPDp23b5on2d+7cHalUiqurW6rH0aBBI4yNTdI8N/b2DnTq1A2ZTEbv3j8RFBTIixdBuLm5c/78GXQ6Ha9evSQiIpyKFSsD+iSd+fLl5/btW0nG/fvv2xQsWNiwPXnyDDp06IxSqSQyMgJLS2siIyOSyBkd/Ya//77NwIFDkclkVK5clUqVqnD27CmMjU3YunU35cpVIDr6DaDPgP4lebTi4xUYf7Qk2NjYBKVSkWL9ly9fcOPGNVq3/hCs9u3bWI4fP8LgwSP47bdjiEQiVqxYbNhfqFBh7ty5xdfO3JRrfGzCwsJwFNsQWL06z5wg/o9Q6tVzymqxvgqPoh5S3LZEVoshIJCpRBUr8slt4lycSPrOmzHExEQD4OTkRFhYKAEBvpw7d9qwXyKREhISQlRUJJUrVzWUOzu7JOlr7txFbNiwhmHDBmBhYcHAgUNp0uTDFE5ISHASvxRnZxeDRcHa2ibRuClNDaWGre2H1ZKhoaH4+Xmzf/8eQ5lKlYBSqSQsLJSlSxcYHnA6nQ5Ly6RT3eHhYTg4OCYp37lzf4o+NhKJxKAwSiSSVI/jvbxpnRsXlzyI31nXjYyMsLGxISoqkvLlKyKTyfjnn7vcunUdV1c3Qz0AR0cnwsOTWmzCw8NwdPzwbHn8+BGjRw8jPj6ekiVLoVQqEz3o38sZGhpKQkICLVp4GPZpNBoKFiyERCIhIMCP337bi7m5OWXLlken0yWrMHh6NkhSVqFCJRYuXJ6ozMTEGJXqw6pgpVKBqWnKiY39/X2oX79BIkXUyEhGw4bulCpVGoAePfowbNgAw35HR0cUCgVv377FyurruTvkGsXm4cP7aF8bE19Pf9PIDUqNTqdj1Y3lzL08gxUN19KxdJesFklAINdw9epf2Nra4eDgiJ2dPT/+2IaRI8cA+v/ms2dPyZcvP/b2DoSHhxna3b37N0FBzw3bKpWKsLAw5sxZhEql4syZU8yZMy3RdIeDgyOhoSGJxg8Ofk3hwp+u7KXEx1PW9vb29O8/mI4duwJ6592QkNcYGxtjZ2dP//5DcHVtCEBsbCxv3ya1LIhEoiQLODKS9/KmdW4+tp6oVCrevIkyKCYNGzbiwoWz3Lp1g59+ShyLTCKRIBIlnfQQi8WG40pISGDatAksXrzS4CDdt29iZ/L3ctrb22Nubo6392lD2evXr7C0tOLWrZscOrQfL69dODg4IJfL8fZO3hc0vSvDChYsbJiOA70v0MeWpv9y+fKfSVbf5c9f0GBBAtBo1ImUrffnQfyV3TJyzVTUnp3bABBpNJmSIVh29jTW7Vsifvok4zv/DKKVb+jp04XZl6ZR0rYUVZ2rZ7VIAgK5Ap1Ox40b11i3bhU9e/ZBLBbToEEjTp704/79e2i1Wvbt283o0cPQ6XQ0bOjOsWOHefnyBXJ5HBs2rDZYe0D/4Js8eRxnz57GyMgIOzs7TE1Nkck+xMYqU6YcYrGYX3/dhVqt5sqVy/z553mDcpHRNGrUmN9//40XL4JQq9Vs2LCauXNnGvbt2bODiIhwFAoFCxbMZsOGNUn6cHJySqRUfAkymRFarRaFIulUSlrn5vXrVxw79j/UajXbtm2mRIlSuLjkAcDNzYOzZ08RHPzaMA31nsjICBwdk1qcHBwciYjQH1dCgoqEhASMjY3RarV4ex/jwYN7qNVJ8xO6uOShcOGi7NixBbVazbNnT/npp57cvXsHuTwWsViMTCYlPj6e9etXASTbT3pxc3PH1/cET548Ijr6Dbt2bcfdvXGydbVaLQ8e3Kd06cThQNzdm3D+/Bnu3LmFUqlgx44tiX5z4eHhmJqaJpluzGxyjcXmvu8JKFgQLWJMTYsn2icSxaDTmfHZp0OtxmLyOCTPn0EmvoGkl7/D79DHpxvPYp7SpkR7ljRYibnsy6IoCwgIpE6XLm0RiUSIxWKcnV3o2bMvrVq1BaBo0WKMGTOeWbOmEBoaSpEiRVmwYClSqZQ6deoSGPiM4cMHolDE06hRY9q27Yiv7wkAZDIZM2bMZeXKpcyePRVHRydmzJiXaAmtTCZjwYJlLFkyn61bN+Ho6MiUKbMoWrQ4169fzfBjrVmzNh07dmXUqKFER7+hTJlyhrguLVq0IiIinJ9+6olcLqdateqMGpV0WXqpUt8RGRlJTEw0VlbWSc7jx/TtOwBXV7cU5bG3t6datRq0aOHBjh2JVwuldW6cnJy5cuUvVq1aRqlS3zFjxjxD29Kly6BWq6lbt34iq4NWq+Xx44eJlqm/p1KlKty7d5eKFSthZmbO8OGjGTt2JFqtlu++K4ubmweBgc+TtAOYPn0uS5fOp0WLxpiamtKzZ19q1qyNVqulatXqtG/fElNTE+rXd6NYsRIEBj5P5AD8KXz3XVn69x/E2LEjkcvlNGnSzOA/4+fnzY4dWw0hAKKj36BUKrGxSRy8tWzZckyaNJ3582cRFhZG9eo1GDJkpGH/vXv/UrlyyvGHMguR7mt79XxFoqLiUKv1c7CX164kev8eHnXogJlZSXr1+uCQZmnZH7E4kOjo44Akhd5SxmTzeiwn/kLc6HHIx03KKPE/i1OBAfTy7oJGp2Hm9/PoU+6nLF31JBKBg4Ml4eFvM8VSJpB+cuq1yKlyC6TNzJlTqFmzdiJ/oa/J9etXWbBgNvv2/S/FOoMG9WXAgCGJlJhr166wY8dWVqxYm6T+lSuX2bVre7L7chuzZk2lRo1ayV7flP7XUqkYW9svTGf0Ra1zEDUHD6e4l/4NyMbmQ7lEcg8Tk70YGf3J5yg1oogIzBfMRZM3H/JhP2eMsF9ABcdKlHOowOFW3vQt319Yyi0gIJBt6dKlB8ePpx47JasICwvlzJmTREZGJJmGOnr0f3TtmnxU6erVayKXx/Hy5YuvIWa2JSYmhvv379GoUfLTW5lJrlFsAAoXsaFTp57UqvVB87ay0jtDqVSfF7vGfOEcxNFviJs2C8zMMkTOL8HB1IHjbfyp5lIjq0UREBAQSJXixUtQtGixTJku+1KOHz/CggVzGDlybKIXxOfPn6HRaKhRI/mowwCjRv3Cjh1bvoaY2ZZff93JwIFDM9VBPCVyzVQUajlITP+T3kCHo6N+bjc8PAidzjppJ6kgfhGEXfUKqKvV4M0Rn2yZOiGrEaYRsg859VrkVLkFBARSJjOnorLe0/UrYf/gCqIEORFlaqOT2QAglerDcet0kk9WagC0+QsQffAoWmubr67UaHVa4tXxglOwgICAgIDAR+QKxUan06FTmyDSRaCTfqzAmKJSuaFUNk+xbVok1Pn6mUsjFREMDvgJmVjG9qa/Ik4mloKAgICAgEBuJFc8EY+1bkrwvefoeJvIsqJWVyI6+n8oFP2yULpP40bINdz31+dUYAAOpo6otZ8fx0BAQEBAQOBbI1coNiZv3nBx2waCXifw5EnO9IPR6XRsv7uFFr83ISw+lGUNVrOs4WqMJEZpNxYQEBAQEMglfPOKjUapBCCqdGm2PY0hLOxaGi2yHwmaBIadGsjYsyNxscjLiTYBdC2T/FJDAQEBgfSg0+kICQnOajEEBDKcb97HRvUuZPerYkUBsLAwTq16msgu/kFCzdrwFXNfSMVStDotHoWasKbRRmxMbNNuJCAg8FW4e/dvhg8fwLp1XpQsqU8GqFarGTq0P3Xq1KVHjz6Getu3b+bvv++QkKCiYMHCdOrUFQ8PTwC8vDawY8cWjIyM0Ol0GBsb07ChBz//PBaJ5NNjbKXFmjUrsLCwoFev5Kfiz58/w6NHD+nd+6cMHzsj2bp1EwcP7kOr1dGuXUf69OmfpM6OHVvYuXOrYVuj0aJSKfn99xPMnz+b27dvGPYlJCTg4pKXQMMDIwAAKDhJREFUvXsPMWfOdDp27Erx4kIS4ZzEN2+xeXhQHxLa/F069jJlymBt3QIrq/aIxa8/qS/pX5exadkU8+mTM1zO1BCJRCxtsIqdzfYJSo2AQDajbNlydO7cnVmzphqyJW/cuAaZTEa3br0AfYTbn38eQo0atdiz5yAnTpxi4MAhrFu3ijNnThr6atq0Of7+5wkIuMDOnfu5efM6R4/+nilyf5y88L8olUo2b95Ax47ZO3Hu2bOnCAjwZdu2X/Hy2omPz3GuXLmcpF6PHn3w9z9v+DRs6Eb79p1xdHRiyZKVhvKjR/3JmzcfgwYNA6Bnz74sW7bwax+WwBfyzVtswt79yKNFMqLCHBCJRBgZnQXg7dtPSMyl1WIx+Rd0UimKHr0zQ9RUMZGafPUxBQQE0kevXv24ePEPNm5cS82atThx4ihbt+4x5BdasWIx3br1pF27ToY21avXYuLEacTFxSbbp52dPbVrf8+jRw8NZdu3e/G//x0kISGB77+vx4gRozEzMyc2Npblyxdx8eIFTE3NaNOmPZ07d0ckErFr1zYOHPgVnQ7Kl6/A+PFTOXv2JH5+3ojFYuRyOYMHD080tp+fN2XKlMXMTB9O4u7dv1m9ehnPnz9DJIIff2zDgAFDeP36FYMH96NUqdLcvn2L1as3IpVKWLx4Pg8e3KdQocKMGzeJokX1+flOnDjKr7/uJDg4GBsbGwYPHk7Dhu6Jxg4ODqZ79/ZJzkfjxk0ZOzZxzil/fx9at26Hg4M+GWXbth04ceIo1avXTPFaXb58kdu3bxnyIH2MPglmSUMix/z5CyASibh16yYVK1ZKsU+B7MU3r9jIylck9PUrpMZSLCxUhnKt1hKdzjLd/Rjv24Ps5g3kA4eiyUSzpE6nE9IgCAjkMKRSKVOmzOSnn3rg7+/NuHGTcXR0AiA4+DWPHz9iwYLlSdpVq5ZyhPCwsFD++usi/foNBMDb+xh+ft6sXbsZa2trZs2ayooVS5gwYSorVy5BoVBw4MBRoqIiGT16GHZ29pQtW569e3ezc+c+zM0tmDp1PL6+x2nXrhO3bt0kX778yU5F+fgcp0sXvR+fTqdj+vSJDBo0HDc3dx4/fsRPP/WkadMfkMmMCAsLpUePPkydOgsjI2O6d+9A69btWbp0NefOnWHcuFHs3v0bYWGhrFmznHXrtlCgQEGOHz/M8uWLkig2Li4u+PufT9d5DwoKokWL1obt/PkLGpKHpsT69avo338wxsaJXxYjIsI5dOhAkiSa9eq54uNzXFBschDfvGJTf8x4gjp25+jRfRgZ6TAy8gFApfJMdx+itzFYzJ6O1sEB+ZhxmSUql19fYvy50exqto98lvkzbRwBgW+RP3t1TrbcvlpNSg0dmey++6uXE3H1w9RFnW2/fvb4hQsXoUSJUjx//pQqVT5kNA4PDwPAwcHBUDZ4cD+ePHmEVqvDycnZYD3w9T3BmTMn0Wp1yOVxFC9eknLlKgB660SXLj3IkycvAIMGDadXry788sskAgL82LVrP2ZmZpiZmdG5c3f8/LypVq0G8fFyvL2P0bChO/PmLUnzxUmj0fDvv3cpVKiwoWz58rXky5efuLhYYmNjMTc3JyIiAheXPAC4ubljZmbO7ds3UavVdOjQ2VD+6687uHHjGpUrV2XLlt04O7sQERGOTGZERETEZ59vAIUiHmPjD36TJiYmKBSKFOtfv36VuLg43Nw8kuz73/8O4uraEBcXl0TlBQsW5ujR/32RnAJfl29esQEoUCAfgwbpE1Sam+tzQmk06be6mC1bjDgslLdLV6Gz+vQIxWmh0+nYdHsd0y9OxkhsxL3IfwTFRkAgh3Hw4H6io99QrFgJli5dyJQpMwGwsdH7xUVGRuDk5AzA2rWbgQ/Zpd/TpEkzxo+fAoBcLmfFisWMHDmYbdt+JSQkGGfnDw9dZ2cXVCol0dFvUKmUhr7f7wsLC8XBwZHZsxewa9d2NmxYQ9GixRg/fiqlSpVO8ThiYqJRqVQ4Ouqnd0QiETdvXmfEiEGIRGK++64MGo2G99l4JBIJ1tY2gN7KFBoagqdnA0N/arWa0NAQJBIJ+/btxs/PG3t7B4oUKZbs+MHBwfTq1SlJubu7J2PGjE9UZmJiYvBrAlAoFJiappyzz8/PG0/PH5J1xvb392HcuKT+k46OToSFhabYp0D2I1coNoDhLUUkegOAXD4s/Y0V8SRUqoyic7cMlytW9ZafTw/j8ONDFLMpzpYmu/jOvkyGjyMg8K3zOdaWlCw5n8qTJ4/YsGENK1asxcbGlp49O3P6dAANG7qTP38BChQoiL+/D1279kx3n2ZmZrRr15HevbsSFRWJg4NjouXZISGvkclkWFhYIpVKCQ0NIW/efIB++svW1o6oqChsbe1Ys2YTMTExbNu2icWL57Fp0/ZURtbfKyUS6f/bu/OwqKr/gePvARx2BcHAja0S+6qFS6UiiijuZVruWamphWYuFfpzSRM33CXXUkuzNK1MUVzQXFIxRXL5KlqmAoogIusAwyy/P8ip+YI6ioKMn9fzzPN4zz333s/McZyP55x7z9/Xuc78+eGsWvUNHh5eAHTp0rbEI6tWdcHLy4evv/6nLZKSEnF1rUZ09E7i4mJZv34zDg4O/PXXn0RH7yx2Dnd3d3bs2GfSZ1S7tieJiQmGBSkTE68Y9TT9r5iYw8yZs6hYeWJiAjk52cVW8YaixE0hT3evUJ641kpP/y8ZGVGA6Wss5U6fTUbkbnjIt1yeT4+n/abW/HzxRzr7vMquN/ZJUiNEBVNQUMDkyePp27c/zz1Xj+rVazBixChmz55hGIYaPTqUr79exaZN61GpctHpdJw4cZyIiHlUrVq1xPOq1Wp27NhGjRo1cXJyJji4Pd9+u4bk5GuoVLksXRpBy5atUSqVtGkTzNKlEahUKq5eTWL9+m8ICgomJSWZjz4aQULCFRwdHbG3d6Dy373OlSpVQqVSFbuuk5MTSqWS9L8flaFSqVAoFFSqpESj0bB27WoyMjLQaos/9bxevQaoVLlERUWi0+k4efJ3BgzoS3LyNXJzc7GyssLS0pKsrCy+/HI5UNSj86DatAnmhx82kJJyneTka/z440batGlXYt20tDSysrLw9vYpti8+/izPPutrmOz9b+npNw29V6JiMPseG5vUc9hfv0Cui5L8mh0BBYWF/vd/IuXDf8LvX5kXuZT1F5ObT+P9F4bLpGEhKqDFixdgZ2dP//7/3C3ZpctrHDiwn+nTpzB3bgQvvvgyixYtY82aVXz11ZcUFBTg7l6d4OAO9Oz5zy3VUVGR7NmzCwCFwgJf37pMnz4HCwsLOnV6lbS0NIYNG4xKlYu/f0tGjfoEgA8//JiFC2fTs+erWFpa0q1bD7p27Y5CoaBfv3f48MP3ycnJxtf3OcaNmwRAQEAgU6aMR6XK5aOPxhliUCgUPP+8H+fOneWpp9zw9vbhjTd6M2BAP6ysrGjc+EWaNHmJhIQr1KrlYfRZKJVKZs6cx/z54SxcOAcnJ2fGjp2It7cP7u7ViYk5TNeuHbC3t6dLl66cOhVHQsJlw11T96t167ZcuXKZIUPeQavV0LNnX/z9A4CiZ9ecPPk7c+cW9dCkphbdiVXSMFRqasodE8z4+LNGc6bE40+h1/97wXDzcvXMeVRHIvH2aka+yx/k1e5e3iEVk5B1BY/KnuUdxiNzp6XpRdmrqG1RUeOuyLZu3cx//3vaMN/nSTZs2GCGDBkmd0U9ZHf6XltZWeDsbPqISknMeijq/IolHFuwiiN/nOXIGfd7H1AOzDmpEUJUTO3bd+LMmVN3fMbOkyIh4Qo6nU6SmgrGrBMbdWYmAL/cuMrO36wBXfkGJIQQFYBSqWTw4PdZv35deYdSrr7+eiWjRn1c3mGI+2TWiY3O0gKFtTV6wM75OM7OzbGy+u2ex9l/Ngnb5YtBqy11DHq9nkUn5rPx/PpSn0sIIcpKq1ZBDBo0tLzDKFcTJ35mWP9LVBxmndig1UEdXwA8PM9jZXUWvb7kCWK3WZ4+he3ihSi3R5Z6ocvMggze3tGXsJhP+fL0MnR66TESQgghHiXzvitKr0dlV/TYbA+PKwBotXeZfa/X4zCx6AFQOWGzimY3PaAzaacZuONNLmddovuzPZgbuAgLeRaCEEII8UiZdWKjKVSTVFDUS+Lj8xeZmXd/gJcy8meUh38lr/8AtA2ef+Drro9fxyf7R6HVa5nZci4D6r0rt3ILIYQQZcCsuxBce/RBYVO0gneVKpmo1Z3vXDkvD4fJE9BVrkLuuAe/xTFHnc2Mo1OpauPClm47GFh/sCQ1QgghRBkx6x6b51q3wbW+D0eP7sDScsRd5wLbrlyBZWICOVNnoP/XYnX3y0HpyLrOG3G3r46r7YOfRwghhBD3z6wTGwBnZ086dBh6zxuc8t8ZCHo9eQOHlPqa9V0blPocQgghhLh/Zj0UdT/0Do7kfTASKlUq71CEEBVIcvI1WrV6ucR9a9asYt68WWUcUZHhw4cQFNSc4OAA2rZtQZcuwYSHT0Olyr3jMSqVig8/fL9U6zeVhVOnfuett3rRtm0LRo4MMaxr9W/Xr18nODjA6BUQ8CJr1qwCYPv2rfTo8SrBwQF88MFQEhKKbjA5duwoS5YsLNP3Ix4uSWweUHr+TRbEzpFbuIUQd/TWWwMZPTq03K4fGjqB3bsPEh39KytXriUh4QqTJ4+/Y/3Vq7+gU6dXsLJ6fDvzCwrymTAhlEGD3mP79r3UquXB558vKFbP3d2d3bsPGl7Tp8/B3b0Gr732BleuXCYiYj6zZs1j5879vPBCQ8LDpwHw4osvc/58PJcu/VXG70w8LI/v396HwOrCHmxIJ8fn9Yd63riUWAbtfIuknETqudQn2KvDQz2/EMI8rFy5nBs3Uhk7diJvvPEKXbu+zk8/bUSj0dC7dz/69n0LgISEy8yZM5MLF87j6elFaOh4w8KQ27dv5bvv1nL9etEijiEhI2jdui3bt29l587t3Lx5E41Gw7p1G0tc4PE2Nzd3Jk78jB49XuXChfhiD57Lzs5m167tvPvuFgDy8/OZO3cmcXGx3LqVTr16DZg0aSqurtUYPnwI7u7VOXLkV7p378lbbw1k2bIIoqN3UalSJXr37scbb/QGIDExgXnzZvHnn3+gVhcQGNiG0NAJxVbSHjNmBKdOxRWLe/fug0bbx48fw9W1Gq1atQZgyJD3ee21jnz88f9ha2tb4ntXq9WEh09jzJixVK5cmcqVK/PDD5HY2dmRl5dHbm4uTk5Ohvrt2nVkw4Z1slZWBWXWic3pH49Qv4Ur6loPZ3FuvV7PmrOrGX/wExQKBfMDP5ekRojHxHfffV1iee3anrRoEVjivl9/3Udi4hXDdp8+bz+K0AxOnDjGN998z6VLlwgJGUTbtu2pWtWF0NDRdOvWg3nzPufAgX2Eho5m3bpN3LiRyuLFC1i6dBW1a3uwbdvPLFgwm9at2wJw8mQcX3yxhho1atw1qbnNzc0dDw8vTp8+VSyx2b9/Lw0avIC1tTUA69Z9TV6einXrNqHT6Rg//mM2bdrAe+8NB+D69WR+/HEbGo2GtWtXc/58PGvXbkClymPMmOHUquVB06bNmTUrjObNA5g373NSU1MYMuRtjh07yssvNzO6/u1VuO8lKSmB2rX/WVW8cuUq2NracvVqEs8882yJx2ze/AO1axfFc5udnR0nThxn5MgQ7OzsiIhYbtgXENCKBQvm8NFH4x7r3itRMrMeikrxqM3aMyoyMo4X26fIysQiMcHkc6kKVXyw9z0+3j8Sd4cabOu+m37/eethhiuEMHPduvXAzs6eevXqU63aUyQnX+Ps2TNoNBp69uyDlZUVQUFtcXJyIi4ulmrVnmLVqnV4eHiSnn6TSpWU3Lz5z3wSDw9Pnn22Dvb2DibH4OjoiEqlKlZ+5swpPDy8DNtvvNGL0NCJWFpakpqagqNjZaO5LP7+LbG2tsHe3oFdu6IYNGgolStXwd3dnddf70VUVCQAEyZMoWfPPhQUFJCefhNHxyolzokxVV5eniH5us3a2ob8/PwS6+t0OjZuXM+bb75TbF+DBi+wZ88h+vcfQGjoaAoLC4GiZMnGxoYLF+IfOE5Rfsw6FU21siRNraN69bxid0XZzQ3HdvUXZGzbjabBC/c814LYOXx//juCPduzuM0KnGycH1HUQogH8SC9LXfqyXlUqlRxMvzZ0tISrVbLrVvppKam0KHDP7FoNBpSU1OwtLRkw4Z17NoVhYuLK97eTxudz9n57kvElCQrK5OnnnqqWHla2g18fZ8zbGdmZhIePo3Ll//Cy8sHrVZLrVq1/3Xtf/4NTE1NJTR0NBYWRc/s0un01Pl7OZuLF/9kzJgPyMvLo04dXwoKCtDr9cWu/8knIzl16vdi5Tt27DPatrGxQa1WG5UVFORjZ1fyMNTp06cAaNSoSbF9lf6+WaRv37dYt24NFy/+Sd26RZ9BtWrVuHHjRonnFI83s05sAKx0luj1XkZlln/+ge0XS9HUa4Cmnmm3Zn/YeAzuDtV5p94gWRpBCPHQVK3qgpeXD19//c+T0ZOSEnF1rUZ09E7i4mJZv34zDg4O/PXXn0RH7/zX0ff38M+UlOskJibw7LO+xfYpFAqjYZe5c2fRpMlLREQsR6FQ8PnnC8jMzDCqf5uLiwvTpoUbhrdu3bqFVquhsLCQTz8dx5w5i/DzawTAoEH9S4wtPHyBSe/Bw8OL6Ohdhu2srExyc3OpWbNWifVjYg7RsmUro7IjRw4RGbmZadNmA0W9OhqNBgeHf3q+LC0tDYmaqFjM/hf6KVstOl1tozL7SeNQaDTkTAs3eaFL+0r2DKw/WJIaIUSJUlNTjF7/26twJ/XqNUClyiUqKhKdTsfJk78zYEBfkpOvkZubi5WVFZaWlmRlZfHll0XzQB7kduykpESmTp1EQEArfHyeLra/WrWnuHkzzbCtUuWgVCpRKBScO/dfdu7cfsfrtmnTjtWrvyAnJ4fs7GzGj/+YH374nsJCNYWFhVhbW6PT6YiKiuTChfhS3U7euHETUlKus3dvNGq1mhUrltKiRdGwWEnOnz+Hr+9/jMrq1PElNvY4MTGH0Wg0rF79Bd7ePkbJ0c2bN6lWrXjPlnj8mX2Pja1todG2Mnon1tG7yH+9J5qXSn72hBBC3A+tVkv37sZLtsyZY9pkWKVSycyZ85g/P5yFC+fg5OTM2LET8fb2wd29OjExh+natQP29vZ06dKVU6fiSEi4bNK5Z80KY86cGYCCKlWqEBjYhsGD3y+xrp9fI/bu3W3YHjZsJDNnhvHVVyupWbMmHTt2ITb2WInHDhjwLkuWLKJfv9cpLNTQqlUQAwYMRqlUMmLEGD7+eCQ6nY7nnqtHUFCw4ZkxD8La2oaZM+cxe/Z0Zsz4jAYNXmDixM+AomfX9O/fg7VrN+Lu7g4UJZxVqxoP2bm4uBIWNouFC+dw48YNnn/ej7CwWYZeqFu3bqFSqXjmmToPHKcoPwp9SYOdZmL58uXU8Ymhvt/f/8Co1TgHNsPy2lXSD8eiq1Gz2DG/JR+lpkNNajqW3K0p7o9CAa6ujqSlZWO+f9MqhoraFhU17oomOzubN9/swYYNm7GxKbn340kRGfkzZ86cktu9H6E7fa+trCxwdrYv1bnNelylXbuneL7RP8+wsUhKRKHRoBoxulhSo9frWXFyCa/93JERv4SUdahCCFGuHB0dadeuo1GvzZMqKiqS3r3fLO8wxAMy66EoJ6d2aDT/PBlY5/M06Qd/K1YvR53NqF8+4OeLP+JT5Wmm+s8oyzCFEOKx8M47gwgNHU27dh2f2Oe3/PZbDHXr/gcvL+/yDkU8ILMeirp1K9cosSnJhfTzDNjRjz8yLtDZ51UWBS3BUVm5jCI0fzKM8PioqG1RUeMWQtzZoxyKejJT8r+l5F6n/Q+tydfkMbn5NN5/YbjRLYxCCCGEqFie6MTGzd6dkY3G8FL1pjSr4V/e4QghhBCilMw6sSnIyMLS4e6PGv+w8ZgyikYIURpFvakyFiWEOXiUoyNmndik/3EMny++RTViNNq6z937ACHEY0evB61Wh4uL6eshCSEef1qt7pHMmyvTxCY2NpbJkyeTmJhIw4YNmT17Nq6urkZ1bty4wSeffMLvv/9OjRo1CAsLo2HDhg90vaTkdP6zaQOFfo3471NQt6okN0JUROnpuZjT9DeFAlxcHLl5UyZElzdpi/LzqD7vMnuOTX5+PiNGjGDEiBH89ttveHp6MnPmzGL1Jk6cSN26dTl69ChDhgxh9OjRaP93BUsTadIzUD/7DH2q76P9pkDi08+V9m0IIcqJXm9eL3N8TxX1JW1Rfp/7o1Bmic2RI0dwc3MjODgYpVLJyJEj2blzJyqVylAnJyeHgwcPEhISglKppGvXrjg6OhITE/NA13S9do13g7LZlhhFR+8u1Hb0eFhvRwghhBCPoTJLbK5cuYKXl5dh28nJCTs7OxISEgxlCQkJODs74+joaCjz8vLi4sWLD3TNzNSTrK+ZzsyWc1na9kvsK5Xu3nghhBBCPN7KbI6NSqXC2traqMzW1pb8/Py71rGxsTGqcz8OdfRhX5OpPOdS74GOF6V3e16ElZXFI+16FPcmbfH4kLZ4fEhbPF6srErf31JmiY2trS1qtdqoLC8vDzs7O6M6BQUFRnXy8/ON6tyPyb2XPtBx4uFzcpLesseFtMXjQ9ri8SFtYT7KbCjK29uby5cvG7YzMjLIzc3Fw+OfeS+enp5kZGSQk5NjKLt06RI+Pj5lFaYQQgghKrAyS2yaNm1KcnIyUVFRqNVqFixYQFBQEDY2NoY6Dg4O+Pv7s2jRItRqNVu2bCEjI4MmTZqUVZhCCCGEqMDKdBHMkydPMmnSJBISEmjUqBGzZ88mPz+fzp07s23bNmrUqMGNGzcYP348sbGx1KxZk7CwMJ5//vmyClEIIYQQFZhZr+4thBBCiCdLmQ1FCSGEEEI8apLYCCGEEMJsSGIjhBBCCLMhiY0QQgghzIYkNkIIIYQwG5LYCCGEEMJsSGIjhBBCCLNRoROb2NhYXnnlFfz8/BgwYABpaWnF6ty4cYMBAwbQsGFDOnfuTFxcXDlEav5MaYuzZ8/Su3dvGjduTMeOHYmOji6HSM2fKW1x261bt2jevDlHjx4twwifHKa0RX5+PpMmTcLf35+WLVuyadOmcojU/JnSFlevXuXtt9+mcePGdOjQgT179pRDpE+WlStXMn78+BL3PfDvt76CysvL0zdv3ly/a9cufUFBgf7TTz/Vjxkzpli9oUOH6mfOnKkvKCjQb968WR8YGKjXaDTlELH5MqUtNBqNPjAwUP/999/rtVqt/siRI/pGjRrpk5KSyilq82Tq9+K20aNH6+vWrauPiYkpwyifDKa2xYQJE/QffPCBXqVS6ePj4/WNGjXSX758uRwiNl+mtsX777+vj4iI0Ot0Ov2hQ4f0DRo00Ofl5ZVDxOZPrVbrFy5cqK9bt67+//7v/0qs86C/3xW2x+bIkSO4ubkRHByMUqlk5MiR7Ny5E5VKZaiTk5PDwYMHCQkJQalU0rVrVxwdHYmJiSnHyM2PKW2RlpZG/fr16dGjBxYWFjRt2hRPT0/OnTtXjpGbH1Pa4ra9e/eSk5NDrVq1yiFS82dKW6jVarZu3crEiROxtbXF19eX77//HhcXl3KM3PyY+r1ISEhAp9Oh0+lQKBTY2tqWU8TmLywsjDNnztCrV68S95fm97vCJjZXrlzBy8vLsO3k5ISdnR0JCQmGsoSEBJydnXF0dDSUeXl5cfHixbIM1eyZ0hZubm5EREQYtq9du8bFixfx9fUty1DNniltAZCVlcXs2bOZMmVKGUf45DClLS5fvoyDgwORkZG0atWK9u3bc+HCBRwcHMohYvNl6vfi7bffZsWKFTRo0IBBgwYxdepUo4WaxcMzfPhwVqxYccckvjS/31YPLcoyplKpsLa2NiqztbUlPz//rnVsbGyM6ojSM6Ut/i0zM5OQkBB69epF7dq1yyLEJ4apbTFjxgz69++Pu7t7WYb3RDGlLbKyskhPT+fSpUvs3LmTs2fPMnjwYHx9ffHx8SnrkM2Wqd8LnU5HaGgovXr14tdff2Xs2LE0aNCA6tWrl2W4T4Rq1arddX9pfr8rbI+Nra0tarXaqCwvLw87OzujOgUFBUZ18vPzjeqI0jOlLW67du0affr04bnnnmPs2LFlFeITw5S2OHjwIAkJCfTp06esw3uimNIWSqUSrVbLyJEjsbGxoVGjRjRv3pxDhw6VdbhmzZS2SElJYd68efTr1w+lUklQUBANGzZk9+7dZR2uoHS/3xU2sfH29uby5cuG7YyMDHJzc/Hw8DCUeXp6kpGRQU5OjqHs0qVL8j+hh8yUtgD466+/6NWrF0FBQcyYMQMLiwr71++xZUpb7Nq1i7Nnz/Liiy/SpEkTkpKSeO+999i6dWs5RGy+TGkLDw8PFAoF2dnZhjKNRoNery/LUM2eKW2RlpZGYWGh0XGWlpZYWVXYgY0KrTS/3xX2l6Vp06YkJycTFRWFWq1mwYIFBAUFGY2HOjg44O/vz6JFi1Cr1WzZsoWMjAyaNGlSjpGbH1PaQq1WM3z4cHr16sVHH31UjtGaN1PaYurUqcTFxXH8+HGOHz9OrVq1WLZsGa+88ko5Rm5+TGkLJycnWrZsyYIFCygoKCA2NpaYmBhat25djpGbH1Pa4plnnsHe3p4lS5ag0+mIiYnht99+o2XLluUY+ZOrVL/fD/kOrjL1+++/61999VW9n5+ffuDAgfqbN2/qr169qvfz89NfvXpVr9fr9ampqfrBgwfrGzVqpH/llVf0J0+eLOeozdO92iIyMlJfp04dvZ+fn9Fr27Zt5R262THle/Fvbdu2ldu9HxFT2iIjI0M/atQo/UsvvaRv3bq1fCceEVPa4syZM/revXvrGzVqpO/cubP+wIED5Ry1+Vu0aJHhdu+H9fut0Oulz1MIIYQQ5qHCDkUJIYQQQvwvSWyEEEIIYTYksRFCCCGE2ZDERgghhBBmQxIbIYQQQpgNSWyEEEIIYTYksRFCmI0rV66Udwj3lJ+fT2pqanmHIYTZksRGCDMTFBTE888/T8OGDY1ey5Ytu+exERERDB069KHGM3bsWOrXr28US5s2bUyK517effdd1qxZA8A333zDjBkzDPsaNmzI2bNnS32N25KSkvD19TV6H35+fgQFBbFixQqTz9O3b19OnDjx0OISQhiTRTCEMEPh4eF06NChvMMw6NmzJ5MmTTJsX7x4kf79++Pi4kKPHj0e+Lxffvml4c+3bt0yWmMpLi7ugc97N3v27KFq1apA0WrQhw4dIiQkhHr16uHv73/P4zMyMh5JXEKIItJjI8QT5tixY/Tr14/mzZvj5+fHu+++S1paWrF6ly5d4s0336RJkya0adOGGTNmoNVqAcjMzGTcuHG0aNGCgIAAZsyYUWz15Lt5+umnadKkCefOnQPg5s2bhIaG0qxZM/z9/Rk3bpwhAUhPT2fIkCG89NJLBAYGMnbsWPLy8gDo378/K1euZPv27SxfvpyDBw/Svn17AHx9fTl9+jTz588v1gvVv39/Vq1aBcDevXvp2rUrTZo0oUePHvfVm2JhYUFAQADPPvss8fHxAGRlZREaGkqbNm144YUXaN++PdHR0QC89957XLt2jU8++YTFixeX+vpCiOIksRHiCZKfn8+wYcPo06cPhw8fZs+ePaSlpfHNN98Uqztr1ixefvlljh07xtq1a4mKiuLXX38FIDQ0lNzcXKKiovj555+Jj49n/vz5JsWg0Wg4fvw4MTExNG3aFIAPPviAnJwcduzYwfbt28nIyDAslrp48WJcXV05fPgwmzdv5uzZs/z0009G5+zUqRNDhw4lICCAnTt3Gu3r1q0bhw4dIj09HYDr168TFxfHq6++yunTpxkzZgxjx44lJiaGQYMGMXToUJPnwBQWFrJ161b++OMPXn75ZQDmzp1Lbm4uW7duJTY2ls6dOzN16lQAli1bRo0aNQgPD2fYsGGlvr4QojgZihLCDI0dO5YJEyYYtuvUqcO3335LpUqV2LhxI56enuTk5HD9+nWcnZ1JSUkpdo7KlStz6NAhfH19adasGfv27cPCwoK0tDR++eUXDhw4gKOjIwCjRo1iwIABhIaGlhjP999/z5YtWwzb1atXJyQkhHbt2pGYmEhsbCz79++nSpUqAEyaNInAwEBSUlKoXLkyhw8fZvv27bRo0YLNmzdjYWH6/8m8vLyoX78+O3bsoG/fvmzZsoUWLVrg6upKREQEXbp0oVmzZgB06NCBjRs3snXrVgYNGlTi+YKDg1EoFKjVarRaLc2bN2fZsmXUr18fKErSlEolSqWS5ORk7O3tS/x8ATZt2nTf1xdC3J0kNkKYoZkzZ5Y4x8bS0pIDBw6wevVqtFotdevWJTMzEzc3t2J1J02aREREBOHh4SQnJxMQEMCUKVMMP9KdO3c2qq/RaLh58yYuLi7FzvW/c2z+LS0tDSsrK9zd3Q1l1atXx8rKiuTkZEJCQrC0tGTFihWEhobSuHFjpkyZwtNPP23y59G9e3d++ukn+vbty9atWxk+fDgA165d4+jRo0RFRRnqarVavL2973iu3bt3U7VqVZKSkhg5ciTW1taG3hqA1NRUpk+fzoULF/D29sbd3Z07rTX8INcXQtydDEUJ8QSJi4tj3rx5fPHFF+zfv5/ly5ff8Uf03LlzDBs2jOjoaLZv305ubi7z58/Hzc0NhULBvn37OH78OMePH+fgwYNERkYaJtXejxo1aqDRaEhOTjaUXb16FY1Gg6urK/Hx8fTo0YPIyEj27duHi4sLkydPvq9rdOrUifj4ePbv309qaiqtW7cGwM3NjX79+hnex/Hjx9myZQsjRoy45zlr1arF0qVLOXHiBGFhYYbyUaNG0aJFC2JiYtiwYcNdJ0eX5vpCiJJJYiPEEyQ7OxsLCwuUSiU6nY49e/YQHR1NYWFhsbpz585l/vz5qNVqXF1dsbS0xMnJCTc3N/z9/Zk+fTo5OTmoVCo+/fRTRowYgUKhuO+Ybp8vLCyMzMxMMjMzCQsL48UXX6RWrVp89dVXfPbZZ+Tk5ODs7Iy1tTVOTk7FzmNtbU1OTk6J13BwcCAoKIjJkyfTpUsXlEolUDT/5scffyQ2Nha9Xk9sbCxdu3bl6NGjJsVerVo1pk2bxnfffceBAwcAyMnJwdraGgsLC5KSkvj8888BDJOrlUol2dnZD+X6QojiJLER4gkSEBDAa6+9xuuvv06zZs346quv6NOnDxcuXChWNzw8nMuXL9O8eXMCAwNxcXExDOHMnj0brVZLhw4daNWqFZmZmSxZsuSB45ozZw729vZ06tSJtm3bYm9vz6JFiwAYP348CoWCoKAgmjZtSmZmptH8odsCAwNJTEykRYsWJQ79vP7661y7do1u3boZyho3bszUqVOZMmUKjRs3JjQ0lNGjRxMcHGxy7K1bt6Z79+5MnDiR7OxsQ6LTsGFDBg4cSPv27bGxseGPP/4AiobFpk2bxqxZsx7K9YUQxhT6Ow3+CiGEEEJUMNJjI4QQQgizIYmNEEIIIcyGJDZCCCGEMBuS2AghhBDCbEhiI4QQQgizIYmNEEIIIcyGJDZCCCGEMBuS2AghhBDCbEhiI4QQQgizIYmNEEIIIczG/wPserM0MVmv8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict probabilities\n",
    "pred_prob1 = logreg.predict_proba(X_test)\n",
    "pred_prob2 = knn.predict_proba(X_test)\n",
    "pred_prob3 = dTree_clf.predict_proba(X_test) #Decision Tree\n",
    "pred_prob4 = gnb.predict_proba(X_test)\n",
    "pred_prob5 = GB_classifier.predict_proba(X_test) #Gradient Boosting\n",
    "pred_prob6 = dTree_clf1.predict_proba(X_test) #DC T\n",
    "pred_prob7 = clf_entropy.predict_proba(X_test)\n",
    "pred_prob8 = xgb_classifier.predict_proba(X_test)\n",
    "pred_prob9 = lda.predict_proba(X_test)\n",
    "\n",
    "# roc curve for models\n",
    "fpr1, tpr1, thresh1 = roc_curve(y_test, pred_prob1[:,1], pos_label=1)\n",
    "fpr2, tpr2, thresh2 = roc_curve(y_test, pred_prob2[:,1], pos_label=1)\n",
    "fpr3, tpr3, thresh3 = roc_curve(y_test, pred_prob3[:,1], pos_label=1)\n",
    "fpr4, tpr4, thresh4 = roc_curve(y_test, pred_prob4[:,1], pos_label=1)\n",
    "fpr5, tpr5, thresh5 = roc_curve(y_test, pred_prob5[:,1], pos_label=1)\n",
    "fpr6, tpr6, thresh6 = roc_curve(y_test, pred_prob6[:,1], pos_label=1)\n",
    "fpr7, tpr7, thresh7 = roc_curve(y_test, pred_prob7[:,1], pos_label=1)\n",
    "fpr8, tpr8, thresh8 = roc_curve(y_test, pred_prob8[:,1], pos_label=1)\n",
    "fpr9, tpr9, thresh9 = roc_curve(y_test, pred_prob9[:,1], pos_label=1)\n",
    "\n",
    "\n",
    "# auc scores\n",
    "auc_score1 = roc_auc_score(y_test, pred_prob1[:,1])\n",
    "auc_score2 = roc_auc_score(y_test, pred_prob2[:,1])\n",
    "auc_score3 = roc_auc_score(y_test, pred_prob3[:,1])\n",
    "auc_score4 = roc_auc_score(y_test, pred_prob4[:,1])\n",
    "auc_score5 = roc_auc_score(y_test, pred_prob5[:,1])\n",
    "auc_score6 = roc_auc_score(y_test, pred_prob6[:,1])\n",
    "auc_score7 = roc_auc_score(y_test, pred_prob7[:,1])\n",
    "auc_score8 = roc_auc_score(y_test, pred_prob8[:,1])\n",
    "auc_score9 = roc_auc_score(y_test, pred_prob9[:,1])\n",
    "\n",
    "\n",
    "\n",
    "# plot roc curves\n",
    "plt.plot(fpr1, tpr1, linestyle='--',color='yellow', label='Logistic Reg  (area = %0.2f)' % auc_score1)\n",
    "plt.plot(fpr2, tpr2, linestyle='--',color='green', label='KNN (area = %0.2f)' % auc_score2)\n",
    "plt.plot(fpr3, tpr3, linestyle='--',color='red', label='Decision Tree (area = %0.2f)' % auc_score3)\n",
    "plt.plot(fpr4, tpr4, linestyle='--',color='blue', label='Naive Bayes (area = %0.2f)' % auc_score4)\n",
    "plt.plot(fpr5, tpr5, linestyle='--',color='black', label='Gradient Boosting (area = %0.2f)' % auc_score5)\n",
    "plt.plot(fpr6, tpr6, linestyle='--',color='orange', label='Decision Tree1 (area = %0.2f)' % auc_score6)\n",
    "plt.plot(fpr7, tpr7, linestyle='--',color='pink', label='Decision Tree (Entropy) (area = %0.2f)' % auc_score7)\n",
    "plt.plot(fpr8, tpr8, linestyle='--',color='brown', label='XGBoost (area = %0.2f)' % auc_score8)\n",
    "plt.plot(fpr9, tpr9, linestyle='--',color='gray', label='Linear D (area = %0.2f)' % auc_score9)\n",
    "\n",
    "\n",
    "# title\n",
    "plt.title('ROC curve')\n",
    "# x label\n",
    "plt.xlabel('False Positive Rate')\n",
    "# y label\n",
    "plt.ylabel('True Positive rate')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We compare : logistic regression , KNN, 3 Decision Tree,Naive Bayes, Gradeint boosting, XGboost and Linear Discriminant\n",
    "We can conclude that \n",
    "As we can see from ROC curves of 9th models are quite significantly different. \n",
    "\n",
    "* We got higest AUC for Gradient Boosting = 0.78\n",
    "* The lowest AUC for this case is KNN = 0.50\n",
    "#### So we can say taht the best model for this case is Gradient boosting . Even it's so, we still believe that hyperparameters have effected big enough to the accuracy of the model in circumstance situation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now Let's clasify credit card by users from age category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b349490>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorizing of Age into 4 categories\n",
    "\n",
    "df['X5']= pd.cut(df['X5'], bins=4, precision=0) \n",
    "plt.figure(figsize=(15,8))\n",
    "sns.countplot(y='Y', hue='X5', data = df)\n",
    "plt.legend(loc = 'center right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the Graph above we can say that most of customers who are using credit card is in Age between 21 - 36 year old is dominant among other age categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 24)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(21.0, 36.0]</th>\n",
       "      <td>16809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(36.0, 50.0]</th>\n",
       "      <td>10922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 64.0]</th>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(64.0, 79.0]</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 X5\n",
       "X5                 \n",
       "(21.0, 36.0]  16809\n",
       "(36.0, 50.0]  10922\n",
       "(50.0, 64.0]   2159\n",
       "(64.0, 79.0]    110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.X5\n",
    "# Group data by gender\n",
    "df_example1 = df.groupby(by = 'X5').agg({'X5': 'count'})\n",
    "\n",
    "display (df_example1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y  X3\n",
       "0  2     10700\n",
       "   1      8549\n",
       "   3      3680\n",
       "   5       262\n",
       "   4       116\n",
       "   6        43\n",
       "   0        14\n",
       "1  2      3330\n",
       "   1      2036\n",
       "   3      1237\n",
       "   5        18\n",
       "   6         8\n",
       "   4         7\n",
       "Name: X3, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1040x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart Education groupby Y (default to pay)\n",
    "plt.figure(figsize=(13,8))\n",
    "sns.countplot(y='Y', hue='X3', data = df)\n",
    "plt.legend(loc = 'center right')\n",
    "plt.xlabel('Number of Customers by education category')\n",
    "plt.ylabel('Default to pay')\n",
    "plt.title('Education Level Category groupby Credit Card Using')\n",
    "\n",
    "# Table Education group by Y\n",
    "df.groupby('Y')['X3'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X3 = Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
    "\n",
    "Based on some the result of graphs above, we can say that most customers using credit card is in category 2 (University Student - green bar on the Bar Chart), following with (Graduate Student) as second rank on using credit card (Orange colour on Bar Chart)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}