Tree_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0df42d4a",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "0eaaf09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV,train_test_split,cross_val_score\n",
    "from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07fe1da",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "4c2b2466",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('./Data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8daba04",
   "metadata": {},
   "source": [
    "## Explore Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "2c327fb2",
   "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",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.63</td>\n",
       "      <td>498.55</td>\n",
       "      <td>308.70</td>\n",
       "      <td>271.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5</td>\n",
       "      <td>17.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.72</td>\n",
       "      <td>432.08</td>\n",
       "      <td>360.15</td>\n",
       "      <td>181.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>42.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.58</td>\n",
       "      <td>531.79</td>\n",
       "      <td>360.15</td>\n",
       "      <td>271.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>432.08</td>\n",
       "      <td>360.15</td>\n",
       "      <td>181.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>45.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>415.46</td>\n",
       "      <td>334.43</td>\n",
       "      <td>181.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.10</td>\n",
       "      <td>5</td>\n",
       "      <td>25.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>0.67</td>\n",
       "      <td>465.31</td>\n",
       "      <td>257.25</td>\n",
       "      <td>271.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>14.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>0.82</td>\n",
       "      <td>382.22</td>\n",
       "      <td>334.43</td>\n",
       "      <td>150.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>36.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>0.89</td>\n",
       "      <td>348.99</td>\n",
       "      <td>308.70</td>\n",
       "      <td>135.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5</td>\n",
       "      <td>34.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0.58</td>\n",
       "      <td>531.79</td>\n",
       "      <td>360.15</td>\n",
       "      <td>271.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0.65</td>\n",
       "      <td>481.93</td>\n",
       "      <td>282.98</td>\n",
       "      <td>271.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>17.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>576 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       X1      X2      X3     X4   X5  X6    X7  X8  output\n",
       "0    0.63  498.55  308.70  271.7  4.5   2  0.40   5   17.15\n",
       "1    0.72  432.08  360.15  181.1  8.0   2  0.10   2   42.32\n",
       "2    0.58  531.79  360.15  271.7  4.5   2  0.40   4   22.35\n",
       "3    0.72  432.08  360.15  181.1  8.0   2  0.25   2   45.20\n",
       "4    0.75  415.46  334.43  181.1  8.0   3  0.10   5   25.53\n",
       "..    ...     ...     ...    ...  ...  ..   ...  ..     ...\n",
       "571  0.67  465.31  257.25  271.7  4.5   3  0.10   2   14.15\n",
       "572  0.82  382.22  334.43  150.9  8.0   3  0.40   2   36.47\n",
       "573  0.89  348.99  308.70  135.8  8.0   3  0.40   5   34.25\n",
       "574  0.58  531.79  360.15  271.7  4.5   2  0.10   4   19.84\n",
       "575  0.65  481.93  282.98  271.7  4.5   2  0.40   1   17.63\n",
       "\n",
       "[576 rows x 9 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "a2dee774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(576, 9)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "5246485f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   X1      576 non-null    float64\n",
      " 1   X2      576 non-null    float64\n",
      " 2   X3      576 non-null    float64\n",
      " 3   X4      576 non-null    float64\n",
      " 4   X5      576 non-null    float64\n",
      " 5   X6      576 non-null    int64  \n",
      " 6   X7      576 non-null    float64\n",
      " 7   X8      576 non-null    int64  \n",
      " 8   output  576 non-null    float64\n",
      "dtypes: float64(7), int64(2)\n",
      "memory usage: 40.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "3a0ad259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X1        0\n",
       "X2        0\n",
       "X3        0\n",
       "X4        0\n",
       "X5        0\n",
       "X6        0\n",
       "X7        0\n",
       "X8        0\n",
       "output    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "50b184b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X1        0\n",
       "X2        0\n",
       "X3        0\n",
       "X4        0\n",
       "X5        0\n",
       "X6        0\n",
       "X7        0\n",
       "X8        0\n",
       "output    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "74c5db38",
   "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",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
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       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.0000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.695833</td>\n",
       "      <td>455.620833</td>\n",
       "      <td>334.4275</td>\n",
       "      <td>217.591667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>25.382483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.095517</td>\n",
       "      <td>59.761950</td>\n",
       "      <td>45.8187</td>\n",
       "      <td>55.686049</td>\n",
       "      <td>1.751521</td>\n",
       "      <td>1.248303</td>\n",
       "      <td>0.133250</td>\n",
       "      <td>1.551297</td>\n",
       "      <td>9.853758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.570000</td>\n",
       "      <td>348.990000</td>\n",
       "      <td>257.2500</td>\n",
       "      <td>135.800000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.622500</td>\n",
       "      <td>411.305000</td>\n",
       "      <td>308.7000</td>\n",
       "      <td>173.550000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>16.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.680000</td>\n",
       "      <td>457.005000</td>\n",
       "      <td>334.4300</td>\n",
       "      <td>226.400000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.757500</td>\n",
       "      <td>502.705000</td>\n",
       "      <td>360.1500</td>\n",
       "      <td>271.700000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.155000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.890000</td>\n",
       "      <td>548.410000</td>\n",
       "      <td>437.3300</td>\n",
       "      <td>271.700000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>49.490000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               X1          X2        X3          X4          X5          X6  \\\n",
       "count  576.000000  576.000000  576.0000  576.000000  576.000000  576.000000   \n",
       "mean     0.695833  455.620833  334.4275  217.591667    6.250000    3.333333   \n",
       "std      0.095517   59.761950   45.8187   55.686049    1.751521    1.248303   \n",
       "min      0.570000  348.990000  257.2500  135.800000    4.500000    2.000000   \n",
       "25%      0.622500  411.305000  308.7000  173.550000    4.500000    2.000000   \n",
       "50%      0.680000  457.005000  334.4300  226.400000    6.250000    3.000000   \n",
       "75%      0.757500  502.705000  360.1500  271.700000    8.000000    5.000000   \n",
       "max      0.890000  548.410000  437.3300  271.700000    8.000000    5.000000   \n",
       "\n",
       "               X7          X8      output  \n",
       "count  576.000000  576.000000  576.000000  \n",
       "mean     0.234375    2.812500   25.382483  \n",
       "std      0.133250    1.551297    9.853758  \n",
       "min      0.000000    0.000000   11.250000  \n",
       "25%      0.100000    1.750000   16.110000  \n",
       "50%      0.250000    3.000000   22.700000  \n",
       "75%      0.400000    4.000000   34.155000  \n",
       "max      0.400000    5.000000   49.490000  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "bf65e193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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svtpUduJ315iRXNrxLgA+Wv2P333QQExoe+zGzuHfPCilLilOO4JX+IneXK+waIrSjvymTjz5+1aDo5ji5IMUHduNd2RrCg6sddVxpCdQEL8N/zYDyV77ea3FL3VfZcnJZMuyDldQ9nBNBCM1a+nSpSxduhQombw8ZMgQ1qxZQ/PmzcnLyysz/OZXwcHBZGVlERAQwKBBg3jrrbeAksnyubm5WJbFqFGj+Pnnn2uzKbVq6Z4PWbrnQwA6Nh7KkNY3subQ1zSv3428oqxTDuka3+k+/L2D+WjV32s73Fp1uu+p9PR0GjVqRFBQENnZ2bRv357ExERX+a+vz+Unv21Kjqd5SH1ig8JIzM1kfIsu3PFj2aFtR3PSGdCoFbP2rqNVvYb42r1Jyc8hNiiMozkZOCwn0YGhtKzXkLjs8hfynuzkz1OIX0PX8mbhXbAZ2ykTE4AeTcaw5ej3FDtPjPXfnvAjQ9pMcj0xKTasA3Fp2/hg1f1Vjsdg6NHkUl787qozaU6N+2rT864L5jYRfRne7nZXYgKQX5TFvV90c72+Z+hMPt/4NIdSt7A94UcGtb6encd+wWkVExHcnPTcROZsfoE5m1+ocgy9mo7jy03ln9bkaTLzj5OWm0BkcAuOZe2nXWT/csMEwwIa86cBb/LeyrtJyjrgWr7z2M/8+aK3+X7nu2QVpBDgUw8/ryA2xi9kY/zCKsfQq+k4j010qyr/wBq8I1vh1aAZxWlHCOlzDQnTry9TJ3v9HIL7TCTzpxnYgurjHdmGoqT9eIVF48hOwSrKxxYQin+b/qQtetU9DZE6q7LkZKkxZjrwkmVZDgBjTCTwEtAO6FkL8VWrfw+aSL+oFoT7BbJmwoO8tGExM/es/f0Vz0Fbt26lU6dOTJ06lcLCQj744ANX2SOPPOJ6hOuECROIiYkBYN68ea55AG3btuWyyy4DYM+ePeXmDZyrth79gU6NhjD10mUUOvLKDKV4ZNR8pi4YQ6h/FGM63klCxl4eHjUPgCW7P3RN0jxXVeU9lZGRwdy5c7nvvvtwOBykpqYyY8YMV72ePXuesxPhf+WwnDy68mv+N+JmbMbGp3vWsjs9ifu6DWdTcjyL43bw5Op5PN//Cm7rMADLsrhneckjSntHNuMvnQZT7HTgxOLhFV+RVpDr5hadue6xYxjU+noczmKKHPm8/cudrrI7Bs3go9V/d/Ws9GwyloU7/lNm/XnbXmNC9yk8NnohxthIzo475ZO/usaMZGKPJwjyDeeOQe8Tl7ad15beAEDriD6k5R6tc0Mvx3a6h0Opm9l85LsK6/y0byb1A2N4ZNQ8wJBdkMoby28DR9l6TcM78+eBbxHgU4/O0cMY2+lunphf8nCB+oExhAU0Zk/SyhpsTfWZuW4Kt/T7J3a7N8nZh/lg5X1c1Oo6AJbt/R+XdryLQN8w/tDzKQCcTgfPLBpLQuYevt78IncN+QhjbDicxXyy9lFSc8v3Sj499if8vYOx27zpGjOCfy75oysJ6tHkUv61dFKttbdGOB0c/+/fiLnvW7DZyVz+PoVHt1P/8sfJP7COnI3fkLtlIYEdhtP06S3gdJD82T9w5qTi12wYDSe+AJYFxpD27csUxtfdXiRxD2NZpx51ZYwJA6YBFwJ3AZ2Ae4Dngf9YlT3W4yQa1lV1l6ysW3dA3WZwHX08Y21bOsrdEdQZ8/qGuTuEOuESv/PjJoSIp7l3Yby7Q6gz2sxwGHfHUBW1eX0cf9O0OnFMflVhz4llWWnAZGPMXcB3wFGgr2VZ+oSIiIiIiEi1q/AB1MaYUGPMm8BNwChgNvCtMWZobQUnIiIiIiLnj8rmnKwH3gD+allWMbDIGNMVeMMYc8iyrGtrI0ARERERETk/VJacXPTbIVyWZW0ELjTG3FajUYmIiIiIyHmnwmFdlc0tsSzLs/9MrIiIiIiI1DkVJiciIiIiIiK1ScmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh7Bq6Z3cMnKtJrexTljXt8wd4dQJ6x96g53h1An9Lw/xd0h1Bk6T1XRYHcHIHJ+WhcZ7e4Q6ow27g5Azpp6TkRERERExCMoOREREREREY+g5ERERERERDyCkhMRERERkfOUMWaUMWaXMWavMeaBU5S/YozZWPqz2xiTflKZ46Syr6sjnhqfEC8iIiIiIp7HGGMHXgeGA/HAGmPM15Zlbf+1jmVZd59U/06g20mbyLMsq2t1xqSeExERERGR81NvYK9lWfstyyoEZgLjK6l/LfBJTQaknhMRERERkVr0w48v1Nq+zM3P3Q7cftKityzLeqv092gg7qSyeKDPKbdjTFOgOfDDSYv9jDFrgWJgmmVZX51tvEpORERERETOUaWJyFu/W/H3TQRmW5blOGlZU8uyjhhjWgA/GGO2WJa172x2omFdIiIiIiLnpyNA7EmvY0qXncpEfjOky7KsI6X/7geWUnY+yhlRciIiIiIicn5aA7Q2xjQ3xvhQkoCUe+qWMaYdEAasOGlZmDHGt/T3BkB/YPtv1z1dGtYlIiIiInIesiyr2BhzB7AQsAPvWZa1zRjzJLDWsqxfE5WJwEzLsqyTVm8PvGmMcVLS4THt5Kd8nSklJyIiIiIi5ynLsuYD83+z7LHfvH78FOv9AnSq7ng0rEtERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCV2WFxpgQoKFlWft+s7yzZVmbazSys3DNNdfQsWNHCgsLmTFjBnFxceXq9OzZk9GjR2Oz2diyZQtffPEFAOHh4dx4440EBQWRk5PDe++9R3p6ei23wP1e7H8Vw2LbkZyfzbCvXnV3OB7DZ0AzQh68GOyGvNmbyXlndZnygBt7EnBVJ6xiC2daLhmPLMB5NNNN0dauwdFteKLPWOzG8MnuNby+5ccy5Y0D6/HqwAmE+PhjN4Zn1y3gh/hdeBkbLwy4kk71o7EbG7P3ruf1LUvd0obaVJXzVK9evRg9ejSWZZGRkcG7775LTk4OAEOGDGHw4ME4nc4y57C6xhgbD42cS3puIq8vu7lMWVhAY27q+zL+PiHYjI0vNz7H1oQlVd5264a9mdB9CtGh7XjnlztZHzcfgDYR/ZjQ/VFXvaiQlrz9851sOrKoehpVjcICGnFT31cI9msAWCzf+zE/7H6/TJ0u0cMZ1/leLMuJ0+ng0/VPsC95bZX3ERnckkl9XyQ2rANzNr/I4p1vucr8vUP4Y+/niA5tg2XBh6vuZ3/K+upqXrW7uO0tDGg5EcuyOJKxkw9W3k+xs8BVXtF7oqoqOlaRwS24rf+/XfUaBDXhmy0v8/2u96qnYW7QfdwTNG47BEdRHis/u5e0o1vL1bHZvekx/ikiWvQFy8mmhS8Qv/Vbul36GJEt+wFg9/bHL6g+nz/eqbabIHVUhcmJMWYC8CqQZIzxBiZZlrWmtHgG0L3GozsDHTt2JCIigkcffZTmzZtz3XXXMW3atDJ1AgMDufLKK3n66afJzs5m0qRJtGvXjp07d3LVVVexYsUKVq5cSdu2bbn88st5//33K9jbuWvW3nXM2PkLrw6c4O5QPIfNEPLIcNJu/QzHsSzqf/pH8pfsw7EvxVWleMcxkq/eCPnF+F/TleB7B5Fx7zfui7mW2Ixhat/x/GHhuyTkZjBv7B0sOryDPRlJrjp3dRnKNwc289GuVbSuF8GHw2+i3+znuLR5J3xsXgz76lX87N4sufwe5hzYRHx2mhtbVLOqcp6y2WxMmDCBxx9/nJycHK644gqGDBnC3LlzadOmDV26dOGpp56iuLiY4OBgN7Xk7F3c5mYSM/bi5x1UruySDney9vBclu39L41CWnPHoPd5+JsBVd52au5RZqy6l+Htbi+zfHfSCqYuGANAgE89pl66jO2Jy86uITXE4XQwa8NU4tK24usVyMMj57Ij8ScSMve46uw89jObvl0MQHRoO27v/zpT5l1c5X3kFqYzc90UusaMLFd2TY8pbEv4kbd+/jN2mzc+dv+zb1QNCfWPZGibm3h8/sUUOQq4rf/r9Go6lhUHZrvqVPSeqKqKjtWxrP2u95QxNp4bv4oNcQvPvDFu1qjtEIIbNGPuCxdRv0k3el7+NItfH1+u3gVD7yQ/O5l5Lw4GY/DxDwVgw9wnXXVaXziJ8MYdailyORdUNqzrIaCHZVldgZuAj4wxl5eWmZoO7Ex16dKFlStXAnDgwAH8/f0JCQkpU6dBgwYkJSWRnZ0NwI4dO+jWrRsAjRo1YteuXQDs2rWLLl261GL0nmPVsQOkF+S5OwyP4t2pEY7DaTjiM6DISf63O/Eb2qpMncLVcZBfDEDR5qPYI+vuRePp6NogloNZKRzOTqXI6WDO/k2MaHJBmToWEOzjB6X/Hssr6VGyLAjw8sFubPh5eVPkLCa7ML+2m1CrqnKeAjDG4OvrC4Cfnx8ZGRkADBo0iAULFlBcXPJey8rKqqXIq1eofxSdGg/lp/0zT1luYeFfmrT4eweTkVeS7Bpj48quD/HgiK95dPQCBrb8wynXT8mJ50j6TizLWWEMPWLHsDVhKUUOz3zPZeYnEZdWcse6oDiHhMy9hAZElqlTUJzr+t3XHoBlnSgb0W6y6ziN7Xj3KfeRVZDCodTNOJxFZZb7eQfTumEffi79/3E4i8gr8uyeYJux4233w2bs+Nj9Sc87Vqa8svfE2Ryrk7WL7M/x7MOk5h45u8a4UUyHERxc9zkAKYc34OMfgl9wRLl6LXpOYPuS10teWBaFueVvKjXtOo5Dm76u0Xjl3FLZsC67ZVkJAJZlrTbGDAHmGmNiKbnO8EihoaGkpqa6XqenpxMWFkZm5okT6vHjx4mMjKR+/fqkpaXRtWtXvLxKDkV8fDzdunXjhx9+oFu3bvj7+xMYGOgaSiHnL1tkEI7EExeBjsQsvDs3qrC+/xWdKFi+vzZCc7tGASEk5GS4XifmZtCtYWyZOi9v+I6PR97CTe0vxN/Lh2sXvgPAvINbGNHkAtZPfAh/uw9PrJ5LeuG5nRhX5TzldDr5+OOPeeyxxygsLCQpKYlPPvkEgMjISFq3bs1ll11GUVERs2fP5tChQ7XejrM1ofsUPt/4zCl7TQC+2fIq/zfkI4a0mYSPVwCv/lCShAxocQ15RVk8u2gcXjYf/j7sc7YnLiclp/zQuN/Ts+k4vtv59lm1o7bUD4yhSVgHDiRvLFfWNWYkl3f5O8G+Dfj3jzcB0D5qIBHBzXh20TgMhr9c9C6tG/Zmz/HV5dY/lQaBsWQVpHBjnxeJCbuAw6lb+HTd4xQ6PPPzmZ53jMU73+LZcSsocuSzPXE5OxKXV2ndsz1WJ+vVdBxrDtXti3H/kChyMhJcr3MzEgkIiSI/60RvuLdfyQ2VziPvI6JFX7JTDrNuzqPkZye76gSERhMU1oRje3+uveClzqus5yTLGNPy1xelicpgYDxQaf+cMeZ2Y8xaY8zaHTt2VEug1Sk3N5ePP/6Y2267jfvvv5+UlBSczpK7KLNnz6ZNmzY8/PDDtG7dmrS0NFeZSFX5jb0A745R5Ly35vcrnyfGt+jCZ3vW0euzZ7lh8fv886IJGAxdG8bitJz0mPkM/WY/x+0dB9IkKNzd4bqdzWZj0KBBTJ06lb///e/Ex8czevRoV1lgYCDTpk3j888/5/bbz2yIijt1ajyUrIIUDqeVH8f+q95Nx/HLgdk8MKcv/146iZv6vYrB0D7qIvo2u4JHRs3ngRFfEegbRkRws9OOIcQvguh6bdmW4JlDuk7m6xXA5AHT+Wz9k+QXZ5cr3xi/kCnzLuY/y29jXOd7Abgg6iLaRw3kkVHzeXjUPKJCWhIR3LzK+7Tb7DQJ68iPe//L0wvGUFCcy6gL/lJtbapuAd4hdIkZwcPfDODvX/XG18ufPs0u//0VOftj9Su7zZsu0cNYFzfvtNeta4zNTmBoY5IPrWPha5eQfHgdXS95pEydpl3GEbdlXqW9lyK/VVnPyZ/4zfAty7KyjDGjgAcr26hlWW8BbwFMnjy5xntZBg8ezIABJeOQDx48SHh4OPv2lczhDw0NJS2tfDfj5s2b2by5ZE7/wIEDXQlIRkYG06dPB8DX15fu3buTl+eZd4mkdjmPZWOPOjFMyx4VjDOp/EWCT7+mBN3el9QbZ0KRozZDdJuE3EwaBdZzvY4KqEdCTtnhHxNb9+L6xSWTQ9cfP4yv3YtwvwAua9GVpUd2U2w5ScnPYc2xQ3RuEM3h7FTOJad7noqNLel5Sk4uuQu5bt06Ro4sGeeenp7O+vXrXduyLIugoCDXUNW6oGXDnnSJHkbHRoPxtvvi7x3Mzf1e5b0V/+eq07/lNby29AYA9qesx9vuS5BvOMYYZq6bUm6eyPjO99Op8RAA1/j/yvRscgkb4xfitIqrr2E1wGa8mDxgOqsPfsWG+AWV1t1zfDUNgpoQ6BOGMYYF299g+b6Py9QZ3PoGBrScCMC/fpzkGi73W2m5iaTlJnAwZSMA6+PmM6q95yYn7aIGkJwdR3ZBybljQ9wCWjTowaqDX/7uumd7rH7VsdFgDqduJSs/udJ6nqh1vxto2ftaAFLiNxNYrxG/tiKgXhS5mYll6hfmplFcmEvc1m8BiNs8j5a9Jpap07TLWNbOeRSR01FZz8lXwBXGGPuvC4wxkcD7wLgajuu0LF26lKlTpzJ16lQ2btxI3759AWjevDl5eXllhkr86tcJpAEBAQwaNIiffvoJKJksb0xJTjZq1Ch+/lldkVKiaGsC9qZh2KPrgbcNv9HtKFiyt0wdr/YRhEwZQdodX+BMza1gS+eeTcnxNA+pT2xQGN42O+NbdGFx3PYydY7mpDOgUckcnVb1GuJr9yYlP4ejOelc2Kikk9bfy5vuEbHsyzhe622oaad7nkpPT6dRo0YEBZUMeWrfvj2JiSUXBxs3bqRt27YAREREYLfb61RiAvDVpud5YE5fHv5mAO/8cic7j/1SJjEBSM05SrvI/gBEhbTC2+ZLVkEK2xN+ZFDr67GZkvtrEcHN8bH7M2fzC0xdMKZKiQmUDL9ZXQeG39zQ53kSM/fy3a53TlneMKip6/fYsI542XzIKUxjW8KP9G8xAV+vAKBkwniwb32W7vnQdZwqu9jOzD9OWm4CkcEtgJK5FCdPxPc0qblHadGgG972krlt7aL6k5ix93fWKnG2x+pXdXlI154VH7Lgn6NZ8M/RHNm2kGY9rgSgfpNuFOVnlRnS9asjO74jskXJU7kiW/Un49iJ90dww5Z4+9cj+dC62mmAnDOMZZ26Y8MYEwY8C/QH7gI6AfcAzwP/sarYR1cbPSe/de2119KhQwcKCwv54IMPXGOxH3nkEaZOnQrALbfcQkxMDADz5s1j7dqSxy52796dyy67DIA9e/bwySefuCad1rR5fcNqZT9V8e9BE+kX1YJwv0CS87J5acNiZu6p+qMpa9LaF+q7bd8+FzUn5IGhYLOR9+UWct5cSdAd/SnalkjBkn2EvTsBr9YNcCaXzFFyHM0k/Y7fv2tXE3ren/L7larR0Ji2PN77UmzGxqd71vKvzUu4r9twNiXHszhuB63rRfB8/ysI9PbFsiyeXvsty47uIcDLh5cHXEXr0EiMgc/2rGP61todZnPJytp/MlhVzlMXXXQRQ4cOxeFwkJqayowZM8jJycFut3PjjTcSExODw+Fg9uzZrgd51KjBld+1P1NtIvoyvN3tvL7sZsZ2uodDqZvZfOQ7GoW05vre00ovGC0+3/gsOxKXYzCM73wfnaOHAYbsglTeWH4b+UVlHwzQNLwzfx74FgE+9ShyFJCZf5wn5g8HSuZv3D/scx6c0xfLc6dR0rJBT/4+/HPi03e4hsZ8tekFwgMbA7Bs7/8Y2f5P9G1+JQ5nEUWOAmZveNr1KOGhbW5y3fkvKM7l3RV3kZx9uMw+Qvwa8tDIb/DzDsKynBQU5/L4vGHkF2cTE3oBN/R+Drvdm+Tsw3yw8j5yPXhS/NiOd9Oz6aU4nA7i0rbx0ep/MLrDHa73VGXvibM9Vj52f54dv4KHvxlY7r14tgZvrP33aI/xT9Go7WAchXmsmnUfqUdKRpuMuutbFvyzZIhpQGg0/a55FR//EPJzUlk1615y048C0HHY3di9fNm0YFqF+6gJ1z532GMf2nSy3ZPstfaf2maGo04ck19VmJy4KhhzF/AKcBToa1lW/OnswB3JSV3lScmJJ3NnclKX1HZyUpe5Izmpk2ooORGRyrkjOamrlJyUV9eSkwqHdRljQo0xb1LyGOFRwGzgW2PM0NoKTkREREREzh+VTYhfD7wB/NWyrGJgkTGmK/CGMeaQZVnX1kaAIiIiIiJyfqgsObnot0O4LMvaCFxojLmtRqMSEREREZHzToXDuiqbW2JZVt34i1UiIiIiIlJnVPYoYRERERERkVqj5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj+BV43sYvKDGd3GuWPvUHe4OoU7oeX+Ku0OoE9YEv+DuEOqMVwLmuDuEOiEDnc9F3GFD4RvuDqHOuNbdAchZU8+JiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiMh5yhgzyhizyxiz1xjzwCnKJxljjhtjNpb+3HpS2Y3GmD2lPzdWRzxe1bERERERERGpW4wxduB1YDgQD6wxxnxtWdb231T91LKsO36zbjgwBegJWMC60nXTziYmJSciIiIiIrXonbA5tbav5ysv7g3stSxrP4AxZiYwHvhtcnIqI4HFlmWllq67GBgFfHIW4WpYl4iIiIjIucoYc7sxZu1JP7efVBwNxJ30Or502W9daYzZbIyZbYyJPc11T4t6TkREREREzlGWZb0FvHUWm/gG+MSyrAJjzGTgA2BotQR3Cuo5ERERERE5Px0BYk96HVO6zMWyrBTLsgpKX74D9KjqumdCyYmIiIiIyPlpDdDaGNPcGOMDTAS+PrmCMabRSS/HATtKf18IjDDGhBljwoARpcvOioZ1iYiIiIichyzLKjbG3EFJUmEH3rMsa5sx5klgrWVZXwN/M8aMA4qBVGBS6bqpxpinKElwAJ78dXL82VByIiIiIiJynrIsaz4w/zfLHjvp9weBBytY9z3gveqMR8O6RERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8Qjn/IT4a7o/TsfGQyh05DFj5X3EpW0tU+5t92Ny///QMLgJTsvJ5iPf8eWm59wTrJv4DGhGyIMXg92QN3szOe+sLlMecGNPAq7qhFVs4UzLJeORBTiPZropWs/yYv+rGBbbjuT8bIZ99aq7w3Ery4J/zWvAyl0B+HlbPHBlEm2iC8rUyS80PP5JFEdSvbHbLPq1y2XyyBQAEtO8eP6LCNJz7AQHOHn46kQi6jnc0ZQa16ZdQ8Zf3gFjDKtXHWbp9/vKlA8c1JzefZvgdFpkZxcya+Ym0tPyABgztj3tLojAGNizK5mvv9zmjiactRv6vECnxkPJyk/hyW9HlCsf0W4yvZuNB8BmvGgU0op7v+xGbmFGlbYf6BPK5AHTaRremRUHZjNznWtuJ3abN9f2eJI2EX2xcPLVphfZEP9t9TSsBjUN78w/hn/JO7/cyfq4+RXW+8vAd2gQ1OSUx7Uy4zvfT99mVxDgU4+7Zl/gWn51t0dpG9kPAB+7P8F+9bn7885n1ohaVpPvo15NxzH6gr9iYZGRd4x3f/k/cgrTaqQdte33zlG/6tg5ihtu6slrLy8nPi6D2CahXDmhU2mpYfHC3Wzbklh7gcs5odLkxBgTBWBZVqIxpiEwENhlWVad+Dbs2GgIEcHNeXTuIJrX78Z1PacybfFl5eot2vkWu5NWYLd5c/eQj+nQaDDbEpbWerxuYTOEPDKctFs/w3Esi/qf/pH8Jftw7EtxVSnecYzkqzdCfjH+13Ql+N5BZNz7jfti9iCz9q5jxs5feHXgBHeH4nardgcQn+zN/+45zPY4X175uiH/+XN8uXrXDEynW4s8iorhnveiWbUrgD5tc/nPggaM6JbFqO5ZrN/nz9uL6vPw1UluaEnNMgYuv7Ijb09fRUZ6HnfePZDtW4+RdCzbVefokUxee3k5RUVO+l7YlEvGtud/H66nabMwmjUP45XnfwTgL3/rT4uW9dl/0ue1rlixfxZLdn/ATX1fPmX5op1vsmjnmwB0bnwxF7e7tcoXlABFjgLmbH6R6NC2NK7XtkzZmAvuICs/hcfmDcFgCPAJPeN21BZjbFzR9UG2Jy6vtF63mFEUFOee0T42H/mOJbs/4KlLl5ZZPmvDU67fh7SeRGx4hzPavjvU1PvIZuxM6D6Fx+cNI6cwjSu6PsiQNjcyd+ur1d2EWleVcxSAr6+dARc159DBEwlZYkImr738E06nRXCIL3ffdxE7th3D6bRquxlSh1U4rKv0z9OvAFYaY/4MzAUuAb4wxtxSS/GdlS4xw1l58HMADqRswN8nhBC/iDJ1ihz57E5aAYDDWcThtK2EBUTVeqzu4t2pEY7DaTjiM6DISf63O/Eb2qpMncLVcZBfDEDR5qPYI4PdEapHWnXsAOkFee4OwyP8vCOQkd2yMAY6NCkgO99GSqa9TB0/H4tuLUqOl7cXtGlcwPHMknskh5K86V5a1q1FHj/vCKrdBtSS2CahJCfnkJqSi8NhsWnDETp0jCxTZ9/eFIqKnAAcPpRGvVA/ACzLwsvLht3LVvKv3ZCdVVBuH3XBnuOryS1Mr1LdXk3Hs+bQHNfrPs0u54ERc3hk1Hyu6/UMxpT/Kit05LEveS1FjvLH58IWE/h2++sAWFh14m730DaT2BD3LVn5yRXW8fUKYFi7W5m/7V9lljcIasLfBn/AQyPnct/Fs4gMbnnK9Q+kbCAzv/IbAr2ajivzf1GXVO/7yGAw+HoFAODnFURG3rGaDL/WVOUcBTBidFuW/rCP4mKna1lRkdOViHh52VBKImeisjkndwAdKPkT9S8A4y3LugXoC9xZC7GdtVD/KFJzjrpep+cmEhZQ/gP2K3/vEDpHD2Nn4s+1EZ5HsEUG4UjMcr12JGZhi6j4otD/ik4ULN9fG6FJHXM804uG9YpdrxuGFLsSj1PJyrPxy85AurcsucvbMqqQZdsDAVi+PZDcAhsZuefetLh6of5kpOe7Xmdk5BNSz7/C+r36NGHnjpILxsOH0tm3N4VHnxjOI08MZ9fO4yQlZVe47rnA2+5Hh0aDWB9XMuwqKqQVPZtcyvOLr2TqgjE4LSd9ml5W5e35e4cAML7zfTw8ch6393+DYL8GNRF6tQn1j6RrzEh+3PNRpfXGdbqXxTvfptBR9obJH3tNY+baKTyz8FJmb3yaP/SaekZxhAdE0yAolp3Hfjmj9d2put9HTquYj9c+wmNjFvL8ZWtoXK81P+3/tIair11VOUdFx4QQGurPzu3lk9nYJqHc849B3PP3QXwxa4t6TeS0VTasq9iyrFwg1xizz7KsRADLstKMMZW+04wxtwO3Awy8JZz2F3v+HVCbsXPrhf9iye73Sc6Jc3c4Hslv7AV4d4wi9YaZ7g5F6rhiBzz1aSRX9EuncXhJQvPn0cn885uGLFgfQpdmeTQIKcZm3Byom3XrEU1MbD2m/3s7APUbBBARGcTTj38HwG1/7suuFsc5uP+s/yCvx+oSPYx9yWtdQ3HaRfanSVgnHhr5NVBy0VlZb8Jv2Yyd8MDG7Etex6wNTzGs7a1c1fVh3l95d43EXx0mdJ/CFxunYVVyHzom9AIaBjVl1oanqB8Y41ru6xVAiwY9uH3AG65lXjbfM4qjV9OxrI+bj2U5f7+yh6n+95EXg1pdz9QFY0jOPszEHk8y+oK/luu1OhcZA5eO78BnH288ZXnc4XRefu5HIiKCmPCHruzakVSmd0Xk91SWnDiNMd6WZRVRMpwLAGOMH7/zlC/Lst4C3gKY/EnTWk2ZB7e+gQEtJwJwMGVz6ZdQSVloQBRpuafudr2+9zSSsg7w/a5q/SOXHs95LBt71IlhWvaoYJynuBPr068pQbf3JfXGmVB0bk5SltP35cp6zF1Tcie6XUw+xzNOnFKOZ3rRMKT4lOu99FUEMQ2KuLr/ibHfDUIcPHVdycTJ3ALDj9uCCPY/977QMtLzXMO0AOrV8yMzo/zQwFZtGjB0eCum/3sFDkfJcejYKYrDB9MpLCz5DO7akUTTZmHndHLSs8lYVh/62vXaYFhxcDZfbXq+TL2uMSO5tONdAHy0+h8cSt1yyu3lFKZRUJzLhtI76Ovi5tG/5TU1FP2ZO/m7zN87hFsvLLnoDfINp2PjITicxWw6sshVv0WD7jQN78zTY3/CbvMi2Lc+9wydyRvLbiWvKJOpC8aU2b4xNh4eOReATUe+45stp577c7KeTcfxydpHq6uJNebkY/evHyeRkZdU7e+j2LCSBwYkZx8GYN3huYy84C/V3hZ3+L1zlK+vF1FRwUy+o+QhCcHBvky6pRcz3l1DfNyJc3pSUjaFhcVENQous1zk91SWnPwflNymsSzr5Fmt9YHZNRjTWVm650OW7vkQgI6NhzKk9Y2sOfQ1zet3I68o65Tjacd3ug9/72A+WvX32g7X7Yq2JmBvGoY9uh6OpCz8Rrcj4+9zy9Txah9ByJQRpE2ehTP1zCZayrnp8r4ZXN635Etnxc4AvlxZj6Gds9ke50ugr5P6IeUT2XcWh5NTYOP+y8t+FtNzbIT4O7HZ4OMfwxjT49x8Ilx8XAYNGgYSFu5PZkY+XbpF88l/15ep0zg6hCuv7sS7b64mJ7vQtTw9LY/e/Zqw5PuSLqXmLevz07Jzd5iln3cwbSL68t6K/3Mt23nsZ/580dt8v/NdsgpSCPCph59XEBvjF7IxfmGVtrv5yHe0iezHrmO/0C6yPwkZe2qoBWfu5O+yk93Y50W2HP2hTGICsGzvf1m2978A1A+M4a8XvcfLP5RcoCdnx9E9dozrCV8xoe2JT99RLmGpTGRwSwK8Q9ifvO5Mm1RrfnvsauJ9lJ6XSKN6rQnyDSe7IJX2UQNJzNxbE82pdb93jsrPL+aJR0+8/yb/tR/zvt5OfFwGYeElQ8KcTovQMH8iIoJI1XWDnKbKkpMPgOnGmJcsy3IAGGMigeeAdsBTlazrEbYe/YFOjYYw9dJlFDry+GDVfa6yR0bNZ+qCMYT6RzGm450kZOzl4VHzAFiy+0N+3n+eDF1yWGQ+/R1hb18FNht5X26heG8KQXf0p2hbIgVL9hF832BMgDehr5Q8jtFxNJP0O750c+Ce4d+DJtIvqgXhfoGsmfAgL21YzMw9a90dllv0bZvLqt0BXPdyU3y9nfzjihPJxy3/iuXdO+NIyrDz36XhNGlYyG2vxwIlCc6lvTLZeKDkCV0G6Nwsj/8bd9xNLalZTqfFnM+3cevkPthshjWr4jiWmM2IUW2Ij8tg+7ZjXDKuPT6+Xlw/qTtQkpTMeHctmzcl0LJ1A+7++0Vgwa6dx9mxrW4+0eyWC1+jbUQ/gnzDmDZ+Jd9seQW7reQradne/wHQLWYk2xOXlZlDkZC5h683v8hdQz7CGBsOZzGfrH2U1Nwj5fbx9Nif8PcOxm7zpmvMCP655I8kZO7hi43TuLnfK0zo/hjZ+anMOOm7oa759busMu+uuIvrek1lTIc7sdu8WXvoa+LTd5Srd0XXB+nddDw+Xv5MG7+Sn/bNdD19qlfTsaw9XDef0lhT76O5W1/lvotn4bCKSM05woyV99Zms2pMVc5RFWneIpzBF7fC6XBiWfDl7C3k5hTVYvRyLjCWdepRV8aYMGAacCFwF9AJuAd4HviPVcVBp7U9rKsue+KpO9wdQp3Q8/6699hUd1gT/IK7Q6gzXvm5bj59qLZl9P6ru0MQOS/VW/26u0OoM55/5dI6MVvx73fPrbXr47pyTH5VYc+JZVlpwGRjzF3Ad8BRoO9vhniJiIiIiIhUi8r+zkmoMeZN4CZgFCXzTL41xgytreBEREREROT8Udmck/XAG8BfLcsqBhYZY7oCbxhjDlmWdW1tBCgiIiIiIueHypKTi347hMuyrI3AhcaY22o0KhEREREROe9UOKyrsrkllmW9XTPhiIiIiIjI+arSP6YoIiIiIiJSW5SciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgErxrfw9JRNb6Lc0XP+1PcHUKdsCb4BXeHUCf0yrrf3SHUGZfkfuPuEEREKpShc9RpuNTdAchZUs+JiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIyHnKGDPKGLPLGLPXGPPAKcrvMcZsN8ZsNsZ8b4xpelKZwxizsfTn6+qIx6s6NiIiIiIiInWLMcYOvA4MB+KBNcaYry3L2n5StQ1AT8uyco0xfwaeB64pLcuzLKtrdcak5EREREREpBbds3BHLe7t0soKewN7LcvaD2CMmQmMB1zJiWVZS06qvxK4vgaCdNGwLhERERGRc5Qx5nZjzNqTfm4/qTgaiDvpdXzpsorcAnx70mu/0m2uNMZcVh3xqudEREREROQcZVnWW8BbZ7sdY8z1QE9g0EmLm1qWdcQY0wL4wRizxbKsfWezH/WciIiIiIicn44AsSe9jildVoYxZhjwMDDOsqyCX5dblnWk9N/9wFKg29kGdFo9J8aYZyzLeuhsd1rTrrnmGjp27EhhYSEzZswgLi6uXJ1evXoxevRoLMsiIyODd999l5ycHG677TYiIyMB8Pf3Jy8vj6lTp9Z2E2rc4Og2PNFnLHZj+GT3Gl7f8mOZ8saB9Xh14ARCfPyxG8Oz6xbwQ/wuvIyNFwZcSaf60diNjdl71/P6lqVuaUNtsSz417wGrNwVgJ+3xQNXJtEmuqBMnfxCw+OfRHEk1Ru7zaJfu1wmj0wBIDHNi+e/iCA9x05wgJOHr04kop7DHU1xqxf7X8Ww2HYk52cz7KtX3R2O21XlPNWzZ09Gjx6NzWZjy5YtfPHFFwBcffXVtG3bFgAfHx+Cg4O5++67azX+6jC0zU0MaHktxhh+2vcJ3+96r0x5gHcIN/R5gYbBTSlyFPDhqvs5mrG7ytsP9All8oDpNA3vzIoDs5m57jEAfL0CuX/YLFe9sIBGrDr4JZ+tf7J6GlbNIoNbMqnvi8SGdWDO5hdZvPPUN0Bv7vdPmoZ3wuEs5mDqJv67+kGcVnGV99M9dgxjO91NVEgrpi0ax6HULa6y6NB2XN/rWfy8g7AsJ88sHEexs6CSrblHVY/VH3s/T9PwThhjOJZ5gA9W3UtBcW6V9zO49Y1c3PZmIoKbcc/nXckpTHOVtYnoy4Tuj2G3eZNdkMpL319TyZY819lcSwEMGTKEwYMH43Q6y5y/xOOsAVobY5pTkpRMBP5wcgVjTDfgTWCUZVlJJy0PA3ItyyowxjQA+lMyWf6sVJicGGNe++0i4I/GmCAAy7L+drY7rwkdO3YkIiKCRx99lObNm3Pdddcxbdq0MnVsNhsTJkzg8ccfJycnhyuuuIIhQ4Ywd+5c3n77bVe9q666iry8vNpuQo2zGcPUvuP5w8J3ScjNYN7YO1h0eAd7MlzvN+7qMpRvDmzmo12raF0vgg+H30S/2c9xafNO+Ni8GPbVq/jZvVly+T3MObCJ+Oy0SvZYt63aHUB8sjf/u+cw2+N8eeXrhvznz/Hl6l0zMJ1uLfIoKoZ73otm1a4A+rTN5T8LGjCiWxajumexfp8/by+qz8NXJ51iT+e2WXvXMWPnL7w6cIK7Q3G7qpynAgMDufLKK3n66afJzs5m0qRJtGvXjp07dzJr1okL6yFDhhAbG/vbXXi8xvXaMKDltTy7aBwOZxF/G/whm498z/HsQ646ozvcQXz6dqb/NJnI4Jb8oedTvLLkD5VstawiRwFzNr9IdGhbGtdr61peUJzD1AVjXK8fGjmXDXELqqdhNSC3MJ2Z66bQNWZkpfVWH/yK91bcBcAtF77GgJYTWbb3v1Xez9GM3UxfPpnrej1TZrnN2Lm536u8v+Ju4tN3EOgTisMqOv2G1IKqHqtZ658kvzgbgKu7Pcrg1jeycMd/qryffclr2XL0e+4ZOrPMcn/vEK7tOZXXlt5AWu5Rgn3rn34jPMDZXku1adOGLl268NRTT1FcXExwcLCbWiK/x7KsYmPMHcBCwA68Z1nWNmPMk8Bay7K+Bl4AgoBZxhiAw5ZljQPaA28aY5yUjMaa9punfJ2RyoZ1XQ6EA2uBdaX/FpX+vu5sd1xTunTpwsqVKwE4cOAA/v7+hISElKtnjMHX1xcAPz8/MjIyytXp0aMHa9asqdmA3aBrg1gOZqVwODuVIqeDOfs3MaLJBWXqWECwjx+U/nssL7NkuQUBXj7YjQ0/L2+KnMVkF+bXdhNq1c87AhnZLQtjoEOTArLzbaRk2svU8fOx6NaiJJH19oI2jQs4nlmS+x9K8qZ7aVm3Fnn8vCOodhvgIVYdO0B6wbmX7J+JqpynGjRoQFJSEtnZJRdQO3bsoFu38r3lvXr1qpPnqaiQVhxI2UiRIx+n5WB30iq6xY4qU6dRSGt2HvsFgGNZ+6gfGEOwXwMA+jS7nAdGzOGRUfO5rtczGFP+66zQkce+5LUUOSq+wx8R3Jxg3/rsOb66GltXvbIKUjiUuhmHs/KEYGvCiQfqHEzZRFhAIwB87P7c0OcFHhgxh4dHzadL9PBTrp+YuZdjWfvLLb8g6iKOpO8kPr3k6UI5helYlvNMm1Ojqnqsfk1MALztvpR860GQbziTB0znwRFf8+CIr2nZoOcp149L20ZKTvmbVL2bjmdj3ALSco+64qmLzvZaatCgQSxYsIDi4pKeu6ysrFqKXM6EZVnzLctqY1lWS8uyni5d9lhpYoJlWcMsy4q0LKtr6c+40uW/WJbVybKsLqX/vlsd8VQ2rOsC4ClgFHCfZVlHjTFTLMv6oDp2XFNCQ0NJTU11vU5PTycsLIzMzEzXMqfTyccff8xjjz1GYWEhSUlJfPLJJ2W207p1a7KyskhKOvfucDcKCCEh50QylpibQbeGZe+8vrzhOz4eeQs3tb8Qfy8frl34DgDzDm5hRJMLWD/xIfztPjyxei7phef2BefxTC8a1jsxNKJhSDHHM72oH3LqoVlZeTZ+2RnIlRemA9AyqpBl2wO56sIMlm8PJLfARkaujXoBnvnlLjWvKuep48ePExkZSf369UlLS6Nr1654eZU9ZYeHh9OgQQN27txZa7FXl6MZu7ms8/0E+oRS6MinU+MhHErdXKZOfPp2usWMYu/xNTQL70J4YDRh/lEE+oTSs8mlPL/4SpxWMdf2nEqfppex8uDpDxvp1WQsaw/Pra5meQSb8aJvsyv4dP3jAIzpcAe7jv3Ch6vux987hAdHzGFH4k8UOqp27o4MaY5lWfxt8IcE+9ZnzeGvWbTjzRpsQe24sc8LdGw8hISMvczaUDJ8+5ruj/PdznfYl7yWsIDG3DX4Ix6ff3GVtxkZ0hy78eaeoTPx8w7ih13vndH70t3O9loqMjKS1q1bc9lll1FUVMTs2bM5dOhQuf2InEqFyYllWVnA/xljegD/M8bMo4oT6EsfUXY7wMCBA2nfvn11xFptbDYbgwYNYurUqSQnJzNx4kRGjx7N/PnzXXV69erF6tWeeyetpo1v0YXP9qzjrW3L6d6wCf+8aAIXf/kqXRvG4rSc9Jj5DPV8/flizJ9YfnQvh7NTf3+j54FiBzz1aSRX9EuncXhJQvPn0cn885uGLFgfQpdmeTQIKcZm3ByoeLzc3Fw+/vhjbrvtNizLYt++fTRs2LBMnV69erF+/Xosy3JTlGcuMXMvC3dM564h/6WwOJe4tG04rbIJ/4Lt/+GaHlN4ZNR8jqTvKq3jpF1kf5qEdeKhkSV/jNjb7kdWfvIZxdGz6TjeX/F/Z9scj/KHnlPZc3wVe4+X9KhdEHURnaOHM7zdbUBJT0F4YDSJmXurtD2b8aJVw148s3AshY487hn6CYdTt7Lz2M811oba8MGq+zHGxsQeT9KryVh+OTCLdlH9aVSvlauOn3cQvl4BVZ6PYjNeNAnvyCs//AFvux//GPEl+1M2kJR1oKaa4TaVXUvZbDYCAwOZNm0azZo14/bbb+fhhx92d8hSR1Q256SJZVmHLctaZ4wZCvwF+Km0bKBlWcsrWvfkR5ZNnjy5xr81Bw8ezIABAwA4ePAg4eHh7NtX8hSz0NBQ0tLKzof4dXx2cnLJl9m6desYOfLE+FSbzUa3bt14+umnazp0t0jIzaRRYD3X66iAeiTkZJapM7F1L65fXDI5df3xw/javQj3C+CyFl1ZemQ3xZaTlPwc1hw7ROcG0edccvLlynrMXVPShd0uJp/jGSc+KsczvWgYcupJpi99FUFMgyKu7n+iZ6pBiIOnrksEILfA8OO2IIL91Wtyvjnd8xTA5s2b2by5pDdh4MCBOJ1l3zc9e/Ys1+tbl/y8/1N+3v8pAJd1vp+03MQy5fnF2Xyw6n7X66fH/kRy9mFaN+zNioOz+WpT2XmXXWNGcmnHkjkXH63+R5kJ3acSE9oeu7FzOG1rdTSnWg1ufQMDWk4E4F8/TiIjr2q9+Jd2vItgv3CmL3/wxEJjePOnP5UbsnVjnxeIDetAel4S//5xUoXbTMtNYM/xVa5J31uOLqFJWEePSU7O9FgBWJaTtYe+ZkT7P/HLgVnYjI1piy4vN9n/b4M/JMSvAYdSt/DR6n9UuL203ARyCtIodORR6MhjT9JqYkLb14nkpDqvpdLT01m/fr1rW5ZlERQU5BqmKlKZyoZ1LTXGTAdesizLAbxujPncGPNfoB0lzzn2CEuXLmXp0qVAySSuIUOGsGbNGpo3b05eXl6Zbkgo+dA0atTI9UFp3749iYknvhR/fZ2enl6Lrag9m5LjaR5Sn9igMBJzMxnfogt3/Fj2AudoTjoDGrVi1t51tKrXEF+7Nyn5ORzNSefCRi35fN8G/L286R4Ry7vbf3JTS2rO5X0zuLxvSYKxYmcAX66sx9DO2WyP8yXQ13nKIV3vLA4np8DG/ZeX/WJMz7ER4u/EZoOPfwxjTI/McuvKue90z1MAwcHBZGVlERAQwKBBg3jrrRNPHoqMjCQgIID9+8vPEagrgn3rk1WQQlhAY7rFjmLaosvLlPt7h1DoyMPhLGJAy4nsOb6a/OJsdh77mT9f9Dbf73yXrIIUAnzq4ecVxMb4hWyMX1jl/fdqOo41h76u7mZVi6V7PmTpng9Pa53+LSZyQdQgXllyLRYn7gtuT/iRIW0muZ5WFhvWgbi0bWUSv8psT/iRke3/hLfdD4eziDYRffhuV7UMLa8WZ3KsGgY1dT18oXP0cBIzSy7CtycsZ2ibSSzaWTJsLSb0AuLTt/Pa0huqtN1NRxZzbY8nsRk7dps3zet35ftd75xWbO5SnddSGzdupG3btuzevZuIiAjsdrsSE6kyU9FwgNLHg00DLgTuAjoBd1MyY/8/VhVnw9VGz8lvXXvttXTo0IHCwkI++OAD1zjHRx55xPVY4IsuuoihQ4ficDhITU1lxowZrsff3XjjjRw4cIBly5bVatzz+obV2r6GxrTl8d6XYjM2Pt2zln9tXsJ93YazKTmexXE7aF0vguf7X0Ggty+WZfH02m9ZdnQPAV4+vDzgKlqHRmIMfLZnHdO31u5xWhP8Qq3uz7Lgn980YPWeQHy9nfzjiiTaxZTcVbvlX7G8e2ccSRl2JjzfnCYNC/G2l7zlL++bwaW9Mlm6NZC3F9XHAJ2b5fF/447jUwt//rRXVtUuPGrLvwdNpF9UC8L9AknOy+alDYuZuWetu8MC4JKVtf+0uaqcp2655RZiYmIAmDdvHmvXnjhel156Kd7e3nz55Ze1F/Tg6n2i1X0XzyLQNwyHs4jZG6ay89jPXNTqOgCW7f0fLep3Z1Lfl7CwSMjYw4er7ie3qOQCqWeTSxl1wV8wxobDWcwnax/lQMqGcvt4euxP+HsHY7d5k1eUyT+X/JGEzD0ATB27nH8tncSxrLP6e2E1LsSvIQ+N/Mb1GN+C4lwenzeM/OJs7hg0g49W/52MvCTeuGYfqTlHXJO9N8QtYN621/C2+zKh+xRaNuiBMTaSs+N4fdnN5fbTNWYkE3s8QZBvOHlFmcSlnbgo79PsckZd8Bcsy2JrwhK+2PhsrR6DqqrKscrMO859w2bj7x0EGOLTd/DxmofJL84m0CeMP/R8iqh6rbAZL/YkreLjteWHIw1pM4mR7f9EiF9DsvJT2JqwxNWjMqLdZPq1uBrLcvLz/pnlHpF9RpaO+v061exsrqXsdjs33ngjMTExOBwOZs+eza5du2ol7jfffLNODJxOvOCFWrs+jtp+f504Jr+qMDlxVTDmLuAV4CjQ17Ks8o+nqIQ7kpO6qjaTk7qstpOTusrTkhNP5o7kpE6q5uRERKrIDclJXaXkpLy6lpxUOMHdGBNqjHkTuImSJ3bNBr4tnX8iIiIiIiJSrSobXLIeeAP4q2VZxcAiY0xX4A1jzCHLsq6tjQBFREREROT8UFlyctFvh3BZlrURuNAYc1uNRiUiIiIiIuedCod1VTa3xLKst2smHBEREREROV9V6Y8qioiIiIiI1DQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BK+a3sG8vmE1vYtzxiUr09wdQp3wSsAcd4dQJ1yS+427Q6gzdJ6qmkvcHYDIeUrnKDmfqOdEREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpe7A6hug6Pb8ESfsdiN4ZPda3h9y49lyhsH1uPVgRMI8fHHbgzPrlvAD/G76NoghucuvAIAYwwvb/iOBYe3uaMJteaaa66hY8eOFBYWMmPGDOLi4srV6dWrF6NHj8ayLDIyMnj33XfJyckBYMiQIQwePBin08mWLVv44osvarsJtaJNu4aMv7wDxhhWrzrM0u/3lSkfOKg5vfs2wem0yM4uZNbMTaSn5QEwZmx72l0QgTGwZ1cyX3+p91TPnj0ZPXo0NputzPvm6quvpm3btgD4+PgQHBzM3XffXavxe4IX+1/FsNh2JOdnM+yrV90dTo0zxsZDI+eSnpvI68tuLlPWumFvJnSfQnRoO9755U7Wx80/rW1HBrdkUt8XiQ3rwJzNL7J451uly1twW/9/u+o1CGrCN1te5vtd7519g2pARe34rZv7/ZOm4Z1wOIs5mLqJ/65+EKdVXOX9dI8dw9hOdxMV0oppi8ZxKHWLqyw6tB3X93oWP+8gLMvJMwvHUewsOOu2Vbcu0cMZ1/leLMuJ0+ng0/VPsC95bYX1/zLwHRoENeHJb0ec1n7Gd76fvs2uIMCnHnfNvsC1/Opuj9I2sh8APnZ/gv3qc/fnnc+sMR7i966rogNDeWnAVdT3CyS9II+/LZtJQm6mm6KVc0GVkxNjTHOgG7DdsqydNRfSmbMZw9S+4/nDwndJyM1g3tg7WHR4B3syklx17uoylG8ObOajXatoXS+CD4ffRL/Zz7Ez7Rhjvvk3DstJhH8wi8bfxeK4HTgspxtbVHM6duxIREQEjz76KM2bN+e6665j2rRpZerYbDYmTJjA448/Tk5ODldccQVDhgxh7ty5tGnThi5duvDUU09RXFxMcHCwm1pSs4yBy6/syNvTV5GRnseddw9k+9ZjJB3LdtU5eiST115eTlGRk74XNuWSse3534fradosjGbNw3jl+ZIT+V/+1p8WLeuzf1+Ku5pTo6ryngoMDOTKK6/k6aefJjs7m0mTJtGuXTt27tzJrFmzXPWGDBlCbGxsbTfBI8zau44ZO3/h1YET3B1Krbi4zc0kZuzFzzuoXFlq7lFmrLqX4e1uP6Nt5xamM3PdFLrGjCyz/FjWfqYuGAOUJEfPjV/FhriFZ7SP2lBRO35r9cGveG/FXQDccuFrDGg5kWV7/1vl/RzN2M305ZO5rtczZZbbjJ2b+73K+yvuJj59B4E+oTisotNvSC3YeexnNn27GChJqG7v/zpT5l18yrrdYkZRUJx7RvvZfOQ7luz+gKcuXVpm+awNT7l+H9J6ErHhHc5o+56iKtdVj/Yaw+x965m9dz0XNmrJAz1Gcdfyz9wYtdR1FQ7rMsZ8ddLv44EfgLHAHGPMpBqP7Ax0bRDLwawUDmenUuR0MGf/JkY0uaBMHQsI9vGD0n+P5ZVk9/mOIlci4mv3wsKq1dhrW5cuXVi5ciUABw4cwN/fn5CQkHL1jDH4+voC4OfnR0ZGBgCDBg1iwYIFFBeX3JXLysqqpchrV2yTUJKTc0hNycXhsNi04QgdOkaWqbNvbwpFRSXvncOH0qgXWvL+siwLLy8bdi9byb92Q3aW591prC5VeU81aNCApKQksrNLkrsdO3bQrVu3ctvq1asXa9asqfmgPdCqYwdIL8hzdxi1ItQ/ik6Nh/LT/pmnLE/JiedI+k6sU9wkGtFuMg+O+JpHRy9gbMdT97BlFaRwKHUzDmfFF9LtIvtzPPswqblHzqwRtaAq7QDYmrDE9fvBlE2EBTQCSu7g39DnBR4YMYeHR82nS/TwU66fmLmXY1n7yy2/IOoijqTvJD59BwA5hemn/D/xBCcnG772AKwKvsp9vQIY1u5W5m/7V5nlDYKa8LfBH/DQyLncd/EsIoNbnnL9AykbyMxPOmXZr3o1HceaQ3NOrwEepirXVa1DI/k5oWREwS8J+8qVi+czxowyxuwyxuw1xjxwinJfY8ynpeWrjDHNTip7sHT5LmNM5XdQqqiynpOmJ/3+D2CoZVkHjDENgO+BGdURQHVqFBBCQk6G63VibgbdGpa9+/ryhu/4eOQt3NT+Qvy9fLh24Tuusm4NYnlxwFXEBIVy17LPztleE4DQ0FBSU1Ndr9PT0wkLCyMz80RXrNPp5OOPP+axxx6jsLCQpKQkPvnkEwAiIyNp3bo1l112GUVFRcyePZtDhw7VejtqWr1QfzLS812vMzLyiW0SVmH9Xn2asHNHyRfW4UPp7NubwqNPlFwI/PLTQZKSsitct66rynvq+PHjREZGUr9+fdLS0ujatSteXmVPQ+Hh4TRo0ICdOz2yg1aq0YTuU/h84zOn7DWpTPuogUQEN+PZReMwGP5y0bu0btibPcdXn3YMJReQX5/2ep7MZrzo2+wKPl3/OABjOtzBrmO/8OGq+/H3DuHBEXPYkfgThY6qJcGRIc2xLIu/Df6QYN/6rDn8NYt2vFmDLTg7XWNGcnmXvxPs24B//3jTKeuM63Qvi3e+Xe4Y/LHXNP635iGSsg/SrH5X/tBrKq/8cO1pxxAeEE2DoFh2HvvljNrgKapyXbUjNYExTTvy7vafGd20A8E+foT6BpBecGa9UlK7jDF24HVgOBAPrDHGfG1Z1vaTqt0CpFmW1coYMxF4DrjGGHMBMBHoADQGvjPGtLEsy3E2MVWWnJx8v8HLsqwDAJZlJRtjKr1qN8bcDtwOEHrDSAIHdz2bGKvV+BZd+GzPOt7atpzuDZvwz4smcPGXr2JhsSE5jou/eoVW9Rry6sAJLDmyiwJH1cfrnmtsNhuDBg1i6tSpJCcnM3HiREaPHs38+fOx2WwEBgYybdo0mjVrxu23387DDz/s7pDdqluPaGJi6zH93yWf5/oNAoiIDOLpx78D4LY/92VXi+Mc3J9a2WbOabm5uXz88cfcdtttWJbFvn37aNiwYZk6vXr1Yv369VgV3fKUc0KnxkPJKkjhcNpW2kT0Pa11L4i6iPZRA3lkVMkcFF+vQCKCm592cmK3edMlehhfbnrutNbzdH/oOZU9x1ex93hJ7+MFURfROXo4w9vdBoC33ZfwwGgSM/dWaXs240Wrhr14ZuFYCh153DP0Ew6nbmXnsZ9rrA1nY2P8QjbGL6R1w96M63wvry65rkx5TOgFNAxqyqwNT1E/MMa13NcrgBYNenD7gDdcy7xsvmcUQ6+mY1kfN99je5iq01Nr5jG133iubtWDVccOkJCTgfM8aPfZmvLov3+/UjV5k/srK+4N7LUsaz+AMWYmMB44OTkZDzxe+vts4N/GGFO6fKZlWQXAAWPM3tLtrTibeCtLTroYYzIBA/gaYxpZlpVgjPEB7JVt1LKst4C3AGLef6DWrjAScjNpFFjP9ToqoB4JOWUnZU1s3YvrF5dMelx//DC+di/C/QJIyc9x1dmbcZyc4kLahkayOcVzu/pP1+DBgxkwYAAABw8eJDw8nH37SrpiQ0NDSUtLK1P/1zH/ycnJAKxbt46RI0t67NLT01m/fr1rW5ZlERQU5Bquc67ISM9zDdMCqFfPj8yM8ncbW7VpwNDhrZj+7xU4HCUn5Y6dojh8MJ3CwpIbCLt2JNG0Wdg5lZyc7nsKYPPmzWzevBmAgQMH4nSW/RLr2bOnq4dOzl0tG/akS/QwOjYajLfdF3/vYG7u9yrvrfi/313XGMOC7W+wfN/HZZYPbn0DA1pOBOBfP04iI6/yYTcdGw3mcOpWsvKTz7gdNeV02/KrSzveRbBfONOXP3hioTG8+dOfyg3ZurHPC8SGdSA9L4l//zipwm2m5Saw5/gqcgpLPs9bji6hSVhHj0lOKjpWe46vpkFQEwJ9wlyxA7Ro0J2m4Z15euxP2G1eBPvW556hM3lj2a3kFWW65iP9yhgbD4+cC8CmI9/xzZaXfzemnk3H8cnaR6uriW5TleuqY3lZ3PZDydymAC8fxjTtSGZhPuI5Tu40KPVW6bU6QDRw8tNr4oE+v9mEq45lWcXGmAygfunylb9ZN/ps460sOWlhWdapxun4A3892x3XhE3J8TQPqU9sUBiJuZmMb9GFO34se5FzNCedAY1aMWvvOlrVa4iv3ZuU/Bxig8I4mpOBw3ISHRhKy3oNicsuf2FVly1dupSlS5cCJZOXhwwZwpo1a2jevDl5eXllht9ASQLSqFEjV9LRvn17EhMTAdi4cSNt27Zl9+7dREREYLfbz7nEBCA+LoMGDQMJC/cnMyOfLt2i+eS/68vUaRwdwpVXd+LdN1eTk13oWp6elkfvfk1Y8r0BoHnL+vy0rPx47rrsdN9TAMHBwWRlZREQEMCgQYN4660TTx6KjIwkICCA/fvPreMk5X216Xm+2vQ8AG0i+jK83e1VSkwAtiX8yPhO97L60FcUFOcS6h+Jw1nM0j0fsnTPh1WOwZOHdJ1uWwD6t5jIBVGDeGXJtWXmTW5P+JEhbSYxc91jAMSGdSAubRsfrKr0bmqZ9Ue2/xPedj8cziLaRPThu13vnlZsNenkY9Uw6MSI9NiwjnjZfMokJgDL9v7X9aCA+oEx/PWi93j5h5LkJjk7ju6xY1xPhosJbU98+o5yCUtlIoNbEuAdwv7kdWfVLk9QleuqMN8A0gvysLC4o/NgPt1T8dPRxD1O7jSoCypLTpYYY6YDL/06dswYEwm8BLQDetZCfKfFYTl5dOXX/G/EzdiMjU/3rGV3ehL3dRvOpuR4Fsft4MnV83i+/xXc1mEAlmVxz/KSJwT1jmzGXzoNptjpwInFwyu+Iu0cHi+5detWOnXqxNSpUyksLOSDDz5wlT3yyCNMnTqVjIwM5s6dy3333YfD4SA1NZUZM2YA8PPPP3PjjTfy2GOP4XA4XMvPNU6nxZzPt3Hr5D7YbIY1q+I4lpjNiFFtiI/LYPu2Y1wyrj0+vl5cP6k7UJKUzHh3LZs3JdCydQPu/vtFYMGuncfZsa1qdz/roqq8pwAmTJhATEzJUIp58+aRlHTimPTq1Yu1a8/vL7Z/D5pIv6gWhPsFsmbCg7y0YTEzz6Mv+7Gd7uFQ6mY2H/mOpuGd+fPAtwjwqUfn6GGM7XQ3T8wfzo7E5TQKacU/hn8JlEyCfnfFXWQVlH0SXohfQx4a+Y3r8bcXt72Zx+cNI784Gx+7P+2jBvLfNQ+5o5mnpbJ23DFoBh+t/jsZeUlc1+tpUnOOuI7LhrgFzNv2GvO2vcaE7lN4bPRCjLGRnB1X7rHNUDJXY2KPJwjyDeeOQe8Tl7ad15beQG5RJt/teoeHRn6DZVlsTVjC1qM/1PZhqJLusaPp2/xKHM4iihwFvP3ziXupj4ya/7tJxrsr7uK6XlMZ0+FO7DZv1h762vUggJNd0fVBejcdj4+XP9PGr+SnfTOZu/VVoGRI19rD31Rru9ylKtdVF0a14IGeo7Asi1XHDvLwiq/cHbacniPAyROJYkqXnapOvDHGC6gHpFRx3dNmKhrXbYwJA6YBFwJ3AZ2Ae4Dngf9YVRxIWZvDuuq6S1aeWz01NaVewFh3h1AnZOSeG1+OtWFe34ofciAnXOKn4XYi7jAv//Qn5Z+v4m+aZtwdQ1VM/qRprV0fv3ntoQqPSWmysRu4mJLEYg3wB8uytp1U569AJ8uy/lQ6If4Ky7ImGGM6AB9TMs+kMSUPzGpdYxPiLctKAyYbY+4CvgOOAn0ty4o/mx2KiIiIiIj7lc4huQNYSMmc8vcsy9pmjHkSWGtZ1tfAu8BHpRPeUyl5Qhel9T6jZPJ8MfDXs01MoJLkxBgTSsmjwvoAo4AxwLfGmLssy/LM/lwREREREakyy7LmA/N/s+yxk37PB66uYN2ngaerM57K5pysB96gJAsqBhYZY7oCbxhjDlmWpT5GERERERGpNpUlJxf9dgiXZVkbgQuNMbfVaFQiIiIiInLesVVUUNncEsuy3q6ZcERERERE5HxVYXIiIiIiIiJSm5SciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEr5rewSV+n9T0Ls4dg90dQN2QwQJ3hyDnmEvcHUAdMS//WneHUGfou0+q09oX6rs7hLrjJncHIGdLPSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRlJyIiIiIiIhHUHIiIiIiIiIeQcmJiIiIiIh4BCUnIiIiIiLiEZSciIiIiIiIR1ByIiIiIiIiHkHJiYiIiIiIeAQlJyIiIiIi4hGUnIiIiIiIiEdQciIiIiIiIh5ByYmIiIiIiHgEJSciIiIiIuIRvNwdwOlqE9GXvwx8m+ScOAA2xC1g3rbXytW77+JZ+HkHAhDs24CDqRv5z/Lbq7yf7rFjGNvpbqJCWjFt0TgOpW4BoHfTyxjR/sR2okPb8/SCS4hP3342zaoRxth4aORc0nMTeX3ZzWXKwgIac1Pfl/H3CcFmbHy58Tm2Jiyp8rZbN+zNhO5TiA5txzu/3Mn6uPkAtInox4Tuj7rqRYW05O2f72TTkUXV06hqdkOfF+jUeChZ+Sk8+e2IcuUj2k2md7PxANiMF41CWnHvl93ILcyo0vYDfUKZPGA6TcM7s+LAbGaue8xVZrd5c22PJ2kT0RcLJ19tepEN8d9WT8NqwNA2NzGg5bUYY/hp3yd8v+u9MuUB3iHc0OcFGgY3pchRwIer7udoxu4qb7+iY+XrFcj9w2a56oUFNGLVwS/5bP2T1dOwGlDZZ6+iz05VRQa3ZFLfF4kN68CczS+yeOdbpctbcFv/f7vqNQhqwjdbXi73/3SueLH/VQyLbUdyfjbDvnrV3eHUmLCARtzU9xWC/RoAFsv3fswPu98vV69NRF8mdH8Mu82b7IJUXvr+mirv41z67Pl7h/DH3s8RHdoGy4IPV93P/pT15eo1De/MP4Z/edqfwXP58+czoBkhD14MdkPe7M3kvLO6bAVvO/WmjcG7QyTO9Dwy7vkGx9FMTD0/Ql8dj3enKPK+3ErW09+7VvEb1ZbAyf3AbihYuo/sl5fVcqukLqowOTHGNAGSLMvKN8YYYBLQHdgOvG1ZVnHthFjenuNryn3h/9aL31/t+n3ygOlsij+9i+OjGbuZvnwy1/V6pszy1Ye+YvWhrwBoXK8tfxn4tkcmJgAXt7mZxIy9+HkHlSu7pMOdrD08l2V7/0ujkNbcMeh9Hv5mQJW3nZp7lBmr7mV4u7IJ3+6kFUxdMAaAAJ96TL10GdsTPfdktGL/LJbs/oCb+r58yvJFO99k0c43Aejc+GIubndrlRMTgCJHAXM2v0h0aFsa12tbpmzMBXeQlZ/CY/OGYDAE+ISecTtqWuN6bRjQ8lqeXTQOh7OIvw3+kM1Hvud49iFXndEd7iA+fTvTf5pMZHBL/tDzKV5Z8ocq76OiY1VQnON6TwE8NHIuG+IWVE/Dakhln72KPjtVlVuYzsx1U+gaM7LM8mNZ+13HyRgbz41fxYa4hWe0j7pg1t51zNj5C68OnODuUGqUw+lg1oapxKVtxdcrkIdHzmVH4k8kZO5x1fH3DuHanlN5bekNpOUeJdi3/mnt41z67F3TYwrbEn7krZ//jN3mjY/dv1wdY2xc0fVBticuP+3tn7OfP5sh5JHhpN36GY5jWdT/9I/kL9mHY1+Kq4r/lZ2wMvNJHvUOfqPbEXTvIDLu/QYKHWT/6ye8WjfAq1UDV31Tz4/g+weTfNWHWGl51HtmND59m1C48rA7Wih1SGXDuuafVD4NuARYBfQC3qrhuKqNn1cQbSMvZGNpcuJj9+eGPi/wwIg5PDxqPl2ih59yvcTMvRzL2l/ptns3Hceaw99Ue8zVIdQ/ik6Nh/LT/pmnLLew8C+9cPL3DiYjLwkoOale2fUhHhzxNY+OXsDAlqe+uEzJiedI+k4sy1lhDD1ix7A1YSlFjvyzbE3N2XN8NbmF6VWq26vpeNYcmuN63afZ5TwwYg6PjJrPdb2ewZjyH6dCRx77ktdS5CgoV3Zhiwl8u/11oOT/I6cw7cwaUQuiQlpxIGUjRY58nJaD3Umr6BY7qkydRiGt2XnsFwCOZe2jfmBM6d3esz9Wv4oIbk6wb332HF9dYR13+73PXmWfnRHtJrs+e2M73n3K9bMKUjiUuhmHs6jCGNpF9ud49mFSc4+cWSPqgFXHDpBekOfuMGpcZn4ScWlbgZJkISFzL6EBkWXq9G46no1xC0jLPQqUvEd+dT599vy8g2ndsA8/l372HM4i8ooyy9Ub2mYSG+K+JSs/uczy8/nz592pEY7DaTjiM6DISf63O/Eb2qpMHb+hrcj7ahsA+Yt24du3CQBWXhFF649AQdl71vbYUIoPpWGllXxOC1Ycwm94m1pojdR1lSUnNsuyckt/HwZMsCzrv5Zl3Qz0qPnQKtaiQXceGfUtdw76gEYhrSut2zVmBDsTfya/OBuAMR3uYNexX5i2aDwvfz+RK7s+dMo7K1XRs8nYMhernmRC9yl8vvGZCpOHb7a8Sp9mlzNt/EruGDzD1Y0/oMU15BVl8eyicTy7cBwDW15L/cDYM4qhZ9NxHnt8Tpe33Y8OjQaxPq5k2FVUSCt6NrmU5xdfydQFY3BaTvo0vazK2/P3DgFgfOf7eHjkPG7v/4brQt4THc3YTeuGvQj0CcXb7kenxkMID2hcpk58+na6xZQkLM3CuxAeGE2Yf9RZH6uT9WoylrWH555tc2rU7332KtI+aiARwc14dtE4pn47mibhnWjdsPcZxdCr6TjWHPr6jNYVz1U/MIYmYR04kLyxzPLIkOYE+NTjnqEzeWjkXPo2uwI4+/PUyerCZ69BYCxZBSnc2OdFHh41nz/2fq7c93uofyRdY0by456Pyiw/3z9/tsggHIlZrteOxCxsEUGnqFOa7DksnFmFmNCKr58ch9PwahaOvXEI2A1+F7fGFhVcI/HLuaWyOSdxxpihlmX9ABwEYoFDxpjf7S82xtwO3A4w8JZw2l9cfmjDmTqcupWHvr6QguJcOjYawp8vepvH5g6usH6vpuP5ad+JO5gXRF1E5+jhDG93GwDedl/CA6NJzNx7WnE0q9+VQkfeaY2pry2dGg8lqyCFw2lbaRPR95R1ejcdxy8HZvPdzrdpUb87N/V7lSfnD6d91EXEhLaje+xooOQiOiK4GSmlc3yqKsQvguh6bdmW4LlDuk5Hl+hh7Ete6xrS1S6yP03COvHQyJIvIG+7X7m7cJWxGTvhgY3Zl7yOWRueYljbW7mq68O8v/LUd+vcLTFzLwt3TOeuIf+lsDiXuLRtOC1HmToLtv+Ha3pM4ZFR8zmSvqu0jvOsj9XJejYdx/sr/u9sm1NjqvLZq8gFURfRPmogj4wqGf/u6xVIRHDz075Tbbd50yV6GF9ueu601hPP5usVwOQB0/ls/ZOum22/shkvmoR35JUf/oC33Y9/jPiS/SkbzqvPHoDdZqdJWEdmrpvCwZSNTOg+hVEX/IWvt7zkqjOh+xS+2DgNC6vMuvr8VT8rs4DMJxdT7+Wx4ISijUewx4a6OyypAypLTm4FPjTGPA5kABuNMRuBUOCeyjZqWdZblA79mvxJU6uyulUxuPUNDGg5EYB//TjJNQRpa8ISrjVPEegTdsohMYE+YTSr36XsRHhjePOnP5UbsnVjnxeIDetAel4S//5x0u/G1KvJWI+9M9KyYU+6RA+jY6PBeNt98fcO5uZ+r/LeSV8s/Vtew2tLbwBgf8p6vO2+BPmGY4xh5rop5eaJjO98P50aDwEoMwa5Ij2bXMLG+IU43Tc1qVr1bDKW1Sf9fxsMKw7O5qtNz5ep1zVmJJd2vAuAj1b/w/Ughd/KKUyjoDiXDaU9Mevi5tG/ZdUnsLrDz/s/5ef9nwJwWef7SctNLFOeX5zNB6vud71+euxPJGcfpnXD3md1rH4VE9oeu7FzuHSIiyeqymevIsYYFmx/g+X7Pi6zvKLzX0U6NhrM4dStZ3wRKp7HZryYPGA6qw9+xYb48nM+0nITyClIo9CRR6Ejjz1Jq4kJbX/W56lf1YXPHkBabiJpuQkcTNkIwPq4+Yxq/5cydZqGd+bWC/8FQJBvOB0bD8HhLD7vP3/OY9nYT+rVsEcF40zKPkWdEJzHssFusAX7YKVXPrSyYOk+CpbuA8D/6s5YjrO+JJTzQGXJibEsa4gxpj3QBpgBxANrgP61EJvL0j0fsnTPhwCE+DV0LW8W3gWbsVU4Vr9HkzFsOfo9xc4T42i3J/zIkDaTXMOYYsM6EJe2rcxF1e8xGHo0uZQXv7vqTJpT477a9Lzry6hNRF+Gt7u93MVRas5R2kX2Z8WB2USFtMLb5ktWQQrbE35kUOvr2XnsF5xWMRHBzUnPTWTO5heYs/mFKsfQq+k4vvzNF2Jd5ecdTJuIvmWO4c5jP/Pni97m+53vklWQQoBPPfy8gtgYv5CN8VWbBLn5yHe0iezHrmO/0C6yPwkZe35/JTcK9q1PVkEKYQGN6RY7immLLi9T7u8dQqEjD4eziAEtJ7Ln+Gryi7Or5VhB3RgqUZXPXkW2JfzI+E73svrQVxQU5xLqH4nDWVzm/FcVdeE4yem5oc/zJGbu5btd75yyfNORxVzb40lsxo7d5k3z+l35ftc7JGTsOW8+ewCZ+cdJy00gMrgFx7L2l5xXM8ueV09+8MuNfV5ky9Ef2HRkEYWOvPP681e0NQF70zDs0fVwJGXhN7odGX8vO4yvYMk+/C/rQNGmo/iNaEvBqt+f2G4LD8CZmosJ8SXg2m6k3133jo3UvsqSk6XGmOnAS5Zl7QAwxkQCHwLtgJ61EF853WPHMKj19TicxRQ58nn7lztdZXcMmsFHq//uurPRs8lYFu74T5n15217jQndp/DY6IUYYyM5O+6UT/7qGjOSiT2eIMg3nDsGvU9c2nZXT0PriD6k5R51Pc64rhjb6R4OpW5m85HvmL1hKtf3nsbFbW8BLGasuheAn/bNpH5gDI+MmgcYsgtSeWP5bVB2BA9Nwzvz54FvEeBTj87Rwxjb6W6emF/ycIH6gTGEBTRmT9LK2m3gGbjlwtdoG9GPIN8wpo1fyTdbXsFuK/lYLNv7PwC6xYxke+IyCh0n7hAlZO7h680vcteQjzDGhsNZzCdrHz3lBMinx/6Ev3cwdps3XWNG8M8lfyQhcw9fbJzGzf1eYUL3x8jOT2XGqvtqp9FnaPKA6QT6huFwFvHJ2sfIK8rkolbXASXHqlFIKyb1fQkLi4SMPXxYmvBXx7EC6NHkUv61dFKttbc6nfzZq+izsyNxOY1CWvGP4V8CUFCcy7sr7iozuRlKbtA8NPIb/LyDsCwnF7e9mcfnDSO/OBsfuz/towby3zUPuaOZterfgybSL6oF4X6BrJnwIC9tWMzMPWvdHVa1a9mgJ/2aX0l8+g7XkKOvNr1AeGDJnK9le/9HYuZetiX8yKOjF2JZTn7eP9M15Ph8++zNXDeFW/r9E7vdm+Tsw3yw8r4y56mKnPefP4dF5tPfEfb2VWCzkfflFor3phB0R3+KtiVSsGQfuZ9vJvS5S2iw4Fac6flk3HfigUANF9+OCfIBbzt+F7cm9bZZOPalEPzgULzbldxUzn5jBY5DnvvgF/EcxrJO3cVmjAmj5CldFwJ3AZ0oGc71PPAfq4qzPatjWJeIiLjfvPxr3R1CnXGJ3yfuDkHOIU88dYe7Q6gzorbfb9wdQ1XU5vXxm9ceqhPH5FcV9pxYlpUGTDbG3AV8BxwF+lqWFV9bwYmIiIiIyPmjwkcJG2NCjTFvAjcBo4DZwLfGmKG1FZyIiIiIiLiHMSbcGLPYGLOn9N+wU9TpaoxZYYzZZozZbIy55qSyGcaYA8aYjaU/XX9vn5X9nZP1wB6gp2VZiyzL+j/gj8BUY4z6q0VEREREzm0PAN9bltUa+L709W/lAjdYltWBkg6NV40xoSeV329ZVtfSn42/t8PKJsRf9NshXKUbvNAYc9vvbVhERERERMp73LvOzJIYDwwu/f0DYCnwj5MrWJa1+6TfjxpjkoCGQPqZ7LDCnpPK5pZYlvX2mexMRERERERqjzHmdmPM2pN+bv/9tVwiLctKKP09EYj8nX31BnyAfSctfrp0uNcrxhjf39thZT0nIiIiIiJSh538x9FPxRjzHRB1iqKHf7MdyxhT4VPGjDGNgI+AG096qu+DlCQ1PqUx/AN4srJ4lZyIiIiIiJynLMsaVlGZMeaYMaaRZVkJpclHUgX1QoB5wMOWZbn+0N1JvS4Fxpj3gd/9o26VTYgXEREREZHz19fAjaW/3wjM+W0FY4wP8CXwoWVZs39T1qj0XwNcBmz9vR0qORERERERkVOZBgw3xuwBhpW+xhjT0xjzTmmdCcBFwKRTPDL4f8aYLcAWoAEw9fd2qGFdIiIiIiJSjmVZKcDFp1i+Fri19Pf/Av+tYP3T/vuI6jkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEI3i5OwAREakbLvH7xN0h1Bnz8q91dwh1wtoX6rs7BBHxMOo5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj6DkREREREREPIKSExERERER8QhKTkRERERExCMoOREREREREY+g5ERERERERDyCkhMREREREfEISk5ERERERMQjKDkRERERERGPoOREREREREQ8gpITERERERHxCEpORERERETEIyg5ERERERERj+BVUYExZhywyLKs/FqMp0qMsfHQyLmk5yby+rKbT1mnW8xo/jRwOs8svJRDqVuqvO3WDXszofsUokPb8c4vd7I+bj4AbSL6MaH7o656USEtefvnO9l0ZNHZNaYGhAU04qa+rxDs1wCwWL73Y37Y/X6ZOl2ihzOu871YlhOn08Gn659gX/LaKu8jMrglk/q+SGxYB+ZsfpHFO99ylfl7h/DH3s8RHdoGy4IPV93P/pT11dW8GtM0vDP/GP5lmf/3U/nLwHdoENSEJ78dcVrbH9/5fvo2u4IAn3rcNfsC1/Kruz1K28h+APjY/Qn2q8/dn3c+s0bUoMr+z092c79/0jS8Ew5nMQdTN/Hf1Q/itIqrvJ/usWMY2+luokJaMW3RuDKf3+jQdlzf61n8vIOwLCfPLBxHsbPgrNtWnXScqqYq5ymANhF9mdD9Mew2b7ILUnnp+2uqvI9An1AmD5hO0/DOrDgwm5nrHgPA1yuQ+4fNKhPLqoNf8tn6J8+6XZ7oxf5XMSy2Hcn52Qz76lV3h+NWPgOaEfLgxWA35M3eTM47q8uUB9zYk4CrOmEVWzjTcsl4ZAHOo5kAhL15Fd5dGlG4/gjpf/nCHeG7xdkcM5EzUWFyAnwK5BhjvgU+ARZaluWonbAqd3Gbm0nM2Iufd9Apy329Arm47U3sTz79C+LU3KPMWHUvw9vdXmb57qQVTF0wBoAAn3pMvXQZ2xOXnX7wtcDhdDBrw1Ti0rbi6xXIwyPnsiPxJxIy97jq7Dz2M5u+XQyUXMjc3v91psy7uMr7yC1MZ+a6KXSNGVmu7JoeU9iW8CNv/fxn7DZvfOz+Z9+oGmaMjSu6Psj2xOWV1usWM4qC4twz2sfmI9+xZPcHPHXp0jLLZ214yvX7kNaTiA3vcEbbr2mV/Z+fbPXBr3hvxV0A3HLhawxoOZFle/9b5f0czdjN9OWTua7XM2WW24ydm/u9yvsr7iY+fQeBPqE4rKLTb0gN03Gqmqqcp/y9Q7i251ReW3oDablHCfatf1r7KHIUMGfzi0SHtqVxvbau5QXFOa7zOcBDI+eyIW7B2TfKQ83au44ZO3/h1YET3B2Ke9kMIY8MJ+3Wz3Acy6L+p38kf8k+HPtSXFWKdxwj+eqNkF+M/zVdCb53EBn3fgNAzvurMX7e+E/o4qYGuMFZHjORM1HZsK6dQGtgGXAvcNQYM90YM6hWIqtAqH8UnRoP5af9MyusM77zvSzYMZ0ix4k7hcbYuLLrQzw44mseHb2AgS3/cMp1U3LiOZK+E8tyVrj9HrFj2JqwlCKHx3UqAZCZn0Rc2lag5Es4IXMvoQGRZeqcfIHtaw/Ask6UjWg32XWcxna8+5T7yCpI4VDqZhzOshc9ft7BtG7Yh59L/38cziLyijz/DsrQNpPYEPctWfnJFdbx9QpgWLtbmb/tX2WWNwhqwt8Gf8BDI+dy38WziAxuecr1D6RsIDM/qdI4ejUdx5pDc06/AbWgov/z39qasMT1+8GUTYQFNAJKeoVu6PMCD4yYw8Oj5tMlevgp10/M3MuxrP3lll8QdRFH0ncSn74DgJzC9Eo/p+6i41Q1VTlP9W46no1xC0jLPQqUHNtf9Wl2OQ+MmMMjo+ZzXa9nMKb811mhI499yWvLfBf8VkRwc4J967Pn+OoK69R1q44dIL0gz91huJ13p0Y4DqfhiM+AIif53+7Eb2irMnUKV8dBfkkPZtHmo9gjg0+UrTyMlVNYqzG729keM5EzUVlyYlmWlWZZ1tuWZV0MdAG2A9OMMXG1E155E7pP4fONz1T4ZRsb1pGwgMZsPfpDmeUDWlxDXlEWzy4ax7MLxzGw5bXUD4w9oxh6evAF5G/VD4yhSVgHDiRvLFfWNWYkT1zyPXcMep8PV90PQPuogUQEN+PZReOY+u1omoR3onXD3lXeX4PAWLIKUrixz4s8PGo+f+z9nMf3nIT6R9I1ZiQ/7vmo0nr/396dx0dR338cf313s7lIQhKOBIEEEBAQEBUQBOUQOayAV/FqBVuF1tqfrYpHPavY4m1ta71oPVqlivcFooI3IPdtATkChEDIfSe7398fu6wJuRZIskvyfj4ePNiZ+c7MZ76Zncxnvt/vZFK/m1i4+TnK3FV/yf980GzmLr+HPy04n3mrH+CKQbOOKo7E6I60jenM5oxvjmr9UOMwYQzpchEb0hcDcN7J1/N9xjfM/ngyj316GRcP+MMRnRtJcV2x1vJ/I1/ijnEfMLb3jEaKvGmpnmq/TiXFdSU6vDU3jp7LH8a9z5AuFwGQHNedgSnn89DCi5k1/zw81sMZqRcc1b4HpUxk+a73j/EI5HjgSIrBvS/fP+3el4+jfc09MACiLupH6ZfVHwC0JKozCYa6unWZyhPW2n3Ak8CTxpjUujZqjJkOTAc465eJ9D6n9hP5SPQ7YTT5pQfZlb2enu2H1BCw4aen3smLS2+utqx38tl0iu/FaZ0nAN7uAu1ju3Cw8MjyrLjI9nRsfRIb0kOzS1dlEWHRzBj+NK+tvI+SioJqy1fvXsDq3Qvo0W4wk/rfxBOLrqRP8tn0Tj6LO8d/6NtGK9rHdg34qaLT4SQloS9zV9zDjoOrmXLaPYzvcx3vrnu0QY+tIU057R7eXD0bi621TKf4PrSLSeX1VffTplUn//yIsGi6tT2d6cOf8s8Lc0QcVRyDUieyMu3DkHzKfTSuGDiLLQeWsvXAd4D3iX7/judybq9rAXA5I0hs1ZF9eVsD2p7DhNG93SD+tGAiZe5ibhz9Kruy1rM54+tGO4am0NLrqa7rlMOEkZLYl8c/uwKXM5Jbx77FDwdX0StpGCkJ/fjDuHcBcDkj62z1rMvA1En869vfHethSDMTObEPrr7JZF1Vey8NqUp1Jg2lruTkiTqWpQA7a1torX0WeBZgxquptd/xHaET2w3klI5j6NthJC5nBFGuWH4x9An+6fvFEuGKoWP8Sdw42vvFaB3VjuvOmsNTX/4SYwxzV9xTbZzI5P4z6XfCKIAqfZBrMzDlJ6zeveCIBq4Gg8OEMWP40yzb8Tardtfdl3rLgWW0jUmhVXgCxhjmb3yKL7e9UqXMyB5XMfzEywD46+fTyC2uuXtSdtE+sovS2XFwNQAr0z5kfO/rjv2AGljl44lyxXHNmd6uWjERifQ9YRRuT0WVlx10a3saqYn9eWDiVzgdYcRGtOHG0XN56otrKC7Pq3buGOPgjnHep7Fr9nzCe+seqzemgamTeHX5XfWWa0qB/twPd37fG4iNTOTpL2//caYxPPPVr6p1RZp6xsN0TjiZnOL9/O3zabVuM7sonS0HllJYlg3Aur2LSEnoGxI33aqno1PfdSq7KJ3C0mzK3MWUuYvZsn8ZneJ7YzB8u2Meb695qEr5AZ3GcX5f7ziel5fdWu/LUDrF98ZpnOzydS+T5s2TUYAz+ccuR87kWDz7qz+4Cx+aSsz0IWRNnQvlITHUNmhUZxIMdSUn9xhjkoBHDw2EPzQN9AIGNkF8Vby95iH/L6Oe7Ydwbq/p/sQEoKQ8n5vePNU/fePoubyx+gF2Zq1jY/rnjOjxMzZnfIPHVtA+tis5Rft4Z+3DvLP24YBjGJQ6ibcO+4UYiq464yH25W3lk++fr3F5u5hUDhR488vOCX0Jc4RTWJbNhvTPmdzvJpbtfJvSiiLio5JweypYvOUlFm95qd795pUcILsonaTYbmTk/0CvpGFVBriGitqOZ+oZj7Bu72fV3sL2xdZ/+wcrt2nVid+c/U8e+8x7M5pZkMZpnc/zv+GrU3xvdudsCijZPSQp9kSiXXH8kLniaA+pUQT6c69sWLfL6JM8gscXXV6lNWpj+ueM6jnN/8akzgknk5a9gRd9XQrrszH9c8b1/hUuZyRuTzk925/BJ9/POaLYGovq6ejUd51as2chl59+Hw7jxOlw0bXNAD79/nnSc7fw67Of49PNc8gvPUh0eGsiw2L8rcGB8o7xerehDkdCXPn6dJypCTg7tsa9P5/ICb3IvaVql76w3u2Ju2cs2TNex5N1dC8/aU5UZxIMxtqaGzaMMfHAg8CZwA1AP+BG4CHgHzbAvicN2XJS2aHk5O9f/IKJ/W5kZ9Za1u75pEqZysmJwTC5/8307zgGMBSUZvHUl9dSUp5fZZ3UxP78+qxniQ5vTbm7lLySA/zxQ++A1DatOjFzzBvc/s6QOrsABduJbQdyy7lvsDtnk7+L0NtrHiax1QkAfLH1P4zr/SuGdL0Yt6eccncp81Y94H+V8OieV/ufApdWFDHn2xvILNhVZR9xke34w7j3/K8qLa0o4t4PxlBSUUCn+D5cNfhBnE4XmQW7eHHJzRQdB4Pi4cfk5FCicef4D6slGYeSk0OvEm7TqjNXDppFXGR7nA4Xy3e+ywcbnqy27YsG3M7g1Mm0jkoitziDr7bN5f31TwBwft/f4XJG8NaaBxv3AI9BXT/z60e8wMvLbiG3eD9PXbqNrMI9/i46q9Lm88GGJ3E5I5hy2j2c2PZ0jHGQWZBW46vAB3Qax2Wn/5GYiESKy/NIy97Ik4uvAryDoMf3uQ5rLevTF/Hm6j83aR0EQvUUmECuU+B9QcfQbj/FWg9f/zCXT7//JwADU85nfJ/rMMaB21PBq8vvYvvBVdX288DEr4hyxeJ0uCguz+Mvi37uf2Aya+KX/HXxNDLytzX48X1QcnmDb/No/W3EZQxN7kZiZCsyiwt4dNVC5m4J/NXxjWn5w0f2BrZjFX52V+JuGw0OB8VvraPwmSXEXD+M8g37KF20jYQ5Uwjr0RZPZiEA7r155Fz/FgCJL19OWNdETLQLT04JuXfNp+zrHU0afzAcS50FQ/LGmab+UsGXPs/ZZDeSHS5xHxd1ckityYm/gDE3AI8De4Eh1trdR7KDxkpOREREQlUoJSehrKmTE2n+lJxUd7wlJ7W+rcsYE2+MeQa4GhgPzAM+MsaMbqrgRERERESk5ahrzMlK4CngN9baCuBjY8wA4CljzE5rrR4LiYiIiIhIg6krOTn78C5c1trVwJnGmGsbNSoREREREWlxau3WVdfYEmvtc40TjoiIiIiItFR1/YV4ERERERGRJqPkREREREREQoKSExERERERCQlKTkREREREJCQoORERERERkZCg5ERERERERKoxxiQaYxYaY7b4/k+opZzbGLPa9+/dSvO7GmOWGmO2GmP+a4wJr2+fdf2dExERERERaWCLv+vYZPu6/JJjWv024FNr7WxjzG2+6VtrKFdsrR1Qw/wHgcettXONMU8DvwT+UdcO1XIiIiIiIiI1mQy86Pv8InBBoCsaYwwwGph3JOsrORERERERaaaMMdONMcsr/Zt+BKsnWWvTfZ/3AUm1lIv0bXuJMeYC37w2QI61tsI3vRuot8lI3bpERERERJopa+2zwLO1LTfGfAIk17DojsO2Y40xtpbNpFpr9xhjugGfGWPWAblHE6+SExERERGRFspaO6a2ZcaYDGNMB2ttujGmA7C/lm3s8f3/gzFmMXAq8AYQb4wJ87WedAL21BePunWJiIiIiEhN3gWm+j5PBd45vIAxJsEYE+H73BYYBmy01lpgEXBJXesfTsmJiIiIiIjUZDZwrjFmCzDGN40xZqAx5nlfmd7AcmPMGrzJyGxr7UbfsluBG40xW/GOQZlT3w7VrUtERERERKqx1h4Ezqlh/nLgGt/nb4B+taz/AzD4SPaplhMREREREQkJSk5ERERERCQkKDkREREREZGQoORERERERERCgpITEREREREJCUpOREREREQkJCg5ERERERGRkKC/cyIiItLAlj/cJtghHBcGzjwY7BCOCzqfpCVRy4mIiIiIiIQEJSciIiIiIhISlJyIiIiIiEhIUHIiIiIiIiIhQcmJiIiIiIiEBCUnIiIiIiISEpSciIiIiIhISFByIiIiIiIiIUHJiYiIiIiIhAQlJyIiIiIiEhKUnIiIiIiISEhQciIiIiIiIiFByYmIiIiIiIQEJSciIiIiIhISlJyIiIiIiEhIUHIiIiIiIiIhQcmJiIiIiIiEBCUnIiIiIiISEpSciIiIiIhISFByIiIiIiIiIUHJiYiIiIiIhAQlJyIiIiIiEhKUnIiIiIiISEhQciIiIiIiIiFByYmIiIiIiISEsGAHcKSiXHH8fPCDdIzvibXw0tKZ/HBwpX/54NQLGNfnVxgMJeWFvLL8DnbnbAp4+63C45kx/GlSE/vz7fZ5zF1xNwARYa2YOeZ1f7mE6A4s3fEWr628r+EOrgGdc9IvGX7iZVhr2ZO7mReXzKTCU+pf3qPdYKacdg8d43vx/De/ZWXah0e0/aTYE5k25BE6J5zMO2sfYeHmZ33zu3HtsL/5y7WNSeG9dY/x6ff/bJgDawJje81gcJfJADhMGB3iunPTW6dSVJYb0Pq1nUMAg1InMaHPb7BYcoszmPPN7ygsy26U42hotf3MD/fzwQ+RmtgPYwwZedt5celNlFYUBbyfkT2mcs5Jv6B9bBdufGNAlfrp2X4IU067G6fDRUFpFo9+eukxH1djOKXjuUzqfxPWevB43Px35R/Zlrm81vLXnfU8bWNSuO+jsUe0n8n9ZzKky0VEh7fmhnl9/PN/eupdnJQ0FIBwZxSxkW34/Rv9j+5gGlF91/NDUhP7c+u5bx3xtao5X6cqCx/ehbjbzwGnoXjeWgqfX1ZlefTUgURf0g9bYfFkF5F753w8e/MASHjmElyndKBs5R5yrnszGOGHjEeGXcKYzr3ILClgzNtPBDucJlffeYTLSevZ5+E6OQlPTjG5N76He28e4UNTib3xbHA5odxN/iOfU7Z0l28dB3F3jCF8cGfwWPL/8hWlC//X9Acnx5U6kxNjzNlAhrX2e2PMMGAosMla+0GTRFeDS0+/hw3pn/Ps17/G6XAR7oyqsjyzMI1HP5lCUXkeJ3cYyc8G/ZnZCy8IePvl7lLeWfsIHeNP4oTWJ/nnl1YUMmv+ef7pP4x7n1Vp84/5eBpDfFQSo3tezb0fnkO5u5Rrh/2dQakT+Xb7PH+ZrKK9vLD0Js7tNf2o9lFUlsPcFfcwoNO4KvMz8n/w15MxDh6cvJRVaQuO/mCC4OPNz/Dx5mcA6H/COZzT65qAExOo/RxyGCdTTruHez8YQ2FZNhcNuJ1RPafy/vonGvoQGkVtP/PDvb7yPkoqCgDvTfLIHlNZsOkfAe9nW+Zy1u39lBtHz60yP8oVx+UDZ/Hk4qvILtpLbESbIz+IJrI542vWfLQQgI7xvZg+7O/c88E5NZY9tdP4I0reKlu75xMW/e9F7j9/cZX5r6+63/95VI9pdE48+ai239jqu56D9zpy0YDb2bjvyyPefnO+Tvk5DHF3nkv2Na/hzsinzX9/Tsmibbi3HfQXqdiUQeZPV0NJBVGXDiD2phHk3vQeAIX/WoaJdBE15ZQgHUDoeH3rCl7Y/A1PnDUl2KE0vQDOo6iL+2HzSsgc/zyRE3oR4zuPPDnFZF/3Jp4DhYR1b0vCc5dwYNTTAMTMGIonq4jM8+aAAdO6+ndc5HC1JifGmCeAwUCYMWYBcA7wEfB7Y8xIa+3MpgnxR5GuWHq0O4MXltwEgNtTTrGnvEqZHzJX+D9vz1xJfHQH//QZXS5kVM9phDlcbD+4mleW34m1nirrl7mL2Za5nPaxXWqNo31sV2Ij2rDlwLJaywSbwzhxOSNxeyoId0aRU5xRZfnBwt0A1Y4fvC0Hp6f8hDBnOKvTFvDe+serlckvPUh+6UH6nTC61hh6JQ3jQMEusor2HOPRBM+g1Ml8t/Md//SxnUMGgyEiLJrCsmwiw2I4kL+j0Y+hoQTyMwf8iQmAyxkBWABiIhK5ctCfSIw+AYDXVt5XY2tCWvaGGrc7OHUyq9Pmk1201x9PqKqcbEQ4o7G25nIRYdGM6XUN/152O9cO+7t/ftuYFK4YeD8xEW0oqyjm5WW3kZG/rdr62w+uqjeWQamTeG/9Y0d+EI0skOs5wOie01iV9hGpiVVbfnSd8nL164B7Vzbu3d4HKCUfbSZydHcKK91Uli1L838uX7uXqIk/trKVLdlF+KDOTRdwCFuasZ1OMQnBDiMoAjmPIkd3p+Dv33iXf/w9cXd6H7hUbNrvL1OxNRMTGeZvRYm6sC+Z5/taJC3YnOImOiI5ntXVcnIu0BeIAvYAHa21RcaY2cAqoMmTk7atOpNfepCpZzxCp4Q+7Mpax39X3EuZu+aTfdiJl7EhfTEAyXHdGZhyPg8tvBiPreDygbM4I/UCluw48mbsQSkTWb7r/WM5lEaVU5zBws3P8udJ31LuLmHjvi/ZFOBTx97JZ9E+tgt//ngSBsN1Z8+hR7vBR5WIDUqdxHc73z3i9UKFyxnJyR1G8OqKu4BjP4c8toJXlt/J3ectoKyimP352/3bbm6mnvEwfU8YRXruVl5fNQuAS0+7l082P8+2zOUkRJ/ADSNf5t4Pa25NqElSXFecxsWNo+cS6Yrhs+//eVTf36YyoNM4LjzlFmIj2vK3z6+uscykfjexcPNz1a5hPx80m/989wf2F+ygS5sBXDFoFo9/dvkRx5AY3ZG2MZ3ZnPHNUR1DYwrkeh4flcSATuN47NPLuOqMh/3zdZ36kSMpBve+fP+0e18+rv4dai0fdVE/Sr/8oSlCk+NIIOeRt0yer4DFk1+GiY+qknBEjO1J+cb9UO7GxEYAEPPb4YQP7ow7LYe8WZ/gOXh0LcXSctSVnFhrrTXGHHosfOjZn4d6BtIbY6YD0wHO+mUivc+JOeZAAZwOJykJfZm74h52HFzNlNPuYXyf63h33aPVyvZsP5Rh3S7l4U8uBrxPx1IS+vGHcd5fQi5nJPklmUcVx8DUSfzr298d9XE0tmhXHKd0Gssd7w2nqCyPGcOf4owuF7J0x1v1rtsn+Wx6J5/FneO9/bojwlrRPrbrEf/SdzpcnNJxDG+tefCojiEUnNJxDNsyl/u7dB3rOeQwYYzo/jNmzT+PzIJdXHb6fUzo8xs+3PDXRok/mF5cOhNjHFx2+n0MSpnIN9tfp1fyMDq07u4vE+mKISIsOuAuTQ4TRkpiXx7/7ApczkhuHfsWPxxcxf787Y11GMdk9e4FrN69gB7tBjOp/008sejKKss7xfehXUwqr6+6nzatOvnnR4RF063t6Uwf/pR/Xpgj4qhiGJQ6kZVpH9bYQhpsgVzPp5x2D2+uno2latOTrlNHJ3JiH1x9k8m6am79hUWOUFj3NsTeOILsa33jc50OnB3iKFu9h/yHFhE9dSCxM0eSe9uRjXGVlqeu5OQDY8xXQATwPPCaMWYJMBL4vK6NWmufBZ4FmPFqai0dGo5cdtE+sovS2XFwNQAr0z5kfO/rqpXrGN+LqwY/yJOfT6WwLAfwdqj5dsc83l7zUJWyAzqN4/y+NwDw8rJb2Zm1rs4YOsX3xmmc7Mpef+wH1Eh6JQ8nsyCNgtIsAFalzadb29MDSk6MMczf+BRfbnulyvyRPa5i+ImXAfDXz6eRW7y/ptX9+nYYya6s9UedADa1mo5vYMpEllV6onqs51DnBG9XiswC70DBFbveZ1yf6udvKDnSn3tl1npYvvNdxvb+Fd9sfx2HcTD74wurvJgB4P9GvkRcZFt2Zq3j5WW31rq97KJ0CkuzKXMXU+YuZsv+ZXSK7x0yyUltdbXlwDLaxqTQKjyhyuD+bm1PIzWxPw9M/AqnI4zYiDbcOHouT31xDcXleVXGuIF3bMQd47wttmv2fMJ76+rvqjUwdRKvLg/N1rlAruepif255kxv8h4TkUjfE0bh9lS02OtUTTwZBTiTY/3TzuRYPPsLqpULH5pKzPQhZE2dC+XupgxRjgOBnEfeMnF4MgrAaXDEhvtbTRxJMcQ/eQG5t3+IOy0H8Hbh8hSV+QfAlyz4nqiL+zXNAclxra7k5O/A23hbUJYYY04ELgSeA9KbILZq8koOkF2UTlJsNzLyf6BX0jDS87ZUKZMQfQK/Gv4M/1zy+yo3LZszvubXZz/Hp5vnkF96kOjw1kSGxfifbgbqeOgCkFW0l25tT8XljKTcXUKv5GHsPFh30nXIhvTPmdzvJpbtfJvSiiLio5JweypYvOUlFm95KeAYjod6quzw44t0xdKz/RD+WamF7FjPoZzifXRo3YOYiEQKSrPonXwW+/K2NsbhNJgj/bkDtItJ5UDBTgD6dzyXfXnesRIb079kdM9p/pcNdIrvw+6cjTy5+KqAtrtmz0IuP/0+HMaJ0+Gia5sBfPr980cUW2OqXFftYlL98zsn9CXMEV7trWxfbP03X2z9NwBtWnXiN2f/k8c+895YZxakcVrn8/xvpuoU35vdOZuqJSx1SYo9kWhXXJVxeKEkkOv5He8N93+eesYjrNv7GWv2fEyZu7hFXqdqUr4+HWdqAs6OrXHvzydyQi9yb6na7Tisd3vi7hlL9ozX8WSpS41UF8h5VLpoG1EXnEz5mr1Ejj2JUt8buUxsBAn/uJj8x76gfFXVsVuli7cRPjiFsqW7iBiSUmWAvUhtjK1lpKYx5gfgaeBRa63bNy8JeBToZa0dGMgOGrLlBLw3NFcNfhCn00VmwS5eXHIzA1MnAvDF1v/w88EPcmrnCWT5Bnx7PG7+9LF3+cCU8xnf5zqMceD2VPDq8rtqHFD6wMSviHLF4nS4KC7P4y+Lfu7/pTlr4pf8dfG0GgenhpKJfX/PwNTzcXvcpGVv4OVltzLh5OvZmbWWtXs+ITWxP78+61miw1tT7i4lr+QAf/zwXABG97za//SxtKKIOd/e4H/af0hcZDv+MO49Il0xWOuhtKKIez8YQ0lFAeHOKP48+VvueO8sSsrzq8V2PBja9RJO7jCC57/5bZX5x3oOnd39Skb3/AVuW05W4R5eWHKTv3Uv1NX1M79+xAu8vOwW8ooPcPOYeUS5YgDD7pxNvPLdHZRUFNAqPIErBt5PcuvuOEwYW/Yv5ZXld1Tbz6ie0xjX+1fERbYjv+Qg69MX+VtUxvaawdBuP8VaD1//MDdkX/06rvevGNL1Ytyecsrdpcxb9YB/8P+d4z+slmQcSk4OvUq4TavOXDloFnGR7XE6XCzf+S4fbHiy2n4uGnA7g1Mn0zoqidziDL7aNtf/9rfz+/4OlzMipLss1Xc9r+xQcnIoYQv169Qf77++wbdZm/CzuxJ322hwOCh+ax2Fzywh5vphlG/YR+mibSTMmUJYj7Z4MgsBcO/NI+d6b0t64suXE9Y1ERPtwpNTQu5d8yn7ekeTxT5wZujcrP5txGUMTe5GYmQrMosLeHTVQuZuqf0V4E1p+cON/3bC+s4jwp3EP/gTwnq3954rN7+He3curWYModW1Z+DelePfVvY13kTYcUIc8bPPw8RGel9jfcdHeNIb974geeNM06g7aCCv3prSoPfHdbn8wV3HRZ0cUldykgD8GRgG3AD0A24EHgL+YQPsxNzQyYmIiEioa8rk5HgWSslJKGuK5KS5UHJS3fGWnNTarctamw38yhhzA/AJsBcYYq3d3VTBiYiIiIhIy1HrW7eMMfHGmGeAq4HxwDzgI2NM3X/kQERERERE5CjUNSB+JfAU8BtrbQXwsTFmAPCUMWantfbIX7ovIiIiIiJSi7qSk7MP78JlrV0NnGmMubZRoxIRERERkRan1m5ddY0tsdY+1zjhiIiIiIhIS1XnX3oXERERERFpKkpOREREREQkJCg5ERERERGRkKDkREREREREQoKSExERERERCQlKTkREREREJCQoORERERERkZCg5EREREREREKCkhMREREREQkJSk5ERERERCQkKDkREREREZGQoORERERERERCgpITERERERGpxhiTaIxZaIzZ4vs/oYYyo4wxqyv9KzHGXOBb9oIxZnulZQPq22dYwx+GiIiIiIjUZnHOhCbb1+XHtvptwKfW2tnGmNt807dWLmCtXQQMAG8yA2wFPq5UZKa1dl6gO1TLiYiIiIiI1GQy8KLv84vABfWUvwT4yFpbdLQ7VHIiIiIiItJMGWOmG2OWV/o3/QhWT7LWpvs+7wOS6il/GfDqYfMeMMasNcY8boyJqG+H6tYlIiIiItJMWWufBZ6tbbkx5hMguYZFdxy2HWuMsXVspwPQD1hQafbteJOacF8MtwL31RWvkhMRERERkRbKWjumtmXGmAxjTAdrbbov+dhfx6amAG9Za8srbftQq0upMeZfwM31xaNuXSIiIiIiUpN3gam+z1OBd+ooezmHdenyJTQYYwze8Srr69uhkhMREREREanJbOBcY8wWYIxvGmPMQGPM84cKGWO6AJ2Bzw9b/z/GmHXAOqAtMKu+HTZ6t66bFuxu7F00GyuSOgY7hOPCqrKngh3CcSG36L1gh3Dc+GBItde2Sw2WP9wm2CFIM6NzKjADZx4MdgjHDd11Nixr7UHgnBrmLweuqTS9A6h2I2utHX2k+1TLiYiIiIiIhAQlJyIiIiIiEhKUnIiIiIiISEhQciIiIiIiIiFByYmIiIiIiIQEJSciIiIiIhISlJyIiIiIiEhIUHIiIiIiIiIhQcmJiIiIiIiEBCUnIiIiIiISEpSciIiIiIhISFByIiIiIiIiIUHJiYiIiIiIhAQlJyIiIiIiEhKUnIiIiIiISEhQciIiIiIiIiEhoOTEGHNDIPNERERERESOVqAtJ1NrmDetAeMQEREREZEWLqyuhcaYy4ErgK7GmHcrLYoFshozMBERERERaVnqTE6Ab4B0oC3waKX5+cDaxgpKRERERERanjqTE2vtTmAnMLRpwhERERERkZaqvpYTAIwx+YD1TYYDLqDQWhvXWIGJiIiIiEjLElByYq2NPfTZGGOAycCQxgpKRERERERaniP+OyfW621gXMOHIyIiIiIiLVWg3bouqjTpAAYCJY0SkYiIiIiItEgBJSfAxEqfK4AdeLt2iYiIiIiINIhAx5xc3diBNJTofuNof8Xj4HCS+8Ucsj94qFqZmEE/pc0FdwOW0l1r2ffMzwhrk8IJv30DHA6M00XOJ38nd9EzTX8ATei0SX/khJNG4S4vZslrN5G9d321Mg6ni9Mn30/7bkPAeliz4GF2r/+IU8+/m6QTvS9xc7qiiIxpwxv39mvqQ2gSPXu1Y/KFJ2OMYdnSXSz+dFuN5fr2T+aqqwfy5GNfsjstl84p8Vw85VCdGBYu+B8b1u1rusCD4NJLL6Vv376UlZXxwgsvkJaWVq3MoEGDmDBhAtZacnNzmTNnDoWFhQCMGjWKkSNH4vF4WLduHW+++WZTH0KTG9mxJ388YyJOY3j1f9/x93WfV1nesVU8jw6/hDaRrcgpLeb/vphLelFekKJtfOHDuxB3+zngNBTPW0vh88uqFnA5aT37PFwnJ+HJKSb3xvdw783DtI4k/onJuPolU/zWevIf+NS/SuT4k2g1Yyg4DaWLt1Hw2BdNfFRNp776i546kOhL+mErLJ7sInLvnI9nb/M9n+Doz6nwoanE3ng2uJxQ7ib/kc8pW7rLt46DuDvGED64M3gs+X/5itKF/2v6gwuSR4ZdwpjOvcgsKWDM208EOxxpZgLt1tUN+AveQfAW+Bb4vbX2h0aM7cgZB+1//lf2PDyO8qzdpN6zlMJV71G2d5O/iCupO4nn30raA2fhKcrBGdsOgIqcdNJmDcNWlGEiWtHlgbUUrHoXd056sI6mUXU4aRSxbbvw/sNn0yblVAZe+AAL/169MazP6N9SUpDJB4+MBGMIj4oHYNX79/nL9DhzGoknnNxEkTctY+DCi/vy3NNLyc0p5re/P4uN6zPYn1FQpVxEhJPhZ3dl545s/7x96Xk8+dhXeDyW2LgIfn/z2WzakIHHYw/fTbPQt29f2rdvz1133UXXrl258sormT17dpUyDoeDKVOmcO+991JYWMhFF13EqFGjeP/99+nZsyennHIK999/PxUVFcTGxtayp+bDYQyzhkzmigVzSC/K5YOJ1/Pxrk1syd3vL3PXoPOYt20l87au5MwOJ3Lb6eO54cvXghh1I3IY4u48l+xrXsOdkU+b//6ckkXbcG876C8SdXE/bF4JmeOfJ3JCL2JuGkHuTe9BmZuCv35FWI+2hHVv6y9vWkcSO3MkmZe8hM0upvWfJhA+JIWyJbuCcYSNK4D6q9iUQeZPV0NJBVGXDiD2UP01V8dwTnlyism+7k08BwoJ696WhOcu4cCopwGImTEUT1YRmefNAQOmdVSwjjAoXt+6ghc2f8MTZ00JdijSDAU6IP4V4DWgA3AC8DrwamMFdbQiuw2mPGMb5Qe2g7ucvKX/pdWpk6qUaT3iGnI+/QeeohwA3PkHvAvc5diKMgBMWASYI35XwHGl08lj2bHiDQAO7lpFeFQckbHtq5XrNnAKGxf93TthLWVF2dXKpA6YxM417zZqvMHSOSWezMxCsg4W4XZb1qzaw8l9k6qVGzvhJBZ/to2KCo9/Xnm5x5+IhIU5aJ4pyY9OOeUUlixZAsD27duJiooiLq7628aNMURERAAQGRlJbm4uACNGjGD+/PlUVFQAkJ+f30SRB8+Atp3ZkX+QXQVZlHvcvPPDGsam9KlSpkd8El+ne1vrvknfVm15c+Lq1wH3rmzcu3Oh3EPJR5uJHN29SpnI0d0pfnsDACUff0/EkBQAbHE55Sv3QGlFlfLOzvFU7MzGZhcDUPrtTiLP7dkER9P0Aqm/smVpUOKto/K1e3EmNe+HAMdyTlVs2o/ngLdVt2JrJiYyzNuKAkRd2JfC55Z6N2DB5hQ30RGFhqUZ28kpbVnHLE0n0DEn0dbalytN/9sYM7MxAjoWYQkdqcj6sRtJRfYeoroNrlImPNn7S6nzHV+Aw8nBt++jaN0C7/qJnej4+/dwte/OgdduabatJgBRcckU5v54fEW5+4iOS6Yk/8cntq5I741l/3E3077bEAoO7mLFO3dRUpDpLxMd35GYhBQytn7ddME3odbxUeTm/Pjuh9zcEjqnJFQp07FTHPHxUWzeuJ8Ro06ssqxzSjw/vfwUEhKimPuf1c221QQgPj6erKws/3ROTg4JCQnk5f3YZcTj8fDKK69w9913U1ZWxv79+3n1Ve9zjqSkJHr06MEFF1xAeXk58+bNY+fOnU1+HE2pQ3Qc6YW5/ul9Rbmc2q5zlTKbstI5L7UvczZ+zYTUk4kNjyQ+Ipqc0qKmDrfROZJicO/7MSl178vH1b9DDWV855Tb4skvw8RH1Xpz6N6VTViXRJwnxOHOyCfynB7gap4PnwKpv8qiLupH6Zeh1QGioTXUORUxtiflG/dDuRsT6324EvPb4YQP7ow7LYe8WZ/gOdj8vpMiwRDoFfojY8xtxpguxphUY8wtwIfGmERjTOLhhY0x040xy40xy//7vxC7GXOEEZ7Ug7TZo0n/x5UkTXsGR3RrACqydrPzrlPZfmtPWg+7Cmdc9ZaElsQ4nLSKP4HMnStY8ORPyNy1ggE/ubNKmdRTJpG27gOs9dSylebNGDh/8sm8/87GGpen7crhsQc/56+PfcWoc7oTFtY8b4oC5XA4GDFiBLNmzeKWW25h9+7dTJgwwb+sVatWzJ49mzfeeIPp06cHOdrQcP93HzAkuSvzJ/0fQ5K7kV6Yi6eFft+Ohs0rJe++hbR+bCKJL1+Be28uNOOHBIGKnNgHV99kCv/5XbBDCXlh3dsQe+MI8u792DvD6cDZIY6y1Xs4eMlLlK3eS+zMkUGNUaQ5CbTl5FCnwhmHzb8M7xiUbpVnWmufBZ4F+N80Z5P9FqjI3kNY4o9PHcMSOlKeveewMrsp2bYM3BVUZO6gPON/uJJ6ULp9ub+MOyed0t0biOp5FgXL32iq8Btdj6FXceLgywE4uHstrVp34FAbSHTrZIryqg7WLivKpqKsiLT1HwGQtvYDThx0WZUyqadMZPk7dzV67MGSm1NM6/hI/3Tr1pHk5VZ6mhYRRnJyLDOu974cIDY2gmm/HMQLc75jd9qPT8T37y+grKyC5A6xVeYf70aOHMnw4cMB2LFjB4mJiWzb5u2CFB8fT3Z21W6AnTt7v5+Zmd4zb8WKFYwb5/2TSTk5OaxcudK/LWstMTExFBRUHd/TnKQX5dGhVWv/dHJ0a9ILqw5OzijO59rP/g1AdFg456X2Ja+seb7J3ZNRgDP5x25GzuRYPPsLaigThyejAJwGR2x4vV1qShdvo3Sx97yM+ml/rLt5JieB1B9A+NBUYqYPIWvqXCh3N2WITe5YzylHUgzxT15A7u0f4k7LAbxduDxFZf4B8CULvifq4ub5QhiRYAj0MW5va23Xyv8qzetW79pNpGT7d7iSuhPWtgs4XcSdcSmFq6oO9CtY+Q5RvUYA4IhpgyupJ+X7fyAsoSPG5b0JdUTHE9VzGGX7vm/qQ2hUW759ifl/mcD8v0xgz4YFdDn9YgDapJxKeUl+lS5dh+zZ9AlJ3bw33kndh5GbscW/LLbdibiiWpO5c0XTHEAQ7E7LpW27ViQkRuF0Gk45tSMbN2T4l5eUVPDHuz5m9v2fMfv+z9i1M8efmCQkRuFwGADiE6Jo3z6GrKzm1ey/ePFiZs2axaxZs1i9ejVDhgwBoGvXrhQXF1fp0gXeBKRDhw7ExMQA0Lt3b/bt8ybFq1ev5qSTTgKgffv2OJ3OZp2YAKzJ3E3XuDZ0jknA5XAyudspLEyr2gqXEBGNwXseXd9/JP/dsrymTTUL5evTcaYm4OzYGlwOIif0onTR1iplShdtI+oC7ws4IseeROnS+ge2OxKjATBxEURffirF89Y2fPAhIJD6C+vdnrh7xpJ9/Zt4mtn1qCbHck6Z2AgS/nEx+Y99Qfmqqg86SxdvI3ywd2xKxJCUKgPsReTYBNpy8g1wWgDzgsvj5sC//49ON38EDid5X/6Lsr0baXPhvZRsX0Hh6vcoWreAViefS+oD68DjJvO1W/EUZhHZZQztLnsYrAVjyP7oMcp2V3+1bnOxd/NndDhpFOff8iXusmKWvn6zf9n4Gz5i/l+8XW1Wf/hnhl76BKdNvIeSwiyWvn6Tv1zqKZPYtaYZv+UF8Hgs77yxgWtmnIHDYfhuaRoZ+woYO74nu9NyqyQqh+vaLZGR53TH4/ZgLbw1bx1FheVNGH3TWr9+Pf369WPWrFmUlZXx4osv+pfdeeedzJo1i9zcXN5//31uvvlm3G43WVlZvPDCCwB8/fXXTJ06lbvvvhu32+2f35y5rYe7lrzLf8b+Aodx8N8ty/lfzn5uPvVc1mTuZmHaJs5M7sZtA8djrWVpxg7u+PbtYIfdeNyWvAc+IeG5S8DhoPitdVRsPUjM9cMo37CP0kXbKHpjLfEP/oS286/Bk1NC7s0/XoPaLZyOiQkHl5PIc3qQde3ruLcdJPb20bh6ed/MWPDUt7h3Vn+xR7MQQP3F3jwSE+0i/nHv2xnde/PIuf6tIAfeiI7hnIq+4lScKfHEXHcmMdedCUD2Na/jySoi/7EviJ99Hua20d5XMt/xUTCPssn9bcRlDE3uRmJkK76bcjuPrlrI3Gb84ESalrG29uZtY0wy0BH4N3AF+B7fQRzwtLW2V307aMpuXce7FUkdgx3CcWFV2VPBDuG4kFvUvBPHhvTBkIT6CwnLH24T7BBEWqSBM9UyE6jdV8829ZcKvhkzZjTZ/fEzzzxzXNTJIfW1nIwDpgGdgMcqzc8H/tBIMYmIiIiISAtUZ3JirX0ReNEYc7G1tvmMDBcRERERkZAT6JiTvsaYan8C3Fp7X02FRUREREREjlSgyUnlV+ZEAucDmxo+HBERERERaakCSk6stY9WnjbGPAIsaJSIRERERESkRTraP1cdjXeQvIiIiIiISIMIqOXEGLMO71+CB29C0x64v7GCEhERERGRlifQMSfnAwnAWUA88KG1tvn+WXAREREREWlygXbrmgy8DLQFXMC/jDG/bbSoRERERESkxQm05eQaYIi1thDAGPMg8C3w18YKTEREREREWpZAW04M4K407fbNExERERERaRCBtpz8C1hqjHnLN30BMKdRIhIRERERkRYp0L9z8pgxZjEw3DframvtqkaLSkREREREWpxAW06w1q4EVjZiLCIiIiIi0oId7R9hFBERERERaVBKTkREREREJCQoORERERERkZCg5EREREREREKCkhMREREREQkJSk5ERERERCQkKDkREREREZGQoORERERERERCgpITEREREREJCUpOREREREQkJCg5ERERERGRkGCstcGOockZY6Zba58NdhzHA9VVYFRPgVNdBUb1FBjVU+BUV4FRPQVOdSWNoaW2nEwPdgDHEdVVYFRPgVNdBUb1FBjVU+BUV4FRPQVOdSUNrqUmJyIiIiIiEmKUnIiIiIiISEhoqcmJ+kcGTnUVGNVT4FRXgVE9BUb1FDjVVWBUT4FTXUmDa5ED4kVEREREJPS01JYTEREREREJMUpOREREREQkJDTr5MQY09kYs90Yk+ibTvBNdzHGzDfG5Bhj3g92nMFWRz0NMMZ8a4zZYIxZa4y5NNixBlsddTXCGLPSGLPaV1+/CnaswVTXd883HWeM2W2M+VtQAw0B9Vyn3L5zarUx5t1gxxpM9dRTijHmY2PMJmPMxkPnWUtURz1dXelcWm2MKTHGXBDkcIOqnnPqId+1fJMx5kljjAl2vKHAGDPNGHPCMazfxRhzRUPGJM1Psx9zYoy5BehurZ1ujHkG2GGt/bMx5hwgGphhrT0/uFEGX031BLwBWGvtFt/FaAXQ21qbE7xIg6+WunoU7/ep1BgTA6wHzrTW7g1iqEFV23fPt+wvQDsgy1p7fTDjDAV1XKcKrLUxwY4vVNRRT4uBB6y1C33fP4+1tiiowQZRXd893/JEYCvQqSXXE9R6Pf8ceBg421fsK+B2a+3ioAQZQnzftZuttcuPcv2RvvVb/H2X1K5Zt5z4PA4MMcb8DhgOPAJgrf0UyA9iXKGmWj1Za/9nrd0C4LvJ3o/3hrKlq6muyqy1pb7lEbSM71Z9avzuGWNOB5KAj4MXWsipsa6kmmr1ZIzpA4RZaxcCWGsLWvoNN/WfT5cAH6megJrrygKRQDje67kLyAhWgI3NGHOjMWa979/vfK0b6ystv9kYc68x5hJgIPAfX+tblDFmh6+VaZ0xZpkxprtvnRd85Q9to8D3cTZwlm/93zflccrxIyzYATQ2a225MWYmMB8Ya60tD3ZMoai+ejLGDMZ7od4WjPhCSW11ZYzpDHwAdAdmtuRWE6i5nowxDrytTD8DxgQ1wBBSx/cv0hizHKgAZltr3w5WjKGglnOqJ5BjjHkT6Ap8AtxmrXUHM9ZgCuD33mXAY00fWeippa6+NcYsAtIBA/zNWrspmHE2Ft/DoquBM/Ae61K8LUfVWGvnGWOup1LLia+3W661tp8x5irgCaCuVpHbUMuJ1KOlPN2dgPci0zfYgYS4GuvJGNMBeBm42lrrCUZgIahaXVlr06y1/fEmJ1ONMUnBCi6EHF5P1wEfWmt3By+kkFXT9y/VWjsQuAJ4whhzYlAiCy2H11MYcBZwMzAI6AZMC0pkoaWu63k/YEEwggpRVerK9/S/N9AJ6AiMNsacFbzwGtVw4C1rbaG1tgB4E+/36Ui8Wun/oQ0ZnLRMzT45McYMAM4FhgC/912Y5TC11ZMxJg5va8Ad1tolwYswdNR3TvlaTNZz5Bf4ZqWWehoKXG+M2YG3+8RVxpjZQQsyRNR2Tllr9/j+/wFYDJwapBBDQi31tBtYba39wVpbAbwNnBa0IENAPdeoKXhvRtWLgFrr6kJgia+LYAHwES3rpjueqveHkfWUtzV8rji0DV+LeXhDBSfNX7NOTnxv1/gH8Dtr7S68A9zUl/swtdWTMSYceAt4yVo7L5gxhoo66qqTMSbKVyYB79Oo74MXaXDVVk/W2iuttSnW2i54n3S/ZK29LYihBl0d51SCMSbCV6YtMAzYGLxIg6uO6/l3QLwx5tB4uNGonur6vXc5Pz7pbtHqqKtdwAhjTJgxxgWMAJplty7gS+ACY0y0MaYV3sTsI6C9MaaN7xpUuQtWPhB72DYurfT/t77PO4DTfZ8n4R23U9v6IlU06+QEuBbYdWigJPAU0Nt4X/v6JfA6cI7xvtJ0XNCiDL4a6wm4He/bSqaZH18/OSBIMYaK2urql8BSY8wavP11H7HWrgtSjKGg1u9eEGMKVbWdU/2B5b5zahHeMSct9qab2utpON5E91NjzDq8/eafC06IIaGu33tdgM7UMqagBartnNqHd3zlOmANsMZa+15wQmxc1tqVwAvAMrzjTZ631n4H3OebtxDYXGmVF4CnDw2I981LMMasBW4ADg1yfw5vgrcGb6tToW/+WsBtjFmjAfFSm2b/KmERERERaXi+LroDrbWZwY5Fmo/m3nIiIiIiIiLHCbWciIiIiIhISFDLiYiIiIiIhAQlJyIiIiIiEhKUnIiIiIiISEhQciIiIiIiIiFByYmIiIiIiISE/welfoD1iE5cKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.corr(),annot=True,cmap='Dark2_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "05dd6c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=df.drop('output',axis=1)\n",
    "output=df['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "30b644d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data,test_data,train_output,test_output = train_test_split(features,output,test_size=0.2,random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63bb1d1",
   "metadata": {},
   "source": [
    "## Decision Tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "a4792bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_para={\n",
    "    'splitter':['best', 'random'],\n",
    "    'max_depth':range(3,15),\n",
    "    'max_features':['auto', 'sqrt', 'log2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "14d5ac75",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=GridSearchCV(DecisionTreeRegressor(),tuned_para,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "ea88232e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'max_depth': range(3, 15),\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(train_data,train_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "d3e5fe94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9568109790075419"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "900a351c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7, 'max_features': 'log2', 'splitter': 'best'}"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "8f1b72a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree=DecisionTreeRegressor(max_depth=6,max_features='auto',splitter='random',random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "aebe35ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=6, max_features='auto', random_state=100,\n",
       "                      splitter='random')"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.fit(train_data,train_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "1f51398e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = tree.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "b71fb454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 3.05193237e-05, 6.90657748e-05, 1.72500000e-04,\n",
       "       1.83423913e-04, 3.63483437e-04, 4.00181159e-04, 4.63768116e-04,\n",
       "       4.70769788e-04, 1.06889674e-03, 1.32628261e-03, 1.36148188e-03,\n",
       "       1.54948478e-03, 2.24698068e-03, 2.40581633e-03, 3.10069807e-03,\n",
       "       3.89971014e-03, 4.00048309e-03, 7.30537500e-03, 8.07246611e-03,\n",
       "       8.39250242e-03, 9.71750000e-03, 1.32611371e-02, 2.03793439e-02,\n",
       "       2.61671243e-02, 2.79210548e-02, 2.86468533e-02, 3.54566989e-02,\n",
       "       3.71705797e-02, 4.56613116e-02, 4.82927189e-02, 6.15223309e-02,\n",
       "       6.34205271e-02, 7.24450001e-02, 8.02087459e-02, 9.41447270e-02,\n",
       "       1.04517103e-01, 1.10110306e-01, 1.10391751e-01, 1.97244557e-01,\n",
       "       2.89044291e-01, 3.45389298e-01, 3.46336109e-01, 3.61482386e-01,\n",
       "       4.16970594e-01, 4.27561417e-01, 5.72480799e-01, 6.39360169e-01,\n",
       "       9.28026956e-01, 1.06112858e+00, 1.07379741e+00, 6.91697101e+00,\n",
       "       7.96726998e+01])"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=tree.cost_complexity_pruning_path(train_data,train_output)\n",
    "alpha=path['ccp_alphas']\n",
    "alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2465c0",
   "metadata": {},
   "source": [
    "## Evalution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "8ce39890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.187503190434306"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(p,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "6b5a3559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3805323974397248"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(p,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "8d6d9027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9514700097102008"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(p,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "d01c413c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.046338972515137"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(p,test_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fde0c22",
   "metadata": {},
   "source": [
    "## Train the model with the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "29efe9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = pd.read_csv('./Data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "d2a730ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=6, max_features='auto', random_state=100,\n",
       "                      splitter='random')"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.fit(features,output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "382286a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = tree.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "5743e945",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = pd.DataFrame({'Id':range(1,193),'output':pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "5ee6c6e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.to_csv('./Data/prediction.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881771a9",
   "metadata": {},
   "source": [
    "## RandomForest Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "a635d34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_para={\n",
    "    'n_estimators':[30,35,40,45,50,55,60],\n",
    "    'max_depth':range(3,15),\n",
    "    'max_features':['auto', 'sqrt', 'log2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "3862b547",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest=GridSearchCV(RandomForestRegressor(),tuned_para,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "9889e11b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': range(3, 15),\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [30, 35, 40, 45, 50, 55, 60]})"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(train_data,train_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "d1110bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968009785116213"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "850194f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=10, max_features='sqrt', n_estimators=50)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "b53e832f",
   "metadata": {},
   "outputs": [],
   "source": [
    "randomForest=RandomForestRegressor(n_estimators=30, max_depth=13,max_features='sqrt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "0f69deeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=13, max_features='sqrt', n_estimators=30)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomForest.fit(train_data,train_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "88996889",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=randomForest.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab16245",
   "metadata": {},
   "source": [
    "## Evalution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "55f73de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.009897841847591"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "268c5a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1713371647509576"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(pred,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "eb1797d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9652649282245304"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(pred,test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "3a0bdf88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7349057155498655"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(pred,test_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bfac9a7",
   "metadata": {},
   "source": [
    "## Train the model with the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "e6d3b94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=13, max_features='sqrt', n_estimators=30)"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomForest.fit(features,output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "449b4b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=randomForest.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "8d4a612e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = pd.DataFrame({'Id':range(1,193),'output':pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "89891a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.to_csv('./Data/prediction_new.csv',index=False)"
   ]
  }
 ],
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