Linear Regression - IPL Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd                   # for data manipulation\n",
    "import seaborn as sns                 # for data visualisation\n",
    "import matplotlib.pyplot as plt       # for data visualisation\n",
    "from sklearn.model_selection import train_test_split           # for splitting the dataset\n",
    "from sklearn.preprocessing import StandardScaler               # for scaling the dataset\n",
    "from sklearn.metrics import r2_score                           # accuracy measure for the model\n",
    "from sklearn.linear_model import LinearRegression              # Linear Regression model\n",
    "from sklearn.metrics import mean_squared_error                 # accuracy measure for the model\n",
    "import numpy as np                                             # for mathematical operations \n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the linear regression model, the target variable is \"Player Cost USD\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Is Captain(1=yes)</th>\n",
       "      <th>Is Wktkeeper(1=Yes)</th>\n",
       "      <th>Nationality(1=Overseas)</th>\n",
       "      <th>Team</th>\n",
       "      <th>IPL 4 Franchise</th>\n",
       "      <th>Player cost USD</th>\n",
       "      <th>matches</th>\n",
       "      <th>innings played</th>\n",
       "      <th>no</th>\n",
       "      <th>runs scored</th>\n",
       "      <th>...</th>\n",
       "      <th>half_centuries_ipl</th>\n",
       "      <th>fours_ipl</th>\n",
       "      <th>sixes_ipl</th>\n",
       "      <th>matches_bowl_ipl</th>\n",
       "      <th>balls_bowled_ipl</th>\n",
       "      <th>total_runs_given_ipl</th>\n",
       "      <th>wickets_taken_ipl</th>\n",
       "      <th>bowling_avg_ipl</th>\n",
       "      <th>economy_ipl</th>\n",
       "      <th>3wickets_ipl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Is Captain(1=yes)  Is Wktkeeper(1=Yes)  Nationality(1=Overseas)  \\\n",
       "0                0.0                  0.0                        1   \n",
       "1                0.0                  0.0                        1   \n",
       "2                0.0                  0.0                        1   \n",
       "3                0.0                  0.0                        1   \n",
       "4                0.0                  0.0                        1   \n",
       "\n",
       "           Team              IPL 4 Franchise  Player cost USD  matches  \\\n",
       "0     Australia               Mumbai Indians          20000.0     40.0   \n",
       "1  South Africa  Royal Challengers Bangalore          20000.0     18.0   \n",
       "2     Australia              Kings XI Punjab          20000.0     23.0   \n",
       "3  South Africa  Royal Challengers Bangalore          20000.0     20.0   \n",
       "4  South Africa             Delhi Daredevils          20000.0     25.0   \n",
       "\n",
       "   innings played   no  runs scored  ...  half_centuries_ipl  fours_ipl  \\\n",
       "0            40.0  3.0        817.0  ...                 0.0        0.0   \n",
       "1            18.0  1.0        356.0  ...                 0.0        0.0   \n",
       "2             8.0  5.0         33.0  ...                 0.0        0.0   \n",
       "3            20.0  0.0        343.0  ...                 0.0        0.0   \n",
       "4            22.0  8.0        213.0  ...                 0.0        0.0   \n",
       "\n",
       "   sixes_ipl  matches_bowl_ipl  balls_bowled_ipl  total_runs_given_ipl  \\\n",
       "0        0.0               0.0               0.0                   0.0   \n",
       "1        0.0               0.0               0.0                   0.0   \n",
       "2        0.0               0.0               0.0                   0.0   \n",
       "3        0.0               0.0               0.0                   0.0   \n",
       "4        0.0               0.0               0.0                   0.0   \n",
       "\n",
       "   wickets_taken_ipl  bowling_avg_ipl  economy_ipl  3wickets_ipl  \n",
       "0                0.0              0.0          0.0           0.0  \n",
       "1                0.0              0.0          0.0           0.0  \n",
       "2                0.0              0.0          0.0           0.0  \n",
       "3                0.0              0.0          0.0           0.0  \n",
       "4                0.0              0.0          0.0           0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the cleaned dataset\n",
    "df = pd.read_csv(\"Clean_Data.csv\")\n",
    "\n",
    "# displaying the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specifying the figure size of the plot\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "# plotting the heatmap\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable shares a moderate significant correlation value with only few of the variables like 'matches_bat_ipl', 'total_runs_scored_ipl', etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding the categorical variables with multiple categories\n",
    "data = pd.get_dummies(df, columns=['Team', 'IPL 4 Franchise'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129, 68)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Is Captain(1=yes)', 'Is Wktkeeper(1=Yes)', 'Nationality(1=Overseas)',\n",
       "       'Player cost USD', 'matches', 'innings played', 'no', 'runs scored',\n",
       "       'highest score', 'Is star batsman', 'Is batsman', 'Batting avg',\n",
       "       'Balls faced', 'Strike rate', '100 runs made', '50 runs made', '4s',\n",
       "       '6s', 'Catches per match', 'Catches taken', 'stumping made',\n",
       "       'Is bowler?', 'Number of balls bowled', 'runs given', 'Is star bowler',\n",
       "       'wkts taken', 'Bowling average', 'Bowling econ', 'sr', '4w', '5w',\n",
       "       'Retained', 'matches_bat_ipl', 'total_runs_scored_ipl',\n",
       "       'balls_faced_ipl', 'batting_avg_ipl', 'highest_score_ipl',\n",
       "       'strike_rate_ipl', 'centuries_ipl', 'half_centuries_ipl', 'fours_ipl',\n",
       "       'sixes_ipl', 'matches_bowl_ipl', 'balls_bowled_ipl',\n",
       "       'total_runs_given_ipl', 'wickets_taken_ipl', 'bowling_avg_ipl',\n",
       "       'economy_ipl', '3wickets_ipl', 'Team_Australia', 'Team_Bangladesh',\n",
       "       'Team_England', 'Team_India', 'Team_Netherlands', 'Team_New Zealand',\n",
       "       'Team_South Africa', 'Team_Sri Lanka', 'Team_West Indies',\n",
       "       'IPL 4 Franchise_Chennai Super Kings',\n",
       "       'IPL 4 Franchise_Deccan Chargers', 'IPL 4 Franchise_Delhi Daredevils',\n",
       "       'IPL 4 Franchise_Kings XI Punjab', 'IPL 4 Franchise_Kochi',\n",
       "       'IPL 4 Franchise_Kolkata Knight Riders',\n",
       "       'IPL 4 Franchise_Mumbai Indians', 'IPL 4 Franchise_Pune Warriors',\n",
       "       'IPL 4 Franchise_Rajasthan Royals',\n",
       "       'IPL 4 Franchise_Royal Challengers Bangalore'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the target and predictor dataframes\n",
    "X = data.drop('Player cost USD', axis=1)\n",
    "y = data['Player cost USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(129, 67)\n",
      "(129,)\n"
     ]
    }
   ],
   "source": [
    "# printing the shapes of the target and predictor dataframes\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        20000.0\n",
       "1        20000.0\n",
       "2        20000.0\n",
       "3        20000.0\n",
       "4        20000.0\n",
       "         ...    \n",
       "124    1900000.0\n",
       "125    2000000.0\n",
       "126    2100000.0\n",
       "127    2100000.0\n",
       "128    2400000.0\n",
       "Name: Player cost USD, Length: 129, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the predictor variable names to be used later \n",
    "cols = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling and Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the variables\n",
    "X = StandardScaler().fit_transform(X)\n",
    "y = StandardScaler().fit_transform(np.array(y).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.28988552, -0.36273813,  0.76980036, -0.89519161, -0.30074073,\n",
       "       -1.06482497, -0.21017582,  0.74098358, -0.44095855,  0.67040152,\n",
       "        0.11282486, -0.34955847,  1.36968313, -0.40440899, -0.52591768,\n",
       "        0.26577671, -0.23293425,  0.22862799, -0.54750376, -0.28886651,\n",
       "       -1.23669388, -1.06446459, -1.0789597 , -0.38959686, -1.04260135,\n",
       "       -1.32784239,  1.23900783, -1.39599675, -0.6168809 , -0.35899621,\n",
       "       -0.32025631, -0.91655287, -0.67084335, -0.70110046, -0.94453662,\n",
       "       -0.83673312, -1.23304101, -0.17888544, -0.49834449, -0.59794914,\n",
       "       -0.55250839, -0.72567821, -0.68140716, -0.69522571, -0.64595605,\n",
       "       -0.89301793, -0.89441656, -0.47809144,  1.70560573, -0.08838835,\n",
       "       -0.23953507, -0.76980036, -0.08838835, -0.23953507, -0.42835294,\n",
       "       -0.27386128, -0.15430335, -0.40269363, -0.33476703, -0.37628835,\n",
       "       -0.30532006, -0.34891135, -0.30532006,  3.122499  , -0.33476703,\n",
       "       -0.23953507, -0.34891135])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.04087012],\n",
       "       [-1.04087012],\n",
       "       [-1.04087012],\n",
       "       [-1.04087012],\n",
       "       [-1.04087012],\n",
       "       [-1.04087012],\n",
       "       [-0.98657483],\n",
       "       [-0.98657483],\n",
       "       [-0.98657483],\n",
       "       [-0.98657483],\n",
       "       [-0.98657483],\n",
       "       [-0.94132876],\n",
       "       [-0.93227955],\n",
       "       [-0.93227955],\n",
       "       [-0.93227955],\n",
       "       [-0.93227955],\n",
       "       [-0.92323033],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.89608269],\n",
       "       [-0.87798426],\n",
       "       [-0.85988583],\n",
       "       [-0.8417874 ],\n",
       "       [-0.82368897],\n",
       "       [-0.80559054],\n",
       "       [-0.80559054],\n",
       "       [-0.80559054],\n",
       "       [-0.78749211],\n",
       "       [-0.73319683],\n",
       "       [-0.7150984 ],\n",
       "       [-0.7150984 ],\n",
       "       [-0.7150984 ],\n",
       "       [-0.7150984 ],\n",
       "       [-0.69699997],\n",
       "       [-0.64270468],\n",
       "       [-0.62460625],\n",
       "       [-0.62460625],\n",
       "       [-0.58840939],\n",
       "       [-0.58840939],\n",
       "       [-0.55221253],\n",
       "       [-0.55221253],\n",
       "       [-0.53411411],\n",
       "       [-0.53411411],\n",
       "       [-0.53411411],\n",
       "       [-0.53411411],\n",
       "       [-0.53411411],\n",
       "       [-0.53411411],\n",
       "       [-0.48886803],\n",
       "       [-0.48886803],\n",
       "       [-0.44362196],\n",
       "       [-0.44362196],\n",
       "       [-0.39837589],\n",
       "       [-0.35312981],\n",
       "       [-0.35312981],\n",
       "       [-0.35312981],\n",
       "       [-0.35312981],\n",
       "       [-0.35312981],\n",
       "       [-0.30788374],\n",
       "       [-0.30788374],\n",
       "       [-0.30788374],\n",
       "       [-0.26263767],\n",
       "       [-0.2173916 ],\n",
       "       [-0.2173916 ],\n",
       "       [-0.2173916 ],\n",
       "       [-0.2173916 ],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.17214552],\n",
       "       [-0.08165338],\n",
       "       [ 0.09933091],\n",
       "       [ 0.09933091],\n",
       "       [ 0.09933091],\n",
       "       [ 0.18982306],\n",
       "       [ 0.18982306],\n",
       "       [ 0.18982306],\n",
       "       [ 0.18982306],\n",
       "       [ 0.18982306],\n",
       "       [ 0.2803152 ],\n",
       "       [ 0.37080735],\n",
       "       [ 0.37080735],\n",
       "       [ 0.46129949],\n",
       "       [ 0.46129949],\n",
       "       [ 0.46129949],\n",
       "       [ 0.46129949],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.55179164],\n",
       "       [ 0.64228379],\n",
       "       [ 0.64228379],\n",
       "       [ 0.64228379],\n",
       "       [ 0.73277593],\n",
       "       [ 0.91376022],\n",
       "       [ 0.91376022],\n",
       "       [ 0.91376022],\n",
       "       [ 0.91376022],\n",
       "       [ 1.09474451],\n",
       "       [ 1.2757288 ],\n",
       "       [ 1.2757288 ],\n",
       "       [ 1.2757288 ],\n",
       "       [ 1.45671309],\n",
       "       [ 1.63769739],\n",
       "       [ 1.81868168],\n",
       "       [ 2.18065026],\n",
       "       [ 2.18065026],\n",
       "       [ 2.18065026],\n",
       "       [ 2.18065026],\n",
       "       [ 2.18065026],\n",
       "       [ 2.18065026],\n",
       "       [ 2.36163455],\n",
       "       [ 2.54261884],\n",
       "       [ 2.72360313],\n",
       "       [ 2.72360313],\n",
       "       [ 3.266556  ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting into train and test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding the baseline predictions\n",
    "y_pred_bl = np.repeat(np.mean(y_train), len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805, 0.03628805,\n",
       "       0.03628805, 0.03628805, 0.03628805, 0.03628805])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_bl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285636773921593"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics for the Baseline model\n",
    "mean_squared_error(y_test, y_pred_bl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.015760026342164668"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics for the Baseline model\n",
    "r2_score(y_test, y_pred_bl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model 1 - All variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising the model \n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding predictions for the test set\n",
    "y_pred_lr = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.205207911302471"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics\n",
    "mean_squared_error(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3183824109623956"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics \n",
    "r2_score(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is even worse than the baseline model. \n",
    "\n",
    "The feature importances of each of the variables in the model is plotted to understand which of the predictors might have some influence over the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finetuning with RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFE(estimator=LinearRegression(), n_features_to_select=15)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the method\n",
    "rfe = RFE(estimator=LinearRegression(), n_features_to_select=15)\n",
    "\n",
    "# fit the data\n",
    "rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: Is Captain(1=yes), Selected True, Rank: 1.000\n",
      "Column: Is Wktkeeper(1=Yes), Selected False, Rank: 7.000\n",
      "Column: Nationality(1=Overseas), Selected False, Rank: 16.000\n",
      "Column: matches, Selected False, Rank: 15.000\n",
      "Column: innings played, Selected False, Rank: 5.000\n",
      "Column: no, Selected False, Rank: 27.000\n",
      "Column: runs scored, Selected True, Rank: 1.000\n",
      "Column: highest score, Selected False, Rank: 32.000\n",
      "Column: Is star batsman, Selected False, Rank: 17.000\n",
      "Column: Is batsman, Selected False, Rank: 43.000\n",
      "Column: Batting avg, Selected False, Rank: 18.000\n",
      "Column: Balls faced, Selected True, Rank: 1.000\n",
      "Column: Strike rate, Selected False, Rank: 33.000\n",
      "Column: 100 runs made, Selected False, Rank: 36.000\n",
      "Column: 50 runs made, Selected False, Rank: 44.000\n",
      "Column: 4s, Selected True, Rank: 1.000\n",
      "Column: 6s, Selected True, Rank: 1.000\n",
      "Column: Catches per match, Selected False, Rank: 39.000\n",
      "Column: Catches taken, Selected False, Rank: 38.000\n",
      "Column: stumping made, Selected False, Rank: 6.000\n",
      "Column: Is bowler?, Selected False, Rank: 13.000\n",
      "Column: Number of balls bowled, Selected True, Rank: 1.000\n",
      "Column: runs given, Selected True, Rank: 1.000\n",
      "Column: Is star bowler, Selected False, Rank: 31.000\n",
      "Column: wkts taken, Selected False, Rank: 34.000\n",
      "Column: Bowling average, Selected True, Rank: 1.000\n",
      "Column: Bowling econ, Selected False, Rank: 14.000\n",
      "Column: sr, Selected True, Rank: 1.000\n",
      "Column: 4w, Selected False, Rank: 8.000\n",
      "Column: 5w, Selected False, Rank: 24.000\n",
      "Column: Retained, Selected False, Rank: 45.000\n",
      "Column: matches_bat_ipl, Selected True, Rank: 1.000\n",
      "Column: total_runs_scored_ipl, Selected False, Rank: 40.000\n",
      "Column: balls_faced_ipl, Selected True, Rank: 1.000\n",
      "Column: batting_avg_ipl, Selected False, Rank: 12.000\n",
      "Column: highest_score_ipl, Selected False, Rank: 9.000\n",
      "Column: strike_rate_ipl, Selected False, Rank: 42.000\n",
      "Column: centuries_ipl, Selected False, Rank: 19.000\n",
      "Column: half_centuries_ipl, Selected True, Rank: 1.000\n",
      "Column: fours_ipl, Selected False, Rank: 28.000\n",
      "Column: sixes_ipl, Selected False, Rank: 53.000\n",
      "Column: matches_bowl_ipl, Selected True, Rank: 1.000\n",
      "Column: balls_bowled_ipl, Selected False, Rank: 3.000\n",
      "Column: total_runs_given_ipl, Selected True, Rank: 1.000\n",
      "Column: wickets_taken_ipl, Selected True, Rank: 1.000\n",
      "Column: bowling_avg_ipl, Selected False, Rank: 11.000\n",
      "Column: economy_ipl, Selected False, Rank: 22.000\n",
      "Column: 3wickets_ipl, Selected False, Rank: 2.000\n",
      "Column: Team_Australia, Selected False, Rank: 48.000\n",
      "Column: Team_Bangladesh, Selected False, Rank: 46.000\n",
      "Column: Team_England, Selected False, Rank: 30.000\n",
      "Column: Team_India, Selected False, Rank: 4.000\n",
      "Column: Team_Netherlands, Selected False, Rank: 20.000\n",
      "Column: Team_New Zealand, Selected False, Rank: 10.000\n",
      "Column: Team_South Africa, Selected False, Rank: 47.000\n",
      "Column: Team_Sri Lanka, Selected False, Rank: 37.000\n",
      "Column: Team_West Indies, Selected False, Rank: 49.000\n",
      "Column: IPL 4 Franchise_Chennai Super Kings, Selected False, Rank: 26.000\n",
      "Column: IPL 4 Franchise_Deccan Chargers, Selected False, Rank: 23.000\n",
      "Column: IPL 4 Franchise_Delhi Daredevils, Selected False, Rank: 21.000\n",
      "Column: IPL 4 Franchise_Kings XI Punjab, Selected False, Rank: 29.000\n",
      "Column: IPL 4 Franchise_Kochi, Selected False, Rank: 25.000\n",
      "Column: IPL 4 Franchise_Kolkata Knight Riders, Selected False, Rank: 41.000\n",
      "Column: IPL 4 Franchise_Mumbai Indians, Selected False, Rank: 51.000\n",
      "Column: IPL 4 Franchise_Pune Warriors, Selected False, Rank: 50.000\n",
      "Column: IPL 4 Franchise_Rajasthan Royals, Selected False, Rank: 35.000\n",
      "Column: IPL 4 Franchise_Royal Challengers Bangalore, Selected False, Rank: 52.000\n"
     ]
    }
   ],
   "source": [
    "# summarize all features\n",
    "for i in range(X.shape[1]):\n",
    "    print('Column: %s, Selected %s, Rank: %.3f' % (cols[i], rfe.support_[i], rfe.ranking_[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialise empty list to store the predictors\n",
    "variables = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering out the variables which had a high coefficient value\n",
    "for i in range(len(cols)):\n",
    "    if rfe.support_[i] == True:\n",
    "        variables.append(cols[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Is Captain(1=yes)', 'runs scored', 'Balls faced', '4s', '6s', 'Number of balls bowled', 'runs given', 'Bowling average', 'sr', 'matches_bat_ipl', 'balls_faced_ipl', 'half_centuries_ipl', 'matches_bowl_ipl', 'total_runs_given_ipl', 'wickets_taken_ipl']\n"
     ]
    }
   ],
   "source": [
    "# displaying the variables\n",
    "print(variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are 15 such variables\n",
    "len(variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model 2 - 15 variables selected using RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the predictor and target dataframes\n",
    "X1 = data[variables]\n",
    "y1 = data['Player cost USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the predictor variables\n",
    "X1 = StandardScaler().fit_transform(X1)\n",
    "y1 = StandardScaler().fit_transform(np.array(y1).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting into train and test datasets\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising the model \n",
    "model1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model1.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding predictions for the test set\n",
    "y_pred_lr1 = model1.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4730937943566677"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics\n",
    "mean_squared_error(y_test1, y_pred_lr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4824805484879059"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics\n",
    "r2_score(y_test1, y_pred_lr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.04531956],\n",
       "       [ 0.09308429],\n",
       "       [-0.74180249],\n",
       "       [-0.37632088],\n",
       "       [ 0.88533739],\n",
       "       [-0.02646064],\n",
       "       [ 0.95458274],\n",
       "       [-0.31389413],\n",
       "       [ 0.25632927],\n",
       "       [ 0.08213079],\n",
       "       [ 2.0546575 ],\n",
       "       [-0.90850313],\n",
       "       [-0.34778027],\n",
       "       [-0.67672313],\n",
       "       [ 0.47771748],\n",
       "       [ 0.25215937],\n",
       "       [ 0.33372865],\n",
       "       [-0.67832387],\n",
       "       [ 0.60510942],\n",
       "       [-0.42492165],\n",
       "       [-0.66888149],\n",
       "       [-0.60924003],\n",
       "       [ 1.61681612],\n",
       "       [-0.71123933],\n",
       "       [ 1.06453999],\n",
       "       [-0.76155446],\n",
       "       [-0.37035203],\n",
       "       [ 0.34786662],\n",
       "       [-0.53897703],\n",
       "       [-0.99756975],\n",
       "       [-0.12871552],\n",
       "       [-0.57771077],\n",
       "       [ 1.09917603],\n",
       "       [-0.51576544],\n",
       "       [-0.11476931],\n",
       "       [-0.40217707],\n",
       "       [-0.06155636],\n",
       "       [-0.72009374],\n",
       "       [ 0.09465046]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inverse transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = data['Player cost USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(np.array(y1).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler.inverse_transform(y_pred_lr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finetuning using Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Is Captain(1=yes), Score: 0.09843\n",
      "Feature: Is Wktkeeper(1=Yes), Score: 0.00014\n",
      "Feature: Nationality(1=Overseas), Score: 0.02368\n",
      "Feature: matches, Score: 0.01617\n",
      "Feature: innings played, Score: 0.00923\n",
      "Feature: no, Score: 0.03184\n",
      "Feature: runs scored, Score: 0.01430\n",
      "Feature: highest score, Score: 0.00844\n",
      "Feature: Is star batsman, Score: 0.00346\n",
      "Feature: Is batsman, Score: 0.00005\n",
      "Feature: Batting avg, Score: 0.03326\n",
      "Feature: Balls faced, Score: 0.00900\n",
      "Feature: Strike rate, Score: 0.01122\n",
      "Feature: 100 runs made, Score: 0.00131\n",
      "Feature: 50 runs made, Score: 0.00911\n",
      "Feature: 4s, Score: 0.01937\n",
      "Feature: 6s, Score: 0.00538\n",
      "Feature: Catches per match, Score: 0.02142\n",
      "Feature: Catches taken, Score: 0.02746\n",
      "Feature: stumping made, Score: 0.00120\n",
      "Feature: Is bowler?, Score: 0.00081\n",
      "Feature: Number of balls bowled, Score: 0.00648\n",
      "Feature: runs given, Score: 0.00531\n",
      "Feature: Is star bowler, Score: 0.00055\n",
      "Feature: wkts taken, Score: 0.00455\n",
      "Feature: Bowling average, Score: 0.01733\n",
      "Feature: Bowling econ, Score: 0.00876\n",
      "Feature: sr, Score: 0.02397\n",
      "Feature: 4w, Score: 0.01325\n",
      "Feature: 5w, Score: 0.00241\n",
      "Feature: Retained, Score: 0.00644\n",
      "Feature: matches_bat_ipl, Score: 0.18135\n",
      "Feature: total_runs_scored_ipl, Score: 0.00993\n",
      "Feature: balls_faced_ipl, Score: 0.10911\n",
      "Feature: batting_avg_ipl, Score: 0.00952\n",
      "Feature: highest_score_ipl, Score: 0.01020\n",
      "Feature: strike_rate_ipl, Score: 0.04583\n",
      "Feature: centuries_ipl, Score: 0.00149\n",
      "Feature: half_centuries_ipl, Score: 0.00532\n",
      "Feature: fours_ipl, Score: 0.05368\n",
      "Feature: sixes_ipl, Score: 0.00632\n",
      "Feature: matches_bowl_ipl, Score: 0.02075\n",
      "Feature: balls_bowled_ipl, Score: 0.00733\n",
      "Feature: total_runs_given_ipl, Score: 0.02278\n",
      "Feature: wickets_taken_ipl, Score: 0.00949\n",
      "Feature: bowling_avg_ipl, Score: 0.00329\n",
      "Feature: economy_ipl, Score: 0.01425\n",
      "Feature: 3wickets_ipl, Score: 0.00345\n",
      "Feature: Team_Australia, Score: 0.00220\n",
      "Feature: Team_Bangladesh, Score: 0.00033\n",
      "Feature: Team_England, Score: 0.00297\n",
      "Feature: Team_India, Score: 0.01868\n",
      "Feature: Team_Netherlands, Score: 0.00000\n",
      "Feature: Team_New Zealand, Score: 0.00082\n",
      "Feature: Team_South Africa, Score: 0.00096\n",
      "Feature: Team_Sri Lanka, Score: 0.00047\n",
      "Feature: Team_West Indies, Score: 0.00001\n",
      "Feature: IPL 4 Franchise_Chennai Super Kings, Score: 0.00130\n",
      "Feature: IPL 4 Franchise_Deccan Chargers, Score: 0.00263\n",
      "Feature: IPL 4 Franchise_Delhi Daredevils, Score: 0.00045\n",
      "Feature: IPL 4 Franchise_Kings XI Punjab, Score: 0.00057\n",
      "Feature: IPL 4 Franchise_Kochi, Score: 0.00413\n",
      "Feature: IPL 4 Franchise_Kolkata Knight Riders, Score: 0.00553\n",
      "Feature: IPL 4 Franchise_Mumbai Indians, Score: 0.00385\n",
      "Feature: IPL 4 Franchise_Pune Warriors, Score: 0.00031\n",
      "Feature: IPL 4 Franchise_Rajasthan Royals, Score: 0.00317\n",
      "Feature: IPL 4 Franchise_Royal Challengers Bangalore, Score: 0.00318\n"
     ]
    },
    {
     "data": {
      "image/png": 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0eydgdXffI3vMOcDB7n5Nun0pEUwtWrWumb3g7nNk23je3ecsef7PA59PN5cG7p3I1zox5gGenUzLp9S6U+t+vR1fk/Zr6tm29mvaWFf7NfVs+39xvyaHRdx9TOkSd6/9AbYhcreK2zsBR/Q85lxgzez2pcB769YFXujZxvNN+9L2DzB2ci2fUutOrfv1dnxN2q+pZ9var2ljXe3X1LPt/8X9avunnzIUjwMLZbcXBP7e52Pq1n0qdVOSfj/dx76IiIiITPP6CcBuBpYys8XMbHpgO+CsnsecBXzSwhrAi+7+ZMO6ZwE7p793Bs4c5msRERERmSY0JuG7+xtmtgdwIVFK4jh3v9PMdk3LjwLOI0pQ3E+Uodilbt206YOBU8zsM8CjRHfl1OaYybh8Sq07Obet19TetrVf7a07Obf9dtyvt+Nrmpzb1n61t24/y1vTmIQvIiIiIpOWpiISERERaZkCMBEREZGWKQATmcaZ2Qgze/+U3g8RaVf67o+e2OVTytS6X21TDlhiZjMCHwXWAt4JvArcAZxbDBwws1VKll/i7s8N43m3rFvu7qfXrLtvw7qHpccZsAOwuLsfaGYLA/O5+01mNgOwFVE0d1S27oF9v4iJZGZrAku5+/FmNgaY1d0fypbPBCzs7oOK75rZO4AfAe90943MbFngfe5+bMljR6Rtv1SxHwPL+3k/zGwBYJGe5VfV7RdwN/AfT3Ok9jz/ypVvUmx70Dol27je3d/X8JiRwDt69vtRM3sXMX3YO9x9OTNbHtjU3X+Q1nsX8NWS17x+eu+2dvdTKp5zRnf/T80+1S4XqZOOG18HlgVmLO539/Wzx5R+7ifBc28DXODuL5vZt4GVgR/0833tY9vLMfg1/T4t+yOwK/AmcAswO3CYu/+kabmZ/Rj4AXH+ugBYAdjH3f+Q1v0AMMHd/21mO6bXdLi7P5KWrwysCThwrbuPM7OvufuPzeyIdH8Xd9+rj/1aAnjc3f9rZusCyxOz67yQ1t0bOB54GfgtsBKwn7tf1Mc+1x7fpiQFYICZHQB8jJgO6RaiJtmMwLuA9YDFiNbCu0uWf4AIxL6T7q8M4sxsFuBVd38rfSiWAbYkPrTzAu8HLku7tR5whbtvmfZxduCAtG2AK4H/pp+lgVXplPj4GHCVd2YgOBJ4C1jf3f/PzOYELnL3Vc3sAuDF9LreLN4Td/9pWndL4JC0f5Z+3N1Hm9nLDP7CvQjMB/ynZFmx7dFmtj+wCrC0u7/LzN4JnOruH0jP+zHgUGB6d1/MzFYEDnT3TdPy84kv5LfcfQUzGwWMd/f3pOUTdZACPtjwfhwCfJyYTuvNzuLa/XqK+OzMCDwA7OTurxXbNrPL058zpvfk1vQ+Lw/c6O5r1v0f0ja+B9wGnO4lX2oz2xPYP+3LW9l+L29mVxIB1tHuvlJ6/B3uvlz6+1bgqJL35Ja0/Cp3X7v3OdOy+9NzXg1cRRy0X2xa3nRhAjxcs2x0ei+qLNqw7Tq12667GDOzs6n4ThQPqVm+OHBczbpX1Cwbrq9RXadxBuAbNeuu27Dt9ah+zesQx7lS7r6XmV0E/Bn4CvF93hl4xt2/DpWf+2WBf9dst/hOLQUcxOBAaPG0/Lb0/VkzPe5Q4PQ+t70GcATwf8D0RJWAf2fHxnXT855HzLV8jbtvndad4O4rmtkORMHzrwO3uPvyTcuzZVsAmwNfAi539xWK10QEZcsDJwLHAlu6+zpm9l2iWkHRKLA5cCpwq7ufbWZFWane13xCP/tFHPsWJaomnEWcGzZO696ajqcfAXYnzrfHu/vKdfuc1q09vk1J/cwF+b/gZnc/oGLZYWb2deILcG3ZA1Jw8H1gOeJAeCPdQdrBqYVtAWCVFABdCowFXnH3HSymc1o21U8ritP+Knua44hgbtt0eydgBXffMh2EVnb3l9O6BxBfjMLq6YM6HsDdn7eoywawoLtvWPPe/Bj4mLvfXfbeEIV1/0icPLYjgq97gS8SJ9R/EF+KohVutrTuFsRVzLi0T383s9mybR9AzEN6RVo+wcwWzZbP4+6nmNk30vI3zOzNbPmyqUVrB+Ig9nUigPhJw/IRDe/H5sSB4b8Vy8v2axZ3XxPAzL4PjE0HhWOBD7v7emnZycDn3f32dHs54sQC9f8HgH2BWYA3zexVegI0YO+03/8sWXdmj9bQ/L438r/d/ciK5wW42My+QpwIB04+7v6cuy+ZWlzXIi5Ofm0xDdmK6TGly4HxaTOlFybAoGnLMmsATxLvQS8HHqlZ1yvW62vbqUUmDyiKoMqIi7iNa7ZdZ2c6350yP61Z1vSampa/gwj+yxwKbFCxvlMTQPWxfEbiO1lnbnc/1sz2dvcrgSvTd6tQ+bk3swOpPj5BXEjtD/yM+NztQvfrLI43mwBHuvuZKShbqY9t/5I4Xp5KBB6fBJZMy7YmAorx7r6LRav6b7N1pzOz6Yhj0S/d/XUz8z6XT5d+bwz8yd2f6/3eu7ub2WZEK9KxWWC1PbCSpxZrMzsYGFe0JBWBVo26/XorHSu3AH7u7kcU56uk2MmNicDrVuvseN0+Q/PxbcrxqaAc/9T4QxwsRw/h8Zs0LJ8XuCf9vSfwtfT3+PT7jpLnvyO7PaFkmxPS73uAGbL7ZyieK92+kbjCGpduj8me9xjgPTX7fW3NshtL7rsh/b61YvmN6fdN6XexT7MAt5U8bnx2X778CmDubP01gCuz5XcSB5tTgXWKfWpYflsf78f5RHdl1fKy/foXMEv2mOWJA/KiRFDVz/+48v/Q5+fzcmBUzWtaItvnrYHzs+UHALsB8wNzFT/Z8oeynweLn7RsQeLAfRRwPTFt2TeydZuWnwPMn92en2jlm+LHiGH+P6ZPn4P3EK28Q1o+hfd9NDBb29suW0bneHMhEQitBDyQLa/73Fcen9Lft6Tft2f3XZ39fQ5wNNGqPQdx3L21z22PTb/zY9p16XdxbLwlvWYD7swetxfwBHHhaERqwNX9LCda6u4hLnCmI84F+X5dSbRo/o24mB5ZvH7iODFH9tg5gHOy2+8ijp8XERdMlwGXZcv3rNmvG9Nx4A5gsXRffv47Pm33PmBmIpi9pWmfs/2uPL5N0e/SlN6BqemHaMkZTQQD9xBXul/Nlv84LZ+OaMF6FtixZntdQVz60L8PuAF4d7qv+HD/kjiIfIq42j2fbM5N4uSUz7f5AeD69Pe3iIDnAOKKbQLwzeyxOxBNuo8DPyRaqLZJy+4CXkv33QbcTvdB4XCiZWN7ort0S6J5t9inbdPrHJH+Lg6IE4Dr0nOPTMt3oHOQ+Qpx8HoQ+Fza1l7Z8x4LfCLt01JEc/1R2fKVgWuJ7sJr05dv+Wz5UA9S9xCtN3cBr9e8H6cRBYePBn5R/DTs1975/67m8/In4kp3XaL75TfEVWrt/yEtN2BH4Dvp9kLAaj3v5zXEgWrf4ictWxy4hCii/ER63KLZug+V/DyYLd+W9DknugbOIFpkIbp9bgQ2q3jNTcubLkxmTK/l9PS/2QeYMVu+JdFS+1Ng855tNa070dvOPgt7ESeelbL7NwEeI4L1K4lC1Bv1szz9r84GniFa2c8kcjsn1WuqXE601NxOdP8+Qhxz3tvnez038V0ZRwQWhxOtVzRtu2HZR4n0geWIYOsWYFM6n/G6z33l8SktvzbdfzqwB9Fqf2+2fOb0mpdKt+cnWrT72fZVRJD9e+K88iU6wduvieBmVyLgGE+0+tQdO0qDzHx52o/3E63HI9P9sxD5wMXj5kvv0Vrp9sLAxel/91fi+PA7IiB6HDg5W/dWoudjNaKL8b3Z/2kEsG3PPlmx30R36y+A7dPtxYgcr+JxCxHfpzmyz9PyNfv8yex5yo5vizQdj9v4UQ5YZgh966X95+kxdYmG6wBfJlozDjGzxYkEyCJJcQugyKW5yt3PyLa7InBC2h7A88DO7n5bWr4ynfywq9x9fLp/BNEK8xyR32TApZ66ssxskbL3wjsJjMeXL/ZPp/0/nAgqnQgsv0R8yN9LfEEPJ4JFJw5o+7j7w2nbGwAfTvt0obtfnL3emYnAcmA58H3PErZTftXSafm97v562Wsxs9+7+yfT498L3O3R/TgzsB9x1fw40QrzQsP7sXPF8oHm97L9skgEPsHddyxbP603I3EAG/gMEF0b/6n7P6R1K/P80vL9K/b7e9nzz0J0wb5ctY8V+53nwvyIOAF/091XN7MViKTdtYkD431ES+Wxad2m5b8kAvA/EZ+h7YD73X3PtPwUIjH3D2l3tgfmdPdtzOzXRLfOn9KyjxOtI7s3rTsJtl2aL+PuPzCze4CPuvv96bFLEHmiy6TblcvN7AYiNaF43u2APd199eG+pj5e823A7u5+dXrsmsCv0/++6f24mPg8F9vdAVjX3T+Ultdtu3IZFao+7wV3/15Kaag7Pq1K5G7OQaSYzE4ES39r2PZzfWx7ESIvbXrimDl7ek3397yORYmL/ns9pT1YwwAkKx+cVeS1HukNg3V6VR3zstdb5Hjd4u7vrdlOZa5oH/tQu+2a9UYCB7v7Vyf2+DY5KQDLmNmdwIpES9gv3f1KS8l/xXJ3f7eZ/QY4zd0vyJenx9QGcekxs7j7oETN9KVcyt0vScHBSO/kdc1ANJ0uQRwQXiROwAem5ZUjCq2/EXLz0p1o2jhKyMzGuPszTY+rWPcQT4mydfeVrNeUnP2pkvvWp5NDtASRO/eGmR1DtHqdRgSnKxAH2Duz9302Il/sxmwfBo3ObNovdz/dzC4kuhxfq3pc2bb7YWbjPOX5eSfRtOuzme4b9NmrO2B75N5NR3dgeAWR0Pp6Wn+8R97LQUSL7h979mNWIshai2ilc3dfNHv+puV1FyZlr7FI2L0TWM7TQS5djNzu7u9uWncSbPtuuvNlZiK6QP6v90RkkZxyZXFf3XIzu7EItrLlN7j7GsN9TX285ms9DZLJll3r7h/o4/0YdAI1s7Huvkq+nYptly172N0XtYaRdz3r1I6G7peZPUQnb25h4mLYiOPyo+6+WB/bGBiQlW6PJNJIXjGz4zxdWKVlswJnuvsH0+1+BiCtQrSUQrSo3kwM+nqBuNDsGqxj5QOqSK/LvZNLWvZa5kp/7kW0yp5BDA4DOgNTzOw7xMC0QbmiFiMZD6Az0rp43mLAw6+A37n7zUPdZzO7zLNRsVMTJeF3O5po5r4VuCoFRPmX9ex0dfoqsFsKdHqH0FcmGprZ+4gm8VmBhS2u/r/g7ruZ2eeAzxP5NUsQCftHEYEBRFfDC0QT/hP5E1o2opD4Yk5HXGkWB62LzGwrSkbImdmmRIvFO4kvzyLEVV9x4JwR+Ey6nQdonwauSwejPxMB6Qs9264b/rsBEZzmiqu5uquCIpm2Kjl7QaIb8bd0DpKr0klSPsrdiwTMVdy9KAFxjcVInCOJpu7Cv/P7LBudCSxmaXRmH/t1OvHZutbMzqL7AFSUC9mUGCTQtW1337ThvQQoWtmKz9oYOqO+aj97xGen7IC9q5mdSuR2TEd0jUAMADkS+Gy6/YSZHQ18CDgkXSyMSM87lsiNuY4Y6bi2p9bEfpYn44CXiwsTM5stu4odb2ZruPsNaXurE60NEN3IC9NJul+I7hGMdesOd9sPE9+X4vgwA5ErBHCnmZ0HnEL8v7YBbs6C+LrlT5jZfsDJadnHgXOzk+BwXlPT8pvS/7lojfw4cIVF6/tTDe/H5Wa2XXpNEBeT52bL67b9cMmyG9OyZ6gZVGElPRJmVvRIjCHSHxal+1z4krvvYxWjVosAy8yOAs5y9/PS7Y2I7wBV284Cq0vTY/+Vbs9E5Di9n/gfH+nuX7RoyT6XSEcoNA1AmptIAfhX2pf9gb8QFzH/Is4pvYN16gZ4FO9l6ajQYhvpN8SIw4GXTHQBAhSvffeS5ccSLYFdI60z6xHHo4eJY6cBD3lNK2hmfDrmnkr3cbeyxFNb1ALWwMxGZSds0hfiJXd/M13FzObu/8iW70UEFrcSJ7KFgT+4+1pmdiNx4DnLe4bDppP/akRCZLHs9uyqpnLYbFp3JeIKu1j3Nu90nb5MGiFH54TgHkOebyVaiC5JrRjrEf3wn0/rnkrkR32CCDR2ILrw9k7LVyO6QTYnAp+TvVNT5koGD//9OxGsLE7nhASRVHktnQPNlkTfft4V8rC7fzNt5xzgcz541OjWRM7VxkT+3gQzezC7kjoVOM+jpfB44FfuPjYFOCcB03kaoZe9v/l7eUt6v66o+D+V7pfHaNXabsCKbRfde2XvZV4qYgfixLQy0VW9NfBtdz81La/77F0IbJUdsGclDthbEAfE1xtaTWYGNiRaPO5Lr/k9HjV6BlpJLXUF92xnjLs/Y2ZrEYHyHe5+UbZ84MLE3ZdIJ4GjstaAu4kLj6LFdmHiAuItOuVjbkrLViVyDV9Jt5eqWdeJoHNit/1Wuu/itK0NiNyTp4kT70CLao/8RFZmayL3tGrdoizNxLymfl7zQAtEjxWJILPq/ViPzjHIiPeuOBk69aMdVyRySst4XeuG1Zc+KIL+3pP+w+5+i0XKSNkTXpm2XdmqV7Vtdz8t36+yfU1/H0J0S76X6EI7LXvcFUStwos9Wr3XAA7xTtmFu4lW/tfS7RmIwTz/Z1nLdM9zj/ZIy5ird1na7+fM7Bo6o0I/RhoV6u613b39sJKW3Z7ltaky6TGlvTjWkL4xJakFLGPVRTSLvvWZieh9YeKk8E7iYHVOsQ13LxKzC4+koKZY/ph1D4ctvpz/dffXimUWzcp5dHydmb3HU4mCHq+5u1unpW2WfGHD1c3r7v5Pi8rEI9z98vTlLyzpkf+xmbufkK4oL8y2fRNx9fojIgH3BDpBU9nw32eJJNmDiPyrwsue1VAys+97d77A2WZ2VXZ70SLISZ4C3uXRpP+zFGj9zMyeovtz/lngcIvCic8C15vZY0TS82eB/VMQXZRd2I0YKFB4w6NOVf6a8v9T6X5BV6BV2gVdse1C7VBqdz8pBXBFnt/m3lOyouaztzAxEKPwOpGk+qqZ/Zcor7CEuz+Q9n9xuk8qr9DJdcLdnzSzo81sj/T4YtH6ZjZH+ns+d18tBV+fJb5XfyXe/5Xd/eD0uN1JFyZp2/elA22hrmTI6sT7X+XhmmVNmra9KNEVU7gi+/sWbx6yX2WXuoVVJ6pJxQe3ThbPWxqsZOttOin3w7LWqdS6UfV8daUPZvaalIci0KrxbDqO/CHty450WsJrtw38O33Ox6XX8F5gJuu0gt5EDGi5ifj+bZm12OxLDKpawsyuJUYybp1t+4/ADWZ2Zrr9MeBP6bxwl0Wx0sXc/ftmthAxeOC7xICGskC4aKWayd0vNTNLn4MDzOxqIijDzHYHTvJO8dQ5iYv5X6fbM6d9X9jdP58uppZ293OIFtKfEMeRvPuyKFP0iEWrfZHnfLW735q2W9uL4+6135kpSQFYt9+R+tbT7b8R3WvHptvHEx/QYtqXx4lmzXPImNkm9HTZEa1Hj1lMGeMWdbj2Ij4oEPVrvkl8CTcgTvxnZ+uvCXzKosvvv3SajpcHTrFoop8jtRh8mu4m6+JDOpDDkz70AC+kFo+rgJPM7Gm6a6S8nj1uOaK2zaJpm6OJVpLtiG7TM4iTZeFZiwTi4kC5NfCERyLq9um+4qplVjOb1Tu5Z2PMbHF3fzA9bjHiQFO4IrXc5MnZlxcL3f1xYJv0v3gpu//F9D7ORhxURhEVmJ9Kz7MrEUB/O233UqIroXCHmX0CGJkOIHsR3WeN+2X13YBN2y57L5/suWJ9mk4SNGY2VxbU1n32ag/YRA26y83sQRgYUdp0UGvqCv559tgvEKPHnjGzQ4nBHEUAVnphUlyxEwnjg6TXXRUs1F7tE4FwbWtA1bYzg07ellUMTyfcXo97dUXxBVMralWu4SV17wcNr6lh+TbufrSZ7Vt2ceDuh1UFK2a2jLvfY9WzPSzr7n+w8jzE93rUSCxb9gjdtQ6r1KWVnGNmG3vqQsz2+XZq0iC80+21PRF8FIH2Vem+ym1n9gFOtegRgAiC7iK+e4XxRIvkx9L+nJ6ef1wKeEsHIKXA6nwiBcWAXd19bHptLxGNCusTAwv+RbTQr5rWrctf+49FHt196eLqCSLlovA5dx+oXelRb/JzdFIX6s6fRevXKtn2PO0nFpXwP0fnQu8PZnaMux+RXsca9PTiFBsxswWJUfTFgIhrgL3TOWKKUhdkxsxu9qgOP9BMa93NwkXzcr68Nwn/KGJ48nrEyWdroq7LZ8xsHmJkzIeIL8ZFxAfhn+mD/Rm6R/391n0gqbXqyvZR4mS3DNUjCg8mTn4npbu2J67C90sn2f+k9XYgmr1P8lS4MLVOnEbUJDqeCB6+6+5HpWDwr8Ap7n59yfu5OFEX5v1EoupDRNmOhy1yqQ6j56rFO0m7G6Z1i9anRYlg5cJs+1vSPfIzb3GYKGb2Ae8puJvfZ92jMyH+hwd6Vpi1ar+sphuwZNtdIz8r3ssdiJaVvNuq+EL3JrGWffb28k6C7Cp0DtjXFAfs7DXNQOeAf49XF6ItHj+C+q7gW4lyGyOIz+sq2br59+vHRO7jJ4lyDrsRJ6oV3P2j1p0QXZjf3We0wUm6Ra7KVTXrOnDXMLZ9gbtvW3ESn81jVoedK96257y6ovgW7r65VXSnAPMO4zU1LZ/V3cdYeRf6p919kZr34+TU2nF5yboO/DkFd2Xbfq9H/mNt1/1QWUorsU5qxn+JC81in9+THlrkKp2Yfu9AFM4+sI/nKN22Z8nsFi1z+XeqdBR39vj13f2yqiA8ayHDqqcdqx2sky7CTiaS/l/Jt28Vo0K9ky94G/G9LM5ZI4kSPsUxvfH8WfPabyNGev473Z6FKMO0fLbdW4mBL2+Z2U3uvlp67MXERWbxf9wR2MHdN2h63snOp4JaGFPLD83FPa8jkiWL5UuQiuZlj7mt5/esRDmApuf+KDFEdmL2+5aG5bfl2yZq09zW85jRlBTZbNiu9fm4Ilcuv+/W9F6PT7fXA47pecwMxMjEFcgKzU7mz8C4uvuAz5QsP7jPbZcVl721z3Xzuj1DLoJJVmcqu2/XntvzEt2RCxPdBMX9u9NdgHFOYLc+n3dB4ir3l8QIseL+h4ng+qH0e750/6xkBWmJAO1zaRt/SX/39blL67+7ZtmcE7tu1XJS0VjiJL5I70+f+3zExC4nStNM0tc0hPfzGzXLmt7rDRqWl26byOO7nQjKHyQrApyWv4NodT4/3V627Dtcse1BxY/z+2goPFqxzfXT7y3LftKyMcA307aPy36+l5YfX/JzXPYcexLpFXfSU8uQmqLc6fY6RItV0cK4NVmduIbX9pO0zgeJlqtTgJ9my2vPn0TO9NeI7tDvEhf6xbLbGVyvrqiheQlx3DiC6AE4nO6aaxNK9nXQfVPiR12Q3b5MSd+6dUYFHUBMYLqQmZ1EtBh8qmcbr6bfr1jMb/hPInkVqx8Zsx2Rm3QaUXSvK3+nwQ1mtqpnQ3RLzEHUAoNOLTHM7AtE9+irRKJtcRVYtFTMQbQ+9O7zXsA8ZvY1Bo+QLJqNf0RcIb2Qbs8JfNndv01z7hl0kqRnBFYwM7wzIW3lXGqN71YJi+7B9xNdn3mXx+i07cLWZvYfdz8prfer/LU37FdpN6A1zBHokc/ykMW8nX+mM8Iy3//fE0m/V7v7PSWb+Y6Z/dfdL0uP/xoR9B5lg3MoFiYGXrw7rdvUtVDJq7uCF61Y5S2iW7t43FtEd/pvKh7f5ES6R7XmLq1Z1rRu6XLv5P+d4J0RtoNYfWmYD1Tc38/yvYk8zCpDfk1DWL4NkdtZpum9PoQYsFClatvHE9+/V6F0uqDfUZNWko5JS9F9/CpyTWcxszXd/Zr02PcTF0CFU4mR6r+lZORe2baJAOcyursaB56a6GI7k/guX0JJAr835zTtTfW0Y78gukznNbMfkgbrZM9xJZEOM5IIoj5H9JzMUHGccuK8cjQxwOELRMmaopU9n0JpfyrOn1U9R9m6xxMjX89Itzenkxq0GdGL8yU6vTh5K+WzFnlvRXrG9nRy9aYoBWAZ74x86S2iOd7MbiI+YFsSLWNGdB/2jkg6JwUtPyGGzzudD2HpFys9944WOVXbA8dbJIoeT1RCbyoctx6Dh+i6d3IVDiKG4l6elq1NZwLdrxBXtVUjq84jcnJuJytrkJxEHMw+SjYRbrZ8I0+jFtNrfN7MNia+8LW5Z1YxIS1RORrq51KbGNMTV1Gj6J6z7SW6E1y3BM4ys7fSPj3nqdhkH/u1K3F1tgCR/3AR0brUz6SwSxMH7d2BYy1GW55cnByIE82awBGpu3IC0dV2eFq+KfHZ/CqRuL5Mug8aciiAEWZmni4d08F5eobA3c+lu+xA1eNeIVrFSM9VWx+oD3UjCuuWDXd507ozNiyfWJPzNTUtnxLv9UxEHtMoL0kMp6Zkg0V6xd5EK+0E4jtwPSnviEgJOc7MigvWF+iUUoCaOVKrtu3u66fu+fPd/ZSydalI4LfyXLgBnsrZEAOKXqx4TONgHYuadR+jM6q6CHoOrXjqeYgWuGXNrJh5wInzZx5AXmxm4yg/f77fU9FdjyK5P6V7YM9hFqM/10zr7uKp2DgRdJ/k7s9TfvHxaeK4/LO0X9fR/X+ccqZ0E9zU9ENMjr0bPc3lRDfIPsTV005D2N4MwOzZ7Ql9rDNPeq6HiemI7iMqXdetM6irg57uDiLJc1PiaiGfeuIC4gtfte1BXXLZsmIurnyqnrzL9ja656iciTSnGXElOYI4qe5MtAblU5PcnpYX03O8Azg7/1+VPPd1Vfs6hP/ZIhX3z5X9LEIkyP6SwfMiTvR+EQf+yv9F9rg5iUD0zZ77RxIHt28QXQj39CyfN/1Pjifrxsv2+VZSVzXdXQO1XQuT84doidso7fvcxc8Q1q/7/FYuG+7yaXHdyfnck+t5idI146ieLugKKtJK0jFmRjrzrS5D5KT1PsdosuN4dv8BVMyR2rRt4uKo6rX+ANi45P79088fifPCT9PP34h84eJxddMvzVXyM1227p+Jc89R6bveV1oMEbCtSxx3riQurB8iLvZXrvtJ6xfpGTcQLfEzpNc4uma/58rer/uJ49KGDCFFYUr/qAWs23ZENH2zRYHI44n8rbeAn5vZRUTZgl/TSVR1706snJnoylzY3T9nZgub2Voeow4rR8ZYJKV/mugXP5GYx+/ptL27iW6tUl4zRDdtewsiN+GsdHsOM9vc3f9KfEmvs0gQz4f/7pX+PDF1N53D4OrGRdLok6mL6e/EFV/hD8ClFonDnl7fCakF5UyPaUjeovyq5VWPZMo3Usvg03QK+kF08U4PTLBI1H6S7u6BifWKxXDo3lGsi5H9z9PvTdLPQJdtxX4tZWa/qHrC7L3+FNEl+E9SdyKREP88UAz1/zgRkNxMzMFIWnYp8fqvT+utmj4/vcnR06d93To1ao2meSRsU9fC5PSiu5/f0nNNLSZny9vkNDmfu2rb+xCfxb2Iltz1iAu6wr5Ul2z4j8cAF8xsBo/RmksPPGEMPNmKlH5haQSod5Lwi+cpKzxau23gYjP7CiVV4YmWs29alIAZlMCfzkMre2e2jgPoHhH6aPqZnsEt1eOIIrnPp+3OQRy/nya6G48HPuFZy1X2flS2RnsMHrmFGM18b3r8u4huvyL1YEaiV+DWtO7yRE7amlT3HP2RTnmM/Dg2kCrj7t+2qLL/YeL8/UuLKbUWoKIlELqOu1OMArCMxzxc30r/zI8SiY9vmdlxRJ/xnkQuwa88hd4ljic+LEV+x+PAPWZWdA1WfbG2AX7mnfyDYp9eMbPa5lKrH6ILsL9nIwTd/YXUxfdXou/+Msq7GCHqQ/0kve7iNRcHmR+k5vkvEwHiaKIfvnieH1uMBiuau7/vaRSjmb1iZrN7lIUoMzZ9IX9DvJ//ojsnYCeixWeP9JwLEQfL4SrtVvX+p7Io268fEwFkLU9FSi1yB7cmCsu+kzj4P0R0ZZxCjCrsrSN2G1G0cTnioPNCyjNqrHBNtIq+SkUORQqEf0cE8UOaImkSqK0P1IfKaZ9oDhjq1h3utg0GRov2To9z+MCDGpaXuLZmGQzvNTUtrysL0fR+PNGwfNC204XctkTL0uOUlEbx+pINj6djzF+JgOh54iKycCadORQHjfr1+pINTdsujumDqsL38Z3trdv3Gqk0UNqvutGhFwBnZMfhDxOtRqcQOZ3rAd8ws7JaXU3V6qfLjw/u/jczm87d10vPdTLweU+1LC3KGn0lPfb7abXTUnrFjMW5wSLyXcdrpsdzdzezfxBlkt4gegnWBe6gM/vCVEdlKHpYTPGyCzF8/kI6J+TZgf/zrOp9xfoTPdR2GPtcOUS3WO49UzZYqt5uZte5+/sHb3XgcQ8Aq3t1jtjE7vMpRHfAxXRfAQ66KrE0Ia2niccnJ0vVra27+v2VnqpMp9vL0TMdh6fBAQ3bHqhrVrF8R6IV8z3EKKZriNbM661T96rpOWYlPr9fIbqaZ8iWlSUFX0uUgfhQzTY3JU2R5FFGYUXSFElN+zNcVlG+wDsDPS71VBU/W+dKskT+Eoum36Ppnmqs2HhRHLMsafxF4BGPXKK6+VfnInIJi7ldZyLylF62KGZ8OHFyuIU4thzm7j9J6w6aPqdYni62jifqff2WmAFjP0+zB6RWignu/u/0eVo5PdfcNe9HoxTI1E6vYxWlD9KyT9KZdid/L29PLbX3ERcYxxP5UQMnptSS/APiIuECYlT0Ph71wy4DPth7QWx9zM3a8/h1iPf5Au9UkK+cfSRbr/FYkG8b2MzdT+3jWFA5OMDMvkUEnmcQQdsWRJAxr9dPobSpZfNvZs9VnLMmEFNs3QJ80mOWjJmIc8mK1lyt/rj0vHm5h5GeBg1YefX/+919yar/V/F/sprJuC0KZ+9MHDN/C/zVI3d7BHCfuy9Rtc9TmlrAMqkJ9QUi0t/PO7PPfw74YlPwlbyWPrSe1l2CdPVkna7AIrKfA1jX3f9qwxvVZ3RfkRTTfRTGmtlhRIuKEy15RcXjy83s80TR10ETqBJDmbvqwfSr4TU1JmWnYHhR0ufUzJbMvpAfJbocepvDJ2oUZKa2W9UaBgc07NfvzGwBovvwKiK4ymc2+DkxPdNRwOUe9dK+RnQr/sDKi2DulZ53DyJ4ey+Rh3Ec0RVZ7HddUnBTa+T+RIHdK9JzTkhB8WRXXD33spijdGZiJO6cdD7vo4nW57HpvoXpmSyZTgX8ui4RiBaBlYnWRSNaF28D5raYlmVOqudf3YruuV0XpDO367LufqPF9DjnkabHIYJc0vKXKpZ/2t0PN7OPEN1pu6TnL6ZvOpIYMbwCMaT/WOKzWda6XSi61Jvek8pBRGa2J/E5eSp7Lk/rQ3Sdv49OseR1iXyfd5nZgURJhw8RLUNHmNmficmX/0Z0a30tHT8fJ3oLLk/v93jgTIuZL/JW4WKUYdncrNdaJHTniu/hrHRGi9fNPlJ1LLgxteBUbfsbdEqqlI4KrfqukgYHuPsPLUZEF5/TXdx9vHWK+1YlywM8Z2ZfJ2p9Qfxfnk/B81vAEu7+cTPbPj3Xq9Y58JS2RhNB9OtEisLuRHewEce4C7LH3W1mv6V75oCiakDdqFCoH+k/D1HC45GulaPl/qPpwuHrDA6U++3VmHx8KkhEm1p+iObfpsfsmP8uWb4BkYT4DNF69jARZEF5PZLx6fdYYrTceCJQ2QX4YZ/7vS9xwDwg/UwgrhCL5bMQlcXHEgfyg4BZ0rKHSn7yOjpnEEmeRxNDmH8B/KLP/RrOazourX8C5bVu7icO7pM04ZLoepydONlent6vTbPlTYMDaveLCEQ/QHTpPkqMosyXv5s4kJ1EdLlelu7fuewnW++rRDXpURXPW5kUTFw9P0qcrAf9jymvX3Zb2fNMwv9D8T3bt+Jn7/RZ/S+demIPpe/BHmndo8iSmYkTZF6X6GRizsri9nLEST9f/u7s9rLpc7g4ceKwqveE+A5O37O8qFt0JxGwnUp0rUBWD65uOZ16TocTxVl7/y9Fsvl3SfWuaEhw73nfK98TagYREZ/7ysERxAXeO7Lb7yBOrnMR83/mj12P6JJ8gTiWPpju/w2wYc/7cXzJT36cOIdUmy3dnp8I1PLPTNWx7y6ie+9eeupp1RwLXqnbNtHifzlxUXBW70/TdzV77pFEesLC2U9lLTVirkiIYOUI4pg8Pv09hvisLklNra60370/lxGDxaYvec4ViHk1i9szEl2YZ6SfL9F/jbG7iK7FByr+FysQaR97EMVg83UvIka03k2UATmueD+m9I9awDLu/qB1qg2v76lmUo99iQi++N27jbqhtiNKtpc3199vZiM9EiCPt5jQtZ/9rhuii0fX5H4w0E0wMBeh1+cxQOQw/LWf/ajYt4l6TcAa7r5szfLHiAO3T+y+lfHOFE0vEieCXk2DAyr3K3VZrZV+5iBODnkr1WjiQLoI0fI3O2m6IG+YO9Cje2oFohwJ9AzEoD4puKk1smn6pcmhGFBRmg/jkedyuJnt6Z1cx16ruvuu2Trnm9n3s+XLeNa64e53pO7VfPmd2fK7zGyldJx4y716/lXq53atmx6nafktFknYixG5OrPR3br1skXJhZ2AtdL3fbpioUWJhquIz921PrjETd17Uje9TmXpg2RRT9N9JU8Tc7c+Z2avm9ncRIvITkQr2p5EULIicImZ3UMEvbulFo3/pO381ktmruh53t65WR/yhq7FZKOG5WXHgoc8VX4vYzFAZ2Wim+6nPYuLz0fT4IC8tbHo7XBgRjP7kkfJl+KxI4iAY770Wfi5u+9YsXv3p1a90lpdXt0a/QPgfDP7mKfq+Rbdrn8gK/fg7v8hSkH8LFu3dHqrbJ3DUgvcrlRPLbYX0dpclQM9t7sfa2Z7e6fO2ZWVT9oiBWCDHUp8QYrfVUo/NdbJwTjXIgfjm2Z2uEfzaF1X4ESP6ktdfXd6J3dlNjNb3d1vTLcH5ZSYWZFTsg2R9/CyxcSyKxPJ8uOh+8SfunoW8pSLZeV1aV4kylNMGM5rIkabLuvud1Us/xpwXvoi5V2nh1U8vi8Wc07uyeA8lyLfqWlwQN1+XUm06h0EnOcp1yRzTfbzS8/mKrPyvI4X0/aOJg5AdQehyqRgj0nWZyJG7pYl2ReDT/5LjEq6kMjJmWzc/ej0uy6hGHc/wqrzcOomS4byLpG8JtK9ZnYk3d01f7MYHfec1c+/eqVVzO3q7kUrY+ERi9pr1C1PJ6LvEi0WD3oM0Jmb7uTzjwOfILoq/2FmC9Pp2oRoOV2T6CL9icWAoKvdvRg8U/ee7E0cz14jWoby7vUHiXlQz6X8+3i1RddckUy/FRFczkK0dF1PBCWbe/ccfWPTseNI4CV3f9PMXqFTw+4IBh+n8/uusOq5Waty/F4ijpVN9RdrjwVWncd1g5m9392fSY+bhcjj2p4IjpsS+PempNCqRVrABSloOz19p09Nr+dj6b0bY2bTlxx7iv2rbEAws+9WrPNti7y0C81sI+AjRJC1hbuPNbNTvHp6rnmJ/+3SxHR5Z6X7P5beC9KFzs+8IgcM+CyRp1zkQB9CfJ6KY1/TaP0pRkn4PaxkrqwhLr+NaA5dnsi9OI7on14nfdG+Q+Q6QDSN/iAdSBchrqCmI5pmZwd+7TEys2mfxxPDkour8RFEbaeV0+0JHkmUOxA5Ql8ngqSi8N3yFq0zBxGB5zc9JVtatKxtSgQjE4iu1Svdfd8U2K1CZ9LwTYj8pmWIL/6fiau06atek5kNtMb1vKa103b/weDJx7FoBfgXPaM3m07YTSzmEzu2ZLuDrpisZHBA3X6lg+oHiNo4q6bl17v7d3q2O1t6rf/K7jucOPEW1Zw/Trw3MxF5TytQMxCjZ/vrEP+L8z2SVT9G/N9bT7JvYpHr9RkGz7ZQJH7vT0lOnrtvbZEIvz/xfjtxQD/QO/Nfzkh0966dNnsVcGS6UiedwHaj07J8DZEX9h/ixLow1fOvjqBnbld3/01a9g7gR8A73X0jM1uW+N8d27TcapKRs+dehE7y/8xEIvTL2fL5ia6YtYhW3kfdfcN+3pOa59y/7P7i+5iCxy173svTsmPWQKHfkm2P855ZBczsXmKqnn3IWlSI78IW3j0/75aUz816A905fu8hWh1XJBK6y16ze0kR4N5jgdXkXKbl0xMDvT5BjEI8DTjd3c/u2W7XdzXddznR3ZiXiikevyBxgXQE0Zp4o7vvmy0/Or3ms+jOmbui5LXmL3qcmX05u2tGIl3j7uy7uC9RrsaIrv/70/3zu/uTVjGfsUcZpYuArbxTWmM24NTsc/kroit8UA5YCuxWzb63MwI3u/t70u2PEi2+C9EZrf89T2WZpiifCvpBp6YfOn3f44e5fFAOBrBNyeMH3TcR+zyh5L68f7wup2R8+n0QUf+l67Vlyz9LZy6yIg/lQmLkV/HYWYnm68uJPvvKfnYiMfYu0vyARADx62z5/UTgtxglxWVJxUMnw///xobll9bd17RfxICEXYkcr4foLly7HJGX8QiRk3ULsFxaNqhwY3Ff+v/eTsVcaen2iSXrn5h+FyPt8v/77XWvo62f9Jn9PpH7sTNx0XJ4vp/U5OQVn8ua7c9EtCaULduCijlIaZ5/de+q+4icmW2zfR7V87+qXE60nq9a87yfIy6CHki3l+r5fD5AJNXvTZyIBxXarHpPiBPrjsB30u2FiHqF+WNmqdivLwEL1uz3KkRe0Dg6OT53EReMdxOjPVdOP+um78j+RKv6/tnPvkTw2c9nqy7Hb0LNesV+bE11YdHSPC4iR/g4IsftD0RLz8N97Gs+j2ppodVsHzYhWnpPLtmv/St+Lk8/1xMtRkW+8OvEBU3ZPs1AnAPOJgK6s4kBDDfRk9dWsf5IYlJsiILLM/Rs+57sdmUOGOU50F/q5zMwpX/UBTnpFTkYOwJrW3cORjECJvd7i7pjpbykBaPEgxb94MW0GLsRXQKFupySJ9JV0YeAQ1L3Sp6rNipdMW9LZz61Qm89mteJQMmJ17ypRe2Xru5aj67SnxFN1Wel+25NrV6FR73+CuUSM/uwp+H3k9Dh6Wr+IrpH+txF9ai7d/azXxYlPe4lDp5HEbl6+ft3DFGx+vL0+HXTfcUclQt7Z1j/wkRCLcT/4EQ6c6UZUdvr2GzbXXkp6XNZtKS84e4vWncuRmlrxBSwpLtvY2abeXSVFl2ghcqcPIu5+35LXBgsbJEj9wV33y0tHyivAZS1/G1KFGC+ijiZXeidVoem+Vd3ZnC9rk+l+yqnx0nqlq8HfMHMHqF82rHdiRGrN6Z17zOzebNt/4JohdqeCGquNLOr3P2BPt6TXxOttusTQfG/SAGhxVyqx1a918T35EIzey69l3/x7pywk4iBJHnL8VZEy+yCQJ5a8BJxgj3dzH7nafSbldRMs/qR2HU5ftQocrdWIQKVnKf3pyqP60KiNWZN75Qs6f2clMl36FHKC63mOWW3ERcjxX1OTARe1UNQtFRW1uoqMTPxXftsdt+gEZjpe7k7URj1LGIgwh5puxOI//2JwE3p+OXExc/vs81U5uN5SQ40MXCseP5flKz2InGxfGbVdlsxpSPAqe2H5hauk/PfJcvnIyLytdLthYkP5RFEd9wvsp/fER/ARap++tzneYmD2tPpOf5I1ISperyRRssRX6ItSVeNxCihD2eP3Zr4Mv863V6c6DqA6E4dR+cqaizR8rcD0c/+MjFK5vLspxjVVzayLh8J9uv0OrZP+7cl0ZVbLH+ZOFC/ShyQXyZyRIb7/z+IGOp+Jd0jffamYdRd037RMK1H/vp77yO6Kx5N+3MF0QKwCZFTt096zMpEgvxewErpvm+kfXgj7U+xT/8EDkqPOZboCrmNaDE5AjhqSn8X074VI7CuIloI56F7pNqviQENuxJTl4wnJrOHCEIW6vmM3ZH9Xdbyd1vP809HBGInpff8t+n+u4g8oa4r8vR5PZvBo9wuJ+bahJrpcZqW03CcoOd7RbSeDRqxSgRKe6bX9GY/7wklx8bs81n7Xmf3LQ/8kGjxuCS7v7SVJS3bqmbZH4ngbpa0zSeJQsXF8sqR2ESL1JFEd+w66bN0CtH6cnPD53IE8Lea5WcQn8sDiM/umUQX+UrExOMPEIHIZ4i6ck3fg0ebHtPn92kMEWCfRxzXLiMdk9PyCSXrTEi/i8/4bUSr+9Nkx76a5zyTONeNS+/vxcTxdcWex72XOM7uTTp+lWxrXrpHfi5ABMLTZ8t/BPw9W+eY9D/YM/1cQVw4nEUMSphixze1gA1W5N2UJmC6+3b571xqVfiDZ0Ut3f1RMzuRyCvYlE7SffEcX/LOVDPzEVevThwA+qk7hrs/TSSX9sXjU1lcyR/t7jtly560SHotWnCe9KwVzuPq8LD09/fN7Hwir8mAXd29uCI8ycy+450Kx70eSy0UnvIh9qI7AXomItj5cL7rpCRz76/C+8TYgihHUpakerjVj7pr2q93mtkRxPvlREvY3t5JOn4wtYaemG7vSJqY2t3PsxiBuAzxXt/jnbycn2fPYUQAaGm9g4CDzOwgd/9GxX7VJtlP4SvIY1KL47eJA+asROAPgHdaWI6yqI3UlZPn7o/1tGbkLU1lLX9dPHLkzif+XzMRLYufpX6E3JNEoJi3SLxMnLigfnqcpuVNLZNXWkXyP4DFJMdrEu/jDcQF09XZ+nXvyevpGOdpW2PoznOse68LTxO5i/8kTpaF/S2S/y+lO4n/dKJu17GU58zV1UwrtlE1EvtT6f3Zh05e2leIlvzSEX/ZNt+ymLGiavkW6c8DUs7W7HSKvI4Hvm4xYGt7YPr0GXuZ+J/0MmBWqyiwmj1nPzmbpTN9ZMvrBmF8NHvcG8BTXpKHVmJxj6Lf44nX+ywx4Kf3HDuB+O4UdR/zFv9Nie/TO4nP0CJEY8MoIl1lhtSSeBjRcpbnSS5JtP69kbZ1JHF+24BOjbYpY0pGf2+HH6IZO799FiUTt6Zl09Vs57NEC8fviNpXDxMjmfrZhx8TV4HTEQewZ6moU1ay7rie2yOBu6qW995HST2aPp93HuJg8BTxhfoD2aTWU/D/+WdqWg+Hue2LiSvwUennU8DF2fI5iZbRccRB+nB6Joav2fZ3iYPJAUR3wq3At/tct/RqM1s+xa4ggcWa7iNaRw8jDtBbZPf/hei+HUd01XyFrOWahpY/Ijn6d0Qr0QlEK+SobPkKVNQe6uN1jSK6hZcrOy5ULafTCnE70eL3BmmC+7R8BJEHVhT7/FzPdrchq8dV8ryV7wnRsn0W0UL8Q6I7fZs+3+svps/NnenzuWzP8/6Birp/1OfENdVUuyrtz++J4+SXKGlpnsjP5pNEN2lVzb81iTQDiCB60Gc5+599hPjO71/zs07dT5/7fEv6necI562vZbW6FiIu8s4heghGD/F9Glf2u+cxexLnrTspr/N1K9EqPD7dXo9oZS4m5C7SYdYo2fa9ZOdkIhi+J/09flJ8Fib6MzQln3xa+KEmgTctf7TndmVRS+KA9hei++LB4if7kMydbWduYt6yfvZxQvq9BXEAm6vnIDQokZhoUajsmiKqVn+ZqO+TJ3oeQOdAWPuladjnjUru23Uq+H9fQSSSXkgfiaRD3PaEPu+bHZhtiNu+m+4k/JmIEUr9rHs50X3zfbKk5Gz5ZXQHHqPSfV3B+mT6f5QdrG/J/v41cTW7S/q5gJirFcqD/Pw7NjMRSNycfn7Q8x6eDGxe8f3Zm5hK6MD0czuwZ7Z8jbTNfxEnhjeJ3CPIutR7ftavW17x/qxMtGIXtz9U8pide25vSqRFHEqUJ6DiPRmb/s7fk2WIfJ49iKnZivtrL6iIQtAr1vyfKwd9kLoD6e7enJB+70UktJ9HtBQtQpTVKB63CJ2RwvsTgfqSadkHiIuiv9FzPO7zs/km0QL4OoPTDfYnWh7/lm6/k6i7Ntm+K+l5jCgVVLX8hvT7QiKFYSXSgI3sMV2DMIjv1A+JIPEIsmLFQ3ifXqJT2qM45+TvV1Mh37Hp962kVA7S9yl7zKAu73T/Z4iehOOJC6oHiQaPWYCfTO7/Sd2PuiCb3WVmf61YZkQ/f66uqOXxxBfzZ0QEvwud5MrH6e72fJkIfvpRJPlvDPzJo7hhvvx6BtfK2dLdZ6vqmrIY/jwrcbLNu9VeotMdsjcl9Wj69B0z+6+nYrcWU+6sRySnT0n7T8ZtP2tRG64oJbE9WV0qM1uVGCE1W7r9ItEKekvvhko8TFy9Ft2SMxB5Jo3cfb3U/b0t0eU3mqi8XXRDLkAcrF5Mt2chuoPetKgjNcmZ2TJEC9Ds1j1P3Gi657Jchxgp6mm9E4DbrVN0coeq5/AoGvktBg8uKZbXdet/hvraQ78k0gJOJXJUPkmnXlfVtCuPEYFt07Qs+T6OS5+bwnfNbCvi4mk2YhDCf4kLM8zsICLN4aT0+L0salIVCf+V70nq4vmzu/+qZP9W7X2vzWxX0vfZ3YtC0PPSXU7k0fTnDVZd9+/fFvXOiv/xGqTPojfXVHsk/fkqKdE8cyz1k0tjPdOhpW0WaRAjy9ZJtiCCm3HpsX+3KK0wWbm7p/NVVamSH5jZ7MTnoyjJ8KViYcUgjPd5KgdBDKQYN8R9Gpm2Pd5LSjclj1FfyPcFi3luryLSW54mum7z//28+W1P07R5lG85j/jcG1Fmqair9tWhvJZJTQEYlQVFIfW9EwfOL9M9Kq6wfX7D64tazuTul5qZpQPDARaVqfcnruJuNLMziQPNZsSokH3TdusKjJ5tJZWi00l1ASIfZCW6R+7NnLb7DSsvGHglkU/yO++ZYytT+6WxmAfzcXf/r8WIvuWB37v7C8RV+Dlm9lWiq2cZOsUVGzVse6J5Sb2vnuctnezYOyOx5ipZ7WWPGj6fJk7MP0v3X0tWKZo4Iezm7lenba1JBO3LVz0v0dXjxGfzTjO7ON3egMhpKfa79v3yyDf8RcpX+RrRpVkEYD8miuleQXyG1gZ+ZFFr7JK692sYliZyTuagOyB5mehiK9xLdD8Un9GFiFbYxqKT6b3apngP0vfgZHf/SLpdN4LOqJ9/FR+ce/Q5i5F657v7KRX71LQ8P1aNID4HeQ7POsSx6tZ0+7vu/qds+SZES9RbaXsnAOMtRrt52XOm17IpEUx828zeRXRP/dk7OZ+1F1QWteYOozuH5246o3PXBHY2s4cYXPevMifOKoqDWhSYrns9ywMvuvv5VY+xmFx6eaKFv8h1GwiELa5ydyC6Fr9vZgsR0x7dBLyWgqEiaOy3APWkUDlC15tn+tifwfO+Tm/dI79H5re9M29wUTuvqqDz12v2+UFKCvkSA2qeJ86HrxLB4g5EL8HedNcyq7tQHUF8T0YBS1rMK3xVzeNboQAs/IiI+ssSCkcQzfF3uPug6VfM7ICe2wNFLRk8jPs/6QB7n8XkyU/QSUR9gO4WiyK5ufGqyd33S1fgeaXozYgm408Rw7h/SucL9DLwzbS/tRO/EtXsf8LgQpjr01z9+jRgFTNbkgguziKSvDd292fT1dYlxBdn66IVI+3X3kTw8TJxFb8SMUH6RU3bbnq/hqlqsuN10vJxRBDwPJ0W0ifTFdvnvD5R9uUi+AJw92vMrGgVrXre49LyW4iTYuGKnm1Xvl9m9n9EYdet6dQPGii6OCWuID2S+880s/e5+/W9y62TkDw7kTh8U7q9Op1pkh4mErjPIjtQZ5/PefKA3d2ft+6SDWWtWEumZcfTKfsB0VWZl/0onQXCI3l7DyJVoex11y6n+3jwBtHaflp235zpPXiA+E4vki748mBkDjoTTs+efg8qH1CybycAJ6SLjK2IsjULu/tSNF9Q/YA4tlzi7iulVqr84nVDqj1HfL+WJj5/9xKDmqD7BDxQHJTuhPEqpZNLe5pRhObp0CrLcgCn2OCZEn6br2zRSvsOulvXHqWBmW3j7qfW3LceMSXZw/SUKkkX559jcKtecSFYNghjJHF8ye8s3iOnU/al7tyH15cMepTy0hr3mtkzxHf6WuA6b5iWrVc6N36cwYH0FA/AVAkfsBgVs2dZV4+ZPUYk2/4nNc83besW4gt5RdHcama3e4wCWZU4OMxBfGFHAz/2NGXQ5GJmW7n7aRXLbicOGDd4VMtfhii4+vG0/CIiMf0rZKNm3P3r1lz9upg14KvE+3eERT2jgYMC8WV7I/3tqXUBM7vV3Vcws48QOSffIa6GVq7Zdl0T9ySRPe93gSdScDIu26+jgDPc/cJ0+8PEyeUUojXgMeJE5ESg+yU63dg7ES2TxbQpHweed/dvNT3vEPZ70PtlURH8T0Tl6b9XrL8A0WqRH7Qn+wEstbYcSSSOL2fRJbQp3SP3BnH3K/v4fN5CJO0XI60WIf53xf9yrLuvYmm2iHTfde7+/vT3ynRqD13l2fyraVuls0BYjHR9lfhe5YHhc2nd2uXpMbPRM1tCuv9vwMHuflxqjTgEWCXb5+2JfKzL6bRmfsPdT07L1yeOBZXHOjNbjfhsbk7kAH4s3T8vnQuqT/dcUBXv5a3EoI+3zOwmd18te8wKdCrWD8xlmv5Pm7r7E+n22kSe33tK9m0GImfzI1X7nz328pK73TvV6o8lJm8vnQ7NSmZFKY5b6e8NqJ4pYU868zkWQcHs7r6wVfTIFBcOZd/7nmPQIhXrP5LOdVfT0+1anB/Sa76UmDt4KyLHbjrP5lStUnHuG/juTKx0DHh/9jOGGC16rbv/uI/17wWWd/fJki4xLD4FE9Cmlh/iymqeimWVI4YqHl9W36qoo5NXe76dISStD/P17U0Ee0ZchY0j1fqik+A6gZRsTJYYTsOomab3grjKvYM0AoiKRMmSdYv37HDSyLae93Sit13znEUZkbrHXEnU1rqPqPk2ku4q5oMq4dNJIP03EWQVoyB3TK/j8pqfy3qe929lzzuZ/xeHEK1J5xKJxWcziQYm9PHcVxItb/n/vu//MxHkzkbJYBoiMH6UKPtxItGN+ZFseeUIOiKIni177GxETljj54hODbn858F+ltM9W8IjZLMlpOWDRiEDa/fcnp8IYjcF5utZ9vv0Gbs+veaPkUbips/BfURS9qeJC4eX6QzieYnIQfwXPXX5iMBsVqJL90/E9/q6bPneVAxqIC4Qb06f+42JY1VpojnRAnhfdrt3/14iLoLOIMoj1H121ia66u6lfGTejel/XYzuGwPcX7O9bbK/ByWdE4VroXoU5EZU15O8qWdbpSMwqanwn5bXDkxpWLfy3Fd8fkrWuZ/uenldPyWPX4IY/HU/UYS5n/06n4bBdFPqZ4rvwLTwQ5xwPkb5cPHF0wHj0+l23TDue6mZXmcy7n9x0igqz6+QHTTOoKRgYLZu2aiZF7L3pfJLQ0zt8Qtg+3R7MeKKEkqm8CBNl5GWH0+MbrsvHRRmo3v0W9m295sE79WFpKJ+FcvLCu1+Mlt+EZHrUPx/v0aMtCryh3q3d0Of+1X7vH2sX/l+UTM6N/vclk7J08Jnt3IEXB/rLge8Qnew8u6ex8xDdFd9jJ6LsPT/m5HyEXTjycoPEKkKeXmW2s/RMN6P64D1stvrpvuWSbdrv1fpMVtSUraj5zHvJFo/HiW6pSBawEsvVPvY71nSezSKaEXfi+4RqbeRTWOUHp+fvN+XHnMTMCa7v6w4aD4a9XvE/ISzpf/j54ng8uNEN/3ePfv5u+zvpunQyspyvElcOC1Q8h7kn4/LyUYWZ/ePpGIaHeK4vXP6LO+c/WxJVq6GmhGYREC1ccX2R5IVx52I/3FTWZeyEc0PUF9e4/1E78tp6X9/MlG3bQ36/H6lde8nZoQZCFwn9XdzYn7UBQmY2THAEZ6mX+hZNgvRZ/5B4iT2HJHMNyPRj/4A8EtPBSktJr/9Fp0CohcSE27/x8yucfc1J8P+NyWGFxNuH040D59R1l1nnYlfi4KBWPlEpn9w95+lxw/iNYnsZnaMu3++j+b/EUSex4Pu/oLFKKgFPCuyOTlYxWS1ng2CsJrJjs1sHuIAmE86fDhx5f09oiXpZDpdjDN4dbHafvd5JNHtVJmPZWZ7u/vhZfeZ2TV0Rud+jDQ61933T487n7h6/1fvdie39Nx7EN2jK5vZ1sQcq3WFUIt1ryMChnel2+sCP/LUHZfuKxuA0ti1amYTiOH7W2X35V2VlZ8jM/tk2Tbd/fdp3crleRdX9ry3Eq0Pvd8rz9Ytvle/JvLY8kndH3D33dPyHYluwPcQJWauIUrtnJy6XMv2a1xad6Ley7Tuv4mALJ9Q+Wm6cxmXJXLpnk/b3rSnu21QcVAzu9HdV+99LnefJb1vb3rWndfTlXdZ8b7V7PcyxLnBiK67PxK5Yd8lphU7NXvseDpFlt9N9LwMyp81s8vdvSxBvtjOdN6ZmHtOojXwtmz5BNIITO90BeapH7Ok53ydTn5YkfpxFrCTu9eNSKzar/zcZ8S57/vE8WQ3orEiz3OejQgMd6zZ5ltEj81hwF+9pGvcGvLazGznsm37EHPJJgcFYIBFsuA3iYPOHXQCrKWIgOM4IpL/r8Ws9/MTORp/6/1AWMwlNr7ieT5IdAMNqvZskaj7g7TdC4irnX3c/Q997P9t6fHLE1/wY4kyE+uk5ccToyEXS48bSQRi703Li1wWJ74QfQ0zrjqpEzVW6j5YKxJDm69t2H5l3lEKOg/IlhcHksX72fea59y/7H7v5A19jriKnsvdl7CoTn+Uu3+wZpsPEe+HlSzua58tSjEcQgzaMAYfOC8DPugVX+iKvJHxHjlgt7j7ey3lKqZlV7v7Wunv04jPTe/ndq+m/R4uM1ucznyYzxPdcTt49cjcfN2yE2yeo1M6AKXppJvWPZ3oGl0s3bUb0TK1eVpe+TmymA2hMCNxAh/n7sXIvsrlFkn/4+ieLWGV7Hm3JS6gXrLIJVsZ+H4WJN1Jd9mOEURX9rvT7WeJk+RRwOXu/nA/F03DeS/T8z5ODAA5I921OXHh95eqdXov9NLF8ubAJ9x9k3Tf9cSFRbGdrYlc0llSoOL5hWhPAFZMc3U2g6vzF4/vTaQ/1yPf911EqY87gN3d/RWL8g3F4KqKl+QHmtkPiQvh3hzA4n94BdEyN4p4r58h0kL2TctvcvfVrJOjNgvxv2jMxTKzU4j/3cXZc89ApD9U7fRzvfel92WW9DmcnegaPojILSu8XLZuz3bmo5P7tVp6zeOIVszrPWZmqc1rm6r5VNAMN7X8EDkK6xJB0uZ0F6Prq3mWmqKW1Fd7npB+b0FJMdWG5yy6E79LtA4M3Jf+HkEciOdIt+cmkhKLdW4nWme+R08FdeKq5Wziavhp4gCyeO9zZI8fT83clqQmfOLLU/eairyj8yjJO0rv8UZEQDJ38TMJPwuzVNw/gcgLGp/dl+eAvYsIGC6iZK61YezP/WSFL0uW/5RobdmJ7gKe/cxNeG36jJxOtDZtQVYEmO7ujoGftr6Xxf+DuGIeRQRg/axzBjEn6aLp59vEVfTA/40IcIrv3jJEaYV+tj0v0Rre1/yrJesfkf09OzU5dWn5Y+nvfemeLeHndHc/FbmTaxIpBZuRcnPS/afT3Y22CFE7MH++dxOV608iun1O7OP1TPR7mR4/jqhdtRc9cwESQW5vkeFF09/TE8fqU4hW5uPJisvSffx6Jv19N3ERvTFxvJuTON52HXPpHKPzn+Oy5WWFqF/Nlo8iBjzcS4xMzY/J25S8B8WsApeX/OTzNY5Pvz9LDJga+L+nv79CdLc9SLQMXU+Mzty65Dk/AWyQ3S77rj9D9/y3+U+eqlA7L2d6zEgmYuaUbP2ZiWPU/aQ5TGnOa6tNsZiSP1N8B6alH2qmGep53HzpQHJt+lJ+O91fV+35zvT7N8CG6e9+A7DaBG2itWRHoiYQ6YO/Wvq7toI6MdqkN3H8fhpO6n3s8/eon8ajNu+I7KQyif/H70tf1EfT7RVIE5Hnz0vFZMfEAf2LxNXae4ufPp97G1JiNxEsnE7K36GhijYVJwviBLsucRBeJ/tZmc6E7KsSFx8LpvVOo2RKjzZ/iAP5N4hSEBukz/AeRFB+Zva4WehUxn4X0TIwXbo9J3HRMy79/JzuYKV2AEof+zjoAmRi1iUKKVfOWpCW/zf9L28lAoW56QQNecX54nN5ENESBBGoFfmaVxJ5cVek7+srdE+KPZq4sDmYOH7dC5yQlm1Z8vNBIhgd9ntJxcmZuGidPnvs9Gm/jiNK+fyB6Dp/uM/nepg+Aoo+tlOWSD++5HHrpud7ue6z0+/niTinzE9c5K2a7uudRH4DorTST9LfN5DlzmWPm4+Gi+EhvB8T0u8diC7D6eg+Nu5BJ2DtaxAacfGxIZFnfQnRSnoj0aq5dXpMZV5bWn5N+pzeRnyHDiAFrlP6R3XAMn10a/2HqLKdN8/iPV0xXl3Usq7ac2kx1T53/ePElcxn3P0fZrYw2WS0dNerOZAYGXQaceJ9mPoK6ubuJ2a3/2AxTPqnVEw4bFG7ykv2M+8225c4eb5hZv/pWQZxwCpOPGWaavhMrJ/TGayAu99qMey9cKXVTHZMJCwfOZHP/R13P9WiAOtHiJo6RxJXz2PN7M/AXynpDnH3XWq2+4iZHeiDu2x2JbpPi4KN/6JTrR0zO8Xdt7UoVTLo/+nDHF7e4EQiwL+euIr/Gqm1w90nZI+7Clgr5cJcSpysP060kj1PXDBUedzM5iDe04vN7HmixaxRShkYaWbTMfSUgbOBxVO+zQgit+nUnuXF+10svzo9z+LpNQ48nKwWE/BEyj/7EFGna4a0jcY6X8k12c8vvTNRPET1//cRgRtEYHEDEfi+OLHvZTKGaEl8ik5RWyfSKkZ5VkzX3V9LXXz/ANZ094cALHJcu1TkB13mnbpXlSyKV7+fwTUQi3UfY3Ah6u/1bsfdrzCz9wJfMLONiJa3Bay7ivtoYsLtRbyTu/td4iL1EWAvd384PfZAIr/qWne/OXXT39fztLcTF9Oe/p7Z3Z/peQzpfDFQJDalVBxEfOaK1zyLu7+j5P2ZHvi6d3JYp0vfh82Jz87rlgrRJvvQM3OKmf08/c4/87n3EZ+x64hepZvc/dWex+wNfNNiVo5BeW3UF0CfohSAdWuamqJumiEArFPUchsi2s+LWlZWe/bqYqqNUsB3WHb7UWI4eWF1T/Vq0vLnzWzRdAAoraBunYrul5vZfnQnjp+RDipXlZzUD3H3forHNj3mFaKIZVXeUZFYu0q+WToFZCeauz9m3YUI88/CfsSJ6HZidNV5dBdYPNvMdiO6v/L9rs116HmeTYAj3f1M6xT6HU28Jx/OHu90qnLPmPar6mTRW6n868QJtG7qp73T736KWk5qi3snH+23xHdpYU+DHTLmkV/zGaJb78fF59wqKt0Tc44+5O5bpG0ckC6WZieCnH58mChC+1FiFNw2RGDSGIARwdDSxMXLG8AjPYFOHix1LTezI939izXb3pZoMTjUY/DK/EQ3UOXAmB7/dvfdLNUZ61n2FtEN/lTal3fQuUC4Kr3P+XtZWWW+hFM9rdkzZrapu5+Vnnczomv0BuASM3uQ+L+OLFn3TCJ4vYSK6YZqXEa0wH6ECHp2IIr+7puWP0h59fbBLy4uBg62qHU2lmipzetOvkxcED+TXuNHiYuH7YmE+qPTfuCR2H9qtu0HiUCNtO5niYv+y4hzzBHAjGY2yrMBCumx0xGBWuF4Bk+Xt6PFYJjd03ORAsmf0f19OZq4oL8VuMpigMRL2fKygLW4uC+9QOjnc9vHuaSuAPoUpST8jJWMmCl5TN1UC1hNUUurL5C3ZcmiF4muxKcb9qkpQftG4kru5hSIjSEOAt+t2ewBNCSOW3li96DCe1Yy/5uZXeo9iev5fTaFRq6Y2V+IYPaXRDLqXkSSc928gPn6D5XcXbxfTaNVzyEODh8iui5fJa74VujjeU8l8i4+QXaycPe90/J5gHOIoKGoVL6dp9FUU5vez1bZZy3dP55ohfwZ0QJ8p3UKH4/3wSN9xwNveQw6GPQZ7HlsWcvfi8R3ZyN3X8bMfgOc5u4XWMkIxYrtHkLU4cuTvw9x96/3/l22fHIys7uJ1vC5iO/+M0S+3x2WDdJIjzXi+LScmT3n7nP1bOtEd98p/V3WKl68l18mLn436A0Q0rpLEPlo70z79BhRguX+tPwDRKCyFdEFeoa7H5OWTXD3FSfyvXjF3We2zijy6YiWp7qgwN39wD62XRYM5QNEjiPyMA9Jt/PBAaXFiT3N3WpRePT9RTBrMYL8b0Sr/h7emb90FiKf8Nnss1c6IIfoRfkBkee1HNFiubunYrl1r5M4hkLNyM+m96uJ1YzAtcEF0GcnCqDfMNznHS61gHWr7dayhqkWkvWIYnFzpoPSf7Lt1I3cqmzet+g+OrFqRVLBRHe/u2L5L4gWmXktRthsDXzFe6az6FEZ6JjZF9PJaQmLEZiF2Yi8keJxmxJX+fn8b/dYdLHNY93zi41OjwOaAy2rmAOun4Nfg12JshELEMHQhUQl/uJ5a7up3X0xqjVNY1TaepGe93jKuwGLFq4l3X0bM9vMYz7SP6Z9Lx5XO/VTmeykWfyPisf3NvFPDiuYWXH1bESX70slz70PkSt2Rgq+FqfzHXrLYqqcR9PrWSS9hhEWoxTfZSVVx7MTwvlEq8kf0+0iCH+J6H5sTBkws1mKE15mA+IzltuIzlx5GzB43ryNSu6bHBYijiWXA1iU7ihGoV6dLhKK48ZWREvHLES6wACLUXDvze46jOiS/CPxP9yOyD8qcrkeoGJaM3d/AFjDYjJm620F9RhNfa2Z7UW8d9ulfYZI+9jY3c+biPei+Ly/YDFX5j+I5P+ipEfplEB9bvs+6+6eg+iWnJVo6f4gEfQUZsz+/g1xXDgawN1vS9/3Yu7Wx4kWtcLLRGD6FJGOUJyHFiaOQd/JHlvVWnQKEUB9CXgBWN/d/5Ze847u/oey71JStFA9Svl0Q1Vdn3h/I8Rrp9PzihSLqYFawDLWXJuqbpqhUcSckrsQH7IRdJKav9XU0mDRB/5ZH9y8/1mieX+5mnWvdfcPNGy/q15NTbDWyPocVmxRBmB9uud/+yGRQPpO4stdnNxfAn7j7r9M6z5EecCxeFr+5ezugTngvI/cjuFIJ92ybuoV3P0yK2/JxKPUSOl0QkSLVyV3f87MtsrumpEYqfh3T12y1hl6fhXRIvQPUtFKOkGUUzL1k3XnohReJCr41w2bn2qUBTpmtiFxIi5aLNYmSog8TOSp7ENJF6x3So4M+l4V96ULkLXppAzMQgyg+Ed63N5EF/WsHtPLrEBcCM1FRT2k9DNR9ZImlaLVp+e+YlowIxLv8xp3SxElfGYiAgfSsteAY9z9G2kbZfW4bnD3NdJx4nQGW97dt6o6sffTcmJR/8qoqHvVsO4jRMmc5Ynj+KzEQKaj0vLKKYHM7NPEdEq9uVnF4+bObs5IdGGvTwQgLwFPu/uG6bErERdlRe/Aze6+qnVPgTTQ0mdmvydKKp1JfM+LLtu/EReNRdfw/d6TT1XRWnQBkUB/LfG/XofocfkzcTz/lLsfbQ0lfOpYQy3ChnVvp2Q6PeLCeXcil/Q4Ii96LeK79eWiBXVKUgtYxmuK3yVlE5UWQcJPiAPl4t4pyjmaaDE7lE4+TZVFi+AreRp4Vzr5NnUT1SZoJ/cRX+xRad8GWgaGyqNI34ukyXSt08U4q5nNmm33dXf/p5mNMLMR7n65RdP7Yma2p7sfUfEU0J3bVRygBro43D1P/sfMDiUlzg9HakE5nJ75Gj3lPgAvuvug3BaLorSXEQePXkWu1stm9g0it2Pt1EowHRHMVXb3Ep+prpo2ZvYnojWrcExqUfw28T7MSiT1H93Hy56R6JLMWzbuBD6TguaXiGT360tac6YoM3sfcRU/K1AEOl9w9908ugVXJv6XRvwfn02rHmLRtVSXp7SQma3uaa5WizkQZ03L3vDI6wEgvS/5e/Mj4j3NB3OMIRKwSy9c0oXN+VXL+35Thuc1i/pheZ2xhyCiFmLwTm99pYPM7CBgXXd/X8V2Z7CoUZbX4yp4FvQOzHFpZl9IyxtzSmvc5hM/P+yT6X98JZ1BDkX+U1UifdGtuCiRO7UI8f2+mgjIJgD44Fy3n1sUGV6HaHHKu/b+QXfLzX8sumU97c/WRMmHwgN0B/DFRdRs6blvr3rBZa1FZjaWaBy4KS37q8UcwfsTI/WXSevWBlpWnmhfdEPP7DWJ8lbf7fofj0LnmNkM7n6PmS1NtLaOJS4SbiKC6MOJIOy3RC/TFKUWMJqbUL0zCWrlRKVmdh8RMHW9oekke4+7L9WwD78mmoTzk+DjRFPzOXXBoUX3VMluD1QCzid+zUcYrURDBfWGff4Y0bWQdzHe7Z2ijpcQLQ0HESMmnya6aD+ctRR8ks5InwPqTjRWM5NACj5uanqf+3hNNxA1c4pK4dsR9X6KxOdtiWTfIY++tCgq+AkiF+9qi9Gq63qqgD7E/VyaKPq4ZLq9mKfRYNljBu5LV8XFSeCensddRvxP3ki3RxFD3DcgBhscSrR6vI/ozriaaJWd4q1jFvmNWxN1tIrWgDvSQbo2566Pbd9DtJrMSnxnXiJapO8ENnH3U2rWLaqt560UeY5PbwHPYvAM/SyfWKml7lWPybDfRQSJ53unsvr7ic980cp1FfG9fL5qm9m2B15rybI7iEDufcSx5waiJfkJoqvyBSLoKy6yniXyvO40sxk9S+UY4usdR7T8V+UHLc/gCurFwJZ3EIH0O919IzNbNu3/WKJl7EC682hfJorXDrxXFjnDnyNqcy3g7iPT/XnL2QjigvOL3l8O4R1EUDbk4sQN292ZaChYOt11N9Fq+wd3f6tinf/z1JtizRXpDyda5PNZGP5BtJ5uQ3RJ/4W4kH2CODctnda9ktTtWvI9P4MIFvchWhGfJy5sF/BOy+0j7r5wtt8TnRs4KakFLBTDcJuutPYkplr4L/EhKqZagAh4yrrM3rTBff1ldicCkQ8QB77fE8m9TgQtlby+BAGkL1XJVRdm9t501VG6jw0nsR8QrQt5F+P22eqbETkyXyKSwmcnrjqKaY7WJuoN7Ukc0I4hXRlXHKAG/j/WnSA9kvhiDzf/Cygtu7EH3eU2oGL0pUXXwv50Zha4hsgT/KdXjFa1iilesseNs8FJzP+gOyfoNOJ/k/sLnTyc36V9OiK18k0ggqgi320WOiOUZiFOOm9ajJw8DjguBZDbEieTzzO8lolJxqtHreY5d18luiHynLsmr6bP9ezE5+KFbFll8JW8loIZtxiuvxdxQiN9ng4gLoiKE5sTXV2Ny4epsmwHgLtfB1yXXvNbPnjUaZ2649zr7l7WOgwx6vo6YuqePPfsN0SQcYeZPUUK/Inu2N7RdFXmTusMyg+ySHRfngio8/e56Dn4HSmFJN3+G1Fc9j3ArRZ5V6MoGZRlZt8mjuWzEnXYvpL2v5AfT94gusW3rXshFiUbrgVw9w+lYHpE7//IIpWm7FxUOUI8XQjvQ5QHGkecg1amU86o9CLRu1NZmkacruTueUmfsy1G0q9tkS4wM/E9+T5xPM0HYs3s7jf1fM/fSPuwRbp9gHWPZr4hLXeLGR5ypQFl2xSAAZ66aZqaUD2mHfqWxSgm7/ng32Vmn+xtzUhBS1eLQ8W2nThhVk69UcWaE7TLhv8WxgNnWoyiy2ubFQehusTxsi7GQ7JtFNt7yyK59p/ESLWilevjRJ7IacBpFlODFHoPUA/RfYD6aM/yrjnghqGs7Ma5RKtYP+UkTiYO+EXO1g7AUxZlRXoVLZG3lCwruMUUVu8uawGxyHd4NzC7deefjab7iv+ydBW5KhHQ75rWO5wYxDHBYooTI3KbfpQO8JdYlIFYlggIriaC5OHWW5tUHqsKdIhuQrcoW/ALj5y7nSu3VMLMPkG6oi8O/t7fQI9HiYuqBYiW7IuI/C4oqYfUo2n5cFSW7YCBHKDjSMG1mb0IfNrd6z6j/RhpUT9vUUpaR4haU5dn91+RPn+4+5KptXgt4nv/azN7oc8WjDFEi84N7r6edfKDIIoNL9u7gnVGKM7j7qdYpA3g7m9Y5JQVNqR6UNaWxHHpXKIL8wbvHpDVlO5S5n4i93MpM3uYqI11bQpeb81aqb6SrTMjcSwaODZayRRvxGdzC+/UGgO4zCL39GQqArAeM3v9SN0x1j0oZmGiZwQil/JfFgMA9ioJ/J+1mm7XrMW46AWYj06tPcv+Jt2uGyzVGgVgGWuopdRwcNodON0i+bLI6VmVaF7dggbWUEqiwTnZ3wMJ2tZQr8aja3UuIjDKr47yq8D8JHZ4z0nsBYtRO1cBJ5nZ00Rh1TWIlq3niKuZE4kv2gjglewA90GiJaVQ5KeNIAqE/rnivRpBdL9VDkwYho+n31/ouf/TxEn+ZGII8wtpX+YkEjq/nR43l3dPrv0DM9vc3VdhGFIz+3tLFi1NnJTmoDv/7GWiO6BY/1KiZet6Ioha1VN5k/Q/PY+o3m/AN71TQuWr6blHEt1EzxHD1idFsDsp5KNWi0CnGLValXPXryWIVtxb6KnzZJFrUnffv9x9h57luxJJ/3UXRPSxfDjMIm9uB+JYB93ngWOB3dz96vTgNYlWoH5a38pyGAuLEy0TVa0jD1pF7pmZLUi0Jq1FFLy9k2hZLl5QZTciMcNIWX4QwPVWXhj7JqL159+pRbs46a9B9//lAOI7c0V6zgkWcwXjkYg/G9HqvAHwGzN7ylMKRWph3J+42IEI0g6sa9nzGKD0S4tu1U3Se/J+otVqDHHRRUmwfG26+CJdIH+cmO2j+D84MLon+Cqe82GLXOZ+nGP1I06/TLR2PkAnCNotBdqXWfRqVAX+uxM9JMuY2RPEZ2PH9Ng8xSZvydwse+7eOmP9FiaerBSAdTuRaK3qKryXLa88OLn7E8DqZrY+EcAZkVtxaZ/P3VRKopJXJ2gXV9CVw3/76L6sO4mVdTEeSHTNfjPdvoyomXRDuvq8jKgm/2xat3gvlyQd3DzyU3YnRtmUvd63zOxWG8ZAgipeX0aiyHP5Zvb4581sYyL5HaIFbTs6XVRb01C8t2f7yzF4KPbvieH0q3onSbZYdibRgvk+d7++ZtO3EQHccsT7/IKZXe+dUVAjiJpPo4AlzWxJT3kyRRO/RZHhj6TXONLdF+z3dU0uHkn1O1Qsbpohosnr7v7ximXXM7jLN7/PzGx97xS+/RrR8ngU9RdE9LF8OPahumwHRML/QFeZu19j0f3dmD9GTFlWxRpaRz5NtEwVgdNVdBLPHwVuBn7k7rt2bbS5G7FutoMTiCDsH2SFsekEJvsSgyiWMLNriSAnHzxQNiir2K/liIBxHSJd4TG6uyCPIybqLlr1dyLOJaWjqLPtGvEd3YwIwJYlBlj9PnvMXNkqI4jv/Xzp9uZE62rvxUNdC+er6TGl6SjpdRSDiCor0rv7eRblJpZJy+4pWgUtzSpSFfh7DIKq6natTLGZ2ikJP5NOritZT+E975ShqByWPgmee5JsJ22rK0G74bFNrX59JY5bFPr8Z2oty4dE3+3u/5c9bjyR0D4/cJF3igK+ixiyX9Rc+w7xxf8z3V2jz6XllxEtjDf1LM9rsk1yFnXPVi0OYBZJtmO9M/DgZaKlqTgZjMj2r7ZF02IY97rEQfU8ovbTNe6+tZndRUz58kjaXnFwK/KGfkzk5L1KzdQ4qcVyF6KbYj53nyG7Ku46iRXvpUVV7rWIq/U5Sa1oHrlhU5Q1JP42rPsQ8T69TIyKWgnYz90vSsuPIbrpbs/WmY9obfsD8b0ozr6jiVbbZdLjKgvfWsNw/ablk4L1lO2wTh7iTkQuzp/odME/7+7fSifptYjPwA1E/tgr7r5D1cnZO0WGfwBcV9M6UrevKxAtSWsTA5XuA65MLbd3eUk3YsV21iHlB3lMZ3Q/EWTdTndO0LV0cjVHENOzGRGkven9Dco6lwgiryaOnV0j2a0kCbznuDmoVY84bo4mctluILo1B12wW6eEj9FJ3zgwBdPnE7ND/KtnnVeILs5BmyNGYc+Sjn0rEEHRiUSDxJbuXptTWVyIWH2Jntpzq8WUWlv1vifufqBF3ldpEd+pnVrAuhVfkhcsK7yXLb/JYp61/OB0RXHwyoKHa9x9TasZtVein1ISpawhQdvqh/+uTTRHl7b6eUniuJn9zSJfaFAXo0UyZ34w6523y72kArGnon6Z4gS6e/4wOsPBJ9nJaIj+AFxqnby7T5NdfXof0zDV2Jo4wI13910sRmH9Ni3bqGHdD7v718xsC0qmxrFI7F6LuBp+hLgCL67IN6fkqjizEXEyOdx7ZneYCjQl/taZz91fMrOPEK0buxBX3Rel5WsCn7LuqcPmIE5UCxJ5ikUA9jLR6gtEy5xVFL7NAq2yIq2Ny4fDKsp2AP/X89A8CCyOHXX5Y01FhvempnXEKqaNcvePeJTwKEorrEVqjU/PUdWN2Pu6ZyYuXO7yzrySj3qa3qjnsSPpjHzNzdxzu3JQlrtvYp1ZU8rKCL1qZmu6+zXpOT9Ap6WpqlXvQeL4sBTRu/GsmT3jndIqpOeua8UvneKNwf//MnXpKKRjz2WeulFTy+O6xIVNVYmeuSzy2UrPrdnjziTOWXk6wNoWaTaTs8V4slIA1q20llK2fMX0u/cK9f1kI+HofFFnoX+jqZnrr0pqki5N0M48yODhv08RLSrreRQ73MyzCuolQd3AUxKvbyPKuxj/BCxvnarlRQXzYt0ZS7Y5SMNBBO9/brtJKp10biOKpxrwfXe/MH9M2dVrP4E0ne6dNyzyLp4mBZzePMS86BbeGPiTR12pfPlMRCB9S8mV4oPUTHzu7runYHDVdLFxkzdMj9WipsTffmwMHJ9O9PmbVhr0ekwdtpX3dP1D18VQ0Z01PfE/3NpisPHoqiDI3XdL26hdPkw/p2Syee8vn9LM7EDiGNWbP1Z7cu7jwmQez0aZenTtz5uedCzRCnUdEWyvnX0fqroRv02UUHgu/f0r4pi3qJl93WOmjXvS8e5suj/7T3ofAy08DcqiM0pygDXPmrIrMQJ69rTPzwGfSstKBwdk2x5NjOZ8P7B7agW+g5g+7TGvL/FzFiX1Evs4vkBzTuX+7n5Gts0XzGx/T2UjvCTdJbVe5QOuygJ/gAU9FabN1p2VyBkrS7Ep7dqzyB+e1d1fKlveNgVg3S71qOFyFenEZ2YDgYBP3MiVvpR9OPtcz606QbtQN/y3eN6uVr+6A2ZqKi+6aQ4sWrQ8ElzxVOtmWmTNXSnFnHwXZOvk8/g15aTUGZuuGn9DXOn9i+hi7cfZVjM1jrvX5T7VTnxuMb3KocQVqRGlLL7q7kMesTsZNCX+1nnFoqDkYsA3LJKm3zKz0ekAXVeCYcF0InyZ+H+tTHRf9tMC+nNKgqAhLB8Wr59svs7eaZ8O8sH5Y6UnZzNbJh0XSkuteKd+Xum0URbFb7/o7jeb2buJ9+XdRFAB0ZK7E4O7Ec8iAsXZ0z4u7+4PpqDuUiJwm4n4vPde9NYNJsAqRp13XpJ/hpoE/XT7VqLFsMiPygOCpla9/xLf2VfT3wsSgcfRpFk1rKLEjw9vLt2mnMoRJesMxBhW3o14eT/BLlEa5T2epQNkLcW1U0KlIHtX4nN+CzFi/LCGY2IrFIB1q62lZBWF+dz92Il9QjP7WmpVOYLyUhJ79bGZ0gTtTN3w32cbWv3K1HYx9rG/U7OmrpSmefpqr17rZC0cR5nZBcTIpNv6XHc/i1yuYmqcV+geBVSn9Ko4822yUZMpuLuEiSiZMqn0tDRVdm01eJTI33nQo2ttbqIb8o/EyNKyGQqKbvBPu/vhFt2X86b1jjezR/sJOJqCoGEESU3qynbUcverzOwhTxNEeyRGF8enqpPzl4kcvd46etDda/AtYoRc0bK9NjEy8BdECZCLiYDmSmA/M1vJ3X9IdTfiW96Zq/ChtK+4+9NmVtSPKr3ote4k9jLnlNy3MDHAobj4LE3QTy1jt2UtTvsAW1lMe7S3R+Hkqla9S4lWr6WIPLDriKBr59TadKs3lPixYcy56BV1DLOHjDWzw4jWRieCvzy5v6wbsXhf5gA+yeCeg+LzVZYO4B45sN+gU8C8kN+3rEeqwQ5Ebu3X0z4oAJsaWJ+1lKgozEecpCdWcfAbO4xtrAd8IX2JByVoUz/893d1rX4ViomSJ7qLcSpW2pViZl8kzdNnNROQ02dOSpX0+cuLuPYVgEF022R/906NU7de01XxCO/ucvwn5Ve7remnpckqRu0R8+RBXExB/E/zbX80/a77HhQr9HZf7kuUVqkLOJqCoIkOkvpQV7ajVnoPF0qthvlJcn1imqevZ/c9ambvdvfPpdtNvQcXEoH+nkQu6reI9Ialie7HfxDdUC+Z2U+AG4l5CKu6EUekC8sRROvanHT+ZyPS66kdgFTFs67n1Ar4TSJgPJjOueAOixpyI1PQsxcRMP2Q6D4sBrfsSBSvXokYIfsRqlv1NgNOInJEywLykRazWCxNRYkf4vy1PzHn4nqkORfrXm/2WsvSUopc4i8T/7vvEOdEY/Bna1A3YuY8YmBB72sulKUDrJsaLuqmhIJoiZ2OyHX9pcdAmKmioUABWOirlhLNhfkKfX2g0zbOTr+H0zRcm6Dt9cN/P0r3dBrQXUG9bHuTtYsxnchWI04STgwbvykFRrMTVzebE3ltELlSZxJTV7wwzKcvulJ2IiqGF3kOf6S/efpKr16zYLiSxXRUS9LJ1fuCmX3I3fs6QQ6VmZ3i7tta96wCA7J9vsDMLqQ7h3BiuvwmOatI/HX3v1Jd9f2dNZscaJUxs0s9TYCcPV9x3y1W0n3p7p+3yDP5trtf27vxpCwI2q1h+ST5DHh92Y4mpxIt3t9mcItcacuwxVRRdftTdM3/mjjxzuruZ6f/2fwp0HjFzB4ouunc/VUzK07SVd2IsxOtHMWxeFzPcmguO1TJoiTLt4jA6SfArt6dW1mVoH+jR+4YRMmJYz1qXd1iZsVnoLRVj2gNrPMnooXw3cTIx0ElfoCZvGTORYsphcqOBfnx6zDiWPzHdP92RHmLe4Hj3H1duo+NvQZ1I2ZmdPd9a9YtC5j+QXynN6W7pe1lojRS4ShipoFbgassurenihwwlaHIWEMtJYuRf1sBF3sU2lsDOMR7huGa2boelZzXdfcr+nzudxGlARZl8NXlRLH64b8LEF+mHxND5Qujga96KqvQNjP7MHEwvo+YDwwix2FJ4iT1ZeLK+ATvJJvOR0xb8SF332CYz99v2Y1iAnJgoDkeqxja7n0kuZrZncBynr6U6UR+++T6X5jZ/O7+ZDogDZLvc9YyZ8QURmeUrdM2Kx/OP96jnMy49D3dkzjx/Nhq5ivM1p+RGGhyOTGKqziJjybqXv1f+t+sSHRfvmDRfbmApy5jixprpRNTm9kJRImQ59PtOYGfNrW8TAo2vLIdtxATsu+R3TfQMkz3BNCzES0+r1HNvVPupvhfDfx/zOzfwBiP7uERniq9p4uwy929tJu3XxYjOHckBq0MKjtUs96pRG2vQ4l6f73dx8+VrZfWvY3oRnyFCJK2cvexadld7r5suhCbg55WPe9vRPwaRID0Hi8p8WNRz2wteuZcJI5xtccCM7vR3Vfveb4b3H0NM7uVGHldeQ6zKKWzZHrdXRenZvYlIuf1nJ7XXJQdKgLDopdlMeBed3+3mU3nnblM5wQWyr6HI4jct4Gpw9IF/kifCspWqAWs21MWJRvWIP7Z1xNN6w+m5ftSX5gPgCLo6jf4Sk4lIvXfMunyPdahevjvQkRxwDmob/Vr2+FEIPVwfqdFt+h5RHfYIfmyFIgdYjELwbB45K+cRuRZQEwKPBBsWMUE5MRVJ1RfvfbjXiKXpAh8FqLPLkibiImn3f3J9LsxOEwH/9Mt1XvrZ59aUpf4a1ZS9b3igiS3EJGb8066W1FeIk1JRRwfliVazg8kRjzn3e8XWUzjcnoRUGeW9+7u4ufNbCAoTAHa3t5dkmFSBWhnUlO2w8z2JrqpymqjnQ08bWbz0zlJnkd/LcNNXk+tzcXFxxjgvqK1yLsng56ONE+gTWQ3YvGcRCtY1wCkPtZbNe3nV4gLQuh8RuZJAVoZJwZYTCA+S3dnwddKdKbWqWrVawzAPEajv+BZ+RLvLvGzD91zLq5H5JD1cyx4y8y2pZP7mZ/7nOZzWFlPTfE/e41oSfwWndaugbJDHvNvDrDIsSxmK7nYouTLKOK9fcbMrnT3fT3SD/Ygm7s1fR+nePAFgLvrJ/0QfdA7Ef/IUcTV0Y09jxlFfNmXI4ru9bPdY/p4zC1T6DW/b0q/7z37cx8wquT+6Yn6SxcRyfHvyJa9g+j+uGQSPP/niKrbD6TbSxGjY4vltxIT/I5Pt9fL/79E690fibyOLYufhuc8mwjsrySujK8gWl9e6fc1EYGaEXWCbiNGrF3ZsM7LxIng5ezvl7K/10j7cjpxIr6DOEk9DWw4pT8r6TUcRwTESxAH658ReY0QeTlnAV9PtxcnunGOr/k5Ltv2XiXPN0P6fSQRjN2dbs9JtJrm7+1bxIll4D3NPkNzZo+di2jpLG6PL3neQfdN5Ps1oWH5rel3MQpzBWBcuu+hkp8Hs3XXBHZJf88DLJYte4DIX9qVSIrufd4d0vM9TuRJ3UvUBWt6PacSgcQDRFB2EXHh0c978dn0XVmHKMXyNNGVOJz3d6vsZ5/0+0vERdXj6TELpO/TiGy9+YmaYcXtdw9jH8YNY90tiWPwiyWf28WJY9WzxKwZZxMtWjOl/33pOQz4TsX9o4Erss/HPBPzOukciz8LfC/9fVv+/ESwvFD6rs1FTBk37O/TcH/UBZmpa2JNf89MtIIt4u6fs8irWtrdz7HqkTNGHNRqp20xswOIA8AZlDTBDofVVxHuq4J6Wyzyr7YlJoB9LN29EJFvcApxhbUfkZA6b1r+FHHwPmS475fFaKHViMC76Aq53dMVmJmNdfdVUpP7Sh5XWDe5+2pp+fElm3WvuSK3qNBdyfuoeZZ14XwXeMJj8MA4H0Y3jUX9pWJKqWPoqffmDV15bbBItP8Oafg9cQL+gXfybIaz7UHvX/Y+l3WZ3eruK/Sx3U8SeYx/Ia7ytwV+6O4nFtshuoSKLsq5iGD6PRWbHMprqq1Ib51ZQA4nTo5n9Nltuz/RLbe0u7/LzN4JnOrdlcxXJ7q/PkDko97qaZqr9JhliORxIy56GvOxrGH2kj7WH9Z3pGHbdxAX9WsTFwbHeqcIbN16MxIDvM5h6K16XeeskmWVBW/T7fuZyCnxqs5hxLH8Znf/VvbY+Yi8uNPSeegsYqaI0u+tdeY1hmj1XhmY290/kronP0zk337Lo2TJbd6ZJeShkk269zHyc3JTF2S3y81sP+ID40TC7rlZcHUk0SVR5HY8TlyBnUNcETwCg4asG51Aoc7O6XeejzXQBDtMlcN/aaig3jZ3P8jMziQSK99HvH+PAzt4Z2Th1xmc8Dup/NdjmhIALEYV5VcpL1jJBOTZ/pcOba/TT4DVh4meeNqiuOZVRH5PPnJylNfUe5tKbOzuXYm/FjWATrU+8irNbBN6uq+IYHMBYoTvSnTngBVFlsu6zAa6yawmgd/df5+C2/XTtrf07lGzPyUSlrsCtP7fksGs/7IdpYML0jY+WbZtj/zILYhWnaLMxt/TuoU30/O9mbb3FHGyzrdzD5EUPxRNs5dUsigrtLCZne+TqKxQ2m6RoL8EkSfWm6Df5ESixb9ycIDVFHuuCr6SyoK3yVNVwZc15w9WncOWBf5iUXtr39RwcT7wE3c/Oj3uTaIW4eWU1CIkTdKdvEHMr1uMRj2QCOauScHX4kQrXrGNplH9U4xawDIVkXLBgedS68egq14zuw/4oJdUpDezx9x9ocm0243M7A6vqHRtZnd6JDL+hrgauaDfK/mpgcWkrasBdxTBwjC392PgBaImzZ5EgvFdxdVbanF5lbgKKyYgP8nTRLBWUaSx36vXYex3X4MHKtb9NNGF8D6iy+FqIiDbv2gd6G0pmJwtB0PR0Ep1K9FiegtZTorHqDPM7CgioFqPyFvZmih8exVRlXwVusvDvEx0b55uUVPo48SV+Alp3W8T3TK1Cfx9vq5l6QRol+YBmpnN6VkO2aRkNYMLLIb8F2YkWqvGecxVepO7r5a997MQAX3RCvEKMTDlMKJbfZLkEZrZZ4kT8fJEF/KswHfd/ag+1j2fCJJeTcfwUUR31kS3NFp3gv4XiM/AgH5a6C0GBzhxAVQ2J3FVsed3eUyB11suYiDIthhIsYV3F7w9g+gFgeiOnY+SKfHMrJiJoPf7NGhGiJLXNB3RsPE6cZzZx7OBPJbNmpDzmJ1lJeL/dOdEtswVPVcLe4xSHui5Guq2JjUFYEOQPoAfBK5NB5kliK6Y1cxsdyICv7VkvT3d/Yje+0se934GX100nkD72O6gSYWzZQcTJR1eJQKZOYBzvKcrti3WUGaCmMC76O77HDE8/wyiCfpsdz94mM8/gkjq/TBx4LqQODkvQeSdXdvz+LWJLr8H0u2tssUzEi0Df/f+CupOUSmI25ZoNZqTCCSKunIzETlppNszuntfLWyTg5ltRNTg2paoO1QYTeQYrWZmt7h7ZTmVrNuq+D0rkTT/4bS8dLqhbP1BXWYWSez7EAn8T6RlTgRvx7j7ryo217fhBL9WPV/foAvHnHcq1ufbmh040d03NbOvEPmSGxAJ+Z8mjo2/SI/djAjyVyPy4q4jRtNeOjGvYyhKWotGuPtfzOxmd1+154J6gveMqh3icz1MJ/jJPwPQZ7eXmd2U9vVfxAXgP4gyPIun5X1PQF6y7Q2JFt6i1X1tol7YdjWrubt/up/3JrVCdhV5pVP0ezoif7e4wCs2fhgVLFIqdiSCvtWJWRh+0/OY2oEYFnMs3wJ80t2Xs5ij8/rh/J8nFQVgPco+QEUQZGYbEFe5yxK5Jh8APuVDG+1Y9bwnEif5CXSuLnw4J27rDN0dRRwcH6SkNpVFHkBRQX0WYDZPJR7aZlFvqqzMxKeIk9082cHyZqIL6pm03zcM5+q1Yb/OAb7pPZXpzWwVoqWobKRpEdBd4jU5KUXXlGVTGg1hv+rm7HTvoyK8mf2W+Ew/RRwcryFaNqaOkUIlLGYqWJHofsjr2L1MlCh43hryKi3lfJrZDUTy8T+JltRiBGxpF6WnqVMsuiDfQfcFU9Gy8F3g5x6FQ79DtJR9vyyQmYjXPhAwTMS6g06iqcXlhZrVvOzzm1o1bita9dLxMb9wucp7JnhPQetGRJA6r7vPNDGvI9te7ewkFa1FH3P3eazPskLD2Le6XKx3u/udFcs+S7SefYWSVj0zO5YYFds0AXlVqZx5iAE2RgQiz5ZvYdD2mvIH9yeC+WWJ0bEbEceSsrpfA9z9exa9T2XHsVeJWTiKmSoucPdVe573VKLr+hNkXbbuvndaXuTt5oH2VNHLowAsU/UBcvet0/K5iA9t8eG9gQhWHrJsZvuKbY8mmkDvqFh+N3HlPsn+IVZR06Xg/U3A2iozu9fdl65aRsxvuC7RBXihu6+SLZ/oE1O2jQ8Q87gtQpxYixaMV7y6G3cgSb9k2dLAue6+ZM1z3gV8kegu+wR05RGWtj5MShZzib4TuIu4Mr7KO6VXpmqW1QAqWVabfJsCoyOIrr6iZeq37v6dtLy0i9LdP2NRW2x/Imh9k8EXNUWr2ppEgPBTIoAfdsvyMFvABpKTs/sqP789jzubzklyBHGcPMVjGqzjPOtmT62JZ3rKg7Mo7bIiMZL5GqIF5EZPBaEnlkU34vFE8vWgbsSy1iLrJO6vTPz/lyNG+I4hakb1VfolbasyF6thvXHEReXErLs20dVdWuzZoiTDT+kpleORalLVAlo7aMHd90oXe7Ok5xyUP5gu+Fcg3v8VUnD826qL057XNHd2c0YiF3kuYBPPWrHLWrWtYSCG1fRcNe3X5KYk/G5b0/kA7VJ8gLLlZxMjwc4FsEi2PJX4Am9lkT90AdHc+QzxQVqSOIAvQqdmTJk7iL73J2seMyRTY4DVh0fM7GtEC9hTMHCV+yliVOSSdGozuZnN51G7a1Z6ApeJdCwxbLwrz4GY+qTKwFV8SYvUP2geMPBdYmTngmRzrSVOw8FxuDyNREuf548Qg1FGesPI3anEomZWOredNyffHkoEvmsRNf+uJgbaFN7vnS7K75nZT+nUYtqbyCOpymUqPjubAEe5+5mpRW5KK52vzxpqo6XA4NDsrjeAR9z98XT7CTM70t2/mFrUzyUmKS8cTLSqTpIah2Y2KrXQNs1OUjY12BjrjKo7g7jYNiKw+BD9196rysVqDKKI88FxZes2tepRPVVR4ftEI8ElKTBZjyiLA9FaP5B75ZHntz9Rn6yWN0//VUz79UZqcHiaPgeRlXyPfm5m1xA1N4u6itZzG3fflOaBGPsT5+WFzOwkUs9VP/s1uSkA69b0AfoRcLaZbUwMo/49aVoPd/9SOvBsTUTv8xPNp3cDR9e1jiXzAHdZ9P/n3SWbTpqXNs34OBGMXGmd0TlFmYltvTqJ9S0i32q4XnT383vvNLObzexzPjj/4DNk02D0cZAaxN3/QowS+o67f39idno4LKajWovIB5mT6AK+uu39mEjHUzG3ndWP2oNInn+ZzhQv2xPf6W3T7WKi+Vcsyir8kxgdCHEx8GLNfj1hZkcTJ/RDLMowlBWNHWBmi3lMxtxkOBca+Xx9EKkU36LTAljGLUYmf8fdP1T6APfvmNkhqdXwvcS0YHn+3ARg99R6A9HSelRV62UfbiK6df+dWk+K0ahr0P1/OYHBU4O9g+jW630fZ2Zo1uhtXRuCmfPW+x6/o37O4aZiz6+7+z/NbITFDAKXm1lRvLrsMzjKe6bCM7NZvFNJf5n0u7TVNWuhH5ta1H5DHBP/RfyfGvVsewQxkGE2otxQ7lAGOyade79DnCdmJUtLcPeLU4tj0XO1d7/drpObuiAzFlNAfJNISPwy8QGa4FlpATPbnEgknI0YPn5fzzZGTsxVnlXUgvJJU6Kg7nmHXEH97Sg7AGwLjCSuYvP8lSeIq+XX6ARcqxDDxbfwTr5a3fyB/ezHpkQgBFGHabKP1DGzXxFdQle7+98n9/NNSkWXhHXXarva3deymlF76XGD8kDy+yq6KG8nrqbfTcwhey7dF0yHpXVnBjYkCqzeZ1E9/j1eM1I3ey21nxczm6vmQqSWmW3j7qc23Vex7lnATkX3Vbovbzkz4iR4E/EeDXSpWeQZTkcERBAtOG+6+2cn8nX01Y1o5VODnesV6QRD3Ie+crEq1n0WWNu7R7eOSi14tYMDrGGqIjO7hBjEdBBxYf80kUf1/tRq9wLdLaBzuvun0rrvIwK9Wd19YYtcyz95TJF0eclLcXdf38yMmGz7sbSdRYHR/Xbn9mz7DWLuxkPd/d4+1z/N3beqWFYWOL5ItOBO0TxXBWAV8g9QOpDnb9T6REL7w9BVq6TIO/kLUVF7yENm22YxN9kKRFP6icSXb0ufRImok5KZ7eLuZYVOJ8W2yw4uBc/yCdYjDvYQw6IvS/c3zh/Yxz4cRIwSOyndtT0w1t2/MbRXM3Sp26NIbr3J3Z+ue/zUwirmtvOSPELLRu2l278jWmFuSLdXJ6Zl2S3dnolOF6UTrYJzUTONibt/bxivZTwx/P+zRIte77YrR4sN4Tkqy3Zkt0sHHpjZKUQrwsXE6FiIFr6q7nn3zki02mB3Il7H43S660cAM9DpRnwzC4Qv854BBDYJckXTdmpzsRrWvZNIOcjXXdLdZ7KGwQHWUOzZYkDSf9I2u0rlWHnh4h9mrV03Er04Z2XBX10Zo4FWW2sYdZweUzmoZajMbEd3/4NFd/K+dKdvOPAc0SJ2PtGwcBvxniyX/p6bqNE27PJFE0tdkICZfYRIpi/muMLdHzazHdKJaWzPKrdQbXmiBe1YixFwxxGVhl+qeO5rvKF2y0S8pKF4w93dYpj44R4V1EtrskwFvkc0zU9y7r5en4/7//bOPNyusjz7900IUyCACtRLCAmjihDEQnGiTFoRoVgc0SqIVKxFrZYPKqIoWD8JBgdUEFBQhK+iBQUpWBSICAgBwqRYKEPwc8BqgUgYw90/nnflrL3PmvZ8zs79u65c2Xut9a73Xefss9eznuF+rkAYWe28B/X9A+vYF8COmmg6fDaAmxGyHAODIVx6EqLtEAF8keSR+b+HKcwHMbm3XWHoESGjsTUnqoNnAngHyaXp/eaIQoSMohDlBpLehMHwFoTnYnW0Ck/2DCdkO55H8gu5XbORMyhZro0GhLfvB22nvrE9fFXCCpJbakKuZQv01vN2BpqFEe8keS5avUWTjNsuqczFYnWC/poFY7Of7YdQ0XNYNWLPahVTPrtg39Ek15X0p5LxD7BVaHkOyTXUpuKfru/7mMi3uo7kzpJuKDpvyWdrOcnCdaS1VD10zEr/r4cwwtv/ZuYhHqDuA3CoUtUpI6fuSMT3xb8hjNCRYA8YAEYZ+n6Sft+2/c8AXCDppen9ByR9vu2YSdty+3YDcB7CXfwdRBn63QO4hK4heRUiXHAIIvT1e0RIciByDg3WU+ayJkJocM0Bz/8vAE5Ua6uOD0v6aMPxjTTfSsbeihBPzWQSnoUIQ9Y+UfcCQ7D0VZnXi6F4fXm33olhwpABOQZhPGW6ZFIkzxdW7aE10X4SSuH3Kq9N27kzHkY8rJ2mHqr7SO6jgjzEXmAD2Y50XKU2WsX5z0aEsjLv4obINRAnuRfi4Smrrp2L6BtZ5Xmumq9RJWidt6gXirxruX2FCfq5n0eRZ66pV69O9+pvAHwG0YGFyD3MM7Qmz0BriPE9Oa/vd9IaTkF4O9+PuDf8FnGPXJ6O2x3RLeUQSf+Rtv0cEZa/DxP6gSs9giWfrWWIh8x/x4QncCVNPcplnwdGl4/9NVl6ZYmkHdmj7luv2AMWrNNufAGAorpuVm7TOwG0G1sH57cxtIH2RXxo5yLKgb+FCGNcAmCbfi68D7wZIX1waLreOYiu9KNiE0QlXrvSNxHijYNmH0kfyd4o9KRei9B/a8JvSa4naRnJjyJc3yeomZTEpwHcnMKhRBjEAw8/IoQp8yHHP6AmYXwK8S3E02yRF6Kqaq8JN5PctS1EmQnx3oPwTJyX3r8ZUSyyDSIJ+W87uYh0/g/lXk8KWfcSglQIRN9C8lxVJ75XFR5U0fKQkP5u8mG+nwI4DZGHh/T62kaLL6ZRIUKdt6hHirxrmZerLkG/aOwsNPPqfROhe1XWquhElPdzPBkTjdYh6RZOFEYA0Sz984hWXL9CeId2S9svS57Uv0rnOUDSYpJzFBpj+1RcL1D82Vqa5tgXETk4DyFq3NgzlAzSjRi5cS0GqaSPkdyW5FcQSvxA/K3+J6MwptsikL5gAyxYixNlzSth6ImsTfKtCCNlHnMlsAiXZ3v57F2IENUCSXmD4TttH/QpgSJ5fGHu/VJEJdiouBjxdLakfUfKjRg0M0iuqSQgycgD6sTrdqyk8xn6T3+FMAK+glBxrkTSeekad0Z8CR+l4QjiXsoQwM0bE4Vii1OQ36ugIiw9CJVW7VXRMET5Ykn5v+eLSC6StBsjv6cb+hp2LGEuS2Q70suLGZVsJ2Ii1eIM1NNisCfvbf7+8g1EOD6r8n0rwpB4Y4frz2ha1FLpLeqRtRHGU947mMlQFMlf1I1d0TAfaitJbyT514pWPecihG8zSvs5AoUhxnxbof9Gquxv41MkH8NEesWeuWjOhQB2knQ/K5LhMfHZWoDoGSpM6O4dnbxzb0WkQBzV/nedvovnaHJi/jcRRURlBunBiI4CH0xrvxohcvsUIhw6MmyABf8G4HSS/6CJZMRZiNyPf0N4Xn6DqCj5bG7cMkzWjHmH2iQnSL5c0k81hdrRsA8K6oNA0qEV+w4awhLOAfAjTvR0fBc6M0jz+k9fUYf6T5J+g/R0OiwkHZnCFq9A/P6/qpxW0BTn44wKux9hcu+65STXV65qryGva3DMRrknfyTPcdZy5cnyYeU0Dbf0SKlsR6JOG62MzwI4jWRmYL0RrQ3Et20L516RQt9doeZVoHXeoq6p8a4VyV+sDMcVjWUUYTShTvdqMaP9zoWY7Jl7IBk6IrkGIsT4izT/HoiqyKyA5ReIUOSHgZWN3DdCiOkuzBlx+c9Ple7XienB9ruMziJrIYoFsrSHFwPYHuF5aykCIrkf4rO5BsIRsiOAT6aQ91aaEGKdZJBKegzx+czfuzNK88+GgXPAADDUk09AVB9l8gtzEBWBx2Yu+6LcDJKHK9f4tSgW3TRfwUwNGP3S9kZ8sfxQ0mU1Q/JjL0ZU4u2N0EN6DFFVOOXzqQCA0abkD52EAEYJyXMQmnyTcm1YXLXXUrXcw7yvRXQu+C/E52Qe4in7SgCHSfpcD+cemNeGFbId6fW3EQ+W56QhjQsPGN08vgQUNhA/CxUVp4OCNSrpPZ679PfEYvmLfH5h0dg1JL29wbyVDchZkfeW/r4/j9z3G0JUeFeEsfVJhHeKiPSJjyI+51VC1Cdn97eqe13JvfE+hIG8FiJP+tsqqMBmNBHfE5ETm1VnZr/TrBH8IhT3zmzvbpL9QBqJxA4Se8AQ6skIF+gnEErrAHB3spzzHEvyCU1IDxyFkBw4laGf8jK0qiwDUWU0Y6AXYPoGJ/oxXlqwrQlvQug/naRQmX4uIkdpysEob/+/iHLt4xHegucAWI3kOyRdWjV+ijBf5QUjRVV7fUHSJSS3Rhh/BHCnJhLvP9fj6QfmtQHwOKM6+y6S/4B4WNg4t78jTxXJ2Yp+l89C3KyvyO17FkJwtUnF6aCo8xb1QtXvqU4stevfsaQsJHwVCjxOVZ65shAjySMROV353/USkosBfFHSiWXnJDmf5COIsCrTawAr27htg8gpW5uRF5iX6NkcUbyyFPGzeHU+PKoJIfKnJT3cFjrNyIRYP4oJIdZjc/vLupuMHHvAOiA9PVyMuKG+BvHl+xZJTzGEVHdHJCuemhu2DMBFahNsNVOTkqe0Sf3zBjDvaojGxj0LRHYw52KE8PD6AL6KKEC4jqF8fZ76oJU0aEiejngC70YMsypfpWzMnpJ+zJLWPWrQy6/BHIP02uyMuNFvgDC6ZyNCQz9L+8/CZE/VJQhDbdLpAGyuqK7LN1PO7pJCTY6NBiz4XOct6vHcpb8n1ouldv07Zn0D8o0AHIY2CQyE0TcpxCjpSpJ3Snp+yXyl+4p+HgXb34nIw/pztEo6LUNInJR23VASImeI3v4I0SXlQETodKakw0vWsnnO2/gz9aEH6yCwB6wDJP03Q6n8coQ1/YYsVJM+KFeRPGvQXyqm/5B8L8J9vQVbpTDWw0Tl28BQtMC6hbm8oiGwupIIIclPZjddSXeWPGlORV4B4J3JAOhIDBMN+9S18ZcIwdeswXDe6GjaB7COQXpthLgR52U7Tmf8wss8VXdh4nrLyBps/0TSnX1aa8/UeYt6pOr3VJWgXze2jrNQ3aroewij5nJMeHx2QuiWfRKhp5iFGL+WPKF57bB2qvblKfTmKHTiziZ5oFrbU3XCsxHh2icQxUKXATg+RZ6eB2CRpAcZ2mRHI3IYN0tjryC5AG3dTdSsMn2g2APWAE4krGdfUmsgytqFCX2Vz0n6IIv1gVbFno7TCoZK+oYIKYijc7uWqcu2L12s4ceICsjr0ZqzNJDPTt7b1+75q8rlmEqQ3Lxoe5OHoF6uMeXwHIhWL4PUpap327kzr832iJvtuohc1NP6cO5foly2o5S6nyfJPRHG8CsRhs7NCGOsUCNxWNR5i3o8d9fetW7GsnmroknaVozq6g+0hRiRDJYvIrqhLCqaFsArJG3Y4Jpq/57YpRJ+27XOQEh2HIsomFmCSB26GPEQ/S/IafGxooVS3byDxgZYDvbQF5HkSyTdyBH1dDT9hdEIPP8lMXCv1LA/OyRXYEIwcW2EUjzS+7UkzSwbOw70aIBdiuipdxMmvAxSf9oFrYkJ4y4vLtsP4+5qSa/ocuyuiJv1CxAPoTMAPKpUMZ1ujDsjwo6HA3isSehqkJD8dyRvkUJAd3UAN2vAQtMcQCFF9nllfauiEwBcI+mS3NjKECOik0cpTb6DykKQuf2FXRZUUfmeG/tHxN/DCkT0aX2E9Mnmkh5POWC/BrDDdEr3sQGWgyV9ERGioD9FyFHcoLaWDGZ8YJQ7L0S0FHoQEYL5haTthjT/5gC2lnQ5o6HzDEnLhjH3qkbdDaNmbGl/vF5Jxt3DaEsallRURt/pufdCVDZOku1oMHYxol3S+Yh8nncgJACOIfkjhFcik664WiPsJ9rUW9TjHKXeNZLnI5LsD0IuyV7SB+rGVsyX5Y3thOoG5MsQv4sn0z8ixMYLi8HYoIdj7tgLABytgibZJF+dS2nYqT3Exy67LKSxyyWtQ/JtiOryowA8LGmd3DGFv9dBekF7xTlgrZT1RVyAqHD8FIAd0hNDZpBdI+l3nBBvLKRhTooZPScgSrIvT192eyBuWAOH5GEA/g7R8HlLRG7DqWgoOmk6pmllaxHXkNxe0m19W80Em0p6zQDOC4Tu1/MRnrWVsh1omLsm6W6SMyStAPB1kpnY9K2IG+OLEMbjQySv1eRK8mFxPSKC8SjJZyN9NydvUae6cGWchfJcrDqx1KqxZeQr7C9AFEdkrYr2RtKklDRJ0JfkQ2wVEV+5Cw1z49LD6T6IllbzmLS4EBIPAPDfyTgkgO+l45kzxLrtspCm50xEr9RTFIVva7Zd09z8+1zqxlno/Gc9FGyAtbKM5D8DeDuA3ZJLfaakixHx5czN/mJExeMCxAdoBpqJN5qpz1OS/kByNZKrSbqC5GeGNPf7AOyCpLkj6a4UCjUdUPEw1C6G2XET3ty5VwdwCMl70Hnyfx2DNO7m9xB+W84Q71xC8kSEOPUsAJD0jwCQvBqHIG54f4bOukj0k6yK5EOoaGzd1YknuqY8R9K30z0DyeOWeSwLk+wbji2jUQNykkR43OZJOp7kZoiHjbLiiJNKtrdzXDpHVni2hORcRGXjdch5VBFJ8wvTsVmuVaESfm7dZUr3QBQQ3AfgFgCLUqTgDrSKq5Z5iLv5WQ8FG2CtlPZFZEhQvCz92xUR178cqZ9ZkzwxMy14KN1EFgH4FskHEQUXw+AJSU8yVSCmfBXnCHTOIB+GBnbuIRl317G6RU4Vf4vIu/kHhK7SZogUDTAq6V6J8ILdj7hhlsoLDIFG3qIuaeJdy7SpjsWENtXHGo4t4zcN8wC/jPBu7omQGvkTgHdL2rluIKulWZ4GVjb3zvMmhLzFgizvjOS9ktolSIqU8Gek46uU7iHpfYgH1Iz7U5izyXfz3AF6QXvCOWANIHkX4hf2XYSlf4OkwhYGrElUNVMbRguqxxA3mrchkj2/Jam95+cg5j4Rkdj9DsQX2t8D+LmkY6rGmfGAJRWdGf14yGOo1W8JoGPZDpIfUFtVY7aNIeS5CMCNDW+KA4XkbxAtlAr1VNRD26emuViDGttgfVmyfj7v7RY16MZRNQdDi2svRE/PFi2u9NB6PIBNEa2LrlSb0jzJHyDEXrPOMn8G4AeKzgyFSvcIo+0ctoqbr0QNil5SytAf0MHPeljYA4aVSYulIQuENMGuiA/d9gBeRPJaRDVNuyvzFBQkqg5o6aZPkNwKwCaSMs2vZxDaNbshBBUHboAh5C8ORUgEvAfx1N6kEbIpYLo9DA3Ji95Lbtk7EW1s8hyMyJdd0MN5B0FTb1E31HrXWJL43WRsxbxNc0GfSqkymcdnIzSXHFFFKPAIxPfR1shpcQFAckj8Y/JcnY3w+LVzIYDzSR6I8J5+H9EUGyhXup+V/u+lUf1yhH7ftoif9S8zI3DU2ABDcdJiGSS3QYQhDwPwSpK/Vyr/zZ2vLFHVTF0+h1CFb2d52lcnRNkzCjHWsxE5YEJ8UdhF3T1+GGqjGyOP5FsRqRnz2pKeZ2M4DybdMEgl4Sa5WGehOPG7UR5XEWquR/gFhHG3MclPIXLePtpw7PoIXa1JoUBJyxGfg5VaXJpov5WtcQlDE27SPVXS6SmH8EKEpMR7JGX3xttJHgRgBqPF1/sRBW6npbEdeyw50a1iAwB5LcVtSPala0Wv2ADrAJJbIJKk/wLhEdsIwD1th5Umqpopzdwil7SkxSnRdOAwRApbGjyTfI/aGsCb5vhhqC9cg/geew5aE52XobdcqkEyyMrhUu9aytsEyhO/B+mZQ5rrWymktxfie+QASU17iT43/bsynStLtAejkvNw5LS4SC5s936mh8asHyTawodEeL+WANiV5K4pjHgEwlhtUbrPnaOwvZKqddWyh+b1MfkBul9dK3rCBlgDGNonWeLetQgJii+WJLIWJap21G/OjIS1KvatPaQ1fBbAHpLuBgCSWyKaSdsA6w4/DPWB5DW7H8BLU2gtS+b+xVTI9yqiA29RN1R51+qS7IcV/r4LYQStnuZf2eKsptpwaUkoEABeqGi8/jZE6PQohCFWF35u94Zd0L49edeOAXBMiXetqL3SSoquSakpOXP6ZFMNG2DN+DqAwxSd5CvJufgfR/TcMtODG0geJun0/EaShyK+ZIbBg5nxlbgHIQZruqO0as90Dsk3IirVrkQYIV8keaSk74x0YcOnyrtWJ3/xqwGvDSSPAPBxAL9DGCtZLvMOddWGAH5aFApM+2ZyshZXZYoEo/9j7X2wgXdtHUmFun1l14TkxUvHTBJNbpLAP2hcBdlnGO2MjkMoqOddpf1uBGv6SHqyvwChHJ0ZXH+O+KN+vaTfDnDuzCh4FeJz823EF+YbEXlgHx7U3ONMVdXeqNY0nSF5C4BXKSncp7DQ5U2q61YVSP4KoX8FhPG/JiaS7FcM46ZP8m4Af1FUuV1WbZhVwTK6bxyDaCJOpFCgot3P+xFer1sA7AtgDoBzJL2yYi1L0/FVIuX7M6nYs1Xp/sbcuia1V6q7JoRqQSm9VML2C3vA+s+ZiKftljYiZmoj6XcAXsZQvs+eln4g6cdDmD6fn/A7RMUOAPwe0SDcdEdp1d7wlzIWrKbW9kJ/QBgZZoKuk+z7yAMo17kqqzYEUB0KlPQFRIJ/xv3p+7IKopnQa6F3LadQQAAfIfkEQuQ2k0+ZXXZNU8HAqsMGWP952EnT0xdJVwC4YshzHjLM+cadaVq1Nx34d5KXIZKkgRCunuSRWMUZeJJ9Gblk93sAXMnQ3cr3+1yIkmrD3DmKQoFXSDqIJVpcmPD4FSHlGnmnnMxt0tu8HMRpmKx0/0hDhYK6a+p7Y/R+YQOsA1J4cYmkR0m+HZFs+fm20u4rSC5AVFjkP/w3wZgKSM5DVAPNRWv4ev+yMaaQ6Vi1Nx0Q4kb5CoQH4quI4iQzwSDlL+rIjJWl6d8a6V+eympDFCfa39t2/hZY3fprk9xxuyM0wu5L+zYj+U5Ji+q8ayRfD+DHkh5O7zcAsLukCxtc0zcRLZT+CrnG6EXXMmycA9YBKa48H8AOiF/qmQD+Jq8DRrLIeyJJexZsN2YlKcfmTIQQ60rhxPwTpOmMtqq969tCaKYDmBTW27atzB8yAMlnDbgCs2rutQGs1/4ZT38DD7drduVCjHnJiDsQzbbPRYQCr2KNij4bdnBIuVoHZZWKDE3NyyTNK/OuZTlzWY5Y27w3q021v+SablZ0H7hV0g4p1HnZVLgn2wPWGU9LEsm/Rni+ziT5zvwBmtz/ypimPJ6eBE0fcNVefyD5XkRbrC3SQ2jGeghJHpMYlfGV+DyASzFZ32pvhNfyvQ2qDQtDgUC1FhfJAxAix7dJuqxkfTPbZCL+MxlDQL3SfVGuYSaxUXdNhY3Ra+YbCvaAdQDJqxAf8EMA7IZIkl4iafvcMesjSoB3S5uuQpT5Tonmn2bqkvIYtgbwQzh83TOu2usP6TttQ0RLtqNzu5aN2OAwOUj+XNILS/bdIWm7umrDkrGrK4Rkr0FocbUXmO2FyK+6Jr2+SNLxBef5GiJU+c206e0AZjTJgU1jHwLwpXSOIwBsKOngBhWU70ZURO6AkJRaF8DHJJ1aN++gsQesM96MSO49VNJvSc7BZBG6ryEafr4pvf9bxC/d+kOmju0Rn5c9MRGCVHpvOsdVe30gPTw+DOCto16LqaQq/yz73JdVG75dFU2vEYn2hVpcJD8BYL6kFUnG4idoVbGfmZLt3wvgfYgkeSKat1+ajqlTuj8CwLGIdk5EPKS+r+qacufI+uleBWBKyUHZAOuApAW1MPd+KYBvtB22paS88v0nSC4ZwvLM9Of1ALaQ9OSoFzImuGrPrEo8SHIXSdfnN5LcGRGtAcpDjE2aXl9M8rUFWlxPKlp9QdJyTta4+D7Jv5b0BOL+meV1zUd0+ZiLGqV7SY+i1fuapzRsmuZZE9GNZi5ajbuRVKvmcQiyATktkkm7MKFFkh17LYAjJV2d3r8cwEmSXjqUxZppC8l/BXCEE8X7A8nPIBqbZ1V7iwDsWvQUb8x0h+QuCBHns9AqJv0OAG+R9LOScauroqVUmxbXLER6xEotLoRRk3XwIIAt0/ts//cBvBTAfklnDCT/EsA5AN4l6T+Kkuzb1rANgH/CZCOqMDqQvyaSlyI8uC2hU0mfLRo7TGyA9RlGG4SzEQ1ACeCPAA6WdMso12WmPiSvROQp3IDWHDDLUHSBq/bMqgbJjRGhuUxM+g4ApwB4dVWIMVdt2HHT6yZVkCSPAfAaAPsg5CBORigILE7nKFW6T/tvAXAqWo2ofSSd0OCabpc0qRXRVMAhyD4jaQmA+SRnp/ePVI8wZiUfH/UCxgFX7ZlVleQ9n/Q9QjIfYnwzIpeqiNJQYI0WV926PkXyMYQBRQB7SrqbzZTugVAg+Erbev48d01VXENye0m31a1z2NgD1ifqkhg1BRp/GrMq4Ko9Y8op0s/K7SsNBZZpcSHkJ0pTdBDJ75mR9XJEeHJlb90mHn6SxwF4ENGvNx8dKP175oRA7OqI6vJ70tjMuBu5J9wesP5RlcRoK9fU0pZruAaAmQAezecYmnpctWdMMSnEuDHJr6I4xFiWaA+UaHGppl1QyvfKKOwL2cC7lultHpkbJoSXuzBsCuB1VeuaCtgD1mdIvlzST+u2GVNHEjfcRdJHRr0WY8z0J2l5bYGQgsiHGM9CRaK9pNlVWlx9WNcSAP+VVxCo8tQVXFORPtlSAM9RW29mkvsB+LWkGzFibID1mZLE30nbjGkCyeskud+eMaZnkqGjJoZNwdhZCC2uvTGhxXVCkojodV23AliRXxfJ29pEzl8E4IVobaj9jbKwaSpqOljSfW3btwLw1bIKymHiEGSfIPlSAC8DsFFbHthsADNGsyoznSCZF+tdDVFC7ickY0xHMPpCzlGu9U/iYgCPVYwrDQXWaHH1ymIA+5LcEhPetZUeKpIfB7A7wgC7BFFNeTVCh7MsbPrsduMLAFLy/7MHcRGdYg9Yn0hx7t0RPanyLQ6WIVoz3DWKdZnpA8mv594+jRAXPN26YMaYpqQQ20kA1lA0ut4RYeAsR0WIMY0tbXrdqRZXh2ueBeC/APwKBd61lFA/H8DNkuYzGoz//6prAvCgpK1K5ru7bN8wsQHWZ0hurtT93RhjjBkmJG9EtC+7MgvpNdW/KzouCwWWaHGhX7lUNdWZ10vaJV3bHgjHxu2Stqs436mI9mMfVc7QSa2Tnivp7/qx7l5wCLL/LCe5ANGcNB+rHnm82UxNSH6sYrdU0NjWGGNKeFrSw5M7AjWqNlxMciFaE+0zA2uSFlenlIVGk3ft1yR/iGLv2uK01tPTev4E4PqqawLwYQBnALibE+0A5yO8ge/u5Tr6hT1gfSZ9gP4V4ao9HFE++3u3PzFlkPxwweZZAA5F5DGsO+QlGWOmKSTPBPAjRL7WgYiKx5mSDq8KMabXpYn23Whxtc1TFBr9pKT9q7xrDEtyU0kPpPPMBTBb0q3pfd01bYFwiADAHZLuabLeYWADrM+QvFHSS/KuXJJXSfrLurHGkFwPwAcQxte3AXzWOWDGmKaQXAfAMQBejTCiLgNwvKTHq0KMDc57b8FmSdqi4bpKQ6PZfbNqbNn+Xq5p1DgE2X+eSv//huS+AH4NYNMRrsdMA0g+C8CHALwN0Ut0J0n/M9pVGWOmG4qG18cAOIbkDACzJD2edleFGCubXkua1+PSSkOjAC4i+fco965dR3JnSTcUjK28pqmMPWB9huTrEKJwmwH4IkKG4hOSvj/ShZkpS8oZ/BsAXwXwJUl/GvGSjDHTFJLnItJfViAMkfUBLJS0oE7Lqy7RvkyLq+G6qkKjld41kj8HsC2iMvxR5NoJDVKfbNDYADNmxJB8BvHU9zRadb/aG9IaY0wlWU4UybcBeAmAowDc2LAKsirUV6jFJekNDddVGhqtGDNH0lKSmxftr1IcSFGFUprmrg0SG2B9wpVsxhhjRg3JOwDsCOBcAKdIuorkLUk/q1LLqyrRvkSL6wxJ+3Wxxiw0+khu2yTvGoAPKnWRIfld5VoV5cYVXhOAeZhor9RO49y1QeIcsP5R5O5cWckGwAaYMcaYQXMaIlR3C4BFyXuUGTrnI0KMZ6C1b2JGadNrAI9Jeobk0yRnIwy1xkZMUWiUZBYaLVO6zxtPZXMVXlO/9MkGiT1gA8CVbMYYY6YKJFeX9HRdtWHNOb4M4CMA3oLQ2PoTgCWSDmk4vjQ0WuZdA/C8nAessKdyk2siuSGArdGau7aoyboHiT1gfcSVbMYYY0YBybdLOoetvYjzLER9tWFZKPCbAD4t6SEAp5K8FDktrobMJDkTwAGI0OhTJDMPUJl37fkkH0F4wtZOr4HW/NjKayL5boRDZFMASwDsCuBahCTGSLEB1ifaKtm2dyWbMcaYITIr/b9exTFVIcaqRPtvkLwQ4bmCCppcN6AqNFqodF/VaqjpNSGMr50BXCdpD5LPB/CJLtbfdxyC7BOuZDPGGDOdqUq0J/klAGeVaHF1O9/qiLytUqX7Psxxg6SdUzuiv5D0RJF6/iiwB6xPSFpt1GswxhizakNyIwCHYXKl47vS/iotr6pE+z0AHE7yPrRpcdWspzI0Kmlhr961mmv6VfKuXQjgP0j+D0IgfeTYADPGGGPGh+8hxMAvR1ulY0W1YWasFIUC70j79ulyPU1Co1VK95XUXZOk16dDjyN5BUKY9tJO5xkEDkEaY4wxY0JVeK0mxFjY9BoRdqzU4urDmkuV7guO3UnSTU2uKe0/CcDXJP283+vuFXvAjDHGmPHhYpKvlXRJwb7SEKMkFYUC2dq8sWvx0pLQ6CxJb0aJd41ku+wEAXyP5H4IB9JNVdeUuBPA6Snf7OsAzpP0cLfX0U9sgBljjDHTHJLLMKH8/hGSTwB4Cq2FYIXVhrnTFIUCVfK6U4pCowsQi7u/yLuWekReh5y8BELYfGFay5511yTpDABnkNwWwCEAbiX5UwCnS7qih+vpGYcgjTHGmDGnLMSYrzYsCQW+CMCy9HptAMuzw9FBhX9RaJTkzZJe3P46t/8NAI4A8JnMo0fyXknzml5T2j4DwOsQBthmCIH0VwB4VNJbmqx/ENgAM8YYY8YEkq8H8OMszJa8Q7tLurBMNZ49NL3uYF0nALgmHxrNq9tXKN2vi2jltylCgf/KfB/HOiV8kgsB7A/gRwDOlHR9bt8vJW3b67V1iw0wY4wxZkyo8jSVaXm1GUJ9TbRvC43OQoQTs9DoumjoXSO5I4CTAWwnaePc9kp9MpLvAvD/JC0v2Lf+KPPBbIAZY4wxYwLJW9urB0neJmn7smpDACuqQoFThRRyXE/SI7lttRWUJPcHsFt6e5Wki4a26AqchG+MMcaMD4tT2O1LCOPqCEQlIFCu5XVB7vVAvDJVodGm51B4jB5J4+dIWooafTKSnwawC4BvpU3vJ/kySf/c8UX0GXvAjDHGmDGB5CwAxwLYG+EN+iGAfbKwZEm14QpMeI9qE+3btbgarqsyCb9TmoZNSd4KYEdJz6T3MxCaYZUK/sPAHjBjjDFmTJD0KICj89tIvib3dpKWl6QZZecjuVObHleRFlcTitr19WKDbJB7XadPtgGAP6bX6/cwZ1+xAWaMMcaMCSS3AfBPaBU83Sp3SKdhr8Wo1+JqdJ6C0OiN7Qd14F3bNPe66po+DeDm1IaIiFywkYcfAYcgjTHGmLGB5C0ATkUYN5ng6fXoIMTYdr5KLa4O1lUUGr0IwOP5wxCCrbXeNZJC8wrK5wLYOe37GYA1+yGt0Ss2wIwxxpgxoU4Xq8tzVmpx9XDeZzDZu7Zr2iZJpd41kkslzak5/0sBPA/AIkkPktwBEZ59paTNel1/r9gAM8YYY8YEksch+iFegJxhI+mPZWNKzjMpFFimxdXwfEWh0Y0QuVllSve3oTi8SADbSFqzYr4FCPX7JYgQ7MUA/h7AvwA4TdLjZWOHhQ0wY4wxZkxI/RPbUZXHqqzpNQpCgUVaXA3XVRQaBYBfosS7VqbMn1EVRkz6YDtJepzkhgB+DWAHSXd1su5BYgPMGGOMWYXpJRTYwRx1LYN2RLHS/QEID9Ztki7rdr4iGYxRYwPMGGOMGSNIvgjACwGslW2T9I22Y1aGGPuVaF+zpuNQExpt966R/DKA7QBcA2AvABdJOr5ijvw1PQRgUW73bvn3kvbv+aJ6xAaYMcYYMyaQ/DiA3REG2CUIpfjbAfyf/GFoCzF2k2hP8kBJ3224rm5Co7cDmC9pBcl1APwk82rVhU0BrFe1HklXNVn3ILEBZowxxowJKXF9PkLtfT7JTQD8FsC1aBBi7CTRvkklYi/k1e7b3/crbNrv5uOdYCFWY4wxZnx4TNIzJJ8mORsR9vsVgKcALGgLMe7RPljSEpJ7osaDlGAnC2sSGs0deyCA56dWQtlcW6b3BPAAGl5TDT3LaXSLDTBjjDFmfFicGl2fjqg4/BOAyxE5XseTPAQRYiwNf+WbXtfQOIRWEhq9GkChAYbwwr2g5rR/QMNrqmBkYUCHII0xxpgxICWxbyrpgfR+LoDZkm7NHbMjOtDy6kWLq+A87aHRMyTtV3L8A03FUnvUJ2sJcw4Te8CMMcaYMUCSSF4I4CXp/X0Fx3QSYgSAUwBc2oflFYVGq8J/IrkM5cbfynZDXVxT+7lGgg0wY4wxZny4juTOkm4oO6CDECMAHAPgd+hCi6uNotDoc3M5XnkIYJOm3jWg+ppIrg1gjqRfFuw+qukc/cYhSGOMMWZMSArw2wK4DxMNuCVphy7PtwzATWioxVVyjsLQKICHq8b1o2E2yf0AnARgDUnzUrjyk9YBM8YYY0zPkJwjaWlZ+54yY6ZOy4vkkwDWLtLi6nB9hUr43Srd18y18ppI3ghgT4Su2YvTtlu7NUj7iUOQxhhjzPTnQkTvw/s71LY6OanUl+VarS5pBQBIWp68Wd0wKTTapnR/PMldOvWulXAygMyofFrSw90ve3DYADPGGGOmP3kLoxNtKwJ4XcX+Oyu0uDoJbe4B4HCS92EiNLo1gFl57xpCjb9X8j+L20keBGAGya0BvB9h8I0cG2DGGGPM9Eclr2vHJa/ZASgIBZJ8fi+LykKjCN2vdn7QJ+9aO/nrPwJRSPAEgPMAXIb+GHk94xwwY4wxZppDcgUmPEtrA1ie7QKwDoA7ioYB2AbAmeig6XWH68q3D2oJjZJcDuDu3Fq2TO9rvWvd6JORnIHwuDWtAB0o9oAZY4wx0xxJM8r2lSXm5/gB2ppeI3mJmmpxVVAVGq1Tuq+iKmw6MTl5LoDDAaxAyF+sT3KhpAU9zN0XbIAZY4wxY0xViBGISseyUKCkbsRNW6Yved2TzETdNeV4oaRHSL4N0QLpKIQhZgPMGGOMMYOjQbVhVdPrrjXEEvNJPpLOtXZ6nc2zLoBlRUtGjXetgwrKmSRnAjgAwCmSniI5JXKvbIAZY4wx481uKAkxJnoJBVZSFRrtkbpryjgNIUp7C4BFKRzrHDBjjDHGDAaSO0m6CUBpiDFt61lxfgRUXlOGpC8A+EJu0/0k9xjGAutwFaQxxhgzzSG5U/smAN8DsB8iTHdXbntLtSGAeegt0X7o1FVQAjhR0jkkP1Q0XtLCoSy0AnvAjDHGmOnPYgDXIfSuMp4NYCGil+NBZQOnqQesLmz6mvR/r0UEA8MeMGOMMWaaQ/INCNHRz0i6JG27V9K80a5sMrnQ6CqNDTBjjDFmDCC5LiIRfVMAH0Y0oN6iD1pevaypKjTKbg2xptdEciMAhwGYi1zUT9K7upm3n9gAM8YYY8YIkjsiGlJvJ2njEa/lGUwOje6atknSngOe/xpEheSNCDFWICb+bumgIWEDzBhjjBkzUlXgeqNuuzPq0CjJJZJ2HMZcnbLaqBdgjDHGmP6iYOR6V5K+A2BfAK8ieT7JOeisWXivXEzytUOcrzH2gBljjDFm4AwzNJrLESOAWYgQ6FOYQtIaNsCMMcaYMYbkgVMh5wmYOqHRqYBDkMYYY8x4c/KoF5Ax7NAoydeTXD/3foPUxHvk2ANmjDHGjDEkH5C02ajXMQqKkvBJ3izpxSNa0krsATPGGGPGmynpaSF54BCmKbJzpkQXIHvAjDHGmGkOydtQLky6jaQ1h7ykWkgulTRnwHN8DcBDAL6E+PkcAWBDSQcPct4m2AAzxhhjpjkkN6/aPxX7PQ4jNEpyFoBjAeyNMEZ/COAESY8Oct4m2AAzxhhjxoCUXL4VgNskXTbi5dQyDA/YVMYGmDHGGDPNIfllANsBuAbAXgAuknT8aFc1+tAoyW0A/BMm94IcaAukJtgAM8YYY6Y5JG8HMF/SCpLrAPiJpJdMgXWNNDRK8hYAp2JyL8gbBzlvE6ZEJYAxxhhjeuJJSSsAQNLyJHg6ciTdP+LQ6NOSvjLkORthD5gxxhgzzSG5HMDd2VsAW6b3WeudHUa0rpGGRkkeB+BBABcg2hEBACT9cVhrKMMGmDHGGDPNGXWor4xRh0ZJ3luwWZK2GNYaynAI0hhjjJnmTEWZicRIQ6OS5g1zvk6wB8wYY4yZ5pBchvJqQ0maPeQlxeRTIDRK8kUAXghgrWybpG8Met467AEzxhhjpjmS1hv1Gkp4wSgnJ/lxALsjDLBLAOwD4GoAIzfA7AEzxhhjzFiSdMjmA7hZ0nySmwA4Q9J+I16aPWDGGGOMGQxTIDT6mKRnSD5NcjaiInLkCfiADTBjjDHGDIgpEBpdTHIDAKcjxFj/BOD6ka4o4RCkMcYYY8aOVHG5qaQH0vu5AGZLunWkC0vYADPGGGPMWELyxqnQkqmI1Ua9AGOMMcaYAXEdyZ1HvYgi7AEzxhhjzFhC8ucAtgVwH4BHMeLWTHlsgBljjDFmrCA5R9LSshZNU6FzgA0wY4wxxowVJG+StFN6/V1JB456Te04B8wYY4wx40a+5+SU0P1qxwaYMcYYY8YNlbyeMjgEaYwxxpixguQKTCTdrw1gebYLI2xOnscGmDHGGGPMkHEI0hhjjDFmyNgAM8YYY4wZMjbAjDHGGGOGjA0wY4wxxow9JHca9RryOAnfGGOMMWNFgbFFAN8DsB/C9rlp+KtqxQaYMcYYY8YKks8AuA7AE7nNu6ZtkrTnSBaWwwaYMcYYY8YKkm8AcASAz0i6JG27V9K80a5sAueAGWOMMWaskPQdAPsCeBXJ80nOwRRTxLcHzBhjjDFjC8kdAZwMYDtJG494OSuxAWaMMcaYsYYkAawn6ZFRryXDBpgxxhhjzJBxDpgxxhhjzJCxAWaMMcaYVQaSB456DYBDkMYYY4xZhSC5VNKcUa/DHjBjjDHGrEpw1AsAbIAZY4wxZtViSoT+Vh/1Aowxxhhj+gnJ21BsaBHAJkNeTiHOATPGGGPMWEFy86r9ku4f1lrKsAFmjDHGmLGD5AEAtgJwm6TLRrycSdgAM8YYY8xYQfLLALYDcA2AvQBcJOn40a6qFRtgxhhjjBkrSN4OYL6kFSTXAfATSS8Z9bryuArSGGOMMePGk5JWAICk5Zgi0hN57AEzxhhjzFhBcjmAu7O3ALZM7wlAknYY1doyLENhjDHGmHHjBaNeQB32gBljjDHGDBl7wIwxxhgzVpBchnIhVkmaPeQlTV6IPWDGGGOMMcPFVZDGGGOMMUPGBpgxxhhjzJCxAWaMMcYYM2RsgBljjDHGDBkbYMYYY4wxQ8YGmDHGGGPMkPlfc7RnhdQyU5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the model\n",
    "model = RandomForestRegressor()\n",
    "# fit the model\n",
    "model.fit(X_train, y_train.reshape(-1))\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "    print('Feature: %s, Score: %.5f' % (cols[i],v))\n",
    "# plot feature importance\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.bar([x for x in range(len(importance))], importance)\n",
    "plt.xticks(range(len(cols)), cols, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialise empty list to store the predictors\n",
    "variables = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering out the variables which had a high coefficient value\n",
    "for i in range(len(cols)):\n",
    "    if importance[i] >= 0.02:\n",
    "        variables.append(cols[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Is Captain(1=yes)', 'Nationality(1=Overseas)', 'no', 'Batting avg', 'Catches per match', 'Catches taken', 'sr', 'matches_bat_ipl', 'balls_faced_ipl', 'strike_rate_ipl', 'fours_ipl', 'matches_bowl_ipl', 'total_runs_given_ipl']\n"
     ]
    }
   ],
   "source": [
    "print(variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model 3 - variables selected using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the predictor and target dataframes\n",
    "X1 = data[variables]\n",
    "y1 = data['Player cost USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the predictor variables\n",
    "X1 = StandardScaler().fit_transform(X1)\n",
    "y1 = StandardScaler().fit_transform(np.array(y1).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting into train and test datasets\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising the model \n",
    "model1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model1.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding predictions for the test set\n",
    "y_pred_lr1 = model1.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45705592633108466"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics\n",
    "mean_squared_error(y_test1, y_pred_lr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5000244452014733"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the metrics\n",
    "r2_score(y_test1, y_pred_lr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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