wholesale_customers_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d65ae7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import seaborn as sb\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.feature_selection import RFECV, RFE\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import decomposition\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9426c55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Downloading xgboost-2.0.3-py3-none-win_amd64.whl.metadata (2.0 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\ramya\\anaconda3\\lib\\site-packages (from xgboost) (1.24.3)\n",
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      "   ------------------------------------- -- 93.3/99.8 MB 8.8 MB/s eta 0:00:01\n",
      "   ------------------------------------- -- 94.1/99.8 MB 8.7 MB/s eta 0:00:01\n",
      "   ------------------------------------- -- 94.4/99.8 MB 8.6 MB/s eta 0:00:01\n",
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      "   ------------------------------------- -- 94.4/99.8 MB 8.6 MB/s eta 0:00:01\n",
      "   ------------------------------------- -- 94.4/99.8 MB 8.6 MB/s eta 0:00:01\n",
      "   ------------------------------------- -- 94.4/99.8 MB 8.6 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 95.4/99.8 MB 7.4 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 95.4/99.8 MB 7.4 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 96.2/99.8 MB 7.0 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 96.5/99.8 MB 7.4 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 96.7/99.8 MB 7.0 MB/s eta 0:00:01\n",
      "   ---------------------------------------  97.5/99.8 MB 7.4 MB/s eta 0:00:01\n",
      "   ---------------------------------------  98.0/99.8 MB 7.0 MB/s eta 0:00:01\n",
      "   ---------------------------------------  98.6/99.8 MB 8.5 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.2/99.8 MB 8.0 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.6/99.8 MB 8.5 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.6/99.8 MB 8.5 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.7/99.8 MB 7.9 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.7/99.8 MB 7.9 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.7/99.8 MB 7.9 MB/s eta 0:00:01\n",
      "   ---------------------------------------  99.7/99.8 MB 7.9 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 99.8/99.8 MB 6.7 MB/s eta 0:00:00\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.3\n"
     ]
    }
   ],
   "source": [
    "!pip3 install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416dc667",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9c2685d",
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9413</td>\n",
       "      <td>8259</td>\n",
       "      <td>5126</td>\n",
       "      <td>666</td>\n",
       "      <td>1795</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12126</td>\n",
       "      <td>3199</td>\n",
       "      <td>6975</td>\n",
       "      <td>480</td>\n",
       "      <td>3140</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7579</td>\n",
       "      <td>4956</td>\n",
       "      <td>9426</td>\n",
       "      <td>1669</td>\n",
       "      <td>3321</td>\n",
       "      <td>2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5963</td>\n",
       "      <td>3648</td>\n",
       "      <td>6192</td>\n",
       "      <td>425</td>\n",
       "      <td>1716</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6006</td>\n",
       "      <td>11093</td>\n",
       "      <td>18881</td>\n",
       "      <td>1159</td>\n",
       "      <td>7425</td>\n",
       "      <td>2098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0        2       3  12669   9656     7561     214              2674   \n",
       "1        2       3   7057   9810     9568    1762              3293   \n",
       "2        2       3   6353   8808     7684    2405              3516   \n",
       "3        1       3  13265   1196     4221    6404               507   \n",
       "4        2       3  22615   5410     7198    3915              1777   \n",
       "5        2       3   9413   8259     5126     666              1795   \n",
       "6        2       3  12126   3199     6975     480              3140   \n",
       "7        2       3   7579   4956     9426    1669              3321   \n",
       "8        1       3   5963   3648     6192     425              1716   \n",
       "9        2       3   6006  11093    18881    1159              7425   \n",
       "\n",
       "   Delicassen  \n",
       "0        1338  \n",
       "1        1776  \n",
       "2        7844  \n",
       "3        1788  \n",
       "4        5185  \n",
       "5        1451  \n",
       "6         545  \n",
       "7        2566  \n",
       "8         750  \n",
       "9        2098  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Wholesale_customers_data.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad9d88fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of rows and columns in the data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bc3e25",
   "metadata": {},
   "source": [
    "### Check the unique values in Channel and Region since they are nominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c5aa019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Channel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e71505c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dff5aca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             int64\n",
       "Region              int64\n",
       "Fresh               int64\n",
       "Milk                int64\n",
       "Grocery             int64\n",
       "Frozen              int64\n",
       "Detergents_Paper    int64\n",
       "Delicassen          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "baab6e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "1    298\n",
       "2    142\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of channels belonging to each category\n",
    "data['Channel'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588f9c81",
   "metadata": {},
   "source": [
    " ### Explore continuous features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73ee0cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "513c0020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             0\n",
       "Region              0\n",
       "Fresh               0\n",
       "Milk                0\n",
       "Grocery             0\n",
       "Frozen              0\n",
       "Detergents_Paper    0\n",
       "Delicassen          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for any missing value\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4218b1",
   "metadata": {},
   "source": [
    "#### Since the sum of missing values is 0, there are no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6a46d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062028</td>\n",
       "      <td>-0.169172</td>\n",
       "      <td>0.460720</td>\n",
       "      <td>0.608792</td>\n",
       "      <td>-0.202046</td>\n",
       "      <td>0.636026</td>\n",
       "      <td>0.056011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>0.062028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055287</td>\n",
       "      <td>0.032288</td>\n",
       "      <td>0.007696</td>\n",
       "      <td>-0.021044</td>\n",
       "      <td>-0.001483</td>\n",
       "      <td>0.045212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>-0.169172</td>\n",
       "      <td>0.055287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.244690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>0.460720</td>\n",
       "      <td>0.032288</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.406368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>0.608792</td>\n",
       "      <td>0.007696</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>0.205497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>-0.202046</td>\n",
       "      <td>-0.021044</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>0.390947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>0.636026</td>\n",
       "      <td>-0.001483</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicassen</th>\n",
       "      <td>0.056011</td>\n",
       "      <td>0.045212</td>\n",
       "      <td>0.244690</td>\n",
       "      <td>0.406368</td>\n",
       "      <td>0.205497</td>\n",
       "      <td>0.390947</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
       "Channel           1.000000  0.062028 -0.169172  0.460720  0.608792 -0.202046   \n",
       "Region            0.062028  1.000000  0.055287  0.032288  0.007696 -0.021044   \n",
       "Fresh            -0.169172  0.055287  1.000000  0.100510 -0.011854  0.345881   \n",
       "Milk              0.460720  0.032288  0.100510  1.000000  0.728335  0.123994   \n",
       "Grocery           0.608792  0.007696 -0.011854  0.728335  1.000000 -0.040193   \n",
       "Frozen           -0.202046 -0.021044  0.345881  0.123994 -0.040193  1.000000   \n",
       "Detergents_Paper  0.636026 -0.001483 -0.101953  0.661816  0.924641 -0.131525   \n",
       "Delicassen        0.056011  0.045212  0.244690  0.406368  0.205497  0.390947   \n",
       "\n",
       "                  Detergents_Paper  Delicassen  \n",
       "Channel                   0.636026    0.056011  \n",
       "Region                   -0.001483    0.045212  \n",
       "Fresh                    -0.101953    0.244690  \n",
       "Milk                      0.661816    0.406368  \n",
       "Grocery                   0.924641    0.205497  \n",
       "Frozen                   -0.131525    0.390947  \n",
       "Detergents_Paper          1.000000    0.069291  \n",
       "Delicassen                0.069291    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c72eb6b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jc8jU/iZtUNIJk1buoK9H+OjZxG38AcuB3bAa0fudcwZQmJqj0NdHlRCnUa56FofCQnveenaO6Feojl6JMiStW0DqnvUY1HPHqM/YAsnpXTg62APw5InmtfPkSRSOb3HtLF3yKWFhEZx66ctAQVGYZJ/zrGfxGuWqZ/HomVtqraNn44B+uaroO5UmeesSUg5tQlm7IUbdR6lj9MtWweCDVqTuXfvOOeb1nvI0+imWdtpztLKz5Gn0U42yF+8p5n+9p4QFhvH11K9YNmwxK8etID01jaUHPsepjJO6joOLI236tSP84WMWDpjPiZ1HGbpgOB92bfbOz0sUPGmI5ZNKpQKy74p8nZo1a6p/NjExwczMjMjISHXZhg0bcHNzw87ODlNTUzZt2kRwsOaHcLVq1dB/af6Ik5OTxj5ePY5CocDR0VEd4+3tTUBAAGZmZpiammJqaoq1tTUpKSkEBr7Z5M1ly5ZhYWGhsX3+1YY3qvuv8dff9QWFQqH+W2sPfzVecz8KPQUJMU/59pN1PLgdyKVfznNw7f9o3b+t1v0NXTSSUpVLs3rcl2+Vdk+PzkRE3lZvBgYGWvNDoUBF3s/nbQ0Y2JMTJ84SER75+uA3pSVnrSnr6WG//BPivvme9CAdNzboKciMjSdq/lek+Qbw/OhZ4jbuxtyjQ8HlDLnzJo+8ARR6oFKRvPkzsh7dI+P2VVL2fYtBw9Z/e69Y795diI/1V28GBtlTh3Nf27pfCy+bOmU0vTw608NjeCF/WdNyfb/unH+/gqzg+2T6eZP64xaU77XIPueGxhj1m0LK3rWoEhMKLsNc17OWS0VH/IvPmBfl/j73OHfoDI/8HuF31ZcVnst5/CCM9oNyhu8Vegoe3Alk1xff8/DOA078cJxTu0/Qpr/2YdtCJ0OTOslk/XyqUKECCoUCPz+/1y758OJD8QWFQkHWX12s+/btY9KkSXz55Zc0aNAAMzMzvvjiC/7888833sebxGRlZVGvXj127dqVKz87uzf7ljtz5kwmT56sUab37N3v7PsneBaXQGZGZq7eLwsbC56+8o32hfioeKxyxVuSkZ7Bs7hn2TGRcWRkZGr8rUIDQrGyt0ZpoCQjPUNdPmTBcNxavs+8njOJjYh5q/yP/HoKr6vX1b8bGmZ/mDs42PHkpUnudnY2RD7JPUk4P1xcStCseSP69B79+uA3kBmXgCojE6WtFS9/dOtbW5AZE5crXs/EGKPqlTCs7Irtp2P+KlSg0NOj3PUjhI+YSfKVG2RGxaLKyNQY1kh/EIzSzgaUSsjIyLXvt6F6noAqMxOFhbVGucLMMlcv2QtZT2PRi4+G5KScsvBgFHp66FnZkhX5+J1yehu//HKCK1dyhsNeXDuOjnZEROQ0sO3tbXkS+fprZ/KkkXwyYxxt2vbi1q3cc/YKgiox+5zrmVnx8rugwtQC1Su9ZOo6CbGonsZAykvn/EkICj09FBY2KAyN0LNxwHjYSzep/NUIMv3yRxKXjkIV8+ZzrF68p+R+j8j7PSUuKj5Xb5mFjYXGe0qu56VSEXDzPk5lndVl8ZFxhN4P0YgLDQjlg3Z/3x3oGv6jQ4oFRRpi+WRtbU2bNm345ptvGD9+fK55YvHx8RrzxPJy/vx5GjZsiKdnzu32b9pD9Tbq1q3L3r17sbe3x9zcPF/7MDQ0xNDQUKMsPa1gPtSLWkZ6Bg9uBVLTvRZXjufcaFHTvTZXT2ifhO5/7S5uLd/XKKvlXpsHtwLIzMi+Xfuulx+NOzfR6E1wLutM7JNYjUbY0IUjeL/NB8zzmEVkyNv3Lj1/npjrbraIiEiaN3fn5o3sCeAGBgY0blyfuXM+e+v9a9N/QHeiomI4dvS3AtkfGRmk+t7HuEFdEk9fUhcXb1CXxN//yBWe9TyJkI9HaJSZ9+qE8fu1eTJ5Eelh2R+aKdd9MW3/4V89a9l/A4MyJcmIjHnnRhgAmRlkBd1HWaUuGT4X1cXKqnXJuJ47byD7zsp67mBoBKkpAOg5lESVlUlW3N/7mtJ27YSHP6FliyZcv34HyL52mrh/wMxPl+rc15TJo/h05gTad+iL97WbhZYzmRlkhQagX6kOGbdyXq/6lWqTcVv76zXzoR/K2o2hmBGk/XXO7UqgyspE9TQGlUpF4mdjNOoU69AfhaExqQc3oop/u79LRnoGgbcCqOVemz9fek+p5V6bKyeuaK2T/Z7ynkZZLfc6BL70nqJN2arlCLoXpP7dz9sP53IlNGKcyzoTFVaAPdeiwMjQ5DtYt24dmZmZvP/++xw4cID79+/j5+fHmjVraNCgwRvtw9XVFS8vL44fP46/vz9z5szh6tWCW9vohb59+2Jra0vnzp05f/48Dx8+5OzZs0yYMIHQ0NACP15+JSUlc9c/kLv+2Y3RsMdPuOsfSHhE4b+BHN78Ey08WtGsZwtKuJZk4Jyh2Drbqtfw6TO9P2NXTlTHn9x1DNsSdgycM4QSriVp1rMFzT1a8vPGH9UxJ3Yew8zKnMHzh+FU1pm6zevRZUwPjn93RB0zbPFI3D9uylfjvyQlMRlLO0ss7SwpZvhuQ1TfrN3K1GmedPqoNVWrVuTbjV+QnJzMvr05d+1u3PQl8xdMU/9uYGBAjZpVqFGzCsWKGeDs7EiNmlUoV660xr4VCgX9+vdg184DZGYW3BpB8d8dxLxbW8y6tMagnAs200eidLInYW/20hjWEwdjv/SvfFUq0gKCNLbM2HhUaWmkBQSh+mvpj6d7D6NvaY7tJ6MxKF2C4k3ex3J4L57u+aXA8k49eQAD97YYNGqDnqMLhj1HoWdtT9rZwwAYdhmC0ZCc85x+5TdUic8wHjQVPadS6FeogWGP4aRfOA7pf908o69Ez6Ucei7lQGmAwsoWPZdyKOyctaVQoNZ8vZlPZoyjc+e2VKtWia1bVpGUlMzuPTnrmG3b+hVLFn+i/n3qlNEsXDCdYSOm8CgoBAcHOxwc7DAxKV4oOaad+RGDD1qhrN8SPYeSGH48DD0rO9IvHgWgWMcBGPWdpI5P9z6LKjEBoz4T0HNwQb9cNQw/Gkz6n6eyz3lGOlkRwRobyYmoUpOzf858+0b7L3+9pzTv2ZISriUZPGcots52nNiVnWPf6QMY/9J7yvFdx7ArYc+gv95TmvdsSQuPlvy0Mee895zQi9pN6uDg4kCZqmUZ88V4ylQty/GdR9Uxhzf/RMU6leg2pgeOpZ1w79yEVn3acOyl952/k0qVWWDbf5H0iL2DsmXLcu3aNZYsWcKUKVMIDw/Hzs6OevXqsX79+jfax6hRo7h+/ToeHh4oFAp69+6Np6cnR48efX3lt1C8eHHOnTvHjBkz6Nq1K8+ePaNEiRK0aNEi3z1kheH23fsMGTdD/fvyr7OX4OjcriVLZk8p1GNfOnwBUyszuo/3wMremhD/IJYOWkh0WPbQnpW9FbbOtur4yJBIlg1ayMC5Q2nTvz1xkbFsnb9ZvYYYQEx4NIv7z2PgnKGsOPYVsU9iOLLtF35af1Ad06Z/9q3sC/Zp9jZ8M+UrzuzPf2/TqpXfYmxsxKrVi9QLunbuNECj98PFxVlj2NTJyZ4/Lue8WU+cNIKJk0Zw/txl2rXNmdzerHljSpUqUTB3S74k8dhZoi3MsBrVF6WdNWn3gwgfPZuMv+ag6dtao3R6u5sNMiOieDziU2ynj6TkwQ1kRkbzdOePxG/Z9/rKbyjD6ywppuYYduyrXtA1ac1sVLHZeSssrdGzts+pkJpC0qpPMOo9BpNZa1ElPiPd6yypP25XhygsbTCdmzMH07BNDwzb9CDj3g2SVuQ06grDFyvWYWxsxNo1S9ULurbr0Efj2in1yrUzauRADA0N+d/eTRr7WrjoSxYuWlngOWb4XCC1uDmGbXqhMLcmKzyI5G8XoIrLfr3qmVujsHrpWklLIXn9XAy7jaD4lFWoEhPIuH6B1CPa77YtCBcPX8DMyoyef72nBPsHsWTQQqI03lNycowMecLiQQsYMncY7fp3IDYyli3zN2msIWZibsLoZWOwtLMi6VkiD+48YHbPmQTcyFmoOeBmAJ+PWEq/GQPoMd6DyNAnbF2wmXM/5twJ+7f6j87tKigK1ZvOvhRCi/TogluL6e/Up96k1wf9Ax2NKsThnkJ0o1zFok4h3+wa/DvfIq133CnqFPIlzrNuUaeQLwMPvf7GrX+ig0E/vz7oHaVcK7hjGNXNex3EfysZmhRCCCGEKCIyNCmEEEKIwiNDkzpJQ0wIIYQQhUf+6bdOMjQphBBCCFFEpEdMCCGEEIVHhiZ1koaYEEIIIQqPrKyvkwxNCiGEEEIUEekRE0IIIUThkaFJnaQhJoQQQojCI0OTOsnQpBBCCCFEEZEeMSGEEEIUHukR00kaYkIIIYQoNCqVLOiqizTEhBBCCFF4pEdMJ5kjJoQQQghRRKRHTAghhBCFR5av0EkaYkIIIYQoPDI0qZMMTQohhBBCFBHpERPvpE+9SUWdQr784L2qqFPIF496E4s6hXwZl5BR1CnkW+rRf2fu79lVLOoU8qXT/uSiTiFfLPSMijqFfy4ZmtRJGmJCCCGEKDwyNKmTDE0KIYQQQhQR6RETQgghROGRoUmdpCEmhBBCiMIjQ5M6ydCkEEIIIUQRkR4xIYQQQhQe6RHTSRpiQgghhCg8MkdMJ2mICSGEEKLwSI+YTjJHTAghhBCiiEiPmBBCCCEKjwxN6iQ9YkIIIYQoPFlZBbe9pXXr1lG2bFmMjIyoV68e58+f1xm/a9cuatWqRfHixXFycmLw4MHExMTk95m/EWmICSGEEOI/Z+/evUycOJFZs2bh4+ODu7s77dq1Izg4WGv8hQsXGDBgAEOHDuXOnTv873//4+rVqwwbNqxQ85SGmBBCCCEKjyqr4La3sHLlSoYOHcqwYcOoUqUKq1evxsXFhfXr12uNv3z5MmXKlGH8+PGULVuWxo0bM3LkSLy8vAriLORJGmJCCCGEKDwFODSZmppKQkKCxpaamprrkGlpaXh7e9O6dWuN8tatW3Pp0iWtaTZs2JDQ0FCOHDmCSqXiyZMn7N+/nw4dOhTKaXlBGmJCCCGE+FdYtmwZFhYWGtuyZctyxUVHR5OZmYmDg4NGuYODAxEREVr33bBhQ3bt2oWHhwfFihXD0dERS0tLvv7660J5Li9IQ0wIIYQQhacAe8RmzpzJ06dPNbaZM2fmeWiFQqHxu0qlylX2gq+vL+PHj2fu3Ll4e3tz7NgxHj58yKhRowr0dLxKlq/4Dzhz5gzNmjUjLi4OS0vLok5HCCGEyKFSFdiuDA0NMTQ0fG2cra0t+vr6uXq/IiMjc/WSvbBs2TIaNWrEtGnTAKhZsyYmJia4u7uzePFinJyc3v0JaCENsUI2aNAgduzYAYC+vj7Ozs506NCBpUuXYmVlVSDHaNiwIeHh4VhYWBTI/gpC6/7t6DyyC5Z2VoTeD2bbgi3cveqbZ3zV+tUYOGcIJSuUIi4ylp82HOLkrmMaMcXNTeg9rR/1236AibkpkaFP+G7xNnx+9wbgY89u1G/bgBLlS5KWkso977vs+uw7Hj8IK9TnCuB1/RbbftiP790AomJi+WrZHFo0aVjox32hbf92dB7ZFSs7K0LuB7N1wWb8XnO+B88ZikuFUsRGxvLjhoOceOl8N+venHFfTsxVz6NiN9JT07N/ntgbj0m9NR6Pi4xj6HsD3zjvDv070G1kN6ztrQm6H8TGBRu5c+VOnvHV61dn+NzhlK5QmpjIGA5sOMCRnUfUjzds2xCPsR44lXZCaaAk7GEYhzYd4reDv6ljeo7pScO2DSlZviRpKWn4efuxddlWwnRcJx8N6ETPUT2wsbfmkX8Q6+av59aV23nG1/ygBqPnjqJMxdJEP4lh7/p9HN75q0aMe/vGDJ46EKfSToQHhbNl+XYuHruofnzXH9/h6OKYa98/bf+ZNbPXAnA69ITW43+7eBPb1/+QZ35DJw+kc9+OmFuYccfHjxWzvuKh/6M84wE+bN+EEdMGU6K0M2FBj/n28y2cPXZB/fiAsX1o2s6d0q6lSE1J5ZbXHdYt3UhwYIg6pmk7dz7u14nKNStiaW3BgNbDuH8nMM9jDpo8gI592mNmaYafz11Wz1rDI/8gnXk2ae/OkKmDcC7txOOgcDYv38qFl84rQOcBH9FrVA9s7G146P+ItfPXafw93ds1plPfjlSqWQELawuGtR5JgK9mnqv/9yW1G9TSKDv/8zlWjF2uUdauf3u6jOyKlb01wfeD2bJgE746rvFq9aszZO4wSv312jy04QDHdh7VGuveqQlTv5nO5eN/sGz4EnV5r0l96D2pj0ZsXGQcg9z653nc/5pixYpRr149Tp48SZcuXdTlJ0+epHPnzlrrJCUloVRqNov09fWB7J60wiINsb9B27Zt2bZtGxkZGfj6+jJkyBDi4+PZvXt3gez/xVj2P0XDjo0ZPHcom+Z8yz0vP1r1acOsHXOZ1HIs0Y+jc8Xbu9gzc/tcTu8+wZqJq6jkVoXhi0aSEPuUP4/+AYDSQMmcnQtIiHnKl6M/JyY8BlsnW5ITk9X7qVa/Ose/O0LAjfvoK/XpPa0fs7+fz6SWY0lNzj2ZsyAlJ6dQybUcH7dvzaRZiwv1WK9q1LExg+cOY9OcDfh5+dGmT1tm75jHhJZj8jjfDszePo9Tu0+weuJKqrhVYfiiUSTEPuXyX+cbIDEhkXHNR2vUfdEIeyH4XhDz+85R/56V+eZ3NTXp1IQR80awbvY6fL18ade3HQt3LGRUi1FEPY7KFe/g4sDCHQs5tvsYKyasoKpbVTwXe/I05ikXj2Z/0D6Lf8aer/cQGhhKeno69VvUZ9KKScRHx3Pt3DUguzF3eMdh/G/6o6+vz8DpA1mycwkjW4zUep182KkpnvNHsWbW19y+eoeO/Tqw7PslDGk2jEgteTq6OLL0uyUc+eEIy8Z/RvX3qjF+yTiexj7l/JHshkvVulWYs24W277YwYVjF2ncthFz189iQtfJ3PW5C4Bnh3Ho6efMHilbqQxf7Pmcs7+eU5d1r+Ohcez3m73H1BWTOX8k77WS+nn2oveIHiya9DkhD0IYNKE/X+3+gl5NBpD00uvpZdXrVWXR+rls+mIrZ4+ep2k7dxZvmMfILuPx9fEDoM4HtTiw40f8rt9DX6nPqBlDWf3Dcvp8OJiU5BQAjIsbcevqbX47fIZPV0zLM0eA3p4e9Bjejc8mf0Hog1D6j+/Lih8+p3/TwRqv+5dVrVuFeetms+WL7Vw4doHGbRszf/0cxnWdiN9f57VZpw8ZO380q2et4dbVO3zUrwPLv1/GwGZDiXwcCYBRcSNue93m7K9nmfbFlDxz/GXXr2xbsR0AMz1D0lLSNB5v3MmdofOG8+3s9fh5+dKmbzvm7pjP2BaeRGu5duxdHJi7Yz4ndh9n1YQVVHGrysjFo3ka85Q/jmpOMLcrYceg2UO486f2LwRB94KY22eW+ve3eW0WuCL6F0eTJ0+mf//+uLm50aBBAzZu3EhwcLB6qHHmzJmEhYXx3XffAdCpUyeGDx/O+vXradOmDeHh4UycOJH3338fZ2fnQstT5oj9DQwNDXF0dKRkyZK0bt0aDw8PTpzI+Sa7bds2qlSpgpGREZUrV2bdunUa9S9dukTt2rUxMjLCzc2NH3/8EYVCwfXr14HsoUmFQkF8fLy6zoEDB6hWrRqGhoaUKVOGL7/8UmOfZcqUYenSpQwZMgQzMzNKlSrFxo0bC+T5dhzWmd/2nuK3PScJCwhl+8ItRIdH07pfO63xrfq2JfpxFNsXbiEsIJTf9pzkt32n+WjEx+qYZj1bYmppyvLhS7nndZfosCjuevkR5PdIHbNk4ALO7P+N0PshBPk9Yt3UNdiVtKdcjfIF8rx0cW/wHuNHDKTVh40K/Viv6jSsM6f3nuLUX+d768LNxIRH06Zfe63xbf4631sXbiYsIJRTe07y275TdB7RRTNQpSI+Kl5je1VmRqbG4wmxCW+cd5dhXTix9wTH9xwnJCCEjQs2EvU4ig79td+h1L5feyLDItm4YCMhASEc33Ock/tO0nVEV3XMrcu3+OP4H4QEhBARFMFPW3/iod9Dqr1XTR0zd8BcTu0/RbB/MA/9HrJyykrsS9pToUYFrcftPqIbR/cc48juYwQHhLBu/gYiH0fRaUAnrfGd+ncgMiySdfM3EBwQwpHdxzi29zg9R3ZXx3Qd1gXv89fY/c0eQgJD2P3NHq5d9KHb0Jy/wdPYp8RFxam3D1rWJ+xRGDf+uKmOefnxuKg4GrVuyPVLNwgP1j4ZGcBjWHe2r9nJ2aPneXDvEYsmfoaRsRGtu7TUWefqOS++W/sDQYEhfLf2B7wuXMNjWDd1zKR+Mziy7zgP/R8R4BvI4kmf41TSkco1K6pjjh04ydbV33H1vHeex3qh+9Cu7Pz6B84fvcDDe49YNmk5RsZGtPy4ed51hnXD67w3P3yzm+DAEH74ZjfXLvrQfWjONdJjRDeO7DnGr7uPEhwQzNr564l8HEnnl/6eJw+c4rvVO/E+f01njqnJKcRGxREbFUd8VDxJz5I0Hu887GNO7T3JyT0nCA0IZcuCTUQ/jqZdf+2vzbb92hEVFsWWBZsIDQjl5J4TnN53io9fusYB9PT0mLxmKrtX7iIij7/1u7w2C1wRLejq4eHB6tWrWbhwIbVr1+bcuXMcOXKE0qVLAxAeHq6xptigQYNYuXIla9eupXr16vTo0YNKlSpx8ODBAj0dr5KG2N/swYMHHDt2DAMDAwA2bdrErFmzWLJkCX5+fixdupQ5c+aohzOfPXtGp06dqFGjBteuXWPRokXMmDFD5zG8vb3p2bMnvXr14tatW8yfP585c+awfft2jbgvv/wSNzc3fHx88PT0ZPTo0dy9e/ednp/SQEm5GuW5cf66RvnNc9epVK+y1joV61bm5jnN+BvnfChXwxV9ZXa3sFur9/C/do9hi0ayyWsHX55YQ5cx3dHTy/sSLm5WHIDn8c/z/4T+4ZQGSsrXcOXGeR+N8uvnfKis43xfP5c7vvxL5xvAyMSYby9uZtPlrXy6dQ5lq5XLtS+nss5svrKN9Rc2MfnrqTi4aJ97oS1v1xqu6l6qF3zO+1ClXhWtdarUrYLPK8/T+6w3FWpW0Mj7ZbUa1aJk+ZLc1jGMaGJmAmT3pmnLs2KNCni9kqf3OW+quVXVur+qdavifU6zoXH1rDcVa1ZU51m1XlW8zmrGeJ3Je59KAyUtu7bg2J7jeT4PK1tL6rd4n6N7juUZ41zKCVsHG66czVkXKT0tHZ/LN6jhVi3PetXrVeXKOc21lP48e1VnHVPz7POaEP/2DQCnUk7YONhw9aVzlJ6WzvXLN6mm45jV6lXVqANw5YyXuo7SQEmlGhW5+spzuarj76lLyy4t+OnmAbad3sygWUMwNjFWP/bitZnrtXY+79dm5bqVuf7KNe5z9hquNTVfmx4Te5EQk8CpvSfzzM25rDPbru5g44XNTF07HYdSb/baLBRFtI4YgKenJ48ePSI1NRVvb2+aNGmifmz79u2cOXNGI37cuHHcuXOHpKQkHj9+zM6dOylRosS7ngGdZGjyb3D48GFMTU3JzMwkJSW7i37lypUALFq0iC+//JKuXbO/8ZQtWxZfX1++/fZbBg4cyK5du1AoFGzatAkjIyOqVq1KWFgYw4cPz/N4K1eupEWLFsyZkz1kVLFiRXx9ffniiy8YNGiQOq59+/Z4enoCMGPGDFatWsWZM2eoXFn7m0Rqamqu9VoyVZnoK3LeIMyszNFX6hMfHa8RFx8dj6Wd9jlxlnaWWuOVBkrMrM2Jj4zDwcWR6g3sufDTWZYNWohjWWeGLRqBvr4++9fs1brfgXOG4nflDiH+2ldR/i/I63w/jX6KpZ2l1jpWdpZcj36qUfbifJtbmxMXGUdYYBhfT/2K4LuPMDYtTschnVh64HMmtx1P+KNwAPyv32PN5FU8fvAYS1tLuo/rydKDy5nQaizPtTRqXmZurT3vuKg4rPK4TqzsrIiLitOZN2Q3wL+/8j0GxQzIyszim9nf5GrAvWz43OHcvnKbIC1zj17k+epx46LisM4jT2t7K+LO5I5XGiixsLYgNjIWazsr4qJfiYnO+7k3atMQU3NTjv9P+5wwgNY9WpGUmMT5oxfyjLGxtwYg9pVjx0bF4Vgy7w9qGztrYqNy17Gxs86zzvh5nlz/8yYP7j3KMyYvL86ttnPkUCLvPPM6ry/2Z2FtoePvmfdz0ebkodNEBEcQGxVL2UplGPnJMMpWLcu8v4bqc67xV67ZqDis7Opq3aelnRXxua7xOI1rvLJbFVp6tGZi2/F55ubvc4/Vk1by+EEYlnaW9BjXi88PrmBcS0+tXzhE0ZKG2N+gWbNmrF+/nqSkJDZv3oy/vz/jxo0jKiqKkJAQhg4dqtGwysjIUE+8v3fvHjVr1sTIyEj9+Pvvv6/zeH5+frkmIzZq1IjVq1eTmZmpnnxYs2ZN9eMKhQJHR0ciIyPz3O+yZctYsGCBRlkV84pUs9TScHtlYqNCodA52fHVx9R3F/9VrtBTkBDzlG8/WUdWVhYPbgdi7WDFRyO7aG2IDV00klKVSzOne963Nf+X5Dq3Ct03KuU+3wqNcn+fe/j73FM/ftfLjxW/rqL9oI5smb8JAJ8zOb1EwfeCuHftLuvObaRZ9+b8svmnfOX9uuvkVerb0F+qkvw8mbFtx2JsYkytRrUYPmc4EcER3Lp8K1d9z0WelK1clqndpr4u0VcP/Jrzmyv8r3LVSzFa/gZ57LNdr7Zc+f0qMU9i8zxmW4+2nD70m8Y8vtZdWjLj88nq36cOmJnHsbUk/Yrc11je52Dqkgm4VinPyC7jdO5TW54K4JOBs/LIU0GeJymPPLVV0X7u324y9q8/5Nwk8vDeI+KDolj561eUq16eB7dzJvbnPm2veS98teCl16axiTGTV0/hmxlf8ywu757Ga2dyegWD7gVx1/su357fTLPuLfh584+vfW4FrojmiP1bSEPsb2BiYoKrqysAa9asoVmzZixYsICxY8cC2cOT9evX16jz8p0a2tZB0eVN67wYHn1BoVCQpeMFM3PmTCZPnqxRNqi65p05z+ISyMzIzNX7ZWFjwdNXej9eiI+Kz9UTYGFjSUZ6Bs/isr+9xUfGkZGRqZFfaEAoVvbWKA2UZKRnqMuHLBiOW8v3mddzJrERhfvPWovai/Od+/zlfb7jouJz9ZZZ2FhonO9XqVQqAm7ex6ls3hNWU5NTCb4XhFOZ109qTYjVnrelbe7e0Zy8c/cYvcg74aUPJZVKRXhQdq/dA98HlHItRc8xPXM1xEYtGEX9VvWZ3mM6MXlcJ+o87TV7S6xsLXP1vLwQGxmHtf2rz8tKI89YLT0wljba92lfwp667nWYP3yh1uMB1Hi/OqVcXVg0eolG+YUTF/H1ybl71qBYMSC7hysmMqdRZ2VrlauX7GUxUbHq3rQXrG0tiY3O3TCcvGgcjVs3ZHTXCUSF575ZRJuX8yym0MegWPZ7k7WdNbEv5WlpY5mrZ+5l2s9rznN7GvuUzIxMrF95Lpa2ljqf/5sIvBVIelo6zmWdeXA7MM9r3ELHNR6v5Rq3fOm9sFTFUjiUcmT21rnqxxV62e/1Bx/8hGezkUQE5Z4zlpqcStC9RzjreP0WqkK84/C/QOaIFYF58+axYsUKMjMzKVGiBA8ePMDV1VVjK1u2LACVK1fm5s2bGkOCr/u/V1WrVuXCBc3hiUuXLlGxYkV1Ay8/DA0NMTc319heHpYEyEjP4MGtQGq6a97WXdO9Nve8tc8/8792l5rutTXKarnX5sGtADIzMoHsHhnH0o4aDUznss7EPonVaIQNXTiC+m0bsKD3bCJD8u7d+6/ISM8g8FYAtbScv7s6znfu+DoEvnS+tSlbtZx6+E8bZTElJV1LEheZd6/Ny3kH3AqgjnsdjfI67nXw8/bTWsfvml+u+LpN6nL/5n2deaNA/cH+wuiFo2nYriEze83kScgTnXn637pPPXfNoaR67nW546V9eRDfa7654t2a1MX/pr86T19vX+o1eSWmaT2t+2zr0Yb46Hgun/4zzzzb9WrLvRv+PPB7oFGelJhM6KPH6u2h/yOin8TwXhM3dYzSQEmdD2pxyyvvJRVue/vynns9jbL3m7jlqjNl8Xg+bOfO2J6TCQ/J+4aBV72cZ9ijxzzyDyLmSQxuL50jpYGS2h/U5I6OPO94+2rUAXivaT11nYz0DO7d8sftlefi5q793L+NUhVLY1DMgNi/ei3zem3W1vHavHvtLrVfjW9Sh4Cb2a/N0MBQxrUcw8S249XblZN/cuuPW0xsO17rXdLw4rXp8kavTfH3k4ZYEfjwww+pVq0aS5cuZf78+SxbtoyvvvoKf39/bt26xbZt29RzyPr06UNWVhYjRozAz8+P48ePs2LFCiD3isEvTJkyhdOnT7No0SL8/f3ZsWMHa9euZerU1wy/FJDDm3+ihUcrmvVsQQnXkgycMxRbZ1v1OlV9pvdn7MqJ6viTu45hW8KOgXOGUMK1JM16tqC5R0t+3vijOubEzmOYWZkzeP4wnMo6U7d5PbqM6cHx73KGB4YtHon7x035avyXpCQmY2lniaWdJcUMixX6c05KSuaufyB3/bOHJMIeP+GufyDhEYXfGPzlr/PdvGdLSriWZPCcodg623FiV/baQ32nD2D8S+f7+K5j2JWwZ9Bf57t5z5a08GjJTxsPqWN6TuhF7SZ1cHBxoEzVsoz5Yjxlqpbl+EvrGQ2cNZiq9ath7+JAhdoVmbb+E4xNi3PmQM6aXboc2nyINr3a0KpnK1xcXRg+dzh2znbqdcEGzRjElFU5Swcc2XkE+xL2DJ8zHBdXF1r1bEVrj9Yc3JhzR1PPMT2p414Hx1KOlCxfki7DutCiWwt+P/S7OsZzsSfNujRj+bjlJCcmY2VnhZWdVZ7Xyf6NB2jfuy1tPdpQytWF0fNGYV/Cnl++PwzA0E+GMGN1zlIMv3z/K/YlHRg9dySlXF1o69GGdr3asu/b/eqYg1t+xK1JPXp59sSlvAu9PHtSt3EdDmw5pHFshUJB256tObH/ZJ7LDxQ3LU6Tjk04slv7WlOv2rt5PwPH9aVp28aUq1SGOas+ISU5hROHTqlj5n41k9GfDFP/vm/LAd5v+h79PHtRurwL/Tx78Z57PfZuPqCOmbp0Im26tmLe2CUkPU/C2s4KazsrDI1yzqu5pRkVqpWnbMUyAJQqX4oK1cprnW+3f8tB+o3tQ+O2jShbqQyfrJpOSnIKp37Mub5mrp7B8E+Gqn8/sOUg7zVxo7enB6XKu9Db04N6jeuyf0vONfK/jQfo0Lsd7TzaUsq1FGPmjcahhD0/f/+LOsbM0gzXquUpXTH77jqX8i64Vs3J07m0EwMm9qNSzYo4lnSgfvP3mb7+EwJvBXDXK+eLxE+bf6RVr9a06NmKkq4lGTp3GLbOdhz76xrvP2MgE1fljDIc23kUuxL2DJkzjJKuJWnRsxUtPVrx41/XeHpqOsH+QRpbYkIiyc+TCPYPUn8pHTRrCNXqV8fexYGKtSsyY8OnFDctzm/7T+d9YRSmIrpr8t9ChiaLyOTJkxk8eDABAQFs3ryZL774gunTp2NiYkKNGjWYOHEiAObm5vzyyy+MHj2a2rVrU6NGDebOnUufPn005o29rG7duuzbt4+5c+eyaNEinJycWLhwocZE/cJ06fAFTK3M6D7eAyt7a0L8g1g6aCHRYdnr5ljZW2HrbKuOjwyJZNmghQycO5Q2/dsTFxnL1vmb1WuIAcSER7O4/zwGzhnKimNfEfskhiPbfuGn9TlvsG3+uiV8wb6lGvl8M+Urzux/s8ZBft2+e58h43LuZl3+dfZSIJ3btWTJ7LzXISoIFw9fwMzKjJ5/ne9g/yCWDFpIlMb5tlPHR4Y8YfGgBQyZO4x2/TsQGxnLlvmbNNYQMzE3YfSyMVjaWZH0LJEHdx4wu+dMAm7cV8fYONow+eupmFmZkxCbgL/PPT7pMk193Nc598s5zCzN6DOhD9b21jzyf8S8gfOIDItU5233Ut5PQp4wd+BcRswdQccBHYl5EsO3879VryEGYGRshOdiT2ydbElLSSMkIIQVE1dw7pectbc6DugIwPL/aS68uXLySk7tP8WrzvxyFnMrc/pP7Jud570gZg6Yrc7Txt4a+xL26viIkAg+HTALz3mj+GhgJ2KexLJ27jr1GmKQ3SO2eMxSBk8bxKCpA3kcFM4izyXqNcReqOteF4eSDjrvlmzW+UMUCvj9p9/zjHnZznV7MDQyZOrSiZhZmOHr48fEPtM01hBzcLbXmAZwy+sOcz0XMnL6UEZMG0JY0GNmj16oXkMMoNvA7Hmp6w6s1jjeokmfcWRfdv6NWzdkzqpP1I8tXp89xLb5y+1sWblDo97udXsxNDJk0pLx2Xle92Na30801hBzKGGP6qU873j7snDMYoZOG8yQqYN4HPSYBZ6L1WuIAfz+yxnMrcwZOLEf1vbWPLz3iBkDPuVJWM6XpkatGvDJqunq3+etnw3A9pXfsX3ld6SnZVC3cR26De2KcXEjosKjuPabN3tW/aBx3i78ch4zSzM8JvTKXrTYP4iFA+frfG0uHDifoXOH0X5AB2KfxLB5/sZca4i9jq2TLVPXTlO/Nu9du8v0j6e88WuzwP1HG1AFRaEqzOViRaHYtWsXgwcP5unTpxgbG7++QiHqUVr7CsX/dD94ryrqFPLFo97Eok4hX1JUGa8P+odKzfp35p6kSn990D+QoSL/0yeKkoWe9i/G/3Q/BR8u9GMkbym40RjjoSsKbF//FNIj9i/w3XffUa5cOUqUKMGNGzeYMWMGPXv2LPJGmBBCCPFa+Vj/6/8TaYj9C0RERDB37lwiIiJwcnKiR48eLFmy5PUVhRBCiCKmypKBN12kIfYvMH36dKZPn/76QCGEEEL8q0hDTAghhBCFRybr6yQNMSGEEEIUHpkjppM0xIQQQghReGSOmE6yoKsQQgghRBGRHjEhhBBCFB6ZI6aTNMSEEEIIUXikIaaTDE0KIYQQQhQR6RETQgghROGR/6SokzTEhBBCCFF4ZGhSJxmaFEIIIYQoItIjJoQQQojCI+uI6SQNMSGEEEIUHllZXycZmhRCCCGEKCLSIyaEEEKIwiNDkzpJQ0y8k6NRN4s6hXzxqDexqFPIl73eq4s6hXwZ6Ta9qFPIt/uZcUWdQr4syHAo6hTyZWSGX1GnkC/Titco6hT+sVRy16RO0hATQgghROGRHjGdZI6YEEIIIUQRkR4xIYQQQhQeuWtSJ2mICSGEEKLwyNCkTjI0KYQQQghRRKRHTAghhBCFR+6a1EkaYkIIIYQoPDI0qZMMTQohhBBCFBHpERNCCCFE4ZG7JnWShpgQQgghCo8MTeokQ5NCCCGEEEVEesSEEEIIUWjkf03qJg0xIYQQQhQeGZrUSRpiQgghhCg80hDTSeaICSGEEEIUEekRE0IIIUThkeUrdJKG2P9TgwYNIj4+nh9//LGoUxFCCPFfJkOTOklD7B9k0KBB7NixI1f5/fv3cXV1LYKMCt6nsyYweEhvLC0t8Lp6ncmT5uLndz/P+CpVKjB7ziRq16lB6dIlmT5tIeu+2aYRc8fvPKVLl8xVd+O33zN50ty3yq9t/3Z0HtkVKzsrQu4Hs3XBZvyu+uYZX7V+NQbPGYpLhVLERsby44aDnNh1TP14s+7NGfflxFz1PCp2Iz01Pfvnib3xmNRb4/G4yDiGvjfwrXLPD6/rt9j2w3587wYQFRPLV8vm0KJJw0I/7tto1q8NbUd+hKW9FWH+IexeuJ37V/20xlrYWeIxeyBlqpfDvqwTp7cfYffC7YWW2+DJA/iobwfMLMzw9fFj5aw1PPIP0lmnaXt3hk0bjHNpJx4HhbPx8y2cP3ZR/Xit+jXoPdqDSjUqYOtoy6dD5nL+eM7j+kp9hk8fwgfN38e5tBOJCYl4XbjGhqWbiXkS89bPwWVQK8qM6UQxe0sS74Vyd853xP95V2us5fuVqDCnDyauzugbG5ISGkXI96cJ/vaIOsbZoynV14zOVfdUqf5k/XXNF6YJ00fRe2A3LCzMue59i7nTl3H/XmCe8RUqlWfSTE9q1KpCyVIlWPjpcrZ9u6tQc6w2oCV1RranuL0lsf5hXFywk/Ar97TGlmvrRrX+LbCtVhr9YgbE+odyddVBQs7e0hrv+tEHtP5mLA+Oe3Fs2OpCfBaioMgcsX+Ytm3bEh4errGVLVtWIyYtLa2Isns3kyaPZOy4oUyZPI+m7p158iSKnw9/j6mpSZ51jIsb8/BhCPPmfE5ERKTWmKbunSlX9j311rFDPwAOHfz1rfJr1LExg+cO48DafUzpMBG/K77M3jEPW2dbrfH2Lg7M3j4Pvyu+TOkwkYPf/I+h84fzQbsGGnGJCYkMcRugsaW/8oEUfC9I4/FJbca9Ve75lZycQiXXcnw62fNvOd7beq9jQ3rPHcThtQeZ334a96/6MWn7p1jn8TdRGhrwLDaBw98cJMRPd4PoXfXx7IXHiO6smv01wzt4EhsVx6rdyzE2Mc6zTrV6VZm/fg7HD5xkcKsRHD9wkoUb5lK1TmV1jFFxYwJ8A1k1+2ut+zAyNqJijQrs+GonQ9uOYtbw+biUK8ln2xa99XNw6NyASosG8mD1IS63/IS4P+9Sd/cnGJWw0RqfmZRKyNbjXP14ARfdp/Bg1SEqfNKTEv1baMSlJyRxpvpIje3vaISNHD+YoZ79mTfjMzq37EtUZAzfH9yAiWnxPOsYFzci5FEony9cQ2REVKHn6NqpPo3n9cP765/5X7vZhF+5R8fvpmHqrP2cO9WvTMj52/w6cAX/az+bsD/8aL91CrbVSueKNS1hQ8PZfXicR0O6qKiyVAW2/RdJQ+wfxtDQEEdHR42tRYsWjB07lsmTJ2Nra0urVq0A8PX1pX379piamuLg4ED//v2Jjo5W72v//v3UqFEDY2NjbGxsaNmyJYmJiRrHW7FiBU5OTtjY2DBmzBjS0wvvzXLM2CF8sfwbfv7pOL6+/owYPhVjY2N6enyUZ51r3jeZPWsZ+/cfJjVVewM0OjqWyCfR6q1du+YEBj7i/Pk/3yq/TsM6c3rvKU7tOUlYQChbF24mJjyaNv3aa41v07ct0Y+j2LpwM2EBoZzac5Lf9p2i84gumoEqFfFR8RrbqzIzMjUeT4hNeKvc88u9wXuMHzGQVh82+luO97baDOvE+X2/cX7vacIDw9i9cDux4TE069daa3xMaBS7F2zj0sGzJD9LKtTceg7ryndrfuDc0Qs8vPeIJRM/x9DYiFZdWuRZp8ewrnid82bn2t0EB4awc+1uvC9co8ewbuqYP3+/wubl2zh39ILWfSQ+S2Ry7+n8/stZQgJD8b3mx+rZa6lcqxL2zvZv9RzKjOpA2A+/E7brdxLvP+benO9ICYuh5KBWWuOf3X5ExKFLJN4LJSUkivADF4j+/SZW9StrBqpUpEU91dj+DkNG9uWblZs5fvg0/ncDmDpmNsbGRnzUTftrGOCmzx2WzV/F4UPH/pYvubWGt8Nv7xn89pwhLuAxFxfs5PnjGKr3137dXFywk+sbfiXyxgOePnrCn5/v4+nDCMq0rKMRp9BT0GqNJ1e/PEBCsPYvrUUmS1Vw23+QNMT+JXbs2IFSqeTixYt8++23hIeH07RpU2rXro2XlxfHjh3jyZMn9OzZE4Dw8HB69+7NkCFD8PPz48yZM3Tt2hWVKudC/v333wkMDOT3339nx44dbN++ne3btxdK/mXKuODoaM/p0+fVZWlpaVy48Cf169crsOMYGBjQq9fHfP/d/96qntJASfkartw476NRfv2cD5XrVdZap2Ldylw/lzu+fA1X9JX66jIjE2O+vbiZTZe38unWOZStVi7XvpzKOrP5yjbWX9jE5K+n4uDi8Fb5/xfpGygpXb0cd87f0Ci/c/4GrvUqFVFW2ZxKOWHjYMPVs17qsvS0dK5fvkF1t2p51qterypXz3lplF0566WzzpswMTchKyuL5wnP37iOwkAfs5pliTlzU6M85uxNLN0qvtE+zKqXwfK9isT9oTl8r29ihLvX1zTx+YY6O6djVr3MG+eVXy6lS2DvaMf53/9Ql6WlpfPnJW/qvV+r0I//JvQM9LGrUZaQc7c1ykPO3cbBrcKb7UShwMDUiJR4zS/VbhO7kBz7DL+9ZwsqXfE3kTli/zCHDx/G1NRU/Xu7du0AcHV1Zfny5eryuXPnUrduXZYuXaou27p1Ky4uLvj7+/P8+XMyMjLo2rUrpUtnd2HXqFFD41hWVlasXbsWfX19KleuTIcOHTh9+jTDhw/XmltqaiqpqakaZSqVCoVC8drn5eBgB0BkZLRGeVRkNC4uJV5b/0116tQaC0tzdu7c/1b1zKzM0VfqEx8dr1H+NPoplnaWWutY2VlyPVrzm358dDxKAyXm1ubERcYRFhjG11O/IvjuI4xNi9NxSCeWHvicyW3HE/4oHAD/6/dYM3kVjx88xtLWku7jerL04HImtBrL8/hnb/U8/kvMrMzQV+rz9JXelISop1jYWhZNUn+xsbcCIDY6TqM8LioOx5J5N6Kt7ayJjdKsExsVh7WdVb5zKWZowKiZwzh16DeSnr95L2Axa3P0lPqkvnJ+06KeYmhvqbNuE59vKGZjjkKpT+AX+wnb9bv6scSAMO6MX88zv2CUZsUpPbwd7/+ygD+azyDpYcRbPbe3YWefPVwdHaU5Ty46MoYSLs6Fdty3YWRthp5Sn+RXznlS9FNc8nifeVXtEe0xKG5I4OGcHn9HtwpU6fUh+9p8WpDpFhxZWV8n6RH7h2nWrBnXr19Xb2vWrAHAzc1NI87b25vff/8dU1NT9Va5cnbPTWBgILVq1aJFixbUqFGDHj16sGnTJuLiND8AqlWrhr5+Ts+Nk5MTkZF5d2kvW7YMCwsLjS09I15rbE+PzkRE3lZvBgYGABo9cgAoFKgouO7mAQN7cuLEWSLC89c1nzs/eLVIV/yLRumLcn+fe5w7dIZHfo/wu+rLCs/lPH4QRvtBHdV1fM5c4/LRPwi+F8TNizdYMnghkD3RXwC8eo5fLSl8rbq04Lj/YfWmVP71HVbL3z/XNfQKbdfMa6rkSV+pz/x1c9DT0+PLT7/K305ePZsKdF/0wNXO87nc5lN8p2+m1Ih2OHbJucHjqXcA4Qcu8Nw3mPg/73Jj+GoSH4TjMqxNPvPTrnP39twO+kO9GRhk/020n99/1pBW7hx57TkHcO3cgPcmd+GE51qSY7KnLxiYGNHyq9Gcmb6ZlLg37xH9W8nQpE7SI/YPY2JiovUOSRMTzQntWVlZdOrUic8//zxXrJOTE/r6+pw8eZJLly5x4sQJvv76a2bNmsWff/6pnvz/onH0gkKhIEvHN5eZM2cyefJkzWM51NQae+TXU3hdva7+3dCwGJDdM/bkpQmxdnY2RD6JfrV6vri4lKBZ80b06Z37jq3XeRaXQGZGJlav9ExY2Fjw9JVeshfiouJz9ZZZ2FiQkZ7BszjtPVkqlYqAm/dxKpv3N/TU5FSC7wXhVOaf8S2+qDyLe0ZmRiYWr5xjM1sLEvL4mxSWCycu4euTc6emQbHs1461nTUxkbHqcktbS2J15BYbFYuNvbVGmZWtJXGv9Ky9CX2lPgs3zMWplCMTek59q94wgLTYBLIyMjF85fwWs7XI1Uv2quTg7Nfwc78QitlZUH5qdyIOXdIerFKRcD0Qk7JOb5Xf65w6dobr3jl3DhYrlv0eY2dvS9RL7yk2dta5esmKSkrsM7IyMin+So+jsY0FSdG6z7lrp/o0+2IYJ0Z9TeiFO+py89L2mJeyp/22KeoyhV72F8JRD3fww4fTSAgq4jlj/9EGVEGRHrF/qbp163Lnzh3KlCmDq6urxvai0aZQKGjUqBELFizAx8eHYsWKcejQoXwf09DQEHNzc40tr2HJ588TefAgSL35+d0nIiKS5s3d1TEGBgY0blyfP//0zndOL+s/oDtRUTEcO/rbW9fNSM8g8FYAtdxra5TXcq/NXW/tdyD5X7urJb4OgbcCyMzIzPNYZauWIy4y7w9eZTElJV1LEvfSB/z/R5npGQTdfkDVxpqN/WqNaxLgrf1W/8KSnJhM2KPH6u2RfxAxT2J4r0nO/EalgZLaH9TittedPPdz29sXN3fNOZHvNXHTWUebF42wkmVLMMljGglxb39zhyo9k2c3H2LTVHPKgk2TGsR7+b/xfhQo0CtmoDPGrFoZUnVc8/mR+DyJoIch6u3+vUAiI6Jw//ADdYyBgZL6DevhfeWGjj39fbLSM4m69RAX9+oa5SXdq/PEK+9lfFw7N6D5ypGcGreOoN+uazwWHxjOnpafsK/tLPX28OQ1wi75sa/tLJ4//mc0QkXepEfsX2rMmDFs2rSJ3r17M23aNGxtbQkICGDPnj1s2rQJLy8vTp8+TevWrbG3t+fPP/8kKiqKKlWqFFnO36zdytRpngQGPiQw4BFTp3mSnJzMvr0/q2M2bvqSx48jmD/vCyC7sVa5SnYPYbFiBjg7O1KjZhUSnyfx4EHO8gQKhYJ+/Xuwa+cBMjPzbgTp8svmnxi/ahIBNwO4d+0urXu3wdbZjhO7jgLQd/oAbBytWTN5NQDHdx2j3cAODJozhJO7T1CpbmVaeLRk1fgV6n32nNALf597hD98jLFZcToM7kSZqmXZOGeDOmbgrMFcPXWF6MfRWNhY0H1cT4xNi3PmwNs3KN9WUlIywaGP1b+HPX7CXf9ALMzNcHJ8uzvwCsPxzb8wfOU4Ht18QOC1ezTt0wprZ1vO7DoBQLfpfbBysGHzlJylHlyqlgHAqLgRptYWuFQtQ2ZaBo8DQgs0t32bD9JvXB9CHoYS+jCM/uP6kJqcwslDp9Uxs76aQXR4NN9+tgWA/VsO8vWB1fTx7MWF4xdp3KYRbu51GdNlgrqOcXEjSpTNmTfpVMoR12rlSYh7RuTjSPT19Vi0cR4Va1RgxsBZ6OnrqeeYJcQ/IyM9442fw6MNv1Jj7Rie3njAUy9/SvZviVFJW0J3nALAdVYvjBytuT1uHQAug1uTEhZN4v3sa8ayfiVKe3YkZEvO2nnlpnTjqfd9kh5GoDQ1ptTwtphVL43fzK1ve4rf2tZvd+E5aSgPHwTzKDAYz0lDSU5O4ecDOeucfbluMRHhkXyxKHvah4GBEtdK5bN/LmaAo5M9VapXIikxu6FX0G5sOkqL1aOJvPmAJ94BVO3bDLMSNtzemX3dfDCjJyaOVpye9C2Q3QhrsWokF+bvJOJaAMZ2FgBkpqSR9iyZzNR0Yu9pXttpCdm9o6+WF5V/2tDwP400xP6lnJ2duXjxIjNmzKBNmzakpqZSunRp2rZti56eHubm5pw7d47Vq1eTkJBA6dKl+fLLL9WT/4vCqpXfYmxsxKrVi9QLunbuNIDnz3Pu/nFxcdYYHnVysuePyzlvohMnjWDipBGcP3eZdm1zFkFt1rwxpUqVeOu7JV928fAFzKzM6DneAyt7a4L9g1gyaCFRYdnDMFb2Vtg626njI0OesHjQAobMHUa7/h2IjYxly/xNXD6ac9eWibkJo5eNwdLOiqRniTy484DZPWcScCPn26+Now2Tv56KmZU5CbEJ+Pvc45Mu09THLUy3795nyLgZ6t+Xf70RgM7tWrJk9pS8qv1trh6+hKmlGR9N6I6FnRVh/sGsHryUmLDsoScLeyusS2iuKbbgSE5DuEzN8jT42J3o0EimNy7YtdJ+WLcHQ6NiTFk6AVMLM/x8/JjcZwbJicnqGAdne421j257+bLAczHDpg9m2LRBhAU9Zt7oRfj65PS6VqpVia/3r1T/Pm5+dt5H9x1n6aTl2DnZ4d4me7mR7Sc3aeQ0rvtkrv/x5r0/T376g2JWppSf3A1DB0ue3w3Bp89npIRmn19DeyuMXj6/egoqzOqNcSk7sjKySH70hPuLdxP63Sl1iIGFCVVXDMfQ3pKMZ0kk3HrE1Y8XkOCT96KqBeXbNdswMjJk0fJPsbDMXtB1QLfRJL40bOtcwlHjPcbe0Z4jZ/epfx8xbhAjxg3i8oWr9O48rMBzDPjlTwytzHCb0AUTe0ti7oVyeOAXPA/L7rkq7mCJ6UvnvFrf5ugbKGm6ZBBNlwxSl9/93zl+m7yxwPMrFEU4NLlu3Tq++OILwsPDqVatGqtXr8bd3T3P+NTUVBYuXMjOnTuJiIigZMmSzJo1iyFDhhRajgqVNFXFOzAtXvb1Qf9Are1qvD7oH2iv9+qiTiFfRrpNL+oU8u1+esEOqf1d5mTYvT7oH2hkhvb/mvBPN634v/M9xTNkZ6EfI2G49nX/8sN804k3jt27dy/9+/dn3bp1NGrUiG+//ZbNmzfj6+tLqVKltNbp3LkzT548YfHixbi6uhIZGUlGRgYNGxbefxyRHjEhhBBCFJ4i6hFbuXIlQ4cOZdiw7J7N1atXc/z4cdavX8+yZctyxR87doyzZ8/y4MEDrK2zb6opU6ZMoecpk/WFEEIIUWgK8l8cpaamkpCQoLG9ur4lZC8Y7u3tTevWmr1xrVu35tIl7Xf4/vzzz7i5ubF8+XJKlChBxYoVmTp1KsnJyVrjC4o0xIQQQgjxr6BtPUttvVvR0dFkZmbi4KC5wLKDgwMREdoXFn7w4AEXLlzg9u3bHDp0iNWrV7N//37GjBlTKM/lBRmaFEIIIUThKcChSW3rWRoaGuYZ/+oSS7r+G0xWVhYKhYJdu3ZhYZF9d+rKlSvp3r0733zzDcbGxu+YvXbSEBNCCCFE4SnA/3BkaGios+H1gq2tLfr6+rl6vyIjI3P1kr3g5OREiRIl1I0wgCpVqqBSqQgNDaVChTf8f6BvSYYmhRBCCPGfUqxYMerVq8fJkyc1yk+ePJnnHZCNGjXi8ePHPH+e86+i/P390dPTo2TJkoWWqzTEhBBCCFFoCnKy/tuYPHkymzdvZuvWrfj5+TFp0iSCg4MZNWoUkD3MOWDAAHV8nz59sLGxYfDgwfj6+nLu3DmmTZvGkCFDCm1YEmRoUgghhBCFqYiWr/Dw8CAmJoaFCxcSHh5O9erVOXLkCKVLlwYgPDyc4OBgdbypqSknT55k3LhxuLm5YWNjQ8+ePVm8eHGh5ikNMSGEEEIUngKcI/a2PD098fTU/l81tm/fnquscuXKuYYzC5sMTQohhBBCFBHpERNCCCFEoXnbuV3/30hDTAghhBCFpwiHJv8NZGhSCCGEEKKISI+YEEIIIQqNDE3qJg0xIYQQQhQeGZrUSYYmhRBCCCGKiPSICSGEEKLQqKRHTCdpiIl3cqNcxaJOIV/GJWQUdQr5MtJtelGnkC/fei0v6hTyLX33iqJOIV+mfhld1Cnky63erkWdQr7473xW1Cn8c0lDTCcZmhRCCCGEKCLSIyaEEEKIQiNDk7pJQ0wIIYQQhUcaYjpJQ0wIIYQQhUZ6xHSTOWJCCCGEEEVEesSEEEIIUWikR0w3aYgJIYQQotBIQ0w3GZoUQgghhCgi0iMmhBBCiMKjUhR1Bv9o0hATQgghRKGRoUndZGhSCCGEEKKISI+YEEIIIQqNKkuGJnWRhpgQQgghCo0MTeomQ5NCCCGEEEVEesSEEEIIUWhUctekTtIQE0IIIUShkaFJ3WRo8j/sww8/ZOLEierfy5Qpw+rVq9W/KxQKfvzxx789LyGEEP9/qLIUBbb9F0mP2L/MoEGD2LFjByNHjmTDhg0aj3l6erJ+/XoGDhzI9u3bOXjwIAYGBkWUqSZzj45YDu6Bvp016QFBRH++gZRrt19bz6hOVZy3rSAt4BGh3T01HtMzM8F6/CBMWjZCz9yMjLAIYr7YSNL5q/nOs0P/DnQb2Q1re2uC7gexccFG7ly5k2d89frVGT53OKUrlCYmMoYDGw5wZOcR9eMN2zbEY6wHTqWdUBooCXsYxqFNh/jt4G/qmJ5jetKwbUNKli9JWkoaft5+bF22lbAHYfl+Hto069eGtiM/wtLeijD/EHYv3M79q35aYy3sLPGYPZAy1cthX9aJ09uPsHvh9gLN5115Xb/Fth/243s3gKiYWL5aNocWTRoWWT77boWy41oQ0UlplLc2Yap7Beo6W2mNnXvKl1/uhucqL2dtwoE+HwBwOjCSLV6PCHmaTEZWFqUsi9O/dik6VnYq0Lyb9GtNy5EfYWFvSbh/KP9buJ3Aq3e1xprbWdJt9gBKVS+HXVlHzmw/yv6FOzRiGvVqQf2uTXCu5AJA8K0H/PTFboJuBBZo3gDKD9pQzL0zCjMrsiJDSD28jaxH2q9pAPSVFGvRE2XtJijMLFE9jSHt9wNkeOe8HjEqTrHWfVBW+wCFsQmquEhSj+wg8961AsvbdkA7HEZ2wcDeihT/YEIWbCHxiu9r65m4Vabi/5aSfC+Iu20n5aRc0QWnKX0oXqM8hi4OhMzfTNSWXwosX1G4pCH2L+Ti4sKePXtYtWoVxsbGAKSkpLB7925KlSqljrO2ti6qFDWYtG2K7SejiFq8lhSfO5j36IDThsWEfDScjIioPOvpmRbHfuk0kv/0Qd/mlQ80pRKnTcvIjI3nyeTFZEREo3S0IyspOd95NunUhBHzRrBu9jp8vXxp17cdC3csZFSLUUQ9zp2ng4sDC3cs5NjuY6yYsIKqblXxXOzJ05inXDx6EYBn8c/Y8/UeQgNDSU9Pp36L+kxaMYn46Hiunct+Y69evzqHdxzG/6Y/+vr6DJw+kCU7lzCyxUhSk1Pz/Xxe9l7HhvSeO4jv52wmwOsuH/ZtxaTtnzK71SRiH0fnilcaGvAsNoHD3xyk1dCOBZJDQUtOTqGSazk+bt+aSbMWF2kux+8/4Yvz/sxsWonaTpYcuBPG2F9ucKDPBziZGeWKn+ZekfENyqt/z1Sp8Nj9J63K26vLLAwNGOZWhjJWJhjoKzj/KJr5p/2wNi5Gw9I2BZJ3vY4N6D53EHvmbOaB1z0a923JmO2fsqjVJOIex+SKVxoa8Dw2gWPfHKT50A5a91nhg6p4/XyRB9fukZ6aTquRnRn3/WwWtZrM0ydxBZI3gLJGQww7DCb1p01kBt3FoH5rjAfNImnVRFRPc1/TAEZ9pqAwtST1wDqyYsJRmFqAnn5OgL4S46HzUD1/SsoPX6B6GovCwgZS8/++8iqrTo0pOW8oIbO+JdHLD9u+bXD9bi6+zceSruW1+IKeWXHKrJ7Is4s3UdpaaD5mbEha8BPif71EyblDCizXgqJSFXUG/2wyNPkvVLduXUqVKsXBgwfVZQcPHsTFxYU6deqoy14dmnydhQsX4uDgwPXr1wswW7Ac0JWEg8d5duAY6Q9CiPl8AxkRUZj30v0BbztvAs9+/Z2UG7m/4Zp3bYO+hRkR4xeQ4uNLRngkKT53SLv3IN95dhnWhRN7T3B8z3FCAkLYuGAjUY+j6NBf+wdO+37tiQyLZOOCjYQEhHB8z3FO7jtJ1xFd1TG3Lt/ij+N/EBIQQkRQBD9t/YmHfg+p9l41dczcAXM5tf8Uwf7BPPR7yMopK7EvaU+FGhXy/Vxe1WZYJ87v+43ze08THhjG7oXbiQ2PoVm/1lrjY0Kj2L1gG5cOniX5WVKB5VGQ3Bu8x/gRA2n1YaOiToWd14P5uKozXauVoJy1CdPcK+Joasj/boVqjTczVGJrYqjefCOfkZCawUdVcnq73Epa0by8PeWsTXCxKE6fWqWoYGuKT3h8geXdfFhHLu37jUt7fyMiMIz9C3cQHx5Nkzyui9jQKP63YDt/HjyX53WxfeLXnNt5glDfIJ4EPmbXJxtQKBRUblSjwPIGMHDvRIbXb2R4nUYVFUba4W2onsZg8EEbrfH6FWujX7YayduXkBl4E1V8FFmhAWQF31PHKOs1R2FsSsr3n5MVdC87JuguWRFBBZa3/fDOxOw9Rcyek6QEhBK6YAvpj6Ox699OZ71Sn3kS++M5Er1z91Ym3QggbMl24n4+T1ZaeoHlWlBkaFI3aYj9Sw0ePJht27apf9+6dStDhuTvm5BKpWLChAls2bKFCxcuULt27QLKElAqMaxageRL3hrFSZe8MapVNc9qZh+3xsDFibj1O7U+XvzDD0i54YfdrLGUPrsHl0PfYjm8F+jl75JWGihxreGq7qV6wee8D1XqVdFap0rdKvic99Eo8z7rTYWaFdBX6mutU6tRLUqWL8ntK3kPy5qYmQDZvWkFQd9ASenq5bhz/oZG+Z3zN3CtV6lAjvH/WXpmFn6Rz2jgotkD/YGLNTcinr7RPn70fUx9F2uczY21Pq5SqfgzJJZHcYnUc7Z815QB0DfQp1T1cvi9cl34nb9JuQK8LooZG6JvoCQx/nmB7RN9JXrO5cm4f12jOOP+DfRLac9dWeU9MsMCKdbkY4p/spHiU76mWLsBoCyWE1P1PTKD72HYeTjFP92C8YRVGHzYFRQF81GpMFBSvEZ5Es5p5p1w7jombpXzrGfdswWGpR0JX7WnQPIQ/ywyNPkv1b9/f2bOnMmjR49QKBRcvHiRPXv2cObMmbfaT0ZGBgMGDMDLy4uLFy9SsmTJPGNTU1NJTdUcKkvNysJQR+NH38ochVKfjJh4jfLMmHj0bbXPnzEo5Yz1pCE8HjAFMrXfbmNQ0gllido8//U3wkfPpljpEtjOGotCX5+4DbvyzCcv5tbm6Cv1iY/WzDMuKg4rO+15WtlZERelOdQSHx2P0kCJubU5cZHZjxU3K873V77HoJgBWZlZfDP7m1wNuJcNnzuc21duE+RfMN/CzazM0Ffq8zRKs1GQEPUUC1vLAjnG/2dxyelkqlRYFy+mUW5T3JCYpNjX1o9KTOViUAxLW1fL9diz1AzabL9AemYWegoFM5tW4oNSBTMsaWqVfc0/03JdmBfgdfHxjL7ER8Ry9+KtAtunorgZCn19VM81c1c9j0dhZqm9jrUD+qUrQ0YaKTuXozAxx7DzcBTFTUk9sA4APSsHFOWqk3H9PCnbl6Bn64Rh5+Ggp0/6b/9757yV1n+9H0bFa5SnR8djnsf7jGEZJ0p8MgD/bjPzfD/8p/uv9mQVFGmI/UvZ2trSoUMHduzYgUqlokOHDtja2r71fiZNmoShoSGXL19+bf1ly5axYMECjbJxduWYYO/6+gO9OklAoQBt8wb09LBf/glx33xPepCOyep6CjJj44ma/xVkZZHmG4C+nQ2Wg7vnqyGWk6ZmUgqFIleZLgrFX284L1VJfp7M2LZjMTYxplajWgyfM5yI4AhuXc79weS5yJOylcsytdvUfOWv26vPTfufQOSPAs0PGxUq3uTj52e/cMwMlTQrZ5frMZNi+uzxeJ/k9Ez+DI3lywv3KWlujFtJ7R/a+aHSel0UzJXRauRHuH3UiNW95pORWhhDZq/mqeOiVigAFSl7voLU7GHV1F+3Y9RnKqk/bYaMNNBToEp8SuqhDaDKIuvxAxTm1hi4dy6QhlhO2rnfZ7ROpNLTo8zaKYSv3E3qw8cFd/y/mcwR000aYv9iQ4YMYezYsQB88803+dpHq1at2L17N8ePH6dv3746Y2fOnMnkyZM1ykI/6KazTmZcAqqMTJS2Vrzcl6ZvbUFmTO6Ju3omxhhVr4RhZVdsPx3zV6EChZ4e5a4fIXzETJKv3CAzKhZVRiZk5XxDTH8QjNLOBpRKyMjQ/cRfkRCbQGZGZq7eL0tby1y9ZC9o6y2zsLEgIz2DhLgEdZlKpSI8KPsOuQe+DyjlWoqeY3rmaoiNWjCK+q3qM73HdGIick+Uzq9ncc/IzMjEws5So9zM1oKEPJ6beHNWxgboKxTEJGn2FscmpeXqJXuVSqXiJ7/HdKjkiIF+7p5lPYWCUpbFAahkZ8bDuCS2ej8qkIbY87jsa95cy3XxLPrNhlR1aTm8E23GdGFN30WE3Q1+5/29TJX0DFVmJgpTS41yhakFqufx2us8i0OVEKtuhAFkRYai0NNDYWGDKiYcVUIcqqxMjYWvsiJD0TO3An0lZL7d+8qrMmL/ej+01/z7KW0sSNfyWtQ3NcakVgWKVyuHy6IR2YV/vR/WeXiQ+33n8fxSwfU0iqIhc8T+xdq2bUtaWhppaWm0aaN9gurrfPTRR/zwww8MGzaMPXt0zz8wNDTE3NxcY9M1LAlARgapvvcxblBXo7h4g7qk3Mh9u3bW8yRCPh5BaPfR6i1h36+kPQghtPtoUm5lT1RNue6LQSmnv77lZjMoU5KMyJi3boQBZKRnEHArgDrudTTK67jXwc9b++3wftf8csXXbVKX+zfvk5mRmffBFGBQTHNZkdELR9OwXUNm9prJk5Anb52/LpnpGQTdfkDVxjU1yqs1rkmA9708aok3ZaCvRxV7My6HaA5DXg6JpZajRR61snmHxRPyNJmPqzq/0bFUKhVpBTQ8lZmeSfDtB1R55bqo3LgmD97xumg5ohPtxnVj7cClBN/K/w00ecrMIOtxIMoKtTSKla41yQzWnnvmo3sozKyhWM5drHq2zqiyMlE9zf7ikxl0Fz0bR433FT1bZ7ISYt+5EQagSs8g6VYg5u6aeZu51ybRK/ck/MxnSfi2HIdf24nqLXrnMVICQvFrO5EkH/93zunvIJP1dZMesX8xfX19/Pz81D/nV5cuXfj+++/p378/SqWS7t27F1SKAMR/dxCHZdNIveNPyg0/zLu3R+lkT8LeXwGwnjgYpb0tkZ9+ASoVaQGac6MyY+NRpaVplD/dexiLPh9h+8lonv7wEwalS2A5vBdPd/2U7zwPbT7ElFVTuH/zPnev3aVtn7bYOdup1wUbNGMQNo42fDnpSwCO7DxCp4GdGD5nOMd2H6Ny3cq09mjN8nHL1fvsOaYn92/eJzwoHKWBkveavUeLbi34ZlZOD6bnYk8+7PwhC4ctJDkxWd3LlpiQSFpqWr6fz8uOb/6F4SvH8ejmAwKv3aNpn1ZYO9tyZtcJALpN74OVgw2bp3ytruNStQwARsWNMLW2wKVqGTLTMngcoP1OwL9bUlIywaE5wzVhj59w1z8QC3MznBztddQseP1ql2L2yTtUtTenpqMFB++EEfE8le7VSwCw5lIAkYmpLG6lOQ/sR7/H1HAwx9XGNNc+t3g9opq9GSUtipOelcWFRzH8ei+CmU0LbiL9b5sPM3DlOIJuPuDhNX8a9WmJlbMt53edBKDz9N5YOlizY0rO9VqyamkADIsbYWptTsmqpclIyyAiIHsqQauRH9FxsgfbJqwhNjQSc7vsxmhqYgqpSQWzHAtA+vlfMOw5nszQQDKD72HwfisUlrak/5l9TRdr0xeFuTWp/8u+pjNunKdY8+4YdR9D6qm9KIqbY9h+ABlev2UPSwLpfx7HoGF7inUcQvofR9CzccLgw66kXzqSZx5vK3LTT5RePZGkmwEket/Dpm8bipWwJXrnMQCcZ/THwNGGoEmrQaUi5Z5mb2JG9FOyUtM0yhUGSowqZK/bpihmQDFHG4yrliUrKZnURxEFlnt+yb840k0aYv9y5ubmBbKf7t27k5WVRf/+/dHT06Nr166vr/SGEo+dJdrCDKtRfVHaWZN2P4jw0bPJCI8EQN/WGqVT7vkxumRGRPF4xKfYTh9JyYMbyIyM5unOH4nfsi/feZ775Rxmlmb0mdAHa3trHvk/Yt7AeUSGZedpZW+FnXNOnk9CnjB34FxGzB1BxwEdiXkSw7fzv1WvIQZgZGyE52JPbJ1sSUtJIyQghBUTV3Dul3PqmI4DspfxWP6/nAYcwMrJKzm1/1S+n8/Lrh6+hKmlGR9N6I6FnRVh/sGsHryUmLDsdYss7K2wLqE5R3DBkRXqn8vULE+Dj92JDo1kemPNhXWLyu279xkybob69+VfbwSgc7uWLJk95W/NpU0FB56mpLPx6kOiE1NxtTHl64611HdBRielEfEsRaPOs9QMTgdGMs29otZ9pmRksvTsPSKfp2Ko1KOMlQmLW1WjTQWHAsvb+/AfmFia0X5CN8ztrAj3D2Hd4GXE/nVdmNtbYfXKdfHpkS/UP5euWZ73P3YnJjSSOY2zp0k06d8aA0MDRmzQ/Bv8uvp//Lq64OZZZdy6BCZmFGvRI3tB1yfBJG9fiio+e80/hZkVepYv5Z6WQvLWhRh2GkrxMctRJT0j49Yl0k7sVoeonsZkx3QYjMH4lagSYkm/9CvpZ38ssLzjfrmAvpUZjhM8MLC3JuVeEIEDF5IWlp23gYMVxUq83XxfAwdrqhxfrf7dYVQXHEZ14dkft7jfc3aB5S4Kh0L1NjORhXhFYPX8DYkWtXEJ+e9BLEqOesWLOoV8+dZr+euD/qHSd694fdA/0NQv814c9J9seW8dw/r/YP47333osijUDcn/KMKbCqhacJ8Trr7HC2xf/xTSIyaEEEKIQpMlQ5M6SUNMCCGEEIVG5ojpJndNCiGEEEIUEekRE0IIIUSh+a8uO1FQpCEmhBBCiEIjtwTqJkOTQgghhBBFRHrEhBBCCFFoZGhSN2mICSGEEKLQyPIVusnQpBBCCCFEEZEeMSGEEEIUGllHTDdpiAkhhBCi0Mhdk7rJ0KQQQgghRBGRHjEhhBBCFBqZrK+b9IgJIYQQotCoVIoC297WunXrKFu2LEZGRtSrV4/z58+/Ub2LFy+iVCqpXbv2Wx/zbUlDTAghhBCFRqUquO1t7N27l4kTJzJr1ix8fHxwd3enXbt2BAcH66z39OlTBgwYQIsWLd7hWb85aYgJIYQQ4j9n5cqVDB06lGHDhlGlShVWr16Ni4sL69ev11lv5MiR9OnThwYNGvwteUpDTAghhBCFJkulKLAtNTWVhIQEjS01NTXXMdPS0vD29qZ169Ya5a1bt+bSpUt55rpt2zYCAwOZN29egZ+HvMhkffFO7Br8O+9LTj2aUdQp5Mv9zLiiTiFf0nevKOoU8s2g99SiTiFfriwdWNQp5ItB37lFnUK+HP9hR1GnkC91/4ZjFOQ6YsuWLWPBggUaZfPmzWP+/PkaZdHR0WRmZuLg4KBR7uDgQEREhNZ9379/n08++YTz58+jVP59zSNpiAkhhBDiX2HmzJlMnjxZo8zQ0DDPeIVCsxGoUqlylQFkZmbSp08fFixYQMWKFQsm2TckDTEhhBBCFJqCXL7C0NBQZ8PrBVtbW/T19XP1fkVGRubqJQN49uwZXl5e+Pj4MHbsWACysrJQqVQolUpOnDhB8+bNC+ZJvEIaYkIIIYQoNEUxgaVYsWLUq1ePkydP0qVLF3X5yZMn6dy5c654c3Nzbt26pVG2bt06fvvtN/bv30/ZsmULLVdpiAkhhBDiP2fy5Mn0798fNzc3GjRowMaNGwkODmbUqFFA9jBnWFgY3333HXp6elSvXl2jvr29PUZGRrnKC5o0xIQQQghRaIpqZX0PDw9iYmJYuHAh4eHhVK9enSNHjlC6dGkAwsPDX7um2N9BGmJCCCGEKDQFedfk2/L09MTT01PrY9u3b9dZd/78+bnuxiwMso6YEEIIIUQRkR4xIYQQQhSarKJO4B9OGmJCCCGEKDQqim5o8t9AGmJCCCGEKDRZ/85/wPK3kTliQgghhBBFRHrEhBBCCFFosmRoUidpiAkhhBCi0MgcMd1kaFIIIYQQoohIj5gQQgghCo0sX6GbNMSEEEIIUWhkaFI3GZoUQgghhCgi0iNWQCIiIli2bBm//voroaGhWFhYUKFCBfr168eAAQMoXrx4UadYpAw+7IRhmx4oLKzJehxEyt71ZN6/nXcFpQGGHfti8EELFOZWqOKiST2ym/SLxwHQcy6N4UcD0C9dAT1bR1L2rCft9KG3zuujAZ3oOaoHNvbWPPIPYt389dy6kndeNT+owei5oyhTsTTRT2LYu34fh3f+qhHj3r4xg6cOxKm0E+FB4WxZvp2Lxy6qH9/1x3c4ujjm2vdP239mzey1AJwOPaH1+OsWfcvuDfvyzG/w5AF81LcDZhZm+Pr4sXLWGh75B+k8B03buzNs2mCcSzvxOCicjZ9v4fxL+daqX4Peoz2oVKMCto62fDpkLueP5zyur9Rn+PQhfND8fZxLO5GYkIjXhWtsWLqZmCcxOo+tzb5boey4FkR0UhrlrU2Y6l6Bus5WWmPnnvLll7vhucrLWZtwoM8HAJwOjGSL1yNCniaTkZVFKcvi9K9dio6Vnd46t4Lgdf0W237Yj+/dAKJiYvlq2RxaNGlYJLm8bMSUwXTp9xFmFmbc8fHl85kreeD/SGed5h2aMmr6MEqWdiY06DHrPtvImaPntcYOGtePsZ+O5IdN+1g59+sCyXnPsXNs/+k00XFPKe/ixPTB3ahX1TXv+KNn2X30HI+jYnG0tWJ4tzZ89GF99eP7T17kl7NXCAh+DEDVcqUY37cTNSqUKZB8X6jbvyX1R7bH1M6SqPthnFqwk9Cr97TGVmzrRt1+LXCoWhr9YgZE3w/l/KqDPDx3Sx1jW6EE7lO64Vi9LJYudpxa8D1Xtx4v0JzfhQxN6iY9YgXgwYMH1KlThxMnTrB06VJ8fHw4deoUkyZN4pdffuHUqVNa66Wnp//NmWZLS0v7W4+ndGuKkccoUn/9gcSFo8m4f4vi45egsLbLs47xyFnoV6lD8o6VPJ8zlKRNy8iKCMkJKGZIVnQEKQe3khX/9h/2AB92aorn/FH88PUPjGw7mltXbrHs+yXYO2vPy9HFkaXfLeHWlVuMbDua3Wt3M3ahJ+7tG6tjqtatwpx1szh54DQjWo/m5IHTzF0/i8p1KqtjPDuMo3sdD/U2rdcMAM7+ek4d8/Lj3et4sHzyCrKysjhzRPuHHEAfz154jOjOqtlfM7yDJ7FRcazavRxjE+M861SrV5X56+dw/MBJBrcawfEDJ1m4YS5VX8rXqLgxAb6BrJqt/cPTyNiIijUqsOOrnQxtO4pZw+fjUq4kn21blOdx83L8/hO+OO/PULcy7PZ4nzrOloz95Qbhz1K0xk9zr8jJwY3V27FBjbAwVNKqvL06xsLQgGFuZdjR3Y19vevTuYoT80/7cSkof9fNu0pOTqGSazk+naz9HxEXhYFj+tBnpAfLZ61iYLvhxETG8s3eVRTXce3UqFeNpRvmc2T/cXq3HMyR/cf57NuFVKtTNVds1VqV6dKvE/53Agos52MXvVm+7QDDu7Vh34pPqFulPJ5L1hEeFas1fu+x83y16xdGe7Tn0KpZeHq0Z+mmfZy5mtOg8bpzn3aN67FlwQR2Lp2Ck50VoxZ+w5OY+ALLu0rH+rSc249La39ma4fZhF65h8eOaZg722iNL/V+ZR6ev82+QSvY1nE2QZf86LFlCg7VSqtjDIwNiQ+O4szne3keWXC5FpSsAtz+i6QhVgA8PT1RKpV4eXnRs2dPqlSpQo0aNejWrRu//vornTp1AkChULBhwwY6d+6MiYkJixcvBmD9+vWUL1+eYsWKUalSJb7//nuN/cfHxzNixAgcHBwwMjKievXqHD58WP34pUuXaNKkCcbGxri4uDB+/HgSExPVj5cpU4bFixczaNAgLCwsGD58OM2bN2fs2LEax4mJicHQ0JDffvutQM+PYatupF84RvqFY2RFhJC6dwNZcVEUa9pJa7x+NTeUFWuS9NVsMv18UMU8IevRPTIDfdUxWY/8Sd2/iYyrZyAjfw3a7iO6cXTPMY7sPkZwQAjr5m8g8nEUnQZoz6tT/w5EhkWybv4GggNCOLL7GMf2HqfnyO7qmK7DuuB9/hq7v9lDSGAIu7/Zw7WLPnQb2kUd8zT2KXFRcertg5b1CXsUxo0/bqpjXn48LiqORq0bcv3SDcKDc/f+vNBzWFe+W/MD545e4OG9RyyZ+DmGxka06tIizzo9hnXF65w3O9fuJjgwhJ1rd+N94Ro9hnVTx/z5+xU2L9/GuaMXtO4j8Vkik3tP5/dfzhISGIrvNT9Wz15L5VqVsHe211onLzuvB/NxVWe6VitBOWsTprlXxNHUkP/dCtUab2aoxNbEUL35Rj4jITWDj6rk9Ha5lbSieXl7ylmb4GJRnD61SlHB1hSf8Pi3yq2guDd4j/EjBtLqw0ZFcnxteg/vybavvuP3I+cIvPeQeROWYGRsSNuurXTU6cGf57zY/vVOggKC2f71Tq5c8KbP8B4accbFjVn0zVyWTF3Os6fPCizn7375jS7NG9CtZUPKlXRkxpDuONpYse+49i8rh89doXurRrRtVI+Sjra0a+xGlxYN2PbjSXXMZxMH0attEyqXLUnZko7MG9WHLJWKP29p763Kj/eHtePG3jPc2HOGmIDHnFq4k4TwGOr00/46PbVwJ39++yvhNx8Q9+gJZ7/YR+yjCFxb1FHHhN98wO9Ld+P3y2UyUovmC77IP2mIvaOYmBhOnDjBmDFjMDEx0RqjUORMVJw3bx6dO3fm1q1bDBkyhEOHDjFhwgSmTJnC7du3GTlyJIMHD+b3338HICsri3bt2nHp0iV27tyJr68vn332Gfr6+gDcunWLNm3a0LVrV27evMnevXu5cOFCrkbWF198QfXq1fH29mbOnDkMGzaMH374gdTUVHXMrl27cHZ2plmzZgV3gvSV6JWuQIbvNY3ijDve6JfP/c0ZwKBWAzIf+WPYtgemy3/AZPFWDLsPB4NiBZaW0kBJxRoV8DqnmZf3OW+quWnPq2rdqnif89You3rWm4o1K6KvzP57VK1XFa+zmjFeZ/Lep9JAScuuLTi2J+9hBCtbS+q3eJ+je47lGeNUygkbBxuunvVSl6WnpXP98g2qu1XLs171elW5es5Lo+zKWS+ddd6EibkJWVlZPE94/sZ10jOz8It8RgMXa43yD1ysuRHx9I328aPvY+q7WONsrr0nR6VS8WdILI/iEqnnbPnGuf2XlSjlhK2DDZfPXlWXpaelc+2P69R0q55nvZpu1fnzpToAl89coeZ7mnVmLJvExdN/cOW85uviXaSnZ+AXGELD2lU0yhvUqsL1ew+11klLz8CwmIFGmVExA24FBJGekam1TkpaGhmZmViYFszUEj0DfRxrlOXhec3pDw/P3aZkvQpvthOFgmImRqQ8TXx97D+ECkWBbf9FMkfsHQUEBKBSqahUqZJGua2tLSkp2cMpY8aM4fPPPwegT58+DBkyRB3Xp08fBg0ahKdn9jDF5MmTuXz5MitWrKBZs2acOnWKK1eu4OfnR8WKFQEoV66cuv4XX3xBnz59mDhxIgAVKlRgzZo1NG3alPXr12NkZARA8+bNmTp1qrqei4sL48aN46effqJnz54AbNu2jUGDBmk0HF+Wmpqq0XADSM3MwlA/7/a8wtQchb4+qoQ4jXLVszgUFtrn/ejZOaJfoTqqjDSS1i1Az9Qco77jUJiYkbJjZZ7HehsW1uboK/WJi9LMKy4qDms77XlZ21sRdyZ3vNJAiYW1BbGRsVjbWREX/UpMdBxWeeyzUZuGmJqbcvx/2ueEAbTu0YqkxCTO59EjBWBjn73/2FePHRWHY0mHPOtZ21kT+8o5iNVxDt5EMUMDRs0cxqlDv5H0POmN68Ulp5OpUmFdXLPBbVPckJgk7cNNL4tKTOViUAxLW+duRD5LzaDN9gukZ2ahp1Aws2klPiilfSjo/xsb++zzEPPKkF5MdBxOJXPPZVTXs7POXScqFhu7nIZ0684tqFyjIgPajSjAjCHu2XMys7KwsTDTzMnSjOj4BK11GtauwsFTl2j+fk2qlHPBNzCYQ79dJiMjk/hnz7GzsshVZ/XOn7C3tuCDmpW17PHtFbcyQ0+pT2K05heLxOinmNhZvtE+6o9oT7Hihvgd/rNAcvo7ZP03208FRnrECsirjZcrV65w/fp1qlWrptF4cXNz04jz8/OjUSPNIYpGjRrh5+cHwPXr1ylZsqS6EfYqb29vtm/fjqmpqXpr06YNWVlZPHyY883w1eMaGhrSr18/tm7dqj7OjRs3GDRoUJ7PcdmyZVhYWGhsK69r//aZi+rV//qqgLz+EaxCD1Qqkjd/Rtaje2TcvkrKvm8xaNi6QHvFtOalUOROVXf4X+Wql2JUr8Tk/Vzb9WrLld+vEvMk74ZGW4+2nD70G+kvDTm06tKC4/6H1ZtSqdSaoEKhyJVP7uekrY7OKnnSV+ozf90c9PT0+PLTr/K1D8Ur33pVb/g9+Ge/cMwMlTQrl3uOn0kxffZ4vM/OHu8x5oNyfHnhPl6hcVr28t/XtmsrzgUcV29Kg+xrJ/e1/fprR9f15uBsz5RF45kzdhFpqYUzL/XV74wqlSrX9fPCyO5taVy3Kv1mrqBuzwlM+HwjnZtlT9TX08v9Ubj1x5McveDNqmnDc/WkvbNc5y13mTZVP2pA44ld+HHMWpJitDc4/4myUBTY9l8kPWLvyNXVFYVCwd27dzXKX/RaGRtrDpFoG758tRGnUqnUZa/Wf1VWVhYjR45k/PjxuR4rVaqUzuMOGzaM2rVrExoaytatW2nRogWlS5fOFffCzJkzmTx5skZZ6sSuOvNTPU9AlZmJwkJzuElhZpmrl0z9nJ7GohcfDck5vSlZ4cEo9PTQs7IlK/KxzmO+iaexCWRmZGJlr5mXla1lrh6tF2Ij47C21+wpsrS1IiM9g4S47DfF7N4kzX1a2mjfp30Je+q612H+8IV55lnj/eqUcnVh0eglGuUXTlzC18dP/bvBXx8U1nbWxETmNOosbS2JjY7Pc/+xUbHYvMU50EVfqc/CDXNxKuXIhJ5T36o3DMDK2AB9hYKYJM1e19iktFy9ZK9SqVT85PeYDpUcMdDSQ6unUFDKMnt4qZKdGQ/jktjq/Qi3kvnv+fu3Onf8Arev5cy3LPbXtWNrb01MZM4NDNY2lsTmMfEd/ur9stfsVbS2tVL3ylauWQkbO2u+P75Z/bhSqaTOB7XoObgrDUu3ICsrf9OvrcxM0dfTIzpec85Z7NPn2Fiaaa1jZFiMhWP6MWdkb2KeJmBnacH+kxcxMTbCykzz/XH7T6fYcuAEG+eNpWKZEvnKUZukuGdkZWTm6v0qbmORq5fsVVU61qf98mEc8vyaRxfvFFhOouhJj9g7srGxoVWrVqxdu1ZjgvybqlKlChcuaA45Xbp0iSpVsuc+1KxZk9DQUPz9/bXWr1u3Lnfu3MHV1TXXVqyY7g+vGjVq4ObmxqZNm/jhhx80hky1MTQ0xNzcXGPTNSwJQGYGWUH3UVapq1GsrFpXY/K9RpWAOygsbMDQSF2m51ASVVYmWXHRuo/3hjLSM/C/dZ967pp51XOvyx0v7Xn5XvPNFe/WpC7+N/3J/GuOia+3L/WavBLTtJ7Wfbb1aEN8dDyXT+c9xNCuV1vu3fDngd8DjfLkxGTCHj1Wb4/8g4h5EsN7TeqpY5QGSmp/UIvbXnm/ad/29sXNvZ5G2XtN3HTW0eZFI6xk2RJM8pimbpi+DQN9ParYm3E5RPPD/3JILLUccw8bvcw7LJ6Qp8l8XNX5jY6lUqlIy/yv3oOlW1JiMqGPwtTbA/9HRD+JoX6T99QxSgMldRvU5qZX3ku53PS6Tf0mmj3t9Zu+x82r2XWunvfC48MB9G05RL3due7HsYMn6dtySL4bYQAGBkqqlHfhjxuaX4Av37xL7UpldddV6uNoY4W+vh7HLnrTpF41jR6xbT+eYuP+Y6yb40k117y/mOZHVnomEbceUtZdcx5dWffqhHrfz7Ne1Y8a0OHLkfw0fh2Bv10v0Jz+DqoC3P6LpCFWANatW0dGRgZubm7s3bsXPz8/7t27x86dO7l79656Yr0206ZNY/v27WzYsIH79++zcuVKDh48qJ7P1bRpU5o0aUK3bt04efIkDx8+5OjRoxw7lj1xe8aMGfzxxx+MGTOG69evc//+fX7++WfGjRv3RrkPGzaMzz77jMzMTLp06fL6CvmQevIABu5tMWjUBj1HFwx7jkLP2p60s9l3fhp2GYLRkGnq+PQrv6FKfIbxoKnoOZVCv0INDHsMJ/3CcUj/a4hDX4meSzn0XMqB0gCFlS16LuVQ2L3ZBzHA/o0HaN+7LW092lDK1YXR80ZhX8KeX77PzmvoJ0OYsTonr1++/xX7kg6MnjuSUq4utPVoQ7tebdn37X51zMEtP+LWpB69PHviUt6FXp49qdu4Dge2aK5xplAoaNuzNSf2nyQrjwZBcdPiNOnYhCO7j77R89m3+SD9xvXBvW0jylYqw6erppOanMLJQ6fVMbO+msHIT4bmnIMtB3mvqRt9PHtRqrwLfTx74eZel/9tPqCOMS5uhGu18rhWKw+AUylHXKuVV98Rqa+vx6KN86hUqyILxy1FT18PazsrrO2s1MNeb6pf7VIc8n3Mj76PeRCbyIrz/kQ8T6V79exeiTWXAph9Mncj8Ue/x9RwMMfVxjTXY1u8HnE5OIbQp8k8jEvke59gfr0XQftKec9/KkxJScnc9Q/krn8gAGGPn3DXP5DwiMgiyQdg96Z9DB7fjw/buVO+Ulnmr/6UlORUjh3MuaNwwZpZjPl0pPr3PZv3U7/pewwc04fSrqUYOKYP9d3d+GHT/4DsBl/gvYcaW0pSCvFxTwnMY0L92xjQqTkHT1/i0Ok/eBAawfJtBwiPjqVHa3cAvtr5E5+u+U4d/+jxEw6fvULQ40hu3X/E9JVbCQh+zPi+H6ljtv54krW7D7PAsy8l7GyIjksgOi6BpOTUXMfPryubj1LL40Nq9myCjaszLeb0xdzZBp9d2a/TptN70nFlznmu+lEDOq4cyW+Lf+CxTwAmdhaY2FlgaJYzWqJnoI991VLYVy2FfjElpo7W2FcthVXpvOeH/p1k+QrdZGiyAJQvXx4fHx+WLl3KzJkzCQ0NxdDQkKpVqzJ16lT1RHxtPv74Y7766iu++OILxo8fT9myZdm2bRsffvihOubAgQNMnTqV3r17k5iYiKurK5999hmQ3WN29uxZZs2ahbu7OyqVivLly+Ph4fFGuffu3ZuJEyfSp08f9cT+gpbhdZYUU3MMO/ZVL+iatGY2qtjsDx6FpTV61i8tc5CaQtKqTzDqPQaTWWtRJT4j3essqT9uV4coLG0wnbtB/bthmx4YtulBxr0bJK3IaTzpcuaXs5hbmdN/Yl+s7a15dC+ImQNmExmWnZeNvTX2JXLyigiJ4NMBs/CcN4qPBnYi5kksa+eu4/yRnB5NX29fFo9ZyuBpgxg0dSCPg8JZ5LmEuz6a39zrutfFoaSDzrslm3X+EIUCfv/p9zd6Pj+s24OhUTGmLJ2AqYUZfj5+TO4zg+TEZHWMg7M9qqyc75W3vXxZ4LmYYdMHM2zaIMKCHjNv9CJ8X8q3Uq1KfL0/5yaJcfOzr+ej+46zdNJy7JzscG+TPc9x+8lNGjmN6z6Z63/ceKP8AdpUcOBpSjobrz4kOjEVVxtTvu5YS30XZHRSGhGvrCn2LDWD04GRTHPXPo8yJSOTpWfvEfk8FUOlHmWsTFjcqhptKhTNh9Ttu/cZMm6G+vflX28EoHO7liyZPaVIctrxzQ8YGhnyybIpmFmYctvHj7G9JpP00rXjWMKBrJeunZtet5k1agGjPxnGqOnDCA0KY+aoedzx0d6jXNDaNqpH/LNEvv3fUaLiEnAt5cQ3n3ri/NdQe1RcAhHROb2rWVkqvvvlNx6FPUGp1Oe9ahX5bukUSrw0vLrv2HnSMzKYsmKLxrFG9WyHp0eHAsnb7/CfGFuZ0Wh8F0ztLYnyD2XfoC9ICMseFja1t8Tc2VYdX7tPc/QNlLRZPIg2iwepy2/+7xy/Ts2+dswcrBh6dKn6sQ9GduCDkR0I+sOPH3ppTmsQ/zwK1WtnY4r/spCQEMqUKcPVq1epW7fu6yu8ImF460LIqvB1ebNOpn+cNJX22+z/6Y5/Uun1Qf9QBr2nvj7oH6hBjYFFnUK+XDw5t6hTyJeVHXYUdQr5MjNoZ6EfY79T3wLbV/fwXQW2r38K6RH7fyo9PZ3w8HA++eQTPvjgg3w1woQQQojXkd4e3WSO2P9TFy9epHTp0nh7e7Nhw4bXVxBCCCFEgZMesf+nPvzww9evESSEEEK8o//qJPuCIg0xIYQQQhQaWVlfNxmaFEIIIYQoItIjJoQQQohC81/910QFRRpiQgghhCg0MhtZN2mICSGEEKLQyBwx3WSOmBBCCCFEEZEeMSGEEEIUGlm+QjdpiAkhhBCi0MgcMd1kaFIIIYQQoohIj5gQQgghCo1M1tdNGmJCCCGEKDQyR0w3GZoUQgghhCgi0iMmhBBCiEIjPWK6SUNMCCGEEIVGJXPEdJKhSSGEEEKIIiI9YuKdWO+4U9Qp5Mt7dhWLOoV8WZDhUNQp5MvUL6OLOoV8u7J0YFGnkC9/3NpR1Cnki4trh6JOIV9+Kl6lqFP4x5KhSd2kISaEEEKIQiMNMd2kISaEEEKIQiMr6+smc8SEEEIIIYqI9IgJIYQQotDIyvq6SUNMCCGEEIVG5ojpJkOTQgghhBBFRHrEhBBCCFFopEdMN2mICSGEEKLQyF2TusnQpBBCCCFEEZEeMSGEEEIUGrlrUjfpERNCCCFEockqwO1trVu3jrJly2JkZES9evU4f/58nrEHDx6kVatW2NnZYW5uToMGDTh+/Hg+jvp2pCEmhBBCiP+cvXv3MnHiRGbNmoWPjw/u7u60a9eO4OBgrfHnzp2jVatWHDlyBG9vb5o1a0anTp3w8fEp1DxlaFIIIYQQhaaoJuuvXLmSoUOHMmzYMOD/2rvvuKauPo7jn7CRvUHEiYp771VHFVddtdaF1r1nXa3bqh1aR7WtdaHVOup8WvfGUScqqIiCDEEQEMXFJs8f1GAkICoYpL93X3lVTs69+SbchJNzzj0XFi9ezIEDB/jll1+YP39+pvqLFy9W+3nevHns3r2bv/76i2rVquVZTmmICSGEECLPpOViUywxMZHExES1MkNDQwwNDdXKkpKSuHTpEpMnT1Yrb9myJWfOnMnRY6WlpfHkyROsra3fLfRryNCkEEIIIfJMbs4Rmz9/PhYWFmo3Tb1bMTExpKam4uDgoFbu4OBAZGRkjnIvXLiQZ8+e8dlnn735k34D0iMmhBBCiA/ClClTGDdunFrZq71hL1Mo1E/ZVCqVmco02bRpEzNnzmT37t3Y29u/XdgckoaYEEIIIfJMbs4R0zQMqYmtrS26urqZer+ioqIy9ZK9asuWLfTv358///yTFi1avFPenJChyfekb9++KBSKTLeAgABtR9Oq6dPGERp8iSdxARw59Cfly5fJtn7/fj04fnQH0fevE33/Ogf2baZWzaq5mqn/uD7879KfHA/Yz/I/F1GiTPHXbvNRm8b8cWwtJ+4c4I9ja2ni3lDtfo8RPVi95xcO++9hz9UdfLt6DkVLuajVadK6EYs2fs8+3138E36M0hVKvdPzcOn7MY0uLKV5yHrqHpyHZR23LOta1i5Lrb9m8ZHfSpoHr6fBqYUUHdxGrU7hbk1oeX9zppuOof475XxV414tmX1yGUv8NzD5r28pVSvr3OZ2lnyxZBQzjixm2Z3NfDq9T6Y6DT5vzrits1hwdQ0Lrq5h1IapFKvybq9tVgaN/4J9l3dy6s5hVmxfSskcHDvN2jZh64nfORN8hK0nfuej1o2yrNt3ZC8uRpxk3OyRuZg6Zy5e8WX4xBk0/aQnFRu05ohXzubZ5LUvJw/nit8JgiIus+PvdZR1c822flk3V1atX8IFn8NEPvJj4FCPTHX69Puco6d3cTv0ArdDL/D3wU00a5H17+VN2fdxp8rZX6h1ZzMV9/+AWe1yWdY1re1G+d3zqH5tHbUCN1HZaymOA9up1VHo6eI8titVzvycvs9DP2LxUd5NLn9T2li+wsDAgBo1anDo0CG18kOHDlG/fv0st9u0aRN9+/bljz/+oG3btm/wiG9PGmLvkbu7OxEREWq3EiVKqNVJSkrSUrr3b8KXwxgzehCjxkylbv22RN6PZv/eTZiammS5TZMm9di8ZTctWn5Gw8afEHo3nH17/6BwYcdcydRr2Od0H9SVhVOX0q/tEB5Ex7Jk0w8UMjHOcpuKNcoz55fp7N9+CI+PB7B/+yG++XUG5atlfLhWq1uF7et2MbD9cEZ3n4Ceni6L//geI2MjVR3jQkb4XrjGz/N+e+fn4dChHmXn9OHO4p2cbTGZh+duUn3TZIycbTTWT32eyN01B7jQcRanG43nzqKdlJ78Gc69m6vVS378nOMVB6vd0hKT3znvCzXa1ePT6X3Zv2wH89tMIuCCH8M9v8KqsObceob6PI19zP7lOwj3C9FYp3Td8lz832kWd5/FD52nEnvvASN/n4qFg1Wu5QboM7wHPQZ34/uvF9Gn9UAeRMWyfMuibI+dSjUqMO/XmezddoDuLb5g77YDfLtiNhWqlc9Ut3wVNzr1as+t69r58hYfn0BZ15J8NW6YVh5fkxGjBzB4WF++mvgNrZt9RtT9GLbsXI2JaaEstzE2NiI0+C7fzPyR+5HRGuvcuxfJ3Jk/0qppV1o17copr7N4/rHstY28nLD+pAHFZn3BvaXb8W05nsfn/Ci7cSoGzrYa66c9T+T+2r34dZ7K1SajCF+8jSKTemDX82NVnSKTemDfqyXBU1fh89Foon4/QJnVEylUsYTGff5XjBs3jlWrVrFmzRr8/PwYO3YsoaGhDBkyBEgf5vTwyGiIb9q0CQ8PDxYuXEjdunWJjIwkMjKSuLi4PM0pDbH3yNDQEEdHR7Vb8+bNGTFiBOPGjcPW1paPP05/c504cYLatWtjaGiIk5MTkydPJiUlBYDg4GCNvWsfffSR6rHOnDlD48aNMTY2xsXFhVGjRvHs2TPV/cWLF2fevHn069cPMzMzihYtym+/vXsD4E2MGjmA+d8uZdeufVy/7s8X/cZQqJAx3T/vlOU2Hn1G8uuKdVy9eh1//0AGD5mAjo4OzZo1zHKbN9FtwKd4Lt3AiX0nueMfzJwx32JkbETLTll3T3cb8CkXvC6yftkfhATeZf2yP7h4yptuA7qo6oztNYm9Ww8QdCuYgBuBfDP2O5yKOOJWOaMHcP/2Q6xZvJ4LJy+98/MoPqQt4X8cI3zjMZ7dvof/tPUkhD+gSN+PNdZ/ci2YyJ1neOYfRsLdaCK2nyLmmA9Wr/aiKZUkRcep3XJTswHtOLP1KGe2HCUyMJxts9fxKCKGxr1aaqwfGxbNn7M8ObfDi/gnzzXW8RzzE14bDhJ2I4T7gffYOPlXFAoFbg0q5Wr27gM/Y+2S9Rzb60WgfxAzRs/FyNgQ986aX/P0bbpyzusinj9tICQgFM+fNnD+1CV6DOyqVs+4kDFzlk9n7pff8yTuSa7mzqlG9WoxalAfPv6ogVYeX5OBQz1YsnAFe/86xE2/24waOhnjQkZ0/rRdlttcuXyN2dMXsHvH3iy/+B7af5wjh7y4ExjMncBgvv1mCc+ePad6rSrvnNlpUHuiNx0h+o/DJASEEzpjDUn3HuDg0Upj/efXgniw6xTxt+6SFBbNgx1exB2/gnmdjC96tl2acO+n7cQd9SYx9D5R6w/w6MQVnAZ/8s55c0OaIvdub6Jbt24sXryY2bNnU7VqVby8vNi7dy/FihUDICIiQm1NsRUrVpCSksLw4cNxcnJS3UaPHp2bL0cm0hDLB9atW4eenh6nT59mxYoVhIeH06ZNG2rVqsXVq1f55ZdfWL16Nd988w0ALi4uar1qly9fxsbGhsaNGwPg6+tLq1at6Ny5Mz4+PmzZsoVTp04xYsQItcdduHAhNWvW5PLlywwbNoyhQ4dy8+bN9/KcS5QoipOTA4cOn1CVJSUl4XXyLPXq1czxfgoVMkZfX4+HsY/eOVPhok7YOthw/sRFVVlyUjKXz16lUs0KWW5XsUZ5zntdVCs7d+JCttuYmqf3+j1+9PgdU2em0NfFrHIJHhz3USt/cMIHy5rZD/2+YFaxOJa1yvDwnxtq5bomRjS6+BONLy+n2oaJmFUsnlux0dXXpWjFkvidvKpW7nfSh5I1yuba4xgYG6Krr8ezR09zbZ/O/x47Z09cUJUlJyXj/c8VKtesmOV2lWtW5NxL2wCcPX6eyrXUt5k0fyynj/zD+VxopBcURYsVwcHRjuPHTqvKkpKS+ef0BWrVyb1hOR0dHTp0bkOhQoW4dP7KO+1Loa+HSeVSxJ1QP8bjTlzBtGbWQ/AvK1SxBKY1y/L4bMZ7U2Ggn6lnOi0hKdshz/cpDWWu3d7UsGHDCA4OJjExkUuXLqn+TgJ4enpy/Phx1c/Hjx9HqVRmunl6eubCq5A1maz/Hv3999+Ympqqfm7dujUArq6ufP/996ryr7/+GhcXF5YtW5b+zd3NjXv37jFp0iSmT5+Orq4ujo7pQ3EJCQl07NiRevXqMXPmTAB++OEHevTowZgxYwAoXbo0S5cupUmTJvzyyy8YGaUPh7Vp04Zhw9KHGSZNmsSiRYs4fvw4bm6aPxA0rd+S0zNQXuXokH4Wyv37MWrl9+9HU6xokRzvZ97crwgPj+TwkawvW5FTNvbpa8XExjxUK4+Nfohjkawnd9rYWRMbnXkbG7us154ZNWMYV875cMc/+O0DZ8HA2hwdPV0SX+mtSoqOw9DeMtttG19ejoGNOQo9XQJ/2Eb4xmOq+54FhHN91C888QtFz6wQxQa2pvZfs/in2SSeB+XsdPDsmFqZo6uny5NXcj+OjsPcNvvcb6LjpJ48iozl5mnfXNunjX360OmD6Fi18gcxD3EqkvWwuY2ddeZtomPVjp2WHZrjVqkMHq0H5VregsDeIX0oLzpK/TMkOuoBRVwKv/P+3cqXZs/BTRgaGfLs2XP69RrJLf/Ad9qnnrUZCj1dkmMeqZUnR8eh/5r3ZrWLK9GzMUehp0PYwq1E/3FYdV/cics4DmrP47M3SAyOxLxRZaxa1UahI30tHwL5Lb1HTZs25cqVK6rb0qVLAahZU70HyM/Pj3r16qk1cBo0aMDTp08JCwtTq9u/f3+ePHnCH3/8gc6/b7pLly7h6emJqamp6taqVSvS0tIICgpSbVu5cmXVvxUKBY6OjkRFRWWZX9P6Lcq0nA2TdO/eiUext1Q3ff307wBKpfo3HIVCkaksK1+OH8rn3TrQtdvATA3EnGjZqQVHbu1V3fT0ssoEvCZTpswKRZabfDl3NK7lSjF9+Jw3zvxmXs3Ea5/HhQ4zOdvqK25MXEXRQa1x7JQxqTXuUgAR20/x9EYoj87d5OrAxTy7E4HLAM1DKm+fOvPr/2rZ2/p48CfU/KQBK4csIOUd5ra5d/4Yr4ADqpue6nhWr5ej4zmb94BDYXvGzxnFtBFzSEr878wf1aRz13YEhl1U3fT1008SeavXPAcCbwfTvFFn2rb4nHWrN7P0l/mUKZtLJ3lkCq2h7BU3On3NtdYTCJq0AscB7bDpmDEdI2TaGhKCIqjitZTaIVspPncAMVuOokx7m6sz5j5lLt4KIukRe49MTExwdc082dPERH1yuqZephcfLC+Xf/PNN+zfv5/z589jZmamKk9LS2Pw4MGMGjUq02MVLVpU9e8XH2QvKBQK0rJ542pav8XKJmfd6X/9dZDz5zOu12VoaACAo6MdkZEZjT97e1vuv/INV5NxYwczedJIWrl/jq+vX44yvOrUwdPcuJzRva9vkJ7Jxs6aB1EZvRRWtlaZesle9iA6VtWb9oK1rSWxMbGZ6o6bM5KGLesztPNooiNe/zzfRlLsY9JSUjG0s1QrN7C1yNRL9qr40PTJy0/97mJgZ0GpLz8lcmcWZ8cplTy+EohJCafciM3Th49JTUnF/JXcZrYWPIl597loLQa2p9XwTiztOYfwm5qvNZdTXgdOcc0749gxMEh/L9naW/Mg6oGq3NrGktjozMfBC+nHjvqJCNYvHW9ulctiY2fN7wdWqe7X09OjWt0qfPZFZ+oXa57te7YgObDvKN4XM4bbX3yG2DvYEnU/Y9K9rZ01MdEPMm3/ppKTkwkOSj9Orl65TtXqlRgwpDcTx858632mxD5BmZKKvp36iSL6thYkv+a9mXg3/XMy/mYo+naWOI/vxoNdp/7d72Nu9/sOhaE+elZmJEfG4vJ1bxJD77911tz03zhC3540xPKh8uXLs337drUG2ZkzZzAzM8PZ2RmA7du3M3v2bPbt20epUurf0qpXr87169c1Nvrehab1W3I6LPn06TOePn2mVhYRcZ8WzRtz5cp1IL1h2LhRXaZ8NS/bfY0fN4SvpoymTdueXPL2ybZudp4/i+f5s3i1spj7D6jVuKbqzDQ9/fQ/etmdyXjt0g1qNarB5pXbVGW1G9fE9+J19dzfjKKJe0OGdR1LxN13H8rLijI5lSc+Qdg0qUTUvoz5RzaNKxF14GI2W6pToEDHIPulKcwqFOfpOzZqXkhNTiX02h3KNazM1QMZud0aVsbn0IVstny9FoPa03pEF37qM5dQ3zvvGvXfYydcrSzm/gPqNK6F/7XbQPqxU71eVX6a+2uW+/G5eI06jWvyx29bVWV1mtTC58I1AC6cvEi3j9SXV5i+eAohAaGsW7bxP9MIA3j29DnPnqofa/cjo2nyUX2u+aR/GdPX16deg1p8M2Nhrj++QpHR+HtbyuQUnvkEYtG4Cg/3n1OVWzSuwsMD598ojKb3pjIxmeTIWBR6uli3qcuDv/LHEiMie9IQy4eGDRvG4sWLGTlyJCNGjMDf358ZM2Ywbtw4dHR0uHbtGh4eHkyaNIkKFSqoFqwzMDDA2tqaSZMmUbduXYYPH87AgQMxMTHBz8+PQ4cO8dNPP2n52WVY+tMqJk8aye2AIAICgpg8aSTPn8ezafNOVZ21a5Zw714EX0/9Fkgfjpw1cwK9PEYQHHIXBwc7IL2h9+yZ5rPm3sSWVdvoM7InYUFh3A0Ko8/IXiTEJ3BwZ8Z8jOlLphAdEc0v36b3UmxdvZ2fty+h17DPOXngNI1aNaBWoxoM7pTRI/nlvDG07NicSf2m8vzpc6z//Ub87MkzEhPSh5zMLc1wcLbH9t+5L0VLpfdePoiKzTQH7XWCf91DpWXDibt6h7iLtyjSuwVGRWwJW5f+PFy//hwjR2uujfwZAJcvWpIQHsOz2/cAsKxTlmLD2nF39X7VPkuO70Lcpds8D4pEz9SYogPdMatYDL8pa94oW3aOrvqbPj+OJMTnDkHet2jQowVWhW05uTF9LaAOE7tj6WDNuvHLVdsUKZ9+BpRhISNMrc0pUr4YKUkpRAakN5Q+HvwJ7cZ1Y+3opcSGRWFuZwFA4rMEEp+/+ZB2Vjat3MoXo3oRGnSXu3fC+GJUbxLiE9m/I2Mdo1lLvyYqMobl81YAsHnVNn7b+RN9hvfg+IFTfNSqIXUa1aR/h+FAeoMv0D9I7XESnifw6GFcpvK89vx5PKFh91Q/h9+7z81bgViYm+HkmLcrj2dl5S/rGTV+EHfuhBAUGMKocYOIf57Ajm1/q+r89Ou3RNy7z7zZi4D0xloZt1Kqfzs52VOhkhvPnj5X9YBNmTaGo4dPci88AhNTEzp2bkP9hrXp3uXd5+lF/PYXpZaO4plPAE8u+mPfqyUGzrbcX38QAJcpPdF3tOHO6PSpKw593UkMjyH+3+PZrHY5nIZ8wv01e1X7NKlWGgNHa55fD8bA0Rrn8d1AR0HEzzszB9CC3LzWZEEkDbF8yNnZmb179zJhwgSqVKmCtbU1/fv3Z+rUqQBcvHiR58+f880336jOpARo0qQJx48fp3Llypw4cYKvv/6aRo0aoVQqKVWqFN26ddPWU9LohwU/Y2xsxLKl87CysuD8+cu0bttDreesqEthtW/9Qwb3wdDQkD+3rFTb1+w5C5k958d3zrTh580YGhny5bwxmFmYceOyH2N6TFDrOXMobK+WyffidaYPm83gif0ZNKEf4SH3mDp0NjcuZwyZdunTAYCfty9We7w5Y79l79YDADRsWZ9pizIuUPvNL9MBWLXQk9U/rnuj53F/9z8YWJlSalwXDB0seXrzLpd7fEtCWPpwqKG9FUYvr1uko6D0190xLmpHWkoa8cH3uf3NJsLWZzRA9S1MKL9gIIb2lqQ8ec5j32AudJzF48vvNoH5ZZf+/gcTSzPajO6CuZ0VEbfu8vMX84kNT89tbm+F1SvrLX219wfVv4tVLkXtjo14EBbFtIbpZwk37t0SfUN9Bv06Xm27PYv/ZM/iP3Mt+7rlf2BoZMjk+eMxszDl2mU/Rnw+Tu3YcXR2IC0t44+Sz8VrfD1kFkMnD2DIxAGEhYQzZcgMrl++oekhtOrazdv0GzlJ9fP3P6X3Endo3YK5U8dntVmeWrZkFUbGhny7YDoWluZcvuTD550H8Oxpxpcy5yJOau9XRyc7jpzMaKAMG9WfYaP6c+bUeTq3S18Q2M7elmUrvsPewY4nj59w4/otuncZhNfxd+9hiv3fafSszHAe+xn69lbE+4fi32suSeHpw6v69lYYqr03dXCZ0gvDovYoU1JJDLnP3XkbiPr9YEYVQ31cJvXAsKgDqc8TeHTEm8BRS0h9/O5fTnODNMOyp1DmxqxG8Z+lZ+Cs7QhvpZZdzpZxyG9mpWR/aY78apfxhzuEdj4xQtsR3so/vm/WeM8vXFzfz2rmuW13ofyxVMSbqnNvR54/xpfFu+favhYEb8q1feUXctakEEIIIYSWyNCkEEIIIfKMzBHLnjTEhBBCCJFnpBmWPRmaFEIIIYTQEukRE0IIIUSe+XBP1Xk/pCEmhBBCiDyTW5coK6hkaFIIIYQQQkukR0wIIYQQeUaGJrMnDTEhhBBC5BlZviJ7MjQphBBCCKEl0iMmhBBCiDwj/WHZk4aYEEIIIfKMDE1mTxpiQgghhMgzMlk/ezJHTAghhBBCS6RHTAghhBB5RhZ0zZ40xIQQQgiRZ2RoMnsyNCmEEEIIoSXSIybeycNh1bUd4a203xav7QhvZXCKn7YjvBXf7q7ajvDW9HtO13aEt+Li2lbbEd7K3YA92o7wVnrVGKftCG9ly3t4DBmazJ40xIQQQgiRZ2RoMnsyNCmEEEIIoSXSIyaEEEKIPJOmlKHJ7EhDTAghhBB5Rpph2ZOhSSGEEEIILZEeMSGEEELkGbnWZPakISaEEEKIPCPLV2RPGmJCCCGEyDOyfEX2ZI6YEEIIIYSWSI+YEEIIIfKMzBHLnjTEhBBCCJFnZI5Y9mRoUgghhBBCS6RHTAghhBB5RibrZ08aYkIIIYTIM0q5xFG2ZGhSCCGEEEJLpEdMCCGEEHlGzprMnjTEhBBCCJFnZI5Y9t6oIda3b1/WrVuXvqGeHtbW1lSuXJnu3bvTt29fdHRyNtI5c+ZMdu3axZUrV944sDYVL16cMWPGMGbMmHfeV3BwMCVKlFD9bGlpSaVKlZgzZw5NmjR55/3nN/oN2mDQrDMKcyvSIkNJ3LmS1Ds3st5AVw+DVt3Rr/kRCnMrlI9iSDy0lZRzhzNV1avWCOM+E0n2PUvC6rlvnK3vOA/a9WiDmaUZfpdvsvjrpQTfCsl2m8ZtGtHvy74ULubEvZAIVn2/hlP7T6vV6eDxCZ8P6YqNvQ1Bt4JZNvNnfM9fU93fqHVD2vdsR9nKpbGwtmBAy8EE3AhU28fiPxdStV4VtbK/duxn1MBJb/w8AUZPHEL3Pl2wsDDnyiVfpk+cz23/wCzrly5birFThlGpSjmKFHVm9lffs3bFxrd67JzSq9sKg0YdUJhZkRZ1l8S/15IW7Jf1Brp6GDT/DL2qjVGYWaKMe0DSse2kXDqaUceoEAYte6BXoS4KYxOUD6NI3LuOVH/vXMu9eb8XnruPEPMwjlIuTkz8ogs1yrtmXX/fCTbt8+JedCyOtlYM7NKKTz6qo7p/26HT/HXiPAGh9wAoX7Ioo3q2p1Lp4rmW+WVfTh5Orz6fYWFpzuVLPkz5cg7+NwOyrF/WzZUJX42kStUKuBR1ZtqU+az8Zb1anT79PqdP/89xcXEGwP9mAD9+/zNHD5/Mk+egycUrvqz9Yxs3bgYQ/SCWJfOn0bxx/ff2+C17t6b94I5Y2lkRdvsu62at5uaFrD/7ytWpgMe0fhQp7cLDqFj+9+tODm88oFankLkJn0/oSW33upiYmxIddp/133hy5dglAIxMjOg2vie1WtXBwtaCoOtBrJu5ikCfrH+fQnveeI6Yu7s7ERERBAcHs2/fPpo2bcro0aNp164dKSkpeZExS8nJye/18fLC4cOHiYiI4MSJE5ibm9OmTRuCgoK0lic1NZW0tNz9/qJXrSGGnQaQdGgrzxeMJvXOdYwHz0RhaZflNkZ9J6FXpjIJm5fybN4Q4tf/QNr9sEz1FFZ2GHboR0rgNQ17eb3uw7rRdWAXlkxbxpC2w4mNimXBH99hbGKc5Tblq5djxs9TObj9MANaDubg9sPM/GUa5aq5qeo0bf8RI2YOZcNPfzDAfQi+5335/vf52Be2z3iOhYy4dvEav81flW3GvzbuoXO1rnSu1pVa5Zrx9bg5b/VcB4/6gv7DejNj0rd0aNGT6KgH/L7jV0xMC2W5jXEhI+4Gh/Hd7KVERUa/1eO+Cb1K9TFs+wVJx7bz/KcvSQ32w7jv1ygsbLPcxqjHeHRLVSJx+888XziShM2LSIsOz6igq4dx/xnoWNmT8McPPP9xFAk7fkEZ9yDXcu8/fYnv125nYJdWbF0wmerlSjFs7s9ERMdqrL9l/0mWbPyLod3asHPR1wzr1oZ5K7dy/IKvqs7F67dp3bAGq2eNZsO88TjZWTFk9nLuP3iUa7lfGDF6AIOH9eWrid/QutlnRN2PYcvO1dkfG8ZGhAbf5ZuZP3I/i2Pj3r1I5s78kVZNu9KqaVdOeZ3F849llHXLuoGa2+LjEyjrWpKvxg17b4/5Qr12DegzvR87l/3J5LbjuHn+BlPWTcOmsObj2c7Fnsme07h5/gaT245j1/JtfDFzALVb11PV0dXXY+qGmdgVsWfR0O8Z22w4Kyb9zMPIjON58HcjqNSoCsvHLubLlqPx8brC1I2zsHKwzvPnrIkyF/8riN64IWZoaIijoyPOzs5Ur16dr776it27d7Nv3z48PT0BiIuLY9CgQdjb22Nubk6zZs24evUqAJ6ensyaNYurV6+iUChQKBQ52g7Se9KqVq3KmjVrKFmyJIaGhiiVSm7evEnDhg0xMjKifPnyHD58GIVCwa5du1TbhoeH061bN6ysrLCxsaFDhw4EBwer7u/bty8dO3ZkwYIFODk5YWNjw/Dhw1WNvY8++oiQkBDGjh2ryg0QEhJC+/btsbKywsTEhAoVKrB3794cv542NjY4OjpSuXJlVqxYwfPnzzl48CAPHjyge/fuFClShEKFClGpUiU2bdqktu1HH33EiBEjGDFiBJaWltjY2DB16lS1M1SSkpKYOHEizs7OmJiYUKdOHY4fP66639PTE0tLS/7++2/Kly+PoaEhISHZ9wa9KYOPOpJ87hDJZw+Sdj+MxJ2rSHsUg37D1hrr67pVR8+1Is9/m0XqrasoY6NIC71NWvBN9YoKHYx6f0nSvj9QPrj/Vtk+7d+ZDT/9wcl9pwjyD2b+2O8xMjaiRcdmWW8zoAsXT17ij+WbCA28yx/LN+F9+jKf9u+sqtN1UBf2bt7Pnk37CA0IZdnMX4i6F0UHj/aqOoe2H2b94g1cOpl9r0xifAKx0Q+JjX5ITNQDnjx5+lbPtd/gniz/cRUH/j7CrZsBfDl8KsbGRnzSpU2W2/hcvs78mYv4e+d+kpKS3upx34R+o/akXDxKysUjKKPDSfp7Lcq4B+jXbaWxvm6ZquiWqEC851xSA31QPoomLSyAtFB/VR29Gs1QGJuS8Pt3pIX4p9cJuUlaZO4d5+v/OkqnZvXo0qI+JYs4MqnfpzjaWLH1gOaen7+9zvPpxw1wb1CDIo62tG5Yk07N67F21yFVnW/H9OVz98a4lShCiSKOzBjSgzSlknO+/hr3+S4GDvVgycIV7P3rEDf9bjNq6GSMCxnR+dN2WW5z5fI1Zk9fwO4de7M8Ng7tP86RQ17cCQzmTmAw336zhGfPnlO9VhWN9fNCo3q1GDWoDx9/1OC9PeYLbQd04OiWwxzdfJjwgDDWzV7Ng4gYWvZy11j/457uPLgXzbrZqwkPCOPo5sMc23qE9oM6qOo0/aw5JpZmLBg4H/+LN4kJj8b/oh8hfsEA6BsaUKd1PTbOX4ff+RvcD4lk2+LNRN2NomVvzY+b19JQ5tqtIMqVsyabNWtGlSpV2LFjB0qlkrZt2xIZGcnevXu5dOkS1atXp3nz5sTGxtKtWzfGjx9PhQoViIiIICIigm7dur12uxcCAgLYunUr27dv58qVK6SlpdGxY0cKFSrEuXPn+O233/j666/V8j1//pymTZtiamqKl5cXp06dwtTUFHd3d7UPkGPHjhEYGMixY8dYt24dnp6eqkbijh07KFKkCLNnz1blBhg+fDiJiYl4eXnh6+vLd999h6mp6Vu9joUKpX/7TE5OJiEhgRo1avD3339z7do1Bg0aRO/evTl37pzaNuvWrUNPT49z586xdOlSFi1axKpVGT0sX3zxBadPn2bz5s34+PjQtWtX3N3duX37ttrrM3/+fFatWsX169ext7cn1+jqoVPEldSbl9WKU29eRrd4OY2b6FWsQ2poAAbNumAy0xOTr37F8JN+oG+gVs+g1econ8aRfO6Qxv28jlNRJ2wcbLhw4pKqLDkpmStnfahQs0KW21WoUV5tG4Dzxy+qttHT16NspTJc8LqoVueC1yUq1Cz/xjlbdGrObp/trD2yiq9mjcu2lyIrLsWcsXe04+Sxf1RlSUnJnDtziRq1398fxWzp6qFTuBQpt6+oFafcvopu0bIaN9ErV4vU8EAMGnek0OTfKDT+Jwxae4BexrGiV74WqaH+GHYYSKGvVmM8ehH6H3UGRe6cNJ6cnIJf4F3qV1U/nutVKccVf82920nJKRga6KuVGRno4xsQQnJKqsZtEpKSSElNxeItfv/ZKVqsCA6Odhw/ljG0npSUzD+nL1CrTrVcexwdHR06dG5DoUKFuHT+Sq7tN7/S1dejZKVS+Jy8olZ+1esKZWq4adymTPWyXPV6tf5lSlZyRVdPF4CaH9fmtvdN+s0ZzIqLniw4uISOwz9F8e/UIF09HXT1dElOVB8xSkpMpOxbfP7kBqVSmWu3gijXJuu7ubnh4+PDsWPH8PX1JSoqCkNDQwAWLFjArl272LZtG4MGDcLU1BQ9PT0cHR1V2x89evS120F6D8/vv/+OnV36sNb+/fsJDAzk+PHjqv3NnTuXjz/+WLXvzZs3o6Ojw6pVq1Q9WWvXrsXS0pLjx4/TsmVLAKysrFi2bBm6urq4ubnRtm1bjhw5wsCBA7G2tkZXVxczMzO13KGhoXTp0oVKlSoBULJkybd6/Z49e8aUKVPQ1dWlSZMmODs78+WXX6ruHzlyJPv37+fPP/+kTp2MeSQuLi4sWrQIhUJB2bJl8fX1ZdGiRQwcOJDAwEA2bdpEWFgYhQsXBuDLL79k//79rF27lnnz5gHpDb+ff/6ZKlWy/4OcmJhIYmKiWllSSiqG/35AaKIwMUehq0vak0dq5conj9Axt9S4jY6NA7oly0NKMvFr5qIwMceo61AUJqYkbFoKgG6JcujX/ZjnP4zONnN2rO2sAHgY81Ct/GHMQxycHbLdTtM2L/ZnYW2Brp4uD6NfqRP9EGu7NxsaOLTzCJGhkcRGx1KibHG+mPQF5SqWoXeXIW+0Hzv79KGQmGj14biYqAc4uxR+o33lFUUhMxS6uiifxqmVK58+QmFmqXkbawd0i7lBShIJG75HYWKOYYeBKAqZkrj9ZwB0rBxQlKxIypWTJHjORcfWCcMOA0FHl+Sjf75z7odPnpKaloaNhZlauY2lGTGPHmvcpn7Vcuw4fIZmtStTrqQLNwJD2Xn0LCkpqTx68hQ7K4tM2yzesBt7awvqVtb8R/xt2TukHxvRUTFq5dFRDyiSC8eGW/nS7Dm4CUMjQ549e06/XiO5lc28xILC3MoMXT1d4mIeqZXHxcRh+e9nxass7CyJi4l7pf4j9PT1MLM251HUQ+xdHKhQrxKndnvxbd85OJVwot+cQejq6rB96VYSniXgf+kmnUd+RvjtuzyKiaNBh0a4Vi1DZFBEXj1d8Q5yrSGmVCpRKBRcunSJp0+fYmNjo3Z/fHw8gYFZv/lyul2xYsVUjTAAf39/XFxc1BpHtWvXzrTvgIAAzMzUPygTEhLU9l2hQgV0dTMaFU5OTvj6+pKdUaNGMXToUA4ePEiLFi3o0qULlStXznabl9WvXx8dHR2eP3+Ok5MTnp6eVKpUidTUVL799lu2bNlCeHi4qhFkYmKitn3dunVVjUuAevXqsXDhQlJTU/H29kapVFKmTBm1bRITE9VeZwMDgxxlnj9/PrNmzVIrm1ynNF/V1dxboe6VbzIKBVl+uVHogFJJ/O8LIOF5euZdqzHqOxm2/Qo6uhj1Gk/ClmUon2n+Q6eJXo0mGH02HIB9c2Fyn/Se01e/ZaW/ntl/88q8TeZNNO73Db/R7fkjY5g7yD+YCzev8dfRzVSo7MZ1n5tZbtfh0zbMXThN9XP/7iOyzJT/vmW+mkeR9a/j3xc+YfMSSPz3WNnjiVGPL0ncvQpSkkBHgfJZHIk7fwVlGmn37qAwt0a/UYdcaYipRXn5WSiVKFBorDv4U3cePHpMrykLUCrTG20dmtZh7a7DGk96WrPrEPtOXWLNrNGZetLeVOeu7fhh0UzVz726Df0376vPJ3eOjcDbwTRv1BkLCzPaftKSpb/Mp1Nbj/9EYww0va6vWeBU4+dRxjYKHQWPH8Tx2+SfUaalEXQtECsHa9oP7sj2pVsBWD5mMUN+GMGvF9aSmpJK0LVATu/2okTFUrn3xN6AnDWZvVxriPn5+VGiRAnS0tJwcnJSm4f0gqWlZZbb53S7VxsiLxqA2UlLS6NGjRps3Jj5bK+XG3X6+uofcAqF4rUT1wcMGECrVq3Ys2cPBw8eZP78+SxcuJCRI0dmu90LW7ZsoXz58qo5Xi8sXLiQRYsWsXjxYipVqoSJiQljxox5o3k6aWlp6OrqcunSJbUGJqA2fGpsbPza1xBgypQpjBs3Tq0s6avPs91G+ewxytRUdMys1N6MClMLlK/0kqm2eRybPpH630YYQNr9uyh0dFBY2KAwNELHxgHjARkNjRd/BU0X7uLZvCEoH0Rm2m/KtfM8C7kFwOD9Cej/+wfN2s6a2KiM4W9LG0tiX+nNelmshp4tSxsrYv/tJYuLjSM1JRVr+1fq2Fqq6ryta1f9SEpKpnjJYtk2xA7vP86VSxlfIgwM0ofq7Oxtib6f0fNhY2edqZdMW5TPn6BMTUVhaqlWrjC1QPn0keZtnjxE+ThW1QgDSIsKUx0rygcRKB8/RJmWCso0tTo65lagqwep73aSkZWZKbo6OsQ8eqJWHhv3FBtLM43bGBkaMHt4L6YN7s6DuMfYWVqw7dBpTIyNsDJT/4zz3H2Y1dsP8tuMEZQp7vxOWQEO7DuK90Uf1c+GhunHhr2DLVH3Mybd2+bSsZGcnExwUCgAV69cp2r1SgwY0puJY2e+877zs8cPn5CakoqlnaVaubmNRaZeshfioh9hoaF+SnIKTx+mH1+Poh6SmpKK8qW/TeEBYVjZW6Orr0dqcgr3QyOZ1W0qhsaGGJsV4lHUQ0Yv+5Kou283l/ZdFdRJ9rklVxpiL4YVx44dS5EiRYiMjERPT4/ixYtrrG9gYEBqqvo8iOrVq792O03c3NwIDQ3l/v37ODikDydduHAh0763bNmiOgngbWnKDenDg0OGDGHIkCFMmTKFlStX5rgh5uLiQqlSmb+lnDx5kg4dOtCrVy8gvVF1+/ZtypVTn4dy9uzZTD+XLl0aXV1dqlWrRmpqKlFRUTRq1CinTzNLhoaGqmHjF55kMywJQGoKaWEB6JatRopvRlbdslVJuXZO8yZBfuhVbQgGRpCUAICOnTPKtFSUcQ9QKpU8+3a42jYGbXujMDQmccdvKB/FaNotJMajTIwHIDw4/f8P7j+gZuPqBFxPP61bT1+PqnUrs2Leyiyf0vVLN6jZuDrbVm1XldVqUoPrF68DkJKcgr/vLWo2qqG2pEXNRjU4ffBMlvvNiTJurhgY6BN9P/szGJ89fc6zp8/VyqIio2n0UV1u+KY34PT19ahTvwbfzlryTplyTWoKafcC0StdhdQb51XFeq6VSfG7oHmTYH/0KtZXP1ZsC6uOFYDUkJvoVW30oitCVSftcew7N8Ig/XUsV8qFf67epHmdjOH9sz43aVqrUvbb6uniaJM+TLX/9CUa16ig1iO2dtdhVm7fzy/ThlPBtdg7Z4UXx0aoWtn9yGiafFSfaz5+/z4nfeo1qMU3MxbmymO+TKHIaPwVZKnJKdzxDaRyo6pcOJDxWVe5UVUuHtT82XfL258aLWqplVVuVJU7vgGk/jt30P/iTRp0aKzWY+lUojCx92NJTVY/nhPjE0mMT8TE3IQqjauxcf663HyKIpe88WzVxMREIiMjCQ8Px9vbm3nz5tGhQwfatWuHh4cHLVq0oF69enTs2JEDBw4QHBzMmTNnmDp1Khcvpk9eLl68OEFBQVy5coWYmBgSExNztJ0mH3/8MaVKlaJPnz74+Phw+vRp1WT9F708PXv2xNbWlg4dOnDy5EmCgoI4ceIEo0ePJiws85IIWSlevDheXl6Eh4cTE5P+x37MmDEcOHCAoKAgvL29OXr0aKbG0ttwdXXl0KFDnDlzBj8/PwYPHkxkZOZenrt37zJu3Dj8/f3ZtGkTP/30E6NHp8+bKlOmDD179sTDw4MdO3YQFBTEhQsX+O67797ozM53lXR8F/p1P0avTgt0HIpg2HEAOlZ2JJ/eB4BBOw+Meo5V1U++dALls8cY9RiNjoMLuiUrYPjJFySfOwzJSZCSTFpkqNqN+GcoE+PT//0Gf1y3rd5BrxE9aOjegBJlizN50UQS4hM4vCtjDaopiycxcHJ/1c/bV++gVuOadB/WjaKlXOg+rBs1GlZn2+odqjp//radtt1b07qbO0VdizJ8xlAcnO353+9/qeqYWZrhWr4Uxcqk/4F1KeWCa/lSqrlmhYs54TGmF2Url8GxiAN1mtVm+dofuHbVj4vnrrzZLwFYs2Ijw8b2p2XbZpRxc+WHZXOIj0/gf9szjoWFP3/DhGmjVD/r6+tRrmJZylUsi76BPo5O9pSrWJZiJVze+PFzIvnkX+jVbJ5+pqOdMwZt+6KwtCX53EEADFr1xLBrxpeclKsnUT5/gtGnw1HYF0GneHkM23iQcvFo+rAkkHzuAIpCZhi064fC1gndstXR/6gzyf/sz7XcHu2bsePIGXYe+Yc7YZF8v3Y7ETGxdG2Z/gVoyYbdfLU0Y42t4Hv3+fvEeULuReF7O5iJP64hIPQeo3p+oqqzZtchlm36m1nDeuJsZ0PMw8fEPHzM8/jETI//rlb+sp5R4wfRul0L3MqVZsnP84h/nsCObX+r6vz067d8NT3jfaqvr0+FSm5UqOSGvr4+Tk72VKjkRvESRVV1pkwbQ516NXApWhi38qWZPHU09RvWZvvWv3lfnj+P5+atQG7eSh8KDb93n5u3AomIjMrzx96zajfNurXgo8+a4+xaBI9p/bAtbMuhf9cF6z6xF8N/zJjnemjjfmyd7eg97QucXYvw0WfNadatBX/9tjujzob9mFmZ0XfmAJxKFKZasxp0HP4pB9dnvI+rNK5KlSbVsHOxp1LDKkzf/A337oRz/M8jef6cNdHmWZM///wzJUqUwMjIiBo1anDyZPZr2J04cYIaNWpgZGREyZIl+fXXX9/2aefYG/eI7d+/HycnJ/T09LCysqJKlSosXbqUPn36qL7J7d27l6+//pp+/foRHR2No6MjjRs3VvVYdenShR07dtC0aVMePXrE2rVr6du372u300RXV5ddu3YxYMAAatWqRcmSJfnhhx9o3749RkZGQPrZiF5eXkyaNInOnTvz5MkTnJ2dad68+Rv1kM2ePZvBgwdTqlQpEhMTUSqVpKamMnz4cMLCwjA3N8fd3Z1Fixa96cuaybRp0wgKCqJVq1YUKlSIQYMG0bFjR+Li1Cdyenh4EB8fT+3atdHV1WXkyJGqExsg/aSEb775hvHjxxMeHo6NjQ316tWjTZuslyzIbSmXT5FYyBzDVp+jMLcmLSKE+BWzUD5M79XRMbdGYfXSmmJJCcT/Mh3DLoMoNH4RymePSblyisS9G3I926aft2BoZMjYuaMwszDjxhU/JvScTPyzeFUdB2d7tWGA65duMHv4N/Sf8AX9vuzLvZB7zBr2DX6XM4YKj/11HHMrc/qM6YW1vTVB/sFM8viK++EZH/4NPq7H5EUTVT/P+GUqAJ4/rsfzx/UkJ6VQvWE1uvTvjHEhI6Ijojl44DhLvv/1rdZ6W7F0LUZGhsz5/issLNMXdPXoMlSt56yws6Pavu0d7dl7Yqvq50Ej+zJoZF/OnrpA9w4D3jjD66T4ngETMwyad01f0PV+KPGe81A+Sj9WFGZW6Fi+tAZTUgLxa2Zj2L4/hYZ/j/L5E1J8z5B0MGOpF2Xcg/Q6bb9Af9SPKB/HknxmD8knduVabvcGNXj05Bkr/txH9MPHuBZ1YvlXwyj87/B09MPHRMZkDH+npSlZ/9dRgsPvo6enS60KZVg/bzzO9hnTE7buP0lySgrjF6xWe6whn7VmWLe2uZYdYNmSVRgZG/LtgumqBV0/7zxA7dhwLuKkdmw4Otlx5ORO1c/DRvVn2Kj+nDl1ns7t+gDpQ+HLVnyHvYMdTx4/4cb1W3TvMgiv4+/WM/wmrt28Tb+RGQsgf//TbwB0aN2CuVPH5+lj//P3acyszOkyqhtW9lbcvRXKt33nEBOefjxb2ltjUzjjsy/6bhTf9p1Dn+n9aNW7DQ+jYlk7cxXn92Wc7fwgIoa5vWfSZ1o/vt+/mNj7sexb+ze7f8n4ImhsZkL3Sb2xcbThadwTzu37h80/bFT1qr1v2pqHumXLFsaMGcPPP/9MgwYNWLFiBa1bt+bGjRsULVo0U/2goCDatGnDwIED2bBhA6dPn2bYsGHY2dnRpUuXPMupUOa/mbrv7PTp0zRs2JCAgACNw34FxUcffUTVqlVZvHix1jI8GdP+9ZXyofbb4l9fKR8Kic/7RVXzgu+g97eAZ27T7/lmZ6jmF8Uajnp9pXzobsAebUd4K71qjHt9pXxoS8iuPH+M5kVa5tq+joQdzHHdOnXqUL16dX755RdVWbly5ejYsSPz58/PVH/SpEn873//w88v40oeQ4YM4erVq/zzzz+Z6ueWAnGtyZ07d2Jqakrp0qUJCAhg9OjRNGjQoEA3woQQQogPQW4uxKppGSVN85eTkpK4dOkSkydPVitv2bIlZ85o7pH9559/VMtZvdCqVStWr15NcnJyphP6ckvurGioZU+ePGHYsGG4ubnRt29fatWqxe7du1+/YR4aMmQIpqamGm9DhnyY37CFEEKIN5WblziaP38+FhYWajdNvVsxMTGkpqZmmtrk4OCgcb41QGRkpMb6KSkpqnnheaFA9Ih5eHjg4eGh7RhqZs+erbYg68ve5czNl2la6kMIIYQoqDQto/Rqb9jLXl2a6XVLXmmqr6k8NxWIhlh+ZG9vn7uXChJCCCE+QGm5OBVd0zCkJra2tujq6mbq/YqKisryBEBHR0eN9fX09DItNp+bCsTQpBBCCCHyJ2Uu3nLKwMCAGjVqcOiQ+rWIDx06RP369TVuU69evUz1Dx48SM2aNfNsfhhIQ0wIIYQQeUhb64iNGzeOVatWsWbNGvz8/Bg7diyhoaGqedpTpkxRm9Y0ZMgQQkJCGDduHH5+fqxZs4bVq1dnOc0ot8jQpBBCCCEKnG7duvHgwQNmz55NREQEFStWZO/evRQrlr6IdkREBKGhGVeZKFGiBHv37mXs2LEsX76cwoULs3Tp0jxdQwykISaEEEKIPJSby1e8qWHDhjFs2DCN93l6emYqa9KkCd7e3nmcSp00xIQQQgiRZwrguvG5SuaICSGEEEJoifSICSGEECLPaHNo8kMgDTEhhBBC5BmlNMSyJUOTQgghhBBaIj1iQgghhMgzMlk/e9IQE0IIIUSekTli2ZOhSSGEEEIILZEeMSGEEELkGRmazJ5CKa+QeAedi32i7QhvJVWZpu0Ib6WV0lLbEd5KXZ5oO8JbO6Bjpu0Ib6VZ8nNtR3grP+p/mO/NDZd+1HaEt6JvWzLPH6OKo+aLbL+Nq5Fncm1f+YX0iAkhhBAiz8jyFdmTOWJCCCGEEFoiPWJCCCGEyDNpMgMqW9IQE0IIIUSekaHJ7MnQpBBCCCGElkiPmBBCCCHyjAxNZk8aYkIIIYTIMzI0mT0ZmhRCCCGE0BLpERNCCCFEnpGhyexJQ0wIIYQQeUaGJrMnQ5NCCCGEEFoiPWJCCCGEyDMyNJk9aYgJIYQQIs/I0GT2pCEmhBBCiDyjVKZpO0K+JnPEhBBCCCG0RHrEhBBCCJFn0mRoMlvSI/YOZs6cSdWqVVU/9+3bl44dO2otjxBCCJHfKJXKXLsVRP/JHrG+ffuybt06APT09LC2tqZy5cp0796dvn37oqPzdu3TJUuWFNgD5U25925Nh8GdsbKz4u7tUNbMWoXfhRtZ1i9fpwJfTOuPS+mixEbFsuvXHRzcuF91f9NPmzFy4ZhM23Ur04XkxGTVz9YO1vSe0pfqH1XHwMiQe3fCWT7xJ+5cC8xR7ta929BpcGes7K0JvR3K6lkruXH+epb1K9SpSL/pAyj6b+6dv25n/4Z9Gus2at+YL5dP5OyBf5g/cK6q/POxPeg+toda3YdRD+lbs3eOMmeZzaMF1Qa3oZC9JbG3wjk9awMR5/011i3pXpMKvZtjW6EYugb6xN4K48KiHdw94auxvusndWm5fAR3Dlxk/4DF75TzVbYerXEY3Al9eysSboVyd9Zqnp3P+th5waSmG2X+nEe8fwg33ceqyo3KuOA0vgeFKpXC0MWBuzNXEb36r1zNDFC9dwvqDG6DqZ0l0bfDOTxrA2EXNL/eZdxrUr1XcxzKp7/eMbfDOLloB0FeGa+3bWlnGo3vgmPFEli62HF41u9cWHMg13Pb93HHaWgHDOytiL91l5Dpa3hy3k9jXdPabhT92gOjUs7oGhuQGB5N1O8HiVz5t6qOQk+XwiM7Y9u1KQaO1sQH3uPu3N+JO375nXK27N2a9oM7YmlnRdjtu6ybtZqb2XymlKtTAY9p/ShS2oWHUbH879edHN6o/voVMjfh8wk9qe1eFxNzU6LD7rP+G0+uHLsEgJGJEd3G96RWqzpY2FoQdD2IdTNXEegT8E7PJScuXvFl7R/buHEzgOgHsSyZP43mjevn+eOK9+8/2yPm7u5OREQEwcHB7Nu3j6ZNmzJ69GjatWtHSkrKW+3TwsICS0vL3A36AWrQriFfTB/A9mVbGd92DH7nbzB13QxsC9tqrG/v4sBUzxn4nb/B+LZj2LH8T/rPHEjd1vXU6j17/Ix+NT3Ubi83wkzMTZi3/TtSk1OY02cWo1oMx3PuGp49fpaj3A3bN6L/jIH8uWwrY9uM4sb560xfNxPbwnZZ5p6+biY3zl9nbJtRbFu2lQEzB1GvdeYPSztnO/pO7cf1c9c07ivEP4Q+NXqpbqNaDs9R5qy4tq9Dwxm9uPTT//iz9VQizvvTbv0ETAvbaKzvVMeNuyevsafPAv5sM5Xwf/xos2Y8thWKZapr6mxD/ak9uHfu5jtl1MSqfUOKzOhP5E9/crP1WJ6ev4Hr+unoZ3HsvKBjVojii8fw5LRP5vuMDUkKvc+9b38n+X5srmcGKNeuDi2m9+LMsv+xpu1Uws77023dBMyzeL2L1nYj6OQ1tvZdwNp2Uwk540fX1eNxeOn11jc25FFoNMe/28LTqEd5ktv6kwYUm/UF95Zux7fleB6f86PsxqkYOGt+vdOeJ3J/7V78Ok/lapNRhC/eRpFJPbDr+bGqTpFJPbDv1ZLgqavw+Wg0Ub8foMzqiRSqWOKtc9Zr14A+0/uxc9mfTG47jpvnbzBl3TRssjgu7Fzsmew5jZvnbzC57Th2Ld/GFzMHUPulzxRdfT2mbpiJXRF7Fg39nrHNhrNi0s88jHygqjP4uxFUalSF5WMX82XL0fh4XWHqxllYOVi/9XPJqfj4BMq6luSrccPy/LHyWhrKXLsVRP/ZhpihoSGOjo44OztTvXp1vvrqK3bv3s2+ffvw9PQEIC4ujkGDBmFvb4+5uTnNmjXj6tWrWe7z1aHJtLQ0vvvuO1xdXTE0NKRo0aLMnZvREzJp0iTKlClDoUKFKFmyJNOmTSM5OaNhcfXqVZo2bYqZmRnm5ubUqFGDixcvAhASEkL79u2xsrLCxMSEChUqsHfvXtW2N27coE2bNpiamuLg4EDv3r2JiYlR3f/RRx8xatQoJk6ciLW1NY6OjsycOfMdX9V07Qd04MiWwxzefIjwgDDWzF7Fg4gYWvVqo7F+q57uxNyLZs3sVYQHhHF48yGObj1Mh0Gd1CsqlTyKfqR2e1mnoV2IiYhh2YSlBFy9TXRYFL6nfbgfGpmj3B0GdOTwlkMc2nyQsIAwVs9aScy9GFr31pzbvVdrosOjWT1rJWEBYRzafJAjWw/TcVBntXo6OjqMW/olm37cSGQWWVJTUtWe1+PYxznKnJUqA1vjt+U4fpuP8zDgHqdnbeDpvQdU7N1cY/3TszZw5dc9RF29Q1zwfc59t5W4oEiKt6imVk+ho+DjpcO4sHA7j0Oj3imjJvYDO/Bgy2EebD5EQkAYYbNWk3wvBrverbPdrui3w4jd5cWzS5kbh8+vBhA+15OH/ztJWlKyhq3fXe0Brbm65ThXNx/nQcA9Ds/ewOOIB1Trpfn1Pjx7A+dW7CHC5w4Pg+9z4oetxAZH4to84/WO8LnDsXmb8PvrLCmJeZPbaVB7ojcdIfqPwyQEhBM6Yw1J9x7g4NFKY/3n14J4sOsU8bfukhQWzYMdXsQdv4J5nXKqOrZdmnDvp+3EHfUmMfQ+UesP8OjEFZwGf/LWOdsO6MDRLYc5uvkw4QFhrJu9mgcRMbTs5a6x/sc93XlwL5p1s1cTHhDG0c2HObb1CO0HdVDVafpZc0wszVgwcD7+F28SEx6N/0U/QvyCAdA3NKBO63psnL8Ov/M3uB8SybbFm4m6G0XL3pofNzc1qleLUYP68PFHDfL8sfKaDE1m7z/bENOkWbNmVKlShR07dqBUKmnbti2RkZHs3buXS5cuUb16dZo3b05sbM6+VU+ZMoXvvvuOadOmcePGDf744w8cHBxU95uZmeHp6cmNGzdYsmQJK1euZNGiRar7e/bsSZEiRbhw4QKXLl1i8uTJ6OvrAzB8+HASExPx8vLC19eX7777DlNTUwAiIiJo0qQJVatW5eLFi+zfv5/79+/z2WefqeVbt24dJiYmnDt3ju+//57Zs2dz6NChd3oN9fT1KFXJlasn1Ychrnhdxq2Gm8ZtylR344pX5vqlKrmiq6erKjMyMWbF6VWsPLuGr9ZMo0SFkmrb1Pq4NoE+AXz58yTWXlrPgr2LafF5yzfKnSnHyaxzu1V348orz/PyCW9cK6vn7jbmcx4/eMzhLVm/toVLFGbthXX8dmoVXy6biENRhyzrvo6Ovi52lUpw10u99+2u1zUcapbO2U4UCvRNjUh4pN6bWHNMJ+Jjn+C35cRb58vyIfX1KFSpFI+9rqiVP/a6gklNzb8DAOvPmmNYzJGIRZtzPVNO6Ojr4lipBEEn1V/vIK9rFKmR89fbwMSIhLic9d7mBoW+HiaVSxF3Qv3LZdyJK5hm83q/rFDFEpjWLMvjsxlDhAoDfdJeaTimJSRhVrvcq5vniK6+HiUrlcLn5BW18qteVyiT5WdKWa56vVr/MiVf+kyp+XFtbnvfpN+cway46MmCg0voOPxTFP9OTdHV00FXT1et1x0gKTGRsjXLv9VzEUKT/+Qcsey4ubnh4+PDsWPH8PX1JSoqCkNDQwAWLFjArl272LZtG4MGDcp2P0+ePGHJkiUsW7aMPn36AFCqVCkaNmyoqjN16lTVv4sXL8748ePZsmULEydOBCA0NJQJEybg5pb+YVO6dMaHemhoKF26dKFSpUoAlCyZ0Sj55ZdfqF69OvPmzVOVrVmzBhcXF27dukWZMmUAqFy5MjNmzFDte9myZRw5coSPP84YZnhZYmIiiYmJamWpylR0FRmNDjMrc3T1dHkU80itXlxMHJZ2lhr3a2VnyZWYOLWyRzGP0NPXw9zanIdRDwkPDOenL5cQejMYY9NCtOvXnnnbv2Oc+ygigiMAcHBxpFWv1vy1ajfbl/9J6Sql6T9rIClJyRzfcUzjY79gbv0i90P1HNEPsbKrrnEbSzsrHkW/Uj/moVput5rlaNGtJWPcR2X52Lcu+7N47I/cuxOOpZ0lXUd+znc7FjCyxTCePHqSbW5NjKzN0NHTJT5a/TV9HhOHSxa/g1dVHdQG/UKGBP59TlXmWLM05T7/iK2tvnrjTDmhZ22OQk+XlFd6OpNjHmFuZ6VxG8PiTjhP9uBWlymQqp21igpZpb/ez145hp/FxGGSw9e7zqA2GBQyxO+l1zuv6VmbodDTJfmV92pydBz69pbZblvt4kr0bMxR6OkQtnAr0X8cVt0Xd+IyjoPa8/jsDRKDIzFvVBmrVrVVDZw3ZW5lhq6eLnEaP1M0HxcWdpbEvfL7iPv3M8XM2pxHUQ+xd3GgQr1KnNrtxbd95+BUwol+cwahq6vD9qVbSXiWgP+lm3Qe+Rnht+/yKCaOBh0a4Vq1DJFBEW/1XP6rZGX97ElD7BVKpRKFQsGlS5d4+vQpNjbqczzi4+MJDHz9xG8/Pz8SExNp3lzz0ATAtm3bWLx4MQEBATx9+pSUlBTMzc1V948bN44BAwbw+++/06JFC7p27UqpUqUAGDVqFEOHDuXgwYO0aNGCLl26ULlyZQAuXbrEsWPHVD1kLwsMDFRriL3MycmJqKish5vmz5/PrFmz1MrczMtQzrJsprqZupAVkN178dX6CoVCrfzWZX9uXc6Y+Hzzoh8L9iyiTd92rJ65Mn0bHQWBvgFs/OF3AIKu38GlTFFa9W792oZYRo5XYisU2XaHZ7rnpdzGJsaMWzye5ZN+4snDrIcavY9fUv07xD+Em5dusuLkKpp+2pz/rdqVo9was2V6Tcn+l/Av1w71qDWuE/v6LyL+QXpufRMjWiwZyvGJq0h4+PStM+WIpmNBU24dHYovG0/Ej5tIDLqXt5ly4i1f7/Kf1KPhmE5sH7CI5w/ebUj6rWh4r74u941OX6NjYoRp9TK4fNWbxOAIHuw6BUDItDWUWDCUKl5LQQkJIZHEbDmKbbdmuRtToeFzJpsNXv1MUegoePwgjt8m/4wyLY2ga4FYOVjTfnBHti/dCsDyMYsZ8sMIfr2wltSUVIKuBXJ6txclKpZ6p+fyXyMr62dPGmKv8PPzo0SJEqSlpeHk5MTx48cz1cnJhHxjY+Ns7z979iyff/45s2bNolWrVlhYWLB582YWLlyoqjNz5kx69OjBnj172LdvHzNmzGDz5s106tSJAQMG0KpVK/bs2cPBgweZP38+CxcuZOTIkaSlpdG+fXu+++67TI/r5OSk+veLYc4XFAoFaWlZ9ypMmTKFcePGqZX1rthd7ecnDx+TmpKK1SvfVC1sLDJ9o33hYfSjTL1lFjYWpCSn8OSh5h4hpVJJgM9tnEoUVpU9inpI2O27avXCAsKoq2Hy/Ksex2aR29YyU++e6vGiH2aqb2ljqcpdtExRHIo6MnXNdNX9Cp30PwY77uxmWNPBRIZknjOWGJ9IiH8whV96bm8iIfYJaSmpFHqlV8PYxoLnr/QSvMq1fR2a/jCAg0N+IuxUxtmi5sXsMS9qT5u14zM9lyFB6/jjowk8Dnm3OWMpsY9RpqSiZ6/+murZWGTqtQHQNTXGpEppClUoicucf3uodRQodHSoFrSD2z1n8PSM5rM+c9Pzh+mv96u9X4VsLDL1kr2qXLs6tPl+ADuH/UTw6azPzs0LKbFPUKakov/KMaxva0FydPa5E++m/67jb4aib2eJ8/huqoZYSuxjbvf7DoWhPnpWZiRHxuLydW8SQ++/Vc7HD5+QmpKa6TPCPJvPlLjoR1hoqJ+SnMLTfz9THkU9JDUlFeVLn3nhAWFY2Vujq69HanIK90MjmdVtKobGhhibFeJR1ENGL/uSqLtv91z+qwrq3K7cIg2xlxw9ehRfX1/Gjh1LkSJFiIyMRE9Pj+LFi7/xvkqXLo2xsTFHjhxhwIABme4/ffo0xYoV4+uvv1aVhYSEZKpXpkwZypQpw9ixY+nevTtr166lU6f0SewuLi4MGTKEIUOGMGXKFFauXMnIkSOpXr0627dvp3jx4ujp5d6v2NDQUDVM+8LLw5IAKckpBPoGUKVRVc4dOKsqr9KoKucPnte431veN6nZopZaWZVG1Qj0DSA1JTXLPCXKlyTEP+M187vkR+GSzmp1CpcoTHT46xsIL+c+e+AfVXnVRlU5d1DzcNFN75vUblFbraxq42oE+KTnDgsMY2QL9bMfe07ohbFpIVbN+I2YezFoomegRxFXl2yXzchOWnIq0b5BuDSqSND+i6ryIo0qEnzwUpbbuXaoR7MFAzk0YjkhR6+o3fcoMILNLSarldWe8CkGJsacmvk7T+894F0pk1N47huIeaMqxO3POHbMGlUlTsPvIPXJc260GKlWZufRGrP6lbkz5DuS3vIP/5tKS04l0jeIEo0qcutAxutdolFFbmXzepf/pB5tfhjI7pHLCXzl9X4flMkpPPMJxKJxFR7uz3h9LRpX4eEBze9VjRQKdAz0MxUrE5NJjoxFoaeLdZu6PPjrzFvlTE1O4Y5vIJUbVeXCgYyclRtV5WIW781b3v7UeOUzpXKjqtx56TPF/+JNGnRorNbr7VSiMLH3Y0lNVj9zPjE+kcT4REzMTajSuBob5697q+cihCb/2YZYYmIikZGRpKamcv/+ffbv38/8+fNp164dHh4e6OjoUK9ePTp27Mh3331H2bJluXfvHnv37qVjx47UrFkz2/0bGRkxadIkJk6ciIGBAQ0aNCA6Oprr16/Tv39/XF1dCQ0NZfPmzdSqVYs9e/awc+dO1fbx8fFMmDCBTz/9lBIlShAWFsaFCxfo0qULAGPGjKF169aUKVOGhw8fcvToUcqVS58MO3z4cFauXEn37t2ZMGECtra2BAQEsHnzZlauXImurq7GzLnlr1W7GbVoLAE+Afh736Rl91bYFrbj4Mb09bV6TvTAxtGapeMWA3Bg435a92lL32n9OLTpIGWru9G8WwsWjVqg2udnoz/n1mV/IoLuYWxWiLZftKd4+RL8Nu1XVZ2/V+1m3o7v6TK8K6f/PkXpqqX5uEcrfp2yPEe5d6/axZhF4/7N7UerHu7YFrZj/4b0s1F7T+qDjaMNi8f+CMD+Dfto26cd/aYN4OCm/ZStXo4W3T5m4cgfAEhOTCb0lnrj+sVSGi+X9/26HxcOnyf6XjSWNhZ0HfU5hUwLcXTbkTd52dVcXbmP5ouHEuVzh/uXAijfsylmzjZc25C+z7qTPsPE0YojY1cA6Y2w5osGc2rmBiK9AzC2swAgNSGJpCfxpCYmE+sfpvYYSY+fA2QqfxdRK3dTbPEYnvsE8OySPzY9W2HgbEvMhvQ15QpP6o2+ow0hYxeDUkmCf6ja9ikxcaQlJqmVK/T1MCrtkv5vA30MHG0wLl+CtOfxJAbn7Iza1zm/ah/tFw0lwucO4d4BVO3eFPPCNlzemP56N5n4GWaOVvw9Lv31Lv9JPdr9OJjDszZw73IAJv++3ikJSSQ+iQfSTwKwLZ3+xULXQA9TR2vsyxcl+VkiD0Nyp5EZ8dtflFo6imc+ATy56I99r5YYONtyf/1BAFym9ETf0YY7o5cC4NDXncTwGOIDwgEwq10OpyGfcH9NxhnbJtVKY+BozfPrwRg4WuM8vhvoKIj4eWfmADm0Z9VuRiwaQ6BPALe9/WnevSW2hW059O+6YN0n9sLa0Ybl45YAcGjjflr1aUPvaV9wdNMhSlcvS7NuLVgy6kfVPg9t2I9737b0nTmA/Z57cCzhRMfhn7LfM2NNtCqNq4JCwb074TgWc6LXV325dyec43++/Xszp54/jyc0LGPIPfzefW7eCsTC3AwnR/s8f/zcVFCXncgt/9mG2P79+3FyckJPTw8rKyuqVKnC0qVL6dOnj2pB17179/L111/Tr18/oqOjcXR0pHHjxmpnPmZn2rRp6OnpMX36dO7du4eTkxNDhgwBoEOHDowdO5YRI0aQmJhI27ZtmTZtmmoJCV1dXR48eICHhwf379/H1taWzp07q+ZopaamMnz4cMLCwjA3N8fd3V11xmXhwoU5ffo0kyZNolWrViQmJlKsWDHc3d3ferHaN3H671OYWZnx2ahu6Quj3gphbt/ZRIdHA2Blb6W2NlfU3ft803cW/aYPoHXvtsRGxbJ65krO7svomTIxN2Ho/OFY2lnx/Mkz7ly/w9TPphBw9baqToBPAN8NmkevSR50HdWNqLD7rJm1Cq9dOTvD79RfJzGzNKPb6M+xtrcm5FYIs/vMzDb37D4z6T99AG082hJ7/wGrZv7GP/ve7Ju/rZMtXy6bgJmVOY9jH+PvfZOJHcerHvdtBPx1DkMrM2qO7oSJvSUP/MP4u88PPA1P77kq5GCJ6UtrRVXo2QxdfT2azO1Lk7l9VeU3//Ti6Ljf3jrHm3r41yl0rcxwHN0NfXtrEvxDCOwzm6R/Xwt9B6ss17jKir6DNeUOLFb97DCkEw5DOvHkH19ufzY16w3fgN/f5zC2MqPBqE6Y2lsSfSuMrX1/4PG/r7epvSXmL615VbVH+uvd6pu+tPqmr6rc508v9nyZ/nqbOVjRf1/GCTd1B7el7uC2hPzjxx+fZyyD8y5i/3caPSsznMd+hr69FfH+ofj3mpvxettbYfjy662jg8uUXhgWtUeZkkpiyH3uzttA1O8HM6oY6uMyqQeGRR1IfZ7AoyPeBI5aQuq/Dfe38c/fpzGzMqfLqG5Y2Vtx91Yo3/adQ8y/OS3trbF56b0ZfTeKb/vOoc/0frTq3YaHUbGsnbmK8y99pjyIiGFu75n0mdaP7/cvJvZ+LPvW/s3uX3ao6hibmdB9Um9sHG14GveEc/v+YfMPG7Ptqc8t127ept/ISaqfv/8p/bjo0LoFc6eOz2qzfEmGJrOnUMorJN5B52JvvzaQNqUqtXOG3btqpbTUdoS3Upc3P/szvzigY6btCG+lWfLbN3y06Uf9D/O9ueHSj6+vlA/p25Z8faV3ZGteJtf2FfP4Vq7tK7/4z/aICSGEECLvyfIV2ZOGmBBCCCHyjAy8ZU9W1hdCCCGE0BLpERNCCCFEnpGzJrMnDTEhhBBC5BkZmsyeDE0KIYQQQmiJ9IgJIYQQIs/IWZPZk4aYEEIIIfKMXPQ7e9IQE0IIIUSekR6x7MkcMSGEEEIILZEeMSGEEELkGTlrMnvSEBNCCCFEnpE5YtmToUkhhBBCCC2RhpgQQggh8oxSqcy1W155+PAhvXv3xsLCAgsLC3r37s2jR4+yrJ+cnMykSZOoVKkSJiYmFC5cGA8PD+7du/fGjy0NMSGEEELkmQ+hIdajRw+uXLnC/v372b9/P1euXKF3795Z1n/+/Dne3t5MmzYNb29vduzYwa1bt/jkk0/e+LFljpgQQggh/rP8/PzYv38/Z8+epU6dOgCsXLmSevXq4e/vT9myZTNtY2FhwaFDh9TKfvrpJ2rXrk1oaChFixbN8eNLQ0wIIYQQeSY3+7ESExNJTExUKzM0NMTQ0PCt9/nPP/9gYWGhaoQB1K1bFwsLC86cOaOxIaZJXFwcCoUCS0vLNwugFCIfSkhIUM6YMUOZkJCg7ShvRHK/fx9qdsn9fknugmHGjBlK0tt2qtuMGTPeaZ9z585Vli5dOlN56dKllfPmzcvRPuLj45U1atRQ9uzZ840fX6FUygIfIv95/PgxFhYWxMXFYW5uru04OSa5378PNbvkfr8kd8HwJj1iM2fOZNasWdnu78KFCxw8eJB169bh7++vdl/p0qXp378/kydPznYfycnJdO3aldDQUI4fP/7GvycZmhRCCCHEB+FNhiFHjBjB559/nm2d4sWL4+Pjw/379zPdFx0djYODQ7bbJycn89lnnxEUFMTRo0ffqrEsDTEhhBBCFDi2trbY2tq+tl69evWIi4vj/Pnz1K5dG4Bz584RFxdH/fr1s9zuRSPs9u3bHDt2DBsbm7fKKctXCCGEEOI/q1y5cri7uzNw4EDOnj3L2bNnGThwIO3atVObqO/m5sbOnTsBSElJ4dNPP+XixYts3LiR1NRUIiMjiYyMJCkp6Y0eXxpiIl8yNDRkxowZ73QmjDZI7vfvQ80uud8vyS2ys3HjRipVqkTLli1p2bIllStX5vfff1er4+/vT1xcHABhYWH873//IywsjKpVq+Lk5KS6nTlz5o0eWybrCyGEEEJoifSICSGEEEJoiTTEhBBCCCG0RBpiQgghhBBaIg0xIYQQQggtkYaYEEIIIYSWSENMCCGEEEJLpCEmhBDvwfHjx7Ud4T9HqVQSEhJCfHy8tqMIkSVZR0xoVefOnXNcd8eOHXmY5N3dunWL48ePExUVRVpamtp906dP11KqgsnHx4fKlStrvG/Xrl107Njx/QbKASMjI5ydnfniiy/o06cPLi4u2o70Rj7E4zstLQ0jIyOuX79O6dKltR1HCI2kISa06osvvshx3bVr1+ZhknezcuVKhg4diq2tLY6OjigUCtV9CoUCb29vLabLWmpqKp6enhw5ckTjH9ijR49qKVn2nJycOH36NCVLllQr3759Ox4eHjx79kxLybIWGxvLhg0b8PT0xMfHh+bNm9O/f386duyIgYGBtuNl60M9vgEqVKjA6tWrqVu3rrajvLEjR45k+d5cs2aNllKJ3CYNMSFyQbFixRg2bBiTJk3SdpQ3MmLECDw9PWnbti1OTk5qf2ABFi1apKVk2Zs9ezZr167lzJkzODk5AbBlyxb69euHp6cnXbt21XLC7F25coU1a9awadMm0tLS6NmzJ/3796dKlSrajqbRh3p8A+zZs4dvv/2WX375hYoVK2o7To7NmjWL2bNnU7NmTY3vzRfXPBQfPmmIiXwlJSWF48ePExgYSI8ePTAzM+PevXuYm5tjamqq7XhZMjc358qVK5l6aPI7W1tb1q9fT5s2bbQd5Y2NHj2aw4cPc/LkSfbv38+AAQP4/fff6dKli7aj5ci9e/f47bff+Pbbb9HT0yMhIYF69erx66+/UqFCBW3HU/OhHt8AVlZWPH/+nJSUFAwMDDA2Nla7PzY2VkvJsufk5MT3339P7969tR1F5DE9bQcQ4oWQkBDc3d0JDQ0lMTGRjz/+GDMzM77//nsSEhL49ddftR0xS127duXgwYMMGTJE21HeiIGBAa6urtqO8VaWLFlC7969qVu3LuHh4WzatIkOHTpoO1a2kpOT2b17N2vWrOHQoUPUrFmTZcuW0b17d2JjY5k0aRJdu3blxo0b2o6q5kM9vgEWL16s7QhvJSkpifr162s7hngPpEdM5BsdO3bEzMyM1atXY2Njw9WrVylZsiQnTpxgwIAB3L59W9sRszR//nx+/PFH2rZtS6VKldDX11e7f9SoUVpKlr2FCxdy584dli1blmnoI7/53//+l6ksOTmZsWPH0rJlSz755BNV+cv/zi9GjhzJpk2bAOjVqxcDBgzINFQWGhpK8eLFM80H0rYP9fj+kE2aNAlTU1OmTZum7Sgij0lDTOQbtra2nD59mrJly2JmZqZqiAUHB1O+fHmeP3+u7YhZKlGiRJb3KRQK7ty58x7TZO/VM1WPHj2KtbU1FSpUyPQHNj+dqaqjk7PVdhQKBampqXmc5s01b96cAQMG0KVLlywn56ekpHD69GmaNGnyntNl70M6vjUJDAxk7dq1BAYGsmTJEuzt7dm/fz8uLi75bhj4hdGjR7N+/XoqV65M5cqVM703f/zxRy0lE7lNhiZFvpGWlqbxD2hYWBhmZmZaSJRzQUFB2o6QYxYWFmo/d+rUSUtJ3kx+6yV6E8nJyRQtWpQ6depke4aknp5evmuEwYd1fL/qxIkTtG7dmgYNGuDl5cXcuXOxt7fHx8eHVatWsW3bNm1H1MjHx4eqVasCcO3aNbX78nvvtXgz0iMm8o1u3bphYWHBb7/9hpmZGT4+PtjZ2dGhQweKFi2ar5eveNmLt5R8WIqXWVpa4u3t/UFOeH8hKSmJoKAgSpUqhZ7eh/E9vl69enTt2pVx48ap9bRfuHCBjh07Eh4eru2I4j9OGmIi37h37x5NmzZFV1eX27dvU7NmTW7fvo2trS1eXl7Y29trO2K21q9fzw8//KCay1amTBkmTJiQr896io+PR6lUUqhQISD9hImdO3dSvnx5WrZsqeV06pYuXZrjuvlxztIXX3xBpUqVGDdunLajvLHnz58zcuRI1q1bB6Qv7lqyZElGjRpF4cKFmTx5spYTZs3U1BRfX19KlCiRacqDm5sbCQkJ2o6YrYCAAAIDA2ncuDHGxsYolUr5klfAfBhfacR/QuHChbly5QqbNm3C29ubtLQ0+vfvT8+ePTOdcp7f/Pjjj0ybNo0RI0bQoEEDlEolp0+fZsiQIcTExDB27FhtR9SoQ4cOdO7cmSFDhvDo0SNq166NgYEBMTEx/PjjjwwdOlTbEVVyuqaZQqHIlw0xV1dX5syZw5kzZ6hRowYmJiZq9+fHzC9MmTKFq1evcvz4cdzd3VXlLVq0YMaMGfm6IWZpaUlERESmeW6XL1/G2dlZS6le78GDB3z22WccO3YMhULB7du3KVmyJAMGDMDS0pKFCxdqO6LILUohxDsrXry4ct26dZnKPT09lcWLF9dCopyxsbFRXrt2TalUKpUrV65UVq5cWZmamqrcunWr0s3NTcvpCpbixYtneStRooS242WraNGiyn/++UepVCqVpqamysDAQKVSqVTevn1baWZmps1orzVhwgRlw4YNlREREUozMzPl7du3ladOnVKWLFlSOXPmTG3Hy1Lv3r2VrVq1Ut69e1ftNT9w4ICyfPnyWk4ncpP0iIl85UO8nh1ARESExjV/6tevT0REhBYS5czz589VJ0IcPHiQzp07o6OjQ926dQkJCdFyuoLlQ57wHh0drXFqwLNnz/L9MNncuXPp27cvzs7OKJVKypcvT2pqKj169GDq1KnajpelgwcPcuDAAYoUKaJWXrp0aXlvFjDSEBP5xuuuZ5efG2Kurq5s3bqVr776Sq18y5Yt+fpiw66uruzatYtOnTpx4MAB1RBqVFQU5ubmWk6nbty4ccyZMwcTE5PXzrPKz6f2f4gT3mvVqsWePXsYOXIkkHEiysqVK6lXr542o72Wvr4+GzduZPbs2Vy+fJm0tDSqVauWr9+XkN7IfTF382UxMTEYGhpqIZHIKx/Gp4D4T/jmm2+YO3fuB3k9u1mzZtGtWze8vLxo0KABCoWCU6dOceTIEbZu3arteFmaPn06PXr0YOzYsTRr1kz1R/XgwYNUq1ZNy+nUXb58meTkZNW/PzQf8oT3+fPn4+7uzo0bN0hJSWHJkiVcv36df/75hxMnTmg7Xo6UKlVKdcZqfu/FA2jcuDHr169nzpw5QHrmtLQ0fvjhB5o2barldCJXaXtsVIgXzMzMVPMgPkQXL15U9uzZU1m9enVltWrVlD179lR6e3trO9ZrRUREKL29vZWpqamqsnPnzin9/Py0mKrgGTVqlLJGjRrKkydPKk1MTFTH+u7du5VVq1bVcrrX8/HxUXp4eCgrVKigLFeunLJnz55KHx8fbcfKkVWrVikrVKigNDAwUBoYGCgrVKigXLlypbZjZev69etKOzs7pbu7u9LAwED56aefKsuVK6d0cHBQBgQEaDueyEWyfIXIN/r370+tWrU+yOvZfeg+lFPk+/Xr99o6CoWC1atXv4c0b6ZYsWJs2bKFunXrqi2jEBAQQPXq1Xn8+LG2IxZI06ZNY9GiRYwcOVLV4/vPP/+wbNkyRo8ezTfffKPlhFmLjIzkl19+4dKlS6SlpVG9enWGDx+Ok5OTtqOJXCQNMZFvfGjXs3v8+LFqHtXr/ojmt/lWL2R1inz//v3z5SnyOjo6FCtWjGrVqpHdR9fOnTvfY6qcKVSoENeuXaNkyZJqDbGrV6/SuHFj4uLitB0xS82aNaNJkybMmDFDrfzhw4d06dKFo0ePainZ69na2vLTTz/RvXt3tfJNmzYxcuRIYmJitJRMiHTSEBP5xod2PTtdXV0iIiKwt7dHR0dHYw/Si56l/HjtQwAPDw+ioqJYtWoV5cqVUzUODh48yNixY7l+/bq2I6oZNmwYmzdvpmjRovTr149evXphbW2t7Vg50qRJEz799FNGjhypunJEiRIlGDFiBAEBAezfv1/bEbOko6ODjY0NDRo0YOPGjao10O7fv0/hwoXz7fENYGVlxfnz5zNNzr916xa1a9fm0aNH2gn2Gvv378fU1JSGDRsCsHz5clauXEn58uVZvnw5VlZWWk4ocos0xIR4SydOnKBBgwbo6em9dsJyfrx+IICjoyMHDhygSpUqar00QUFBVKpUiadPn2o7YiaJiYns2LGDNWvWcObMGdq2bUv//v1p2bJlvhxOfeHMmTO4u7vTs2dPPD09GTx4sNqE9xo1amg7YpZ0dHS4fPkygwcP5tmzZ/z1118UL178g2iIjRw5En19/Uxn0n755ZfEx8ezfPlyLSXLXqVKlfjuu+9o06YNvr6+1KxZk/Hjx3P06FHKlSv3wVzyTbyeNMSE+A8zMzPD29ub0qVLZ7oOn7u7Ow8ePNB2xGyFhITg6enJ+vXrSU5O5saNG5iammo7VpZ8fX1ZsGCB2pyfSZMmUalSJW1Hy5aOjg6RkZFYWFjQr18/Dh48yJ9//km5cuU+iIbY+vXrcXFxoW7dugCcPXuWu3fv4uHhoTYFIj8te2Jqasq1a9coXrw4M2fO5Nq1a2zbtg1vb2/atGlDZGSktiOKXCLLV4h8IzU1FU9PT44cOaJxQdf8PA/Fx8dHY7lCocDIyIiiRYvmy7V/PvRT5BUKBQqFAqVSmel4yY8qVaqkWr7iQ/Kip9HQ0JCNGzfyzTff4O7u/kEsNXPt2jWqV68OQGBgIAB2dnbY2dlx7do1Vb381ptqYGDA8+fPATh8+DAeHh4AWFtby4kdBYw0xES+MXr0aDw9PWnbti0VK1bMdx+M2alatWq2efX19enWrRsrVqzAyMjoPSbL3g8//MBHH33ExYsXSUpKYuLEiVy/fp3Y2FhOnz6t7XgavTw0eerUKdq1a8eyZctwd3dHR0dH2/GytHfvXnR1dWnVqpVa+YEDB0hLS6N169ZaSvZ6rw6cTJ06lXLlytGnTx8tJcq5Y8eOaTvCW2nYsCHjxo2jQYMGnD9/ni1btgDpc9teXW1ffOC0sGSGEBrZ2Ngo9+zZo+0Yb2XXrl3KsmXLKletWqX08fFRXr16Vblq1SpluXLllJs3b1Zu2LBBWaRIEeX48eO1HTWTiIgI5fTp05Vt27ZVtm7dWvn1118r7927p+1YGg0dOlRpZWWlrFKlinLx4sXKmJgYbUfKsUqVKmk8vvft26esXLmyFhLlXHBwsNo6cy/4+voqPT09tZCo4AsJCVG2bdtWWblyZeWqVatU5WPGjFGOHDlSi8lEbpM5YiLfKFy4MMePH6dMmTLajvLGateuzZw5czT2dkybNo3z58+za9cuxo8frxoe0bbk5GRatmzJihUrPpjXXEdHh6JFi1KtWrVseyB37NjxHlPljLGxMX5+fhQvXlytPDg4mAoVKvDs2TPtBHtDYWFhKBQKnJ2dtR0lxy5cuMCff/5JaGgoSUlJavflx2NF/Lfk33588Z8zfvx4lixZku36UPmVr68vxYoVy1RerFgxfH19gfThy/x0AXB9fX2uXbv2QQ0Be3h40LRpUywtLbGwsMjylh9ZWFhoXIIlICBAtRxEfpWWlsbs2bOxsLCgWLFiFC1aFEtLS+bMmZPv5+Zt3ryZBg0acOPGDXbu3Kk6qePo0aP59lgB8Pb2Vn12AOzevZuOHTvy1VdfZWpMig+clnvkhFDp2LGj0sLCQlmiRAllu3btlJ06dVK75WdVq1ZV9unTR5mYmKgqS0pKUvbp00d1+ZpTp04pixcvrq2IGo0bN045adIkbcf4Txg4cKCyUqVKapenuX37trJy5crK/v37azHZ602ePFlpZ2en/Pnnn5VXr15VXrlyRbl8+XKlnZ2d8quvvtJ2vGxVqlRJuWzZMqVSqVSampoqAwMDlWlpacqBAwcqp0+fruV0WatZs6Zy27ZtSqVSqQwMDFQaGRkpu3fvrnR1dVWOHj1au+FErpKhSZFvfPHFF9nen5/XzTlz5gyffPIJOjo6VK5cGYVCgY+PD6mpqfz999/UrVuX33//ncjISCZMmKDtuCovTu13dXWlZs2amXpm8tPp/B+6uLg43N3duXjxomqydVhYGI0aNWLHjh1YWlpqN2A2ChcuzK+//sonn3yiVr57926GDRtGeHi4lpK9nomJCdevX6d48eLY2tpy7NgxKlWqhJ+fH82aNctXvdQvs7CwwNvbm1KlSvHdd99x9OhRDhw4wOnTp/n888+5e/eutiOKXCJnTYp8Iz83tF6nfv36BAcHs2HDBm7duoVSqeTTTz+lR48emJmZAdC7d28tp8xw584dihcvrnZq/61bt9TqfEhDlh8CCwsLzpw5w6FDh7h69SrGxsZUrlyZxo0bazvaa8XGxuLm5pap3M3NjdjYWC0kyjlra2uePHkCgLOzM9euXaNSpUo8evRItTxEfqR8aUmWw4cP065dOwBcXFzkskwFjPSICfEf9PLlmQC6devG0qVLcXBw0HIykR/VqVOHOnXqsHTpUrXykSNHcuHCBc6ePaulZK/Xo0cPatasybhx45g7dy5LliyhQ4cOHDp0iOrVq+fbyfrNmjXDxcWFFi1a0L9/f27cuIGrqysnTpygT58+BAcHazuiyCXSEBP5yrZt29i6davGs5u8vb21lCpnfv/9d1asWMGdO3f4559/KFasGIsWLaJkyZJ06NBB2/HUvFgp/UVDzNzcnCtXrlCyZEktJyvYTpw4wYIFC/Dz80OhUFCuXDkmTJhAo0aNtB0tWydOnKBt27YULVqUevXqoVAoOHPmDHfv3mXv3r35On9sbCwJCQkULlyYtLQ0FixYwKlTp3B1dWXatGn59pqNPj4+9OzZk9DQUMaNG6e64PrIkSN58OABf/zxh5YTitwiZ02KfGPp0qV88cUX2Nvbc/nyZWrXro2NjQ137tzJ14tdAvzyyy+MGzeO1q1b8/DhQ9UlX6ysrFi8eLF2w+WAfB/Lexs2bKBFixYUKlSIUaNGMWLECIyNjWnevHm+/6PapEkTbt26RadOnXj06BGxsbF07twZf3//fN0ICwkJYfv27ezatYvr16+jo6PDxIkT+d///sePP/6YbxthAJUrV8bX15e4uDhVIwzSF2H+EK/OILKhtdMEhHhF2bJllX/88YdSqcw4u0mpVCqnTZumHD58uDajvVa5cuWUO3fuVCqV6tl9fX2VNjY2WkymmY6OjjIqKkr1s6mpqfLOnTtaTFTwubm5KX/88cdM5QsXLlS6ublpIVHOJCUlKT/66COlv7+/tqO8kRMnTihNTEyUCoVCqVAolPr6+qrPFyHyExmaFPlGoUKF8PPzo1ixYtjb23Po0CGqVKnC7du3qVu3br6+ALWxsTE3b96kWLFiahfPvn37NpUrVyY+Pl7bEdXo6OjQunVr1fUv//rrL5o1a5bprMn8On/mQ2RoaMj169dxdXVVKw8ICKBixYokJCRoKdnr2dnZcebMGUqXLq3tKDnWpEkTzM3NWbFiBcbGxkyZMoU9e/Z8MGcbpqamsmjRoiynauT3kyREzsnQpMg3HB0dVY2tYsWKqSYABwUF5fuhsxIlSnDlypVM5fv27aNcuXLvP9Br9OnTB3t7e9UCqL169aJw4cIfxMKoHyoXFxeOHDmSqfzIkSO4uLhoIVHOeXh4sHr1am3HeCO+vr7Mnz+fwoULY2VlxcKFC7l37x4PHz7UdrQcmTVrFj/++COfffYZcXFxjBs3js6dO6Ojo8PMmTO1HU/kIlm+QuQbzZo146+//qJ69er079+fsWPHsm3bNi5evEjnzp21HS9bEyZMYPjw4SQkJKBUKjl//jybNm1i3rx5+fIP2Ie8VMiHavz48YwaNYorV65Qv359FAoFp06dwtPTkyVLlmg7XraSkpJYtWoVhw4d+mDWm3v06JHqZBRIX0+sUKFCPHr0KF/PDXth48aNrFy5krZt2zJr1iy6d+9OqVKlqFy5MmfPnmXUqFHajihyiQxNinwjLS2NtLQ09PTSvx9s3bpVdXbTkCFDMDAw0HLC7K1cuZJvvvlGNfTh7OzMrFmzaNWq1Qd1XT6Rd3bu3MnChQvx8/MDUJ01md/Oqn3hxXpzzZs3z7KOQqHg6NGj7zFVzujo6HD06FGsra1VZfXr12fr1q2qBXUhfVJ8fmRiYoKfnx9FixbFycmJPXv2UL16de7cuUO1atWIi4vTdkSRS6QhJkQui4mJIS0tjdTUVObNm8eqVavy3Rwx8X6lpKQwd+5c+vXrl++HIV/2Ia83p6Ojg0Kh0Dit4UW5QqFQneGc35QtW5b169dTp04dGjVqRNu2bZk8eTJbtmxh5MiRREVFaTuiyCUyNCnylUePHnH+/HmioqIyXUzYw8NDS6my9ujRI4YPH87BgwfR19dn8uTJjBgxglmzZrFgwQLKly/PmjVrtB1TaJmenh4//PADffr00XaUN/JqI2bfvn08e/ZMS2neTFBQkLYjvJNOnTpx5MgR6tSpw+jRo+nevTurV68mNDSUsWPHajueyEXSIybyjb/++ouePXvy7NkzzMzM1C6xo1Ao8uVZQsOGDeOvv/6iW7du7N+/Hz8/P1q1akVCQgIzZsygSZMm2o4o8omOHTvSsWNH+vbtq+0oOfbqwr8vnxFc0AwbNozZs2dja2ur7SganTt3jtOnT+Pq6prpmp/iwyYNMZFvlClThjZt2jBv3jwKFSqk7Tg5UqxYMVavXk2LFi24c+cOrq6ujBo16oNYxFW8XytWrGDmzJn07NmTGjVqZJrwnh//uOrq6hIZGYmdnR2Q3hDz8fGhRIkSWk6W++TqEkJbpCEm8g0TExN8fX0/qA9CfX19QkJCKFy4MJC+Ftr58+epWLGilpOJ/EZHJ+vVgvLrXKX/0npz+a23b/78+Tg4ONCvXz+18jVr1hAdHc2kSZO0lEzkNllHTOQbrVq14uLFi9qO8UbS0tLQ19dX/ayrq5vpj5QQkHFWsKZbfmyEgaw3p00rVqzAzc0tU3mFChX49ddftZBI5BWZrC+06n//+5/q323btmXChAncuHGDSpUqqTVwIH8O3SiVSvr27avqMUhISGDIkCEFssdAvJ34+HiOHDlCu3btAJgyZQqJiYmq+/X09Jg9ezZGRkbaipglWW9OeyIjI3FycspUbmdnR0REhBYSibwiDTGhVR07dsxUNnv27Exl+XXo5tWz4Hr16qWlJCK/Wr9+PX///beqIbZs2TIqVKiAsbExADdv3sTR0ZFx48ZpM6bIZ1xcXDh9+nSm+XinT59WTYUQBYM0xIRWvbpExYdGegzE62zcuDHTcgN//PGHai7Shg0bWL58uTTEhJoBAwYwZswYkpOTadasGZB+OayJEycyfvx4LacTuUnmiAmtO3r0KOXLl+fx48eZ7ouLi6NChQqcPHlSC8mEeHe3bt2iTJkyqp+NjIzUJu7Xrl2bGzduaCOaeEmvXr0wNzfXdgyViRMn0r9/f4YNG0bJkiUpWbIkI0eOZNSoUUyZMkXb8UQukrMmhdZ98sknNG3aNMtFCpcuXcqxY8fYuXPne04mxLszNjbmypUrlC1bVuP9N2/epGrVqiQkJLznZP8N+/fvx9TUlIYNGwKwfPlyVq5cSfny5Vm+fHm+v+7k06dP8fPzw9jYmNKlS6vmo4qCQ3rEhNZdvXoVd3f3LO9v2bIlly5deo+JhMg9RYoU4dq1a1ne7+Pjo3btQ5G7JkyYoOpt9/X1Zfz48bRp04Y7d+58EMPBpqam1KpVi4oVK0ojrICSOWJC6+7fv5/pDMmX6enpER0d/R4TCZF72rRpw/Tp02nbtm2mMyPj4+OZNWsWbdu21VK6gi8oKIjy5csDsH37dtq1a8e8efPw9vamTZs2Wk6nrnPnznh6emJubk7nzp2zrStnYhcc0hATWufs7Iyvry+urq4a7/fx8dF4GrcQH4KvvvqKrVu3UrZsWUaMGEGZMmVQKBTcvHmTZcuWkZKSwldffaXtmAWWgYEBz58/B+Dw4cOqa9ZaW1trnJeqTRYWFqpLu8n6bP8dMkdMaN3IkSM5fvw4Fy5c0NhjULt2bZo2bcrSpUu1lFCIdxMUFMTQoUM5dOiQ6kLaCoWCjz/+mJ9//jnfrOZeEH3yySckJSXRoEED5syZQ1BQEM7Ozhw8eJARI0Zw69YtbUcU/3HSEBNad//+fapXr46uri4jRoygbNmyKBQK/Pz8WL58OampqXh7e+Pg4KDtqEK8k9jYWAICAgBwdXXF2tpay4kKvtDQUIYNG8bdu3cZNWoU/fv3B2Ds2LGkpqbKFzyhddIQE/lCSEgIQ4cO5cCBA2o9Bq1ateLnn3+mePHi2g0ohBB5rFq1aqqhydfx9vbO4zTifZE5YiJfKFasGHv37uXhw4cEBASgVCopXbp0vj+1XAiRv+nq6hIREYG9vb1a+YMHD7C3t89XV+zQdKURUfBJj5gQQogCS0dHh8jIyEwNsXv37lGqVCni4+O1lEyIdNIjJoQQosB5MfdLoVCwatUqTE1NVfelpqbi5eWFm5ubtuLlyKNHj9i2bRuBgYFMmDABa2tr1XxZZ2dnbccTuUR6xIQQQhQ4Ly6WHRISQpEiRdDV1VXdZ2BgQPHixZk9ezZ16tTRVsRs+fj40KJFCywsLAgODsbf35+SJUsybdo0QkJCWL9+vbYjilwiDTEhhBAFVtOmTdmxY8cHN9+0RYsWVK9ene+//x4zMzOuXr1KyZIlOXPmDD169CA4OFjbEUUukaFJIYQQBdaxY8e0HeGtXLhwgRUrVmQqd3Z2JjIyUguJRF6RhpgQQogCKzU1FU9PT44cOUJUVBRpaWlq9x89elRLybJnZGSkceV/f39/7OzstJBI5BVpiAkhhCiwRo8ejaenJ23btqVixYo5XqdL2zp06MDs2bPZunUrkH7SQWhoKJMnT6ZLly5aTidyk8wRE0IIUWDZ2tqyfv36fHeB79d5/Pgxbdq04fr16zx58oTChQsTGRlJvXr12Lt3LyYmJtqOKHKJ9IgJIYQosAwMDHB1ddV2jDdmbm7OqVOnOHbsGJcuXSItLY3q1avTokULbUcTuUwaYkIIIQqs8ePHs2TJEpYtW/bBDEumpaXh6enJjh07CA4ORqFQUKJECRwdHVEqlR/M8xA5I0OTQgghCqxOnTpx7NgxrK2tqVChAvr6+mr379ixQ0vJNFMqlbRv3569e/dSpUoV3NzcUCqV+Pn54evryyeffMKuXbu0HVPkIukRE0IIUWBZWlrSqVMnbcfIMU9PT7y8vDhy5AhNmzZVu+/o0aN07NiR9evX4+HhoaWEIrdJj5gQQgiRT7Rs2ZJmzZoxefJkjffPmzePEydOcODAgfecTOQVHW0HEEIIIfJSSkoKhw8fZsWKFTx58gRIv+j306dPtZwsMx8fH9zd3bO8v3Xr1ly9evU9JhJ5TYYmhRBCFFghISG4u7sTGhpKYmIiH3/8MWZmZnz//fckJCTw66+/ajuimtjYWBwcHLK838HBgYcPH77HRCKvSY+YEEKIAmv06NHUrFmThw8fYmxsrCrv1KkTR44c0WIyzVJTU9HTy7qPRFdXl5SUlPeYSOQ16RETQghRYJ06dYrTp09jYGCgVl6sWDHCw8O1lCprSqWSvn37YmhoqPH+xMTE95xI5DVpiAkhhCiw0tLSSE1NzVQeFhaGmZmZFhJlr0+fPq+tI2dMFixy1qQQQogCq1u3blhYWPDbb79hZmaGj48PdnZ2dOjQgaJFi7J27VptRxT/cdIQE0IIUWDdu3ePpk2boqury+3bt6lZsya3b9/G1tYWLy8v7O3ttR1R/MdJQ0wIIUSBFh8fz6ZNm/D29lZds7Fnz55qk/eF0BZpiAkhhBBCaIlM1hdCCFFg/e9//9NYrlAoMDIywtXVlRIlSrznVEJkkB4xIYQQBZaOjg4KhYJX/9S9KFMoFDRs2JBdu3ZhZWWlpZTiv0wWdBVCCFFgHTp0iFq1anHo0CHi4uKIi4vj0KFD1K5dm7///hsvLy8ePHjAl19+qe2o4j9KesSE59FYdAAABZlJREFUEEIUWBUrVuS3336jfv36auWnT59m0KBBXL9+ncOHD9OvXz9CQ0O1lFL8l0mPmBBCiAIrMDAQc3PzTOXm5ubcuXMHgNKlSxMTE/O+owkBSENMCCFEAVajRg0mTJhAdHS0qiw6OpqJEydSq1YtAG7fvk2RIkW0FVH8x8lZk0IIIQqsVatW0bFjR4oUKYKLiwsKhYLQ0FBKlizJ7t27AXj69CnTpk3TclLxXyVzxIQQQhRoaWlpHDx4kFu3bqFUKnFzc+Pjjz9GR0cGhYT2SUNMCCFEgZSSkoKRkRFXrlyhYsWK2o4jhEbydUAIIUSBpKenR7FixUhNTdV2FCGyJA0xIYQQBdbUqVOZMmUKsbGx2o4ihEYyNCmEEKLAqlatGgEBASQnJ1OsWDFMTEzU7vf29tZSMiHSyVmTQgghCqyOHTtqO4IQ2ZIeMSGEEEIILZE5YkIIIQq0R48esWrVKrW5Yt7e3oSHh2s5mRDSIyaEEKIA8/HxoUWLFlhYWBAcHIy/vz8lS5Zk2rRphISEsH79em1HFP9x0iMmhBCiwBo3bhx9+/bl9u3bGBkZqcpbt26Nl5eXFpMJkU4aYkIIIQqsCxcuMHjw4Ezlzs7OREZGaiGREOqkISaEEKLAMjIy4vHjx5nK/f39sbOz00IiIdRJQ0wIIUSB1aFDB2bPnk1ycjKA6qLfkydPpkuXLlpOJ4RM1hdCCFGAPX78mDZt2nD9+nWePHlC4cKFiYyMpF69euzduzfTAq9CvG/SEBNCCFHgHT16FG9vb9LS0qhevTotWrTQdiQhAGmICSGEKMDWr19Pt27dMDQ0VCtPSkpi8+bNeHh4aCmZEOmkISaEEKLA0tXVJSIiAnt7e7XyBw8eYG9vT2pqqpaSCZFOJusLIYQosJRKJQqFIlN5WFgYFhYWWkgkhDq56LcQQogCp1q1aigUChQKBc2bN0dPL+PPXWpqKkFBQbi7u2sxoRDppCEmhBCiwOnYsSMAV65coVWrVpiamqruMzAwoHjx4rJ8hcgXZI6YEEKIAmvdunV069ZN7fJGQuQn0hATQghRoD169Iht27YRGBjIhAkTsLa2xtvbGwcHB5ydnbUdT/zHSUNMCCFEgeXj40OLFi2wsLAgODgYf39/SpYsybRp0wgJCWH9+vXajij+4+SsSSGEEAXW2LFj6du3L7dv31YbnmzdujVeXl5aTCZEOpmsL4QQosC6ePEiv/32W6ZyZ2dnIiMjtZBICHXSIyaEEKLAMjIy4vHjx5nK/f39sbOz00IiIdRJQ0wIIUSB1aFDB2bPnk1ycjIACoWC0NBQJk+eLMtXiHxBJusLIYQosB4/fkybNm24fv06T548oXDhwkRGRlKvXj327t2LiYmJtiOK/zhpiAkhhCjwjh07xqVLl0hLS6N69eq0aNFC25GEAGSyvhBCiAIqLS0NT09PduzYQXBwMAqFghIlSuDo6JjlNSiFeN+kR0wIIUSBo1Qqad++PXv37qVKlSq4ubmhVCrx8/PD19eXTz75hF27dmk7phDSIyaEEKLg8fT0xMvLiyNHjtC0aVO1+44ePUrHjh1Zv349Hh4eWkooRDrpERNCCFHgtGzZkmbNmjF58mSN98+bN48TJ05w4MCB95xMCHWyfIUQQogCx8fHB3d39yzvb926NVevXn2PiYTQTBpiQgghCpzY2FgcHByyvN/BwYGHDx++x0RCaCYNMSGEEAVOamoqenpZT4PW1dUlJSXlPSYSQjOZrC+EEKLAUSqV9O3bF0NDQ433JyYmvudEQmgmDTEhhBAFTp8+fV5bR86YFPmBnDUphBBCCKElMkdMCCGEEEJLpCEmhBBCCKEl0hATQgghhNASaYgJIYQQQmiJNMSEEEIIIbREGmJCCCGEEFoiDTEhhBBCCC35PwZvujN44TlVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(data.corr(), square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b09f62",
   "metadata": {},
   "source": [
    "<li>There's a positive correlation of 0.64 between Channel and Detergents_Paper and negative correlation of -0.2 between Channel and Frozen</li>\n",
    "<li> There's a huge positive correlation of 0.92 between Grocery and Detergent_Paper</li>\n",
    "<li> There's a good amount of positive correlation of 0.73 between Milk and Grocery</li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73049684",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a38d320",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "210b7b22",
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0    12669   9656     7561     214              2674        1338\n",
       "1     7057   9810     9568    1762              3293        1776\n",
       "2     6353   8808     7684    2405              3516        7844\n",
       "3    13265   1196     4221    6404               507        1788\n",
       "4    22615   5410     7198    3915              1777        5185\n",
       "..     ...    ...      ...     ...               ...         ...\n",
       "435  29703  12051    16027   13135               182        2204\n",
       "436  39228   1431      764    4510                93        2346\n",
       "437  14531  15488    30243     437             14841        1867\n",
       "438  10290   1981     2232    1038               168        2125\n",
       "439   2787   1698     2510      65               477          52\n",
       "\n",
       "[440 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "260b7bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/n48++ojo6GjuvvvuEsdZrVaMhf8oXU0WiwWTyXTVzytKJz1aQvio0nq0ZA14Ia6+J598EoPBwJYtWxg8eDAtWrSgdevWDBo0iG+//Za77roLAE3TeO+99xgwYACBgYG89tprAMydO5fGjRtjMpm44YYb+PTTTz3qP3fuHI8//jgRERH4+fkRFxfHN998496/ceNGunXrhr+/PzExMYwePZoLFy649zds2JDXXnuN4cOHExISwogRI7jtttsYNWqUx3lOnz6N2Wzmp59+ulKXSpRCerSE8FFKeQZa4Ay+NL3eW00Sosoopciz5Xnl3P4GfzRNK1fZ06dPs2LFCqZOnUpgYGCpZYrXNWXKFKZNm8bMmTPR6/UsW7aMMWPGMGvWLHr37s0333zDww8/TL169ejZsycOh4N+/fpx/vx5/vOf/9C4cWP27NmDvvBzvnPnThISEvj73//OvHnzOHXqFKNGjWLUqFHMnz/ffd5//vOfTJ48mRdffBGAzZs3M2rUKN58803MZjMAn332GdHR0fTs2bNS101UjgRaQvgo5R46LAq0HHY7Ogm0xDUgz5ZHp887eeXcmx7YRIAxoFxlDx48iFKKG264wWN7eHg4+fn5ADz11FPMmDEDgAceeIBHHnnEXe6BBx5g+PDhPPnkkwCMHz+eX375hTfeeIOePXuyatUqNm/ezN69e2nWrBkAjRo1ch//z3/+kwceeICxY8cC0LRpU9555x26d+/O3Llz8fPzA+C2225jwoQJ7uNiYmJ4+umn+eqrrxg8eDAA8+fPZ/jw4eUOMkXVkKFDIXyUwz10WJRrIXlaQnjHxcHJ5s2b2b59O61ataKgoMC9vWPHjh7l9u7dyy233OKx7ZZbbmHv3r0AbN++nXr16rmDrItt3bqVBQsWUKNGDfcjISEBh8NBSkrKJc9rNpv5y1/+wkcffeQ+z44dOxg+fHjF3rj406RHSwgf5c7RMhT1aMnC0uJa4W/wZ9MDm7x27vJq0qQJmqbx+++/e2x39Tr5+3vWVdrw4sVBmlLKve3i4y/mcDh44oknGD16dIl99evXL/O8jz32GO3ateP48eN89NFH9OrViwYNGpR5PlH1JNASwkddnAwPoCTQEtcITdPKPXznTWFhYdx+++3Mnj2bp59++pJ5WpfSokUL1q9fz7Bhw9zbNm7cSIsWLQBo06YNx48fZ//+/aX2anXo0IHdu3fTpEmTCre9devWdOzYkQ8//JDPP/+cf/3rXxWuQ/x5MnQohI9yJcPrdDp3XpZdhg6FuOrmzJmDzWajY8eOLF68mL1797Jv3z7+85//8Pvvv7sT10vzt7/9jQULFvDee+9x4MAB3nrrLZYuXerOp+revTvdunVj0KBBrFy5kpSUFL777ju+//57ACZOnEhSUhJPPfUU27dv58CBA3z99dc8/fTT5Wr7Y489xvTp07Hb7dx7771//mKICpNASwgf5crR0jQNnd7Z+azsjrIOEUJcAY0bNyY5OZnevXszadIk2rZtS8eOHfnXv/7FhAkT+Pvf/37JY++55x7efvtt/vnPf9KqVSvef/995s+fT48ePdxllixZwo033sj9999Py5YtefbZZ91pAm3atGHt2rUcOHCArl270r59eyZPnkxUVFS52n7//fdjMBh44IEH3Inz4urSlEzM41XZ2dmEhISQlZVFcHCwt5sjfMje9WtY/q83qB/XloxD+7Hk5fHI2x8QGhnt7aYJUWH5+fmkpKQQGxsrf/CvomPHjtGwYUN+/fVXOnTo4O3mVDtl/dyW9++35GgJ4aNcOVqaTufu0XLYJEdLCHF5VquV9PR0nnvuOW6++WYJsrxIhg6F8FGuocPiOVrKIYGWEOLyNmzYQIMGDdi6dSvvvfeet5tzXZMeLSF8lCsZXtPp0Omc/xO5gi8hhChLjx49ZMkuHyE9WkL4qOJDh65ldxwyvYMQQlQrEmgJ4aPcgZZWNHQogZYQQlQvEmgJ4aM8crR0hYGW5GgJIUS1IoGWED7K867DwmR46dESQohqRQItIXyUR6DlSoaXCUuFEKJakUBLCB9VajK8DB0KIUS1IoGWED6qtHm0JBleCCGqFwm0hPBRnkOH0qMlhLcMHz4cTdNKPA4ePOjtpolqQCYsFcJHSTK8EL6jb9++zJ8/32Nb7dq1PV5bLBZMJtPVbJaoBqRHSwgf5Tl06EqGl0BLCG8wm81ERkZ6PHr16sWoUaMYP3484eHh3H777QCsXbuWm266CbPZTFRUFM899xw2mw2AI0eOlNo71qNHD/e5Nm7cSLdu3fD39ycmJobRo0dz4cIF9/6GDRsydepUHnnkEYKCgqhfvz4ffPDBVb0eovwk0BLCR7mXz9B0aO6hQ7nrUFwblFI4cnO98qjKpWk+/vhjDAYDGzZs4P333yctLY3+/ftz4403smPHDubOncu8efN47bXXAIiJiSE9Pd39SE5OJiwsjG7dugGwc+dOEhISGDhwIL/99huLFy9m/fr1jBo1yuO8b775Jh07diQ5OZknn3ySv/71r/z+++9V9r5E1ZGhQyF8lJJkeHENU3l57OsQ75Vz37BtK1pAQIWO+eabb6hRo4b7db9+/QBo0qQJ//jHP9zbX3jhBWJiYpg9ezaaptG8eXNOnDjBxIkTeemll9Dr9URGRgKQn5/PPffcQ+fOnXn55ZcB+Oc//8kDDzzA2LFjAWjatCnvvPMO3bt3Z+7cufj5+QHQv39/nnzySQAmTpzIzJkzWbNmDc2bN6/UNRFXjgRaQvgoVZj4rkmgJYTX9ezZk7lz57pfBwYGcv/999OxY0ePcnv37qVz585omubedsstt5CTk8Px48epX7++e/ujjz7K+fPnWblypXuuvK1bt3Lw4EE+++wzdzmlFA6Hg5SUFFq0aAFAmzZt3Ps1TSMyMpKTJ09W7ZsWVUICLSF8lKOUuw6V3HUorhGavz83bNvqtXNXVGBgIE2aNCl1e3FKKY8gy7UN8Nj+2muv8f3337N582aCgoLc2x0OB0888QSjR48uca7iQZrRaPTYp2mapBb4KAm0hPBRxYcONenREtcYTdMqPHxXHbRs2ZIlS5Z4BFwbN24kKCiIunXrArBkyRJeffVVvvvuOxo3buxxfIcOHdi9e3epQZ2oniQZXggfVfy/YFmCR4jq4cknn+TYsWM8/fTT/P7773z11VdMmTKF8ePHo9Pp2LVrF8OGDWPixIm0atWKjIwMMjIyOHPmDODMt0pKSuKpp55i+/btHDhwgK+//pqnn37ay+9MVJYEWkL4qKJ5tPRFgZYMHQrh0+rWrcvy5cvZvHkzbdu2ZeTIkTz66KO8+OKLAGzZsoXc3Fxee+01oqKi3I+BAwcCztyrtWvXcuDAAbp27Ur79u2ZPHkyUVFR3nxb4k/weqA1Z84cYmNj8fPzIz4+nnXr1pVZfu3atcTHx+Pn50ejRo147733SpRZsmQJLVu2xGw207JlS5YtW1ah81qtViZOnEjr1q0JDAwkOjqaYcOGceLECY86evToUWIulKFDh1bySgjhyVHaWocydCjEVbdgwQK+/PLLEtvXrFnDrFmzSmzv3r07mzdvpqCggPT0dKZPn47B4MzUGT58OEqpEo81a9a4j7/xxhtZsWIF58+fJycnhx07dvD888+79x85csR9V6LL9u3b3XcuCt/i1UBr8eLFjB07lhdeeIHk5GS6du1Kv379SE1NLbV8SkoK/fv3p2vXriQnJ/P8888zevRolixZ4i6TlJTEkCFDSExMZMeOHSQmJjJ48GA2bdpU7vPm5uaybds2Jk+ezLZt21i6dCn79+/n7rvvLtGmESNGeMyJ8v7771fxVRLXK1fiu05mhhdCiGpLU1U5c1sFderUiQ4dOnjcMtuiRQvuuecepk2bVqL8xIkT+frrr9m7d69728iRI9mxYwdJSUkADBkyhOzsbL777jt3mb59+xIaGsrChQsrdV6AX3/9lZtuuomjR4+67/zo0aMH7dq1K/U/mvLKzs4mJCSErKwsgoODK12PuPaseP8ddv60gluGJJJz5jQ7Vi6n83330+X/HvR204SosPz8fFJSUtwjCUJUB2X93Jb377fXerQsFgtbt26lT58+Htv79OnDxo0bSz0mKSmpRPmEhAS2bNmC1Wots4yrzsqcFyArKwtN06hZs6bH9s8++4zw8HBatWrFhAkTOH/+/KXfNFBQUEB2drbHQ4jSeCTDu3q05PZtIYSoVrw2vUNmZiZ2u52IiAiP7REREWRkZJR6TEZGRqnlbTYbmZmZREVFXbKMq87KnDc/P5/nnnuOBx54wCNqffDBB4mNjSUyMpJdu3YxadIkduzYwcqVKy/5vqdNm8Yrr7xyyf1CuHguKi1rHQohRHXk9Xm0SpvY7eJtlyt/8fby1Fne81qtVoYOHYrD4WDOnDke+0aMGOF+HhcXR9OmTenYsSPbtm2jQ4cOpbZ/0qRJjB8/3v06OzubmJiYUsuK61vxRaVlrUMhhKievBZohYeHo9frS/QinTx5skRvk0tkZGSp5Q0GA2FhYWWWcdVZkfNarVYGDx5MSkoKP/3002VzqDp06IDRaOTAgQOXDLTMZjNms7nMeoSAi6Z3kLsOhRCiWvJajpbJZCI+Pr7EMNvKlSvp0qVLqcd07ty5RPkVK1bQsWNH93IElyrjqrO853UFWQcOHGDVqlXuQK4su3fvxmq1ynwnokp4LMEjgZYQQlRLXh06HD9+PImJiXTs2JHOnTvzwQcfkJqaysiRIwHnMFtaWhqffPIJ4LzDcPbs2YwfP54RI0aQlJTEvHnz3HcTAowZM4Zu3boxY8YMBgwYwFdffcWqVatYv359uc9rs9m477772LZtG9988w12u93dA1arVi1MJhOHDh3is88+o3///oSHh7Nnzx6eeeYZ2rdvzy233HK1LqG4lrkDLU3WOhRCiGrKq4HWkCFDOH36NK+++irp6enExcWxfPlyGjRoAEB6errHnFqxsbEsX76ccePG8e677xIdHc0777zDoEGD3GW6dOnCokWLePHFF5k8eTKNGzdm8eLFdOrUqdznPX78OF9//TUA7dq182jz6tWr6dGjByaTiR9//JG3336bnJwcYmJiuOOOO5gyZQr6wt4HIf6M4jla0qMlhBDVk1fn0RIyj5a4tC//+XcObdnE7Y+PIj8nh3WfL6BV9970fXKst5smRIXJPFqiOqrW82gJIcqmSsvRkqFDIa664cOHu5dZMxqNREREcPvtt/PRRx9V6E7gl19+ucQoSXXQsGHDPzUxd3FHjhzxWLYuNDSUbt26sXbt2iqp3xdJoCWEjyoaOpS7DoXwtr59+5Kens6RI0f47rvv6NmzJ2PGjOHOO+/EZrNd1ba4JuiuzlatWkV6ejpr164lODiY/v37k5KS4rX22O32KzZ9jgRaQvgojx4tnax1KIQ3mc1mIiMjqVu3Lh06dOD555/nq6++4rvvvmPBggWAcwWRxx9/nDp16hAcHMxtt93Gjh07AOfC1K+88go7duxw9+aU5zgo6gn76KOPaNSoEWazGaUUv//+O7feeit+fn60bNmSVatWoWmaxwLYaWlpDBkyhNDQUMLCwhgwYABHjhxx7x8+fDj33HMPb7zxBlFRUYSFhfHUU0+5g7kePXpw9OhRxo0b5243wNGjR7nrrrsIDQ0lMDCQVq1asXz58nJfz7CwMCIjI2nTpg3vv/8+ubm5rFixgtOnT3P//fdTr149AgICaN26tccNb642jRo1ilGjRlGzZk3CwsJ48cUXKZ4JZbFYePbZZ6lbty6BgYF06tTJY+HuBQsWULNmTb755htatmyJ2Wzm6NGj5W5/RXh9wlIhROncgVaxJXhk6FBcK5RS2CzemYDXYNKVOTF2ed122220bduWpUuX8uijj3LHHXdQq1Ytli9fTkhICO+//z69evVi//79DBkyhF27dvH999+zatUqAEJCQlBKlXlcrVq1ADh48CD//e9/WbJkCXq9HofDwT333EP9+vXZtGkT58+f55lnnvFoX25uLj179qRr1678/PPPGAwGXnvtNfr27ctvv/2GyWQCnDd5RUVFsXr1ag4ePMiQIUNo164dI0aMYOnSpbRt25bHH3/cY5Lup556CovFws8//0xgYCB79uyhRo0albqOAQEBgLOnLj8/n/j4eCZOnEhwcDDffvstiYmJNGrUyOOmto8//phHH32UTZs2sWXLFh5//HEaNGjgbuPDDz/MkSNHWLRoEdHR0Sxbtoy+ffuyc+dOmjZt6r4+06ZN49///jdhYWHUqVOnUu2/HAm0hPBRxXu0NFmCR1xjbBYHH4zxTl7O4293x2iumrvDmzdvzm+//cbq1avZuXMnJ0+edE9K/cYbb/Dll1/yxRdf8Pjjj1OjRg0MBgORkZHu43/66afLHgfOHppPP/2U2rVrA/D9999z6NAh1qxZ467v9ddf5/bbb3fXvWjRInQ6Hf/+97/dgeX8+fOpWbMma9asca/5GxoayuzZs9Hr9TRv3pw77riDH3/8kREjRlCrVi30ej1BQUEe7U5NTWXQoEG0bt0agEaNGlXq+l24cIFJkyah1+vp3r07devWZcKECe79Tz/9NN9//z3/+9//PAKtmJgYZs6ciaZp3HDDDezcuZOZM2cyYsQIDh06xMKFCzl+/DjR0dEATJgwge+//5758+czdepUwBnYzZkzh7Zt21aq7eUlgZYQPspjegdZgkcIn+Ravm3r1q3k5OSUmNw6Ly+PQ4cOXfL48h7XoEEDd5AFsG/fPmJiYjyCn5tuuqlE3QcPHiQoKMhje35+vkfdrVq18piWKCoqip07d5b1thk9ejR//etfWbFiBb1792bQoEG0adOmzGOK69KlCzqdjtzcXKKioliwYAGtW7fGbrczffp0Fi9eTFpaGgUFBRQUFBAYGOhx/M033+zRK9m5c2fefPNN7HY727ZtQylFs2bNPI4pKCjwuM4mk6lCba4sCbSE8FGl3nUoPVriGmEw6Xj87e5eO3dV2bt3L7GxsTgcDqKiojzygFxq1qx5yePLe9zFgcbl1gV21R0fH89nn31WYl/xoM21soqLpmmX/afuscceIyEhgW+//ZYVK1Ywbdo03nzzTZ5++ukyj3NZvHgxLVu2dOdYubz55pvMnDmTWbNm0bp1awIDAxk7diwWi6Vc9YLzfev1erZu3VpiXsviw5v+/v5VMoR8ORJoCeGjlCq51qEkw4trhaZpVTZ85y2uYb9x48ZRr149MjIyMBgMNGzYsNTyJpMJ+0Wf4Q4dOlz2uNI0b96c1NRU/vjjD/c6vb/++muJuhcvXuxOsq+s0toNzuG7kSNHMnLkSCZNmsSHH35Y7kArJiaGxo0bl9i+bt06BgwYwF/+8hfAGTQdOHCAFi1aeJT75ZdfSrxu2rQper2e9u3bY7fbOXnyJF27di3v27xi5K5DIXyURzK8TpLhhfCmgoICMjIySEtLY9u2bUydOpUBAwZw5513MmzYMHr37k3nzp255557+OGHHzhy5AgbN27kxRdfZMuWLYBzPqqUlBS2b99OZmYmBQUF5TquNLfffjuNGzfmoYce4rfffmPDhg288MILAO5emgcffJDw8HAGDBjAunXrSElJYe3atYwZM4bjx4+X+703bNiQn3/+mbS0NDIzMwEYO3YsP/zwAykpKWzbto2ffvqpRDBUGU2aNGHlypVs3LiRvXv38sQTT7iXwCvu2LFjjB8/nn379rFw4UL+9a9/MWbMGACaNWvGgw8+yLBhw1i6dCkpKSn8+uuvzJgxo0J3RlYVCbSE8FHFc7Q0GToUwqu+//57oqKiaNiwIX379mX16tW88847fPXVV+j1ejRNY/ny5XTr1o1HHnmEZs2aMXToUI4cOeLucRo0aBB9+/alZ8+e1K5dm4ULF5bruNLo9Xq+/PJLcnJyuPHGG3nsscd48cUXAdwzmAcEBPDzzz9Tv359Bg4cSIsWLXjkkUfIy8urUA/Xq6++ypEjR2jcuLF7yNFut/PUU0/RokUL+vbtyw033MCcOXMqe3ndJk+eTIcOHUhISKBHjx5ERkZyzz33lCg3bNgw8vLyuOmmm3jqqad4+umn3TcOgDPpf9iwYTzzzDPccMMN3H333WzatImYmJg/3caKkiV4vEyW4BGX8smzT3PqaAqDnn8Vh8POsumvENGoKX+ZNtPbTROiwmQJnitvw4YN3HrrrRw8eLDUYblrRY8ePWjXrl2VzVZflqpYgkdytITwUR7J8IXbZOhQCOGybNkyatSoQdOmTTl48CBjxozhlltuuaaDrOpIhg6F8FGuzmZdsbsOJRleCOFy/vx5nnzySZo3b87w4cO58cYb+eqrr7zappEjR1KjRo1SHyNHjvRq27xFerSE8FEOdzK8DtcdyJKjJYRwGTZsGMOGDfN2Mzy8+uqrHhOOFldV6TGlTYXhyyTQEsJHqcJhQk2nwxVpydChEMKX1alT54otZVNdSaAlhI/yWILHFWjZZWZ4IYSoTiTQEsJHeUzvoCtc61B6tIQQolqRZHghfJQrGV4rFmhJMrwQQlQvEmgJ4aNkrUMhhKj+JNASwkeVGmjJ0KEQQlQrEmgJ4aOK52i51jp0BV9CiOrn5Zdfpl27du7Xw4cPL3V5GXFtkUBLCB9VfHqHoqFDCbSEuNqGDx+OpmlomobRaCQiIoLbb7+djz76yP0PUWW8/fbbLFiwoOoaKnySBFpC+CjlKJYMry+861BytITwir59+5Kens6RI0f47rvv6NmzJ2PGjOHOO+/EZrNVqs6QkBBq1qxZtQ0VPkcCLSF8lCo2M7xr6FBytITwDrPZTGRkJHXr1qVDhw48//zzfPXVV3z33XfuXqmsrCwef/xx6tSpQ3BwMLfddhs7duy4ZJ0XDx06HA5mzJhBkyZNMJvN1K9fn9dff929f+LEiTRr1oyAgAAaNWrE5MmTsVqt7v07duygZ8+eBAUFERwcTHx8PFu2bAHg6NGj3HXXXYSGhhIYGEirVq1Yvny5+9g9e/bQv39/atSoQUREBImJiWRmZrr39+jRg9GjR/Pss89Sq1YtIiMjefnll//kVb0+yDxaQvgoVTxHq3DoEKVQDod7ugchqiulFLaCAq+c22A2uycB/jNuu+022rZty9KlS3n00Ue54447qFWrFsuXLyckJIT333+fXr16sX//fmrVqnXZ+iZNmsSHH37IzJkzufXWW0lPT+f333937w8KCmLBggVER0ezc+dORowYQVBQEM8++ywADz74IO3bt2fu3Lno9Xq2b9+O0WgE4KmnnsJisfDzzz8TGBjInj17qFGjBgDp6el0796dESNG8NZbb5GXl8fEiRMZPHgwP/30k/v8H3/8MePHj2fTpk0kJSUxfPhwbrnlFm6//fY/fS2vZRJoCeGjHKXcdejcbkcvgZao5mwFBbzz0H1eOffoj7/A6OdXJXU1b96c3377jdWrV7Nz505OnjyJ2WwG4I033uDLL7/kiy++4PHHHy+znvPnz/P2228ze/ZsHnroIQAaN27Mrbfe6i7z4osvup83bNiQZ555hsWLF7sDrdTUVP72t7/RvHlzAJo2beoun5qayqBBg2jdujUAjRo1cu+bO3cuHTp0YOrUqe5tH330ETExMezfv59mzZoB0KZNG6ZMmeKue/bs2fz4448SaF2GBFpC+CiP6R10xQItux29weitZgkhilFKoWkaW7duJScnh7CwMI/9eXl5HDp06LL17N27l4KCAnr16nXJMl988QWzZs3i4MGD5OTkYLPZPBZqHj9+PI899hiffvopvXv35v/+7/9o3LgxAKNHj+avf/0rK1asoHfv3gwaNIg2bdoAsHXrVlavXu3u4Sru0KFDHoFWcVFRUZw8efKy7+16J4GWED5KqWJL8BTv0ZI7D8U1wGA2M/rjL7x27qqyd+9eYmNjcTgcREVFsWbNmhJlypPw7u/vX+b+X375haFDh/LKK6+QkJBASEgIixYt4s0333SXefnll3nggQf49ttv+e6775gyZQqLFi3i3nvv5bHHHiMhIYFvv/2WFStWMG3aNN58802efvppHA4Hd911FzNmzChx3qioKPdz1zCki6Zpf+quy+uFBFpC+CCP+bI0DZ2+aKhQEuLFtUDTtCobvvOWn376iZ07dzJu3Djq1atHRkYGBoOBhg0bVriupk2b4u/vz48//shjjz1WYv+GDRto0KABL7zwgnvb0aNHS5Rr1qwZzZo1Y9y4cdx///3Mnz+fe++9F4CYmBhGjhzJyJEj3flgTz/9NB06dGDJkiU0bNgQg0HCgqomiR5C+KDi/yXqdHo0reijKusdCnH1FRQUkJGRQVpaGtu2bWPq1KkMGDCAO++8k2HDhtG7d286d+7MPffcww8//MCRI0fYuHEjL774ovvOv7L4+fkxceJEnn32WT755BMOHTrEL7/8wrx58wBo0qQJqampLFq0iEOHDvHOO++wbNky9/F5eXmMGjWKNWvWcPToUTZs2MCvv/5KixYtABg7diw//PADKSkpbNu2jZ9++sm976mnnuLMmTPcf//9bN68mcOHD7NixQoeeeQR7PL75k+T0FUIH1S8R0vT6dA0DZ1ej8Nul7m0hPCC77//nqioKAwGA6GhobRt25Z33nmHhx56CF3hzSnLly/nhRde4JFHHuHUqVNERkbSrVs3IiIiynWOyZMnYzAYeOmllzhx4gRRUVGMHDkSgAEDBjBu3DhGjRpFQUEBd9xxB5MnT3ZPsaDX6zl9+jTDhg3jjz/+IDw8nIEDB/LKK68AYLfbeeqppzh+/DjBwcH07duXmTNnAhAdHc2GDRuYOHEiCQkJFBQU0KBBA/r27et+b6LyNKWU8nYjrmfZ2dmEhISQlZXlkdQorm/W/Hz3HVmuO6Te/stAbFYLI979iODwOl5uoRAVk5+fT0pKCrGxsfhV8yFDcf0o6+e2vH+/JVQVwgc5LurRAtwJ8ZIML4QQ1YcEWkL4INcdhwCazjmxok6W4RFCiGpHAi0hfNDFOVqAey4tJXcdCiFEtSGBlhA+yCPQKrzjUOceOpRASwghqgsJtITwQY5iC0q71mTTJNASQohqRwItIXxQ8eV3XFxDhzJhqajO5EZ3UZ1Uxc+rBFpC+CBXMrwrER6KJ8PLXYei+nEt35Kbm+vllghRfq6f14uXH6oImbBUCB9UVo+WzAwvqiO9Xk/NmjXdixAHBAS4h8WF8DVKKXJzczl58iQ1a9ZEX2y92YqSQEsIH+TK0So+K7M7GV6GDkU1FRkZCeAOtoTwdTVr1nT/3FaWBFpC+KDSerQkGV5Ud5qmERUVRZ06dbBard5ujhBlMhqNf6ony8XrgdacOXP45z//SXp6Oq1atWLWrFl07dr1kuXXrl3L+PHj2b17N9HR0Tz77LPutaBclixZwuTJkzl06BCNGzfm9ddfd69eXp7zWq1WXnzxRZYvX87hw4cJCQmhd+/eTJ8+nejoaHcdBQUFTJgwgYULF5KXl0evXr2YM2cO9erVq8IrJK5HRYFW0Yfc1bslPVqiutPr9VXyB0yI6sCryfCLFy9m7NixvPDCCyQnJ9O1a1f69etHampqqeVTUlLo378/Xbt2JTk5meeff57Ro0ezZMkSd5mkpCSGDBlCYmIiO3bsIDExkcGDB7Np06Zynzc3N5dt27YxefJktm3bxtKlS9m/fz933323R3vGjh3LsmXLWLRoEevXrycnJ4c777xTVjsXf5o70CqWw+K+61CS4YUQovpQXnTTTTepkSNHemxr3ry5eu6550ot/+yzz6rmzZt7bHviiSfUzTff7H49ePBg1bdvX48yCQkJaujQoZU+r1JKbd68WQHq6NGjSimlzp07p4xGo1q0aJG7TFpamtLpdOr777+/ZD0Xy8rKUoDKysoq9zHi2vdHyiH1xuA71NzH/+Le9vnkv6k3Bt+h9v+ywYstE0IIoVT5/357rUfLYrGwdetW+vTp47G9T58+bNy4sdRjkpKSSpRPSEhgy5Yt7vH+S5Vx1VmZ8wJkZWWhaRo1a9YEYOvWrVitVo96oqOjiYuLK7OegoICsrOzPR5CXKzUuw71MnQohBDVjdcCrczMTOx2OxERER7bIyIiyMjIKPWYjIyMUsvbbDYyMzPLLOOqszLnzc/P57nnnuOBBx4gODjYfR6TyURoaGi56wGYNm0aISEh7kdMTMwly4rrV5kTlsrQtBBCVBten7D04nlUlFJlzq1SWvmLt5enzvKe12q1MnToUBwOB3PmzCnjnZSv/ZMmTSIrK8v9OHbs2GXrFNefsqZ3KL4OohBCCN/mtUArPDwcvV5fovfn5MmTJXqbXCIjI0stbzAYCAsLK7OMq86KnNdqtTJ48GBSUlJYuXKluzfLdR6LxcLZs2fL3X4As9lMcHCwx0OIixX9A1HKPFrSoyWEENWG1wItk8lEfHw8K1eu9Ni+cuVKunTpUuoxnTt3LlF+xYoVdOzY0T09/qXKuOos73ldQdaBAwdYtWqVO5BziY+Px2g0etSTnp7Orl27Ltl+IcpLFeZhecyjJWsdCiFEtePVebTGjx9PYmIiHTt2pHPnznzwwQekpqa658WaNGkSaWlpfPLJJwCMHDmS2bNnM378eEaMGEFSUhLz5s1j4cKF7jrHjBlDt27dmDFjBgMGDOCrr75i1apVrF+/vtzntdls3HfffWzbto1vvvkGu93u7gGrVasWJpOJkJAQHn30UZ555hnCwsKoVasWEyZMoHXr1vTu3ftqXUJxjSozGV6mdxBCiGrDq4HWkCFDOH36NK+++irp6enExcWxfPlyGjRoADh7iIrPqRUbG8vy5csZN24c7777LtHR0bzzzjsMGjTIXaZLly4sWrSIF198kcmTJ9O4cWMWL15Mp06dyn3e48eP8/XXXwPQrl07jzavXr2aHj16ADBz5kwMBgODBw92T1i6YMECmYhP/Gml5mhJMrwQQlQ7mnIlgwivyM7OJiQkhKysLMnXEm5HdmxjydSXqN0glmH/+BcAy//1BnvXr6HHsMeIv+Me7zZQCCGuc+X9++31uw6FECW5k+FLW1RaerSEEKLakEBLCB9UtARPKcnwEmgJIUS1IYGWED6o9Hm0ZGZ4IYSobiTQEsIHlTa9Q9HQodx1KIQQ1YUEWkL4oLKW4FHSoyWEENWGBFpC+CBXMnzxoUNNkuGFEKLakUBLCB/kcPdoFa2bKXcdCiFE9SOBlhA+qGjosGjyW50swSOEENWOBFpC+CBZgkcIIa4NEmgJ4YNcvValLcGjZOhQCCGqDQm0hPBBylFyZnh3MrwMHQohRLVRqUArJSWlqtshhCimaGZ4SYYXQojqrFKBVpMmTejZsyf/+c9/yM/Pr+o2CXHdcwVaOWctfP7yL/yRkl2UDC+BlhBCVBuVCrR27NhB+/bteeaZZ4iMjOSJJ55g8+bNVd02Ia5brukdTqXmcDYjl3X/3V9sCR5JhhdCiOqiUoFWXFwcb731FmlpacyfP5+MjAxuvfVWWrVqxVtvvcWpU6equp1CXFdcPVpKOYcOc7Ms7qFDSYYXQojq408lwxsMBu69917++9//MmPGDA4dOsSECROoV68ew4YNIz09varaKcR1RanCXivN+RHNv2BB02RRaSGEqG7+VKC1ZcsWnnzySaKionjrrbeYMGEChw4d4qeffiItLY0BAwZUVTuFuK4o9/Cgs0fLWuDg3JdfA5KjJYQQ1YmhMge99dZbzJ8/n3379tG/f38++eQT+vfv757zJzY2lvfff5/mzZtXaWOFuF64l+Ch6K7DnB17ITpQAi0hhKhGKhVozZ07l0ceeYSHH36YyMjIUsvUr1+fefPm/anGCXG9urhHC8Ch9yvcJ4GWEEJUF5UKtFauXEn9+vU9Zq0GUEpx7Ngx6tevj8lk4qGHHqqSRgpxvSkKtIo+Y3adCbDLEjxCCFGNVCpHq3HjxmRmZpbYfubMGWJjY/90o4S43hUlwxf1aCm9CZAcLSGEqE4qFWgppUrdnpOTg5+f359qkBCi9KFDu84MyF2HQghRnVRo6HD8+PGAc1mQl156iYCAAPc+u93Opk2baNeuXZU2UIjrkaOUQMvVo6VkwlIhhKg2KhRoJScnA84erZ07d2Iymdz7TCYTbdu2ZcKECVXbQiGuQ8VztHR2Cw69CYerR0uGDoUQotqoUKC1evVqAB5++GHefvttgoODr0ijhLjeFe/R8is4S25ABHadc1heAi0hhKg+KpWjNX/+fAmyhLiSiiXDmwvOAuDQu3K0ZOhQCCGqi3L3aA0cOJAFCxYQHBzMwIEDyyy7dOnSP90wIa5nxXu0TAXZANhd82hJj5YQQlQb5Q60QkJC0ApvNQ8JCbliDRJCeN51aLKeB4r3aEmgJYQQ1UW5A6358+eX+lwIUfWUewkeHUbrBQAcOhM4JEdLCCGqk0rlaOXl5ZGbm+t+ffToUWbNmsWKFSuqrGFCXM+KDx0abcUCLcBhs3mpVUIIISqqUoHWgAED+OSTTwA4d+4cN910E2+++SYDBgxg7ty5VdpAIa5Hxad3MFid/9TI9A5CCFH9VCrQ2rZtG127dgXgiy++IDIykqNHj/LJJ5/wzjvvVGkDhbgeuQMtjaKhQ4NM7yCEENVNpQKt3NxcgoKCAFixYgUDBw5Ep9Nx8803c/To0SptoBDXI7s7mNIVDR3qJdASQojqplKBVpMmTfjyyy85duwYP/zwA3369AHg5MmTMr+WEFXAYSuWo+Xq0dJcS/BIoCWEENVFpQKtl156iQkTJtCwYUM6depE586dAWfvVvv27au0gUJcj4p6rTQMtrzCp86Pq0xYKoQQ1UeFluBxue+++7j11ltJT0+nbdu27u29evXi3nvvrbLGCXG9stucgZamFHp7QeFWZ6CllEIp5Z7XTgghhO+qVKAFEBkZSWRkpMe2m2666U83SAhRlAyvUw40FHqjDluBzmO/ptd7q3lCCCHKqVKB1oULF5g+fTo//vgjJ0+eLDGUcfjw4SppnBDXK3thjpamFJqfH0aTHpulKNBy2O3oJNASQgifV6lA67HHHmPt2rUkJiYSFRUlQxhCVDHXMjuacqDz98dg1kGOVmK/EEII31apQOu7777j22+/5ZZbbqnq9gghAIe9aOhQ5++P0aSn+L0rMsWDEEJUD5W66zA0NJRatWpVdVuEEIVcgZSmFFqAP0azBFpCCFEdVSrQ+vvf/85LL73ksd5hZc2ZM4fY2Fj8/PyIj49n3bp1ZZZfu3Yt8fHx+Pn50ahRI957770SZZYsWULLli0xm820bNmSZcuWVfi8S5cuJSEhgfDwcDRNY/v27SXq6NGjB5qmeTyGDh1asQsgRCmUQwGgOezo/AMwmPTOIXrl2i9TPAghRHVQqUDrzTff5IcffiAiIoLWrVvToUMHj0d5LV68mLFjx/LCCy+QnJxM165d6devH6mpqaWWT0lJoX///nTt2pXk5GSef/55Ro8ezZIlS9xlkpKSGDJkCImJiezYsYPExEQGDx7Mpk2bKnTeCxcucMsttzB9+vQy38OIESNIT093P95///1yv38hLsXVY6VTyjl0aHYmvmsX7RdCCOHbNKWUquhBr7zySpn7p0yZUq56OnXqRIcOHTwWom7RogX33HMP06ZNK1F+4sSJfP311+zdu9e9beTIkezYsYOkpCQAhgwZQnZ2Nt999527TN++fQkNDWXhwoUVPu+RI0eIjY0lOTmZdu3aeezr0aMH7dq1Y9asWeV6v6XJzs4mJCSErKwsmVVfuM0fP54zafupqdrRJzST3fGjOLTtJAVnZqI0xWP/mkdInQhvN1MIIa5b5f37Xalk+PIGUmWxWCxs3bqV5557zmN7nz592LhxY6nHJCUluZf7cUlISGDevHlYrVaMRiNJSUmMGzeuRBlXMFSZ85bls88+4z//+Q8RERH069ePKVOmuNeBLE1BQQEFBQXu19nZ2RU+p7j2uZbZ0SmHM0fL5Ox81nCOHspdh0IIUT1UaugQ4Ny5c/z73/9m0qRJnDlzBoBt27aRlpZWruMzMzOx2+1ERHj+Vx4REUFGRkapx2RkZJRa3mazkZmZWWYZV52VOe+lPPjggyxcuJA1a9YwefJklixZwsCBA8s8Ztq0aYSEhLgfMTExFTqnuD44HMXvOnTmaAGgnIOHMnQohBDVQ6V6tH777Td69+5NSEgIR44cYcSIEdSqVYtly5Zx9OhRPvnkk3LXdfEcXJdbWqS08hdvL0+dFT1vaUaMGOF+HhcXR9OmTenYsSPbtm27ZK7apEmTGD9+vPt1dna2BFuihKJkeAc6v0AM7h4t58+okkBLCCGqhUr1aI0fP57hw4dz4MAB/Pz83Nv79evHzz//XK46wsPD0ev1JXqRTp48WaK3ySUyMrLU8gaDgbCwsDLLuOqszHnLq0OHDhiNRg4cOHDJMmazmeDgYI+HEBdzuIcO7Wj+fkU9WsjC0kIIUZ1UKtD69ddfeeKJJ0psr1u3brmH30wmE/Hx8axcudJj+8qVK+nSpUupx3Tu3LlE+RUrVtCxY0eMRmOZZVx1Vua85bV7926sVitRUVF/qh4h3GsdOuzozGZ3jxaaDB0KIUR1UqmhQz8/v1KTuPft20ft2rXLXc/48eNJTEykY8eOdO7cmQ8++IDU1FRGjhwJOIfZ0tLS3EORI0eOZPbs2YwfP54RI0aQlJTEvHnz3HcTAowZM4Zu3boxY8YMBgwYwFdffcWqVatYv359uc8LcObMGVJTUzlx4oT7vUHRYtqHDh3is88+o3///oSHh7Nnzx6eeeYZ2rdvLzPmiz/NY1Fpk7lEj5a9IN9LLRNCCFERlQq0BgwYwKuvvsp///tfwJnvlJqaynPPPcegQYPKXc+QIUM4ffo0r776Kunp6cTFxbF8+XIaNGgAQHp6usfcVrGxsSxfvpxx48bx7rvvEh0dzTvvvONxzi5durBo0SJefPFFJk+eTOPGjVm8eDGdOnUq93kBvv76ax5++GH3a9dEpFOmTOHll1/GZDLx448/8vbbb5OTk0NMTAx33HEHU6ZMQS+L/Yo/qahHy4FmMhUuwQOaa+gwXwItIYSoDio1j1Z2djb9+/dn9+7dnD9/nujoaDIyMujcuTPLly8nMDDwSrT1miTzaInSvPvoI+TnnKTB+Rhue/BWMpv1YuW8PdjOfoCNHAY+/Syxt3bzdjOFEOK6dUXn0QoODmb9+vWsXr2arVu34nA46NChA7179650g4UQRVw9WpoqzNEyXjR0mPfnl78SQghx5VU40HI4HCxYsIClS5dy5MgRNE0jNjaWyMjISk2RIIQoSSlXjpYdzWx2Dx0qTQ8KHHl53myeEEKIcqrQXYdKKe6++24ee+wx0tLSaN26Na1ateLo0aMMHz6ce++990q1U4jriivQ0jtshcnwrrsOnQGXXXK0hBCiWqhQj9aCBQv4+eef+fHHH+nZs6fHvp9++ol77rmHTz75hGHDhlVpI4W43hRNWGpHM5uK7jp0BVrSoyWEENVChXq0Fi5cyPPPP18iyAK47bbbeO655/jss8+qrHFCXK/cQ4cOm8c8WsqVoyU9WkIIUS1UKND67bff6Nu37yX39+vXjx07dvzpRglxvXPdDKx3OHO0SvRoFVuYXAghhO+qUKB15syZMpepiYiI4OzZs3+6UUJc9wp7tDSHHc1UlAwPzq8OmbBUCCGqhQrlaNntdgyGSx+i1+ux2Wx/ulFCXO8URcnwOrMJvSsZvnBRaenREkKI6qFCgZZSiuHDh2M2m0vdXyC//IWoGoVDh7rCoUO90XXXoWsJHvmsCSFEdVChQOuhhx66bBm541CIKuDO0bKimc1omobBpMPiWoLHIoGWEEJUBxUKtObPn3+l2iGEKMY1dKhTdjSTCQCjWU/RotIWbzVNCCFEBVQoGV4IcZW4hw5t6AoDLecyPM4cLYdFAi0hhKgOJNASwie5kuGdQ4eAcy6twhwth1UCLSGEqA4k0BLCxzhnhS/s0UKhFd7pazDp0VxDh1art5onhBCiAiTQEsLH2Kx293ODvugj6pwd3tWjJYGWEEJUBxJoCeFjbJaiuegMxqKPqNGkLzZ0KIGWEEJUBxJoCeFjrAVFgZbeqHc/dy7DU5gMb5NASwghqgMJtITwMTZrsUDLVDQ5sOfQoazAIIQQ1YEEWkL4GFvxHi2z0f3c2aNVGGjJUldCCFEtSKAlhI+xWR3u57qLe7RcOVp2CbSEEKI6kEBLCB9TPBleKxZoGYvnaNntqMJJTYUQQvguCbSE8DHFc7R0ZpP7efF5tBSg8vOvdtOEEEJUkARaQviY4j1aOnPpyfBK03Dk5V3tpgkhhKggCbSE8DE2i2vCUu2iHC09aIVDhxo4ciXQEkIIXyeBlhA+xm4tCrQ0c/EcrWI9WmiovNyr3zghhBAVIoGWED7GnaOlQLsoR6to6BAZOhRCiGpAAi0hfIwr0NLQLsrRKh5oaTJ0KIQQ1YAEWkL4GHuxRaW1S8yjpQBHrgwdCiGEr5NASwgfYy/Wo6WV6NFyJsM77zqUQEsIIXydBFpC+Bhb8R4tjxwtXdE8WhooydESQgifJ4GWED7GZnMGWhqe82gZS+RoSY+WEEL4Ogm0hPAx9sIJSzWlXZSjpS9a6xBJhhdCiOpAAi0hfIy9jKFDd46WTnq0hBCiOpBASwgfY3cNHaqLl+ApGjp0aDqZR0sIIaoBCbSE8DHuQAuFZirq0dLpNHR6PVAYaOVe8Er7hBBClJ8EWkL4GPf0DgqP6R0A9EYD4EyGl7sOhRDC90mgJYSPsRe767B4MjyAoTDQcmgajguSoyWEEL5OAi0hfIzd5nA+UQpdsWR4AL3ROXSoJEdLCCGqBQm0hPAxDrtrZvhLDx06ZFFpIYSoFiTQEsLHuHq0dEqVCLSMpqIcLZneQQghfJ8EWkL4GIfNlQzvedchgKHwtdKQtQ6FEKIa8HqgNWfOHGJjY/Hz8yM+Pp5169aVWX7t2rXEx8fj5+dHo0aNeO+990qUWbJkCS1btsRsNtOyZUuWLVtW4fMuXbqUhIQEwsPD0TSN7du3l6ijoKCAp59+mvDwcAIDA7n77rs5fvx4xS6AEBdxL8GjlMc8WgAGkxFwDh0qSYYXQgif59VAa/HixYwdO5YXXniB5ORkunbtSr9+/UhNTS21fEpKCv3796dr164kJyfz/PPPM3r0aJYsWeIuk5SUxJAhQ0hMTGTHjh0kJiYyePBgNm3aVKHzXrhwgVtuuYXp06dfsv1jx45l2bJlLFq0iPXr15OTk8Odd96J3W6/5DFCXI7D7hw61MoaOkRytIQQojrQlFLKWyfv1KkTHTp0YO7cue5tLVq04J577mHatGklyk+cOJGvv/6avXv3ureNHDmSHTt2kJSUBMCQIUPIzs7mu+++c5fp27cvoaGhLFy4sMLnPXLkCLGxsSQnJ9OuXTv39qysLGrXrs2nn37KkCFDADhx4gQxMTEsX76chISEcl2D7OxsQkJCyMrKIjg4uFzHiGvb+099QE7m14TkOXj48/+ir1HDuePsEb5/9UN2n9iBpjT6/XaQ5rt2ohkM3m2wEEJch8r799trPVoWi4WtW7fSp08fj+19+vRh48aNpR6TlJRUonxCQgJbtmzBarWWWcZVZ2XOW5qtW7ditVo96omOjiYuLq5C9QhxMYej2NBh8RytVS9jdOQAoHD+fyS9WkII4du8FmhlZmZit9uJiIjw2B4REUFGRkapx2RkZJRa3mazkZmZWWYZV52VOe+l2mIymQgNDa1QPQUFBWRnZ3s8hCjOUZijpVMKjM6cLCwX4PdvMegsztda4fCh3HkohBA+zevJ8JqmebxWSpXYdrnyF28vT50VPW95Xa6eadOmERIS4n7ExMT86XOKa4s7R4tiP6dH1oPdgskvoKicTPEghBA+z2uBVnh4OHq9vkTvz8mTJ0v0NrlERkaWWt5gMBAWFlZmGVedlTnvpdpisVg4e/ZsheqZNGkSWVlZ7sexY8fKfU5x7VMOhUMVzqNVPF4/5ryZwxhez73JIesdCiGEz/NaoGUymYiPj2flypUe21euXEmXLl1KPaZz584lyq9YsYKOHTtiLBxiuVQZV52VOW9p4uPjMRqNHvWkp6eza9euMusxm80EBwd7PIRwcU5W6uyl1RWPtDJ2AmCqFeXe5NDAcSb9ajZPCCFEBXn1dqXx48eTmJhIx44d6dy5Mx988AGpqamMHDkScPb+pKWl8cknnwDOOwxnz57N+PHjGTFiBElJScybN899NyHAmDFj6NatGzNmzGDAgAF89dVXrFq1ivXr15f7vABnzpwhNTWVEydOALBv3z7A2ZMVGRlJSEgIjz76KM888wxhYWHUqlWLCRMm0Lp1a3r37n3Fr524NtmsDigcDtcVH4JO/w0AY+0YnP8fOZyzwx/bBfS66u0UQghRPl4NtIYMGcLp06d59dVXSU9PJy4ujuXLl9OgQQPA2UNUfG6r2NhYli9fzrhx43j33XeJjo7mnXfeYdCgQe4yXbp0YdGiRbz44otMnjyZxo0bs3jxYjp16lTu8wJ8/fXXPPzww+7XQ4cOBWDKlCm8/PLLAMycORODwcDgwYPJy8ujV69eLFiwAL1ef0Wul7j2OXu0CocOXT1aeWchxznUbQirC+gBBw6dhuPE3lLrEUII4Ru8Oo+WkHm0hKfszDzmT5iNLW8tMXYdg7/4Gk4kwwc9oEYER/psZMnUJ0AV0O33VJom1KHmP1Z5u9lCCHHd8fl5tIQQJRXv0dL0hR/Ps0ecX0MbYjDpcPZoFd51eDrtqrdRCCFE+UmgJYQP8UyGLxyCPpPi/Boai8GkxxVoKQ1UTjbky1xsQgjhqyTQEsKHFE+G11y5fh49Wno0zfmxdWgaDpsGp/Z5oaVCCCHKQwItIXyIo3gyfKmB1kVDhzYNTklCvBBC+CoJtITwITarA3WpQKtWLEaTHrSLAq2Tv3uhpUIIIcrDq9M7CCE82W0KV46WpteDwwHZhQnvIfUwGHW4/j+y6/TSoyWEED5OAi0hfIi9eI6WwQC5p8FhAzSoEYFBFSXD2/VG6dESQggfJ0OHQvgQ+8U5WuedKxMQWBv0RnR6Da1w6NCuM+Cw6pxlCs57qcVCCCHKIoGWED7Ebi02vYPBCOcLFz8Pdq5xqGma+25Em96Iw25y7s88cLWbKoQQohwk0BLCh9isxXq0DAbILuzRCipaTFqnc4742/UG7A4JtIQQwpdJoCWED/GYsNRgKOrRKhZo6Q3OQMumN+GwFq6HmClzaQkhhC+SQEsIH1I8GV5nNBTlaBXv0XIHWkYcBXbnxsz9V7WdQgghykcCLSF8iMdah6XkaAEYjEZnWZ0BZbWj7MjQoRBC+CgJtITwIR7J8EYjnE937qgR6S6jdwVa+sKvNg1OHwK77aq2VQghxOVJoCWED7HbimaG1xsMcCHTuaNGbXcZg7EwGd7oTIR3qABwWOHc0avbWCGEEJclgZYQPsRWLBleMxqLAq2AcHcZg6mwJ8vg/OoIqO/cIXlaQgjhcyTQEsKHOIonw+tw9lQBBDoDrfOW8xzNOwaAVef8+Dr86znLSKAlhBA+RwItIXyIrfjM8BTmXJlqgNEfgNd+eY1TVmcvl1VzBmR2c4SznARaQgjhcyTQEsKHFE+G11PYmxUQBsCx88f4LuU7bHpnAObQOWeItxpCneXkzkMhhPA5EmgJ4UM81jp0BVqBzkT4JfuXoFDUCqoFgNKck5X+fiHLWe7UPvewoxBCCN8ggZYQPqT4hKWaynduLMzP+unYTwA0rB3r3K45P757Mg8BGuSfg9zTV7O5QgghLkMCLSF8SPEeLb2jMNAKCCctJ42UrBT0mp4GtRsWlnb2aJ3MTOVCzRjnJsnTEkIInyKBlhA+xFZ8wtJiPVq/nPgFgLa12xIcFAKAQzlQgF++g01h0c6yp2TNQyGE8CUSaAnhQ+wWO+5Ay37BuTEwnJ2ZOwHoENEB/xp+haVt2PV+BBTAOrNzElNJiBdCCN8igZYQPsRmLTYzvCvQCghnV+YuAOLC4jAFOKd6UMqOxRREYD6ss55xhmcydCiEED5FAi0hfIjdagdVeNehLRuAPL8QDp47CECr8FbuRaXBhtVYg6B8jT9sOaQaDBJoCSGEj5FASwgf4szRKuzRsjgDrX2OHOzKTphfGBEBERgK1ziksEcrzOp8vdXPDOdSwZrnjaYLIYQohQRaQvgQu03hnkerwDk/1u68kwDEhcehaRp6U1GPlqWwRwtga2AQoOD0oavcaiGEEJcigZYQPkIp5Qy0XEOHyjlh6f68dACa12oO4O7RUsqG1RiE6YIFlGKrvzN3S4YPhRDCd0igJYSPcNhcs7oXBlqaAlMNDmUfBaBJzSYA6F1Dh9ixmGqg2ez423SkaXYy9Hq581AIIXyIBFpC+AjngtJQtASPQgWGcfjcYQAa1WwEgME1dKhsWM3BALQzNwZgm59ZerSEEMKHSKAlhI9wLigNShX1aGUG1OK89Tw6TUfD4IZA0dAh2LH41QSgg38zoDAhXgItIYTwGRJoCeEjbFY7ABrOrzpNccg/EICYoBhMemeAVTR0CBZTDQBaGusDkOxnhtMHweFACCGE90mgJYSPcPVoFc/ROlw4TNgopJG7nHvoECgwBADQWKsDwEGjkWx7HmQfvwotFkIIcTkSaAnhI2wXDx2iOKw5nxcPtHR6A5rm/OhaNT12nYmAPEVMUAxK09hhluFDIYTwFRJoCeEjSuvROqosADQMaeguV3wuLaVs5PuFYs/Kon2d9kDh8KHceSiEED5BAi0hfERpgdYxew4A9YPqe5QtnhCfbw7FnnWuKNCSHi0hhPAZEmgJ4SOKpndwzqflwEF64TI89YMvDrSKpngoMIdiP3eODnU6ALDLbMIqgZYQQvgECbSE8BF2qwOlFK5A6w+jHgcKf4M/YX5hHmX1JlePVuHQ4ekzNAxpSIgxkHydjr2Fi1ALIYTwLgm0hPARzukdiqZlOGHUA1AvqB6apnmULb4MT4G5FrbMTHSajva12wGQ7LgAeeeuRrOFEEKUQQItIXyEM0erKNA6bnIGWhfnZwHoXUOH2Mn1r43t9GkA2kXeCEhCvBBC+AoJtITwERcHWif8nItExwTFlCjrToZXNnID6mDLzEQpRYcIZ55Wsp8Z9ceuK95mIYQQZfN6oDVnzhxiY2Px8/MjPj6edevWlVl+7dq1xMfH4+fnR6NGjXjvvfdKlFmyZAktW7bEbDbTsmVLli1bVuHzKqV4+eWXiY6Oxt/fnx49erB7926PMj169EDTNI/H0KFDK3EVhCicR0sVBVqpfmbgEoGWqahHy2oKxqoMOLKyaBnWEiM6zuj1pKb9cjWaLYQQogxeDbQWL17M2LFjeeGFF0hOTqZr167069eP1NTUUsunpKTQv39/unbtSnJyMs8//zyjR49myZIl7jJJSUkMGTKExMREduzYQWJiIoMHD2bTpk0VOu8//vEP3nrrLWbPns2vv/5KZGQkt99+O+fPn/do04gRI0hPT3c/3n///Sq+SuJ6YbcV79FSHCvM0Sot0HItw2Pyc77O86+D7fRpzHozcYF1AUjOlB4tIYTwNq8GWm+99RaPPvoojz32GC1atGDWrFnExMQwd+7cUsu/99571K9fn1mzZtGiRQsee+wxHnnkEd544w13mVmzZnH77bczadIkmjdvzqRJk+jVqxezZs0q93mVUsyaNYsXXniBgQMHEhcXx8cff0xubi6ff/65R5sCAgKIjIx0P0JCQqr+Qonrgq3Y0KEOxfHCWeFL79FyBlr+NZxJ8hcCIrCdygSgnWv4sOAkOOxXutlCCCHK4LVAy2KxsHXrVvr06eOxvU+fPmzcuLHUY5KSkkqUT0hIYMuWLVit1jLLuOosz3lTUlLIyMjwKGM2m+nevXuJtn322WeEh4fTqlUrJkyYUKLH62IFBQVkZ2d7PIQA1/QOzuBKQ2FFYdAZiAyMLFHWVJi/ZXYudejM0zrtDLQ61L8NgG1GPZw+dBVaLoQQ4lK8FmhlZmZit9uJiIjw2B4REUFGRkapx2RkZJRa3mazkZmZWWYZV53lOa/r6+Xa9uCDD7Jw4ULWrFnD5MmTWbJkCQMHDizzfU+bNo2QkBD3IyamZG+FuD4V79GicDaHujXqYtAZSpQ1mJ1jhiZ/55xbFwKisBd+BtpHxqNTcMRkJCN1/RVvtxBCiEvzejL8xfMDKaVKbLtc+Yu3l6fOqigzYsQIevfuTVxcHEOHDuWLL75g1apVbNu27ZLtnzRpEllZWe7HsWPHLllWXF+K33WoNOfPdWnDhgDGwkBr98kzAOTUqMfh/akopQgxh9DKGAxA0vG1V7jVQgghyuK1QCs8PBy9Xl+i9+rkyZMlepJcIiMjSy1vMBgICwsrs4yrzvKcNzLSOVRTkbYBdOjQAaPRyIEDl56/yGw2Exwc7PEQAsBmsbvvOiwr0Cqw2Vl5wBlgHTnnHHrO9w8naUsKjyz4lcycAm6u2QKApHOyFI8QQniT1wItk8lEfHw8K1eu9Ni+cuVKunTpUuoxnTt3LlF+xYoVdOzYEWPhBI6XKuOqszznjY2NJTIy0qOMxWJh7dq1l2wbwO7du7FarURFRZX11oUolc1S1KNlL/xkXhxoORyK0QuT2XWyAIBm4WaMRhsAtTCyet8p7vrXeprWcv6cbnKcxyEJ8UII4TUlkz+uovHjx5OYmEjHjh3p3LkzH3zwAampqYwcORJwDrOlpaXxySefADBy5Ehmz57N+PHjGTFiBElJScybN4+FCxe66xwzZgzdunVjxowZDBgwgK+++opVq1axfv36cp9X0zTGjh3L1KlTadq0KU2bNmXq1KkEBATwwAMPAHDo0CE+++wz+vfvT3h4OHv27OGZZ56hffv23HLLLVfrEopriNVStASPrXCEul6Neh5l/r3+MD/s/oO2Buc/Fs1rm9HZ/Ek9YqW2fyiNagdy+NQFXl0Vjn89B2d0Og4cW88NDbpfzbcihBCikFcDrSFDhnD69GleffVV0tPTiYuLY/ny5TRo0ACA9PR0j7mtYmNjWb58OePGjePdd98lOjqad955h0GDBrnLdOnShUWLFvHiiy8yefJkGjduzOLFi+nUqVO5zwvw7LPPkpeXx5NPPsnZs2fp1KkTK1asICgoCHD2jP3444+8/fbb5OTkEBMTwx133MGUKVPQ6/VX+tKJa5CtwIYr0LIW/ggV79FKz8rjrZXOocC74huS9d1aLPn5xDQJJfXISc5rIfz30RtJ/Hgbe9Oz6Z6vZ1uA4uf9yyTQEkIIL9GUK5tceEV2djYhISFkZWVJvtZ1bvHff+GPI/uw5vyPrEALy7qns/nBzfgbnFM5jFu8nWXJadzYMJSpHeDrN6cS1aw5nQc9y/L3dhFwIZ2/TO/BueBw7nl3AzfrprIi4g/iDDVZ+GDZKy4IIYSomPL+/fb6XYdCCCdbQdHQoV0Htf1ru4OsfRnnWZachqbBS3e2wuTnnEDLmp9PZOOaAOQGRnH+8HFqB5lZ8PCNOPLboinFLts5/rjwhzfekhBCXPck0BLCRxTP0XJoymPY8N/rDgPQLy6S1vVCMBaug2gtyMc/yESgIwuAE3tPAdA0IoiBCUNoW2ABYM4vRctUCSGEuHok0BLCR9gsjmLTO0C9IGci/Mnz+Xy1/QQAj3VtBICxcGZ4a34+AGH+FwD441iuu76uN93IrfnOzIAVB77m2JmifUIIIa4OCbSE8BHFZ4Z36JQ70Fq46RgWu4MO9WvSoX4oUDRhqSvQiqjj/CifOlPsRgxNo29YKwBy/NL466I1WO0OhBBCXD0SaAnhAxwOhd0ORUOHzjsOlVIs2XYcgMTORXfFmvwLe7QK8nHY7UTdEA7AWVtQ4QzzTg3q30rb/ALQYF/Oz8xcKROYCiHE1SSBlhA+wGYpnFRUOb86dIp6Nerx65GzpJ7JJdCkJ6FV0eLS5oBA9/OCvFxqt2uE0ZKNQzOQfvhcUcUxN3F3jnNY0RiyjblrD7HhYOYVfz9CCCGcJNASwgc4Z4UHhTPQsuucyfBLtjp7s/q3jiLAVDTtnd5gcA8fFuTkYG7QgFrnnEs/pW4ptn5mvRtJyLdiVAq9Xzqa6QRjF2/ndE7B1XhbQghx3ZNASwgf4OrR0hzOAEjTa/jrQvh2ZzoAg+LrlTjGXKMGAAW5F9CMRmobnD1Vx/acKSpk9Cek7k30yM0DIDx6K6fOFzDhfztwOGQKPSGEuNIk0BLCB1gLAy2dwzkdg5/RzOp9p8gpsFG3pj83NaxV4hi/wuHD/JwcAKIjnev2nD7tIP+CtahgbDfuzz4PgCNwK2ZTAav3neKjDSlX7P0IIYRwkkBLCB9gKyhMYC8MtPzNgXy/OwOAO9tGodNpJY4xBzp7tPIvOAOtmk3rEXAhA4XGif3nigrGdqNjfgFNrA4K7Pkk3OwcWpzx/e/sSsu6Qu9ICCEESKAlhE9wDR3qlLMnKsAvmJ/2OmdzL54EX5w50NmjVZDrDLT827Sm1tnfATi2t9jwYXQHNGMgD2SdBWBf3nJubxmG1a54emEyOQW2qn9DQgghAAm0hPAJlgJXjpazR8tOIBcsdiKCzbSrV7PUY/xcPVqFQ4d+bdoQ6gq09hS7s9BgggZduDMnl1p6P07knOC2jmlEhfiRknmBKV/tvkLvSgghhARaQvgAa76zV0lTzkArK98EOHuzShs2hGI9WoVDh4bQUOoE5aEpO1mZBWSfzisqfENf/JXiIYsRgM/3LWDmkDboNFiy7ThfJqddkfclhBDXOwm0hPABlnxXj5Zz6DA9yxkQ9b3EsCGAf5Bztfjc7Gz3tqA2LQjKPgpA2r6zRYWb9QNgyLG9hJiCOZJ9hLPaFkb3agrAC8t2cvT0hSp6N0IIIVwk0BLCB1jyCvOkCnO0LljM1AwwclNsybsNXWqEhjnLnivKxwro2JHQc/sAOF480AqpC1FtCVQO/hLaFoA52+cwsntDbmpYiwsWO099vo0818SpQgghqoQEWkL4AItr6LCwR8uuGejdIgKD/tIf0RqhziAs52xRoFWjWzdqnS0MtPaeQalic2UV9mo9ePYMoeZQjmQf4evDXzJraDtCA4zsSstm/H+3y/xaQghRhSTQEsIHuIYOwRlw2TVdmcOGAIGFgdaFYoGWMTKS2rX16OwWcrOtnPsjt+iAFncCEHTwJ55o+RAA725/l5BAB+8ndsSk1/HdrgxmfP97Fb0rIYQQEmgJ4QNcyfAo51e9wcitTcPLPKZGLefQYW7WOey2oikaQnrcSkj2YQCO/15s+DAiDmo3B3sBg60G6gfV50z+GT7e/TE3xdZixn2tAXj/58N8+PPhqnprQghxXZNASwgfYMnzXFS6WXQofkZ9mcf41whCp3euf1g8Tyv4jjsIPbsfgGO7ThYdoGnQ+v8AMO76gjEdxgCwYPcCTuWe4t729fhbwg0AvL58L58mHfnT70sIIa53EmgJ4QMsF1yLPDsDrbj6YZc9RtPpCKkTAcDZEyfc2/2aNyciyDlkmPb7GVTxnKvCQIuUddwe2oo2tduQZ8vjza1vAvBUzyY81bMxAJO/2i3BlhBC/EkSaAnhA3JdaxHiXIqnTYOyhw1dwurFAHA67ZjH9ph+N6O35WGx6cg8nlO0I7QB1O8CKLTkT3n+pufR0Pj28Lf8mvErABP63MCjt8YCzmDr7VUHPJPqhRBClJsEWkL4gLwLzslF7TpnoBVcI6Bcx9Wq6wy0zqSlemwPveduQrMPAZCydq/nQR0fcX7dMp9Woc34v2bOXq6pm6ZidVjRNI0X72jhnmNr5qr9vPz1buxyN6IQQlSYBFpC+ABL4aLSDs0ZzBhM5nIdFx7TAIA/Ug55bDeEhxMV6ZxR/ujmo54HtbwbAmtDTgb8/g2jO4wm1BzKwXMH+Xzv5wBomsb425vx8l0tAfg46SiPf7KF8/nWyr1BIYS4TkmgJYQPsFudSe3uQMtcvkCr7g3OQOiPwwex5Od57Gsy4GYAMvODKMgsdvehwQwdnNM7sPnfhJhDGBc/DnBO95CaXdQ7NvyWWGY/0B6zQcePv59k0NyNpJ4uNmWEEEKIMkmgJYSX2ax2wLm2oSpc1tBgMpXr2ODadQiuHYFyODjx+x6PfZE94zHbc3DoTRz6z/eeB3Z8GDQ9HF0PadsY0GQAN0beSJ4tj8kbJmN3FM0Qf2ebaP77RGfqBJnZ/0cOA95dzy+HT1f6/QohxPVEAi0hvOzCeecdh5qyowoXkDaWs0cLIKalc/6r1N2/eWzX6XRERTt7yo4mHUI5HEU7Q+pB6/ucz9e9iU7T8fdb/k6AIYBtJ7fxn73/8airbUxNvh51K63rhnA218qD/97Ev9cdliR5IYS4DAm0hPCyXw8eAEBvycGBM9Aqb44WQIPWzrULj/62vcS+Rre1AuCUPpoLGzZ67rx1vPPr79/Ayb3UrVGXZ298FoB3tr3DntMX9ZCF+PHfJzpzT7to7A7Fa9/u5anPt5FTYEMIIUTpJNASwstW7doJgMl63r2tIoFW/dbtADh55BC52Vke+2LaOJfxOR9Unz8++5/ngXWaQ4u7nc9/fgOAgU0H0qNeDywOC2NXj+Vs/lmPQ/xNemYOacerA1ph1Gss35nB3bPXc+CP8wghhChJAi0hvEgpRVp6YY+WNdu9vbw5WgCBNUOp3cA579XRnds99tUINVMzzAiajmN7MrEcT/M8uNsE59ddX8CJ7WiaxutdX6d+UH3SL6QzYe0ELHaLxyGapjGsc0MWPd6ZyGA/Dp+6wIB3N/Bl8kV1CyGEkEBLCG/acvQsmnL2BhlszkBLp9ejNxgqVE+DNu0BOPpbcol9sR2iADgV3pZzixd57oxqC60HO5+veBGUItgUzKyes/A3+LM5YzN/W/s3rI6S0zrENwjlm9G30qVxGLkWO2MXb2fMomSy8mQKCCGEcJFASwgv+u+Ww2iFeVk6mzPgMgUEVrieBoXDh0d/Sy6RoN64Qx0ATteK4/SSr3AUFHge3Gsy6M1wZB3sd96d2DS0KW/3fBuTzsRPx35izE9juGC9UOK84TXMfPLITYzp1RSdBl9tP0H/t9fJXYlCCFFIAi0hvORCgY3vD/yKn80ZWOnszkDGHFC+WeGLq9uiFXqjkZwzpzlz0XI8dRoGUSPUjN3gxyldFOe/v2iqh5r14eaRzufLn4UC55I9naM7M7PnTPz0fqxLW8eD3z7I3tMXzTIPGPQ6xt3ejP+N7EL9WgGknctj6Ae/8OwXOzh7wVKivBBCXE8k0BLCS5ZuO06B4TBB+SEA6AuHDk3+FQ+0jCYzdZs77zC8ePhQ0zSadHQuPn0i6lbOfP55yQq6PesMuLJS4cdXizbX68b8vvMJ9w/nUNYhHvj2AaZumkpmXmaJKuIbhLJ8TFfuv6k+AP/dcpzb3lzDp78cxWp3lCgvhBDXAwm0hPACh0OxYOMRjIG/E5JfEwC95pzZvTI9WgANXXlaFyXEA7TqGg0anA5rxZn9GeTt2u1ZwFwD7nrb+XzzB3B4rXtXXHgcX9z1Bbc3uB2bsrHw94X0W9KPlze+zL4z+zyqqWE2MG1ga74Y2ZkbIoI4m2tl8pe76P3WWr7anoZNAi4hxHVGAi0hvGDt/lMcOn0aXcBRAi3OHi1DgDMBvjI9WlCUEH9s907sNs+E9Jp1AmgYFwbA4dg7ObuwlF6txrcVLs2j4ItHIKvoLsIw/zDe6vEW/+7zb9rUbkO+PZ8lB5Zw3/+7j4e+e4jvj3yPzVE0n1bHhrX4ZvStvHJ3K8JrmDl6Opcxi7bT8801fLQ+RebeEkJcNyTQEuIqU0oxc9V+DIEH0ACTo3DoMMgIgLkSyfAAtes3JCCkJtaCfE7s/73E/pvuboSmwck68fz+yx8lp3oA6DsdIlpDbiYsegDysz12d4rqxH/6/Yf5CfNJaJiAXtOz7eQ2/rb2b9y57E4W/76YArsz2d6o1/FQl4b8/GwP/pZwA7UCTRw7k8er3+yh87QfmfLVLnafyCrZBiGEuIZIoCXEVbZq70l+O56FOWQvgZaaaOjRHDao4Zyk1C+wRqXq1XQ6j7sPL1Y7Joj2CQ0A2Nv0Af43bTPr/ruffb+kc+6PwoWiTQEw5FMICIP07bDwfrB43m2oaRodIzvyRvc3+GHQD4xsO5JafrVIy0njtU2vkfBFAh/t+ogcizOpPsBk4KmeTdj43G28fm8cjcIDOZ9v4+Oko9zxznru+td6Pv3lqEwLIYS4JmlKFivzquzsbEJCQsjKyiI4ONjbzRFXWL7VTr+315Fy+gw1m08l6lxD7tz7JAEX0gmr8wsHjqdw69BhdLp3cKXq3732R76fM5Pa9Rsy7J+zS+xXDsW6D39h57YLoHn+n9X85ki6Dm2Gyc8AJ5JhwV1gOQ/RHeCB/0KN2pc8b54tj2UHlrFg9wLSL6QDEGQK4oHmD/BgiwcJ9Qt1l3U4FOsPZrJ4yzFW7M7Aanf+CjIZdPS8oTZ3tY2mV/MI/E36Sl0DIYS4Gsr791sCLS+TQOv68taKfbzz00FqRSRjrbWYbsdvoeWxwYSf2o7W5CjHjhyizxOjaX1bn0rVn5dznvceT8RhtzHsn7OpXb9hqeUOP/cKR5IOcyHiBgra9OCPtHxQULt+EHc81YbAEDMc2wyfD4a8sxAUBffMceZxlcHqsPLt4W+Zt3MeR7KPAOBv8Gdg04H8X7P/o3HNxh7lz1ywsCw5jf/+eox9xZbxCTTpub1lBP1aR3FLk3BqmCs2gasQQlxpEmhVExJoXT82p5zhgQ9/weaw07j9e5zMT+XpnQOw5txGg6M/cLbVBU4eO8K9E6fQqMONlT7PV2+8xsFff6Fdwh30euSvpZax51zgyODBWA4fRh8ejvbYRH7eGUz+BRtBYX7c9XRbQiMDUSf34fjkAXTZB50dYM3vhC6jIeYm0LRLtsHusPPTsZ/48LcP2XumaO6tuLA4+jfqT7d63WgQ3MC9XSnFvj/O8/X2E3y94wTHz+a59xl0Gh0bhtK9WR06NgylZVQwgRJ4CSG8TAKtakICrevDsTO53DtnA5k5Fm6MO8Tv9g+p4VA8tekZsmhA27T/8VtEFnnns/nL9LeJiG18+Uov4ejO7Xzx2ovoDQYenvkeIXUiSy1nPXmSY48+RsEB51qLeTXrsSPur+QaamK059HmyEJC0pLB4UBnNhBYO4daN+QQUNsCNSIhuh0ER4N/LQgMh8Dazq9hTSC4LmgaSik2ntjI4n2LWXd8HTZVdLdh/aD6dI7uTPs67Wlfpz1RgVFohcckHzvH/9txgtW/n+TI6VyPdmsaNKldg8a1a1Av1J96of7UCfajpr+RkAAjNQNMhPgbCTTp0coIBoUQ4s+oNoHWnDlz+Oc//0l6ejqtWrVi1qxZdO3a9ZLl165dy/jx49m9ezfR0dE8++yzjBw50qPMkiVLmDx5MocOHaJx48a8/vrr3HvvvRU6r1KKV155hQ8++ICzZ8/SqVMn3n33XVq1auUuU1BQwIQJE1i4cCF5eXn06tWLOXPmUK9evXK/fwm0rn0HT54ncd5m0rPyaRrloCDin5wtOMvYU1byD8wGTcdtgT+y/PgOAJ7++H+Y/PwrfT6lFF+89gKpu36jdoNYBj3/KoE1Q0st6ygo4MxHH3F28X+xZWRgMdbgt7gnyA5pBEDY6Z2EZKVg15vI96tFnl84toBgavidpr7fDloErCJYf6pkxX41ISIOotpAZGuIbMPpoNp8d3Qla46tYesfWz2CLoA6AXVoE96GpqFNaRralGahzahXox7HzuSzdv8p1h/MZOfxLDKy88t1HYx6jRB/EzUDjNT0N1IzwEiIv4nIEDPRNf2JrulPvcKv0kMmhKioahFoLV68mMTERObMmcMtt9zC+++/z7///W/27NlD/fr1S5RPSUkhLi6OESNG8MQTT7BhwwaefPJJFi5cyKBBgwBISkqia9eu/P3vf+fee+9l2bJlvPTSS6xfv55OnTqV+7wzZszg9ddfZ8GCBTRr1ozXXnuNn3/+mX379hEUFATAX//6V/7f//t/LFiwgLCwMJ555hnOnDnD1q1b0evLl8grgda1y2p3sHBzKtOW/06e1U6jCEXN2I85kPU7N1gdTN7XgvXnn6RGznFuvq2Ab374iqCw2jw+Z/6fPnd25kn+M2kcedlZBIXX5q6xzxHV9IZLlld2OyeSNrD+y/+Scz4bvak+5/NaoenrlNkrpNM5aBlzjPZ1txDsOAo5f8DpQ6DsANiUEbsyotesGHR2CAiHoAhyAsL4xWxgq87Kdls2v1vOYqPkZKZGnZG6NepSL6ge9WrUIyYohiB9BDkXgsi5EEhmloHj5/I4c8HCuVwrWXlWzuVasVRwYtQQfyN1azp7x+qG+hc9rxlA3VB/QgOM0jsmhPBQLQKtTp060aFDB+bOneve1qJFC+655x6mTZtWovzEiRP5+uuv2bu3KOdj5MiR7Nixg6SkJACGDBlCdnY23333nbtM3759CQ0NZeHCheU6r1KK6Ohoxo4dy8SJEwFn71VERAQzZszgiSeeICsri9q1a/Ppp58yZMgQAE6cOEFMTAzLly8nISGhXNdAAq1rS77Vzp70bH7ae5Ivt6c5c410udzQKIX8Gss5U3CaWg7497FzbEx7kxwtnKZp3+E/pAWb/99SmnXuyl1jJ1ZJW86cSOPLf7zC2fQT6PR6br3/ITrecQ+azvNuQ4fdztZvv2TD4k+x2zx7mfxqhGH0DycwJIqoZu2JCA4mf+F8sv7IJT2yE+dCncGbpoOGrcMJjwnCXmDhTOpJzqTnkZ2th8JFswN0ZwjRp1PTcIKahhOE6tMINaQRpP+DAp2D3WYTe00m9gcEccA/kENYKSgWfPnnK2L/UDT8AyLPKmrlgA4New0zubVr4qgdg65mXcwB0ZgMgei0QDQCQO+HMvpToDeRTwE5BRc4d/482dnZ5ORkoSx5+FGAHT15mMhTZmxWRciFMwQXnCNLH8yRkKbkh9Qj2N9IkJ+BQLMBvU5Dp2nurwadhl6vYdRpGPQ6jHoNvQY6vZ0AMwT6aQSbjdT09yM0wI/QADMhyk5AdhZ+ljwM/v7oQ4Ix1KqFZjJVyc+AEOLKKe/fb6/1l1ssFrZu3cpzzz3nsb1Pnz5s3Lix1GOSkpLo08fzbqyEhATmzZuH1WrFaDSSlJTEuHHjSpSZNWtWuc+bkpJCRkaGx7nMZjPdu3dn48aNPPHEE2zduhWr1epRJjo6mri4ODZu3HjJQKugoICCggL36+zs7FLL/Vnf/HaCLUfOlrrv4tj64kj74tC7wJHNvoKlJcsqVey1KqUuVeK5UlDLkkaQ7TQK0AqLKO3imkArVn/XNefROS5qWLGXWvEXyrkrmxCPwgrNfT6roT5oelAaSlNogCoMCJwXQHPFB8UaVMrb0rSi8sV2mYDBhecEIAUUjdHRGCOK73RB2HWb0dktZDe2sfeHbwFoeuPNVJVa0XV54PW3WPnBbPb/sp6f//MR23/4lqimN2D2D0DTaeTl5PDHof1knfwDgEYdbqRV914c2JzEgc0byc85TX7Oac6f2kfGwTUE1gylXudWcDwN887F1MyuQXZQAwr8QtmfBPuTil8oz4uVBWShSEUH1Ct8AMqBURVgcFxAp6w0VA5iCwMsh9JwKM1ZjUPD9X1RaFzwM+DQGbFrJhxnDHAGwEYBqZT2s+diVIra2KitLIAVlKVYGYVy5KFUgccx7bN/QztmRK8LdM575nqPmkZpP/vO51rxFziAc8B5B5xwKAyOop9/l8ALGZisOVj1YDFqWIxgNWhYL/pNXeK/Y63oi6tODdCh0GMjWDuHXoEehV65glcdDjRU8Yd28Wtd0c9w6acs0S7nt0uVfO7epnAUlv2jlsbaDgbsKOwodGgY0WFUOgxo7q8GNPRKhw7Qo6FTzu+CDg29Kr19Ja5RKV0Kiot+3ylnO/M0f1KMTVCFr5Vy/d50nkvTQENzftWcV0Mrtt3160Nzfy1rf/HXReVc1/hK9qRqaLQNHHbF6vcld7aJIr5BLa+c22uBVmZmJna7nYiICI/tERERZGRklHpMRkZGqeVtNhuZmZlERUVdsoyrzvKc1/W1tDJHjx51lzGZTISGhl6yntJMmzaNV1555ZL7q8rGQ6f5fFNqldSlGc9Qo8kPVVIXQKoOZyRSAU9tt2GowGiQVaexsnUpE3+6fmc5zlSsAVXEDlgBHCedzQCOOqedIqZla5p1vrVKz+cXWIM7x07kt1VtWff5ArJP/UH2qT9KLdftL48Q1/N2NE2j2c23kp+Tw6mjhzmbcYK0vbs5tHUzF86dZd8v650Hhbqu71EoOPqn2mkva2dhfFXq9MoKuCKr+WjoDPXQGerjcJzGYTmIworNce5P12wD53sp5f1k1TQANSteaRnjEjUv5NPu4LnCVxpwcVqDKruCK+i3hhr/61jmd9+Lqub3pwd10VcvU0rHtu3dvN2Mq6JpnaDrL9ByuThaV0qVGcGXVv7i7eWps6rKXOxyZSZNmsT48ePdr7Ozs4mJiSmzzsro0aw2tQI8o5nizSrRwovfe7HnBY4cdl0YVGyfdtnnRdWW3F8z7whBBaUkUF+yXtjX6SQaGq5/Xt2dS4X/Abp/bxWeTykItlg86lCF/zUqwEoMyvUHRyvlepRokme3lnZRV0Tx/0R1hf/G6tAwaDpMmh6jTg96E5gC0HQ6/Pz11LqhLsbCQD0oLIzGHW9Gp6v6STo1TaPt7f1o2a0nqbt2cPZEGjaLBYfDjl9gDYJrR1C/ddsSCfh+NWoQ06oNMa3a0KZXX+w2K6m7fuP08VQKcnOhsGdEORS2zEzsWedw5OaibHZwOFAOR+G11YquXFEXgGsDNoeeArsei02PQodS4HAdpdOhM5nQmYzo/EzozGbQNDSdHr1Rj9nfgMnfgDnAgMGoR9nt2M+cxX46E0d+AcpSADYbyuFAKTsKDatDh9WhYbHpsCk9NrvB3cOCBkZDAH7mWuh0JhwOO6ia2O0x5BVkYrVewO6wuPpHnW1Bc/6ca7rCnwNdYUeXKowRVWEvk3L+YOoAveb87avToTRwKIVDKUyB2WhcQBXYwWoDix0sdjRbaX+ZtRLPXZ1/zp4jZz+vPjCAgw2bY8cZ2Ds0Z9+SphSachR26Tic30/lKNxWuN31motig8KenpItKvwcoKFzv3b2JBdtA005e6OCavrxnC0CXWEvlR0HNhzYNAdWFDYcWHFg1xw4Cnu97Cjnc63wq2t42eNz7PqZK9bbqBW1sdQrWKyT0maoQXZoK3Ra4Wf7ot8RSrl6w1TRc5Tndly9ZcW3O187VGn7io5x9aRd6jpXHR3te1b+DufqpFW091JzvBZohYeHo9frS/T+nDx5skRPkktkZGSp5Q0GA2FhYWWWcdVZnvNGRjpvh8/IyCAqKuqSZSwWC2fPnvXo1Tp58iRdunS55Ps2m82YzeZL7q8qfVpF0qdV6bf1V06HKqyrEh727umvBUazH43jO0F85Y7XG4zEtosntl0lKxBCiOuQ19Y6NJlMxMfHs3LlSo/tK1euvGSg0rlz5xLlV6xYQceOHTEajWWWcdVZnvPGxsYSGRnpUcZisbB27Vp3mfj4eIxGo0eZ9PR0du3aVWagJYQQQojriPKiRYsWKaPRqObNm6f27Nmjxo4dqwIDA9WRI0eUUko999xzKjEx0V3+8OHDKiAgQI0bN07t2bNHzZs3TxmNRvXFF1+4y2zYsEHp9Xo1ffp0tXfvXjV9+nRlMBjUL7/8Uu7zKqXU9OnTVUhIiFq6dKnauXOnuv/++1VUVJTKzs52lxk5cqSqV6+eWrVqldq2bZu67bbbVNu2bZXNZiv3NcjKylKAysrKqtQ1FEIIIcTVV96/314NtJRS6t1331UNGjRQJpNJdejQQa1du9a976GHHlLdu3f3KL9mzRrVvn17ZTKZVMOGDdXcuXNL1Pm///1P3XDDDcpoNKrmzZurJUuWVOi8SinlcDjUlClTVGRkpDKbzapbt25q586dHmXy8vLUqFGjVK1atZS/v7+68847VWpqaoXevwRaQgghRPVT3r/fXp8Z/non82gJIYQQ1U95/357LUdLCCGEEOJaJ4GWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVIoGWEEIIIcQVYvB2A653ron5s7OzvdwSIYQQQpSX6+/25RbYkUDLy86fPw9ATEyMl1sihBBCiIo6f/48ISEhl9wvax16mcPh4MSJEwQFBaFpmlfbkp2dTUxMDMeOHZN1F32AfD98j3xPfIt8P3zP9fQ9UUpx/vx5oqOj0ekunYklPVpeptPpqFevnreb4SE4OPia/4BUJ/L98D3yPfEt8v3wPdfL96SsniwXSYYXQgghhLhCJNASQgghhLhCJNASbmazmSlTpmA2m73dFIF8P3yRfE98i3w/fI98T0qSZHghhBBCiCtEerSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSuMQ0bNkTTNI/Hc88951EmNTWVu+66i8DAQMLDwxk9ejQWi8WjzM6dO+nevTv+/v7UrVuXV199tcR6TmvXriU+Ph4/Pz8aNWrEe++9V6I9S5YsoWXLlpjNZlq2bMmyZcuq/k1fo+bMmUNsbCx+fn7Ex8ezbt06bzep2nn55ZdLfB4iIyPd+5VSvPzyy0RHR+Pv70+PHj3YvXu3Rx0FBQU8/fTThIeHExgYyN13383x48c9ypw9e5bExERCQkIICQkhMTGRc+fOeZQpz+fuWvPzzz9z1113ER0djaZpfPnllx77fe36l+f3XnV3ue/J8OHDS3xmbr75Zo8y8j2pICWuKQ0aNFCvvvqqSk9Pdz/Onz/v3m+z2VRcXJzq2bOn2rZtm1q5cqWKjo5Wo0aNcpfJyspSERERaujQoWrnzp1qyZIlKigoSL3xxhvuMocPH1YBAQFqzJgxas+ePerDDz9URqNRffHFF+4yGzduVHq9Xk2dOlXt3btXTZ06VRkMBvXLL79cnYtRjS1atEgZjUb14Ycfqj179qgxY8aowMBAdfToUW83rVqZMmWKatWqlcfn4eTJk+7906dPV0FBQWrJkiVq586dasiQISoqKkplZ2e7y4wcOVLVrVtXrVy5Um3btk317NlTtW3bVtlsNneZvn37qri4OLVx40a1ceNGFRcXp+688073/vJ87q5Fy5cvVy+88IJasmSJAtSyZcs89vvS9S/P771rweW+Jw899JDq27evx2fm9OnTHmXke1IxEmhdYxo0aKBmzpx5yf3Lly9XOp1OpaWlubctXLhQmc1mlZWVpZRSas6cOSokJETl5+e7y0ybNk1FR0crh8OhlFLq2WefVc2bN/eo+4knnlA333yz+/XgwYNV3759PcokJCSooUOHVvr9XS9uuukmNXLkSI9tzZs3V88995yXWlQ9TZkyRbVt27bUfQ6HQ0VGRqrp06e7t+Xn56uQkBD13nvvKaWUOnfunDIajWrRokXuMmlpaUqn06nvv/9eKaXUnj17FODxD0RSUpIC1O+//66UKt/n7lp38R91X7v+5fm9d625VKA1YMCASx4j35OKk6HDa9CMGTMICwujXbt2vP766x5dsUlJScTFxREdHe3elpCQQEFBAVu3bnWX6d69u8eEcwkJCZw4cYIjR464y/Tp08fjvAkJCWzZsgWr1VpmmY0bN1bp+73WWCwWtm7dWuLa9enTR65dJRw4cIDo6GhiY2MZOnQohw8fBiAlJYWMjAyP62w2m+nevbv7Om/duhWr1epRJjo6mri4OHeZpKQkQkJC6NSpk7vMzTffTEhIiEeZy33urje+dv3L83vverFmzRrq1KlDs2bNGDFiBCdPnnTvk+9JxUmgdY0ZM2YMixYtYvXq1YwaNYpZs2bx5JNPuvdnZGQQERHhcUxoaCgmk4mMjIxLlnG9vlwZm81GZmZmmWVcdYjSZWZmYrfb5dpVgU6dOvHJJ5/www8/8OGHH5KRkUGXLl04ffq0+1qWdZ0zMjIwmUyEhoaWWaZOnTolzl2nTp0yPy8Xf+6uN752/cvze+960K9fPz777DN++ukn3nzzTX799Vduu+02CgoKAPmeVIbB2w0Ql/fyyy/zyiuvlFnm119/pWPHjowbN869rU2bNoSGhnLfffe5e7kANE0rcbxSymP7xWVUYfJhVZQp7fyiJLl2f16/fv3cz1u3bk3nzp1p3LgxH3/8sTvBtzLX+XKfl8qWuR750vUvz++0a92QIUPcz+Pi4ujYsSMNGjTg22+/ZeDAgZc8Tr4nlyY9WtXAqFGj2Lt3b5mPuLi4Uo91/TE5ePAgAJGRkSX+Ezh79ixWq9X9n0JpZVxdx5crYzAY3AHdpcpc/B+K8BQeHo5er5drdwUEBgbSunVrDhw44L77sKzrHBkZicVi4ezZs2WW+eOPP0qc69SpU2V+Xi7+3F1vfO36l+f33vUoKiqKBg0acODAAUC+J5UhgVY1EB4eTvPmzct8+Pn5lXpscnIy4PywAHTu3Jldu3aRnp7uLrNixQrMZjPx8fHuMj///LNHbteKFSuIjo6mYcOG7jIrV670ONeKFSvo2LEjRqOxzDJdunT5E1fj2mcymYiPjy9x7VauXCnX7k8qKChg7969REVFERsbS2RkpMd1tlgsrF271n2d4+PjMRqNHmXS09PZtWuXu0znzp3Jyspi8+bN7jKbNm0iKyvLo8zlPnfXG1+7/uX5vXc9On36NMeOHXP/DZHvSSVc7ex7ceVs3LhRvfXWWyo5OVkdPnxYLV68WEVHR6u7777bXcZ1S22vXr3Utm3b1KpVq1S9evU8bqk9d+6cioiIUPfff7/auXOnWrp0qQoODi51eodx48apPXv2qHnz5pWY3mHDhg1Kr9er6dOnq71796rp06fL9A7l5JreYd68eWrPnj1q7NixKjAwUB05csTbTatWnnnmGbVmzRp1+PBh9csvv6g777xTBQUFua/j9OnTVUhIiFq6dKnauXOnuv/++0udXqBevXpq1apVatu2beq2224r9Vb2Nm3aqKSkJJWUlKRat25d6q3sZX3urkXnz59XycnJKjk5WQHu30+uaUp86fqX5/fetaCs78n58+fVM888ozZu3KhSUlLU6tWrVefOnVXdunXle/InSKB1Ddm6davq1KmTCgkJUX5+fuqGG25QU6ZMURcuXPAod/ToUXXHHXcof39/VatWLTVq1CiP22eVUuq3335TXbt2VWazWUVGRqqXX365xO20a9asUe3bt1cmk0k1bNhQzZ07t0Sb/ve//6kbbrhBGY1G1bx5c7VkyZKqf+PXqHfffVc1aNBAmUwm1aFDB7V27VpvN6nacc3LZDQaVXR0tBo4cKDavXu3e7/D4VBTpkxRkZGRymw2q27duqmdO3d61JGXl6dGjRqlatWqpfz9/dWdd96pUlNTPcqcPn1aPfjggyooKEgFBQWpBx98UJ09e9ajTHk+d9ea1atXK6DE46GHHlJK+d71L8/vvequrO9Jbm6u6tOnj6pdu7YyGo2qfv366qGHHipxveV7UjGaUtVtilUhhBBCiOpBcrSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa4QCbSEEEIIIa6Q/w//433GSGoozgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53be5ea5",
   "metadata": {},
   "source": [
    "#### Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e23615a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.44865163,  0.59066829,  0.05293319, ..., -0.58936716,\n",
       "        -0.04356873, -0.06633906],\n",
       "       [ 1.44865163,  0.59066829, -0.39130197, ..., -0.27013618,\n",
       "         0.08640684,  0.08915105],\n",
       "       [ 1.44865163,  0.59066829, -0.44702926, ..., -0.13753572,\n",
       "         0.13323164,  2.24329255],\n",
       "       ...,\n",
       "       [ 1.44865163,  0.59066829,  0.20032554, ..., -0.54337975,\n",
       "         2.51121768,  0.12145607],\n",
       "       [-0.69029709,  0.59066829, -0.13538389, ..., -0.41944059,\n",
       "        -0.56977032,  0.21304614],\n",
       "       [-0.69029709,  0.59066829, -0.72930698, ..., -0.62009417,\n",
       "        -0.50488752, -0.52286938]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_standard = StandardScaler().fit_transform(data)\n",
    "scaled_standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b7e7e3f",
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.614870e-17</td>\n",
       "      <td>3.552714e-16</td>\n",
       "      <td>-3.431598e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.037175e-17</td>\n",
       "      <td>3.633457e-17</td>\n",
       "      <td>2.422305e-17</td>\n",
       "      <td>-8.074349e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>-1.995342e+00</td>\n",
       "      <td>-9.496831e-01</td>\n",
       "      <td>-0.778795</td>\n",
       "      <td>-8.373344e-01</td>\n",
       "      <td>-6.283430e-01</td>\n",
       "      <td>-6.044165e-01</td>\n",
       "      <td>-5.402644e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>-7.023369e-01</td>\n",
       "      <td>-7.023339e-01</td>\n",
       "      <td>-0.578306</td>\n",
       "      <td>-6.108364e-01</td>\n",
       "      <td>-4.804306e-01</td>\n",
       "      <td>-5.511349e-01</td>\n",
       "      <td>-3.964005e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>-2.767602e-01</td>\n",
       "      <td>-0.294258</td>\n",
       "      <td>-3.366684e-01</td>\n",
       "      <td>-3.188045e-01</td>\n",
       "      <td>-4.336004e-01</td>\n",
       "      <td>-1.985766e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.448652e+00</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>3.905226e-01</td>\n",
       "      <td>0.189092</td>\n",
       "      <td>2.849105e-01</td>\n",
       "      <td>9.946441e-02</td>\n",
       "      <td>2.184822e-01</td>\n",
       "      <td>1.048598e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.448652e+00</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>7.927738e+00</td>\n",
       "      <td>9.183650</td>\n",
       "      <td>8.936528e+00</td>\n",
       "      <td>1.191900e+01</td>\n",
       "      <td>7.967672e+00</td>\n",
       "      <td>1.647845e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Channel        Region         Fresh        Milk       Grocery  \\\n",
       "count  4.400000e+02  4.400000e+02  4.400000e+02  440.000000  4.400000e+02   \n",
       "mean   1.614870e-17  3.552714e-16 -3.431598e-17    0.000000 -4.037175e-17   \n",
       "std    1.001138e+00  1.001138e+00  1.001138e+00    1.001138  1.001138e+00   \n",
       "min   -6.902971e-01 -1.995342e+00 -9.496831e-01   -0.778795 -8.373344e-01   \n",
       "25%   -6.902971e-01 -7.023369e-01 -7.023339e-01   -0.578306 -6.108364e-01   \n",
       "50%   -6.902971e-01  5.906683e-01 -2.767602e-01   -0.294258 -3.366684e-01   \n",
       "75%    1.448652e+00  5.906683e-01  3.905226e-01    0.189092  2.849105e-01   \n",
       "max    1.448652e+00  5.906683e-01  7.927738e+00    9.183650  8.936528e+00   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count  4.400000e+02      4.400000e+02  4.400000e+02  \n",
       "mean   3.633457e-17      2.422305e-17 -8.074349e-18  \n",
       "std    1.001138e+00      1.001138e+00  1.001138e+00  \n",
       "min   -6.283430e-01     -6.044165e-01 -5.402644e-01  \n",
       "25%   -4.804306e-01     -5.511349e-01 -3.964005e-01  \n",
       "50%   -3.188045e-01     -4.336004e-01 -1.985766e-01  \n",
       "75%    9.946441e-02      2.184822e-01  1.048598e-01  \n",
       "max    1.191900e+01      7.967672e+00  1.647845e+01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_standard_dataframe = pd.DataFrame(scaled_standard, columns = data.columns)\n",
    "scaled_standard_dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "35c4424d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(scaled_standard_dataframe).plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae98f31",
   "metadata": {},
   "source": [
    "#### MinMax Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b107731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 1.00000000e+00, 1.12940044e-01, ...,\n",
       "        3.10630465e-03, 6.54271997e-02, 2.78473091e-02],\n",
       "       [1.00000000e+00, 1.00000000e+00, 6.28990263e-02, ...,\n",
       "        2.85484189e-02, 8.05898491e-02, 3.69837297e-02],\n",
       "       [1.00000000e+00, 1.00000000e+00, 5.66216072e-02, ...,\n",
       "        3.91164289e-02, 8.60523222e-02, 1.63558615e-01],\n",
       "       ...,\n",
       "       [1.00000000e+00, 1.00000000e+00, 1.29543104e-01, ...,\n",
       "        6.77141542e-03, 3.63462669e-01, 3.88819358e-02],\n",
       "       [0.00000000e+00, 1.00000000e+00, 9.17270036e-02, ...,\n",
       "        1.66491355e-02, 4.04174015e-03, 4.42636629e-02],\n",
       "       [0.00000000e+00, 1.00000000e+00, 2.48243393e-02, ...,\n",
       "        6.57418973e-04, 1.16108172e-02, 1.02211097e-03]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_minmax = MinMaxScaler().fit_transform(data)\n",
    "scaled_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f059bfff",
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.322727</td>\n",
       "      <td>0.771591</td>\n",
       "      <td>0.106977</td>\n",
       "      <td>0.078173</td>\n",
       "      <td>0.085671</td>\n",
       "      <td>0.050078</td>\n",
       "      <td>0.070510</td>\n",
       "      <td>0.031745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.387136</td>\n",
       "      <td>0.112774</td>\n",
       "      <td>0.100491</td>\n",
       "      <td>0.102430</td>\n",
       "      <td>0.079789</td>\n",
       "      <td>0.116790</td>\n",
       "      <td>0.058826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.027863</td>\n",
       "      <td>0.020124</td>\n",
       "      <td>0.023174</td>\n",
       "      <td>0.011788</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>0.008453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.075802</td>\n",
       "      <td>0.048636</td>\n",
       "      <td>0.051225</td>\n",
       "      <td>0.024670</td>\n",
       "      <td>0.019927</td>\n",
       "      <td>0.020077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150968</td>\n",
       "      <td>0.097154</td>\n",
       "      <td>0.114821</td>\n",
       "      <td>0.058005</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>0.037907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region       Fresh        Milk     Grocery      Frozen  \\\n",
       "count  440.000000  440.000000  440.000000  440.000000  440.000000  440.000000   \n",
       "mean     0.322727    0.771591    0.106977    0.078173    0.085671    0.050078   \n",
       "std      0.468052    0.387136    0.112774    0.100491    0.102430    0.079789   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.500000    0.027863    0.020124    0.023174    0.011788   \n",
       "50%      0.000000    1.000000    0.075802    0.048636    0.051225    0.024670   \n",
       "75%      1.000000    1.000000    0.150968    0.097154    0.114821    0.058005   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "       Detergents_Paper  Delicassen  \n",
       "count        440.000000  440.000000  \n",
       "mean           0.070510    0.031745  \n",
       "std            0.116790    0.058826  \n",
       "min            0.000000    0.000000  \n",
       "25%            0.006216    0.008453  \n",
       "50%            0.019927    0.020077  \n",
       "75%            0.095997    0.037907  \n",
       "max            1.000000    1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_minmax_dataframe = pd.DataFrame(scaled_minmax, columns = data.columns)\n",
    "scaled_minmax_dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bd8caaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(scaled_minmax_dataframe).plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730d6885",
   "metadata": {},
   "source": [
    "### Plotting for outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9c9f9073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "scaled_standard_dataframe.boxplot(column = [col for col in list(scaled_standard_dataframe)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7bd5ae36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "scaled_minmax_dataframe.boxplot(column = [col for col in list(scaled_minmax_dataframe)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365ab523",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "Scaling the data using StandardScaler method bought the data into uniform units across all the columns. Because of this scaling, each feature will now have almost equal weight and will be considered equally important without any bias and hence it becomes easier for the model to make predictions. \n",
    "Scaling the data using MinMaxScaler method also bought the data into uniform units across all the columns. As we can see for data using MinMaxScaler, the range of values are between [0,1]. MinMaxScaler works well if the data is not normally distributed(Gaussian). Comparatively, there are a lot of outliers in MinMaxScaler and since the data is normally distributed, we'll be choosing StandardScaler in the further predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "23eb1396",
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9413</td>\n",
       "      <td>8259</td>\n",
       "      <td>5126</td>\n",
       "      <td>666</td>\n",
       "      <td>1795</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12126</td>\n",
       "      <td>3199</td>\n",
       "      <td>6975</td>\n",
       "      <td>480</td>\n",
       "      <td>3140</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7579</td>\n",
       "      <td>4956</td>\n",
       "      <td>9426</td>\n",
       "      <td>1669</td>\n",
       "      <td>3321</td>\n",
       "      <td>2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5963</td>\n",
       "      <td>3648</td>\n",
       "      <td>6192</td>\n",
       "      <td>425</td>\n",
       "      <td>1716</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6006</td>\n",
       "      <td>11093</td>\n",
       "      <td>18881</td>\n",
       "      <td>1159</td>\n",
       "      <td>7425</td>\n",
       "      <td>2098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0  12669   9656     7561     214              2674        1338\n",
       "1   7057   9810     9568    1762              3293        1776\n",
       "2   6353   8808     7684    2405              3516        7844\n",
       "3  13265   1196     4221    6404               507        1788\n",
       "4  22615   5410     7198    3915              1777        5185\n",
       "5   9413   8259     5126     666              1795        1451\n",
       "6  12126   3199     6975     480              3140         545\n",
       "7   7579   4956     9426    1669              3321        2566\n",
       "8   5963   3648     6192     425              1716         750\n",
       "9   6006  11093    18881    1159              7425        2098"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b724ef26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>,\n",
       "        <Axes: ylabel='Frequency'>],\n",
       "       [<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>,\n",
       "        <Axes: ylabel='Frequency'>],\n",
       "       [<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>,\n",
       "        <Axes: ylabel='Frequency'>]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='hist', bins=50, subplots=True, layout=(3,3), legend=True, figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1c78dd",
   "metadata": {},
   "source": [
    "#### The histograms show an exponential decline in the number of orders for the respected products. Hence this could be a cluster consisting of larger size companies with higher purchase quanitites for these particular items."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e89a25",
   "metadata": {},
   "source": [
    "### RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a28d30e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "1    298\n",
       "0    142\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = scaled_standard_dataframe.drop(['Channel'], axis=1)\n",
    "#since Channel is our target variable\n",
    "Y = data['Channel']\n",
    "Y[Y == 1] = 1\n",
    "Y[Y == 2] = 0\n",
    "Y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a444ed18",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "223acf40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression:  0.464938511811836\n",
      "K-Nearest Neighbors:  0.8909090909090909\n",
      "Support Vector Classifier:  0.8818181818181818\n",
      "Gradient Boosting Classifier:  0.9090909090909091\n",
      "Random Forest Classifier:  0.9\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression: \", LinearRegression().fit(X_train, Y_train).score(X_test, Y_test))\n",
    "print(\"K-Nearest Neighbors: \", KNeighborsClassifier().fit(X_train, Y_train).score(X_test, Y_test))\n",
    "print(\"Support Vector Classifier: \", SVC().fit(X_train, Y_train).score(X_test, Y_test))\n",
    "print(\"Gradient Boosting Classifier: \", GradientBoostingClassifier().fit(X_train, Y_train).score(X_test, Y_test))\n",
    "print(\"Random Forest Classifier: \", RandomForestClassifier().fit(X_train, Y_train).score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24919fee",
   "metadata": {},
   "source": [
    "#### Since Gradient Boosting Classifier is giving the best accuracy score of all, we will be use Gradient Boosting Classifier as the estimator for RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a531886f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFECV(estimator=GradientBoostingClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RFECV</label><div class=\"sk-toggleable__content\"><pre>RFECV(estimator=GradientBoostingClassifier())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RFECV(estimator=GradientBoostingClassifier())"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv = RFECV(estimator=GradientBoostingClassifier())\n",
    "rfecv.fit(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9fbb79fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_test_score': array([0.88636364, 0.90681818, 0.90681818, 0.90681818, 0.92045455,\n",
       "        0.90909091, 0.89090909]),\n",
       " 'std_test_score': array([0.01016395, 0.02317736, 0.02530802, 0.02426609, 0.01607061,\n",
       "        0.01607061, 0.04906144]),\n",
       " 'split0_test_score': array([0.875     , 0.90909091, 0.88636364, 0.90909091, 0.92045455,\n",
       "        0.93181818, 0.93181818]),\n",
       " 'split1_test_score': array([0.89772727, 0.90909091, 0.88636364, 0.875     , 0.89772727,\n",
       "        0.88636364, 0.89772727]),\n",
       " 'split2_test_score': array([0.875     , 0.93181818, 0.93181818, 0.92045455, 0.93181818,\n",
       "        0.92045455, 0.79545455]),\n",
       " 'split3_test_score': array([0.88636364, 0.92045455, 0.94318182, 0.94318182, 0.94318182,\n",
       "        0.89772727, 0.92045455]),\n",
       " 'split4_test_score': array([0.89772727, 0.86363636, 0.88636364, 0.88636364, 0.90909091,\n",
       "        0.90909091, 0.90909091])}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf19b50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_scores = len(rfecv.cv_results_['mean_test_score'])\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score\")\n",
    "plt.plot(range(1, n_scores+1), rfecv.cv_results_['mean_test_score'])\n",
    "plt.title(\"Optimal Number of features: {}\".format(rfecv.n_features_))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9fe2e0",
   "metadata": {},
   "source": [
    "#### K-Means for 2 to 15 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c2971d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for i in range(2,16):\n",
    "    clustering = KMeans(n_clusters=i, random_state=5)\n",
    "    clustering.fit(scaled_standard)\n",
    "    wcss.append(clustering.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "162307dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2599.3873849123083,\n",
       " 2149.283956221759,\n",
       " 1851.7595764506573,\n",
       " 1547.092741153285,\n",
       " 1313.961987424017,\n",
       " 1191.0653549805397,\n",
       " 1064.9511210223977,\n",
       " 979.1114496637405,\n",
       " 913.4639657997401,\n",
       " 851.7041522431481,\n",
       " 803.4009723994486,\n",
       " 777.1657522570288,\n",
       " 721.0902919400475,\n",
       " 686.7440851024853]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1db36072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(2,16)), wcss, marker='x')\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6601d9",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "In the above graph, the elbow point comes at around number of clusters(n)=6 which will be the optimal number of clusters for the above data. After n=6, as we keep on increasing the number of clusters, the wcss decreases slightly.\n",
    "Hence, the optimal number of clusters=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cad86477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=6)\n",
    "kmeans.fit(scaled_standard_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "13a35f5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the squared error: 1314.1997560278032\n",
      "Centroids: [[ 1.44865163e+00  1.69928497e-01 -3.14722179e-01  4.52466342e-01\n",
      "   6.66146634e-01 -3.51066687e-01  6.83203927e-01  4.65876480e-02]\n",
      " [-5.95232699e-01  3.03333796e-01  1.85960912e+00 -1.24058137e-01\n",
      "  -2.57815536e-01  1.46400382e+00 -4.26267584e-01  4.13676499e-01]\n",
      " [-6.90297086e-01  5.90668285e-01  1.96681731e+00  5.17550306e+00\n",
      "   1.28721685e+00  6.90059988e+00 -5.54861977e-01  1.64784475e+01]\n",
      " [-5.90811099e-01 -1.58939862e+00 -1.17369902e-01 -3.50992288e-01\n",
      "  -4.02741180e-01  1.33513200e-02 -4.22747572e-01 -1.62015752e-01]\n",
      " [-6.90297086e-01  5.90668285e-01 -2.26969330e-01 -3.81612608e-01\n",
      "  -4.75234627e-01 -1.72435121e-01 -4.42861386e-01 -1.86419955e-01]\n",
      " [ 1.44865163e+00 -5.58343155e-02  3.13830315e-01  3.92190593e+00\n",
      "   4.27561037e+00 -3.57419457e-03  4.61816580e+00  5.03365339e-01]]\n"
     ]
    }
   ],
   "source": [
    "print('Sum of the squared error: {}'.format(kmeans.inertia_))\n",
    "print('Centroids: {}'.format(kmeans.cluster_centers_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "22dbaa60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    172\n",
       "0    126\n",
       "3     86\n",
       "1     45\n",
       "5     10\n",
       "2      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(kmeans.labels_).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9393c177",
   "metadata": {},
   "source": [
    "#### PCA with 2 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cdef17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "wholesale_data_pca_2 = pca.fit_transform(scaled_standard_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ae8edfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.38750123, 0.22374588])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022ec32e",
   "metadata": {},
   "source": [
    "#### Variance of 1st component is 38.75% and 2nd component is 22.37% i.e The principal component 1 holds 38.75% of the information whereas the principal component 2 holds 22.37% of the information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9e9a56ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZEhERkaCmZEhERESCmpIhERERCWohgQ5A5GRkbS7k/AQ2HUIaY5xxgQ5JREQKoWRIpBRZayF9BjbtFfDsObTVYF1dMJUfwThrBzQ+ERE5lprJREqRTX0Je/DxIxIhAAtZy7F7r8S6kwIWm4iIFEzJkEgpse4dkDa5kL1u8OzHpk7xa0wiIlI0JUMipSVjHmB8FHBDxgdYm+2ngEREpDiUDImUEuveTtH/pTLBk+KPcEREpJiUDImUFkc1wBZRyAmOSv6IRkREiknJkEgpMeGXAG4fJZwQ3gNjwv0VkoiIFIOSIZFSYkKbQnhfCu435ABCMVG3+zkqEREpSoVJhho0aIAx5pjHsGHDCiy/dOnSAsv//vvvfo5cgomJeRIiBgLOw1u8/zjrYKpN9yZMIiJSrlSYSRdXrVqF2/1PE8TPP//MRRddxJVXXunzuPXr11O5cuW85zVr1iyzGEWMCcPEjMNWugOyloHNgJAmENYeY3yNNBMRkUCpMMnQ0UnMU089RePGjencubPP42JjY6lSpUqxr5OVlUVWVlbe85QUjfyRkjPOGhB5eaDDEBGRYqgwzWRHys7OZsaMGdx4441F/rXdunVr4uPj6datG0uWLCny3BMmTCAmJibvUbdu3dIKW0RERMqhCpkMzZs3jwMHDnD99dcXWiY+Pp6pU6cyZ84cPvjgA0455RS6devG8uXLfZ57zJgxJCcn5z0SExNLOXoREREpT4y1tqiJUcqdiy++mLCwMD788MMSHdenTx+MMSxYsKDYx6SkpBATE0NycnK+vkciIiJSfpXk/l3haoa2bt3K4sWLufnmm0t87DnnnMOGDRvKICoRERGpqCpcMjRt2jRiY2Pp3bt3iY9ds2YN8fHxZRCViIiIVFQVZjQZgMfjYdq0aQwePJiQkPyhjxkzhu3btzN9+nQAXnzxRRo0aECLFi3yOlzPmTOHOXPmBCJ0ERERKacqVDK0ePFitm3bxo033njMvp07d7Jt27a859nZ2YwaNYrt27cTERFBixYt+Pjjj+nVq5c/QxYREZFyrkJ2oPYndaAWERGpeMq0A/W6det44oknmDx5Mnv27DnmwgXV2oiIiIiUVyWqGVq4cCF9+vShadOmHDx4kPT0dN599126du0KwN9//01CQkK+ZTMqOtUMiYiIVDxlVjM0btw4Ro0axc8//8yWLVu47777uOSSS/jf//53QgGLiIiIBEqJOlD/8ssvvPXWWwAYY7j33nupU6cOV1xxBbNmzeLss88ukyBFREREykqJkiGXy8WBAwfybRs4cCAOh4OrrrqK559/vjRjExERESlzJUqGWrVqxZIlS2jbtm2+7QMGDMDj8TB48OBSDU5ERESkrJUoGbrtttsKXeh04MCBAEydOvXEoxIRERHxE80zVASNJhMREal4ymw02f79+5k4cSIpKSnH7EtOTi50n4iIiEh5VaJkaNKkSSxfvrzADCsmJoYvv/ySiRMnllpwIiIiImWtRMnQnDlzGDp0aKH7b731Vt5///0TDkpERETEX0qUDG3atImmTZsWur9p06Zs2rTphIMSERER8ZcSJUNOp5MdO3YUun/Hjh04HCVe7kxEREQkYEqUubRu3Zp58+YVun/u3Lm0bt36RGMSERER8ZsSzTN0xx13cNVVV1GnTh1uu+02nE4nAG63m8mTJ/Pvf/+bmTNnlkmgIiIiImWhxPMMPfjgg0yYMIHo6GgaNWqEMYZNmzaRmprKvffey1NPPVVWsQaE5hkSERGpeEpy/z6uSRdXrVrF22+/zYYNG7DW0qxZMwYNGnRSLtSqZEhERKTiKcn9u0TNZOnp6dx7773MmzePnJwcunXrxsSJE6lRo8YJBSwiIiISKCXqQD127FjeeOMNevfuzcCBA1m8eDG33XZbWcUmIiIiUuZKVDP0wQcf8Prrr3PVVVcBcPXVV3PuuefidrvzOlOLiIiIVCQlqhlKTEzkvPPOy3t+9tlnExIS4nPuIREREZHyrETJkNvtJiwsLN+2kJAQcnNzSzUoEREREX8pUTOZtZbrr78el8uVty0zM5OhQ4cSFRWVt+2DDz4ovQhFREREylCJkqHBgwcfs+2aa64ptWBERERE/K1EydC0adPKKg4RERGRgNCqqiIiIhLUlAyJiIhIUFMyJCIiIkFNyZCIiIgENSVDIiIiEtSUDImIiEhQUzIkIiIiQU3JkIiIiAQ1JUMiIiIS1JQMiYiISFBTMiQiIiJBTcmQiIiIBDUlQyIiIhLUKkwyNG7cOIwx+R5xcXE+j1m2bBlt27YlPDycRo0aMWXKFD9FKyIiIhVFSKADKIkWLVqwePHivOdOp7PQsps3b6ZXr14MGTKEGTNm8PXXX3P77bdTs2ZNLr/8cn+EKyIiIhVAhUqGQkJCiqwNOmzKlCnUq1ePF198EYDTTjuN77//nueee07JkIiIiOSpMM1kABs2bCAhIYGGDRty1VVX8eeffxZaduXKlXTv3j3ftosvvpjvv/+enJycQo/LysoiJSUl30NEREROXhUmGWrfvj3Tp0/ns88+47XXXiMpKYmOHTuyd+/eAssnJSVRq1atfNtq1apFbm4ue/bsKfQ6EyZMICYmJu9Rt27dUn0dIiIiUr5UmGSoZ8+eXH755Zx++ulceOGFfPzxxwC8+eabhR5jjMn33Fpb4PYjjRkzhuTk5LxHYmJiKUQvIiIi5VWF6jN0pKioKE4//XQ2bNhQ4P64uDiSkpLybdu1axchISFUr1690PO6XC5cLlepxioiIiLlV4WpGTpaVlYWv/32G/Hx8QXu79ChA4sWLcq3beHChbRr147Q0FB/hCgiIiIVQIVJhkaNGsWyZcvYvHkz3377LVdccQUpKSkMHjwY8DZvXXfddXnlhw4dytatWxk5ciS//fYb//3vf3n99dcZNWpUoF6CiIiIlEMVppnsr7/+YuDAgezZs4eaNWtyzjnn8M0331C/fn0Adu7cybZt2/LKN2zYkE8++YS7776bl19+mYSEBF566SUNqxcREZF8jD3cq1gKlJKSQkxMDMnJyVSuXDnQ4YiIiEgxlOT+XWGayURERETKQoVpJpNjWWsh5wfI3QaOaHB1wpiIQIclIiJSoSgZKqesOwmbPtub7ODEuDpBxGUYR1Xv/uxV2OQHwL31n4NMFETdDlE3+5xLqUzjtjmQ+Sk2/R1wJ4KjKibiUoi4EuOIDkhMIiIivqjPUBEC0WfIZv4Pe2Ak4Dn0ADBgIjFVpwJh2H2DAPcR+/9hKt2JqXSHX2I9krWZ2P23QPY3eFtgPcChpMwRj6k+E+NM8HtcUv7s3Pw3n01bQtLmXVSqEkXXgZ1o3qFZwJJ4ETn5lOT+rWSoCP5OhmzOH9i9ffEmEkf/ahxgwiHkNMhZQ0GJkFcIJvYrjKNamcZ6NE/KeEifTsFxOSG0JY7q7/k1JilfrLXMeOx9pj/2Lg6Ht8uiMQZ3rpuzerTi4ffuISIqPMBRisjJQB2oKzCbfnh5kYJyVA/YjENNZ4UlQgBuyPy09IPzwXrSIH02hcflhpx12Jyf/BmWlDOf/udzpj/6LljwuD143B7cuW4Aflj0I8/d+HKAIxSRYKRkqLzJWoq3+aswxanIc2LdhS9GWyZy/wAyiijkgOzv/RGNlEMej4e3n5xT+H63h+Xvf8OOTUmFlhERKQtKhsobm1sKJ3FjnLVK4TwlUZy+HraY5eRktOXnRHZt852kG2P45sMf/BSRiIiXkqHyJqw14PRRwAmOhCLKhEJ4z7xn1lpszo/YjI+xWV97R3yVttBTvKPZfLIQ1r70ry0VQlZGdpFlHA5TrHIiIqVJyVA5YyKvxXczmQcqPwSEUtivz0TfjXHEAIeG4O/pjd17BTb5buz+G7C7zvMOfS/NuE0ERF5N4TU/Tghthwk9rVSvKxVHnWbxhIT6SuLBneuh0Rn1/BSRiIiXkqFyxrjO9c4VBOSv/XECBlP5MRzhF2Kqz/SOKjuSoxqm8qOYqJsAsNlrsfsGg/vP/OXsPmzKI9i06aUbe6U7wdX1qNgPJUfOupgqL5bq9aRiia5aia6DOuEIKfhrx+Ew1KhTnXY9Wvk3MBEJehpaX4RArU1ms5Zh096AnNWAA8I6YaJuwIS1yV8u53dwbwMTDWHtMCY0b59n71WQs5bCR3hFYGK/xjgqlV7c1gNZX2DT3/VOCOmohonoC+GXYByRpXYdqZgO7E7mrnMfImnzLjzufz6XzhAHzpAQnl70MC3PPTWAEYrIyULzDJWiirpQq81NxO7pVmQ5E/M0JqKfHyIS8UrZd5D3nl3Ax1MXcXB/Gs5QJ136d+Sq+/vRoEXdQIcnIieJkty/tRzHycqzuxiFnODeVeahiBypcrVobppwNTc8OZCM1EzCI104Q3z3JRIRKUtKhk5WjhrFKOQGZ80yD0WkIA6Hg6jKajoVkcBTB+qTlAmpB6Gt8P0rDgdXdz9FJCIiUj4pGTqJmejReH/FBQ939w7BL73O0yIiIhWRkqGTmAlri6k6DZxHzdtiYjCVx2KibghMYCIiIuWI+gyd5IyrPdRY6B1i704ERwyEdcCYsECHJiIiUi4oGQoCxphDy3y0DnQoIiIi5Y6SoSBn3X9Dxjys+y9wVMGE98GENgt0WCIix2X3X3s5uC+VGrWrUbl6dKDDkQpCyVAQs6mvYlP/feiZA7DYtFex4b29kzGqKU1EKoh1y37hvw/O4tcV6wEwDsO5l57NzU9dTe0m8QGOTso7daAOUjb9fWzq83iX6vAAueQtEJv5KTZlXKBCExEpkW8++oH7LnyM37/5I2+b9VhWzF/FHe3H8NcfOwIYnVQESoaCkLUebOpEHyU8kPEB1p3kt5hERI5Hbk4uz900Gevx4PHkX13K4/aQnpLB5LvfCExwUmEoGQpGub+DZ2cRhSxkfeGXcEREjtd3n6wheXcKha2y6XF7WPW/Nez+a69/A5MKRcnQSc7mbsNmfYPNWU/emrw2oxhHOsCml2lsIiInavuGnTicRdzKLCRt1jqMUjh1oD5J2ZxfsSlPQs6qfzY6G0P0vRB6Jt482OPjDG4IaVLGUYqInJioKlF43L6+yw6Vi9E6eFI41QyVA9Z6sJlL8Oy/E8/e/nj234nNXIK17uM7X86v2L1XQc4P+Xe4/8QeuA2yvwHXRUBhK4U7wBEHYecd1/X9zeb8ik2bgU2fhc3dHOhwRMSPOvZthzOksO8ywEDtpvE0PL1e4WUk6KlmKMCszcTuHwrZK/AmJ27Aic36H4R1hKqvYExEyc6ZMh7I5tiaHwsY70ix6h94Z6X27CFvFBkcisGJqfIcxvj4gikHrHsH9sDdkLOGf9Zfs9iwzpgqz2AcVQMZnoj4QZWaMfS7qxfvv/Ch9yvuaBZuePwq7+SzIoVQzVCA2ZQnvTU1wD9JyaF/s7/BpjxRsvPlJkLOdxTeBGbBJmNyf8VUnwORA4DDyZYDXBdgqr+LCTu7RNf1N+tJxu4dBDk/Ht5C3jdh9lfYfYOxNjtQ4YmIH9381NX0u7MXxmEwDoMz1Ikx4IoI4+5Xb6Vz/46BDlHKOWNtYX3wBSAlJYWYmBiSk5OpXLlyqZ7bevZhd3XCO8dPYUIwsV9hHNWKd86sb7H7ry2ilAMTPTpvoVZrs8FzAEwljKNitKvb1KmH5kkq/ONrYp7DRFziv6BEJKD2bN/LsndXkrL3IHENY+ncvyOR0SWrWZeTR0nu32omC6TsVfhOhPDuz14F4RcX75yOKsUo5MlXzpgwcMYW7/zlhM2Yg69ECBzYjHlKhkSCSI3a1bn87n8FOgypgNRMFlBFj4AAwBaVMB0hpJl31Bi+2sfDwHVh8c9ZHnn2F1XgUH8oERER35QMBVLI6fhOWvDuDz2j2Kc0xmCi7/NdptLtGEfxFzC01mIzFuDZcwWepOZ4ks7wjnjLXlfsc5Q6Z218v3dOcNb1VzQiIlKBKRkKIBNSB1xdKXyIuxPCOmNCjr2pW2ux2euwmZ9jc37hyK5fJrwrJubfYKoc2nL41+zCVLobom4rdozWWmzyGGzyKMj9GW+zXiZkLcLuG4DNWFDsc5UmEzkA381kbkzklf4KR0REKjD1GQowU/kJ7L6B4E4kf7OZA5y1MTFPHnOMzVyMPfgUuLf9s9HZBCo/hHF5R02YiF4QfiFkLQX3dnBUBVe3EtUIAZD5IWR+cOjJkfF5R7zZ5NEQdjbGGVey856oiH6QMQdyfuLY5kbjTTLDzvdvTCIiUiGpZijAjLMGpvoHmOhR4GwAJgqcDTDRozDV52KcNfOVt5mfYQ8MO5Q8HcG9Cbv/RmzWin/ObcIw4d0xUTdgIi4teSIE2LTp+P6YWGz6uyU+74kyxoWp+gZEXAmEHbEjEqJuxlR5CWP08RYRkaJpaH0RynJofUlZm4vd3Rk8uwspYcDZGFPj41KbYMyTdBr5J2UsQFhnHNVeyx9r7kbI/BxrMzGhzby1UiaskBOcGOtJhpzfwTggpEWFmR5ARETKTknu3xXmT+cJEyZw1llnER0dTWxsLJdeeinr16/3eczSpUu9HYqPevz+++9+irqUZX/jIxECsODeCLm/lOJFi2pJNXBEkmM9aXj2D8Pu6YVN/TekvYo9cBd213nYrK9LMa4jInDEYFztMWFnKRESEZESqzDJ0LJlyxg2bBjffPMNixYtIjc3l+7du5OWllbksevXr2fnzp15j6ZNm/oh4jLgTipmuZ2ld01XZwrv4A1gMa7O3p+sxR64E7I+P7TPQ948SvYAdv8t2JzSTNREREROXIXpQP2///0v3/Np06YRGxvLDz/8wPnn++4oGxsbS5UqVcowOj9xVC9muRqldkkTdRM2a1FhFwJHNQg/NMlZzjrI/rKQshbwYFNfwVSdVGrxiYiInKgKUzN0tOTkZACqVSt6mYrWrVsTHx9Pt27dWLJkic+yWVlZpKSk5HuUG65zwcT4KGDAWQdCz/R5GpvzGzZjATZzEdaT6rOsCWuNiXkab+3Q4Y/Lof5IjmqYqm/kNU3ZzE/wXYvkhqzFWJvl85oiIiL+VGFqho5krWXkyJF06tSJli1bFlouPj6eqVOn0rZtW7Kysnjrrbfo1q0bS5cuLbQ2acKECTz66KNlFfoJMSYMou/DpjxYSAnrXXOskFFUNmcDNvl+yP3piK3h2KgbMZWGF7pKvYm4FMLOwabPhpyfwYRiXF0g/F/5++jY4iSOHrAZYFzFKCsiIlL2KuRosmHDhvHxxx/z1VdfUadOnRId26dPH4wxLFhQ8GSBWVlZZGX9U3ORkpJC3bp1y8VossNs+nvYg8+ATf5no6M6Jvph7/xCR5a1OZDzIzZ3Gxx8DGwmBY4OixiEI2bcicWV+go29f/wucyIicbEfldo4iUiIlIaTuqFWocPH86CBQtYvnx5iRMhgHPOOYcZM2YUut/lcuFyle9aCxN5JUT0hayvvOtvOeMgrCPG/PPrtNZC+hvY1Clgi1rHC8iYiY0ajAlpePyBRfSD1P/zUcAJkQOUCImISLlSYZIhay3Dhw9n7ty5LF26lIYNj++mvWbNGuLj40s5urJhbTZk/s+75IU94J2MMbI/hJ7lbTILv6DwY1Ofg7TXCt1/LCc2Yz4mesTxxereA9mrvZ2pMxfg7Vd0ZKWjd60wE3XrcZ1fRESkrFSYZGjYsGHMnDmT+fPnEx0dTVKSd5h5TEwMERERAIwZM4bt27czffp0AF588UUaNGhAixYtyM7OZsaMGcyZM4c5c+YE7HUUl3Xvxe6/DnI34O247IGcX7CZCyC8H8SMz1fDYj0p3iYwRzXv8hslSoQO8ewqeZyedGzKY5A5n/zNb+FAxqGfwyCiHyZ6JMbhqwO4iIiI/1WYZOiVV14BoEuXLvm2T5s2jeuvvx6AnTt3sm3bP+t1ZWdnM2rUKLZv305ERAQtWrTg448/plev/P1qyiN74E7I/fPQs8N9cA4lG5lzIaQRVLoVm/UVNnUy5Hzv3WeqePcdTqBKwlGrZDFaN3b/LYeuffS1ssARD1X+jQlphnFUKlksIiIiflIhO1D7UyCW47A5v2L3Xuq7kKMaRI2Egw/jbZIqYeJTAFNjESakfrHL28xF3nXSCj8jJvpeTNTNJxybiIhISZyUy3EElewVFPmr8eyDg49weDLDExZxbYkSIQCbMZeiF3F974TCEhERKWsVppksqFg3eRMb+lQKSRBhmEq3QdRtJT/U/XfRMXj2HFdUIiIi/qKaofIotBVFrhRfarIhtHWhEzX65IzH94zTBhw1jzcwERERv1AyVB6FnQ3OxvhONEqLA3s8I88AE3EZRSVtJrL/cZ1bRETEX5QMlUPGGO9ipqYyxWsuOxEeyF6BtbklP9TVGcI6UvDHyAnOBhChZEhERMo3JUPllAlpDNGjKPtkCLydsEveLGeME1N1yqGE58juZwZcF2CqzyrTIfXWvQub/j42bQY2+3s0MFJERI6HOlCXQzbnD2zW15D6NKXTSboIzgaY41w41ZhwTMxj2Oi7IfsHwA2hZ2CcZTfLt7VZ3okeM+bgfX8OzXbtbAxVnseENi+za4uIyMlHyVA5YnP/9K4qn7PWj1c1mKjBJ34WR1UIv7AU4vHNWos9MBKyPuefRPFQjZB7C3bfNVB9HiakXpnHIiIiJwc1k5UT1r0du3cA5Pzk3wu7LoKIq/x7zROR8yNkLaLgGjM32Axs2lR/RyUiIhWYkqFywOZuwiY/BjaVkvfdOZ4+RQacTTGVn8BU+b8KtYq8zfwQ36Ps3JAxH2v90LwoIiInBTWTBZDN/h6bMh5yfz6Rs0DIqZC7nvyrxBcirAum6qsYU3QSZa0FdyLYLHDWxjgiTyDOUuLZR9GvMwub9SW4zi/W6xQRkeCmZChAbPZ32H3XUyodpHP/pFiJEEDOT9hd52MdLnB1w0ReXWD/GpvxETZ1ErgPLxYbjo28HFNpRGBXnnfGU6zasANDsKFnQtVXMY5qZR6WiIhUXGomCwBrLTZ5LN5EqDSac7JLcPG9YP8G9zZIn47d08s7cu3IImmvY5NHgnvzEVszIf0d7L6BWM/BUoj5+BRnosc8OT9j992sJjMREfFJyVAg5P4E7k34Zdi8T24gB7v/dqxnPwDWnYQ9+Oyh/UfXNrkh909s2n/8GWQ+JqQxRF5fzNJubxNk9sqyDElEirD1t7+YNPx1bjnzHoa2vpfXx7zN31t3BzoskTxKhgLBvSPQERzBApmQ8YH3acb7RTS4eSB9VkBrW0z0GEz0aKA4zXUh2MzPyjokESnEp69/zpCWI/no1YVs/mkbm9Zt4d3nFnD9KXfy7cc/BDo8EUDJUGCYqoGO4CgWm73K+1PuFqyniP5H9gDY9LIPqxDGGEzUTVBzYTFK24DGKhLM/vhhE/++5VWstbhz//kDyuP24M7J5dErnmNX4p4ARijipWQoEMLagqNGoKM4guFwp+TMjDA8bt/JkNtt4DhnrC5NxlEFHDWLKGUxIU38EY6IHGXuS5/gcBZ8m7EWcnPcfDx1kZ+jEjmWkqEAMCYEE31voMPIx4SdA8D3yxIICS28XG4ufPVxZXb+uTffdmszsbkbsbnb/LZGmDEGE3kNvj/GBiIuL9b5rLWsXfIzrz8wk9dGz2DF/FW4c0u+ZpuIeP2wcJ3P/0PWY1kw+TNysnP8GJXIsTS0PkBMRD+wudiDEw5NtnhofS2/c4CJgIh+AGz9I5ZKoZU4vX0qzqM+HR43WI9h9qRY7m6dCo3BelKxqf8HGe+CzfAWdDaASrdjIi4tdhTWWtYt/YWPX1tE4m/bia4WzQWDOtF1YCfCI33UQkVdD1lLvDNT5+uQ7gA8mMrjMM6iao/g7627efiSp9j80zacIU4w8O6z86lRpzqPzx9Nk9YNi/1aRMSrOH8Xpe5P4/X732boC9eXeTwihVHNUACZyCsxsSswVf4PKt3Fca9QH3rWCUQRiqk6FeOoDEBC43gevaEB3y+NBsCdCzmHRu4fPODk4Wsb8uevUcTWr4n1pGH3XQ3pb/2TCAG4t2KT78OT/Bi2GJ3FPR4Pz900mXu7PcqX73/DpnVbWbf0F14YMoVbW41iz/a9hR5rTASm2psQdRuYKke8rNaYqv/BRA4o8vqZ6VmMumAc237769BrduPO8f41u2/nfkZdMM5nDCJSsDO7NC+0mexIH05ZSFpymh8iEimYkqEAsDYXm7UUmz4LspaBqwuOSreD6+LjO2F4LwjrfHzHhp2FCfsnmTq339ngqMQj1zXi1gua8dZzcbw3OZbxQ+sxqE1z1q2IoUOfdlSNjYH06Ydmvj56ZNmhPwczZmB3d8Gz5zJs1vJCQ3j/+Q9Z+OZSgLxOloeb2v7esotxlz3rs+nNmAgc0Xd5E8uaX2Niv8dRfRbGdX6x3oIvZn5F0uZd+Tp4HuZxe8hIzWT+pP8V61wi8o9+d/bG4y565Gl2Zg4/f73eDxGJFEzJkJ/ZzM+wu8/D7r8FmzIWe2A4dldHbNp0ON6ZkrO+g9BTOa6apeyvsO5/aj1cjqUMn7AHjGXr+nBmvVSLN5+JZ9mCqng8IURWjmDIM9d6X0v62xxOhNIOOtiy3sXuHQV0OMr9Fbt/CDbjw2N2uXPdvP/Ch4W2ELpzPaxftYnfvt1Q5EsxJgTjrJlXy1Vcy9792ueyHR63hy9mfVWic4oItOh4Cl0HdipW2cO1sSKBoGTIj2zmEuyBO8FzVJOLTcMefAIyZh/fiXNWY4ocVeWDJ8kbRsZc7IHhXNBvE49O20L9UzLzihhjOevipkz8ZgJ1msZjbTZ4drE3KYRn76pL/5YtuLXrqVzTrjl39mrCqiXRR14AsNiUh7Ge/MPc//pjB/v/TvYZnsPpYM3in47/9RUh9UB6kZ2+0w9m+NwvIgW7+qGiBzAYY2jSRv3yJHDUgdpPrLXeztI+HedfRvZvbGhrvLltyc9hAWwGNuXxvC3ndE+h/UUp/LXJRVqKk9g6bqrVqYyjWvyhMqHs2RnJnb0bsH93CB73PzUrf/wYyUPXNGT0xG1ccNmBIy6UDpn/g8jL8jYVpwrdGG+/ouNlrSUnO5fQsJACa4DqN6/DprWbC2wmA3A4DHWbJRz39UWCWf3T6nBml+b8/NXvBf4fc4Y4aN+7LbF1y9N0IxJsVDPkL7m/gXsLZTZi7OAEiLrluA41OCBz8aFRbUdsN1C3SRantkmnWmwWZH+NdScd2md447lWxyRC4B1xhoX/u68OmelHfsRCsO4t+crWbpZAVEykz/jcuR5adDylxK9r559/8+Ktr9Kn0jX0jhjEFbE38d8HZ5KyL//aar1vuajQRAjA47H0uf04+3OJCPe9cQdV46oe05na4TDUql+TEVOO77tLpLQoGfIXz76yPX/O95C7E1PpXqCEEyI6qoF7O0V/HCwcSobSUtJZMifzmEToH4bMdAfLFhy5ZIbnmP48Ya5QLrn9Yoyj4PM4nA7qNIundbfTi/VSDtu4djND29zL/6Z9QVaGdzhcyt6DzH5mPsPOup9PX/+cl+/6L5NHTGP/3we45HCyc1QYxmE4u1drLihmvwcROVZsvZpMWf0Mgx64jBq1qxESFkJsvRpc9+gAXl71NFVrVQl0iBLkjPXXDHkVVEpKCjExMSQnJ1O5csk65h7J5m7E7ulVipEVzFT/EELqYTO/9s69kz6VwheENRB6NqbSzdjkseApehi8qbEQE9KArb8mcnPLkT7LhoR6uPzW3dz4wKE+SRb2ZM2mVoPW+cplZ+XwcJ8JrF78E8Zh8pYDcTgcRFeL4vllj1H/tDpFxnaYtZYbm49gx8Ykn81wzlAn4O24WbNudboP7sLnb39J0uZdAFSJrcyld/RiwOi+hISqRbm8y8rIYsk7K/j87eWk7DlIQpM4eg25kHbdz/TZQV5ETk4luX/rG95PTEgTbEhLyP2Vslyt3u6/BVP5AUx4d0zEhXgMkDalsNKQuxW7fwhFj0QzENIcE9IAa3OJDPmgyFg8HkNUtOfQz7Do3aq8MHI8rbudzp0v30ydQ/1wwlyhPPnxAyyZ9TUfTlnI9g07qVQlkguv6cy/hl5U4r8af/ryN/5aX3Rid+Tolb079vPRlEX855cXyEzLwp3rJq5BrHcCRin39iXtZ9QFj5L4+/a8hHrLr4l89cG3dL6yA2Pevivf79Lj8bBh9WYO7kslrmEsdZrG+zi7iJzsVDNUhNKqGQKw2Wu8kxSSWzrB+RJxJabyE4CFtMnY1FeBrBM4ocFUfR3COmAP3AFZX3Bn78b8sS7S20eoEK9/+Ru1G2az8N2qvDS6Drk5DjAQVTmSyd8/TULjuBOIqWBzX/qEV0a+UfSCs0cxDsNN469mwH19Sz0mKVsjOz/CryvXF9j3yxjDtY9cybVjrwRg6eyv+c+Yt/l7y+68Mi3OPYU7Jt5Ek1Ya0SRysijJ/Vt9hvzIhLWG6NH+uVjGe5A5F2McmEp3QGirEzufqQQ5v2AzPoKszwHLdff+fWi6/WOTDuOARs3T+XRmdQZ3OJUXRtbzJkKHiqclp/Pfh2adWEyFCHWFHtf6aNZjWfru12UQkZSljWs389OXvxXaCd5aywcvfUx2Vg7/++8XPDnwxXyJEMBv32xgRKeH+fPHrf4IWUTKGSVDfmZCm/vtWvbgS95/czdCzrcnejJs6gtwcDyHPzbtuhzk/knbiIjyziMUEurB4fAmIWHhYfz5ayTvvxLL34kFd+j+8r2VpKV45x2y1u1d58ye+MRrZ/dshTnOpU0yUjSfUEWz9oufcRTSAf+w1P1p/P7dBiaPmFbgfo/bQ05WDlPvnV4WIYpIOac+Q/4WegYQAfjhpuvZgcedDCmPltIJLdj9+bZ07XeADhensGxBDNs3u4iq5KHR6bk8NKhu0eF5LCm7/iCCeZA+F+97EoGNvAwTdSvGeXxNaLH1atLt6vP4YuaXeErQVOYMcdDg9HrHdc2iuN1unE71PyoL1mO980AUMW3F2i9+JiM1s9D9HreHHxb9yO6/9lKzTvVSjlJEyjMlQ/6Wsw4o/Au51B0cD9knWCtUhPBIDxdf5U2SrIUfV1Qq1nG1G2VRs9IQSE/ln8kiMyD9HWzGJ1D9XUxI/eOK6a4pt5Cy7yDffbIGZ4gTj9tzaPLGwm+Y7lwPl9xWevMJJW3ZxXvPLWDRW8vIOJhJ1bgq/OuWi7hsRG8qVYkqtescbe/O/Xw4+TMWv72ctOR06jSNp89tF3PBoE4+R8Xt//sA3368mozUTOqdVpvW3U7H4Sj/lcfNO55S5OSd4VEurPUmvL7mlALyJUO5Obl8PW8VKxesIisjm0an16fHTRcoWRI5yagDdRFKswM1gGfvQMhZQ1mOKAska+Gl0XX4ZEbRN4v/+2gTp7bJoOBZs50Q2gZH9bcB2LVtN/MmfsrS2SvybtbdB3chtn4NnE4nrkgXi6YvY/XnP2KMoU2307lkWA/SDqTx+dtfkrz3ILF1a5C0ZRdfz/3OO3ju0CffGIO1lt63XMhdr9xSKsOw//xxKyM7P0JmWma+m6/D6SC+US1e/OpxqtSM8XGG47P5523c02UsacnpeQnC4dFVbS48ncc/HEOYK//6cbk5uUy5500+nLIQT64nr3xs/ZqMeWs4LTudVupxliZrLUPb3MvWXxIL7kDtMFx2V2/qnVqbfw99tch5T9/cMJGExnHs2rab+y56nO0bduJwOrAeD+ZQcnjX5CH0GnJhWbwcESklJbl/KxkqQqmOJnMnYXcXbyX18s2BN5s4OolxsntHCDef35TMdN9NQvWbZTJ1adGrVJsan7J+tYf7LnqMrPRsnzUAh5Ma8NYAeDyWe6cN46JrO+eV8Xg8fPjKQt5/4cO8+YQSmsRx5T2X0PuWC0slEbLWclOLu9m+YWeB8TqcDrpedS73v3XnCV/rSB6PhxtOuZOkLbsLvK5xGAbe348bnhiYb/uzN77MojeXHdPp3DgMIaFOXlo5vtyPstqxKYm7z3+E/X8fyBtFeDipO6Nzc8Z/8gBZ6dkMqH0LudkFj+Y0DkPTNo14+buncLvd3NxyJDs3JRVak/TUZw/R9qIzy+w1iciJ0Wiy8spzINARlI7Qs8FxeB2hEPJaW50NeHF0RzIziu4b065b8WbJzs38jUf6Pk1Wmu9ECMh3M3fnerAey7M3vMzWXxPztjscDvoO68H0jZN4Z/tUZu+YyhvrX+Jft15UahPz/fTlbyT+vr3QeD1uD0tnr+DAbt8L1JbUD4t+ZMemvwu9rvVYFkz+jOysHMDbnPZ/t01l4RtLCxx9Zz0Wd66HGY+9X6pxloWExnG89uPz3PD4QOqekkBMjWhOOasJo/57O0999hCuCBeVq0czaMxlBR5/+Fd/04SrAfj249X8tX5H4evVOR288/S8sngpIhIA6jPkT85aePPPCt5ElvMNVHoIExKHzV4DxoEJ6whhHTir96f88MWbWB9tES06nsLgx9pDzj1FXuq3b7YWuaq9Lw6HYf7LnzF80k2sXPA98yZ9yobVfxIaFkLHvmfT765eVIuretznL8jGNZvzzaRdEHeum62//kWVzqXXVPbrivU4Q5y4cwsfkZd6II0dG5Ow1nJPl7Ec3J9aaFnwJm4rF6wi/WAGkdERpRZrWahcPZqBY/oxcEy/Qstc88gVOJwOZo6fQ3ZmTl5tYpVaVRg5dShtDi378u1HP/h8Lz1uD2u/+JnsrJxjmh1FpOJRMuRHxlEV67oIshZz3CvUlxdpL0LsChzh3fNt7nHTBXz06kK2F7AUhnEYmrVrzPNLH8XhyMDucuFrIkhLON98FoIjxIGniE6vhXHnelj9+Y9MvOM/fPjKQhxOR15c//vv53w27QvGzb2P9r3aHNf5CxLqCoVitD6HlvJN1LsIZjFavQ2M7fsMacnpxSru8VhSD6SV+2SoOIwxXP3Q5Vx6Z0++/egHUvalEt+oFu26n5lvhuqc7NxizVWVm52rZEjkOO3ZsY8ls75mf9J+qidU44JBnQK2Tp2ayfzMRI8C43uV9grBpmIzPjzmhhEZHcHzyx7j7J6t863w4XA66Hb1eTy7+BGcIU6MoxJEXUdhy4B4PDB7YhU2rv27xDNJHy3jYAYfvrLQe94jEjR3rgd3rpvHrnw+30r2SVt2sXjGchbPWM7fW3cfc76inNWjVZE5RkyNaJq2Kd1+OG0uPKPIkVLVE6ry9+Zd7Pyz8Oa0oxmHIaZGdGmEWG5EVY7kgkHncekdPWnfq80xy640adWwyM9drfo1iagUXpZhipyUrLW8/sBMrq5/G/8Z/RZzX/qEV++dzsC6t/LWY+8d16S5J6rCJUOTJ0+mYcOGhIeH07ZtW7788kuf5ZctW0bbtm0JDw+nUaNGTJlS2Dpd/mFC6kPkDQGN4ViGotcmK0DKg9g9F2HTZ+X78FaNjeHxBfczfeMkxrx9Fw+9czezEqcw+s3hRFT6p3bBVLobwr19OHJzwJ3r/Rfgs1nVePPpWNZ8/tMJJUOHh1KbQiblsxZysnL4bNpSUvYe5JFLn+HaxsN4+rqJPH3dRK5pdDtj+z2TL1kqSlyDWLr073iopqZg/e/tS2hY6dYoNO/QjGbtGuMMKfy6V95zCb9/t7FEa645HA5cEcXr43WyuGhwZ0JdIYX2IzPGcOnwnloAVuQ4zJowl3eemovH7cHjseTmuPP6KE4f9y5z/+8Tv8dUoZKh2bNnM2LECB588EHWrFnDeeedR8+ePdm2bVuB5Tdv3kyvXr0477zzWLNmDQ888AB33nknc+bM8XPkR8n9ObDXz+fwR+A4Ew53IjZlLDblkWOy+fiGtbhgYCc69+9YYL8cY0L4K+kOhnZrxrzXa7JsQRXm/qcmt3Rtxov31sXjMRiHKTSRKQ5rIXl3iu+EysLPX//GPV3H8s1H3+d/Kyx889EP3NftUbIzs4t93ZGvDaVV1xYAecnJ4X/73tGDK+7pU+LXUhRjDOM+uJdaDWK9zw+9b4ev2+OmC+h3V6/iN6cdEhoefK3p0VUrcf+MuzAOkz+5NN73ud3FZ3Lp8J6BC1CkgspIy+Sdp+b6LDPj8ffyBnr4S4UaWt++fXvatGnDK6+8krfttNNO49JLL2XChAnHlB89ejQLFizgt99+y9s2dOhQ1q1bx8qVKwu8RlZWFllZ//RjSUlJoW7duqU2zxCAZ9+NkP1VqZyr5Fx4O3C7Ke2O3KbqNIzr3GO2b/3tLz6ZuphN67YQXimcTpeeTZerziU80sXM8R/w5tjZxW6yKS5niANr4d43hvHs4ElFzkTduFUDNq3d4rPMPa/fTo8buhY7BmstPy77lcUzlpOy9yC16tek500X0PD045tIsriyMrJY8s4Klsz6koP706h3am16DbmQ0887DWMMv6xYz4hODxXrXA6n4dxL2/PIe0V3dj8ZrV+1kdnPzGfFglW4c9zUbhZPv+G96H3LhT4nsBSRgq2Yv4qx/Z4pslxpTF1RkqH1FeZ/c3Z2Nj/88AP3339/vu3du3dnxYoVBR6zcuVKunfP38H34osv5vXXXycnJ4fQ0GObKSZMmMCjj5bW8hWFCD0jgMnQiaxc74sDmz7zmGRo9jPz+c/9M/I1V3370Q9Mf/Rdnv18LCn7DhYrEaoeX5W9O/f7LBNbrwYhYSE4Qxy06XYGlwzrQb1Ta7PozaWsXfKLz+sUtUCncRg+e2NJiZIhYwxndmnBmV1aFPuY0uCKcNHjhq6Fxtq8QzOatm3En+u2FNnHyOO2XDaid1mEWSGcclYTHnnvHqy1eDweLakicoIOr0dZZLnk4pUrLRUmGdqzZw9ut5tatWrl216rVi2SkpIKPCYpKanA8rm5uezZs4f4+PhjjhkzZgwjR47Me364Zqi0WJsLWd+U2vlKn8H7sShpFaUHsr7G5m7EhDQB4Ot53/Gf+2cA5N10DzdX7d2xnzE9nqRBy6LfW2O8c+IYh8V68jeZOUOd/OvW7lw6vCd1mh77+wS4clRfVi/+yec1iuqXZD2Wvdv3FRlrRWCM4dG59zHqgnHs2JiUbzbuw5whDtxuD3e8dBMtzz01EGGWK8YYJUIipaDuKQmlWq60VJhk6LCjOyxaa312YiyofEHbD3O5XLhcZdhZNO11yF1dduc/YRZCTsNEjwB3EtZRDRw14cBw8Owo4th07J5Lodo0TNhZzH5mHg6HKbCJyuP2sPPPv9m5+e+iIzp0+NGJkHEYKleP5pZnr803vDkzPYs3x87m09c/Jz259BbEja1Xo+hCFUTNOtV5aeWTvHbvDFb9bw1ZGdm4IsNwRYThinRxeqfT6HP7xTRsWTYL14pIcDrlrCbUb1GXxN/+KvDe4HA6aNK6YZl3JzhahUmGatTwrkF1dC3Qrl27jqn9OSwuLq7A8iEhIVSv7v+FFq3Nxab91+/XLRknhDTDuDp5n1o31rohpAlkF5UMAeRiD9xJZuRCfvtmQ9HFT6DHmvVY9icd4Mv3v6Hb1ecBsGHNn4w8fyyZaaW/GG7Pm7rle777r718Oecb0g6kk9Akjk6XnV1hRl398cMmHug5nuQ9KXmj3tJS0omMjuCxebf7vWmvpNy5blZ++D2/rliPw+mgzYVn0Lrb6RrdJVLOGWO45z+3MeqCceRm5+brvuBwOggLD+XuV2/1e1wVJhkKCwujbdu2LFq0iH79/plhdtGiRfTt27fAYzp06MCHH36Yb9vChQtp165dgf2Fypw7Eazvfi+B54bI/tj097Hpb0Ju0euHHcnj8ZB24ADkLiyb8I7icBq+X7SWblefx5LZXzF+4P+VyXWiYiI5/8pzAO+NeOLw1/l46qJDMXgnhYwcFsHdr95KlwHHdiIvT5L3pDC6++Okp3hrzY78MspIzeTB3uN57ecXiG9Y8B8Z4J3JOisjmyo1K5domH5p2LD6Tx7p+zR7tu/DGeoE6+2bVr9FXZ748H7iDo2mE5Hy6bT2TXlpxZO88cg7fPvRam8Lj8PQ4ZJ23PD4VdRvXnpdU4qrQo0mmz17Ntdeey1TpkyhQ4cOTJ06lddee41ffvmF+vXrM2bMGLZv38706dMB79D6li1bcuuttzJkyBBWrlzJ0KFDmTVrFpdffnmxrlmqC7XmbsbuufiEzlF2DnUcibwRPLsg8yMK7ExSiLSDDt6bHMtH06tzcH8IxkBYRBhZ6cUfjn68HE4Hoa6QMr1WWHgoz34xjiatGjCi00NsWL250LLjP3mAs3q0LrNYTtQ7T8/jvw/OLLSflMPp4LK7enPrc9cds+/7heuY+eQcfvrSO0Izulol/nXrRQwc0y/fHFJlZfdfexly+kgyUjOP6RDvDHFQo051XvvpBSKiNBmiSEWQsu8gybtTqBIbQ3TVSqV77pNxNBnAgAED2Lt3L4899hg7d+6kZcuWfPLJJ9Sv721b3LlzZ745hxo2bMgnn3zC3Xffzcsvv0xCQgIvvfRSsROhUuesC47q4NkbmOv7YqLA1QkcsZB+uCmveIlQThaM7NuEbX+E4znUr8dayM70zzwRHrenzJOu3JxcHuj5JLWbxvtMhACeu2kysxJfxeEon9N4fT3vO58dxj1uD19+8M0xydBnbyzhuZsm4zD/vK6D+1KZ/cx8vl+4jueXjCvzhGj+pE8LTITA20n/7627WTLzK3oNubBM4xCR0lG5WjSVqwV+hvsKVTMUCKVZMwRgU1/Fpj5fCpGVheLXBB0pcaOLW7qegsfto7/GEac+PFLpRPoLBUQJ3p4WnU6lYcu65Ga7iWsYS8e+Z+V1Rk4/mIE7102lKlEn1MclJzuHr+d+x+IZyzmwO4WExrXoeVM3WnVt6fO8t7S6h80/FjxR6WFV46rw7o7X8p4n70nhqtq3kJtT8Jp6DqeDQQ9cxuBHBxzfiymmqxvcxq5tewrdb4yhVdcWPLN4bJnGISLl30lbM3RSiLoJcn6FrE8DHUkBSp6dWAt//RnmOxEC4urHsv/vA4RFhHHZ7Y1ofNov7Nu5hb8Tw1j0blX27Aw73qD9pwRvzy9f/c4vX/2e9/yNh9/xrkVmDBt++BOA2Po1ufyu3vS9o0eJ+92k7D3IfRc9xqa1W/JG7G1c/SdLZn1NlwEduf+tOws8Z+L67bgiXBiH8dlMdvS6aQvfXOZNYAvhcXuY8fj7vPvsfDr378iVoy4pk5FoGQd9jw601vp9fhIRqfiUDPmZMSFQ5d/Y5OqQOSPQ4ZwwY+DLD6v4LBMS6uT8K87h5qf7Yw/cB1lvkZsLWO/x141KYvpzccz6v8I77J4Mjm5e27V1N6+MfIOF05fy/LLHiCrBqvAPXfIUm9ZtAcgbnnp4Lqdl766g7im1uWxEb+ZN/JRPXlvMnh37cIY4yc3OLfLcHreHvsPyLzWx7ddEHA6Du4j5mLIzc/hi5pcsnf01T3z0AG26nV7s11QcdU+tze/fbih0RnFniIP6Lfzf+VJEKrby2anhZJf2+kmRCIF3cdXlH1XxWcZaS1hEGDZ5HGR9BkBICISEgjMEHE6oXK3om3RpMg5Dk9YNMM7AD8XetHYL1zUaRtKWXXnbdmxKYs0XP7Fp3ZZ8a76lHkhjeIcH+G3lH4XWVFkLc178iNvbjeatR99j9197sR5bZCJ0eC2zvsN6cFaPVvn2hUeFU9zFfN25HnJz3Dx25XNkZZTujOeX3N7D59Iq7lwP/7r1olK9poic/JQM+ZnNXotNfTbQYZQaZwjUa5KJcfi+QZ17SWPI/ICC1kNLTXbw3ycLnj26rFiPZeOaLUXOPH2kspzDJmXvQR761wT++GETd5//MIObDue+Cx9jaOt7ubH5CFbMX4W1lrH9nmH9qo1Fni89JYOkLbvweIq/5lvjMxswevpwhr104zGvtdNl7XHnFtxfqCDWY0k7kM6ydwteA/B4dbmqI+f8q+2xv4tDTy8d3pPmHU4p1WuKyMlPyZCf2fQZFPcv7Iri6pF/HzM79GEOp4M2F52BO/3TY1a1P2z5h1XIzQnQe1KCfkD5Vi8vA1t//YsRnR7i15V/5Nu+/Y8djL3sGaY/+h4/Lvu12AlcSRI9gC2/JPL5jOX8sOjHY/ad0bk5p3VoljdBY3E4Q51sWP1niWIo8pxOJ2PnjOL6x6+iaq2YvO3xDWtx1yu3cPuLN5Tq9UQkOGg0WRFKezSZZ1eXYixrUfHMf706rzxSGwyHhpQ7cOe6adyqATv//Jteg7Zy4wM7cRbQS+3NZ2oxe1Is7twgz83NoQFrhfyPDA0LITfHXWhSWRocTgcet4ebn7qGAffln8w0Ze9BHun7NL+sWJ+38K4vzhAnl4/ozZBnri2TWN25bnYl7sHpdFCzbg3NPi0i+Wg0WXlmTr633O2GDj1SaH9RMsvmVyPxz2iiYi/n1Pan8OwNL+POcbNlfXiBiRBAlRq5RY5GCwrWd0VVTjE6PwPHO0MC8M9s1P+5fwatu7WkWdvGefsqV4/m318+zi9f/87X875j4ZtLSdmXWui13Lluzu7d5vgCKQZniNPnLNkiIsUV5H+KB4CrKydTM5nbDR43xNbOIa5eLgOG72LUvzdx+7OtWfvFT7gPzUuzelk0u7aH4i6g20nnS5Ipp/MTVkhNWjfEFXliUxU4Q5wsmPzZMduNMbTsdBq3PjeYOyffUmgi5Axx0LRtI844v/kJxSEi4g+6BfmZibwa8O9aTmXJ6YTQgu67NpMvZn6V99TjMTw9rB7uHOMdVn+E6Cq5XHLD7rIN1E/qnpIQ0OvXO60OL375OP3u7H1CzUbuXDe/ffOHzzKdr+zAzU9d420aPdSX6PC/dU+tzRMf3q+mKxGpEE6+NptyzoQ0wEYOgvTpgQ6lSJbjrcMyWGejY5bj+Pm7StzZuykDR/xNp57JOEMgI83BwtlVWbqgCi3PTuWXVVFYWzFvoBHR4aQmp51QM9WJ6j64C64IF9c/NoBd23bzxcyv8vr3+JposSChrqIXMx5wX1/Ou7w9n7y2mG2/byeiUjjnXX4OHfq08/sCriIix0vJkJ9ZmwEZcwIdRrEUlpLYQ5MlHv75p2+i+OKDqqTsd1KrTi4XX9eIRnHxVIuvyr6d+/Mdu/m3CMbf2oCwcA8RUW5Sk0Nw53pPtn9XqI+rln8D7u3LG4/MDmgMp59/GuBt5rr/rTvpO6wH//vvEnb/tYeqtarQdWAnKlerxPvPL2DZ+98UPgu1w0GHPu2Kdc2ExnHeGiIRkQpKyZC/ZX4ONs1/1wvvD7gg861SO+XhRCgz3cljN9Xjh2WVcYZYPG5wOOCDqSn0u3Malw7vyX8fmFngObIzHWRnHt1KW3ESocO1LMaAcToY/eZw9u3cnzcay98cIQ4atKjLae2b/hOjMTTvcEqB8+7c8OQgvl7wPblZuceMTnM4DGERofTW5IUiEiTUZ8jf3NvxW5+h6LE4qjyBiexVJqf/z4RWrPnSu9qwO9dgrcF9aFTY3Jc+AaBOs8OTKZ48MzjUalCTlp1OpcMl7Rj86FXM3DqFCwZ2IjI6okSTHJYWh9NBdNVKPDx7ZLH76CQ0juOxeaMJiwjNm3naGG8C5YoK58mPH6BGQrVix7Bx7Wbef+FD3ntuAb+sWF+mw/9FREqbaob8zVGNgmZhLgsm9FTvD7m/U9odWax1snBWFh5P4YndnH9/xLTfX+T/hr7G8ve/OWlukLsT9/L3lt00PrM+d04eQvX4qrhz3bii/LfYrDPESUiok0pVK3Hx9V3oe0cPqsVVLdE52nU/k5lbp/C//37BT1/+dmjF95Z0v74LlapEFesc+5L283j/F/j5q99xOAwYg8ftoXGrBjzy3j0kNI47npdX4R3cn8rHry7iszeWkrw7mZr1atB7yEVcfIO3T5eIlC+adLEIpT3povUkY3edC2SfeHA+hWNiV2AclbDp72NTHijBsQ58J2xOtie25sZzcorMr15aOZ7T2jclKyOL/gm3kH4SrSjuDHFQp1kCVz90Ba/cPY39fyf79fqf5c4+NMFlYGRlZHFbm/vYvjHpmKZBh9NBldgYXvvxeSpXjw5QhIGxa9tu7j7/kbw14cBb62aBZm0a8cznY4mqHBnYIEWCQEnu32om8zPjiMFUGlrGV3FAZH+Mo5L3qes8iv5VR0BYRwhrD1E3Q8SVhZRzgong1x8vKlaTTHamN+lzRbgIjzy5/iJ253rY+utfjB/0YrETofrN6xBRKfyEr+2KCAtoIgSwZNbXJK7fUWAfKY/bw/6/D/Dx1MUBiCywxl/9Ent27MvXOd1awMLGtVuYMvLNwAUnIgVSMhQIUcMwle4GDicHh5IKU9K/oI9eSfzQz6GtMdH3/LPVWQvC++Hr122ih+Oo9gaOam/hiB6Fqfy4N0ZzVHNJSAtMtVnENTm7yGHazhAH9ZvXyXtes061oJ93JiM1kw/2TuOW56477nM4Qxx0uercY7Yn70nhw1c+461H3+PT1z8nLaVsa+E+f/vLvP5GBbEey6LpS8s0hvLmzx+38svXv+MpZKkSj9vD4reWkbL3oJ8jExFf1GcoAIwxUOk2iLwWsr4AzwFw1sWGdYK0FyHtNfI3VTnAREL0GIzNAhMGrvPBhEP6u9iMD7znCKmDiRgIEX0wJn//FRMzDmsPQNbneDtwH55FyA2RgyHypqNidHhjjLoesr4Bmw4hjfP6IZ1xvqXOKQnsKKCJBLyjmzr370iVmv8sptnzpm6sX7Wp+O+Tw9Cx71mMePVW+te6qdA1uyqSXdv25HV4Ph4Oh8EZ4uTKUZfkbbPW8taj7zFrwge4cz04nA7cbjcv3/lfhjxzLX2H9Sit8PNJ3pNSZEKcsi+1TK5dXv2yYn2RZXJz3Gxcs5k2F57hh4hEpDiUDAWQcVSCiH9uagYg+l6sqws2fSbk/AImHBN+MUT0xzhrHnuSSrdgKt1S9LWMC6pMhpx12Mz54NkHjgRM5OWYkCY+jouA8K4FbDc89M7d3NNlLJlpmfkW7XQ4HcQ1iOW2f1+f75gLrz2fj15dxJ8/bvU9/PxQX+/zrziH+964g7DwMJq2bczG1X/iKeFK7OWNK9KFw+GgVdcWxe7TbozBGeIgN8dNdPVoHnnvHuqf9k+N26wJc3nrsffynrtzvWueZGVkM2n460RUCqf74C6l/EogoUkc2377q9AFW43DBF0HaqezeJXtjmKWExH/0P/IcsiEnYWjyr9x1FyIo8YCTKVhBSdCJT2vMZiwVjgqj8VR5f9wVB7tMxEqSuMzG/DK6mfodfOFhEd5m/yqxMYwcEw/Jn07IV+tEHj7DT37+Vhq1C5iyLaFR+fey0PvjCQs3FvDdfmI3ieUCPW48QIatqx73MeXBmeIg66HmrfiGsTSse9ZRR5jHIbTzzuNy+/+Fw/NHsmsxCn51vtKP5jBzPEf+DzHtIdm4S5oUbgT1HvIhT5Xrrcey7+CbK6iVhe0LLJMeJSLU85qXGQ5EfEfJUNyQuIb1uLOyUNYkPIWn2bN4r2k/3D9Y1cV2gwU6gphz/Z9Ps/pDHHw+3cb823rOrATl9x+MYDPfioFufiGrtzzn9v4vxVP0u2a80t0bFGqxVcpVjzGYQgJDcnXvHXvf4dRtVaMj6O8CUWvId24+alr6HxlB0LD8i+RserTNWSlZ/k8x57t+/jtmw1FxlhS7S5uxflXnFNgPzCHw3BmlxZcMKhTqV+3PEtoHEeHPu0KrfkxDsMlt11MRKUIP0cmIr4oGZJSYYz3Zl+UtOT0YszQbEjenXLM+e+YeBOPL7ifNt1OJyI6Im97Yeo0i+fhd0cy6vXbAYioFMHoN++gx40XFBlnUeqeWpvH5o/mgbdH4HCYQhOiwzfFKjUr89RnD1Hv1Np5+ypVieLl758ucuj504MnsWLBqgL3HdxfvNnM0w6U/qznxhgemDmCax6+It+8RBGVwrlsxL948uMxxfpMnGzufWMYTVo1ALxJIfzzOWjfuw3XP3FVoEITkUJonqEilPY8Q8EuJzuHS6tcnzfkviAOp4Prxvbn6ocu93murb/9xXvPzmfJO1+TnZlDtfiqXHLbxfS+9UKiYiKPqUU5zOPxcHPLu0n8fUfJX4CBNhedwYRPHswb2r5u6S9MuvN1tvycmFesRp1qnN7pNOIb1aJJm0Z06NO20MRg78793HLmPaTsKXiEkTFQpVYVZm2bcszip6s//4nRFz1WZNiv//pivkSstGVn5bD5p21Yj4f6LeoSEXXi0wdUZDnZOXw99zsWTl/G/qQDxDWMpdfN3Wjb/cyAT4kgEixKcv9WMlQEJUOl78WhU/n0v58XOvzYGMOMLZOJrVujWOez1pKbk1to8lOQG5uPIPH37cUuDxAWHkqf2y7mxvGDCDtqRXdrLZvWbWF34l6q1orhlLOaFHsagb/+2MENp95VZLknPhpD+15t8m3zeDxc23gYuxP3Fjiyy+F0cMrZTXjp6yeLFYuIyMmiJPfv4KvDloC75uHLWTH/O5L3HCywyezqhy4vdiIE3uSpJIkQeJutipMMDXygH/ENaxFbrwantm9a6MzBxhiatGpIk1YNSxQHwM7Nu4osY4whqYByDoeDe167jQd6PYmFfJ3MHU4Hoa5Q7px0c4ljEinIrsQ9zJ/0P76Y+SUZqZnUaRZPn9su5sJrzj+m1lKkIlF9rfhdjdrVmfjNBM7u2Tpf7UnVWjHcMfEmrhvXv8xjaNW1RZFlYmpU5sYnBtHzpm60vejMMltCoTjLVVhrCy3X5sIzeO6LcTTveMTq9Iea8yaufJImrUueoIkcbcPqP7nljHt4/4UP2bN9H2nJ6fzxw588d+NkHurzFDnZOYEOUeS4qZmsCGomK1u7/9pL4u/bDw03buK3vy53/7WXqxvcVvikgQZuGn81V42+tMxj8Xg8XN9sODv/LLyGyBXp4t2drxEZ7XsU0q5tuzmwO4XqCdWoHl+yhVtFCuPOdXNNo2Hs27m/wNpc4zBc+/CVXDu2sGV8RPxPa5NJhVGzTnXaXHgGzTuc4tdq9pp1qnP7v28Ajh2RZhyG5uc0o9+dPf0Si8Ph4KYJ1/gsc81DlxeZCAHE1qtJs7aNlQhJqfr249Xs+WtvoSNBrccy/+VPyc3J9XNkIqVDyZAErUuH9+TRuffRrN0/E+BVrh7NoAcu45nFj+CK8N/Csp2v7MC904YRWdmb8DicDjDeeZmuf/wqBvihhkqkML+uXI8z1PcfK8l7DvL31t1+ikikdKkDtQS1jn3PomPfs0jZe5DszGyq1qoSsI6g3Qd3oXP/DqyY/z27tu0hpmZlOvU7O98cPiKB4HA6irV0jJYZkYpKyZAIxevE7A+uCFfekh0i5UXb7mcya8LcwgsY7xIzteqf+LJBIoGgNF5ERHw64/zmNG7VAEdIIbcMC/1HXaIJJaXC0idXRER8Msbw2PzRxB2q+Tm8/IzzUHJ06fCe/Gto94DFJ3Ki1EwmIiJFiq1bg6k/Ps+Sd1aw7N2vST2QTv3TatPrlotofk6zQIcnckI0z1ARNM+QiIhIxaN5hkRERESKScmQiIiIBDUlQyIiIhLUKkQytGXLFm666SYaNmxIREQEjRs3ZuzYsWRnZ/s87vrrr8cYk+9xzjnn+ClqERERqQgqxGiy33//HY/Hw6uvvkqTJk34+eefGTJkCGlpaTz33HM+j+3RowfTpk3Lex4WFlbW4YqIiEgFUiGSoR49etCjR4+8540aNWL9+vW88sorRSZDLpeLuLi4sg5RREREKqgK0UxWkOTkZKpVq1ZkuaVLlxIbG0uzZs0YMmQIu3bt8lk+KyuLlJSUfA8RERE5eVXIZGjTpk1MnDiRoUOH+izXs2dP3n77bb744guef/55Vq1axQUXXEBWVlahx0yYMIGYmJi8R926dUs7fBERESlHAjrp4rhx43j00Ud9llm1ahXt2rXLe75jxw46d+5M586d+c9//lOi6+3cuZP69evzzjvvcNlllxVYJisrK1+ylJKSQt26dTXpooiISAVSkkkXA9pn6I477uCqq67yWaZBgwZ5P+/YsYOuXbvSoUMHpk6dWuLrxcfHU79+fTZs2FBoGZfLhcvlKvG5RUREpGIKaDJUo0YNatSoUayy27dvp2vXrrRt25Zp06Yd1+rIe/fuJTExkfj4+GIfc7jiTH2HREREKo7D9+1iNYDZCmD79u22SZMm9oILLrB//fWX3blzZ97jSKeccor94IMPrLXWHjx40N5zzz12xYoVdvPmzXbJkiW2Q4cOtnbt2jYlJaXY105MTLSAHnrooYceeuhRAR+JiYlF3usrxND6hQsXsnHjRjZu3EidOnXy7bNHZHzr168nOTkZAKfTyU8//cT06dM5cOAA8fHxdO3aldmzZxMdHV3sayckJJCYmEh0dDTGmHz7DvcnSkxMDNr+RHoP9B4cpvdB7wHoPThM70Pg3wNrLQcPHiQhIaHIslq1/gRoRXu9B6D34DC9D3oPQO/BYXofKtZ7UCGH1ouIiIiUFiVDIiIiEtSUDJ0Al8vF2LFjg3oovt4DvQeH6X3QewB6Dw7T+1Cx3gP1GRIREZGgppohERERCWpKhkRERCSoKRkSERGRoKZkSERERIKakqFSsGXLFm666SYaNmxIREQEjRs3ZuzYsWRnZwc6tDI1efJkGjZsSHh4OG3btuXLL78MdEh+NWHCBM466yyio6OJjY3l0ksvZf369YEOK6AmTJiAMYYRI0YEOhS/2759O9dccw3Vq1cnMjKSVq1a8cMPPwQ6LL/Jzc3loYceyvsebNSoEY899hgejyfQoZWZ5cuX06dPHxISEjDGMG/evHz7rbWMGzeOhIQEIiIi6NKlC7/88ktggi1Dvt6HnJwcRo8ezemnn05UVBQJCQlcd9117NixI3ABF0DJUCn4/fff8Xg8vPrqq/zyyy/8+9//ZsqUKTzwwAOBDq3MzJ49mxEjRvDggw+yZs0azjvvPHr27Mm2bdsCHZrfLFu2jGHDhvHNN9+waNEicnNz6d69O2lpaYEOLSBWrVrF1KlTOeOMMwIdit/t37+fc889l9DQUD799FN+/fVXnn/+eapUqRLo0Pzm6aefZsqUKUyaNInffvuNZ555hmeffZaJEycGOrQyk5aWxplnnsmkSZMK3P/MM8/wwgsvMGnSJFatWkVcXBwXXXQRBw8e9HOkZcvX+5Cens7q1at5+OGHWb16NR988AF//PEHl1xySQAi9aHYK5ZKiTzzzDO2YcOGgQ6jzJx99tl26NCh+badeuqp9v777w9QRIG3a9cuC9hly5YFOhS/O3jwoG3atKldtGiR7dy5s73rrrsCHZJfjR492nbq1CnQYQRU79697Y033phv22WXXWavueaaAEXkX4CdO3du3nOPx2Pj4uLsU089lbctMzPTxsTE2ClTpgQgQv84+n0oyHfffWcBu3XrVv8EVQyqGSojycnJVKtWLdBhlIns7Gx++OEHunfvnm979+7dWbFiRYCiCrzDiwSfrL93X4YNG0bv3r258MILAx1KQCxYsIB27dpx5ZVXEhsbS+vWrXnttdcCHZZfderUic8//5w//vgDgHXr1vHVV1/Rq1evAEcWGJs3byYpKSnf96TL5aJz585B/T0J3u9KY0y5qjmtEKvWVzSbNm1i4sSJPP/884EOpUzs2bMHt9tNrVq18m2vVasWSUlJAYoqsKy1jBw5kk6dOtGyZctAh+NX77zzDqtXr2bVqlWBDiVg/vzzT1555RVGjhzJAw88wHfffcedd96Jy+XiuuuuC3R4fjF69GiSk5M59dRTcTqduN1unnzySQYOHBjo0ALi8HdhQd+TW7duDURI5UJmZib3338/gwYNKleLt6pmyIdx48ZhjPH5+P777/Mds2PHDnr06MGVV17JzTffHKDI/cMYk++5tfaYbcHijjvu4Mcff2TWrFmBDsWvEhMTueuuu5gxYwbh4eGBDidgPB4Pbdq0Yfz48bRu3Zpbb72VIUOG8MorrwQ6NL+ZPXs2M2bMYObMmaxevZo333yT5557jjfffDPQoQWUvif/kZOTw1VXXYXH42Hy5MmBDicf1Qz5cMcdd3DVVVf5LNOgQYO8n3fs2EHXrl3p0KEDU6dOLePoAqdGjRo4nc5jaoF27dp1zF9BwWD48OEsWLCA5cuXU6dOnUCH41c//PADu3btom3btnnb3G43y5cvZ9KkSWRlZeF0OgMYoX/Ex8fTvHnzfNtOO+005syZE6CI/O/ee+/l/vvvz/vOPP3009m6dSsTJkxg8ODBAY7O/+Li4gBvDVF8fHze9mD9nszJyaF///5s3ryZL774olzVCoGSIZ9q1KhBjRo1ilV2+/btdO3albZt2zJt2jQcjpO30i0sLIy2bduyaNEi+vXrl7d90aJF9O3bN4CR+Ze1luHDhzN37lyWLl1Kw4YNAx2S33Xr1o2ffvop37YbbriBU089ldGjRwdFIgRw7rnnHjOtwh9//EH9+vUDFJH/paenH/O953Q6T+qh9b40bNiQuLg4Fi1aROvWrQFvf8tly5bx9NNPBzg6/zqcCG3YsIElS5ZQvXr1QId0DCVDpWDHjh106dKFevXq8dxzz7F79+68fYf/OjjZjBw5kmuvvZZ27drl1YRt27aNoUOHBjo0vxk2bBgzZ85k/vz5REdH59WUxcTEEBEREeDo/CM6OvqYPlJRUVFUr149qPpO3X333XTs2JHx48fTv39/vvvuO6ZOnXpS1xAfrU+fPjz55JPUq1ePFi1asGbNGl544QVuvPHGQIdWZlJTU9m4cWPe882bN7N27VqqVatGvXr1GDFiBOPHj6dp06Y0bdqU8ePHExkZyaBBgwIYdenz9T4kJCRwxRVXsHr1aj766CPcbnfed2W1atUICwsLVNj5BXYw28lh2rRpFijwcTJ7+eWXbf369W1YWJht06ZN0A0pL+x3Pm3atECHFlDBOLTeWms//PBD27JlS+tyueypp55qp06dGuiQ/ColJcXeddddtl69ejY8PNw2atTIPvjggzYrKyvQoZWZJUuWFPgdMHjwYGutd3j92LFjbVxcnHW5XPb888+3P/30U2CDLgO+3ofNmzcX+l25ZMmSQIeex1hrrX/SLhEREZHy5+Tt2CIiIiJSDEqGREREJKgpGRIREZGgpmRIREREgpqSIREREQlqSoZEREQkqCkZEhERkaCmZEhERESCmpIhERERCWpKhkTkpHL99ddjjMEYQ2hoKI0aNWLUqFGkpaXllZkzZw5dunQhJiaGSpUqccYZZ/DYY4+xb98+AHbu3MmgQYM45ZRTcDgcjBgxIkCvRkT8QcmQiJx0evTowc6dO/nzzz954oknmDx5MqNGjQLgwQcfZMCAAZx11ll8+umn/Pzzzzz//POsW7eOt956C4CsrCxq1qzJgw8+yJlnnhnIlyIifqC1yUTkpHL99ddz4MAB5s2bl7dtyJAhfPTRR8yfP5/27dvz4osvctdddx1z7IEDB6hSpUq+bV26dKFVq1a8+OKLZRu4iASMaoZE5KQXERFBTk4Ob7/9NpUqVeL2228vsNzRiZCIBAclQyJyUvvuu++YOXMm3bp1Y8OGDTRq1IjQ0NBAhyUi5YiSIRE56Xz00UdUqlSJ8PBwOnTowPnnn8/EiROx1mKMCXR4IlLOhAQ6ABGR0ta1a1deeeUVQkNDSUhIyKsJatasGV999RU5OTmqHRKRPKoZEpGTTlRUFE2aNKF+/fr5kp5BgwaRmprK5MmTCzzuwIEDfopQRMoT1QyJSNBo37499913H/fccw/bt2+nX79+JCQksHHjRqZMmUKnTp3yRpmtXbsWgNTUVHbv3s3atWsJCwujefPmAXwFIlIWNLReRE4qBQ2tP9q7777Lyy+/zJo1a/B4PDRu3JgrrriC4cOH540oK6hvUf369dmyZUvZBC4iAaNkSERERIKa+gyJiIhIUFMyJCIiIkFNyZCIiIgENSVDIiIiEtSUDImIiEhQUzIkIiIiQU3JkIiIiAQ1JUMiIiIS1JQMiYiISFBTMiQiIiJBTcmQiIiIBLX/B9Fj8htcXcJsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(wholesale_data_pca_2[:,0], wholesale_data_pca_2[:,1], c=data['Channel'])\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692d3626",
   "metadata": {},
   "source": [
    "#### PCA with 4 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc84301f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)\n",
    "wholesale_data_pca_4 = pca.fit_transform(scaled_standard_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64ec4e38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.38750123, 0.22374588, 0.12647173, 0.09229904])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11e0882",
   "metadata": {},
   "source": [
    "#### Variance of 1st component is 38.75%,  2nd component is 22.37%, 3rd  component has 12.64% and 4th component has 9.22% i.e The principal component 1 holds 38.75% of the information, the principal component 2 holds 22.37% of the information, component 3 holds 12.64% and component 4 holds 9.22% of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "92dbed77",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=6, init='k-means++', n_init=100)\n",
    "y_means = kmeans.fit(scaled_standard_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69b90642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.448652</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>0.052933</td>\n",
       "      <td>0.523568</td>\n",
       "      <td>-0.041115</td>\n",
       "      <td>-0.589367</td>\n",
       "      <td>-0.043569</td>\n",
       "      <td>-0.066339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.448652</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>-0.391302</td>\n",
       "      <td>0.544458</td>\n",
       "      <td>0.170318</td>\n",
       "      <td>-0.270136</td>\n",
       "      <td>0.086407</td>\n",
       "      <td>0.089151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.448652</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>-0.447029</td>\n",
       "      <td>0.408538</td>\n",
       "      <td>-0.028157</td>\n",
       "      <td>-0.137536</td>\n",
       "      <td>0.133232</td>\n",
       "      <td>2.243293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.690297</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>0.100111</td>\n",
       "      <td>-0.624020</td>\n",
       "      <td>-0.392977</td>\n",
       "      <td>0.687144</td>\n",
       "      <td>-0.498588</td>\n",
       "      <td>0.093411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.448652</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>0.840239</td>\n",
       "      <td>-0.052396</td>\n",
       "      <td>-0.079356</td>\n",
       "      <td>0.173859</td>\n",
       "      <td>-0.231918</td>\n",
       "      <td>1.299347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-0.690297</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>1.401312</td>\n",
       "      <td>0.848446</td>\n",
       "      <td>0.850760</td>\n",
       "      <td>2.075222</td>\n",
       "      <td>-0.566831</td>\n",
       "      <td>0.241091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>-0.690297</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>2.155293</td>\n",
       "      <td>-0.592142</td>\n",
       "      <td>-0.757165</td>\n",
       "      <td>0.296561</td>\n",
       "      <td>-0.585519</td>\n",
       "      <td>0.291501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>1.448652</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>0.200326</td>\n",
       "      <td>1.314671</td>\n",
       "      <td>2.348386</td>\n",
       "      <td>-0.543380</td>\n",
       "      <td>2.511218</td>\n",
       "      <td>0.121456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.690297</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>-0.135384</td>\n",
       "      <td>-0.517536</td>\n",
       "      <td>-0.602514</td>\n",
       "      <td>-0.419441</td>\n",
       "      <td>-0.569770</td>\n",
       "      <td>0.213046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>-0.690297</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>-0.729307</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.573227</td>\n",
       "      <td>-0.620094</td>\n",
       "      <td>-0.504888</td>\n",
       "      <td>-0.522869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
       "0    1.448652  0.590668  0.052933  0.523568 -0.041115 -0.589367   \n",
       "1    1.448652  0.590668 -0.391302  0.544458  0.170318 -0.270136   \n",
       "2    1.448652  0.590668 -0.447029  0.408538 -0.028157 -0.137536   \n",
       "3   -0.690297  0.590668  0.100111 -0.624020 -0.392977  0.687144   \n",
       "4    1.448652  0.590668  0.840239 -0.052396 -0.079356  0.173859   \n",
       "..        ...       ...       ...       ...       ...       ...   \n",
       "435 -0.690297  0.590668  1.401312  0.848446  0.850760  2.075222   \n",
       "436 -0.690297  0.590668  2.155293 -0.592142 -0.757165  0.296561   \n",
       "437  1.448652  0.590668  0.200326  1.314671  2.348386 -0.543380   \n",
       "438 -0.690297  0.590668 -0.135384 -0.517536 -0.602514 -0.419441   \n",
       "439 -0.690297  0.590668 -0.729307 -0.555924 -0.573227 -0.620094   \n",
       "\n",
       "     Detergents_Paper  Delicassen  \n",
       "0           -0.043569   -0.066339  \n",
       "1            0.086407    0.089151  \n",
       "2            0.133232    2.243293  \n",
       "3           -0.498588    0.093411  \n",
       "4           -0.231918    1.299347  \n",
       "..                ...         ...  \n",
       "435         -0.566831    0.241091  \n",
       "436         -0.585519    0.291501  \n",
       "437          2.511218    0.121456  \n",
       "438         -0.569770    0.213046  \n",
       "439         -0.504888   -0.522869  \n",
       "\n",
       "[440 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_standard_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "30e60ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sb.scatterplot(x=wholesale_data_pca_4[:,0], y=wholesale_data_pca_4[:,1], hue=y_means.labels_, palette='pastel', data=scaled_standard_dataframe).set_title(\"KMeans Clusters derived from Elbow Method\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bde0d2c",
   "metadata": {},
   "source": [
    "### XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0543a560",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Channel', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0d620cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Channel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6233ae09",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y[Y == 2] = 0\n",
    "Y[Y == 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e535a618",
   "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>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>9413</td>\n",
       "      <td>8259</td>\n",
       "      <td>5126</td>\n",
       "      <td>666</td>\n",
       "      <td>1795</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>12126</td>\n",
       "      <td>3199</td>\n",
       "      <td>6975</td>\n",
       "      <td>480</td>\n",
       "      <td>3140</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>7579</td>\n",
       "      <td>4956</td>\n",
       "      <td>9426</td>\n",
       "      <td>1669</td>\n",
       "      <td>3321</td>\n",
       "      <td>2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>5963</td>\n",
       "      <td>3648</td>\n",
       "      <td>6192</td>\n",
       "      <td>425</td>\n",
       "      <td>1716</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>6006</td>\n",
       "      <td>11093</td>\n",
       "      <td>18881</td>\n",
       "      <td>1159</td>\n",
       "      <td>7425</td>\n",
       "      <td>2098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0       3  12669   9656     7561     214              2674        1338\n",
       "1       3   7057   9810     9568    1762              3293        1776\n",
       "2       3   6353   8808     7684    2405              3516        7844\n",
       "3       3  13265   1196     4221    6404               507        1788\n",
       "4       3  22615   5410     7198    3915              1777        5185\n",
       "5       3   9413   8259     5126     666              1795        1451\n",
       "6       3  12126   3199     6975     480              3140         545\n",
       "7       3   7579   4956     9426    1669              3321        2566\n",
       "8       3   5963   3648     6192     425              1716         750\n",
       "9       3   6006  11093    18881    1159              7425        2098"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3bbd3f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "5    0\n",
       "6    0\n",
       "7    0\n",
       "8    1\n",
       "9    0\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e7abf3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since it is a classification task, we will use logloss as evaluation metric\n",
    "xgbc = XGBClassifier(eval_metric='logloss')\n",
    "kfold = KFold(n_splits=5)\n",
    "result = cross_val_score(xgbc, X, Y, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "855e0cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 92.04545454545455%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Score: {}%'.format(result.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aea2d3f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation: 3.3709993123162114%\n"
     ]
    }
   ],
   "source": [
    "print('Standard Deviation: {}%'.format(result.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c844f7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Precision_Score = cross_val_score(xgbc, X, Y, cv=kfold, scoring='precision_weighted')\n",
    "Recall_Score = cross_val_score(xgbc, X, Y, cv=kfold, scoring='recall_weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "35e4734c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score: 92.72498120032316%\n"
     ]
    }
   ],
   "source": [
    "print('Precision Score: {}%'.format(Precision_Score.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "88c504e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score: 92.04545454545455%\n"
     ]
    }
   ],
   "source": [
    "print('Recall Score: {}%'.format(Recall_Score.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "729391ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "P = (Precision_Score.mean())*100\n",
    "R = (Recall_Score.mean())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4f198dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "F1_Score = 2*(P*R)/(P+R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "432d039b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 92.38396833187647%\n"
     ]
    }
   ],
   "source": [
    "print('F1 Score: {}%'.format(F1_Score))"
   ]
  }
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