Gradient_Boosting_XG_Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc53ba21-4d7e-48ee-9e36-3d421f11c48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "# Load your dataset\n",
    "df = pd.read_csv('startups_data_cleaned_step1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be2f98a5-ae18-4efc-a775-ca738c1b7318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12306 entries, 0 to 12305\n",
      "Data columns (total 39 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   name                  12306 non-null  object \n",
      " 1   market                12306 non-null  object \n",
      " 2   funding_total_usd     12306 non-null  float64\n",
      " 3   country_code          12306 non-null  object \n",
      " 4   state_code            12301 non-null  object \n",
      " 5   region                12306 non-null  object \n",
      " 6   city                  12291 non-null  object \n",
      " 7   funding_rounds        12306 non-null  int64  \n",
      " 8   founded_at            12306 non-null  object \n",
      " 9   founded_month         12306 non-null  int64  \n",
      " 10  founded_quarter       12306 non-null  int64  \n",
      " 11  founded_year          12306 non-null  int64  \n",
      " 12  first_funding_at      12306 non-null  object \n",
      " 13  last_funding_at       12306 non-null  object \n",
      " 14  seed                  12306 non-null  int64  \n",
      " 15  venture               12306 non-null  int64  \n",
      " 16  equity_crowdfunding   12306 non-null  int64  \n",
      " 17  undisclosed           12306 non-null  int64  \n",
      " 18  convertible_note      12306 non-null  int64  \n",
      " 19  debt_financing        12306 non-null  int64  \n",
      " 20  angel                 12306 non-null  int64  \n",
      " 21  grant                 12306 non-null  int64  \n",
      " 22  private_equity        12306 non-null  int64  \n",
      " 23  post_ipo_equity       12306 non-null  int64  \n",
      " 24  post_ipo_debt         12306 non-null  int64  \n",
      " 25  secondary_market      12306 non-null  int64  \n",
      " 26  product_crowdfunding  12306 non-null  int64  \n",
      " 27  round_A               12306 non-null  int64  \n",
      " 28  round_B               12306 non-null  int64  \n",
      " 29  round_C               12306 non-null  int64  \n",
      " 30  round_D               12306 non-null  int64  \n",
      " 31  round_E               12306 non-null  int64  \n",
      " 32  round_F               12306 non-null  int64  \n",
      " 33  round_G               12306 non-null  int64  \n",
      " 34  round_H               12306 non-null  int64  \n",
      " 35  category_count        12306 non-null  int64  \n",
      " 36  clean_name            12306 non-null  object \n",
      " 37  is_operating          12306 non-null  int64  \n",
      " 38  category_updated      12306 non-null  object \n",
      "dtypes: float64(1), int64(27), object(11)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a125dc4-0661-4195-a7ab-b9612253cc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ee80b88-afd9-4c40-94d4-f215a423bc9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create copy of dataframe\n",
    "df_processed = df.copy()\n",
    "\n",
    "# Convert date columns to datetime\n",
    "date_columns = ['founded_at', 'first_funding_at', 'last_funding_at']\n",
    "for col in date_columns:\n",
    "    df_processed[col] = pd.to_datetime(df_processed[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2d25bce-5d14-4559-a424-b3335132ad9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create temporal features\n",
    "df_processed['days_to_first_funding'] = (df_processed['first_funding_at'] - df_processed['founded_at']).dt.days\n",
    "df_processed['funding_duration'] = (df_processed['last_funding_at'] - df_processed['first_funding_at']).dt.days\n",
    "df_processed['company_age'] = (pd.Timestamp('2024-01-01') - df_processed['founded_at']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07ebc01d-319e-4a32-bad8-7182078e2d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill NaN values\n",
    "df_processed['days_to_first_funding'] = df_processed['days_to_first_funding'].fillna(-1)\n",
    "df_processed['funding_duration'] = df_processed['funding_duration'].fillna(0)\n",
    "df_processed['company_age'] = df_processed['company_age'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9310e7ac-9c6f-4463-b163-3d9a65b8c67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now check for negative values in 'days_to_first_funding'\n",
    "negative_days_to_funding = df_processed[df_processed['days_to_first_funding'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac5cc9e9-33fa-4ec7-8982-99d7da8175dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>founded_at</th>\n",
       "      <th>first_funding_at</th>\n",
       "      <th>days_to_first_funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>2014-10-07</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>2014-09-14</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>2014-09-14</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2013-08-06</td>\n",
       "      <td>-391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>-593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12253</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>1996-12-31</td>\n",
       "      <td>-731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12275</th>\n",
       "      <td>1998-11-28</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>-331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12280</th>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>1997-09-30</td>\n",
       "      <td>-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12299</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>-365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12305</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>-1096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      founded_at first_funding_at  days_to_first_funding\n",
       "6     2014-10-10       2014-10-07                     -3\n",
       "7     2014-10-01       2014-09-14                    -17\n",
       "12    2014-09-18       2014-09-14                     -4\n",
       "23    2014-09-01       2013-08-06                   -391\n",
       "24    2014-09-01       2013-01-16                   -593\n",
       "...          ...              ...                    ...\n",
       "12253 1999-01-01       1996-12-31                   -731\n",
       "12275 1998-11-28       1998-01-01                   -331\n",
       "12280 1998-01-01       1997-09-30                    -93\n",
       "12299 1996-01-01       1995-01-01                   -365\n",
       "12305 2010-01-01       2007-01-01                  -1096\n",
       "\n",
       "[920 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_days_to_funding.loc[:,['founded_at','first_funding_at','days_to_first_funding']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e621f300-80cd-4b17-b638-f5dcbf20a61a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where 'days_to_first_funding' is negative\n",
    "df_processed = df_processed[df_processed['days_to_first_funding'] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0405dd00-d24f-4e2b-92f0-b95028afb378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'market', 'funding_total_usd', 'country_code', 'state_code',\n",
       "       'region', 'city', 'funding_rounds', 'founded_at', 'founded_month',\n",
       "       'founded_quarter', 'founded_year', 'first_funding_at',\n",
       "       'last_funding_at', 'seed', 'venture', 'equity_crowdfunding',\n",
       "       'undisclosed', 'convertible_note', 'debt_financing', 'angel', 'grant',\n",
       "       'private_equity', 'post_ipo_equity', 'post_ipo_debt',\n",
       "       'secondary_market', 'product_crowdfunding', 'round_A', 'round_B',\n",
       "       'round_C', 'round_D', 'round_E', 'round_F', 'round_G', 'round_H',\n",
       "       'category_count', 'clean_name', 'is_operating', 'category_updated',\n",
       "       'days_to_first_funding', 'funding_duration', 'company_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62a188d3-2bcf-4b86-9509-741c4775a4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>funding_total_usd</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>funding_rounds</th>\n",
       "      <th>founded_at</th>\n",
       "      <th>founded_month</th>\n",
       "      <th>...</th>\n",
       "      <th>round_F</th>\n",
       "      <th>round_G</th>\n",
       "      <th>round_H</th>\n",
       "      <th>category_count</th>\n",
       "      <th>clean_name</th>\n",
       "      <th>is_operating</th>\n",
       "      <th>category_updated</th>\n",
       "      <th>days_to_first_funding</th>\n",
       "      <th>funding_duration</th>\n",
       "      <th>company_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le HerbÈ</td>\n",
       "      <td>Medical Marijuana Patients</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WA</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>le herbè</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kandid</td>\n",
       "      <td>Apps</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kandid</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Manly Man Can</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>30.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MI</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>Pinckney</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>the manly man can</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MyKidsCalendar</td>\n",
       "      <td>Software</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Newark</td>\n",
       "      <td>Long Valley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-22</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>mykidscalendar</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peach</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-19</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>peach</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3361</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12300</th>\n",
       "      <td>XO Group</td>\n",
       "      <td>Media</td>\n",
       "      <td>19600000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>3</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>xo group</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>10227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12301</th>\n",
       "      <td>eBay</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>6700000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>1</td>\n",
       "      <td>1995-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>ebay</td>\n",
       "      <td>1</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>639</td>\n",
       "      <td>0</td>\n",
       "      <td>10349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12302</th>\n",
       "      <td>Arkansas Children's Hospital</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AR</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>arkansas children's hospital</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12303</th>\n",
       "      <td>Northcentral Technical College</td>\n",
       "      <td>Education</td>\n",
       "      <td>6200000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WI</td>\n",
       "      <td>WI - Other</td>\n",
       "      <td>Antigo</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>northcentral technical college</td>\n",
       "      <td>1</td>\n",
       "      <td>Education</td>\n",
       "      <td>627</td>\n",
       "      <td>0</td>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12304</th>\n",
       "      <td>EATON</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>eaton</td>\n",
       "      <td>0</td>\n",
       "      <td>Transport &amp; Automotive</td>\n",
       "      <td>941</td>\n",
       "      <td>0</td>\n",
       "      <td>4748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11386 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name                        market  \\\n",
       "0                            Le HerbÈ   Medical Marijuana Patients    \n",
       "1                              Kandid                         Apps    \n",
       "2                   The Manly Man Can                      Fashion    \n",
       "3                      MyKidsCalendar                     Software    \n",
       "4                               Peach                      Fashion    \n",
       "...                               ...                           ...   \n",
       "12300                        XO Group                        Media    \n",
       "12301                            eBay            Business Services    \n",
       "12302    Arkansas Children's Hospital                Biotechnology    \n",
       "12303  Northcentral Technical College                    Education    \n",
       "12304                           EATON                   Automotive    \n",
       "\n",
       "       funding_total_usd country_code state_code         region  \\\n",
       "0               100000.0          USA         WA        Seattle   \n",
       "1              1400000.0          USA         CA    SF Bay Area   \n",
       "2                   30.0          USA         MI        Detroit   \n",
       "3               110000.0          USA         NJ         Newark   \n",
       "4               500000.0          USA         CA    SF Bay Area   \n",
       "...                  ...          ...        ...            ...   \n",
       "12300         19600000.0          USA         NY  New York City   \n",
       "12301          6700000.0          USA         CA    SF Bay Area   \n",
       "12302          3500000.0          USA         AR    Little Rock   \n",
       "12303          6200000.0          USA         WI     WI - Other   \n",
       "12304          2400000.0          USA         OH      Cleveland   \n",
       "\n",
       "                city  funding_rounds founded_at  founded_month  ...  round_F  \\\n",
       "0            Seattle               1 2014-12-01             12  ...        0   \n",
       "1      San Francisco               1 2014-11-19             11  ...        0   \n",
       "2           Pinckney               1 2014-10-26             10  ...        0   \n",
       "3        Long Valley               1 2014-10-22             10  ...        0   \n",
       "4           Berkeley               1 2014-10-19             10  ...        0   \n",
       "...              ...             ...        ...            ...  ...      ...   \n",
       "12300       New York               3 1996-01-01              1  ...        0   \n",
       "12301       San Jose               1 1995-09-01              9  ...        0   \n",
       "12302    Little Rock               1 2012-01-01              1  ...        0   \n",
       "12303         Antigo               1 2012-01-01              1  ...        0   \n",
       "12304      Cleveland               1 2011-01-01              1  ...        0   \n",
       "\n",
       "       round_G round_H category_count                      clean_name  \\\n",
       "0            0       0              3                        le herbè   \n",
       "1            0       0              1                          kandid   \n",
       "2            0       0              1               the manly man can   \n",
       "3            0       0              1                  mykidscalendar   \n",
       "4            0       0              3                           peach   \n",
       "...        ...     ...            ...                             ...   \n",
       "12300        0       0              3                        xo group   \n",
       "12301        0       0              6                            ebay   \n",
       "12302        0       0              1    arkansas children's hospital   \n",
       "12303        0       0              1  northcentral technical college   \n",
       "12304        0       0              3                           eaton   \n",
       "\n",
       "       is_operating        category_updated  days_to_first_funding  \\\n",
       "0                 1              Healthcare                      0   \n",
       "1                 1              Technology                     13   \n",
       "2                 1     Fashion & Lifestyle                      1   \n",
       "3                 1              Technology                      0   \n",
       "4                 1     Fashion & Lifestyle                      4   \n",
       "...             ...                     ...                    ...   \n",
       "12300             1                   other                      0   \n",
       "12301             1       Business Services                    639   \n",
       "12302             1              Healthcare                    628   \n",
       "12303             1               Education                    627   \n",
       "12304             0  Transport & Automotive                    941   \n",
       "\n",
       "       funding_duration  company_age  \n",
       "0                     0         3318  \n",
       "1                     0         3330  \n",
       "2                     0         3354  \n",
       "3                     0         3358  \n",
       "4                     0         3361  \n",
       "...                 ...          ...  \n",
       "12300              1200        10227  \n",
       "12301                 0        10349  \n",
       "12302                 0         4383  \n",
       "12303                 0         4383  \n",
       "12304                 0         4748  \n",
       "\n",
       "[11386 rows x 42 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e46ab524-fc60-4d6f-9ff8-fd46e2ff00aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selected features (reduced set)\n",
    "selected_features = [\n",
    "    'funding_total_usd',\n",
    "    'funding_rounds',\n",
    "    'days_to_first_funding',\n",
    "    'funding_duration',\n",
    "    'category_count',\n",
    "    'company_age'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83bec845-caa1-4f10-b589-9f4b3783f35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables (reduced set)\n",
    "categorical_features = ['category_updated', 'state_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78711978-cd4e-4c10-8ff1-bc38c37fc1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot Encoding with pandas\n",
    "df_processed = pd.get_dummies(df_processed, columns=categorical_features, drop_first=False)\n",
    "\n",
    "# Identify dummy columns\n",
    "dummy_columns = [col for col in df_processed.columns if any(prefix in col for prefix in categorical_features)]\n",
    "\n",
    "# Update feature_columns\n",
    "feature_columns = selected_features + dummy_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6feca37-5d59-4b2c-980d-9a6b5fba27ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix\n",
    "X = df_processed[feature_columns]\n",
    "y = df_processed['is_operating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92aba4fc-8b9c-49bc-b6da-1d18e6044e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11386 entries, 0 to 12304\n",
      "Data columns (total 68 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   funding_total_usd                        11386 non-null  float64\n",
      " 1   funding_rounds                           11386 non-null  int64  \n",
      " 2   days_to_first_funding                    11386 non-null  int64  \n",
      " 3   funding_duration                         11386 non-null  int64  \n",
      " 4   category_count                           11386 non-null  int64  \n",
      " 5   company_age                              11386 non-null  int64  \n",
      " 6   category_updated_Business Services       11386 non-null  bool   \n",
      " 7   category_updated_Education               11386 non-null  bool   \n",
      " 8   category_updated_Energy & Environment    11386 non-null  bool   \n",
      " 9   category_updated_Fashion & Lifestyle     11386 non-null  bool   \n",
      " 10  category_updated_Finance                 11386 non-null  bool   \n",
      " 11  category_updated_Healthcare              11386 non-null  bool   \n",
      " 12  category_updated_Media & Entertainment   11386 non-null  bool   \n",
      " 13  category_updated_Retail & E-Commerce     11386 non-null  bool   \n",
      " 14  category_updated_Technology              11386 non-null  bool   \n",
      " 15  category_updated_Transport & Automotive  11386 non-null  bool   \n",
      " 16  category_updated_other                   11386 non-null  bool   \n",
      " 17  state_code_AK                            11386 non-null  bool   \n",
      " 18  state_code_AL                            11386 non-null  bool   \n",
      " 19  state_code_AR                            11386 non-null  bool   \n",
      " 20  state_code_AZ                            11386 non-null  bool   \n",
      " 21  state_code_CA                            11386 non-null  bool   \n",
      " 22  state_code_CO                            11386 non-null  bool   \n",
      " 23  state_code_CT                            11386 non-null  bool   \n",
      " 24  state_code_DC                            11386 non-null  bool   \n",
      " 25  state_code_DE                            11386 non-null  bool   \n",
      " 26  state_code_FL                            11386 non-null  bool   \n",
      " 27  state_code_GA                            11386 non-null  bool   \n",
      " 28  state_code_HI                            11386 non-null  bool   \n",
      " 29  state_code_IA                            11386 non-null  bool   \n",
      " 30  state_code_ID                            11386 non-null  bool   \n",
      " 31  state_code_IL                            11386 non-null  bool   \n",
      " 32  state_code_IN                            11386 non-null  bool   \n",
      " 33  state_code_KS                            11386 non-null  bool   \n",
      " 34  state_code_KY                            11386 non-null  bool   \n",
      " 35  state_code_LA                            11386 non-null  bool   \n",
      " 36  state_code_MA                            11386 non-null  bool   \n",
      " 37  state_code_MD                            11386 non-null  bool   \n",
      " 38  state_code_ME                            11386 non-null  bool   \n",
      " 39  state_code_MI                            11386 non-null  bool   \n",
      " 40  state_code_MN                            11386 non-null  bool   \n",
      " 41  state_code_MO                            11386 non-null  bool   \n",
      " 42  state_code_MS                            11386 non-null  bool   \n",
      " 43  state_code_MT                            11386 non-null  bool   \n",
      " 44  state_code_NC                            11386 non-null  bool   \n",
      " 45  state_code_ND                            11386 non-null  bool   \n",
      " 46  state_code_NE                            11386 non-null  bool   \n",
      " 47  state_code_NH                            11386 non-null  bool   \n",
      " 48  state_code_NJ                            11386 non-null  bool   \n",
      " 49  state_code_NM                            11386 non-null  bool   \n",
      " 50  state_code_NV                            11386 non-null  bool   \n",
      " 51  state_code_NY                            11386 non-null  bool   \n",
      " 52  state_code_OH                            11386 non-null  bool   \n",
      " 53  state_code_OK                            11386 non-null  bool   \n",
      " 54  state_code_OR                            11386 non-null  bool   \n",
      " 55  state_code_PA                            11386 non-null  bool   \n",
      " 56  state_code_RI                            11386 non-null  bool   \n",
      " 57  state_code_SC                            11386 non-null  bool   \n",
      " 58  state_code_SD                            11386 non-null  bool   \n",
      " 59  state_code_TN                            11386 non-null  bool   \n",
      " 60  state_code_TX                            11386 non-null  bool   \n",
      " 61  state_code_UT                            11386 non-null  bool   \n",
      " 62  state_code_VA                            11386 non-null  bool   \n",
      " 63  state_code_VT                            11386 non-null  bool   \n",
      " 64  state_code_WA                            11386 non-null  bool   \n",
      " 65  state_code_WI                            11386 non-null  bool   \n",
      " 66  state_code_WV                            11386 non-null  bool   \n",
      " 67  state_code_WY                            11386 non-null  bool   \n",
      "dtypes: bool(62), float64(1), int64(5)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f504cbf8-b2d0-49e5-9603-af5064645731",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a DataFrame to store all the evaluation metrics\n",
    "# Initialize an empty DataFrame to store results\n",
    "results_df = pd.DataFrame(columns=['Model', 'Features Selected', 'Validation Technique', 'Accuracy', 'TPR', 'FPR', 'Precision', 'F1 Score',\n",
    "                                   'ROC-AUC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ee1a6e-582d-49ca-8509-55c7f9165a53",
   "metadata": {},
   "source": [
    "## Build Model - Gradient Boosting (without CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f444cc76-7b7f-4d40-847a-97a51bfe0bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ff0913e3-9dad-4d30-85f7-ece1ac63a543",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#Split the data into training and testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ec05bbc3-feee-4ffe-bd5f-70be5c47bf4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.91\n",
      "Confusion Matrix:\n",
      "[[  92  272]\n",
      " [  76 1838]]\n",
      "Accuracy: 0.85\n",
      "True Positive Rate (TPR): 0.96\n",
      "False Positive Rate (FPR): 0.75\n",
      "Precision: 0.87\n"
     ]
    }
   ],
   "source": [
    "#Build the model \n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score,f1_score\n",
    "\n",
    "# Initialize the Gradient Boosting Classifier\n",
    "gbm = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "gbm.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = gbm.predict(X_test)\n",
    "y_pred_proba = gbm.predict_proba(X_test)[:, 1]\n",
    "\n",
    "#Evaluation Metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, roc_curve, auc\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8c3f40cf-ddad-4438-a2a0-c30ab9d45dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model', linestyle='--')  \n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2fefead2-8970-4320-9ae2-ba2b67428a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cd/jz0sn5850b5ckqbp3mgr9xv00000gn/T/ipykernel_23222/2531870861.py:18: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, new_row], ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# For Model 1 (without cross-validation), before feature selection\n",
    "model_1_results = {\n",
    "    'Model': 'Gradient Boosting',  \n",
    "    'Features Selected': 'All Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row = pd.DataFrame([model_1_results])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabb0cef-ea5b-49eb-9e8f-bd8d624112cb",
   "metadata": {},
   "source": [
    "## Feature Importance for Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1960cacb-0a99-4e6c-8519-f90e3a6ec988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lisaPH6+srCxVr15dxhgdPXpUdrtd//jHP/S3v/2tUL3kxZPt07NnzwK3jc1m03fffZfvMsnJyUpPT1fbtm1d07777js9/fTTOnPmjOuEn8LatWuX67P66dOn1bdvXw0aNEgDBgwocF1fbGNffZ50Hi8KMmrUKLfzfLWvnD788EO9//77+vXXX2Wz2dSgQQPddtttuvHGGz1aPzY2VsHBwXrnnXfUunVrj9Yp1wzKhX79+pk77rjDZGZmmkqVKpmdO3caY4xZtmyZiYuL87jOsGHDTHh4uKlbt64ZOHCgGTBggImJiTERERHm5ptvNo0aNTJBQUFm5cqVbmt89tlnJjQ01Pj5+Znw8HATERHhulWuXDnf+7fZbDlufn5+uX523jzVqlUrM3v2bGOMybFtNmzYYCIjIz2uc/bsWTNr1izTtWtXExAQYPz8/MyLL75oTp065XEN52M8cuRIrukbN24scPtkN3z4cPOvf/2rUPedvYe8tq/NZjOBgYGmYcOG5vPPP/e4XlhYmElOTjbGGDN16lTTp08fY4wxK1euNLVr1y5w/f/+97/Gbrebhx56yBw4cMA1/cCBA+bBBx80/v7+5osvvijUY1y2bJkZMmSIqVixovHz8zOPPvqo2bFjh8frR0ZGmo0bNxpjcj5vdu3aZUJCQjyuk5aWZoYNG2ZCQkLMSy+9VKjHkF1ISIjZvXu3McaY66+/3kydOtUYY8zevXtNhQoVClzf3f7OPt2T15XNZjOHDx/2+nFcatKkSSY2Nta88847Jjg42LWd582bZzp27Fjg+qmpqebpp582DRo0MH5+fiYxMdH85z//MadPn/aqH1+9zo25+JoeNWqUueKKK0zlypXN/fffb9avX+/x+itWrDDDhg0zlSpVMvHx8cZut+d77L2Ur/aVzWYzoaGhplevXub48eO55h86dMjYbDaPajVp0sR8/PHHxpicr6ukpCRTtWrVAte///77TWBgoBk3bpw5d+6ca/qOHTtMYmKiqVatmnnvvfc8ekzZt032XgrzmGJiYszSpUtdP//zn/809evXN+fPn3f9nJCQUGCdbdu2mWrVqplHH3001/TIyEgzYMAAc+HChQLr9O7d27z55ptu57/99tuu43N+WrZsab799lu387/99lvTqlWrfGv46pizY8cOU7FiRdOzZ0/zySefmO3bt5uff/7ZfPTRR6Z79+4mJCQkx75zZ9asWcZms5mqVasam81mrrjiCjN37lwTGhpqhg8fbpKSkgqskV1RX5+DBw82cXFxeb5Gn3vuORMQEGDmz59fYB1fvM6HDx+e4xYYGGgGDRqUa3phewkNDfXqdWWMMfv27TOTJ0829evXN35+fqZr165m9uzZ5uzZsx4/LpvNZmJiYsz9999vxowZ4/ZWkIkTJxZ4Gz9+vLnnnntM9erVzb333ptvvaJ8PtmzZ4/rtnv3bhMSEmK+//77HNP37NlTYJ3sn80vvVWuXNkEBgZ69PpMTU01ffv2NREREWbOnDkePYa8ZGZmmrZt25qgoCDTv39/M27cOPPoo4+aG264wQQGBpqOHTvmOOa74+4ztjfuv/9+ExISYm6++WYzevToQj9vYmJiTExMjLHZbCY6Otr1c0xMjGnYsKHp06ePWbNmjUe91KlTx/zvf/8zxuR8v9qxY4cJDQ31/kFms3HjRo/2+datW029evVc38cGDBhgDh06ZLp162bCw8PNQw89ZFJSUgqs44v3mYJ4+pg8eY1PnDjR4/st6vvDkiVLjJ+fn5kwYYI5ceKEa/rx48fNk08+aex2u/n+++89rueOJ9snv2PnnXfeaYKDgz3axv379zd///vfXT/v2rXLBAcHmz59+phRo0aZSpUqmX//+98e9f3bb7+ZuXPnmu7du5ugoCDzpz/9ydjt9kK9j/tqG3vyedKT7ZP9+ODuVq9evXxr+GpfORwOc/PNNxubzWYaNWpk+vXrZ2644QbTsGFD4+fnZwYPHmyysrIKrHPmzBlz3333maCgIDN58mTjcDgKXKc8I7gtJ6pWrWq2b99ujMl5wNi9e7cJDg72uM6jjz5q7r333hwvLIfDYR544AHz2GOPmaysLPOXv/zFJCYmuq3RoEEDM3r0aHPmzBkvH81FixcvNm3atDFfffWVSU9PNxkZGearr74y7dq1M998843HdYKDg12BV/Zts3PnThMUFFTg+mvXrjV//vOfTVhYmGnXrp2ZNm2aOXTokPH39zdbt271uI9WrVqZ1q1bGz8/PxMfH29at27turVo0cKEhoaam266yeN6Tz/9tImIiDCDBg0y//jHP8yLL76Y4+aJmJgYc/ToUY/v053Q0FDz66+/GmOMueqqq8y0adOMMZ6Hit26dTNPPPGE2/lPPPGE6datW4F1Dhw4YJ555hlTv359U6NGDfPggw+adevWFXpfGXPxueJ8TNmfNz/88IOpUqVKoWoZY8yHH35o7Ha7CQsLM5UrV85x80SHDh3Mo48+apYvX24qVKjgCpX/97//maioqALXv/RLnbtbQWw2m1m6dKnZtGlTvjdP1a9f3/XBPft2/vnnn01ERITHdYy5+GV46NChplKlSiY0NNSMHDnSrF692qN1ffU6z8u5c+fMtGnTTFBQkPHz8zMtWrQwb7/9ttsPPc8++6xp1KiRifo/9s47LIrr+/9nlr50EBEL1YoVYtcoYMdEsUXF3jX2XmJD7L3FilJsMRojRhM7omiwA1ERseLHWGJXUBF4//7wt/tlYcvs7mW5Kq/nmeeBmZ0zZ+b2c889t1QpjBs3Tp7W2uoiCALmzJmTr27Qtq6QSCSIj49H9erV4eHhka9jLLZTCgDm5ubyfJY7vW/cuCGqrgCAY8eOwc3NDVWqVMH58+exdOlSWFhYICgoCI8ePRIlg1VHO/f7AECrVq0wbtw4+f8pKSmi64tz587B2toaCxcuBPCpDJQoUQJt2rQRZbQFABcXF7UGoNTUVLi4uGiUY2VlhXv37qm8fu/ePY3GAlZ1ztChQxEQEKD0Wk5ODgICAjBs2DCNcqpXr4558+YB+DQxJAgCfH195ZOOYmFVPj9+/IiWLVuievXqePnypfz84sWLYWxsjB07doiSw9JQJSNveRALq3KVl+PHj6N79+6wtLSEjY0NBg4cKMrgtWDBAlSqVAnFixfH6NGjtTbO60JsbKxSpwCW/ZPc6JpWqvj3338xaNAgmJiYoEWLFqLvCw8Ph729Pdq1a4eLFy9q3SdYvnw5nJ2d5WOZ3CQnJ8PZ2VnU5LcgCAgMDES7du3UHmJwdHTU2mlAGX5+fgqGIV3IPbGdO80TEhJgY2Ojt44yWWLK5/fff4+AgAD88ccf6NKlCwRBQLly5RASEoLXr1+Lfh6LdkYTYt+JFazahx9++AEDBw5UeX3AgAHo0qWL3vrq+n0+fvyI5cuXw8nJCWXLlhXVXpUuXVqhTx4aGorq1avL/w8LC1P4XxVDhgyBvb096tati9WrV+Pp06cACu8bF1S7xwpd0mrJkiVwcHBQ6sQVHR0NBwcH0UZ24FMb7uHhgdq1a2PPnj2Ijo5WOIr4RJHh9ivB3t5eXlnlrjBOnTqF4sWLi5ZTrFgxpKSk5DufkpIi94ZKSkqCra2tShlSqZRJJ7Jy5co4depUvvMnT55ExYoVRcvx9PTEkSNHACh+m8jISFSqVEnj/UZGRhg1alS+zqS2DYRstlYQBIwbN05hBnfu3LnYvn07Pnz4IFqePjNyrPH390fPnj0RFRUFExMTueHgxIkTcHNz03i/tbW10s66jOvXr8PKykqjHDMzM3Tv3h0HDx5UmHzQZWAUGBgonxm2srLC7du3kZ2djU6dOqFDhw5ayTp37hwqVqyISpUqISwsDBEREQqHGGJiYmBnZweJRII+ffrIz0+ePFn0IIQFrLzoZKgy5F29elUrz+bcvHnzBhs3bkT9+vUhkUjg7e2t8R5W5Tw3mZmZ2LlzJ1q2bAkjIyO5N/Ds2bNRokQJdO3aVaUuU6ZMyWes08Vwm9fLR5e6QtYpzcjIwA8//AArKyv89ttv8uvadEorVaqEvXv3AlBM7xUrVsDX11f0u71+/Rpt2rSBRCKBlZUVtm3bJvpegF1Hu3jx4vKBGfBpoL979275/zdu3NAqHx87dgwWFhaYMWMGSpYsie+++06Ul5kMc3NzJCcnq7x+7do1UQZyW1tbuXeXMv7++2+1/QCWVK5cGfv27VN5fd++fahcubJGObJ6HPg0IW1sbIwTJ05orQ+r8gl88vBv0KABGjZsiHfv3mHZsmUwNjbWKj8LgiD3klR3aANvhlsZr1+/xrp16+Dg4AAjIyPR9505cwb9+/eHjY0NatWqhbVr1+LVq1c666GON2/eYPTo0fnOs+yf5IaV4fb169f46aefYGVlhTp16uD48eNayzhy5AiMjIwU+gJi+wSNGjXC6tWrVV5fuXKlqAl8QRDQuXPnfN7i2nqPA58mwpSNiQqDRo0ayQ3XueuyoUOHamVgV4dYI56zs7N89dCLFy8gCAI2bNig9fMM0c4Y2nDLqn1wd3dXOgaWcfLkSbi7u+uspwxdvs/WrVvh6ekJFxcX/Pzzz/IVRpowNzdX8MQOCAhQ8MC9efOmqPSWfeO8kwSF9Y15NtzqmlZVq1bFpk2bVF4PCwtDlSpVtNJl7969MDIyUjp2LOITxoUdqqEIw9CsWTNavny5PD6eIAj09u1bmjFjBgUGBoqWk5WVRdevX88Xi/P69evy2Czm5uZq46e0aNGCLly4oHeA+1u3binEXpVha2tLd+/eFS1n0KBBNHLkSNq8eTMJgkD//vsv/f333zRu3DiaPn26xvsDAgJo06ZN9OTJE+rRowe1aNFCVBykvMyYMYOIPm1c1LlzZzI3N9daRm7u3Lmj1/0yjh07pjJ+1+bNm0XJWL58OXXr1o327t1LP/30E5UtW5aIPm1OUL9+fY335+TkkImJicrrJiYmojahcXNzo7i4OHJ1dSU3Nze94p0uWrSI/Pz86MKFC5SZmUkTJkygq1ev0vPnz+n06dOiZGRlZdGMGTNo8eLFNHToUJo7d67O6e7n50dPnz6l169fk729vfz8wIEDRcV9K1WqFAUEBJC/vz/5+/uTh4eHTnoQEZ09e1Yes1BfKleuTKdOnSI3NzeF87t27dI5HpKVlRX5+/vT3bt36fr163Tjxg2N97Aq50SfYqCFh4fTjh07yMjIiHr06EHLli1TyI/NmzdXGZdz1qxZFBERQVu2bKGuXbtSjx49qEqVKjrpcuHCBWbxiC0sLGjnzp00b9486ty5M02ZMoVCQkK0kjF+/HgaOnQovX//ngDQuXPnaMeOHTRv3jwKCwsTLWfHjh0UExNDderUoYsXL9LRo0fp+++/J2tra9Eyrl27Ro8ePSKiT/Ehr1+/Lt/M8+nTp6Jk1K5dm1auXEkbN26kPXv20Js3bxTiat64cYPKlCkjWqeAgADavn07derUiZo3b0579uxRWzfmxd3dnS5cuKCy7rtw4UK+sqYMHx8f2rt3L9WtW1fp9d9//13veGV79uyhmTNnaoyFm5aWRlWrVlV5vUqVKvJN7tSRnp5OlpaWRPQpDqa5ublWaSODZfm0sLCgAwcOUOPGjembb76hGzduUHh4OAUHB2slJyQkRGl/ydAIgpCv3tS1Hs3L7du3KSIigiIiIujVq1fUtGlT0ffWq1eP6tWrRytWrKBdu3bRzz//TOPGjaN///2XbGxsNN6vTdxAKysreczr3LDsn7AkMzOTVq9eTXPnzqVixYpReHi46PiFuZHFF+/evTtNmzaNjI21G4Jeu3aN/Pz8VF739/cXvRHrypUrmbR7Y8eOpRUrVtDq1av1ysf6xsol+hSLs2XLlnTt2jXKysqiFStW0NWrV+nvv/+m2NhYUXrY29urfY+srCxRcp48eSLfYMvOzo6kUik1btxY1L25YdHOaIoLKnaDY1Yxblm1D48fP1YZ/5zo0+azsv6LOlh9HyKigwcP0qRJk+jOnTs0btw4GjNmjLxNFYODgwM9fPiQypQpQzk5OXThwgWFWL+ZmZmixnpRUVEUHh5OLi4u1Lp1a+rRowe1bNlStB4yWH1jIqKwsDB5DOusrCyKiIigYsWKERGJ3jyUVdxxIv3TKjU1VW0b27RpUxo2bJgoWe/evaOJEyfShg0baNq0afTTTz9p3T58LRR9la+EZcuWkb+/P3l7e9P79+8pODiYUlNTqVixYrRjxw7Rcnr06EH9+vWjKVOmUK1atUgQBDp37hzNnTtXXlHExsZS5cqVVcpo3bo1jR8/nq5du0ZVq1bNN+gUu6lYrVq1aNSoUbR161ZycXEhIqJHjx7R2LFjqXbt2qLfacKECfTq1Svy9/en9+/fU6NGjcjMzIzGjRsnqtI5fPgw3b9/n8LDw2nIkCH07t076ty5MxHpNiDp1asXEX1qoJR14FxdXbWSl5mZSXfu3CEvLy+tK8KQkBCaNWsW1axZk1xcXHTumFarVo3++eeffOcXLVpERkZGGu+vXLkyRUdHqwzWv3fvXrV5TkZKSgqdPn2aNm3aRLVq1aLy5ctT9+7diUj7tPL29qakpCRau3YtGRkZUXp6OrVv356GDh0qz4+a8PX1pbdv39Lhw4d16tTmBQBdvHiRbt26RcHBwWRtbU2mpqaiDLeDBw+m2NhYGj58OL1//57KlCmjYMjVZlMRV1dXZsbAGTNmUI8ePejBgweUk5NDe/bsoZSUFIqKiqL9+/drJSsjI4N27dpFmzdvpri4OPL09KQxY8ZQ7969Nd7LspzXqlWLmjVrRmvXrqWgoCClhjdvb2/q0qWL0vunTJlCU6ZModjYWNq8eTPVrVuXvLy8CAC9ePFCtB6sDCZ5mTx5MlWvXp26detGSUlJtGjRItH39unTh7KysmjChAmUkZFBwcHBVKpUKVqxYoXK75GbBw8eUN++fen8+fO0cuVK6t27NyUmJlKvXr2ocuXKtGnTJmrWrJkoXZo0aaIwSPjuu++IiBQ2+NFEaGgoNW3alLZu3UpZWVk0ZcoUhYmVX375RVTZVzagPnXqFDk7Oyuce/78uVo57du3p59++omaNWuW795Hjx7R1KlT5XWiOoYNG0ZdunSh0qVL05AhQ+T1eHZ2Nq1Zs4aWLVtG27dv1yhn48aN8s3ARo4cSXXq1KHjx4/T2LFjKSUlReOmbUSfNndRV8dJpVKNm13JOHTokNzAmZOTQ8eOHaMrV64o/EZTH4VV+cy9GeuQIUNo5MiR1K5dO7KxsVG4JqbP1KVLF2Z1sj4AoCZNmsj7Iu/evaPvv/9evlGXWMOQjHfv3tGuXbsoPDycTp48Sa6urtS/f3/q06ePTkb3S5cuUWxsLCUnJ1OVKlVET4rUqFFD5TXZBmOaNmJi2T/Jiy73A6CoqCiaPn06ZWVl0dy5c6lfv36i+my5uX37NvXs2ZNu3bpF27dvp7Zt22qtC9GnTY0dHR1VXnd0dKRXr15plMOy3YuLi6OYmBj666+/qHLlyvnyi6bN+mSMHDmSIiIiqHXr1lSlShWddKxfvz6dPn2aFi9eTF5eXnT48GHy9fWlv//+W+3EVm7EbBQsBkEQFDYqlUgkWk0wymDRzixbtkzjc8SMq4KCguR/A6B58+bR4MGD5RstioVV+/D+/Xu1GxyamJhQZmamRjksvs+5c+do4sSJFB8fT4MHD6ajR4/KjZLa0LhxYwoNDaU1a9bQrl27KCcnh/z9/eXXr127ptaQKiM4OJiCg4Pp7t27FB4eTkOHDqWMjAzKycmha9eukbe3tyh9WH1jV1dX2rhxo/z/EiVK0JYtW/L9RhO9e/cmKysrMjY2VmnAFgRBreGWVVpZWFjQy5cvVer9+vVrsrCw0CjnzJkz1KtXLzIzM6PTp08rbExXRH4EiJm6KOKL4N27d7Rjxw66dOkS5eTkkK+vL3Xr1k1UwZKRnZ1N8+fPp9WrV9Pjx4+JiMjZ2ZmGDx9OEydOJCMjI0pLSyOJRKLS0KNs13EZYna6lXHz5k1q164dpaSkyCuOtLQ0Kl++PO3du1fu1anpfeLi4qhq1apkbm5O165do5ycHPL29lbY3VUbjhw5Qps3b6a9e/dSmTJlqGPHjtSxY0fy9fUVdX9qair17duXzpw5o3AeIncClpGRkUHDhw+nyMhIIvrk2eXp6UkjRoygkiVL0qRJkzTKcHFxoYULF4oaPBckkZGRNGTIEFq8eDENHDhQPujLysqi9evX0/jx42nNmjWiDHAy3rx5Q7/88gtt3ryZzp49S40bN6bg4GAKCgpi5i2qif79+9Py5ct1zmu5uXfvHrVs2ZLS0tLow4cP8vQeNWoUvX//ntatWydKzsePH+nvv/+mEydOUGxsLP3999/04cMH8vT0pICAAFq/fr3a+1nu2Czj0KFDNHfuXLp48aK87po+fTo1b95c1P2nT5+mzZs3065duygrK4vat29P/fr1U+gQaos+5fzevXuivBrF8ubNG9q2bRuFh4fTxYsXqXbt2tSxY0caM2aM2vtYpZWqXetv3LhBQUFBlJGRQffv3xddd8l4+vSpfCdfsdjb21OdOnUoLCxMoQ36+PEjhYSE0MKFC6lfv360du1atXLEeGgSkah0/O+//+jMmTNUokQJqlOnjsK1AwcOkLe3t0YPd1k9rgnZxJ8q3rx5Q/Xq1aO0tDTq3r07VahQgQRBoOTkZNq2bRuVKVOG4uPjRXkm//TTTzRv3jyytrYmT09PEgSBbt26RW/fvqXx48fT/Pnz1d6/ePFimjJlClWrVo2Sk5PlMpcuXUrDhw+noUOHihpQSCQSOn78uMoB9NOnT6lZs2Ya85+6vokMbdpgGfqUTxb6qCqf2pDbUExE1LVrV1q+fHk+478mI7JYD3zZCiRVnDlzhsLDw+nXX3+lzMxMCgoKon79+mnlZSvj33//lXvqvn79mrp37059+/YVPbhXRVZWFv388880Z84csrW1pdDQUFGTT0SfJiN27NihU/8kr2dgUlISVaxYMZ8BQpNnYLVq1ejWrVs0fPhwGjVqlMrJEU0eyVZWVtSyZUtat26dTgYCGUZGRvTo0SOV7y92Z3aWfZQ+ffqovR4eHi5KTrFixSgqKkqrFZCFRVZWlkYnEIlEQra2tvJ8+PLlS7KxsclXp2maaCTSv50pKKytrSkxMVHv1aP6tA+zZ89WOX548+YNTZ8+Xev2ShckEglZWFjQoEGD1BpWR4wYoVbOnTt3qFmzZnTnzh2SSCS0cuVKGjJkiPx6UFAQeXh4iDI25wYAHTp0iDZv3kz79u2jYsWKUfv27TV6G/P0jYk+OTE9fvxY3kZVq1ZNaxms0qp169bk6uqqsi89ePBgun//Ph04cECtHFNTUxo+fDjNnTuXzMzMNOr/tVNkuC1CZ16/fk1EmjttBQkAOnLkCF2/fp0AkLe3NzVt2lSr2Wpzc3NKTk7Wa2m4Ml68eEFbt26lzZs3U1JSkuiKvUGDBmRsbEyTJk1S6uVavXp1UXJGjhxJp0+fpuXLl1PLli0pKSmJPD09ad++fTRjxgy6fPmyRhmOjo507tw58vLyEvXM3GhacpUbMZ23cePG0dKlS8na2lquj6zzNmLECK0b8twkJyfTpk2baMuWLfT8+XP6+PGjxns8PDyoe/fucuNHYRMUFETW1ta0adMmcnR0lHcoY2NjqX///pSamqqT3BcvXtCSJUto1apV9PbtW435uEGDBnTgwAGys7PT6XmsKV++PN26dYt8fHyoX79+FBwczHTJsK7lvKD4559/aNOmTbR9+3Z68uSJ2t+GhITQ+PHjRXlkq0PdQPjNmzcUHBxMBw4cyLd6QBl37tyhrKwsKleunML51NRUMjEx0ehtsXbtWoWOfl4uXLhAvXv3zudF+SUgZkBNRPTq1SuaPHky7dy5U+7hY29vT507d6a5c+dqVXbPnTtH27Zto5s3bxIAKl++PAUHB4ta9VKpUiUaP3489e3bl06cOEEBAQEUEBBAu3fv1koHiUQi94LOS27v6MIum0TalU9WsDBUFZRRW1ckEglVr16d+vXrR926dVPwYteGwMBAiomJoebNm1Pfvn2pdevWTJZobtu2jaZPn07v3r2jqVOnKkw4q0JV+dW2f8LKOJ47zZX15cSWq6ioKI1Ld3/88Udas2aNRn2qVKmi8jtmZWXR1atXNeoTGxsr72fzQsmSJenEiRP5wtBpQjYOE4O+Y7Vr167Rpk2baOvWrXLHHVWwmmiUoU87U1CwMtzmRpv2wd3dXdQYi1XYPH11EQSBbt++rVHWx48f6dq1a+Tk5EQlS5ZUuJaYmEilS5dW63mviefPn1NUVBRFRERQQkKC2t/y9I1lnD17ljZv3kw7d+6ksmXLyttAseWbVVqdOXOG/Pz8KCgoiMaNG0cVK1YkAJScnExLliyh6OhoiomJoQYNGqiVc/LkSZVh4YrIT5Hh9ishr7eEDEEQyNzcnMqWLcvccFkYvHz5UmuDUa1atWj+/PnUpEmTglGKPnk2iPW4tbS0pIsXL+od38zNzY127txJdevWVehg3Lx5k3x9fUV1+CZOnEhWVlY0bdo0rZ+fu+P27Nkzmj17NrVo0YLq1atHRER///03HTp0iKZNm6YyBEJe4uPjaceOHXIjZPny5alLly4q418pY9euXbR37176+PEjNW3alAYOHCi/lpWVRfv27aP27dtrlLN06VLasWMHXbx4kXx8fKhHjx7UuXNn0WESiEgh1qUqZHHxNFGsWDE6ffo0VahQQSG97969S97e3qKXCr9//55Onz5NJ06coBMnTtD58+fJ3d2dGjVqRI0bN6Zu3bqpvd/JyYl69epF/fr1o0qVKol6pjrOnz9POTk5+TwVz549S0ZGRlSzZk21948YMYL69esnesJDH8SWc1WTGrnr4969e2v05lHHx48fNS5NTE1NpenTp9P69evzdfpevXpFQ4YModmzZzMZmKSlpYlaCta4cWPq27dvvgHd1q1bKSwsjE6cOKH2/qioKOrcubPamfvMzEy1y99yk5qaStHR0XT37l0SBIE8PDwoKChI9DdhGZNMFdoMqHMDgJ4+fUoAyMnJqcBCZ6hCKpXS9evX5fnCzMyMTp48ma+sa4KldzQLxMQLF1M+i1CNNn0qdUgkEnJxcaHixYurzf+avFNl6BM3UNZ2qvL21aZ/wgKxcVE1hXqxtbWlmJgYlek1dOhQ2rp1q8YwB6wM0gVRJ//333+UkpJCgiBQ+fLltV61tWTJErp9+7bWsXJlk1bq0Gfi6u3bt/TLL7/Qpk2b6Pz581S3bl3q0KGD6D67OsRONOqLq6srXb58WW7sW716NfXs2VNvQ3ZBGG5lGLJ90OR1KkOTByYLTpw4oTaONZG4SR5NvHjxgv744w+9+l3aUBB1Tu4wQefOnaOgoCDavHmzQb1Wf//9dxo4cGA+5yt7e3tav349dejQQaOMUqVKUZMmTcjPz0/v/VW+BooMt18JqjxScnujNGzYkPbu3avWc+Hx48c0btw4eQD9vPLEdgxiY2Np8eLFlJycTIIgyD1vvv32W9HvtGDBAvlGXkREP/zwA/32229UokQJ+vPPP0Ubag4fPkwTJ06k0NBQ+uabb/J1sjU17qyNH7Vq1aJly5ZRw4YNRf1eFVKplK5cuUKenp4KHYzExERq1KiRqFhgI0eOpKioKKpWrRpVq1YtX0dC2QYbyujQoQP5+/vnixm8evVqOnr0KO3du1f0e+nDhg0baPDgwVSuXDkyNzenK1eu0IQJE2jevHk6y7xx4wZt27aNfvnlF7p9+zb5+/tT9+7dRTXA6jq/uePiiSlXDg4OFBcXR97e3grpHRcXRx06dNBo0JkxYwbFxMTQ+fPnydPTkxo3biw/SpQoofH5MubNm0cRERF08+ZNql27NvXv3586d+6scziI2rVr04QJE/JthrJnzx5asGABnT17Vie5uvD69Wt5Gf/zzz8V4jEaGxuLXua4bNkymjNnDrVq1Ypq165NAOj8+fN08OBBGj16NN25c4e2bNlCq1atogEDBuS7X0wnUBAEjSFOBg0aRLa2trRw4UKl1ydOnEivX7/WGFZAHdoaFW1sbOjSpUv5Qt3cvHmTatasSS9fvlR7P4tl4TLmzZtH06dPl4drAED//fcfGRkZ0dy5c2ncuHEaZUgkElExycSsOshNQQ6oxfD8+XPKyMhQCEdx9epVWrx4MaWnp1NQUJDGDbTyeoIW5EBYG3bt2kU7duygGzdukCAIVK5cOQoODha9IVNoaKg8zIy+8cJZ6KNqgtDW1pYqVKhAQ4cO1SkerC5os4mXOmSDPX9/f/Lz89N5sMfKGJg3buBPP/2kdVgA1m2nvoiZBBPD2LFjaevWrXTq1Kl8HqXDhg2jyMhI+vPPP7Xq/+sDyzo5PT2dhg8fTlFRUfIVJUZGRtSzZ09atWqV6NUs7dq1o5iYGHJwcNAqVq5Y4zqRZgN7buLi4igsLIx+++038vDwoGvXrsk9lfVFl4nGvBOonp6e1LZtW1FtRd52xsbGhhISEvRuZ3Rtr8QYSgVBoOHDh+uqmlaIqTvFesvqC6tJHk0kJiaSr6+vQVeIFEQ/kOiTx+qMGTPo5MmT9PTpU51Xn+hKRkYGHTp0SMGhqnnz5qLrPtb9pS+dIsPtV8KxY8fop59+ojlz5siXlpw7d46mTp1K06ZNI1tbWxo0aBDVqVOHNm3apFJOq1atKC0tjYYNG6Z0Gb+YTQe2bt1Kffr0ofbt21ODBg0IAJ05c4Z+//13ioiIEL1bsqenJ23dupXq169PR44coR9++IF27txJv/76K6WlpdHhw4dFyVG1HEzsLPXAgQPJzs6OmfHj+PHjNHXqVJo7d67SzdvEzhI3btyYOnbsSMOHDydra2tKSkoiDw8PGjZsGN28eZMOHjyoUYa6GKCCIIja6ZboU4yzhISEfMaY1NRU8vHxke/UrgpWxvGqVatSUFAQhYaGEhFRREQEDR8+XPSOnpqIj4+nIUOG6LVkXte4eJ07dyZbW1vasGGDPL2dnJyobdu25OrqqjHWmkQiIVdXV5o0aRJ16tRJr6VIRJ82Ttq8eTPt3r2biIg6duxI/fv317rjb2VlJQ/zkZs7d+5QtWrVNKYdK6/m/fv307Rp0+QhRqytrSk9PV1Bxs6dO0UZUzp06EDNmjWjwYMHK5xfv349HT58mH777TdatWoVbdiwQemmfqw6gRUqVKCtW7dSrVq1lF6/ePEiBQcHU0pKisZ3yo0+RkVbW1s6ceJEvp2iL168SH5+fhrTm1X8wpiYGGratClNmzaNRo4cKe8MP3/+nJYvX05z586l48ePa1zixSImWW70HVCzMpx17dqVXFxc5JN3T548oYoVK1LJkiXJy8uL/vrrL9q0aZPayYO88eMmTpxI48ePz2fw0uTpw8KITPRpM7KuXbvSrl27qHz58vKlf9evX6ebN29Sp06daMeOHaI94vSNF85KH1Xl7uXLl3Tx4kW6ffs2xcXFqd1gS4a+RmRWk5W8DfZYxQ0k0r/tZFXGWU6C9e3bl44dO0ZnzpyhUqVKEdGnb7F582Y6cOAAk81ZxcKyTh40aBAdPXqUVq9eLU+fuLg4GjFihHwDUjGwipWrLwsXLqTNmzfT27dvqWvXrtS9e3eqXr06mZiYUGJios5xn/XpE+g7gcpqgjCvwVXX9oqVoZQnT1lWuhhqkkes4ZaVpyzrfuCDBw8oMjKSwsPDKT09XS5XzEpdQ6wC0wV9+0tfDSjiq6By5co4ffp0vvNxcXHw9vYGABw5cgRlypRRK8fKygqXL1/WS5eKFSti6dKl+c4vWbIEFStWFC3H3NwcaWlpAIARI0Zg4MCBAICUlBTY2dmJlnPixAm1hyYqVKiAc+fOqbx+4cIFlC9fXrQ+giBAEARIJBKFQ3ZOLKdPn4a1tTUGDx4Mc3NzjBw5Ek2bNoWlpSUuXLggWg4LXF1dsXDhwnznFy5cCFdXV433DxgwAOPHj1d5fcKECRg8eLBGOVKpFLdu3ZL/n5WVBRMTEzx8+FDjveo4e/YsRo4ciRIlSsDCwgI//PCDTnK2bt0KT09PuLi44Oeff8bHjx9F3/vgwQOUL18elSpVgrGxMerWrQtHR0dUqFABjx8/1nj/X3/9hYkTJ6JOnTowNTVFlSpVMGzYMOzatQtPnjzR6X0A4O3btwgLC0PDhg0hCALKly+PBQsWiL7fwcEBZ86cyXf+9OnTosr5qFGjVB59+/aFhYWFqHL1/fffIywsTP6/lZWVQl5asGABWrVqJeqdLC0tkZqamu98amoqLC0tAQA3b96EVCpVer+3tzccHR0xcuRIJCYminqmMszNzXH37l2V1+/evQsLCwvR8k6dOoVevXrBysoKVatWhZGREeLi4rTSqXXr1ujUqROysrLk57KystChQwe0bNlS4/2CIOiVX2X88MMP8jZFGQMGDECXLl1EyYqPj8fAgQNha2uLb775BmvWrMGrV6+00mfBggWoUKECSpUqhXHjxiEhIQEAYGxsjKtXr4qWw6o8uLu7IyYmRv7/okWL4OXlJa+zFi1ahDp16qiV4ebmBnd3d7WHh4eHRl26dOmC0aNHy/9//Pgx7O3tUblyZbRp0wYmJiaIiorSKGfJkiVwcHDAH3/8ke9adHQ0HBwcsGzZMo1yVPH8+XP89NNPsLGxEfWNC1ofGT/++KPGuis7Oxs//PADBEFAhQoV0LZtW7Rp0wbly5eHRCJB586dkZOTo9PzP378iOXLl8PJyQlly5bFjh07RN+bmZmJ2NhYhISEICAgQJ5/y5Ytq7b8soZVXs6Nrm0nqzIuCIKovoMYsrOz0a5dO1SsWBH//fcfRo0aBalUiuPHj4uW4efnB39/f7VHQECAKFks6mQAcHR0VKgHZRw/fhzFihXTWp4+uLu7IyQkBPfu3dNZhpGREaZMmaLQ/gLatzMy9O0THD9+HBKJBDNmzMDz58/l5589e4Zp06bByMgIsbGxamXkzcd5+25i0VS+dSnj+lBY+lSpUkU+9i4IXfr06QNXV1f873//k58bPnw4LC0tRY3JxZCQkCC6DrS2toa9vT3s7OyUHvb29qKeyaLO2blzJ1q2bAkLCwsEBQUhOjo6X1k11DtFRkaKOnRF2/7S10KR4fYrwdzcHP/880++80lJSTA3NwcgbpBeqVIlXLp0SS9dTE1NVRotzMzMRMtxcXGRG6PLly+PX3/9FQBw/fp1WFtb66WjNrA2fuhrSM5NUlISevbsicqVK6NSpUro1q0bkpKStJIBfEqbgwcPIiMjAwC0HqCFh4dDIpEgMDAQoaGhCA0NRevWrWFkZITw8HCN97MyjisbiOjaiUtJScH06dNRtmxZGBsbo1mzZoiIiMDr16+1lvXXX3+hevXqsLGxwaxZs/D27VutZQBARkYGNm/ejKFDh2LIkCHYuHGjPM204fXr1zhw4AAmTJiAWrVqwdTUFN7e3hg6dKhOesnYv38/HBwctGqEO3fujMaNG+Ply5fycy9evEDjxo3RqVMnnfTQxVDg5uaG8+fPy//Pm2+SkpLg5OQk6vllypRROnm1dOlS+eRZYmIinJ2dVcpg0Ql0dnbGsWPHVF4/evSoWh1ksDIqAsCVK1fg6OgILy8v9O7dG71794aXlxecnJyUtmF5EQQBgYGBaNeundpDE+7u7jh16pTK6ydPnoS7u7tW75aRkYHIyEj4+flBKpUiODgY79+/F3Uv6wF1bnQpD3nbvVatWmHcuHHy/1NSUuDg4KCXXmJhYUQGgKpVq2LTpk0qr4eFhaFKlSqi9Xr37h2OHj2KqVOnomHDhjAzM0OFChUwYMAAbN261eD6qCIxMRElSpRQ+5uCMiLrM1mpDG0HeyyNgQWJLm1nbnQp46wmwWR8+PABTZs2hZOTE6RSKY4eParV/awM0rnRp04GAAsLC1y7di3f+StXrqiceFXFx48fceTIEaxbt07ej3zw4AHevHkj6v6VK1fC19cXRkZGaNq0KXbs2KHVuwDAnDlzUK5cOZQpUwYTJkyQt7natjOs+gQsJlAFQcCcOXOwYsUKrFixAubm5pg2bZr8f9nBK8oMpYWJrmMmsbCY5NGEWMMtKyeJ3OhT5wiCADc3N0yZMiVf/hWbl1m9E0ujNqB/f+lrochw+5XQoEEDtGzZUqET9uTJE7Rs2RLffvstgE8et+XKlVMr59ChQ2jevDnu3Lmjsy5eXl5Yt25dvvPr1q1D2bJlRcsZOnQo3Nzc0LRpUzg6Oso7N7/88gt8fHy01is9PR3JyclITExUODTByvjBI0+fPkVAQIDc21fWWPft2xdjxozRSlZ8fDyCg4Ph4+ODGjVqIDg4GPHx8aLuZWUcz9uBU9WJE4MgCKhVqxaWLVums8fu2bNn4efnB3Nzc4waNQr//fefTnIKiqysLJw5cwaTJk3SedYzPT0dmzdvxrfffguJRIJy5cph3rx5ou//3//+B09PT9ja2sLPzw9+fn6ws7NDhQoVdOrM6mooMDMzw+3bt+X/nz9/HpmZmfL/b9++DVNTU1GyNmzYACMjI3z//fcIDQ3F7Nmz0aZNGxgbG8u9ehcvXizKc1ufTmCnTp0QFBSk8nqbNm3QsWNHjXJYGxUfPHiAyZMnIzAwEB06dEBISAiePXsm6l5BENC5c2e50VfVoQkLCwvcv39f5fX79+/LJz21JTY2Fn5+fpBIJApeROpgNaDOi67loXjx4vLBOPDJ82z37t3y/2/cuCH3HmeFqgEsKyOyubm5Wm+1u3fvikrz6dOn49tvv4W5uTm8vb0xZMgQ/PLLL1q3E6z00cSNGzc0rl5gbURmNVmp72CvIIyBYhBjjNG37ZShaxlnNQmWu281e/ZsmJmZoU2bNkwMZ/p4a+dGlzoZAAICAtCpUye8e/dOfi4jIwOdOnVCkyZNRMu5e/cuKlasCKlUCiMjI3lfe+TIkRg0aJD4F8Eno9SIESPg5OQEe3t7DB06FBcvXtRKxokTJ9CzZ09YWlqiWrVqWnvKsuoTsJhALQhveDGwMriyMpTypI8mXfSd5FFn1FyxYgUmTJggul5n5Z2fF13qHFZ5mcU7sTIAs+ovfS0UGW6/Eq5fv44KFSrA1NQUXl5eKFu2LExNTVGxYkWkpKQAAH7//XeNywnt7OxgamoKiUQCKysr2NvbKxxiWLNmDUxNTTF48GBERUVhy5YtGDRoEMzMzJQadFWRmZmJRYsWYcSIEQpewMuWLcPGjRtFy3ny5Alat26dLzSB7NAEK+OHjNjYWLWHNmRnZyMlJQWnTp3SSU6PHj3QokUL3L9/X6GxPnTokDzEhiFgZRxn2YGTlRt9EAQBUqkUo0eP1nn2VMbcuXOVDqw3bdqE+fPni5KRnZ2Ns2fPYv78+WjZsiWsra0hkUjg6uqKXr16ISIiQvS7nTx5En369IG1tTWkUil69uypdf6V8fbtW6xfvx4//vgjxo4di8jISAWjqRj0NRS4uLjgyJEjKq8fOnRIo9dabuLi4tClSxf5REaXLl2UhrMRiy6dwEuXLsHMzAwdOnTA2bNn8fLlS7x8+RLx8fFo3749zMzMRA34WBkVMzMz4efnp1fZYrW8V5OcR48eaWXQ+d///oc5c+agbNmycHFxwfjx45GcnKy1XvoOqGXoWx6+++479O3bF9nZ2di1axdMTU0V8t3+/fu1Cn0kBlUDRlZGZHt7e7UDkKSkJFH9HJlXzNq1a/H06VONvy9ofTSxevVq1K5dW+1vWBmRWU1WFuRgj5UxUB3qjB+s2k59yzirSbCCWtKtr7c2izr5n3/+QalSpeDo6IiAgAA0adIEjo6OKFWqFK5cuSJaTtu2bdG9e3d8+PBBIW+cOHFCK4eW3GRmZmL58uUwMzODRCJBtWrVsGnTJq1WzL1+/Rpr165F7dq1YWRkhHr16mHJkiUa72PVJyjICdSChpXB9UuUo0oGq0keMXWOIVdMyWDVD2SFvu/EwgDMqr/0tVBkuP2KyMnJwV9//YUVK1Zg+fLlOHjwILKzs7WSERERofYQy549e9CgQQM4ODjAwcEBDRo0wN69e7V9JSYEBwejfv36OHfuHCwtLXH48GFs2bIFFSpUwP79+zXez8r4IUMW4zb3oY0hWcbff/8NDw8PeXzcvPLE4OzsLB8Q525ob9++rbU3lT5GZNbGcV5gaUh2c3NTaviLj48X1UFp1aoVbGxsIAgCSpUqhW7duiEsLEzrDpqswy6RSFC7dm2sW7eOyey0rrAyFHTu3Bnff/+9yuutW7fWObaxrrDoBP7xxx9wcnLKN2Hl5OSE6OhorWSxMCoWK1YMN27c0Oqe3EgkEmaG27ze+XkHFWLqURYxyZSh64CaVXm4fPkyHB0d5RO5U6dOVbjevXt3rT3FNKFqsMfKiBwYGKg2VvqgQYMQGBioUQ6reOGs9ImOjlZ6REVFYfjw4ZBKpfJQU6pgadRmMVlZUIM91qEbVKEsL7NqO1mVcZYxblmir0GadZ2ckZGBDRs2YMyYMRg9erROIaocHR1x/fp1AIp5486dO1qFWgM+GWxl72hkZIQGDRpg8+bNmD17NkqUKIGuXbtqJU9GUlISRo4cKTokFKB/n4D1BKoYePJM/VLlqJLBWxxhZejiJFFQ/UBNiM3Luq44kKGPAbig9lf5UhEAFVtSF1EE52jaGVHsjoguLi4UHR1NtWvXJhsbG7pw4QKVL1+e9u3bRwsXLqS4uDiNMvbv3099+/alZ8+eKZx3dHSksLAwatOmjShdiIhevXql8P/Hjx/p8uXLNG3aNJozZw41adJElJwaNWpQ+fLlKSQkhFxcXPLtMmxra6tRhrW1NV26dInKlSunsBPr+fPnqWXLlvneVxXx8fEUHBxM9+7do7xVjiAIGnf2vHz5MtWrV4++++47mjBhAlWoUIGIiK5fv04LFy6kAwcO0JkzZ8jX11eUPmKpWrUq/fnnn1SmTBkiInJwcKAbN25QsWLFyN7eXu3Ozc+fP2eqiybMzc0pOTk53461t2/fJm9vb3r//r3a+7t27SrfkbtcuXI66+Hk5ETdu3enfv36UZUqVXSWk5sbN27QiRMn6MmTJ5STk6Nwbfr06WrvZbXbtywPfv/99zRhwgT5jrcpKSm0YMECrfNgdnY27d27l5KTk0kQBPL29qY2bdqQkZGRxnt//fVXCg8Pp9jYWGrRogX16dOHWrduLepeZbx7944OHjxIN2/eJABUvnx5at68OUmlUp3kvXnzhrZt20bh4eF08eJFql27NnXs2JHGjBmj8d6xY8eSiYkJzZ8/X6dn591BWlfc3d017sxORHTnzh2N+ri6ulK3bt3I2dlZ5e/02fn5n3/+oU2bNtH27dvpyZMnGvVhUR6IiP777z86c+YMlShRgurUqaNw7cCBA+Tt7S1qB22xqNoNPCEhgZo2bUpv3ryhrKwsmjJlCoWGhsqv9+jRgywtLWndunVq5Z85c4b8/PwoKCiIxo0bRxUrViQAlJycTEuWLKHo6GiKiYmR7x4vhjdv3tCpU6coNjaWYmJiKDExkcqWLUv+/v60evVqg+gjkUiUnre2tqaKFSvSuHHjqFOnTmpltG7dmlxdXWnt2rVKrw8ePJju379PBw4cUCtHTLkSs6P6wYMH6cSJE3TixAm6fPkylS9fnvz8/Khx48bUuHFjcnJyUnu/MnmTJk2iO3fu0Lhx42jMmDFkaWmplQxtUJaXWbWdrMq4kZERPXz4UO+6VFvy9rtknDt3jiZOnEjx8fE0ePBg+umnn6hYsWJayzdEnawtDg4OFBcXR97e3gp5Iy4ujjp06ECPHz/WKOPSpUsUHh5OO3bsICMjI+rRowf1799fYaf58+fPU6NGjejdu3c66/rx40cyMTEhItVplRdd+wQSiYRmz55NVlZWKuVOnz5d4xhCG1S1M0Vy2MlhpQsrNOXjBw8eUGRkJIWHh1N6ejp1796d+vbtq1C21FFYdY6676zvOynj5MmTNGPGDDp58iQ9ffqU7O3ttbpfn/7S10KR4fYrIj09nWJjYyktLY0yMzMVrqmrLF6/fk02Njbyv9Uh+50hyFshfPz4kTIyMsjU1JSkUqlow5mNjQ0lJSWRu7s7ubu707Zt26hBgwZ0584dqly5MmVkZIiSw9r4kZeTJ0/S6NGj6eLFi6J+b2lpKa/0dKV169bk6+tLoaGhZG1tTUlJSeTm5kZdunShnJwc2r17tyg5LIzILI3jYsnb6EVGRlKXLl3IzMyMIiIi1A5Ae/XqxVwfdZ2LcuXK0YwZM6h79+4K57ds2UIzZszQOBBmpUvuTr0+cmRs3LiRhgwZQsWKFaMSJUoofHNBEOjSpUtq5bMyFBARRUdHU//+/fPVLfb29hQWFkZBQUEaZRAR3bx5kwIDA+nBgwdUoUIFAkA3btygMmXK0IEDB8jLy0vt/YXVCRQ7SMuNNkZFIqLhw4dTVFQUlS1blmrWrJnPeLJ06VK198fGxlKDBg3I2NhYlH66vJM2sMx/mhAzoDakPrlh8Z3VDUJYGZF///13GjhwoNIyvn79eurQoYNOumdnZ9O5c+do3759tGbNGnr79q0oY0NB6aMtBWHUZoU+gz1WxkBtUZaXWbWdrMo4q0kwbVFVzlkZpPX9Pvv27aNWrVqRiYkJ7du3T60csf3Szp07k62tLW3YsEHe13ZycqK2bduSq6srhYeHa5RhZGREzZo1o379+lFQUJDSvJSenk7Dhg0TJU8MuhjgtOkTsJpA1QaeDJxfqhxWurDqv6nSh5WTRGH1u5S9F2vHj4IwAOvaX/oaKDLcfiVcvnyZAgMDKSMjg9LT08nBwYGePn1KUqmUihcvrrayyD3rLpFIlFY+ANR6ThrKUzE1NZWGDBlC48ePpxYtWoi6p1atWjR79mxq0aIFBQUFkY2NDc2bN49WrlxJu3fvplu3bumsjzJ0bWiSk5OpVq1a9PbtW1G/DwgIoAkTJlDLli11UZOIiK5du0Z+fn70zTff0PHjx6lNmzZ09epVev78OZ0+fVqjgUkGCyMyUcEbx/PC26ywOn0WLFhAixYtokWLFlFAQAARER07dowmTJhAY8eOpcmTJxtMF5Zy3Nzc6Mcff6SJEyfq9RxWZGRk0KFDhyg1NZWIPhnMmzdvrpV3VmBgIAGgbdu2kYODAxERPXv2jLp3704SicRgXmvaok+ai/XS8ff3VylDEAQ6fvy41s9WB28DCFZ8TnWXIWUQaU6rvGVcl3YmJyeHLly4QDExMXTixAk6ffo0paenU+nSpeUrG8RO7rHQRxtUfZ/CMCJrU650Geyx9EDXBp7KgyoKaxJM1XsVVruXl9wGbVXe7DJdxBob/v33X/L39ycjIyNKTU2lmjVrUmpqKhUrVoxOnjwpynh+7949cnNzE/0eLDBEn8DQ8GTgLCw579+/J3Nzc6XXtm/fTm3bttVrRcLn8m149M7XBmXvxeqdWBqAWfaXvnTEtcZFfPaMHj2avv/+e1q7di3Z2dlRfHw8mZiYUPfu3WnkyJFq7z1+/LjcuBATE6PT85ctW0bW1tbyv8XMoOpCuXLlaP78+dS9e3e6fv26qHtGjRpFDx8+JCKiGTNmUIsWLWjbtm1kampKERERzHW8e/cuffz4UeX1pKQkhf8B0MOHD2n+/PlUvXp1tbJz3zt8+HAaO3YsPXr0iKpWrZpv9r1atWoadfX29qakpCRau3YtGRkZUXp6OrVv356GDh1KLi4uGu+XUadOHbp586behlsLCwtq166dxt8ZohOoahnhs2fPqHjx4gafHZwwYQI9f/6cfvzxR7lHvbm5OU2cOJG50daQvHjxQuMyXpZoyjtSqVTvPBgbG0vx8fHyepXok+f4/PnzRXms3b17V+NveCN3/aOuDtS1jSlsNNXrYuFpAEvEnz4s0JRW+pbxwMBAOn36NL1584ZKlixJfn5+tGzZMvL399dpgMmiztEGVd+nXbt21KJFC4MakdWllbrBXrt27dROAslwdXUlQRDo999/V/kbQRC4HZizQFW+ady4sVZyWNWB6uQXBnm/T+5wTXlDN+lKyZIlKSEhgXbs2EGXLl2inJwc6tevH3Xr1o0sLCxEyfD396fz58+To6OjwvmXL1+Sr69vgRu0tUVsn0AbPtf2Sp2hdP369WoNbKzIycmhOXPm0Lp16+jx48d048YN8vT0pGnTppG7uzv169ePiIiCg4MLXBdekLUP27dvV/mbgmgfCjIfs3qnLl26kKurK40ePZqcnZ3p7t279PPPP+f7nSY5rPtLXzpFhtuvhISEBFq/fj0ZGRmRkZERffjwgTw9PWnhwoXUq1cvat++vcp7c3fetO3Iycg9U9K7d2+dZIjFyMiI/v33X9G/79atm/xvHx8funv3Ll2/fp1cXV0NsmQuLzVq1CBBEPLFgq1bty5t3rxZ63v79u0r/1t2TRtPgBIlSlBISIgWb5AfFkZkbSjoAQQR5UsfGR8+fCBTU9MCfbYyBEGgBQsW0LRp0yg5OZksLCyoXLlyZGZmZnBdWNKpUyc6fPgwDR482CDPY5V31MkxMzOjN2/e5Dv/9u3bAsk7n+Ng5ubNm3Tr1i1q1KgRWVhYyOutLx1D1F3aYGh9eBjAikXVt7G1taVFixbpHS+clT4sMbQRWRWsBnsFaQz8XPIyb3UOKwp6IoPo014bnTt3ztfPyszMpF9++UX0XhtEnxwT+vbtq9Bn11ZPZf36Dx8+0IMHD3SS+bnxObVXBWEo1Uef2bNnU2RkJC1cuJAGDBggP1+1alVatmyZXB9D6MILhTVZVJD5mNU7sTIAF1Z/6XOlyHD7lWBiYiIf9Do7O1NaWhpVqlSJbG1tKS0tTe29eT1A1SHGAMfKUzFvbCmZZ+rq1av1irMmlUqZb3KlDXljNUkkEnJyclLZAKq7lwXv37+npKQkpRtDiY3fJVtCqa8RmQdWrlxJRJ/0DwsLU9g0ITs7m06ePKlXbB99sbKyolq1ahXa81lTtmxZmjZtGsXHxys1+n+OnlDfffcdDRw4kDZt2kS1a9cmIqKzZ8/S4MGDCyRW8+c0MH/27Bn98MMPFBMTQ4IgUGpqKnl6elL//v3Jzs6OlixZUtgqFsGYL83TZ8eOHVr9/nOcWNFEQdc5hTXY05RWX1pe/pwxRLvXp08fatmyZb7xzJs3b6hPnz5aGW4fPHhAp0+fVtrXVtfPyT0WOnTokMKeEdnZ2XTs2DG1YUCK0A5WZZyVoZSVPlFRUbRhwwZq0qSJgqNEtWrVRK9g/drrvy+xLVf1TqwMwEX9Je0oMtx+Jfj4+NCFCxeofPny5O/vT9OnT6enT5/Sli1bqGrVqmrvze3FqcnjSYwBjpWnYt6NgARBICcnJwoICNBqcN+xY0eqWbMmTZo0SeH8okWL6Ny5c7Rr1y7RsligT4yq3PeePHmS6tevny8+WVZWFp05c0bUcw4ePEg9e/akp0+f5rumjcG1IAzKhcWyZcuI6FM+XrdunUI8H1NTU3J3d9e4ezkr2rdvTxEREWRjY6PWa56IaM+ePQbRiTUbNmwgKysrio2NpdjYWIVrn+sS1pUrV1KvXr2oXr16ckN0VlYWtWnThlasWFHI2hUuo0ePJhMTE/nkoozOnTvT6NGjiwy3XyCsPX0+Nz6niRVeKKzBnqa0+trz8teGqnHR//73P1Gb7soIDw+nwYMHk6mpKTk6OubbhFVdP0c2FhIEIV8cSBMTE3J3dy9qN/PAg2cqC0MpS30ePHigNJxdTk6O6Pbpa6//CqMtL2jPZt7CmHzt/aUiw+1Xwty5c+VLc0NDQ6lXr140ZMgQKlu2rMbdRXMb3S5fvkzjxo2j8ePHU7169YiI6O+//6YlS5bQwoUL1cph7anIKrZUbGwszZgxI9/5li1b0uLFi5k8Q1tiY2Np8eLFlJycTIIgUKVKlWj8+PH07bffipbh7++v1LP51atX5O/vL8roOmzYMOrUqRNNnz5dr8rf0BsmaIO2jZ6sPPj7+9OePXvI3t6+wHVUha2trbyDr80g4XPiSzL6y7Czs6Po6GhKTU2l69evEwDy9vbWOwb0l8Dhw4fp0KFDVLp0aYXz5cqVo3v37hWSVkUUJKwGsEUUoQpDDfaK8rI4Pvdl1D4+PiQIAgmCQE2aNFFwkMjOzqY7d+5otTHw9OnTafr06TR58mS1G54pQzYW8vDwoPPnzzMP8fa5p5UMnjxTidgYSlnqU7lyZTp16lS+8dquXbvIx8fHoLoUBF9KPib6PD2bv3aDKyuKDLdfCTVr1pT/7eTkRH/++afS350+fZpq1qypEK8pdyXeqVMnWrlyJQUGBsrPVatWjcqUKUPTpk3L5wWbm8LyVLSxsaGEhASVcc9UxZU0MTGh169fM9dHE1u3bqU+ffpQ+/btacSIEQSAzpw5Q02aNKGIiAjRFbEqT4Bnz56J3g30yZMnNGbMGL0btKioKLXXtVlOxgIWjV7uTZRkXuQsYnBq07nIPemiaQKmoHUxhJwvjXLlynEX06mw0yo9PV3pRkdPnz7VOV5zYb9TQcHbe+mqD6sBbBFFFDas8zJvZVwfPkdjgypkY52EhARq0aKFgiOKbDwjCxEmhoyMDOrSpYvWRtvcKJvkfvnyJdnZ2Wkt60tKKxk8eaYSsTGUstRnxowZ1KNHD3rw4AHl5OTQnj17KCUlhaKiomj//v0G1SU3vMUR5oGv3bP5qwZFFJELa2tr3Lp1S+V1c3NzXLt2Ld/5a9euwdzcXNQz/Pz88Pz5c5111BYrKyu171SzZk2EhITkOz9jxgz4+vrq9Mx3796pvLZt2za8fftW5fWKFSti6dKl+c4vWbIEFStW1Pjsdu3aoV27dpBIJAgMDJT/365dO7Rp0wbu7u5o0aKFqPfo06cPwsLCRP1WHXZ2dgqHpaUlBEGAmZkZ7O3t9ZafF01pHhISAk9PT2zduhUWFhby3+7cuRN169YV/ZzIyEhUqVIFZmZmMDMzQ9WqVREVFaW1vtnZ2Zg1axZKliwJIyMjuT5Tp04V/f1nzpyJmzdvav3sgtCFpRwAuH//Pn7++WdMnDgRo0ePVjhYoynv6Conr97qDtZoeieWaSVD1zowMDAQU6dOlet9+/ZtZGdno1OnTujQoYPo5/P0TtpQGGlV2Pp888032LJlS77nzZw5Ew0bNtRJbx7Simc5Bf19eNJFW330kcMiL/NWxnOjT1qx6neJxRBpHhERofabiGX8+PGYN2+eXjLmz5+PX375Rf5/x44dIQgCSpYsiYSEBK1kFURaFXY59/LywtGjR/P9Ljk5GXZ2dqKfwaq92rdvH2xtbTF//nxIpVIsWrQI/fv3h6mpKQ4fPmxwfQDg4MGDaNSoESwtLWFhYYEGDRrg0KFDBteFVR34JdY5ALu8zEKXz1XO50qR4bYIBTQVCB8fHwQHBys0wO/fv0dwcDB8fHwMoaLWaHqn6OhoGBsbo2fPnoiIiEBERAR69OgBY2Nj/P7776Kfw6qhMTU1RWpqar7zqampMDMz03h/79690bt3bwiCgM6dO8v/7927NwYOHIi5c+fiv//+E6VLeno6AgMD0atXLyxevBgrVqxQOPThxo0baNKkCQ4ePKjT/fp0Alk0ekuWLIFUKsWECRMQHR2NvXv3Yvz48ZBKpUoN7+pg0bmoWrUqJBIJ6tSpg1WrVuHJkyda6cBSF5Zyjh49CqlUisqVK8PY2Bg1atSAnZ0dbG1t4e/vr93L/X9YDSC0kePn5yfq0PWd1GGoiQwWdeDVq1fh5OSEli1bwtTUFB07dkSlSpXg7Oys1cQET++UF96MH4WtD6sBLG9pJRZDGc4MaRD83CYgWMlhkZd5K+Os0sqQxoa8z9CEIcq5OrKystCyZUs0btwYw4YN02ky18PDA6dPnwYAHD58GHZ2djh06BD69euHZs2aaaUPq7TiqU42NzfH3bt3ASi+09WrV2FpaSn6+azaK0B/QylrffSFlS6s6sAvtc5hlZfFwJvBtchwW0QRudBUIM6ePYvixYujWLFiaNKkCZo0aYJixYrByckJZ8+eFf0c3jzo9u/fj/r160MqlcLR0RH+/v44ceKEVs9h2dCsW7cu3/l169ahbNmyouXMnDlT787mxo0bYWRkBCsrK7i5ucHd3V1+eHh46CUbAM6fP48KFSqI/j2rTiCLRs/d3R2RkZH5zkdERMDd3V20LgC7zsWVK1cwefJkeHh4wMTEBK1atcK2bduQnp5ucF1YyalVqxamTZumIOfNmzdo06YN1qxZI1oOj57ELCnsiQyAXR348OFDTJ8+Ha1bt0arVq3w008/4d9//xV9P8DfO/Fm/OBNHxYDWN7SSiyGMnKyNggWtsFfGww5aNQ3L/NWxlmlVUEYGwrbIJ2VlYVFixahVq1acHZ2hr29vcIhllmzZkEQBFSsWBGNGzfWaTLX3NwcaWlpAIARI0Zg4MCBAICUlBStjVSs0oqnOpknz1TW8KQPC11Y1YGGNHDmfYYyWNXJBbFKSRW8GVyLDLdFFJELMQUiPT0d69evx+jRozFq1Chs2LBBKwNhQXjQqcNQhZxVQ7NmzRqYmppi8ODBiIqKwpYtWzBo0CCYmZkpNehq4smTJzh16hTi4uK09sJ0dnbGnDlzkJ2drfVzxXDp0iVYW1uL/j2rTiCLRs/MzEypZ/SNGzdEeUbnpiA6F3Fxcfjxxx/h5OSk1TdmpQsrOVZWVnJPSzs7O1y5cgUAkJCQADc3N9FyePMklnH//n3873//0/o+gK+JDMDw3g3q4O2deDN+8KYPC3hLq9zwYORk8X14M/iL5XPyvuStjPO2xJwng/S0adPg4uKCRYsWwdzcHKGhoejXrx8cHR21WpVmZ2eH8PBw0b9XhouLi9zjtnz58vj1118BANevX9eqDwiwSyue6mSePFN5wc7OLt9kg6rDkLCqA3kLw8SqTmadl7+klUVfOkWG2yIUMISRk5UHnVg0xe2V8eHDB9y/fx/37t1TOMTCcgC7Z88eNGjQAA4ODnBwcECDBg2wd+9erWSkp6ejT58+MDIygiAIEAQBxsbG6Nu3r2gPTHt7eyZxU6OjoxWOvXv3Yu3atahcuTJatmwpWg6rTiCLRq9y5cqYM2dOvvOhoaGoUqWKaF2AgulcXL58GWPHjkWpUqVEx59mqQsrOc7Ozrh69SoAwNvbG9HR0QA+GW4LwwDHyvgREhICGxsbSCQSSCQS2NraYtasWVpNkvA0kQGwqwPfvXuHs2fP4o8//shXd4iFt3fizfjBmz4s4C2teDNysvg+PBv8C9v7khW8lXHelpjzZJD29PTE/v375TJk/eUVK1aga9euonVxdnbGjRs3RP9eGUOHDoWbmxuaNm0KR0dHvHnzBgDwyy+/aB3KjlVa8VYnF7ZnKm+GUll4wIiICCxZsgT29vbo0qWLPBxely5dYG9vr3X4N33hLY4wb205oH9e5imMSUHp8yVSZLgtQgExRs6UlBSsX78eoaGhCAkJUTjEwMqDTiyajNE3btxAw4YN5QYU2SEIAiQSiejn8DSABYCBAwfC09MTf/75J169eoVXr17hwIED8PLywuDBg0XJGDVqlFLjpLbIDMeyQyKRwNnZGV27dtVqCTTLwZ6+jd7u3bthZGSEFi1aYNasWQgNDUWLFi1gbGyMPXv2aKULq87F7du3MXv2bFSqVAlGRkbw9/fHxo0b8fLlS4PrwkpO27ZtsWHDBgCfNvAoW7YsZs+eDV9fXzRp0kS0HJ48iSdNmgQnJyesWbMGiYmJSEhIwM8//wwnJydMmTJFtC48TWQAbOrAv/76C05OTvnqDG3rY57eCeDP+FHY+hTEAJa3tOLNyMni+/Bm8OfB+5J1XuatjPO2xJwng7RUKpU7eJQoUQIXL14EANy6dQs2NjaidZk7dy6GDx8u+vfKyMzMxKJFizBixAhcunRJfn7ZsmXYuHGj1vJYpBVvdbKusCrjrAylBdF+tm/fHqtWrcp3ftWqVWjbtq1BdeEtjjBvbTkLeApjwlKfL50iw20RCmgycm7YsAFGRkZwdnZG9erVUaNGDfkhdkaXlQedWE6dOoX379+rvF6/fn00atQIf/75Jy5fvoyEhASFQyysGppz584hPj4+3/n4+HicP39etBxHR0fExMTkO3/8+HEUK1ZMlIzhw4fD1tYWjRo10nnDBJbwZhy/cOECunXrBl9fX/j4+KBbt24KHWZt0LdzUbduXUgkElSvXh0LFy7Uefk9C11Yyrl16xYSExMBfPIiHzJkCKpWrYp27drJO0Bi4MmT2MXFRan36N69e1GyZEnRuvA0kQGwqQO9vLzw448/4tGjR1o9Wxm8vBPAn/GjsPUpCE8f3tKKNyMni+9T2Ab/vPDgfVkQeZmnMs7bEnOeDNLly5eX99cbNmyIefPmAfjk5erk5CRal6CgINjY2MDDwwPfffcd2rVrp3B8rvBWJ+tKQZRxXQ2lBaWPpaWlyvBv6spVQXntFrZ3dG54a8tZwFMYE5b6fOkUGW6/EmbMmKGVkUMVrq6umD9/vl4y9PGgy2s4VHeIRSqVIjk5Wa93ksGioalVqxZ27dqV7/xvv/2G2rVri5ZjYWGBa9eu5Tt/5coVSKVSUTKU7Xqv7YYJecnJyUFOTo5O9/I2gOCJyZMny73Xi8gPT57EZmZmSElJyXf++vXrhRLWgiX61oHW1tZMwrOwhCcDMCt40kefAWxeeEor3oycgP7fp7AN/nnhyfsSYJuX9YWnfMMSngzSEydOlK9K27VrF4yNjVG2bFmYmppi4sSJonXp3bu32kMsUVFRaNCgAVxcXOT5edmyZVqHWmNJYdbJPHmm5kVXQ2lB6ePq6oqFCxfmO79w4UK4uroaVBfe4KEtZ52XeQtjwpM3Ms8UGW6/Enx9fWFkZISAgABs27ZNbRwSdYiNF6sOfTzo1BkSdTUq1qxZE6dOndLrnVhiaWmp9Bvfvn0bVlZWouUEBASgU6dOCmmdkZGBTp06abXEnBWRkZGoUqUKzMzMYGZmhqpVqyIqKkprObp2AgsqttTjx4/xzz//IDExUeEwJJmZmfDw8JB7sn+J6BuDGuDHk7h27dpKl0UOGzYMderUES2HJ+MbK/r06fPFxrPiyfjBkz6sBrAs4cnjkZU+LOCtzuHJ+xLgLy8Xdr75GpZR5yY+Ph5LlizRKh47K9asWYNixYph9uzZCp5v4eHh8PPz03g/b/FX88LLyg5WZZyFoZSlPuHh4ZBIJAgMDERoaChCQ0PRunVrGBkZid44j4f6j+cwTIDudQ7rvMxbGBMeHVF4pMhw+xWRmJiIUaNGoXjx4rCzs8PgwYNx7tw5rWT07dsXa9eu1VmHrKwsnDhxAs+fP9dZBgtkMV9fvXqFY8eOoV69eoiJicHTp08Vrr169crgujk4OODMmTP5zp8+fVqr2at//vkHpUqVgqOjIwICAtCkSRM4OjqiVKlSBvfMXLJkCaRSKSZMmCDfnGz8+PGQSqUGC3rPutG7cOECKleuLI+HrG0sTtadi5IlSyr1sBYDK10KosOUkpLCJAY1T5w4cQKWlpaoVKkS+vbti379+qFSpUqwsrLCyZMntZLF20SGvqSnpyMwMBC9evXC4sWL5eVTdqiD13fSF97ei7U+rAawvMGbkZMVhW0MzA1P3peA7nmZtzLOiq9hGTUvVKpUCb///jsAxbLwzz//wNHRUeP9vG5UxQqePFMBNoZSlvoAnyYegoOD4ePjgxo1aiA4OFhp6L6C0IW3OMK54a0tZ5GXeQtjwts35hUBAKiIr4qsrCz6448/KDw8nA4ePEgVKlSg/v37U+/evcnW1lbtvfPmzaOlS5dS69atqWrVqmRiYqJwfcSIERqfb25uTsnJyeTh4aHXe+iDRCIhQRDk/wNQ+D/3uezsbJVy7O3t892niufPn4v6XZcuXejRo0cUHR0tT4+XL19SUFAQFS9enH799VdRcoiI3r17R1u3bqXr168TAPL29qZu3bqRhYWFqPvT09Np/vz5dOzYMXry5Anl5OQoXL99+7YoOR4eHhQSEkI9e/ZUOB8ZGUkzZ86kO3fuiHshRnTo0IH8/f1p2LBhCudXr15NR48epb1792qUUa1aNSpbtixNnDiRnJ2d8+UDNzc3tfdHRkbK/3727BnNnj2bWrRoQfXq1SMior///psOHTpE06ZNo9GjR2vUZ/78+XT9+nUKCwsjY2Njjb8vCF1YvxMRUYMGDcjY2JgmTZpELi4u+b5z9erVRb8nT/z777/0888/K5TNH3/8kUqWLGmQ57NKK9Z1YFhYGA0ePJgsLCzI0dFRQbYgCGrrHF7fSV8KolzxpE9ERAT169ePWrZsKZcRHx9PBw8epLCwMOrdu7fKe3lLq7wcOnSI5s6dSxcvXqScnBzy9fWl6dOnU/PmzQ3yfJ6+T0Ho8scff1CPHj1o8uTJNGvWLAoJCaGUlBSKioqi/fv3U7NmzUTrxyKtdM3LvJXxgkgrFv0u3pg3bx45OztT3759Fc5v3ryZ/vvvP5o4caIoOY8fP6Zx48bJ+9p5h+bqxiEyLCws6Pr16+Tm5kbW1taUmJhInp6elJqaStWqVaN3796Jfi990oqnOic3VlZWlJCQQGXLllU4n5qaSj4+PvT27VtRcvRpr/Jy9uxZWrlyJSUnJ8v7gSNGjKA6deqIlsFSH33RR5eCqANZ1jmF3ZbnhlVeZvFOvLXBXzpFhtuvkMzMTPr9999p8+bNdPz4capfvz49fvyY/v33X9q4cSN17txZ5b3qjK2aBtUyatWqRfPnz6cmTZropH9uzp8/T7t27aK0tDTKzMxUuLZnzx6V98XGxop+RuPGjVVeK4iG5sGDB9SoUSN69uwZ+fj4EBFRQkICOTs705EjR6hMmTKidRdD69atKSwsjFxcXPJd69q1K8XGxlKPHj2UGs1Gjhwp6hnm5uZ05coVpY1M1apV6f379yrvLYhOIItGz9rami5fvpxPhi6w6Fy0a9eOjh07RlZWVlS1alWytLRUuK6uPLDWhaUcS0tLunjxIlWsWFHU73PDKu/wOhBhhT5pxboOLFGiBI0YMYImTZpEEolEtxciPt6JN+MHb/rkRtcBLM9ppQ+s9OHJIFhQuvA22NPXGMNDGS+ItGJlbNAH1uXc3d2dtm/fTvXr11c4f/bsWerSpYtop4RWrVpRWloaDRs2TGlfu23bthpleHt707x586ht27YKhtuVK1dSZGQkXbx4UZQuRPqlFa91spubGw0bNozGjx+vcH7RokW0evVqunfvnqhnEbExuLKElT7Z2dm0d+9eSk5OJkEQyNvbm9q0aUNGRkYG1YVVv+JLrHOI2OZlFvDWBn/JFBluvyIuXrxI4eHhtGPHDjIzM6OePXtS//795RXakiVLaOHChfT48eMC1ePw4cM0ceJECg0NpW+++SafgcnGxkaUnF9++YV69uxJzZs3pyNHjlDz5s0pNTWVHj16RO3ataPw8HBRctLS0qhMmTJKPW7v379Prq6uouSwnNlLT0+nbdu2UWJiIllYWFC1atWoa9eu+TycWZC7g5cXOzs7OnDgADVo0ECvZ1SpUoWCg4NpypQpCudnz55NO3fupH/++UflvQUxgGDR6AUFBVGPHj2oQ4cOop6pDhadiz59+qi9LrY8sOrosJJTq1YtWrZsGTVs2FDU73PDsyfxy5cv6dy5c0o92fN6pueG14kMIjZ1oIODA50/f568vLxEPVMVPLwTb8YP3vRhDW9ppQ88eR/xbPD/EuGtjLNKK336XbwapFWtILx9+zZ5e3urdUrIjbW1NZ06dYpq1Kgh6vfKCA8Pp2nTptGSJUuoX79+FBYWRrdu3aJ58+ZRWFgYdenSRbQsVoYhnupknjxTZbAwlLLi5s2bFBgYSA8ePKAKFSoQALpx4waVKVOGDhw4oHefTBtY9St4MHAWRJ3MY14uwkAYODRDEYVE1apVYWxsjMDAQPz+++/IysrK95snT55AEIQC1yVvLFBdY1ZWrVoVq1evBvB/cVVycnIwYMAATJ8+XbQciUSCx48f5zv/9OlTrfQxdFD2wMBA/Pvvv3rLyR2TJi/u7u46x03Nze7du2FkZIQWLVpg1qxZCA0NRYsWLWBsbIw9e/aIlsMqRhWL2FL//fcfAgMDMXPmTOzevRvR0dEKhzbwFOORlS6s5LCKQc0q77CKLWVtbQ2JRAJbW1vY2dnJj8KI38XTJhmjRo2S79KtDzy9E8BffD3e9MnKysLu3bsRGhqK2bNnY8+ePUr7KergIa2+xF3Med7gRx8KKq6svnmZtzLOw+ZHBZEHWXyfsmXLyuM75iYqKgoeHh6idalUqRIuXbok+veq2LBhA1xdXeVjrNKlS+u02Ser+Ks81Mm50TeGqwwW7VVqairKlSsHqVQq10cqlaJChQq4efOmwfVp1aoVWrZsiWfPnsnPPX36FC1btkRgYKBBdeEhjjDPbTmgW17mLZY6b/p8DhQZbr8SZs2ahf/97396y+nTp4/aQwwnTpxQe4hFKpXizp07AABHR0ckJSUBAK5du4YSJUqIliMIAp48eZLv/N27dyGVSkXLMbTxTZ3BlZWcLVu2oGPHjkhPT9f7ORcvXkS3bt3g6+sLHx8fdOvWTeuOKsvBnr4duOjoaNjY2OTbmEyXTbNYdZI/fvyII0eOYN26dXj9+jUA4MGDB3jz5o3BdWElR9kkjy4TPazyDgs55cqVw8iRI/UuVzxNZABs6sDhw4fD1tYWjRo1wrBhwzB69GiFQyw8vRPAh/GDV31YDWB5SCuejZwsvg8PBn+eN7FhkZd5K+M8bX4E8GWQnj9/PhwdHbF582bcvXsXd+/exaZNm+Do6Ii5c+eK1uXQoUNo3ry5fEwjlujoaGRmZuY7/99//yl1SNEGFmnFQ53MGlbtFStDKSt9pFKpfBydm4SEBNHfmJUurOpAQPd8zHNbrius3onnNvhLp8hwW4RWBAUFKRytW7eGm5sbbG1t0a5dO4PqUrp0aXkjU61aNWzfvh0AcObMGdjY2Gi8X2YIkEgkGDRokIJxYMSIEahTpw7q168vWh+WDY0YDGG4rVGjBqytrWFlZYUqVarAx8dH4RBDZmYmevfuzURXnjxT3dzcMHToUDx69IiJPH07yXfv3kXFihUhlUphZGQk/94jR47EoEGDDKoLSzmsJnp48iSWSqVMygNPExkAmzrQz89P5eHv7/9ZvhPAn/GDJ31YDWB5SysejJy5YfF9eDD48+p9CbDLyzyVcUP3bTXBk0E6JycHEyZMgLm5uXxSWSqVIiQkRLQewCdDiKmpKSQSCaysrEQbPyQSidzxRNXqwcKEtzqZJ89UFoZSlvrY29vj9OnT+c7HxcWJ9nhkpQvAbhzCAt7ackD/vKzPO/HcBn/pFMW4/UrIzs6miIgI+Y6leWMqHj9+XGfZOTk59OOPP5KnpydNmDBB4+9Pnjyp9nqjRo1EPTc4OJhq1qxJY8aMoTlz5tCKFSuobdu2dOTIEfL19dW4GZO/vz8RfdqorF69emRqaiq/ZmpqSu7u7jRu3DgqV66cKH2IDBuwXl1sWlZyQkJC1N47Y8YMUc+ws7OjS5cu6a0ry7g++saWsra2poSEBIPGfVJHUFAQWVtb06ZNm8jR0VGeprGxsdS/f39KTU0tbBULFVZ5h4Wc9u3bU5cuXeiHH37Q9XWIiI/4XXnhbdMOFnxpOz/zpo+lpSXFx8dT1apVFc4nJiZSgwYNtIqTy1NafYm7mPO2wQ9vm9iwzMv6wlO+kcEipierPMjy+7x9+5aSk5PJwsKCypUrR2ZmZqLvJVKMg6mMXr16KT1fokQJ2rhxI33//fckkUjo8ePH5OTkpNWzVcEq/iovdTKrGK6syriDgwPt378/38Z2p0+fpu+//150vHBW+vTs2ZMuXbpEmzZtotq1axPRp7QbMGAAffPNNxQREWEwXVjCIh/z1pazyMs87ANREPp88RSOvbgIQzN06FBYWlrihx9+wMiRIzFq1CiFQ1+uX78uOjyBqqXlskMsz549w4MHDwAA2dnZWLBgAb7//nuMHj0az58/Fy2nd+/eWsXK5AVDeNyyonfv3liyZAkTWSxmYVks5+nZsyc2btyoyysoRd/ZU0dHR1y/fh2AYpreuXMHFhYWBtVFXzmJiYnIzs6W/63u0AZePInDwsLg6uqKGTNm6BUfmaUnFKs05wne3omn+Ho86cPC04c1PHk8stKHBUXel+phlZd5K+Ms+JKWUfPCjBkzlIaSUnZoA8v4q6zgZWUHqzLeo0cPVK5cGfHx8cjJyUFOTg7+/vtvVKlSBb169TK4Pi9evECbNm0gCAJMTU3lHuBBQUF4+fKlQXUB+IojzFtbziIv8xbGhKcVtTxT5HH7lVCsWDGKioqiwMDAApH/559/Uq9evei///7T+NtXr14p/P/x40e6fPkyTZs2jebMmUNNmjQpEB0NgSF3CGXlcTtv3jwaMmQI2dnZsVFMCXPmzKHFixdTkyZN6JtvviFLS0uF6yNGjCiwZysjMDCQANC2bdvIwcGBiD7t9tm9e3eSSCR04MABjTLmzJlDy5cvp9atW1PVqlXJxMRE4bo278Ri9tTBwYHi4uLI29tbIW/ExcVRhw4d6PHjxwbTRV85EomEHj16RMWLFyeJREKCIJCypkoQBMrOzhalD09IJBKV17R9JxbeLCx3E9a3Dnz//j2tWrWKYmJilK4OuXTp0mf3TizhaednVvqw8PSRwVNa8eTVLIPF9ynyvlQNi7zMWxknYpNWLPpdMnhZ2eHv70+CIKi8rs1qRl2/8fXr1+nmzZvUpk0bCg8PV9mXb9u2rWhdWKYVL3UyT56pREQvX76kXr160R9//CEfP2RlZVGbNm0oIiKCbG1tDaqPjJs3byqUq7wekIbQhVUdyCof89aWs8jLrN6Jtzb4S6fIcPuVULJkSTpx4gSVL19eLzljxoxR+B8APXz4kA4cOEC9evWi1atX6yz75MmTNHr0aLp48aKo3xsZGdHDhw+pePHiCuefPXtGxYsXV2v8aN++vWi9NIVckGHozrYyw+2+fftE39+mTRul5x0cHOjGjRtUrFgxsre3V9spFbuUx8PDQ+U1QRDo9u3bouQQsekEsmj0WL4Ti85F586dydbWljZs2EDW1taUlJRETk5O1LZtW3J1daXw8HCD6aKvnHv37pGrqysJgqCx0XdzcxOlDxG7AQQvAxFWsEpzFnVgcHAwHTlyhDp27EjOzs756h+x4Vl4eicZvBk/eNGH1QCWt7Qi4svIyZNBkJUuvIUDYJGXeSvjvC0xZ4m+32f06NEK/3/8+JESEhLoypUr1KtXL1qxYoUoOSy+cUhICI0fP56kUqmoZ6qDVVrxVCezCk3Aqr2SoY+htCD00QdWurCqA3kLw0TEpk5mlZd5CWPCUp8vngLw4i2CQxYvXowff/wROTk5eslp3LixwmYxAQEB6Ny5M9avX4+PHz/qJfvatWtaudULgqA0EP+DBw9gbm6u9t7evXuLPsTCahnO27dvRf1u7ty5ePHihcI5ZSEotA1JERERgffv38v/VncYGlbLXnhbmstik4IHDx6gfPnyqFSpEoyNjVG3bl04OjqiQoUKWm1YwWrDBH3lfPvttwr5Ozo6GhkZGaKfnxdWeYe3JYQslpPxtEmGjY0N4uLiRD9TFTy9E8DXzs886iPTad++fYiOjla69E4TvKUVK3jbxZynDX4APpe765OXeSvjvC0xB/haRq2MGTNmYOzYsaJ/z7I8sIBVWvFUJ7MKTZBbJ33aK9boq0+HDh0wb968fOcXLlyIjh07GlQXVnUgb2M9VnUO67ysLzy2wV8qRYbbr4SgoCDY2trCw8MD3333Hdq1a6dwqCM6OhqZmZnMdMkbozIhIQF//fUXGjdujPr162u8X7ZroUQiwZw5c+T/r1ixAkuXLkVQUBBq1KjBTF+xsGpoLC0t0adPH5w6dUovfY4cOQJfX18cPHgQr169wuvXr3Hw4EHUrFkThw8f1kt2QWFtba023i6rTiDrRk8mQ1dYdS4yMjKwefNmDB06FEOGDMHGjRu1Nnay0kVfOXknZjTlDU2wyjus5Bw9ehSTJ09Gv3790KdPH4VDLLxNZLCoAytVqqR17GJl8PROAH/GD970YQFvaQXwZeRk8X14NPizgpeY2LyVcVZpxarfxZtBWpWOhmhnfHx85Ht61KhRAz4+PioPbWCVVjzVySxiuLKEpaGUBcWKFVOaVklJSShevLhBdeEtjjDAV1vOKi/z0ubxqg+PFBluvxL08SqVSCR48uSJ/G9tvPeUocwTVBAE1KtXD8nJyRrvd3d3h7u7OwRBQJkyZeT/u7u7o3z58mjevHmhzPSwamj27duH9u3bw9TUFOXKlcO8efPkm7BpQ+XKlZUaf0+ePImKFStqLS8jIwOvXr1SOFijaaM0Vp1AVo1eZGQkqlSpAjMzM5iZmaFq1aqIiooSfb8MnmZPWemir5y8hlt9N9HjxZMYAGbOnAmJRILatWujbdu2CAoKUjjEwttEBos68M8//0TLli1x9+5d0c9VBk/vBPBn/OBJH1YDWN7SijcjJ4vvw6PBnyfvSxZ5mbcyztPmRwB/BmllREVFwcXFRfTvdf3GM2fORHp6uvxvdYc2sEor3upkgB/PVFaGUlb6mJubyzc4zk1ycrLGVaysdWFVB7LKx7y15bn10jUvs1xxwFMb/KVTZLgtQiPOzs7Yt28fgE/GFJkRV1fu3r2rcKSlpeHdu3day/Hz85PPNOvLrl270KlTJ9SpU0fnWWrWxrenT59i6dKlqFatGoyNjdG6dWv89ttvokNSmJubK20gEhMTRTfCb9++xdChQ+Hk5KT3LrVi0GScY+3dpU+jt2TJEkilUkyYMAHR0dHYu3cvxo8fD6lUiqVLl2olS5/ORWpqKi5cuKBw7ujRo/Dz80OtWrUwZ84cg+nCUg5rwy0vnsQAUKJECZ0M/HnhbSKDRR345MkT+Pn5QSKRwMrKCvb29grH5/hOAH/GD570YTWA5S2teDNysvg+PBn8Af68L1nkZd7KOG9LzHkySOddvRgUFIQ6derAyMhIK2NpQU7eZ2Zm4t69ezrdq29a8VYns4BVe8XCUMpSn5o1ayIkJCTf+RkzZsDX19egurD2juYlDBNP+ZinMCYs9fnSKTLcFqGRGTNm5IuPquooLPRZqr5ixQpYWVlh6NChMDU1xaBBg9C0aVPY2tpiypQpouUU5DKclStXwszMDIIgwMnJCdOmTZPPsqvi22+/RUBAAP7991/5uYcPH6Jp06Zo1KiRqOf++OOPqFSpEnbt2gULCwts3rwZoaGhKF26NLZu3arXOylDk3GOJ89Ud3d3REZG5jsfEREBd3d3nWTq0rkICgrC1KlT5f/fvn0bFhYWaN68OUaMGAErKyssW7bMILqwlCMIAqKiohAdHY3o6GhIpVJs2LBB/r/sEAsvnsQA4ODgwGQGmaeJDIBNHdikSROUK1cO8+fPR3h4uN5xtXl4J4A/4wdP+rAawPKWVrwZOVl8H54M/gB/3pes8jLATxnnbYk5TwbpvKsX+/bti4kTJ+LQoUOi9QAK9hsnJCQU2viMpzqZJ89UgI2hlKU+0dHRMDY2Rs+ePeX9rR49esDY2Bi///67QXWRwUscYd7achZ5macwJiz1+dIpMtx+RejjVZqcnIw//vgDgiAgIiICe/fuVXqoInccWk2HNrBYql6hQgVs374dgKLhcNq0aRg6dKhWsgB2Dc3Dhw+xYMECVKxYEVKpFN26dcPx48exdetWVKlSBc2aNdOoR5UqVWBiYgIvLy94eXnBxMQElStXFq1XmTJlEBMTA+BTjFHZfVFRUWjVqpXO76YKTYZbVp1AFo2emZmZ0u9448YNmJmZidZFX0qXLo0zZ87I/w8NDUX16tXl/4eFhSn8/7mQN5SKskObwQgvnsQAMGHCBMyaNUv0M1XB00RGbvSpAy0sLJCQkFBAmukOLwZgVvCkD6sBrAxe0oo3I6cMfb4PTwZ/gC/vS4B9XtYHnvIN8OUuo9aXnJwc3L17F+np6QViqNLFcMs6/ioPdTJPnqkAG0MpS30AYP/+/ahfvz6kUikcHR3h7++PEydOFIouLOAtDBOrOodFXuYtjAlP3sg8U2S4/Upg5VWaO56SOuLi4vD+/Xv5/7nj0Lq7u8PS0hKCIMiXvwqCAEtLS3h4eIjWhdVSdQsLC3k8RScnJ7nR4MaNG3BwcBAthxW//fYbvvvuO5iYmKB69epYtWoVXrx4ofCbK1euwMTERKOsnJwcHDp0CCtWrMDy5ctx+PBhrTyTLS0t5d+mVKlSOHv2LIBPXp0FMQMmdgMqfTuBLBq9ypUrKw1DEBoaiipVqmiljz6dC3Nzc6Slpcn/DwgIUPDAvXnzJmxtbQ2iS0HIYU1heRKPHj1afowcORJ2dnZo1KgRhg0bpnBt9OjRonXgaSKDFT4+Pvj777/1lsPTO+WGF+MHT/qwGsCyhhePR1b6sIAXY5cMnrwvATZ5mbcyzoovdRm1vmRnZ8PExAQ3btwoEPm6GG552qgqNzys7GDZXulrKGWtjxi2b9+Ot2/fFqguvMUR5q0tZ5GXeQtjwqsjCm8UGW6/Elh7lWpCnQFu27ZtaNCggUKlc/36dXz77bdaLb9ntVTdw8MDFy9eBPBptnDdunUAgEOHDmlV6bBqaGxsbDBo0CCcO3dO5W8yMjK0ip/17t07nUJJVK1aVd6JaNasGcaOHQvg00RAqVKltJanCX3jmIqFRaO3e/duGBkZoUWLFpg1axZCQ0PRokULGBsbY8+ePVrpo0/nomTJknKDenZ2NmxsbPDHH3/Ir1+7dg02NjYG0aUg5IglMDBQISwIb/j5+Yk6/P39tZbNw0QGwKYOPHToEOrXr4+YmBg8ffpU5w0ReXonlvA2oGalD4sBLG9pxZuRk+X34cHgD/DpfalvXuatjPO2xFxGYRlc7ezs8sVeV3WIxdvbm8mEpTJ0MdyySiue6mSePFO1QZ2htDD00eRgw0IX3uII89aWs8jLPIUxYanPl06R4fYrwdBepeoMcJ6enrh06VK+8xcuXNDK4MpqqXq/fv3kRtC1a9fCwsICTZs2hZ2dHfr27StaDouG5uPHj/j555/x8OFD0c9VRXZ2NmbNmoWSJUvCyMhInh5Tp05FWFiYKBlLly6Vh684fvw4LCws5JXp8uXL9dYxL6dOnVLw1M4Lq04gqw7chQsX0K1bN/j6+sLHxwfdunVTmrc1oU/nomvXrvjuu++QlpaGJUuWwMrKSqGTt3v3blSrVs0guhSEHLFoMvp/6Z7E+sDTJhm5w2DkPrQNjcHTOwH8GT9400cMmgawvKWVDF6MnDwZBL9270t1eZm3Ms7bEnNW6Pp9csdcX7JkCezt7dGlSxd5uLcuXbrA3t5eq5V/+/fvR8OGDfHPP/9o/R6JiYlqj507d2ptuGWVVjzVyTx5pmqD2JWIhtKHhYONJl14iyMsg5e2nGVe5iGMCUt9vnSKDLdfCay8SsWirmK3sLCQewjm5uzZs7CwsBD9DFZL1bOzs/Hx40f5/zt37sTw4cOxYsUKfPjwQbQcVg1NbiO7PoSEhMDT0xNbt26FhYWFPD127tyJunXr6iTz3r17+O2337SOQZl3KbjsGDNmDKZMmYLNmzcrBDZXBatOIG9Lc/XpXNy+fRteXl6QSCQwNjbGmjVrFK63bdsWo0aNMoguBSFHLJo6kzx5Er98+VJpfn/27JlWXqW8TWSwqANPnDih9hALT+8E8Gf84E0fMWgawPKWVqzgyfuIV4P/5zbYU5eXeSvjvC0x58kg3b59e6xatSrf+VWrVqFt27aidbGzs5MbPMzNzbXy3JVNaqraB0DbSU+AXVrxVifz5JkqFlYrEXnSR5MuvMURZgXLvoUh87IYPrc2+HOlyHD7lcDKq1Qs6ir27777DtWqVcP58+fly/fPnz+PGjVq4Pvvvxf9DJZL1VnAqqHx8/Nj0qB4eXnh6NGjABTTIzk5GXZ2dqJkREZGKvWA/fDhg9IwFarw8/ODjY0NLC0t5d6pVlZWsLW1RZ06deRLz65evapWDsvBnr6NnkQiwePHj/Odf/r0qcE7yZmZmUhISMCDBw/yXUtISMDTp08NpgtrOWLR1JnkyZO4ZcuW+Pnnn/OdX7t2rVab/vE2kcGTRxVv78Sb8YM3fcSgqYzzlla8GTlZfJ/P0eAvBkOvpFCXl3kr47wtMefJIG1paaly5Z82+0Dk9uJVdqjj7t27og5tYZFWvNXJYuDJM/VLlaNJBm9xhHlry8WiLi/ztnqQN314pchw+5XAyqtULOoq5SdPnqBVq1b53OpbtWql1BCmDlZL1U+ePIlu3bqhbt26+N///gcAiIqKwqlTp0TLYNXQ/Prrr/D09MSqVatw5syZfMuexGJubi7vrOVOj6tXr4ruULIyTi5btgzt27dX8CZ89eoVOnbsiOXLlyM9PR1t27ZF8+bN1cox9GBPXaMnCILSb/PgwQOdGmCeZk9Z6WLIdzKUUYeFHHt7e1y7di3f+eTkZK1C1/A0kQGwqwNfvHiBxYsXo1+/fujfvz+WLl2q05Irnt6JN+MHb/qIwVCDPd48HnnyPuLN4M+T96U2aMrLPJVx3paY82SQdnV1xcKFC/OdX7hwIVxdXUXr8rmiKa14q5PFwJNn6pcqR4wMnuII89aWi0VdXuYpjAlLfb50igy3RRQIYhq+GzduIDo6Gnv37kVKSoqBNMvP7t27YWFhgf79+8PMzEyu988//6yV9xvApqFhtdzpm2++wZYtWwAoNpIzZ85Ew4YNRevy5MmTfOcTEhK0CrFRsmRJpd60V65cQcmSJQEAFy9ehKOjo1o5hh5AKMvHsjhmEokEc+bMkf+/YsUKLF26FEFBQahRowZzXQB2ngAsYKWLobwbePIklkqlKjso2oSL4WkiQ4a+deD58+fh4OCAUqVKoV27dggKCkLp0qXh6OgoD/fDEkNt/MGb8YM3fcRgqMEebx6PPHkf8Wbw58n7UhsMESeSp3yjDZ/TMurw8HBIJBIEBgYiNDQUoaGhaN26NYyMjBAeHq723ryODOoOXhEz1uOpThYDTwbOL1UOK10MFUeYx7ZcDOq+M29hTAzdBn+uFBluvyKeP3+ORYsWoW/fvujXrx8WL14sKq6oLhgiBg4rb9AaNWrIl/3n1vvy5ctwdnbWUmvNaGpoWC132rdvH2xtbTF//nxIpVIsWrQI/fv3h6mpKQ4fPqz23ho1asDHxwcSiQRVq1aFj4+P/KhWrRqsra3RqVMn0bpYWloiJiYm3/mYmBhYWVkBAG7dugVra2uNsgrbi9Pd3R3u7u4QBAFlypSR/+/u7o7y5cujefPmiI+PLxB9WHkCsICVLob0buDFk7hx48YYNmxYvvM//vij6EkVgI+JDF1QVwc2bNgQvXv3Vlgh8vHjR/Tq1Qvffvut3s/OiyE3/uDJ+MGjPpow5GCPJ49HVvqIRd334c3gz5P3pTYYoo8M8JNvtOFzW0YdHx+P4OBg+Pj4oEaNGggODhbVB8w9hlG2Iaeu8WkNyedWJ4uBJwMnj3IqV66MtLQ0LnQx1Pjhc23L1b0Xb2FMeAufxCtFhtuvhBMnTsDW1hZlypRBu3bt0K5dO7i6usLGxkarCsPf3x8vXrzId/7Vq1fw9/dnqLHmipTVUnULCwvcuXMn3zNv3boFMzMz7ZQWgSGNbwcPHkSjRo1gaWkJCwsLNGjQAIcOHdJ438yZMzFz5kwIgoBx48bJ/585cybmzp2L7du3axViIzg4GB4eHtizZw/u37+P//3vf9izZw88PT3RvXt3AMCOHTvwzTff6PyuuTHEAMLPzw/Pnz/X+xms9DE0vHUm586dq7Ru0hZDeBLHxcXB3Nwc3377rbxcffvttzA3N8fJkye1eg4vnUBtUFcHmpubIzk5Od/5q1evauWNLBbeBhC8xdfjSZ/PLa14M3KKRdP34cngz5P3pTbwVB4MlW/E8rkto9aVEydOyCcoY2JimGzKaWg+tzpZDLz1bVkYSsXq8+LFC2zcuBGTJk2SO3ZdvHhRHkKQFbx9Y0NOFonBEP0u3sKY8LaRHK8UGW6/EipXrowBAwYgKytLfi4rKwsDBw5E5cqVRctRZSx9/PgxjI2NmegqQ1WFw3qpuqenJ44cOZLvmZGRkahUqRKbl8mFmIbm5s2bGDZsGJo0aYKmTZti+PDhuHnzJnNd1JGVlYXw8HD8+++/est68+aN3NtX5klgamqKAQMGyBuny5cv4/Lly3o/C+AvRhUreNKnIDtM0dHRog/WGMqT+PLly+jatSu8vb3xzTffoE+fPrhx44bez1UGT8Y3TXKKFy+udHLp4MGDhRJr0tByeKu7eNLHkANYMfDm8chTWn2t3pdi+Rq91gwtpyAN0jt37lRwXrhz547C+Co9PR0LFizQ+9m8w1ua87SyQ0wZN5ShVIw+iYmJcHJyQtmyZWFsbCz/BlOnTkWPHj0MqotYDJn/vsS2nLcwJjzt9cIrRYbbrwRVruzXr18X5Z0q2xhLEATExMQobJZ16dIlzJ07F25ubkx1VlXhsF6qvmDBAnh7eyM+Ph7W1tY4deoUtm7dCicnJ6xatYrlKwHQXJEePHgQpqamqF27NkaPHo1Ro0ahdu3aMDMz0xjiIDceHh54+vRpvvMvXryAh4eHKBlmZma4ffu26Gdq4s2bN0hMTERCQgLevHnDTG5eDNGY9+nTR+1REKjT56+//lLYTG/16tWoXr06unbtWiCewQX5jVXFeM79v+xgDW8DERZ8TgPz4cOHo3Tp0vjll1+QlpaG+/fvY8eOHShdujRGjhyp97O10aVIjuHkGHIAy9u3+ZwmVsTyNXtfFnmt8SGnIPNg3lBteZ/16NEjrfonHh4emDp1qtJxmrZcunRJIdQQAOzZs0dvucrgLc0N5cnOooyzNJSy0KdJkyYYP348AMVvcPr0aa3G90Vt+ZfVlvMUxkSsPl8yRYbbr4T69esrnfn4/fffUbduXY335zaWKNs8SyqVYtOmTUx11lThsFyqPmXKFFhYWMjfx9zcHFOnTmUiOy+a3qtGjRqYOHFivvMTJ06Ej4+P6Oeo8o5+9OgRTE1NRcmoWbMmjh49KvqZvGCIRi8oKEjhaN26Ndzc3GBra4t27drp/Wxt9alSpQoOHDgA4FNQeDMzM0yePBl16tRB7969DaoLSzlHjhyBr68vDh48iFevXuH169c4ePAgatasqdVEBit9WMm5efMmfvrpJ3Tt2lVeTv/66y9cuXJF72drqwtPcj58+IARI0YoeOebmZlh1KhReP/+vd7P1kaXIjmGkWNITx9NuhSGnM9pYuVz1AUwXDgAQ+Zl3r7x1yQnb/8672+0NdwuWbIENWvWhCAI8PX1xbJly3Re7SYIAmxtbTF06FA8evQICxcuZL4iUsbnkFbaYijPVFaGUlb62NjYyFd25tbn7t27osMGFrXlfLXlLDybeQpjwlKfzxVjKuKrYMSIETRy5Ei6efMm1a1bl4iI4uPj6eeff6b58+dTUlKS/LfVqlXLd/+dO3cIAHl6etK5c+fIyclJfs3U1JSKFy9ORkZGTHUWBEHt9ZiYGFFybGxsKCEhgTw9PVX+Zs6cOfTTTz/RtWvXKCcnh7y9vcnKykorfVmRnJxMv/76a77zffv2peXLl2u8f9++ffK/Dx06RLa2tvL/s7Oz6dixY+Tu7i5Klzlz5tC4ceMoNDSUvvnmG7K0tFS4bmNjI0pOeno6zZ8/n44dO0ZPnjyhnJwcheu3b98WJcfQuLm5kYmJidJrv//+e75zOTk59OOPP6rNawXFnTt3yNvbm4iIfvvtN/ruu+9o7ty5dOnSJQoMDDS4PqwYNWoUrVu3jho2bCg/16JFC5JKpTRw4EBKTk4uRO10IzY2llq1akUNGjSgkydP0uzZs6l48eKUlJREYWFhtHv37sJWsdAwNTWlFStW0Lx58+jWrVsEgMqWLUtSqbSwVSuigBgzZgz17t2bFi5cSNbW1vLzrVq1ouDg4ELUzDAAKGwVvngGDRpEderU0btt1pRWX3teZoG6fteXypgxY2jMmDF048YN2rZtG61du5bGjx9P/v7+1L17d+rZs6doWU+fPqWkpCRav349eXh4EBFRREREgej9uaXVy5cvaffu3XTr1i0aP348OTg40KVLl8jZ2ZlKlSpFRERXrlxRK4NVGT9//jytX78+3/lSpUrRo0ePRMthpY+5uTm9fv063/mUlBSFMb8hdDE0rPKxIdtyFnlZDKzeyVBt8JdOkeH2K6Fr165ERDRhwgSl1wRBIAAkCAJlZ2fn+42bmxsRUT6DW0HCqnCKlSOVSqlmzZpMnqkPTk5OlJCQQOXKlVM4n5CQQMWLF9d4f1BQEBF9Mnz36tVL4ZqJiQm5u7vTkiVLROnSsmVLIiJq06aNgiFdXV5RRv/+/Sk2NpZ69OhBLi4uGo3yhqAgGj2JREKjR48mPz8/pWVNX9R1LkxNTSkjI4OIiI4ePSrv6Ds4OCjtjBWkLizl3Lp1S2HyQYatrS3dvXtX7+cXBpMmTaLZs2fTmDFjFDq3/v7+tGLFikLUTD0FOUjLzs6mq1evUrly5cjCwoKkUilVrVqViIjevXtHSUlJVKVKFZJIJEyf+7kNPMXC23up04fVALaIIlRhqMGeIfMyb2VcDIYyNnyulC9fnkJCQigkJITi4+NpyJAh1KdPH7WG2/j4eDI2NpaPXxwcHMjPz49++eUXMjc3p48fP1LZsmW11uVLS6ukpCRq2rSpvO84YMAAcnBwoN9//53u3btHUVFRouSwKuMsDKUs9Wnbti3NmjVL7jgkCAKlpaXRpEmTqEOHDgbVRSxi6sAvLR8TscvLhuRrN7iyoshw+5Vw584dJnIiIyOpWLFi1Lp1ayL6ZAjesGEDeXt7044dO+QGXhb89ddf8kq1IGjfvr2o3+3Zs4fpczU1NAMGDKCBAwfS7du3qX79+iQIAsXFxdGCBQto7NixGuXLjOseHh50/vx5KlasmM66ivVq1sRff/1FBw4coAYNGjCRpy8F2ejdunWLsrKytL5P385Fw4YNacyYMdSgQQM6d+4c7dy5k4iIbty4QaVLlzaoLizl1KpVi0aNGkVbt24lFxcXIiJ69OgRjR07lmrXrq3Ve/HCP//8Q9u3b8933snJiZ49e1YIGhm2c6usDtyyZQutXr2azp49m+/3pqam1LdvXxo1ahR1795d9HMK+50KCt4GIvrqw2oAK5bP0eBlSHj6PjzpIgZWeZm3Mi4GTWn1ORob1JF7RVtOTg4dO3ZMniYvX77UWe65c+do+/bttHPnTnr16hV17NhR7e9HjRpF06ZNUzg3depUio6OphMnTtAff/xBs2bNogMHDojWwdBpZYhyzpNnKhEbQylLfRYvXkyBgYFUvHhxevfuHTVu3JgePXpE9erVozlz5hhUFyI2deCXVufI+Fw9m4tgQKEEaCjis6V8+fI4duwYAODMmTOwsLDA+vXr8f3334uO6zl69Gilx5gxYzBlyhRs3rxZHtCcBapixfTu3VvhMDU1RYcOHfKd1wYWQdlzcnKwdOlSlCpVSh5zt1SpUli+fDlycnK00ocX3N3dce3aNYM9T1NcHxaxpfLm31GjRqFz586wsrLC0KFDtdKXRVyoe/fuoXXr1qhWrRrCwsLk50eNGoXhw4cbVBeWclJTU1GlShWYmJjAy8sLXl5eMDExQeXKlZGamipajlhY7XarTk6pUqVw+vRpAIr5b8+ePfD09NT72XnRFC+Lh00yGjZsiB07dqi8vnPnTnz77bei9eDhnXSBp52fDaXPgAEDEBQUhMzMTFhZWeH27du4d+8efHx8tN6Qjqe0EsuXuIu5oTb4YakPCzks8jJvZRzga/MjsRRkuVK214eyTVXFkpKSgunTp8vTvFmzZoiIiMDr16813mtlZYU7d+7I/1++fDlcXV1x48YNAMDVq1dhY2MjWheAbVrxUieziOEKsGuvXr16hQYNGsDOzg5GRkYoU6YMTExM0KhRI61igbJsPwHg2LFjWLRoERYsWIAjR45odS8rXXiLIywWQ7XlrPKyGHiLI8zTxs+FQZHh9ivif//7H3bu3IlVq1ZhxYoVCodYLCwscO/ePQDAhAkT5BXolStXUKxYMVEy/Pz8YGNjA0tLS/j6+sLHxwdWVlawtbVFnTp1YGdnB3t7e1y9elX7l1SC2EKub2VQEJ3t169fi+q4qeLt27c4cOAA1q5dq3Oanzx5Et26dUO9evXkHa2oqCicOnVKtIwtW7agY8eOSE9P1/od8sKiE8ii0fPz81M4AgIC0LlzZ6xfvz7fjr6aMHTnwhC6sHynnJwcHDp0CCtWrMDy5ctx+PBhnSYxWA0gdJUTGxuLzMxMjB8/Hg0bNsTDhw9hbW2N1NRUxMXFwdPTEzNnztT6vTRhiIkMQL860MnJSWHwmZfbt2+LbmMAPt4pL7wZP3jRh9UAlre0EsvnuIu5JnibgDDUoJFFXuatjPO0+ZE28DSRoQlBEFCrVi0sW7YMDx8+1OpeJycnuYFt8+bNqFChgnycBgCXLl3Squ0E2KUVT3Vy8eLFcenSJQCK73To0CGULl1atB6s2isZ+hhKWeoTGRmpdAPYDx8+IDIy0qC6sKoDDV3nGKotZ5WXxcCbwbXIcFvEV8HmzZthamoKKysruLm5wd3dXX54eHiIluPk5CSvLGrUqCGvzG/evAlLS0tRMpYtW4b27dvj1atX8nOvXr1Cx44dsXz5cqSnp6Nt27Zo3ry5Fm+oGrE7EOpbGfBkfAM+ddZKlCgBGxsbGBkZwcnJCYIgwNLSUnSa7969GxYWFujfvz/MzMzk7/Tzzz+jVatWonWpUaMGrK2tYWVlhSpVqsDHx0fhEAurTqAhGz0xsOhcXLx4EUlJSfL/9+7di7Zt22Ly5Mn48OGDQXVhKSc3796909nrnAdPYolEgsePHyMzMxPBwcGQSCQQBAEmJiaQSCTo3r07srKytHovXiYyAP3qQKlUisTERJXXExMTIZVKRevCwzvlhjfjB2/6APoPYHlLK7F8bruY82Lw1wZD79KtT17mrYyzSiuW/S6eDNJiCAwMxL///qvyekpKis6y+/XrB3t7ezRo0ABGRkb46aef5NdycnLwww8/aNVfB9ilFU91Mk+eqQAbQylLfWT907w8ffpUK+9xFrqwqgMNPdYzVFvOOi+r43NcWfQlU2S4/UooXbo0Zs+ejezsbL3kBAcHw9fXF/369YNUKsXTp08BANHR0ahcubIoGSVLllTqTXvlyhWULFkSwKcOmKOjo166yjCUxy2rhqZGjRr5DJs+Pj7w9fVF/fr10bNnTxw/flyjnMaNG2PAgAHIysqS65OWloZGjRrht99+E62LrAOR+50uX74MZ2dn0e80Ry3QigAArwNJREFUc+ZMtYdYWHUCC6rRe/78OVauXInq1atrdR+LzkXNmjWxe/duAMCtW7dgbm6Orl27omzZslq9E6uODis52dnZmDVrFkqWLAkjIyOFjk7ukBCa4MGTWBAEhY7xzZs3sWvXLuzcuVO+rFEbeJvI0KcOrF69OtauXavy+s8//6xVueLhnXLDm/GDJ31YDWB5SyuALyMni+/Do8FfDIbyvmSRl3kr47wtMefNIC0GseOLDx8+4P79+7h3757CoY53794hJCQEs2fPRkJCAlxcXFCnTh0MHjwY1apVg1QqRUJCglb6skornupknjxTAXaGUlb6CIKAJ0+e5DufkJAAe3t7g+rCqg7kLQwTqzqHVV7mJYxJYejzuVJkuP1KcHBwkDee+vDixQsMGzYMbdq0wV9//SU/P336dMyePVuUDEtLS8TExOQ7HxMTAysrKwCfDE/W1tZ66wsAp06dUtqQ5EVfwy2rhmbSpEmwtbVFw4YNMWbMGIwePRrffvstbG1tMXLkSDRr1gwSiQR79+5VK8fW1hbXr1+X/y2LMRsfH48KFSqI0sXCwkK+fDn3O926datABleaYNUJZL3U6ciRI+jSpQvMzc1RunRpjBgxQqv7WXQucn+b+fPnyz3W4+LiCqWjw0pOSEgIPD09sXXrVlhYWMjTfOfOnahbt65oOTx4EqvqGOsKbxMZ+tSBCxYsgKOjo1Kv24SEBDg6OmLBggWf1TvlhjfjB0/6sBrA8pZWvBk5WXwfngz+MnjyvmSRl3kr47wtMefNIC0GTeOLGzduoGHDhpBIJAqHtrFyAeDx48cYP348WrZsiX79+qldyaIKVmnFW50M8OOZysJQykIfmbOQRCJB1apVFRyGqlWrBmtra3Tq1MkgusjgLY4wb225DH3yMk9hTFjr8yVjXNiboxVhGPr160e7du2iSZMm6SwjKyuLVqxYQRMmTKAyZcooXAsJCREtp23bttS3b19asmQJ1apViwRBoHPnztG4ceMoKCiIiD7tqlq+fHm1csaMGaP0vCAIZG5uTmXLlqW2bdtSw4YNlf5u3759Cv/n3RVWRps2bUS/F4sdQp8+fUpjx47Nt0vs7Nmz6d69e3T48GGaMWMGhYaGUtu2bVXKMTExIUEQiIjI2dmZ0tLSqFKlSmRra0tpaWmidHFxcaGbN2+Su7u7wvm4uDjy9PQU/U6sYLVjqY2NDcXFxdHx48fp0qVLlJOTQ76+vtS0aVPRMtLS0ig8PJzCw8Pp7du39OLFC/r111+1SmsZLHZzBUA5OTlERHT06FH67rvviIioTJky9PTpU4PqwlJOVFQUbdiwgZo0aUKDBw+Wn69WrRpdv35dtBxWeUdfOdOmTSOpVKr2N0uXLhWly/nz52n9+vX5zpcqVYoePXokSgYRu7TSpw4cPXo0/fXXX/TNN99Q06ZNqWLFiiQIAiUnJ9PRo0epQYMGNHr06M/qnXLD087PvOkDQN5W5eZ///uffNd2MfCWVl/iLuas6hxWacVq13BWacUiL/NWxlmlFYt+FxG7PMjq+7Cgd+/eZGxsTPv37ycXFxeleUgsxYsXp4ULF+qlD6u04qlOjoqKos6dO1NAQAAFBATIz2dmZtIvv/xCPXv2FCVH3zLu4+NDgiCQIAjUpEkTMjb+P1NMdnY23blzh1q2bClKFxb6yMbdCQkJ1KJFC7KyspJfMzU1JXd3d9FpxaotZ1UHssrHvLXlLPIyq3firQ3+4ilMq3ERhiMrKwstW7ZE48aNMWzYMIwePVrhEIulpaXaDWTE8ObNG/Tv3x+mpqbyWWVTU1MMGDBAPgN2+fJlXL58Wa0cfTc5Y70rLKuZPRsbG6SmpuY7n5qaKt8ZNjk5We6drIpmzZph27ZtAIBBgwahdu3a2Lp1K1q0aIHatWuL0mXBggXw9vZGfHw8rK2tcerUKWzduhVOTk5YtWqV2nvt7e3x33//AYA8LVQdYmE1C6vPcp6dO3eiWbNmkEql6NixI/bu3YsPHz7A2NhY7w319Jk99ff3R8+ePREVFQUTExN5Hjpx4oROy/709UpgJcfc3Bx3794FoDhDffXqVdFxtQE+PIkFQUD9+vXzbWyX+/D39xetC+v4XYW9SUZmZiYWLFiA6tWrQyqVwsLCAtWrV8eCBQu0itOcm8J+Jxm8xdfjQR+Wnj4Af2nFk1czwOb7FHlfKod1Xgb4KeO8LTHncRm1JjR53EqlUiQnJzN9pj6wSiue6mRePFNlIeIEQcC4ceMUwsbNnTsX27dvF9XfYV3nRERE4N27d6J/X5C6yOAljjBvbTkLz2aewpiw1OdLp8hw+5Uwa9YsCIKAihUronHjxjobCtq2bYvw8HAmOr158waJiYlISEjAmzdvtL7f0JuciUXfhqZ48eJKG5TIyEgUL14cwCejlaYYwOfPn5fHwn3y5AlatWoFa2tr+Pj4aBXvasqUKbCwsJAbs83NzTF16lSN90VERMgbzIiICLWHWFh1AvVp9IyMjDB58mS8fv1a4bw+hlsWnYvExERUqVIFNjY2CnGDhw0bhq5duxpUF5ZyvvnmG2zZsgWAYmM+c+ZMNGzYULQcVnlHHzl5Y9zqCw8TGcpgZfTXB97eiTfjBw/6sBrA5oWXtOLNyClDn+/Dg8E/N7yEA2CZl3kr4zJ4WWLOm0FaDJoMtzVr1sSpU6eYPlMfWG5UBfBRJ+sbmoB1e6WPobQg9NEH1rrwFkeYt7acRZgN3sKY8LZpOK8UGW6/Euzs7JgYXNetW4cSJUpg7Nix2L59O6KjoxUOQ2LoTc407QrLqqEJDQ2FhYUFRowYgS1btmDr1q0YMWIEpFKpPI7w0qVL0bRpU+1fQkfS09Nx/vx5nD17VicjO2v07QTq0+gNGDAAtra2qF+/PtauXYvnz58D0M9wy7qTnJt3794hMzPT4LqwkrNv3z7Y2tpi/vz5kEqlWLRokdxj//Dhw6LlyChMT2JV30RXeJjIyA1rY+mQIUPkXvvawus78WL84EkffQewMnhLK96MnCy+Dw8G/9zw5n3JIi/zVsZ52vwI4M8gLQZNhttjx46hXr16iImJwdOnT/Hq1SuFw9CwSise6mSePFMLAlb6ZGVlYdGiRahVqxacnZ11WhnJShfe4gjz0pazzMu87QNhyBUQnzNFhtuvBGdnZ512Lc8Li7ACb9++xdSpU1GvXj14eXnBw8ND4RCLoTc509TxYtnZ3rp1K+rWrStvMOvWrSsPewAAGRkZBuk49OnTJ59nKfApDfv06aP23rwdT3WHWPTtBLJq9DIyMhAREYFGjRrBzMwMbdq0gZGREf755x/R75IbVp0LFgHiWenCSg4AHDx4EI0aNYKlpSUsLCzQoEEDHDp0SCsZPHgSs/a4lVGYExm5YW1wsLa21nnDSN7eiTfjB2/6sIC3tOLNyMmyfPJg8Ac+T+9LTfBWxnlZYp4XXgzSYpg7dy5evHih8nrucZS+m5PpA+u04qFO5skzNTcsDKUsmTZtGlxcXLBo0SKYm5sjNDQU/fr1g6OjI1asWGFQXfStA3kNw6RvncMyL/MUxoSlPl86AgAUdpzdIgqeefPm0cOHD2nlypWFrQp17dqVYmNjqUePHkqD8I8cOVKUnG7dutHff/+tdJOz+vXr05YtW+iXX36hxYsX04ULF/TW29ramhITE1VuyiWRSOjx48f5AownJiaSv78/PX/+XG8dNCELei+GS5cuafyNkZERPXz4kIoXL65w/unTp1SiRAnKyspSea9EIhGtS3Z2tqjfqdLn2bNnVLx4cY1yZJvohYSE0NixY1UG4Tc1NRWlDxFRamoqbd68maKioujt27fUunVr6tixI7Vv317jvbL0SkxMpMqVK6vcpEC2sYM6kpKSqEmTJmRnZ0d3796llJQU8vT0pGnTpokKEM9KF5bvxBJ98w4LOZGRkdSlSxcyMzMT9azWrVtTWFgYubi4KL0u26AgrzyxGxSwTivWdaCmOlcZvL6TvvmP9XvxpE92djYtW7aMfv31V0pLS6PMzEyF62K/MS9plRd9N0ZhpQ+L76NvncNSFyKi169fU2BgIF29epXevHlDJUuWlG9i8+eff5KlpaUoOTL0TSt98jJvZVyGvmnFut/FKg/q+n3ybmysDrGbG8fGxqq93rhxY9HP1AfWacVTnRwZGUmdO3cmc3NzUc9UBav2avr06RQWFkZjxoyhadOm0U8//UR3796lvXv30vTp02nEiBEG1cfLy4tWrlxJrVu3Jmtra0pISJCfi4+Pp+3btxe4LqzqwIIY6xHx05azystE+r8Tb23wl46x5p8U8SVw7tw5On78OO3fv58qV65MJiYmCtf37NljMF3++usvOnDgADVo0EAvOevXr6fRo0dTly5d5AZEY2Nj6tWrFy1btoyIiCpWrEhhYWF666wO1juEEhG9fPmSdu/eTbdv36Zx48aRg4MDXbp0iZydnalUqVIq75PtDkpE9P79e1qzZg15e3tTvXr1iIgoPj6erl69Sj/++KPa579+/ZrwySOf3rx5o9A4ZGdn059//pmv4clLTEyM/O+7d+/SpEmTqHfv3nJd/v77b4qMjKR58+aplZMb6Llj6YwZM4iIyN3dnVmjV65cOZo3bx7NmTOHDhw4QJs2baKuXbvShw8fNN7LcjfXMWPGUJ8+fXTekZOVLizfiYjI09OTzp8/T46OjgrnX758Sb6+vnT79m1RcvTNOyzk9OrVS/RziIhOnjxJ7969U3m9T58+1LJly3xl8c2bN9SnTx+NA1hWaVUQdaCu8PpO+uY/1uWKJ31CQkLUDmA1wVtayfgSdzHXt85hnVasdg1nlVb65GXeyjirtGLd79I3D8rQ9fvk7mcTEQmCQLl9oHLLFGuIYWGYTU9Pp/nz59OxY8foyZMnlJOTo3BdTF+JVVrxWCdr2/9Shb7tlYxt27bRxo0bqXXr1hQSEkJdu3YlLy8vqlatGsXHx4s23LLS59GjR1S1alUiIrKysqJXr14REdF3331H06ZNM4gurOpA1nUOL225DBZ5mdU78dYGf+kUedx+JfTp00ft9fDwcJXXVq5cSQMHDiRzc3ONHrtiGhoPDw/6888/qVKlShp/K4a3b9/S7du3CQB5eXkpVPQsUeX9xXpmLykpiZo2bUq2trY6eU7K6N+/P7m4uFBoaKjC+RkzZtD9+/dp8+bNKu/V5C0rCAKFhITQTz/9JEqXJk2aUP/+/alr164K57dv304bNmygEydOqL2fVy9OVTx58kSjYTs3LGZPbW1t6dKlS+Tl5aWQV+/du0cVKlSg9+/fG0wXlnIkEgk9evQo3/d8/Pgxubq6ajSQf86exIby8tc3rQrKu0EfeHkn1vlG3/fiTR8i/T19eE0rXryaWZbPIu9L9bDwWuOljPNYrxPpnwdZlvOjR4/SxIkTae7cuVSvXj0SBIHOnDlDU6dOpblz51KzZs3U3r9w4UIaPnw4WVhYENGnydo6derI8/ObN29o4sSJtGbNGo26sFrNyAIe62SePFOJiCwtLSk5OZlcXV3JxcWFDhw4IHdG8PHxkRtODaVPhQoVKCoqiurUqUPffvsttW7dmiZNmkQ7d+6k4cOH05MnTwymC0uPUhbw0pbnvkffvMyqzeOtDf7SKfK4/UpQZ5jVxLJly6hbt25kbm4u92RVhiAIogy3oaGhNH36dIqMjCSpVKqzXjKsrKyoWrVqesvRFdYze2PGjKHevXvr7DkpY9euXUpDRHTv3p1q1qyp1nAbExNDACggIIB+++03cnBwkF8zNTUlNzc3KlmypGhd/v77b1q3bl2+8zVr1qT+/ftrvJ+1JwqrDpwqtDHaErGZPTU3N6fXr1/nO5+SkpJvgFPQurCQk3s54qFDhxRmo7Ozs+nYsWPk7u6uUQ6vnsT6wNqbRd+0YlEH/vrrrxQUFCQfyN29e5fKlClDRkZGRESUkZFBq1evpgkTJoiSx8M7EbHPN/q+F2/6EOnv6cNrWvHi1czi+xR5X4qDhdcaL2WcdVqxWkatbx5kWc5HjRpF69ato4YNG8rPtWjRgqRSKQ0cOJCSk5PV3j958mTq3bu33HD73XffUUJCgnyyNiMjg9avXy/KcMtqNSOR/mnFY53Mk2cqEVHp0qXp4cOH5OrqSmXLlqXDhw+Tr68vnT9/XnQ4LZb6tGvXjo4dO0Z16tShkSNHUteuXWnTpk2UlpZGo0ePNqgurMYhrMZ6vLTlMljkZVZtHm9t8JdOkeH2K+O///6jlJQUEgSBypcvL8qgk5CQIC80d+7c0VuHJUuW0K1bt8jZ2Znc3d3zhW0QE3uViM2yIJawamjOnz9P69evz3e+VKlS9OjRI9FyLCwsKC4ujsqVK6dwPi4uTmMnSrZs686dO1SmTBmSSCSin6uMMmXK0Lp162jJkiUK59evX09lypTReD/rAQSrDhwrWHQu2rZtS7NmzZLP2AqCQGlpaTRp0qRCMWrrK0fW0REEIV/ZMjExIXd393z5SRms8k5BhNnQFV4nMvSpA7t27aow216tWjWFAeybN29o8uTJog23PLwTEX/GD970IWI3gOUlrXg1curzfXg0+BPxEw5ABou8zFsZ52WJOY8G6Vu3binNZ7IVc5rIu+hVn0Ww9vb2Ck4W+sCqj8xLnUzELjQBq/aKhaGUpT7z58+X/92xY0cqU6YMnT59msqWLSs6VjMrXVj133gJw8S6TtYnL/MWxoSnUGufA0WG26+E9PR0Gj58OEVFRckNnEZGRtSzZ09atWqVWs9XBwcH+YA6ICCA9uzZQ3Z2djrrkjc+lK70799f7bIg1kyZMkVtp4hVQ8PKc3LUqFE0ZMgQunjxItWtW5eIPsW43bx5s+iOl5ubG718+ZLOnTun1DgudiZt2bJl1KFDBzp06JCCLrdu3aLffvtN9DuxGkCw6sCxgkUnefHixRQYGEjFixend+/eUePGjeUB4ufMmWNQXVjIkeU1Dw8POn/+PBUrVkz0s5XBiycxC3idyNCnDmQ5gCXi451yw4vxg0d9WA1geUkrXo2c+nwf3gz+PHpfErHJy7yVcVblSt9+F48G6Vq1atGoUaNo69at8k1EHz16RGPHjqXatWvrLV8bWK5mZNVH5qVOJuLLM5WIjaGUpT4nT56k+vXry+vSOnXqUJ06dSgrK4tOnjxJjRo1MpguvMQR5rUt1ycv87YPBE+rGT8LUMRXwcCBA+Hp6Yk///wTr169wqtXr3DgwAF4eXlh8ODBau+1sbHBtWvXAACCIODJkyeGUFkjtra2iIuL0+ne6Oho0YdYpk2bBhcXFyxatAjm5uYIDQ1Fv3794OjoiBUrVoiWM2DAAAQFBSEzMxNWVla4ffs27t27Bx8fH4wcOVKr99y5cyfq168Pe3t72Nvbo379+ti5c6fo+/ft2wdra2tIJBLY2trCzs5Oftjb22uly/379zF58mS0a9cOQUFBmDJlCtLS0rSSkZWVhUWLFqFWrVpwdnaWv5fsEItUKsW9e/cAACVKlMDFixcBALdu3YKNjY1WOrHA09MT+/fvBwBYWVnh5s2bAIAVK1aga9euWsk6duwYFi1ahAULFuDIkSOFpgvLd2IBq7zDSo4YrKyscOvWLaYylcEqrfSpAwVBwOPHj+X/5333R48eQSKRfFbvlBtW+YbVe/GmT27i4+OxZMkSrdpfgL+0ioiIwLt377R6h4LUh9X3YYG+usycORMzZ86EIAgYN26c/P+ZM2di7ty52L59Oz58+CBaH1ZplRdd8jJvZZxVvuGt38Xi+6SmpqJKlSowMTGBl5cXvLy8YGJigsqVKyM1NVXj/SzbvRo1asDa2hpWVlaoUqUKfHx8FA5tYJVWPNXJ5cuXR3x8PACgYcOGmDdvHgDgl19+gZOTk2hd8qJrexUbG4uPHz/mO//x40fExsYaXB+JRKKQF2U8ffpUq74XC11Y1YGs8jFvbTmLvKzvO30ubfCXRpHh9ivB0dERMTEx+c4fP34cxYoVU3tv+/bt4ezsDD8/PwiCgAYNGsDf31/pYUjc3d3lBmVtEQRB4ZBIJPn+lx1iYdXQvHr1Cg0aNICdnR2MjIxQpkwZmJiYoFGjRnj79q12L6on5cqVw8iRI5Genm7Q56qCVSeQZQduz549ePTokcK5ZcuWaSWDpwENK11YvtPbt29x4MABrF27FitWrFA4xMIq7xjS+DF37ly8ePFC5XXeJjL0qQNZG255eKfc8Gb84EkfVgNY3tKKFaz0YfF9eDP48zbYY5GXeSvjrNKKVb+LN4N0Tk4ODh06hBUrVmD58uU4fPgwcnJyRN0rCALmzJkj78+Ym5tj2rRp8v9nz54tut3LbThRdmgDq7TiqU6eOHEi5syZAwDYtWsXjI2NUbZsWZiammLixImidWHVXrEylLLSR5VjVkpKCqytrQ2qC6s6sKCM9brCqs5hlZdZwFsb/KVTZLj9SrCwsFBq5Lxy5QqkUqnaezMyMrB27VqMGzcOgiBg4MCBGDVqlNJDFfb29vjvv/8AQO6tqeoQy5YtW9CxY0e9jYpHjhyBr68vDh48iFevXuH169c4ePAgatasicOHD4uWw9r4pq/npIzz588jKioKW7ZswYULF7S6VyqVMvP4e/HiBQ4dOoQtW7YgMjJS4RALq04gy0bPxsYGRkZGaN26NS5fvowff/wRpqamWslg1bk4evQoJk+ejH79+qFPnz4Kh6F1YSXn0qVLKFGihPw7Ozk5QRAEWFpawsPDQ7ScwvYk5tnLn1Va6VMHCoKAqKgo+TeQSqXYsGGD/P/IyEitBjM8vFNueDN+8KQPqwEsb2nFm5GTxffhyeDPElZpxSIv81bGWaUVq34XbwZpGe/evRNtsJXh5uYGd3d3jYehYZVWvNXJuSlsz1QWhlIW+rRr1w7t2rWDRCJBYGCg/P927dqhTZs2cHd3R4sWLQyiiwxWdSCrfMxbW54XXfKyIVcPfo768EqR4fYrISAgAJ06dVKYFcnIyECnTp3QpEkT0XL8/PzUeoCpIiIiAu/fv5f/re4QC6tlQZUrV8apU6fynT958iQqVqwoWg6rhiYyMlL+rXLz4cMHrYyc9+/fR8OGDSEIgrzik3lMiw1R0K5dO61CK6iCVciFghrs6dqBk3Hnzh2MGTMGEokEVlZWWi9zYtG5mDlzJiQSCWrXro22bdsiKChI4TCkLizlNG7cGAMGDEBWVpbcCzMtLQ2NGjXCb7/9JlpOYXsS8+zlzyqt9KkD834fZYc234aHd8oNb8YPnvRhNYDlLa14M3Ky+D48GfwB/rwvWeRl3so4b0vMeTJIZ2dnY9asWShZsiSMjIzkjg5Tp05FWFiYaF1YcuHCBWzZsgVbt27FpUuXmMjUNa14qpN58UxlaShloU/v3r3Ru3dvCIKAzp07y//v3bs3Bg4ciLlz58odrwpaFxkF5VFa2GGYWNXJLPIyT2FMWOrzpVNkuP1KSEpKQqlSpeDo6IiAgAA0adIEjo6OKFmyJK5cucL8edbW1gUem5HVsiBzc3MkJSXlO5+YmAhzc3PRclg1NKxmLJs1a4Y6derg+vXr8nPXr19H/fr10axZM1EywsLC4OrqihkzZmD37t06ewayCrnAqhOoT6O3b98+HDt2TOFcdnY2vv/+e7i7u8Pe3h4HDx4UrYsydOlclChRAlFRUXo9l5UuLOXY2trK87Ctra185UB8fDwqVKggWg5PnsS8evnL0DWteFq+lZfCfifejB886MN6AMtbWvFm5GTxfXgy+AP8eF+yzsu5KewyztsSc54M0iEhIfD09MTWrVthYWEhH/fs3LkTdevWFa0LCx4/fgx/f3+5s4adnR0EQUBAQIDWe5OwSiue6mRePFNZGUpZ1znjx49XGKPduXMHy5YtEzWeKcj6Dyj8OMK8teUs7AQ8hTFhqc+XTpHh9isiIyMDGzZswJgxYzB69Ghs3LgRGRkZBfKsvPEJZRuiiTkMzbfffouAgAD8+++/8nMPHz5E06ZN0ahRI53l6trQqJqxTEhI0Gr2ytzcXOls+8WLF0UbpFl5v7EKucCDcbx69er5DLe9evVCxYoVcf/+faxevVrrfMOic+Hg4CBv6PSBVUeHlZxixYohJSUFwKdOj6wTmZycDAsLC9FyePIk5s3Ln7dNMljA2zvxZvzgQR+Wnj7KKOy04s3ImRddvg8PBv/c8OJ9yTIv81bG81LYS8x5Mkh7eXnh6NGjABTHPcnJybCzsxOtCwt++OEHfPPNNwph8a5evYqaNWuiS5cuWskqiI2qgMKtk3nyTAX0M5QWhD5NmzbF2rVrAXwKbefs7IzSpUvD3Nwca9asMaguvMUR5q0tZ+HZzFsYE97CJ/FKkeH2K2Hu3LnYtGlTvvObNm3C/PnzmT8vr+E271JgdYeh0XdXWBn6NjQ1atSAj48PJBIJqlatqhD2oVq1arC2tkanTp1E61O+fHmcPXs23/mzZ8/Cy8tLtBwWsAq5kBfWxnExjZ6FhYW8cQGAsWPHonr16nJ5KSkpsLS01EofFp2LCRMmYNasWVo9t6B0YSmnWbNm2LZtGwBg0KBBqF27NrZu/X/s3XlYVGX7B/DvARdANlFQUAFxhERRAXHPQlFMc8F6Vcxk1dReWcQFNcAVcAM0y41QMJfI1DQtMKwMSATZUgEXhEkFcwkxQdnO7w9+zOs4gAMc5hzg/lwXl8wzM8/cxxlmztznOff9NWtvb88OHTpU7nlex+dK4ta6yr+5kqWNIfRt4jv5IaR4mvoFtobQniuhJTm5+P8RQsL/VUJafcmy3LyWhfY3LpRTzGsIKSGtoqLC5uXlsSwr/b3n2rVrDd4PbCpNTU328uXLMuNJSUmslpZWg+bi6rkSwnuykFamvqopidLmiKdLly6SM3D379/PDhw4kK2srGSjo6PlXlDAVSxCqyMslM9yLl/LQipjwmU8rR0lbtsIIyMjNiEhQWb80qVLzVL4/vXE7a+//ir5OXjwINu9e3fW19dXcsq9r68vq6+v/8Yat83R5Ixlm9YVtkZTP2hqSjwwDMMuW7ZMquxDYGAge+TIEfbly5dyx3Pq1Cl26NChbHJysmRbkpOT2eHDh7MnT56Uex4ucFVyoak7gVx86BkYGLDR0dEsy7Ksv78/O2LECKm6z4mJiay+vr7c28Sy3OxceHh4sNra2uyYMWPY//73v6y3t7fUjyJj4XKe5ORk9sKFCyzLsuzff//Nvvfee6yGhgZraWnJpqenyz2PkFYSt5RV/opuksEloW2T0JIfQoqHqy+wQnuuhJbkbI6/T74PQAhp9SXLcvNaFtrfuFBOMa8Lnwlpa2tr9tChQyzLSn/vWbt2LTt69OgGxdNU6urqbFpamsx4amqq3K8brp8rIbwnC2ll6qu4SJRyGc+ri1L+85//SEoOisViuc9u4yoWodURFspnOZevZSGVMeEyntauHUibUFhYCH19fZlxXV1dFBQUNPvjv/POO5Lf169fj5CQEDg6OkrGpk6dCgsLC+zbtw9OTk51zhMaGgoNDQ0AQFhYGGfxMQyDCRMmYMyYMejYsSMYhmnwHCzL1nq/x48fo1OnTm+8f0BAAADA2NgYs2bNgoqKSoNjeJWzszNKSkowbNgwtGtX/adeUVGBdu3awdXVFa6urpLbPnnyROq+kyZNwtGjR6GlpQUA2LRpEz799FNoa2tLtuntt9/G9evX5Ypl/vz5AKqf+9cxDIPKykq55rG1tUVBQQH09PSkxp8+fQpbW9s3zlOzPSzLQkNDA6qqqpLrOnTogOHDh0tircvcuXMxd+5cLF26FEVFRfjoo48kr8nnz5/js88+w9tvvy3X9syYMQNA9f+Bs7MzOnbsKLmusrISmZmZGDlypFxzZWZmYvDgwQCAq1evSl0nz+uZq1i43CYAGDJkiOR3XV1dnDt3Tu77vqqprx0u54mIiICDgwOMjIxgaGgIABCLxTA1NcWpU6fk2yAAFy9exMiRIyV/38OGDcOwYcNQUVGBixcvYsyYMfXen+vnqqnvgVwQ6jY19XXD9XYJKZ60tDTJ5/nx48fRrVs3pKWl4bvvvoO/vz8WLVok1zxCea5qBAcHS37/8MMP0atXLyQkJEAkEmHq1KkKj4eL/5+mvudwGQsAODg4IC4uDsOGDYOnpyccHR3x1VdfQSwWw9vbW+55uHqumvJaFtrfeI2mPldc7He9iqvXIBf/PwEBAfj4449x7949VFVV4cSJE8jJyUFUVBR++OEHubeJC2PHjoWnpyeOHj0KAwMDAMC9e/fg7e2NcePGyTUH18+VEN6TDxw4AKB6/3Ht2rVQU1MDAOTl5eHUqVPo168funbtKncsXH1elZSUSL47xMbGYsaMGVBSUsLw4cORn5+v8HhEIhFOnToFBwcHxMTESN4///77b2hqaiokFq7eA7l+HQvls5zL1zJX2yS0z+DWjhK3bUTNH0Dv3r2lxhMSEiQf8FyqL1H0xx9/YM+ePTLjQ4YMgbu7e73zvprUrS/B2xBVVVXYtGkT9uzZgwcPHuDGjRswMTGBn58fjI2N4ebmVu/9ud7Z5mq7mpLYjomJwcuXLyWXN2/eDEdHR0nitqKiAjk5OXLPV1VV1ehYXtXUnUAuPvSCg4MxYMAAKCkpYdy4cRg/fjx69+6N/v37Iz09HZWVlbh8+bJc28PlzsUvv/wi1+2aOxaud5i4wtUXCC7mEYlEyMzMxPnz55GdnQ2WZWFubg47O7sGHTQSwoEMgPv3wKYQ6jYJLfkhpHia+gVWaM9VDaEkObn8/xFSwh/g7sseV89VU17LQvsb5+q54jpxJpSENABMmTIF33zzDQIDA8EwDPz9/WFlZYUzZ85g/Pjxcs3xqtDQULz99tuSA9Ysy8Lb21uu/fldu3Zh2rRpMDY2Rq9evcAwDMRiMSwsLPD111/L9fhcPVdCfE9OS0tDVFQUFi5ciKKiIgwfPhzt27fHo0ePEBISovCEKxeJUi7j8ff3x5w5cySJ/hEjRkjmtLS0VEgsXL0Hcv2eI5TP8hpcvJa52iahfQa3dpS4bSPc3d3h5eWF8vJyjB07FgAQFxeHFStWwMfHh/PHY1m2zut69eqFPXv2YPv27VLje/fuRa9eveqdt7i4WO4Y5P3g27hxIyIjI7FlyxapDwQLCwuEhoa+MXHL9c52ZWUlQkNDER0dDbFYjLKyMqnrX18dW5emJIBff/7qez4VgeudwKZ86DEMg48//lhy+fLlyzhw4AD+/PNPjBo1Cm5ubrWubq8N1zsXTcFVLFzMY2lpKXcCMzU1td7rhbqSWAir/Ll6zpsjWX/y5EmMHDkS3bp1k4yFhYXBy8urRW2T0JIfQosHaPoXWKE9VzWEkuTk8u9TSAl/QFirL4GmvZaF9jfO9XPFVeJMKAnpGvb29rC3t5f79vWJiIjAmjVr0KdPH2zcuBFfffUVLl68KFfitlevXkhNTa31gHBDNfW5EuJ7spBWpgLcJEq5jOfDDz/E6NGjUVBQgEGDBknGx40bBwcHB4XE0hyro7l4zxHKZ/mr29XU1zJXn3lC+wxu7Shx20asWLECT548weLFiyWJQBUVFaxcuRKrVq1q1Jw1ybzadqB+/PFH9OjRo9b7hYaG4oMPPkBMTAyGDx8OALh06RJu376N7777rt7H1NbWljvBIe8feVRUFPbt24dx48Zh4cKFkvGBAwciOzv7jffn+oNm3bp1CA8Px9KlS+Hn54c1a9ZI5vL395d7HrFYXO/1NadoK0JtJRJe9abtao4vEFzswAGAqqoqFi9eLPft64qnMTsXM2bMwMGDB6GpqSnZMajLiRMnmjUWLueZPn265PcXL17gyy+/hLm5uWTH9tKlS7h27Zpc/+9CXEkstFX+TX3Om+MAhLOzM54/f46JEydi48aN2L9/P8LDw9+YuBXaNgkt+SG0eICmf4EV6nMllCQnF/8/Qkz4A8JafQlwk4wRyt+40E4xF2JC2sTEBMnJyejSpYvUeFFREaysrJCbmyvXPDX+/PNPvHjxAjt27ICDgwPU1dWRnJzcoDnGjx/fqNW+r2rqcyXE92QhrUwFuEmUchkPAHTv3h3du3eXGhs6dKjCY+Hye4gQyjBxvW/BxWtZCGVMmiOeVq+pRXJJy/Ls2TP28uXL7J9//sm+ePGiUXNERkayAwYMYDt27Mh27NiRtbCwYKOioho0x19//cWuWrWKdXBwYKdPn86uXr2aFYvFb7wfV03OXsVVV1iuirKbmJiwP/zwgySeW7dusSzLsjt27GAdHR3lnodhGFZJSanOn/ooKSlJFYZXV1dnc3NzJZcLCwsb1Fhg8ODBUj/9+/dn1dTUWE1NTdbS0lLuebjqWMpFEX4uNbZJgbOzM1tcXCz5vb6f5o6lueZxc3NjP/vsM5lxf39/1sXFRe55uHrtcDHPunXrWBMTE/brr79mVVVVJe8533zzDTt8+PA33p/rZhtCa5JR486dO+zSpUtZJSUlVl1dvUENdYS2TULq/CzEeAoKCtjU1FS2srJSMpaUlMRmZWXJPYdQnqvW2MVcqA1+hNbEhmWb/loW2t+4UJofcf0a5OL/h2GYWptvFRYWsh06dHjj/ffv38+eOnVKaqykpIQdPnw4a2Vlxerr67OHDx+u8/47duxgS0tLJb/X99MQXO0jC+U9mWVZ1sLCgt2xYwcrFotZTU1NNjExkWVZlk1JSWG7desm9zwsy83nFZeEFA8XsXD1HtjU17FQP8ub8lrmepuE+BncmlHiljTI9u3bWTU1NXbFihXs999/z546dYpdvnw5q6amxoaEhCg0lrFjx7JHjhyRGT98+DD7zjvvyD0PV11hufqgUVNTk3zQdO/enb1y5QrLsix7+/ZtVlNTU+550tPTpX6Sk5PZffv2sW+99Rb73Xff1XtfhmGk3kDbtWvHTpgwQXJ50qRJTe4S//TpU9bBwaFBSX+udgK53IHjgpASyVzFwtU8mpqa7I0bN2TGb9y40aC/B65eO1zM06dPH/bnn39mWVb6PScrK4vV1taWOxahHchoynvg6dOn2bi4OKmxyspKdsqUKayxsTHbuXPnBm2XELbpVUJJfgg1Hi4I5bkSapKTi/8foST8ufqyx/VzxQWh/Y1z9XfF1X6XEBLSNYtFGIZho6KiJJe///579sSJE+ynn37KmpqavjGGt956i42Pj5dcLisrY+3t7dnRo0ezT58+ZQ8dOsQOGTKkzvsbGxuzjx49kvxe10/v3r3l+S+R4Oq5Esp7MstWd6pv3749q6SkxI4fP14yHhgYyE6cOFHuWEjz4+o9sKmvY6F+ljfltczVNrXmz2Aho8QtaRBjY2M2MjJSZvzgwYOssbGx3PP8888/bExMDHvo0CE2MjJS6kdeqqqqtSZ0cnJyGvTGfvr0aVZLS4sNDg5m1dTU2K1bt7Lu7u5shw4d2NjY2AbFw8UHjampKXvp0iWWZVl29OjRbFBQEMuyLHvs2DFWV1dX7nnq8sMPP7wxsf2m1ZsNXcVZlz///JM1MjKS+/Zc7QQKbQeOi53kkpISqS8zeXl5bGhoKBsTE6PwWLicp1u3bmxERITMeEREBKunpyf3PEJaSSy0Vf5cPVdNeQ8cNGiQTOLWycmJfeutt9i//vqL3bVrFztmzJgWtU2vElryQ2jxcEFoz5VQkpw1uPj/EUrCX4irL7kitL9xrv6uuNrvEkJCmmEYyVltNb/X/HTo0IE1NTVlz5w588YYXv2/raqqYmfPns2OHz9e8lq8desWL2eBcfVcCe09WUgrU0nduHoP5Op1LLTPcpZt+mu5qdvUmj+DhYwSt6RBOnbsyN68eVNm/MaNG2zHjh3lmuP06dOshoYGq6SkxGppabHa2tqSn86dO8sdi6mpKbt06VKZ8aVLl8p1pPtVP/30EztmzBi2U6dOrKqqKjtq1CjeEl4rV65kN23axLJs9YdOu3btWJFIxHbo0IFduXJlg2KqzY0bN1g1NbUmz8OF33//vUErDLlc3SWkHTgudi7Gjx8v9WVGT09P4Udym2OeoKAgtmPHjuynn37KHjp0iD106BD76aefsqqqqpKDGvIQ0kpioa3y5+q5asp74Kv/ryzLsj4+PuygQYMkp2Dl5OQ0KKkthG16ldCSH0KLhwtCe66EkuSswcX/j9AS/kJYfck1of2NC+0UcyElpI2NjZu0GszY2Jj94osv2NLSUtbFxYV1cHBgX758Kbn+559/Zg0NDRs9f1Nw8VwJ7T2ZtAxc7lcIqQyTkF7HQipjwmU8rR0lbkmD9O/fX5JUfNWGDRvYAQMGyDVH3759WU9PT6k/9MY4e/Ysq6Kiwvbv3591c3Nj3dzc2P79+7MqKirs2bNnmzR3YzTXF9hLly6x27dvZ7///vsG3e/p06dSP0VFRWxWVhY7a9YsdtCgQY2OpzFer7UVFhbGrly5kjUwMGBnz54t9zxCWt1la2vL/vPPPzLjT58+ZW1tbRs8n1Dq4nERC9fzfPPNN+zIkSPZzp07s507d2ZHjhzJfvPNNw2aQ0griYW2yp9luXmumvIeaGBgwEZHR7MsW12/eMSIEVJ/X4mJiay+vr7csbAs/9v0KqElP4QWDxeE9lwJLcnJxf+P0BL+Qlh92RyE9DcupIMzLCvMhHRjbdu2jWUYhm3fvj1rZGTETps2jS0qKmJZlmXv3bvHDh48mHV3d5drroqKCjY8PJx1dHRkx40bx9ra2kr98EFo78mk5RDKfgXLCu+znAtCKmPCZTytHSVuSYMcP36cVVZWZu3t7dn169ezGzZsYO3t7dl27dqxJ06ckGsONTU1yQqzpmpsk7NX9e7dW1Ij6lX//PNPg+tCcfFBExgYyH711Vcy41999RUbHBws9zy1NSdjGIY1NDSUfFg0p4yMDMn/w+v1tkxMTNhhw4axq1atkjTXkoeQvkDU1ZTiwYMHbLt27RQaC8sK60iuEAltJbGQVvlzqbHvgStWrGA7dOjA9uzZk1VXV2c/+eQTtqKigmVZlv3333/ZsWPHsjNnzmzW2OsipAQwV4QWD1eE9FwJLcnJsk3//xFawl9Iqy+FRkivGy4JLSH977//smfPnmV3797dqIZgFy9eZOPj49nnz5+z48aNY9XU1Ni33nqL7dixI9u3b1+5V/R++umnbKdOndiZM2eynp6erJeXl9QPX4T0nkxIYwjxs7yphFbGpDV+BjcHStySBktJSWE/+ugj1srKirW0tGQ/+ugjNjU1Ve77Ozg4NHi1XHNqaldYrhkZGbEJCQky45cuXWpQHeFff/1V6ufixYtsVlYWW15ezmW4dVJSUpL8vzb1dLJX8f0FIiMjg83IyGAZhmF/+eUXyeWMjAw2NTWVDQwMbFDdXq4IMYnHpeTkZDYqKoo9dOgQm5KS0qg5hLaSuKmEtBPYVFVVVWxUVBT79ddfswUFBeyAAQPYXr16sRMnTmS7d+/O6urqsnfu3OE7zCYRyutGqPEIiZBWPHIVDxeE9p7TmlZfNgehvG64JKSEdGpqKtu9e3dWU1OTVVZWZnV1dVmGYdhOnTo1eOEHy1Z/Dp49e5YNDg5mDx06xJaUlMh93y5duvBytqGitMbXMmkZWuNnudDKmLTWz2CuUeKWKFx4eDhraGjIBgQEsMePH5fqxtrQcgBNaXLGVVdYrnXs2JHNzc2VGb99+7bcdYQbYtKkSez9+/c5n1dHR0fSZE1JSUlSq7Kle3Ul8+tNKRiGYdXU1GpdMd3chPaFmit//fUXO3r0aJZhGEmpBIZh2FGjRjV4db1QCG2VvxCVlJSwX3zxBbtw4UJ2w4YNzfIeRUhzaq3vyUJ6zxHa6kuiGEJ5Db7zzjvs/Pnz2YqKCkm9erFYzI4ZM4b97rvvFBqLvr4+m5OTo9DHJKQtaI2fD0IrY9Ia/4+bA8OyLAtC5KSsrIyCggLo6elJjT9+/Bh6enqorKx84xxKSkp1XscwjFxzAMCZM2fw0Ucf4fnz59DQ0ADDMFLzPHnyRK44GIbB638G7du3h7GxMbZv3473339frni40rdvXwQEBGDu3LlS44cOHUJAQAByc3M5fTwNDQ1kZGTAxMSkQfd7+fIlOnbsWOf1CxYsQFRUFPT19SEWi9GzZ08oKyvXeluut6k55efng2VZmJiY4PLly9DV1ZVc16FDB+jp6dW5nc2tsLAQBQUFGDRokOT1ffnyZWhqauKtt97iJaammjBhAoqLixEZGQkzMzMAQE5ODlxdXdGpUyfExsbyHGHDKSkpobCwUOZ99MGDBzA0NMTLly95iowQwqXW+J4sJMePH8ecOXNQWVmJcePGST4PgoKCcPHiRfz4449yz0XPFWkobW1tJCUlwczMDNra2vjjjz/Qr18/JCUlwcnJCdnZ2QqLZfv27cjNzcWuXbukvg8RQpquNX4+cLFN9BmsWJS4JQ1SV8Lh/v376NOnD0pLSxUWi6mpKSZNmoTAwECoqak1ep7evXsjOTkZXbt25TC6xtu8eTO2bt2KrVu3YuzYsQCAuLg4rFixAj4+Pli1ahWnjydP4nbatGl4++238dFHH0FfXx8PHz7EtGnTkJiYWO/cP/30E27dugUPDw+sX78eGhoatd7O09OzSdugaOXl5Zg/fz78/f0bnPAmDaOqqorExERYWlpKjaempmLUqFEKfc9pqtOnTwMApk+fjsjISGhpaUmuq6ysRFxcHM6fP4+cnBy+QiSEkBaFvuwRvujq6iIhIQGmpqYwMzPDzp07YW9vj+zsbFhZWaGkpKRZH3/GjBlSly9cuAAdHR30798f7du3l7ruxIkTzRoLIaRtos9gxWnHdwCkZdi5cyeA6tWp4eHhUFdXl1xXWVmJixcvKvyP8969e/Dw8GhS0hYA7ty5w1FE3FixYgWePHmCxYsXo6ysDACgoqKClStXcp60lZehoSHOnj0Lf39/fPLJJzh9+jQ0NTXfeL+JEycCAK5cuQJPT886E7ctTfv27fH999/D39+f71AkbG1t611lceHCBQVGwx1DQ0OUl5fLjFdUVKBHjx48RNR406dPB1D9Purk5CR13aur/AkhhMine/fu6N69u9TY0KFDeYqGtCWWlpZISUmBqakpbG1t4e/vj0ePHuHQoUOwsLBo9sd/9eAvADg4ODT7YxJCyKvoM1hxaMUtkUvv3r0BVJ8m/vop7x06dICxsTHWr1+PYcOGvXGu9evX13u9vMmwGTNmYPbs2Zg5c6Zct6/P8+fP8dtvv0EsFkuSpTU8PDyaPH9j/Pvvv8jKyoKqqir69u1bb1mCpmhIqYRjx45hzpw5UFdXR35+Pjp37twsMbUELi4usLCwwNKlS/kOBQDg7e0tdbm8vBzp6em4evUqnJycsGPHDp4ia5rvv/8egYGB+OKLL2BtbQ2GYZCSkoIlS5Zg5cqVkmRoSyK0Vf6EEEIIaZiUlBQ8e/YMtra2ePjwIZycnBAfHw+RSIQDBw5g0KBBfIdICCGklaDELWkQW1tbnDhxokkJu9dPeS4vL8edO3fQrl079OnTB6mpqXLN89VXX2H9+vWSBNrrpwVNnTpVrnnS0tIwadIklJSU4Pnz59DR0cGjR4+gpqYGPT29FlV/tTFqS9z+97//xeDBg+Hu7i4Zu337NkaPHo33338fmZmZmDx5sqBWnCrapk2bsG3bNowbNw7W1tbo1KmT1PV8Jfxft3btWvz777/Ytm0b36E0SufOnVFSUoKKigq0a1d9kkjN76//n7+prjUhhBBCiJA4OzvD1dUVY8aMafQcpaWlYFlWchZifn4+Tp48CXNzc0yYMIGrUAkhhPCEErekWWhqaiI9PV3u+p/FxcVwdnaGg4MDPv74Y7nuw1WTs3fffRempqbYvXs3tLW1kZGRgfbt22Pu3Lnw9PSUqSHV2tSWuO3VqxfOnj2LgQMHAgAKCgowatQoTJs2DaGhoTh//jwWL16Mmzdv8hU272pWodeGYRjBJPxv3bqFoUOHttikZmRkpNy3fb38gJAJcZU/IYQQQhTrgw8+wNmzZ9GrVy+4uLjAycmpwaWgJkyYgBkzZmDhwoUoKiqCmZkZOnTogEePHiEkJASLFi1qpugJIYQoAiVuSbNoyOn3Na5evYr3338feXl5zRdYLYTUFZZLz58/l1mRWJugoCAsWrQI2trakjE1NTVcvXoVJiYm+Oeff/DOO+/AwcEB69atAwDk5uZiwIABzd54gTTdoUOHsHLlSty/f5/vUMj/a+ur/F9naWkpdxdsec/IIIQQQrjWXJ9Xjx8/xtdff42DBw/i6tWrsLOzg5ubG6ZNmyZzRmFtunbtit9++w39+/dHeHg4Pv/8c6SlpeG7776Dv78/srKy5I6FEEKI8FBzMiIYRUVFePr0qcIft3379pKdsG7dukEsFqNfv37Q0tKCWCxWeDxc6datG2bOnAlXV1eMHj26ztvV1vDsrbfewsaNGzFnzhysWLEC06dPlyRtASAhIQFGRkbNEndLVHP8S96d+ebw+spwlmVRUFCAlJQU+Pn58RRV073pb9DQ0FBBkXDH29sbU6ZMkazyv3TpktQq/7amJdYpJoQQ0va8+nn14sULfPnllzA3N8eIESMAAJcuXcK1a9ewePHiBs3bpUsXeHp6wtPTE2lpaYiIiMDHH38MdXV1zJ07F4sXL0bfvn3rvH9JSYmkAXBsbCxmzJgBJSUlDB8+HPn5+Q3fUEIIIYJCiVuicDt37pS6XJNgOnToECZOnCj3PFw1OeO7K2xzOXr0KA4ePIhx48bByMgIrq6umDdvHgwMDN5438DAQHzwwQf47rvvMGHCBBw9ehSjRo3C4MGDcfHiRfj4+Mg0w2qLoqKisHXrVknJCFNTUyxfvlzuch9cer27sJKSEszMzLB+/foWXd/M2Ni43oS4vCVRhCQ9PR179+6FsrIylJWV8fLlS5iYmGDLli1wcnJq9eVZXhcQEMB3CIQQQsgbvfp55e7uDg8PD2zYsEHmNn/99Vej5i8oKEBsbCxiY2OhrKyMSZMm4dq1azA3N8eWLVvq3PcWiUQ4deoUHBwcEBMTI7nd33//DU1NzUbFQgghRDioVAJpFq+XSsjMzMSAAQOgpKQkUxtUSUkJurq6GDt2LFatWiU5YvwmXDU5a+1dYR8/foyoqCgcPHgQ169fh729PVxdXTF16lRJs6favHz5EgDQsWNHrFu3DsHBwSgrKwPLspg9ezaioqLqvX9rFxISAj8/P/z3v//FqFGjwLIsEhIS8MUXX2Djxo2U2OZIRkaG1OXy8nKkpaUhJCQEmzZtapFJTl1dXSQkJMDU1BRmZmbYuXMn7O3tkZ2dDSsrKypBQgghhAiclpYWUlJSZFbC3rx5E0OGDJH7LMLy8nKcPn0aBw4cQGxsLAYOHAh3d3d89NFHku9Ex44dw6JFi/DPP//UOsfx48cxZ84cVFZWYty4cYiNjQVQXQ7t4sWL+PHHH5uwpYQQQvhGiVvSLF5vTqasrIyCggLo6emhd+/eSE5ORteuXTl/3MY0OWtLPv/8cyxfvhxlZWXo2rUrFi5cCF9fX0kX2voUFRUhJycHPXr0QM+ePRUQrbD17t0b69atw7x586TGIyMjsXbtWty5c4enyKrl5uaitLQU/fr1q7eRX0t19uxZbN26Fb/++ivfoTTYhAkT4OzsjDlz5mDhwoVIS0uDh4cHDh06hH/++QdJSUl8h6hQOjo6uHHjBrp27YrOnTvXu8K6pTbZI4QQ0rp0794dQUFBcHFxkRo/cOAAfH198eDBA7nm6dq1K6qqquDo6Ij58+dj8ODBMrf5559/YGVlVe++ZWFhIQoKCjBo0CDJft/ly5ehqamJt956S/4NI4QQIjhtd7kcaVavHw/Q1tbGnTt3oKenB7FYLHM9VzQ1NbF+/Xq8//77lLj9f4WFhYiKisKBAwcgFovx4Ycfws3NDffv30dwcDAuXbokOTJfH21tbQwbNkwBEbcMBQUFGDlypMz4yJEjUVBQoLA4ysvLsXHjRqSmpmL48OHw9fXF3LlzER0dDQAwMzPDuXPnYGxsrLCYFMHU1BTJycl8h9EogYGBePbsGQBgw4YNcHJywqJFiySr/Nua0NBQyaqisLAwfoMhhBBC5ODl5YVFixbhypUrGD58OIDqGrcRERFyl2sDqj8D//Of/0BFRaXO23Tu3PmNCwK6d++O7t27S40NHTpU7jgIIYQIF624JU1SWVmJP//8E0ZGRujcubNkPD4+HjY2NujYsSMAYMGCBYiKioK+vj7EYjF69uwJZWXlWudsakf1+Ph4TJkypc7TiYC20cX8xIkTOHDgAGJiYmBubg53d3fMnTsX2trakttcu3YNlpaWKCsr4y/QFmrAgAGYM2cOVq9eLTW+ceNGfPPNN/jzzz8VEoePjw8OHTqEqVOn4pdffsGAAQOQk5ODdevWQUlJCRs2bICFhQUOHz6skHi4VlxcLHW5pib22rVrkZ2djfT0dH4CI4QQQkibFh0djR07diArKwsA0K9fP3h6emLmzJkKjyU5ORnffvstxGKxzH79iRMnFB4PIYQQ7tCKW9IgXl5esLCwgJubGyorK/HOO+8gMTERampq+OGHH/Duu+8CAEaPHi11v3379mHGjBm4desWPDw8MH/+fLlr2dalKU3OmqsrrJC4uLjA0dERCQkJsLGxqfU2JiYmWLNmjYIjax3WrVuHWbNm4eLFixg1ahQYhkF8fDzi4uIkq10V4fjx4zh48CAmTZqEGzdu4K233sLZs2fx3nvvAQD09PTw0UcfKSwermlra8scZGFZFr169cKxY8d4ioooQmlpKcrLy6XGqMkKIYQQoZg5c2aTk7R11epnGAYqKioQiUSYM2cOzMzM6pzj2LFjmDdvHiZMmIDz589jwoQJuHnzJgoLC+Hg4NCk+AghhPCPVtySBunZsydOnTqFIUOG4NSpU/j000/xyy+/ICoqCr/88gsSEhLeOIeLiwt27tzZqMRtczQ5c3d3h76+fp1dYSMiIhocJ98qKiokyfLXT5si3Lly5QpCQ0ORlZUFlmVhbm4OHx8fmcZ5zal9+/bIy8tDjx49AACqqqrIzMyUNMsoKChAr169UFFRobCYuPTbb79JXa75OxeJRC2qOV5bWOXPhefPn2PlypWIjo7G48ePZa6vrKzkISpCCCGkdikpKcjKygLDMOjXrx+sra0bdH9nZ2ecOnUK2trasLa2BsuySEtLQ1FRESZMmICMjAzk5eUhLi4Oo0aNqnWOgQMH4pNPPsGnn34qaRDdu3dvfPLJJ9DX18e6deu42FRCCCE8ocQtaRAVFRXcunULPXv2xIIFC6CmpoawsDDcuXMHgwYNkjmtmWvN0eSMq66wQqOmpoasrCwYGRnxHQppRkpKSigsLISenh4ASHbYaxoDPnjwAAYGBq0+4TV58mSEh4dDX1+f71Bq9eqXpjet8g8KCuIrTN7VHAxcv3495s2bhy+++AL37t3D3r17ERwc3KJXjxNCCGk97t69KzmzraYMWVFREUaOHImjR4+iV69ecs3j6+uL4uJi7Nq1S9JUrKqqCp6entDQ0MCmTZuwcOFCXLt2DfHx8bXO0alTJ1y7dg3Gxsbo2rUrfvnlF1hYWCArKwtjx45VaO8FQggh3Gs5y5WIIHTr1g3Xr1+Hvr4+fvrpJ3z55ZcAgJKSkjpr1nKpOZqcqaqqIj4+XiZxGx8fX2+jAKEbNmwY0tLSOEnclpSU1Foza+DAgU2euyWrrKzEyZMnpVZaTJs2TeErQWNiYqClpQWgemc/Li4OV69eBVD9JaItuHjxIkpLS/kOo04BAQGS393d3eHh4VHnKv+27MyZM4iKisK7774LV1dXvP322xCJRDAyMsLhw4cpcUsIIUQQXF1dUV5ejqysLEkZg5ycHLi6usLNzU2uxr8A8NVXXyEhIUGStAWqD8ovWbIEI0eORGBgIP773//i7bffrnMOHR0dSdPTHj164OrVq7CwsEBRURFKSkqasJWEEEKEgBK3pEFcXFwwc+ZM6Ovrg2EYjB8/HgCQlJSEt956q9kf/4MPPsA777wjWVU3ZMiQJjc546orrNAsXrwYPj4+uHv3LqytrdGpUyep6+VJuj58+BAuLi748ccfa72+ta/irM/Vq1cxbdo0FBYWSnbYb9y4AV1dXZw+fRoWFhYKi8XJyUnq8ieffCJ1Wd5T9IlifPvtt0hJSZEZnzt3LoYMGdIiy7Nw5cmTJ5IyOJqamnjy5AmA6rrpixYt4jM0QgghROL3339HYmKiVO1ZMzMzfP7553WWNKhNRUUFsrOzYWpqKjWenZ0t2c9WUVGpd1/u7bffxvnz52FhYYGZM2fC09MTFy5cwPnz5zFu3LgGbhkhhBChocQtaZC1a9diwIAB+Ouvv/Cf//wHHTt2BFBdwsDX17fZH785mpz5+vrCxMQEO3bswJEjRwBUd4U9ePAgL11huTJr1iwAgIeHh2SMYRiwLAuGYeRKunp5eeGff/7BpUuXYGtri5MnT+LBgwfYuHEjtm/f3myxtwTu7u7o378/UlJS0LlzZwDAP//8A2dnZyxYsAB//PGHQuKoqqpSyOMQ7rTWVf5cMDExQV5eHoyMjGBubo7o6GgMHToUZ86ckZyKSgghhPDN0NBQpoEmUJ2Irek7II+PP/4Ybm5uWL16NWxsbMAwDC5fvozAwEDMmzcPQHW9//79+9c5x65du/DixQsAwKpVq9C+fXvEx8djxowZ8PPza+CWEUIIERqqcUsa5M6dOzJNwfjSlCZnbUF+fn6918tTQkFfXx/ff/89hg4dCk1NTaSkpMDU1BSnT5/Gli1b6qy11RaoqqoiJSVFZkf66tWrsLGxEexp+0KvBdtYr9f2FbLg4GCsXbsW7u7uta7yV8RBMKEKDQ2FsrIyPDw88Msvv2Dy5MmorKxERUUFQkJC4OnpyXeIhBBCCL7//nsEBgbiiy++gLW1NRiGQUpKCpYsWYKVK1di+vTpcs1TWVmJ4OBg7Nq1Cw8ePABQXZquZh5lZWWIxWIoKSmhZ8+eUvcdM2YMTp8+LTmwefr0aYwfPx6qqqpcbiohhBCeUeKWNIiysjLGjBkDNzc3fPjhh61qdVhTu8K2RpqamsjMzISxsTGMjY1x+PBhjBo1Cnfu3EH//v3bdN2swYMHIyQkBGPHjpUav3DhAjw9PfHnn3/yFFn9WlKCsyFa2nZFR0djx44dyMrKAlC9yt/T07NFr/JvDmKxGCkpKejTpw8GDRrEdziEEEIIAKBz584oKSlBRUWFpLdBze+vlyerKfvzuoqKChw+fBj29vbo3r27pMmzpqamXDG83qBWU1MT6enpLWZfiBBCiHyoVAJpkIyMDERERMDHxwf//e9/MWvWLLi5uWHo0KF8h9ZoXHWFFaLbt28jLCxMKiHt6emJPn36yHV/MzMz5OTkwNjYGIMHD8bevXthbGyMPXv2tLoVmw0VGBgIDw8PrF27VmrV5Pr167F582bJzjcg/w44aTtmzpxJSdpaREVFYdasWZIyPIaGhjA0NERZWRmioqIkp40SQgghfAoLC2vyHO3atcOiRYskB3Gbur9I67EIIaR1ohW3pFEqKipw5swZHDx4ED/++CP69u0LNzc3fPzxx9DV1eU7vAaZMGECiouLERkZKdMVtlOnTnJ3hRWamJgYTJ06FYMHD8aoUaPAsiwSExORkZGBM2fOSBrL1efw4cMoLy+Hs7Mz0tLSYG9vj8ePH6NDhw44ePCgpI5uW/Rq99+ahhE1b6evXpa3nrCitLSVqc+fP5dZuVKboKAgLFq0qEXVQaVV/rKUlZVRUFAgWT1U4/Hjx9DT0xPU3xIhhBDSVLa2tvD09JS7tMKrXl9x29L28QghhMiHErekSV6+fIkvv/wSq1atQllZGdq3b49Zs2Zh8+bNLWZFpqqqKhITE2FpaSk1npqailGjRgm2VumbWFpawt7eHsHBwVLjvr6+iI2NRWpqaoPnLCkpQXZ2NgwNDdG1a1euQm2RfvvtN7lv+8477zRjJA3T0nbq1dXVMXPmTLi6umL06NF8h8OJ1rzKv6mUlJTw4MEDmQOAGRkZsLW1rfN0U0IIIUSRxGJxvdcbGhrKNc+3334LX19feHt7w9raWuZg9cCBA+u8r5KSEiIjI6GlpQUAcHR0RFhYGLp16yZ1u6lTp8oVCyGEEGGixC1plJSUFERERODYsWPo1KkTnJyc4Obmhvv378Pf3x/Pnj3D5cuX+Q5TLmZmZjh06JBMuYfLly9jzpw5uHXrFk+RNY2Kigr+/PNPmc71N27cwMCBAyXdZ0nb0tIStzUr+3/44QcYGRnB1dUV8+bNg4GBAd+hNVprXeXfFJaWlmAYBhkZGejfv7+kXiBQ3bjlzp07mDhxIqKjo3mMkhBCCKmmpKQkOcOqNvKeIfLqGVw1GIaR66yt2u5b21x0tgohhLRsVOOWNEhISAgOHDiAnJwcTJo0CVFRUZg0aZJkx6F3797Yu3cv3nrrLZ4jld+WLVuwZMkSma6wnp6e2LZtG9/hNZquri7S09NlErfp6ekypyG/aunSpdiwYQM6deqEpUuX1vsYISEhnMTaEv30009QV1eXrAL94osvsH//fpibm+OLL75A586deY6wdZgyZQqmTJmCx48fIyoqCgcPHoSfnx/s7e3h6uqKqVOnSiX5WoLff/8diYmJkqQtUH0A6fPPP8eoUaN4jIw/NaeIpqenw97eHurq6pLrOnToAGNjY3zwwQc8RUcIIYRIS0tLk7pcXl6OtLQ0hISEYNOmTXLPc+fOnUbHUFVV1ej7EkIIaTloxS1pkL59+8LV1RUuLi7o3r17rbcpKyvD0aNH4eTkpODoGoeLrrBCtH79eoSGhsLX1xcjR44EwzCIj4/H5s2b4ePjg88++6zW+9na2uLkyZPQ1taGra1tnfMzDIMLFy40V/iCZ2Fhgc2bN2PSpEn4888/MWTIEPj4+ODChQvo168fDhw4wHeItWqJtWBf9/nnn2P58uUoKytD165dsXDhQvj6+kJNTY3v0OTSWlf5cyEyMhKzZs2CiooK36EQQgghDXb27Fls3boVv/76K9+hyJg8eTLCw8NbTDk7Qggh1ShxS9q8yMhIuW/bUpLRQHVjrLCwMGzfvh33798HABgYGGD58uXw8PCo9/Qu8mbq6uq4evUqjI2NsXbtWly9ehXHjx9HamoqJk2ahMLCwmaP4fTp03LftqXXNyssLERUVBQOHDgAsVgMBwcHSXmW4OBg6Ovrt5gSA99//z0CAwNlVvkvWbIEK1eubFSDktakqKgIx48fx+3bt7F8+XLo6OggNTUV3bp1Q48ePfgOjxBCCKnTzZs3MXjwYDx//lzu+xw6dAh79uzBnTt38Mcff8DIyAhhYWHo3bs3pk2bxllsLa1cFiGEkGqUuCWNUlJSArFYjLKyMqnx+groE/48e/YMQPUOG+GGjo4O4uPjYW5ujtGjR2PevHlYsGAB8vLyYG5ujpKSkmaP4fXaZjU10V69XKOl1jc7ceIEDhw4gJiYGJibm8Pd3R1z586VWjF87do1WFpayrwfCVVrXeXPhczMTNjZ2UFLSwt5eXnIycmBiYkJ/Pz8kJ+fj6ioKL5DJIQQQlBcXCx1mWVZFBQUYO3atcjOzkZ6erpc8+zevRv+/v7w8vLCpk2bcPXqVZiYmODgwYOIjIzEL7/8wlnMlLglhJCWqWUVBiS8e/jwIZydnfHTTz/Ven1LTA5x1RVWyBqSsJ0xY4bctz1x4kRjwmkVRo8ejaVLl2LUqFG4fPkyvvnmGwDVzd969uypkBherW32888/Y+XKlQgMDMSIESPAMAwSExPx2WefITAwUCHxNAcXFxc4OjoiISEBNjY2td7GxMQEa9asUXBkjRcWFsZ3CILl7e0NZ2dnbNmyRep967333sOcOXN4jIwQQgj5H21tbZmz11iWRa9evXDs2DG55/n888+xf/9+TJ8+HcHBwZLxIUOGYNmyZZzFSwghpOWixC1pEC8vLxQVFeHSpUuSWqgPHjzAxo0bsX37dr7DaxRjY2NOusIKTU2X9tcxDAMVFRWIRCI4OzvL1LHV0tKS/M6yLE6ePAktLS0MGTIEAHDlyhUUFRU1KMHbGu3atQuLFy/G8ePHsXv3bskp3D/++CMmTpyo8Hi8vLywZ88eSbM0ALC3t4eamhoWLFiArKwshcfUVBUVFQgKCsKMGTPqrKkNAKqqqggICFBgZE3TkkquKFpKSgr27dsnM96jRw+FlB8hhBBC5PH6SlglJSXo6upCJBI1qGnqnTt3YGlpKTPesWPHBpVbIIQQ0npR4pY0yIULF/D999/DxsYGSkpKMDIywvjx46GpqYmgoCBMnjyZ7xAbjKuusEIzceJE7N69GxYWFhg6dChYlkVKSgoyMzPh7OyM69evw87ODidOnJCqn/VqU62VK1di5syZ2LNnD5SVlQFUJ7IXL14MTU1NhW+TkBgaGuKHH36QGQ8NDeUhGuD27dtSSfcaNaect0Tt2rXDsmXLWuT7Sn3awir/xlJRUZE5/RQAcnJyoKury0NEhBBCiKx33nlHrtu9qSFY7969kZ6eDiMjI6nxH3/8Eebm5k2OkxBCSMtHiVvSIM+fP4eenh6A6hqfDx8+hKmpKSwsLJCamspzdI0zaNAgmbEhQ4bAwMAAW7dubbErSx89egQfHx/4+flJjW/cuBH5+fmIjY1FQEAANmzYUGfjg4iICMTHx0uStgCgrKyMpUuXYuTIkdi6dWuzboPQVVVV4datW/j777+lyhYAwJgxYxQai42NDby8vPD1119LvhwUFhbCx8cHQ4cOVWgsXBo2bBjS0tJkvtC0ZK11lT8Xpk2bhvXr1yM6OhpA9RkCYrEYvr6++OCDD3iOjhBCCGmYixcvorS0tM7rly9fjk8//RQvXrwAy7K4fPkyjh49iqCgIISHhyswUkIIIUJFiVvSIGZmZsjJyYGxsTEGDx6MvXv3wtjYGHv27KnzSHJLZWpqiuTkZL7DaLTo6GhcuXJFZnz27NmwtrbG/v374ejoiJCQkDrnqKioQFZWFszMzKTGs7KyZBKVbc2lS5cwZ84c5Ofn4/UejwzDKDz5FhERAQcHBxgZGUlWbIrFYpiamuLUqVMKjYVLixcvho+PD+7evQtra2uZ5l0tsSFia13lz4Vt27Zh0qRJ0NPTQ2lpKd555x0UFhZi+PDhbf7/hhBCSOvj4uKCiooKrFixAiUlJZgzZw569OiBHTt2YPbs2Zw+1urVq6Gjo8PpnIQQQpofJW5Jg3h5eaGgoAAAEBAQAHt7e3z99dfo0KEDIiMjeY6ucerrCtu3b1+eomo6FRUVJCYmQiQSSY0nJiZCRUUFQPWK0Y4dO9Y5h4uLC1xdXXHr1i0MHz4cQHXCMjg4GC4uLs0XfAuwcOFCDBkyBGfPnoW+vn69KygVQSQSITMzE+fPn0d2djZYloW5uTns7Ox4j60pZs2aBQDw8PCQjDEMA5ZleUmQc6G1rvLngqamJuLj43HhwgWkpqaiqqoKVlZWsLOz4zs0QgghpFnMnz8f8+fPx6NHj1BVVSU5u7E+p0+flnv+qVOnAgBWrVrV6BgJIYTwh2FfXypGSAOUlJQgOzsbhoaG6Nq1K9/hNIqSklK9XWFHjBjBU2RNs3HjRgQGBmL+/PmwsbEBwzC4fPkywsPDsXr1aqxZswahoaE4d+4czp8/X+scVVVV2LZtG3bs2CFJ2Ovr68PT0xM+Pj5SJRTamk6dOiEjI0MmMS4EL168QMeOHVt0wrZGfn5+vde3phIKN2/exODBg6kZSS1SU1Ph7+9fa11pQgghRKg0NDSQkZEBExMTTudVUlKSulxzUPvVyzVa4kFuQggh/0OJW/JGS5culfu29Z12L1S//fab1OXGdoUVosOHD2PXrl3IyckBUF3qYsmSJZgzZw4AoLS0FAzDSFbg1qdmZXJbb0pWY+zYsVixYgUmTpzIdygAqpPsmzZtwp49e/DgwQPcuHEDJiYm8PPzg7GxMdzc3PgOkfy/+lb5Z2dnIz09nZ/AeHb+/HnExsaiffv2cHd3h4mJCbKzs+Hr64szZ85g/Pjx+Omnn/gOkxBCCJHbmxK3Dx48wLJlyxAXF4e///5bpvyWPEnXn3/+GStXrkRgYCBGjBgBhmGQmJiIzz77DIGBgRg/fjwn20IIIYQfLTsrRRTi9XqMV65cQWVlpaTu6Y0bN6CsrAxra2s+wmsyrrrCCtFHH32Ejz76qM7rVVVV3zhHRUUFfv31V9y+fVuS8L1//z40NTWhrq7OWawtzZIlS+Dj44PCwkJYWFigffv2Utcruvbqxo0bERkZiS1btmD+/PmScQsLC4SGhrboxO3t27cRFhaGrKwsMAyDfv36wdPTE3369OE7tEbR1taud5V/WxQZGQkXFxfo6OjgyZMnCA8PR0hICBYvXowPPvgAGRkZGDBgAN9hEkIIIZxydnaGWCyGn59fo0tveXl5Yc+ePRg9erRkzN7eHmpqaliwYAGysrK4DJkQQoiC0Ypb0iAhISH49ddfERkZic6dOwMA/vnnH7i4uODtt9+Gj48PzxE2n+Y61ak5FRUV4fjx48jNzcWyZcugo6OD1NRUdOvWDT169Hjj/fPz8zFx4kSIxWK8fPlSsorTy8sLL168wJ49exSwFcL0+ilqAL+1V0UiEfbu3Ytx48ZJvVazs7MxYsQI/PPPPwqNhysxMTGYOnUqBg8ejFGjRoFlWSQmJiIjI0OyCrOlac2r/Btr8ODBmD17Nnx9fREdHY3Zs2fD0tIS0dHRLTZBTwghpPV6/vy5TMPU2gQFBWHRokXQ1tau9XoNDQ38/vvvGDx4cKNjUVVVxeXLl2FhYSE1npmZiWHDhqG0tLTRcxNCCOEfJW5Jg/To0QOxsbHo37+/1PjVq1cxYcIE3L9/n6fIml9LS9xmZmbCzs4OWlpayMvLQ05OjuTU+fz8fERFRb1xjunTp0NDQwNfffUVunTpItn+3377De7u7rh586YCtkSYhFZ7VVVVFdnZ2TAyMpJ6rV6/fh1Dhw7Fv//+q9B4uGJpaQl7e3sEBwdLjfv6+iI2Nhapqak8Rdb8WuIq/8bS0NBAZmYmevfuLWma+PPPP8t9RgQhhBCiSOrq6pg5cyZcXV2lVro2lLm5OQ4fPgxLS8tGzzFmzBi0b98eX3/9tWSfobCwEB9//DHKyspkDhgTQghpWWSXjBFSj+LiYjx48EBm/O+//8azZ894iIjUZenSpXB2dsbNmzelati+9957uHjxolxzxMfH47PPPkOHDh2kxo2MjHDv3j1O421pjIyM6v1RtP79++P333+XGf/222+b9GWAb1lZWbWWeXB1dcX169d5iEhxLl682GZWyby6cklJSQkqKiro1asXz1ERQgghtTt69CiePn2KcePGwdTUFMHBwY1awBIWFgZfX1/k5eU1OpaIiAj8/fffMDIygkgkgkgkgqGhIQoKCvDVV181el5CCCHC0DbPySSN5uDgABcXF2zfvh3Dhw8HAFy6dAnLly/HjBkzeI6OvCo5ORl79+6VGe/RowcKCwvlmqOqqqrWU/7v3r0LDQ2NJsfYGly/fh1isRhlZWVS41OnTlVoHAEBAfj4449x7949VFVV4cSJE8jJyUFUVBR++OEHhcbCJV1dXaSnp6Nv375S4+np6dDT0+MpKtIcYmJioKWlBaD6vScuLg5Xr16Vuo2i/64IIYSQ2kyZMgVTpkzB48ePERUVhYMHD8LPzw/29vZwdXXF1KlT5Sp/NGvWLJSUlKBPnz5QU1OT6Znw5MmTN84hEomQmZmJ8+fPIzs7GyzLwtzcHHZ2do2qmUsIIURYKHFLGmTPnj1YtmwZ5s6di/LycgBAu3bt4Obmhq1bt/IcHXmVioqKTPd6AMjJyYGurq5cc4wfPx5hYWHYt28fgOoarv/++y8CAgIwadIkTuNtaXJzc+Hg4IA///xTUtsWgGQHWdE1bqdMmYJvvvkGgYGBYBgG/v7+sLKyarF1YGvMnz8fCxYsQG5uLkaOHAmGYRAfH4/Nmze36prabZGTk5PU5U8++UTqMh+1owkhhJD6dOnSBd7e3vD29sbnn3+O5cuX49y5c+jatSsWLlwIX19fqKmp1Xn/sLAwTuJgGAYTJkzAmDFj0LFjR0rYEkJIK0I1bkmjPH/+HLdv3wbLshCJRHIV52/pWlqN2wULFuDhw4eIjo6Gjo4OMjMzoaysjOnTp2PMmDFy7Sjev38ftra2UFZWxs2bNzFkyBDcvHkTXbt2xcWLF9v0iscpU6ZAWVkZ+/fvh4mJCS5fvozHjx/Dx8cH27Ztw9tvv813iK0Cy7IICwvD9u3bJacgGhgYYPny5fDw8GjVX0xa2nsOIYQQ0tYUFhYiKioKBw4cgFgshoODA9zc3HD//n0EBwdDX18fsbGxzRpDVVUVNm3ahD179uDBgweSZsJ+fn4wNjauteQUIYSQloMSt6TN46orrNAUFxdj0qRJuHbtGp49ewYDAwMUFhZixIgROHfunNzJ9tLSUhw9ehSpqamoqqqClZUVPvroI6iqqjbzFghb165dceHCBQwcOBBaWlq4fPkyzMzMcOHCBfj4+CAtLU2h8ZiYmCA5ORldunSRGi8qKoKVlRVyc3MVGk9zqKmj3VbKdFDitm5tqXEbIYQQ4Tlx4gQOHDiAmJgYmJubw93dHXPnzpX6nnDt2jVYWlrKlNMCgOjoaEyfPl3SRyIvLw+9evWCsrIyAKCkpAS7du3CihUr3hjL+vXrERkZifXr12P+/Pm4evUqTExMEB0djdDQUPzxxx/cbDQhhBBeUOKWtHlcdYUVqgsXLkglXe3s7PgOqVXo3Lkzrly5AhMTE/Tp0wfh4eGwtbXF7du3YWFhgZKSEoXGo6SkhMLCQplV0A8ePIChoSFevnyp0HhI01Hitm70f0MIIYRPWlpacHR0hJubG2xsbGq9TWlpKbZs2YKAgACZ65SVlVFQUCDZb9PU1ER6errkc+3BgwcwMDCQq0SQSCTC3r17MW7cOKnPx+zsbIwYMQL//PNPE7aUEEII36jGLWnzjh49ioMHD2LcuHEwMjKCq6sr5s2bBwMDA75Da5KoqCjMmjULY8eOxdixYyXjZWVlOHbsGObNm1fnfS9evCjXY4wZM6bJcbZUAwYMQGZmJkxMTDBs2DBs2bIFHTp0wL59+xSaTDp9+rTk91ebOwHVdXbj4uJgbGyssHi4ZmlpWWs5BIZhoKKiApFIBGdnZ9ja2vIQXePIu8p/9erV0NHRUUBEhBBCCJFXRUUFgoKCMGPGDHTv3r3O26mqqtaatAWA19dONWUt1b179yASiWTGq6qqJD1JCCGEtFy04paQ//dqV9jr1683uCus0Lx+JL/G48ePoaenV+8RfCUlJUmyrK63iLbeKCgmJgbPnz/HjBkzkJubi/fffx/Z2dno0qULvvnmG6lkeXNSUlICAKkGaTXat28PY2NjbN++He+//75C4uHaqlWrsHv3blhYWGDo0KFgWRYpKSnIzMyEs7Mzrl+/jri4OJw4cQLTpk3jO1y5tPZV/opAK24JIYTwSU1NDVlZWTAyMmrU/V8/U+r1z7WGrLgdMmQIvLy8MHfuXKl51q1bh59//hm///57o2IkhBAiDC0vG0VIM2lqV1ihYVm21pWKd+/elVqVWZvOnTtDQ0MDzs7O+Pjjj9G1a9fmCrPFsre3l/xuYmKC69ev48mTJ+jcubNCG2ZVVVUBAHr37o3k5ORW91w9evQIPj4+8PPzkxrfuHEj8vPzERsbi4CAAGzYsKHFJG5b6yp/QgghpK0YNmwY0tLSGp245VJAQAA+/vhj3Lt3D1VVVThx4gRycnIQFRWFH374ge/wCCGENBGtuCXk/wmhKywXak4tz8jIQP/+/aVWC1dWVuLOnTuYOHEioqOj65yjrKwMJ0+eREREBH7//XdMmjQJbm5umDhxokKTkkJVUVEBFRUVpKenY8CAAXyH06ppaWnhypUrMqcA3rp1C9bW1nj69Cmys7NhY2MjaV7WUrS2Vf6KRCtuCSGE8Onbb7+Fr68vvL29YW1tLVMCaeDAgfXeX0lJCZGRkZLFFI6OjggLC0O3bt0AVDeXdXFxkfvstpiYGAQGBuLKlSuSvhb+/v6YMGFCI7aOEEKIkFDilrR5Te0KKzTr1q2T/Ovj4wN1dXXJdR06dICxsTE++OADSRfbN/nrr79w4MABREZG4uXLl3BycsK6devafGKpT58+OHHiBAYNGsR3KBLPnz/Hb7/9BrFYLPNa9fDw4CmqpunWrRu2bt0qU5M5KioKy5cvx4MHD3D9+nWMGTMGjx494inKpqtZ5V9WVtZiV/krEiVuCSGE8KmmVNWraspWyVNOrLb71zZfWy5LRgghpBolbkmb19SusEIVGRmJWbNmQUVFhZP57ty5Azc3N/z22294+PBhm2+adODAAXz77bf4+uuvBfF/kZaWhkmTJqGkpATPnz+Hjo4OHj16BDU1Nejp6SE3N5fvEBtl48aNCAwMxPz582FjYwOGYXD58mWEh4dj9erVWLNmDUJDQ3Hu3DmcP3+e73AbpLWs8udDUFAQFi1aJHWAjRBCCFGU/Pz8eq9XZAkFExMTJCcno0uXLlLjRUVFsLKyarH7gIQQQqpR4pa0aRUVFdi3b98bu8K2VS9fvsR3332HiIgI/PHHH5g8eTJcXV0xceJEvkPjzcWLFzFixAgMHToUt27dQnl5OYyMjGROkUtNTVVoXO+++y5MTU2xe/duaGtrIyMjA+3bt8fcuXPh6emJGTNmKDQeLh0+fBi7du1CTk4OAMDMzAxLlizBnDlzAFQfWGEYhrODFM2tta3yb6rTp0/LfdupU6c2YySEEEKIME2ePBnh4eHQ19eXue71Rmc1Hjx4AENDQ7x8+VJRYRJCCGkGlLglbV5Tu8IKVWVlJUJDQxEdHV3rqfNPnjyp876XL1/GgQMHcOzYMfTu3RvOzs6YO3euIFaW8k1ZWRkFBQXYvXt3vbdT9OpsbW1tJCUlwczMDNra2vjjjz/Qr18/JCUlwcnJCdnZ2QqNh9Stta7yb6zXTxetOdX01cs16JRRQgghQnH79m2EhYUhKysLDMOgX79+8PT0RJ8+fTh/rNpKBNUc+Jw+fbpUvVyg+vMyLi4O58+flxz4JoQQ0jK17SKVhEBYXWG5tG7dOoSHh2Pp0qXw8/PDmjVrkJeXh1OnTsHf37/e+w4fPhyGhobw8PCAtbU1ACA+Pl7mdm1x9VtNQkloCbX27dtLElzdunWDWCxGv379oKWlBbFYzHN0TVNUVITjx48jNzcXy5Ytg46ODlJTU9GtWzf06NGD7/AapKKiAkFBQW9c5a+qqiq411hzqaqqkvz+888/Y+XKlQgMDMSIESPAMAwSExPx2WefITAwkMcoCSGEkP+JiYnB1KlTMXjwYIwaNQosyyIxMRH9+/fHmTNnMH78+GaPYfr06QCqD3A6OTlJXde+fXsYGxtj+/btzR4HIYSQ5kUrbkmb19SusELVp08f7Ny5E5MnT4aGhgbS09MlY5cuXcKRI0fqvC81TKibkpISHjx4AF1dXb5DkTJhwgQ4Oztjzpw5WLhwIdLS0uDh4YFDhw7hn3/+QVJSEt8hNkpmZibs7OygpaWFvLw85OTkwMTEBH5+fsjPz0dUVBTfITZYa13lz4UBAwZgz549GD16tNT477//jgULFiArK4unyAghhJD/sbS0hL29PYKDg6XGfX19ERsby3nJrPqacvbu3RvJycno2rUrp49JCCFEGChxS9q8pnaFFapOnTohKysLhoaG0NfXx9mzZyUNCiwtLfH06VO+Q2yRlJSUsGDBAqipqdV7u5CQEAVFVC0lJQXPnj2Dra0tHj58CCcnJ8THx0MkEuHAgQMYNGiQQuPhip2dHaysrLBlyxapLy2JiYmYM2cO8vLy+A6xwWxtbeHp6SlZKUP+R1VVFZcvX4aFhYXUeGZmJoYNG4bS0lKeIiOEEEL+R0VFBX/++Sf69u0rNX7jxg0MHDgQL1684PTx6kvcEkIIad2oVAJp8+7cucN3CM2iZ8+eKCgogKGhIUQiEWJjY2FlZYXk5GR07NiR08eqr2FCa/Tnn3+iQ4cOdV7/ak1ORRkyZIjkd11dXZw7d07hMTSH5ORk7N27V2a8R48eKCws5CGiplu8eDF8fHxw9+7dVrXKnws2Njbw8vLC119/LXk/KSwshI+PD4YOHcpzdIQQQkg1XV1dpKenyyRu09PTZZqEKcLz58/x22+/1drXwsPDQ+HxEEII4Q4lbkmb11pPV3ZwcEBcXByGDRsGT09PODo64quvvoJYLIa3tzenj3Xx4sU2tRLu5MmTvOyUt0UqKiooLi6WGc/JyRFcuQp5zZo1C4D0F6nWsMqfCxEREXBwcICRkREMDQ0BAGKxGKampjh16hS/wRFCCCH/b/78+ViwYAFyc3MxcuRIMAyD+Ph4bN68GT4+PgqNJS0tDZMmTUJJSQmeP38OHR0dPHr0CGpqatDT06PELSGEtHBUKoEQKLYrLF+SkpKQkJAAkUjEeVOxtnT6lrKyMgoKCgSRuLW0tJR7dS/XtdYUZcGCBXj48CGio6Oho6ODzMxMKCsrY/r06RgzZgzCwsL4DrHB8vPz672+tR5MkhfLsjh//jyys7PBsizMzc1hZ2fHy0p2QgghpDYsyyIsLAzbt2/H/fv3AQAGBgZYvnw5PDw8OP/MCgoKwqJFi6CtrS1z3bvvvgtTU1Ps3r0b2trayMjIQPv27TF37lx4enpixowZnMZCCCFEsShxS9q8urrCZmRkKKwrbHMICgpCt27d4OrqKjUeERGBhw8fYuXKlZw9VltK3CopKaGwsFAQidt169ZJfn/x4gW+/PJLmJubY8SIEQCAS5cu4dq1a1i8eDGCgoL4CrNJiouLMWnSJFy7dg3Pnj2DgYEBCgsLMWLECJw7d06mzABpPV68eIGOHTtSwpYQQoigPXv2DED1/rA8Tp8+Lffc8iy20NbWRlJSEszMzKCtrY0//vgD/fr1Q1JSEpycnJCdnS334xFCCBEeStySNk/RXWEVxdjYGEeOHMHIkSOlxpOSkjB79mxOa/u2pcRtZGQkZs+eLXedYEXV/3V3d4e+vj42bNggNR4QEIC//voLERERzfr4ze3ChQtITU1FVVUVrKysYGdnx3dITdIWVvk3RlVVFTZt2oQ9e/bgwYMHuHHjBkxMTODn5wdjY2O4ubnxHSIhhBDSJK83Rq4pl/Tq5RrylE/S1dVFQkICTE1NYWZmhp07d8Le3h7Z2dmwsrJCSUkJd8ETQghROKU334SQ1i0rK6vWZICrqyuuX7/OQ0TcKCwsrDVZqKuri4KCAh4iah2cnJwa1NxNUfV/v/32W8ybN09mfO7cufjuu++a/fGbS1RUFF6+fImxY8di2bJlWLFiBezs7FBWVoaoqCi+w2uUmJgYmJub4/Llyxg4cCAGDBiApKQk9O/fH+fPn+c7PF5t3LgRBw8exJYtW6QaAFpYWCA8PJzHyAghhJD/sbS0hJWVlcyPtbU1Ro0aBScnJ/zyyy+13reqqkryExsbi8GDB+PHH39EUVERnj59inPnzsHKygo//fST3LGkpKQAAGxtbeHv74/Dhw/Dy8sLFhYWnG0zIYQQflDilrR5NV1hX8dXV1iu9OrVCwkJCTLjCQkJMDAw4CEi0pxUVVURHx8vMx4fHw8VFRUeIuKGi4sLnj59KjP+7NkzuLi48BBR0/n6+sLb2xtJSUkICQlBaGgokpKS4OXlxWkJk5YoKioK+/btw0cffQRlZWXJ+MCBA+lUT0IIIYIxceJE5ObmolOnTrC1tcW7774LdXV13L59GzY2NigoKICdnR2+//77eufx8vLCjh07YG9vD01NTWhoaMDe3h4hISFyNxULDAyULNbYsGEDunTpgkWLFuHvv//Gvn37mrythBBC+NWO7wAI4ZuQusJyyd3dHV5eXigvL8fYsWMBAHFxcVixYgXn27V69Wro6OhwOidpGC8vLyxatAhXrlzB8OHDAVTXuI2IiIC/vz/P0TUey7K11ji9e/cutLS0eIio6bKyshAdHS0z7urq2iKbrXHp3r17EIlEMuNVVVUoLy/nISJCCCFE1qNHj+Dj4wM/Pz+p8Y0bNyI/Px+xsbEICAjAhg0bMG3atDrnuX37dq37M1paWsjLy5MrliFDhkh+19XVxblz5+TbCEIIIS0CJW5Jm+fn5wcNDQ1s374dq1atAlDdFXbt2rVyH+kWohUrVuDJkydYvHgxysrKAAAqKipYuXKlZDtr05iGCfXNRxTD19cXJiYm2LFjB44cOQIA6NevHw4ePIiZM2fyHF3DWVpagmEYMAyDcePGoV27/31cVVZW4s6dO5g4cSKPETZezSr/vn37So239FX+XOjfvz9+//13GBkZSY1/++23sLS05CkqQgghRFp0dDSuXLkiMz579mxYW1tj//79cHR0REhISL3z2NjYwMvLC19//bVk1WxhYSF8fHwwdOjQZomdEEJIy0KJW9LmMQwDb29veHt7N7grrJAxDIPNmzfDz88PWVlZUFVVRd++fd9Yn3X69Oky8zSlYQJRnJkzZ7bIJG1tal6H6enpsLe3h7q6uuS6Dh06wNjYGB988AFP0TVNa13lz4WAgAB8/PHHuHfvHqqqqnDixAnk5OQgKioKP/zwA9/hEUIIIQCqF0MkJibKnCWSmJgoKVFVVVX1xv3uiIgIODg4wMjICIaGhgAAsVgMU1NTnDp1qs771RzglkdLbbRMCCGkGiVuCXlFa0jYvk5dXR02NjZy376qqkry+88//4yVK1ciMDAQI0aMAMMwSExMxGeffYbAwMDmCJc0UUpKCrKyssAwDPr16wdra2u+Q2qUgIAAAICxsTFmzZrVouv0vq61rvLnwpQpU/DNN98gMDAQDMPA398fVlZWOHPmDMaPH893eIQQQggAYMmSJVi4cCGuXLkCGxsbMAyDy5cvIzw8HKtXrwZQ3Yz0TWeLiEQiZGZm4vz588jOzgbLsjA3N4ednV29idlXF1q8ePECX375JczNzTFixAgA1eWyrl27hsWLFzd9YwkhhPCKYV9dSkdIG1TXEWuGYaCiogKRSARnZ2fY2tryEB2/BgwYgD179mD06NFS47///jsWLFiArKwsniJrOYKCgrBo0SJoa2s36+PcvXsXjo6OSEhIkDxWUVERRo4ciaNHj6JXr17N+vikcVrTKn9CCCGkLTl8+DB27dqFnJwcAICZmRmWLFmCOXPmAABKS0sl3yfk8eLFC3Ts2FHulbQ13N3doa+vjw0bNkiNBwQE4K+//kJERESD5iOEECIslLglbd6qVauwe/duWFhYYOjQoWBZFikpKcjMzISzszOuX7+OuLg4nDhxot7mAq2RqqoqLl++DAsLC6nxzMxMDBs2DKWlpTxFxo/G1P9VlAkTJqC4uBiRkZEwMzMDAOTk5MDV1RWdOnVCbGysQuPhSmVlJUJDQxEdHQ2xWCyp11zjyZMnPEVGmoOJiQmSk5PRpUsXqfGioiJYWVkhNzeXp8gIIYQQ7lVVVWHTpk3Ys2cPHjx4gBs3bsDExAR+fn4wNjaGm5vbG+fQ0tJCSkqKTO38mzdvYsiQIXj69GlzhU8IIUQBqFQCafO46grbGlHDBGlCrv/7+++/IzExUZK0BapXfnz++ecYNWqUQmPh0rp16xAeHo6lS5fCz88Pa9asQV5eHk6dOgV/f3++w2sUWuVft7y8vFr/dl6+fIl79+7xEBEhhBBSu6KiIhw/fhy5ublYtmwZdHR0kJqaim7duqFHjx5yzbFx40ZERkZiy5YtmD9/vmTcwsICoaGhciVuVVVVER8fL5O4jY+Pb1WlpgghpK2ixC1p87jqCtsaNbZhQmsl5Pq/hoaGKC8vlxmvqKiQ+8uDEB0+fBj79+/H5MmTsW7dOjg6OqJPnz4YOHAgLl261CJrwk6cOPGNq/zt7Oza1Cr/V1ezx8TEQEtLS3K5srIScXFxMDY25iEyQgghRFZmZibs7OygpaWFvLw8uLu7Q0dHBydPnkR+fj6ioqLkmicqKgr79u3DuHHjsHDhQsn4wIEDkZ2dLdccXl5eWLRoEa5cuYLhw4cDqK5xGxER0WIPchNCCPkfStySNo+rrrCtUWMbJrQFXl5eMvV/7e3toaamxkv93y1btmDJkiX44osvYG1tDYZhkJKSAk9PT2zbtk2hsXCpsLBQUqpDXV1dcrrf+++/L7NKvqWgVf6yalazMwwDJycnqevat28PY2NjbN++nYfICCGEEFlLly6Fs7MztmzZIlWn/r333pPUuJXHvXv3ZL6DANXfPWo7IF8bX19fmJiYYMeOHThy5AgAoF+/fjh48CBmzpwpdyyEEEKEiRK3pM3jqitsa8UwDCZMmIAxY8Y0qmFCa3X79m2pVYE1alZeKJqzszNKSkowbNgwtGtX/dZeUVGBdu3awdXVFa6urpLbtqS6sD179kRBQQEMDQ0hEokQGxsLKysrJCcnt9iDKbTKX1bNavbevXsjOTkZXbt25TkiQgghpG7JycnYu3evzHiPHj1QWFgo9zz9+/fH77//DiMjI6nxb7/9tkHfPWbOnElJWkIIaaUocUvavM8++wy9e/fGrl27cOjQIQDVtUH3798vOWK+cOFCLFq0iM8wecFFw4TWSmj1f8PCwhT+mIrg4OCAuLg4DBs2DJ6ennB0dMRXX30FsVgMb29vvsNrFFrlX7c7d+7wHQIhhBDyRioqKiguLpYZz8nJga6urtzzBAQE4OOPP8a9e/dQVVWFEydOICcnB1FRUfjhhx8aFFNKSgqysrLAMAz69esHa2vrBt2fEEKIMDHsq511CCHkFevXr0dkZCTWr1+P+fPn4+rVqzAxMUF0dDRCQ0Pxxx9/8B0ib27dugUHBwfk5OTUWv+3ttPeSNMlJSUhISEBIpEIU6dO5TucRtm4cSMCAwMxf/78Wlf5r1mzBqGhoTh37hzOnz/Pd7gK9/z5c/z2228Qi8UoKyuTuq4l1jQmhBDS+ixYsAAPHz5EdHQ0dHR0kJmZCWVlZUyfPh1jxoxp0AH1mJgYBAYG4sqVK6iqqoKVlRX8/f0xYcIEue5/9+5dODo6IiEhAdra2gCqG6eNHDkSR48eRa9evRqxhYQQQoSCEreEgJuusK2RSCTC3r17MW7cOGhoaCAjIwMmJibIzs7GiBEj8M8///AdIq9YlhVM/V+xWFzv9TXJ5ZYmKCgI3bp1kyr1AFQ3znv48CFWrlzJU2RNc/jwYezatQs5OTkAqlf5L1myRLLKv7S0FAzDtLlu0GlpaZg0aRJKSkrw/Plz6Ojo4NGjR1BTU4Oenh5yc3P5DpEQQghBcXExJk2ahGvXruHZs2cwMDBAYWEhRowYgXPnzqFTp04Ki2XChAkoLi5GZGQkzMzMAFSv/HV1dUWnTp0QGxursFgIIYRwjxK3pM17vStsTk6OpBxAQ7rCtkaqqqrIzs6GkZGRVOL2+vXrGDp0KP7991++QxSEFy9e8F7/V0lJqd7Hr6ysVGA03DE2NsaRI0cwcuRIqfGkpCTMnj2bTq1vZd59912Ymppi9+7d0NbWRkZGBtq3b4+5c+fC09MTM2bM4DtEQgghROLChQtITU2VrJS1s7Nr0P1NTEyQnJyMLl26SI0XFRXByspKrgOWqqqqSExMlKmJm5qailGjRqG0tLRBMRFCCBEWJb4DIIRvNV1hb968KbW67b333sPFixd5jIx/NQ0TXtfQhgmtUVVVFTZs2IAePXpAXV1dkkD08/PDV199pfB40tLSkJqaKvlJSkrCnj17YGpqim+//Vbh8XClsLBQUkP4Vbq6uigoKOAhIm4UFRVJSiPUNItLTU3FvXv3eI6MX+np6fDx8YGysjKUlZXx8uVL9OrVC1u2bJE0iySEEEL4FhUVhZcvX2Ls2LFYtmwZVqxYATs7O5SVlTVo0UdeXl6tB9dfvnwp9z6BoaEhysvLZcYrKira9JmDhBDSWlBzMtLmcdUVtjXismFCa7Nx40ZERkZiy5YtmD9/vmTcwsICoaGhCm/cNmjQIJmxIUOGwMDAAFu3bm2xKxV79eqFhIQE9O7dW2o8ISEBBgYGPEXVNK+v8nd3d4eOjg5OnjzZ5lf5t2/fXrJyvFu3bhCLxejXrx+0tLTeWA6EEEIIURQXFxdMnDgRenp6UuPPnj2Di4sL5s2bV+/9T58+Lfk9JiYGWlpaksuVlZWIi4uDsbGxXLFs2bIFS5YswRdffAFra2swDIOUlBR4enpi27Zt8m8UIYQQQaLELWnzuOoK2xpNmTIF33zzDQIDA8EwDPz9/WFlZYUzZ85g/PjxfIfHq6ioKOzbtw/jxo3DwoULJeMDBw5EdnY2j5FJMzU1RXJyMt9hNJq7uzu8vLxQXl6OsWPHAgDi4uKwYsUK+Pj48Bxd49Ss8t+yZQs0NDQk4++9956kxm1bZWlpiZSUFJiamsLW1hb+/v549OgRDh06BAsLC77DI4QQQgBU9zmorUTV3bt3pZKwdZk+fToAgGEYODk5SV3Xvn17GBsbY/v27XLF4uzsjJKSEgwbNgzt2lV/va+oqEC7du3g6uoq1Seg5iwfQgghLQclbkmbN23aNKxfvx7R0dEAqnegxGIxfH198cEHH/AcHf/s7e1hb2/PdxiCc+/ePYhEIpnxqqqqWk9Xa26vH3xgWRYFBQVYu3Yt+vbtq/B4uLJixQo8efIEixcvRllZGYDqgy0rV67EqlWreI6ucWiVf90CAwPx7NkzAMCGDRvg5OSERYsWQSQS4cCBAzxHRwghpK2ztLQEwzBgGAbjxo2TJEqB6pWyd+7cwcSJE984T1VVFQCgd+/eSE5ORteuXRsdU1hYWKPvSwghRPgocUvavG3btmHSpEnQ09NDaWkp3nnnHUlX2E2bNvEdHq+4aJjQWtXU/zUyMpIa56v+r7a2tszKD5Zl0atXLxw7dkzh8XCFYRhs3rwZfn5+yMrKgqqqKvr27YuOHTvyHVqj0Sr/ug0ZMkTyu66uLs6dO8djNIQQQoi0mpWy6enpsLe3h7q6uuS6Dh06wNjYuEELP7hosvr6il1CCCGtCyVuSZunqamJ+Pj4JneFbY24aJjQWgmt/u8vv/widVlJSQm6uroQiURSq0FaKnV1ddjY2PAdBidolT8hhBDSMgUEBAAAjI2NMWvWLKnGxo31/Plz/PbbbxCLxZKzi2p4eHi88f5vqgFvaGjYpPgIIYTwi2FZluU7CEL4FBUVhVmzZsms4CsrK8OxY8fe2FygNappmDB9+nRERkbW2jDh/PnzyMnJ4StEQYiJiUFgYCCuXLkiSfj7+/tjwoQJfIdWp8mTJyM8PBz6+vp8h9JmFRcXY9KkSbh27RqePXsGAwMDySr/c+fOoVOnTnyHqFA1p53KIzU1tZmjIYQQQhQnLS0NkyZNQklJCZ4/fw4dHR08evQIampq0NPTk+vsNiUlpXo/R2tbhEEIIaTloMQtafOUlZVRUFAg0xX28ePH0NPTa5M7O0pKSgCqVwK+/hbxasOE999/n4/wSBNoaGggIyMDJiYmfIfS5tEq/2rr1q2T/P7ixQt8+eWXMDc3x4gRIwAAly5dwrVr17B48WIEBQXxFSYhhBAiUVlZidDQUERHR9e6UlbeJmDvvvsuTE1NsXv3bmhrayMjIwPt27fH3Llz4enpiRkzZrxxjoyMDKnL5eXlSEtLQ0hICDZt2iTXHIQQQoSLErekzVNSUsKDBw9kaktmZGTA1ta2TXdf5aJhQmvVUuv/UuKWf7TKv27u7u7Q19fHhg0bpMYDAgLw119/ISIigqfICCGEkP/x9/dHeHg4li5dCj8/P6xZswZ5eXk4deoU/P395SpxAFT3KEhKSoKZmRm0tbXxxx9/oF+/fkhKSoKTkxOys7MbHePZs2exdetW/Prrr42egxBCCP8ocUvarJrTczMyMtC/f/86u8LW1KEk5FVKSkooLCyUWan94MEDGBoa4uXLlzxFVj9K3PKPVvnXTUtLCykpKejbt6/U+M2bNzFkyBA8ffqUp8gIIYSQ/+nTpw927tyJyZMnQ0NDA+np6ZKxS5cu4ciRI3LNo6uri4SEBJiamsLMzAw7d+6Evb09srOzYWVlhZKSkkbHePPmTQwePBjPnz9v9ByEEEL41/I71hDSSFx3hW2tmtowobWpqf8LVNe4ra3+r7GxMQ+RkZaCZdlaa9HdvXtX6vXUFqmqqiI+Pl4mcRsfH89JAxhCCCGEC4WFhbCwsABQ3UC15sDi+++/Dz8/P7nnsbS0REpKCkxNTWFrawt/f388evQIhw4dksz/JsXFxVKXWZZFQUEB1q5dK/N5SgghpOWhxC1ps5qjK2xr86aGCW0xcVuT8GcYBk5OTlLXvVr/l5DX1azyZxgG48aNq3OVf1vm5eWFRYsW4cqVKxg+fDiA6hq3ERER8Pf35zk6QgghpFrPnj1RUFAAQ0NDiEQixMbGwsrKCsnJyTKlkOoTGBiIZ8+eAQA2bNgAJycnLFq0CCKRCAcOHJBrDm1tbZkDwizLolevXjh27Jj8G0UIIUSQqFQCIaROXDRMaK1aav1fKpXAn5omXOvWrYOPj0+dq/w7dOjAV4iCEB0djR07diArKwsA0K9fP3h6emLmzJk8R0YIIYRU8/X1haamJlavXo3jx4/D0dERxsbGEIvF8Pb2RnBwsMJi+e2336QuKykpQVdXFyKRSOogMSGEkJaJErekzeOqK2xr1JwNEwi3nj9/jk6dOr3xdkFBQVi0aBG0tbWbPyhSq8jISFrlTwghhLQiSUlJSEhIgEgkwtSpU/kOp1aTJ09GeHg49PX1+Q6FEEJIA1DilrR5XHWFbY2as2FCayCk+r/q6uqYOXMmXF1dMXr0aIU+NiFcS0lJQVZWFhiGQb9+/WBtbc13SIQQQohEUFAQunXrBldXV6nxiIgIPHz4ECtXrqzzvjWlk+SRmprapDhfRWddEUJIy0TnTpA27/Dhw9i/fz8mT56MdevWwdHREX369MHAgQNx6dKlNp245aJhQmsltPq/R48excGDBzFu3DgYGRnB1dUV8+bNg4GBgULjIG9Gq/zrdvfuXTg6OiIhIUGyKryoqAgjR47E0aNH0atXL34DJIQQQgDs3bsXR44ckRnv378/Zs+eXW/itqZfAgC8ePECX375JczNzTFixAgA1bXdr127hsWLF3MeNyGEkJZHie8ACOFbfV1hz549y2dovAsMDJScTrVhwwZ06dIFixYtwt9//419+/bxHB2/vL29MWXKFDx58gSqqqq4dOkS8vPzYW1tjW3btik8nilTpuC7777D/fv3sWjRIhw9ehRGRkZ4//33ceLECVRUVCg8JlK7devWISQkBDNnzsTTp0+xdOlSzJgxA0pKSli7di3f4fHK1dUV5eXlyMrKwpMnT/DkyRNkZWWBZVm4ubnxHR4hhBACoPr7Q20lB3R1dVFQUFDvfQMCAiQ/Dx8+hIeHB/744w+EhIQgJCQEiYmJ8PLywoMHD5orfEIIIS0IlUogbZ6ZmRmioqIwbNgwvP3225g8eTJ8fX3xzTffYMmSJfj777/5DpEIUEuo//v5559j+fLlKCsrQ9euXbFw4UL4+vpCTU2N79DatD59+mDnzp2YPHkyNDQ0kJ6eLhm7dOlSrSt42gpVVVUkJibC0tJSajw1NRWjRo1CaWkpT5ERQggh/9O3b18EBARg7ty5UuOHDh1CQEAAcnNz5ZpHS0sLKSkp6Nu3r9T4zZs3MWTIEMmCEi5QqQRCCGmZaMUtafMcHBwQFxcHAPD09ISfnx/69u2LefPmydStIqRG+/btJfXJunXrBrFYDKB6B7zmdz4UFhZiy5Yt6NevH3x9ffHhhx8iLi4OoaGhOHnypNTpeYQftMq/boaGhigvL5cZr6ioQI8ePXiIiBBCCJHl7u4OLy8vHDhwAPn5+cjPz0dERAS8vb0xf/58uedRVVVFfHy8zHh8fDw1MSWEEAKAatwSguDgYMnvH374IXr16iX4rrDNia+GCS2N0Or/njhxAgcOHEBMTAzMzc3x6aefYu7cuZI6oQAwePBgmZWMRPF69uyJgoICGBoaQiQSITY2FlZWVkhOTkbHjh35Do9XW7ZswZIlS/DFF1/A2toaDMMgJSUFnp6evJQgIYQQQmqzYsUKPHnyBIsXL5bUqldRUcHKlSuxatUquefx8vLCokWLcOXKFQwfPhxAdY3biIgI+Pv7N0vshBBCWhYqlUDavKZ0hW2N1q1bJ/n9TQ0TgoKC+AqTdykpKXj27BlsbW3x8OFDODk5IT4+HiKRCAcOHMCgQYMUGo+WlhYcHR3h5uYGGxubWm9TWlqKLVu2ICAgQKGxEWm+vr7Q1NTE6tWrcfz4cTg6OsLY2BhisRje3t5SB5Pams6dO6OkpAQVFRVo16762HLN7506dZK6bVtu4kYIIUQY/v33X2RlZUFVVRV9+/Zt1AHY6Oho7NixA1lZWQCAfv36wdPTEzNnzpTr/s+fP5f5jKxNUFAQFi1aJHVQnxBCiPBR4pa0ecbGxjhy5AhGjhwpNZ6UlITZs2fjzp07PEXGP3d3d+jr62PDhg1S4wEBAfjrr78QERHBU2TkVRUVFdi3bx9mzJiB7t278x0OaaCkpKQ2vcr/VZGRkXLf1snJqRkjIYQQQloGdXV1zJw5E66urhg9ejTf4RBCCOEYJW5Jm6eiooKsrCz07t1bajw3Nxfm5uZ48eIFT5HxT5ENE0jTqKmpISsrC0ZGRnyHQt6AVvkTQggh5FUpKSnIysoCwzDo168frK2t5b7vmTNncPDgQfzwww8wMjKCq6sr5s2bBwMDg2aMmBBCiKJQjVvS5tXUtH09cZuQkNDmd3hqGia8nrhtqw0ThFz/d9iwYUhLS6PEbQuwd+9eHDlyRGa8f//+mD17dptO3L6psZ+hoaGCIiGEEEKa3927d+Ho6IiEhARJCYOioiKMHDkSR48eRa9evd44x5QpUzBlyhQ8fvwYUVFROHjwIPz8/GBvbw9XV1dMnTpVUn6IEEJIy0Pv4KTNq+kKW15ejrFjxwIA4uLisGLFCvj4+PAcHb+oYYK06dOnS35/U/1fRVu8eDF8fHxw9+5dWFtby9Q6GzhwoMJjIrUrLCyEvr6+zLiuri4KCgp4iEg4jI2N6z04UllZqcBoCCGEkObl6uqK8vJyZGVlwczMDACQk5MDV1dXuLm5ITY2Vu65unTpAm9vb3h7e+Pzzz/H8uXLce7cOXTt2hULFy6Er68v1NTUmmtTCCGENBMqlUDaPJZl4evri507d8p0hW2LycnXNbVhQmsltPq/SkpKMmMMw4BlWTAMQwkvAenbty8CAgIwd+5cqfFDhw4hICAAubm5PEXGv4yMDKnL5eXlSEtLQ0hICDZt2oQZM2bwFBkhhBDCPVVVVSQmJsLS0lJqPDU1FaNGjUJpaanccxUWFiIqKgoHDhyAWCyGg4MD3NzccP/+fQQHB0NfX79BiWBCCCHCQIlbQv4fF11hSdshtPq/+fn59V5PJRSEY/Pmzdi6dSu2bt1a6yr/VatW8Ryh8Jw9exZbt27Fr7/+yncohBBCCGfMzMxw6NAhDB06VGr88uXLmDNnDm7duvXGOU6cOIEDBw4gJiYG5ubmcHd3x9y5cyWlFwDg2rVrsLS0lCxSIYQQ0nJQqQRC/p+6ujpsbGz4DkOQmtIwobUSWv1fSsy2HCtWrMCTJ0+wePFimVX+lLStnampKZKTk/kOgxBCCOHUli1bsGTJEnzxxRewtrYGwzBISUmBp6cntm3bJtccLi4ukjq5dX2XMTExwZo1a7gMnRBCiILQiltCSJ24aJjQWgUHB2Pt2rVwd3evtf6vr6+vwmO6ffs2wsLCpJLsnp6e6NOnj8JjIW9Gq/xlFRcXS11mWRYFBQVYu3YtsrOzkZ6ezk9ghBBCSDPo3LkzSkpKUFFRIWkgVvP76/0Knjx5InP/iooK7Nu3DzNmzED37t0VEjMhhBDFosQtIaROEyZMQHFxMSIjI2UaJnTq1KnN18kSUv3fmJgYTJ06FYMHD8aoUaPAsiwSExORkZGBM2fOYPz48QqPiZCGUlJSkmlOxrIsevXqhWPHjkkaARJCCCGtQWRkpNy3dXJyqnVcTU0NWVlZdPYVIYS0UpS4JYTUicuGCaR5WVpawt7eHsHBwVLjvr6+iI2NRWpqKk+RESK/3377TeqykpISdHV1IRKJJCuRCCGEEPI/tra28PT0xPTp0/kOhRBCSDOgb0GEkDoZGhqivLxcZryiogI9evTgISLhEUr936ysLERHR8uMu7q6IiwsTPEBEdII77zzjly3mzx5MsLDw6Gvr9/MERFCCCHNRywW13u9oaHhG+dYvHgxfHx8cPfuXVhbW8uUWBg4cGCTYiSEEMIvStwSQurERcOE1kpo9X91dXWRnp4u0ywtPT0denp6Co2FkOZ28eJFWvFPCCGkxTM2NpYpEfSqysrKN84xa9YsAICHh4dkjGEYsCwLhmHkmoMQQohwUeKWEFInZ2dnlJSUYNiwYTINE1xdXeHq6iq5bW0NE1ozV1dXlJeXIysrS6b+r5ubm8Lr/86fPx8LFixAbm4uRo4cCYZhEB8fj82bN8PHx0ehsRBCCCGEkDdLS0uTulxeXo60tDSEhIRg06ZNcs1x586d5giNEEKIQFCNW0JInbhomNBaCa3+L8uyCAsLw/bt23H//n0AgIGBAZYvXw4PD496V3MQ0tJoaGggIyMDJiYmfIdCCCGEcO7s2bPYunUrfv31V75DIYQQwjNacUsIqVNbS8Y2hNDq/zIMA29vb3h7e+PZs2cAqpNbhBBCCCGkZTE1NUVycrLct799+zbCwsKk+i54enqiT58+zRglIYQQRaDELSGkTlw0TGithFz/lxK2hBBCCCHCV1xcLHWZZVkUFBRg7dq1Mn0L6hITE4OpU6di8ODBGDVqFFiWRWJiIvr3748zZ85g/PjxzRE6IYQQBaFSCYSQOikpKTW5YUJr1blzZ5SUlEhq/gL/q//7ejdfRdT/tbS0rPW5YhgGKioqEIlEcHZ2hq2tbbPHQkhzo1IJhBBCWoPa9rVZlkWvXr1w7NgxjBgx4o1zWFpawt7eHsHBwVLjvr6+iI2NRWpqKqcxE0IIUSxacUsIqRMXDRNaq7CwML5DkDJx4kTs3r0bFhYWGDp0KFiWRUpKCjIzM+Hs7Izr16/Dzs4OJ06cwLRp0/gOl5BaPX/+XObAR21Wr14NHR0dBURECCGENJ9ffvlF6rKSkhJ0dXUhEokkCwPeJCsrC9HR0TLjrq6ugttfJYQQ0nC04pYQ0mDUMEF45s+fD0NDQ/j5+UmNb9y4Efn5+di/fz8CAgJw9uxZpKSk8BQlIfVTV1fHzJkz4erqitGjR/MdDiGEECIIkydPRnh4OPT19WWu69WrF0JCQvCf//xHajw6OhrLli17Y+kzQgghwkYrbgkhDdbQhgmtkdDq/0ZHR+PKlSsy47Nnz4a1tTX2798PR0dHhISEKDQuQhri6NGjOHjwIMaNGwcjIyO4urpi3rx5MDAw4Ds0QgghhDcXL15EaWlprdfNnz8fCxYsQG5uLkaOHAmGYRAfH4/NmzfDx8dHwZESQgjhGiVuCSF14qJhQmtlbGwsqPq/KioqSExMhEgkkhpPTEyEiooKAKCqqgodO3ZUaFyENMSUKVMwZcoUPH78GFFRUTh48CD8/Pxgb28PV1dXTJ06Ve5TRwkhhJC2wM/PDxoaGti+fTtWrVoFADAwMMDatWvh4eHBc3SEEEKair79EELqpK2tXW/DhLZMaPV/lyxZgoULF+LKlSuwsbEBwzC4fPkywsPDsXr1agDVXYctLS0VHhshDdWlSxd4e3vD29sbn3/+OZYvX45z586ha9euWLhwIXx9faGmpsZ3mIQQQgjvGIaRfGY+e/YMQHUTT0IIIa0D1bglhNTpt99+k7rcmIYJbQ2f9X8PHz6MXbt2IScnBwBgZmaGJUuWYM6cOQCA0tJSMAwjWYFLiFAVFhYiKioKBw4cgFgshoODA9zc3HD//n0EBwdDX18fsbGxfIdJCCGEKISGhgYyMjJgYmLCdyiEEEIUjBK3hJAmq69hQltz8+ZNDB48GM+fP+c7FEJanBMnTuDAgQOIiYmBubk53N3dMXfuXGhra0tuc+3aNVhaWqKsrIy/QAkhhBAFqi9xa2lpWWv5rpqD9SKRCM7OzrC1tVVEqIQQQjimxHcAhJCWr76GCa1VcXGx1M/Tp0+RnZ0NPz8/3ur/FhUVSUojPHnyBACQmpqKe/fu8RIPIQ3l4uKCHj16ICEhAenp6fjvf/8rlbQFABMTE6xZs4afAAkhhBCBmThxInJzc9GpUyfY2tri3Xffhbq6Om7fvg0bGxsUFBTAzs4O33//Pd+hEkIIaQQ615kQQhpBaPV/MzMzYWdnBy0tLeTl5cHd3R06Ojo4efIk8vPzERUVpfCYCGmIiooKBAUFYcaMGejevXudt1NVVUVAQIACIyOEEEKax/Pnz9GpU6c33m716tXQ0dGp9bpHjx7Bx8cHfn5+UuMbN25Efn4+YmNjERAQgA0bNmDatGmcxE0IIURxqFQCIaTJ2mLdLaHV/7Wzs4OVlRW2bNki9XwkJiZizpw5yMvLU3hMhDSUmpoasrKyYGRkxHcohBBCSLNTV1fHzJkz4erqitGjRzdqDi0tLVy5cgUikUhq/NatW7C2tpacFWZjYyNpXkYIIaTloFIJhBDSCO+8847Uz9tvv4233npLJmk7efJkFBQUNHs8ycnJ+OSTT2TGe/TogcLCwmZ/fEK4MGzYMKSlpfEdBiGEEKIQR48exdOnTzFu3DiYmpoiODgY9+/fb9AcKioqSExMlBlPTEyUNKStqqpCx44dOYmZEEKIYlGpBEIIaUaKqv+roqKC4uJimfGcnBzo6uo2++MTwoXFixfDx8cHd+/ehbW1tczpowMHDuQpMkIIIYR7U6ZMwZQpU/D48WNERUXh4MGD8PPzg729PVxdXTF16tQ3nsm1ZMkSLFy4EFeuXIGNjQ0YhsHly5clfQ8AICYmBpaWlorYJEIIIRyjUgmEkCZri6US5KWo/5sFCxbg4cOHiI6Oho6ODjIzM6GsrIzp06djzJgxCAsLa9bHJ4QLSkqyJwIxDAOWZcEwDCorK3mIihBCCFGczz//HMuXL0dZWRm6du2KhQsXwtfXF2pqanXe5/Dhw9i1axdycnIAAGZmZliyZAnmzJkDACgtLQXDMJIVuIQQQloOStwSQuokb8OEoKAgLFq0SKb7O1Fc4ra4uBiTJk3CtWvX8OzZMxgYGKCwsBAjRozAuXPn5HoeCeFbfn5+vddT7VtCCCGtUWFhIaKionDgwAGIxWI4ODjAzc0N9+/fR3BwMPT19REbG8t3mIQQQnhAiVtCSJ24aJjQ1il6NfKFCxeQmpqKqqoqWFlZwc7OTiGPSwghhBBCGubEiRM4cOAAYmJiYG5uDnd3d8ydO1dqMcS1a9dgaWmJsrKyOucpKirC8ePHkZubi2XLlkFHRwepqano1q0bevTooYAtIYQQ0lyoxi0hpE5Hjx7FwYMHMW7cOBgZGcHV1RXz5s2DgYEB36GR10RFRWHWrFkYO3Ysxo4dKxkvKyvDsWPHMG/ePB6jI0R+t2/fRlhYGLKyssAwDPr16wdPT0/06dOH79AIIYQQTrm4uMDR0REJCQmwsbGp9TYmJiZYs2ZNnXNkZmbCzs4OWlpayMvLg7u7O3R0dHDy5Enk5+cjKiqqucInhBCiALTilhDyRq82TLh+/XqDGia0dYpacausrIyCggLo6elJjT9+/Bh6enpUG5S0CDExMZg6dSoGDx6MUaNGgWVZJCYmIiMjA2fOnMH48eP5DpEQQgjhREVFBfbt24cZM2age/fujZ7Hzs4OVlZW2LJli9R+Z2JiIubMmYO8vDzugiaEEKJwlLglhDRIYxomtEZCq/+rpKSEBw8eQFdXV2o8IyMDtra2ePLkSbM+PiFcsLS0hL29PYKDg6XGfX19ERsbi9TUVJ4iI4QQQrinpqaGrKysJtVw19LSQmpqKvr06SOVuM3Pz4eZmRlevHjBYcSEEEIUjZbKEULe6PWGCR9++KFUw4RLly61uYYJ3bp1k6v+76pVq5o1DktLSzAMA4ZhMG7cOKkV0JWVlbhz5w4mTpzYrDEQwpWsrCxER0fLjLu6uiIsLEzxARFCCCHNaNiwYUhLS2tS4lZFRQXFxcUy4zk5OTIH9AkhhLQ8lLglhNTp9YYJn376qUzDhMGDB8PS0pK/IHkilPq/06dPBwCkp6fD3t4e6urqkus6dOgAY2NjfPDBBwqNiZDG0tXVRXp6Ovr27Ss1np6eLlMGhBBCCGnpFi9eDB8fH9y9exfW1tYyZ3MNHDjwjXNMmzYN69evlxz4ZBgGYrEYvr6+tA9ICCGtAJVKIITUSUtLC46OjnBzc6uzYUJpaSm2bNmCgIAABUcnDEKp/xsZGYlZs2ZBRUVFYY9JCNfWr1+P0NBQ+Pr6YuTIkWAYBvHx8di8eTN8fHzw2Wef8R0iIYQQwhklJSWZMYZhwLIsGIaRq0dBcXExJk2ahGvXruHZs2cwMDBAYWEhRowYgXPnzslV2osQQohwUeKWEFIrrhomtCVU/5eQpmFZFmFhYdi+fTvu378PADAwMMDy5cvh4eEBhmF4jpAQQgjhTn5+fr3XN6SEwoULF5CamoqqqipYWVnBzs6uqeERQggRAErcEkLqxEXDhNbu9fq/Dg4OUvV/9fX1FVL/t7KyEqGhoYiOjoZYLEZZWZnU9dScjLQ0z549AwBoaGjwHAkhhBAiXFFRUZg1axY6duwoNV5WVoZjx45h3rx5PEVGCCGEC7LnZhBCyP+raZhAZJ04cQJTpkyBoaEhjhw5gk8//RT37t3D119/DVtbW3z00Uc4duwYfv31V4XEs27dOoSEhGDmzJl4+vQpli5dihkzZkBJSQlr165VSAyEcElDQ4OStoQQQlq927dvY8mSJbCzs8P48ePh4eGB27dvy31/FxcXPH36VGb82bNncHFx4TJUQgghPKDmZISQOnHRMKG1cnFxgaOjIxISEuqs/2tiYoI1a9YoJJ7Dhw9j//79mDx5MtatWwdHR0f06dMHAwcOxKVLl+Dh4aGQOAhpCktLy1rLITAMAxUVFYhEIjg7O8PW1paH6AghhBBuxcTEYOrUqRg8eDBGjRoFlmWRmJiI/v3748yZMxg/fvwb56iph/u6u3fvQktLqznCJoQQokBUKoEQUicuGia0RkKs/9upUydkZWXB0NAQ+vr6OHv2LKysrJCbmwtLS8taV2IQIjSrVq3C7t27YWFhgaFDh4JlWaSkpCAzMxPOzs64fv064uLicOLECUybNo3vcAkhhJAmsbS0hL29PYKDg6XGfX19ERsbi9TU1HrvyzAMMjIy0L9/f6mmuJWVlbhz5w4mTpyI6OjoZoufEEJI86MVt4SQOt25c4fvEASpXbt2WLZsGSZPnsx3KBI9e/ZEQUEBDA0NIRKJEBsbCysrKyQnJ8vUPCNEqB49egQfHx/4+flJjW/cuBH5+fmIjY1FQEAANmzYQIlbQgghLV5WVlatiVVXV1eEhYXVe9/p06cDANLT02Fvbw91dXXJdR06dICxsTE++OADLsMlhBDCA0rcEkLqRE3J6lZT/1co/0cODg6Ii4vDsGHD4OnpCUdHR3z11VcQi8Xw9vbmOzxC5BIdHY0rV67IjM+ePRvW1tbYv38/HB0dERISwkN0hBBCCLd0dXWRnp6Ovn37So2np6dDT0+v3vsGBAQAAIyNjTFr1iyoqKg0W5yEEEL4Q4lbQki9bt++jbCwMGRlZYFhGPTr1w+enp7o06cP36HxSmj1f189xe7DDz9Er169kJCQAJFIhKlTpyo0FkIaS0VFBYmJiRCJRFLjiYmJki+kVVVVtIqcEEJIqzB//nwsWLAAubm5GDlyJBiGQXx8PDZv3gwfHx+55nBycmrmKAkhhPCJatwSQupUV8OEjIwMuRsmtFZU/5cQ7m3cuBGBgYGYP38+bGxswDAMLl++jPDwcKxevRpr1qxBaGgozp07h/Pnz/MdLiGEENIkLMsiLCwM27dvx/379wEABgYGWL58OTw8PGptOva6yspKhIaGIjo6GmKxGGVlZVLXP3nypFliJ4QQohiUuCWE1KkpDRNau/z8/HqvV3QJhaCgIHTr1g2urq5S4xEREXj48CFWrlyp0HgIaazDhw9j165dyMnJAQCYmZlhyZIlmDNnDgCgtLQUDMPQKaGEEEJalWfPngEANDQ0GnQ/f39/hIeHY+nSpfDz88OaNWuQl5eHU6dOwd/fHx4eHs0RLiGEEAWhxC0hpE4qKir4888/Zepu3bhxAwMHDsSLFy94ioy8ztjYGEeOHMHIkSOlxpOSkjB79mxqNEcIIYQQ0gr16dMHO3fuxOTJk6GhoYH09HTJ2KVLl3DkyBG+QySEENIEVOOWEFKnpjRMaAuEVP+3sLAQ+vr6MuO6urooKChQeDyENFZRURGOHz+O3NxcLFu2DDo6OkhNTUW3bt3Qo0cPvsMjhBBCOGNpaVlrOYSaM0tEIhGcnZ1ha2tb5xyFhYWwsLAAAKirq+Pp06cAgPfffx9+fn7NEzghhBCFkS3SSAgh/6+mYcLmzZvx+++/Iz4+HsHBwfjkk0+wYMECvsPjVUxMDMzNzXH58mUMHDgQAwYMQFJSEvr3789L7c2aZmSvS0hIgIGBgcLjIaQxMjMzYWpqis2bN2Pr1q0oKioCAJw8eRKrVq3iNzhCCCGEYxMnTkRubi46deoEW1tbvPvuu1BXV8ft27dhY2ODgoIC2NnZ4fvvv69zjp49e0oO0otEIsTGxgIAkpOTqZknIYS0AlQqgRBSJy4aJrRWQqv/W5Po2rp1K8aOHQsAiIuLw4oVK+Dj40NJL9Ii2NnZwcrKClu2bIGGhgYyMjJgYmKCxMREzJkzB3l5eXyHSAghhHBm/vz5MDQ0lFkZu3HjRuTn52P//v0ICAjA2bNnkZKSUuscvr6+0NTUxOrVq3H8+HE4OjrC2NgYYrEY3t7eMvuqhBBCWhZK3BJC5NLYhgmtldDq/7IsC19fX+zcuVPSTVhFRQUrV66Ev7+/QmMhpLG0tLSQmpqKPn36SCVu8/PzYWZmRnW1CSGEtCpaWlq4cuUKRCKR1PitW7dgbW2Np0+fIjs7GzY2NpJ98TdJSkpCQkICRCIRpk6d2hxhE0IIUSCqcUsIkQslbKUJrf4vwzDYvHkz/Pz8kJWVBVVVVfTt25dOkSMtioqKCoqLi2XGc3JyoKury0NEhBBCSPNRUVFBYmKiTOI2MTERKioqAICqqqoG7c8NGzYMw4YN4zROQggh/KHELSGkTlw0TGitaur/5ubmYuTIkWAYBvHx8di8eTN8fHx4i0tdXR02Nja8PT4hTTFt2jSsX78e0dHRAKrfa8RiMXx9ffHBBx/wHB0hhBDCrSVLlmDhwoW4cuUKbGxswDAMLl++jPDwcKxevRpAdV8FS0vLOucICgpCt27d4OrqKjUeERGBhw8fYuXKlc26DYQQQpoXlUoghNRp1apV2L17NywsLDB06FCwLIuUlBRkZmbC2dkZ169fR1xcHE6cOIFp06bxHa5CUf1fQrhXXFyMSZMm4dq1a3j27BkMDAxQWFiIESNG4Ny5c+jUqRPfIRJCCCGcOnz4MHbt2oWcnBwAgJmZGZYsWYI5c+YAAEpLSyWLJmpjbGyMI0eOYOTIkVLjSUlJmD17Nu7cudO8G0AIIaRZUeKWEFInLhomtAVU/5cQbl24cAGpqamoqqqClZUV7Ozs+A6JEEIIESQVFRVkZWWhd+/eUuO5ubkwNzen+vCEENLCUeKWEFKn5miYQAghdYmKisKsWbNkavmVlZXh2LFjmDdvHk+REUIIIc2jqKgIx48fR25uLpYtWwYdHR2kpqaiW7du6NGjxxvv37dvXwQEBGDu3LlS44cOHUJAQAByc3ObK3RCCCEKQDVuCSF1ao6GCa0F1f8lhHsuLi6YOHGiTIO/Z8+ewcXFhRK3hBBCWpXMzEzY2dlBS0sLeXl5cHd3h46ODk6ePIn8/HxERUW9cQ53d3d4eXmhvLwcY8eOBQDExcVhxYoVvPZdIIQQwg1K3BJC6sRFw4TWauLEiW+s/2tnZ9cm6/8S0lgsy9Z6QOTu3bvQ0tLiISJCCCGk+SxduhTOzs7YsmWLVMmt9957T1Lj9k1WrFiBJ0+eYPHixSgrKwNQvfhi5cqVWLVqVbPETQghRHGoVAIhpF5NbZjQWlH9X0K4U7OCPSMjA/3790e7dv87rlxZWYk7d+5g4sSJiI6O5jFKQgghhFtaWlpITU1Fnz59oKGhgYyMDJiYmCA/Px9mZmYNqk/777//IisrC6qqqujbt2+bPCOOEEJaI1pxSwip10cffYSPPvqozutVVVUVGI1wREdH48qVKzLjs2fPhrW1Nfbv3w9HR0eEhITwEB0hLcv06dMBAOnp6bC3t4e6urrkug4dOsDY2BgffPABT9ERQgghzUNFRQXFxcUy4zk5OdDV1W3QXOrq6rCxseEqNEIIIQJBiVtCSL2a2jChtaL6v4RwJyAgAABgbGyMWbNmtbkV/IQQQtqmadOmYf369ZIzShiGgVgshq+vLx2wJIQQAoASt4SQenDRMKG1ovq/hHDPycmJ7xAIIYQQhdm2bRsmTZoEPT09lJaW4p133kFhYSFGjBiBTZs28R0eIYQQAaAat4SQOtnZ2cHKykrSMKGm7lZiYiLmzJmDvLw8vkPkFdX/JYRblZWVCA0NRXR0NMRisaTJSo0nT57wFBkhhBDSfC5cuIDU1FRUVVXBysoKdnZ2fIdECCFEIChxSwipE5cNEwgh5E38/f0RHh6OpUuXws/PD2vWrEFeXh5OnToFf39/eHh48B0iIYQQwpmoqCjMmjVLprRWWVkZjh07hnnz5vEUGSGEEKFQ4jsAQohwcdkwoTUqKiqSlEaoWQmYmpqKe/fu8RwZIS3T4cOHsX//fixbtgzt2rWDo6MjwsPD4e/vj0uXLvEdHiGEEMIpFxcXPH36VGb82bNncHFx4SEiQgghQkOJW0JInWoaJpSXlwOghgmvyszMhKmpKTZv3oytW7eiqKgIAHDy5EmsWrWK3+AIaaEKCwthYWEBoLo7ds2X2ffffx9nz57lMzRCCCGEcyzLgmEYmfG7d+9CS0uLh4gIIYQIDSVuCSF12rZtGx4+fCjVMEEkEkFDQ6PNN0xYunQpnJ2dcfPmTakatu+99x4uXrzIY2SEtFw9e/ZEQUEBAEAkEiE2NhYAkJycLHMaKSGEENJSWVpawsrKCgzDYNy4cbCyspL8DBo0CG+//TbVuSWEEAIAaMd3AIQQ4dLU1ER8fDw1TKhFcnIy9u7dKzPeo0cPFBYW8hARIS2fg4MD4uLiMGzYMHh6esLR0RFfffUVxGIxvL29+Q6PEEII4cT06dMBAOnp6bC3t4e6urrkug4dOsDY2LjNn91GCCGkGjUnI4TUiRom1K1bt2746aefYGlpKdW4LTY2Fm5ubvjrr7/4DpGQFi8pKQkJCQkQiUSYOnUq3+EQQgghnIqMjMSsWbOkzt4ihBBCXkWJW0JInZSVlVFQUAA9PT2p8cePH0NPTw+VlZU8Rca/BQsW4OHDh4iOjoaOjg4yMzOhrKyM6dOnY8yYMQgLC+M7REIIIYQQQgghhLRgVOOWEFInaphQN6r/Swj3goKCEBERITMeERGBzZs38xARIYQQ0nwqKyuxbds2DB06FN27d4eOjo7UDyGEEEI1bgkhMiwtLcEwjKRhQrt2/3urqKysxJ07dzBx4kQeI+Qf1f8lhHt79+7FkSNHZMb79++P2bNnY+XKlTxERQghhDSPdevWITw8HEuXLoWfnx/WrFmDvLw8nDp1Cv7+/nyHRwghRACoVAIhRMa6desk//r4+NTZMKFDhw58hcg7qv9LCPdUVFSQlZWF3r17S43n5ubC3NwcL1684CkyQgghhHt9+vTBzp07MXnyZGhoaCA9PV0ydunSpVoPZhJCCGlbKHFLCKkTNUyoG9X/JYR7ffv2RUBAAObOnSs1fujQIQQEBCA3N5enyAghhBDuderUCVlZWTA0NIS+vj7Onj0LKysr5ObmwtLSEk+fPuU7REIIITyjUgmEkDo5OTnxHYJgUf1fQrjn7u4OLy8vlJeXY+zYsQCAuLg4rFixAj4+PjxHRwghhHCrZ8+eKCgogKGhIUQiEWJjY2FlZYXk5GSZs7oIIYS0TZS4JYTUqbKyEqGhoYiOjoZYLEZZWZnU9U+ePOEpMv5Q/V9Cms+KFSvw5MkTLF68WPJ+o6KigpUrV2LVqlU8R0cIIYRwy8HBAXFxcRg2bBg8PT3h6OiIr776CmKxGN7e3nyHRwghRACoVAIhpE7+/v71Nkzw8PDgO0SFo/q/hDS/f//9F1lZWVBVVUXfvn1p1REhhJA2ISkpCQkJCRCJRJg6dSrf4RBCCBEAStwSQupEDRPqRvV/CSGEEEIIIYQQ0pyU+A6AECJchYWFsLCwAACoq6tLGiS8//77OHv2LJ+h8c7JyYmStoQQQgghpNGCgoIQEREhMx4REYHNmzfzEBEhhBChocQtIaRONQ0TAEgaJgCghgmorme7bds2DB06FN27d4eOjo7UDyGEEEIIIfXZu3cv3nrrLZnx/v37Y8+ePTxERAghRGgocUsIqVNNwwQA8PT0hJ+fH/r27Yt58+bB1dWV5+j4tW7dOoSEhGDmzJl4+vQpli5dihkzZkDp/9q7Q5ZWwzAMwA/jhAliE1FQZlgxCjIw+isUjEsmRdimoLNp9gdYBJN/wbjgL1ibsCQIJoNo8KSzwxjfUdSP95VzXfFJd755ud9KJU5OTlLHAwAgc/f39zE/Pz9xn52dHT2eAOD/ZuMW+DAfJvxl/xcAgK+o1+vR7XZje3t77H55eRndbjcGg0GiZADk4lfqAMDP0Wg0otFopI6RhX/t/x4dHaWMBgDAD9BsNmN3dzdeX19jY2MjIiJubm6i1WrF/v5+4nQA5EBxCxQ6PT2Nubm5iVmEi4uLeHh4iHa7nShZen/2f5eWlkb7v6urq/Z/AQD4kFarFY+Pj7GzsxMvLy8REVGtVqPdbsfBwUHidADkwFQCUKhWq8XV1VWsr6+P3W9vb2NzczPu7u4SJUuv0+nEzMxMHB4exvX1dWxtbUWtVovhcBh7e3txdnaWOiIAAD/A09NT9Pv9mJqainq97hEAACOKW6BQtVqNfr8fy8vLY/fBYBArKyvx/PycKFl+7P8CAAAA38lUAlBocXExer3eRHHb6/ViYWEhUao82f8FAAAAvpPiFijkw4Ri9n8BAACAMplKAAq9vb1Fp9OJ8/PziQ8Tjo+PE6dLy/4vAAAAUCbFLfAuHyZMsv8LAAAAlMlUAvCu6enpWFtbSx0jK/Z/AQAAgDIpbgE+wf4vAAAAUCZTCQCfYP8XAAAAKJPiFuAL7P8CAAAAZVDcAgAAAABkppI6AAAAAAAA4xS3AAAAAACZUdwCAAAAAGRGcQsAAAAAkBnFLQAAAABAZhS3AAAAAACZUdwCAAAAAGRGcQsAAAAAkJnf9JFot8XCBe0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature importance\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Extract feature importance from the model\n",
    "feature_importance = pd.DataFrame({\n",
    "    'Feature': X.columns,\n",
    "    'Importance': gbm.feature_importances_\n",
    "})\n",
    "\n",
    "# Sort features by importance\n",
    "feature_importance_sorted = feature_importance.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Vertical Line Plot\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Colors to differentiate important features\n",
    "colors = ['green' if imp > 0.01 else 'red' for imp in feature_importance_sorted['Importance']]\n",
    "\n",
    "# Plot vertical lines\n",
    "for idx, (feature, imp, color) in enumerate(zip(feature_importance_sorted['Feature'],\n",
    "                                                feature_importance_sorted['Importance'],\n",
    "                                                colors)):\n",
    "    plt.plot([idx, idx], [0, imp], color=color, linewidth=2)\n",
    "    plt.scatter(idx, imp, color=color, s=60, edgecolor='black')  # Marker at the tip of the line\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(len(feature_importance_sorted)), feature_importance_sorted['Feature'], rotation=90, fontsize=10)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.ylabel(\"Feature Importance\", fontsize=14)\n",
    "plt.title(\"Feature Importance in Gradient Boosting Model (Vertical Line Plot)\", fontsize=16)\n",
    "\n",
    "# Optimize layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "003ca531-df17-45ea-a404-9c256febfd24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Top 10 Features: ['company_age', 'days_to_first_funding', 'funding_duration', 'funding_total_usd', 'category_updated_Healthcare', 'state_code_CA', 'category_count', 'category_updated_other', 'category_updated_Media & Entertainment', 'funding_rounds']\n"
     ]
    }
   ],
   "source": [
    "#Select top 10 features\n",
    "top_n = 10 \n",
    "selected_features_top_10 = feature_importance_sorted.head(top_n)['Feature'].tolist()\n",
    "print(\"Selected Top 10 Features:\", selected_features_top_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0ccdb063-ce0b-4494-a9d4-2c49e1b9dcfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features based on threshold: ['company_age', 'days_to_first_funding', 'funding_duration', 'funding_total_usd']\n"
     ]
    }
   ],
   "source": [
    "# Set a threshold for feature importance\n",
    "threshold = 0.05\n",
    "\n",
    "# Filter features based on importance\n",
    "important_features = feature_importance_sorted[feature_importance_sorted['Importance'] >=threshold]['Feature'].tolist()\n",
    "print(\"Selected Features based on threshold:\", important_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa52e76-4d96-449c-a44b-f4f887a3e959",
   "metadata": {},
   "source": [
    "## Gradient Boosting with Top 10 features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c8a066cf-bea8-4151-aadf-65ab9055d76b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[  90  274]\n",
      " [  77 1837]]\n",
      "Accuracy: 0.85\n",
      "True Positive Rate (TPR): 0.96\n",
      "False Positive Rate (FPR): 0.75\n",
      "Precision: 0.87\n",
      "F1 Score: 0.91\n"
     ]
    }
   ],
   "source": [
    "#Build the model using those features\n",
    "\n",
    "X_important = X[selected_features_top_10]\n",
    "\n",
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled_imp = scaler.fit_transform(X_important)\n",
    "\n",
    "#Split data into training and testing\n",
    "X_train_imp, X_test_imp, y_train, y_test = train_test_split(X_scaled_imp, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Initialize and train the model\n",
    "gbm = GradientBoostingClassifier(random_state=42)\n",
    "gbm.fit(X_train_imp, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = gbm.predict(X_test_imp)\n",
    "y_pred_proba = gbm.predict_proba(X_test_imp)[:, 1]\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8968713f-d451-4202-8078-d21b9f406dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model', linestyle='--')  \n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "65892aa0-3e65-48e0-809f-7f8a97e19246",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 1 (without cross-validation), after feature selection (top 10 )\n",
    "model_1_results_1 = {\n",
    "    'Model': 'Gradient Boosting',  \n",
    "    'Features Selected': 'Top 10 Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_1 = pd.DataFrame([model_1_results_1])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_1], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe783138-2040-4b1c-a46b-116c7eea3f62",
   "metadata": {},
   "source": [
    "## Gradient Boosting with Features by threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9e2d5adc-df7e-409a-84af-2977aaac1835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[  81  283]\n",
      " [  76 1838]]\n",
      "Accuracy: 0.84\n",
      "True Positive Rate (TPR): 0.96\n",
      "False Positive Rate (FPR): 0.78\n",
      "Precision: 0.87\n",
      "F1 Score: 0.91\n"
     ]
    }
   ],
   "source": [
    "#Build the model using those features\n",
    "\n",
    "X_important = X[important_features]\n",
    "\n",
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled_imp = scaler.fit_transform(X_important)\n",
    "\n",
    "#Split data into training and testing\n",
    "X_train_imp, X_test_imp, y_train, y_test = train_test_split(X_scaled_imp, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Initialize and train the model\n",
    "gbm = GradientBoostingClassifier(random_state=42)\n",
    "gbm.fit(X_train_imp, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = gbm.predict(X_test_imp)\n",
    "y_pred_proba = gbm.predict_proba(X_test_imp)[:, 1]\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4ed216a4-738f-42e4-a0fb-047ea7cfe18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model', linestyle='--')  \n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8225d832-4197-4c24-bd9c-8c6d37deab40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 1 (without cross-validation), after feature selection (based on threshold )\n",
    "model_1_results_2 = {\n",
    "    'Model': 'Gradient Boosting',  \n",
    "    'Features Selected': 'Thresholded Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_2 = pd.DataFrame([model_1_results_2])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfff446-9c75-437f-8f00-4ef38470b7f7",
   "metadata": {},
   "source": [
    "## XGBoosting without CV - No feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "cde105e4-ee06-4012-92ba-f86deb382b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the XGBooster to implement weights on the dataset for more balancing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a1ab8aa2-3bd2-4537-99e6-5bc7eae66371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of negative to positive class:  0.19038159958180867\n"
     ]
    }
   ],
   "source": [
    "# Calculate the count of each class (0 and 1)\n",
    "class_counts = df_processed['is_operating'].value_counts()\n",
    "\n",
    "# Calculate the ratio of negative (0) to positive (1) classes\n",
    "ratio_of_negative_to_positive_class = class_counts[0] / class_counts[1]\n",
    "\n",
    "print(\"Ratio of negative to positive class: \", ratio_of_negative_to_positive_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "cffd7f7c-aa6e-43e8-9c54-5e9098dc967b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 251  113]\n",
      " [ 389 1525]]\n",
      "Accuracy: 0.78\n",
      "True Positive Rate (TPR): 0.80\n",
      "False Positive Rate (FPR): 0.31\n",
      "Precision: 0.93\n",
      "F1 Score: 0.86\n"
     ]
    }
   ],
   "source": [
    "#Build the model \n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# XGBoost with scale_pos_weight\n",
    "xgb = XGBClassifier(scale_pos_weight=ratio_of_negative_to_positive_class, random_state=42)\n",
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "\n",
    "#Evaluation Metrics\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred_xgb)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred_xgb)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred_xgb)\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred_xgb)\n",
    "\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7f928fc1-0d12-4792-8bff-88db07c68af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "y_prob_test = xgb.predict_proba(X_test)[:, 1]  # For probabilities\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model (AUC = 0.50)', linestyle='--')  # Diagonal line for random guess\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "9a41366b-c1ce-41eb-acf6-221e16398df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (without cross-validation), no feature selection \n",
    "model_2_results = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'All Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_3 = pd.DataFrame([model_2_results])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_3], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8382304a-a0ed-489c-ae09-4d34b3d09504",
   "metadata": {},
   "source": [
    "## Feature importance of XGBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4bde367c-c38c-4365-8d37-97dc1aa02b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Initialize the XGBoost model\n",
    "xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "# Fit the model on training data\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# Extract feature importance from the trained model\n",
    "feature_importance = pd.DataFrame({\n",
    "    'Feature': X.columns,\n",
    "    'Importance': xgb_model.feature_importances_\n",
    "})\n",
    "\n",
    "# Sort features by importance\n",
    "feature_importance_sorted = feature_importance.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Vertical Line Plot\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Colors to differentiate important features\n",
    "colors = ['green' if imp > 0.01 else 'red' for imp in feature_importance_sorted['Importance']]\n",
    "\n",
    "# Plot vertical lines\n",
    "for idx, (feature, imp, color) in enumerate(zip(feature_importance_sorted['Feature'],\n",
    "                                                feature_importance_sorted['Importance'],\n",
    "                                                colors)):\n",
    "    plt.plot([idx, idx], [0, imp], color=color, linewidth=2)\n",
    "    plt.scatter(idx, imp, color=color, s=60, edgecolor='black')  # Marker at the tip of the line\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(len(feature_importance_sorted)), feature_importance_sorted['Feature'], rotation=90, fontsize=10)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.ylabel(\"Feature Importance\", fontsize=14)\n",
    "plt.title(\"Feature Importance in XGBoost Model (Vertical Line Plot)\", fontsize=16)\n",
    "\n",
    "# Optimize layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c5c695-c7c6-4ac1-b09e-2d0986a98ebc",
   "metadata": {},
   "source": [
    "## XGBoosting Model with Top 10 features - No CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7e544042-6dd3-47d5-ae18-aeb960f618f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Top 10 Features: ['category_updated_Healthcare', 'company_age', 'funding_duration', 'state_code_UT', 'state_code_OH', 'days_to_first_funding', 'state_code_CA', 'category_updated_Transport & Automotive', 'state_code_CO', 'category_updated_Retail & E-Commerce']\n"
     ]
    }
   ],
   "source": [
    "#Select top 10 features\n",
    "top_n = 10 \n",
    "selected_features_top_10 = feature_importance_sorted.head(top_n)['Feature'].tolist()\n",
    "print(\"Selected Top 10 Features:\", selected_features_top_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4cb9b6aa-500c-4db2-b5b8-3c7f00588a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_important = X[selected_features_top_10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3e49ee89-935b-4525-b0fd-d65b8f760e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of negative to positive class:  0.19038159958180867\n"
     ]
    }
   ],
   "source": [
    "# Calculate the count of each class (0 and 1)\n",
    "class_counts = df_processed['is_operating'].value_counts()\n",
    "\n",
    "# Calculate the ratio of negative (0) to positive (1) classes\n",
    "ratio_of_negative_to_positive_class = class_counts[0] / class_counts[1]\n",
    "\n",
    "print(\"Ratio of negative to positive class: \", ratio_of_negative_to_positive_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "274c6066-5edc-4687-9b3d-4691e67db435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled_imp = scaler.fit_transform(X_important)\n",
    "\n",
    "#Split data into training and testing\n",
    "X_train_imp, X_test_imp, y_train, y_test = train_test_split(X_scaled_imp, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "b1777adf-343f-4d34-bec4-7e3dc952a147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 268   96]\n",
      " [ 506 1408]]\n",
      "Accuracy: 0.74\n",
      "True Positive Rate (TPR): 0.74\n",
      "False Positive Rate (FPR): 0.26\n",
      "Precision: 0.94\n",
      "F1 Score: 0.82\n"
     ]
    }
   ],
   "source": [
    "#Build the model \n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# XGBoost with scale_pos_weight\n",
    "xgb = XGBClassifier(scale_pos_weight=ratio_of_negative_to_positive_class, random_state=42)\n",
    "xgb.fit(X_train_imp, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred_xgb = xgb.predict(X_test_imp)\n",
    "\n",
    "#Evaluation Metrics\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred_xgb)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred_xgb)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred_xgb)\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred_xgb)\n",
    "\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "83374365-7f98-42d1-8211-22c1406db04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get predicted probabilities for the positive class (class 1)\n",
    "y_prob_test = xgb.predict_proba(X_test_imp)[:, 1]  # For probabilities\n",
    "\n",
    "# Compute ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "\n",
    "# Compute AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model (AUC = 0.50)', linestyle='--')  # Diagonal line for random guess\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4d06d9cf-f96e-4dd9-b09e-d71ee465ff0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (without cross-validation), Top 10 feature selection \n",
    "model_2_results_1 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Top 10 Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_4 = pd.DataFrame([model_2_results_1])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_4], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d51135-e833-41ca-9cf8-a19bff9291a5",
   "metadata": {},
   "source": [
    "## GridSearch for Top 10 features - XGBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4bba560a-cac4-4cba-9cb8-60d53a91262f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n",
      "Best Parameters: {'colsample_bytree': 1.0, 'learning_rate': 0.2, 'max_depth': 7, 'n_estimators': 200, 'scale_pos_weight': 0.19038159958180867, 'subsample': 0.8}\n",
      "Best Cross-Validation Accuracy: 0.7708\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2],\n",
    "    \"subsample\": [0.8, 1.0],\n",
    "    \"colsample_bytree\": [0.8, 1.0],\n",
    "    \"scale_pos_weight\": [ratio_of_negative_to_positive_class],\n",
    "}\n",
    "\n",
    "# Create the XGBoost model\n",
    "xgb = XGBClassifier(random_state=42)\n",
    "\n",
    "# Perform Grid Search with Cross-Validation\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=xgb,\n",
    "    param_grid=param_grid,\n",
    "    scoring=\"accuracy\",\n",
    "    cv=5,  # 5-fold cross-validation\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train_imp, y_train)\n",
    "\n",
    "# Display the best parameters and best score\n",
    "print(f\"Best Parameters: {grid_search.best_params_}\")\n",
    "print(f\"Best Cross-Validation Accuracy: {grid_search.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "407e745f-45eb-419d-a322-020a59c8e113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the final model with the best parameters\n",
    "final_xgb_Grid= XGBClassifier(\n",
    "    colsample_bytree=1.0,\n",
    "    learning_rate=0.2,\n",
    "    max_depth=7,\n",
    "    n_estimators=200,\n",
    "    scale_pos_weight=0.19038159958180867,\n",
    "    subsample=0.8,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "final_xgb_Grid.fit(X_train_imp, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "442eed89-faf2-4c2c-8551-2e61f5f8e2c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Test Data):\n",
      "[[ 229  135]\n",
      " [ 426 1488]]\n",
      "Accuracy: 0.7537\n",
      "True Positive Rate (TPR): 0.7774\n",
      "False Positive Rate (FPR): 0.3709\n",
      "Precision: 0.9168\n",
      "F1 Score: 0.84\n",
      "ROC-AUC: 0.8037\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict on test data\n",
    "y_pred_test = final_xgb_Grid.predict(X_test_imp)\n",
    "y_prob_test = final_xgb_Grid.predict_proba(X_test_imp)[:, 1]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n",
    "\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_test)\n",
    "TP, FP, FN, TN = conf_matrix[1, 1], conf_matrix[0, 1], conf_matrix[1, 0], conf_matrix[0, 0]\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(y_test, y_pred_test)\n",
    "TPR = TP / (TP + FN)  # True Positive Rate (Recall/Sensitivity)\n",
    "FPR = FP / (FP + TN)  # False Positive Rate\n",
    "precision = TP / (TP + FP)\n",
    "f1 = f1_score(y_test, y_pred_test)\n",
    "auc = roc_auc_score(y_test, y_prob_test)\n",
    "\n",
    "# Print metrics\n",
    "print(\"Confusion Matrix (Test Data):\")\n",
    "print(conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"True Positive Rate (TPR): {TPR:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {FPR:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"ROC-AUC: {auc:.4f}\")\n",
    "\n",
    "# Plot AUC/ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {auc:.4f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle=\"--\", label=\"Random Guess\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.title(\"AUC/ROC Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "1084b47d-14e0-4954-b44b-f63562ac7b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (with GridSearch cross-validation), Top 10 feature selection \n",
    "model_2_results_2 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Top 10 Features',  \n",
    "    'Validation Technique': 'GridSearch CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': TPR,\n",
    "    'FPR': FPR,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_5 = pd.DataFrame([model_2_results_2])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_5], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d144ac94-8701-4858-b1f3-b74788a901a9",
   "metadata": {},
   "source": [
    "## Random Search CV for Top 10 features - XG Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "1073f67d-1f2f-4930-9049-295364c83d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Best parameters found: {'subsample': 0.8, 'scale_pos_weight': 0.19038159958180867, 'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.1, 'colsample_bytree': 0.8}\n",
      "Best AUC score: 0.8356254481814904\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# Define parameter grid\n",
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2],\n",
    "    \"subsample\": [0.8, 1.0],\n",
    "    \"colsample_bytree\": [0.8, 1.0],\n",
    "    \"scale_pos_weight\": [ratio_of_negative_to_positive_class]\n",
    "}\n",
    "\n",
    "# Initialize model\n",
    "xgb = XGBClassifier(random_state=42, eval_metric=\"logloss\")\n",
    "\n",
    "# RandomizedSearchCV\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=xgb,\n",
    "    param_distributions=param_grid,\n",
    "    n_iter=50,  # Test 50 random combinations\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=5,\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Fit\n",
    "random_search.fit(X_train_imp, y_train)\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best parameters found:\", random_search.best_params_)\n",
    "print(\"Best AUC score:\", random_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "e256e20a-0837-4c9e-a67a-95ebbbc17442",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = random_search.best_estimator_\n",
    "y_test_proba = best_model.predict_proba(X_test_imp)[:, 1]\n",
    "y_test_pred = best_model.predict(X_test_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "f013731b-df4e-4554-9a4a-b7c55432c5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Test Data):\n",
      "[[ 285   79]\n",
      " [ 532 1382]]\n",
      "Accuracy: 0.7318\n",
      "True Positive Rate (TPR): 0.7220\n",
      "False Positive Rate (FPR): 0.2170\n",
      "Precision: 0.9459\n",
      "F1 Score: 0.84\n",
      "ROC-AUC: 0.8293\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
    "TP, FP, FN, TN = conf_matrix[1, 1], conf_matrix[0, 1], conf_matrix[1, 0], conf_matrix[0, 0]\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(y_test, y_test_pred)\n",
    "TPR = TP / (TP + FN)  # True Positive Rate (Recall/Sensitivity)\n",
    "FPR = FP / (FP + TN)  # False Positive Rate\n",
    "precision = TP / (TP + FP)\n",
    "f1 = f1_score(y_test, y_pred_test)\n",
    "auc = roc_auc_score(y_test, y_test_proba)\n",
    "\n",
    "# Print metrics\n",
    "print(\"Confusion Matrix (Test Data):\")\n",
    "print(conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"True Positive Rate (TPR): {TPR:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {FPR:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"ROC-AUC: {auc:.4f}\")\n",
    "\n",
    "# Plot AUC/ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_test_proba)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {auc:.4f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle=\"--\", label=\"Random Guess\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.title(\"AUC/ROC Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "a81cfff4-9d52-432a-9e7c-eba525916607",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (with RandomSearch cross-validation), Top 10 feature selection \n",
    "model_2_results_3 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Top 10 Features',  \n",
    "    'Validation Technique': 'RandomSearch CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': TPR,\n",
    "    'FPR': FPR,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_6 = pd.DataFrame([model_2_results_3])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_6], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c248962-3599-496f-9192-ded4d8c17acb",
   "metadata": {},
   "source": [
    "## XG Boosting Model with Features selected based on threshold value - No CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8bb16797-5d8a-4787-9e83-74186f76b4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a threshold for feature importance\n",
    "threshold = 0.02\n",
    "\n",
    "# Filter features based on importance\n",
    "important_features = feature_importance_sorted[feature_importance_sorted['Importance'] >= threshold]['Feature']\n",
    "X_important = X[important_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "170880b1-6363-4f5b-a869-6ee4133882fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 261  103]\n",
      " [ 431 1483]]\n",
      "Accuracy: 0.77\n",
      "True Positive Rate (TPR): 0.77\n",
      "False Positive Rate (FPR): 0.28\n",
      "F1 Score: 0.85\n",
      "Precision: 0.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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LFDs7O/Tt2xc7duzQ/zS6bt06lChRAm3atDE4xvfff290jGrVqhnEq6P7OfVZdD8t637yNOXatWsAgNKlSxu0+/j4GO2ra7t//77F4t6+fTu6deuGkiVL4uuvv8axY8dw8uRJDBo0CImJiVl6nBlxcXGBk5OTQZujo6PB/d6/f98gadcx1WZKmTJlMn1+TSlatKhRm6OjIx4/fqzfNvd5MfXcnjhxAq1btwYArFq1CkeOHMHJkycxbdo0ANAf7+7duwDwzPdCRiIjI/Ho0SM4ODgYvRYiIiKy/fqdOnUqFi5ciOPHj6Ndu3YoWrQoXnrppQynvHqWu3fvwtfX16DkJ6seP35s9FpK64MPPsDJkydx8OBBTJs2DZGRkejcuTOSkpL0+2Tl/RgfH4979+7p349ZuY0puljTvqZMyc7r1xym+trT0xOvvPIK1q9fj9TUVADK52K9evX0nx33799HSkoKPvnkE6PXlK4MIbPPXsqfWDNL+UaVKlVQt25dAECLFi2QmpqK1atX49tvv0XXrl1RrFgxAMofwi5dupi8j8qVKwNQ6lp1o4wZKVasGJydnfHFF19keH1mBg4ciA8//FBfs7tr1y6MGzcOtra2BvdRs2ZNvPfeeybvw9fX12A7q6M2rVq1wjvvvIOdO3cajTzq6OajbNWqlUF7RESE0b66Nl0yZom4v/76a/j7+2PLli0G16dNAnJS0aJFceLECaN2U4/flDZt2uCTTz7B8ePHLVp3aO7zYuq53bx5M+zt7bF7926DRCz9HKReXl4AgFu3bhl9qcmKYsWKoWjRoti3b5/J6wsVKvTMWE2xs7NDcHAwgoOD8ejRI/z0009455130KZNG9y8eRMuLi5mxenl5YXffvsNWq3W7IS2WLFiePDgQYbXlytXTv+59OKLL8LZ2RnTp0/HJ598gokTJwIAgoKCUKRIEezatQvz5883+Tzs2rULWq1W/36sW7cuPD098d1332V4G1N0sT7r88nc16+Tk5PJ1+C9e/dMHiujeAcOHIhvvvkGISEhKFOmDE6ePIkVK1bory9SpAhsbW3Rt2/fDH8x8Pf3f2a8lM+oXOZA9Nwyms3gwYMH+jOEdbWwFStWlPbt2z/zPnU1s5nVlL777rvi4uIiV69efeb9ZVRPVr9+falXr54sW7bMZA3rkCFDxNfX95k1YLra0w8//PCZsejopuZKP3elyNOpudq2bWvQjkxqZsuXL2/RuLt06WJQwyqizJDg5uYm6T+6PD09pVu3bkb3kdlsBunpzoDX0dXMpq19Fsl6zWxWpjbavn27fjujqbn69+9vUI9qzvOC/2YzSC84OFjc3NwM6pQTEhKkTJkyBnWmYWFhYmtrK3379s30sXbp0kW8vb2N2r/++mt9Pfuz6GYzyOi6Z029tnjxYoN6bF39pam694xqZtesWfPMONMbNGiQeHp6GrVnNJvBkydPpEKFClK0aFGJiYnRt+um5vrggw+M7isyMlI/NVfa19KzpuaKjIw0en9v2LBBAMgff/yR6eMy9/Xbpk0bqVq1qsE+//zzj9jZ2ZmsmU3/vOikpKRIyZIlpVu3bjJx4kRxcnIyOv7LL78stWrVyvJ8uZT/cWSW8q0iRYpg6tSpmDx5MjZu3Ig+ffrgs88+Q7t27dCmTRsMGDAAJUuWxIMHD3DhwgWcOXMG33zzDQBgzpw52Lt3L1588UW88847qFGjBh49eoR9+/YhODgYAQEBGDduHLZt24YXX3wR48ePR82aNaHVanHjxg3s378fEyZMQP369TONcdCgQRg+fDju3LmDRo0a6UeGdebMmYOQkBA0atQIY8eOReXKlZGYmIhr165hz549WLlyZbZ/Al6/fj1efvlltG7dGmPHjsVLL70EQKmlXLJkCQICAgxq13SKFSuGli1b4n//+59+NoOLFy9i8+bNFo27Y8eO2L59O0aOHImuXbvi5s2bmDt3LkqUKGG0sluNGjXw66+/4vvvv0eJEiVQqFAho+fSXP3798fHH3+MPn364N1330WFChWwd+9e/PjjjwDwzBE8f39//ah7YGAgRo8ejdq1awNQzsb+4osvICJ47bXXzIrLnOclIx06dMBHH32EXr16YdiwYbh//z4WLlwIR0dHg/3Kli2Ld955B3PnzsXjx4/Rs2dPeHh44Pz587h37x5mz54NQHn+t2/fjhUrViAoKAg2NjaoW7cuevTogQ0bNqB9+/Z46623UK9ePdjb2+PWrVs4cOAAXn31VbMfP6Cc2V+9enXUrVsXXl5euH79OhYvXgw/Pz/9DB41atQAACxZsgT9+/eHvb09KleubDQaDCh10GvXrsWIESPwzz//oEWLFtBqtfj9999RpUoV9OjRI8NYmjdvji+++AKXLl3KUr2vvb095s2bh27dumHJkiWYPn06AODtt9/GH3/8of+3e/fu8PDwwJ9//okPP/wQsbGx2L17Nzw8PPT3NWnSJFy4cAEzZ87EiRMn0KtXL5QuXRrR0dE4dOgQPv/8c8yePRuNGzfW3+b48eMoWrSo/vnJiLmv3759+6JPnz4YOXIkXn/9dVy/fh0LFizQj+5nla2tLfr164ePPvoI7u7u6NKli8FjBpQ+bdKkCZo2bYo333wTZcuWRWxsLP799198//33+OWXX8w6JuUDamfTRM8ro5FZEWVOzjJlykjFihUlJSVFRET++OMP6datm3h7e4u9vb34+PhIy5Ytjc4KvnnzpgwaNEh8fHz0c8h269ZNIiMj9fvExcXJ9OnT9XNo6ua7HD9+vMHoZUYjS9HR0eLs7JzpmdR3796VsWPHir+/v9jb24unp6cEBQXJtGnT9PPZZmdkVhf/vHnzJDAwUFxcXMTFxUVq1qwp7777rtFcuSJPR/qWL18u5cuXF3t7ewkICDA5Cbsl4n7//felbNmy4ujoKFWqVJFVq1YZjaCKiJw9e1YaN24sLi4uWZ5nNj1T93vjxg3p0qWLuLm5SaFCheT11183OedlZq5cuSIjR46UChUqiKOjozg7O0vVqlUlODjY4Ez7rI7MmvO8IIORWRGRL774QipXriyOjo5Srlw5mT9/vqxZs8bkDADr16+XF154QZycnMTNzU1q165tMDL94MED6dq1qxQuXFg0Go1BHMnJybJw4UKpVauW/vYBAQEyfPhwuXz5sn4/c0ZmFy1aJI0aNZJixYqJg4ODlClTRgYPHizXrl0zuN3UqVPF19dXbGxsnjnP7OPHj2XGjBn6+ZOLFi0qLVu2lKNHj5qMSSc6Olrc3NxkwYIFBu3PGoGsX7++FClSxGDUUavVyoYNG6R58+ZSuHBhcXBwEH9/f3nzzTeNZgZJ67vvvpMOHTqIl5eX2NnZSZEiRaRFixaycuVKg9FLrVYrfn5+MmbMmEwfU1pZff1qtVpZsGCBlCtXTpycnKRu3bryyy+/ZDibQUbPi4gyhyz+mxs4JCTE5D5hYWEyaNAg/TzWXl5e0qhRI3n33Xez/Ngo/9CI/HfKMhHRM2g0GowaNQrLli1TOxTVzJs3D9OnT8eNGzeyPSpO+cuYMWPw888/4++//37u2QZy0s8//4zWrVvj77//RkBAgNrhEFkMywyIiDKgS9oDAgKQnJyMX375BUuXLkWfPn2YyJLe9OnTsX79emzbtk2/cEhe9O6772LQoEFMZCnfYTJLRJQBFxcXfPzxx7h27RqSkpJQpkwZvP322/o6RyJAma5tw4YNePjwodqhZOjhw4do1qyZfhpCovyEZQZEREREZLW4aAIRERERWS0ms0RERERktZjMEhEREZHVKnAngGm1Wty5cweFChXK01OoEBERERVUIoLY2Fj4+vo+c5GaApfM3rlzJ1trjBMRERFR7rp58+Yzp0IscMmsbinDmzdvwt3dPVeOqdVqcffuXXh5eT3z2wXlPew/68c+tH7sQ+vG/rN+ud2HMTExKF26tMklqNMrcMmsrrTA3d09V5PZxMREuLu7801shdh/1o99aP3Yh9aN/Wf91OrDrJSE8hVFRERERFaLySwRERERWS0ms0RERERktZjMEhEREZHVYjJLRERERFaLySwRERERWS0ms0RERERktZjMEhEREZHVYjJLRERERFaLySwRERERWS0ms0RERERktZjMEhEREZHVYjJLRERERFaLySwRERERWS1Vk9lDhw6hU6dO8PX1hUajwc6dO595m4MHDyIoKAhOTk4oV64cVq5cmfOBEhEREVGepGoyGx8fj1q1amHZsmVZ2j8sLAzt27dH06ZNERoainfeeQdjx47Ftm3bcjhSIiIiIsqL7NQ8eLt27dCuXbss779y5UqUKVMGixcvBgBUqVIFp06dwsKFC/H666/nUJREREREBYsIkJDwdFv7JAUJCRqIqBdTRlRNZs117NgxtG7d2qCtTZs2WLNmDZKTk2Fvb290m6SkJCQlJem3Y2JiAABarRZarTZnA/6PVquFiOTa8ciy2H/Wj31o/diH1o39l/elTV5FgGbNNDh7VgMA6IRd+AjBeBk/4Y9HWhQqlPPxmPNasapkNiIiAsWLFzdoK168OFJSUnDv3j2UKFHC6Dbz58/H7Nmzjdrv3r2LxMTEHIs1La1Wi+joaIgIbGx4zp21Yf9ZP/ah9WMfWjf2X94kAjx+rIy2du7sib/+MhwUtMcTvI8pCMbHAICpmI+7d2fh8WNNjscWGxub5X2tKpkFAI3G8AmU/8a707frTJ06FcHBwfrtmJgYlC5dGl5eXnB3d8+5QNPQarXQaDTw8vLim9gKsf+sH/vQ+rEPrRv7L2elLwnI6m3Sjr6mVxZh2OXSAzUSTgAAkt4ci5fHTEAZPy/Y2uZ8Hzo5OWV5X6tKZn18fBAREWHQFhUVBTs7OxQtWtTkbRwdHeHo6GjUbmNjk6tvKI1Gk+vHJMth/1k/9qH1Yx9aN/af+bKSpIoATZsCZ88+//ECA4HDhwG7XdvhOHIQNNHRQJEiwLp1sO/YEa5RUbC1zZ0+NOcYVpXMNmzYEN9//71B2/79+1G3bl2T9bJEREREeYG5o6eWTFIzo0tgNRrAxQXQPHoIjBoMREcDDRsCmzYBfn5AHq53VjWZjYuLw7///qvfDgsLw9mzZ+Hp6YkyZcpg6tSpuH37NtavXw8AGDFiBJYtW4bg4GAMHToUx44dw5o1a7Bp0ya1HgIRERGRSboENjcS07RJqTlcXNLdpkgRYO1a4Ngx4N13ASsYLFQ1mT116hRatGih39bVtvbv3x/r1q1DeHg4bty4ob/e398fe/bswfjx4/Hpp5/C19cXS5cu5bRcREREZFHZqUNNf3tLJLBZTVKNklJzbN0KuLsDbdsq2507KxcroWoy27x5c/0JXKasW7fOqK1Zs2Y4c+ZMDkZFREREBYWppDWnRlKzM3r6XEnqszx+DAQHAytXAkWLAn/+Cfj65tDBco5V1cwSERERZUduJq1pGdWk5vysVlnzzz9At25KAqvRACNGAN7eakeVLUxmiYiIKF9Jn7hmN2nNbh1qWnkqgdXZsAEYPhyIj1cS2K+/Blq1UjuqbGMyS0RERPmGVgsEBZmXuGaUtObJRPR5pKYqSeyaNcp2ixZKYmti0SlrwmSWiIiIrJKpEdg6dYDLl03vX2CS1ozY2ir/ajTAzJnA9OlP26wYk1kiIiLK07JT71qxInDmjGGSWmCS1vQSEwHdilpLlwIDBgBNmqgakiUxmSUiIqI8wxL1roGBwOnTQIFfbCwuDhg1CoiIAPbuVZ4QF5d8lcgCTGaJiIhIJZZKXNOXDhTYEdi0zp1TZiu4eFFJYo8fBxo1UjuqHMFkloiIiCwubaKq1QIJCRrExz8dLTU3cS3w9a5ZJQKsXg2MHauUF5QsqSxJm08TWYDJLBEREVmY8YwCNgCKZ/n2HG3NppgYZbaCzZuV7XbtgPXrgWLF1I0rhzGZJSIiomwzd0aB9Ji4WlCPHkptrK0tMH8+MGFCgSgcZjJLRERUAJiaEcAS95mVGQVEtLh79y68vLxgky65YuJqQe+9B/z7L/Dll0DDhmpHk2uYzBIREeUjai3bml7aGQW0WiA+XuDqWiAGCnNPdDRw9KhSTgAAtWsD588DdgUrvStYj5aIiMjKZTbCqkbSCrBUQBWnTgHduwM3byoJbd26SnsBS2QBJrNERER5Uk6MsGY0I8DzYuKai0SUhQ8mTQKSk4GyZdWOSHVMZomIiFRmiflW0+I0VvnUw4fAoEHAzp3KdpcuwJo1QOHCakalOiazREREucRSo63PGmFl0poP/f67UlZw/Trg4AAsWqSs7sWOZjJLRESUk3QJrCWTViarBdDBg0oiW748sHWrMv8ZAWAyS0REZBHPO+rKk6goUxMnKi+G4cMBd3e1o8lTmMwSERFlgaVmEeBoK2XJb78Bc+cC27dDP6fZpElqR5UnMZklIiJ6BuPlWc2TNoFl0kqZ0mqBDz4A/vc/IDVVWQhh3jy1o8rTmMwSERFlQASIj8/68qwcdaXnEhUF9O0L7N+vbPfpA7zzjroxWQEms0RERGlkdsKWbnlWziJAFvfrr0CvXkB4OODsDCxbBgwcyBdUFjCZJSKiAsncE7bSLs9KZFFffw3076+UGFStqsxWUK2a2lFZDSazRERUoOhKB8w9YcvVlYNklENatgSKFgU6dgQ++UR5sVGWMZklIqJ86XmmyuIJW5TjLl0CKlVS/u/rC/zxB1CihLoxWSn+WEJERPmGbtQ1Lk45acvNzfBSqJBhIhsYCMTGKvunvZw5o+zP0ViyuJQUYMYMoEoVYNu2p+1MZLONI7NERGQVdIlqRjWr2Znrlckq5arbt5WTvA4dUraPHwdef13dmPIBJrNERJQnZLYoQWoq0Lp1Ufz1l3k/KHKqLMoz9u1Tpt26d08Z9l+1CujRQ+2o8gUms0RElOvSJ67PHlW1QVYr41jvSnlKcrKyAMIHHyjbtWsDW7Yo87yRRTCZJSKiXPU8q2kFBgoOH9ZkmqAygaU85dChp4nsqFHAwoWAk5O6MeUzTGaJiChXZGU1rYzKArRaLe7evQs/Py/Y2jJTJSvy0kvKKl61awNdu6odTb7EZJaIiJ5bZvWuuuuzsppWRqOqWi0QHy8ccaW878kTYO5cYMQIoGRJpe2999SNKZ9jMktERNlm7gIEOlxNi/Kla9eA7t2BEyeUnxgOHGDNSy7gxwgREWWLCNCkifHcrZnRzet65gwTWcpnduxQSglOnAAKFwbGj2cim0s4MktERGbRlRTExwNHjz5tz6jeNS2enEX5TlISMGmSsgwtADRoAGzeDPj5qRtXAcJkloiIskw3Gps2iQWAyEjAy4uJKhUwt28Dr76q1MwASlL73nuAvb26cRUwTGaJiOiZMhqNBYDGjZnIUgFVuDDw+DFQtCjw5ZdAhw5qR1QgMZklIiqAnjX7QPp9TZ3gFRmpLAfL0gEqUBITAQcHpejb1VWplXVxAUqVUjuyAovJLBFRPmf+alvPxtFYKpD++Qfo1g3o2ROYMkVpq1RJ3ZiIySwRUX72PKttpcdlYqlA27ABGD5cqbW5excYM0YZmSXVMZklIrJCWSkTEMnealsZYQJLBVJCAjB2LLBmjbLdvLmS2DKRzTOYzBIRWZnsjLaas9oWEf3nwgWlrOCvv5Q3y4wZwP/+B9jaqh0ZpcFklojISuhW28pstNUUrrZFlA0xMUpx+MOHgI+PMhrbsqXaUZEJTGaJiKyAqdFYU6OtpnAEligb3N2BOXOA774Dvv4aKF5c7YgoA0xmiYjyKF1drKnaV462EuWAc+eA1FTlDQYAo0YBI0fyjZbHsXeIiPIYESAuTklg3dyAQoWeJrIVKwKxscqILP++ElmICLBqFVCvHtC1q1JiACg/afCNludxZJaIKA/JaLlYgKOxRDkiNlaZcmvTJmW7YkUgOVndmMgsTGaJiFRianqt9MvFcm5Xohx09qwyW8Hly8oMBe+9B0yaxG+MVobJLBFRLkpbB/usVbgiI7nKFlGOEAFWrgTGjweSkoDSpYHNm4FGjdSOjLKBySwRUQ4zJ4HV4XKxRDlIBNi1S0lkO3UC1q4FihZVOyrKJiazREQ5RDcvbGYJbEarcLGkgCgH2dgA69cD33wDvPkm32xWjsksEVEOeNaJXKyDJcpFIsDSpcDFi8CKFUqbl5cy7RZZPSazRETZZOoELh2eyEWURzx8CAwaBOzcqWy/8QZX8spnmMwSET2DqaTVnPpXnshFpJLffwe6dweuXwccHIBFi4AWLdSOiiyMySwRkQnZOWnLFJ7IRaQCEeCjj4ApU4CUFKB8eWDLFmVNaMp3mMwSEf0nOwlsRidw6bCkgEgFgwYB69Yp/+/WDfj8c8DDQ9WQKOcwmSWiAi2rCSxnHSCyIt27KyOxH32krO7FN2m+xmSWiAqc7CSwTFqJ8jCtFrh0CQgIULbbtgXCwoDixdWNi3IF12sjogJFN2WWmxtQqJBxIhsYqCzVHhcHnDmj7OfqykSWKM+KigLatwcaNFASWB0msgUGk1kiKlASEoznfmUCS2SlDh5U3sA//gg8eQKcO6d2RKQClhkQUYGhW5FLJzJSSVpZQkBkZVJTgffeA2bPVkoMqlQBtm4FqldXOzJSAUdmiahA0GqBunU1Br88urpyBJbI6kREAG3aADNnKm/sAQOAkyeZyBZgHJklonxPBGjTpij++utp1tq4sTIiS0RWZskS4OeflTfwihVAv35qR0QqYzJLRPmWbtaC2Fjgr7/sAQAVKyp1sRyRJbJSM2cCt24B06Y9nb2ACjSWGRBRvqGriY2PV07mqlNHOZmrRImnH3W6E7yYyBJZidu3gYkTlZW8AMDJCfjqKyaypMeRWSKyauas2tW4scDVlVkskdXYtw/o2xe4dw9wdwdmzFA7IsqDODJLRFZHNwKbdvTV1JyxgDJrT3S0FleuROLgQeGILJE1SE4Gpk4F2rVTEtnAQKBHD7WjojyKI7NElGfpRl3Tt5mz7KyLi+5+mMgSWYWbN5XEVTch9MiRwKJFSnkBkQlMZokoT9JqgaCgzMsGdJ617KxITkRIRBb3889At27AgwdKWcHq1cAbb6gdFeVxTGaJKFeZGm01tU+dOsDlyxnv86wEloiskI8P8Pix8k12yxagfHm1IyIrwGSWiHKNOaOtOrqptNInq0xgifKJ+HhlrjwAqFZNGZ2tUwdwdFQ3LrIaPAGMiHJU2pO1AgLMS2QDA4GLF5UTvHSrdXHVLqJ8ZOdOoGzZp/WxANCwIRNZMgtHZokox4gATZoY/p0CMh5tTY+jr0T5VFISMHkysHSpsv3xx0CjRurGRFZL9ZHZ5cuXw9/fH05OTggKCsLhw4cz3X/Dhg2oVasWXFxcUKJECQwcOBD379/PpWiJ6FnSLlxw965xIpvZaCtHX4kKgCtXlPWkdYnsxInAxo3qxkRWTdVkdsuWLRg3bhymTZuG0NBQNG3aFO3atcONGzdM7v/bb7+hX79+GDx4MP7++2988803OHnyJIYMGZLLkRNRWqbmfXVzA4oXf7pPZKRy/ZkzgI3qX6OJSBVbtwK1awOnTwOensDu3cCHHwL29mpHRlZM1T8pH330EQYPHowhQ4agSpUqWLx4MUqXLo0VK1aY3P/48eMoW7Ysxo4dC39/fzRp0gTDhw/HqVOncjlyItLRap+9cEHjxoCXF0dbiQoyhyNHYNOzJxAbq3wonD0LdOigdliUD6hWM/vkyROcPn0aU6ZMMWhv3bo1jqb/XfI/jRo1wrRp07Bnzx60a9cOUVFR+Pbbb9EhkzdDUlISkpKS9NsxMTEAAK1WC61Wa4FH8mxarRYikmvHI8ti/ykyWsCgbl0NLl82zFADA8VgtS3dwgVqzffKPrR+7EPrptVqkdSwIbRdukBTqRJk9mzAzk75NkxWIbffg+YcR7Vk9t69e0hNTUXxtL9DAihevDgiIiJM3qZRo0bYsGEDunfvjsTERKSkpOCVV17BJ598kuFx5s+fj9mzZxu13717F4mJic/3ILJIq9UiOjoaIgIb/r5qdQpi/4kAjx9rDLY7d/bEX39l/FNguXIp2L//PjQawNlZDBLf+PicjPbZCmIf5jfsQ+vkuHs3njRrhlRXV6X/liyBjZ2dsigCWZXcfg/GxsZmeV/VZzPQpPvNUUSM2nTOnz+PsWPHYsaMGWjTpg3Cw8MxadIkjBgxAmvWrDF5m6lTpyI4OFi/HRMTg9KlS8PLywvu7u6WeyCZ0Gq10Gg08PLy4oewFSoo/acbeRUBmjXT4OzZrNcDBAYKTp60gY2NVw5GmH0FpQ/zM/ahlUlIgGbcOGjWrIF064bUr79m/1m53H4POpmxfLFqyWyxYsVga2trNAobFRVlNFqrM3/+fDRu3BiTJk0CANSsWROurq5o2rQp3n33XZQoUcLoNo6OjnA0MV+djY1Nrr6hNBpNrh+TLCc/9V9G5QJNmz57Dti0q26l5eKiyfBLaF6Rn/qwoGIfWokLF5Qlaf/6C9BooAkIgI1Gw/7LB3KzD805hmqvKAcHBwQFBSEkJMSgPSQkBI0ymGsuISHB6MHZ2toCUEZ0iShzunlfdbMN6C6mTtwKDFTO04iLe3o5c4YLGBBRJr78EqhbV0lkixcHQkKA2bM5hQnlKFXLDIKDg9G3b1/UrVsXDRs2xOeff44bN25gxIgRAJQSgdu3b2P9+vUAgE6dOmHo0KFYsWKFvsxg3LhxqFevHnx9fdV8KER5nojpeV/TSjvyygULiCjL4uOBUaOUZBYAXnoJ+PprwMdH3bioQFA1me3evTvu37+POXPmIDw8HNWrV8eePXvg5+cHAAgPDzeYc3bAgAGIjY3FsmXLMGHCBBQuXBgtW7bEBx98oNZDILIKplbiiox8uhy6DhNYIsqWhARg/35lBHb2bGDqVOC/X06JcppGCtjv8zExMfDw8EB0dHSungAWFRUFb29v1gpZofzQf3FxSimBTuPGpmtf86v80IcFHfvQChw+rEy11ayZ0VXsP+uX231oTr6m+mwGRJSzdCd36URGKgsYFJRElohyQGwsMGIE0L490Lu30pb2g4YoF/HrEVE+pquT1Z3cFRjIRJaIntPZs0BQELBxIzB6NPDfYkREamEyS5RPiCjnYOgucXHKMrNpZ7orSKUFRGRhIsCKFUCDBsDly0CpUsDu3UAulewRZYRlBkT5gFarDJRkNk9s48bGJ3wREWVJdDQwdCjwzTfKdseOwLp1QNGiqoZFBDCZJbJ6IpknsrrptjgfLBFlS3y88iFz5QpgZwd88AEwfjw/UCjPYDJLZMXS18RWrKgsbJD2bwyn2yKi5+LqCrz+OrBli3KpX1/tiIgMsGaWyEpptcY1saZW6GIiS0Rme/gQuHXr6fa77wKhoUxkKU9iMktkhUyVFrAmlogs4vffgdq1ga5dgeRkpc3eHihSRN24iDLAZJbIypgqLYiN5UwFRPScRIBFi5TlAq9fVz5obt9WOyqiZ2IyS2RFMistYCJLRNl2/z7wyivAxIlASgrwxhvKh0vZsmpHRvRMPAGMyAro5pCtU0eZ3lGHpQVE9NyOHAF69FBqZB0dgcWLgeHD+Q2ZrAaTWaI8SgRISHi6HG3a+ljdrAU8wYuInouIMs3WrVvKB8vWrcp8fkRWhMkskcp0SWv6tvQJrE5gIHD6NGDDIiEiel4aDbBhgzJ37McfA4UKqR0RkdmYzBKpRFc6kFHSmh4XPyAiizh4UPnQeestZbtiRWD1alVDInoeTGaJVCCinDB89Gjm++kSWI2Gix8Q0XNKTQXmzQNmzVI+hOrUUb5NE1k5JrNEKoiPN0xk0yataTGBJSKLiIgA+vQBfv5Z2e7fX0lmifIBJrNEuSTtCV1p/4ZERgJeXkxaiSiH/Pwz0Lu38mHj4gIsX64ks0T5BE8hIcphIkBcnJLAurkp51foptcKDGQiS0Q5aP58oFUrJZGtXh04eZKJLOU7TGaJcpBukYNChYxP8tLNSsBElohyjLe38o16yBBlmdqqVdWOiMjiWGZAlENEgKAgwySWJ3QRUY6Li1N+BgKAQYOAypWVM06J8imOzBLlkISEp4lsxYpAbOzTpWc5vRYRWVxKCjB1qlJO8OCB0qbRMJGlfI/JLFEu0CWxTGCJKEfcvAk0bw68/z5w/TqwbZvaERHlGiazRLmASSwR5ZgfflBqmI4cAdzdgS1bgKFD1Y6KKNcwmSXKAbrVvYiIcsyTJ8DEiUDHjkpZQVCQ8jNQt25qR0aUq5jMEllQ2mm4ihdXOxoiytdmzQIWLVL+P3asMjJbvryqIRGpgckskYXolqhNPw1X48bKzAVERBY1cSJQqxawfTuwZAng6Kh2RESqYDJLZCEJCcZL1MbGml6mlojIbElJwPr1yjdnAPD0VMoKXntN3biIVMZ5Zomeg26JWsCwRpZL1BKRRV29qtTCnj6tJLW6E7xsOCZFxHcBUTakX6LWzc2wRpbzyBKRxXz7LVC7tpLIenoCJUqoHRFRnsJklshMGdXG6rBGlogsIjERGDUKeOMNICYGaNRI+dDp2FHtyIjyFJYZEJkpPt64NjZtXSyXqSWi53b5slJWoPvGPGUKMGcOYG+valhEeRGTWaIsSltaoMPaWCLKEbduAX/8ARQrBnz1FdC2rdoREeVZTGaJMqA7uUurBeLjNWjXTmNQVhAYyESWiCxI5OkHSosWwLp1wEsvASVLqhoWUV7HmlkiE3R1sW5ugLu7DSpUKI6zZ59mrYGByrkYTGSJyCIuXFA+dC5detrWrx8TWaIsYDJL9B/dErTx8cDdu4Z1sTq6uWPPnOGMOERkIV9+CdStq3zojB2rdjREVodlBkR4OhJrKoEND9ciIeEuvLy84OZmw9FYIrKM+HhltoIvv1S2W7ZUSguIyCwcWyKC8epdOo0bK3WxLi7CuWOJyHL++gt44QUlkbWxUWYq2L8f8PFROzIiq8ORWSI8XR0SUGYocHVV/u/iYngdEdFz+/135QSvx4+VBRA2bgSaN1c7KiKrxWSWCjRdnWza6bZcXZ8ms7p9iIgspk4doFYtwN1dmXbL21vtiIisGpNZKnB0U26JAE2bwmi6La7eRUQWd/48ULGisuiBvT3www9A4cI8k5TIAvguogJFq1UGRdzcjJej5XRbRGRxIsDKlcoHz7RpT9s9PZnIElkIR2apwBABgoIME1jg6XK0PMGLiCwqJgYYOhTYulXZvnABSE0FbG3VjYson2EySwVGQsLTRLZiRWWuWI1GKStgEktEFnX6NNC9O3DlCmBnB8yfDwQHczSWKAcwmaUCQXeil86ZM0qpARGRRYkAy5YBEycCT54Afn7A5s1AgwZqR0aUb/ErIuVbugQ2Lk4pVyte/Ol1HIklohxx+zbwzjtKItu5MxAaykSWKIdxZJbypcxW9GrcmDMWEFEOKVUKWLUKiIoCxozhN2eiXMBklvIF3XRbOvHxxoksT/QiIosTAT7+GKhdW1kIAQB69FA3JqIChsksWT2t1vQsBTq6Fb14ohcRWdSDB8CAAcD33yvL0J4/DxQponZURAUOk1myWmlX77p82fQ+jRsDXl5MYonIwo4eVUZgb94EHB2BGTOURRCIKNcxmSWrZKomNu10WzocjSUii9JqgQ8/VBZASE1VPni2blXqmIhIFUxmySqlr4nVrd7FKRyJKMc8fgy8/jqwd6+y3bMn8NlnynKCRKQaJrNkdUSApk2fbkdGspSAiHKBk5NSSuDkBCxdCgwZwg8eojyA41hkddKu5BUYyESWiHJQaurTFVc0GmUk9uRJZZlafvAQ5QlMZsmqpF/J6/Bh/j0hohwSGQm0bQv06aN8+ABKSUH16urGRUQGmMyS1dBquZIXEeWSX35Rfvr56Sdg/37g4kW1IyKiDDCZpTxPRFmSNiDAcC5ZruRFRBaXmgrMnAm8/DIQEQFUq6aUFVSponZkRJQBngBGeZqpBRF0U3BxJS8isqg7d4DevYFff1W2Bw9WTvTit2aiPI3JLOVZIsaJLKfgIqIcIQK8+ipw6pTyTfmzz5TElojyvGylBCkpKfjpp5/w2WefITY2FgBw584dxMXFWTQ4KtjSzlpQsSIQG6uMyDKRJSKL02iUUdigIOWDhokskdUwe2T2+vXraNu2LW7cuIGkpCS0atUKhQoVwoIFC5CYmIiVK1fmRJxUwJ05A7i5qR0FEeUrt24p35g7dlS2GzZU6mNZv0RkVcwe43rrrbdQt25dPHz4EM7Ozvr21157DT///LNFgyPS4d8WIrKoPXuUuqVu3YC//nrazg8bIqtj9sjsb7/9hiNHjsDBwcGg3c/PD7dv37ZYYERERBaXnAxMmwZ8+KGyXacOkGZghoisj9nJrFarRWpqqlH7rVu3UIjrUxMRUV51/TrQowdw/LiyPWaMktQ6OqobFxE9F7PLDFq1aoXFixfrtzUaDeLi4jBz5ky0b9/ekrFRAZZ+pS8ioufy3XdA7dpKIuvhAWzbppzwxUSWyOqZPTL78ccfo0WLFqhatSoSExPRq1cvXL58GcWKFcOmTZtyIkYqYESAJk2Ao0fVjoSI8o0zZ4CHD4F69YDNmwF/f7UjIiILMTuZ9fX1xdmzZ7F582acPn0aWq0WgwcPRu/evQ1OCCPKroQEw0SWK30RUbaIPD2ha8YMwNsbGDoUSHfOBxFZN7OT2UOHDqFRo0YYOHAgBg4cqG9PSUnBoUOH8OKLL1o0QCo4RJRENm15QWQk4OXFE4yJyEzbtgHLlgF79wJOToCtLTBqlNpREVEOMLtmtkWLFnjw4IFRe3R0NFq0aGGRoKhgEQHi4pSTit3cgOLFn17HJWuJyCyJicDo0UDXrsqytJ9+qnZERJTDzB6ZFRFoTGQX9+/fh6urq0WCooJDqzVeslaH5QVEZJbLl4Hu3YHQUGX77beBsWPVjYmIclyWk9kuXboAUGYvGDBgABzTnAGampqKP//8E40aNbJ8hJRviRgnsoGBwOHDymisiwtHZYkoizZvVuph4+KAYsWA9euBdu3UjoqIckGWk1kPDw8AyshsoUKFDE72cnBwQIMGDTB06FDLR0j5kghw9+7TRLZiReVkY5YVEJHZFi0CJk5U/t+0KbBpE1CypLoxEVGuyXIyu3btWgBA2bJlMXHiRJYUULaZmnrrzBmlXpaIyGyvvw7MmweMHAnMnAnYmV1BR0RWzOx3/MyZM3MiDipA4uONp97idyMiMktoqLIIAgCULavUy3p6qhoSEanD7NkMAODbb79Ft27d0KBBA9SpU8fgYq7ly5fD398fTk5OCAoKwuHDhzPdPykpCdOmTYOfnx8cHR1Rvnx5fPHFF9l5GJTL0s5aoBMZ+bRGlojomeLjgUGDlA+SPXuetjORJSqwzE5mly5dioEDB8Lb2xuhoaGoV68eihYtiqtXr6KdmcX2W7Zswbhx4zBt2jSEhoaiadOmaNeuHW7cuJHhbbp164aff/4Za9aswT///INNmzYhICDA3IdBuUyrVf72FCqkDKAAyslenEOWiLLK7p9/oGnQAFi7FrCxAf75R+2QiCgP0IiImHODgIAAzJw5Ez179kShQoXwxx9/oFy5cpgxYwYePHiAZcuWZfm+6tevjzp16mDFihX6tipVqqBz586YP3++0f779u1Djx49cPXqVXhm81t4TEwMPDw8EB0dDXd392zdh7m0Wi2ioqLg7e0NG5tsDYZbNRElkU0/a8Hp08rfo7yuoPdffsA+tHIi0K5ZA82YMdAkJgI+PspJXs2bqx0ZZRHfg9Yvt/vQnHzN7JrZGzdu6KfgcnZ2RmxsLACgb9++aNCgQZaT2SdPnuD06dOYMmWKQXvr1q1xNG1BZRq7du1C3bp1sWDBAnz11VdwdXXFK6+8grlz52a4lG5SUhKSkpL02zExMQCUTtFqtVmK9XlptVqISK4dL6+JjwfOnlVe+BUrCk6dEn2NrDU8JQW9//ID9qEVi4uDZuRI2GzYAADQtmqlTLvl7W0dHyAEgO/B/CC3+9Cc45idzPr4+OD+/fvw8/ODn58fjh8/jlq1aiEsLAzmDPLeu3cPqampKJ52uScAxYsXR0REhMnbXL16Fb/99hucnJywY8cO3Lt3DyNHjsSDBw8yrJudP38+Zs+ebdR+9+5dJCYmZjne56HVahEdHQ0RKZDfSBMSNACUft67NwoJCYKEBHVjMkdB77/8gH1ovRx/+AFFNmyA2Nri7pgxSJkwQamPi4pSOzQyA9+D1i+3+1A3WJoVZiezLVu2xPfff486depg8ODBGD9+PL799lucOnVKv7CCOdKvJpbRCmOA8kRqNBps2LBBP+/tRx99hK5du+LTTz81OTo7depUBAcH67djYmJQunRpeHl55WqZgUajgZeXV4F6E4vAKGn19vayupkLCmr/5SfsQys2cCDk33+R2qYNUitXhjf70CrxPWj9crsPnZycsryv2cns559/rh/6HTFiBDw9PfHbb7+hU6dOGDFiRJbvp1ixYrC1tTUahY2KijIardUpUaIESpYsqU9kAaXGVkRw69YtVKxY0eg2jo6OBquV6djY2OTqG0qj0eT6MdUkosxdnr5iRHkO1InpeRS0/suP2IdWIiYGmDpVmS/W21tpmz8fNlotNFFR7EMrxveg9cvNPjTnGGZHY2NjA7s0E1J369YNS5cuxdixY3H37t0s34+DgwOCgoIQEhJi0B4SEpLhsriNGzfGnTt3EBcXp2+7dOkSbGxsUKpUKTMfCeUU3epe6RPZxo2VJWqJiEw6c0Y5W3T5cmDwYLWjISIrYZHUOiIiAmPGjEGFChXMul1wcDBWr16NL774AhcuXMD48eNx48YN/Qjv1KlT0a9fP/3+vXr1QtGiRTFw4ECcP38ehw4dwqRJkzBo0KAMTwCj3KVb3Svt4HpkpDK/LOeTJSKTRIBly4CGDYErV4AyZYB33lE7KiKyEllOZh89eoTevXvDy8sLvr6+WLp0KbRaLWbMmIFy5crh+PHjZi9e0L17dyxevBhz5sxBYGAgDh06hD179sDPzw8AEB4ebjDnrJubG0JCQvDo0SPUrVsXvXv3RqdOnbB06VKzjks5w9SIbOPGylyyrq5MZInIhEePgK5dgTFjgCdPgFdeUVb3athQ7ciIyEpkeZ7ZkSNH4vvvv0f37t2xb98+XLhwAW3atEFiYiJmzpyJZs2a5XSsFsF5ZnOGVgsEBRnOJRsZmT8WRSgI/ZffsQ/zqIsXgfbtgbAwwN4e+PBDYOxYkx8a7EPrxv6zfvlintkffvgBa9euxcsvv4yRI0eiQoUKqFSpEhYvXvy88ZKVEzFOZHUjstaeyBJRDvL1BWxtAX9/YMsW4IUX1I6IiKxQlpPZO3fuoGrVqgCAcuXKwcnJCUOGDMmxwMh6JCQ8TWQrVlTO4WBZARGZFBOjrGut0QDu7sDu3UqRfeHCakdGRFYqy+PEWq0W9vb2+m1bW1u4WtuEoZTjzpwB3NyYyBKRCceOAdWrKyd76VSuzESWiJ5LlkdmRQQDBgzQz9mamJiIESNGGCW027dvt2yEZFWYxBKREa0WWLhQmaEgNRX47DNgxAilTpaI6DllOZnt37+/wXafPn0sHgxZHxEgPl7tKIgoz7p7F+jfH9i7V9nu0UNJZpnIEpGFZDmZXbt2bU7GQVZIN6ds+sURiIgAAIcOAT17AnfuAE5OwNKlwJAh/AmHiCzK7OVsqWATUU74ApQR2fRzynKFLyICAISHA61bA0lJSl3s1q1AzZpqR0VE+RCTWcqyzEZi88ucskRkISVKALNnA3//rSxP6+amdkRElE8xmaUsS0gwnchyTlkiAgAcOAB4ewPVqinbkycr//LDgYhyEJNZylDakgLA8ESvyEhlLllAKS3g3yqiAiw1FZg7F5gzB6hSBThxgpNNE1GuYTJLJj3r5C5X16fJLBEVYOHhQO/eyqgsADRowCSWiHJVthbX/eqrr9C4cWP4+vri+vXrAIDFixfju+++s2hwpJ6MSgoAnuhFRP8JCQECA5VE1tUV+OorYM0afkAQUa4yO5ldsWIFgoOD0b59ezx69AipqakAgMKFC2Px4sWWjo/ygMhIIC7u6eXwYQ68EBVoKSnA9OlAmzZAVJQyS8GpUwDnHyciFZidzH7yySdYtWoVpk2bBltbW3173bp1ce7cOYsGR3mDrqRAd2EiS1TAaTTAb78p9UjDhwPHjwMBAWpHRUQFlNk1s2FhYahdu7ZRu6OjI+K5FBQRUf4loiSytrbAxo1KQtutm9pREVEBZ/bIrL+/P86ePWvUvnfvXlStWtUSMRERUV6SnKxMszVu3NM2X18mskSUJ5g9Mjtp0iSMGjUKiYmJEBGcOHECmzZtwvz587F69eqciJFymYjhNFxEVIDduAH06AEcO6ZsDxoE1KqlbkxERGmYncwOHDgQKSkpmDx5MhISEtCrVy+ULFkSS5YsQY8ePXIiRsoFujllRYCmTQETg+9EVNDs2gUMGAA8fAh4eCgzFTCRJaI8JlvzzA4dOhRDhw7FvXv3oNVq4e3tbem4KBdlNqcsp+EiKoCePAHefhvQzVDzwgvAli2Av7+qYRERmWJ2zezs2bNx5coVAECxYsWYyOYDpuaUDQwEYmM5DRdRgSMCdOr0NJEdP1450YuJLBHlUWYns9u2bUOlSpXQoEEDLFu2DHfv3s2JuEglujllz5wB3NyYyBIVOBqNMt1WkSLAd98BH30EODioHRURUYbMTmb//PNP/Pnnn2jZsiU++ugjlCxZEu3bt8fGjRuRkJCQEzFSLuJcskQFUGIikHae8C5dgKtXgVdeUS8mIqIsytZyttWqVcO8efNw9epVHDhwAP7+/hg3bhx8fHwsHR/lAhG1IyAi1fz7L9CoEdCyJXD79tP2woVVC4mIyBzZSmbTcnV1hbOzMxwcHJCcnGyJmCgX6WYvIKICaMsWoE4dIDRU+TAIC1M7IiIis2UrmQ0LC8N7772HqlWrom7dujhz5gxmzZqFiIgIS8dHOSwh4ek0XIGBnLmAqEB4/BgYMUKZPzY2VpnO5OxZ5V8iIitj9tRcDRs2xIkTJ1CjRg0MHDhQP88sWT/OXEBUAPzzj7Jy159/Km/4d94BZs0C7LI1UyMRkerM/vRq0aIFVq9ejWrVquVEPJSL0q/0xUSWqABYskRJZL29ga+/Blq1UjsiIqLnYnYyO2/evJyIg3JZZgslEFE+9uGHQEoKMHs2UKKE2tEQET23LCWzwcHBmDt3LlxdXREcHJzpvh999JFFAqOclX6hBK70RZRP/f038NlnyiIINjbK3Huff652VEREFpOlZDY0NFQ/U0FoaGiOBkS5LzIS8PJimQFRviICrFsHjBqlnPBVrhwwbpzaURERWVyWktkDBw6Y/D/lD1wkgSifiYsDRo4EvvpK2W7dGujVS92YiIhyiNlTcw0aNAixsbFG7fHx8Rg0aJBFgiIiomz680+gbl0lkbWxAd57D9i7Vznhi4goHzI7mf3yyy/x+PFjo/bHjx9j/fr1FgmKiIiyYcsWoH59ZfqtkiWBX39Vpt6yee71cYiI8qwsz2YQExMDEYGIIDY2Fk5OTvrrUlNTsWfPHnjzmz8RkXoqVAC0WqBdO2D9eqBYMbUjIiLKcVlOZgsXLgyNRgONRoNKlSoZXa/RaDB79myLBkdERM/w6BFQuLDy/6Ag4NgxZTk/jsYSUQGR5WT2wIEDEBG0bNkS27Ztg6enp/46BwcH+Pn5wdfXN0eCJCKidESA5cuVMoIDB4A6dZR23b9ERAVElpPZZs2aAQDCwsJQpkwZaHj6u1USUeaYTbvyFxFZmUePgKFDgW+/VbbXrWMSS0QFVpaS2T///BPVq1eHjY0NoqOjce7cuQz3rVmzpsWCI8viql9E+cDJk0D37kBYGGBvDyxYALz1ltpRERGpJkvJbGBgICIiIuDt7Y3AwEBoNBqIiNF+Go0GqampFg+SLCM+3jiR5cpfRFZCBFiyBJg8GUhOBvz9ldkLXnhB7ciIiFSVpWQ2LCwMXl5e+v+TdRFREtm0v0JGRiqLJbi4cMEEIquwbRswfrzy/9dfB1avfnriFxFRAZalZNbPz8/k/ynvM1VaEBjI5WuJrE6XLsArryireY0cyTcwEdF/srVowg8//KDfnjx5MgoXLoxGjRrh+vXrFg2Onl/60oLAQOD0af4dJMrztFpg1SrljE1AmWpr505g1Ci+gYmI0jA7mZ03bx6cnZ0BAMeOHcOyZcuwYMECFCtWDON1P4FRniACNG36dDsyEjhzhtNPEuV59+4BnToBw4YBY8Y8bWcSS0RkJMtTc+ncvHkTFSpUAADs3LkTXbt2xbBhw9C4cWM0b97c0vFRNokAd+8CZ88q2ywtILIShw8DPXsCt28DTk7K8rQifPMSEWXA7DE6Nzc33L9/HwCwf/9+vPzyywAAJycnPH782LLRkdlEgLg45WSv4sWfth8+zL+FRHmaVgvMmwe0aKEkspUrA7//rozO8s1LRJQhs0dmW7VqhSFDhqB27dq4dOkSOnToAAD4+++/UbZsWUvHR2bQapXVLHWjsTqNGyszFxBRHhUVBfTtC+zfr2z36QOsWAG4uakbFxGRFTB7ZPbTTz9Fw4YNcffuXWzbtg1FixYFAJw+fRo9e/a0eICUNSLGiWxgIBAby1FZojwvOVkpaHd2BtasAdavZyJLRJRFZo/MFi5cGMuWLTNqnz17tkUCIvOlr4+tWFH5u+jqyiSWKM9KWwdbsiTwzTdKYXu1aurGRURkZcxOZgHg0aNHWLNmDS5cuACNRoMqVapg8ODB8PDwsHR89AymSgvOnOGgDlGeFhEB9O4NjB4NvPaa0sYTaImIssXsMoNTp06hfPny+Pjjj/HgwQPcu3cPH3/8McqXL48zZ87kRIyUAVOlBayPJcrjfvoJqFUL+OUXYOxY4MkTtSMiIrJqZo/Mjh8/Hq+88gpWrVoFOzvl5ikpKRgyZAjGjRuHQ4cOWTxIMi0hgaUFRFYjJQWYNUuZsUAEqFkT2LoVcHBQOzIiIqtmdjJ76tQpg0QWAOzs7DB58mTUrVvXosFR1rG0gCgPu31bmTv28GFle/hw4OOPlRO+iIjouZhdZuDu7o4bN24Ytd+8eROFChWySFBkPo7GEuVRd+8qU4scPgwUKgRs2gSsXMlElojIQsxOZrt3747Bgwdjy5YtuHnzJm7duoXNmzdjyJAhnJqLiCg9Ly+ge3egdm3g9GmgRw+1IyIiylfMLjNYuHAhNBoN+vXrh5SUFACAvb093nzzTbz//vsWD5CIyOrcuAHY2wMlSijbixYpdbJOTurGRUSUD5mdzDo4OGDJkiWYP38+rly5AhFBhQoV4OLikhPxERFZl++/B/r3V07w+uknwM4OcHRUOyoionwry2UGCQkJGDVqFEqWLAlvb28MGTIEJUqUQM2aNZnIEhE9eQJMmAC88grw8KEy3cjDh2pHRUSU72U5mZ05cybWrVuHDh06oEePHggJCcGbb76Zk7EREVmHsDCgaVPgo4+U7fHjgd9+U+pliYgoR2W5zGD79u1Ys2YNevx38kKfPn3QuHFjpKamwtbWNscCJCLK07ZvBwYNAqKjgSJFgHXrlNFZIiLKFVkemb158yaaNm2q365Xrx7s7Oxw586dHAmMiCjPS04G/vc/JZFt2BAIDWUiS0SUy7KczKampsIh3Uo1dnZ2+hkNiIgKHHt7YMsWYOpU4OBBwM9P7YiIiAqcLJcZiAgGDBgAxzRn5SYmJmLEiBFwdXXVt23fvt2yERIR5SVbtwJRUcDo0cp29erKErVERKSKLCez/fv3N2rr06ePRYMhIsqzHj9WTuz67DPA1hZo3FhZCIGIiFSV5WR27dq1ORkHEVHe9c8/QLduwJ9/KmtHT5kC1KihdlRERIRsLJpARFSgfP01MGIEEB8PeHsr261aqR0VERH9J8sngBERFTgjRwJ9+yqJbIsWwNmzTGSJiPIYJrNWSkT5+0pEOSggQCkrmDULCAkBSpRQOyIiIkqHZQZWSKsFgoKUQSIisrAHDwBPT+X/Y8YAzZoBtWqpGxMREWWII7NWRsQ4kW3cGHBxUS0kovwhLg7o3x+oXx+IiVHaNBomskREeVy2ktmvvvoKjRs3hq+vL65fvw4AWLx4Mb777juLBkfGEhKeJrIVKwKxscDhw8rfXCLKpnPngBdeANavB65eBQ4cUDsiIiLKIrOT2RUrViA4OBjt27fHo0ePkJqaCgAoXLgwFi9ebOn4KBNnzgBubkxkibJNBFi1CqhXD7h4EShZEvj1V+DVV9WOjIiIssjsZPaTTz7BqlWrMG3aNNja2urb69ati3Pnzlk0OMock1ii5xAbC/TuDQwbBiQmAu3aKT97NG2qdmRERGQGs5PZsLAw1Dax6o2joyPieXo9EVmLCROATZuU1bwWLAB27waKFVM7KiIiMpPZyay/vz/OmjiNfu/evahatarZASxfvhz+/v5wcnJCUFAQDh8+nKXbHTlyBHZ2dggMDDT7mEREePddoEEDpeh80iTAhufDEhFZI7On5po0aRJGjRqFxMREiAhOnDiBTZs2Yf78+Vi9erVZ97VlyxaMGzcOy5cvR+PGjfHZZ5+hXbt2OH/+PMqUKZPh7aKjo9GvXz+89NJLiIyMNPchEFEBpImJAb77Dhg+XGnw9gaOHmW9DhGRlTM7mR04cCBSUlIwefJkJCQkoFevXihZsiSWLFmCHj16mHVfH330EQYPHowhQ4YAUGZE+PHHH7FixQrMnz8/w9sNHz4cvXr1gq2tLXbu3GnuQyCigubUKRR94w3Y3LgBFCoE9OqltDORJSKyetlaNGHo0KEYOnQo7t27B61WC29vb7Pv48mTJzh9+jSmTJli0N66dWscPXo0w9utXbsWV65cwddff4133333mcdJSkpCUlKSfjvmv/kjtVottFqt2XFnh1arhYhY5HjKXdjo7zeXHkKBZsn+o1wmAnzyCTSTJ8MuORlStiykXDnwjWN9+D60buw/65fbfWjOcZ5rBbBiz3GyxL1795CamorixYsbtBcvXhwREREmb3P58mVMmTIFhw8fhp1d1kKfP38+Zs+ebdR+9+5dJCYmmh94Nmi1WkRHR0NEYPOcdXkJCRoAynN29+5dxMeLBSKkzFiy/yj3aB49gsf48XDatw8AENOqFeKXLIGmSBEgKkrl6MhcfB9aN/af9cvtPoyNjc3yvmYns/7+/tBk8tPc1atXzbq/9PclIibvPzU1Fb169cLs2bNRqVKlLN//1KlTERwcrN+OiYlB6dKl4eXlBXd3d7NizS6tVguNRgMvL69svwBElAUT0vLy8oKrqwUCpExZov8ol/3+OzQ9e0Jz/TrEwQHaBQsQ/8Yb8PL2Zh9aKb4PrRv7z/rldh86OTlleV+zk9lx48YZbCcnJyM0NBT79u3DpEmTsnw/xYoVg62trdEobFRUlNFoLaBk6KdOnUJoaChGjx4N4OmQt52dHfbv34+WLVsa3c7R0RGOjo5G7TY2Nrn6htJoNNk+pogy9WX66gvl/iwUIGXqefqPVPDwIXD9OlC+PDRbt0ITGAhNVBT70MrxfWjd2H/WLzf70JxjmJ3MvvXWWybbP/30U5w6dSrL9+Pg4ICgoCCEhITgtdde07eHhITgVROr77i7uxstyrB8+XL88ssv+Pbbb+Hv75/lY1ubhATjRLZxY8DFRZ14iPIkkacndLVvD2zcCHToALi7s0aWiCgfs1hq3a5dO2zbts2s2wQHB2P16tX44osvcOHCBYwfPx43btzAiBEjACglAv369VMCtbFB9erVDS7e3t5wcnJC9erV4VpAfm+PjATi4pSpMXkiNtF/fvsNqFVLGY3V6dlTSWSJiChfe64TwNL69ttv4enpadZtunfvjvv372POnDkIDw9H9erVsWfPHvj5+QEAwsPDcePGDUuFmC+4uoJ1skQ6Wi3wwQfA//4HpKYC06cDX32ldlRERJSLNCJi1unwtWvXNjhBS0QQERGBu3fvYvny5Rg2bJjFg7SkmJgYeHh4IDo6OldPAIuKioJ3Nk8+iY8H3NyU/8fFMZnNbc/bf5RDoqKAvn2B/fuV7T59gBUrnr5Z0mAfWj/2oXVj/1m/3O5Dc/I1s0dmO3fubLBtY2MDLy8vNG/eHAEBAebeHRGR+X79VVn4IDwccHYGPv0UGDCAtTdERAWQWclsSkoKypYtizZt2sDHxyenYiIiytjevUDHjkqJQdWqwNatQLVqakdFREQqMWuc2M7ODm+++abBilpERLmqRQugZk1g4EDgxAkmskREBZzZRQ/169dHaGhoTsRCRGTa778rJ3gBgJMTcOgQ8MUXLCAnIiLza2ZHjhyJCRMm4NatWwgKCjKaEqtmzZoWC46ICriUFGD2bOC994AZM4BZs5T2QoVUDYuIiPKOLCezgwYNwuLFi9G9e3cAwNixY/XXaTQa/TK0qbrREyKi53H7tnKS16FDynZkpOHCCERERDAjmf3yyy/x/vvvIywsLCfjISIC9u1Tpt26d0+ZamvVKqBHD7WjIiKiPCjLyaxuOlrdggZERBaXnKyUE7z/vrJduzawZQtQsaK6cRERUZ5l1glgGv68R0Q56epVYPFi5f+jRgFHjzKRJSKiTJl1AlilSpWemdA+ePDguQIiogKscmXgs88AFxega1e1oyEiIitgVjI7e/ZseHh45FQsZIKIspwtUb705AkwfTrw2mtAw4ZKW79+6sZERERWxaxktkePHvD29s6pWCgdEaBJE+WXVqJ859o15aSu339XVvG6eFGZQ5aIiMgMWa6ZZb1s7ktIMExkGzdWfn0lsno7dignd/3+O1C4MLBkCRNZIiLKFrNnMyB1REYCXl6cYpOsXFISMGkS8MknynaDBsDmzQBnSSEiomzKcjKr1WpzMg56BldXJrJk5R4+BFq1Ak6fVrYnTVJW9rK3VzcuIiKyamYvZ0tElC2FCwOlSim1sl9+CXTooHZERESUDzCZJaKck5gIpKQoq3hpNMAXXyjF4KVKqR0ZERHlE2YtmkBElGWXLik1scOGKVNzAICnJxNZIiKyKCazRGR5GzcCQUHAH38AP/0E3L6tdkRERJRPMZklIstJSACGDgV69wbi4oDmzYGzZzkaS0REOYbJLBFZxoULQP36wOrVSn3szJnKqKyvr9qRERFRPsYTwIjo+aWkAJ06AVeuAD4+wIYNQMuWakdFREQFAEdmiej52dkBn38OtGmjlBUwkSUiolzCZJaIsufcOWD37qfbLVsCe/cCxYurFxMRERU4TGaJyDwiSl1svXpAz57A5ctPr+MydURElMuYzBJR1sXGAn36KDMWJCYCTZooK3sRERGphMksEWXN2bPK3LEbNwK2tsAHHwA//AB4eakdGRERFWCczYCInm3lSmDcOCApCShdGti8GWjUSO2oiIiIODJLRFlw5YqSyHbqBISGMpElIqI8gyOzeZhuOXsiVWi1gM1/33fnzQNq1VJW9uJJXkRElIdwZDaPEgGaNlU7CiqQRIAlS5SptpKTlTZ7e+XELyayRESUxzCZzaMSEpTzbQAgMBBwcVEzGiowHj4EunRR6mMPHgQ2bVI7IiIiokyxzMAKHD7MATHKBb//DnTvDly/Djg4AIsWAX37qh0VERFRpjgyawWYyFKO0mqVxLVJEyWRLV8eOHoUGD2aLz4iIsrzmMzmQSJAfLzaUVCBMXkyMHEikJICdOsGnDmjzCdLRERkBZjM5jEiygAZl7enXDN0KFCsmDKX7ObNgLu72hERERFlGWtm85iEBOUXXp3GjXnyF1mYVqu8yJo0UbYrVwauXQNcXVUNi4iIKDs4MpuHRUby5C+ysKgooH17oFkz4Ndfn7YzkSUiIivFkdk8zNWViSxZ0MGDQM+eQHg44Oys/EtERGTlODJLlN+lpgJz5yqLIISHA1WqACdOKIktERGRlePIbB7CWQzI4iIilJW7fv5Z2R4wAFi2jGUFRESUb3BkNo/gLAaUI/buVRJZFxfgyy+BtWuZyBIRUb7Ckdk8grMYUI4YMAC4ehXo1UspLyAiIspnODKbB3EWA8q2O3eUsoKHD5VtjUapl2UiS0RE+RRHZvMgzmJA2bJvH9C3L3DvnrL99dfqxkNERJQLODJLZO1SUoCpU4F27ZRENjAQmDlT7aiIiIhyBUdmVSai1MtyFgPKlps3lSm2jhxRtkeOBBYtApyc1I2LiIgolzCZVZFuBoO0J34RZdnx40CHDsCDB4C7O7BmDdC1q9pRERER5SomsypKP4MBwFkMyAyVKikF1uXKAVu2KP8SEREVMExm84jISCUvcXHhyV+UiagowMtLeZF4eipzyJYpAzg6qh0ZERGRKngCmIpEnv7f1ZWzGNAz7NgBVK4MfPHF07aKFZnIEhFRgcZkViUiQNOmakdBViEpCRg7FujSBXj0CNiwwfCbEBERUQHGZFYlCQnA2bPK/wMDWSdLGbhyRSmk/uQTZXviRODHHzmET0RE9B/WzOYBXO2LTPrmG2DIECAmRqmPXb9emb2AiIiI9JjM5gFMZMnIpUtAjx6AVquMzG7aBJQurXZUREREeQ6TWaK8qFIlYMYMpV52zhzAjm9VIiIiU/gXkiiv2LQJqFtXmaEA4JK0REREWcATwFQgwuVrKY2EBKU2tlcvoHt3IDFR7YiIiIisBpPZXKZbwrZ4cbUjoTzhwgWgfn1lKVqNBujUCbC3VzsqIiIiq8Eyg1yWfglbLl9bgH35JTBypPKiKF5cmT/2pZfUjoqIiMiqMJlVUWTk05VJqQBJSADefFOZagtQEtivvwZ8fNSNi4iIyAqxzEBFXL62gLKzAy5eBGxsgLlzlUUQmMgSERFlC0dmiXKDiHKxsQEcHIAtW4Dr14FmzdSOjIiIyKpxZJYop8XGAn36AFOnPm0rW5aJLBERkQVwZJYoJ509C3TrBly+rJQXvPmmksgSERGRRXBkligniAArVgANGiiJbKlSwK+/MpElIiKyMI7MElladDQwdCjwzTfKdseOwLp1QNGiqoZFRESUHzGZJbIkrVaphf3jD6Ws4IMPgPHjOW0FERFRDmGZQS4TUTsCylE2NsCkSYCfH/Dbb0BwMBNZIiKiHMRkNheJAE2bqh0FWdzDh8qJXjq9ewPnzyvL1BIREVGOYjKbixISnuY8gYFcxjZf+P13oHZtoH174O7dp+3sXCIiolzBZFYlhw/z12erJgIsWgQ0aaIsfuDsDERFqR0VERFRgcMTwFTCRNaK3b8PDBgA7N6tbL/xBrBqFeDhoWpYREREBZHqI7PLly+Hv78/nJycEBQUhMOHD2e47/bt29GqVSt4eXnB3d0dDRs2xI8//piL0VKBd+SIUiOyezfg6AgsX64sTctEloiISBWqJrNbtmzBuHHjMG3aNISGhqJp06Zo164dbty4YXL/Q4cOoVWrVtizZw9Onz6NFi1aoFOnTggNDc3lyKnAWrECuHULqFgROH5cWdGLw+xERESq0YioN1lU/fr1UadOHaxYsULfVqVKFXTu3Bnz58/P0n1Uq1YN3bt3x4wZM7K0f0xMDDw8PBAdHQ13d/dsxW0urVaLqKgouLp6w91d+f4QFwe4uubK4ek56frP29sbNnFxwOzZwKxZQKFCaodGWWTQhzaq/yBF2cA+tG7sP+uX231oTr6mWs3skydPcPr0aUyZMsWgvXXr1jh69GiW7kOr1SI2Nhaenp4Z7pOUlISkpCT9dkxMjP62Wq02G5GbT6vVQkT+O55NmuPnyuHpeRw8CGzZApkxQ+k/Nzfgww+V69iBVsPwPUjWiH1o3dh/1i+3+9Cc46iWzN67dw+pqakoXry4QXvx4sURERGRpftYtGgR4uPj0a1btwz3mT9/PmbPnm3UfvfuXSQmJpoXdDZptVpER0cjPh4ASuiPHx/PFRTyrNRUuC5ZArdFi6DRaiFlyiBqwACOKFgp3XtQRNiHVop9aN3Yf9Yvt/swNjY2y/uqPpuBJl29oYgYtZmyadMmzJo1C9999x28vb0z3G/q1KkIDg7Wb8fExKB06dL6k8hyg1arhUajgYuLl77Ny8uLZQZ5VUQENP37Q/PzzwAAbd++sOneHcX485jV0r0Hvby82IdWin1o3dh/1i+3+9DJySnL+6qWzBYrVgy2trZGo7BRUVFGo7XpbdmyBYMHD8Y333yDl19+OdN9HR0d4ejoaNRuY2OTq28ojUZjcDzl+Ll2eMqqn39WVvCKjFQWPli+HOjbF4iKyvXXDFmW7j3IPrRe7EPrxv6zfrnZh+YcQ7VXlIODA4KCghASEmLQHhISgkaNGmV4u02bNmHAgAHYuHEjOnTokNNhUkGyZAnQqpWSyFavDpw6BfTvr3ZURERElAlVywyCg4PRt29f1K1bFw0bNsTnn3+OGzduYMSIEQCUEoHbt29j/fr1AJREtl+/fliyZAkaNGigH9V1dnaGB+f5pOf1wguAjQ0wcKCS2HJJWiIiojxP1WS2e/fuuH//PubMmYPw8HBUr14de/bsgZ+fHwAgPDzcYM7Zzz77DCkpKRg1ahRGjRqlb+/fvz/WrVuX2+FTfhAZCejKWho1Av76CwgIUDcmIiIiyjLVTwAbOXIkRo4cafK69Anqr7/+mvMBUcGQkgL873/AJ58Av/8OVKumtDORJSIisiqqJ7NEue7mTaBnT2VpWgD4/vunySwRERFZFSazuUQE/80zS6r64QegXz/gwQPA3R1YtQrIZJ5iIiIiyts4P0YuEAFefdUTJUrw6VZNcjIwcSLQsaOSyAYFAWfOMJElIiKycsyuckFCAnDypIN+u3Fjniif69asARYtUv4/dqxSYlC+vLoxERER0XNjmUEui4wEvLyALCxyRpY0ZAjw449KicFrr6kdDREREVkIR2ZzmasrE9lc8eQJ8OGHQFKSsm1nB+zYwUSWiIgon+HILOU/V68C3bsrK3jduKFMv0VERET5EkdmKX/59lugdm0lkfX0BNq0UTsiIiIiykFMZil/SEwERo4E3ngDiIlRzrI7e1aZvYCIiIjyLSazZP2uXAEaNgRWrFC2p0wBDhwASpdWNy4iIiLKcayZJetnYwOEhQHFigFffQW0bat2RERERJRLmMySdUpNBWxtlf/7+yszFVSqBJQsqW5cRERElKtYZkDW58IFoE4dYN++p20tWjCRJSIiKoCYzJJ1Wb8eqFsX+PNPYNIkQKtVOyIiIiJSEZNZsg7x8cDAgUD//sr6wC1bAiEhSr0sERERFVjMBCjv++sv4IUXgHXrlOR1zhxg/37Ax0ftyIiIiEhlPAGM8rarV4F69YDHj4ESJYCNG4HmzdWOioiIiPIIJrOUt5UrB/ToAdy5o9TLenurHRERERHlIUxmKe/54w/A1xfw8lK2V6wA7O1ZH0tERERGmB1Q3iECrFwJ1K8P9Ov3dKYCR0cmskRERGQSMwTKG6KjlXKCN98EkpKUBRESEtSOioiIiPI4JrOkvtOngaAgYOtWwM4O+PBDYNcuwM1N7ciIiIgoj2PNLKlHBFi2DJg4EXjyBPDzAzZvBho0UDsyIiIishIcmSX1xMcDS5YoieyrrwKhoUxkiYiIyCwcmSX1uLkBW7YAv/0GjB0LaDRqR0RERERWhsks5R4RYPFiwNkZGDFCaQsKUi5ERERE2cBklnLHgwfAgAHA998DDg5Aq1ZA+fJqR0VERERWjsks5byjR5Vpt27eVOaM/fhjZWUvIiIioufEE8Ao52i1wAcfAC++qCSyFSsCx48rc8myPpaIiIgsgCOzlDO0WqBzZ6WsAAB69gQ++wwoVEjVsIiIiCh/4cgs5QwbG6BhQ8DJCVi1CtiwgYksERERWRxHZslyUlOBe/eA4sWV7bffBt54A6hQQd24iIiIKN/iyCxZRmQk0LYt8NJLQEKC0mZjw0SWiIiIchSTWXp+v/wC1KoF/PQTEBYGnDmjdkRERERUQDCZpexLTQVmzgReflkZma1WDTh5EmjSRO3IiIiIqIBgzSxlz507QO/ewK+/KtuDBwNLlwIuLqqGRURERAULk1nKnjFjlETW1VWZcqt3b7UjIiIiogKIySxlz9KlQHQ08OmnQOXKakdDREREBRRrZilrbt1SEledkiWVE76YyBIREZGKODJLz7ZnD9CvH3D/vpLEdu6sdkREREREADgyS5lJTgYmTwY6dFAS2Tp1gBo11I6KiIiISI8js2Ta9etAjx7A8ePK9pgxwIcfAo6O6sZFRERElAaTWTK2ezfQty/w6BHg4QF88QXQpYvaUREREREZYTJLxpKSlES2Xj1g82bA31/tiIiIiIhMYjJLipQUwO6/l8PrrwPbtgEdOwIODurGRUQFWmpqKpKTk6HVapGcnIzExETY2PB0D2vD/rN+OdGHDg4OFrkvJrMEfPst8M47yiIIvr5KG8sKiEhFIoKIiAg8evRIv63VahEbGwuNRqNucGQ29p/1y4k+tLGxgb+/Pxyec+CMyWxBlpgITJgALF+ubH/4IfDxx+rGREQE6BNZb29vuPy3THZKSgrs7OyYDFkhEWH/WTlL96FWq8WdO3cQHh6OMmXKPNd9MpktqC5fBrp3B0JDle233wbmzlU3JiIiKKUFukS2aNGiAJgMWTv2n/XLiT708vLCnTt3kJKSAnt7+2zfD5PZgmjzZmDoUCAuDihWDFi/HmjXTu2oiIgAAMnJyQCgH5ElovxJV16QmprKZJbMsH490L+/8v+mTYFNm5RVvYiI8hiO4BHlbxarvbXIvVCmRNSOII3XXweqVQOmTwd++YWJLBEREVk1JrM5TARo1kzl0YWQEECrVf7v6gqcOqXUx9pxYJ6IiPKWJ0+eoEKFCjhy5IjaoeQbUVFR8PLywu3bt9UOJUcwmc1hCQnA2bNKMhsYKMjVErD4eGDgQKB1a2DRoqftTk65GAQRUcExYMAAaDQaaDQa2NnZoUyZMnjzzTfx8OFDo32PHj2K9u3bo0iRInByckKNGjWwaNEipKamGu174MABtG/fHkWLFoWLiwuqVq2KCRMmPDM5CQ0NxRtvvIHixYvDyckJlSpVwtChQ3Hp0iWLPWZL+/zzz+Hn54fGjRsbXTds2DDY2tpi8+bNRtcNGDAAnTt3Nmo/e/YsNBoNrl27pm8TEXz++eeoX78+3NzcULhwYdStWxeLFy9GQkKCJR+OgYcPH6Jv377w8PCAh4cH+vbtq59+LiNxcXEYPXo0SpUqBWdnZ1SpUgUrVqzQX//gwQOMGTMGlStXhouLC8qUKYOxY8ciOjpav4+3tzf69u2LmTNn5tRDUxWT2Vx08KAg10rA/v5bWcFr3TrAxgb474QKIiLKWW3btkV4eDiuXbuG1atX4/vvv8fIkSMN9tmxYweaNWuGUqVK4cCBA7h48SLeeustvPfee+jRowckTX3aZ599hpdffhk+Pj7Ytm0bzp8/j5UrVyI6OhqL0g5UpLN79240aNAASUlJ2LBhAy5cuICvvvoKHh4e+N///pftx5ecw39PPvnkEwwZMsSoPSEhAVu2bMGkSZOwZs2a5zpG3759MW7cOLz66qs4cOAAzp49i//973/47rvvsH///ue678z06tULZ8+exb59+7Bv3z6cPXsWffv2zfQ248ePx759+/D111/jwoULGD9+PMaMGYPvvvsOAHDnzh3cuXMHCxcuxLlz57Bu3Trs27cPgwcPNrifgQMHYsOGDSa/WFk9KWCio6MFgERHR+fK8eLiRJRiA5GYmNScP6BWK7JmjYizs3JQHx+RAwdy/rj5WGpqqoSHh0tqai70H+UI9qF1efz4sZw/f14eP36sb0tN1crDh08kNlYrcXGSqxetNuux9+/fX1599VWDtuDgYPH09NRvx8XFSdGiRaVLly5Gt9+1a5cAkM2bN4uIyM2bN8XBwUHGjRtn8ngPHz402R4fHy/FihWTzp07Z3q7tWvXioeHh8F1O3bskLTpwcyZM6VWrVqyZs0a8ff3F41GIytXrhRfX1+j91SnTp2kX79+Bo+nTp064ujoKP7+/jJz5kxJTk42GZOIyOnTp8XGxsbk3+h169ZJgwYN5NGjR+Ls7CxhYWEG15t67kVEQkNDBYB+/y1btggA2blzp9G+Wq1WHj16lGF8z+P8+fMCQI4fP65vO3bsmACQixcvZni7atWqyZw5cwza6tSpI9OnT8/wNlu3bhUHBwej57ps2bKyZs2abMWv1WrlyZMnojXnDfEMpt7rOubkaxyZzU/i4oC+fYHBg4HHj5Xygj/+AJo3VzsyIqLnkpAAFClij0KFNHBzQ65enudX56tXr2Lfvn0G0w7t378f9+/fx8SJE43279SpEypVqoRNmzYBAL755hs8efIEkydPNnn/hQsXNtn+448/4t69e2bfLiP//vsvtm7dim3btuHs2bPo2rUr7t27hwMHDuj3efjwIX788Uf07t1bH0OfPn0wduxY/P333/j000/x5Zdf4r333svwOIcOHUKlSpXg7u5udN2aNWvQp08feHh4oH379li7dq1Zj0Fnw4YNqFy5Ml599VWj6zQaDTw8PDK8rZubW6aXdplMc3ns2DF4eHigfv36+rYGDRrAw8MDR48ezfB2TZo0wa5du3D79m2ICA4cOIBLly6hTZs2Gd4mOjoa7u7usEt3bky9evVw+PDhDG9nrXgGUH5y6RKwdStga6uc4PX220qJARER5Zrdu3fDzc0NqampSExMBAB89NFH+ut19apVqlQxefuAgAD9PpcvX4a7uztKlChhVgyXL1/W35clPHnyBF999RW8vLz0bW3btsXGjRvx0ksvAVASb09PT/32e++9hylTpqB///4QEZQpUwZz5szB22+/nWHt5rVr1+CrW1Y93eM5fvw4tm/fDgD6JHnmzJmwMfPv3OXLl1G5cmWzbqNz9uzZTK93dnbO8LqIiAh4e3sbtXt7eyMiIiLD2y1duhRDhw5FqVKlYGdnBxsbG6xevRpNmjQxuf/9+/cxd+5cDB8+3Oi6kiVLIlS3WFI+wmQ2P6lTB/jsM6BiRSCDFzkRkTVycQEePkxWZQUpc0/cbdGiBVasWIGEhASsXr0aly5dwpgxY4z2kwzmbRQR/WNM+39zZHTf2eXn52eQyAJA7969MWzYMCxfvhyOjo7YsGEDevToAVtbWwDA6dOncfLkSYORWF2Cn5CQYHJRjMePH8PJxEnKa9asQZs2bVCsWDEAQPv27TF48GD89NNPaN26tVmPJbvPKQBUqFAhW7fTMXXcZ8WzdOlSHD9+HLt27YKfnx8OHTqEkSNHokSJEnj55ZcN9o2JiUGHDh1QtWpVk18YnJ2dc/QEN7UwmbVmMTHA6NHA+PFA7dpK28CB6sZERJQDNBplZkE7O+TeibTZ5Orqqk96li5dihYtWmD27NmY+9+S4ZUqVQIAXLhwAY0aNTK6/cWLF1G1alX9vtHR0QgPDzdrdFZ3jIsXL6Jhw4YZ7mdjY2OU+Jo6wcvV1dWorVOnTtBqtfjhhx/wwgsv4PDhwwYj0FqtFrNnz0aXLl2MlkI1lbACQLFixXDu3DmDttTUVKxfvx4REREGP5unpqZizZo1+mTW3d0d169fN7pP3WwBuvKBSpUq4cKFCyaP/yxubm6ZXt+0aVPs3bvX5HU+Pj6IjIw0ar979y6KFy9u8jaPHz/GO++8gx07dqBDhw4AgJo1a+Ls2bNYuHChQTIbGxuLtm3bws3NDTt27DC5otaDBw+MvpTkB/wN2lqdOaOMxH71FdC7N2BiKhciIlLfzJkzsXDhQty5cwcA0Lp1a3h6epqciWDXrl24fPkyevbsCQDo2rUrHBwcsGDBApP3ndG0Tq1bt0axYsWeeTsvLy/ExsYiPj5ef92zfkrXcXZ2RpcuXbBhwwZs2rQJlSpVQlBQkP76OnXq4J9//kGFChWMLhmVBtSuXRsXL140SLD37NmD2NhYhIaG4uzZs/rLN998g507d+L+/fsAlJKKv/76S1/aoXPy5El4eXmhSJEiAJQZBS5duqSfDSAtETGY0iq9tMc3dVm9enWGt23YsCGio6Nx4sQJfdvvv/+O6Ohok19qAOWLRXJystHzZWtrC61u/ngoI7KtW7eGg4MDdu3aleGXhb/++gu1dYNf+YmlzkizFlY/m4FWK/LJJyIODsqdlikjcvTo898vZYhnwls/9qF1MXWGc06cSZ0TMjqjPigoSEaNGqXf/uabb8TW1laGDh0qf/zxh4SFhcnq1aulSJEi0rVrV4PH+emnn4pGo5FBgwbJr7/+KteuXZPffvtNhg0bJsHBwRnGsnPnTrG3t5dOnTpJSEiIhIWFycmTJ2XSpEnSvXt3ERG5f/++uLq6ytixY+Xy5cuyYcMG8fX1NTmbgSn79+8XR0dHqVy5ssydO9fgun379omdnZ3MnDlTzp07J3/88Yds2rRJpk2blmHM9+7dEwcHBzl37py+7dVXX9XHm5ZWq5WSJUvK4sWLRUTk0aNH4uPjI127dpWTJ0/Kv//+K1999ZUUKVJEFixYYHC77t27i7Ozs8ybN09Onjwp165dk++//15atmwpO3bsyDC+59W2bVupWbOmHDt2TI4dOyY1atSQjh07GuxTuXJl2b59u367WbNmUq1aNTlw4IBcvXpV1q5dK05OTrJ8+XIREYmJiZH69etLjRo15N9//5Xw8HD9JSUlRX8/8fHx4uzsLIcOHcpW7Hl5NgMmsznMosnsw4ciXbo8vcNXXhG5f98icVLGmAhZP/ahdcmPyeyGDRvEwcFBbty4oW87dOiQtG3bVjw8PMTBwUGqVq0qCxcuNEhAdEJCQqRNmzZSpEgRcXJykoCAAJk4caLcuXMn03hOnjwpXbp0ES8vL3F0dJQKFSrIsGHD5PLly/p9duzYIRUqVBAnJyfp2LGjfP7551lOZlNSUqREiRICQK5cuWJ0/b59+6RRo0bi7Ows7u7uUq9ePfn8888zjblHjx4yZcoUERGJiIgQOzs72bp1q8l9x4wZIzVq1NBvX758WV5//XUpWbKkuLq6So0aNWTZsmVG7/3U1FRZsWKFvPDCC+Li4iLu7u4SFBQkS5YskYSEhEzjex7379+X3r17S6FChaRQoULSu3dvo+nVAMjatWv12+Hh4TJgwADx9fUVJycnqVy5sixatEj/Xjhw4IAAMHlJO33Zxo0bpXLlytmOPS8nsxoRC1eJ53ExMTHw8PDQT1uR0+LjlaldlGNrUahQNis7bt0CmjYFrl0D7O2BDz8Exo7N+8Vj+YBWq0VUVBS8vb3NPmuW8gb2oXVJTExEWFgY/P399T+XSrqaS7Iu5vTfuXPn8PLLL+Pff/9FoUKFcinC/K9evXoYN24cevXqla3b58R70NR7XcecfI2f6tbC11eZpcDfHzhyBHjrLSayRESU79SoUQMLFiwwWH6Wnk9UVBS6du2qr8XObzibQV724AHg5KTMC2NjA2zcqJzKa+Zk10RERNakf//+aoeQr3h7e2e4gEZ+wJHZvOroUSAwUBmB1SlWjIksERERURpMZvMarRZYsAB48UXg5k3g11+BDKZeISIiIiromMzmJXfvAh07KsvQpqYCPXoAp09zNJaIiIgoA6yZzSsOH1aS1zt3lDrZJUuAoUN5khcRERFRJpjM5gUJCcAbbwCRkUDlysDWrUDNmmpHRURERJTnscwgL3BxAb74AujbFzh1ioksERERURZxZFYtBw4Ajx8D7dsr2+3bP/0/EREREWUJR2ZzW2oqMGsW8NJLQO/ewI0bakdERET5TNmyZbF48WL9tkajwc6dO1WLx5LMfSwDBgxA586dn7lf3759MW/evOwHRkZeeOEFbN++PcePo3oyu3z5cv0yZkFBQTh8+HCm+x88eBBBQUFwcnJCuXLlsHLlylyK9PlpIsKBVq2A2bMBEaBLF2XuWCIiyhcGDBgAjUajvxQtWhRt27bFn3/+qWpc4eHhaNeuXY4eY926ddBoNKhSpYrRdVu3boWDgwP8/f1zNIbs+vPPP/HDDz9gzJgxRtdt3LgRtra2GDFihNF169atQ+EMZhwqXLgw1q1bZ9B24MABtG/fHkWLFoWLiwuqVq2KCRMm4Pbt25Z4GCaJCGbNmgVfX184OzujefPm+PvvvzO9ja4v015sbGyQmJhosN+zcrj//e9/mDJlCrRarcUfV1qqJrNbtmzBuHHjMG3aNISGhqJp06Zo164dbmQwWhkWFob27dujadOmCA0NxTvvvIOxY8di27ZtuRy5+VphP1wa1VbKC1xdga++AtasUepliYgo32jbti3Cw8MRHh6On3/+GXZ2dujYsaOqMfn4+MDR0THHj+Pq6oqoqCgcO3bMoH3t2rUoU6ZMjh8/u5YtW4Y33ngDhQoVMrruiy++wOTJk7F582YkJCRk+xifffYZXn75Zfj4+GDbtm04f/48Vq5ciejoaCxatOh5ws/UggUL8NFHH2HZsmU4efIkfHx80KpVK8TGxmZ6O3d3d/3rODw8HHfu3IGTk5P++qzkcB06dEB0dDR+/PHHHHt8AABRUb169WTEiBEGbQEBATJlyhST+0+ePFkCAgIM2oYPHy4NGjTI8jGjo6MFgERHR5sfcDbExWrlXbwjqdCIACI1a4pcvJgrxybLSE1NlfDwcElNTVU7FMom9qF1efz4sZw/f14eP36sb9NqtfLkyRPRxsaKxMWZvqTZX0Qy3i8uTiQhIWv7mql///7y6quvGrQdOnRIAEhUVJS+bfLkyVKxYkVxdnYWf39/mT59ujx58kR//dmzZ6V58+bi5uYmhQoVkjp16sjJkyf11x85ckSaNm0qTk5OUqpUKRkzZozEpYnXz89PPv74Y/02ANmxY4eIiISFhQkA2bZtmzRv3lycnZ2lZs2acvToUYO4n3WM9NauXSseHh4yevRoGTJkiL795s2b4ujoKJMmTRI/Pz+D2yxfvlzKlSsn9vb2UqlSJVm/fr3B9ZcuXZKmTZuKo6OjVKlSRfbv32/wWEREbt26Jd26dZPChQuLp6envPLKKxIWFqa/3lSfpJWamiqFCxeW3bt3G10XFhYmzs7O8ujRI6lfv758+eWXJh+zKR4eHrJ27Vr9c+Dg4CDjxo0zue/Dhw8zjO95aLVa8fHxkffff1/flpiYKB4eHrJy5coMb2fqcenfg1qtiGQ9hxswYID07dvX5HFMvdd1zMnXVBuZffLkCU6fPo3WrVsbtLdu3RpHjx41eZtjx44Z7d+mTRucOnUKycnJJm+TlJSEmJgYgwsAaLXa3LmIoAgewgaCpIFDoT16FNqKFXPv+LxY5CIiqsfAC/uwIF1ExOACKD+XagoVAtzcTF7k9dcNb+ftnfG+7doZ7lu2rOn90sXxrIuObjs2NhZff/01KlSoAE9PT327m5sb1q5di7///huLFy/GqlWr8NFHH+mv7927N0qVKoUTJ07g1KlTePvtt2FnZwcRwZ9//ok2bdrgtddewx9//IHNmzfjt99+w+jRo42er8y2p02bhgkTJiA0NBSVKlVCz549kZycnOVjZPT4Bw0ahC1btiA+Ph4igrVr16Jt27bw9vY2iGP79u146623EBwcjHPnzmHYsGEYOHAgfvnlF4gIUlNT0aVLF9ja2uLYsWNYsWIF3n77bYP7iI+PR4sWLeDq6oqDBw/i8OHDcHNzQ9u2bZGUlGSyT9Jf/vjjDzx69AhBQUFG161ZswYdOnSAu7s7evfujTVr1mT42sysfevWrXjy5AkmTZpkcl8PD48M42vXrh3c3NwyvWR026tXryIiIgKtWrXStzk4OKBZs2Y4evRopn0ZFxcHPz8/lCpVCh07dkRoaKj+cSUlJeH06dMG9ysiaNWqldH9vvDCCzh8+HCmx8rocyCrVJvN4N69e0hNTUXx4sUN2osXL46IiAiTt4mIiDC5f0pKCu7du4cSJUoY3Wb+/PmYPXu2Ufvdu3eNaj9yQkKCBsH4CHvQHovfCYJbbCzwjKF9ylu0Wi2io6MhIrCxUb3MnLKBfWhdkpOTodVqkZKSgpSUFADQJzeZERGk/rc/kPkfuKzum5Jmn6zQarXYvXu3/ufq+Ph4lChRAjt37jT4Az1lyhT9bUqVKoVx48Zh69atCA4OBgDcuHED48ePR4UKFQBAX2uakpKCBQsWoEePHhg9erT+uo8++ggvvfQSli5dqv8pWPcc6qSmpho8p+PHj0ebNm0AANOnT0dgYCAuXryIgICALB8j/WMHgOrVq6NcuXLYsmUL+vTpgy+//BIffPABrl69avCcLly4EP369cOwYcMAAGPHjsWxY8ewcOFCNG3aFCEhIbhw4QIuX76MUqVKAQDmzJmDTp066R/Lxo0bodFosHLlSmj+W2To888/h5eXF37++We0atVK/7xn1JdXrlyBra0tPD09DfbRarX48ssv8fHHHyMlJQVdu3bFhAkTcPHiRX2/6B5zRvetO+6lS5fg7u4OLy8vs19TK1aswOPHjzPdJ6P71NXiFi1a1GAfLy8v3LhxI8PbVaxYEatXr0b16tURGxuLTz75BE2aNMHvv/+OypUrIyIiAqmpqShWrJjR/YaHhxu0+fj44MaNG3jy5InR529KSgq0Wi3u378Pe3t7g+ueVQaRlupTc2nSrXAlIkZtz9rfVLvO1KlT9R8OABATE4PSpUvDy8sL7u7u2Q07y0SAyEda3LtXF2XKFIOtLf+QWhutVguNRgMvLy8mQlaKfWhdEhMTERsbCzs7O9jZGf6Zkkz+wGlsbQ33j4yEZLSvjY3hvmFhJvdNf/xnsbGxQYsWLbB8+XIAwIMHD7BixQp06tQJv//+O/z8/AAA3377LZYsWYJ///0XcXFxSElJgbu7u/5448ePx4gRI7Bp0ya89NJLeOONN1C+fHkAQGhoKP79919s2rRJf1zd6NbNmzf1J2DZpHuMtv89P7q2wMBA/f9Lly6tj9fOzi7Lx0j/2HXP2aBBg/DVV1/B398fcXFxeOWVV7BkyRKD5/TixYsYNmyYQYxNmjTB0qVLYWdnh0uXLqFMmTIoW7aswfVpH0toaCiuXLkCT09Pg1gSExNx7do12NnZwcbGxui5SOvJkydwdHQ0Sqb27duH+Ph4dOzYEXZ2dvDx8UHr1q2xfv16/awHaR+zKWmPq9FozH49AdC/ZrLD1tYWAGBvb2907Myek8aNG6Nx48b67RdffBFBQUFYuXIlli1bpr9d+veo7kSxtG1ubm7QarVITU2Fg4ODwXF0/VO0aFGjL0imvjBlRLVktlixYrC1tTUahY2KijIafdXx8fExub+dnR2KFi1q8jaOjo4mi951L+7cUKiQMqWsrW3uHZMsS/cGZf9ZL/ah9bCxsTE4ixpIM9Dh6prpgIcBN7esH9ScfZ/B1dUVFStW1G/XrVsXHh4eWL16Nd59910cP34cPXv2xOzZs9GmTRt4eHhg8+bNWLRokf6xzZ49G71798YPP/yAvXv3YtasWdi8eTNee+01aLVaDB8+HGPHjjU6dpkyZfT3kfb5S7uta3NwcND/X/e+0D3PWT1GWmmP26dPH7z99tuYPXs2+vXrBzs7O4PrdXR9nf5+0seddv+0+4gIgoKCsGHDBqN4vLy8Mryf9PslJCQgOTnZINlau3YtHjx4AFdXV32bVqtFaGgo3n33Xdja2sLDwwNxcXHQarX6xBFQRsHj4uJQuHBhaDQaVK5cGdHR0YiIiDD5K3Jm2rVr98yZnuLi4ky2644VGRkJX19fffvdu3dRvHjxLL+XbG1tUbduXVy5cgWA8pzZ2toiMjLS4D5M3e/Dhw/h4uICFxMnvKedKSH9Z7M5n9Wqfao7ODggKCgIISEhBu0hISFo1KiRyds0bNjQaP/9+/ejbt26Rt+oiIiI8gLdH2vdT8VHjhyBn58fpk2bhrp166JixYq4fv260e0qVaqE8ePHY//+/ejSpQvWrl0LAKhTpw7+/vtvVKhQweiSfuQru573GJ6ennjllVdw8OBBDBo0yOQ+VapUwW+//WbQdvToUf2ob9WqVXHjxg3cuXNHf336WRLq1KmDy5cvw9vb2yhODw+PLD3WwMBAAMD58+f1bffv38d3332HzZs34+zZswaXuLg47N27FwAQEBCA1NRUhIaGGtznmTNnkJqaisqVKwMAunbtCgcHByxYsMBkDI8ePcowvtWrVxvFkP6SEX9/f/j4+BjkTk+ePMHBgwczzLVM0dUW+/j4ADAvh/vrr79Qp06dLB8rW0RFmzdvFnt7e1mzZo2cP39exo0bJ66urnLt2jUREZkyZYrBGXBXr14VFxcXGT9+vJw/f17WrFkj9vb28u2332b5mLk9m4EIz6S2duw/68c+tC6Zzmbw35nUeVX//v2lbdu2Eh4eLuHh4XL+/HkZOXKkaDQaOXDggIiI7Ny5U+zs7GTTpk3y77//ypIlS8TT01N/9nhCQoKMGjVKDhw4INeuXZPffvtNypcvL5MnTxYRkT/++EOcnZ1l5MiREhoaKpcuXZLvvvtORo8erY8jK7MZhIaG6q9/+PChANDHmJVjpJf+DPiEhAS5d++eiCj9t3DhQoPZDHbs2CH29vayYsUKuXTpkixatEhsbW31MaSmpkrVqlXlpZdekrNnz8qhQ4ckKCjI4LHEx8dLxYoVpXnz5nLo0CG5evWq/PrrrzJ27Fi5efOmvk8ym81ARKROnTryySef6Lc//vhjKVGihMnPjF69eknnzp312+3atZMaNWpISEiIXL16VUJCQqRGjRrSrl07g9t9+umnotFoZNCgQfLrr7/q+3bYsGESHBycaXzP4/333xcPDw/Zvn27nDt3Tnr27CklSpSQmJgY/T59+/Y1mIVg1qxZsm/fPrly5YqEhobKwIEDxc7OTo4cOaJ/Dz4rh9Np1qyZzJkzx2RslprNQNVkVkTpXD8/P3FwcJA6derIwYMH9df1799fmjVrZrD/r7/+KrVr1xYHBwcpW7asrFixwqzjMZklc7H/rB/70LpYezILQH8pVKiQvPDCC0aDLpMmTZKiRYuKm5ubdO/eXT7++GN9IpiUlCQ9evSQ0qVLi4ODg/j6+sro0aMNno8TJ05Iq1atxM3NTVxdXaVmzZry3nvv6a9/3mQ2K8dIL7NpqkwlsyLPnprrn3/+kSZNmoiDg4NUqlRJ9u3bZzQ1V3h4uPTr10+KFSsmjo6OUq5cORk6dKj+73xWktmVK1caTPNZo0YNGTlypMl9t23bJnZ2dhIRESEiSl4xfvx4qVChgjg5OUmFChVk3Lhx8ujRI6PbhoSESJs2baRIkSLi5OQkAQEBMnHiRLlz506m8T0PrVYrM2fOFB8fH3F0dJQXX3xRzp07Z7BPs2bNpH///vrtcePGSZkyZcTBwUG8vLykdevWcuTIEaP3YGY5nIgybZq9vb3+i0V6lkpmNSKSUX18vhQTEwMPDw9ER0fnyglggFJjExUVBW9vb9brWSH2n/VjH1qXxMREhIWF6VcWApSfOVNSUgxqL8l65PX+S0xMROXKlbF582Y0bNhQ7XDypOz04aRJkxAdHY3PP//c5PWm3us65uRrqs9mQERERKQmJycnrF+/Hvfu3VM7lHzF29sbEydOzPHjMJklIiKiAq9Zs2Zqh5DvTJo0KVeOw9/biIiIiMhqMZklIiIiIqvFZJaIiPKkAnZ+MlGBY6n3OJNZIiLKU3SL4CQkJKgcCRHlpCdPngCAwepp2cETwIiIKE+xtbVF4cKFERUVBQD6ZTDz8tROlLm8PjUXPZul+1Cr1eLu3btwcXGBnd3zpaNMZomIKM/RLZupS2hFBFqtFjY2NkyGrBD7z/rlRB/a2NigTJkyz31/TGaJiCjP0Wg0KFGiBLy9vZGcnAytVov79++jaNGiXPjCCrH/rF9O9KGDg4NF7ovJLBER5Vm2trawtbWFVquFvb09nJycmAxZIfaf9cvLfZi3oiEiIiIiMgOTWSIiIiKyWkxmiYiIiMhqFbiaWd0EvTExMbl2TK1Wi9jY2DxZZ0LPxv6zfuxD68c+tG7sP+uX232oy9OysrBCgUtmY2NjAQClS5dWORIiIiIiykxsbCw8PDwy3UcjBWy9QK1Wizt37qBQoUK5NtddTEwMSpcujZs3b8Ld3T1XjkmWw/6zfuxD68c+tG7sP+uX230oIoiNjYWvr+8zR4IL3MisjY0NSpUqpcqx3d3d+Sa2Yuw/68c+tH7sQ+vG/rN+udmHzxqR1WHhChERERFZLSazRERERGS1mMzmAkdHR8ycOROOjo5qh0LZwP6zfuxD68c+tG7sP+uXl/uwwJ0ARkRERET5B0dmiYiIiMhqMZklIiIiIqvFZJaIiIiIrBaTWSIiIiKyWkxmLWD58uXw9/eHk5MTgoKCcPjw4Uz3P3jwIIKCguDk5IRy5cph5cqVuRQpZcScPty+fTtatWoFLy8vuLu7o2HDhvjxxx9zMVoyxdz3oc6RI0dgZ2eHwMDAnA2QnsncPkxKSsK0adPg5+cHR0dHlC9fHl988UUuRUvpmdt/GzZsQK1ateDi4oISJUpg4MCBuH//fi5FS+kdOnQInTp1gq+vLzQaDXbu3PnM2+SZfEbouWzevFns7e1l1apVcv78eXnrrbfE1dVVrl+/bnL/q1eviouLi7z11lty/vx5WbVqldjb28u3336by5GTjrl9+NZbb8kHH3wgJ06ckEuXLsnUqVPF3t5ezpw5k8uRk465fajz6NEjKVeunLRu3Vpq1aqVO8GSSdnpw1deeUXq168vISEhEhYWJr///rscOXIkF6MmHXP77/Dhw2JjYyNLliyRq1evyuHDh6VatWrSuXPnXI6cdPbs2SPTpk2Tbdu2CQDZsWNHpvvnpXyGyexzqlevnowYMcKgLSAgQKZMmWJy/8mTJ0tAQIBB2/Dhw6VBgwY5FiNlztw+NKVq1f+3c/8xVdX/H8Cf93K5er2IJhWgFyHQq7ZpieQPnDmNlMGk0UBLpug0JSUKU7OteXFlrZxoOn/0h4NkEJCBc1PzNyTqFiCkgDNQYpmQI3+EoCLw+v7Rl/PxwlW4V7hw8fnYzh/vH+ec19vXLr7uueecl2XDhg1dHRp1kq05nDdvnnz22WdiMplYzPYwa3N4+PBhGTRokPzzzz/2CI86YG3+Nm3aJL6+vmZ927ZtE4PB0G0xUud1ppjtTfUMbzN4Co2NjSgsLMSsWbPM+mfNmoWzZ89a3OfcuXPt5s+ePRsFBQV4+PBht8VKltmSw7ZaWlpQV1eHIUOGdEeI1AFbc5iUlIQrV67AZDJ1d4jUAVtyeODAAQQEBOCbb77BsGHDYDQasXr1aty7d88eIdMjbMlfYGAgrl27hkOHDkFE8Pfff2Pfvn0IDQ21R8jUBXpTPaOx69n6mNraWjQ3N8Pd3d2s393dHTU1NRb3qampsTi/qakJtbW18PT07LZ4qT1bctjW5s2bUV9fj7lz53ZHiNQBW3JYXl6OdevW4fTp09Bo+Gewp9mSw6tXryIvLw/9+/dHdnY2amtrsWLFCty8eZP3zdqZLfkLDAxEamoq5s2bh/v376OpqQlhYWHYvn27PUKmLtCb6hleme0CKpXKrC0i7fo6mm+pn+zH2hy2+uGHH5CQkICMjAy8+OKL3RUedUJnc9jc3Iz58+djw4YNMBqN9gqPOsGaz2FLSwtUKhVSU1MxceJEhISEIDExEcnJybw620OsyV9ZWRni4uKwfv16FBYW4ueff0ZlZSViYmLsESp1kd5Sz/CSxFN4/vnn4eTk1O6b540bN9p9W2nl4eFhcb5Go4Gbm1u3xUqW2ZLDVhkZGViyZAl+/PFHBAUFdWeY9ATW5rCurg4FBQUoKipCbGwsgP8KIxGBRqPB0aNHMXPmTLvETv+x5XPo6emJYcOGYdCgQUrfmDFjICK4du0aRo4c2a0x0//Ykr+vvvoKU6dOxZo1awAA48aNg16vx7Rp0/DFF1/wV0oH0JvqGV6ZfQparRYTJkzAsWPHzPqPHTuGwMBAi/tMmTKl3fyjR48iICAAzs7O3RYrWWZLDoH/rsguWrQIaWlpvMerh1mbQ1dXV1y8eBHFxcXKFhMTg1GjRqG4uBiTJk2yV+j0/2z5HE6dOhXXr1/H3bt3lb7ff/8darUaBoOhW+Mlc7bkr6GhAWq1eQni5OQE4H9X96h361X1jN0fOetjWl9HsmfPHikrK5OPPvpI9Hq9/PHHHyIism7dOlmwYIEyv/VVFvHx8VJWViZ79uzhq7l6mLU5TEtLE41GIzt27JDq6mplu337dk8t4ZlnbQ7b4tsMep61OayrqxODwSARERFSWloqubm5MnLkSFm6dGlPLeGZZm3+kpKSRKPRyM6dO+XKlSuSl5cnAQEBMnHixJ5awjOvrq5OioqKpKioSABIYmKiFBUVKa9X6831DIvZLrBjxw7x9vYWrVYr/v7+kpubq4xFR0fL9OnTzebn5OTI+PHjRavVio+Pj+zatcvOEVNb1uRw+vTpAqDdFh0dbf/ASWHt5/BRLGZ7B2tzeOnSJQkKChKdTicGg0FWrVolDQ0Ndo6aWlmbv23btsnLL78sOp1OPD09JSoqSq5du2bnqKnVqVOnnvh/W2+uZ1QivJ5PRERERI6J98wSERERkcNiMUtEREREDovFLBERERE5LBazREREROSwWMwSERERkcNiMUtEREREDovFLBERERE5LBazREREROSwWMwSEQFITk7G4MGDezoMm/n4+GDr1q1PnJOQkIBXX33VLvEQEdkLi1ki6jMWLVoElUrVbquoqOjp0JCcnGwWk6enJ+bOnYvKysouOX5+fj6WLVumtFUqFfbv3282Z/Xq1Thx4kSXnO9x2q7T3d0dc+bMQWlpqdXHceQvF0RkPyxmiahPCQ4ORnV1tdn20ksv9XRYAABXV1dUV1fj+vXrSEtLQ3FxMcLCwtDc3PzUx37hhRcwYMCAJ85xcXGBm5vbU5+rI4+u8+DBg6ivr0doaCgaGxu7/dxE9OxhMUtEfUq/fv3g4eFhtjk5OSExMRFjx46FXq+Hl5cXVqxYgbt37z72OL/99htmzJiBgQMHwtXVFRMmTEBBQYEyfvbsWbz++uvQ6XTw8vJCXFwc6uvrnxibSqWCh4cHPD09MWPGDJhMJpSUlChXjnft2gU/Pz9otVqMGjUKKSkpZvsnJCRg+PDh6NevH4YOHYq4uDhl7NHbDHx8fAAA4eHhUKlUSvvR2wyOHDmC/v374/bt22bniIuLw/Tp07tsnQEBAYiPj0dVVRUuX76szHlSPnJycrB48WLcuXNHucKbkJAAAGhsbMTatWsxbNgw6PV6TJo0CTk5OU+Mh4j6NhazRPRMUKvV2LZtG0pKSvD999/j5MmTWLt27WPnR0VFwWAwID8/H4WFhVi3bh2cnZ0BABcvXsTs2bPx9ttv48KFC8jIyEBeXh5iY2Otikmn0wEAHj58iOzsbHz44Yf4+OOPUVJSguXLl2Px4sU4deoUAGDfvn3YsmULvvvuO5SXl2P//v0YO3asxePm5+cDAJKSklBdXa20HxUUFITBgwfjp59+Uvqam5uRmZmJqKioLlvn7du3kZaWBgDKvx/w5HwEBgZi69atyhXe6upqrF69GgCwePFinDlzBunp6bhw4QIiIyMRHByM8vLyTsdERH2MEBH1EdHR0eLk5CR6vV7ZIiIiLM7NzMwUNzc3pZ2UlCSDBg1S2gMHDpTk5GSL+y5YsECWLVtm1nf69GlRq9Vy7949i/u0Pf6ff/4pkydPFoPBIA8ePJDAwEB57733zPaJjIyUkJAQERHZvHmzGI1GaWxstHh8b29v2bJli9IGINnZ2WZzTCaTvPLKK0o7Li5OZs6cqbSPHDkiWq1Wbt68+VTrBCB6vV4GDBggAASAhIWFWZzfqqN8iIhUVFSISqWSv/76y6z/jTfekE8//fSJxyeivkvTs6U0EVHXmjFjBnbt2qW09Xo9AODUqVP48ssvUVZWhn///RdNTU24f/8+6uvrlTmPWrVqFZYuXYqUlBQEBQUhMjISfn5+AIDCwkJUVFQgNTVVmS8iaGlpQWVlJcaMGWMxtjt37sDFxQUigoaGBvj7+yMrKwtarRaXLl0ye4ALAKZOnYpvv/0WABAZGYmtW7fC19cXwcHBCAkJwZw5c6DR2P5nPCoqClOmTMH169cxdOhQpKamIiQkBM8999xTrXPgwIE4f/48mpqakJubi02bNmH37t1mc6zNBwCcP38eIgKj0WjW/+DBA7vcC0xEvROLWSLqU/R6PUaMGGHWV1VVhZCQEMTExODzzz/HkCFDkJeXhyVLluDhw4cWj5OQkID58+fj4MGDOHz4MEwmE9LT0xEeHo6WlhYsX77c7J7VVsOHD39sbK1Fnlqthru7e7uiTaVSmbVFROnz8vLC5cuXcezYMRw/fhwrVqzApk2bkJuba/bzvTUmTpwIPz8/pKen4/3330d2djaSkpKUcVvXqVarlRyMHj0aNTU1mDdvHn755RcAtuWjNR4nJycUFhbCycnJbMzFxcWqtRNR38Filoj6vIKCAjQ1NWHz5s1Qq/97VCAzM7PD/YxGI4xGI+Lj4/Huu+8iKSkJ4eHh8Pf3R2lpabuiuSOPFnltjRkzBnl5eVi4cKHSd/bsWbOrnzqdDmFhYQgLC8PKlSsxevRoXLx4Ef7+/u2O5+zs3Km3JMyfPx+pqakwGAxQq9UIDQ1VxmxdZ1vx8fFITExEdnY2wsPDO5UPrVbbLv7x48ejubkZN27cwLRp054qJiLqO/gAGBH1eX5+fmhqasL27dtx9epVpKSktPvZ+1H37t1DbGwscnJyUFVVhTNnziA/P18pLD/55BOcO3cOK1euRHFxMcrLy3HgwAF88MEHNse4Zs0aJCcnY/fu3SgvL0diYiKysrKUB5+Sk5OxZ88elJSUKGvQ6XTw9va2eDwfHx+cOHECNTU1uHXr1mPPGxUVhfPnz2Pjxo2IiIhA//79lbGuWqerqyuWLl0Kk8kEEelUPnx8fHD37l2cOHECtbW1aGhogNFoRFRUFBYuXIisrCxUVlYiPz8fX3/9NQ4dOmRVTETUh/TkDbtERF0pOjpa3nrrLYtjiYmJ4unpKTqdTmbPni179+4VAHLr1i0RMX/g6MGDB/LOO++Il5eXaLVaGTp0qMTGxpo99PTrr7/Km2++KS4uLqLX62XcuHGycePGx8Zm6YGmtnbu3Cm+vr7i7OwsRqNR9u7dq4xlZ2fLpEmTxNXVVfR6vUyePFmOHz+ujLd9AOzAgQMyYsQI0Wg04u3tLSLtHwBr9dprrwkAOXnyZLuxrlpnVVWVaDQaycjIEJGO8yEiEhMTI25ubgJATCaTiIg0NjbK+vXrxcfHR5ydncXDw0PCw8PlwoULj42JiPo2lYhIz5bTRERERES24W0GREREROSwWMwSERERkcNiMUtEREREDovFLBERERE5LBazREREROSwWMwSERERkcNiMUtEREREDovFLBERERE5LBazREREROSwWMwSERERkcNiMUtEREREDuv/ACkQqAVBo6m+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_scaled_imp = scaler.fit_transform(X_important)\n",
    "\n",
    "#Split data into training and testing\n",
    "X_train_imp, X_test_imp, y_train, y_test = train_test_split(X_scaled_imp, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "#Build the model \n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# XGBoost with scale_pos_weight\n",
    "xgb = XGBClassifier(scale_pos_weight=ratio_of_negative_to_positive_class, random_state=42)\n",
    "xgb.fit(X_train_imp, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred_xgb = xgb.predict(X_test_imp)\n",
    "\n",
    "#Evaluation Metrics\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred_xgb)\n",
    "\n",
    "# Extract TN, FP, FN, TP from the confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred_xgb)\n",
    "\n",
    "# True Positive Rate (TPR) or Sensitivity\n",
    "tpr_value = tp / (tp + fn)\n",
    "\n",
    "# False Positive Rate (FPR)\n",
    "fpr_value = fp / (fp + tn)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred_xgb)\n",
    "\n",
    "#F1 score\n",
    "f1 = f1_score(y_test, y_pred_xgb)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"True Positive Rate (TPR): {tpr_value:.2f}\")\n",
    "print(f\"False Positive Rate (FPR): {fpr_value:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get predicted probabilities for the positive class (class 1)\n",
    "y_prob_test = xgb.predict_proba(X_test_imp)[:, 1]  # For probabilities\n",
    "\n",
    "# Compute ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "\n",
    "# Compute AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', label='Baseline Model (AUC = 0.50)', linestyle='--')  # Diagonal line for random guess\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "11c3bd37-2dd5-4d84-85bf-3d08ffccd74f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (with no cross-validation), feature selection based on threshold\n",
    "model_2_results_4 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Thresholded Features',  \n",
    "    'Validation Technique': 'No CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': tpr_value,\n",
    "    'FPR': fpr_value,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': roc_auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_7 = pd.DataFrame([model_2_results_4])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_7], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557874e9-99d6-4d0b-9de8-be9528addfba",
   "metadata": {},
   "source": [
    "## GridSearchCV for threshold features - XG Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e63b472e-a34a-4d18-bfe0-1f34df03de78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n",
      "Best Parameters: {'colsample_bytree': 1.0, 'learning_rate': 0.2, 'max_depth': 7, 'n_estimators': 200, 'scale_pos_weight': 0.19038159958180867, 'subsample': 0.8}\n",
      "Best Cross-Validation Accuracy: 0.7964\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2],\n",
    "    \"subsample\": [0.8, 1.0],\n",
    "    \"colsample_bytree\": [0.8, 1.0],\n",
    "    \"scale_pos_weight\": [ratio_of_negative_to_positive_class],\n",
    "}\n",
    "\n",
    "# Create the XGBoost model\n",
    "xgb = XGBClassifier(random_state=42)\n",
    "\n",
    "# Perform Grid Search with Cross-Validation\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=xgb,\n",
    "    param_grid=param_grid,\n",
    "    scoring=\"accuracy\",\n",
    "    cv=5,  # 5-fold cross-validation\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train_imp, y_train)\n",
    "\n",
    "# Display the best parameters and best score\n",
    "print(f\"Best Parameters: {grid_search.best_params_}\")\n",
    "print(f\"Best Cross-Validation Accuracy: {grid_search.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "75d43cb4-bdab-4996-9421-b1765ab8b495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the final model with the best parameters\n",
    "final_xgb_Grid_1 = XGBClassifier(\n",
    "    colsample_bytree=1.0,\n",
    "    learning_rate=0.2,\n",
    "    max_depth=7,\n",
    "    n_estimators=200,\n",
    "    scale_pos_weight=0.19038159958180867,\n",
    "    subsample=0.8,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "final_xgb_Grid_1.fit(X_train_imp, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "dca5c2b1-dda0-452f-9664-e638c028e1ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Test Data):\n",
      "[[ 225  139]\n",
      " [ 334 1580]]\n",
      "Accuracy: 0.7924\n",
      "True Positive Rate (TPR): 0.8255\n",
      "False Positive Rate (FPR): 0.3819\n",
      "Precision: 0.9191\n",
      "F1 Score: 0.85\n",
      "ROC-AUC: 0.8176\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict on test data\n",
    "y_pred_test = final_xgb_Grid_1.predict(X_test_imp)\n",
    "y_prob_test = final_xgb_Grid_1.predict_proba(X_test_imp)[:, 1]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n",
    "\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_test)\n",
    "TP, FP, FN, TN = conf_matrix[1, 1], conf_matrix[0, 1], conf_matrix[1, 0], conf_matrix[0, 0]\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(y_test, y_pred_test)\n",
    "TPR = TP / (TP + FN)  # True Positive Rate (Recall/Sensitivity)\n",
    "FPR = FP / (FP + TN)  # False Positive Rate\n",
    "precision = TP / (TP + FP)\n",
    "f1 = f1_score(y_test, y_pred_xgb)\n",
    "auc = roc_auc_score(y_test, y_prob_test)\n",
    "\n",
    "# Print metrics\n",
    "print(\"Confusion Matrix (Test Data):\")\n",
    "print(conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"True Positive Rate (TPR): {TPR:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {FPR:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"ROC-AUC: {auc:.4f}\")\n",
    "\n",
    "# Plot AUC/ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {auc:.4f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle=\"--\", label=\"Random Guess\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.title(\"AUC/ROC Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "7f8a5d40-fcba-453c-bcf1-6ff6ca0af6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (with GridSearch cross-validation), feature selection based on threshold\n",
    "model_2_results_5 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Thresholded Features',  \n",
    "    'Validation Technique': 'GridSearch CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': TPR,\n",
    "    'FPR': FPR,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_8 = pd.DataFrame([model_2_results_5])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_8], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a31c7b0-b2a4-46f7-be32-0ea5e47c79eb",
   "metadata": {},
   "source": [
    "## RandomSearchCV for features based on Threshold - XG Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "7908ebd2-2fed-4d2f-8ff8-688fa08a2a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Best parameters found: {'subsample': 0.8, 'scale_pos_weight': 0.19038159958180867, 'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.1, 'colsample_bytree': 1.0}\n",
      "Best AUC score: 0.8410318172092701\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# Define parameter grid\n",
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2],\n",
    "    \"subsample\": [0.8, 1.0],\n",
    "    \"colsample_bytree\": [0.8, 1.0],\n",
    "    \"scale_pos_weight\": [ratio_of_negative_to_positive_class]\n",
    "}\n",
    "\n",
    "# Initialize model\n",
    "xgb = XGBClassifier(random_state=42, eval_metric=\"logloss\")\n",
    "\n",
    "# RandomizedSearchCV\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=xgb,\n",
    "    param_distributions=param_grid,\n",
    "    n_iter=50,  # Test 50 random combinations\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=5,\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Fit\n",
    "random_search.fit(X_train_imp, y_train)\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best parameters found:\", random_search.best_params_)\n",
    "print(\"Best AUC score:\", random_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "55675bd1-e4a6-4b83-a1cf-5955d4182dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = random_search.best_estimator_\n",
    "y_test_proba = best_model.predict_proba(X_test_imp)[:, 1]\n",
    "y_test_pred = best_model.predict(X_test_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "15765418-72ce-4e3f-8e31-ae27fbb9964b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Test Data):\n",
      "[[ 283   81]\n",
      " [ 498 1416]]\n",
      "Accuracy: 0.7458\n",
      "True Positive Rate (TPR): 0.7398\n",
      "False Positive Rate (FPR): 0.2225\n",
      "Precision: 0.9459\n",
      "F1 Score: 0.83\n",
      "ROC-AUC: 0.8331\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
    "TP, FP, FN, TN = conf_matrix[1, 1], conf_matrix[0, 1], conf_matrix[1, 0], conf_matrix[0, 0]\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(y_test, y_test_pred)\n",
    "TPR = TP / (TP + FN)  # True Positive Rate (Recall/Sensitivity)\n",
    "FPR = FP / (FP + TN)  # False Positive Rate\n",
    "precision = TP / (TP + FP)\n",
    "f1 = f1_score(y_test, y_test_pred)\n",
    "auc = roc_auc_score(y_test, y_test_proba)\n",
    "\n",
    "# Print metrics\n",
    "print(\"Confusion Matrix (Test Data):\")\n",
    "print(conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"True Positive Rate (TPR): {TPR:.4f}\")\n",
    "print(f\"False Positive Rate (FPR): {FPR:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"ROC-AUC: {auc:.4f}\")\n",
    "\n",
    "# Plot AUC/ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_test_proba)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label=f\"ROC Curve (AUC = {auc:.4f})\")\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle=\"--\", label=\"Random Guess\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.title(\"AUC/ROC Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "eb5e20e8-dd00-4891-93ef-2bbcf347e9c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Model 2 (with RandomSearch cross-validation), feature selection based on threshold\n",
    "model_2_results_6 = {\n",
    "    'Model': 'XG Boosting',  \n",
    "    'Features Selected': 'Thresholded Features',  \n",
    "    'Validation Technique': 'RandomSearch CV',  \n",
    "    'Accuracy': accuracy,\n",
    "    'TPR': TPR,\n",
    "    'FPR': FPR,\n",
    "    'Precision': precision,\n",
    "    'F1 Score':f1,\n",
    "    'ROC-AUC': auc\n",
    "}\n",
    "\n",
    "# Create a DataFrame for the new row\n",
    "new_row_9 = pd.DataFrame([model_2_results_6])\n",
    "\n",
    "# Adding the row to the DataFrame\n",
    "results_df = pd.concat([results_df, new_row_9], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "ce64e9ff-a546-4b3d-b9b6-f7bac6d397b9",
   "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>Model</th>\n",
       "      <th>Features Selected</th>\n",
       "      <th>Validation Technique</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC-AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>All Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.847234</td>\n",
       "      <td>0.960293</td>\n",
       "      <td>0.747253</td>\n",
       "      <td>0.871090</td>\n",
       "      <td>0.913519</td>\n",
       "      <td>0.829107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>Top 10 Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.845917</td>\n",
       "      <td>0.95977</td>\n",
       "      <td>0.752747</td>\n",
       "      <td>0.870204</td>\n",
       "      <td>0.912795</td>\n",
       "      <td>0.830315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>Thresholded Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.842406</td>\n",
       "      <td>0.960293</td>\n",
       "      <td>0.777473</td>\n",
       "      <td>0.866572</td>\n",
       "      <td>0.911029</td>\n",
       "      <td>0.825321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>All Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.779631</td>\n",
       "      <td>0.796761</td>\n",
       "      <td>0.310440</td>\n",
       "      <td>0.931013</td>\n",
       "      <td>0.858671</td>\n",
       "      <td>0.826646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Top 10 Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.735733</td>\n",
       "      <td>0.735632</td>\n",
       "      <td>0.263736</td>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.823874</td>\n",
       "      <td>0.818381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Top 10 Features</td>\n",
       "      <td>GridSearch CV</td>\n",
       "      <td>0.753731</td>\n",
       "      <td>0.777429</td>\n",
       "      <td>0.370879</td>\n",
       "      <td>0.916821</td>\n",
       "      <td>0.841391</td>\n",
       "      <td>0.803657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Top 10 Features</td>\n",
       "      <td>RandomSearch CV</td>\n",
       "      <td>0.731782</td>\n",
       "      <td>0.722048</td>\n",
       "      <td>0.217033</td>\n",
       "      <td>0.945927</td>\n",
       "      <td>0.841391</td>\n",
       "      <td>0.829300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Thresholded Features</td>\n",
       "      <td>No CV</td>\n",
       "      <td>0.765584</td>\n",
       "      <td>0.774817</td>\n",
       "      <td>0.282967</td>\n",
       "      <td>0.935057</td>\n",
       "      <td>0.847429</td>\n",
       "      <td>0.822554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Thresholded Features</td>\n",
       "      <td>GridSearch CV</td>\n",
       "      <td>0.792362</td>\n",
       "      <td>0.825496</td>\n",
       "      <td>0.381868</td>\n",
       "      <td>0.919139</td>\n",
       "      <td>0.847429</td>\n",
       "      <td>0.817613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XG Boosting</td>\n",
       "      <td>Thresholded Features</td>\n",
       "      <td>RandomSearch CV</td>\n",
       "      <td>0.745830</td>\n",
       "      <td>0.739812</td>\n",
       "      <td>0.222527</td>\n",
       "      <td>0.945892</td>\n",
       "      <td>0.830255</td>\n",
       "      <td>0.833132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model     Features Selected Validation Technique  Accuracy  \\\n",
       "0  Gradient Boosting          All Features                No CV  0.847234   \n",
       "1  Gradient Boosting       Top 10 Features                No CV  0.845917   \n",
       "2  Gradient Boosting  Thresholded Features                No CV  0.842406   \n",
       "3        XG Boosting          All Features                No CV  0.779631   \n",
       "4        XG Boosting       Top 10 Features                No CV  0.735733   \n",
       "5        XG Boosting       Top 10 Features        GridSearch CV  0.753731   \n",
       "6        XG Boosting       Top 10 Features      RandomSearch CV  0.731782   \n",
       "7        XG Boosting  Thresholded Features                No CV  0.765584   \n",
       "8        XG Boosting  Thresholded Features        GridSearch CV  0.792362   \n",
       "9        XG Boosting  Thresholded Features      RandomSearch CV  0.745830   \n",
       "\n",
       "        TPR       FPR  Precision  F1 Score   ROC-AUC  \n",
       "0  0.960293  0.747253   0.871090  0.913519  0.829107  \n",
       "1   0.95977  0.752747   0.870204  0.912795  0.830315  \n",
       "2  0.960293  0.777473   0.866572  0.911029  0.825321  \n",
       "3  0.796761  0.310440   0.931013  0.858671  0.826646  \n",
       "4  0.735632  0.263736   0.936170  0.823874  0.818381  \n",
       "5  0.777429  0.370879   0.916821  0.841391  0.803657  \n",
       "6  0.722048  0.217033   0.945927  0.841391  0.829300  \n",
       "7  0.774817  0.282967   0.935057  0.847429  0.822554  \n",
       "8  0.825496  0.381868   0.919139  0.847429  0.817613  \n",
       "9  0.739812  0.222527   0.945892  0.830255  0.833132  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb17fb1-6385-4ae2-9a64-86cee51d7ba0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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