Flight Holiday Booking Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive modeling of customer bookings\n",
    "\n",
    "This Jupyter notebook includes some code to get you started with this predictive modeling task. We will use various packages for data manipulation, feature engineering and machine learning.\n",
    "\n",
    "### Exploratory data analysis\n",
    "\n",
    "First, we must explore the data in order to better understand what we have and the statistical properties of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/knigh/Downloads/customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.head()` method allows us to view the first 5 rows in the dataset, this is useful for visual inspection of our columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         50000 non-null  int64  \n",
      " 1   sales_channel          50000 non-null  object \n",
      " 2   trip_type              50000 non-null  object \n",
      " 3   purchase_lead          50000 non-null  int64  \n",
      " 4   length_of_stay         50000 non-null  int64  \n",
      " 5   flight_hour            50000 non-null  int64  \n",
      " 6   flight_day             50000 non-null  object \n",
      " 7   route                  50000 non-null  object \n",
      " 8   booking_origin         50000 non-null  object \n",
      " 9   wants_extra_baggage    50000 non-null  int64  \n",
      " 10  wants_preferred_seat   50000 non-null  int64  \n",
      " 11  wants_in_flight_meals  50000 non-null  int64  \n",
      " 12  flight_duration        50000 non-null  float64\n",
      " 13  booking_complete       50000 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.info()` method gives us a data description, telling us the names of the columns, their data types and how many null values we have. Fortunately, we have no null values. It looks like some of these columns should be converted into different data types, e.g. flight_day.\n",
    "\n",
    "To provide more context, below is a more detailed data description, explaining exactly what each column means:\n",
    "\n",
    "- `num_passengers` = number of passengers travelling\n",
    "- `sales_channel` = sales channel booking was made on\n",
    "- `trip_type` = trip Type (Round Trip, One Way, Circle Trip)\n",
    "- `purchase_lead` = number of days between travel date and booking date\n",
    "- `length_of_stay` = number of days spent at destination\n",
    "- `flight_hour` = hour of flight departure\n",
    "- `flight_day` = day of week of flight departure\n",
    "- `route` = origin -> destination flight route\n",
    "- `booking_origin` = country from where booking was made\n",
    "- `wants_extra_baggage` = if the customer wanted extra baggage in the booking\n",
    "- `wants_preferred_seat` = if the customer wanted a preferred seat in the booking\n",
    "- `wants_in_flight_meals` = if the customer wanted in-flight meals in the booking\n",
    "- `flight_duration` = total duration of flight (in hours)\n",
    "- `booking_complete` = flag indicating if the customer completed the booking\n",
    "\n",
    "Before we compute any statistics on the data, lets do any necessary data conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri'], dtype=object)"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {\n",
    "    \"Mon\": 1,\n",
    "    \"Tue\": 2,\n",
    "    \"Wed\": 3,\n",
    "    \"Thu\": 4,\n",
    "    \"Fri\": 5,\n",
    "    \"Sat\": 6,\n",
    "    \"Sun\": 7,\n",
    "}\n",
    "\n",
    "df[\"flight_day\"] = df[\"flight_day\"].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('route',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 3, 4, 1, 7, 2, 5], dtype=int64)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.591240</td>\n",
       "      <td>84.940480</td>\n",
       "      <td>23.04456</td>\n",
       "      <td>9.06634</td>\n",
       "      <td>3.814420</td>\n",
       "      <td>0.668780</td>\n",
       "      <td>0.296960</td>\n",
       "      <td>0.427140</td>\n",
       "      <td>7.277561</td>\n",
       "      <td>0.149560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.020165</td>\n",
       "      <td>90.451378</td>\n",
       "      <td>33.88767</td>\n",
       "      <td>5.41266</td>\n",
       "      <td>1.992792</td>\n",
       "      <td>0.470657</td>\n",
       "      <td>0.456923</td>\n",
       "      <td>0.494668</td>\n",
       "      <td>1.496863</td>\n",
       "      <td>0.356643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.670000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>778.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
       "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
       "mean         1.591240      84.940480        23.04456      9.06634   \n",
       "std          1.020165      90.451378        33.88767      5.41266   \n",
       "min          1.000000       0.000000         0.00000      0.00000   \n",
       "25%          1.000000      21.000000         5.00000      5.00000   \n",
       "50%          1.000000      51.000000        17.00000      9.00000   \n",
       "75%          2.000000     115.000000        28.00000     13.00000   \n",
       "max          9.000000     867.000000       778.00000     23.00000   \n",
       "\n",
       "         flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
       "count  50000.000000         50000.000000          50000.000000   \n",
       "mean       3.814420             0.668780              0.296960   \n",
       "std        1.992792             0.470657              0.456923   \n",
       "min        1.000000             0.000000              0.000000   \n",
       "25%        2.000000             0.000000              0.000000   \n",
       "50%        4.000000             1.000000              0.000000   \n",
       "75%        5.000000             1.000000              1.000000   \n",
       "max        7.000000             1.000000              1.000000   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \n",
       "count           50000.000000     50000.000000      50000.000000  \n",
       "mean                0.427140         7.277561          0.149560  \n",
       "std                 0.494668         1.496863          0.356643  \n",
       "min                 0.000000         4.670000          0.000000  \n",
       "25%                 0.000000         5.620000          0.000000  \n",
       "50%                 0.000000         7.570000          0.000000  \n",
       "75%                 1.000000         8.830000          0.000000  \n",
       "max                 1.000000         9.500000          1.000000  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "739"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49261 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0                   2      Internet  RoundTrip            262              19   \n",
       "1                   1      Internet  RoundTrip            112              20   \n",
       "2                   2      Internet  RoundTrip            243              22   \n",
       "3                   1      Internet  RoundTrip             96              31   \n",
       "4                   2      Internet  RoundTrip             68              22   \n",
       "...               ...           ...        ...            ...             ...   \n",
       "49995               2      Internet  RoundTrip             27               6   \n",
       "49996               1      Internet  RoundTrip            111               6   \n",
       "49997               1      Internet  RoundTrip             24               6   \n",
       "49998               1      Internet  RoundTrip             15               6   \n",
       "49999               1      Internet  RoundTrip             19               6   \n",
       "\n",
       "       flight_hour  flight_day booking_origin  wants_extra_baggage  \\\n",
       "0                7           6    New Zealand                    1   \n",
       "1                3           6    New Zealand                    0   \n",
       "2               17           3          India                    1   \n",
       "3                4           6    New Zealand                    0   \n",
       "4               15           3          India                    1   \n",
       "...            ...         ...            ...                  ...   \n",
       "49995            9           6      Australia                    1   \n",
       "49996            4           7      Australia                    0   \n",
       "49997           22           6      Australia                    0   \n",
       "49998           11           1      Australia                    1   \n",
       "49999           10           4      Australia                    0   \n",
       "\n",
       "       wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                         0                      0             5.52   \n",
       "1                         0                      0             5.52   \n",
       "2                         1                      0             5.52   \n",
       "3                         0                      1             5.52   \n",
       "4                         0                      1             5.52   \n",
       "...                     ...                    ...              ...   \n",
       "49995                     0                      1             5.62   \n",
       "49996                     0                      0             5.62   \n",
       "49997                     0                      1             5.62   \n",
       "49998                     0                      1             5.62   \n",
       "49999                     1                      0             5.62   \n",
       "\n",
       "       booking_complete  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     0  \n",
       "...                 ...  \n",
       "49995                 0  \n",
       "49996                 0  \n",
       "49997                 0  \n",
       "49998                 0  \n",
       "49999                 0  \n",
       "\n",
       "[49261 rows x 13 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sales_channel',hue='booking_complete',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49261 entries, 0 to 49999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         49261 non-null  int64  \n",
      " 1   sales_channel          49261 non-null  object \n",
      " 2   trip_type              49261 non-null  object \n",
      " 3   purchase_lead          49261 non-null  int64  \n",
      " 4   length_of_stay         49261 non-null  int64  \n",
      " 5   flight_hour            49261 non-null  int64  \n",
      " 6   flight_day             49261 non-null  int64  \n",
      " 7   booking_origin         49261 non-null  object \n",
      " 8   wants_extra_baggage    49261 non-null  int64  \n",
      " 9   wants_preferred_seat   49261 non-null  int64  \n",
      " 10  wants_in_flight_meals  49261 non-null  int64  \n",
      " 11  flight_duration        49261 non-null  float64\n",
      " 12  booking_complete       49261 non-null  int64  \n",
      "dtypes: float64(1), int64(9), object(3)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_booking_origin = pd.get_dummies(df['booking_origin'], prefix='booking_origin')\n",
    "encoded_booking_origin = encoded_booking_origin.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df = pd.concat([df, encoded_booking_origin], axis=1)\n",
    "df=df.drop('booking_origin',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>...</th>\n",
       "      <th>booking_origin_Timor-Leste</th>\n",
       "      <th>booking_origin_Tonga</th>\n",
       "      <th>booking_origin_Tunisia</th>\n",
       "      <th>booking_origin_Turkey</th>\n",
       "      <th>booking_origin_Ukraine</th>\n",
       "      <th>booking_origin_United Arab Emirates</th>\n",
       "      <th>booking_origin_United Kingdom</th>\n",
       "      <th>booking_origin_United States</th>\n",
       "      <th>booking_origin_Vanuatu</th>\n",
       "      <th>booking_origin_Vietnam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>49995</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49261 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0                   2      Internet  RoundTrip            262              19   \n",
       "1                   1      Internet  RoundTrip            112              20   \n",
       "2                   2      Internet  RoundTrip            243              22   \n",
       "3                   1      Internet  RoundTrip             96              31   \n",
       "4                   2      Internet  RoundTrip             68              22   \n",
       "...               ...           ...        ...            ...             ...   \n",
       "49995               2      Internet  RoundTrip             27               6   \n",
       "49996               1      Internet  RoundTrip            111               6   \n",
       "49997               1      Internet  RoundTrip             24               6   \n",
       "49998               1      Internet  RoundTrip             15               6   \n",
       "49999               1      Internet  RoundTrip             19               6   \n",
       "\n",
       "       flight_hour  flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
       "0                7           6                    1                     0   \n",
       "1                3           6                    0                     0   \n",
       "2               17           3                    1                     1   \n",
       "3                4           6                    0                     0   \n",
       "4               15           3                    1                     0   \n",
       "...            ...         ...                  ...                   ...   \n",
       "49995            9           6                    1                     0   \n",
       "49996            4           7                    0                     0   \n",
       "49997           22           6                    0                     0   \n",
       "49998           11           1                    1                     0   \n",
       "49999           10           4                    0                     1   \n",
       "\n",
       "       wants_in_flight_meals  ...  booking_origin_Timor-Leste  \\\n",
       "0                          0  ...                           0   \n",
       "1                          0  ...                           0   \n",
       "2                          0  ...                           0   \n",
       "3                          1  ...                           0   \n",
       "4                          1  ...                           0   \n",
       "...                      ...  ...                         ...   \n",
       "49995                      1  ...                           0   \n",
       "49996                      0  ...                           0   \n",
       "49997                      1  ...                           0   \n",
       "49998                      1  ...                           0   \n",
       "49999                      0  ...                           0   \n",
       "\n",
       "       booking_origin_Tonga  booking_origin_Tunisia  booking_origin_Turkey  \\\n",
       "0                         0                       0                      0   \n",
       "1                         0                       0                      0   \n",
       "2                         0                       0                      0   \n",
       "3                         0                       0                      0   \n",
       "4                         0                       0                      0   \n",
       "...                     ...                     ...                    ...   \n",
       "49995                     0                       0                      0   \n",
       "49996                     0                       0                      0   \n",
       "49997                     0                       0                      0   \n",
       "49998                     0                       0                      0   \n",
       "49999                     0                       0                      0   \n",
       "\n",
       "       booking_origin_Ukraine  booking_origin_United Arab Emirates  \\\n",
       "0                           0                                    0   \n",
       "1                           0                                    0   \n",
       "2                           0                                    0   \n",
       "3                           0                                    0   \n",
       "4                           0                                    0   \n",
       "...                       ...                                  ...   \n",
       "49995                       0                                    0   \n",
       "49996                       0                                    0   \n",
       "49997                       0                                    0   \n",
       "49998                       0                                    0   \n",
       "49999                       0                                    0   \n",
       "\n",
       "       booking_origin_United Kingdom  booking_origin_United States  \\\n",
       "0                                  0                             0   \n",
       "1                                  0                             0   \n",
       "2                                  0                             0   \n",
       "3                                  0                             0   \n",
       "4                                  0                             0   \n",
       "...                              ...                           ...   \n",
       "49995                              0                             0   \n",
       "49996                              0                             0   \n",
       "49997                              0                             0   \n",
       "49998                              0                             0   \n",
       "49999                              0                             0   \n",
       "\n",
       "       booking_origin_Vanuatu  booking_origin_Vietnam  \n",
       "0                           0                       0  \n",
       "1                           0                       0  \n",
       "2                           0                       0  \n",
       "3                           0                       0  \n",
       "4                           0                       0  \n",
       "...                       ...                     ...  \n",
       "49995                       0                       0  \n",
       "49996                       0                       0  \n",
       "49997                       0                       0  \n",
       "49998                       0                       0  \n",
       "49999                       0                       0  \n",
       "\n",
       "[49261 rows x 116 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49261 entries, 0 to 49999\n",
      "Columns: 116 entries, num_passengers to booking_origin_Vietnam\n",
      "dtypes: float64(1), int32(104), int64(9), object(2)\n",
      "memory usage: 24.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder,OrdinalEncoder,LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "x=df.iloc[:,:-1].values\n",
    "y=df.iloc[:,-1].values.reshape(-1,1)\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'Internet', 'RoundTrip', ..., 0, 0, 0],\n",
       "       [1, 'Internet', 'RoundTrip', ..., 0, 0, 0],\n",
       "       [4, 'Internet', 'RoundTrip', ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [5, 'Internet', 'RoundTrip', ..., 0, 0, 0],\n",
       "       [1, 'Internet', 'RoundTrip', ..., 0, 0, 0],\n",
       "       [1, 'Internet', 'RoundTrip', ..., 0, 0, 0]], dtype=object)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "x_train[:,[0,11,3,4,5,6,]]=sc.fit_transform(x_train[:,[0,11,3,4,5,6]])\n",
    "x_test[:,[0,11,3,4,5,6,]]=sc.transform(x_test[:,[0,11,3,4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[1,2]\n",
    "ct=ColumnTransformer(transformers=[('encoder', OrdinalEncoder(), cols)], remainder='passthrough')\n",
    "x_train=ct.fit_transform(x_train)\n",
    "x_test=ct.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 2.0, -0.5800203618151143, 2.9309869517246616,\n",
       "        0.14723123304604843, 1.6527520967028726, 1.5967423887999306, 1,\n",
       "        0, 1, 8.83, -0.4207860880538252, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0],\n",
       "       [0.0, 2.0, -0.5800203618151143, -0.8396663857372578,\n",
       "        0.11736754258784937, -0.9349556988237041, -0.918831601961757, 0,\n",
       "        1, 1, 5.62, -0.4207860880538252, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0],\n",
       "       [0.0, 2.0, 2.368276348338014, -0.10986251397043469,\n",
       "        0.17709492350424752, 1.4679158255938314, 1.093627590647593, 1, 1,\n",
       "        1, 7.57, -0.4207860880538252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0],\n",
       "       [0.0, 2.0, -0.5800203618151143, -0.4637068154331368,\n",
       "        0.14723123304604843, -0.3804468854965805, 0.5905127924952556, 1,\n",
       "        0, 0, 5.0, -0.4207860880538252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0],\n",
       "       [0.0, 2.0, -0.5800203618151143, -0.7069747726887445,\n",
       "        -0.5993610284089284, -0.5652831566056217, -0.41571680380941944,\n",
       "        1, 0, 1, 5.0, -0.4207860880538252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0]], dtype=object)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\knigh\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "rf=RandomForestClassifier(n_estimators=30,max_depth=2,min_samples_split=3,max_leaf_nodes=5,random_state=42)\n",
    "model=rf.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " ...\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9914067257595236"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14652,     0],\n",
       "       [  127,     0]], dtype=int64)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\knigh\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\knigh\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\knigh\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.99      1.00      1.00     14652\\n           1       0.00      0.00      0.00       127\\n\\n    accuracy                           0.99     14779\\n   macro avg       0.50      0.50      0.50     14779\\nweighted avg       0.98      0.99      0.99     14779\\n'"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     14652\n",
      "           1       0.54      0.93      0.68       127\n",
      "\n",
      "    accuracy                           0.99     14779\n",
      "   macro avg       0.77      0.96      0.84     14779\n",
      "weighted avg       1.00      0.99      0.99     14779\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "\n",
    "# Address class imbalance using SMOTE\n",
    "smote = SMOTE(random_state=42,sampling_strategy=0.5, k_neighbors=5)\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(x_train, y_train)\n",
    "\n",
    "class_ratio = sum(y_train == 0) / sum(y_train == 1)\n",
    "# Define the XGBoost classifier and set its parameters\n",
    "xgb_classifier = xgb.XGBClassifier(\n",
    "    objective='binary:logistic',\n",
    "    random_state=42,\n",
    "    eval_metric='logloss',\n",
    "    scale_pos_weight=float(class_ratio)\n",
    ")\n",
    "\n",
    "# Fit the classifier on the resampled training data\n",
    "xgb_classifier.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred1 = xgb_classifier.predict(x_test)\n",
    "\n",
    "# Evaluate the model's performance using classification metrics\n",
    "print(classification_report(y_test, y_pred1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14551,   101],\n",
       "       [    9,   118]], dtype=int64)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9611203006872299"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importance = xgb_classifier.feature_importances_\n",
    "\n",
    "# Create lists of feature names and corresponding importances\n",
    "features=df.columns\n",
    "importances = list(importance)\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(range(len(importances)), importances, align='center')\n",
    "plt.yticks(range(len(features)), features)\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = xgb_classifier.feature_importances_\n",
    "\n",
    "# Create lists of feature names and corresponding importances\n",
    "features = df.columns\n",
    "importances = list(importance)\n",
    "\n",
    "# Sort the feature importances in descending order\n",
    "sorted_indices = np.argsort(importances)[::-1]\n",
    "sorted_importances = np.array(importances)[sorted_indices]\n",
    "sorted_features = np.array(features)[sorted_indices]\n",
    "\n",
    "# Plot the top 10 feature importances\n",
    "top_n = 10\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(range(top_n), sorted_importances[:top_n], align='center')\n",
    "plt.yticks(range(top_n), sorted_features[:top_n])\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Top {} Feature Importances'.format(top_n))\n",
    "plt.show()\n",
    "\n",
    "# Save the top 10 features and their importances in a dictionary for reference\n",
    "top_features = sorted_features[:top_n]\n",
    "top_importances = sorted_importances[:top_n]\n",
    "importance_dict = dict(zip(top_features, top_importances))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OnIG3t/dtt3U77dq1Q4sWLbBs2TI88cQTAEq7GHh5eZl+MKenpyMnJwffffcdvvvuuyrtIyQk5I6Zy5T9osrPzy93yvNWtyt4mzZtWmHdZs2aYfny5QDMe5/vlLu4uBhz587FokWLkJSUVG6osH8Xbeb6d+FSVpBkZ2cDgGnM0CZNmpRbz87O7ranv2/l6uoK4J/3sDpylW1z3759mDVrFvbv34+ioqJy6+fm5sLNzc10/3afh6psIyEhAQDQunVrs15DGXO/H1X97H700UeYOHEiAgMDERYWhiFDhmDChAlo1KiR2RnL/ni519cI4LZD2AUHB+P777+H0WhEQkIC5syZg/T09Ap/GLi4uNy1wPz3d9HV1dWU3dysdyvS7/WzW1WVHWc7OztER0dj6dKl0Gg0UKvVWLlyJXQ6Xbli9n5+5lLtxWKWapXOnTujU6dOAEpbD3v27ImHHnoI586dg7Ozs2l8x+nTp1faWgVULF7uxGg0ok2bNvj0008rfTwwMPCOzx83bhzmzJmDjIwMuLi4YM2aNRg/frypJbAs7yOPPFKhb22Ztm3blrtflVZZoLRP6erVq/H333+jd+/ela7z999/A0CVWstudS/vc2W5n3/+eSxatAgvvfQSunXrBjc3N8hkMjz44IO3Hauzqm43LNPtChNztWjRAgBw4sQJtG/fvsrPu1uuhIQEDBgwAC1atMCnn36KwMBAqFQqbNiwAZ999lmF96Wy99Xcbdwrc78fVf3sjh07Fr169cKqVauwZcsWfPzxx/jwww+xcuVKDB48+L5zV1W9evUA/PMH0L85OTmV62veo0cPdOzYEW+88Qa+/PJL0/KWLVvi2LFjuHr1aoU/Zsr8+7vYokULHD16FNeuXbvrz5lbZWdnV/rH6K3M/ezerjg2GAyVLr/dcX7wwQcxf/58bNy4ESNHjsTy5cvRokULtGvXzrTO/f7MpdqJxSzVWgqFAnPnzkW/fv3w1Vdf4fXXXze13CiVynK/ZCrTuHFjnDx58q7rHD9+HAMGDKjSadd/GzduHGbPno24uDj4+voiLy/PdKEDAHh7e8PFxQUGg+Guec01bNgwzJ07F0uWLKm0mDUYDFi6dCk8PDzQo0ePco9duHChwvrnz583tVia8z7fSWxsLCZOnIhPPvnEtKykpAQ5OTnl1ruX9/5uygbAv3jxIvr162dartfrkZiYWOGPiH8bPHgwFAoFfv7552q9kGbt2rXQaDRYs2ZNucLHnNOrVd1G48aNAQAnT5684x95t3v/7/f7cSf169fHs88+i2effRZpaWno2LEj5syZYypmq7q/ss/q3b7rlSkr+i5fvlyl9du2bYtHHnkE8+fPx/Tp003v/bBhw/Drr79iyZIleOuttyo8Ly8vD7///jtatGhhOg5RUVH49ddf8fPPP2PGjBlV2r9er8e1a9cwfPjwO65n7mfXw8OjwncSgNkzovXu3Rv169fHsmXL0LNnT2zfvh1vvvlmuXVq8jNFtot9ZqlW69u3Lzp37ozPP/8cJSUl8PHxQd++fTF//nwkJydXWD89Pd10Ozo6GsePH8eqVasqrFfWSjZ27FgkJSXh+++/r7BOcXGx6ar822nZsiXatGmDZcuWYdmyZahfv365wlKhUCA6OhpxcXGV/rK9Na+5unfvjvDwcCxatKjSGYbefPNNnD9/Hq+++mqFlpTVq1eX6/P6119/4cCBA6ZCwpz3+U4UCkWFltL//e9/FVp8nJycAKDSX6j3qlOnTqhXrx6+//576PV60/Jffvnlti1xtwoMDMTkyZOxZcsW/O9//6vwuNFoxCeffILr16+blaus5fbfXS4WLVpU7dsYOHAgXFxcMHfuXJSUlJR77NbnOjk5Vdrt436/H5UxGAwV9uXj44MGDRpAo9HcNdO/eXt7o3fv3li4cCGuXr1a7rG7tdL7+/sjMDDQrNmwXn31Veh0unItizExMQgNDcUHH3xQYVtGoxHPPPMMsrOzMWvWrHLPadOmDebMmYP9+/dX2E9+fn6FQvD06dMoKSlB9+7d75jR3M9u48aNkZuba2o9BoDk5ORKf3beiVwuR0xMDNauXYuffvoJer2+XBcDoGY+U2T72DJLtd4rr7yCMWPGYPHixZgyZQq+/vpr9OzZE23atMHkyZPRqFEjpKamYv/+/bh+/bppusdXXnnFNLPU448/jrCwMGRlZWHNmjWYN28e2rVrh0cffRTLly/HlClTsGPHDvTo0QMGgwFnz57F8uXLsXnzZlO3h9sZN24cZs6cCXt7ezzxxBOQy8v/jfnBBx9gx44d6NKlCyZPnozQ0FBkZWXhyJEj2Lp1K7Kysu75vVmyZAkGDBiAESNG4KGHHkKvXr2g0WiwcuVK7Ny5E+PGjcMrr7xS4XlNmjRBz5498cwzz0Cj0eDzzz9HvXr18Oqrr5rWqer7fCfDhg3DTz/9BDc3N4SGhmL//v3YunWr6fRumfbt20OhUODDDz9Ebm4u1Go1+vfvDx8fn3t+b1QqFd555x08//zz6N+/P8aOHYvExEQsXrwYjRs3rlKr0CeffIKEhAS88MILWLlyJYYNGwYPDw9cvXoVK1aswNmzZ8u1xFfFwIEDoVKpEBUVhaeffhoFBQX4/vvv4ePjU+kfDvezDVdXV3z22Wd48skn8cADD+Chhx6Ch4cHjh8/jqKiIvz4448AgLCwMCxbtgzTpk3DAw88AGdnZ0RFRVXL9+Pf8vPzERAQgJiYGNMUrlu3bsXBgwfLteDfLlNlvvzyS/Ts2RMdO3bEU089hZCQECQmJmL9+vU4duzYHfOMGDECq1atqlJfVKC0m8CQIUPwww8/4O2330a9evWgUqkQGxuLAQMGoGfPnuVmAFu6dCmOHDmC//u//yv3WVEqlVi5ciXCw8PRu3dvjB07Fj169IBSqcSpU6dMZ1VuHVosPj4ejo6OiIiIuGtOcz67Dz74IF577TWMGjUKL7zwAoqKivDtt9+iWbNmZl+oOW7cOPzvf//DrFmz0KZNmwpD7NXEZ4pqAcsPoEBU/W43aYIQpTPMNG7cWDRu3Ng09FNCQoKYMGGC8PPzE0qlUvj7+4thw4aJ2NjYcs/NzMwUU6dOFf7+/qbBuSdOnFhumCytVis+/PBD0apVK6FWq4WHh4cICwsTs2fPFrm5uab1/j00V5kLFy6YBnbfu3dvpa8vNTVVPPfccyIwMFAolUrh5+cnBgwYIL777jvTOmVDTq1YscKs9y4/P1+88847olWrVsLBwUG4uLiIHj16iMWLF1cYmujWSRM++eQTERgYKNRqtejVq5c4fvx4hW1X5X2+07HLzs4WkyZNEl5eXsLZ2VlERkaKs2fPVvpefv/996JRo0ZCoVBUadKEf79PtxtM/8svvxRBQUFCrVaLzp07i3379omwsDAxaNCgKry7pbMl/fDDD6JXr17Czc1NKJVKERQUJCZNmlRu6KPbzQBW9v7cOlHEmjVrRNu2bYW9vb0IDg4WH374oVi4cGGF9comTahMVbdRtm737t2Fg4ODcHV1FZ07dxa//vqr6fGCggLx0EMPCXd39wqTJlT1+4Gbg+lXBrcMzaXRaMQrr7wi2rVrJ1xcXISTk5No165dhQkfbpfpdsf55MmTYtSoUcLd3V3Y29uL5s2bi7fffrvSPLc6cuSIAFBhqKjbTZoghBA7d+6sMNyYEEKkpaWJadOmiSZNmgi1Wi3c3d1FeHi4aTiuymRnZ4uZM2eKNm3aCEdHR2Fvby9at24tZsyYIZKTk8ut26VLF/HII4/c9TWVqepnVwghtmzZIlq3bi1UKpVo3ry5+Pnnn+84acLtGI1GERgYKACI9957r9J1qvqZorpDJkQ1Xe1ARLVeYmIiQkJC8PHHH2P69OlSx5GE0WiEt7c3Ro8eXempTqp7BgwYgAYNGuCnn36SOsptHTt2DB07dsSRI0fMuiCRyBawzywR0W2UlJRU6De5ZMkSZGVloW/fvtKEIqvz/vvvY9myZWZf8GRJH3zwAWJiYljIUq3EPrNERLfx559/4uWXX8aYMWNQr149HDlyBAsWLEDr1q0xZswYqeORlejSpQu0Wq3UMe7ot99+kzoCUY1hMUtEdBvBwcEIDAzEl19+iaysLHh6emLChAn44IMPys0eRkRE0mGfWSIiIiKyWewzS0REREQ2i8UsEREREdmsOtdn1mg04saNG3BxceFUeERERERWSAiB/Px8NGjQoMJkQv9W54rZGzduIDAwUOoYRERERHQX165dQ0BAwB3XqXPFrIuLC4DSN8fV1bXG96fT6bBlyxYMHDgQSqWyxvdH1Y/H0PbxGNo+HkPbxuNn+yx9DPPy8hAYGGiq2+6kzhWzZV0LXF1dLVbMOjo6wtXVlV9gG8VjaPt4DG0fj6Ft4/GzfVIdw6p0CeUFYERERERks1jMEhEREZHNYjFLRERERDaLxSwRERER2SwWs0RERERks1jMEhEREZHNYjFLRERERDaLxSwRERER2SwWs0RERERks1jMEhEREZHNYjFLRERERDaLxSwRERER2SwWs0RERERks1jMEhEREZHNkrSY3b17N6KiotCgQQPIZDKsXr36rs/ZuXMnOnbsCLVajSZNmmDx4sU1npOIiIiIrJOkxWxhYSHatWuHr7/+ukrrX758GUOHDkW/fv1w7NgxvPTSS3jyySexefPmGk5KRERERNbITsqdDx48GIMHD67y+vPmzUNISAg++eQTAEDLli2xd+9efPbZZ4iMjKypmDVLCEBXJHUKuhOdDgqDBtAWAkIpdRq6FzyGto/HsEalFZTg+NWcGtu+Xm/A6XQNVMcvw85OUWP7oZqj0+pwOl2DPiU6uCut6zsoaTFrrv379yM8PLzcssjISLz00ku3fY5Go4FGozHdz8vLAwDodDrodLoayXmrsn1Uui8hoFgyFPLrf9V4Drp3SgDDAOBviYPQPeMxtH08hjXLB0BEDe9jMABcr+GdULUTAI6iNf5ER7yMZbiS0Q1O9jVfzJpTo9lUMZuSkgJfX99yy3x9fZGXl4fi4mI4ODhUeM7cuXMxe/bsCsu3bNkCR0fHGsv6b/Hx8RWWKQwaDGMhS0RERFZIAyXWIQInZS0AAAdFW1w9sB9n/1bX+L6Liqp+1tqmitl7MWPGDEybNs10Py8vD4GBgRg4cCBcXV1rfP86nQ7x8fGIiIiA8tZmeSGAogxTK4PupTOA0nLFNVWdTqfH9u3b0b9/fyiVtf4rUyvxGNo+HsPbMxoFxH1uo8fHu1CkNWD91O4I9KjYMHS/ePxsT2paOlat3YCs7BzIZDL06t4VObktMGrQUChVqhrff9mZ9KqwqU+Un58fUlNTyy1LTU2Fq6trpa2yAKBWq6FWV/wLQqlUli8ua1i5/QkBLIwErh3453FHN0DlZLE8ZAadDgaFGkonN4t+Zqga8RjaPh7DSm06mYwXfzsGjd54n1tSAlBC5eQGpVMNNKzw+NkMIQQOHTqEzZs3w2AwwNXVFTExMfDz88OGDRugVKkscgzN2YdNFbPdunXDhg0byi2Lj49Ht27dJEp0j3RF5QpZBHZlqywREZltz4WMaihkSzXycoKPi321bItsV1ZWFjZt2gSj0YhmzZphxIgRcHR0tMh1RvdK0mK2oKAAFy9eNN2/fPkyjh07Bk9PTzRs2BAzZsxAUlISlixZAgCYMmUKvvrqK7z66qt4/PHHsX37dixfvhzr16+X6iXcv+kXAScvQCaTOgkREdmox7oH46Xwpve1DRd7JRRy/i6q6+rVq4fIyEgYDAZ07doVMhuoTyQtZg8dOoR+/fqZ7pf1bZ04cSIWL16M5ORkXL161fR4SEgI1q9fj5dffhlffPEFAgIC8MMPP9jusFwAoHJkIUtEVEcZjQJzNpzBpfSCe3r+uZR8AIC7oxLujjXfj5FqHyEE/vrrLwQFBcHPzw8A0LlzZ4lTmUfSYrZv374Q4vbd1iub3atv3744evRoDaYiIiKyjFM38rBg7+X73o6Xc81fXU61T3FxMdasWYOzZ8/C09MTTz/9NFQWuLirutlUn1kiIqLaJF9T2g/R11WN/xvY/J624Wpvh/4tfO++ItEtrl+/jtjYWOTm5kKhUKBLly42e3Eei1kp3KE1moiI6g6NrvTiLW8XNcZ2CpQ4DdUFQgjs378f27Ztg9FohIeHB2JiYtCgQQOpo90zFrOWJgSwaJDUKYiIyApo9AYAgD2neCUL0Gq1iIuLw/nz5wEArVq1QlRUVKVDmNoSFrOWpisCUk6U3vZrwyG5iIjqsJKbLbP2ShazVPOUSiX0ej0UCgUGDRqEsLAwmxit4G5YzEpp0iaOZEBEZMNKdAbkl+jv+fkZBRoAgL1SXl2RiMoRQsBgMMDOzg4ymQyjRo1CQUGBaeSC2oDFrJRYyBIR2azLGYUY9uUeFGoN970tNVtmqQYUFhZi1apVcHNzQ1RUFADA2dkZzs7OEierXixmiYiI7sHZ5DxTIXs/bRNqOznCW/pUUyqiUomJiYiLi0NBQQHs7OzQs2dPeHh4SB2rRrCYJSIiug+dgz2xfIqNTatOtZbRaMSePXuwa9cuCCHg5eWFMWPG1NpCFmAxS0REVM6F1Hy8s/YUCjR37j6QW6S1UCKiqikoKMDKlStx+XLpRBzt27fH4MGDbXIiBHOwmCUiIrrFyqNJ2Hcxs8rr13e3r8E0RFUjhMCSJUuQnp4OpVKJoUOHol27dlLHsggWs0RERLfIKSqdlWtE+waIanvngeQVChm6hHhaIhbRHclkMoSHh2P79u2IiYmBl5eX1JEshsUsERHRTYUaPX796yoAoG2AO8JDOU0sWa/8/HxkZWUhKCgIANCsWTM0adIEcnndGuqNxSwREdFNcUeum257OdfufoZk2y5evIhVq1bBaDTi6aefhru7OwDUuUIWYDFLRERkklesM92OYKssWSGj0Yjt27dj3759AAA/Pz8YjUaJU0mLxSwREdUJQghczSqCRn/7X/wZBaUjFIzvHAhHFX9FknXJzc1FXFwcrl27BgDo1KkTIiMjYWdXtz+rdfvVExFRnfHtrgR8tOmc1DGI7sn58+exevVqFBcXQ61WIyoqCq1atZI6llVgMUtERHXCqaQ8AICTSgH7O0wfa69UYFDr+paKRVQlFy5cQHFxMRo0aICYmJhaPQmCuVjMWpoQUicgIqqTNPrSSRDeHhaKBzs3lDgNkXkiIyPh7u6OLl261PluBf/Gd8OShAAWDZI6BRFRnVCiM+Cpnw4jMaMQAJCWXwIAd2yVJbIWZ8+exd9//42YmBjI5XLY2dmhR48eUseySixmLUlXBKScKL3t1wZQOkqbh4ioFvv7ei52n0+vsLyxt7MEaYiqRq/XIz4+Hn/99RcA4OjRowgLC5M4lXVjMSuVSZsAmUzqFEREtVZ2UenIBM19XTA3ug0AwNtZjUBPNiSQdcrKykJsbCySk5MBAN26dUP79u2lDWUDWMxKhYUsEREA4NSNXPx+7AYMxttfU2A0GnE5UY5jG89VeVD4C2kFAID67vbo2JAXy5B1O3XqFNauXQuNRgMHBweMHDkSzZo1kzqWTWAxS0REkvrPutP481JWFdaUY2fyFbO3X9/N3vxQRBa0Z88ebN++HQAQGBiI6OhouLm5SZzKdrCYJSIiSRVpS0cZGNqmPhrWq7wLgNFgREJCAho3bgy5ourTdart5Bj3QGC15CSqKc2aNcOePXvQpUsX9OvXr05OSXs/WMwSEVG1K9EZ8Pf13Dt2HShTUKIHAMSEBaBfC59K19HpdNiw4QKGDGwKpVJZrVmJpJCZmYl69eoBAHx9ffH888/DxcVF4lS2icUsERFVu5eXHcPGkylmPYeXElBdoNPpsGnTJhw7dgyTJk1CQEAAALCQvQ8sZomIqNqdS80HAPi7O8BBdfdxXRu4O6BTsGdNxyKSVHp6OmJjY5GWlgYASEpKMhWzdO9YzFoSZ/8iojoi/2bXgfmPhqG1Py9kITp27Bg2bNgAnU4HJycnjB49Go0aNZI6Vq3AYtZSOPsXEdURp27kIj1fAwBwtWf/VqrbtFotNmzYgOPHjwMAQkJCMHr0aDg7c/KO6sJi1lI4+xcR1RHbzqSZbvu6qSVMQiS9kydP4vjx45DJZOjbty969uzJ0QqqGYtZKXD2LyKqxbIKS2feeqJnCNR2d+8vS1SbdejQAUlJSWjTpg2Cg4OljlMrsZiVAgtZIrIyf1zMQNyRJIhq6Nt/6Eo2AE5WQHWTRqPB7t270bt3b6jVashkMkRFRUkdq1ZjMUtERHhv/RmcTs6r1m029GR3KqpbUlJSEBsbi8zMTBQWFmLkyJFSR6oTWMwSERGKdaWzcE3oFoRAj/svQr1cVOh/mwkQiGobIQQOHz6MTZs2wWAwwNXVFR07dpQ6Vp3BYpaIyMYIIXAwMRsZBZpq22bZUFoj2vsjLMij2rZLVNuVlJRg3bp1OHXqFIDSqWlHjBgBR0eembAUFrNERDZmf0ImHvrhQI1sW23Hq6yJqiotLQ2//fYbsrOzIZfLER4ejq5du0LGa2MsisUsEZGNOXkjFwDg5axGIy+nattuI28ntKzvWm3bI6rtHB0dodVq4ebmhpiYGM7mJREWs0REVkZnMN7x8SuZRQCAsZ0C8OqgFpaIREQ36XQ6KJWlk4E4Ozvj4Ycfhru7OxwcHCROVnexmCUisiLvrDmFxX8kVmndQI4WQGRR169fR2xsLMLDw9G6dWsAQP369SVORewcRURkRdb9nVyl9VzUdujWqF4NpyEioPSiy/3792PRokXIzc3Fvn37qmVMZqoebJklIrISJTqDaYSCPa/2g6uD8rbrOigVUPFiLaIaV1RUhN9//x3nz58HAISGhiIqKooXeVkRFrNERBJKyS3BnA1nkFesg0ZfOtarg1KBAA8H/rIkkti1a9cQGxuLvLw8KBQKDBo0CGFhYfxuWhkWs0REElp59DrWHr9RbllzPxf+siSSWHZ2NhYvXgyj0QhPT0+MGTMGfn5+UseiSrCYJSKSUGpuCQAgItQXka38IAPQo4mXtKGICB4eHujSpQsKCgowdOhQqNVqqSPRbbCYJSKSUPrNPrI9GtdDTBjHqCSSUmJiIjw8PODm5gYACA8Ph0wm45kSK8erB4iIJJSRrwUAeLvYS5yEqO4yGo3YtWsXlixZgtjYWBgMpf3X5XI5C1kbwJZZIiIJlbXMervwFCaRFAoKCrBy5UpcvnwZAFCvXj0YjUYoFAqJk1FVsZglIrKgEp0BecU60/20vNI+syxmiSzv8uXLiIuLQ2FhIZRKJYYMGYL27dtLHYvMxGKWiMhCUnJLEPHpLuRr9BUeYzFLZDll3Qp2794NAPDx8UFMTAy8vb0lTkb3gsUsEZGFnEvNNxWyCvk//fD6t/CBk4qnNIksxWg04ty5cwCADh06YPDgwVAqbz9JCVk3FrNERBZiNJZOf9nG3w1rn+8pcRqiusvOzg4xMTFITk5GmzZtpI5D94nFLBGRGYq1Bry87Bhu5Bab/dyyvrJyOa+OJrIko9GI7du3Q6VSoXfv3gAALy8veHlxTOfagMUsEZEZdl9Ix6ZTKfe1jQB3h2pKQ0R3k5ubi7i4OFy7dg0ymQytWrVCvXr1pI5F1YjFLBGRGa5lFQEAujbyxFO9G5n9fLlMhs4hntUdi4gqcf78eaxevRrFxcVQq9WIiopiIVsLsZglolpty6kUnL6Ri/PXZbi88xIU8vubK2bX+XQAQPtAD/Rv4VsdEYmomhkMBmzbtg379+8HANSvXx8xMTHw9OQfkrURi1kiqrVSckvw1E+Hb95TYMO1i9W27RAvx2rbFhFVHyEEfv75ZyQmJgIAOnfujIiICNjZseSprXhkiajWKtD8MzlBdx8jAhs2hPw+W2YBwNNJiah2De57O0RU/cr6xaakpGD48OFo2bKl1JGohrGYJSKbUaIzIDGzsMrrX80s7d/q7qDEuMbFGDIklGNJEtVCer0eeXl5pm4EYWFhaNGiBZydnSVORpbAYpaIbIIQAsP+txcX0wrMfq6MI2ER1VrZ2dlYsWIFioqK8PTTT8PBwQEymYyFbB3CYpaIbIIQMBWy9ZxUkJlRoUZ3aADoL9RUNCKSyOnTp7FmzRpoNBo4ODggMzMTAQEBUsciC2MxS0Q2J35aH3g6qaq8vk6nw4YNLGaJagu9Xo/Nmzfj0KFDAIDAwEBER0fDzc1N4mQkBRazRGTVVh9Nwudbz0NnEFJHISIrkJmZidjYWKSklE5e0qNHD/Tr1w8KhULiZCQVFrNEZNV+OXAFiTcv5AIAL2c1nNX80UVUV+3cuRMpKSlwdHTEqFGj0KRJE6kjkcT4G4GIrFp+iR4A8O6IVmgb4I4QLyeo7O5/eC0isk2DBw8GAERERMDV1VXiNGQNWMwS0X27nl2En/+8Co3eUO3bTsouBgC0DXBH+0D3at8+EVm39PR0nDx5En379oVMJoOjoyOio6OljkVWhMUsEd23b3cm4JcDV2t0H94u6hrdPhFZn+PHj2P9+vXQ6XTw9PREu3btpI5EVojFLBHdt+wiLQCgdzNvtPGv/tN+Lfxc4e/uUO3bJSLrpNVqsXHjRhw7dgwAEBISgsaNG0sbiqwWi1kiMkuBRo+/r+cAtwwukJxbAgAY2sYP4x5oKE0wIqoV0tLSsGLFCmRkZEAmk6FPnz7o1atXtUxFTbUTi1kiMsuEBQdw5GpOpY85qPgjhYju3YkTJ7BmzRro9Xo4OzsjOjoawcHBUsciK8ffPERklrJZuEK8nKBU/DMLl4+LPXo28ZIqFhHVAk5OTtDr9WjcuDFGjRoFJycnqSORDWAxS0RVJoRAkbZ0xIJfJ3eFn5u9xImIyNZptVqoVKUz+jVq1AiPPfYYGjZsaNaU1VS3sZgloju6llWERxYcQEa+BgKA3ljaWdZRzdl2iOjeCSFw+PBh7NixA0888QQ8PT0BAEFBQRInI1vD3tREdEe7zqfjSmYRCrUGU6tsEx9nOLN/LBHdI41Gg7i4OKxfvx5FRUU4dOiQ1JHIhklezH799dcIDg6Gvb09unTpgr/++uuO63/++edo3rw5HBwcEBgYiJdffhklJSUWSktU92QUaAAAw9s1wO5X+mH3K/2w4YVekMt5CpCIzHfjxg3Mnz8fp06dglwuR0REBCIiIqSORTZM0qaVZcuWYdq0aZg3bx66dOmCzz//HJGRkTh37hx8fHwqrL906VK8/vrrWLhwIbp3747z58/jscceg0wmw6effirBKyCyTTqDEf/dcg6puXf/Q/DvpFwAQLCXExrWc6zpaERUSwkhcPDgQWzfvh0GgwFubm6IiYlBQECA1NHIxklazH766aeYPHkyJk2aBACYN28e1q9fj4ULF+L111+vsP4ff/yBHj164KGHHgIABAcHY/z48Thw4IBFcxPZuu1n0zB/1yWznhPMQpaI7kNWVhaOHz8OAGjRogWGDx8OBwdOhkL3T7JiVqvV4vDhw5gxY4ZpmVwuR3h4OPbv31/pc7p3746ff/4Zf/31Fzp37oxLly5hw4YNePTRR2+7H41GA41GY7qfl5cHANDpdNDpdNX0am6vbB86nR7KW5fJan7fVD3+OYa155j9fS0bANApyB0DQ33vur67gxKRLb1t9j2ojcewruExtG06nQ4eHh4wGo0IDQ1Fp06dIJPJeDxtiKW/g+bsR7JiNiMjAwaDAb6+5X+R+vr64uzZs5U+56GHHkJGRgZ69uwJIQT0ej2mTJmCN95447b7mTt3LmbPnl1h+ZYtW+DoaLmWpu3bt2PYzdubN2+BQcF55m1NfHy81BHumcEInM+VoaT0+i3sTJYDkCFIlgnfnIy7byAH2Jpckwktw5aPIZXiMbQdQghkZ2fDw8MDMpkMcrkcXl5eSE9Px8aNG6WOR/fIUt/BoqKiKq9rU5cj79y5E++//z6++eYbdOnSBRcvXsSLL76I//znP3j77bcrfc6MGTMwbdo00/28vDwEBgZi4MCBcHWt/jnk/02n0yE+Ph79+/cH/i5dFhk5EFBxIGhbUXYMIyIioFQq7/4EK/TLgauYd6DiH4mjB3RB52BPCRJZVm04hnUdj6FtKS4uxrp163D16lX4+/ujZ8+eiI+Px8CBA3n8bJSlv4NlZ9KrQrJi1svLCwqFAqmpqeWWp6amws/Pr9LnvP3223j00Ufx5JNPAgDatGmDwsJCPPXUU3jzzTcrnbdZrVZDra7YCqpUKi36hVIq7W65rQT4ZbY5lv7MVKeMQr3pdpeQ0uK1sY8zujb2gaIOjUpgy8eQSvEYWr9r164hNjYWeXl5UCgU8PDwMB0zHj/bZ6ljaM4+JCtmVSoVwsLCsG3bNowcORIAYDQasW3bNkydOrXS5xQVFVUoWBWK0oHbhRA1mpfI0oQQ0BqM1bKtsokOHusejHeGt6qWbRIR3UoIgX379mH79u0QQsDT0xNjxoyBn58f+8ZSjZK0m8G0adMwceJEdOrUCZ07d8bnn3+OwsJC0+gGEyZMgL+/P+bOnQsAiIqKwqeffooOHTqYuhm8/fbbiIqKMhW1RLWBEAKPLDiAfRczpY5CRHRXhYWFWL16NS5evAgAaN26NYYNG1bpmVGi6iZpMTtu3Dikp6dj5syZSElJQfv27bFp0ybTRWFXr14t1xL71ltvQSaT4a233kJSUhK8vb0RFRWFOXPmSPUSiGqERm+s9kJWqZChc0jt7x9LRJZXXFyMK1euwM7ODoMHD0aHDh0gk9WdLkwkLckvAJs6deptuxXs3Lmz3H07OzvMmjULs2bNskAyIunobulecPitcKjs7n+yPqVCDnslz2AQUfXz8vLC6NGj4eHhUWGUIqKaJnkxS1RXJeUU4/31Z5BXUrEvmd7wTx9wD0cVp44lIqtSUFCA1atXo1evXggKCgJQOhECkRRYzBJJZNWR61h/4s6Dt/q4qFnIEpFVuXTpElauXInCwkJkZ2fjueeeq3Q0ISJLYTFLJJHc4tIW2f4tfDCsbf1K1+nY0MOSkYiIbstoNGLXrl3YvXs3AMDb2xtjxoxhIUuSYzFLJJECTenYr+0D3TG6Y4DEaYiIbi8/Px8rV65EYmIiAKBDhw4YPHgwx4wlq8BilkgiBZrSuWWd1fwaEpH1ys3NxXfffYeioiIolUoMGzYMbdu2lToWkQl/ixJJpPBmyyyLWSKyZq6urggJCUFGRgbGjBmDevXqSR2JqBz+FiWyECEE0vM1uDkZF7IKtQAAJxazRGRl8vLyoFKpYG9vD5lMhqioKMjlcnYrIKvE36JEFvJq7N9Ycfh6heVOao79SkTW4/z581i9ejWCg4MxZswYyGQyzuRFVo3FLJGFHLqSDQBQyGUoG20rwMMRHQI5YgERSc9gMGDbtm3Yv38/ACAnJwcajQb29vYSJyO6MxazRBZSoiu94Gv1sz3QJsBN4jRERP/IyclBXFwcrl8vPXvUuXNnREREwM6OZQJZP35KiSxg/q4EJOeWAADUSo7JSETW4+zZs/j9999RUlICtVqNESNGoGXLllLHIqoyFrNEFvD1joum276uPGVHRNZBp9Nh48aNKCkpgb+/P6Kjo+Hhwa5PZFtYzBJZQNkIBj890RluDrwamIisg1KpRHR0NM6ePYsBAwZAoeAFqWR7WMwS1bBircE021egh6PEaYiorjt9+jT0er1p4oOGDRuiYcOGEqciuncsZolq2OpjSabbDiq2ehCRNPR6PTZv3oxDhw7Bzs4O/v7+nACBagUWs0Q1LC1PAwBQKeTsL0tEksjMzERsbCxSUlIAAF26dIG7u7u0oYiqCYtZovtQqNEjMbPwjutcyy4CAEzsHmSJSERE5Zw8eRJr166FVquFo6MjRo4ciaZNm0odi6jasJglukdGo8CgL3bjWlZxldZ3UPHrRkSWI4TA+vXrcfjwYQClfWOjo6Ph6uoqcTKi6sXfrkT3KKNAYypkfVzuPNWjq4MSA0N9LRGLiAgAIJPJ4OhYetFpr1690LdvX8jlHOeaah8Ws0T3KCWvdBIEX1c1DrwRLnEaIqJSWq0WKpUKANC3b180bdoUgYGBEqciqjksZonM8O7a09h8qvQCirLpaf14URcRWQGtVouNGzciNTUVjz/+OOzs7CCXy1nIUq3HYpbIDEv2J0JfNgPCTe0D3aUJQ0R0U1paGmJjY5Geng6ZTIbExEQ0adJE6lhEFsFilsgMZWXsDxM6wcdVDTu5HC38XCTNRER1lxACx44dw4YNG6DX6+Hs7Izo6GgEBwdLHY3IYljMElWREAKGm62ybQPc4MPuBUQkIY1Gg/Xr1+PEiRMAgMaNG2PUqFFwcnKSOBmRZbGYJaqiC2kFptv2nMmLiCS2bt06nDx5EjKZDP369UPPnj0hk8mkjkVkcSxmiaqoSGsw3Xa1V0qYhIgI6N+/P1JTUzFs2DA0bNhQ6jhEkmExS3XetawiXMksuu3jeoMe53JkyEjKAwAEejpYKhoRkYlGo8HFixfRqlUrAICHhweeeeYZtsZSncdiluq0zAIN+n+yEzqDuMuaCuDMWQCAnL84iMjCkpOTsWLFCmRnZ0OtVptGKmAhS8Riluq4tHwNdAYBhVyGpj7Ola4jhEBefj5cXVwgk8nwcNcgC6ckorpKCIGDBw9iy5YtMBgMcHNzg709Lz4luhWLWSIAnk4qbHqpd6WP6XQ6bNiwAUOGdIdSyb6yRGQZJSUlWLNmDc6cOQMAaN68OUaMGAEHB3Z1IroVi1mqs2IPX8fsNaekjkFEVEFSUhJiY2ORk5MDuVyOiIgIdOnShd0KiCrBYpbqrHV/30C+Rg8AaOvvJnEaIqJ/ZGRkICcnB+7u7oiJiYG/v7/UkYisFotZqrMKSkoL2TmjWmP8AxzWhoikJYQwtby2a9cOWq0Wbdq0YR9ZortgMUu1UmaBBl9su4CcIt1t1zmfmg8ACPJ0glzOU3dEJJ1r165hy5YtGD9+PBwdHQEADzzwgMSpiGwDi1mqldafSMaS/VeqtG4Dd7Z6EJE0hBD4448/sG3bNgghsH37dgwbNkzqWEQ2hcUs1UoanREA0DbADSPb376vWYiXExp5Vz4kFxFRTSosLMTq1atx8eJFAEDr1q0REREhcSoi28NilmqVEp0Bey5k4NSNXABAE29nPN4zROJURETlXblyBXFxccjPz4ednR0GDRqEjh07crQConvAYpZqlW92XMSX2y+a7ivYF5aIrMzZs2exfPlyCCFQr149jBkzBr6+vlLHIrJZLGapVknL1wAAAj0d0MTbGY9wti4isjLBwcFwd3dHYGAghg4dCpVKJXUkIpvGYtZihNQB6gSdofR9HtcpEFP7N5U4DRFRqdTUVPj4+EAmk8He3h5PPvkkHBwc2K2AqBrIpQ5QJwgBuyVRUqeo9Q5fyUbcketSxyAiMjEajdi5cyfmzZuHQ4cOmZY7OjqykCWqJmyZtQCFUQtZ6onSO35tAKWjtIFqqSNXsk23w4I8JUxCRATk5+dj5cqVSExMBACkpaVJG4iolmIxa2mTNgH8a7xGDWtbH90a15M6BhHVYQkJCVi1ahUKCwuhVCoxbNgwtG3bVupYRLUSi1lLYyFbbX4/loTYw9chbnZHTsopBgCoFOw9Q0TSKOtWsGfPHgCAr68vYmJi4OXlJXEyotqLxSzZrI82nTMVsLfydlVLkIaIqPRCr7179wIAwsLCEBkZCaVSKXEqotqNxSzZrPwSHQDgzSEt4e1SWsDaK+Xo08xHylhEVIfVr18fERERcHFxQevWraWOQ1QnsJglm6Q3GJFXogcARLVrAD83e4kTEVFdZDAYsHPnTrRt2xbe3t4AgG7dukmciqhuYedCsknbzv5zVbCzPf8mIyLLy83NxeLFi7F3717ExsbCYDBIHYmoTmIVQDYp/eZMXwDgrObHmIgs69y5c1i9ejVKSkqgVqvRp08fKBQKqWMR1UmsAsgmafVGAKVdDIiILMVgMCA+Ph4HDhwAADRo0AAxMTHw8PCQOBlR3cVilmyS5mYxy2G4iMhSCgsLsXTpUty4cQMA0LVrV4SHh7NFlkhiLGbJJpW1zKqVLGaJyDIcHBxgZ2cHe3t7jBw5Es2bN5c6EhGBxSzZKI2+9EILtswSUU3S6/WQyWRQKBSQy+WIjo6G0WiEu7u71NGI6CYWs2RTfthzCSuPJCE1rwQAW2aJqOZkZWVhxYoVCAoKwqBBgwAArq6uEqcion9jMUs2Zd6uS8go+GckgyBPJwnTEFFtdfLkSaxduxZarRZ5eXno3bs3HB0dpY5FRJVgMUs2RQgBAJgzqjVa+LmiQ6C7tIGIqFbR6XTYtGkTjhw5AgBo2LAhoqOjWcgSWTEWs2Qzcot1yCzUAgAeCPZEM18XiRMRUW2SkZGBFStWIC2tdFKWXr16oW/fvpDL2Z2JyJqxmCWb8cuBK6bbDkoOhUNE1Uev12PJkiXIz8+Hk5MTRo0ahcaNG0sdi4iq4L6K2ZKSEtjb21dXFqI7yi/RAygtZAM9ecqPiKqPnZ0dIiMjcejQIYwePRouLjzzQ2QrzD53YjQa8Z///Af+/v5wdnbGpUuXAABvv/02FixYUO0BicrkFOkAAA91aShxEiKqDdLS0nDlyj9nfFq1aoUJEyawkCWyMWYXs++99x4WL16Mjz76CCqVyrS8devW+OGHH6o1HFGZAo0ev/51FQAgkzgLEdk2IQSOHj2K77//HsuXL0d+fr7pMZmMP2GIbI3ZxeySJUvw3Xff4eGHHy43hV+7du1w9uzZag1HVCY5p9h0e1BrPwmTEJEt02q1WL16NdasWQO9Xg8/Pz9e4EVk48zuM5uUlIQmTZpUWG40GqHT6aolFNG/6QylQ3L5uKjRKdhT4jREZItSU1OxYsUKZGZmQiaToV+/fujZsydbY4lsnNnFbGhoKPbs2YOgoKByy2NjY9GhQ4dqC0Z0K53BCABQcvpaIjKTEAJHjhzBpk2boNfr4eLigujo6Aq/x4jINpldzM6cORMTJ05EUlISjEYjVq5ciXPnzmHJkiVYt25dTWQkMhWzKjsWs0RkHplMhmvXrkGv16NJkyYYNWoUJ0EgqkXMLmZHjBiBtWvX4t1334WTkxNmzpyJjh07Yu3atYiIiKiJjETQmlpmeTqQiKpGCGHqQjBkyBAEBAQgLCyM3QqIapl7Gme2V69eiI+Pr+4sRJVKyS3Bf9adAcBuBkR0d0IIHDx4EImJiRgzZgxkMhlUKhU6deokdTQiqgFmVwaNGjVCZmZmheU5OTlo1KhRtYQiutWiPy7jTHIeAMDDUXWXtYmoLispKUFsbCw2btyIM2fO4MyZM1JHIqIaZnbLbGJiIgwGQ4XlGo0GSUlJ1RKK6FY5haWjZKgUcrwzvJXEaYjIWiUlJSE2NhY5OTmQy+WIiIhAy5YtpY5FRDWsysXsmjVrTLc3b94MNzc3032DwYBt27YhODi4WsNR3ZWerzG1xl7LLgIAvDqoOZr4OEsZi4iskBACBw4cQHx8PIxGI9zd3RETEwN/f3+poxGRBVS5mB05ciSA0qtCJ06cWO4xpVKJ4OBgfPLJJ9Uajuomg1FgyJd7kJ6vKbfcQaW4zTOIqC7buHEjDh48CABo2bIlhg8fDnt7e4lTEZGlVLmYNRpLryYPCQnBwYMH4eXlVWOhqG4r0OhNhWwLPxfIZDLUc1JhQAtfiZMRkTVq164djh8/jgEDBuCBBx7gaAVEdYzZfWYvX75cEzmITDT60j7ZMhmw8cVe/MVEROUIIZCamgo/v9Kprf39/fHSSy/BwcFB4mREJIV7GueosLAQGzZswLx58/Dll1+W+2eur7/+GsHBwbC3t0eXLl3w119/3XH9nJwcPPfcc6hfvz7UajWaNWuGDRs23MvLICv1+dYLAAB7OwULWSIqp6ioCL/++it++OEHpKSkmJazkCWqu8xumT169CiGDBmCoqIiFBYWwtPTExkZGXB0dISPjw9eeOGFKm9r2bJlmDZtGubNm4cuXbrg888/R2RkJM6dOwcfH58K62u1WkRERMDHxwexsbHw9/fHlStX4O7ubu7LICt29GoOAMBOzkKWiP5RUFCABQsWID8/HwqFAhkZGabWWSKqu8xumX355ZcRFRWF7OxsODg44M8//8SVK1cQFhaG//73v2Zt69NPP8XkyZMxadIkhIaGYt68eXB0dMTChQsrXX/hwoXIysrC6tWr0aNHDwQHB6NPnz5o166duS+DrFjZ1LXzHw2TOAkRWQMhBPbt24eLFy8iPz8f9erVw+TJk9G6dWupoxGRFTC7ZfbYsWOYP38+5HI5FAoFNBoNGjVqhI8++ggTJ07E6NGjq7QdrVaLw4cPY8aMGaZlcrkc4eHh2L9/f6XPWbNmDbp164bnnnsOv//+O7y9vfHQQw/htddeg0JR+ZXuGo0GGs0/V8Xn5ZUO96TT6aDT6ar6su/Zv/eh0+kAWc3v19ZkFmjwxfYE5BXrkZxTDABQyIRFjtHdlGWwhix0b3gMbVdhYSHWrFljul4jNDQUQ4YMgUql4vG0IfwO2j5LH0Nz9mN2MatUKiGXlzbo+vj44OrVq2jZsiXc3Nxw7dq1Km8nIyMDBoMBvr7lr1D39fXF2bNnK33OpUuXsH37djz88MPYsGEDLl68iGeffRY6nQ6zZs2q9Dlz587F7NmzKyzfsmULHB0dq5z3ftxaZm/evAUGhdoi+7Ulu5JlWJlY/g+Sk4f+QMpJiQJVglM42z4eQ9uTlpaGGzduQCaTISAgAEqlElu3bpU6Ft0jfgdtn6WOYVFRUZXXNbuY7dChAw4ePIimTZuiT58+mDlzJjIyMvDTTz/V+Ckfo9EIHx8ffPfdd1AoFAgLC0NSUhI+/vjj2xazM2bMwLRp00z38/LyEBgYiIEDB8LV1bVG8wKlf1ls37TOdD8yciCgcqrx/dqaa7svA4kX0C7ADcPb1UdIPUf0amodw7/pdDrEx8cjIiICSqVS6jh0D3gMbZcQAps3b0a7du1w9OhRHkMbxe+g7bP0MSw7k14VZhez77//PvLz8wEAc+bMwYQJE/DMM8+gadOmWLBgQZW34+XlBYVCgdTU1HLLbx1u5d/q168PpVJZrktBy5YtkZKSAq1WC5VKVeE5arUaanXFllClUinJF0qpVAL8IgMAjEaBPxIykVmowZmUAgBAy/queKJXY4mTVU6qzwxVHx5D65efn49du3YhMjLSdKyioqKg0+lw9OhRHkMbx+Nn+yx1DM3Zh9nFbKdOnUy3fXx8sGnTJnM3AQBQqVQICwvDtm3bTLOLGY1GbNu2DVOnTq30OT169MDSpUthNBpNXR3Onz+P+vXrV1rIknXbdSEdkxYdLLdMbXdPo8URUS2QkJCAVatWobCwEHK5HEOGDJE6EhHZgGqrHI4cOYJhw4aZ9Zxp06bh+++/x48//ogzZ87gmWeeQWFhISZNmgQAmDBhQrkLxJ555hlkZWXhxRdfxPnz57F+/Xq8//77eO6556rrZZAFJeeUAADqOanQs4kXwlv6YnyXhhKnIiJLMxqN2L59O37++WcUFhbCx8cHnTt3ljoWEdkIs1pmN2/ejPj4eKhUKjz55JNo1KgRzp49i9dffx1r165FZGSkWTsfN24c0tPTMXPmTKSkpKB9+/bYtGmT6aKwq1evmlpgASAwMBCbN2/Gyy+/jLZt28Lf3x8vvvgiXnvtNbP2S9ZBe3Omr66N6uHrhztKnIaIpJCXl4e4uDhcvXoVANCxY0cMGjSIp6KJqMqqXMwuWLAAkydPhqenJ7Kzs/HDDz/g008/xfPPP49x48bh5MmTaNmypdkBpk6dettuBTt37qywrFu3bvjzzz/N3g9ZF6NR4J21pwEAdgpOjkBUF129ehXLli1DUVERVCoVoqKiOHYsEZmtysXsF198gQ8//BCvvPIK4uLiMGbMGHzzzTc4ceIEAgICajIj1UJ5Jf+MH9e9cT0JkxCRVNzc3CCEgJ+fH2JiYlCvHn8WEJH5qlzMJiQkYMyYMQCA0aNHw87ODh9//DELWbpvMWGBUkcgIgspKSmBvb09gNJidsKECfDy8oKdndnXIxMRATCjmC0uLjZNMiCTyaBWq1G/fv0aC0a102fx53EwMQt6g5A6ChFZ2Llz5/D7779jxIgRaN68OQDcdihGIqKqMutP4R9++AHOzs4AAL1ej8WLF8PLq/zA9i+88EL1paNaJaNAgy+2XSi3zMtZBTm7zBLVagaDAVu3bjVd73Dw4EFTMUtEdL+qXMw2bNgQ33//vem+n58ffvrpp3LryGQyFrN0WxkFGgCAi70d3htZepFH+0B3yGSsZolqq+zsbMTFxSEpKQkA0KVLF0REREiciohqkyoXs4mJiTUYg2qjsyl52J+Qabp/JbN0nmVfV3uMaO8vVSwispAzZ87g999/h0ajgb29PUaMGIEWLVpIHYuIahn2uKca8/iig7iRW1JhuY9LxemFiah2SU5OxvLlywEAAQEBiI6Ohru7u7ShiKhWYjFLNSajUAsACG/pAwdV6UfNTi7DI12DpIxFRBZQv359dOrUCSqVCv3794dCoZA6EhHVUixm6b5p9Aak52sqLDcaS0cseG9kG/i52Vs6FhFZ2OnTp9GwYUPThcJDhgxhn3giqnEsZum+lOgM6PffnUiupDtBmVtmJCaiWkin02Hz5s04fPgwQkJC8Mgjj0Aul7OQJSKLYDFL9+VGTrGpkLVXVqxaOwV5wsuJfWSJaquMjAzExsYiNTUVAODvz4s7iciy7qmYTUhIwKJFi5CQkIAvvvgCPj4+2LhxIxo2bIhWrVpVd0ayYvklegBAAzd7/DFjgMRpiMiS/v77b6xbtw46nQ6Ojo4YPXo0GjduLHUsIqpjzD4BvGvXLrRp0wYHDhzAypUrUVBQAAA4fvw4Zs2aVe0ByXplF2oxZt5+AICLvVLiNERkKTqdDmvWrMGqVaug0+kQHByMKVOmsJAlIkmYXcy+/vrreO+99xAfHw+VSmVa3r9/f9PsLlQ3/JGQCa3BCABo4uMscRoishQhBK5duwYA6NOnDx599FG4uLhInIqI6iqzuxmcOHECS5curbDcx8cHGRkZ1RKKbINRCNPtT8a2kzAJEVmCEAIymQwqlQoxMTEoLCxEo0aNpI5FRHWc2S2z7u7uSE5OrrD86NGj7Phfx1zOKAQAdGtUD/ZKjiFJVFtptVqsXr263Nk3X19fFrJEZBXMLmYffPBBvPbaa0hJSYFMJoPRaMS+ffswffp0TJgwoSYykpVafax0rvVinUHiJERUU1JTU/H999/j+PHj2L59u+k6CSIia2F2N4P3338fzz33HAIDA2EwGBAaGgqDwYCHHnoIb731Vk1kJCulUpT+LTS0TX2JkxBRdRNC4MiRI9i0aRP0ej1cXFwQHR1tmhCBiMhamF3MqlQqfP/993j77bdx8uRJFBQUoEOHDmjatGlN5CMrklukw5Wswn/uF+sAAB2D3CVKREQ1QaPRYN26dTh58iQAoEmTJhg5ciScnJwkTkZEVJHZxezevXvRs2dPNGzYEA0bNqyJTGSFSnQG9PnvDuQU6So85qji3BtEtYXBYMCCBQuQnp4OmUyGAQMGoHv37pzNi4isltl9Zvv374+QkBC88cYbOH36dE1kIiuUVag1FbIN3OxN//o190ZTDstFVGsoFAp06NABrq6umDRpEnr06MFCloismtlNajdu3MBvv/2GX3/9FR988AHatm2Lhx9+GOPHj0dAQEBNZCQrolLIOdMXUS1TUlKCwsJC1KtXDwDQtWtXdOjQAfb29hInIyK6O7NbZr28vDB16lTs27cPCQkJGDNmDH788UcEBwejf//+NZGRJKbVG/HIDwekjkFENeDGjRuYP38+fv31V2g0GgCATCZjIUtENuO+OjuGhITg9ddfR7t27fD2229j165d1ZWLrMjZlDxcujmmLGf6IqodhBA4cOAA4uPjYTQa4e7ujvz8fKjVaqmjERGZ5Z6L2X379uGXX35BbGwsSkpKMGLECMydO7c6s5GVKNb+M47syme7S5iEiKpDcXEx1qxZg7NnzwIAWrRogREjRrA1lohsktnF7IwZM/Dbb7/hxo0biIiIwBdffIERI0bA0dGxJvKRBHafT8eW0ymm+ym5JQCAlvVdOdMXkY27fv06YmNjkZubC4VCgYEDB+KBBx7gRV5EZLPMLmZ3796NV155BWPHjoWXl1dNZCKJvR73N27cLGBv5eWskiANEVWnXbt2ITc3Fx4eHoiJiUGDBg2kjkREdF/MLmb37dtXEznIipRNT/to1yDUu1nAKmQyDG3Lmb6IbN2IESOwc+dOREREsH8sEdUKVSpm16xZg8GDB0OpVGLNmjV3XHf48OHVEqx2EVIHMEv2zfFkJ3QLQlNfF4nTENH9uHr1KhISEtCvXz8AgLOzM4YNGyZxKiKi6lOlYnbkyJFISUmBj48PRo4cedv1ZDIZDAbDbR+vk4RAzwvvSZ2iynKKtKbbcjn70BHZKiEE9u7dix07dkAIgfr166NFixZSxyIiqnZVKmaNRmOlt6kKdEVwL75aetuvDaC07gvlknKKTbdD6nEediJbVFhYiFWrViEhIQEA0LZtWzRq1EjiVERENcPsSROWLFliGlj7VlqtFkuWLKmWULXWpE2AlV8xXHRzGK4QLye2zBLZoMTERMybNw8JCQmws7PD8OHDMXLkSKhUvICTiGons4vZSZMmITc3t8Ly/Px8TJo0qVpC1VpWXshOX3Ec47/7EwDgqOIQXES2Zv/+/ViyZAkKCgrg5eWFyZMno0OHDhx2i4hqNbNHMxBCVPqD8fr163Bzc6uWUCSNdX/fgN5YerHaA8GeEqchInN5enpCCIH27dtj8ODBbI0lojqhysVs2V/3MpkMAwYMgJ3dP081GAy4fPkyBg0aVCMhybLinumOsCAPqWMQURWUlJSYZu5q3rw5Jk+ezLFjiahOqXIxWzaKwbFjxxAZGQlnZ2fTYyqVCsHBwYiOjq72gGQZiRmFKNGVXtzn48KxJ4msndFoxM6dO3H48GE89dRTpjNjLGSJqK6pcjE7a9YsAEBwcDDGjRvHObxrmV8OXDHddrVXSpiEiO4mLy8PK1euxJUrpd/b06dPo1u3bhKnIiKShtl9ZidOnFgTOUhiWn1pq2y3RvXg5shilshaXbx4EatWrUJRURFUKhWioqLQunVrqWMREUmmSsWsp6cnzp8/Dy8vL3h4eNzxytisrKxqC0eWcfpGHhLSCwEADwSzryyRNTIYDNixY4dpSnE/Pz/ExMSgXr16EicjIpJWlYrZzz77DC4uLqbbHOal9sgr0WHkN/tMLbN2CrNHayMiCzhw4ICpkH3ggQcwcODAchfiEhHVVVX6SXhr14LHHnusprKQBLILtdDqjVDIZRjUyg8j2/tLHYmIKvHAAw/g3Llz6NKlC0JDQ6WOQ0RkNcxuhjty5AhOnDhhuv/7779j5MiReOONN6DVaqs1HNUsg1Egt1gHAHCxt8PXD3dEw3rWPd0uUV1hMBhw6NAh0xTiSqUSjz32GAtZIqJ/MbuYffrpp3H+/HkAwKVLlzBu3Dg4OjpixYoVePXVV6s9INWMQo0evT/ageFflZ62tJOzewGRtcjJycGiRYuwfv167Nmzx7ScXbyIiCoyu4I5f/482rdvDwBYsWIF+vTpg6VLl2Lx4sWIi4ur7nxUQy5nFCIpp9h0v38LbwnTEFGZM2fOYP78+UhKSoK9vT18fX2ljkREZNXuaTrbstNeW7duxbBhwwAAgYGByMjIqN50VOM8HJXYP2MA7JUKqaMQ1Wl6vR7x8fH466+/AAABAQGIjo6Gu7u7tMGIiKyc2cVsp06d8N577yE8PBy7du3Ct99+CwC4fPkyWxBsxKfx57HjbBoAQG2nYCFLJLGsrCzExsYiOTkZANCtWzcMGDAACgW/m0REd2N2Mfv555/j4YcfxurVq/Hmm2+iSZMmAIDY2Fh079692gNS9cot1uHLbRdM931dOXUtkdS0Wi3S0tLg4OCAkSNHolmzZlJHIiKyGWYXs23bti03mkGZjz/+mK0INsBoFKbbX47vgK6NPCVMQ1R3CSFMF3SVTYBQv359uLm5SZyMiMi23POI24cPH8aZM2cAAKGhoejYsWO1haKak1+iN90e1qY+5HJeHU1kaZmZmVi5ciWGDBkCf//SsZ1btGghcSoiIttkdjGblpaGcePGYdeuXaYLE3JyctCvXz/89ttv8PbmVfHW7OcDV6SOQFSnnThxAuvWrYNWq8XGjRvxxBNPcMgtIqL7YPbQXM8//zwKCgpw6tQpZGVlISsrCydPnkReXh5eeOGFmshI1UijMwAAAj0d2CpLZEE6nQ5r1qzBypUrodVqERwcjHHjxrGQJSK6T2a3zG7atAlbt25Fy5YtTctCQ0Px9ddfY+DAgdUajqpfap4GADCK09YSWUx6ejpiY2ORllY6ikifPn3Qu3dvyDlZCRHRfTO7mDUajVAqlRWWK5VK0/izZJ2+252ATadSpI5BVKekpaXhhx9+gE6ng5OTE6KjoxESEiJ1LCKiWsPsZoH+/fvjxRdfxI0bN0zLkpKS8PLLL2PAgAHVGo6q14mkPNPtPs3Zt5nIEry9vRESEoKQkBBMmTKFhSwRUTUzu2X2q6++wvDhwxEcHIzAwEAAwLVr19C6dWv8/PPP1R6Qqt+sqFCEBXFILqKakpaWBnd3d6hUKshkMkRHR8POzo7dCoiIaoDZxWxgYCCOHDmCbdu2mYbmatmyJcLDw6s9HFWPo1ezMXvtaSSkFUgdhahWE0Lg6NGj2LhxI0JDQzFy5EjIZDKoVCqpoxER1VpmFbPLli3DmjVroNVqMWDAADz//PM1lYuq0aqjSTh2Lcd0P8DDUbowRLWURqPB+vXrTZPKFBUVwWAwwM7unofzJiKiKqjyT9lvv/0Wzz33HJo2bQoHBwesXLkSCQkJ+Pjjj2syH1UDnaH0wrwxYQGY1CMELeu7SJyIqHZJSUnBihUrkJWVBZlMhgEDBqB79+4cdouIyAKq3IHrq6++wqxZs3Du3DkcO3YMP/74I7755puazEbVRGconcK2kbczQhu48hcsUTURQuDgwYP44YcfkJWVBVdXV0yaNAk9evTg94yIyEKqXMxeunQJEydONN1/6KGHoNfrkZycXCPBqPoYjKXFrB0nSSCqViUlJdi1axcMBgOaNWuGp59+2nRhLBERWUaVuxloNBo4OTmZ7svlcqhUKhQXF9dIMKo++rJiVsFilqg6OTg4YPTo0UhNTUXXrl3ZGktEJAGzrkx4++234ej4z8VDWq0Wc+bMgZubm2nZp59+Wn3pyCxCCJxLzUeJrvzkFZkFpbN+sWWW6P4IIfDXX3/BxcUFoaGhAIBGjRqhUaNGEicjIqq7qlzM9u7dG+fOnSu3rHv37rh06ZLpPlslpPXtrgR8tOncbR+3U3CMS6J7VVxcjDVr1uDs2bNQqVQICAiAq6ur1LGIiOq8KhezO3furMEYVB0upJaOI+tqbwcX+/JTDns5q9C7GWf9IroX169fR2xsLHJzc6FQKDBgwAC4uHBUECIia8ABEGuRsiG4Xo5ohkk9OGUm0f0SQmD//v3Ytm0bjEYjPDw8EBMTgwYNGkgdjYiIbmIxW4vsT8gEACjZnYDovhmNRixbtgznz58HALRq1QpRUVFQq9USJyMioluxmK1FGtZzRGahFkVavdRRiGyeXC6Hp6cnFAoFBg0ahLCwMF4XQERkhVjM1iKidAQuhHg5SxuEyEYJIaDRaGBvbw8ACA8PR8eOHeHtzf7mRETWiueja5GbtSw4AheR+QoLC7F06VIsXboUBoMBAKBQKFjIEhFZuXsqZvfs2YNHHnkE3bp1Q1JSEgDgp59+wt69e6s1HJlH3Gya5ZlQIvMkJiZi/vz5uHjxIpKTk5GSkiJ1JCIiqiKzi9m4uDhERkbCwcEBR48ehUZTOiB/bm4u3n///WoPSFVX1s2A/fqIqsZoNGLXrl1YsmQJ8vPz4eXlhcmTJ8Pf31/qaEREVEVmF7Pvvfce5s2bh++//x5K5T9jmfbo0QNHjhyp1nB0dzqDEX8kZGDH2TTklegAACxlie6uoKAAP//8M3bu3AkhBNq3b4/JkyfDx8dH6mhERGQGsy8AO3fuHHr37l1huZubG3JycqojE5nhq+0X8cW2C+WW2cnZFZroblatWoXLly9DqVRi6NChaNeundSRiIjoHphd9fj5+eHixYsVlu/du/ee5yf/+uuvERwcDHt7e3Tp0gV//fVXlZ7322+/QSaTYeTIkfe039rgRk4xAMDXVY02/m4Y1MoPYUEeEqcisn6DBw9GQEAAnnrqKRayREQ2zOxidvLkyXjxxRdx4MAByGQy3LhxA7/88gumT5+OZ555xuwAy5Ytw7Rp0zBr1iwcOXIE7dq1Q2RkJNLS0u74vMTEREyfPh29evUye5+10cTuwVj7fE/MezQMDiqF1HGIrI5Op8OpU6dM9728vPD444/Dy8tLwlRERHS/zO5m8Prrr8NoNGLAgAEoKipC7969oVarMX36dDz//PNmB/j0008xefJkTJo0CQAwb948rF+/HgsXLsTrr79e6XMMBgMefvhhzJ49G3v27KnT3Rv2XsyQOgKR1bt06RLOnj2L06dPw8PDA0FBQQB4sSQRUW1gdjErk8nw5ptv4pVXXsHFixdRUFCA0NBQODubP1C/VqvF4cOHMWPGDNMyuVyO8PBw7N+//7bPe/fdd+Hj44MnnngCe/bsueM+NBqNacQFAMjLywNQ2kqj0+nMzmwunU4Ppem2DpBV7z5D6jkiObcE2QUai7yeuqjsfeX7a3vKRiso+3ni4+MDtVrNY2mD+D20bTx+ts/Sx9Cc/dzzDGAqlQqhoaH3+nQAQEZGBgwGA3x9fcst9/X1xdmzZyt9zt69e7FgwQIcO3asSvuYO3cuZs+eXWH5li1b4OjoaHZmcykMGgy7eXvz5i0wKKp3XveMTDkAOUpSErBhQ8W+zFR94uPjpY5AZtBqtbhy5QoKCwsBlHYr8PPzw4EDByRORveD30PbxuNn+yx1DIuKiqq8rtnFbL9+/e54am779u3mbrLK8vPz8eijj+L777+vcj+3GTNmYNq0aab7eXl5CAwMxMCBA+Hq6lpTUU10hbnA36W3IyMHAiqn+96mEAJf7biEc6n5yNBnA9Chffv2GNKu/n1vmyrS6XSIj49HREREueHoyHpdvHgRa9euRXFxMdRqNSIjI3H16lUeQxvG76Ft4/GzfZY+hmVn0qvC7GK2ffv25e7rdDocO3YMJ0+exMSJE83alpeXFxQKBVJTU8stT01NhZ+fX4X1ExISkJiYiKioKNMyo9EIALCzs8O5c+fQuHHjcs9Rq9VQqyu2hiqVSst8oZT/vMVKpRKohn0mpBfgyx0J5Zb5uDnwB0QNs9hnhu5bQUEBiouLUb9+fcTExMDFxQVXr17lMawFeAxtG4+f7bPUMTRnH2YXs5999lmly9955x0UFBSYtS2VSoWwsDBs27bNNLyW0WjEtm3bMHXq1Arrt2jRAidOnCi37K233kJ+fj6++OILBAYGmrV/W5VZoAUAeDmr8WJ4U/i4qNGjMa/IprpNCGE6a9SpUycolUq0bt0adnZ27KdHRFSL3XOf2X975JFH0LlzZ/z3v/8163nTpk3DxIkT0alTJ3Tu3Bmff/45CgsLTaMbTJgwAf7+/pg7dy7s7e3RunXrcs93d3cHgArLa6usQi1+/CMRABDg4YBHuwZJG4jICpw9exa7d+/GhAkTYG9vD5lMVuEsEhER1U7VVszu378f9vb2Zj9v3LhxSE9Px8yZM5GSkoL27dtj06ZNpovCrl69CjlntDKZsfJvbD5V2i2jnpNK4jRE0tLr9di6davpoq4//vgD/fv3lzgVERFZktnF7OjRo8vdF0IgOTkZhw4dwttvv31PIaZOnVpptwIA2Llz5x2fu3jx4nvap63KuNnFAACm9G18hzWJaresrCzExsYiOTkZANCtWzf06dNH4lRERGRpZhezbm5u5e7L5XI0b94c7777LgYOHFhtwagivcGI/JLSvn/zHw3DA8GeEiciksapU6ewdu1aaDQaODg4YOTIkWjWrJnUsYiISAJmFbMGgwGTJk1CmzZt4OHhUVOZqBIFGj0iPt2F5NwSqaMQSerw4cNYt24dACAwMBAxMTEWGWaPiIisk1mdURUKBQYOHFinp4+VSmJGoamQ9XJWo42/212eQVQ7tWzZEq6urujZsycee+wxFrJERHWc2d0MWrdujUuXLiEkJKQm8tBd1HNS4a83BkAu55zyVHdcu3bNNPSeo6Mjnn322UrHjyYiorrH7GEC3nvvPUyfPh3r1q1DcnIy8vLyyv2j6qczGPFKbOk0YkqFnIUs1Rk6nQ5r1qzBwoULy01hzUKWiIjKVLll9t1338X//d//YciQIQCA4cOHl5vWtmzAcoPBUP0p67gTSbk4k1z6h4Kvm/nDnxHZovT0dMTGxiItLQ1A6XTWRERE/1blYnb27NmYMmUKduzYUZN5qBIGozDdXjCxk4RJiCzj+PHjWL9+PXQ6HZycnDB69Gg0atRI6lhERGSFqlzMClFaUHEcR8tLz9cAAEK8nODlzNOrVHtptVps3LjR1KWgUaNGGDVqFJydnaUNRkREVsusC8Bu7VZAlrNkfyIAQKs3ShuEqIbduHEDx44dg0wmQ9++fdGzZ0/OAEhERHdkVjHbrFmzuxa0WVlZ9xWIKirrZdC7mZe0QYhqWHBwMAYOHIj69esjODhY6jhERGQDzCpmZ8+eXWEGMKpZQgicvXnxV2QrP4nTEFUvjUaDLVu2oEePHvD0LJ3Rrlu3bhKnIiIiW2JWMfvggw/Cx8enprJQJWatOYW8Ej0AwMVeKXEaouqTkpKC2NhYZGZmIi0tDY8//ji7MhERkdmqXMzyl4w0Tiblmm63asCZjsj2CSFw+PBhbNq0CQaDAa6uroiIiODPGCIiuidmj2ZA0pj/aBjslQqpYxDdl5KSEqxbtw6nTp0CUNoPf8SIEXB0dJQ4GRER2aoqF7NGI6+kt7STSbk4cjVH6hhE1SI7Oxs//fQTsrOzIZfLER4ejq5du7JFloiI7otZfWbJstb9nWy6HejBliuyba6urnBwcIDRaERMTAwCAgKkjkRERLUAi1krVta1I7ylL0LZX5ZsUElJCVQqFeRyORQKBcaOHQuVSgUHBwepoxERUS3B0cit1OWMQszffQkA0MjbSeI0ROZLSkrC/Pnzy02B7ebmxkKWiIiqFYtZKzV3wxnTbUcVL/wi2yGEwP79+7Fw4ULk5OTg9OnT0Gq1UsciIqJait0MrFShtnRs2aB6jnika5DEaYiqpri4GKtXr8b58+cBAKGhoYiKioJKpZI4GRER1VYsZq3UpfRCAMC0iGbwclZLnIbo7q5du4bY2Fjk5eVBoVBg0KBBCAsL42gFRERUo1jMWqFNJ5ORnFsidQyiKispKcEvv/wCjUYDT09PjBkzBn5+nH6ZiIhqHotZK3Q5o8h0u1vjehImIaoae3t7DBo0CJcuXcLQoUOhVvNsAhERWQaLWSs2JiwAPi72UscgqtSVK1cgl8sRGBgIAGjfvj3atWvHbgVERGRRLGat0KZTKVJHILoto9GIvXv3YufOnXB2dsaUKVNM09GykCUiIktjMWuFHJWlQ3HlFOskTkJUXkFBAVatWoVLl26OgdyoEezs+GOEiIikw99CVkh+c/TfYW3rSxuE6BaXL19GXFwcCgsLoVQqMWTIELRv317qWEREVMexmLUyBy5lYt/FTKljEJkIIbBz507s3r0bAODj44OYmBh4e3tLnIyIiIjFrNWZvfa06barg1LCJET/yMjIAAB06NABgwcPhlLJzyYREVkHFrNWpkRnAFA6kkGvJl4Sp6G6TAgBmUwGmUyGqKgotGrVCqGhoVLHIiIiKkcudQAq70pW6RizYx8IhJ2Ch4csz2g0YuvWrYiNjYUQAkDpOLIsZImIyBqxZdaKZBdqYTCWFg9yDnFEEsjNzUVcXByuXbsGoHQs2eDgYGlDERER3QGLWSuSXqAx3W7j7yZhEqqLzp8/j9WrV6O4uBhqtRpRUVEsZImIyOqxmLUiZa2yXs5qqOzYxYAsw2AwYNu2bdi/fz8AoH79+oiJiYGnp6fEyYiIiO6OxawVyS7UAgDYVZYsKS4uDmfOnAEAdO7cGREREZwIgYiIbAZ/Y1kR3c2W2dQ8zV3WJKo+Xbp0wZUrVxAVFYUWLVpIHYeIiMgsLGatSNklXy38XCTNQbWbXq9HSkoKAgICAABBQUF48cUXoVKpJE5GRERkPp7QtiJf77gIAJBxJAOqIdnZ2Vi4cCGWLFmC9PR003IWskREZKvYMmsl0vJLcOByFgDAy5mFBVW/06dPY82aNdBoNHBwcEBBQQGnpCUiIpvHYtZKaHRG0+2PY9pJmIRqG71ej82bN+PQoUMAgMDAQERHR8PNjcO/ERGR7WMxayVuTrQER5UCfm720oahWiMzMxOxsbFISUkBAPTo0QP9+vWDQqGQOBkREVH1YDFrJQQ48xdVv7///hspKSlwdHTEqFGj0KRJE6kjERERVSsWs1bi5qhcYC1L1alPnz7QarXo1q0bXF1dpY5DRERU7TiagZUw3uxnwFqW7kdGRgZWr14NvV4PAJDL5YiMjGQhS0REtRZbZq3E3gsZADgsF92748ePY/369dDpdHB1dUX//v2ljkRERFTjWMxaifT80lm/cot1EichW6PVarFx40YcO3YMABASEoLOnTtLG4qIiMhCWMxaCcPNbgaTegRLG4RsSlpaGmJjY5Geng6ZTIY+ffqgV69ekMvZg4iIiOoGFrNWQAiBb3cmAAAU7GZAVXT27FnExcVBr9fD2dkZ0dHRCA4OljoWERGRRbGYtQJXMotMt+u7O0iYhGyJj48PFAoFgoKCMGrUKDg5OUkdiYiIyOJYzFoBfdm4XAAmdAuSMAlZu8LCQlPR6unpiSeeeAJeXl68cJCIiOosdqyzAhfT8gEA7o5KKBU8JFSREAKHDh3C559/joSEBNNyb29vFrJERFSnsWXWCsz8/RQAQKs3SpyErFFJSQnWrVuHU6dKPycnT55E48aNJU5FRERkHVjMWgGFvLRlbXKvRhInIWtz48YNxMbGIjs7G3K5HAMGDEC3bt2kjkVERGQ1WMxaAZ2htEU2ItRX4iRkLYQQ+OuvvxAfHw+DwQA3NzfExMQgICBA6mhERERWhcWsxIQQyCjQ3rwtcRiyGpcvX8amTZsAAC1atMDw4cPh4MCRLoiIiP6NxazEbhnIAD6uaumCkFVp1KgROnbsCB8fH3Tu3JkXeREREd0Gi1mJiVuaY9V2HMmgriobraBVq1ZwdHQEAERFRUmcioiIyPqxepLYrS2zbH2rm4qKivDbb79hw4YNWL16dbk/cIiIiOjO2DIrMYF/ChfWsnXPtWvXEBsbi7y8PCgUCjRt2lTqSERERDaFxazEbm2EYy1bdwghsG/fPmzfvh1CCHh6emLMmDHw8/OTOhoREZFNYTErMcMt/QzkbJqtE4qKirBq1SpcvHgRANC6dWsMGzYMajUvACQiIjIXi1mJnUzKNd22U7CYrQvkcjkyMjJgZ2eHwYMHo0OHDuwvTUREdI9YzEpMc8sUtmo7hYRJqCaVXdQlk8lgb2+PsWPHQi6Xw9eXE2UQERHdD45mILErmYUAgND6rhInoZpSUFCAn3/+GYcOHTItq1+/PgtZIiKiasCWWQn9eSkTb/9+CgBHMqitLl++jLi4OBQWFiI5ORlt27Zl31giIqJqxGJWQmWtsgDwSNcgCZNQdTMajdi1axd2794NAPD29saYMWNYyBIREVUzFrNWILylD8Z3bih1DKom+fn5WLlyJRITEwEAHTp0wODBg6FUKqUNRkREVAuxmJXQikPXpY5A1Uyr1eK7775DQUEBlEolhg0bhrZt20odi4iIqNZiMSsh/c0xZgs1BomTUHVRqVR44IEHcPr0aYwZMwb16tWTOhIREVGtxmJWQvKbF3091iNY0hx0f/Ly8qDT6UyFa8+ePdG9e3fY2fHrRUREVNM4NJeEjlzNAcBpbG3Z+fPnMW/ePCxfvhw6nQ5A6aQILGSJiIgsg79xJWQnl0FvFDAKcfeVyaoYDAZs27YN+/fvBwC4u7ujuLiYF3kRERFZGItZCTnb2yGnSIfG3s5SRyEz5OTkIC4uDtevl17A17lzZ0RERLA1loiISAJW0c3g66+/RnBwMOzt7dGlSxf89ddft133+++/R69eveDh4QEPDw+Eh4ffcX1rllNUelpaxhkTbMbZs2cxf/58XL9+HWq1GmPHjsXgwYNZyBIREUlE8mJ22bJlmDZtGmbNmoUjR46gXbt2iIyMRFpaWqXr79y5E+PHj8eOHTuwf/9+BAYGYuDAgUhKSrJw8vtTovtnBAOFnMWsLRBCYP/+/SgpKUGDBg3w9NNPo2XLllLHIiIiqtMkL2Y//fRTTJ48GZMmTUJoaCjmzZsHR0dHLFy4sNL1f/nlFzz77LNo3749WrRogR9++AFGoxHbtm2zcPL7k1eiM90OrucoYRKqKplMhtGjR6Nnz554/PHH4eHhIXUkIiKiOk/Sc6NarRaHDx/GjBkzTMvkcjnCw8NNF9bcTVFREXQ6HTw9PSt9XKPRQKPRmO7n5eUBAHQ6nenq85qk0+mhNN3WAbLSfWq0pf8r5DLo9foaz0H35syZM0hJSQFQevwcHR3Ru3dvGI1GGI1GidNRVZV91y3xnaeawWNo23j8bJ+lj6E5+5G0mM3IyIDBYICvr2+55b6+vjh79myVtvHaa6+hQYMGCA8Pr/TxuXPnYvbs2RWWb9myBY6ONd8iqjBoMOzm7c2bt8CgUAMAsjQAYAcIIzZs2FDjOcg8RqMRN27cQEZGBgCgcePGiI+PlzgV3S8eQ9vHY2jbePxsn6WOYVFRUZXXtemrVj744AP89ttv2LlzJ+zt7StdZ8aMGZg2bZrpfl5enqmfraura41n1BXmAn+X3o6MHAionAAA17KLgCN7obRTYMiQyBrPQVWXlZWFVatWmQrZzp07Q6PRICIigkNv2SidTof4+HgeQxvGY2jbePxsn6WPYdmZ9KqQtJj18vKCQqFAampqueWpqanw8/O743P/+9//4oMPPsDWrVvRtm3b266nVquhVqsrLFcqlZb5Qin/eYuVSiVwc59yeelyhUzGL7YVOXHiBNatWwetVgtHR0eMGjUKQUFB2LBhg+U+M1RjeAxtH4+hbePxs32WOobm7EPSC8BUKhXCwsLKXbxVdjFXt27dbvu8jz76CP/5z3+wadMmdOrUyRJRq11ybgkAQM6RDKzG5s2bsXLlSmi1WgQFBeHpp59GkyZNpI5FREREdyB5N4Np06Zh4sSJ6NSpEzp37ozPP/8chYWFmDRpEgBgwoQJ8Pf3x9y5cwEAH374IWbOnImlS5ciODjYdHGOs7MznJ1tZ/KBw1eyAAD5Jbz4y1oEBAQAAHr16oW+fftCLpd8sA8iIiK6C8mL2XHjxiE9PR0zZ85ESkoK2rdvj02bNpkuCrt69Wq5ouLbb7+FVqtFTExMue3MmjUL77zzjiWj3xfFzdfUObjyURjIMgoKCkx/BLVq1Qq+vr7w8vKSOBURERFVleTFLABMnToVU6dOrfSxnTt3lrufmJhY84EsoEhb2iIbxDFmJaHVarFx40ZcuHABU6ZMMRW0LGSJiIhsi1UUs3XR0as5AIASPccqtbS0tDTExsYiPT0dMpkMly5duuNFhERERGS9WMxKxM+tdCgxJS8AsxghBI4dO4YNGzZAr9fD2dkZ0dHRCA4OljoaERER3SMWsxLR3myRberrInGSukGr1WLdunU4ceIEgNJJEEaNGgUnJyeJkxEREdH9YDErgdS8Eqw5fkPqGHXK7t27ceLECchkMvTr1w89e/aETMZWcSIiIlvHYlYC51LyTbc7h3A0A0vo3bs3kpOT0adPHzRs2FDqOERERFRNOJCmhFrWd0VYkIfUMWoljUaDP/74A0IIAKUTdDz66KMsZImIiGoZtsxK4Ni1HAAAT3LXjOTkZMTGxiIrq3Riiu7du0uciIiIiGoKi1kJJKQXAACScoolTlK7CCFw8OBBbNmyBQaDAW5ubmyJJSIiquVYzEpAqSjt3fFwFxZa1aWkpARr1qzBmTNnAADNmzfHiBEj4ODgIHEyIiIiqkksZiVwPbsIAOBir5Q4Se1w48YNrFixAjk5OZDL5YiIiECXLl04WgEREVEdwGJWAteySrsXlE1pS/dHCIG8vDy4u7sjJiYG/v7+UkciIiIiC2ExKwFfVzWScooR4MFT4PfKaDRCLi/truHv749x48ahYcOGsLe3lzgZERERWRKH5pJAap4GAODhqJI4iW26du0avvnmG6SkpJiWNWvWjIUsERFRHcRi1sJWHrnOUQzukRAC+/btw6JFi5CZmYnt27dLHYmIiIgkxm4GFnb2ltm/OnLChCorLCzE6tWrcfHiRQBA69atMWzYMIlTERERkdRYzErk6d6N4OWsljqGTbhy5Qri4uKQn58POzs7DBo0CB07duRoBURERMRi1tK2nk6VOoJNuXr1Kn788UcIIVCvXj2MGTMGvr6+UsciIiIiK8Fi1sJcHErHls0r0UmcxDYEBAQgODgYLi4uGDp0KFQqXjRHRERE/2Axa2GKm2fG+zb3kTaIFbt69Srq168PpVIJuVyO8ePHQ6nkBBNERERUEUczsLAzyaUXgLG3Z0VGoxE7d+7EokWLsHnzZtNyFrJERER0O2yZtTCjEAAArcEocRLrkp+fj5UrVyIxMREAYDAYyk2MQERERFQZFrMW5uGoQkpeCQI8HKWOYjUSEhKwcuVKFBUVQalUYtiwYWjbtq3UsYiIiMgGsJi1sJS8EgCAnZwdDYxGI3bs2IG9e/cCAHx9fRETEwMvLy+JkxEREZGtYDFrQZczCk23OURq6UQIhw8fBgCEhYUhMjKS/WOJqAKDwQCNRgM7OzuUlJTAYDBIHYnMpNPpePxsXE0cQ5VKVS3dCVnMWtDV7CLT7Wa+LhImsQ4uLi4YOXIktFotWrduLXUcIrIyQgikpKQgJycHQgj4+fnh2rVrnDDFBvH42b6aOIZyuRwhISH3Pewmi1kLMhpLL/5qF+AGpaLuXdhkMBiwfft2NGzYEM2bNwcANGvWTOJURGStygpZHx8f2Nvbo7CwEM7Ozrww1AYZjUYUFBTw+Nmw6j6GRqMRN27cQHJyMho2bHhfBTKLWQu6klnaMiuvg/1lc3NzERsbi+vXr+Po0aN44YUXYG9vL3UsIrJSBoPBVMjWq1cPRqMROp0O9vb2LIZskNFohFar5fGzYTVxDL29vXHjxg3o9fr76mbIYtaCFDeL2HMp+RInsaxz585h9erVKCkpgVqtRlRUFAtZIrojna50lkRHR478QlRblXUvMBgMLGZtRVl7bL8WdWP2L4PBgPj4eBw4cAAA0KBBA8TExMDDw0PiZERkK9i/kqj2qq7vN4tZC0rKLgZQN2b/0ul0WLx4MW7cuAEA6Nq1K8LDw6FQKCRORkRERLUJO65YUGahpvT/Aq3ESWqeUqmEn58f7O3t8eCDDyIyMpKFLBFRDZPJZFi9erXUMWxOZmYmfHx8TLNQ0v178MEH8cknn1hkXyxmLcheVVrMNferncNy6fV6FBcXm+4PGjQIU6ZMMY1cQERU2z322GOQyWSQyWRQKpUICQnBq6++ipKSEqmj1ahbX/et/y5evChpppEjR1Zp3Tlz5mDEiBEIDg6u8FhZY8zBgwcrPNa3b1+89NJLFZYvXrwY7u7u5Zbl5eXhzTffRIsWLWBvbw8/Pz+Eh4dj5cqVEDenuq8JO3fuRMeOHaFWq9GkSRMsXrz4rs/ZvHkzunbtChcXF3h7eyM6Ovq2hf6+fftgZ2eH9u3bl1v+1ltvYc6cOcjNzb3/F3EXLGYtSG8o/bD6uta+i5+ysrKwYMECrFixAkajEUBp66ybm5vEyYiILGvQoEFITk7GpUuX8Nlnn2H+/PmYNWuW1LFqXNnrvvVfSEjIPW1Lq7XcGcyioiIsWLAATzzxRIXHrl69ij/++ANTp07FwoUL73kfOTk56N69O5YsWYIZM2bgyJEj2L17N8aNG4dXX321xgq+y5cvY+jQoejXrx+OHTuGl156CU8++SQ2b958x+eMGDEC/fv3x7Fjx7B582ZkZGQgJiam0tc1YcIEDBgwoMJjrVu3RuPGjfHzzz9X62uqDItZCyobZ7a2TWV78uRJzJ8/HykpKUhJSUF2drbUkYioFhJCoEirt/g/c1vN1Go1/Pz8EBgYiJEjRyI8PBzx8fGmxzMzMzF+/Hj4+/vD0dERbdq0wa+//lpuG3379sULL7yAV199FZ6envDz88M777xTbp0LFy6gd+/esLe3R2hoaLl9lDlx4gT69+8PBwcH1KtXD0899RQKCgpMj5e1Xr7//vvw9fWFu7s73n33Xej1erzyyivw9PREQEAAFi1aVOXXfeu/su5lu3btwoABA+Dg4ID69evj9ddfh16vL/d6p06dipdeegleXl6IjIwEUPr7ZfDgwXB2doavry8effRRZGRkmJ4XGxuLNm3amF5feHg4CgsL8c477+DHH3/E77//bmol3rlzZ6W5N2zYALVaja5du1Z4bNGiRRg2bBieeeYZ/Prrr+XOPprjjTfeQGJiIg4cOICJEyciNDQUzZo1w+TJk3Hs2DE4Ozvf03bvZt68eQgJCcEnn3yCli1bYurUqYiJicFnn3122+ccPnwYBoMB7733Hho3boyOHTti+vTpOHbsmGmUkTJTpkzBQw89hG7dulW6raioKPz222/V+poqwwvALEh/s5itLePM6nQ6bNq0CUeOHAEANGzYENHR0XB1dZU4GRHVRiU6Izp8WLFgq2mn342Eo+refl2ePHkSf/zxB4KCgkzLSkpKEBYWhtdeew2urq5Yv349Hn30UTRu3BidO3c2rffjjz9i2rRpOHDgAPbv34/HHnsMPXr0QEREBIxGI0aPHg1fX18cOHAAubm5FU53FxYWIjIyEt26dcPBgweRlpaGJ598ElOnTi13qnn79u0ICAjA7t27sW/fPjzxxBP4448/0Lt3bxw4cADLli3D008/jYiICAQEBJj9HiQlJWHYsGEYP348fvrpJ5w/fx6TJ0+Gvb19uQL9xx9/xDPPPIN9+/YBKG3169+/P5588kl89tlnKC4uxmuvvYaxY8di+/btSE5Oxvjx4/HRRx9h1KhRyM/Px549eyCEwPTp03HmzBnk5eWZCnFPT89K8+3ZswdhYWEVlgshsGjRInz99ddo0aIFmjRpgtjYWDz66KNmvX6j0YjffvsNDz/8MBo0aFDh8TsVsnv27MHgwYPvuP358+fj4YcfrvSx/fv3Izw8vNyyyMjISrtGlAkLC4NcLseiRYvw2GOPoaCgAD/99BMGDBhQbvisRYsW4dKlS/j555/x3nvvVbqtzp07Y86cOdBoNFCr1Xd8HfeDxawFGWpRy2xGRgZiY2ORmpoKAOjVqxf69u3LwbCJqM5bt24dnJ2dodfrodFoIJfL8dVXX5ke9/f3x/Tp0033n3/+eWzevBnLly8vV8y2bdvW1D2hadOm+Oqrr7Bt2zZERERg69atOHv2LDZv3mwqkN5///1yhc/SpUtRUlKCJUuWwMnJCQDw1VdfISoqCh9++CF8fX0BlBZ5X375JeRyOZo3b46PPvoIRUVFeOONNwAAM2bMwAcffIC9e/fiwQcfvOvrLjN48GCsWLEC33zzDQIDA/Hxxx/Dzc0NoaGhuHHjBl577TXMnDnT9HujadOm+Oijj0zPf++999ChQwe8//77pmULFy5EYGAgzp8/j4KCAuj1eowePdr0x0KbNm1M6zo4OECj0cDPz++Ox+vKlSuVFplbt25FUVGRqZX4kUcewYIFC8wuZjMyMpCdnY0WLVqY9TwA6NSpE44dO3bHdcqOY2VSUlIqPO7r64u8vDwUFxfDwcGhwnNCQkKwZcsWjB07Fk8//TQMBgO6deuGdevWmda5cOECXn/9dezZswd2drcvJRs0aACtVouUlJRyf9BVNxazFlRWzCpsvJgVQmDlypVITU2Fo6MjRo8ejcaNG0sdi4hqOXulHCffibD4H80OSvNGYunXrx++/fZbFBYW4rPPPoOdnR2io6NNjxsMBrz//vtYvnw5kpKSoNVqodFoKkwQ0bZt23L369evj7S0NADAmTNnEBgYWK4I+/ep3jNnzqBdu3amQhYAevToAaPRiHPnzpmKnFatWpV7T319fdG6dWvTfYVCgXr16pn2fbfXXaZsv2fOnEHXrl3LjSnao0cPFBQU4Pr162jYsCEAVGgdPX78OHbs2FFpy2VCQgIGDhyIAQMGoE2bNoiMjMTAgQPvaSzz4uLiSifyWbhwIcaNG2cq1saPH49XXnkFCQkJZv3Ou5+LuxwcHNCkSZN7fv69SElJweTJkzFx4kSMHz8e+fn5mDlzJsaOHYsVK1bAYDDgoYcewuzZs+86JX1ZsVxUVFSjmVnMWpC+lrTMymQyDB8+HNu2bcPw4cPh4lI7R2cgIusik8ngqLKz+jNATk5OpgJk4cKFaNeuXbkLjD7++GN88cUX+Pzzz9GmTRs4OTnhpZdeqnDR079nRJLJZKYLbKtTZfu5l33f+rrvxa1FNwAUFBSYWpH/rX79+lAoFIiPj8cff/yBLVu24H//+x/efPNNHDhwwKwLz7y8vCpc65GVlYVVq1ZBp9OVK9ANBgMWLlyIOXPmAABcXV0rvXgrJyfHdAG0t7c33N3dcfbs2SpnKnO/3Qz8/PxMZ1DLpKamwtXVtdJWWQD4+uuv4ebmVq6V/Oeff0ZgYCAOHTqEsLAwHDp0CEePHsXUqVMBlHalEELAzs4OW7ZsQf/+/QGUvo9A6XtQk1jMWpDh5g8CW2yZTUtLQ0pKiqmlwM/P77ZfHiIiKiWXy/HGG29g2rRpeOihh+Dg4IB9+/ZhxIgReOSRRwCUFgLnz59HaGholbfbsmVLXLt2DcnJyahfvz4A4M8//6ywzuLFi1FYWGgqFPft22fqTmApLVu2RFxcXLkWyn379sHFxeWOfXA7duyIuLg4BAcH3/ZUtkwmQ48ePdCjRw/MnDkTQUFBWLVqFaZNmwaVSgWDwXDXfB06dKhwxf0vv/yCgICACmP2btmyBZ988gneffddKBQKNG/eHFu2bKmwzSNHjphaLeVyOR588EH89NNPmDVrVoUuDQUFBbC3t6/0Nd5vN4Nu3bphw4YN5ZbFx8ff9oItoLQV9d9/MJZdyGc0GuHq6ooTJ06Ue/ybb77B9u3bERsbW+4PiZMnTyIgIABeXl53fA33y7r/vK1ldl8ovQLTTmE7xawQAkePHsX333+P33//HUlJSVJHIiKyKWPGjIFCocDXX38NoLRvaFmL4pkzZ/D0009XaD27m/DwcDRr1gwTJ07E8ePHsWfPHrz55pvl1nn44Ydhb2+PiRMn4uTJk9ixYweef/55PProo3csgKrbs88+i2vXruHVV1/F2bNn8fvvv2PWrFmYNm3aHVvZn3vuOWRlZWH8+PE4ePAgEhISsHnzZkyaNAkGgwEHDhzA+++/j0OHDuHq1atYuXIl0tPT0bJlSwBAcHAw/v77b5w7dw4ZGRkVrsQvExkZiVOnTpVrnV2wYAFiYmLQunXrcv+eeOIJZGRkYNOmTQCAZ555BufPn8cLL7xg2tenn36KX3/9Ff/3f/9n2t6cOXMQGBiILl26YMmSJTh9+jQuXLiAhQsXokOHDuVGmLhVWTeDO/2709nRKVOm4NKlS6b3/ptvvsHy5cvx8ssvm9b56quvyg2tNXToUBw8eBDvvvsuLly4gCNHjmDSpEkICgpC27ZtIZfLK7wvPj4+sLe3R+vWrcu1sO/ZswcDBw68bb7qwmJWAiobmQlLq9Vi9erVWLNmDfR6PYKDgysMAk1ERHdmZ2eHqVOn4qOPPkJhYSHeeustdOzYEZGRkejbty/8/PyqPLh/GblcjlWrVqG4uBidO3fGk08+aTr1XcbR0RGbN29GVlYWHnjgAcTExGDAgAHlLkazBH9/f6xbtw5HjhxBhw4dMGXKFDzxxBN466237vi8Bg0aYN++fTAYDBg4cCDatGmDl156Ce7u7pDL5XB1dcXu3bsxZMgQNGvWDG+99RY++eQT02n5yZMno3nz5ujUqRO8vb1NoyT8W5s2bdCxY0csX74cQOnQVMePHy/Xz7mMm5sbBgwYgAULFgAAGjVqhN27d+Ps2bMIDw9Hly5dsHz5cqxYsQKDBg0yPc/T0xN//vknHnnkEdOFbb169cKvv/5qujCuJoSEhGD9+vWIj49Hu3bt8Mknn+CHH34wXdQGlF6glpCQYLrfv39/LF26FKtXr0aHDh0waNAgqNVqbNiw4bZdEypTUlKC1atXY/LkydX6miojEzU57YQVysvLg5ubG3Jzcy0yhJSuMAfKj0uv4GuvX4wcvQp7Xu2HQE/HuzxTWqmpqVixYgUyMzMhk8nQr18/9OzZs1wH/rpCp9Nhw4YNGDJkSIV+ZGQbeAxtT0lJCS5fvoyQkBDY29vDaDQiLy8Prq6uVt9nliqy9uO3fv16vPLKKzh58qRV5rMG5h7Db7/9FqtWraq0G0aZf3/Pb2VOvcY+sxKw9nFmjxw5gg0bNsBgMMDFxQXR0dE1OqQGERGRlIYOHYoLFy4gKSkJgYGBUsepFZRKJf73v/9ZZF8sZqmCkpISGAwGNGnSBKNGjaowXAwREVFtc6eJBMh8Tz75pMX2xWKWAJSePig7bdCtWzfTwNZ1sVsBERER2Q52DKlx1t0lWQiBv/76C999951pjEOZTIZWrVqxkCUiIiKrx5bZmiQE7JZESZ3itkpKSrBmzRqcOXMGQGlf2a5du0qcioiIiKjqWMzWJF0RZKmlAwsL3zYoTlLDWlpqk5KSEBsbi5ycHMjlckRERKBLly5SxyIiIiIyC4tZC9FPWAvM/RNSF7NCCBw4cADx8fEwGo1wd3dHTEwM/P39Jc1FREREdC9YzFpI7OFkaPTVP6e2uXbv3o2dO3cCKJ1icPjw4RXGdiMiIiKyFSxmLWT+nkv4//buOyyKc/sD+Hd3YQtlATUIKKKIYNdgxd5BE4VY8NpRrprYciUxGjWCeu1GRX8YNVEwuVyxxHYVMZYYEUlDsIGgAlEjaGzUBZbd8/vDy1xXii7C4ur5PM8+PvPOOzNn9rjs4WXmHeDpDVVWipqbtL1du3aIj49Hly5d0KFDB77JizHGGGNGjWczMJCSUdmvx7eHhcxwv0MQkc5j6iwsLDBjxgx07NiRC1nGGGNVLjk5GXZ2dsjJyanpUN4YnTt3xvfff1/TYby2uJg1kCcqNQCgia2FwY6Zn5+PXbt24V//+heuXr0qtJuY8IA8Y4xVNY1Ggy5dumDo0KE67VlZWXB0dMSCBQt02r///nv06dMHNjY2UCgUcHNzw6RJkxAfHy/0CQsLg0gkEl4WFhZo164d9u/fb5BzKtGrV6+XfqjA559/jpkzZ8LS0rLUuubNm0MmkyEzM7PUuoYNG2LDhg2l2oOCgtC2bVudtszMTMycORPOzs6QyWRwdHTE4MGDcerUqZeKsTKuXr2KYcOGoWHDhhCJRGXGWpZLly6he/fukMvlcHR0xOrVq0v12bt3L5o2bQq5XI5WrVohMjJSZ/3ChQsxb948aLU1f7ni64iLWQMzN9Co7B9//IEtW7bg+vXrkEgkUKvVBjkuY4y9rSQSCcLCwhAVFYXw8HChfebMmahVqxYCAwOFtrlz52LkyJFo27YtDh8+jOTkZPz73/+Gs7MzPv/8c539KpVKZGRkICMjA/Hx8fD09ISvry+Sk5MNdm4v69atWzhy5Aj8/PxKrYuNjYVKpcLw4cOxc+fOSh8jPT0d7dq1w+nTp7FmzRpcvnwZUVFR6N27N6ZPn/4K0VcsPz8fzs7OWLlyJezs7F5qm+zsbAwYMABOTk6Ii4vDmjVrEBQUhG3btgl9zp8/j1GjRsHf3x/x8fHw8fGBj48Prly5IvQZOHAgcnJycOzYsSo/rzcCvWWysrIIAGVlZVX/wQpziQKVRIFKajp3H83ff6naD6nVauns2bO0ePFiCgoKok2bNlFmZma1H/dNVlRURAcPHqSioqKaDoVVEufQ+KhUKkpMTCSVSkVERBqNhh4/ekQaVfbTn62GfGm1esUeHBxMNjY2dPfuXTp48CCZmppSQkKCsD42NpYAUHBwcJnba585XmhoKFlZWems12g0ZGpqSnv27BHaHj16ROPGjSNra2tSKBTk5eVFKSkpOtvt27ePmjdvTlKplJycnGjt2rU660NCQsjFxYVkMhnZ2trSsGHDiIhowoQJhKdT8QivtLS0MmNfs2YNtW/fvlS7RqOh0aNH09y5c+nYsWPk6upaqo+TkxOtX7++VHtgYCC1adNGWB44cCDVq1ePcnNzS/V9/PhxmXFVtfJifd7mzZvJxsaGCgsLhba5c+eSm5ubsOzr60vvvfeeznadOnWiqVOn6rRNnDiRxo4d+2qBvwKNRkOPHz8mjUZTZft8/nP+LH3qNf57swGN92hYrfvPy8vD/v37kZqaCgBo3bo13nvvPUil0mo9LmOMGUSxCuKVzQx/3Pl3Aan5S3efOXMmDhw4gHHjxuHy5ctYtGgR2rRpI6zftWsXLCwsMG3atDK3r+h+Bo1Gg2+//RYA4O7uLrT7+fnh+vXrOHz4MJRKJebOnYtBgwYhMTERpqamiIuLg6+vL4KCgjBy5EicP38e06ZNQ+3ateHn54fff/8ds2bNwnfffYcuXbrg0aNHiI6OBgAEBwcjJSUFLVu2xJIlSwAA77zzTpnxRUdHo3379qXac3JycOjQIcTGxqJ58+bIyspCdHQ0unfv/oJ3U9ejR48QFRWFZcuWwdy8dE6sra3L3TY8PBxTp06tcP/Hjh3TO6aKxMbGokePHjrfw56enli1ahUeP34MGxsbxMbGIiAgQGc7T09PHDx4UKetY8eOWLlyZZXF9ibhYtaAxNV8v9Wff/6J1NRUmJiYYNCgQWjbti3f5MUYYwYmEonw1VdfoVmzZmjVqhXmzZunsz4lJQXOzs469y+sW7cOixYtEpb//PNPWFlZAXh6za2FxdP7LVQqFUxNTbFt2zY0btwYAIQiNiYmBl26dAHwtHBzdHTEwYMHMWLECKxbtw59+/bFF198AQBwdXVFYmIi1qxZAz8/P9y6dQvm5uZ4//33YWlpCScnJ7z77rsAACsrK0ilUpiZmb3wz+t//PFHmcVsREQEnJ2d0aJFC4jFYvztb3/D9u3b9S4cb9y4ASJC06ZN9doOAIYMGfLChwNV9ZzrmZmZaNSokU5b3bp1hXU2NjbIzMwU2p7t8/x1xQ4ODrh9+za0Wi3EYr5K9FlczBqQvbWiWvfv6uqKAQMGoHHjxrC1ta3WYzHGmMGZKKCdd8fwX+SmZnpvsmPHDpiZmSEtLQ137txBw4YNK+w/adIkDBkyBL/88gvGjh0Lov89YMfS0hIXLlwA8PS6zZMnT+LDDz9E7dq1MXjwYCQlJcHExESnUKtduzbc3NyEx5UnJSXB29tb55hdu3bFhg0boNFo0L9/fzg5OcHZ2RleXl7w8vLCBx98ADMz/c5dpVKVOXd5WFgYfH19heWxY8eiZ8+e2LRpU5k3ipXn2fdFX5aWlnod63WjUCig1WpRWFgIhaJ66wljw6W9AZlKqnaUNCcnB3v27EFWVpbQ5uHhwYUsY+zNJBI9/XO/oV96/oXr/PnzWL9+PY4cOYKOHTvC399fpwhr0qQJUlNTdW7Mtba2houLS5kjg2KxGC4uLnBxcUHr1q0REBCAXr16YdWqVZV/L59TUjDv2rUL9vb2wqURT5480Ws/derUwePHj3XaEhMT8fPPPyMwMBBSqRQmJibo3Lkz8vPzERERIfRTKpU632clnjx5IoxSN2nSBCKRCNeuXdP7HMPDw2FhYVHhq+TSiqpiZ2eHe/fu6bSVLJeMcpfX5/lR8EePHsHc3JwL2TJwMWtAIlRdMXvz5k1s3boVSUlJ+M9//lNl+2WMMVZ5+fn58PPzw0cffYTevXtj+/bt+PXXX7Flyxahz6hRo5Cbm4vNmzdX+jgSiQQqlQrA06c5FhcX45dffhHWP3z4EMnJyWjevLnQJyYmRmcfMTExcHV1hUQiAfB02sZ+/fph9erVuHTpEtLT03H69GkAgFQqhUajeWFc7777LhITE3Xatm/fjh49eiA6OhoXLlxAQkICEhISEBAQgO3btwv93NzcEBcXV2qfFy5cgKurKwCgVq1a8PT0REhICPLy8kr1raj4HjJkiHDs8l5lXSLxKjw8PHD27FmdX1xOnDgBNzc32NjYCH2en1LsxIkT8PDw0Gm7cuWKcOkHe06V3ZJmJGpyNgN18avfAajRaOjUqVMUFBREQUFBtHnzZvrrr7+qIFhWHr4T3vhxDo1PmbMZVPGd1NVh1qxZ5OLiQnl5eULbli1byMLCQmcGgE8++YQkEgnNnj2boqOjKT09nWJjY2ns2LEkEomE76jQ0FBSKpWUkZFBGRkZlJqaSlu3biWJREKLFy8W9uft7U3Nmzen6OhoSkhIIC8vL3JxcRH+z8fFxZFYLKYlS5ZQcnIyhYWFkUKhoNDQUCIi+s9//kPBwcEUHx9P6enptHnzZhKLxXTlyhUiIpo8eTJ16NCB0tLS6K+//io3D4cPHyZbW1sqLi4moqefvXfeeYdCQkJK5S8xMZEACMeIiYkhsVhM//znPykxMZEuX75M8+fPJxMTE7p8+bKw3c2bN8nOzo6aN29O+/bto5SUFEpMTKTg4GBq2rRpZVP3QoWFhRQfH0/x8fFkb29Pn376KcXHx9P169eFPps2baI+ffoIy0+ePKG6devSuHHj6MqVKxQREUFmZma0detWoU9MTAyZmJjQ2rVrKSkpiQIDA8nU1FTnnImIevbsSUuWLKm283uR13k2Ay5mq9NzxWyxRr/pXZ6XlZVFO3bsEArZw4cP85ezAXAhZPw4h8bHGIvZM2fOkEQioejo6FLrBgwYQH369NGZdmv37t3Uq1cvsrKyIlNTU6pfvz6NHj2afv75Z6FPaGiozpRYMpmMXF1dadmyZULBSPS/qbmsrKxIoVCQp6dnuVNzmZqaUoMGDWjNmjXCuujoaOrZsyfZ2NiQQqGg1q1b0+7du4X1ycnJ1LlzZ1IoFBVOzaVWq8nBwYGioqKEY4rFYrp7926Z+WvWrBnNnj1bWD5+/Dh17dqVbGxsqHbt2tSrVy/66aefSh3n7t27NH36dHJyciKpVEr16tWjIUOG0I8//lhmXFUhLS2t1BRlAKhnz55Cn8DAQHJyctLZ7uLFi9StWzeSyWRUr149WrlyZal979mzh1xdXUkqlVKLFi3o6NGjOuvv3LlDpqamdPv27eo4tZfyOhezIqJXuJraCGVnZ8PKygpZWVlQKpXVe7CiPGC5AwCgWcEOJK4YWunZBTIzM/Htt99CpVJBKpVi8ODBaNmyZVVGy8qhVqsRGRmJQYMGwdTUtKbDYZXAOTQ+BQUFSEtLQ6NGjSCXy6HVapGdnQ2lUsl3cr/mQkJCcPjwYRw/flxo4/y9mrlz5+Lx48c6D1swtOrI4fOf82fpU6/xbAYG9CrTZNWuXRuWlpawsrLC8OHDUbt27SqMjDHGGKsaU6dOxZMnT5CTk2PUswe8TmxtbUvNRcv+h4vZ11hOTg4sLCwgEolgamqK0aNHw9zcXGduQsYYY+x1YmJiggULFtR0GG+UTz75pKZDeK3xWL+B1LWU6dU/OTkZmzdv1pkmxMrKigtZxhhjjLFncDFrINqXvDJZo9Hg+PHjiIiIQEFBAa5fvw6tVlu9wTHGGGOMGSke5jOQv3ILX9jn8ePH+P777/Hnn38CADp16oT+/fvzxfKMMcYYY+XgYtZA2jpaV7g+KSkJhw4dQmFhIeRyOby9vSv17GnGGGOMsbcJF7OvgZycHHz//ffQaDSoX78+hg0bBmtr65oOizHGGGPstcfFrIHceZRf7jpLS0t4eXnh0aNH6Nu3r/BoQcYYY4wxVjEuZg1kuHs9neWrV6/C2toa9eo9ba/q50EzxhhjjL0N+M4iQ/nv8xLUajWOHDmCffv2Yd++fSgoKKjZuBhjjL0xRCIRDh48WNNhMGZQr0UxGxISgoYNG0Iul6NTp0749ddfK+y/d+9eNG3aFHK5HK1atUJkZKSBIn01Dx48wPbt2xEXFwcAaNmyJaRSaQ1HxRhjrCr5+flBJBIJD7xp1KgRPvvsMx68YKya1Hgxu3v3bgQEBCAwMBAXLlxAmzZt4Onpifv375fZ//z58xg1ahT8/f0RHx8PHx8f+Pj44MqVKwaOXD9FD+5g27ZtuHfvHszMzDB27Fj07duXp91ijLE3kJeXFzIyMpCamor169dj69atCAwMrOmwGHsj1XgltW7dOkyePBkTJ05E8+bNsWXLFpiZmWHHjh1l9g8ODoaXlxfmzJmDZs2aYenSpXB3d8f//d//GTjyl1MMCQ5hAPLT4qFWq9GwYUN8+OGHaNy4cU2HxhhjRqmoqKjcV3Fx8Uv3VavVL+xbWTKZDHZ2dnB0dISPjw/69euHEydOAAAePnyIUaNGoV69ejAzM0OrVq2wa9cune179eqFWbNm4bPPPkOtWrVgZ2eHoKAgnT7Xr19Hjx49IJfL0bx5c2H/z7p8+TL69OkDhUKB2rVrY8qUKcjNzRXW+/n5wcfHB8uXL0fdunVhbW2NJUuWoLi4GHPmzEGtWrVQv359hIaGVvq9YKy61egNYEVFRYiLi8Pnn38utInFYvTr1w+xsbFlbhMbG4uAgACdNk9Pz3KvESosLERh4f8eWJCdnQ3g6bWrz/8gq3JqNUygQR7MAADdunVDt27dIBaLq//YrMqU5IpzZrw4h8ZHrVaDiKDVaqHVakH09DGKRIQVK1aUu52LiwtGjRolLK9du7bcvDs5OWH8+PHCcnBwMPLzdWee+eKLL/SOnYiE2AHgypUrOH/+PJycnKDVapGfnw93d3fMmTMHSqUSkZGRGDduHBo1aoSOHTsK+9m5cydmz56N2NhYxMbGYtKkSfDw8ED//v2h1WoxdOhQ1K1bF7GxscjKyhK+G0ves7y8PHh6eqJz58745ZdfcP/+fUyZMgXTp08XilMiwunTp1GvXj2cOXMGMTExmDx5MmJiYtCjRw/ExsZiz549mDp1Kvr27Yv69evr/X6UHKfkX36qpXGqjhyWfLbVanWpmZz0+Xldo8XsgwcPoNFoULduXZ32unXr4tq1a2Vuk5mZWWb/zMzMMvuvWLECixcvLtX+ww8/wMzMrJKRvxyJphDvA/BBFFbarERubi6ioqKq9Zis+pQ16sGMC+fQeJiYmMDOzg65ubk6I6Q5OTkVbldcXCwMWgD/+wKubN9n178stVqNo0ePQqlUori4GIWFhRCLxVi1ahWys7NhaWmJyZMnC/3Hjx+Po0ePIjw8XHhYTnFxMZo3b45//OMfAAAfHx9s2rQJx44dQ6dOnXD69Glcu3YNe/bsgb29PQBg/vz5GDFiBFQqFbKzs7Fz506oVCps2rQJ5ubmaNCgAVauXIlRo0ZhwYIFsLW1hVqthrW1NZYuXQqxWIzhw4dj9erVyMnJwfTp0wEA06ZNw6pVq3DixAkMGzZM7/fjWS/KH3v9VWUOi4qKoFKpcPbs2VJ/VXn+F8uKvPFTc33++ec6I7nZ2dlwdHTEgAEDoFQqq/fgRMjv0wenT5/GPM/3YMo3exkltVqNEydOoH///jA1Na3pcFglcA6NT0FBAW7fvg0LCwvI5XIQEXJycmBpaYm5c+eWu51YLIaJyf++2j755JNy+5bcoFVi1qxZpfpU5iZdU1NT9OrVC5s3b0ZeXh42bNgAExMTjB07FgCg0WiwYsUK7N27F3/++SeKiopQWFgIpVIpfC+ZmJigdevWOt9T9erVQ1ZWFpRKJW7dugVHR0e4ubkJ6/v27QsAUCgUUCqVSE9PR9u2bYViF4Awqnv37l24uLjA1NQULVu21HlQj729PVq0aKFz7Nq1ayM3N7fS35vP5k8kElVqH6xmVUcOCwoKoFAohMtlnqXPL5I1WszWqVMHEokE9+7d02m/d+8e7OzsytzGzs5Or/4ymQwymaxUu6mpqWG+1ERW0EhkMJVK+UvUyBns/wyrNpxD46HRaCASiSAWiyEWi4U/a4pEolJfehWprr4VEYlEsLCwgKurKwAgNDQUbdq0QWhoKPz9/bF69Wps3LgRGzZsQKtWrWBubo5//OMfUKvVOjcFS6VSnWWxWAwiglgsFoqJ59eX/KtPn+ePU15bybEr49n88Y3Pxqk6cljyf7Csn836/Kyu0f9RUqkU7dq1w6lTp4Q2rVaLU6dOwcPDo8xtPDw8dPoDT/90WF5/xhhjrCaJxWLMnz8fCxcuhEqlQkxMDLy9vTF27Fi0adMGzs7OSElJ0WufzZo1w+3bt5GRkSG0/fzzz6X6XLx4EXl5eUJbTEwMxGKxzoguY8auxn89CggIwNdff42dO3ciKSkJH330EfLy8jBx4kQAT68levYGsY8//hhRUVH48ssvce3aNQQFBeH333/HjBkzauoUGGOMsQqNGDECEokEISEhaNKkCU6cOIHz588jKSkJU6dOLfUXxxfp168fXF1dMWHCBFy8eBHR0dFYsGCBTp8xY8ZALpdjwoQJuHLlCn788UfMnDkT48aNK3XvCWPGrMavmR05ciT++usvLFq0CJmZmWjbti2ioqKED9qtW7d0hrO7dOmCf//731i4cCHmz5+PJk2a4ODBg2jZsmVNnQJjjDFWIRMTE8yYMQOrV69GfHw8UlNT4enpCTMzM0yZMgU+Pj7Iysp66f2JxWIcOHAA/v7+6NixIxo2bIiNGzfCy8tL6GNmZobjx4/j448/RocOHWBmZoZhw4Zh3bp11XGKjNUYEVV0q+cbKDs7G1ZWVsJF9NVNrVYjMjISgwYN4mv1jBTn0PhxDo1PQUEB0tLS0KhRI8jlcmi1WmRnZ0OpVPI1l0aI82f8qiOHz3/On6VPvcb/oxhjjDHGmNHiYpYxxhhjjBktLmYZY4wxxpjR4mKWMcYYY4wZLS5mGWOMvbbesnuUGXurVNXnm4tZxhhjr52SWSf0eT47Y8y4FBUVAQAkEskr7afG55lljDHGnieRSGBtbY379+8DePqo2aKiIhQUFPDUTkZIq9Vy/oxcVedQq9Xir7/+gpmZGUxMXq0c5WKWMcbYa8nOzg4AcP/+fRARVCoVFAoFRCJRDUfG9MX5M37VkUOxWIwGDRq88v64mGWMMfZaEolEsLe3h62tLVQqFX766Sf06NGDH3xhhNRqNc6ePcv5M2LVkUOpVFolo7xczDLGGHutSSQSyGQyFBcXQy6XczFkhCQSCefPyL3OOeQLVxhjjDHGmNHiYpYxxhhjjBktLmYZY4wxxpjReuuumS2ZoDc7O9sgx1Or1cjPz0d2dvZrd40JezmcQ+PHOTR+nEPjxvkzfobOYUmd9jIPVnjritmcnBwAgKOjYw1HwhhjjDHGKpKTkwMrK6sK+4joLXtWoFarxd27d2FpaWmQue6ys7Ph6OiI27dvQ6lUVvvxWNXjHBo/zqHx4xwaN86f8TN0DokIOTk5cHBweOH0XW/dyKxYLEb9+vUNflylUskfYCPHOTR+nEPjxzk0bpw/42fIHL5oRLYE3wDGGGOMMcaMFhezjDHGGGPMaHExW81kMhkCAwMhk8lqOhRWSZxD48c5NH6cQ+PG+TN+r3MO37obwBhjjDHG2JuDR2YZY4wxxpjR4mKWMcYYY4wZLS5mGWOMMcaY0eJiljHGGGOMGS0uZqtASEgIGjZsCLlcjk6dOuHXX3+tsP/evXvRtGlTyOVytGrVCpGRkQaKlJVHnxx+/fXX6N69O2xsbGBjY4N+/fq9MOes+un7OSwREREBkUgEHx+f6g2QvZC+OXzy5AmmT58Oe3t7yGQyuLq68s/TGqRv/jZs2AA3NzcoFAo4Ojpi9uzZKCgoMFC07Hlnz57F4MGD4eDgAJFIhIMHD75wmzNnzsDd3R0ymQwuLi4ICwur9jjLROyVREREkFQqpR07dtDVq1dp8uTJZG1tTffu3Suzf0xMDEkkElq9ejUlJibSwoULydTUlC5fvmzgyFkJfXM4evRoCgkJofj4eEpKSiI/Pz+ysrKiO3fuGDhyVkLfHJZIS0ujevXqUffu3cnb29swwbIy6ZvDwsJCat++PQ0aNIjOnTtHaWlpdObMGUpISDBw5IxI//yFh4eTTCaj8PBwSktLo+PHj5O9vT3Nnj3bwJGzEpGRkbRgwQLav38/AaADBw5U2D81NZXMzMwoICCAEhMTadOmTSSRSCgqKsowAT+Di9lX1LFjR5o+fbqwrNFoyMHBgVasWFFmf19fX3rvvfd02jp16kRTp06t1jhZ+fTN4fOKi4vJ0tKSdu7cWV0hsheoTA6Li4upS5cu9M0339CECRO4mK1h+ubwq6++ImdnZyoqKjJUiKwC+uZv+vTp1KdPH522gIAA6tq1a7XGyV7OyxSzn332GbVo0UKnbeTIkeTp6VmNkZWNLzN4BUVFRYiLi0O/fv2ENrFYjH79+iE2NrbMbWJjY3X6A4Cnp2e5/Vn1qkwOn5efnw+1Wo1atWpVV5isApXN4ZIlS2Brawt/f39DhMkqUJkcHj58GB4eHpg+fTrq1q2Lli1bYvny5dBoNIYKm/1XZfLXpUsXxMXFCZcipKamIjIyEoMGDTJIzOzVvU71jInBj/gGefDgATQaDerWravTXrduXVy7dq3MbTIzM8vsn5mZWW1xsvJVJofPmzt3LhwcHEp9qJlhVCaH586dw/bt25GQkGCACNmLVCaHqampOH36NMaMGYPIyEjcuHED06ZNg1qtRmBgoCHCZv9VmfyNHj0aDx48QLdu3UBEKC4uxocffoj58+cbImRWBcqrZ7Kzs6FSqaBQKAwWC4/MMvYKVq5ciYiICBw4cAByubymw2EvIScnB+PGjcPXX3+NOnXq1HQ4rJK0Wi1sbW2xbds2tGvXDiNHjsSCBQuwZcuWmg6NvYQzZ85g+fLl2Lx5My5cuID9+/fj6NGjWLp0aU2HxowQj8y+gjp16kAikeDevXs67ffu3YOdnV2Z29jZ2enVn1WvyuSwxNq1a7Fy5UqcPHkSrVu3rs4wWQX0zeHNmzeRnp6OwYMHC21arRYAYGJiguTkZDRu3Lh6g2Y6KvM5tLe3h6mpKSQSidDWrFkzZGZmoqioCFKptFpjZv9Tmfx98cUXGDduHP7+978DAFq1aoW8vDxMmTIFCxYsgFjMY22vu/LqGaVSadBRWYBHZl+JVCpFu3btcOrUKaFNq9Xi1KlT8PDwKHMbDw8Pnf4AcOLEiXL7s+pVmRwCwOrVq7F06VJERUWhffv2hgiVlUPfHDZt2hSXL19GQkKC8BoyZAh69+6NhIQEODo6GjJ8hsp9Drt27YobN24Iv4gAQEpKCuzt7bmQNbDK5C8/P79UwVryiwkRVV+wrMq8VvWMwW85e8NERESQTCajsLAwSkxMpClTppC1tTVlZmYSEdG4ceNo3rx5Qv+YmBgyMTGhtWvXUlJSEgUGBvLUXDVM3xyuXLmSpFIp7du3jzIyMoRXTk5OTZ3CW0/fHD6PZzOoefrm8NatW2RpaUkzZsyg5ORkOnLkCNna2tI///nPmjqFt5q++QsMDCRLS0vatWsXpaam0g8//ECNGzcmX1/fmjqFt15OTg7Fx8dTfHw8AaB169ZRfHw8/fHHH0RENG/ePBo3bpzQv2Rqrjlz5lBSUhKFhITw1FzGbNOmTdSgQQOSSqXUsWNH+vnnn4V1PXv2pAkTJuj037NnD7m6upJUKqUWLVrQ0aNHDRwxe54+OXRyciIApV6BgYGGD5wJ9P0cPouL2deDvjk8f/48derUiWQyGTk7O9OyZcuouLjYwFGzEvrkT61WU1BQEDVu3Jjkcjk5OjrStGnT6PHjx4YPnBER0Y8//ljmd1tJ3iZMmEA9e/YstU3btm1JKpWSs7MzhYaGGjxuIiIREY/nM8YYY4wx48TXzDLGGGOMMaPFxSxjjDHGGDNaXMwyxhhjjDGjxcUsY4wxxhgzWlzMMsYYY4wxo8XFLGOMMcYYM1pczDLGGGOMMaPFxSxjjDHGGDNaXMwyxhiAsLAwWFtb13QYlSYSiXDw4MEK+/j5+cHHx8cg8TDGmKFwMcsYe2P4+flBJBKVet24caOmQ0NYWJgQj1gsRv369TFx4kTcv3+/SvafkZGBgQMHAgDS09MhEomQkJCg0yc4OBhhYWFVcrzyBAUFCecpkUjg6OiIKVOm4NGjR3rthwtvxtjLMqnpABhjrCp5eXkhNDRUp+2dd96poWh0KZVKJCcnQ6vV4uLFi5g4cSLu3r2L48ePv/K+7ezsXtjHysrqlY/zMlq0aIGTJ09Co9EgKSkJkyZNQlZWFnbv3m2Q4zPG3i48MssYe6PIZDLY2dnpvCQSCdatW4dWrVrB3Nwcjo6OmDZtGnJzc8vdz8WLF9G7d29YWlpCqVSiXbt2+P3334X1586dQ/fu3aFQKODo6IhZs2YhLy+vwthEIhHs7Ozg4OCAgQMHYtasWTh58iRUKhW0Wi2WLFmC+vXrQyaToW3btoiKihK2LSoqwowZM2Bvbw+5XA4nJyesWLFCZ98llxk0atQIAPDuu+9CJBKhV69eAHRHO7dt2wYHBwdotVqdGL29vTFp0iRh+dChQ3B3d4dcLoezszMWL16M4uLiCs/TxMQEdnZ2qFevHvr164cRI0bgxIkTwnqNRgN/f380atQICoUCbm5uCA4OFtYHBQVh586dOHTokDDKe+bMGQDA7du34evrC2tra9SqVQve3t5IT0+vMB7G2JuNi1nG2FtBLBZj48aNuHr1Knbu3InTp0/js88+K7f/mDFjUL9+ffz222+Ii4vDvHnzYGpqCgC4efMmvLy8MGzYMFy6dAm7d+/GuXPnMGPGDL1iUigU0Gq1KC4uRnBwML788kusXbsWly5dgqenJ4YMGYLr168DADZu3IjDhw9jz549SE5ORnh4OBo2bFjmfn/99VcAwMmTJ5GRkYH9+/eX6jNixAg8fPgQP/74o9D26NEjREVFYcyYMQCA6OhojB8/Hh9//DESExOxdetWhIWFYdmyZS99junp6Th+/DikUqnQptVqUb9+fezduxeJiYlYtGgR5s+fjz179gAAPv30U/j6+sLLywsZGRnIyMhAly5doFar4enpCUtLS0RHRyMmJgYWFhbw8vJCUVHRS8fEGHvDEGOMvSEmTJhAEomEzM3Nhdfw4cPL7Lt3716qXbu2sBwaGkpWVlbCsqWlJYWFhZW5rb+/P02ZMkWnLTo6msRiMalUqjK3eX7/KSkp5OrqSu3btyciIgcHB1q2bJnONh06dKBp06YREdHMmTOpT58+pNVqy9w/ADpw4AAREaWlpREAio+P1+kzYcIE8vb2Fpa9vb1p0qRJwvLWrVvJwcGBNBoNERH17duXli9frrOP7777juzt7cuMgYgoMDCQxGIxmZubk1wuJwAEgNatW1fuNkRE06dPp2HDhpUba8mx3dzcdN6DwsJCUigUdPz48Qr3zxh7c/E1s4yxN0rv3r3x1VdfCcvm5uYAno5SrlixAteuXUN2djaKi4tRUFCA/Px8mJmZldpPQEAA/v73v+O7774T/lTeuHFjAE8vQbh06RLCw8OF/kQErVaLtLQ0NGvWrMzYsrKyYGFhAa1Wi4KCAnTr1g3ffPMNsrOzcffuXXTt2lWnf9euXXHx4kUATy8R6N+/P9zc3ODl5YX3338fAwYMeKX3asyYMZg8eTI2b94MmUyG8PBw/O1vf4NYLBbOMyYmRmckVqPRVPi+AYCbmxsOHz6MgoIC/Otf/0JCQgJmzpyp0yckJAQ7duzArVu3oFKpUFRUhLZt21YY78WLF3Hjxg1YWlrqtBcUFODmzZuVeAcYY28CLmYZY28Uc3NzuLi46LSlp6fj/fffx0cffYRly5ahVq1aOHfuHPz9/VFUVFRmURYUFITRo0fj6NGjOHbsGAIDAxEREYEPPvgAubm5mDp1KmbNmlVquwYNGpQbm6WlJS5cuACxWAx7e3soFAoAQHZ29gvPy93dHWlpaTh27BhOnjwJX19f9OvXD/v27XvhtuUZPHgwiAhHjx5Fhw4dEB0djfXr1wvrc3NzsXjxYgwdOrTUtnK5vNz9SqVSIQcrV67Ee++9h8WLF2Pp0qUAgIiICHz66af48ssv4eHhAUtLS6xZswa//PJLhfHm5uaiXbt2Or9ElHhdbvJjjBkeF7OMsTdeXFwctFotvvzyS2HUseT6zIq4urrC1dUVs2fPxqhRoxAaGooPPvgA7u7uSExMLFU0v4hYLC5zG6VSCQcHB8TExKBnz55Ce0xMDDp27KjTb+TIkRg5ciSGDx8OLy8vPHr0CLVq1dLZX8n1qRqNpsJ45HI5hg4divDwcNy4cQNubm5wd3cX1ru7uyM5OVnv83zewoUL0adPH3z00UfCeXbp0gXTpk0T+jw/siqVSkvF7+7ujt27d8PW1hZKpfKVYmKMvTn4BjDG2BvPxcUFarUamzZtQmpqKr777jts2bKl3P4qlQozZszAmTNn8McffyAmJga//fabcPnA3Llzcf78ecyYMQMJCQm4fv06Dh06pPcNYM+aM2cOVq1ahd27dyM5ORnz5s1DQkICPv74YwDAunXrsGvXLly7dg0pKSnYu3cv7OzsynzQg62tLRQKBaKionDv3j1kZWWVe9wxY8bg6NGj2LFjh3DjV4lFixbh22+/xeLFi3H16lUkJSUhIiICCxcu1OvcPDw80Lp1ayxfvhwA0KRJE/z+++84fvw4UlJS8MUXX+C3337T2aZhw4a4dOkSkpOT8eDBA6jVaowZMwZ16tSBt7c3oqOjkZaWhjNnzmDWrFm4c+eOXjExxt4cXMwyxt54bdq0wbp167Bq1Sq0bNkS4eHhOtNaPU8ikeDhw4cYP348XF1d4evri4EDB2Lx4sUAgNatW+Onn35CSkoKunfvjnfffReLFi2Cg4NDpWOcNWsWAgIC8Mknn6BVq1aIiorC4cOH0aRJEwBPL1FYvXo12rdvjw4dOiA9PR2RkZHCSPOzTExMsHHjRmzduhUODg7w9vYu97h9+vRBrVq1kJycjNGjR+us8/T0xJEjR/DDDz+gQ4cO6Ny5M9avXw8nJye9z2/27Nn45ptvcPv2bUydOhVDhw7FyJEj0alTJzx8+FBnlBYAJk+eDDc3N7Rv3x7vvPMOYmJiYGZmhrNnz6JBgwYYOnQomjVrBn9/fxQUFPBILWNvMRERUU0HwRhjjDHGWGXwyCxjjDHGGDNaXMwyxhhjjDGjxcUsY4wxxhgzWlzMMsYYY4wxo8XFLGOMMcYYM1pczDLGGGOMMaPFxSxjjDHGGDNaXMwyxhhjjDGjxcUsY4wxxhgzWlzMMsYYY4wxo8XFLGOMMcYYM1r/D7akYrTpM7wTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_curve, auc, accuracy_score, f1_score\n",
    "\n",
    "# Assuming you have trained and tested the Random Forest and XGBoost models\n",
    "# Obtain the predicted probabilities for each model\n",
    "rf_probs = rf.predict_proba(x_test)[:, 1]\n",
    "xgb_probs = xgb_classifier.predict_proba(x_test)[:, 1]\n",
    "\n",
    "# Compute the ROC curve and AUC score for each model\n",
    "rf_fpr, rf_tpr, _ = roc_curve(y_test, rf_probs)\n",
    "rf_auc = auc(rf_fpr, rf_tpr)\n",
    "\n",
    "xgb_fpr, xgb_tpr, _ = roc_curve(y_test, xgb_probs)\n",
    "xgb_auc = auc(xgb_fpr, xgb_tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest (AUC = {:.2f})'.format(rf_auc))\n",
    "plt.plot(xgb_fpr, xgb_tpr, label='XGBoost (AUC = {:.2f})'.format(xgb_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Calculate and plot accuracy and F1 score for each model\n",
    "rf_accuracy = accuracy_score(y_test, rf.predict(x_test))\n",
    "rf_f1_score = f1_score(y_test, rf.predict(x_test))\n",
    "\n",
    "xgb_accuracy = accuracy_score(y_test, xgb_classifier.predict(x_test))\n",
    "xgb_f1_score = f1_score(y_test, xgb_classifier.predict(x_test))\n",
    "\n",
    "labels = ['Random Forest', 'XGBoost']\n",
    "accuracy_scores = [rf_accuracy, xgb_accuracy]\n",
    "f1_scores = [rf_f1_score, xgb_f1_score]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=labels, y=accuracy_scores)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy Scores Comparison')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=labels, y=f1_scores)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('F1 Scores Comparison')\n",
    "plt.show()\n"
   ]
  }
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