Flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flight Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(\"Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9087.064121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4611.359167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count  10683.000000\n",
       "mean    9087.064121\n",
       "std     4611.359167\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8372.000000\n",
       "75%    12373.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
       "       'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date_of_journey is a object data type , so this has to be converted proper datatype(timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey,format = \"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data.Date_of_Journey,format= \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since there is no use of date_of_Journey column into integers, Now we can drop as it is of no use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract hours and mins from departure time\n",
    "\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "\n",
    "# drop Dep_time\n",
    "\n",
    "train_data.drop([\"Dep_Time\"],axis =1 ,inplace =True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# similarlilly comw up with arrival time\n",
    "\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data[\"Arrival_Time\"]).dt.hour\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data[\"Arrival_Time\"]).dt.minute\n",
    "\n",
    "# drop arrival time\n",
    "\n",
    "train_data.drop([\"Arrival_Time\"],axis =1 , inplace  = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop([\"Duration\"],axis = 1 , inplace = True)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling categorical data\n",
    "\n",
    "\n",
    "1. Nominal data ------> data are not in order ---> One hot encoding is used\n",
    "\n",
    "2. Ordinal data -------> data are in order ----> Label encoder is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot(figsize = (18,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQQAAAGkCAYAAABnzShmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde1hc9YH/8c/cIJehJOBMiBhjbKzRpE2so6u1hdXaABK0smbXgmbb/bXWtD/rZle6mGSTpr/mIbo8Seq62O724jZqa1wVuikQ3dokKl5IvG0sMV5IpKAwDCRcEmCYc35/YGhIAiE6w2E479fz+DDznZmTz8mIwme+3/N1mKZpCgAAAAAAAIAtOK0OAAAAAAAAAGDsUAgCAAAAAAAANkIhCAAAAAAAANgIhSAAAAAAAABgIxSCAAAAAAAAgI1QCAIAAAAAAAA24rY6gFVCoS4Zhml1DAAAAAAAACDqfL6kYR9jhiAAAAAAAABgIxSCAAAAAAAAgI1QCAIAAAAAAAA2QiEIAAAAAAAA2AiFIAAAAAAAAGAjFIIAAAAAAACAjVAIAgAAAAAAADZCIQgAAAAAAADYCIUgAAAAAAAAYCMUggAAAAAAAICNxLQQrKioUG5urnJzc3XPPfdIkurq6pSfn6+srCytWrVK/f39kqSmpiYVFhYqOztby5cvV3d3tySpo6NDt912m3JyclRYWKhgMChJ6uvrU1FRkXJycnTjjTfq3XffjeWpAAAAAAAAABNCzArBo0ePav369dqyZYsqKiq0e/du1dTUqKioSGvWrNH27dtlmqa2bt0qSVq3bp0KCgpUXV2tBQsWqKysTJK0efNmBQIBVVVVaenSpVq/fr0kacuWLZo8ebKqqqq0cuVK3X333bE6lfHt6FGrEwAAAAAAACCOxKwQjEQiMgxDR48eVX9/v/r7++V2u9XT06NFixZJkvLz81VdXa1wOKza2lplZWUNGZekHTt2KC8vT5K0ZMkS7dq1S+FwWDt27ND1118vSbrsssvU1tampqamWJ3O+BQMSb97SmoNWZ0EAAAAAAAAcSJmhaDX69Wdd96pnJwcZWZmKj09XR6PRz6fb/A5Pp9Pzc3Nam9vl9frldvtHjIuSS0tLYOvcbvd8nq9amtrGzJ+7DUffvhhrE5n/DEMafcrA7drXxm4DwAAAAAAAJyGO1YH3rdvnx5//HH94Q9/UFJSku666y49//zzcjgcg88xTVMOh2Pw6/FOvH/8a5xO50mvOTY+Wqmp3jM8o/HlyOt16u7tHbjT26epzU2a8rmLrA0FAAAAAACAcS9mheBzzz2nK6+8UqmpqZIGlgH//Oc/H9wURJJaW1vl9/uVkpKizs5ORSIRuVwuBYNB+f1+SZLf71dra6vS0tLU39+v7u5uTZs2TTNmzFBLS4vOPffcIccarVCoS4ZhRvGMx1BPj/TSa1IkMnC/v1/dL76m7ulnSZMmWZsNAAAAAAAAlvP5koZ9LGZLhufNm6eamhodOXJEpmnqmWee0eWXX67ExETt2bNH0sAuxBkZGfJ4PAoEAqqsrJQklZeXKyMjQ5KUmZmp8vJySVJlZaUCgYA8Ho8yMzNVUVEhSdq9e7cSExN19tlnx+p0xpf3GyXzhDLTNKWGRmvyAAAAAAAAIG44TPPEZil6/v3f/11PPPGEPB6PPvvZz2rt2rWqr6/X6tWr1dXVpfnz56ukpEQJCQlqbGxUcXGxQqGQZs6cqY0bNyo5OVmHDh1ScXGxGhoalJSUpNLSUp1zzjnq7e3VmjVrtHfvXiUkJOhHP/qR5s+fP+pscT9DsOp/lPr1v5IzIUFGX59CDz4u5VzLDEEAAAAAAACMOEMwpoXgeBbXhaAk7X9HvqsuUeRwp1zJSQrWvCZd8GmrUwEAAAAAAGAcsGTJMGJs7vkyTVOu5CSZpil9eo7ViQAAAAAAABAHKATjldMph8OhvvebBnZbPoMdlgEAAAAAAGBftEgAAAAAAACAjVAIxqnU1KmSpIRzB3ZW9vmSBscAAAAAAACA4bitDoCPx+l06uj/vjVkbPJnL7QoDQAAAAAAAOIFMwQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEYdpmqbVIawQCnXJMOLr1FNTpsrpOn2Ha0QMhdq6xyARAAAAAAAAxiOfL2nYx9hUJI44XU51PVd72ud5v3jZGKQBAAAAAABAPGLJMAAAAAAAAGAjFIIAAAAAAACAjVAIAgAAAAAAADZCIQgAAAAAAADYCIUgAAAAAAAAYCMUggAAAAAAAICNUAgCAAAAAAAANkIhCAAAAAAAANgIhSAAAAAAAABgIxSCAAAAAAAAgI04TNM0rQ5hhVCoS4Yxfk89NWWqnK7o9bVGxFCorTtqxwMAAAAAAMD45fMlDfuYewxz4Aw4XU4d/t0zUTtecu41UTsWAAAAAAAA4hdLhgEAAAAAAAAboRAEAAAAAAAAbIRCEAAAAAAAALARCkEAAAAAAADARigEAQAAAAAAABuhEAQAAAAAAABshEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEQpBAAAAAAAAwEbcsTrwY489poceemjw/p/+9CfdcMMNuvbaa1VSUqLe3l7l5ORoxYoVkqS6ujqtWrVK3d3dCgQCWrdundxut5qamlRUVKRQKKQ5c+aotLRUU6dOVUdHh+666y41NDQoJSVFmzdvls/ni9XpAAAAAAAAABNCzGYILl26VBUVFaqoqFBpaalSU1P1rW99SytXrlRZWZkqKyu1d+9e7dy5U5JUVFSkNWvWaPv27TJNU1u3bpUkrVu3TgUFBaqurtaCBQtUVlYmSdq8ebMCgYCqqqq0dOlSrV+/PlanAgAAAAAAAEwYY7Jk+Ac/+IFWrFihhoYGzZ49W7NmzZLb7VZeXp6qq6vV2Nionp4eLVq0SJKUn5+v6upqhcNh1dbWKisra8i4JO3YsUN5eXmSpCVLlmjXrl0Kh8NjcToAAAAAAABA3Ip5IVhTU6Oenh7l5OSopaVlyLJev9+v5ubmk8Z9Pp+am5vV3t4ur9crt9s9ZFzSkNe43W55vV61tbXF+nQAAAAAAACAuBazawge85vf/Ebf+MY3JEmGYcjhcAw+ZpqmHA7HsOPHvh7vxPvHv8bpHH2/mZrqPZPTmBB8viSrIwAAAAAAAMBiMS0E+/r6VFtbqw0bNkiS0tLSFAwGBx8PBoPy+/0njbe2tsrv9yslJUWdnZ2KRCJyuVyDz5cGZhe2trYqLS1N/f396u7u1rRp00adLRTqkmGYUTrT6ItFeRcMdkb9mAAAAAAAABh/RuqWYrpk+K233tJ5552nKVOmSJIWLlyo+vp6HTx4UJFIRNu2bVNGRobS09OVmJioPXv2SJIqKiqUkZEhj8ejQCCgyspKSVJ5ebkyMjIkSZmZmSovL5ckVVZWKhAIyOPxxPJ0AAAAAAAAgLgX0xmCDQ0NSktLG7yfmJioDRs26I477lBvb68yMzOVnZ0tSSotLdXq1avV1dWl+fPna9myZZKktWvXqri4WA888IBmzpypjRs3SpLuvPNOFRcXKzc3V0lJSSotLY3lqQAAAAAAAAATgsM0zfG7bjaG4mHJ8OHfPRO14yXnXsOSYQAAAAAAAJsYackwheAYS02ZIqfLNeZ/7mgZkYhCbUesjgEAAAAAAIBPYKRCMOa7DGMop8ultkcqrI4xrJSCG6yOAAAAAAAAgBiK6aYiAAAAAAAAAMYXCkEAAAAAAADARigEAQAAAAAAABuhEAQAAAAAAABshEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEQpBAAAAAAAAwEYoBAEAAAAAAAAboRAEAAAAAAAAbIRCEAAAAAAAALARCkEAAAAAAADARigEAQAAAAAAABuhEAQAAAAAAABshEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEQpBAAAAAAAAwEYoBAEAAAAAAAAboRAEAAAAAAAAbIRCEAAAAAAAALARCkEAAAAAAADARigEAQAAAAAAABuhEAQAAAAAAABshEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG3GYpmlaHcIKoVCXDOP0p546fYqcbtcYJJq4jP6IQu1HrI4BAAAAAABgGz5f0rCPuccwR1xyul0KPvCQ1THimm/5LVZHAAAAAAAAwEdiumT4mWeeUX5+vnJycvSjH/1IklRTU6O8vDwtXrxYmzZtGnxuXV2d8vPzlZWVpVWrVqm/v1+S1NTUpMLCQmVnZ2v58uXq7u6WJHV0dOi2225TTk6OCgsLFQwGY3kqAAAAAAAAwIQQs0KwoaFBa9euVVlZmX7729/qj3/8o3bu3KmVK1eqrKxMlZWV2rt3r3bu3ClJKioq0po1a7R9+3aZpqmtW7dKktatW6eCggJVV1drwYIFKisrkyRt3rxZgUBAVVVVWrp0qdavXx+rUwEAAAAAAAAmjJgVgk8//bSuu+46paWlyePxaNOmTZo8ebJmz56tWbNmye12Ky8vT9XV1WpsbFRPT48WLVokScrPz1d1dbXC4bBqa2uVlZU1ZFySduzYoby8PEnSkiVLtGvXLoXD4VidDgAAAAAAADAhxOwaggcPHpTH49Htt9+uDz74QH/5l3+pCy64QD6fb/A5fr9fzc3NamlpGTLu8/nU3Nys9vZ2eb1eud3uIeOShrzG7XbL6/Wqra1NM2bMGFW+1FRvtE4VozDShSwBAAAAAAAwdmJWCEYiEe3evVtbtmzRlClTtHz5ck2aNEkOh2PwOaZpyuFwyDCMU44f+3q8E+8f/xqnc/QTHke7yzBFVnQEg51WRwAAAAAAALCNkTqtmC0ZPuuss3TllVcqJSVFkyZN0rXXXquampohm38Eg0H5/X6lpaUNGW9tbZXf71dKSoo6OzsViUSGPF8amF3Y2toqServ71d3d7emTZsWq9MBAAAAAAAAJoSYFYJXX321nnvuOXV0dCgSiejZZ59Vdna26uvrdfDgQUUiEW3btk0ZGRlKT09XYmKi9uzZI0mqqKhQRkaGPB6PAoGAKisrJUnl5eXKyMiQJGVmZqq8vFySVFlZqUAgII/HE6vTAQAAAAAAACYEh2map183+zH913/9lx588EGFw2FdddVVWr16tV566SWVlJSot7dXmZmZuvvuu+VwOLRv3z6tXr1aXV1dmj9/vkpKSpSQkKDGxkYVFxcrFApp5syZ2rhxo5KTk3Xo0CEVFxeroaFBSUlJKi0t1TnnnDPqbGeyZDj4wEOf5K/B9nzLb2HJMAAAAAAAwBgaaclwTAvB8YxCcOxQCAIAAAAAAIwtS64hCAAAAAAAAGD8oRAEAAAAAAAAbIRCEAAAAAAAALARCkEAAAAAAADARigEAQAAAAAAABuhEAQAAAAAAABshEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEQpBIMoija9bHQEAAAAAAGBYFIJAFPXt+bX6dm1W3yu/tjoKAAAAAADAKVEIAlES6TuqyP6nBm6/9ZQifUctTgQAAAAAAHAyCkEgSvqe/tEJ99dblAQAAAAAAGB4FIJAFEQ+/KPU0TR0sKNRkQ/3WRMIAAAAAABgGG6rA4xn9fXvqba2UR1vvWl1lLgWeOcdJSfPsDpGTIVfeWSY8Yfkuu5Hp3wMAAAAAADACswQBKLA8/lbzmgcAAAAAADAKswQHMGcOefr8ssXKniwzeoocc03d66CwU6rY8SUK22e9Kmzhy4b/lT6wDgAAAAAAMA4wgxBIEoSvrL6hPurLEoCAAAAAAAwPApBIEpcCZPl+szigdsXLpYrYbLFiQAAAAAAAE7GkmEgihIu/ZoiaRfLlb7Q6igAAAAAAACnxAxBIMooAwEAAAAAwHhGIQgAAAAAAADYCIUgAAAAAAAAYCMUggAAAAAAAICNUAgCAAAAAAAANuIwTdO0OoQVQqEuGcbpTz11+hQ53a4xSDRxGf0RhdqPWB0DAAAAAADANny+pGEfc49hjrgU7SLL50tS2yMVUT1mNKUU3KBgsNPqGAAAAAAAAIgRlgwDAAAAAAAANkIhCAAAAAAAANgIhSAAAAAAAABgIxSCAAAAAAAAgI1QCAIAAAAAAAA2EtNdhm+99Va1tbXJ7R74Y374wx/q/fff1wMPPKD+/n797d/+rQoLCyVJNTU1KikpUW9vr3JycrRixQpJUl1dnVatWqXu7m4FAgGtW7dObrdbTU1NKioqUigU0pw5c1RaWqqpU6fG8nQAAAAAAACAuBezGYKmaerAgQOqqKgY/CctLU2bNm3SI488ovLycj366KN655131NPTo5UrV6qsrEyVlZXau3evdu7cKUkqKirSmjVrtH37dpmmqa1bt0qS1q1bp4KCAlVXV2vBggUqKyuL1akAAAAAAAAAE0bMCsH33ntPkvR3f/d3uv766/XQQw+ppqZGV1xxhaZNm6YpU6YoKytL1dXVeuONNzR79mzNmjVLbrdbeXl5qq6uVmNjo3p6erRo0SJJUn5+vqqrqxUOh1VbW6usrKwh4wAAAAAAAABGFrMlwx0dHbryyiv1z//8zwqHw1q2bJlycnLk8/kGn+P3+/XGG2+opaXlpPHm5uaTxn0+n5qbm9Xe3i6v1zu4FPnY+JlITfV+wjP8eEzDUErBDZb82aNhGoZ8viSrYwAAAAAAACBGYlYIXnLJJbrkkksG7990000qKSnR8uXLB8dM05TD4ZBhGHI4HKMeP/b1eCfeP51QqEuGYZ7paY0Zny9Jh3/3TNSOl5x7jYLBzqgdDwAAAAAAAOPXSBO+YrZkePfu3XrhhRcG75umqfT0dAWDwcGxYDAov9+vtLS0UY23trbK7/crJSVFnZ2dikQiQ54PAAAAAAAAYGQxKwQ7Ozt17733qre3V11dXXryySf1L//yL3rhhRfU1tamo0eP6qmnnlJGRoYWLlyo+vp6HTx4UJFIRNu2bVNGRobS09OVmJioPXv2SJIqKiqUkZEhj8ejQCCgyspKSVJ5ebkyMjJidSoAAAAAAADAhBGzJcNXX321Xn/9dX31q1+VYRgqKCjQpZdeqhUrVmjZsmUKh8O66aab9LnPfU6StGHDBt1xxx3q7e1VZmamsrOzJUmlpaVavXq1urq6NH/+fC1btkyStHbtWhUXF+uBBx7QzJkztXHjxlidCgAAAAAAADBhOEzTHL8X0oshriEIAAAAAACAicqSawgCAAAAAAAAGH8oBAEAAAAAAAAbidk1BPHJGBFDybnXRPV4AAAAAAAAAIXgOBVq6z5pzOdLUtdztad9rfeLl3G9QAAAAAAAAJwSS4YBAAAAAAAAG2GGYBwxIoa8X7xsVM8DAAAAAAAAToVCMI4cv4zY50vS0f99a8jjkz97IUuFAQAAAAAAMCIKwThl9Pdr8mcvHDpmMDMQAAAAAAAAI+MagvGop0ehXzwm0zQlSaZpKvjvv1aoMWRxMAAAAAAAAIx3FILx6P1GyTRlhsOSNPDVNKWGRouDAQAAAAAAYLxjyXA8OjdderNOoZ9v/fOYyyXNSrcuEwAAAAAAAOICMwTj0aRJ0vx5AyWgNPB1wUUD4wAAAAAAAMAIKATj1dzzpUmJA7cnJUqfnmNtHgAAAAAAAMQFCsF45XRKgc8P3L7s8wP3AQAAAAAAgNNwmMe2qrWZUKhLhjEBTv3oUWnyZKtTAAAAAAAAYBzx+ZKGfYxpZfGOMhAAAAAAAABngEIQAAAAAAAAsBEKQQAAAAAAAMBGKAQBAAAAAAAAG6EQBAAAAAAAAGyEQhAAAAAAAACwEQpBAAAAAAAAwEYoBAEAAAAAAAAboRAEAAAAAAAAbIRCEAAAAAAAALCRURWChmHoZz/7mf7pn/5JXV1d+ulPf6pIJBLrbAAAAAAAAACibFSF4L333qv9+/frjTfekCQ9++yzKikpiWkwAAAAAAAAANE3qkLwhRde0IYNG5SYmCiv16tf/OIXev7552OdDQAAAAAAAECUjaoQdLvdcjr//NSEhAS53e6YhQIAAAAAAAAQG6Nq9T7zmc/o4YcfViQS0XvvvacHH3xQ8+bNi3U2AAAAAAAAAFE2qhmCq1at0ptvvqlQKKSCggIdOXJEK1eujHU2AAAAAAAAAFHmME3THM0TGxoaNGvWLHV1den999/XxRdfHOtsMRUKdckwRnXqAAAAAAAAQFzx+ZKGfWxUMwS3bNmi73znO5Kk9vZ23XHHHXrssceikw4AAAAAAADAmBlVIfjoo4/q17/+tSRp1qxZKi8v169+9auYBgMAAAAAAAAQfaMqBCORiLxe7+D9pKQkORyOUf0B99xzj4qLiyVJdXV1ys/PV1ZWllatWqX+/n5JUlNTkwoLC5Wdna3ly5eru7tbktTR0aHbbrtNOTk5KiwsVDAYlCT19fWpqKhIOTk5uvHGG/Xuu++O/owBAAAAAAAAGxtVIXj++eertLRUDQ0Namho0I9//GOdd955p33dCy+8oCeffHLwflFRkdasWaPt27fLNE1t3bpVkrRu3ToVFBSourpaCxYsUFlZmSRp8+bNCgQCqqqq0tKlS7V+/XpJA0uYJ0+erKqqKq1cuVJ33333mZ43AAAAAAAAYEujKgTXrVunAwcO6Ktf/apuuukmHThwQD/4wQ9GfM2hQ4e0adMm3X777ZKkxsZG9fT0aNGiRZKk/Px8VVdXKxwOq7a2VllZWUPGJWnHjh3Ky8uTJC1ZskS7du1SOBzWjh07dP3110uSLrvsMrW1tampqenMzx4AAAAAAACwGfdonnTWWWfp/vvvP6MDr1mzRitWrNAHH3wgSWppaZHP5xt83Ofzqbm5We3t7fJ6vXK73UPGT3yN2+2W1+tVW1vbKY/14Ycf6uyzzx51vtRU7+mfBAAAAAAAAEwwIxaC69ev16pVqwZn+Z3oJz/5ySnHH3vsMc2cOVNXXnmlnnjiCUmSYRhDrjtomqYcDsfg1+MNd31C0zTldDpPes2x8TMRCnXJMMwzeg0AAAAAAAAQD3y+pGEfG7EQvPLKKyVpcDnvaFVWVioYDOqGG27Q4cOHdeTIETkcjsFNQSSptbVVfr9fKSkp6uzsVCQSkcvlUjAYlN/vlyT5/X61trYqLS1N/f396u7u1rRp0zRjxgy1tLTo3HPPHXIsAAAAAAAAACMbsRC85pprJEnl5eX6z//8z1Ef9Je//OXg7SeeeEIvv/yySkpKtGTJEu3Zs0eXXnqpKioqlJGRIY/Ho0AgoMrKSuXl5am8vFwZGRmSpMzMTJWXl+v2229XZWWlAoGAPB6PMjMzVVFRoUAgoN27dysxMfGMlgsDAAAAAAAAdjWqdbadnZ06cuTIJ/7DSktLVVJSouzsbB05ckTLli2TJK1du1Zbt27Vddddp927d+vv//7vJUl33nmnXnvtNeXm5uqRRx7RmjVrJEm33nqr+vr6lJubq/Xr1+vee+/9xNkAAAAAAAAAO3CYpnnaC+kVFhbqnXfe0YUXXqgpU6YMjg93DcF4wDUEAQAAAAAAMFF97GsIStL+/fv15S9/WV/84heVlpYW1WAAAAAAAAAAxtaIheDjjz+ue+65R7Nnz9b777+v0tJSfelLXxqrbAAAAAAAAACibMRCcMuWLfrv//5vzZgxQ6+++qo2bdpEIQggLvUdbVPC5BSrYwAAAAAAYLnTbioyY8YMSdIll1yi9vb2mAcCgGjrCtVp79PL1RXaZ3UUAAAAAAAsN2Ih6HA4htx3uVwxDQMA0WYaER14tUySdODVMplGxOJEAAAAAABY67QzBI93YkEIAONdS321+nsPS5L6ew8pWL/d4kQAAAAAAFjLYZqmOdyDF198sSZNmjR4v6enR5MmTZJpmnI4HHrllVfGJGQshEJdMoxhTx3ABBDuOaQ3f/89GZHewTGnK1Hzv3yfPJOmWZgMAAAAAIDY8vmShn1sxE1Fnn766aiHAYCx0tb4vEzTGDJmmobaG5+X/9O5FqUCAAAAAMBaIxaC6enpY5UDAKIuJf0qfbDvUR0/F9jhcGp6+lWWZQIAAAAAwGpndA1BAIgnnknTNHPe38jpSpQ0sFz47Hk3s1wYAAAAAGBrFIJAlKWmepWa6rU6Bj7in5Mtd+JAAehOnCbfnCyLEwEAAAAAYC0KQSDKnE6HnE525B4vHE6XzrtkuSTpvEu+I4fTZXEiAAAAAACsNeIuwxMZuwwjVo7t4hMMdlqcBMfrbn9PU6efb3UMAAAAAADGxEi7DDNDEMCE1xWq01vP3q2u0D6rowAAAAAAYDkKQQATmmlEdODVMknSgVfLZBoRixMBAAAAAGAtCkEAE1pLfbX6ew9Lkvp7DylYv93iRAAAAAAAWItCEMCEFe45pA/2PSoj0itJMiK9atr3G4V7DlmcDAAAAAAA61AIApiw2hqfl2kaQ8ZM01B74/MWJQIAAAAAwHoUggAmrJT0q+RwOPXlWx/TtV8vlyQ5HE5NT7/K4mQAAAAAAFjHbXUAAIgVz6Rpmjnvb+ROmCpJcroSdfa8m+WZNM3iZAAAAAAAWIcZggAmNP+c7MHb7sRp8s3JsjANAAAAAADWoxAEMKE5nK7B2+dd8p0h9wEAAAAAsCMKQQC24U2dZ3UEAAAAAAAsRyEIAAAAAAAA2AiFIADAEkd7QlZHAAAAAABbohAEAIy51rY/qmrHt9XaXmd1FAAAAACwHQpBAMCYMoyIXnptoyTp5Vc3yjAiFicCAAAAAHuhEAQAjKl3DvxOvX2HJEk9fe165+DvLE4EAAAAAPZCIQgAGDM9ve168+2Hh4y9uf9h9fQesigRAAAAANgPhSAQZaZpyjRNq2MA49J7DU/LNIcuETbNiOobnrYoEQAAAADYj9vqAMBE43A4rI4AjFt8dwAAAACA9ZghCAAYM3NmLZbDMfSzKIfDrTmzvmJRIgAAAACwHwpBAMCYmZQ4TfMvKDhuxKH5FxRqUuI0yzIBAEcspMgAACAASURBVAAAgN1QCAIAxtTc83I1KXG6JGlS4nTNPe86ixMBAAAAgL3EtBD88Y9/rOuuu065ubn65S9/KUmqqalRXl6eFi9erE2bNg0+t66uTvn5+crKytKqVavU398vSWpqalJhYaGys7O1fPlydXd3S5I6Ojp02223KScnR4WFhQoGg7E8FQBAlDidLl2+cIUk6fJFK+R0uixOBAAAAAD2ErNC8OWXX9aLL76o3/72t3r88ce1ZcsW7du3TytXrlRZWZkqKyu1d+9e7dy5U5JUVFSkNWvWaPv27TJNU1u3bpUkrVu3TgUFBaqurtaCBQtUVlYmSdq8ebMCgYCqqqq0dOlSrV+/PlanAgCIsrNSLlbOX/5UZ02/yOooAAAAAGA7MSsEL7/8cv3qV7+S2+1WKBRSJBJRR0eHZs+erVmzZsntdisvL0/V1dVqbGxUT0+PFi1aJEnKz89XdXW1wuGwamtrlZWVNWRcknbs2KG8vDxJ0pIlS7Rr1y6Fw+FYnQ4AIMomT0q1OgIAAAAA2JL79E/5+Dwej+677z794he/UHZ2tlpaWuTz+QYf9/v9am5uPmnc5/OpublZ7e3t8nq9crvdQ8YlDXmN2+2W1+tVW1ubZsyYMapsqaneaJ0mcEo+X5LVEXAC3hMAAAAAAGJcCErS9773PX3rW9/S7bffrgMHDsjhcAw+ZpqmHA6HDMM45fixr8c78f7xr3E6Rz/hMRTqkmGYZ3g2wOkdK52CwU6Lk+AY3hMAAAAAgN2MNCkmZkuG3333XdXV1UmSJk+erMWLF+ull14asvlHMBiU3+9XWlrakPHW1lb5/X6lpKSos7NTkUhkyPOlgdmFra2tkqT+/n51d3dr2rRpsTodAAAAAAAAYEKIWSH4pz/9SatXr1ZfX5/6+vr0+9//XjfffLPq6+t18OBBRSIRbdu2TRkZGUpPT1diYqL27NkjSaqoqFBGRoY8Ho8CgYAqKyslSeXl5crIyJAkZWZmqry8XJJUWVmpQCAgj8cTq9MBAETZlCkJVkcAAAAAAFtymKYZs3Wz//qv/6qqqiq5XC4tXrxYd9xxh1544QWVlJSot7dXmZmZuvvuu+VwOLRv3z6tXr1aXV1dmj9/vkpKSpSQkKDGxkYVFxcrFApp5syZ2rhxo5KTk3Xo0CEVFxeroaFBSUlJKi0t1TnnnDPqbCwZRqywPHX84T0Zn3y+JN4TAAAAAIiRkZYMx7QQHM8oBBErlE/jD+/J+EQhCAAAAACxY8k1BAEAAAAAAACMPxSCAAAAAAAAgI1QCAIAAAAAAAA24rY6ADDR2PSynAAAAAAAIE5QCAJR5nA4rI4AAAAAAAAwLJYMAwAAAAAAADZCIQgAAAAAAADYCIUgAAAAAAAAYCMUgkAUpaZOPeVtAAAAAACA8YJNRYAocjqd6q77gyRp6kVXW5wGAAAAAADgZMwQBAAAAAAAAGyEQhDAhGeapkzTtDoGjnNsST1L6wEAAABg7FEIApjwHA6HHA6H1TFwHKfTqabmN+R08r8hAAAAABhr/CYGAAAAAAAA2AiFIAAAAAAAAGAjFIIAAAAAAACAjVAIAgAAAAAAADZCIQgAAAAAAADYCIUgAACQJB3uDVkdAQAAAMAYoBAEAACqP1yn9bW3q/5wndVRAAAAAMQYhSAAADYXMSN6dP/9kqRH3/43RcyIxYlwvPbeLqsjAAAAYIKhEASiJDVliiRp6kVXa+pFVw8ZAzCUaRo6e8bnZJqG1VEg6fmmKnWFD0uSuvoOqaap2uJEOOatwx9oRe1D2n/4A6ujAAAAYAKhEASixOly6fBLW4f843S5rI4FjEsOh1NvvfuUHA7+N2S1zr52PXXwN+ozeiVJfUavth/8tTr7DlmcDBHT0M/2PyNJ+o+3/6AIBToAAACihN/EAACwsVeDz8s4oWgyTEOvBZ+3KBGO+Z+mveoIH5UkdfQd0e+b9lqcCMe09x61OgIAAMAnQiEIAICNXeL7opwnzNR0Opxa5LvKokSQpEN9R/TEwZfVa/RLknqNfj1+8GUd7jticTLsPxTSP7z4tPYfZlduAAAQvygEAQCwsaSEaVo8+2YlOBMlSQnORGXN/pqSEqZZnMzeXgq+rYhpDhkzTFMvBt+xKBGkj5Zxv/WqJOln+15lGTcAAIhbFIIAJrTU1KmnvA3gz646O0fejwrApIRp+sLZ2RYnwhW+C+RyOIaMOR0OXeGba1EiSNL/NNaro2/gepsdfb36fWO9xYkAAAA+HgpBABOa0+lU49tPq/Htp+V08p884FRcDpf+5oLvSpL++oLvyuVgQySrJSdMUf7sy5XodEuSEp1u/dXsy5WcwO71Vjnc16Mn6/ep14hIknqNiJ6o36fDfT0WJwMAADhz/HYMAAA0J/kirbrsJ5qTfJHVUfCRa89eoE8lTFHBvC8oOWGKvnz2Aqsj2dqLLY2nXMb9UkujRYkAAAA+PrfVAQAAwPiQnJhqdQQcx+Vw6psXXK0vzv2MzvP45HLwOa6VrvCn68n6feo/rhN0Ohz6C3+6daEAAAA+Jn6yBAAAGKcuTJ4pSfrMR19hneSESbpxzjwlOgeW1Cc4XcqfM0/JCZMsTgZJOussr846y2t1DAAA4gaFIAAAwDiVmuqVecIyVVjn2vQ5+lTCwI7cyQmJ+nL6HIsTAQAAfDwUggAAAOOU0+mQ44TdhmEdl8Opb154iSTpm/MuYRn3OOJw8L0CAMCZ4BqCAAAAwCh9ZlqqNl7xFU1PnGx1FAAAgI8tph9r3n///crNzVVubq7uvfdeSVJNTY3y8vK0ePFibdq0afC5dXV1ys/PV1ZWllatWqX+/n5JUlNTkwoLC5Wdna3ly5eru7tbktTR0aHbbrtNOTk5KiwsVDAYjOWpAAAAAAAAABNCzArBmpoaPffcc3ryySdVXl6uN998U9u2bdPKlStVVlamyspK7d27Vzt37pQkFRUVac2aNdq+fbtM09TWrVslSevWrVNBQYGqq6u1YMEClZWVSZI2b96sQCCgqqoqLV26VOvXr4/VqQAAAACSpP2HQvqHF5/W/sMhq6MAAAB8bDErBH0+n4qLi5WQkCCPx6NPf/rTOnDggGbPnq1Zs2bJ7XYrLy9P1dXVamxsVE9PjxYtWiRJys/PV3V1tcLhsGpra5WVlTVkXJJ27NihvLw8SdKSJUu0a9cuhcPhWJ0OAADAmDNNk01FxpGIaehnb70qSfrZvlcVMQ2LEwEAAHw8MbuG4AUXXDB4+8CBA6qqqtItt9win883OO73+9Xc3KyWlpYh4z6fT83NzWpvb5fX65Xb7R4yLmnIa9xut7xer9ra2jRjxoxR5UtN9X7icwRGw+dLsjoCjsP7Mf7wngCnx/fJ+FC+v04d4V5JUme4Vy8d/kA3XDDP4lQ4Ht8rAACMTsw3FXn77bf17W9/W9///vflcrl04MCBwcdM05TD4ZBhGEN2BTs2fuzr8YbbPcw0TTmdo5/wGAp1yTD4xB3RM9wPoMFg5xgnwfFOfF94P8aH498X3hNgeMe+V/g+sd7hvh5t2fu6eo2IJKknEtGv/vc1zZ+SquSESRanA98rAACcbKQPymK6qciePXv09a9/Xf/4j/+oG2+8UWlpaUM2/wgGg/L7/SeNt7a2yu/3KyUlRZ2dnYpEIkOeLw3MLmxtbZUk9ff3q7u7W9OmTYvl6QAAAMCmXmxpVOSj5dsFF39WkmSYpl5qabQyFgAAwMcSs0Lwgw8+0He/+12VlpYqNzdXkrRw4ULV19fr4MGDikQi2rZtmzIyMpSenq7ExETt2bNHklRRUaGMjAx5PB4FAgFVVlZKksrLy5WRkSFJyszMVHl5uSSpsrJSgUBAHo8nVqcDAAAAG7vCny7XRytVCi7+nCTJ6XDoL/zpVsYCAAD4WGK2ZPjnP/+5ent7tWHDhsGxm2++WRs2bNAdd9yh3t5eZWZmKjs7W5JUWlqq1atXq6urS/Pnz9eyZcskSWvXrlVxcbEeeOABzZw5Uxs3bpQk3XnnnSouLlZubq6SkpJUWloaq1MBhpWaMkVOl2vwfvJf/PVJz/H5kmREIgq1HRnLaAAAIIqSEybpxjnz9GT9PklSgtOl/DnzWC4MAADiksO06dZ1XEMQ0eDzJSn09P2nfV7qV/4v17SxiM+XpMa3n5YkpV/wFd6HcSAldYpcTteQsYgRUVuI0hw4XkrqVLk+uj5yxDDUFuq2OBEipqG7X35Gv1xyo76x7UmVXH6NXI6YXoEHo8Q1BAEAONlI1xCM+aYiAAAcz+V0affrW4aMBRbealEaYPxyOZ36n/f3SpKuPXeBxWkgSS6HU9+88BJJ0jfnXUIZCAAA4haFIIAJKzVliqSBmYHHj7F8GwDwcX1mWurA1+RUi5MAAAB8fBSCACYsp8uld155eMjY3M8XWpQGAAAAAIDxgXUOAAAAAAAAgI1QCAIAAAAAAAA2wpJhABNOasoUOV0Du9ieaonwsZ2WjEiE6wkCAAAAAGyHQhDAhON0ufS/uzae9nmfzfiHMUgDAABiKSV16pDbbaFuC9MAABAfKAQBAAAAxC2X06lnDnwgSbrmvJkWpwEAID5wDUEAAAAAAADARpghCIxSaspkOV0nf8ukfuX/jur1x65bdzwj0q9Q29FPnA0AAAAAAGC0KASBUXK63Gp+fE1Ujznjr34Y1eMBAAAAAACcDoUggLiWkjJZrlPM3BzthiEnztyMRPrVxqzNqEtJmSLXRzs/S1Jg4a0nPcfnS1IkElEbOz8DAAAAQExRCAKIay6XW7W/+6eoHe+y3Huidiz8mcvl0h9qSk/7vKu/cNcYpAEAAAAAe2NTEQAAAAAAAMBGKAQBAIAkKTXVq9RUr9UxAAAAAMQYS4Zhe8PtHnwqsdgE5FS7D5/IbrsRD3ddwOFEe5nvaN4TrjWIicjpdFgdAQAAAMAYoBCE7TldbjX9crnVMUZ09jcesDrCmHK53Nr16NetjjGijL950OoI49Zwhe5orw/IRi8AAACx0ddrKiGRDwABUAgCAKLM5XLrt08VRe141y/+l6gdCwA+rpTUqXI5/3y1nWMfXkQMQ22hbqtiAcCodR4y9NYrYV34eY+SpnH1MMDuKAQBAACA03A5nfrZ63tOGv/mwkstSANYKyVlqlyu6BVKkYihtjaK9VgyDVP1fwxLkg78MawFVyTIwaVCAFujEAQAAAAAjJrL5dRPaoJRO97tX/BF7Vg4tZY/RdTfN3A73Ddwf8a51AGAnfFfAMSd1OmT5XRH91/deLhG32g2uhgto79foXauyQYAAIAB01Omyn0Gs/6iXeKN5mfd/oihdmYSnrFwr6nG9yIyjIH7hiE1vhdRSppLngRmCQJ2RSGIuON0u/X+fTdZHSOunfu9/7I6AgAAAMYRt8up//fUB1bHGNE/L55pdYS4FGqOyDSHjpmS2pojmjGLSgCwK777AXxiw+0q+0nEwy6+0Zy1GQ876Z7J+xztjUBG83cdD3+HwGilpE6VJF177oIhY2xeAQA4U6lpLjW9N1AKXn51sl7+w2E5JKXMcFkdDYCFKAQBfGIul1vbf36d1THiWtb/qbQ6wmm5XG498sStVscYVkH+FqsjAFHjcjr1cN3zQ8YKL7rKojTA+HWsPL/mvJlDxijPgT/zJDiUfr5LF34uWR6PU9fmp+itNw6zXBiwOQpBAAAAAHHJ5XTqN398b8jYzRefb1EaYPzyn+OSx+NUW0tYKX6P/OcwOxCwu+jtFQ8AAOKaaZoyT7zIEAAAiHsOp2PE+7COccSwOgJsikIQAABIkhwOhxwOfkEAAAAYC5GWiI4+eVSRlojVUWBDLBkGAAAAMC5NT5kqt2vkOQynWiI83GZU/RFD7W1cXxD2ZJqmUvweVgOME6ZhqveFXklS7wu9mpw3mZmbGFMUggAwQcVi9+fxvnFHNHd+luy3czG/IAAYb9wup1btfCVqx1uf+fmoHQuINw6HQ431PUqfM8nqKJAUfiss8+jAz17mUVPh/WElzEuwOBXshEIQACYol8utn27JsjpGXPv2rdutjjCmWC4MnOz4GWrfXHjpKZ/j8yUx8+wMjWbm3zHRLvFG++ER7ymAWDGOGgq/EZaOrRSOSOHXw/LM9sgxmZ/HMDYoBBFX6uvfU21to1rfPmR1lLj2hXfeUXLyDKtjAAAw7rldTm148dnTPq/4ii+NQZqJw+1y6luVz1sdY0T/cd1VVkcAMEFFDkR07rf8ciYO/WDECBsKHeKDCIwNCkEAAKCU1KlDbreF+GEUAMar+vr39N5770T1mJ/61GQdeL09qseMtsr+6eroiN6lPM4/f67mzDn5GpQTRWqKV07X0Nlmx5YLHz9T1oiYCrV1jWk2O0hNmSrncDOhfacednqcw85iNiKGQsxaRhRRCCKuzJlzvi6/fKHef+cXVkeJa+fOnatgsNPqGADGEZfTqefef1qS9MVzv2JxGgDA6Uz1JsrlHN2y69H6q4XTo3q8WPjUpyZH71jJ0TvWeOR0OfTGS6f/mf9zfxHdazBjgNPlVHN59Er2GV8d/9+fiC8UggAAAAAQR459SP69JxusjhLX7rtwFh+S44yNOPPvBNEu8UZ7DVRmE2I0Yl4IdnV16eabb9ZPfvITnXPOOaqpqVFJSYl6e3uVk5OjFStWSJLq6uq0atUqdXd3KxAIaN26dXK73WpqalJRUZFCoZDmzJmj0tJSTZ06VR0dHbrrrrvU0NCglJQUbd68WT7fMPNuAQAAAAAAPiGny6nGB4NWxxhR+tfpRnB60Z1jfoLXX39dX/va13TgwAFJUk9Pj1auXKmysjJVVlZq79692rlzpySpqKhIa9as0fbt22WaprZu3SpJWrdunQoKClRdXa0FCxaorKxMkrR582YFAgFVVVVp6dKlWr9+fSxPBQAAIOamp0wZ/PS/8KKrhvwjDcwM8PmSND1lipUxAQAAEOdiOkNw69atWrt2rb7//e9Lkt544w3Nnj1bs2bNkiTl5eWpurpac+fOVU9PjxYtWiRJys/P13333aelS5eqtrZW//Zv/zY4fsstt6ioqEg7duzQww8/LElasmSJfvjDHyocDsvj8cTylACc4NjOz6/XcSHiT+LT7PwMQJLb5dLGPVWnfd4/XJozBmkAAIh/qdOnyumO7lyoeJiBN9rlxaNh9BsKtbMEeaKJaSF44qy9lpaWIct6/X6/mpubTxr3+Xxqbm5We3u7vF6v3G73kPETj+V2u+X1etXW1qYZM/iFGgCAM5GSOjDb7PjNRFJSp6gtdMSqSLYwPWWK3C7XSeOjLftO/EG/PxJRexvvGWAX/RFD9904y+oYca0/YlgdAWPA6XbqwOYPrY4R1877+zSrIyAGxnRTEcMw5HD8edtz0zTlcDiGHT/29Xgn3j/+Nc4z2GUrNdV7humBiSVanxj5fAslLZTrg/ujcjy7mjt3rtURMIxofro6npXXPTTk/lcvusU2526l4md/E7VjbfjSzbxnFuPvf+Kx03saMUy5nKf+XWu8iHZGt8tpq/d4JPw94HT4d+T/t3fnAVFVfR/Av8MACoogOuJehoW4pGUIbqC4sMgOIZq4vKL5FKFPueD6YJppamZamEsuaC4pIiBKKqJoipqpmOaW8qAlyuIGqczy/sEzN5BtkAvDMN/PX8Nwl9/c5dxzf/fcc+qeGk0INm/eHPfv/9P55v3799GsWbMS32dlZaFZs2awtLTE48ePoVAoIJVKhemBwtaFWVlZaN68OeRyOfLy8mBhYaFxLNnZT6BUqsT7cVRjWBCJQ8wR1bhPxCHmPrl58w9kZd3Bld/YtL8qruvJq9xlncMcebF6VUfZyX0mvsrsJ25/zelK3YH7lHQZy69CulLe1HZ1+Ripy8o7/ms0Idi1a1fcvHkT6enpaN26NeLj4+Hv749WrVqhXr16+OWXX9C9e3fs2bMHjo6OMDIywjvvvIOEhAR4enoiJiYGjo6OAAAnJyfExMRgwoQJSEhIwDvvvMP+A4mIiIiIqoGFZQMYScXtg2uNe29Rl1cdxEwkFCiUeJDDB3VENUnd3/m96w+0HYpO63O9h148JNc3NZoQrFevHhYuXIiPPvoIz549g5OTE1xdXQEAS5YswaxZs/DkyRN06tQJI0eOBAD85z//QXh4OCIjI9GiRQt8+eWXAICJEyciPDwcQ4YMgZmZGZYsWVKTP4W0SCmXo23YTm2HodOUcrm2Q6Bq1q7da+jRoysyspdrOxSd1r59ez4NJSICYCQ1gM/OQ9oOQ6fFBAzQdghERESCGkkIJiUlCZ979uyJ2NjYEtN06NABO3eWTPK0atUKUVFRJb63sLDAqlWrxA2UdEJ27t+iLk8mM8Of6/8l6jLF1nJMJJMSRERE1ayxZQMYltIKLtyhr0bzlxzoRYlctggjItIa9UPyW1c5qEhVvNq+Oe9H66AabSFIREREtYOlpSmkRUa49bEdUWIamcwMCoUCORy5lvSEodQAk5MSRVveEmcX0ZZFRFQeS8uGkEpLDrjypr1mr72/+EBDoVAhJ+eJKLERUe3EhCAREZEekkqlWPvL0gqnC+n+SQ1EQ0RERFUhlUqQmvRQtOXZO5uLtiwiqp2YECQiIiISSWNLUxgWaXlZkYV9g0RdvyYDIMgVCuSy1SdVgrpT/kcX0rQdik673u0VdspPRES1BhOCRERERCIxlEoxJvE7bYdRrvUu72s7BCIiIiLSMiYEiajKFAo5XMYmaDsMnaZQcORnIiKqndSd8q/Or6ftUHQaR64nIqLahAlBIqqynBzxR34+un20qMsUm+PQDazUExEREVG1srRsAGkpo5+XRux+/zTphkKhUCKnlo+mrpQr8eqk5toOQ6cp5Upth0DVgAlBIiKiOq6xpQkMpSUv+ZoOGFLaDYFcIUeuyA8DiKpLZfp2FHtkYPbrSERVIZUaIDk+R9thlKmfh6W2Q6hQdq64CUuZzAx3NtwXdZliazVaxsYLVCEmBEnvKRVytBwTqe0wyqXUs9dJFQo5HIdu0HYY5eIrvqRLDKWGWJgyRdRlhvddLOryiKqToVSK0Xt3azuMMm0Y4qvtEIiIiEjPMCFIei9bwxYuMpkZMnfNEXXdVv6f8slNKSrzCrKlpQmkpbR8elkKhVz0V6C1RaGQ4/3gRG2HodOY+K37LCxNYVSJUYE1oQuDdmjSak1TBQoFHrB1GxEREZFOYUKQiHRaack7mcwMaUe/rHDeLo4f1+mEbHX07fhDdLCoyxTTcL+oOr0/qXoYSaUYsputHatir6+4rU+JiIhqM6VCiVajZdoOo1xKBfv8o4oxIUhEdY5SoUAXx481mo6IiGqX6mi1CdT+13LFbLUJsOUmkTZUZgCQyqjt/fSJXX7V9oFKsjWMrYllAxiIeDwoFUqN102kCSYEiajOyf7fDVATS1MYvHBTqVQohP8TEVHtYySVwmPnFm2HofPiA97TdghEekcqNcC+7VnaDkPnuQ1tqu0QRFFa8q6JZUMYSCUVzqtUqJCd86Q6wiISMCFIRHVWdk4+mjRpAAODwidzSqWSyUCqU8oaPbg01TEIiGajp3I0YiIiIiIAQpKv4HIBmttbwKBe8RaEygIlsh+wFSDVDCYEiahOy87OQ9OmDYXPRHWJodQQkxNHajuMci1x2aTtEIiIiIhqFcNXDfHfNfeAouPXGQKmXqaQmFTcgpBIDEwIEmlIqZDDyv9T0ZdJ1S8ri83tiYiIqqJAoURMwABth6HTCtjJf5128+YfOH36Dn5Ne6TtUHTe693fgbm5lbbDqFYSEwmM3jRCwfkCQAFAChh1NWIykGoUE4JEGsouYzTb7AMrK5y3yaBQjn5KOk+hkGO4X5S2wyiTggl2qiT1zdvj8ze0HYpOu97lumg3bsI+ufCbKMvTZ9e7ibdfAOCByB3Zy2RmGJdwXNRlim2Ne2/W34io2hjZGEF+VQ7VE1VhgvANI22HRHqGCUEiItJIjob9wMlkZoj9aYpo6/UavJg3ZERERFSrtWv3Gnr06ArJYw4qUlXt2zfVi7qfxECCej3r4emBp6jXsx4kBmwdSDWLCUEiIqIaUplBQDSlC330aTL4iKbEHKREffP2zTO2RquK9u3bi3bjpt4n3+bnirI8fSbmfiEiouohbSaFia8JDEwNKp6YSGRMCBJVgVKhQJNBoRpNR0RkKDXEmN2u2g5Dp6333a/tEIhIBHKFEmvce2s7jHLJ2ecfEdUAJgNJW5gQJKqC7Jx84bNMZoaHqTuK/d/cPpBP50nvKBRyeA1eLOryiEh/FCgUiA94T9th6LyCWv4wMrcKfRLKZGb4+vSlCqcLs+vIehjVKIVCCbehTbUdhs5TMBlfp6WkJOPIkSRth6HznJyc0bdvvyotgwlBIpEoFQqY2weW+I5I35TW16BMZobDPy+pcN7+vSbX2Zs39WAJWec56nVViDmABdVOD4o8bBOLTGaG0Xt3i75csWwY4ltnyz4ifZIj8uA7QGH5lRyfI/pyxdLPw5LlF5GOYkKQSCTZOfmQycyQd/kwAKCBbf9iLQiJiKj2KVAosNdXvEFw9FFtb4kGAHKFAhuG+Go7jDLJdWAbEpF2KBRK9POw1HYYZWJrPqqsvn37VbllG4mDCUEiIqIa0K7da3i7eye4u7trOxSdJuagIoD4rdEaW5rCUCoVdZlikysUyNWzB1aa/l6ZzAyTkxJFW+8SZxe2nKkGcoUSYXYdNZqOSNdp2upQJjNDatJD0dZr72zO8ouojmNCkIiIqIaImcgC/pe8SBwp6jLFtsRlk17dUFQm0SZ28lAfE31ikyuUWOLsIurySHxV6X+QqK5SKFSwdzYXdXlEVLcxIUgkIqVSiQa2/YXPRFRIoVCgf6/JGk1HpC+YvKt9Sks0yWRmWHgypcJ5wx366lXym4hql5wc9lFMRJXDhCCRiLKz2Bfe7QAAIABJREFU8yCTmQmfiahQzguJD5nMDFdu/AQb68G8gSYiIiIiIqphTAgSiUhx7yog667tMIhIT8gVcixx2aTtMMolV8i1HQJRlcgVSoQ79NVoOiIiIiJdwYQgkUhUSgUKTq4FOnUX/pYY1O6O5Ym0RaVSwsZ6MFQq3kBXhaZ9EspkZliYIu5IuuF9F7N1J+kF9WvEMpkZ1p7/pcT/Q7p257lAREREOocJQSKRyK8ehOrpI6hUqv/9fQhGHQZrOSqi2kkiMcCfmRfQ0upNbYdCRKQRhVKJkK4l3wJQsM9gIiIi0kFMCBKJQPX3Q8gv7AYUz6AqKGyxI0+LhuEr9pCYiDfaF1FdoVQq0dLqTQ6+U0PkCjnC+y4WfZlE+iSHfQMTERFRHcKEIJEIFOknAVXh6Ki3V40s/NLACIr0VBiylSBRCRx0p2Zp+mpx06YNAQBZWRypkIiIiIioLjPQdgBEdYH0FQdA8kJ/gQYGkL5ir52AiIheQlbWEyYDiYiIiIj0ABOCRCKQmJjD8E1fQFqv8AupMQy7+PF1YSIiIiIiIiKqdZgQJBKJ4RsDITFpBOB/CcI3Bmg5IiIiIiIiIiKiknQ6IRgXFwd3d3cMHjwYW7Zs0XY4pOckBlIY2YcAAIwcQiAxkFYwBxERERERERFRzZOoVCqVtoN4GZmZmRg2bBiio6NhbGyMoKAgfPnll2jfvr1G82dnP4FSqZM/nWo5VX4uJKaNtR0GEREREREREekxmcyszP/pbAvBn3/+GQ4ODrCwsICpqSlcXFywf/9+bYdFxGQgEREREREREdVqOpsQvHfvHmQymfB3s2bNkJmZqcWIiIiIiIiIiIiIaj9DbQfwspRKJSQSifC3SqUq9ndFmjRpWB1hERERERERERER1Wo6mxBs3rw5zpw5I/x9//59NGvWTOP52YcgERERERERERHVVXWyD8FevXrhxIkTyMnJwd9//42ffvoJjo6O2g6LiIiIiIiIiIioVtPZFoJWVlb497//jZEjR6KgoAABAQF48803tR0WERERERERERFRrSZRqVR6+d4sXxkmIiIiIiIiIqK6qk6+MkxERERERERERESVx4QgERERERERERGRHmFCkIiIiIiIiIiISI8wIUhERERERERERKRHmBAkIiIiIiIiIiLSI0wIEhERERERERER6RFDbQegLQYGEm2HQEREREREREREVOMkKpVKpe0giIiIiIiIiIiIqGbwlWEiIiIiIiIiIiI9woQgERERERERERGRHmFCkIiIiIiIiIiISI8wIUhERERERERERKRHmBAkIiIiIiIiIiLSI0wIEhERERERERER6REmBImIiIiIiIiIiPQIE4JERERERERERER6hAlBIiIiIiIiIiIiPaLXCcHo6GiEh4eXO83hw4exfv36Mv8fFRWFzp074/79+8J3mZmZGDdunGhxVoWzszPc3d3h7e2NIUOGIDQ0FA8fPnypZXl7e4scXc3Th32uTbdv34aNjQ3mzJlT7PvLly/DxsYG0dHR5c7v7OyM27dvIyMjAzNmzAAApKWlYebMmeXOFx4eXuGyq8u4ceOQmZmplXVr09WrV2FjY4PExEThO022hSbn4IvUx8WhQ4ewfPnyl4q3Ltu/fz/8/Pzg5eUFT09PrF27ttzpZ86cibS0tCqtMzg4GKmpqeVOM336dNy5c6dK66mL5HI5IiMj4ebmBnd3d7i4uGDVqlVQqVQVzsvrj7hsbGwqNf2KFSuwYsUKAMXrRDk5OZg5cyYGDRoENzc3+Pr64tChQ6LGWtcNGzYMe/fuLfZdfn4+7O3tK7y2iFHWsP4iDhsbG3h7e8PHxweenp4ICgrClStXRF0HACxfvlyvz7GXrYOpLVy4EA4ODnj+/HmF09ZUPbe8MiAnJ6dGyoHSBAcHY9CgQfD29hbqWQkJCaKvh3Xc6jV37lx4e3vD3d0dnTt3hre3N7y9vbFr1y6N5tekzNHVeq+htgOo7S5evFju/6OjozFgwADs2rULEyZMAABYWVlhzZo1NRGeRlavXo3WrVsDAD777DN89913mDp1aqWXs2fPHrFDq5Xqwj7XJgsLC6SkpEChUEAqlQIAEhISYGlpqfEy/vzzT2RkZAAAunTpgi5dulRLrGLQ1/2+a9cuuLq6Yvv27XBxcQFQ/dtiwIABGDBgQLWuQ9dkZmZi0aJFiI6ORuPGjZGXl4fg4GC0a9euzG312Wef1Uhsqamp+PDDD2tkXbpk7ty5yMrKwvbt29GoUSM8efIEH374IczMzPDee++VOy+vP7WHuk70/PlzjBo1Ci4uLti/fz+kUin++OMPjB07Fq1atUKHDh20HKlu8Pf3R1xcHIYMGSJ899NPP8He3h5ff/11ufOKVdaw/iKOovcLUVFRmDNnDrZv3y7qOiZOnCjq8nRNVepgcrkc+/btw1tvvYXExER4enqWO31NXV/KKwMsLS0rjKM66xzz58+Hvb09AODKlSsICAhA3759YWZmJto6WMetXv/5z38AFD78GTlyZKXzGpqUObpa79XrFoJFpaenY8yYMfD19cWwYcNw6dIlXL9+Hdu2bcO2bdtKzR7//vvvePjwIcaNG4cdO3ZAqVQCKDzQnJ2dARQ++ZswYQLc3NywcuVKLF68GABw7NgxvP3225DL5QAANzc3ZGVlYd++fQgMDISXlxdcXV1x9uxZpKeno1+/fsLyU1NTERISgrt372LEiBHw8/NDQEAAzp07V+5vVCqVyMvLQ9OmTQEUf9IN/PN08/fff0dgYCD8/PwwbNgw3Lp1C8A/T9JXrFiBWbNmITg4GM7OzoiMjAQAKBQKfP755/D19YWXlxc2bNgAAGXGuWjRInh5ecHHxwcrV66s3A4TgT7sc21o0KABbG1tcfr0aeG748ePo1evXsLfRVtllNZibP78+bh48SLmzp2L1NRUBAcHAyh8SvfZZ5/B19cX7u7uOHbsWIn1x8TEwNfXF97e3pgxYwaePXtWYpq4uDi4u7tjyJAhCA8PR0FBATIzMzF27FgEBgaiX79+wlO66OhoBAcHw9PTE19++WWx/ZuUlCScN2Ud/6mpqQgICICfnx+mTZuGEydOwM/PD35+fhgzZgxycnJefmNrSUFBAeLi4jBp0iT89ttv+O9//wvgnzLkxW1WlvDwcMyfPx/Dhg2Ds7OzcM49ePAA48aNg6enJyZNmiTsw6LHSmnnjT7Kzc1FQUEBnj59CqDw/Fu4cCHat28PZ2dnfPHFF/Dx8YGPjw8uXboE4J/WfSqVCosXL4aLiwvc3d2xceNGAKWXjeUp7ZxbvXo17t27h/HjxyM3N7d6N4IOuXv3LmJjY7Fw4UI0atQIANCwYUPMmTMHTZs2RVZWFt5//314enrC19cXR48eFeat7PUnKSmp5n+gjkpNTcX//d//4YMPPoCLiwvCwsKEljNr167F4MGDMXToUFy4cEGYR30dS0xMRL169RAaGiokkV577TVERERAoVAAAM6dO4d3330XXl5eGDVqFNLT02v4F9Z+bm5uOHv2LB48eCB8FxsbC39//3Lrpy+WNWVdG4KDgxEaGgoXFxdcvnwZmzdvxrvvvgsPDw/4+voiIyOD9ZdqqL/Y29vj6tWrpe6Do0ePIiAgAD4+PggNDRWuFc7Ozli6dCn8/PwQGBiI5ORkjBw5Ek5OTkLLLHXLyqLlH1D83qZ3796YM2cOfHx8EBISgn379mH48OFwdnbGqVOnSsRa1rXvzp07GDlyJDw8PBAQEIDff/8dQGFSzsPDA56enggPD0deXh4AoE+fPpg3bx58fHzg7+8vJIjLKgeCg4Px+eefIyAgAJ6enjhy5AjGjh2Lfv36YcOGDVAqlXB2dsbNmzcBAA8fPsSmTZvwwQcfvFQdLDk5GW3btoWPjw+2bdsmfF/WPYZ6uU+ePEFYWBiGDh2K/v37Y8aMGRq1bNdUeWVA0Tiqsxz4448/KozTxsYGpqamSE9Px4oVKzB27Fi4u7vjhx9+KPMYCg8Px9y5czF06FC4ubnhwIEDCA0NxcCBA7Fw4UIAxcsT9W8FUKIcKe9YeVFeXh6mTZsGPz8/eHt7Iz4+HgDw7NkzzJgxAy4uLvDw8BDOq/KO0S+++AJDhw7FoEGDcOTIEQAos86yYsUKTJ8+HcOGDYOLiwtiYmIwbdo0uLq6YtKkSVCpVJgyZQp27NghxBocHIzz589XuP3F9uI+LPoGzIv1K3Vr7rpW72VC8H+mTZuGKVOmYPfu3Zg3bx7+/e9/o3379ggKCkJQUJBQGBWlfjrTuXNnGBoaIiUlpdRlW1hYYN++fXBzc8OJEycAACdPnkT9+vVx6dIlZGRkwMzMDJaWlti2bRtWrVqF2NhYhISEYPXq1XjllVfQunVr4eCMiYmBn58fdu7ciX79+iE6OhphYWH45ZdfSl3/+PHj4e3tDUdHRxw/fhyurq7lbouNGzdizJgxiI6ORmBgYKlJpytXrmDdunX48ccfsXr1ajx69Eg4qXfv3o2dO3fi0KFDOHPmTKlx3rlzB0ePHkVsbCy2bt2K69evl1rxqU51eZ9rm5ubm/Aaw4ULF2BjYwMjIyON5581axY6d+4sPM0p6smTJ9i9ezeWLl2K8PDwYq87XLt2DTt27MC2bduwZ88eNGnSBOvWrSs2f2ZmJj7//HN8//332Lt3LxQKBY4cOYL4+Hh4eHhgx44diIuLw8aNG4XKbmZmJnbv3o2PP/4YwD/7t2gltKzjHwBu3bqFjRs3YtGiRfj2228RERGB6Oho9OrVq8JkS2105MgRtGzZEu3atcPAgQNLffL/4jYry927d/HDDz8gMjISX3zxBQDg66+/RseOHREXF4f33nsPWVlZxeZRKpWlnjf6qEOHDhgwYAAGDhyIgIAALF68GEqlEq+88goAwNTUFDExMQgLC8O0adOKzbt//36cPXsWcXFx+PHHHxEdHY379++XWjaWpaxzbvz48WjWrBlWr16Nxo0bV+s20CUXLlyAtbU1zM3Ni31vbW0NFxcXzJs3Dw4ODoiLi8PXX3+NGTNmCMd/Za8/Rcsnqtivv/6KOXPmYN++ffjzzz9x7NgxpKWlYdeuXdi9ezfWr1+Pu3fvlpjv/PnzsLOzK/G9k5MTOnXqhOfPn+Pjjz/G7NmzERsbi6CgoArLRX3UoEEDDBgwAPv37wdQeA25efMm+vTpI0xTWv20aFljbm5e7rVB/YplmzZtcPDgQURFRSE+Ph79+vVDTEwMANZfxKy/qFQq7N27F2+99ZbwnXofWFlZYenSpVi3bh1iYmLQp08fLFmyRJiuadOmiI6OhrW1NVavXo3vv/8eixcvrtS1PisrC46OjoiJicGzZ89w8OBB/PDDD/joo4+EB2BFlXXtmzt3LlxcXBAfH4+PPvoIkZGRuHLlClatWoWoqCjExcXBxMREaNxw//599OzZEzExMbCzs8OWLVsqLAdUKhV27twJFxcXzJ8/HytXrsSWLVvwzTffwMDAAD4+PoiNjQUArFy5EhYWFrCxsXmpOlh0dDRcXV3h5OSEy5cv4/r16wBQ4T1GcnIybG1tsX37diQmJuL06dP47bffNN4fFdGkDACqtxzYsmVLhXGqr73t2rUDUNhKPCEhAcOHDy+3/nTv3j1s374d48ePx/Tp0zF37lzExMRgx44dePz4scbbqbxj5UWRkZHo1KkToqOjsWXLFqxatQoZGRmIiopCfn4+9u3bh/Xr1+Obb76p8BgtKCjA9u3bMX36dOGhQ3l1lqtXryIqKgrz5s3D9OnTMW7cOMTHx+PSpUu4cuUK/P39hVZ6d+7cQU5ODrp27arxdhBT0X1YkbpY7+UrwyjMnl+8eBHTp08XvsvPzy83u6tuIfP9998DKKxAbNu2DU5OTiWmffPNNwEUVvifPHmChw8f4syZMxg+fDhOnToFExMTODk5wcDAAN988w2SkpJw8+ZNnDp1CgYGhTlbf39/xMbGolu3bjh58iQiIiJgZWWFjz76CJcvX4aTkxNGjBhRaqxFXxn+/vvvMXbs2HL7PnBycsKnn36KlJQUODs7o3///iWmsbe3h7GxMZo0aQILCws8fvwYJ06cwOXLl3Hy5ElhG165cgU9e/YsEadUKkW9evUQFBSE/v37Y/LkyahXr16ZMYmtru9zbXN2dsZXX30FpVIpJEbF6m8jMDAQAGBrawuZTFasb5rU1FSkp6cL0xQUFKBjx47F5v/111/x9ttvo3nz5gAgtOAECpO269atw7Vr11BQUIC///4bANCxY0cYGv5TXKr3b1FlHf/t27dHu3bthNcKBgwYIDwVHDBgAHr37l3lbVLT1E/FAcDd3R2TJ08u0ZT+xW1Wlt69e0MikeCNN94QngqfOnUKS5cuBQDY2dmhTZs2xeYp77zRR3PnzsUHH3yAY8eO4dixYwgMDBRurNTngrOzM8LDw4u16Dh9+jTc3NxgbGwMY2Nj7Nmzp9yysbQKjibnHBUnkUiEz/v370dkZCSUSiWMjY1x+/ZtzJ8/HwDQpk0bdO3aFefPn4ejo2Olrz9UOa+//rpwXbC2tsbDhw9x8+ZNODk5oUGDBgAAV1dXoWVmWZYsWYKUlBQ8ffoUffv2RWBgIBo1aiTsFzc3N8yZMwePHz8W9XWzusDPzw/Lly9HUFAQ4uLi4OXlJbS6BCqun1Z0bVDvg4YNG2Lp0qXYu3cvbt26hZSUFLRt2xYA6y9A1esv6v41nz9/Dmtra3z66acl1n/+/Hn89ddfGDlyJIDCB31FH5Q4OjoCAFq2bIlmzZrB0NAQLVu2xKNHj0pdZ1nUy2nVqhW6d+8uLPPF5ZR37Tt9+rTQ0s7JyQlOTk7YvHkz+vfvL1wXhw4dWmzevn37AigsV86cOYNbt26VWQ68+Hu7du0KExMTtGrVSohT3Spz4sSJiIuLE17zrWwdLDs7G8ePH8f8+fNRv3599O/fH9u2bcOsWbNKvV8rysPDAxcuXMCGDRvwxx9/4MGDB8jPz9dsR2ioojIAqN5ywNbWttS4Zs2aBVNTUygUCpibm+Orr74Srgvq5VV0b1l0H7/++uto0qQJgMIkfWX696/oWCnq559/xtOnT4U3cPLz83Ht2jWcPn0agYGBMDAwgEwmw969e3H16tVyj9Gix7S6vn7y5MlS6yxAYf1efd7KZDK0b98eQGE3Jw8fPoS9vT1mz56N27dvY8+ePVodq6Ay9aa6WO/Vu4TgmTNn0KZNG1hZWUGlUkEqlQoV8aLvkt+9excWFhZlLufw4cN4/PgxQkNDARQeDNnZ2aU+Pa5fv77wuW/fvjhw4AAkEgmcnZ2xfPlySCQShIWFIS8vDwEBAfDy8oKdnR1sbGyEJxWurq5YtmwZEhMT4ejoiHr16qF79+7Yu3cvkpOTkZCQIDzBLs+7776LRYsWITc3FxKJpFjFtqCgQFjXW2+9hcOHD2PDhg1ITk4WTna1osk7iUQClUoFhUKBKVOmYPDgwQAKO9lu0KAB6tWrV2qcP/74I06dOoWjR48iKCgIUVFRwtMWMen7PteGBg0aoEOHDvjll19w8uRJfPLJJyUq1CqVChKJRHiFWlNFKwZKpbJYhUehUMDNzQ2zZs0CUHhxVr+ypWZoaFjshlydIFm9ejUyMjLg4eGBgQMH4ueffxZehSi6P0v7W73u0o7/c+fOFZt+9OjR6N+/Pw4fPozFixfjwoUL+Ne//lWpbaBN2dnZSElJwW+//YZNmzZBpVLh0aNHOHDgQLHpSttGpVGXJUX3ibpMUXuxMljeeaNvkpOTkZ+fD3d3d/j7+8Pf3x87duzAzp07AaDY+aFUKottyxfPhdu3b8Pc3LzUsrFBgwY4ePAgBg4cCABCWarJOUf/6Ny5M27cuIEnT56gYcOGcHV1haurq9CnzYuvX6mvrS97/SHNlVavebEsMjQ0LNEJf+fOnYu9djd58mRMnjwZ0dHROHXqVKkJRPV+peLs7Oxw//59/PXXX4iNjS3RnUxF9dOKrg3qc+Ovv/5CcHAwRowYAUdHRzRt2lRoEcf6S9XrL+X1zaVenkKhwNtvv41Vq1YBKHyFUf3KLYBirTLLe7j44jkql8uLTW9sbCx8frEuUVR59wVFl6dSqXDjxo0S57VKpSp2PBSt26hUqgrLgYp+b+vWrdGyZUvs2rULubm5SExMxKFDhypdB4uNjYVKpUJAQAAA4OnTpygoKMDkyZMrvMeIiopCYmIiAgMD0atXL1y9elXUV4aBissAoHrLgcuXL5caV9E+BF+kXl5F95aaHtNq6m37YjlTmeUolUosXrwYnTp1AlDYatbc3By7du0qVpakp6dXeIyWVl8vq86iSZwSiQQ+Pj7Yu3cv9u3bV6JFdE168Xwpa9sDmpXVukbvmlTs2rULBw8eBFD42mubNm1gZmaGV199VTiBjx8/LnTsLZVKSz0YoqOjMXHiRCQlJSEpKQkpKSno3r07fvzxx3LX7+TkhO+++w7du3eHra0tbty4gZs3b6Jjx464desWJBIJJkyYAHt7exw4cEA4wExMTODo6Igvv/wSfn5+AIAvvvgCsbGx8PX1xZw5czR69fDEiRNo0aIFLC0t0bhxY6GZ+IULF4RRCydNmoS0tDQEBQVh4sSJGr/S6ODggB07dqCgoAB5eXkYPnw4zp07V2qcly5dwogRI2BnZ4dp06bB2tpa6BtDbPq+z7XFzc0NS5cuFV5vK6px48a4du0aVCpVqf1clbUPAAgV87S0NDx69AhvvPGG8D/1NszOzoZKpUJERESJ10K6dOmCc+fOCcf7ggULcOjQIRw/fhxjx46Fm5sbbt68iczMzApbghRV1vH/onfffRd5eXkYPXo0Ro8eXav3YWn27NkDBwcHHD16FElJSTh8+DAmTJhQ7Ia4qnr27CmcmxcuXBD6x1Er77zRN/Xr18fSpUuFvmZUKhUuX74sPOVWj9h34MCBEq+q2tnZ4aeffhJak4SEhCArK6vMsnHatGlCi4CMjAy0adOm3HNOnTCkf7Rs2RJeXl6YNm2a8DRfLpcjOTkZBgYGcHBwEJK5GRkZOHv2LLp16/bS1x+qmp49ewrJ2GfPnpW46QYKW+j8/fffiIyMFB6sPn78GKmpqTAwMMBrr72GBw8eCP0PJiQkoGXLluU+gNRnPj4+iIyMhLm5udBqT62s+qm6rNH02pCWloZXXnkFo0ePRpcuXXDw4MFi13vWX6q//tK1a1ecO3dOqPt/++23QrchldGoUSM8ePAAOTk5eP78eZldKVSkvPuCd955R7iW/vzzz5g9ezZ69OiBpKQkoaXUjh07ykwYARClHPD398eCBQvw6quvvnQdLDo6GgsXLhSuJceOHYO5uTkSEhIqvMc4fvw4hg4dCi8vLzx79gy///57pY4zTZVXBgDVWw5Upc5S3jFUWUXv0asymraDgwO2bt0KoPCVZS8vL/z111+ws7NDQkICVCoVsrOzMWLECLRq1arSx2hZdRZN+fn5Ydu2bWjRogWsrKxe+neKqei2V+cPiqqL9V69ayE4fvx4TJ06FZs3b0bz5s3x1VdfAShsdh8REYG1a9fCyMgIy5Ytg0QiERJWTZs2FTr0zMrKQmpqKhYsWFBs2WPGjEFERES5TV7t7e1x//599OjRAxKJBLa2tkJz8w4dOsDW1hZubm6QSCTo06dPsf4bhgwZgrNnzwrv1wcHB+OTTz5BdHQ0pFIpFi1aVOZvNjIygoGBAaRSqdDs3d3dHYmJiXB3d0enTp2E5q4TJkzAzJkz8c0338DIyAgREREabdugoCCkp6fD19cXcrkcfn5+sLe3R9u2bUvE2bFjR3Tr1g0eHh4wMTHB22+/LTSBFps+7vPaoH///pg5c2apozJ98sknmDBhApo2bYru3buXeFXb2toajx8/xpQpU4SnmGoZGRnw9fUFACxbtqzYE98OHTogNDQUo0aNglKphK2tLcaPH19sfisrK8ycORNjx46FUqlEt27d4OfnBxMTE0ydOhX169dH8+bN0blzZyHJoomyjn91P5BqH3/8McLDw2FoaAhTU9MSrW9ru927d5foU+69997D2rVr0bBhQ1HWERYWhvDwcAwZMgSvvfZaiVeGKzpv9ImDgwNCQ0MxYcIEIRnRt29ffPjhh4iLi8PZs2exc+dOmJiYCB1Xqw0aNAgXL16En58flEolRo4ciXbt2pVaNhobG2Pq1KkYNmwYCgoKMHz4cFhZWcHKyqrMc65fv34YP3481q5dW2If6rOIiAisX78eI0eOhEKhQF5eHuzt7bFmzRqYmppizpw5QsfV8+fPh4GBwUtff6hqbG1tMWrUKAQEBKBRo0Zo2bJliWmMjY2xadMmfPXVV/Dx8QFQ2ILAxcUFISEhMDY2xrJlyzBv3jz8/fffMDc3x7Jly2r6p+gMPz8/ODs7lzoaeln1U3VZs2bNGo2uDb1798bWrVvh7u4OlUoFOzs7XLx4Ufg/6y/VX3+RyWRYsGABJk2aBKVSCSsrq2KvQGvKzMwMISEhCAgIQPPmzas0qnNZ9wVz5szBrFmz8MMPP8DExATz589H+/bt8f777yM4OBgFBQXo1KkT5s6dW+ayxSgHBg8ejKlTp2LmzJnFvte0DpaWlobc3FwMGjRI+M7AwACjRo3Ctm3bsHz58nLvMUaNGoWIiAisXr0aDRs2xFtvvVWp40xT5ZUBQPWWA9euXatS7GUdQ5UVFhaGefPmYeXKlSX6UKyM0NBQREREwMPDQ2gJ3LZtWwwfPhzz58+Hl5cXAGD27NkwMzOr9DE6c+bMEnWWZs2aaRxfixYt0KJFC6FcrA1CQkIQHh6OXbt2lTrqc3llta7WeyUqsdv6UrVQKBRYtmwZmjRpgjFjxmg7HKoB3OclqUcHK+8pLBEVcnZ2xqZNm4Q+ZImISDtYf6GqUKlUOHr0KLZSgrygAAADhUlEQVRu3Sq8Zk2ky1QqFe7du4fg4GDEx8cXe72/Nvrwww/h6elZ4eCsukjvWgjqKn9/fzRu3BiRkZHaDoVqCPc5EREREZF+W7BgAQ4fPow1a9ZoOxQiUSQmJiIiIgIRERG1PhkYFhaGmzdvokePHtoOpVqwhSAREREREREREZEe0btBRYiIiIiIiIiIiPQZE4JERERERERERER6hAlBIiIiIiIiIiIiPcKEIBERERFVSkFBAfr06YOQkBDhu7S0NISFhZU6fXh4ONatWwcA8Pb2xqNHj2okTiIiIiIqHROCRERERFQpBw4cQIcOHXDx4kXcuHEDANClSxd8/fXXFc67Z88eNGrUqLpDJCIiIqJyGGo7ACIiIiLSLVu3boW7uzvatm2LjRs34tNPP0VqairmzZuH+Ph4hIeH48GDB8jIyEC/fv2KzWtjY4MTJ04gOTkZBw4cgIGBAdLT01G/fn0sWrQI1tbWePz4MT777DNcvXoVBQUF6NmzJ6ZOnQpDQ1ZdiYiIiMTAFoJEREREpLHr16/j119/haurK3x8fLBnzx7k5uaWmO7p06fYu3cvpkyZUuayTp8+jdmzZyM+Ph5du3bF6tWrAQALFixAp06dEB0djZiYGOTm5mL9+vXV9puIiIiI9A0fsxIRERGRxrZu3Yr+/fujcePGaNy4MVq3bo0dO3agW7duxabr3r17hcvq1KkTmjdvDgDo2LEjDhw4AABITk5GWloadu7cCaAwuUhERERE4mFCkIiIiIg0kp+fjz179sDY2BjOzs4AgCdPnmDz5s3o3LlzsWlNTU0rXF79+vWFzxKJBCqVCgCgVCqxfPlyWFtbAwAePXoEiUQi1s8gIiIi0nt8ZZiIiIiINBIXFwcLCwukpKQgKSkJSUlJOHjwIPLz85GTkyPaevr06YMNGzZApVLh+fPn+Ne//oXNmzeLtnwiIiIifceEIBERERFpZOvWrRgzZgykUqnwXaNGjRAcHIwNGzaItp6ZM2ciPz8fnp6e8PT0xBtvvIGQkBDRlk9ERESk7yQq9bsZREREREREREREVOexhSAREREREREREZEeYUKQiIiIiIiIiIhIjzAhSEREREREREREpEeYECQiIiIiIiIiItIjTAgSERERERERERHpESYEiYiIiIiIiIiI9AgTgkRERERERERERHqECUEiIiIiIiIiIiI98v+0LJdENYnsmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \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  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source vs price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</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>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\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",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(\"Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing of test data similar to train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Jet Airways Business                   2\n",
      "Vistara Premium economy                2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n",
    "\n",
    "# EDA\n",
    "\n",
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "            \n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "            \n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\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",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \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  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2671, 28)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection\n",
    "\n",
    "Finding out the best feature which will contribute and have good realtion with target variable. Some of the feature selection methods:\n",
    "* heatmap\n",
    "* feature_importance\n",
    "* SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_train.drop([\"Price\"],axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\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",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3897\n",
       "1     7662\n",
       "2    13882\n",
       "3     6218\n",
       "4    13302\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:,1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAOXCAYAAAAkX95SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd5QUZdbH8W91mJzzAIIEARFQFAFBRUGSLi5J0B0FE4gkQYJkliQKiKuiLoIKviIKmEBXDBh2zYgICAhIHmDyDJNDh/eP3h0YJzXKTLfw+5wz50xV3aq+T3V1dd9+nqo2nE6nExERERERES9j8nQCIiIiIiIiFVGxIiIiIiIiXknFioiIiIiIeCUVKyIiIiIi4pVUrIiIiIiIiFdSsSIiIiIiIl5JxYqIiIiIiJwTubm5/OUvfyExMbHcsj179tCvXz969OjBtGnTsNls1W5PxYqIiIiIiPxh27dv54477uDw4cMVLp84cSIzZ87kww8/xOl0snbt2mq3qWJFRERERET+sLVr1zJr1ixiYmLKLTt+/DiFhYVcccUVAPTr149NmzZVu03LOc9SRERERETOC9nZ2WRnZ5ebHxISQkhISJl58+fPr3Q7KSkpREdHl05HR0eTnJxc7ePXarFiPNihNh/Oa3089UZPp+A1Gg/+l6dT8BphDcM8nYLXOPhV9SevC0VUPT9Pp+A1AqIDPJ2C19j2SbqnU/Aan7zb2dMpeI12Xb7wdApeY0DRXk+n4JY/w2fjp1sksHTp0nLzR40axejRo93ejsPhwDCM0mmn01lmujLqWRERERERkQoNGTKEvn37lpv/216V6sTFxZGamlo6nZaWVuFwsd9SsSIiIiIiIhWqaLjX71G3bl18fX3ZunUrV111Fe+++y7XX399tevpAnsREREREQ8wTIbX//1RQ4cOZefOnQAsXryYBQsW0LNnT/Lz8xk8eHC166tnRUREREREzplPP/209P/ly5eX/t+8eXPWr19/VttSz4qIiIiIiHglFSsiIiIiIuKVNAxMRERERMQDzsU1Iec79ayIiIiIiIhXUrEiIiIiIiJeScPAREREREQ8QMPAqqeeFRERERER8UoqVkRERERExCtpGJiIiIiIiAdoGFj11LMiIiIiIiJeScWKiIiIiIh4JQ0DExERERHxAMPQMLDqqGdFRERERES8kooVERERERHxShoGJiIiIiLiAbobWPXUsyIiIiIiIl5JxYqIiIiIiHglFSsiIiIiIuKVdM2KiIiIiIgH6JqV6qlnRUREREREvJKKFRERERER8UoaBiYiIiIi4gEaBlY99ayIiIiIiIhXUrEiIiIiIiJeScPAREREREQ8QMPAqqeeFRERERER8UoqVkRERERExCtpGJiIiIiIiAdoGFj11LMiIiIiIiJeScWKiIiIiIh4JQ0DExERERHxAA0Dq94FV6ysHDKDnccP8MQnr3k6lVrx87cneffFXdhKHNRtFErC+CvxD7SWiTl+8BTrlm6nIK8Ek9ngjrFtqN803EMZ/z7+Ha4j4v4xYPWh5OA+Uhf9HWd+nvtxJhMRD47Hv10nDLOZU2+8Qs7GdQD4XXE1EQ+OxzCbsWefIuPZhRQf2AdAzOwn8GncFEdBPgCF27aQ8dzi2mt4NSytO+DffxiG1Yr92EHyXn4cCvPdjgl96l0cmamlsUWbXqf4209Kp42AIIJnLadg7T8p2fpF7TTqdwrp3Jm64x/G5ONDwd69HJ46DUde+WOkqrjov91B1IABGH5+5O/axZGp03CWlJSuG9ypE/UmTmBPn7611i53+be/jrD7R2NYfSg+uJ/0xZW8RiqLM5kIHz4e/6s7gtlM9tpXyH1vPQA+zS4jfMRETH7+YDKR/cbL5H3yLwCsDZsQMWoypsAgnA47GU/Oo3j/nlpte1V82nQk8PbhYLViP3qAnGWP4izIdyvG8A8kePhUzHUagGFQ+O8PKNjwKgDmuhcTPPQRDD9/nE7IW/M8JTu+80QTf7eobp25ZPp4TL4+5Ozay66HpmLPLX/M/E/LpY+Rs2cfR559qcx83zpxtP9wLd90/islGZk1nfY5l7Q9nT1vHsJe4iD0okCuuKcZVv/TH5+OfpXEgY8SS6dtBXYKMovovrgDfqE+pfO/X7oLvzAfWt95Sa3mf67F9epMy7njMfv6cGrnXn54YCq2nMqPi7YrHiN71z72PflSuWXXvPEMBSdT+Gns3JpMWf7kLphhYM3jLmbz2KUMaNPF06nUmpysIv5v8Y8MndWBWSu7ExUfyLsrfi4TU1xoY+nkL7lpUFOmLOtKz4TmrFywxUMZ/z6m0HCiJ80hedZ4jg/5KyUnjxMx7KGzigvuPQBrvQYcv6c/J4b/jZABCfg0b4kRGETMnCVkLFvC8ftvI/3JeUTPXARWV8Hn26I1Jx+6lxNDB3Fi6CCvKlSM4FAC751M3rMzyJ56F47UE/gPeMDtGFPcRTjzcsj5+/2lf2cWKgAB903F8A+stTb9XpbwcC5e8CgHR49hV89eFB07Rt0J488qLqxbN6LvvJN999zL7lv+gsnXj5i77wbA8PWlztiHaPTkEgyzuTab5hZTaDiRE2eT+vcJnLi7D7aTiYTfX/FrpLK4oL+4XiMn7htA0ogEQvon4NOsJQDRsxZzatXznHxgEClTRhI+fAKWuvUxfP2Iefx5Tr2xkpPDb+fUq8uJmvporba9KkZwGMHDp5H95FQyH74De8oJAu8Y4XZMwMBh2NNTyJx4J5nT7sO/W18sl7j2SdB9Eyj8/D0yJ99N7rJHCRk7F0zed2xUxhoZTsunF7D9ntF81aEnBUeO0XTmhApjAy9pRNu3VxHbu0e5ZfED/0q7javxi4+t6ZRrRFF2Mdte2svVI1tw04J2BET7s3v9oTIx9TvFcePsttw4uy2dZ1yJb6iV1glNyhQq+z84Svq+U7Wd/jnnExVO2xcW8O3to/mwVU/yDh2j1fyKj4vg5o24ftMq6vUrf1wANB1/P1Gd2tZkunKeuGCKlZGd+7Piqw2s+/FTT6dSa/ZsTaZB0zBi6gUBcF3vhmzZfAyn03lGTApR8UG0bB8HQOuO8dw3o71H8v29/K++hqK9P2M7fhSAnHfXEtT15rOKC7y2Czmb3gWHHUduDnmfbiKo2y1Y69bHkZdD4Y/fA1By7DDO/Fz8WlyOJa4upoBAoibMpO6L64maNAdTcEgttbp61suuxn7oFxwpxwEo+uxdfDvc5HaMpUlLcDgImvwMwbNfwq/3EDBOnzL8eg/Gfvwg9sSDtdSi3y/k2k7k79xJ0ZEjAKSueZ3I3r3PKi6yz19Jfull7KdOgdPJ0VmzyHj33f+udy0mf38OT55cSy06O/5tr6Fo767Tx/6GdQR27XVWcQHXdiH3zNfIZx8SeNPNYPXh1P8to/BHV6+BPS0F+6lMzNEx+LW9BtuJRAq//xKAgq8/J3XupFposXt8Wrej5MAe7Emub8ULPn4L32u7ux2Tt+pJ8l5dCoApLBIsVpz5uQAYJhNGoOt8YPgFQHFxrbTpXIm88VpO/bST/IOu18Kxl9cQN6D8awbgovsSSHx1HUkbNpWZ7xsXQ8zNN7F14H01nm9NSdmVSXjDYIJiAwBoeGMdEr9NLvM+eqb9HxzDN9iHi2+oUzov7ZcsUnZmcvEN8bWSc02KvelaMrfuJPdX13Fx4IU11L+94uOi8fAEDr28jsQ3N5VbFn19O+K6XcfB5a/XaL5/BobJ8Po/T3O7WElPTwfgxx9/5PXXX6eoqKjGkqoJo994gjVbPvJ0GrUqK6WA8JiA0umwaH8K820U5ttK56Uk5hIS4curi7fy+IhPeWbSl9jtDk+k+7tZouOwpSSXTttSkzEFBWMEBLodZ46Jw56SVGaZJSqWksQjmPz88W97DeAa7mK9uDHmyCjM4REUbP2OtCXzOD50II6CfKImza7h1rrPiIjBkZFSOu3ITMUICAK/APdiTGZKdm8ld8lEch8bg7Xl1fje1A8AS4u2WJpdTuHb5bv1vZE1Lp7ipNPPb3FSEubgYEyBgW7H+V58MdbISJqsWM6lG94lfvQo7Dk5AJzavJnEBY9VOUTGk8zRsdhTT7fLXslrpKo4S3QstjOXpSVjiY6FkmJyP3indH7QLf0x+QdQvHsn1noNsGemETlhFnHPrSZm4T+9qufJFBmLI/30OcGRnoopIAjDP8D9GIed4JGziFj0KiW7t2E/8d9C76UnCPjrXUQ8+w6h058i58VF4LDXTsPOAb86cRQeP/18F51IwhoSjDmofE/qL5PnkvTme+XmFyWlsP3u0eQfOFyTqdaogowi/CN8S6f9wn2xFdixFZZ/LotySjjwYSItb298ev3MIna+9itXDWvuFR/6/qiAenHkJ54+LgoSk7CGBmMJLn9c/DR2LsfeKH9c+MXHcPkT0/j+7gk47X+e14R4jlvFypw5c1i8eDEHDhxg3LhxbNu2jRkzZtR0bvIHOSr55sd0xgnTbnOw6/tkOt3SkEee60LnPo15burXlBT/iU4gJgMqaqvD4XacYZR9KRiGAQ47zvw8kmeMIzThPuqsWEtQ994UbtuC01ZC0Z6dpMwchz01GRwOslY9T0CH68DiHZeC/bZNpc7YL1XFFP/7PQpeewqKC3EW5FL40VqsV16HERFDwO0jyXthPjj/HIWtYTK5dYxUFWdYLIR06sjBh8byS/8BWELDqDNubA1lfI5V2i67+3Hllhk4f7P/Qm6/h9Ahw0mZ/hDO4iIwW/Bvdy05771J0ogEct55nZhHl5YOo/Q014fH8u09s13uxOQ8O5u0oTdjCgohoP89YPUh5KG55PxzHhkj+5D19xEED52EKTKmJppRI9x9zZz3KnkfrajwOPLFCeLaRBIY4w+Aw+Zg67I9tLyjMX5hvuXi/4wqOy6cbn7JaVgstH/lCbZPXEBhUmr1K4jg5gX2P/30E2+++SbPPvss/fv3Z8yYMfTv37+mc5M/KCImgMN7Tl/MmJVWSECwFd8zLgwMjfQjrn4wDS+NAODyTnV4bcmPpJ/MI66B9wxp+q2we0YQ0LEzAKaAIIoP7S9dZomOwZ59CmdhQZl1bMlJ+F7aqsI4W8pJzJHRpcvMkdHYUpPBMHAW5JM07v7SZfVe2YDt+DF8W7XBHBxC/tf/u7DcAIfTa97MHenJmBtdWjptCo/CkZsNxYVuxfhc0x37sV/PGOZlgN2Gz9U3gI8vQQ8vBMAcUxfTwAcxgkMp/nxDbTTNLfFjRhPWxXWNmjkoiIJ9+0qX+cTGYsvKwlFQ9hgpPnmCwMtbVxhXkpJK5kcfl15sn75hA3VGlr2+wZuE3v0gAdfcAIAREEjJGa8Rc9T/jv3CMuvYU07i27xlhXG25PKvEXvqf3scrFaiJs3B2qARSaOHYE8+4dpeeiolRw9R/IvrWrmCrz+H8TOxxNfDdrTsuH9PsKclY2lyWem0KSLadfwXFboVY23dHvuxAzgy06CogMKvP8a33Q1YLmqE4eNH8Y9fA2D7dRe2xENYmlxGcfrpnkxv03jyGKJ7uF4zluAgcvecfs34xsdSkpmFPb+gstXPS/6RfmQezCmdLswswhpoweJbvofw+PeptEpoUjqddTiHvNRCfn79AABFp4pxOsBe4qDNPc1qPvlzpMXMMdT5y3+Pi5Agsn8+fVz4142lOMP94yL8qpYENryIyxe6hsz6xUZhmM2YfX3Z+uD0c5+8nBfc6lmx2Ww4nU42b97M9ddfT2FhIfn5+dWvKB516VUxHN6TQUqiawz1lxsP0rpj2TGzLdrFkZ6Ux9F9rqJm/440MAwi4737oumsl58rvaj9xMi78Lu0NZa69QEI7n0b+V99Xm6dgh++qTQu/6vPCe7VB0xmTIHBBHbpSf5Xn4HTSeyCZ/Fp2gKAwBt74CwuovjAPkz+AUSOnlx6nUro7XeT9++PvaZYKdm1BUujFphi6gLgc8OtlPz0ldsx5roN8etzr+s6FasPvl37Uvz9ZxR9uJbsyX8rvejedngvBWuf96pCBeDk08+wp09f9vTpyy8DBxF4+eX4NmgAQNTtt5O1ufz1a9lfflVpXOaHHxLeqyeGr+sb0rCbupK38+dy2/AWp1a6Lng/+cAgkkbfhW+LM4/9Aa7C4TcKfvim0riCrz8n6L+vESMwmMAbe7heI0DUlEcxAoJIGnO6UAEo+P5LLHF18bnEVRD7troSnGA7ebymmn1Wind8j7XJZZjj6gHgf1Mfin/4j9sxvtd0IaD/va5AixXfDl0o2bUVe1Kia+hcU1fhZ4qti6VuQ2yH9+HNDjz2NN/e2Idvb+zD9z0HEnrV5QQ0cr0W6t19OykfbPZwhrUv5rJwMg9mk5vs+sxz+PMTxF0RWS6uOK+EvJQCIhqf/pIvokkoPZ7oUHrx/cU31KFuu+g/VaECsHvO03zSrg+ftOvDZ9cNJKLd5QQ1cR0XjYbezomN7h8XGd/9xL+a3FC6vYPLX+fY+n9d0IWKYRhe/+dpbvWs3HrrrVx33XW0atWKK664gltuuYWBAwfWdG7yBwWH+3HnxKtYMec7bDYH0fGBDH6kLUf2ZrJ6yY9MXdaV0Ag/hs2+htef/oniQjsWq4mhs9pj9fGeceXVcWRlkLpwJjGzF2NYrNhOJJK6YBoAPk1bEDVxFieGDqoyLvvdtVjq1KPui+swLBayN66ncPtWAFLnTyZqwizXrX3TU0me4Rr6U/D9V5x6aw3xz6wCw0TJof2kLfaea1acOVnkvfQYgSPnYJit2FOPk7/iUcwXNyPg7onk/P3+SmMACjasJCBhLCFzXwazheItn1P87/Ljj/8MbBkZHJ4ylUZPP4VhtVJ09BiHH3kEgICWLWkwby57+vStMi71tdewhIZy6VtvYpjN5O/azZHHZnqyWW5zZGWStnAW0bMWYVislJxMJP0x14cDn6YtiBw/i5MPDKoyLmfDOix1LiJ++VoMi5Xc99ZTtGMrPi1aE9i5GyXHDhP31KrSx8xc/g8Kf/iG1JnjiHhoqusWviXFpP79YSjxjovNndmZ5PxzPiHj5oPFij35ODnPzsHSqDnBwyaTOfnuSmMA8v7vGYLun0T4Itftiou2fEHBB2vB6ST7iSkEDRmHYfXBabeTs/xxHMneUaS5ozgtg11jpnD5S09j+FgpOHyUnSNcr4WQK1rS4sl5fHtjHw9nWfN8Q3xoc28ztjy7G4fdSWC0H1fe35zMQzn8tHIvN8523c0qL6UAvzAfTJbz+75FRakZ/DBsCh3WPI3Jx0rewaN8f6/ruAi/siVX/XMen7Q7/48LqV2Gs7JbWvxGSUkJRUVFmEwm8vLyiI6Orn6l3z7Ygx3Oep3z0cdTb/R0Cl6j8eB/eToFrxHWMMzTKXiNg18lVx90gYiq5+fpFLxGQHRA9UEXiG2fpHs6Ba/xybudPZ2C12jXxbt/76o2DSja6+kU3BIyu5unU6hW9qyPPfr4bvWs7N27lylTpnD06FGcTieXXnopCxYs4KKLLqrp/EREREREzkvnw13iappb/ZVTpkxhxIgR/PDDD2zdupWEhAQme+nvCYiIiIiIyPnBrWLF6XRy002nf0yuV69e5OV55+8JiIiIiIjI+cGtYWCdOnXihRde4LbbbsNisfD+++/TuHFjkpNd48pjY2NrNEkRERERkfONhoFVz61iZePGjQCsXr26zPyBAwdiGAaff/75OU9MREREREQubG4VK198obtLiIiIiIhI7XKrWMnMzGTevHl8++232Gw2OnTowMyZM4mMLP/DSCIiIiIiUj0NA6ueWxfYz5o1i2bNmvH+++/zwQcfcOmllzJt2rSazk1ERERERC5gbhUrR44cYdiwYYSFhREREcHw4cNJTEys6dxEREREROQC5tYwMMMwSE5OLr3rV1JSEhaLW6uKiIiIiEgFNAysem5VHKNHj2bgwIG0adMGp9PJtm3bmDlzZk3nJiIiIiIiFzC3ipWuXbvSqlUrduzYgdPpZNq0aURERNR0biIiIiIicgFz65qVO+64g5iYGG666Sa6detGTEwMffv2rencRERERETkAlZlz8rdd9/Nli1bsNvttGzZEgCn0wlA586daz47EREREZHzlK5ZqV6VxcrKlSux2WzMnz+f6dOnl843m801npiIiIiIiFzYqh0Glpuby8MPP4zZbCYlJYVXX32VLVu21EZuIiIiIiJyAauyWPnqq6/o1asXv/zyC+np6fTr14+ffvqJxx9/nPXr19dWjiIiIiIi5x3DZHj9n6dVOQzs6aefZvXq1TRq1IgVK1bQpEkTnnzySXJyckhISGDAgAG1laeIiIiIiFxgquxZKSgooFGjRgB8++23dO3aFYDg4GAcDkfNZyciIiIiIhesKosVp9OJ0+mkoKCArVu30rFjR8BVxBQWFtZKgiIiIiIi5yNPD/H60w8D69q1K6NGjcJut9OsWTOaNm3Kvn37eOaZZ+jevXtt5SgiIiIiIhegKouVsWPHsnHjRtLS0ujXrx8A//nPf2jUqBFjxoyplQRFREREROTCVGWxAtC7d+8y0/fdd1+Z6REjRvDcc8+d26xERERERM5z3jDMyttV+zsr1Tlx4sS5yENERERERKSMP1ysGIYqQhEREREROfeqHQYmIiIiIiLnnr70r94f7lkRERERERGpCX+4WHE6neciDxERERERkTL+cLHy27uFiYiIiIiInAtVXrPSvXv3CsfSOZ1ODMPgww8/LHcrYxERERERqZ5uXVy9KouVFStW1FYeIiIiIiIiZVRZrNSvXx+A4uJivvzyS/Lz83E6ndjtdhITExk1alStJCkiIiIiIhcet25dPGHCBNLS0khMTKRNmzZs2bKFtm3b1nRuIiIiIiLnLQ0Dq55bF9jv3r2b1atX0717d4YPH86aNWv0y/UiIiIiIlKj3CpWIiMjMQyDhg0bsnfvXho0aEBxcXFN5yYiIiIiIhcwt4aBNWnShPnz5zNw4EAmTZpEenq6fl9FREREROQP0DCw6rlVrMyePZutW7dyySWXMGLECL7++msWLVp01g/28dQbz3qd81G3Rz/zdApeY/XnhZ5OwWvE7ErydApeo9mVgZ5OwWsUZhZ5OgWv4RNi93QKXsPfXx9w/mf05Vd6OgWvsc36hadTEDnn3BoG9vjjj9O+fXsAunXrxqxZs1i1alWNJiYiIiIiIhe2KntWZsyYwfHjx9m+fTsHDhwonW+z2cjMzKzx5EREREREzlcmt7oNLmxVFitDhw4lMTGR+fPnM3To0NL5ZrOZJk2a1HhyIiIiIiJy4aqynqtfvz4dO3bk/fffJyYmhsOHD3PgwAFCQ0OJiIiorRxFREREROQC5Fbn03vvvcfQoUM5cOAAhw4d4sEHH+Stt96q6dxERERERM5bZsPw+j9Pc+tuYMuXL2f9+vWlvSkjR45k8ODB9OvXr0aTExERERGRC5dbPSsOh6PMsK+IiAgML6i0RERERETk/OVWz0rTpk15/PHHGTBgAADr16+nadOmNZqYiIiIiMj5zKwfhaxWlT0rb7/9NgBz587F6XQyfvx4xo0bh8PhYPbs2bWSoIiIiIiIXJiq7Fl55ZVX6Nu3LwEBAUyePLm2chIREREREXHvmhUREREREZHaVmXPyv79++natWu5+U6nE8Mw2Lx5c40lJiIiIiJyPvOGWwN7uyqLlQYNGvDCCy/UVi4iIiIiIiKlqixWrFYrdevWra1cRERERERESlVZrFx55ZW1lYeIiIiIyAXFrKvHq1XlLpo5c2Zt5SEiIiIiIlKG6jkREREREfFKbv2CvYiIiIiInFu6G1j11LMiIiIiIiJeScWKiIiIiIh4JQ0DExERERHxAA0Dq556VkRERERExCupWBEREREREa+kYWAiIiIiIh5gNmkYWHXUsyIiIiIiIl5JxYqIiIiIiHglFSsiIiIiIuKVdM2KiIiIiIgHmHXJSrXUsyIiIiIiIl5JxYqIiIiIiHglDQMTEREREfEA3bq4eupZERERERERr6RiRUREREREvJKGgYmIiIiIeIDZ0DCw6qhnRUREREREvJKKFRERERER8Urn5TCwn789ybsv7sJW4qBuo1ASxl+Jf6C1TMzxg6dYt3Q7BXklmMwGd4xtQ/2m4R7K2DNWDpnBzuMHeOKT1zydSo2qc3NnLl8wHrOvD1k79vLtfVOx5eRVGt9h5WNk7dzHL0+8VDqvX+q3FCQmlU7vWfQih1/bWKN517TImzrTZPp4TD4+5O7ey+6xU7HnVr5fWjzzGLl79nH0uZcqjfEmfu2uJfSe0RhWKyWH9pPx5Byc+XnuxZhMhA19GL+214DZQs76V8j715sAmIJCCBsxCWv9Rhi+vmS//hL5m98HwL/jjYTcNRwcDhw52WQ8NRf7ycRab3t1AjpdT9SDYzF8fCj6dR8p82fgyCv/3FcWZ/j6Ej1xOn4tWoFhULhrB6mL5uEsKipd1xQcQv1Va0lbuoTcTz+qzea5zfeqTgTf+SCG1YeSI79yaul8nAV57sX4+BI6bCLWS1oABiX7d3HqhUVQfHofGIHBRC1eRc4rSyn85tNabt3ZC+/SmYaTH8bw8SFvz172T5xW4Tmh0jiTiSbzZhDa/moAMj77N4fmLQTAEhZK4znTCbikMSY/P449809S3tpQq+37vb79z0FeXPoVJSV2GjWJYvzMbgQG+ZaJ+eeSL/jik/2EhPoBUK9BODMeu6V0eW5OIePuX8eEWd1o1iKuVvM/12J6dKbFbNd7R/auvfw0our31DbLHiN71z4OPO167zD5+dJ6ySzC2rrOH1lbdrDj4dk4Cosq3cb5THcDq95517OSk1XE/y3+kaGzOjBrZXei4gN5d8XPZWKKC20snfwlNw1qypRlXemZ0JyVC7Z4KOPa1zzuYjaPXcqANl08nUqN840Kp8PLC/iy/2jea96T3IPHuOKxCRXGhjRvRJfNq6g/oEeZ+cFNG1KckcUHbfqU/v3ZCxVrZDiXPbWAHfeM5puOPSk4cowmMyreLwGXNOLKN1cR+5ceFS73RqbQMCIe/jvpcyeQdH8/bCePE3bPaLdjgm7uj6VufZIeGEjymNZRyAIAACAASURBVDsJ7vs3fJpeBkDEhNnY01JIHvU3Uqc8SPjwiZijYjB8fImYNI/0ORNIHnkHBd/9m/AHJ9Z626tjDgsndvo8Tk4Zy5GBf6HkeCKRIx4+q7iIux/AMJs5mtCXowl9Mfn6ET5kaJn1Y2c+iikouFba9HuYQsIIHT2dzIVTSB01EHvScYLvGuF2TNCAu8FkJm1sAmnjEjB8fAnqP6TM+mFjZmIKDKqtJv0h1ohwmj7xKLuHjWHrDb0oPHqMi6eMP6u4mP5/xb9RQ7Z2u5Ufe/QhtMPVRN3iOm80XbKAopNJbOvVj5133EOj2dPwiYut1Tb+HlmZ+Sye/RGzFv2FlW/dTXy9UFY882W5uF07TjJ9wc0sW3Mny9bcWaZQ+e7LQ4wa8jqJRzJrM/Ua4RMVTpt/LmBLwmg+vbIneYeOcemcit87gpo14pr3VxHfp+x7R9NJD2JYzHze/lY+b38rJn9fLpnwQG2kL39S512xsmdrMg2ahhFTz/UGcV3vhmzZfAyn03lGTApR8UG0bO/6dqN1x3jum9HeI/l6wsjO/Vnx1QbW/ej93/T9UfHdryV9y05yfj0CwP7n13BxQu8KY5uOTODAinUcXbepzPzojm1w2h3c9O/V9Nq+gZYzRmKY/twvncgbriX7p50UHHLtl8SVa4jvX/F+uejeBI6vXkfyxk0VLvdGfldeQ/G+XdhOHAMg9/11BHTp5XaMf8cbyft4AzjsOHNzyP/iIwK63owpKATfNu3JXv0CgKtoGTsYR042/PeYMP774dTk74+zuLhW2ns2Atp3pGjPz5QcOwrAqbdeJ7jnLWcVV/DTD2S8tAycTnA4KNq7B2tcndJ1I+4dTvGBfRT9uq8WWvT7+FzRnpL9e7CfdD3/+Zvewv/6nm7HFO/6idz1L5Xug5KDezFHn/7GPOi2e7EdPUDJkV9rqUV/TNj1ncjdvpPCw65zwsn/e52YPuXPCVXFGSYTpgB/TD4+GD4+GFYrjqJiLGGhhF3XkaNPPgtAcVIy228dhC3rVC217vfb+s0RmraIo15918iL3gNas/mDX8p8pigutvHr3hTeWPUD9w98hb9P3EjyyezS5W+/vo3Jc3sSERVY6/mfa9FdriVr607yDrie/8Mr1lBvYMXvHQ2HJXB05TpOvF32vSP9yy3sW/h86Wsne/seAi6qU+E2RMDNYqW4uJjnn3+eSZMmkZuby9KlSyn2wjdhgKyUAsJjAkqnw6L9Kcy3UZhvK52XkphLSIQvry7eyuMjPuWZSV9itzs8ka5HjH7jCdZs8c5hGedawEVx5B87PXwrPzEJn9BgLMHl3zR+GD2XI2veKzffsJhJ+uRrPut5P59cn0B8j2tpOvquGs27pvnWiaPwxOn9UnQiCUtIMOag8vtl75S5JL9Vfr94M3N0LPbU5NJpe2oKpsBgjIBAt2LKLUtLxhwVi6XORTgy0gjul0DMEy8R+/SrWJs0x1lUiLOwgMxnHiV2ycvUWf0hQb0HcerFp2unwWfBEhuPLfn0c29LScYcFIwpMNDtuPzvvqbkmOvDiiUunrDb7yJ384cABLS7Bv82bUl/YWkttOb3M0fFYk8/4zlOT8EUGIThH+hWTPH277D/t9A1R8cR2Pt2Cr/eDIDP5e3wuawNOWteqKXW/HG+deIpOvOccLLic0JVccnr3sZ2Kpt2W76g/db/UHj4KBmffIbfxfUpTk6l7rC7af3Wa1zx/nqCWrXAUVhYa+37vVKSc4iJO907Fh0TTH5eMfl5pz8Dpafm0ebqi7hnREeWv3EXl7aKZ+b4DaUFzWNL+9H8sj/30K//8a8XR8Hx089/4fEkrJW8p+4cP5fj68q/d6R++hV5vx52be+iOjQaOaRcQXMhMRve/+dpbhUrc+bMoaCggN27d2M2mzl69ChTp06t6dx+F8cZ33acyXTGmEC7zcGu75PpdEtDHnmuC537NOa5qV9TUmyvrTSltphMrm9vfsN5FsXpgRXr2DpmHvb8AkpO5fDLkpep1/emc5llrTMq2y+O86RoNypuH3a7ezHllhngsIPFgiW+Ho78PFLG30v6gimEPzAea5NLsV7chJCEYZx8YAAnEnqQ/fqLRM5YdM6b9ocZRplvhf+n3GvCjTjf5i2ot+z/yFr3GnlffYElNp6ohyaRNGsyePuxZBgVP/8O+1nFWBo1J3L+MvL+tY6iH77CFBVLyD0PkfWPWd6/D87g6i2u/rioKq7BuJGUpGfw3ZXX8n27zljCQqk77B5MFgv+DS7CnpPHjn5/45eRD9No5mSCWl1WQ605d1xPf/lPaybz6Y9P8XVDefTpvjRsEoVhGAy86ypOJp4i6UR2ufX+7Cp97/gdX/iGXnEZnT5azaFlr5K86fNzkJ2cr9y6wH7Xrl28/fbb/Pvf/8bf35/HH3+c3r0r7vbztIiYAA7vOT0uNCutkIBgK77+p5saGulHXP1gGl4aAcDlnerw2pIfST+ZR1yDkFrPWc6tVrPHUO9W1/U41pAgsnaeHoriXzeWoows7PkFbm/v4jv/Stb2X8jaudc1wzBwltiqXskLNXpkDNE9XPvFHBxE3u7T+8U3PpaSzCwcZ7FfvJk9NQnf5i1Lp81RMdhzTuEsKnQrxp6ahDky+vSyyGjsqSnY01MByPvIdWGw7eQxinb9hE+zyzD5+VO866fSC+pzN64lbNh4TCFhOLKzarS91YkYNoqg624EwBQYSNGB/aXLLNEx2E+dwllY9rm3JZ/Er2XrSuOCuvUiZuIMUhfPJ+cj1w0Ggrp2x/Dzo+5TywCw1quPdfR4zKFhnHp7bY228Ww50pLxaXrG8x8ZjeM3x0h1MX7XdiN02EROLV9M4X9cvdX+Hbti+PoRMfMp1zpx9QgeMhpTSCj5H75dG01zW4Pxo4no9t9zQlAQ+XvPOCfExVKSlYWjoOxxUXj8BMFtWlcYF9mrGwdmzMdZUoK9pITk9e8QdXMP0j5w7ZvktW+5tnH4KKe2/EjwFa3I3bmrppv5h8TEBbPn55Ol02mpuQSH+OLvf/qmPQf3p3JgXyrdbmlROs/pdGKx/LmHC/9Ps+ljiLvZdZxYgoPI3nX6OPGrE0vxWb6nAtQZcDOtl8yqtPdF5ExuvZIMw6C4uBjjvz9ck5mZWfq/t7n0qhgO78kgJTEXgC83HqR1x/gyMS3axZGelMfRfa6iZv+ONDAMIuP//ONJBXbOerr0QvgPOwwkssPlBDdpAMAlw28n8d3NZ7W9sJaX0GrOGAyTCbOfL01HJXDkjX/VROo16uDjT/Ndlz5816UPW3oNJKTt5fg3dO2XukNuJ3XT2e0Xb1a49Rt8mrfCUuciAIJu6U/hN1+4HVPwzecEdv8rmMwYgUEEdO5BwTefYU8+QfH+PQTe5PqyxhQWgc+ll1OyfzfFv/6Cb+urMIW5vgTxv+YGbMknPF6oAGS8sJSjd/Xn6F39OXbf3/Br2RrrRfUBCO03iLz/lL9+Lf+7ryuNC7z2BqIfnsLxMUNLCxWArNdWcaR/r9LHKtzzM2nPPOF1hQpA0U/fYW3aEnO86/kP6NGPwu//43aMb9trCbn/YTJmjyktVADyNrxG6oP9SXv4LtIevouSA3vIWfWM1xUqAEeeeIZtPfuyrWdftv91EMFtLsfvYtc5If7O20n/qPxxkfXvryqNy925m+jermt6DIuFyG43krPtJ4qOHSdnxy5iBvQBwBoVSUjbNuTs+Lnc9r3NVR0asGdnEolHXZ8XNq7fQcfOjcvEGIbBs4s+5+Rx1zU4G9btoNElUUTHeu8NJs7G3nlP80XHPnzRsQ//6TKQiHaXE9jY9fxffN/tJL1/du8dsb1upNWi6Xzz1/tUqOC6G5i3/3maWz0rgwcP5p577iE1NZX58+fz8ccfM2rUqJrO7XcJDvfjzolXsWLOd9hsDqLjAxn8SFuO7M1k9ZIfmbqsK6ERfgybfQ2vP/0TxYV2LFYTQ2e1x+pj9nT6co4VpWbw3T1TuHb905h8rOQeOMo3gx8BIOKqlrRfMY8P2vSpchs7Zy+l7dKZ3LxzIyarhaPrNnFgxbraSL/GlKRlsHvMFFq/9DQmq5X8w0fZNcq1X4Ivb0mLJ+fxXZeq94s3c5zKJGPJ34mcvgjDYsV2MpGMRTOwXnIpEWNnkjzyjkpjAHLfW48l/iLinn8dLFby/vUmRTt/BCBtznjCR04m6C8DwDCR/dpyivftBiBn/SvELFyO01aCI+cUabPHeWwfVMaemUHy3OnEL/gHhsVCyfFjJM12Dev1bX4ZsdPmcPSu/lXGRY2ZAIZB7LQ5pdst2LGN1EXzPNKm38NxKpNTz8wlfOICDKsFW9Jxsp6ajbVxc0JHTiPt4bsqjQEIuXsMYBA6clrpNot/2UH2C1449M8NJekZ7Bs/lUuXPYXJaqXgyDH2jXOdE4Jat+SShXPZ1rNvlXEHZz9G43kzuOqzf+G028n66lsSn38RgD1DR9F43kzi77odw2Ti6D+eI3e79xcr4REBTJzVnTmT3sNW4iC+XiiPzOnJ3t1JLJn7CcvW3EnDJlGMmnQjM8a9i8PuJCo2iKnzb/Z06jWiODWDbcOn0PZV13tq3sGjbBvmev5D27Tkimfn8UXHqt87Lpv/CBgGVzx7+nyR8e2P7Hx4ThVryYXMcFY0KLkCv/76K9999x12u5327dvTrFmzs36wT45NOet1zkfdHv3M0yl4jdX//PPfyvFciYmuPuZC0exK9XL+T2HmhfnbAxUJqvPnuA1wbfj1B++/k1ZtuXjPWE+n4DW2xT3p6RS8xq25ez2dglu6vpng6RSqtbn/ao8+vlvDwPbu3cuzzz5LQkICnTp1Ys6cORw8eLCmcxMRERERkQuYW8XKjBkz6Nu3LwCNGzdmxIgRTJs2rZq1RERERESkMmbD8Po/T3OrWCkoKOD6668vne7UqRMFBefHXYNERERERMQ7uVWsREREsGbNGvLy8sjLy2PdunVERkbWdG4iIiIiInIBc+tuYAsWLGD27NksXLgQq9XK1Vdfzfz582s6NxERERGR85Y3DLPydm4VK3Xq1GHZsmU1nYuIiIiIiEipKouVBx54gGXLltGlS5cKfwRy8+bz50fkRERERETEu1RZrMydOxeAf/zjH7pGRURERETkHDK7dfX4ha3KYiUmJgaARx55hA8++KBWEhIREREREQE3r1lp3rw577zzDq1bt8bPz690fp06dWosMRERERERubC5Vaxs376dHTt24HQ6S+cZhqFrVkREREREfqfz7W5gGzdu5Pnnn8dmszFkyBASEhLKLN+1axczZ86kpKSE+Ph4Fi1aREhISJXbrHKkXHJyMuPHjycwMJBOnTrxzjvv8Omnn/Lpp5+qUBEREREREcBVNzz55JO89tprvPPOO7zxxhv8+uuvZWLmz5/PmDFj2LBhAw0bNuTFF1+sdrtVFitTp04lJiaG8ePHU1JSwoIFC/5YK0RERERE5Lzz9ddf06FDB8LCwggICKBHjx5s2rSpTIzD4SAvLw+AgoKCMpeXVKbKYWDJycmlFU+nTp3o06fP781fRERERETOYDZ5/zCw7OxssrOzy80PCQkpM4QrJSWF6Ojo0umYmBh27NhRZp3Jkydz77338uijj+Lv78/atWurffwqixWr1Vrm/zOnRURERETk/LZq1SqWLl1abv6oUaMYPXp06bTD4Sjzu4xOp7PMdGFhIdOmTWPlypW0bt2al19+mUceeYQXXnihysd36wL7/6nohyFFREREROT8NGTIEPr27Vtu/m8vjI+Li+OHH34onU5NTS39GRSAffv24evrS+vWrQEYNGgQTz31VLWPX2Wxsn//frp27Vo6nZycTNeuXUsrJV1kLyIiIiJy/vrtcK/KdOzYkWeeeYaMjAz8/f356KOPSn9gHqBBgwYkJSVx8OBBGjVqxObNm2nVqlW1262yWPnwww/daIKIiIiIiJyt8+nWxbGxsYwbN47BgwdTUlLCgAEDaN26NUOHDmXMmDG0atWKBQsWMHbsWJxOJ5GRkTz66KPVbrfKYqVu3brnrAEiIiIiInL+6t27N7179y4zb/ny5aX/d+7cmc6dO5/VNqu8dbGIiIiIiIinnNUF9iIiIiIicm6Y1W1QLe0iERERERHxSipWRERERETEK2kYmIiIiIiIB5xPdwOrKepZERERERERr6RiRUREREREvJKGgYmIiIiIeIDZpGFg1VHPioiIiIiIeCUVKyIiIiIi4pU0DExERERExAN0N7DqqWdFRERERES8kooVERERERHxSipWRERERETEK+maFRERERERDzCr26Ba2kUiIiIiIuKVarVnpfHgf9Xmw3mt1Z8XejoFr5EwPNzTKXiN3d9keToFr+G0Oz2dgtewBKgD/H+Ks4s8nYLXqNPA6ukUvEZRt6WeTsFrtLraz9MpiJxzehcUEREREfEA3bq4ehoGJiIiIiIiXknFioiIiIiIeCUNAxMRERER8QCzRoFVSz0rIiIiIiLilVSsiIiIiIiIV9IwMBERERERDzDpbmDVUs+KiIiIiIh4JRUrIiIiIiLilTQMTERERETEA3Q3sOqpZ0VERERERLySihUREREREfFKGgYmIiIiIuIBJg0Dq5Z6VkRERERExCupWBEREREREa+kYkVERERERLySrlkREREREfEA3bq4eupZERERERERr6RiRUREREREvJKGgYmIiIiIeIBJ9y6ulnpWRERERETEK6lYERERERERr6RhYCIiIiIiHqC7gVVPPSsiIiIiIuKVVKyIiIiIiIhX0jAwEREREREP0M3AqqeeFRERERER8UoqVkRERERExCtpGJiIiIiIiAfobmDVU8+KiIiIiIh4JRUrIiIiIiLilVSsiIiIiIiIV9I1KyIiIiIiHmAydNFKddSzIiIiIiIiXknFioiIiIiIeCUNAxMRERER8QDdurh6f7pixb/DdUTcPwasPpQc3Efqor/jzM9zP85kIuLB8fi364RhNnPqjVfI2bgOAL8rribiwfEYZjP27FNkPLuQ4gP7AIiZ/QQ+jZviKMgHoHDbFjKeW1x7DT9LdW7uzOULxmP29SFrx16+vW8qtpzy++l/Oqx8jKyd+/jliZdK5/VL/ZaCxKTS6T2LXuTwaxtrNG9PWTlkBjuPH+CJT17zdCrnXNB1nYke/TCGjw9F+/dy8u/TcOSVPxYqizMFBRE/az4+DRtiGCZObXyH9JUrAPBp1Jj4GXMwBQSA00nKU0vI++bL2m5ihfzaXUvYfaMxrD6UHNpP+hOzy50rKo0xmQh74GH823YEs5mc9f9H7nvrAfBp2oLwERMx/PzBZCL7jZXkb/4XAAFdbybktiGAE2dhIZnPLaR43+7abnqFavLcaW3QiKjxMzH8/cEJmcufomDL1wCE3DaY4Jv74LTbcGRlkrZkLrYTibXa9t/yb38dYfe7nvfig/tJX1zJvqgszmQifPh4/K92HR/Za18pPT58r2hL+LBxGBYLzqIiMpYupHjvzwAE33YXQT37gN2GPSuTjCfnYTvp2X1xpoBrriPigbEYPlaKD+wnZcHMCvdLdXHmmFjqLVvNsbsH4DiVBYD14kZET5qFyd91rkj/5z8o+P7rWmvb2QroeD2RD47FsFopPrCP5PmV7ItK4gxfX6InTMf30pZgGBTt3knq4nk4i4qw1ruI6IkzMYeFY1itZG98i6w1qzzQysp56nwRfu9IAm/sgaOwgKJd28l4djHOkuJabbt4nz/VMDBTaDjRk+aQPGs8x4f8lZKTx4kY9tBZxQX3HoC1XgOO39OfE8P/RsiABHyat8QIDCJmzhIyli3h+P23kf7kPKJnLgKrFQDfFq05+dC9nBg6iBNDB3l1oeIbFU6HlxfwZf/RvNe8J7kHj3HFYxMqjA1p3ogum1dRf0CPMvODmzakOCOLD9r0Kf07HwuV5nEXs3nsUga06eLpVGqEOTyc+NmPkjhhDAf79KI48RgxD40/q7joEQ9hS0ni0IBbOZRwG2ED78C/9RUAxE2dRdY7b3JoUF9OzJpG3YVPgtlcq22siCk0nMgJs0mbM5GT9/bFdjKRsPvGuB0TdEt/rPUacHLobSSNupPgvn/Dp9llAETNWkzWqn+SNPx2UqeOIvyB8Vjq1sdSrwHhQ8eSMnUkScNv59RrK4ia5R3niZo8dwJEjp1KzgfvcGLoINIWziJm5kIwmfG7sj3BN/fhxMi7OHH/QPL+s5noSXNqte2/ZQoNJ3LibFL/PoETd/fBdjKR8Psr3heVxQX9xbUvTtw3gKQRCYT0T8CnWUuwWIievpD0JXM4OWwQp1YvJ2rKPAD8rmxPUK8+JI0ezMlhg8j/8lMiJ82u1bZXxRQWTszUuSRPH8exv91KyYlEIh8ce9ZxQT17U3fpSizRsWXWix4/nZz33ybxnttIWTCT2DmLveJcURFTWDgx0+aSNGUsR2/vTcnxRKJGjDuruPAhw8Bs5thd/Th2Vz8MH1/CB98PQMz0+eR+soljQwaQODSBkD634X9Vu1ptY1U8db4I6vlX/K+5nhPDEzgxdBD29DTC7xtVq20X7/SnKlb8r76Gor0/Yzt+FICcd9cS1PXms4oLvLYLOZveBYcdR24OeZ9uIqjbLVjr1seRl0Phj98DUHLsMM78XPxaXI4lri6mgECiJsyk7ovriZo0B1NwSC21+uzFd7+W9C07yfn1CAD7n1/DxQm9K4xtOjKBAyvWcXTdpjLzozu2wWl3cNO/V9Nr+wZazhiJYfpTHS5uGdm5Pyu+2sC6Hz/1dCo1IvCaThTu2knJUdexkLXudUJ6lT8WqopLXjif5CULAbBER2OyWrHn5gBgmEyYQ0IBMAUG4iwuqvE2ucPvqg4U79t1+hywcR2BXXu5HePfqQu5H7rOE87cHPI//5DArreA1YdT//cCRdu+A8CeloL9VCbmqBicJcWkL5mDIyMNgOJ9uzCHR4HF8x3YNXnuBDBMZkxBrnOiKSAAZ7Hrm1B7Rjrp/5hf+o1s0d7dWGLja7ax1fBvew1Fe8943jeUPzaqiwu4tgu5Z+6Lzz4k8KabwWYjcVB3Sn7dC4Alvh727FMA2DPSyPjHo6X7otgL9sWZAq7uSOGeXZQkutqb/fYbpc+vu3HmyGgCr+vCyfHDyz+AyVT6vmkKCCw9RrxRQLuOFJ3RxlNvvUFQjwr2RRVxBT9tJfPlZeB0gsNB0b49WOLqAJC98S1yPnofAEdeLiWJR0uXeQNPnS98m7Yg/8vPcOS53l/y/rOZgM431WxjvYDJ8P4/T6v20+ctt9zCihUrSE1NrY18qmSJjsOWklw6bUtNxhQUjBEQ6HacOSYOe0pSmWWWqFhKEo9g8vPHv+01APg0uwzrxY0xR0ZhDo+gYOt3pC2Zx/GhA3EU5BPlRd+I/VbARXHkHzvdxvzEJHxCg7EEB5aL/WH0XI6sea/cfMNiJumTr/ms5/18cn0C8T2upenou2o0b08Y/cYTrNnykafTqDHW2HhKkk4fCyXJSZiDgzEFBp5dnN1OnfkLabR+I3k/bKH48CEAkhbMIfLeYTT58HMaLHuJpPmzwW6v+YZVwxIdhy319DnAnpqCKbDsuaKqGEt0LPYzltnSUjBHx0BJMXmb3imdH3hzP0wBARTv2Yk9+SSF358eAhf+wHgKvvkCbLaaaqbbavLcCZD21KOEJdzLRWs/Im7xC6T9Yz447JQc/pXC7VtdK1itRAx9iLwvPq7BllbPHB2LPfV0O+yV7Iuq4izRsdjOXJaWfLonwW7DFB5B3Tc+InzYOLLfWAlAyeEDFO04vS/Ch47x+L44kyU2Dttvnl9zRcdIFXH29FSSp42j5NiRcttPW/Io4XfeT4O3PqHOP5aTtniuV5wrKnIu9kXB91+X7gdLXDxhg+4i91PXe03O++/gLCoEIKBDJ/xaXUH+t94xfBY8d74o2rOTgI43YAoJA8MgqHtvLBHRNdxa+TOotlh54YUXKCoqYvDgwQwbNoxNmzZRUlJSG7mVZzJc31L8lsPhdpxhlG2yYRiub0/z80ieMY7QhPuos2ItQd17U7htC05bCUV7dpIyc5zrw4vDQdaq5wnocJ1XfGNaIZOpwvY77Y4Kgit2YMU6to6Zhz2/gJJTOfyy5GXq9T3/v+E477h7LLgRd2LaJPbdcA3m0FCiHhiJ4eND3cef5OTMKfza4wYO33sXcdNnY4mNO+fNOGuVngPs7sVUtD9+c54JGXQPYYOHkzpjbJkeJcPPj6gZC7HUvYj0JV7ypUYNnjsNqw8xMxeS+thMjg3szsmH7iHq4emYzxgGZAoNJ27RP3EU5JOx4ulz0aLfr5JjvcyxUV1cuWUGzjP2pSMzg+ODupM0ZjCRE2djqVf/9GZDw4l9/HkcBflkvfjMH2zMOWS4eYy4G3fmKj4+xM5eRMqj0znS7yaOj7rbdc1GTGyl63iSUelz7zjrON9mLaj7/CucenMN+V99USYsuNetxM56jKRpD2NPTzsnuZ8THjpf5H78HnlffET8kuXEP7OKkqOHcNo89HlTvEq1n7br1q3LyJEjGTlyJB9//DHz5s1j1qxZ3HrrrYwYMYLw8PAaTTDsnhEEdOwMgCkgiOJD+0uXWaJjsGefwllYUGYdW3ISvpe2qjDOlnISc+TpSt0cGe36dtUwcBbkkzTu/tJl9V7ZgO34MXxbtcEcHEL+1/870RjgcFZ5cq5trWaPod6trusurCFBZO3cV7rMv24sRRlZ2PMLKlu9nP9n777jm6r3P46/TkbTPehiL0FZUhQEVFQQL24F5QoOcCLIEhEUQUUBRVGGg+t1A1e4qOhVxJ+IIC6W48pFkSnILB1QOtM2TfL7oxqopU2QZkDfz8cjjwfn5JPk8z0kJ/3kO07TW67l8P82c/in8uEMGAZuR/B/IRbvku4ZQUz3w4dVzwAAIABJREFU8veCKSqakm1H3guWlFScuYcrfWYc6fuJaNf+mHFR53ajZPtWyrIycduLyFv6MTE9e2FrcTqm8HAKvv4CgOKf/kfJju1EnJlGfsYBgqks8wBhrY6cA8xJf5wDin2KcWYeOPZ5AsBqJXHsJKyNm3Pg3ltxZqQfiUuuS/Lk53Ds3knmmLuDOiwuUOdOa7MWmGzh2Nd+BUDJpp8o/e1XbK3PpCgrA2vzlqQ+8RxFX3/OoX/OCMp5M+62e4g8tzsARmQUjqOOxbHeGwDOzHRsv4+x/3NcWUblY+HMysCIiia8wznYV60EoHTbZhy/biWsWUvK9u7G2rwlKZOfo+ibz8l5OTjH4mgJdw4jqlt3oPxcUfrrUe+RpKrfI+Ft2nuNO1pYsxYY4eEUrf79PbJxA6W//Up4m/YUZoZG71KdQcOI6tYDKB/SWvKr98+L40A6tjbH/rwARF9yOcljHyZr+hMULPu/Co9NHDGG6B692DfyLkq3bfFXs3wWCueL4mI7hSs+IXdB+UI/trZpOPbt8VubQ4VZF4X0ymvPSmFhIe+//z633nor06dP58Ybb2TRokU0bdqUO++80+8JHn7zH55J7fuHDSC8dXssDcp/pYq5+u8Urfqi0mPs36+pMq5o1RfEXN4bTGZMUTFEXXwZRatWgttN6tTZhJ3eBoCoHpfiLi2h9NetmCIiSRwxzjPeNq7/bRR+9VnQv2iO9tPE5z0T4T/tegOJXdOIadEEgJZD+rP3wxXH9Xzx7Vpy5qSR5XMSwm2cPvxmdr39f94fKEGX/dIL7OzXh539+vDbgH5EtE/D2rj8vZDQtz/5X1Sen1O4ZlWVcbG9LiNp8DAADKuV2F6XUfTdWkr37MIUE0NE2lkAWBs2wtb8NIo3B3/1q+If1mBrfabnHBB9VV/sa77wOaZo9RdEX3otmMwYUdFE9bgU++ry+5LGPYEpMoqMURULFSMiktTpr1L0zQoOPjku6PN3AnXuLNu3ByM6GlvbNAAs9RsS1uQ0SrdvxpyUQr0Zr3J43ivli5IE6ZyZO+cl0gf3I31wPw6MGICtzdFt7Ov5vz2a/fs1VcbZV39B9O/HwoiKIarHpeXfI04niWMfx9a2fAEKa5PTsDZuSsmmnzAnpZD67Csc/tfL5LwUvGNxtJzXZ7P39r+z9/a/s+/um7G1bY/1916g2N43UPj1ykqPsX+72qe4ozn27cEUFY2t3VHvkabNKdm6qYZb9NcdenU2e27t65n0Ht4uzdPGuD79KPyq8nnT/u3qKuMiu11E0n3j2H/v3ZULleH3E9GhE3vu6BcShQqExvnCdkZbUibNBLMFTGbibryDguUfB6T9EtoMt/tYfXhHdO3alR49enDddddxzjnnePa73W6GDx/O7NmzfX6xnT3S/nqmv4vo0o2EQSMxLFbK9u8la+oEXPl5hJ3ehqSxE9k/qF+1cZjM1LlnNBGdzsWwWMj7aBF578wDIDytI3WGPYBhteI8mEX29EmUpe8Dfl9+88o+YJhw7NxG9rOP4/p9kvHxWvNFsfegE1T/8gtJm3o/pjArBb/uZs3ABynNyaVOx3Z0eW0Kn5zVu0J81zencvjnbZ6li80R4XR68VGSuqZhslrY/e5S/jdhZo3nefMQ//bM+erNgY/w8/7gLl38y5rDfnneqG4XkjJidPnSmnv3sP/hB3Hl5RLeph31Jk5mZ78+1caZYmKoO+ExbC1aAlDw+XKyXnoB3G4iO3Uh5b4xGGE2cJaR9fJsClYeX2F8zJyTI074OcI7dyP+jhEYVgtl+/dycNojWOo1pM7oRzkwpH+VMX+cJ+IH30f42V0xrFYKliwif9G/CGvdnrrPzy1fgOOoYuTwa88R1qI1cbcNxfHb9gp5ZI4djCs/9y+3w1lWM3/U+vXc2eGc35eyteF2Ojk8958UrVpJ4uhHiO51FY49v3nycDscpA+95S+1wVRDMz3DO3cj4a4RGBYrjvS9HHzqYc+xSLx/IumD+1Ubh8lMwpDRhHfsimEpf3/kvVt+LGztO5IweHT50sWOUg6/9jzF67+jzn0PE/W3qyg7aj6H21HKgeF/bS5gWUnNz/eI7HoBdYbcW97efXvInDIeV34etjPakDzucfbe/vdq44522jc/sfPKCzxLF4efdQ6JQ0d7zhWH3vwnRV/XzMImbme1f8b8JZHnXuBZktixbw8Zkx7ClZeHrVVbUh56nD239q02rvHCjzDHxlGWlel5TvtPP5Iz9zWa/mcZZRnpuAoKPPcdfuct8j/+oFIex8scXjMrrAXjfAGQcOdwIi/oCYaJolUryXnt+b9c2Ddb+b8aORb+9tT3x1iQIsSM6/TPoL6+12KloKCA6OjoCvuKi4sJDw8/7heriWLlVBCIYuVkESrFSijwV7FyMqqJYuVUUVPFyqmgpoqVU4E/ipWTlT+KlZNVTRUrp4KTpViZ9kPoFysPdAxuseJ1zsq3337LrFmzKCoqwu1243K5sNvtrF27NhD5iYiIiIhILeW1WJk6dSqTJ0/mzTffZMiQISxfvhy73feJ2iIiIiIiIn+F12IlJiaGrl278t///pf8/HzGjh3LFVdUvjiQiIiIiIj4zqzRrV55XQ0sPDycnTt3ctppp/Htt99SWloavOusiIiIiIhIreG1WBk1ahSzZs2iR48erFmzhvPPP5+ePXsGIjcREREREanFvA4D69y5M507dwbgvffeIzc3l7i4OL8nJiIiIiIitVuVxcqAAQMwqrmq5rx58/ySkIiIiIhIbWDyOsZJqixWRowYAcA777xDeHg4vXv3xmKxsGTJEkpKgntlZhEREREROfVVWaz8MfTr6aef5r333vPs79ChA9ddd53/MxMRERERkVrN65yVkpISdu7cSbNmzQDYsmULZWVlfk9MRERERORUZq5myoWU81qsjBs3jgEDBpCamorb7ebgwYNMnz49ELmJiIiIiEgt5rVY6datG59//jlbt27FMAzOOOMMLJbyh7399tv069fP70mKiIiIiEjt49MaBGFhYbRr1462bdt6ChWAhQsX+i0xEREREZFTmckI/VuwndCCaW63u6byEBERERERqeCEipXqrsMiIiIiIiJyIrzOWRERERERkZpn1u/+Xum6mSIiIiIiEpK8FiulpaVV3hcTE1OjyYiIiIiIiPzB6zCwXr160aNHD/r06UP79u0r3Ddv3jy/JSYiIiIicioLhdW2Qp3XnpVPPvmEtLQ0ZsyYwdVXX83rr79OVlZWIHITEREREZFazGuxEhERQe/evZkzZw4jR45k3rx59OrVi6FDh7Jr165A5CgiIiIiIrWQ12Fgu3btYvHixSxZsoT69eszZswYevXqxdq1axk0aBDLli0LRJ4iIiIiIlLLeC1Wbr/9dq677jreeOMNGjRo4Nl/0UUXsWrVKr8mJyIiIiJyqjLrmoVeeS1WVqxYgd1uZ/fu3bjdbux2O5GRkQCMHz/e7wmKiIiIiEjt5HXOytq1a7n22msZOnQo2dnZ9OjRg2+++SYQuYmIiIiISC3mtViZMWMGCxYsIDY2luTkZObPn8+0adMCkZuIiIiIyCnLZIT+Ldi8Fisul4vk5GTPdosWLfyakIiIiIiICPgwZ6Vu3bqsXLkSwzDIy8tj/vz51K9fPxC5iYiIiIhILea1Z2XSpEl89NFHpKenc8kll7Bp0yYmTZoUiNxERERERE5ZZiP0b8HmtWclMTGRGTNmBCIXERERERERD6/Fytdff82sWbPIzc3F7XZ79q9YscKviYmIiIiISO3mtViZMmUK48aNo2XLlhi6cI2IiIiISI0w6W9rr7wWKwkJCfTo0SMQuYiIiIiIiHh4LVY6duzI1KlTueCCC7DZbJ7955xzjl8TExERERGR2s1rsbJhwwYAfvnlF88+wzCYN2+e/7ISERERETnFhcJqW6HOa7Fy5ZVX0r9//0DkIiIiIiIi4uG1WHnrrbdqrFiJbxZfI89zskvZeCDYKYSMX9YcDnYKIaPNufp8/CEzx+sloGqNvf87FOwUQkad+uHBTiFk7NxaGuwUQkbrLrHBTiFk5O3JD3YKIjXOpyvYDxw4kLS0tApzVoYPH+7XxEREREREpHbzWqx06NAhEHmIiIiIiNQqWrrYO6/FinpQREREREQkGLwWK61atap0MciUlBS+/PJLvyUlIiIiIiLitVjZvHmz598Oh4Ply5ezfv16vyYlIiIiInKq0zAw745ryR2r1crll1/O2rVr/ZWPiIiIiIgI4EPPygcffOD5t9vtZtu2bVgsXh8mIiIiIiJyQrxWHevWrauwnZCQwKxZs/yWkIiIiIhIbaBhYN55LVamTp2Kw+Fg586dOJ1OWrZsqZ4VERERERHxO69Vx88//8zIkSOJj4/H5XKRnZ3N7NmzSUtLC0R+IiIiIiJSS3ktVqZMmcLMmTM9xcn69euZPHkyixYt8ntyIiIiIiKnKpNxXGtd1Upej1BRUVGFXpQOHTpQUlLi16RERERERES8FitxcXEsX77cs/3ZZ58RHx/v16RERERERES8DgObPHkyY8eOZcKECQA0atSIadOm+T0xEREREZFTmVYD867KYuWhhx7y/LtFixbUq1eP6OhomjZtSnJyckCSExERERGR2qvKYqVz586V9rndbrZs2cKoUaN4/fXX/ZqYiIiIiIjUblUWK3369KnyQVdeeaVfkhERERERqS00DMy747q6Y1ZWFsuXLycqKspf+YiIiIiIiAA+rAZ2tF27drFhwwZNsBcREREREb87rp6VTp060alTJ3/lIiIiIiIi4nFcxYqIiIiIiNQMzVnx7riGgYmIiIiIiASKihUREREREQlJGgYmIiIiIhIEJvUbeKUjJCIiIiIiIUnFioiIiIiIhCQNAxMRERERCQKtBuadelZERERERCQkqVgREREREZGQpGFgIiIiIiJBoGFg3qlnRUREREREQpKKFRERERERCUkaBiYiIiIiEgQmQ/0G3ugIiYiIiIhISFKxIiIiIiIiJ+yjjz7iiiuuoFevXsyfP7/S/Tt27GDAgAFcc8013HnnneTm5np9ThUrIiIiIiJyQjIyMpg5cyYLFizggw8+4O2332b79u2e+91uN/fccw+DBg1i8eLFtG7dmldeecXr857Uc1Ys7bsScf3dGFYrzj07KHzzaSgu8jkm7rkPceVkeWJLli6kdO1yz7YRGU3MxFexv/NPHD98GZhG+UHiJRfR4uH7MYWFUfDLFn4ZNR5nQWGV8W1eeIqCTVvZ/Y83AphlzYq+4CKSR4zGCAujZNsW0h+bgKuwcpurijNFR1Nv4hOENWuGYZjI/egDDs55DYCw5qdR75FJmCIjwe0m87kZFK75JtBN9Js5tz7CT/t+ZfryBcFOpUaFnXUeUf2HgNWKc/ev5L/8JG57kU8xRkQUMUPGY67fBAyD4q8+wb74LQCsbc4masAIDJMZV0EuBXOfw7l7+7FSCBmxF11E/fvK3/f2LVvY/fCxPx/VxZ25eg2lBw54YjPfeJ2cJUuIOOMMGj46EXNMNM6CQtKfm0XBunUBa5svIrpcQPxdIzCsYZTu2MbBZx/DXVS5/VXGmUwkDLmfiHPOA7OZvHfmUbBkEQC2Dp1IuPs+DIsFd0kJh16cRumWnwGIv30Ykd0vxV1sp2Tj/zj00rPgKA1o2/+q2vQ9Yut0PnEDh4E1jLLftpHz/BTc9sLjijEnpZL87BtkjrwJV175L8dGdCzxg8diadQMw2Yj/503sK/8JKBt+ytO9Pv0aA2mP09ZVhYZT02usD/q3PNJGTWGnf36+LUtoehkWLo4Ly+PvLy8SvtjY2OJjY31bK9evZquXbsSHx8PwKWXXsrSpUsZPnw4ABs3biQyMpILL7wQgCFDhhzzef/spO1ZMWLiiLpjHIWzHyFv/ABcWfuJ6DvY5xhT3Ua4C/PJf+wuz+3oQgUg8s7xGBFRAWuTP1gTE2j73FQ23D6CNeddhn3XHlo8MuaYsZEtm3P2e3NJverSAGdZs8wJCdR7/En2jhnJjt6XU7p3Dyn33n9ccclD76Us8wA7+17Dzpv/TvwNNxLRvgMAdcdP5PAH77GzXx/2T5xAg2kzwWwOaBv9oVXdpqwY9SJ9z7o42KnUOCMmnpghE8ibOZ6c0TfizNxP1I1DfY6JvOFunAczyRl7CzkT7iTib32wtGyHERFF7OgnKZz/IjkPDqTg9WeJHTUZLNZgNNMnloQEGj/xJDvvHcmmK8rf9/Xvr/z5qC7O1rQZZbm5bLmuj+eWs2QJAM1m/4ODixax+Zpr2DlyBI0mPoYlKSmgbayOKS6BxLGPk/XYGPbf1puy9L0k3HXvccVFX9UXa8Mm7L+zLweG3kzs9TcTdkY7sFhIfngaB2dMIv3ufuTOf5Wkh6YAEHXptUR0vZD0oTeTPrgfzkNZxN8xLKBt/6tq0/eIKTaehHsf5eDUB8m8py9lB/YRe9vw44qJ6HEFSU+9jDkxpcLjEkZNxJmdQdaoW8h+eBjxd4/B9KeYUFMT36d/qHPbnUSe1anCPsNmI3nYvTR4esYp8T16qpo7dy49e/asdJs7d26FuMzMTJKTkz3bKSkpZGRkeLZ3795NUlIS48ePp0+fPkycOJHIyEivr3/SFivWtufg3LkZV+Y+AEpWfoit6yU+x1hatAOXi+hxLxDz+BuEX30rHLUiQ/jVA3Hu24Fz744Atcg/Ert3I2/9T9h37gJg75x/U+/6q48Z2+iOm9k3/10yPloayBRrXNS551O88Sccu8vbfPjdhcReXrnN1cVlTHuCjBnTALAkJ2OyWnEW5ANgmEyYY+MAMEVF4S4t8XubAmHYRdfz2qrFvPvfz4OdSo0La98Zx6+bcB7YC4D9s/exdevlc0zh3JkUvvUiAKb4RLBYcRcVYK7XCLe9EMfPPwDg3L8Lt70I6+ntAtW04xZz/vkU/fwTJbvK3/fZ/15Inasqfz6qi4s66yxwOmn5r7do9cGH1B06FEwmzPHxhNWty6EPPwCgLDsb+5YtxHa7IECt8y6i07mUbNlI2b7dAOQvfpeonpcfV1xkt4spWPohuJy4CvIpXPkpUZdcAWVl7O3XC8f2LQBY6jXE+fuv6mGnt6Zo1UrcheXnkaKvPyfqwr/5vb01oTZ9j9jO6opj2y840/cAUPjJe0RedJnPMaY6SUR07c7BR0dWeIwRHUt4h87kL3wVANfBTLLuvx13gffx+sFUE9+nAJGdOhN93gUcXrSw4uPO64YREcH+R8b5sRVyom699VZWrFhR6XbrrbdWiHO5XBhH9RS53e4K22VlZXz77bfceOON/Oc//6FRo0Y89dRTXl/f6zAwh8PB6tWrycnJqbC/d+/eXp/cn4w6KbgOZXq2XTlZGJHREB7pGeZVbYzJjOOXH7AvehnDbCF61FO4iwsp+WwRljadsJyRRsH0sUSPnRHwttUkW/26FO8/MlSjZP8BLLExmKOjKnXhb3movFs2sfv5Ac2xpllT6+E4aniKI+MA5pgYTFFRFbqkvcY5ndR/Yhoxl1xK/ufLKf1tJwAHpk6i8StzqXPLrVjq1GHfg/eD0xm4BvrJiLenA9CrdZcgZ1LzTImpuA4e+XXHdTALU2Q0RkSkZyiY1xiXk5hhE7F16U7Jd1/h3L8bIzwcwxaOtX1nHBu+xdK8NZaGzTDFh05Pwp+F1a2HI/3I+760is9HdXGGxUz+mjXsnzEdw2Kh+T9fxllQQNa8eZTu3Uud3r059P77hDVsSHTHjth/+SWgbayOOTkVZ9aRdjmzMjBFx2BERlUYClZdnCU5lbKj78vOIKx5y983yjAl1KHePxdijo0na8qDAJRu+omYvreQ/8FCXPm5RPW6CnOd0H2fHK02fY+Yk1NxZh85DzizMzFFRWNERHmGeVUX4zqUzaGpD1R6Xku9hjhzDhJ97c3YOp6HYbVS8J+3KNu/2/+NOgE18X1qiowidex49gwbRHzffhWev2DlCgpWriCyU2f/NyZEnQzDwP483KsqdevW5fvvv/dsZ2VlkZJypPcwOTmZJk2acOaZZwJw1VVXMXLkyErP82dee1buvfdeXnzxRdauXcu6des8t2AzqlqX2uXyKab0qyXYFzwHpcW47QUUL3sH69kXYNRJIbL/MApfeQLcrmM//iRimEzgdlfa73ad/G2rUlVtdrqOO27/hAfY2v1czHFxJA0ehhEWRoOnZ5L+6ENsv7Q7v90xgLoPP44ltW6NN0NqjmEygOo/B77E5M9+nOxBV2CKjiXy+ttx24vIfXYckb0HkvD0XMIvvIzSjT/gLnP4oxk1w2TCfYx28udzQjVxB999l71PTMFlt+PMzydzzhziLinvJdgxbCjxl15Kqw8XU2/ESPK++hK3I4SORxWfe1xO3+Mq3WdUeJ+4cg6xr18vDowcSOLYx7E0bEzh8o8p+vIzUqe/Qt3n5+DY/Vtov0+OUqu+RwwDt7f3hy8xf35aiwVL3Qa47IVkP3gXh56ZQNxdo7Ge1qoGkvajE/w+xTCoP3U6Gc9OpSw7q/L9cko577zzWLNmDYcOHcJut7Ns2TLP/BSAs846i0OHDrF582YAPv/8c9q2bev1eb32rOzYsYOlS0OvO9d1MANz89aebVNCEq6CPCgt9ikm7NxeOPdsP2qYlwHOMsLO6Q5hNqJHlw8BMqc0wHTDPRgxcZR+sTgQTTthzR8cSfKl5fMOzDHRFP6y1XOfrV4qjpzDuIrswUrPL5LuGUFM9/I2m6KiKdl2pM2WlFScuYdxF1dssyN9PxHt2h8zLurcbpRs30pZViZuexF5Sz8mpmcvbC1OxxQeTsHXXwBQ/NP/KNmxnYgz08jPOICEJmd2BpYWR06IpjrJ5eeCkmKfYqztu+Dc8yuunGwosVO8+jNsnbuX/9FSbCd30pHx6gkzF3qGkoWKuiNGENfj93NCdDT2rUc+H9bUVMoOH8Zlr/j5KE3fT2T79seMS7jmGuybN1P8+/MYhgF//OFtMrFj6FBPb+Npr71O7ufBHVoYd9s9RJ7bHQAjMgrHzm2e+8xJKTjzcnEXF1d4jDMzHVurdseMK8tIx5x4ZFy2OTEZZ1YGRlQ04R3Owb5qJQCl2zbj+HUrYc1a4so9TOGKT8j7d/mEc1ubNMr27fFXk09YbfwegfIetLCjhnGaE5Nx5efiPvpc4UNMpec9lA1A0fKPyrfT91L6y3rCTm+L49fNNd2ME1KT36e25i0Ia9iQ1DHlw7wsiUlgMmMKCyN90iMBaI0EUmpqKvfddx8DBw7E4XDQt29f2rdvz6BBgxg5ciRnnnkms2fP5uGHH8Zut1O3bl2mTZvm9Xm99qw0btyY/fv310gjapJj43dYmrfBlNIAgLDu1+BYv8rnGHODZoT3vqN8noo1DFvPPpR+u5KST98hb9xNnkn3Zb9twf7OSydNoQKw4+nnWXdxb9Zd3JvvLr+B2E5pRDRrAkCDW/uTtXRFkDOsedkvvcDOfn3Y2a8Pvw3oR0T7NKyNy9uc0Lc/+V9U/mOpcM2qKuNie11G0uDyya+G1Upsr8so+m4tpXt2YYqJISLtLACsDRtha34axZtDZ5iLVFa64VusLdpirtsQgIhLelP6/dc+x9jOvZjI6+8oD7RYsXW9GMfGH8DtJm7cdCzNW/0e1xNKS0NuNbADL7zgmQi/tX8/otLSsDUpf98n9et/zGIif9WqKuMiWrak3oiRYDJh2Gwk3XwzOZ+Ur2rU6PFJxPcsnxsY1eEswlu0IH/NmkA0s0q5c14ifXA/0gf348CIAdjatMfSoDEAMVf3xb76i0qPsX+/pso4++oviL68N5jMGFExRPW4lKJVK8HpJHHs49jali/GYW1yGtbGTSnZ9BNhp7cledIMMFvAZCb2xtspXPF//m/8X1Qbv0cASn5cS9gZ7TDXawRA1OXXY1/31XHH/JkzYz+l2zcRefGVAJji6xDWuj2l2zf5oRUnpia/T+0b1rP9sh6e58tZ9DZ5yz5RoXIUk2GE/O14XH311SxZsoRPP/2UQYMGAfDqq696hn6lpaWxaNEiPv74Y15//XUSExO9PmeVPSsDBgzAMAwOHTrE1VdfTatWrTAftVLDvHnzjiv5mubOP0zhG08RNWwShtmKM2sfRa89ibnpGUTeNpb8x+6qMgbAvngOkTePInbym2C2UPrdF5R+tSSobfIHR/Yhfhn5EO3feB6T1UrRb7vZOLx8DHVMWjvazJzCuouDO/+opjlzDrF/4ngaPvMchtVK6d497H+4vM3hbdpRb+JkdvbrU21cxoynqTvhMZotKi9SCz5fzqH588DtZu99I0h9YDxGmA2cZaRPfhTH3tD9hVTAnZdD/j+fIPa+J8BixZmxj/zZk7A0b0XM3ePIGXdblTEAhf96gei7HiDhmfLliku++xL7J+8AkP/CRKIHjcOwWHAdPkju9NCeKFp26BC7J4yn2azy933Jnj3sGlf+vo9o247Gkyez5bo+1calz55No4cfodWHizGsFg4v/ZSD774LwJ6Jj9J48mTqDhuGq6iIHUOHVuq1CSbX4Ryyp00keeIzGBYrjvS9HHzqYQDCTm9D4v0TSR/cr9q4/MXvYqnfiHqvvoNhsVKwZBElG8oXWch69D4Sho4tX7rYUUr2Ew/hzM7EmZ1JcVpH6r/6DphMFK1aSd57bwXtOByP2vQ94srNIee5SSQ+9FT5eeDAXg7NeAxri9bEj3iYrHtvrjLGm0NPjiVuyINEXdEXDIO8ha/h2BbaP3TVxPepyIky3McceAnffvtttQ/s3Pn4J0Pl3HHRcT/mVPTDEg0X+kOD+qE/sSxQ2pwbH+wUQkZmzkm7UGGN2/u/Q8FOIWTUqR8e7BRCxrafQqcADLbWXbxP/K0t8vbkBzuFkNF6fWgNr6vKV/sfDnYKXl1Yf0pQX79dDh2CAAAgAElEQVTKnpU/ipHJkyfzyCMVu+sefPDBv1SsiIiIiIhIOVNVi0GJR5XFyoQJE9izZw8///wz27YdmYzodDp9utqkiIiIiIjIiaiyWLnnnnvYt28fTzzxBMOHH1npxmw2c9pppwUkORERERERqb2qLFYaNmxIw4YNWbx4MQUFBeTn53vWFS8qKiI+XuPrRURERET+KhOau+uN1+usvPzyy7z88ssVihPDMFix4tRctlBEREREREKD12Ll3XffZfny5dSpUycQ+YiIiIiIiAA+FCv16tUjLi4uELmIiIiIiNQax3vRxdrIa7HStGlTbrrpJrp06UJYWJhn/9GT7kVERERERGqa12IlNTWV1NTUQOQiIiIiIiLi4bVYGT58OIcOHeJ///sfTqeTDh06kJSUFIjcRERERESkFvN62cyvv/6aa6+9lvfff5///Oc/XHPNNaxcuTIQuYmIiIiInLJMhinkb8HmtWdl5syZLFiwgEaNGgGwZ88ehg8fTo8ePfyenIiIiIiI1F5ey6WysjJPoQLQqFEjXC6XX5MSERERERHx2rNSv3595syZQ9++fQFYtGgRDRo08HtiIiIiIiKnMi1d7J3XnpUnnniC9evXc8kll9CzZ09+/PFHJk2aFIjcRERERESkFvPas5KYmMisWbMCkYuIiIiIiIiH12Jl6dKlvPLKK+Tm5lbYv2LFCr8lJSIiIiJyqtMwMO+8FitPP/0006ZNo379+oHIR0REREREBPChWGncuDEdO3bEZAr+OssiIiIiIlJ7eC1W7rjjDgYOHMg555yD2Wz27B8+fLhfExMREREROZWFwkUXQ53XI/TSSy/RqFGjCoWKiIiIiIiIv3ntWXE4HEydOjUQuYiIiIiIiHh4LVbOP/983nrrLS644AKsVqtnvybci4iIiIj8dVoNzDuvxcqSJUsAeOONNzz7DMPQ0sUiIiIiIuJXXouVzz//vMr73n77bfr161ejCYmIiIiIiIAPE+yrs3DhwprKQ0REREREpAKvPSvVcbvdNZWHiIiIiEitYkJzVrw5oZ4VQ5OCRERERETET3QlGhERERERCUknNAxMRERERET+Gi1d7J3XnpXS0tIq74uJianRZERERERERP7gtVjp1asXjz/+OBs2bKh037x58/ySlIiIiIiIiNdhYJ988gmffvopM2bM4ODBg/Tu3ZtrrrmG5OTkQOQnIiIiInJKMhmaPu6N1yMUERFB7969mTNnDiNHjmTevHn06tWLoUOHsmvXrkDkKCIiIiIitZDXnpVdu3axePFilixZQv369RkzZgy9evVi7dq1DBo0iGXLlgUiTxERERERqWW8Fiu333471113HW+88QYNGjTw7L/oootYtWrVcb3YjlUZx5/hKeiMs6OCnULIcDt1YdE/ZOaoK/gPKQmuYKcQMrbGmoOdQsgwWfUZ+UOX6xt4D6olcrbnBDuFkGGx6XxxstFqYN55LVZWrFiB3W5n9+7duN1u7HY7kZGRAIwfP97vCYqIiIiISO3k9WeqtWvXcu211zJ06FCys7Pp0aMH33zzTSByExERERGRWsxrsTJjxgwWLFhAbGwsycnJzJ8/n2nTpgUiNxERERGRU5ZhmEL+FmxeM3C5XBWWKW7RooVfExIREREREQEf5qzUrVuXlStXYhgGeXl5zJ8/n/r16wciNxERERERqcW89qxMmjSJjz76iPT0dC655BI2bdrEpEmTApGbiIiIiIjUYl57VhITE5kxY0YgchERERERqTVM3vsNaj2vxcrXX3/NrFmzyM3Nxe0+ck2MFStW+DUxERERERGp3bwWK1OmTGHcuHG0bNkSQxeuERERERGRAPFarCQkJNCjR49A5CIiIiIiUmuEwtLAoc5rsdKxY0emTp3KBRdcgM1m8+w/55xz/JqYiIiIiIjUbl6LlQ0bNgDwyy+/ePYZhsG8efP8l5WIiIiIiNR6XouVK6+8kv79+wciFxERERGRWsOkYWBeeT1Cb731ViDyEBERERERqcCnK9gPHDiQtLS0CnNWhg8f7tfERERERESkdvNarHTo0CEQeYiIiIiI1CqGLgrplddiRT0oIiIiIiISDF6LlVatWlW6GGRKSgpffvml35ISERERERHxWqxs3rzZ82+Hw8Hy5ctZv369X5MSERERETnVaTUw747rCFmtVi6//HLWrl3rr3xEREREREQAH3pWPvjgA8+/3W4327Ztw2Lx+jAREREREZET4rXqWLduXYXthIQEZs2a5beERERERERqA60G5p3XYmXq1Kk4HA527tyJ0+mkZcuW6lkRERERERG/81p1/Pzzz4wcOZL4+HhcLhfZ2dnMnj2btLS0QOQnIiIiIiK1lNdiZcqUKcycOdNTnKxfv57JkyezaNEivycnIiIiIiK1l9dipaioqEIvSocOHSgpKfFrUiIiIiIipzotXeyd1yMUFxfH8uXLPdufffYZ8fHxfk1KRERERETEa8/K5MmTGTt2LBMmTACgUaNGTJs2ze+JiYiIiIhI7VZlsfLQQw95/t2iRQvq1atHdHQ0TZs2JTk5OSDJiYiIiIicqgwNA/OqymKlc+fOlfa53W62bNnCqFGjeP311/2amIiIiIiI1G5VFit9+vSp8kFXXnmlX5IRERERERH5w3Fd3TErK4vly5cTFRXlr3xERERERGoFk65g79VxHaFdu3axYcMGTbAXERERERG/O66elU6dOtGpUyd/5SIiIiIiIuJxXMWKiIiIiIjUDK0G5p2OkIiIiIiIhKSTvmcl9qKLaHD/aExhYdi3bOG38RNwFRYeV1zyTTeS1LcvRng4RRs3smv8BNwOh+exMeefT8OxY9jUu+oV0gItvHM34m4fgWG14ti5jUMzJ+EuKvQtxmQiftBowjudC2YL+YvmUfh/7wFgio4lfugDWBs3x7DZyFv4BkUrPgYg4rwexA4YAi4Xrvw8Dj03GWf63oC3/c/CO3cj/s4RGNYwHDu3cXD648c8FseMMZmIHzyaiE7ngdlM/qJ/UbBkEQBhp7chYehYjPAIMJnIe3sORSv+D4DInlcQ+/dbATfu4mJy/jGN0q2/BLrpXoWddR5R/YeA1Ypz96/kv/wkbnuRTzFGRBQxQ8Zjrt8EDIPirz7BvvgtAKxtziZqwAgMkxlXQS4Fc5/DuXt7MJroV3NufYSf9v3K9OULgp1KjYo8/0KS7hmFERZGyfatZD7xyDHPm1XFGTYbyWMfJrzNmeXvjY0byHpmCu6SEsJOa0mj1xbg2Lvb8zzpE+7Hsfu3ALawav46d4Z3uZA6Yx7HmXnA8zyZY+7EbS8i8uIriOk7ENxu3CXF5Lw0Dce2TQFttzfmdl2wXXsXhjUM594dFL/1DBQX+RxjvfAarOdfCdYwXLu3UvzWs1DmwIhLJHzgAxixdcAwKF22kLJvlwejidUKxvdI9LX9ibm2H+7SEhy7d5LzwlRc+XkBb7s3/jxf/MEUE0vjue+Q/eIMCj5fFsjmyUngpO5ZsSQk0HTqk+wYMZKNl11OyZ49NBhz/3HFxf/tbyTfcgtbb7+DX668CpMtnJTbbgPAsNmoP+pems+cgWE2B7Jp1TLFxVNn9GMcnDyGA3ddR1n6PuJvH+FzTPQV12Np0JgDg28gY+QtxPS5ibDT2wKUf9lmZ5Ix/CayHrqHhCFjMSelYITZqPPAFA5OGkPGsBuxr/uKhHvGBrztf2aKSyBxzONkTxpL+h19KEvfS/ydI32Oib7yeqwNm5A+6O8cGP77sTij/FgkTXyWw3P/yYEh/ckaP5yEwfdjadAYS8MmJAwaReb4YRwY0p/cBa+RNPHZgLfdGyMmnpghE8ibOZ6c0TfizNxP1I1DfY6JvOFunAczyRl7CzkT7iTib32wtGyHERFF7OgnKZz/IjkPDqTg9WeJHTUZLNZgNNMvWtVtyopRL9L3rIuDnUqNM8cnkPrwFNIfGsWuG67CsW8viUNHH1dcndsGY5jN7L65D7tv7oPJFk7CrYMACG/fgfxlH7N7wPWeW6gUKv48d9ratCd/0b/IGHaj5+a2F2Fp2IT4u+4l6+HhZAy7kbx/v0bSI6F1vjCi4wgf+AD2Vx6j8LFbcWXvx9Z7kM8xlg4XYO3eh6LnxlA0+Q4IsxF2cV8AbNfeiXPnJoqeGIT9hXGE3zgKIzYh4G2sTjC+R2xpnYjtdxuZDwzmwJD+2L/9hjqjHgl4273x9/niD6mPPokpOiYgbQo1JsMU8rdgC34GJyC22/kU/fQTJbt2AZD174UkXn31ccUl9r6WjDfexJmbC243uydO5NCHH/7+uG6YIiL4bdy4ALXIN+Fnn0vp1o2U7d8DQMHH7xJ58eU+x0Sc14PCzxaDy4m7IJ+iL5cR2fMKTNGx2M7qQt78VwDKi5ZRA8t/6TGVv1WMqGgATBERuEtLA9Le6oR37Frezn3lv+Lmf/QuUT0v9zkm4vyLKfj0wyPH4otPiepZ/utg7r9eoeTHdUD5sXDm5mBOSsHtKOXgjEm4DmUDULp1I+aEJLCEVkdlWPvOOH7dhPNAee+X/bP3sXXr5XNM4dyZFL71IgCm+ESwWHEXFWCu1wi3vRDHzz8A4Ny/C7e9COvp7QLVNL8bdtH1vLZqMe/+9/Ngp1LjIrucR8mmn3HsKf885L6/kJjLKl87q7o4+/rvOfTGy+B2g8tFyZZNWOvWByDizLMIa9qcRnPfpdEbC4nqfkmAWuadv86dAGGt0wjvcA6p/1hIyrOvY2t3NgBuRymHZk0+6nzxS8idL8ytO+H6bQvurH0AOL5ajLVzT59jLF3+hmPFu1CUD243JQtm4lj3WfkDTWaMiN8vdxBmA5ez/H0TQoLxPRLWsjXFP67DmZ0JgP2bFUR0vTCk3hfg//MFQJ07hlD661ZKtm8NQIvkZOTTp2LhwoX079/f37kcN2vdepQeONLlXnrgAOaYGExRURW6KKuLszVtijUxkRavvYo1JYWC779n3zPlv3rlrlhB7ooVRHfuHLhG+cCcnIozK8Oz7czKxBQVgxEZ5em2ri6m0n3ZGVibtcRSvxGuQ9nEXHcz4Z3Ox7CGkffePM/JOeeFJ0md8Sau/FwwmcgcfUeAWlw1S3Jdyrwci+piLH86FmXZmUQ0bwmOUgqXfuDZH3XFdZgiIynd9BPu0hKcGeme+xIG3499zZdQVubPph43U2IqroNH2uY6mIUpMhojItIzFMxrjMtJzLCJ2Lp0p+S7r3Du340RHo5hC8favjOODd9iad4aS8NmmOKTAt5Gfxnx9nQAerXuEuRMap4ltR5lGUfOh2WZGZijK583q4srWrf6SFzdesT3H0Dm1McAcNnt5C/7P3L/8w7Wxk1p+NIc9h9Ip2TzRv83zgt/nTsBXPm5FK38BPs3Kwhr24GkiTPIGNofZ0Z6hfNF/OD7sa8NrfOFKSEFV06mZ9t9OAsjIhrCIz3DvKqLMaU2xLUrnojhT2HEJeL89SdK3i//0avkg1eJvP85LGdfhBETT8l7L+HOPxzYBnoRjO8RnE5i+tyIOaUezsx0oi69FiMsDFNsvKewDQX+Pl9Edj6XiLM6se/eu2nw4uv+b5CclHzqWXnrrbf8ncdfYphMx/6FxuXyOc6wWIg9/zx23DuKzdf3xRIXT/37Rvkp4xpiVNEep9O3mEr3GeW/dlksWOo1xFVUSOb9d3Bw6kMkDL4fa4vWWJu2IPbmu0kf3Jf9N19K3sLXSXzkmRpv2nEzGVX83zp9iznWe+NP75/YfrcTP3AIWY+Mwl16ZIytER5O0iPTsDRoxMEZj59IK/zCMBlA5Xa7j2qfLzH5sx8ne1B5z1vk9bfjtheR++w4InsPJOHpuYRfeBmlG3/AXeao9DwSggwD9zE+D26n67jjbK3a0PDlf3H43QUUrvoSgKxnJpP73kJwuXD8toOC5UuJuqB7jTbhL/PXuRM4OHkM9m9WAFC6cT2lv2wg/KyuRyJt4SROeBpLvUYcmjWpJlpTc0zGsfcffS6sJsYwWzC36oj9tUkUPXUPRmQstmvvBCD89gmUfvY2hQ/dQOHjtxPW60ZMTVrVcANOUBC+R0p+/pHcf71C8mPTSZ09H1wunHmHwRFi51E/ni8sqfVIuvcBDkwcV+l4iRzNp56VunXrMnDgQNLS0rDZbJ79w4cP91tiVak3cgTxF5ePIzdHR2PfeqTbMCw1lbLDh3HZ7RUeU5q+n6i09seMc2RmkbPsM88vBAcXL6b+sIrj+kONM+sAtlZHhtyYk1Jw5ufiLin2KcaZdQBzYvKR+xKTcWZl4jyYBUDhssUAlKXvoWTjesLOaIspPILSjes9E+oLPnqH+LvvL/8VKC94v5KVZR4grNWZnm1zUgrOvFzcxcU+xTgzKx8Lz69nViuJYydhbdycA/feWuHXUXNyXZInP4dj904yx9xdoYgJFc7sDCwt2nq2TXWScRXkwdHvk2pirO274NzzK66cbCixU7z6M2ydu5d/KRXbyZ105POfMHOhZyiZhJ46dw8n+oIeAJiioij5dZvnPktyCs7cXNzFFc+bZRnphLdrX2Vc9N8uJ2XsI2Q9+wT5y8oX4cBkImHgXRx+5y3cRb9PzjYM3CHSi+Cvc6cRFU30VTeQ//YbR17MMHA7y9ttTq5L0uOzKNu9k6wHQ+984TqUiaVpa8+2EZ+MuzAPSot9inEdPkjZ+m88vTCObz/DdsVAjKhYzC3aYX+ufI6oO2sfzk3fY27ZHteuzQFqnXfB+B4xIiIp2fCDp+fFnJhM3G1Dy0cuBFmgzhfRPXthhIfT4LmXAbA2bIx1xP2Y4+LJ/c87fm1jKDEInTnRocqnnpUOHTrQuXPnCoVKsKQ//wKbevdhU+8+bL6hH1FpadiaNAEgqX9/Dq+oPMY875tVVcblfPopCZdfhvF72+Iv6UnhTz8HqDV/TfEPawhrdSaW+o2A8sl9xWu+9DnGvuYLonpdWz6WOCqayIsuxb5mJc6M/ZRu20TUJeXzeUzxdQhrnYZj2y+Ubt+MrX1HTPF1AIg4tztlGfuDWqhAeTttrc/E0qAxANFX9cW+5gufY4pWf0H0pUeORVSPS7GvLr8vadwTmCKjyBhVsVAxIiJJnf4qRd+s4OCT40LuD48/lG74FmuLtpjrNgQg4pLelH7/tc8xtnMvJvL634f6WazYul6MY+MP4HYTN246luatfo/rCaWlp+RqYKeKQ6+86JnsvufOmwhv1x5ro/LPQ9x1/Sj8uvJ5s2jd6irjorp1J3n0Q+wbOehIoQLgchF9QQ/iev8dKB/yEd3jbxSs/MzPLfSNv86dbnsR0VffQMT55T+kWU87g7Az2lL8/WqMiEhSpr2CfdXnHHzqoZA8Xzg3fY+5WWuM5AYAWC+4mrL/rfY5puzHr7B0vAisYQBY0rrh3LUFd2Ee7pxsLGdfBFBevLRsj+u30FoJLRjfI+bEZFKefRUjsnw+T+xNd1G0cqk/m+mzQJ0vDi+Yy67rL/e8VvGmn8l+YXqtKlTEN4b7WP12fvLDGTXf9Rt74YU0uH80htVKye49/Pbggzhzc4ls144mUyZ7lhuuKg6TiXr33EPCFZdjmM0UbfyFXY8+WmEsZnTnzjR+5GF+ufqaGsk5pVnkCT9H+Dnnly+tabFSlr6XQ888grleA+qMepSMYTdWGeMqyAOTmfhB9xF+dhewWCn8v/fIf+9fQPkvgAnDxmGp1wAME/kfLPAszRl99Q1EX90Pd5kDV34uOf94mrJdO06oHW7nib/9wjt3I/6OERhWC2X793Jw2iNY6jWkzuhHOTCkf5Ux5QsHmIkffB/hZ3fFsFopWLKI/EX/Iqx1e+o+PxfHnt8q/HFx+LXnCGvRmrjbhuL4reIf55ljB5/Qr2IRiRF/+bFVCetwLlE3DgGLFWfGPvJnT8Kc2oCYu8eRM+62KmPchfkYkdFE3/UAlkbNASj57kuK3n0N3G6srTsQNXAUhsWC6/BB8l99Glfm/hrLOyUhNIYEvDnwEX7eH9yli7f+N7/GnzPyvAtIGnofhsWCY98eDjw+HldeLrZWbUmdMIndA66vNq7JO0swxcbhzDoyh8G+4UeynpmCtWFjUh58FHOdRDCbOfTaPyhYXjN/hIUnnPgPZv46d1pbtiZh6IOYIiJxO50cfnk6JRu+J6bf7cQNrHy+yBo35ITOF/HN4v/6QTgGc9su2HrfBWYL7uz92Oc8hSmpHuG3jKHoyburjKEoHwwTYVfcgqVjdwyTGefubRQvmAHFRZgaNMfWbyRGVCy4XThW/gfHqo+9ZHN8crbnnPBzBPp7pPj7NURf24+Ya24Aw0TJz+vJefGpEy5mS/JqfuEbf54vjtbgH2+Su+jfNbZ0cct1wZ8n54uDxf8OdgpeJYbfGNTX96lYadWqFYZRcbxqSkoKX375ZRWPODZ/FCsno5ooVk4VNVGsnCr8UaycrEKlWAkF/ihWTlY1UaycKmq6WDmZ1USxcqrwR7FysjpZipWckreDnYJXCbZ+QX19n+asbN58ZGypw+Fg+fLlrF+/3m9JiYiIiIiIHPd1VqxWK5dffjlr1671Rz4iIiIiIiKAjz0rH3xwZJ1wt9vNtm3bsITYhYtERERERE4mxsl9ffaA8KniWLduXYXthIQEZs2a5ZeEREREREREwMdiZerUqTgcDnbu3InT6aRly5bqWREREREREb/yqeL4+eefGTlyJPHx8bhcLrKzs5k9ezZpaWn+zk9ERERE5JRkMjQMzBufipUpU6Ywc+ZMT3Gyfv16Jk+ezKJFi/yanIiIiIiI1F4+lXNFRUUVelE6dOhASUnoXYVXREREREROHT4VK3FxcSxfvtyzvXz5cuLjdUEqEREREZG/yjBMIX8LNp+GgU2ePJmxY8cyYcIEABo1asS0adP8mpiIiIiIiNRuPhUrTZs25d1336WoqAiXy0V0dLS/8xIRERERkVrOp2Jlw4YNvPHGG+Tk5OB2uz37582b57fEREREREROZSZdFNIrn4qVBx98kFtuuYUWLVpgGIa/cxIREREREfGtWAkPD+fmm2/2dy4iIiIiIiIe1RYr+/fvB6B169bMmTOHnj17YjabPffXr1/fv9mJiIiIiEitVW2xcsstt2AYBm63m7Vr11aYo2IYBitWrPB7giIiIiIip6JQWBo41FVbrHz++eden+Dtt9+mX79+NZaQiIiIiIgI+HhRyOosXLiwJvIQERERERGpwKcJ9tU5eiljERERERHxjUnDwLw64SOkpYxFRERERMQfVM6JiIiIiEhIOuFhYCIiIiIicvwM9Rt4dcJHKCYmpibyEBERERERqcCnnpW8vDyef/551q1bh8Vi4cILL+See+4hPDy8wrVXREREREREaopPPStjx47FarXy7LPPMnXqVIqKipgwYYK/cxMREREROWWZDFPI34LNp56Vffv28fLLL3u2J0yYwFVXXeW3pERERERERHwql1q0aMH333/v2d68eTNNmjTxW1IiIiIiIiI+9azs2LGDW265hWbNmmE2m9m5cydxcXFcfPHFGIbBihUr/J2niIiIiMgpRauBeedTsfLSSy/5Ow8REREREZEKfCrnGjRowH//+1/eeecd6tSpw3fffUeDBg08NxERERERkZrmU7Hy7LPP8uWXX7Js2TKcTifvvfceTz31lL9zExERERGRWsynYuWbb77hmWeewWazER0dzZtvvslXX33l79xERERERE5ZwV6W+GRYutinDEymimGlpaWV9omIiIiIiNQknybYX3bZZYwaNYq8vDzmzJnDhx9+qOusiIiIiIiIX/lUrHTv3p2UlBT27NnDDz/8wL333kv37t39nJqIiIiIyKnLCIFhVqGu2mLl4MGDjBw5ku3bt9OkSRMsFgtr166luLiYjh07EhMTE6g8RURERESkljHcbre7qjvHjx9PUlISI0aMwGq1AuBwOHj++efJyso67hXBdvXscGLZniJKCxzBTiFkWCJ96tyrFQ4fKAl2CiEjOtYc7BRCxuln60ehP2Tl6X3xh9xdecFOIWS4nVX+GVPr1L+gYbBTCBmR05YGOwWfuFkZ7BS8MugR1Nev9i/FH3/8kU8++aTCPqvVyujRo7n22mv9mpiIiIiIyKnMOBlqbSO4L1/tQDmbzXbM/YZhaDUwERERERHxq2orDsOoupSq7j4REREREZETVe0wsG3bttGzZ89K+91uN1lZWX5LSkRERETklOd2BTsD74LcP1FtsfLpp58GKg8REREREZEKqi1WGjRoEKg8REREREREKtC6sSIiIiIiwXAyDAMLMi3pJSIiIiIiIUnFioiIiIiIhCQVKyIiIiIiEpI0Z0VEREREJBg0Z8Ur9ayIiIiIiEhIUrEiIiIiIiIn7KOPPuKKK66gV69ezJ8/v8q4L774gosvvtin59QwMBERERGRYDiFhoFlZGQwc+ZM3n//fcLCwujfvz9dunShRYsWFeKys7N5+umnfX5e9ayIiIiIiMgJWb16NV27diU+Pp7IyEguvfRSli5dWinu4YcfZvjw4T4/r3pWRERERETkmPLy8sjLy6u0PzY2ltjYWM92ZmYmycnJnu2UlBQ2bNhQ4THz5s2jTZs2pKWl+fz6KlZERERERILBFfrDwObOncuLL75Yaf/w4cMZMWKEZ9vlcmEYhmfb7XZX2N66dSvLli1jzpw5HDhwwOfXV7EiIiIiIiLHdOutt9KnT59K+4/uVQGoW7cu33//vWc7KyuLlJQUz/bSpUvJysri+uuvx+FwkJmZyU033cSCBQuqfX0VKyIiIiIickx/Hu5VlfPOO48XXniBQ4cOERERwbJly5g8ebLn/pEjRzJy5EgA9u7dy8CBA70WKqAJ9iIiIiIiweF2hf7NR6mpqdx3330MHDiQ3r17c9VVV9G+fXsGDRrETz/99JcPkeH+f/buO8qJsuOPeiUAACAASURBVO3j+HdSt1d2lw4iIFKtCIgKqAhWUAQf6aCINBHEQhUR8UUsiIpiQXmsYBe7YHtAsSvSBYQFtsGyvSSb5P0jGljZksXNboDf55ycs5m5Jrnu2ZlJrtz3zHg8nqNeuop2XXhaTb1VUHPkOWs7haBhCVPn3t+yUotrO4WgERFlru0UgkbLMyJrO4WgkZGj7eJv2buOPNn1ROVx1djXmKBX/7yGtZ1C0Aibf+RVqIJS0Xu1nUHlQq6o1bdXz4qIiIiIiAQl/awtIiIiIlIbjqObQgaKelZERERERCQoqVgREREREZGgpGFgIiIiIiK1QcPAKqWeFRERERERCUoqVkREREREJCipWBERERERkaCkc1ZERERERGqDW+esVEY9KyIiIiIiEpRUrIiIiIiISFDSMDARERERkdqgSxdXSj0rIiIiIiISlFSsiIiIiIhIUNIwMBERERGR2qBhYJU65oqV0HPOI+aG8RhWG44d2ziw4G48Bfn+x5lMxI6eTOjZXcBsJmf5MvJWvg6A7ZQ2xI6ZgikkFEwmcl5bSv5nHwBgPak5cePuxBQegcftIvPhe3Fs21Sjba9I2LnnU+fmiRg2G8V/bCV97gzc+Ueul/LiDLudhCnTCWndDgyDog2/kfHAvXiKi33LmiKjaPzCcvY/9hB5qz+pyeZVKrTTecTdMAGsNpw7tpLxQDnbRXlxJhNxN08mtOO5GGYz2a8tI/e9FQBYmzSjzuSZGKGh4IGDTy+k8Pu1AERdO4TIS/vgcZXgzjrI/ofmULJvT422vSJRF1xA/VsnYdhsFG7Zwu7p08rcLiqKa7f2Gxypqb7Y9Oee5eDKlYSecgoNZ87CHBmBKy+flIWPkLduXY21raoCuY/YTm5Bo2dexrlnt+91UqZNxrn7zxpsYWA9P3QG6/du58HPXq7tVKqN9bTOhA8YDRYbruQ/yHt6Hp7CgirHRE68D/fB/eS/8BAA5kYnEzHiNgx7KHg85C9/Cuev39ZYu45WWOfziLtpIobNimP7NtLnzSzzOFpZnDkxiYZPvUTysH64s7MAsDZtRsLtszCFhoHHw4EnH6Hwu7U11raqCutyPvE3T8SwWnFs30ra3HLWRTlxht1Owm3TsZ/aFgyD4o3ryVjgPV5YGzYiYcpMzDGxGFYrOe+9SdYrL9RCKytnatURW+/hYLHiTtmJY8XDUFzgX0xoBLarx2OqfzI4iij5/hNK1r7rXaZhS6xX3oRhCwHDhPOLFbh+Xl0bTZRjwDE1DMwUHUv8lNlk3H0b+4b1oSRlD7E33FKluIjL+2Ft2IR9I/uROmYgUdcMxHZKWwASZi0g+4XFpNw0gPS7xhI7+jYsDRpj2ENI/L/FZL/2PCmjryP7xaepM/W+Gm17RcwxsSRNv5eUuyayq//lOPfuIX7MpCrFxQ27CcNsZvfAvuwe2BeTPYTYoTeWWj5p5n2YIiJrpE1VYYqOJeH2e0ibNZm9Q6/CmbKXuFFlbxflxUVe4d0u9g6/hn2jryeq30BsrbzbRfzEqeR++Db7bhzA/vmzSJw5H0xmQs44h8hL+7Bv7GD23dCf/K9XkXD7PTXa9opYYmNpPPc+dt4ygU2X9saxJ5n6kydXKc7e9CRKsrPZcnVf3+PgypUAnPT4Exx4/XU2X3klOyeMp9Gsu7HUqVOjbfRXoPeRkPankfvJ++wefI3vcbwUKq3qNmXVxMfod3qP2k6lWhmRMUSOmkbOI9PImvIfXOn7CBtwc5VjQi+/Husp7UtNixwzk8KVL5M1dRi5i+8hcvwcMAf3b4OmmFgSp84hbfqtJF9/Jc59e4i/eWKV4yJ6XUGDx57HkpBUarmEydPJff8t9gy/lvR5M0m6ZwGYzQFv19EwxcSSOG0OqXdNZPd1V+Dcu4c6Y26tUlzs0FFgNpM8+GqSB1+NYbMTO+QGABKnzyXvs49IHtqPPTcOJKrPtYSe2bFG2+iX8Gjs/SdR/N85FD1wA54DKVh7D/c7xnbFTVBcRNGCURQ9NhFzq7Mwneptp23wdJyfvEjRI2Mpfm4GtitGYdSpX9MtlGPEMVWshJ7VmeItGyjZ6/31MvfdFYRf2LtKcWFde5D30TvgduHOyyX/848Jv+hSsNrI/u9TFP3k/WXYtT8dV/ZBzAmJhJzVmZJ9eyj67n8AFK79gow5t9dAi/0Tdk4Xijf9jjPZ297sN18lstdlVYor/OUHMp97CjwecLsp3rIJa91DB464EaNxbN9K8R9ba6BFVRN6dmeKt/x+6P/9znIiLry0SnHhXXuQe/h2sfojIi72rhvDZMYUEQWAKSwMj8MBgCvzAAcemev7ta14y0YsSfUC29gqiDz3XAp+X0/xrl0A7H/lVeIuv6JKceGnnw4uFy3++yKt3n6HumPGgMmEOSYGW926ZL7zNgAl+/dTuGULUV3Pq6HWVU2g95HQdqdja9qMRi+soNFzrxLe7aIaalngjb3gGp5Z8y4rfjq+fvW0tetIyY5NuNO8PaFFn72F/dyeVYqxnno61vadKFr1TqnlsqaNwPHj1wCYkxrgKcgN+hu/hZ3dhaJNG3y9gzlvveY7BvobZ45PIPy8HqRMHn3kG5hMmCL/Po6G+46jwSisYxeKD2tj9puvEXFJGeuigrjCX37k4NLDjhdbN2H563iR896b5H7yPgDu/Dyce3b75gUTc8szcCdvxbN/HwAl376P5R8/WlQUY2rYgpKfVnmHOblKcG36Hku788BixfnZS7j/+BkAT/Z+PHnZGNHB+WNXwHncwf+oZX791JOTk8N7771HVlYWHo/HN33cuHEBS6ws5oQkXBmHhqO4MtIwRURihIWX7oKuIM6SkETJ4fP2p2Fr1gKcDvI+fNs3PeKyazCFhuHYuJ7Ivv/BdXA/8bfNwtqsJe68XLKefiTArfWfJakeJWmH2lSSnoY5IhJTeHipYS4VxRWsO9Qdb6lbj5jrBpM+724Awjp2JvT0s9h7yygaPPZs4BtURZaEupSkp/mel5SzXVQUZ06siys9tdS8sGYtAdi/8D7qPfQ00dcOwhwTR/qcO8DtwvnnHzj/XsBqJe7GW8j/8tOAtrUqbHXr4Uw51CZHWirmyCO3i4riDIuZ3G++Yd9DD2JYLDR78ilceXlkLFuGY88e4vr0IfPNN7E1bEjEmWdSuHFjjbbRX4HeR9yFheR+8gHZby3H2rgpDRc/z77UFIo3bwh84wJs/GsPAtDz1HNqOZPqZYpPxJWZ7nvuzszAFBaBERrmG+ZVUYxhDyN88C1kz59MaI+rSr+42wVA7EPLMSXUJX/ZwqD4wK+IJakuJf84BprLOo5WEOc6kEHatCN7IAD2P3Qf9Rc+Q0z/IZhj40ibNQVcrsA16F+ojnVx+BA3S916xAwYTPr/zQYg9/1D3zXCOp1LSLvTSL9vZiCbdFSM6ATc2Rm+557sDIzQcLCH+YaCVRTj3r0FyxkX4vhzA1ismNud6/2flzhxff+xbxnzOb3BHop71+aaa5wcU/wqVm655RYiIyNp0aIFhmEEOqfymUzeXyn+ye3yP+6IeQaef/ziFXXdcCKvvp70O8ficRSD2UJox66kTb4Rx+bfCe3SjcT7HmPP9b3B6aTWGUapIvJvHpe7ynH2Vq2p93+PkrXiZfLXfIklqR51brmdveNvDN5fBk1GOf9vt99xhlG6k9EwDHC7MKw2EmfOJ+P+mRR++xX2U9uRdN+j7N38O64Mb+Fjio4lcfYC3Hl5ZD7zaHW16t8zmfDgz3opP+7AihWlJqU//zwJgweTsWwZO8aOof7tt5M4dBiFmzeT89WXeIJhfyhLAPcRgIwH5vjmO//cQd5nHxF+Xrfjolg5bhllf06U+jwoJwYMIsfdTf6Lj+LJOlDuWxyc1B9TQj2iZz6Ba+9OnBt/qobEA8Tw8zjqb9zhi9hsJM1+gPT7plOw9ivsbdpT7/5FFG3+HddhPyAFC6Pc7xDuKsfZT2lN3fsXkv3GKxT8dbz4W2TvK6kzYQqp0ybhOrC/WnKvVuX+r11+xThWLsF2+Y2ETHwcT+5BXNt+xtzk1FJhlm79sXbtQ9Gz06AkeHvbpHb5Vazs37+fpUuXBjqXMkUPu5mwzt0AMMLCce7c5ptnrpOIKycbT1FRqWVc6SnY/zrf4J9xJWkpmOMTDs2LT/B96cRqpc7t92Bt0ozU8UNxpXm7NV0HMnDu3olj8++AdxgYk2diqdeQkt07q7/RfogbNY6I87oDYAoPp3j7ofViSUjElZ2Np6iw1DIlaSmEtG1fblzExb1JnDKDjAVzfV3UERf2xAgJocHCpwCwNmyMdfxkzNExZL+1PKBtrEjM8DGEdbkAAFNYBI6d/2h/TlntT8V+arsy40rSj9wuSjLSsJ7UHJM9hMJvvwKgeNN6HH9ux35qOwoy0rA2a0HS3IUUfL2azCcfqvWCru748UR393bBmyMiKNx6aNieNSmJkqws3IWl14sjZR9h7duXGRd75ZUUbt5M0V+vYxgGlPxVkJhM7Bgzxvfr6MnPPEv26uAZKlRT+wgmE7FDbiBr+Yt4Cv468dQw8JSUBLB18m+5D6Riad7a99wUVwd3Xg4UF1UaY27QFFNifcIHTfBOj44DkwnDaiNv6QJsZ1+AY91q8HhwZ6Tg/P0HLE1bBl2xEjtyLOFduwFgCo/Acfg+Uqf842hI6/aVxh3OdlJzjJAQCtb+dRzd8BuOP7cT0ro9+enB0Rsdd+NYwrtWcLwoo43O1BTsrcv+TAGIuKg3CVOmk/HgXPI++aDUsvHjbyOie0/2TrgBx7YtgWrWv+LJysDUuJXvuRFVxzuk0VnsV4wRHoXj/WegMA8AS/cBuA94v1dhtmIbMBlTYmOKHr8Vz8HgK1priscTnD2Mh6vFbgrAz3NWTj31VDZvrp3uueznvSe8p9w0gNTxg7G3bo+lQWPAe1J04dovjlim8Idvyo0rXPsFEb37gMmMER5JePdLKFjzOQB17roPIyyC1AmHChWAwu/+h6VuA2wtvL8I2NudAR4oSdkbqGZXKnPJY74TeZNHXk9I2/ZYG3nbG331APK/PvJLY8G6teXGhXftRsKku9g74cZDX8KArJdfYNc1vX3vVbTpd/YverBWCxWArKVPsO/GAd7H2MGEnHr4//taCtZ8ccQyhT98U25cwZoviPxruzCFRxLeoxcFaz6nZG8yRkQE9jYdALDUb4ityck4/tiMuU4i9R56mqxlS8h8YkGtFyoAqYsW+U6E33rdAMI7dMDepAkAdQZcV2YxkbtmTblxoS1aUG/8BO8XMbudOgMHcvDDDwFoNPseYi70npsRftrphDRvTu4339REM/1SU/sIbjcR53Unus+1gHfIR0T3i8n7PDi+hEnZHOu/w9q8DaakhgCEXNjXd55JZTElf2zg4ISryZo6jKypwyha9TbF364m75n7wVVC+LWjsHf27hummDpYW5+Bc9MvNdtAPxx89nH2DL+WPcOvZe+ogdjbtMfa0LvtR/XpT/7Xnx+xTOF3a/2KO5xzbzKm8AjsbQ87jjZtRvHW4LmiZubTj5M8tJ/vpPeQth18bYzuO4D8r448XhR+t7bcuLCuF1Dn1jvZd8uoIwuVcZMJPe0skkcMCNpCBcC19UfMjVv5Tny3dLoM14Zv/I6xdLoMa88h3sCIGCwde+H6+QsAbP+5HcMedsIXKuIfw1PWmId/6Nu3L5s3byY+Ph673Y7H48EwDFatWlWlN9t14WlHnejfQjp2JfaG8RgWK86UPRy4fzru3BxsLVsTP3kWKTcNqDAOk5nY0ZMIObMThsVK3srXyVmxDFvr9tRbtAxn8p+lLtd78OlHKPrhG+ztziD2plsxQkLxOB0cfHw+xb8f3YePI6/6h8qEdTmPOmNuxbBYcO5NJnX2VNw52dhbtSFp2j3sHnxNhXFNlq/EFBWNK+PQ+OzC334m44F7S71PgyeWkv36K9V26WJLWPVcISf0nK7E3jgBw2KlZN8eMuZN820XdabMYt+NAyqMw2Qm7uZJhJ7VGcNiIee918lZvgyAkNPO/usynXY8LhdZLzxJwZrPiZ80g4iel+NM/tOXh8fpJGXMoKNqQ1ZqceVBVRR1/vneSxJbrRQnJ7PrzjtwZWcT2qYtjefMYcvVfSuMM0JCaDR9BmEdOmBYLWR99DEpjzwMQEiLFjSeMwdTaBjuggKS58yhcGP1DHuKiKr+qwQFch+xNmxM4h0zMcfFg9lM5jNPkPfZR9WSd8szguMKfEuHzOD3fbV76eKMnOrdLqwdOhM+4CbvJVfT95K7eA7mxAZE3HgnWVOHlRvjyc8t9TphV4/AiIw57NLFzYgYNtk7dt/tpuC9F709LdUoe1dOtb4eQFin84gbfYv3c3NvMun3TsWdm4P9lNYk3DmbPcOvrTDucCf/bz07LzvPd+nikNPPJn7MJAybHVwlZC59koIyfjA4Gh5XpV9jqiys83m+SxI79yaTds9duHNysLdqQ+Jds0ke2q/CuMavvoc5KpqSw48X63/m4AvP0PStTyhJS8Gdl+ebl7X8xVLnshyt+uc1/NevcThTq7Ox9RoOZguezBSKX30AU3w9bP0mUvTI2HJjKMwDeyi2AVMw1akPGDg/fw3Xz6sxNT6VkHEP487YU6qXxvHBc7i3/lhtuYfNr55jcKB5sl6s7RQqZcQc3feaant/f4qVvXvL7kFo0KBBld6sOoqV40EgipVjVXUVK8eDQBQrx6pAFCvHqmApVoJBdRcrx7JAFCvHqkAUK8eq6i5WjmUqVqpPbRcrFX5T/Pzzz+nevTvff/99mfOrWqyIiIiIiMhfgmAIebCrsFhZv3493bt3Z105d6Xu06dPQJISERERERGpsFiZMMF7pZN58+bVSDIiIiIiIiJ/8+uEgY8++oglS5aQnZ1danpVT7AXEREREZG/BPkNY4OBX8XK//3f/zF//nzq168f6HxEREREREQAP4uVxo0bc+aZZ2Iy+XVbFhERERERkX/Nr2JlxIgRDBkyhLPPPhuz+dClI8eNGxewxEREREREjmsaBlYpv7pKFi9eTKNGjUoVKiIiIiIiIoHkV8+K0+nUFcFERERERKRG+VWsnHvuubz44oucd955WK1W33SdcC8iIiIicpQ0DKxSfhUrK1euBOC5557zTTMMQ5cuFhERERGRgPGrWFm9enW581577TUGDBhQbQmJiIiIiIiAnyfYV+TVV1+tjjxERERERE4sHnfwP2rZvy5WPB5PdeQhIiIiIiJSyr8uVgzDqI48RERERERESvHrnBUREREREalm7tofZhXs/nXPioiIiIiISCD862IlMjKyOvIQEREREREpxa9hYDk5OTz66KOsW7cOi8XC+eefz80330xISAjLli0LdI4iIiIiInIC8qtnZcqUKVitVhYsWMC8efMoKChg2rRpgc5NREREROT4VduXJT4GLl3sV8/K3r17eeqpp3zPp02bxuWXXx6wpERERERERPzqWWnevDk//PCD7/nmzZtp0qRJwJISERERERHxq2dlx44dDBo0iJNOOgmz2czOnTuJjo6mR48eGIbBqlWrAp2niIiIiMjxJQiGWQU7v4qVxYsXBzoPERERERGRUvwaBtagQQN++uknli9fTlxcHN9//z0NGjTwPURERERERKqbX8XKggUL+PLLL/nkk09wuVy88cYb3H///YHOTURERETk+FXbV/o6Bq4G5lex8r///Y8HHngAu91OREQES5cu5auvvgp0biIiIiIicgLzq1gxmUqHORyOI6aJiIiIiIhUJ79OsO/VqxcTJ04kJyeH559/nnfeeUf3WRERERER+TfctT/MKtj5Vax069aNxMREkpOT+fHHH7nlllvo1q1bgFMTEREREZETWYXFyoEDB5gwYQJ//PEHTZo0wWKx8O2331JUVMSZZ55JZGRkTeUpIiIiIiInmAqLlQcffJAzzzyT559/HqvVCoDT6eTRRx9l7ty5uiKYiIiIiMjRCoKrbQW7CouVn3/+mQ8//LDUNKvVyqRJk7jqqqsCmpiIiIiIiJzYKrykl91uL3O6YRi6GpiIiIiIiARUhRWHYRhHNU9EREREROTfqnAY2LZt27jwwguPmO7xeMjIyAhYUiIiIiIixz2ds1KpCouVjz/+uFrfLCwhrFpf71hli3LVdgpBw5FTXNspBI24+iG1nULQMFk1zPRvGTnm2k4haCTo2Onz0hpHbacQNHoPr1vbKQSN1U/urO0Ugsbl82s7A6kuFRYrDRo0qKk8RERERERESvHrppAiIiIiIlLNdAf7SmmshYiIiIiIBCUVKyIiIiIiEpQ0DExEREREpDa4PbWdQdBTz4qIiIiIiAQlFSsiIiIiIhKUNAxMRERERKQ26GpglVLPioiIiIiIBCUVKyIiIiIiEpQ0DExEREREpDZoGFil1LMiIiIiIiJBScWKiIiIiIgEJRUrIiIiIiISlHTOioiIiIhIbdAd7CulnhUREREREQlKKlZERERERCQoaRiYiIiIiEht0KWLK6WeFRERERERCUoqVkREREREJChpGJiIiIiISG3QMLBKqWdFRERERESCkooVEREREREJShoGJiIiIiJSG3RTyEqpZ0VERERERIKSihUREREREQlKGgYmIiIiIlIbdDWwSqlnRUREREREgpKKFRERERERCUoaBiYiIiIiUht0NbBKqWdFRERERESCkooVEREREREJSipWREREREQkKOmcFRERERGR2qBLF1dKPSsiIiIiIhKUjumeFdvpXQi/bjRYrbh2byf3qfvwFBb4FWOEhhM5eirm+k3AMCj66kMK330RAHODpkTeeAdGSCgeD+S/shjnb+tqo4l+s595LpGDbsaw2nDu+oPsx+biKcz3L8ZmJ3rUFKwtWgMGzm0byF7yADiKfcsa4ZHUWfACucseo+ib1TXcusqFnnMeMTeMx7DacOzYxoEFd+MpyPc/zmQidvRkQs/uAmYzOcuXkbfydQDsp51F7KhbMSwWPMXFZD42H8eW3wGIvHYwEb36gKsEV9ZBMh++l5KUPTXa9n+qrXURM3wsYd0uwVNUSPGGX8lcvACcjhpt++FCOnYlevh4DKsV585tZD58zxHrodwYk4mYGycRclZnMFvIfX0Z+R+84V3mnPOJu202rvRU3+uk3zYST2EBYT0uJbLfEPB48BQXcXDxfJzbNtVou/1hPa0z4QNGg8WGK/kP8p6ed8Sx05+YyIn34T64n/wXHgLA3OhkIkbchmEPBY+H/OVP4fz12xprV015fugM1u/dzoOfvVzbqQRU/UsvoMO8yZjtNrJ+28K3I6dSknvkseRvnZ6/n6z1W9n84HO+aVdnfEvhnkP7yqYHnuXPl98LaN7/lqV9J0KvGYVhteJK3kH+0v+DogK/Y6IXvoP7YIYvtvijV3F8+xmmBs2ImvY4rvS9vnn5T87GnZpcMw2rJomXXECrWZMx2W3k/L6F38ZVvF10ePJ+cjdsZcci73ZhCrHT9sFZxJzZDsMwOPjDb/w+eTbuouJyX0NObMdsz4oRGUPk6GnkPDyVg5P+gyt9H+H/GeN3TFj/UbgOpHNwyiAOThtJ6MV9sbRoC0DEyNso+mIlB+8cRt5T9xE1cQ6YzDXeRn+ZomKIHj+dg/PvImNcf1ype4kcPMbvmIh+w8BkZv/Egey/dSCGzU7ENUNLLR8zYSam8IiaalKVmKJjiZ8ym4y7b2PfsD6UpOwh9oZbqhQXcXk/rA2bsG9kP1LHDCTqmoHYTmkLFgsJ0+dz4KF7SBk1gOyXnqbOXfcCEHLGOUT07kPq+CGkjBpAwf9WE3/77Bpt+z/V1roIv+QqQjudT8qYgaTcNABXZgYxI8bWaNtLty+GuEl3c2DObaTecDUlKXuJGT7e75iIS6/B0qAxqTf1J23CICL7Xo+tZRsA7K3bk/v6f0kb+x/fw1NYgKVhE2JuuIWM6eNIG/sfcl55hjozFtR42ytjRMYQOWoaOY9MI2uK97gYNuDmKseEXn491lPal5oWOWYmhStfJmvqMHIX30Pk+DlgPqZ/EyulVd2mrJr4GP1O71HbqQScvU4snZbO43/XjGdlq17k7UjmtPtvKzM2qlUzeqx6gcb9Lik1PbLlSTgys/jw9D6+R7AXKkZkNOEj7iT/8RnkTB2MO2Mfof1u8jvGVLcRnvxccu++wfdwfPsZAJbmbXCsW1Vq3rFWqNjiY+nwxDx+HDyeL87sRcGfybSaXfZ2EdGyGZ3ee4F6V5XeLlpMuRmTxcxXna/ky85XYg6103zyTWW+xgnB7Q7+Ry3zq1i56667Ap1Hldnad8S5fROuVO+v2IWfvom9a0+/Y/JfeJj8Fx8DwBQTDxYrnoI8AAyTCSM8yvt3SBg4au/XYX/YTjsH57ZNuFK8B72Cj94k9Pxefsc4NvxC3uvPgccDbjfOHVswJ9T1LRtx7QhKdm/HueuPGmpR1YSe1ZniLRso2bsbgNx3VxB+Ye8qxYV17UHeR++A24U7L5f8zz8m/KJLoaSEPQN64vxjCwCWeg1x5WQD4MrcT+Yj9/l+rXds2YglqV7A21uR2loXtpanUrDmczz5uQAUfL2a8PMvDnh7yxNyRmccWzdQss+7vee9v4KwHr39jgnt0p38T98FtwtPXi4FX35C2IWXAmA7tQMhp51N0hOvkrjgWextzwDA43SQ+cgc3Jn7AXBs3Yg5tg5YguvLuq1dR0p2bMKd5j0uFn32FvZze1Ypxnrq6Vjbd6Jo1TullsuaNgLHj18DYE5qgKcgNyg+6KrL2Auu4Zk177Lip+DrXa5u9Xp25cD368n9YxcA2xa/QtOBV5QZ23LsQLY/s4LdKz4qNT2hy+l4XG4u+uolev/6Lm1njMUwBfdvpNY2Z+PauRn3X70fxZ+/g73TRX7HWJq3BbebiDsXETn7OUKu+iNxVwAAIABJREFUGAqGyTfPVK8JkbOeJnL6k1jPOK8GW1Y9Ei7sStZP68nf7t0udj37Cg2uLXu7aDpqILuXrSDl7dLbxYE137PtgcW+7xw5v24itFH9gOcuxy6/PkW3bt1Kfn4+4eHhgc7Hb6b4JNwH0nzP3QcyMIVFYISG+YYqVBrjdhE5dhb2c7pR/P1XuPb99cXtuQeJmb6I0EsHYIqOJWfhTHC7araBVWCuk4TrsHa6DqRjCo/ACA33DQWrKMbx66EhbuaEuoRfcR3Zi+cBYOvQEVub08m85xbiZj9WQy2qGnNCEq6MQ8MMXBlpmCIiMcLCSw37qSjOkpBEyeHz9qdha9biryclmGLjqPfkq5ijYsi49w4AnH9uP5SE1UrsjRPI//LTALXSP7W1Lhyb1hPZbxC5b7+KOzeb8J6XY46rE+DWls/bvsO294x0TOGl10NFMUfM25+G9STvOnDnZlPw+YcU/m8VtjanUWfWQ6SNuQ5XWgqutBTfMjE3Tabw2y+hpCTQza0SU3wirsx033N3ZlnHzvJjDHsY4YNvIXv+ZEJ7XFX6xf86TsY+tBxTQl3yly0Ez/FTrIx/7UEAep56Ti1nEnhhjepSkHzoOFCwJxVbdCSWyPAjhvz8MH4OAPV6nltqumExk/rZWn6580FMVgvd3l+CMyePLQtfCHwDjpIRl4j78G3/YAZGWASEhPmGeVUYYzLj3Pgjha8/hWG2EDHxfjxF+RR/+jqe4iKc61ZR/MW7mOo2IvKOheRlpuP6c0uNt/NohTSoS9Fhw/qK9qZiLWe7+P0273aR0KP0drF/9Rrf36GN6nPSmKH8dsuMAGYtxzq/ihWTyUT37t056aSTsNvtvunLli0LWGKVMUwGcORdPz2H/YrnT0zu47PJfWY+0ZPuI+ya4RS881+ibplD7pP34vhpLZbmbYi+fT4Hd2zCfSD9iNcKCobh/YXinw4vsPyIsTRrRdyd/0f+Byso/mENpjpJRA2/hcy7xwf3r6MmU+XtryzuiHlGqe3EfTCTvQN6YmvRisQHlpC6azsle7zFrSk6loRZD+DOzyPr2UXV0KB/oZbWRf5n72NOSCLpwSV4igrJXfkmnhJn9bTpaBjltM/l8i/miHmGbx0emHNoyINjwy84Nv5GyOmdvD0xgGEPIe622Zjr1CVjeu0NhStXOe0+/H9c7rrBIHLc3eS/+CierAPlvsXBSf0xJdQjeuYTuPbuxLnxp2pIXGpUOccIj8v/z4Ltz6zw/e0CNj+0lJYTBgd3sWKU0/Nz+HeLCmIcX630PfUARZ8sx37RNRR/+jqFLz58KDRlF47vPsfaocsxVawYJhOef7ld/C36tDac9dJj/LnkRdI/+qIasjs2lbU+g41Ry+/vV7EyZcqUQOdRZa79aViat/E9N8Ul4M7LgeIiv2Ks7c/Blbwd98H9UFxI0dpPsXfshqVRMwxbCI6f1gJQ8scGSvbs9I41DdJixb0/DVvLtr7n5vgE3LnZeA5bF5XFhHS9mOhRU8h+egFFX38CQGiXC71fvGYu9C5TtyGRQ8djioqm4OO3aqJp5YoedjNhnbsBYISF49y5zTfPXCcRV042nqKiUsu40lOwt2pbZlxJWgrm+IRD8+ITcGWkYYRHEHLa2RSu+RwAx7bNOLdvxXZSC0r27MbarAWJcxZS8L/VHHzqoVop6oJhXbizs8hf9SE5r3hPoLS37kDJ3tobi+3KSD2yff/YJyqKcWWklrEO0jHCI4i4vD+5rx06gRjDwOPy9p6YE+pSZ/YjlOzeScYdo/A4gu+EUfeBVCzNW/uem+LqHHHsLC/G3KAppsT6hA+a4J0eHQcmE4bVRt7SBdjOvgDHutXg8eDOSMH5+w9YmrZUsXKMaDd7Ag2v9J6PY42KIGv9Vt+80AZJFGdm4Soo9Pv1mg66iqxfN5O1/q8v44aBxxlcPY3/5D6QhrnZqb7npti/9g9HkV8xts49cSX/gWvPjr/mGuAqAcNEyGXXU/TZG1BU6JuFK7jXB0DLaRNI6u3dLiyREeRuPLRdhNRPwnGwatsFQP1rLqXtQ7P4/bY57FuxsvIF5ITm1+BRwzDKfNQmx2/fYW3eBnPdhgCEXtQHxw9f+x1j79yDsGtGeAMtVuydeuDc8COu1D3eoTB/fbE3JTXA0uAkSv7cSrAq/mUd1pZtMddrBEDYJVdT9N3XfsfYz+pK1A2TyJw9wVeoAOS/+zIZN1/D/kmD2T9pMM7tm8h9YVGtFyoA2c8vJuWmAaTcNIDU8YOxt26PpUFjACKv6Efh2i+OWKbwh2/KjStc+wURvfuAyYwRHkl490soWPM5uFzET5mNvc1pAFibnIy1cVOKN63HXCeRpAVLyPrvUxxcvKDWep+CYV3YWrYh4Z6HvCdTm8xE/Wc4+as+CHzjy1H04zfYWrXDUt+7vUdcdg1F33zpd0zhN18Q3vOqv9ZBBGEXXELhN5/jKSwg4or+hJ771xe6k0/Bdkobin5YixEaRuL8JRSuWc2B++8KykIFwLHee1w0JXmPiyEX9vWdZ1JZTMkfGzg44Wqypg4ja+owila9TfG3q8l75n5wlRB+7Sjsnb1j900xdbC2PgPnpl9qtoFy1NbPetR3IvzHnfoT36kDkc2bANBi9HXseWdVlV4vpm0L2t0zAcNkwhxip+W4gex6rfaOC/5wbvgeS7PWmBIbAGDrdiXOX9b4HWNucBIhfUZ4eyetNuwX9sXx3efgcWM97VzsF3jP7zDFJ2E78wIcP35Vg607OlvnPsrXXfvwddc+rLmwP7FndyD8ZO920WTEdaS9X7XtIrFXd9rMn866PiNVqIhfDI8f/U+DBw/2/V1SUsKWLVs466yzWLJkSZXeLOO6LlXPsAK20zoT/p/RYLHiSttL7uP3YE5qQOSoOzl457ByYzz5uRhhEUTccDuWRs0AKP7+SwpWPAMeD9bWZxA+cCyG1YbH5aLgjedw/FB9B5SS4uo//8V+RhciB43BsFooSd1L1sLZWJLqEz12GvsnDS43xpOXQ8JjyzEionBnHrrUomPzb+QseaDUe8TNeYKCD16v1ksXO3Kq5wtdSMeuxN4wHsNixZmyhwP3T8edm4OtZWviJ88i5aYBFcZhMhM7ehIhZ3bCsFjJW/k6OSu8wxzt7c8k9qZJ3sv1Oh1kPfMoRb98T9yt0wm/+HJKknf58vA4HaSOG1xmjjWlNtYFQMyIcYR17QEmEwVrPvcOiTvKAs5k/fcn4Yacfa73ssQWKyUpe8h8YAbmeg2ImziTtLH/KTfGneddBzE33krIGeeAxUr+B2+Q+8Z/AbC2OJXYMXdgCg3D43KR9dSDFP/2A5EDhhM9ZAzOP0tfiCLjztG4c7OPuh2h8aFHvxLKYe3QmfABN4HFijt9L7mL52BObEDEjXeSNXVYuTF/X0Dhb2FXj8CIjDns0sXNiBg2GSM0HNxuCt570dvTUk0SooLj3MGlQ2bw+77avXTxS08eDPh71O99Ph3mTcZks5K3fTffDLkDx8Fs4s5syznP3MuHp/cpFd9p6Tyyft/mu3SxOTSEsx6bSZ1OHTBZLexe8RG/Tnu4rLf6V3oPr1t5UBVY2p1DaL9RGGYrroy9FDxzH6aE+oQNm0Lu3TeUG+PJzwWbnbCBE7Gc3BrMFhzff0HRm08DYEpsQNiQSZiivD2She88j/P7z6s19zWvp1Ye9C8l9jyfVrMmY9isFOzczS833YHzYDbRp7el/aJ7+bpr6e2iw+J55G7c5rt0cbcfP8IaG03RvkPnBR5c9xO/T76nWvO8POfYGF7nXht8o5f+ydTlgcqDAsivYuWfkpOTmTdvHk888USVlqvuYuVYFYhi5VhVXcWKHF+qo1g5XgSiWDlWBUuxEgxqolg5VlR3sXIsq4li5VihYqX61HaxclTfCBo1asSOHTsqDxQRERERETlKfp1g/8/7rGzfvp2WLVsGJCERERERkRNCMF9tNUj4Vax07NjR97dhGPTq1YvOnTsHLCkRERERERG/hoH17duXNm3akJ+fT1ZWFomJidhstkDnJiIiIiIiJzC/ipW3336bMWPGsGfPHvbt28e4ceN4/fXXA52biIiIiIicwPwaBrZ06VJWrFhBbGwsAKNHj2bIkCH069cvoMmJiIiIiBy33MF/B/va5lfPitvt9hUqAHFxcbV+U0gRERERETm++dWzcsoppzB37lxfT8rrr79Oq1atApqYiIiIiIic2PwqVu69914effRRpk6disfj4ZxzzmHWrFmBzk1ERERE5PilSxdXyq9iJSQkhNtvvz3QuYiIiIiIiPj4VawsX76chx9+mKysLAA8Hg+GYbBp06aAJiciIiIiIicuv4qVJ598kmXLltGiRYtA5yMiIiIicmLQMLBK+XU1sPj4eBUqIiIiIiJSoyrsWXn77bcBqF+/PjfffDMXXnghFsuhRfr06RPY7ERERERE5IRVYbGybt06AMLCwggLC+PHH38sNV/FioiIiIjIUdJNIStVYbEyb968Sl9gxowZzJkzp9oSEhERERERAT/PWanI77//Xh15iIiIiIiIlOLX1cBERERERKSa6WpglfrXPSsiIiIiIiKBoGJFRERERESC0r8eBubx6CoGIiIiIiJVpmFglfrXPStdunSpjjxERERERERKqbBnZfDgwRiGUe78ZcuWcfvtt1d7UiIiIiIiIhUWK+PHj6+pPEREREREREqpsFjp2LGj7++NGzdSUFCAx+PB5XKxZ8+eUvNFRERERKQKdAf7Svl1gv306dP57rvvyM7OplmzZmzevJkzzjiDfv36BTo/ERERERE5Qfl1gv3atWt5//33ueSSS5gzZw7Lli2jqKgo0LmJiIiIiMgJzK9iJTExEavVysknn8yWLVto164dubm5gc5NREREROT45XYH/6MK3nvvPS699FJ69uzJSy+9dMT8zz77jKuuuoorr7ySMWPGkJ2dXelr+lWsJCUl8dRTT3H66afz6quv8v777+NwOKqUvIiIiIiIHJ/S0tJ4+OGHefnll3n77bd57bXX+OOPP3zz8/LyuPvuu1myZAnvvvsup5xyCosWLar0df0qVubOnUvDhg1p3749PXv2ZOXKldx9991H3RgRERERETl+rF27lk6dOhETE0NYWBiXXHIJH330kW++0+lk1qxZJCUlAXDKKaeQkpJS6ev6dYL9o48+ypVXXgl4770yePDgo2mDiIiIiIj87Ri4g31OTg45OTlHTI+KiiIqKsr3PD09nYSEBN/zxMREfvvtN9/z2NhYLr74YgCKiopYsmSJXzWFX8VK48aNmTt3LtnZ2VxxxRVcccUVNGzY0J9FRURERETkGPXCCy/w2GOPHTF93Lhxpe7J6Ha7S91M3uPxlHlz+dzcXMaOHUurVq3o27dvpe/vV7EyaNAgBg0aREpKCh988AFjx44lPDycl19+2Z/FRURERETkGDR06NAyi4rDe1UA6tatyw8//OB7npGRQWJiYqmY9PR0Ro4cSadOnZg6dapf7+9XsQLeKmjNmjWsWbMGl8vFueee6++iIiIiIiLyT8fATSH/OdyrPF26dGHRokVkZmYSGhrKJ598wpw5c3zzXS4Xo0ePpnfv3owZM8bv9/erWBk9ejQbNmygZ8+e3HLLLXTo0MHvNxARERERkeNbUlISt956K0OGDMHpdNKvXz/at2/PjTfeyIQJE0hNTWXjxo24XC4+/vhjANq2bcvcuXMrfF2/ipX+/ftz/vnnY7H43REjIiIiIiInkL/PbT/c008/DUC7du3YvHlzlV+zwupj0aJFjB8/nk8//ZRPP/30iPnz5s2r0pv9/NmBqmV3nAoNPfJkoxNV/SbW2k4haOzcqnsX/e2caxrUdgpBI319Rm2nEDReWqN95G8DR8fWdgpBY+1HabWdQtC45P9Oq+0UpKqOgauB1bYKi5U2bdoA0LFjxxpJRkRERERE5G8VFis9evQAYOXKlTz77LM1kpCIiIiIiAj4eQf7oqIiv+4wKSIiIiIiUl38OmP+wIED9OjRg/j4eOx2u+8mL6tWrQp0fiIiIiIixyWPK/gvXVzb/CpW5s+fT3x8fKBzERERERER8fGrWLnjjjv48MMPA52LiIiIiIiIj1/FSqtWrXj77bdp3749ISEhvun169cPWGIiIiIiIse1Y+AO9rXNr2Ll119/5ddffwXAMLz3CHE4HHz99deBy0xERERERE5ofhUrq1evBsDpdPLpp5/yyiuvsH79+oAmJiIiIiIiJza/ipXk5GSWL1/Om2++SXZ2NqNHj2bhwoWBzk1ERERE5Pilq4FVqsL7rHz66aeMHDmSa6+9lqysLObPn09iYiLjxo0jLi6upnIUEREREZETUIU9K+PHj6d379689tprNGnSBDh0zoqIiIiIiEggVVisvPvuu7z55ptcf/31NGjQgMsuuwyXy1VTuYmIiIiIHLc8uhpYpSocBtayZUvuvPNOvvzyS0aNGsW6devYv38/o0aN4ssvv6ypHEVERERE5ARUYbHyN4vFwkUXXcQTTzzBV199RadOnXjwwQcDnZuIiIiIiJzA/Loa2OHi4uIYMWIEI0aMCEQ+IiIiIiInBl0NrFJ+9ayIiIiIiIjUNBUrIiIiIiISlFSsiIiIiIhIUKryOSsiIiIiIlINXO7aziDoqWdFRERERESCkooVEREREREJShoGJiIiIiJSC3QH+8qpZ0VERERERIKSihUREREREQlKGgYmIiIiIlIbdAf7SqlnRUREREREgpKKFRERERERCUoaBiYiIiIiUht0NbBKqWdFRERERESCkooVEREREREJShoGJiIiIiJSCzy6Glil1LMiIiIiIiJBScWKiIiIiIgEJQ0DExERERGpDW53bWcQ9NSzIiIiIiIiQem46lmpc/EFtJg+GZPdRu6GLWy4ZSquvPxy49s+dj+5m7ay6/HnSk2316/LOR8v55sLrsKZeTDQaVeb2B4XcNKdkzBsNvI3bWHblGlltr/cOJOJ5vfOIPqcswHI/Pwrdt47HwBLTDQn3zOdsBYnYwoJIXnRk6S/+W6Ntq8qwjqfR9xNEzFsVhzbt5E+byaegiPXRWVx5sQkGj71EsnD+uHOzgLA2rQZCbfPwhQaBh4PB558hMLv1tZY26pL/EUX0Hz6ZEw2G3kbt7BxYsX7S+tF95O3aSu7n3iu3JhgZm57DvarbsCw2nDt2UHRiw9AUYHfMdbzr8R67mVgteHevZWiFxdAiRMjOp6QIbdjRMWBYeD45FVKvvusNproN+0f5at/6QV0mDcZs91G1m9b+HbkVEpyy98vOj1/P1nrt7L5wUP7xdUZ31K4J9X3fNMDz/Lny+8FNO/a8vzQGazfu50HP3u5tlOpVjHdL6Dx7ZMw2WwUbN7C9jvK/jwtL67lEwsJadLYF2dv2JCc775ny41jDpvWgPbvvcHGITeQv/73GmnXv/XlzmweWZOC0+WhZZ0Q7rmoMRF28xFxHo+HaZ/spkWdUIafmeib3vXJ9SRFWH3Ph5+VyOWt4mokdzl2HTc9K9b4WNo+Oo9fh49nTadeFO5KpuXM28qMDW/RjLPeeoGkKy45Yl69/lfR8b2XCKmXFOiUq5U1LpaWD97HxlET+LFbb4p2J9P0rslViku85ipCm53EjxdfyU+X9CG609nUucy7jlo+NI/ilFR+7n016/8znGazp2GrG5zryBQTS+LUOaRNv5Xk66/EuW8P8TdPrHJcRK8raPDY81gSSrczYfJ0ct9/iz3DryV93kyS7lkA5iMP1sHMGh9Lm4Xz+G34eL7p4t1fms8oe38Ja9GMM954gaTLj9xfjhVGRDQhQ26ncMnd5N89FPf+fdj73Oh3jOW087B260vBwtsomDMCbHZsPfoBYL9qJK6dmyiYeyOFi+4k5D8TMaJia7yN/tL+UT57nVg6LZ3H/64Zz8pWvcjbkcxp95e9X0S1akaPVS/QuF/p/SKy5Uk4MrP48PQ+vsfxWKi0qtuUVRMfo9/pPWo7lWpniYul+fz72HrzBH650Ps52fj2Iz9PK4rbOuYWfrusL79d1pftd83AlZvDzpn3+JY1bDaaP/wAhtV6xOsGq8yCEmZ8kswjl53EyqGn0jDKzsNr9h0Rtz2ziJFvbOfTbdmlpu/MLCI6xMIbg1r5HipUxB9+FSsbN25kwoQJDB06lCFDhvgewSS+e1eyf1lPwY5dACQvfYW6/a4oM7bRyIHseXEFqe9+VGq6vW4iiZdexI/9RwY83+oWc/655P26nqI/ve1P+e+rJPY5sv0VxRkmE6awUEw2G4bNhmG14i52YImJJua8Lux++HEAHKlp/HrlAEqyso94/WAQdnYXijZtwLlnNwA5b71GxMWXVSnOHJ9A+Hk9SJk8+sg3MJkwRUZ5/wwLx+NwBKglgRPfrSs5v6yncKd3O9jz/CvUu6ac/WXEQPa+tIK09z4qc/6xwHzqWbj/3IInYy8Azq/exdrxQr9jLOdcjHPVCijIBY+H4pcfxrnuU++CJjNGaLj3b5sd3C7wBO+lKLV/lK9ez64c+H49uX9494tti1+h6cCy94uWYwey/ZkV7F5Rer9I6HI6Hpebi756id6/vkvbGWMxTMfN74I+Yy+4hmfWvMuKn1bXdirVLua8c8n77dDnZNqLr1LnqjI+T/2IM6xWmi+4nz/vmYcj5VBvW7N7ZpLxxls4D2YFsCXVa+3uHNokhdEk1g7AgPbxvL/5IJ5/HO9e/XU/17SNp2eL6FLTf0nJx2TAkOXb6PviZhZ/m4pLd28Hlyf4H7XMr2Fgd9xxBwMGDKBFixYYhhHonI5KSP26FO09dCAo3peKNSoSc0T4EV23m++cA0B8t3NLTS9OTefXYeMDn2wA2OvXo3jfYe1PScVSRvsriktb8RZ1Lu9Fx++/xLBYyPpqDZmffU7Eae1wpGXQYNQwYrudj8luY+9Tz1G488+abKLfLEl1KUk/1MaSjDTMEZEYYeGlhrBUFOc6kEHatFvLfP39D91H/YXPENN/CObYONJmTQGXK3ANCgB7/boU7Su9v5S1vQBsuavs/eVYYopNxH0w3ffck5WBERoBIWG+YV4VxZiSGuLeFUPouPsxouNxbV9P8ZtLACh++2nCJi/EcsYFGJExFL+xGE9u8H4B0f5RvrBGdSlIPtTmgj2p2KIjsUSGHzEU7Ifx3v2iXs/S+4VhMZP62Vp+ufNBTFYL3d5fgjMnjy0LXwh8A2rQ+NceBKDnqefUcibVz1avHsWHFRbFqWUfH/2JS+x/DY60dDI/OTQ0NHFAPwyrhfRXV9BgbBkFf5BKzXVSN/JQT1BSpI08h5t8h7vUULBp3RsCsHZXTqnlXW7o3DiSiV3rUeLyMOadnUTYTAw+IxGRivhVrISEhDBo0KBA5/KvGCZT2b9mniBXWfD+cndk+z0ut99xTW4di/NAJuvO6IopxE7rZx6nwajh5P70C6FNGuHKzee3q68npGljOrz+IoU7d5G3fkOAWvQvGIZ/24K/cYcvYrORNPsB0u+bTsHar7C3aU+9+xdRtPl3XOlp/zLxmlPe/uI5XvcXUzk/shze3gpiDLMFc6szKXxyBjgdhAy9E/tVIyle8Tghw6fh+PQ1nF+9i5HQgLBJD+PasQn3rs3V347qoP2jfOXtFy7/94vtz6zw/e0CNj+0lJYTBh93xcrxrNzjY1mfp5XE1Rs5jB13zfQ9D2/TmqTrr2PDgOD+TlUWtwfKOkr623HYr138oSdWGHpGAi/9kqFiRSrlV7HStWtX/vvf/9K1a1fsdrtvev369QOWmD9OvnMCCZd4x8taIiPI27TVN89eLwnnwSxcBYW1lV7ANZk8nriLve03R0RQsOWw9tdNwpmVhbuwdPuL9u4j8vT2ZcbF976Y7TPm4nE6cTmdpL3+NnUuvYT9H34CQNryN72v8edusr//icjT2gVNsRI7cizhXbsBYAqPwLF9m2+epU4irpxsPEWl10VJWiohrdtXGnc420nNMUJCKFj7FQDFG37D8ed2Qlq3Jz/902psUfVrdseh/cUcGUH+xiP3F/dxur+4M9OxND3V99yIScCTnwOOIr9i3FkHKPnlf75eGOd3n2K/dAhGeBTm5m0pXOgdp+7J2Itr0w+YW7QPqmJF+0f52s2eQMMrvfuFNSqCrPWH9ovQBkkUZ1btc6TpoKvI+nUzWeu3eCcYBh5nSbXmLNWv0a3jib2o7M9TW90kSsr4PC3et4+I09qXGxfW+lQMs5mcdd/5YhKuvgpzZARt33jFu0xiAi0eeYBd8+Zz8LPPA9a+6lAv0sr61EM9S+l5TqLsZsKs/p2T9u6mTE6pE8opCaGA92dTS3k/Ep1APBoKVym/6uF33nmHpUuXMnLkSAYNGsSgQYMYPHhwoHOr1Pb7H+Xb7n34tnsfvuvVn+gzOxDWrAkADYddR/qHq2o5w8Da9eAifu7Vl5979eXXqwYQeXoHQpp6219v0HUc+OTIscRZX60pNy5v/UYSrugFgGGxEH9xd3J//oXi5L3k/raBxH59ALDWiSfqrNPJ/S14rl5y8NnH2TP8WvYMv5a9owZib9Mea0PvlVii+vQn/+sjPwQKv1vrV9zhnHuTMYVHYG/bAQBL/YbYmjajeOumam5R9dvxf4+yrkcf1vXow/e9+xN1VgdCT/JuBw2GXkfGR8fv/uLa9APmk07FSGgAgPW8Kyj5da3fMSU/f4XlzAvAagPA0qErrl1b8OTn4Dm4H8sZFwB4i5cW7XH/GVzbg/aP8q2f9ajvRPiPO/UnvlMHIpt794sWo69jzztV2y9i2rag3T0TMEwmzCF2Wo4byK7XPghE6lKNkh9e5Dshfv3VA4g47HOy7vXXkflpGZ+nX6+pMC7qnLPJ/ubbUsv8OWcev/To5XsvR3oG2yZOCfpCBaBLk0h+TS3jbCMAAAAgAElEQVRg18FiAF77bT89To6uZKlD/thfxOPfpOByeygqcfPyLxn0ahm8FyOR4OFXz8rq1cF/Ap1jfyYbJtxFh+cexbBZKfxzN+vH3AFA1Gltaf3wvXzbvU8tZxk4zgOZbJ08lVOfWojJaqVwVzJbb/W2P6J9W1rMn8PPvfpWGLdj9v2cfO8Mzvz8AzwuF1lrvmXP4mcB2HTjOE6+dyb1Bl+HYTKx+5EnyPs1eIqVw7myMsm4bwZJ9z6EYbHi3JtM+r1TAf6fvfuOjqL62wD+zNZU0nsoUkMvRqqKFCnyC6EpCAKKiggJCIgaSpCEGCQgCqiADbGggIAC0hIBfUEJIr2XaCoppPfN7r5/RDaGkOwEsiXZ53NOzsnO3N08M5mZ3e/eOzNQtmkHt7eWIPGFp2tsVx1Nfh5uzX8NrrPegqBQAuoypC0PQ1lyojEWrc6oMjJxcWYIOn2+GhK5HIV/x+NCUPl2YN+5fH853r/h7C/avGwUb4qC9dS3AakM2oxkFG1cBkmT1rB67nUUvjO12jYAoDryIwRbe9iErIMgkUIdfw3FP3wMACj6eAGUY2dCMXQioNWgdN9mqK+fM93C6sH9o3ol6Zk4/kIIHt22GhKFHPk34vH7pPL9wvnhDujx6VLs7VrzfnFuyVr4rw3FU+d2QSKXIX7rvkpDw8j8ld3OxI1589H6ow8gyOUo+ScB1+eWbwe2HTugxbJwnB02ssZ2AGD9UFOUJCaZajHqnIuNHEufbILZe+KgUmvR2FGJyMFNcD61EIsPxuOH5/xqfP6rPT0RcSgRI7++jDKNFoNaOWJ0B14NjPQTtHdfxuE/1qxZg+DgYISEhNxzfmRkZK3+2AHXNrVL10BZW7Pb8w7vpvXnso2GFne1/lw1ydB6jPYxdQSzkXYu3dQRzMbxo9xH7pgwjd9I33Fsn/le0MLY/N/obOoIZkP+6vemjiBKyfKRpo6gl/KNHSb9+zX2rLRv3x4A0L17d6OEISIiIiIiuqPGYqV///KTzUaOHIn8/Hzk5ubW1JyIiIiIiKjOiDpn5d1338WWLVvg6OgIANBqtRAEATExDfeEXCIiIiIigzKDmy6aO1HFSkxMDH799VfY2toaOg8REREREREAkZcubtOmDUpLeWIjEREREREZj6ielcDAQAwaNAitW7eGVFpx859NmzYZLBgRERERUUPGm0LqJ6pYWbVqFRYsWGDyO9YTEREREZHlEFWs2NvbY8SIhnODOCIiIiIiMn+iipV27dohODgYjz/+OOTyipv4sYAhIiIiIiJDEVWsFBUVwc7ODn/99Vel6SxWiIiIiIjuk1pj6gRmT1SxEhkZWe28RYsWITw8vM4CERERERERASIvXVyT8+fP10UOIiIiIiKiSkT1rBARERERUd3ipYv1e+CeFSIiIiIiIkNgsUJERERERGbpgYeBabXsviIiIiIiqjU1P0fr88A9K717966LHERERERERJXU2LMyceJECIJQ7fxNmzbhjTfeqPNQRERERERENRYrwcHBxspBRERERGRZeDUwvWosVrp37677/eLFiygsLIRWq4VarUZiYmKl+URERERERHVJ1An2CxcuRGxsLHJyctC8eXNcvnwZ3bp1w5gxYwydj4iIiIiILJSoYuXYsWPYv38/wsPDMWnSJBQVFWHZsmWGzkZERERE1GBpeTUwvURdDczd3R1yuRwtWrTAlStX0LFjR+Tl5Rk6GxERERERWTBRPSseHh5Yv349evXqhaioKABAaWmpQYMREREREZFlE1WsRERE4MiRI+jUqRMGDRqE3bt34+233zZwNCIiIiKiBoxXA9NLVLGyevVqDB8+HED5vVcmTpxo0FBERERERESiipUmTZogIiICOTk5CAgIQEBAAHx9fQ2djYiIiIiILJioE+yfe+45bN68GZ999hkUCgVmzJiB8ePHGzobERERERFZMFE9KwCQl5eHo0eP4ujRo1Cr1ejTp48hcxERERERNWxqjakTmD1Rxcq0adNw4cIFDBo0CLNmzULnzp0NnYuIiIiIiCycqGLlmWeeweOPPw6ZTHRHDBERERER0QOpsfpYs2YNgoODcfDgQRw8eLDK/MjISIMFIyIiIiJqyLS8dLFeNRYr7du3BwB0797dKGGIiIiIiIjuqLFY6d+/PwBg9+7d+Oyzz4wSiIiIiIiICBB5zkpxcTFSUlLg5eVl6DxERERERJZBzWFg+ogqVm7fvo3+/fvDxcUFSqUSWq0WgiAgJibG0PmIiIiIiMhCCVqtVm9Jd/bsWbi4uFSZ7uPjU6s/9sbRqbVq31AFd+5m6ghmo+TJtaaOYDasXa1NHcFsqIvLTB3BbJTmq0wdwWy4tK36PmSpLh9KNXUEs9F7iKOpI5iNH7/KMnUEszE8/4qpI4hSMHOgqSPoZbs62qR/X1TPyptvvom9e/caOgsRERERkcXg1cD0E1Ws+Pn5YefOnejUqROsrKx00729vQ0WjIiIiIiILJuoYuXMmTM4c+YMAEAQBABAaWkpfvvtN8MlIyIiIiIiiyaqWPnll18AACqVCgcPHsTmzZtx7tw5gwYjIiIiImrItLwamF6iipWEhARs2bIF27dvR05ODqZNm4YPPvjA0NmIiIiIiMiCSWqaefDgQbz44ot4+umnkZ2djeXLl8Pd3R1BQUFwdnY2VkYiIiIiIrJANfasBAcHY+jQofj+++/RtGlTABXnrBARERERERlSjcXKTz/9hO3bt2P8+PHw8fHBsGHDoFarjZWNiIiIiKjB4qWL9atxGFjr1q3x1ltv4ciRI5g6dSqOHz+OjIwMTJ06FUeOHDFWRiIiIiIiskA1Fit3yGQyDBw4EB999BF+/fVX9OzZEytXrjR0NiIiIiIismCirgb2X87OzpgyZQqmTJliiDxERERERBZBw0sX6yWqZ4WIiIiIiMjYWKwQEREREZFZqvUwMCIiIiIienC8Gph+7FkhIiIiIiKzxGKFiIiIiIjMEoeBERERERGZgFajMXUEs8eeFSIiIiIiMkssVoiIiIiIyCxxGBgRERERkQloeVNIvdizQkREREREZonFChERERERmSUWK0REREREZJZ4zgoRERERkQnwDvb6sWeFiIiIiIjMEosVIiIiIiIySxwGRkRERERkArx0sX7sWSEiIiIiIrPEYoWIiIiIiMwSh4EREREREZkArwamH3tWiIiIiIjILLFYISIiIiIis8RhYEREREREJqDhMDC92LNCRERERERmicUKERERERGZpQYzDOzWmdu49EMc1CoNHBrbossLbSC3rli8+KO3cONAou5xWZEaRVklGLSiJ6wcFLrpsWsvwMpRgU7PtTJq/rr0x2838dnao1Cp1Gje0hVzQ5+ErZ2yUpt17x3BkehraORgBQDwbeqERcuG6ebn5xVj9ktb8friJ9GmnadR8z8om96Pw+XV1yDI5Si9cRWpEaHQFhaIbicolXB7fSGUbTsAgoCSi+eQvmIptCUlkPs2htu8UEgdnSDI5cjdtR3Zm780wVKKo/TvA4dJMwC5AmV/X0PW6qXQFhXUqo3U1QNuKz5H2szx0OTmAAAEu0ZwfGUeZI0fgqBUIm/L5yg6tNeoy6aPVfdH4fhiMAS5Aqq4a7i9ckmV7aDaNhIJHF+ZA2v/3oBUirxtXyF/9zYAgKJ1OzhNnwfByhqQSJD7/UYUxvwMALALHAf7wLHQlpZAFR+HrDWR0OTlGn3Z9eE+Uk7WqSesR0+FIJdDnXATBV+8CxQXim7j8MGP0GSl69qW7PsOpX9EQ+LTHI0WfAh1WpJuXsG6JdDcSjDOgt0nx3590eSNOZAoFCi8fAU33lwAdX7V7aK6dq0/+gBWTZvo2il9fZEbewJXXp7+n2k+6LTrB1yc9BIKzp03ynIZy8bJi3Au6QZWRn9r6igG5T64L9otmQuJQoHcC1dwevp8lOVV3U7u6Lp+GXIvXMWN1Z8DAGSN7NDlowjYtW4OQSJBwjc7cX3VJ8aKb3Z4U0j9GkTPSkluKU59fgWPzGiHgZHdYeNmjYvb4iq1adLHE/2W+KPfEn/0XdQNSgc5Ok1oWalQubY3Hrev5hg7fp3KzirEiiUHsDjqf9i4/Xl4+Trg0zX/V6XdhbMpWBj5FNZvfg7rNz9XqVA5/n9xCJr8HRL/yTJm9DohcXSC+4Jw3Ap5DfHjAqBKSoTr9Nm1auc0eSoglSJh4igkTBwFQaGE06SXAADuCyOQH70PCZPHIPHlCWg04mlYP9zdqMsolqSRI5xmheJ25JtIe3UMym4lodHzQbVqY93vKbguWw+pi3ul5zm9thjqjFSkv/YcMhbOgOPU1yG5q40pSRyc4PL6EmSEzUPKlJEoS0mE44szRbexGzYact+mSHn5adwKeg72I8dD0aY9AMB18Qpkf7kOt6aNQ/r8IDi9MhcynyZQdvZHo7HPI+2NV3Br2jgUxf4fnF9bZPRl14f7SDnB3gG2U95CwYeLkDt/IjTpybAe84roNhLPxtAW5CHv7Zd0P6V/RAMAZC3bo/R4TKV55l6oyJyd0HL5O7j66kycHjAUxfEJaPLG3Fq1uzp9Fs4OG4mzw0biRsgiqPNyERcapnuuoFCg5aooCHK50ZbLGPw8myHmtbUY07W/qaMYnMLVCV3XReLEhGD80m0ICuIS0Dbs9Xu2tWvTHL32fAmvEYMrTfdbNAtFSak43D0Avz4+Bs1eGgen7l2MEZ/qqQZRrKRdyILTQ/aw87ABADzUzxuJf6RCq713tXptbwKU9go0e8JbNy3jcjbSzmWh2RNeRslsKCd//wet23nCt4kTACBgTCfE7L1caV2Ulpbh+pU0fP/ln3jpmU14e94upKZUfPu747tTeCt8CJxdbY2e/0HZdO+NkksXoEqMBwDkbP8edoOH1apd0emTyPpiPaDVAhoNSq5egsyzfFvJ3bUdeQf2AAA0BflQJcbr5pkbZdeeUF27CHVK+Yekgr0/wKbvENFtJM6usO75BG6HVv6QL9g1glWX7sj7rvybMM3tNKTPfQHafPMp9K0e7onSqxdQllT+/83btRW2A4aKbmPdpz/y9/8IaNTQ5ueh8PB+2A4YBsgVyPlqA0pOHQcAqDPSoM7JgtTVHYpWbVF86jjUGWkAgKL/i4F1z8cBmXl1YHMfKSdv/wjUcZeh+bf3o+TQj1D2HCi6jaxlB0Cjgd1ba2C/5HNYBUwGBIlunsSrKewXfwL7hesg7/aYEZfs/jg+1gf5Z8+h+O9/AACpX38H18CA+2onyOVouWIZ/g6LRGnKLd305mGhSP9hB1RZ2QZcEuOb0Xc0Pj36E7b+9YupoxicW/9HkX3yHApulP////50M3yfqbqdAMBDUycgfuNWJO/YV2n6+XkRuDj/XQCAlacbJEoFVLl5hg1O9dp9Fyv5+fl1meOBFGWWwNq5YpiTlZMSZUVqlBWrq7QtyVPhxv5EdBjXouL5WSU49+11PDzVD4JEMEpmQ0lLzYO7p53usZu7PQoLSlFYUKqbdju9AF0faYwXpvfGJ99PRNuOXgid+5OuoFm2dhT82tevoV93yDw8UZZW8eZYlp4KqZ09BBtb0e2KYo9BlVB+IJZ5esFx7ETk/3IAAJC3Zye0JcUAAJuefWDVsQsK/6jac2UOpG4eUGek6h6rM9IgsbWDYG0rqo0mMwOZkW+gLDm+0uvKvHyhzroNu8AJcH33U7i99yXkLdpAW1Ji+IUSSebmibL0/yxXehoktpW3g5rayNw8oP7PvLKMNEjd3AFVKQr27dRNt31qFCQ2Nii9dA6ll8/DqssjkLqXf+FhOzgQgkIBSSNHQy5qrXEfKSc4u0OTmaZ7rMlKh2BjB1jZiGsjkUJ18STy35uH/GUzIe/wCJQDRwEAtCXFUB2PQV7YKyj4LBI2k+ZC2qyN8RbuPii8vFDyn8Ki5NYtyBrZQ2pnW+t27s+MRmlqGjIPRFdMGzsGglyGtO+2GnApTCP4+5XYfOKAqWMYhbWvJ4qSKv7/xUm3IHewh8y+6peb5+aGI2nr7nu+jlatRrdPo/BE7G5k/BaL/Ktx92xnCbQardn/mJroYuXQoUOIiopCQUEBhg4digEDBmD79u2GzCZeNT0o9yo8/jmSDM+uLrB1twYAaMo0OLn+Ejo82wJWjsoq7eub8lVRdbkl0op/tZePA95ZPRIPtXSFIAh4ZuLDSEnMwa1k8xtbX1uCRHLv7UGjqXU7ZZt28Pl4E3J+2IzCo0cqNbMfOhwei5fh1oI5UN/OqJPsdU4Q7t27qFHXrs3dLyuTQebpA01RATLefAmZUQvg8NIcyFv41UHoOiIRqvn/qsW1udf2cdc21GjsC3CcNA3pi16DtrQEJedPIeerDXB7eyU8PvwG0Gigzs0GVKo6WKC6w32knCBU8/b3n+WrqU3pr7tR9O0HQGkxtEX5KD6wRdeDUvT1KpQc2gloNdCk/IPS2EOQd+5d14tQp6r7f2vV4raL/7bzevF5JK1dp3ts274dPMaPw80Fb9dZXjINsduJGH+9NA/7mvaEwskBbUJm1EU8aqBEj09Yu3YtIiIi8PPPP6NTp04IDQ3FxIkTMWrUKEPmE8XaxQpZNyu6EIuzSiC3lUGmlFZpmxSbjo4TWuoeZ/+dh4L0Ypz/7gYAoCSnFFoNoFZp0PUF8/4m7F7cPe1x6XyK7nFGej7sGylhbV0xRvjmtXTcuJqOJ4e1003TarWQyernqEDnl2fA9tF+AACJrS1KblzTzZO5uUOdmwNtcVGl56hupUDZrmO17ewGDoXbvIVIXxmB/AM/V3quS/DrsOs3CEkzX0LptSuGWqwHpk5PhaJ1B91jqYsbNHk5um+9xbap8rqZ5R88C6N3lT9OSUTpxdNQtG4P1Y3Ldb0Y96Us7RYUfhX/X6nrnf9vsag26rRbkLq4VcxzcavohZHL4TIvDPImzXFr1mSoU8v3N8HaBiVnT+p6XqQubnB4fjo0eaYfHsd9pCrN7VRIm7fVPZY4uUKTnwuUFotqo+g1COqE61An3vx3rgCoywBBAqth41Ec/QNwZ50KKJ9nZhrPDobTwPLzLKR2dii8clU3T+HpgbLsbGiKKm8XJcnJsOvSqdp2Nu3aQpBKkXs8VtfGbVQgpPZ26PDD5vLnuLuh1ftR+CdyObKiDxls+ahutFk4E55PlW8nMns75F6o2E6svD1QmpkNdWFRdU+vwm3Ao8i9cBUlt9KgLihE0tY98AocVOe5qeGo1adTPz8/HD58GP3794etrS1UZvKNoXt7J2TdzEV+avkVWv4+nAzPLi5V2pUWqFCQVgTnFo1005xbOmDwyp66k++bPeENn+5u9bJQAYCHezbFpXO3kBhffnL8rm1n0btvi0ptBEHAh1GHkZJU/iHqp61n0byVK9w87I2ety5kfvIhEiaP0Z3Qa9WhM+S+5VekcRg5FgW/Vh1HXBR7rNp2No/2hevst5A8a2rVD2FBc2HdxR8JU8aa7YewO0pO/QFFmw6QejUGANgOHY2i47/Wus3d1KnJKL1+CTb9y89fkDg6Q9G2E0qvXzLAUtyf4pO/Q9m2I2Q+5f9fu/+NQdHvh0W3KTx2GHaDAwGJFIKtHWz7DUbRsfJ5rm9FQGJji9TXKgoVoLw4cV/xiW44VaPxL6HwUOWx2qbCfaQq1YUTkDVvB4m7DwBA8cRwqE4fFd1G6vMQrEZMKT9PRa6AcsBIlMYeArQayLv0gbJv+Th+iYsHFA/3RenJmvcrU0hYtUZ3Qvy5UWNh17UzrJo1BQB4jh+HzINVt4vs347W2K5Rj0eQ8/sflZ7zd3gkTvcfovtbpWnpuPbaPBYq9cSVpatxpPcIHOk9Ar/1fwbO3TvDtkX5/7/Zi+Nwa09MrV7Pe9RQtJlf3pMiUcjhPWooMo78oedZZMlE96y4uroiPDwc58+fR1RUFJYtWwZvb/M4aVLZSIGuU9rgxIcXoVFrYetmhW4v+SErLg+nN15BvyX+AICCtCJYOSogqac9CGI4Odtg3uJBCHtjN8pUGnj5OuDNsCG4cvEW3guPxvrNz+Ghlq4IeqMfFs3+ERq1Fq4edpgf8ZSpo9cJdVYm0pYuhOc7qyDI5VAlJSA1LAQAoPRrD/eQJUiYPKbGdq5Br0MQBLiHLNG9btG5U8j68lM4jpuEstQU+HxQcZnF7C1fI2/PTpgbTU4Wsj4Ig0vIMkAmh/pWIjLfexvylm3hGLwQ6bMmVNtGn8x35sFh2puwfWoMIAjI/e5TqK5dNPxCiaTJzsLtFW/DdVEUBLkMZcmJuL18ERSt28F5TihuTRtXbRsAyN+1FTJvX3iu/x6CXI783dtQcvYkFG07webxJ6FK+Bse72/U/b3sTz9A8Z+/I/f7L+C5ZhMgSFBy/jSy1i4z0RqoHveRctq8bBR8vgy2M8IgSOVQpyeh8NN3IG3WBjbPz0Pe2y9V2wYAin7aCJsJr6FR+BeAVIbSE4dR+mv5+PyCDUthM2kOlH2GAhIJCjevgSblH1Murl5ltzNxY958tP7oAwhyOUr+ScD1uW8CAGw7dkCLZeE4O2xkje0AwPqhpihJTKruz1A9V5qeiVPTQuD/9WpIFHIU3IzHqanl/3+Hrh3Q5cOlONJ7RI2vcWH+MnT+YAmeiC3vnU/ZFY2bH20yeHZzZQ7nhJg7QVvdJbPukpKSguPHj6Nr165o2rQpvvnmGwQGBsLOzk7/k//1xtGp9x20IQnu3M3UEcxGyZNrTR3BbFi7Wps6gtlQF5vfkBlTKc03jx5sc+DStmqPuaW6fChVfyML0XuIeV3EwpR+/Kr+3XLAUIbnm2/P7n8lBTxi6gh6+ew6YdK/L7pnZcqUKdi7t+KmbxMmTDBIICIiIiIiIqAWxYqfnx927tyJTp06wcrKSjfdXIaCERERERHVJ7yDvX6ii5UzZ87gzJkzlaYJgoCYmNqdWEVERERERCSG6GLll18a/p1ZiYiIiIjIfIguVkJCQu45PTIyss7CEBERERFZCq2m9jfUtDSii5Xu3bvrfi8rK0NMTAyaN29ukFBERERERESii5WRI0dWejxmzBg8++yzdR6IiIiIiIjqn127duHjjz9GWVkZJk+eXOXqwZcuXcKCBQtQUFAAf39/LFmyBDJZzeXIfd8d8caNG0hLS7vfpxMRERERWTStWmv2P2KlpqZi1apV+Pbbb7Fz5058//33uH79eqU28+bNQ2hoKPbv3w+tVostW7bofd1aXbpYEATcuYeks7Mz5syZI3oBiIiIiIiofsnNzUVubm6V6Y0aNUKjRo10j48dO4aePXvC0bH8Rq2DBw/Gvn37EBQUBABISkpCcXExunTpAgAYNWoUVq9ejfHjx9f490UXK5cvXxbblIiIiIiIGoAvv/wSa9eurTI9KCgIwcHBusdpaWlwc3PTPXZ3d8fZs2erne/m5obU1FS9f190sVJUVIS1a9fi999/h1qtRs+ePTFr1izY2NiIfQkiIiIiIvqXVmP+N4WcPHlylXPXAVTqVQEAjUYDQRB0j7VabaXH+uZXR3SxEhYWBmtra7zzzjsAgC1btmDx4sWIiooS+xJERERERFSP3D3cqzqenp74888/dY/T09Ph7u5eaX56errucUZGRqX51RF9gv2FCxcQGhoKPz8/+Pn5ITQ0FBcuXBD7dCIiIiIiaqB69+6N33//HZmZmSgqKsKBAwfw+OOP6+b7+PhAqVTi5MmTAIAff/yx0vzqiC5WtFptpZNrcnNzIZVKa7MMRERERETUAHl4eGD27NmYNGkSRowYgf/973/o1KkTXn75ZZw7dw4AsGLFCkRGRmLIkCEoLCzEpEmT9L6u6GFgzz//PMaMGYP+/ftDq9Xi0KFDmDp16v0vERERERGRBdPUg3NWaiMgIAABAQGVpn3yySe63/38/LBt27ZavaboYmX06NHo2LEjTpw4AY1GgzVr1qBNmza1+mNERERERERiiR4GplKpkJKSAltbW9jb2+PSpUvYuXOnIbMREREREZEFE92zMmvWLKSnp6NFixaVLjM2YsQIgwQjIiIiImrIanOHeEsluli5efMm9u3bZ8gsREREREREOqKHgTVp0gTJycmGzEJERERERKSjt2dl4sSJEAQBmZmZCAgIgJ+fX6VLFm/atMmgAYmIiIiIGqL6cAd7U9NbrAQHB+t9kfT0dLi5udVJICIiIiIiIkBEsdK9e3e9LzJ16lTs2LGjTgIREREREREBtTjBviZaLbuwiIiIiIhqg1cD00/0CfY1+e+ljImIiIiIiOpCnRQrREREREREda1OhoEREREREVHt8Gpg+tVJzwrPWSEiIiIiorpWq56V/Px85OXlVSpOvL29sXDhwjoPRkRERERElk10sbJu3Tps2LABjo6OummCICAmJgb+/v4GCUdERERE1FBxGJh+oouVbdu2ITo6Gs7OzobMQ0REREREBKAW56x4eXnBwcHBkFmIiIiIiIh0RPesNGvWDOPHj0ePHj2gUCh004OCggwSjIiIiIiILJvoYsXDwwMeHh6GzEJEREREZDF4B3v9RBcrQUFByMzMxJkzZ6BWq9GlSxe4uroaMhsREREREVkw0ees/PbbbwgMDMT27duxY8cODB8+HIcOHTJkNiIiIiIismCie1ZWrVqFb7/9Fo0bNwYAJCQkICgoCP369RP9x7r3P1L7hA3QKTnXwx0dH7EydQSzkZuQZ+oIZkOmlJo6gtnw6dvY1BHMxi/r4kwdwWwMfreLqSOYjR/nnTZ1BLMRONHJ1BHMRn0ZXKXhpYv1Et2zUlZWpitUAKBx48bQaDQGCUVERERERCS6WPH29sbGjRuRn5+P/Px8bNy4ET4+PobMRkREREREFkx0sRIREYHTp09j4MCBGDBgAE6dOoWwsDBDZiMiIiIiarA0GvP/MTXR56y4uLjg/fffN2QWIiIiIqohpGsAACAASURBVCIiHb3FyiuvvIL169ejf//+EAShyvyYmBiDBCMiIiIiIsumt1gJDw8HAHz11VcGD0NEREREZCnMYZiVudN7zoq7uzsAYNmyZfDx8an0M3/+fIMHJCIiIiIiy6S3ZyUoKAiXLl1CWloaBgwYoJuuVqvh6elp0HBERERERGS59BYry5YtQ3Z2NiIiIrBw4cKKJ8pkcHFxMWg4IiIiIqKGisPA9NM7DMzOzg6+vr74+OOPkZOTg5SUFCQnJyMuLg47d+40RkYiIiIiIrJAoi9dvHDhQsTGxiInJwfNmzfH5cuX0a1bN4wZM8aQ+YiIiIiIyEKJvinksWPHsGfPHgwePBjh4eHYtGkTiouLDZmNiIiIiIgsmOieFXd3d8jlcrRo0QJXrlzBsGHDkJeXZ8hsREREREQNlkZr6gTmT3Sx4uHhgfXr16NXr16IiooCAJSWlhosGBERERERWTbRw8AiIiLg6+uLTp06YdCgQdi9ezfefvttA0YjIiIiIiJLJrpnZdasWfjss88AABMnTsTEiRMNFoqIiIiIqKHjpYv1E92zUlRUhJSUFENmISIiIiIi0hHds5KZmYn+/fvDxcUFSqUSWq0WgiAgJibGkPmIiIiIiMhCiS5W7gwBIyIiIiKiB8dhYPqJLlZOnDhxz+k+Pj51FoaIiIiIiOgO0cXK8ePHdb+rVCqcPHkS/v7+GDFihEGCERERERGRZRNdrERGRlZ6nJ2djdmzZ9d5ICIiIiIiS8BhYPqJvhrY3WxsbJCUlFSXWYiIiIiIiHRE96xMnDgRgiAAALRaLRITE9G3b1+DBSMiIiIiIssmulgZNGgQ3NzcYGVlBTs7Ozg6OqJly5aGzEZERERE1GBxGJh+eouV27dvY+bMmbh27RqaNWsGAIiLi0PXrl2xcuVK2NvbGzojERERERFZIL3nrKxcuRIPP/wwjh49ii1btmDLli04evQo2rRpg4iICGNkJCIiIiIiC6S3Z+XUqVPYu3dvpWkKhQJz5sxBYGCgwYIREREREZFl01usKJXKe04XBAESyX1fTIyIiIiIyKLxnBX99FYbd64AVtt5RERERERED0Jvz8q1a9cwYMCAKtO1Wi3S09MNEoqIiIiIiEhvsbJ//35j5CAiIiIisigcBqaf3mLFx8fHGDmIiIiIiIgq4RnyRERERERklkTfwZ6IiIiIiOoOh4Hpx54VIiIiIiIySw2qZ8VzaF90CJ8LqVKBnHNX8Ocr81GWV1Bte/9PlyH3wlVcXfV5lXm9vl+DopQ0nH4t3JCRDcZ9cF+0WzIXEoUCuReu4PT0mtdF1/Xl6+LG6vJ1IbFSotN7i+Ho3xEQBGSfOIuzc5ZAU1xirEWoFeuej8H5pZmAXAHVzatIj3ob2sKqy1ttO4kEzq/OhXX3PhCkUuR8vwl5u7YCAORNm8N1bigEa2tAC2R98gGKThwDADhNmQHbfoOhKS5CyYUzyPxwBbSqUqMue03sHusLt+A5EBQKlFy7gpS3F0BTUHW9iGnns3I1ytLTkbqs8j5h26sP3F97HXFjRxp0WR6UTZ/H4frqa+XLeP0q0iIW3XNdVNdOUCrhNm8hrNqV7xPFF84iPWoptCUV+4TEvhGafLkFGWvfQ/4vB4y5eLUi8esOxdAXAJkcmpQ4lG5dBZQUimtjbQfFqGBIvFsApcUoO3EAZcd+Kn+Ob2vIh78CQWEFCBKoDm+F+tQvpljE++Y+uC/8Fs+FRKlA7vkrOBtU87Gz87plyLtwFTfXVBw7O6xcDMeHO0IQBGT9eRbn55rvsbM6R+Jy8P7RFKjUWrR2tULYwCawU0qrtNNqtVhwIB6tXK3xwsPuuumPrjsHDzu57vEL/u74n5+zUbIbwoO+p8oa2aHLRxGwa90cgkSChG924vqqT4wV3+g2Tl6Ec0k3sDL6W1NHoQagwfSsKFyd4L8hEn+MC8b+jkNQEJeAjhGv37OtvV9zPL7vS/iOGnzP+a3nvgTXPv6GjGtQClcndF0XiRMTgvFLt/J10Tbs3uvCrk1z9NrzJbxGVF4Xrd94FYJMisM9huNwj+GQWCvR6vVXjBG/1iQOTnB7Iwypi+ciaXIgVClJcJ46q1bt7APGQO7bFEkvjEbytPFoNGYCFH4dAAAur81H3t6dSH55LDKWL4Z76HJAIoXdkEBY93ocydMmIPnlsVDfzoDTi0FGXfaaSJ2c4LXkHSS+PhM3RwxFaWIC3GfNva92zs+/CJuulfcJQamE24xZ8Hn3PUBa9UOMOZE6OsFj4VKkhLyGf575H1RJiXCZPqdW7ZyffwWCVIr4CSMRP2EkJEorOE1+udLzPULfgcTO3ijLdN9sHaB8Zg5KvgpHcdRL0N5OgXzoC6LbKAJeAUqKUbxiKorXvgapnz8kbbuXz5u4EKoDX6P4/Rko+XwRFAFTIbh6G3sJ75vCxQmdP4rEyYnBOPzwEBT+nQC/JdUcO1s3R89dX8IrsPKxs9W8VyGRSfFrr+E40ms4pNZKtJxrnsfO6mQWlmHRgQS8P+wh7J7cFr6NlFh1NLlKuxuZxXjxhxs4eC2n0vS4zGI4WMnww3N+up/6XKjUxXuq36JZKEpKxeHuAfj18TFo9tI4OHXvYoz4RuXn2Qwxr63FmK79TR2l3tBozP/H1BpMseIx8FFknTyH/Ov/AABubNiMJuMC7tm2xbQJiPtiKxJ/2Fdlntvj3eH55GO4+cl3Bs1rSG79H0X2yXMouFG+Lv7+dDN8n7n3unho6gTEb9yK5B2V18Xt/zuBq8s/BrRaQKNB7plLsGlsnh86rB/phZIr51GWFA8AyPtxC+wGPFWrdraP9kfevh8BjRqa/DwU/LIPdk8OAwAIEikkdo0AABIbG2hLy3tOlK3bofD/DkFTkAcAKPgtBjZ9Bxp2YWvBtlcfFF84B1V8+XaQvfU7NBpadTvQ187Gvzvsej+G7G2V9wnb3o9CsLZG8qK3DLgUdcOmR2+UXDoPVUL5/z5n+3ewHzKsVu2KTv+JzM/X6/aJkiuXIPes2Cecp0xD6Y2rKLl+1QhLdP+krbtBk3AV2ozyD59lf+yB7K4PFjW1kfi2QtlfMYBWA6jLoL50ArKOjwEyOVTR30Bz/RQAQJuTAW1+DgQHVyMu3YNxG/Aosv+qOHb+89lm+Dx972Nns6kTEL9pK1J23nXsPHoC16IqHzutzfTYWZ1j8blo72GDpk5KAMDYTi7YczkLWq22UrvvzmRgdAcXDGrlUGn66ZQCSARg0pZrGPn1ZXz8xy2oNZWfW5/UxXvq+XkRuDj/XQCAlacbJEoFVLl5hg1uAjP6jsanR3/C1r/qV48qmTfRxUp8fDx++uknaLVaLFq0CKNHj8a5c+cMma1WbHw9UZh4S/e4KPEW5A72kNnbVml7+rVwJHy/u8p0Ky93dF65ALHPvw6tWm3QvIZk7euJoqSKdVGcVP26ODc3HElbq66L9F+OouD63+Wv19gbzWdMrnLwNRcyN0+UpaXqHpelp0JiZw/BxlZ0O6m7J9RptyrNk7l6AAAyPngHjhOmoPGWA/BcsQEZ70cAGjVKLp2DTe8nIGnkCAgC7AYFQObsZuClFU/u4QXVrYplUqXegtTeHhJbW9HtZG7u8Jg3H8nz50F719cr+YdikLZi2T2HUpkbmYcXylL/8/9NS4XUruq6qKld4fFjUCWUf1iReXrBcdxE5MeU34fKpnsvWHf1x+0Na42wNA9GcHCDJqfihr7anHQI1raA0kZUG038Fci6DQAkUkBhBWnHPhDsnYEyFdQnKu7LJe0xFFBaQ/PPZeMsWB2w8vFEcaK4Y+f518ORfI9jZ8Zdx86Hpk+uUtCYu1t5KnjaVwzh8rBXIL9Ug4LSyseABf18MczPqcrz1RqgVxN7rBvZHF+OaYmj/+Th29P19ybSdfGeCgBatRrdPo3CE7G7kfFbLPKvxhkss6kEf78Sm0+Y7xBYqp9EFyshISHQaDSIiYnB33//jZCQEERERBgyW60IEkn5N1l30arF9V8JMhl6bFqJM/MiUXyr/h5UgQdfF//l0KU9+hz4BnHrv0bqvsN1kM4AJMI9l7dK32UN7QSh8q4gCAKgUUOQK+Aeuhzpy0KR8MwgpMx6Aa5zFkLq5oH8g7tRcOQAvN77BF5rvoQqPg7aMlUdLtgDErsdVNMOggDvyJVIXRGJsoz6vU9AEKp8KwzcY12IaKf0awff9V8he+u3KDh6BDIPL7jOegO3Fr9lHv3l+gjV7QdqUW1Kd28AoIXVax9COXkx1NdOAerK273siWegeHIiSjYuBsrM5xwufQSJRNx2IoJDl/bove8b/L3ha6SZ67GzGhotINxjukTkJ4YxHV0wv58vbORSNLKSYXI3N8TcyNH/RDNVl++pf700D/ua9oTCyQFtQmbURTyq57Rardn/mJroE+xLSkowYsQILFiwAAEBAfD390dpqWnfhNqFzoT3/8qHJsga2SH3fMXwC2sfD5RmZkNdWCTqtZwe7gDbhxqj8/LyIS1WHq4QpFJIlUqcfHVh3YevY20WzoTnU/+uC3s75F6oWBdW3rVbF3d4j3kKnd5bXOM3Rabi+MJ02PTuCwCQ2NihNO6abp7MzR3q3Bxoiysvb1nqLSjbdrxnu7K0FEhdKnpFpC5uKEtPhfyhlpAorVD0x68AgJJL51D69w0o23ZEcXERCmL2Iufb8hMole07Q5WUYLBlFsP11WDYP/HvcB1bO5Rcq9gOZO4eUOdkV1kvqpRkWHfoVKWdsnlLKHx94fF6+T4hc3EFJFJIFAqkhC0ywtI8GOepQbB7rB8AQGJri5Ibd20jOffaRlJg9d91cVc7uyeHwn3eIqSviEDegT3l0wYMgmBlBZ8P1gMA5L5NIA+eC6mDI3J2bDHoMt4PbXY6JE38dI+FRq7QFuYBqhJRbQTbRijd8ylQlA8AkPUbC83tf89nkMqhGDsXEvcmKP5wNrRZFT2Z5qr1gpnwGFpx7My7eNexM+s+jp2jn0KH9xZX2/ti7rzs5Th3q6LHNC1fhUZKKWzk4s5N++lSJtq4WqONmzUAQAtAJrlX+WO+6vo91W3Ao8i9cBUlt9KgLihE0tY98AocVOe5iRoi0cWKVCrF/v37cfjwYcyaNQvR0dGQiP2axUAuhq3GxbDVAAClmzOePLkLdi2bIv/6P2j+8jgk74oR/VqZx0/j55ZP6B63WxgEhatTvbka2JWlq3Flafm6ULg5o9/xXbBt0RQFN/5BsxfH4dYe8esCADyG9kPHqIX4PfBF5Jw6b4jIDyT7i4+Q/cVHAACJozN8P9sGmU8TlCXFwz7gaRQePVzlOUV//g6XV+fes13h0cOwHzoChceOQGJtA9v+Q3B71VKUJSVAsLODsn1nlFw4A5m3LxRNW6D0+mUo27SH08uzkDz9OUCrhcOzU5AfvceIa6GqjI/XIOPjNQAAqZMzmm/7CfImTaGK/wdOY8Yh73DVccQFvx+Fx9w3q7QrOnsa14f007VznRYEqaNTlauBmavMDWuR+e+wLKmTM5p8uwPyxk2gSoiHw6ixKPit6rooPH4MrrPm3bOd7aNPwG1OCJJmvoySyxd0z8n+9ktkf/ul7rHPR18gZ9tms70amPrqSSj+9zIEV29oM5Ih6zkM6gu/i24j6zkMUNpA9eNHgJ0jZN2HoPSbSACA4tk3IMiVKP5wdqXix5xdjViNqxH/HjtdndH3j4pjZ9Mp45Bay2On+5B+aL98IY6PMM9jpxi9m9oj6rdk/JNVgqZOSnx/NgP9Wzjof+K/rmcUI/paNlb97yGoNFp8ezq93p1gX9fvqd6jhsIr8EmcnbkYEoUc3qOGIv2Xo4aITtTgiC5WwsLCsHHjRoSGhsLd3R179uzB0qVLDZmtVkrSM/Hn1BD03LwaEoUcBTfjETvlTQCAU7cOeHjdUkR3H2HilMZRmp6JU9NC4P91xbo4NbV8XTh07YAuHy7Fkd41r4v2EW8CgoAuH1b8jzP/+Avn5oQZNPv90GRnIn15KNyXrIAgk6MsORHpkQsAAIrW7eA6bzGSXx5bY7vcH7dA5u0Ln8+2QpDJkLtrG4rPnAQApC2aA5egNyAolNCq1chYGYay5ESUJSfCqvPD8PlsKyBIUHj0EHK3fW2y9XA3dVYmkhfPh2/UBxDkcpQmJiB5Yfl2YNWuA7wWhyNu7Mga2zUU6qxMpIYvhFfk+xBkMqiSEnBryXwAgNKvPTwWhCF+4uga27nOfB0QBHgsqNgHis6eQnqU+RwHRSnIQcnW96B8biEglUGbmYKS76Ig8W0FxZjXUPz+jGrbAIDq0PdQjJ0HqznrAAhQHfgKmsSrkDRpC1mnx6BJT4TVjPd0f67058+huXrSRAtbO6UZmTgzPQQPb1oNQSFHYVw8Tr9ScezstGYpfnu05mNnu3+PnZ3WVGwXWcf/wvm55nfsrI6LjRxLn2yC2XvioFJr0dhRicjBTXA+tRCLD8bjh+f8anz+qz09EXEoESO/vowyjRaDWjlidIf6Vaz8V128p16YvwydP1iCJ2J3AQBSdkXj5kebDJ6dzF99GD1saoK2FoPR8vPzkZeXV2n8mre3+KucbFO2qV26Bkoh19/GUnR8xMrUEcxGcVb9+CbaGGT3uJ+DpfLp29jUEczGL+sa3gnJ92vwuw3vsrf3a++806aOYDYCJ1a94IGl0n78h6kjiBLtbv6fjQemXTHp3xfds7Ju3Tps2LABjo6OummCICAmpnZdoURERERERGKILla2bduG6OhoODvX365cIiIiIiKqP0QXK15eXnBwEH+CHRERERERVY/nrOgnulhp1qwZxo8fjx49ekChUOimBwUFGSQYERERERFZNtHFioeHBzw8PAyZhYiIiIiISEd0scIeFCIiIiKiusNhYPrpLVZGjhyJHTt2wM/PD4JQcQdarVYLQRBw6dIlgwYkIiIiIiLLpLdY2bFjBwDg8uXLBg9DRERERER0h+hhYJmZmdizZw9ycnIqTefwMCIiIiKi2uMwMP0kYhu+/PLLuHjxoiGzEBERERER6YjuWQGAyMhIQ+UgIiIiIiKqRHSxMnDgQGzduhU9e/aEVCrVTff29jZIMCIiIiKihozDwPQTXawUFhbinXfegZOTk26aIAiIiYkxSDAiIiIiIrJsoouVQ4cO4ffff4eVlZUh8xAREREREQGoRbHi4+ODnJwcFitERERERHWAw8D0E12sqFQqDBs2DK1atYJcLtdN37Rpk0GCERERERGRZRNdrEybNq3aeenp6XBzc6uTQEREREREREAtipXu3btXO2/q1Km6O90TERERERHVhVrdZ6U6Wq22Ll6GiIiIiMhi8JwV/UTfwb4mgiDUxcsQERERERHp1EmxQkREREREVNfqZBgYERERERHVjoZnUuhVJz0rPGeFiIiIiIjqWq16VvLz85GXl1epOPH29sbChQvrPBgREREREVk20cXKunXrsGHDBjg6OuqmCYKAmJgY+Pv7GyQcEREREVFDxauB6Se6WNm2bRuio6Ph7OxsyDxEREREREQAanHOipeXFxwcHAyZhYiIiIiISEd0z0qzZs0wfvx49OjRAwqFQjc9KCjIIMGIiIiIiBoyDgPTT3Sx4uHhAQ8PD0NmISIiIiIi0hFdrAQFBSEzMxNnzpyBWq1Gly5d4OrqashsRERERERkwUSfs/Lbb78hMDAQ27dvx44dOzB8+HAcOnTIkNmIiIiIiBosjcb8f0xNdM/KqlWr8O2336Jx48YAgISEBAQFBaFfv34GC0dERERERJZLdM9KWVmZrlABgMaNG0NjDuUWERERERE1SKJ7Vry9vbFx40aMGTMGQPl9V3x8fAwWjIiIiIioIeP3/vqJ7lmJiIjA6dOnMXDgQAwYMACnTp1CWFiYIbMREREREZEFE92z4uLigvfff9+QWYiIiIiIiHQErVarranBK6+8gvXr16N///4QBKHK/JiYGIOFIyIiIiIiy6W3WElLS4O7uzuSkpLuOZ/nrRARERERkSHoPWfF3d0dALBs2TL4+PhU+pk/f77BAxIRERERkWXSe85KUFAQLl26hLS0NAwYMEA3Xa1Ww9PT06DhiIiIiIjIcukdBpafn4/s7GxERERg4cKFuukymQwuLi6QyUSfo09ERERERCSa3mLlvy5evIjCwkJotVqo1WokJibq7rtCRERERERUl0R3iyxcuBCxsbHIyclB8+bNcfnyZXTr1o3FChERERERGYTom0IeO3YMe/bsweDBgxEeHo5NmzahuLjYkNmIiIiIiMiCiS5W3N3dIZfL0aJFC1y5cgUdO3ZEXl6eIbMREREREZEFEz0MzMPDA+vXr0evXr0QFRUFACgtLTVYMCIiIiIismyiT7DPz8/HkSNHMGzYMHz11Vc4duwYJk+ejJ49exo6IxERERERWSDRxcqLL76Izz77zNB5iIiIzE5ISAgiIyNNHYOIyOKIHgZWVFSElJQUeHl5GTKPwdy+fRsuLi7466+/cPXqVYwcORJKpdLUsYyutLQUn332GeLi4hAaGoqNGzdi6tSpUCgUpo5mVMOGDcPIkSMRGBgINzc3U8cxKZVKhWPHjiErK6vS9BEjRpgokWl99913GDdunKljmIXc3Fzs2rUL2dnZ+O/3WkFBQSZMZRpXr15FQUEBbG1tTR3F5C5evIh169YhJyen0naxadMmE6YyH/n5+bCzszN1DJOIj4/H6dOnERAQgNDQUFy8eBFvv/02OnbsaOpoVI+JLlYyMzPRv39/uLi4QKlUQqvVQhAExMTEGDJfnQgLC0NRURFeeuklzJ49Gz179sRff/2F5cuXmzqa0YWFhcHZ2RkXL16EVCpFfHw85s+fjxUrVpg6mlFt2LABO3fuxKRJk9C4cWOMGjUKAwYMgFwuN3U0o5s1axbS09PRokULCIKgm26pxcrXX3/NYuVfs2bNgr29PVq1alVp27BEEokE/fr1w0MPPVTpiy5L/ID+5ptvYuzYsdwu/nXo0CH8+eefmD59OsaMGYPMzEy8+eabGDVqlKmjGV1ISAiefvppxMTE4O+//0ZISAgiIiLw3XffmToa1WOii5X6PATs9OnT+OGHH/Dhhx9i9OjRmDlzJkaPHm3qWCZx4cIF7NixA7/++iusra3x7rvvIiAgwNSxjM7HxwczZszAjBkzcPDgQSxduhSLFy/G8OHDMX36dDg5OZk6otHcvHkT+/btM3UMs+Hp6YlJkyahc+fOlT6UWmJvQkZGBr744gtTxzAL8+bNM3UEs2FlZYXnnnvO1DHMxtq1axEREYGff/4ZnTp1QmhoKCZOnGiRxUpJSQlGjBiBBQsWICAgAP7+/rwYEz0w0cXKiRMn7jndx8enzsIYSllZGbRaLWJiYrB48WIUFxejsLDQ1LFMQhAElJaW6r4Ny8rKsshvxgoKCrB//378+OOPSE1NxbPPPothw4bh119/xYsvvojt27ebOqLRNGnSBMnJyfD29jZ1FLPQpUsXU0cwG23btsXly5fh5+dn6igmZ4nHyeo8+uij+Oqrr/Doo49WKugt+Rji5+eHNWvWYPjw4bC1tYVKpTJ1JJOQSqXYv38/Dh8+jFmzZiE6OhoSiei7ZBDdk+hi5fjx47rfVSoVTp48CX9//3oxVGT48OF47LHH0LFjR3Tp0gXDhg3DM888Y+pYJjFp0iS88MILSE9PR0REBA4ePGiR3xgPGDAA/fr1Q1BQEB555BHd9PHjx+PYsWMmTGY8EydOhCAIyMzMREBAAPz8/CCVSnXzLXF4C2CZPSjVuXbtGkaOHFkvh//WtdWrV+t+Lysrw5UrV+Dv71/p+GEpfvzxRwCo1OtmqdsFALi6uiI8PBznz59HVFQUli1bZrGFW1hYGDZu3IjQ0FC4u7tjz549WLp0qaljUT0n+mpgd8vOzsbs2bPrzRABlUqFkpISSCQSFBQUWPRJ1devX8fx48ehVqvRo0cPtGnTxtSRjO5eJ0AWFxfDysrKRImMLzY2tsb53bt3N1IS8+Ln51flW3R3d3ccOXLERIlMJykp6Z7T60OPuqElJCQgMjISH330kamjkImlpKTg+PHj6Nq1K5o2bYpvvvkGgYGBFnuSfX5+PvLy8ipdfMFSizeqG6J7Vu5mY2NT7RuZubly5QpCQkIQHx8PrVaLtm3bIjIyEo0bNzZ1NKO7cuUK1q1bh1WrVuHGjRsIDQ1FeHg4mjdvbupoRhUbG4v3338fhYWF0Gq10Gg0KCoqwh9//GHqaEZzpxgJDw/HokWLKs178803LbZYuXz5su53lUqF6OhonD592oSJjO/QoUPo169fvR7+a2iNGzfGzZs3TR3DqNasWYPg4GCEhITcc76lXtp5ypQp2Lt3r+7xhAkTTJjGtNatW4cNGzbA0dFRN82Se92oboguVu4MGQEArVaLxMRE9O3b12DB6lJISAimT5+OgQMHAgD27t2Lt956C998842JkxnfokWLdMNcWrRogenTp2PBggXYvHmziZMZV2RkJMLDw/HFF19g2rRpiI6ORlFRkaljGdWCBQuQkJCA8+fP49q1a7rparUaubm5JkxmPuRyOYYOHYp169aZOopRnTt3Dv369as0/Pe/6sPw37p29wf0GzduoHXr1iZKYxrt27cHYLm9rtXx8/PDzp070alTp0q985bYm7Bt2zZER0fD2dnZ1FGoARFdrAwaNAhubm6wsrKCnZ0dHB0d0bJlS0NmqzNarVZXqADA0KFDsX79ehMmMp2ioiI8/vjjusd9+vRBVFSUCROZhr29ve4S1nl5eZg3bx6eeuopU8cyqldffRVJSUmIiIiodJ6GVCpFixYtTJjMtHbu3Kn7XavV4tq1a5DJ7rsTul6aOXMmAMv9pvxe/vsBXRAEDBkyBL169TJhIuPr378/AGDkyJHIz8/nlxr/OnPmDM6cOVNpuNNxFQAAHPRJREFUmqX2Jnh5ecHBwcHUMaiB0fsOfPv2bcycORPXrl1Ds2bNAABxcXHo2rUrVq5cCXt7e0NnfGB9+vTBhg0b8PTTT0Mmk2HPnj1o0aIFUlNTAQAeHh4mTmg8zs7O2Lx5M4YPHw4A+Pnnn+Hi4mLiVMZnZWWFuLg4tGjRArGxsejZs6fFXb3F19cXvr6++Omnn6qMMS4sLKzUjW9J7u5NcHJywvvvv2+iNKa1b98+bNiwATk5OZWmW+KHsJEjR+Lq1auIjY1FWVkZ2rRpY3E3073j3XffxZYtW3THCEu+8AIA/PLLL6aOYDaaNWuG8ePHo0ePHpX2D164hB6E3hPs58+fD1dXVwQHB+tumFdaWoo1a9YgPT0dy5YtM0rQB1HTcDVBEHD48GHjhTGx5ORkLFmyBLGxsZDL5XjkkUewaNEieHp6mjqaUcXGxuKbb75BVFQUnn32WcTHx2P06NF46623TB3N6NavX4/169dzjPF/qFQqxMXFQa1Wo1WrVhbXs3JHv379sHz58irDWSzxnJWdO3di7dq1GDhwIDQaDWJiYvDqq69izJgxpo5mdIMGDcKOHTtga2tr6ihmgefwVFi7du09p7NYoQeht1gZOnRopRPH7tBqtQgMDMRPP/1ksHBExpKTk2OxXdcDBw7Eli1bOMb4X+fPn8fMmTPh6OgIjUaDjIwMfPjhh+jcubOpoxnd5MmT8cUXX/A+CQACAwOxceNG3Q1jMzMzMWnSJOzevdvEyYwvODgYYWFhFnXz3Jrs2LHj/9u707CorjMO4P+J4IJaEQQUKyoCUmNhVKgY4wZItKAMmMe4YmJKgg2oJRoVlKQOExUDEjBFfBqlbokWFREXBEOJSlDrw1IXEA3BJUYRFLCgzHL7gTBwBdFUMmeY8/4+MXfy4e8TZrjvPed9j/ZnlUqFkydPwtbWFh999BHDVIQYjuc+Lmx+4FNzEomkw/wBe/DgASIjI5GbmwuVSgU3NzdERERwtf3p/fffR2JiItzd3Vs93IyXp+jNB0W0hsezRWiPsVhkZCQ2bdqkLU7y8/Mhl8uRnJzMOJnuLVy4EAEBAXB1dRWdwcPjU1KNRiO6OTczM+P2oEhfX194eXnBwcGBzmZCwxbB5t58803Mnj2bURo2/Pz8cPDgwRaj3xu3CF65coVhOtLRPbdYaevLuKN8UX/88ccYPnw41qxZA41Gg3379iE8PJyrCT9yuRwAEBsby1WR9rSQkBAAwL59+9C1a1fIZDIYGRkhLS0NT548YZyODdpjLFZbWytaRZFKpdz+biQkJGDw4MGiG1JeDR06FAqFQrvtKzk5GY6OjoxTsbFp0yaEh4dzOe3qRVy/fh337t1jHUOnGleXmo9+J6S9PLdYKSkpgYeHR4vrgiCgvLz8VwnV3srKykSnDwcFBcHHx4dhIt2ztLQE0HB+Rmvb+njRONFnw4YN2L9/v/a6VCqFv78/q1hMWVlZcTVk4nl69eqFzMxM7QTBzMxMbocNKJVKLvfdtyYyMhJxcXEICwuDIAgYPXo0Pv74Y9axmOjZsyeX46ufpXE1oXFXvZmZGUJDQxmnYqOyshJHjhxpMZSD14dfpH08t1hJT0/XRY5flUQiwd27d7U3ZD/99BO3DbM0D77BkydPUFpaisGDBwNoOCxTpVIxTsVGcHAwKisrUVBQALVaDalUij59+rCOxYxcLsfy5csRHh4OoOHwv6ioKMap2Bg7dix27dqFcePGaQesAPx9XwANEwSpB6HBsGHDEBISgvHjx4t+L3gtYGg1oUlgYCAcHBy4HMJBfj3PbbA3BCdPnsTatWsxYsQICIKAvLw8REREiM5e4UVjz0rz/+08Tn46ffo0Vq5cCSsrKwiCgIqKCkRHR8PFxYV1NJ07deoUwsLCIJVKodFokJeXB4VCgUmTJrGOxlRtbS00Gg169OjBOgozjedqNMfj9wXQsHV006ZNePjwIQC+9+LT9Cuxuro6bN68Gd999x3UajXc3NywZMkSmJiYsI6mczNmzBDtWiCkPXBRrADAvXv3UFhYCEEQ4OzsDDMzM65WV+7evYuoqChcvXoVI0aMwLJly/Cb3/yGdSym6uvrcfXqVUgkEgwdOlT7+7B371689dZbjNPpjr+/Pz7//HMMGDAAAHDz5k0EBwfj0KFDjJOxUVhYiG3btuHBgweiop7X5uFn4e1z4u7ujsTERNjb27OOotfWrFmj7ZHkxapVq9CtWzfMnDkTQENhW1NTw+WBywkJCejTpw/c3NxEvW48rsaS9sPF3frs2bPx1VdfiVZSpk2bhsOHDzNMpVthYWFwcHDAtGnTkJ6ejnXr1nH7FKxR586dMXz48BbXv/76a65uwlQqlbZQARq2PWk0GoaJ2FqxYgXmzZsHOzu7DjNEhAXePifm5uZUqLyAixcvso6gc5cuXRId4xAREYE//vGPDBOxU1tbi08//VQ0OY/X1VjSfgy6WHn77bdx/vx5qNVq7U1p45PStg6KNER3797Fl19+CaBhHzqve4tfBCeLjVrW1tZISkoSTTnieb9x165dMXfuXNYx9B4vn5OUlBQADZ+TRYsWwcPDQ7QqT9+lRBAEVFdXa3crVFdXcztBLysrC999952oJ5aQl2XQxUpSUhJUKhUUCgVWr16tvc7jl0jzJkhjY2PRayLG29N0hUIBuVyOLVu2QBAEuLm5Ye3ataxj6dyPP/4IAPjd736HpKQkeHh40DaGNvDyOTl79iwAwMTEBCYmJrhw4YLofSpWyNtvv40333wT7u7uEAQBWVlZeO+991jHYqJ///6oqqqiYoW0K4MuVgDg0aNHCA0NRadOnXDnzh2cOHECw4YNg6urK+toTPFyo0Gez9zcHLGxsaxjMDdv3jzt8Inc3FxRjwptY+DXi2yX5bFPgzSZMWMGfv/73+P8+fPQaDSIj4/H0KFDWcdiQqlUwtvbG/b29qKHotTzR16GQRcrZ86cwbJlyxAXFwdbW1v4+/vDzc0Nhw8fxqxZs7TbXnjw9Hk5d+/ehYeHh3aiDd2I8ev48ePYunVri7n4vP1OfPPNN8/9b3hrKicvhsc+jWfhZXtgc0qlEnfu3EH37t0BAFeuXMGVK1e4XHULCgp65nvl5eWwsLDQYRpiKAy6WImLi8Pu3btha2uLv//977Czs8OmTZtQU1ODuXPnclWsGMJ5Oe2pvr5edFp7cz179tRxGrY2bNiAqKgo2ub0AnhrKm8Lb58T8mJee+011hF0bsmSJSgvL8eQIUNEuxZ4LFYaD15uzXvvvac96Z6QX8Kgi5W6ujrY2toCAHJzc7UrCz179uRu2hHPDdOt8fLywqRJk+Dn5wcnJyfRe7wtV9vY2GDUqFF45ZVXWEfRe7w9Na6urkZcXBzOnj0LIyMjjB8/HosWLULXrl25+5wQYP78+W1uId6xYweXB2d+//33OH78OOsYeo+370/Sfgy6WBEEAYIg4PHjx7hw4YL2S7Surg6PHz9mnI6wdOzYMaSnpyMmJgYVFRWQyWSYPn06l0vUCxcuREBAAFxdXUUN5cHBwQxT6Sfeer2WL18OW1tbfPbZZxAEAfv370d4eDiio6NZRyMMhISEsI6gl2xsbPDjjz/S6vRz8Pb9SdqPQRcrHh4eCA4OhlqtxtChQ+Hg4ICrV68iPj4eXl5erOMRhrp16waZTAaZTIaMjAxERkZi8+bNGDNmDFasWIGBAweyjqgzCQkJGDx4MJdT8kjbbt++jcTERO3r8PBw+Pj4MEykn3h5Ytx8i8/ly5dRW1sLQRCgVqtx69atNrcAGaLGlabKykpMmzYNjo6Oou9RWn0kpH0YdLGydOlSHD58GPfv34e/vz8A4NSpU7C1tcXixYsZpyMslZWVITU1FWlpabC2tsayZcvg5eWF3NxcBAYG4sSJE6wj6oxSqeT+gFDSOjs7O/z73/+Gi4sLAKCoqIirQv5F8dansXr1apw7dw5VVVWwtbVFUVERRo4cyVUfKPBiK03UVE7IyzPoYgVoOKm+uXfffVf0+s9//jP+9re/6TIS0QPvvPMO/P39sW3bNlE/z4QJE3DmzBmGyXRv7Nix2LVrF8aNGycaNUlbGlriran8+++/x7x587Qrb6WlpejVqxfc3d25mSJIfRot5eTkID09HXK5HAEBAairq8P69etZx9K5F1lJoqbyJrysQJL2Z/DFyvM0HgRH+HLy5EnU1dXhxo0bEAQBdXV1MDExAQCEhYUxTqdbaWlpAIBt27Zpr/FyI9oaaipvkpCQwDoCc9Sn0ZKlpSWMjY0xZMgQFBcXw9vbGzU1Naxj6SUeb9AfPXqEmpoa0b/d2tpadDg3Ib8E98UKNXzxKTc3FxEREVCr1di7dy98fHwQHR2N119/nXU0nWvrfBEezxWhpvIm/fv3x+HDh3Ht2jUEBQUhPT2du3Gs1KfRkpWVFRITEzFmzBhs3LgRQMM4eNISb/cYW7ZswdatW2Fqaqq91vjwq3E7KSG/FPfFCuFTTEwM9uzZg8DAQFhYWGD37t0IDQ3lslhpC4/nilBTeZPPPvsMP/30Ey5duoTAwEDs378fRUVFWLlyJetoOkd9Gk0UCgWys7Ph5OQELy8vpKWl4ZNPPmEdi+iB5ORkZGZmwszMjHUUYkDoYAXCJY1GI2p6tLOzY5hGf/G4haGxqbwRz03lp0+fxsaNG9GlSxf06NED27dvx7fffss6FhM5OTk4cuQI3njjDcjlcuzYsYPbEfhxcXHaz8T8+fORkJAANzc3xqmIPujXrx969erFOgYxMNyvrPB4M0aAvn37IisrCxKJBNXV1di9ezc1lLeCty0MADWVN/f0QaH19fXcHh5KfRpNbGxsoFAoUFVVhWnTpmHatGn47W9/yzqWXuLtHmPQoEGYM2cORo8ejc6dO2uv07ld5GVwX6w8PS2M8GHt2rVQKBS4c+cOPD094ebmhrVr17KORfQANZU3mTJlCpYuXYrq6mokJSXh0KFD3G6Joz6NJvPmzcO8efNw584dHD16FB988AG6d++OPXv2sI7GDDWVN7CysoKVlRXrGMTASAQDLvu9vLxafTIsCAIkEgnS09MZpCKk4/Dz8+Ny7CbvTeWNrl69isuXL2PHjh3o378/ZsyYgYkTJ7KOxcSjR4+QnZ0Nb29v7Ny5Ezk5OViwYAG3259qamqQnp6Oo0eP4t69e5g6dSo++OAD1rGYaKupnEeVlZUoKCiAWq2GVCpFnz59WEciHZxBFys3btxo830bGxsdJSH65tSpU4iNjUVVVZXoSRiPf1zq6+tFy/XNBQQEcDeut3lT+T//+U8sWrQIr776KldN5RUVFVi8eDGuXbum7U0oLS2FVCpFTEwMd+fNAMCnn36K6dOnY/jw4ayjMBcUFIRLly7By8sL06dPh7OzM+tITHl6emLfvn3UVI6Gv61hYWGQSqXQaDTIy8uDQqHApEmTWEcjHZhBFyuN6uvrcfr06RYjJ2kPJb/eeOMNrFy5Evb29qLVt+YHRPJi4sSJmDRpEvz8/ODk5MQ6DnMymQwHDx6En58fUlJSoFKpMH36dBw9epR1NJ0JCwtDnz59EBISoj0oVKlUIi4uDuXl5VweALhr1y4cOXKE+jTQMO58/PjxMDLific5gIYhA0lJSejUqRPrKMz5+/vj888/x4ABAwAAN2/eRHBwMA4dOsQ4GenIuPimWbZsGe7fv49bt25hxIgROH/+PM375lzv3r3pSc/Pjh07hvT0dMTExKCiogIymQzTp08XTUvjCTWVA3l5eTh27JjomrGxMUJDQ+Hr68soFVvUpwHEx8cjJCQEGRkZyMjIaPH+unXrGKRij5rKm6hUKm2hAgADBgyARqNhmIgYAi6KlcuXLyMjIwMKhQIzZsxAaGgoPvzwQ9axCEOjRo3CunXrMG7cOHTp0kV73dXVlWEqNrp16waZTAaZTIaMjAxERkZi8+bNGDNmDFasWMHd2F5qKofoM9GcRCLhrnBrrqamBmfOnMGZM2egVqsxduxY1pF06tVXXwUALg/CbAs1lTextrZGUlKS9vyh5ORkLncskPbFRbFibm4OiUSCwYMHo7i4GDKZjNspLqRBYWEhgIZCtpFEIuGuPwMAysrKkJqairS0NFhbW2PZsmXw8vJCbm4uAgMDceLECdYRdWrixImwtLTEzZs3ceHCBSxZsoS7pvK2RlbzOM4aEPdpLFmyhMs+DXd3dwBAWloavvzyS8Zp9EdwcDA1lf9MoVBALpdjy5YtEASBJm2SdsFFsWJnZweFQoGZM2fio48+QkVFBXezz4mYt7c3Zs2axTqGXnjnnXfg7++Pbdu2iZ6ATZgwAWfOnGGYTLeebio3MjJCbm4uHj9+jFGjRnHVVF5SUgIPD48W1wVBQHl5OYNE7M2cOZP6NH72+PFj3LlzB/369WMdRS883VQeERHBbVO5ubk5YmNjWccgBoaLBnuVSoULFy5g9OjRyMjIQE5ODt566y04OjqyjkYY8fHxQVpaGusYekEQBNTV1eHGjRsYOnQo6urqYGJiwjqWzlFTeZPbt2+3+T5P2zoa+zRWrVrV6vs89mlMmTIFZWVlMDc3R5cuXbTHAfA4TRGgpnIAeP/995GYmKg9PPdpvP5ukPbBxSOiDRs2IDw8HAAwefJkTJ48GatWreLyjwxp0LdvXwQEBMDZ2Vm0P5/Hhsjc3FxERERArVZj79698PHxQXR0NF5//XXW0XSKmsqb8FSMPA/1abQUFRUFc3Nz1jH0BjWVA3K5HACwc+dOxkmIITLoYmXNmjW4ffs2CgoKcP36de11lUqFBw8eMExGWJNKpawj6I2YmBjs2bMHgYGBsLCwwO7duxEaGspdsUJN5aQ11KfR0ooVK1oU9jyjpnLA0tISALB+/XrEx8eL3luwYAH+8Y9/sIhFDIRBFyuBgYG4desWFAoFAgMDtdc7deoEOzs7hskIazyuoDyLRqMRjSnm9bNBTeWkLdSn0cTR0REpKSlwcnJC165dtdetra0ZpmKHmsob/qZeuXIF9+7dE/W7qdVq9O3bl2EyYggMulixsbGBjY0Njhw5guvXr+PcuXNQq9VwdXWlk2Y55+jo2OIG1NLSEtnZ2YwSsdO3b19kZWVBIpGguroau3fv5vKmg5rKSVsqKirg7u5OfRoACgoKUFBQAKCpkK+vr8epU6dYxmKGmsobVlQePnwIhUKB1atXa68bGRnRlkHy0rhosE9LS0NMTAzc3d0hCAKysrIQHBwMf39/1tGIHlAqlcjMzER+fv4zm2gNWUVFBRQKBXJycqDRaODm5obVq1drl/V5QU3lpC2FhYWt3nTx/HuhVCqRkZGBr776Cv/5z3+Qn5/POpJOUVN56y5fvoza2loIggC1Wo1bt25pt8gR8v/goljx9fXF9u3btasplZWVCAgIoGlQRMTX15er6S2EkBc3depU6tP42c2bN7Fv3z4cOHAAVVVVCAoKwpw5c7jbsXDv3j1YWlo+80EHj4Xs6tWrce7cOVRVVcHW1hZFRUUYOXIk9XuRl2LQ28AaaTQa0ZeomZkZ7UHnXEpKivZnQRBQUlLC7fkJp06dQmxsLKqqqkTnD/H6VJCQ1lCfBpCRkYGvv/4aly5dwuTJkxEVFYU1a9Zw2wNITeUt5eTkID09HXK5HAEBAairq+Nq7Dv5dXBxd+bg4IANGzaIJnU4ODgwTkVYOnv2rOh17969ud1zHBkZiZUrV8Le3p6KeEKegfo0gJCQEEydOhV79+7FwIEDAfA9fIKayluytLSEsbExhgwZguLiYnh7e6OmpoZ1LNLBGXSxcvDgQfj5+UEulyMuLg4ffvghNBoNxowZg7/+9a+s4xGG1q1bB6VSidLSUqjVatjb23O7stK7d28uT1om5Jf45ptvALTs0+BJamoqDhw4gDlz5qB///7w9vaGWq1mHYsZaipvycrKComJiRgzZgw2btwIoKGoJ+RlGHTPip+fHw4ePMg6BtFDFy9exOLFi2FqagqNRoP79+/jiy++gLOzM+toOrdx40aoVCqMGzdOdNaIq6srw1SE6Bfq02iiUqnwr3/9CwcOHMC3336L1157DXPnzsWECRNYR2OGmsobPHr0CNnZ2fD29sbOnTuRk5ODBQsWwM3NjXU00oFRsUK4NGvWLKxatUpbnOTn5yMyMhLJycmMk+ne/PnzW1yTSCTYsWMHgzSE6Jen+zSmTJmCNWvWaFdaeFdZWYmUlBSkpKQgNTWVdRwmqKm8ybvvvsvlv5v8ugx630tb5ybwOh+fNKitrRWtokilUjx58oRhIna8vb0xa9Ys1jEI0UvUp9E2MzMzLFy4EAsXLmQdhRlqKm9SV1dHh6eSdmfQxcrAgQOxdetW1jGIHurVqxcyMzPh6ekJoOHpqampKeNUbOzatYuKFUKegfo0yPNQU3mTyspKOjyVtDuD3gYmk8lEI2oJafTDDz9g+fLluHHjBgBgwIABiIqKgq2tLeNkuvenP/0J9fX1cHZ2FvWs8DqOlJDWUJ8GeZYlS5Zg2LBh2qbyWbNmIT4+HsePH2cdTefozBnyazDolZWRI0eyjkD0TPMT6u3s7NCvXz/06NEDgwYNgoWFBcNk7EilUtYRCNF7RkZG8PT0hKenp7ZPIzo6mooVAoVCgezsbDg5OcHLywtpaWn45JNPWMdi4vz5861ep2KFvAyDXlkh5GmtDVwQBAHFxcW4du0aNQYSQgj5RaipvEnzB4JKpRIXLlyAi4uLdowxIf8Pg15ZIeRpfn5+z3zP29tbh0n0h6OjY4uGYUtLS2RnZzNKRAghHQc1lTdZt26d6PXDhw/xl7/8hVEaYiioWCHcKy8vR2ZmJrp37846ChNFRUXan5VKJTIzM5Gfn88wESGEdBzUVP5sJiYmz+xjIeRFUbFCuFdWVobCwkJERUWxjsKcsbExpk6dii1btrCOQgghHQJtAWsyf/587Uq9IAi4desW9XWRl0bFCuGei4sLXFxcWMdgpvnEPEEQUFJSAiMj+moghJAXQU3lTby8vGBhYYGuXbuiR48eMDU1hZ2dHetYpIOjOxJCOHf27FnR6969eyM2NpZRGkII6Viaf4c2byqXyWQMU+lWRUUFFi9ejJKSEgwaNAgAUFpaihEjRiA6Oho9e/ZkG5B0aDQNjBACpVKJ0tJSqNVq2Nvb08oKIYT8nxqbyrdv3846is6EhYWhT58+CAkJgbGxMQCgvr4e8fHxKC8vx/r16xknJB0Z3ZEQwrmLFy9i8eLFMDU1hUajwf379/HFF1/A2dmZdTRCCOlweGwqz8vLw7Fjx0TXOnfujNDQUPj6+jJKRQwFFSuEcC4yMhKbNm3SFif5+fmQy+VITk5mnIwQQvQfNZUDXbp0afW6RCLBK6+8ouM0xNBQsUII52pra0WrKFKpFE+ePGGYiBBCOg5qKkeLs7pe9D1CXgQVK4RwrlevXsjMzISnpycAICMjA6ampoxTEUKIfqOm8iYlJSXw8PBocV0QBJSXlzNIRAwJNdgTwrkffvgBy5cvx40bNwAAAwYMQFRUFGxtbRknI4QQ/UVN5U2e16PD4xhn0n6oWCGEU6tWrRK9/u9//4sePXpg0KBBmD17NldPBQkh5JeaOnVqi6ZyoGE1wdfXF6mpqQxSEWJ4aBsYIZz6wx/+0OKaIAgoLi7G0qVL6VRmQghpAzWVE6IbVKwQwik/P79nvuft7a3DJIQQ0vFQUzkhukHFCiFEq7y8HJmZmejevTvrKIQQoteoqZwQ3aBihRCiVVZWhsLCQkRFRbGOQgghei09PZ11BEK4QA32hBBCCCGEEL1EHWCEEEIIIYQQvUTFCiGEEEIIIUQvUbFCCCGEEEII0UtUrBBCCCGEEEL0EhUrhBBCCCGEEL30P4cH2ZPdLWbNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the coreeletion between Independent and dependent attribute\n",
    "\n",
    "plt.figure(figsize = (15,15))\n",
    "sns.heatmap(train_data.corr(),annot = True,cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
       "                    max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                    max_samples=None, min_impurity_decrease=0.0,\n",
       "                    min_impurity_split=None, min_samples_leaf=1,\n",
       "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                    n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                    random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.34040526e-01 1.44396272e-01 5.29724981e-02 2.54817186e-02\n",
      " 2.09748575e-02 2.70456091e-02 1.87470296e-02 1.09933558e-01\n",
      " 1.77228600e-02 1.05965177e-02 1.92274869e-03 1.90081731e-02\n",
      " 1.41986467e-01 6.75705092e-02 1.91811138e-02 8.63326506e-04\n",
      " 3.29514176e-03 1.14598934e-04 5.08210232e-03 9.10294092e-05\n",
      " 5.16791728e-04 1.40532889e-02 3.21691980e-03 6.67365079e-03\n",
      " 1.02911605e-02 1.23545041e-02 6.34516126e-03 4.39672770e-04\n",
      " 2.50821924e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fiiting model using Random forest\n",
    "\n",
    "\n",
    "1.Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "\n",
    "2.Import the model\n",
    "\n",
    "3.Fit the model\n",
    "\n",
    "4.Predict w.r.t X_test\n",
    "\n",
    "5.In regression Check RSME score\n",
    "\n",
    "6.Plot the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf  = RandomForestRegressor()\n",
    "reg_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9533312346561971"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7986311633536207"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1175.8678513818243\n",
      "MSE: 4341926.197700163\n",
      "RMSE: 2083.7289165580446\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7986311633536207"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning\n",
    "I have used RandomisedSearchCV (fast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   3.5s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   3.5s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   3.4s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   3.4s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   3.4s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   5.3s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   5.3s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   5.4s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   5.3s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   5.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   3.2s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   5.8s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   5.8s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   5.8s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   5.7s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   5.7s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   8.9s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   9.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   9.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   9.3s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   9.0s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   8.7s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   8.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   8.4s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   8.5s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   8.3s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.8s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.8s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.8s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.8s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.8s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.9s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  10.9s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  10.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  10.5s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  10.7s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  10.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:  4.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                   iid='deprecated', n_iter=10, n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1165.749214901713\n",
      "MSE: 4052746.115358068\n",
      "RMSE: 2013.1433419799168\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model to reuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you want to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(reg_rf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7986311633536207"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}