ab-demo1-TitanicDisaster-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "train_df=pd.read_csv('titanic_train.csv', quotechar='\"',index_col='PassengerId')\n",
    "test_df=pd.read_csv('titanic_test.csv', quotechar='\"',index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 10 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Ticket      418 non-null object\n",
      "Fare        417 non-null float64\n",
      "Cabin       91 non-null object\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 35.9+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Ticket      891 non-null object\n",
      "Fare        891 non-null float64\n",
      "Cabin       204 non-null object\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Survived']=-888"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df=pd.concat((train_df, test_df),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 11 columns):\n",
      "Age         1046 non-null float64\n",
      "Cabin       295 non-null object\n",
      "Embarked    1307 non-null object\n",
      "Fare        1308 non-null float64\n",
      "Name        1309 non-null object\n",
      "Parch       1309 non-null int64\n",
      "Pclass      1309 non-null int64\n",
      "Sex         1309 non-null object\n",
      "SibSp       1309 non-null int64\n",
      "Survived    1309 non-null int64\n",
      "Ticket      1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 122.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
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       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
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       "                Sex  SibSp  Survived            Ticket  \n",
       "PassengerId                                             \n",
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       "2            female      1         1          PC 17599  \n",
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    "df.head()"
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   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0   NaN        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0   NaN        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0   NaN        S   8.0500   \n",
       "6             NaN   NaN        Q   8.4583   \n",
       "7            54.0   E46        S  51.8625   \n",
       "8             2.0   NaN        S  21.0750   \n",
       "9            27.0   NaN        S  11.1333   \n",
       "10           14.0   NaN        C  30.0708   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "6                                             Moran, Mr. James      0       3   \n",
       "7                                      McCarthy, Mr. Timothy J      0       1   \n",
       "8                               Palsson, Master. Gosta Leonard      1       3   \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      2       3   \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)      0       2   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket  \n",
       "PassengerId                                             \n",
       "1              male      1         0         A/5 21171  \n",
       "2            female      1         1          PC 17599  \n",
       "3            female      0         1  STON/O2. 3101282  \n",
       "4            female      1         1            113803  \n",
       "5              male      0         0            373450  \n",
       "6              male      0         0            330877  \n",
       "7              male      0         0             17463  \n",
       "8              male      3         0            349909  \n",
       "9            female      0         1            347742  \n",
       "10           female      1         1            237736  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
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       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1306</td>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1307</td>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>-888</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked      Fare                          Name  \\\n",
       "PassengerId                                                                \n",
       "1305          NaN   NaN        S    8.0500            Spector, Mr. Woolf   \n",
       "1306         39.0  C105        C  108.9000  Oliva y Ocana, Dona. Fermina   \n",
       "1307         38.5   NaN        S    7.2500  Saether, Mr. Simon Sivertsen   \n",
       "1308          NaN   NaN        S    8.0500           Ware, Mr. Frederick   \n",
       "1309          NaN   NaN        C   22.3583      Peter, Master. Michael J   \n",
       "\n",
       "             Parch  Pclass     Sex  SibSp  Survived              Ticket  \n",
       "PassengerId                                                              \n",
       "1305             0       3    male      0      -888           A.5. 3236  \n",
       "1306             0       1  female      0      -888            PC 17758  \n",
       "1307             0       3    male      0      -888  SOTON/O.Q. 3101262  \n",
       "1308             0       3    male      0      -888              359309  \n",
       "1309             1       3    male      1      -888                2668  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1                                 Braund, Mr. Owen Harris\n",
       "2       Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "3                                  Heikkinen, Miss. Laina\n",
       "4            Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "5                                Allen, Mr. William Henry\n",
       "                              ...                        \n",
       "1305                                   Spector, Mr. Woolf\n",
       "1306                         Oliva y Ocana, Dona. Fermina\n",
       "1307                         Saether, Mr. Simon Sivertsen\n",
       "1308                                  Ware, Mr. Frederick\n",
       "1309                             Peter, Master. Michael J\n",
       "Name: Name, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1305</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1306</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1307</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1308</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1309</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          Name   Age\n",
       "PassengerId                                                         \n",
       "1                                      Braund, Mr. Owen Harris  22.0\n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0\n",
       "3                                       Heikkinen, Miss. Laina  26.0\n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0\n",
       "5                                     Allen, Mr. William Henry  35.0\n",
       "...                                                        ...   ...\n",
       "1305                                        Spector, Mr. Woolf   NaN\n",
       "1306                              Oliva y Ocana, Dona. Fermina  39.0\n",
       "1307                              Saether, Mr. Simon Sivertsen  38.5\n",
       "1308                                       Ware, Mr. Frederick   NaN\n",
       "1309                                  Peter, Master. Michael J   NaN\n",
       "\n",
       "[1309 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name','Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>54.0</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>349909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>237736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "5            35.0   NaN        S   8.0500   \n",
       "6             NaN   NaN        Q   8.4583   \n",
       "7            54.0   E46        S  51.8625   \n",
       "8             2.0   NaN        S  21.0750   \n",
       "9            27.0   NaN        S  11.1333   \n",
       "10           14.0   NaN        C  30.0708   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "6                                             Moran, Mr. James      0       3   \n",
       "7                                      McCarthy, Mr. Timothy J      0       1   \n",
       "8                               Palsson, Master. Gosta Leonard      1       3   \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      2       3   \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)      0       2   \n",
       "\n",
       "                Sex  SibSp  Survived  Ticket  \n",
       "PassengerId                                   \n",
       "5              male      0         0  373450  \n",
       "6              male      0         0  330877  \n",
       "7              male      0         0   17463  \n",
       "8              male      3         0  349909  \n",
       "9            female      0         1  347742  \n",
       "10           female      1         1  237736  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of male passengers : 843\n"
     ]
    }
   ],
   "source": [
    "male_passenger=df.loc[df.Sex == 'male',:]\n",
    "print('Number of male passengers : {0}'.format(len(male_passenger)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of male passengers in first class: 179\n"
     ]
    }
   ],
   "source": [
    "male_passenger_first_class=df.loc[((df.Sex=='male')&(df.Pclass==1)),:]\n",
    "print('Number of male passengers in first class: {0}'.format(len(male_passenger_first_class)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>-283.301757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>414.337413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-888.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-888.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch       Pclass        SibSp  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027     2.294882     0.498854   \n",
       "std      14.413493    51.758668     0.865560     0.837836     1.041658   \n",
       "min       0.170000     0.000000     0.000000     1.000000     0.000000   \n",
       "25%      21.000000     7.895800     0.000000     2.000000     0.000000   \n",
       "50%      28.000000    14.454200     0.000000     3.000000     0.000000   \n",
       "75%      39.000000    31.275000     0.000000     3.000000     1.000000   \n",
       "max      80.000000   512.329200     9.000000     3.000000     8.000000   \n",
       "\n",
       "          Survived  \n",
       "count  1309.000000  \n",
       "mean   -283.301757  \n",
       "std     414.337413  \n",
       "min    -888.000000  \n",
       "25%    -888.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min fare:0.0\n",
      "Max fare:512.3292\n",
      "Fare range : 512.3292\n",
      "25 percentile : 7.8958\n",
      "50 percentile : 14.4542\n",
      "75 percentile : 31.275\n",
      "Variance fare : 2678.959737892894\n",
      "Standerd deviation fare : 51.75866823917414\n"
     ]
    }
   ],
   "source": [
    "#dispersion measures\n",
    "print('Min fare:{0}'.format(df.Fare.min()))\n",
    "print('Max fare:{0}'.format(df.Fare.max()))\n",
    "print('Fare range : {0}'.format(df.Fare.max()-df.Fare.min()))\n",
    "print('25 percentile : {0}'.format(df.Fare.quantile(.25)))\n",
    "print('50 percentile : {0}'.format(df.Fare.quantile(.5)))\n",
    "print('75 percentile : {0}'.format(df.Fare.quantile(.75)))\n",
    "print('Variance fare : {0}'.format(df.Fare.var()))\n",
    "print('Standerd deviation fare : {0}'.format(df.Fare.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b761e6c48>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Fare.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      843\n",
       "female    466\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      0.644003\n",
       "female    0.355997\n",
       "Name: Sex, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Survived != -888].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7735b8c8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HbKqqZ5O8DHigqn5zTRvUsiV5oqp+ba370HDt/703V9VzSbYAdwOfrKqPJnmoqq5a0wZX2Ur8/MCL3emq+ilwKsl3qupZgKr6vyQ/W+PetIgkj5ztELBhNXvRkl1QVc8BVNXjSd4G3J3k1Qz/WZSuGe7j95MkL6+qU8BvzReTvAow3M9/G4DfB55eUA/wH6vfjpbge0murKqHAdoZ/LuAjwO/sbatrT7DffzeWlU/BqiqwTB/CbB7bVrSEnwOeOV8QAxKct/qt6MluBE4PVioqtPAjUn+fm1aWjuuuUtSh7wUUpI6ZLhLUocMd0nqkOEuSR0y3CWpQ/8PTnLDrEdgInMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Pclass.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b761e6948>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Pclass.value_counts().plot(kind='bar',rot=0,title='Class wise passenger count',color='c')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7731b688>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BBoEk7cK22n1UVfcD9yR59ATqkST1aNRvNP8MuD7J5bRnIAFU1cvHUpUkqRejhsL/bAdJ0i5si6GQ5PCqurWqLp5UQZKk/mztmMKnBiNJPjHmWiRJPdtaKGRo/HHjLESS1L+thUJtZlyStAva2oHmo5PcRbPHsFc7TjtdVfWosVYn7cSyfHkv263Fi3vZrnYNWwyFqnr4pAqRJPVvW+6nIEnaxY0tFJK8L8mGJCuH2g5IcnmSm9vH/dv2JHl7ktVJrktyzLjqkiRt3jj3FC4CTt6k7RzgyqpaAFzZTgM8C1jQDkuBd46xLknSZowtFKrqS8D3N2leAgy+CHcx8Nyh9vdX4ys0l+ieN67aJEkzm/QxhYOqaj1A+3hg234IcNvQcmvbtgdJsjTJiiQrNm7cONZiJWm2mZYDzZmhbcbvRVTVhVW1qKoWzZ07d8xlSdLsMulQuGPQLdQ+bmjb1wKHDS13KLBuwrVJ0qw36VBYBpzRjp8BfHqo/fT2LKTjgB8NupkkSZMz6qWzt1mSS4DFwJwka4HXA2+iud/zmcCtwPPbxT8LnAKsBu4BXjKuuiRJmze2UKiq0zYz68QZli3grHHVIkkazbQcaJYkTQFDQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ2x3U9h2mX58t62XYsX97ZtSdoS9xQkSR1DQZLUMRQkSR1DQZLUMRQkSZ1Ze/aRtKvq68w6z6rbNbinIEnquKfQgz6/IyFJW+KegiSpYyhIkjqGgiSpYyhIkjqGgiSp08vZR0nWAHcD9wP3VdWiJAcAHwXmA2uAF1TVD/qoT5Jmqz73FJ5eVQuralE7fQ5wZVUtAK5spyVJEzRN3UdLgIvb8YuB5/ZYiyTNSn2FQgFfSHJ1kqVt20FVtR6gfTxwphWTLE2yIsmKjRs3TqhcSZod+vpG8/FVtS7JgcDlSb456opVdSFwIcCiRYtqXAVK0mzUy55CVa1rHzcAlwHHAnckmQfQPm7oozZJms0mHgpJ9kmy72Ac+ENgJbAMOKNd7Azg05OuTZJmuz66jw4CLksy2P6Hq+pzSb4GXJrkTOBW4Pk91CZJs9rEQ6Gqvg0cPUP7ncCJk65HkvSAaTolVZLUM++nIGmH6PM+Id71bcdxT0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEmd3fouQJIeqixf3st2a/HiXrY7Tu4pSJI6hoIkqWMoSJI6hoIkqTN1oZDk5CQ3JVmd5Jy+65Gk2WSqzj5K8nDgHcAzgLXA15Isq6ob+61Mkh6sr7OeYHxnPk3bnsKxwOqq+nZV3Qt8BFjSc02SNGtM1Z4CcAhw29D0WuD3hhdIshRY2k7+OMlN27mtOcD3tnPdcbKubWNd22Za64LprW0q68pDq+uxm5sxbaGQGdrqVyaqLgQufMgbSlZU1aKH+jw7mnVtG+vaNtNaF0xvbbOtrmnrPloLHDY0fSiwrqdaJGnWmbZQ+BqwIMkRSR4BnAos67kmSZo1pqr7qKruS/Iy4PPAw4H3VdUNY9rcQ+6CGhPr2jbWtW2mtS6Y3tpmVV2pqq0vJUmaFaat+0iS1CNDQZLUmXWhME2X0UjyviQbkqwcajsgyeVJbm4f959wTYcl+WKSVUluSPKKaairrWHPJFcl+UZb2xvb9iOSfLWt7aPtSQqTru3hSb6e5DPTUlNbx5ok1ye5NsmKtm0a3sv9knw8yTfbz9rv911XkiPbn9NguCvJ2X3X1db2X9vP/Mokl7S/C2P5jM2qUBi6jMazgKOA05Ic1WNJFwEnb9J2DnBlVS0ArmynJ+k+4JVV9XjgOOCs9mfUd10APwdOqKqjgYXAyUmOA/4WuKCt7QfAmT3U9gpg1dD0NNQ08PSqWjh0Tvs0vJdvAz5XVb8FHE3zs+u1rqq6qf05LQR+F7gHuKzvupIcArwcWFRVT6A5CedUxvUZq6pZMwC/D3x+aPpc4Nyea5oPrByavgmY147PA27qub5P01yLatrq2hu4huYb798DdpvpPZ5QLYfS/LE4AfgMzZcwe61pqLY1wJxN2np9L4FHAbfQnugyLXVtUssfAl+ehrp44EoPB9CcMfoZ4Jnj+ozNqj0FZr6MxiE91bI5B1XVeoD28cC+CkkyH3gS8NVpqavtprkW2ABcDnwL+GFV3dcu0sd7+lbgVcAv2+nHTEFNAwV8IcnV7SVioP/38nHARuCf2i63f0yyzxTUNexU4JJ2vNe6qup24HzgVmA98CPgasb0GZttobDVy2iokeSRwCeAs6vqrr7rGaiq+6vZvT+U5gKKj59psUnVk+TfAxuq6urh5hkW7etzdnxVHUPTZXpWkn/XUx3DdgOOAd5ZVU8CfkI/XVgzavvmnwN8rO9aANpjGEuAI4CDgX1o3s9N7ZDP2GwLhZ3hMhp3JJkH0D5umHQBSXanCYQPVdUnp6WuYVX1Q2A5zXGP/ZIMvog56ff0eOA5SdbQXNX3BJo9hz5r6lTVuvZxA03/+LH0/16uBdZW1Vfb6Y/ThETfdQ08C7imqu5op/uu6yTglqraWFW/AD4J/AFj+ozNtlDYGS6jsQw4ox0/g6ZPf2KSBHgvsKqq3jItdbW1zU2yXzu+F80vyyrgi8Cf9FFbVZ1bVYdW1Xyaz9O/VNUL+6xpIMk+SfYdjNP0k6+k5/eyqr4L3JbkyLbpRODGvusachoPdB1B/3XdChyXZO/293Pw8xrPZ6yvAzl9DcApwP+j6Yv+y55ruYSmj/AXNP89nUnTH30lcHP7eMCEa3oKzW7odcC17XBK33W1tT0R+Hpb20rgdW3744CrgNU0u/x79PR+LgY+My01tTV8ox1uGHzep+S9XAisaN/LTwH7T0ldewN3Ao8eapuGut4IfLP93H8A2GNcnzEvcyFJ6sy27iNJ0hYYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSer8fxJ28H6z2Q7/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind='hist', title='histogram for Age',color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b774153c8>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind='hist', title='histogram for Age', color='c',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b774b3f08>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind='kde', title='Density plot for Age', color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b775bea88>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Age', y='Fare', color='c', title='scatter plot : Age vs Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b776344c8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Age', y='Fare', color='c', title='scatter plot : Age vs Fare',alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7761e588>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Pclass', y='Fare', color='c', title='scatter plot : Pclass vs Fare',alpha=0.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping and Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.0\n",
       "male      28.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex').Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>15.0458</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fare   Age\n",
       "Pclass               \n",
       "1       60.0000  39.0\n",
       "2       15.0458  29.0\n",
       "3        8.0500  24.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass'])['Fare','Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>87.508992</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>21.179196</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.302889</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare   Age\n",
       "Pclass                 \n",
       "1       87.508992  39.0\n",
       "2       21.179196  29.0\n",
       "3       13.302889  24.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass']).agg({'Fare':'mean','Age':'median'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## crosstabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>female</td>\n",
       "      <td>144</td>\n",
       "      <td>106</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>179</td>\n",
       "      <td>171</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass    1    2    3\n",
       "Sex                  \n",
       "female  144  106  216\n",
       "male    179  171  493"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Sex, df.Pclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7768b148>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Sex, df.Pclass).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>female</td>\n",
       "      <td>37.037594</td>\n",
       "      <td>27.499223</td>\n",
       "      <td>22.185329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>41.029272</td>\n",
       "      <td>30.815380</td>\n",
       "      <td>25.962264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass          1          2          3\n",
       "Sex                                    \n",
       "female  37.037594  27.499223  22.185329\n",
       "male    41.029272  30.815380  25.962264"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='Sex',columns='Pclass',values='Age',aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass\n",
       "female  1         37.037594\n",
       "        2         27.499223\n",
       "        3         22.185329\n",
       "male    1         41.029272\n",
       "        2         30.815380\n",
       "        3         25.962264\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex', 'Pclass']).Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>female</td>\n",
       "      <td>37.037594</td>\n",
       "      <td>27.499223</td>\n",
       "      <td>22.185329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>41.029272</td>\n",
       "      <td>30.815380</td>\n",
       "      <td>25.962264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass          1          2          3\n",
       "Sex                                    \n",
       "female  37.037594  27.499223  22.185329\n",
       "male    41.029272  30.815380  25.962264"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex', 'Pclass']).Age.mean().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data munging: Working with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 11 columns):\n",
      "Age         1046 non-null float64\n",
      "Cabin       295 non-null object\n",
      "Embarked    1307 non-null object\n",
      "Fare        1308 non-null float64\n",
      "Name        1309 non-null object\n",
      "Parch       1309 non-null int64\n",
      "Pclass      1309 non-null int64\n",
      "Sex         1309 non-null object\n",
      "SibSp       1309 non-null int64\n",
      "Survived    1309 non-null int64\n",
      "Ticket      1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 162.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature: Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>38.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>830</td>\n",
       "      <td>62.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked  Fare  \\\n",
       "PassengerId                              \n",
       "62           38.0   B28      NaN  80.0   \n",
       "830          62.0   B28      NaN  80.0   \n",
       "\n",
       "                                                  Name  Parch  Pclass     Sex  \\\n",
       "PassengerId                                                                     \n",
       "62                                 Icard, Miss. Amelie      0       1  female   \n",
       "830          Stone, Mrs. George Nelson (Martha Evelyn)      0       1  female   \n",
       "\n",
       "             SibSp  Survived  Ticket  \n",
       "PassengerId                           \n",
       "62               0         1  113572  \n",
       "830              0         1  113572  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    914\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>30</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Embarked   C   Q    S\n",
       "Survived             \n",
       "0         75  47  427\n",
       "1         93  30  217"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[df.Survived != -888].Survived, df[df.Survived!=-888].Embarked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.loc[df.Embarked.isnull(), 'Embarked']='S'\n",
    "#df.Embarked.fillna('S',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Embarked\n",
       "1       C           76.7292\n",
       "        Q           90.0000\n",
       "        S           52.0000\n",
       "2       C           15.3146\n",
       "        Q           12.3500\n",
       "        S           15.3750\n",
       "3       C            7.8958\n",
       "        Q            7.7500\n",
       "        S            8.0500\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass', 'Embarked']).Fare.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Embarked.fillna('C', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Cabin, Embarked, Fare, Name, Parch, Pclass, Sex, SibSp, Survived, Ticket]\n",
       "Index: []"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 11 columns):\n",
      "Age         1046 non-null float64\n",
      "Cabin       295 non-null object\n",
      "Embarked    1309 non-null object\n",
      "Fare        1308 non-null float64\n",
      "Name        1309 non-null object\n",
      "Parch       1309 non-null int64\n",
      "Pclass      1309 non-null int64\n",
      "Sex         1309 non-null object\n",
      "SibSp       1309 non-null int64\n",
      "Survived    1309 non-null int64\n",
      "Ticket      1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 162.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature: Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1044</td>\n",
       "      <td>60.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>3701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked  Fare                Name  Parch  Pclass  \\\n",
       "PassengerId                                                                 \n",
       "1044         60.5   NaN        S   NaN  Storey, Mr. Thomas      0       3   \n",
       "\n",
       "              Sex  SibSp  Survived Ticket  \n",
       "PassengerId                                \n",
       "1044         male      0      -888   3701  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.05\n"
     ]
    }
   ],
   "source": [
    "median_fare = df.loc[(df.Pclass == 3) & (df.Embarked=='S'),'Fare'].median()\n",
    "print(median_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Fare.fillna(median_fare, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 11 columns):\n",
      "Age         1046 non-null float64\n",
      "Cabin       295 non-null object\n",
      "Embarked    1309 non-null object\n",
      "Fare        1309 non-null float64\n",
      "Name        1309 non-null object\n",
      "Parch       1309 non-null int64\n",
      "Pclass      1309 non-null int64\n",
      "Sex         1309 non-null object\n",
      "SibSp       1309 non-null int64\n",
      "Survived    1309 non-null int64\n",
      "Ticket      1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 162.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature: Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>244373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>330959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>334915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>365237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>-888</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age Cabin Embarked     Fare                             Name  \\\n",
       "PassengerId                                                                 \n",
       "6            NaN   NaN        Q   8.4583                 Moran, Mr. James   \n",
       "18           NaN   NaN        S  13.0000     Williams, Mr. Charles Eugene   \n",
       "20           NaN   NaN        C   7.2250          Masselmani, Mrs. Fatima   \n",
       "27           NaN   NaN        C   7.2250          Emir, Mr. Farred Chehab   \n",
       "29           NaN   NaN        Q   7.8792    O'Dwyer, Miss. Ellen \"Nellie\"   \n",
       "...          ...   ...      ...      ...                              ...   \n",
       "1300         NaN   NaN        Q   7.7208  Riordan, Miss. Johanna Hannah\"\"   \n",
       "1302         NaN   NaN        Q   7.7500           Naughton, Miss. Hannah   \n",
       "1305         NaN   NaN        S   8.0500               Spector, Mr. Woolf   \n",
       "1308         NaN   NaN        S   8.0500              Ware, Mr. Frederick   \n",
       "1309         NaN   NaN        C  22.3583         Peter, Master. Michael J   \n",
       "\n",
       "             Parch  Pclass     Sex  SibSp  Survived     Ticket  \n",
       "PassengerId                                                     \n",
       "6                0       3    male      0         0     330877  \n",
       "18               0       2    male      0         1     244373  \n",
       "20               0       3  female      0         1       2649  \n",
       "27               0       3    male      0         0       2631  \n",
       "29               0       3  female      0         1     330959  \n",
       "...            ...     ...     ...    ...       ...        ...  \n",
       "1300             0       3  female      0      -888     334915  \n",
       "1302             0       3  female      0      -888     365237  \n",
       "1305             0       3    male      0      -888  A.5. 3236  \n",
       "1308             0       3    male      0      -888     359309  \n",
       "1309             1       3    male      1      -888       2668  \n",
       "\n",
       "[263 rows x 11 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7777eb48>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind='hist', bins=20, color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.881137667304014"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.Age.fillna(df.Age.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.0\n",
       "male      28.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex').Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b7781a348>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.Age.notnull()].boxplot('Age','Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#age_sex_median=df.groupby('Sex').Age.transform('median')\n",
    "#df.Age.fillna(age_sex_median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b778cb708>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.Age.notnull()].boxplot('Age','Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetTitle2(name):\n",
    "    first_name_with_title=name.split(',')[1]\n",
    "    title=first_name_with_title.split('.')[0]\n",
    "    title=title.strip().lower()\n",
    "    return title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1           mr\n",
       "2          mrs\n",
       "3         miss\n",
       "4          mrs\n",
       "5           mr\n",
       "         ...  \n",
       "1305        mr\n",
       "1306      dona\n",
       "1307        mr\n",
       "1308        mr\n",
       "1309    master\n",
       "Name: Name, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name.map(lambda x: GetTitle2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mr', 'mrs', 'miss', 'master', 'don', 'rev', 'dr', 'mme', 'ms',\n",
       "       'major', 'lady', 'sir', 'mlle', 'col', 'capt', 'the countess',\n",
       "       'jonkheer', 'dona'], dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name.map(lambda x: GetTitle2(x)).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetTitle(name):   \n",
    "    title_group={ 'mr':'Mr', \n",
    "             'mrs':'Mrs', \n",
    "             'miss':'Miss', \n",
    "             'master':'Master', \n",
    "             'don':'Sir', \n",
    "             'rev':'Sir', \n",
    "             'dr':'Officer', \n",
    "             'mme':'Mrs', \n",
    "             'ms':\"Mrs\",\n",
    "             'major':'Officer',\n",
    "             'lady':'Lady', \n",
    "             'sir':'Sir',\n",
    "             'mlle':'Miss', \n",
    "             'col':'Officer', \n",
    "             'capt':'Officer', \n",
    "             'the countess':'Lady',\n",
    "             'jonkheer':'Sir',\n",
    "             'dona':'Lady'\n",
    "            }\n",
    "    first_name_with_title=name.split(',')[1]\n",
    "    title=first_name_with_title.split('.')[0]\n",
    "    title=title.strip().lower()\n",
    "    return title_group[title]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Title']=df.Name.map(lambda x: GetTitle(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Sir', 'Officer', 'Lady'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b779ac848>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.Age.notnull()].boxplot('Age','Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_age_median=df.groupby('Title').Age.transform('median')\n",
    "df.Age.fillna(title_age_median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 12 columns):\n",
      "Age         1309 non-null float64\n",
      "Cabin       295 non-null object\n",
      "Embarked    1309 non-null object\n",
      "Fare        1309 non-null float64\n",
      "Name        1309 non-null object\n",
      "Parch       1309 non-null int64\n",
      "Pclass      1309 non-null int64\n",
      "Sex         1309 non-null object\n",
      "SibSp       1309 non-null int64\n",
      "Survived    1309 non-null int64\n",
      "Ticket      1309 non-null object\n",
      "Title       1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 172.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b779535c8>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind='hist', bins=20, color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>71.0</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>70.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>494</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>631</td>\n",
       "      <td>80.0</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27042</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>852</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>988</td>\n",
       "      <td>76.0</td>\n",
       "      <td>C46</td>\n",
       "      <td>S</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>Cavendish, Mrs. Tyrell William (Julia Florence...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>-888</td>\n",
       "      <td>19877</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "97           71.0    A5        C  34.6542   \n",
       "117          70.5   NaN        Q   7.7500   \n",
       "494          71.0   NaN        C  49.5042   \n",
       "631          80.0   A23        S  30.0000   \n",
       "852          74.0   NaN        S   7.7750   \n",
       "988          76.0   C46        S  78.8500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "97                                   Goldschmidt, Mr. George B      0       1   \n",
       "117                                       Connors, Mr. Patrick      0       3   \n",
       "494                                    Artagaveytia, Mr. Ramon      0       1   \n",
       "631                       Barkworth, Mr. Algernon Henry Wilson      0       1   \n",
       "852                                        Svensson, Mr. Johan      0       3   \n",
       "988          Cavendish, Mrs. Tyrell William (Julia Florence...      0       1   \n",
       "\n",
       "                Sex  SibSp  Survived    Ticket Title  \n",
       "PassengerId                                           \n",
       "97             male      0         0  PC 17754    Mr  \n",
       "117            male      0         0    370369    Mr  \n",
       "494            male      0         0  PC 17609    Mr  \n",
       "631            male      0         1     27042    Mr  \n",
       "852            male      0         0    347060    Mr  \n",
       "988          female      1      -888     19877   Mrs  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Age>70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b77b10688>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Fare.plot(kind='hist', title='histogram  for Fare', bins=20, color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b77b92f88>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Fare.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>259</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>680</td>\n",
       "      <td>36.0</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>738</td>\n",
       "      <td>35.0</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1235</td>\n",
       "      <td>58.0</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Cardeza, Mrs. James Warburton Martinez (Charlo...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>-888</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Cabin Embarked      Fare  \\\n",
       "PassengerId                                         \n",
       "259          35.0          NaN        C  512.3292   \n",
       "680          36.0  B51 B53 B55        C  512.3292   \n",
       "738          35.0         B101        C  512.3292   \n",
       "1235         58.0  B51 B53 B55        C  512.3292   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "259                                           Ward, Miss. Anna      0       1   \n",
       "680                         Cardeza, Mr. Thomas Drake Martinez      1       1   \n",
       "738                                     Lesurer, Mr. Gustave J      0       1   \n",
       "1235         Cardeza, Mrs. James Warburton Martinez (Charlo...      1       1   \n",
       "\n",
       "                Sex  SibSp  Survived    Ticket Title  \n",
       "PassengerId                                           \n",
       "259          female      0         1  PC 17755  Miss  \n",
       "680            male      0         1  PC 17755    Mr  \n",
       "738            male      0         1  PC 17755    Mr  \n",
       "1235         female      0      -888  PC 17755   Mrs  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Fare==df.Fare.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "LogFare = np.log(df.Fare + 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b77c0c3c8>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LogFare.plot(kind='hist', color='c', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1         (-0.001, 7.896]\n",
       "2       (31.275, 512.329]\n",
       "3         (7.896, 14.454]\n",
       "4       (31.275, 512.329]\n",
       "5         (7.896, 14.454]\n",
       "              ...        \n",
       "1305      (7.896, 14.454]\n",
       "1306    (31.275, 512.329]\n",
       "1307      (-0.001, 7.896]\n",
       "1308      (7.896, 14.454]\n",
       "1309     (14.454, 31.275]\n",
       "Name: Fare, Length: 1309, dtype: category\n",
       "Categories (4, interval[float64]): [(-0.001, 7.896] < (7.896, 14.454] < (14.454, 31.275] < (31.275, 512.329]]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df.Fare, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1        very_low\n",
       "2       very_high\n",
       "3             low\n",
       "4       very_high\n",
       "5             low\n",
       "          ...    \n",
       "1305          low\n",
       "1306    very_high\n",
       "1307     very_low\n",
       "1308          low\n",
       "1309         high\n",
       "Name: Fare, Length: 1309, dtype: category\n",
       "Categories (4, object): [very_low < low < high < very_high]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df.Fare, 4, labels=['very_low','low','high','very_high'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b77cad048>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAARWUlEQVR4nO3cf4xlZX3H8fengD8ipoAMdN3ddKndFtGWhY6Uxh+lQBRI42IrCkmVGOLaFFNtrC2atGIrqcYfJCRKXAu6NhYkKIFYakWEqqmCgy7ICoQtoCy7ZQdFBGmprN/+cc/Gy3Bn5+7MvTOdZ9+v5Oae85znufc7d879nDPPnHtTVUiS2vJLS12AJGn0DHdJapDhLkkNMtwlqUGGuyQ1yHCXpAbNGe5JnpXk5iS3JtmS5L1d+6eS3Jtkc3db17UnyUVJtia5Lcmx4/4hJElPtf8QfZ4ATqyqx5IcAHw9yb92295ZVVfO6H8qsLa7/S5wcXc/q0MPPbTWrFmzV4VL0r7ulltueaiqJgZtmzPcq/cpp8e61QO6254++bQe+HQ37ptJDkqyoqp2zDZgzZo1TE1NzVWKJKlPku/Ptm2oOfck+yXZDOwErquqm7pNF3RTLxcmeWbXthK4v2/4tq5NkrRIhgr3qtpVVeuAVcBxSV4MvAs4EngJcAjw1133DHqImQ1JNiSZSjI1PT09r+IlSYPt1dUyVfVj4EbglKraUT1PAJ8Ejuu6bQNW9w1bBWwf8Fgbq2qyqiYnJgZOGUmS5mmYq2UmkhzULT8bOBm4M8mKri3A6cDt3ZBrgDd2V80cDzyyp/l2SdLoDXO1zApgU5L96B0MrqiqLyT5SpIJetMwm4E/7fpfC5wGbAUeB940+rIlSXsyzNUytwHHDGg/cZb+BZy78NIkSfPlJ1QlqUGGuyQ1yHCXpAYN8w/VZSk33rjUJQylTjhhqUuQ1CDP3CWpQYa7JDXIcJekBhnuktSgZv+hqtHyH9TS8uKZuyQ1yHCXpAY5LSMtgeUwzeUU1/LmmbskNcgzd0nL2nL4KwgW/y8hz9wlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDZoz3JM8K8nNSW5NsiXJe7v2I5LclOTuJJ9N8oyu/Znd+tZu+5rx/giSpJmGOXN/Ajixqo4G1gGnJDke+ABwYVWtBR4Gzun6nwM8XFW/DlzY9ZMkLaI5w716HutWD+huBZwIXNm1bwJO75bXd+t0209KkpFVLEma01Bz7kn2S7IZ2AlcB/wn8OOqerLrsg1Y2S2vBO4H6LY/AjxvlEVLkvZsqHCvql1VtQ5YBRwHvHBQt+5+0Fl6zWxIsiHJVJKp6enpYeuVJA1hr66WqaofAzcCxwMHJdn9rZKrgO3d8jZgNUC3/ZeBHw14rI1VNVlVkxMTE/OrXpI00DBXy0wkOahbfjZwMnAHcAPw2q7b2cDV3fI13Trd9q9U1dPO3CVJ4zPM97mvADYl2Y/eweCKqvpCku8Blyd5H/Ad4JKu/yXAPyXZSu+M/cwx1C1J2oM5w72qbgOOGdB+D73595nt/wOcMZLqJEnz4idUJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg+YM9ySrk9yQ5I4kW5K8rWs/P8kDSTZ3t9P6xrwrydYkdyV51Th/AEnS0+0/RJ8ngXdU1beTPBe4Jcl13bYLq+pD/Z2THAWcCbwIeD7w5SS/UVW7Rlm4JGl2c565V9WOqvp2t/wocAewcg9D1gOXV9UTVXUvsBU4bhTFSpKGs1dz7knWAMcAN3VNb01yW5JLkxzcta0E7u8bto0BB4MkG5JMJZmanp7e68IlSbMbOtyTHAh8Dnh7Vf0EuBh4AbAO2AF8eHfXAcPraQ1VG6tqsqomJyYm9rpwSdLshgr3JAfQC/bPVNXnAarqwaraVVU/Bz7BL6ZetgGr+4avAraPrmRJ0lyGuVomwCXAHVX1kb72FX3dXgPc3i1fA5yZ5JlJjgDWAjePrmRJ0lyGuVrmpcAbgO8m2dy1vRs4K8k6elMu9wFvAaiqLUmuAL5H70qbc71SRpIW15zhXlVfZ/A8+rV7GHMBcMEC6pIkLYCfUJWkBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0JzhnmR1khuS3JFkS5K3de2HJLkuyd3d/cFde5JclGRrktuSHDvuH0KS9FTDnLk/Cbyjql4IHA+cm+Qo4Dzg+qpaC1zfrQOcCqztbhuAi0detSRpj+YM96raUVXf7pYfBe4AVgLrgU1dt03A6d3yeuDT1fNN4KAkK0ZeuSRpVns1555kDXAMcBNweFXtgN4BADis67YSuL9v2LauTZK0SIYO9yQHAp8D3l5VP9lT1wFtNeDxNiSZSjI1PT09bBmSpCEMFe5JDqAX7J+pqs93zQ/unm7p7nd27duA1X3DVwHbZz5mVW2sqsmqmpyYmJhv/ZKkAYa5WibAJcAdVfWRvk3XAGd3y2cDV/e1v7G7auZ44JHd0zeSpMWx/xB9Xgq8Afhuks1d27uB9wNXJDkH+AFwRrftWuA0YCvwOPCmkVYsSZrTnOFeVV9n8Dw6wEkD+hdw7gLrkiQtgJ9QlaQGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDZoz3JNcmmRnktv72s5P8kCSzd3ttL5t70qyNcldSV41rsIlSbMb5sz9U8ApA9ovrKp13e1agCRHAWcCL+rGfCzJfqMqVpI0nDnDvaq+CvxoyMdbD1xeVU9U1b3AVuC4BdQnSZqHhcy5vzXJbd20zcFd20rg/r4+27o2SdIimm+4Xwy8AFgH7AA+3LVnQN8a9ABJNiSZSjI1PT09zzIkSYPMK9yr6sGq2lVVPwc+wS+mXrYBq/u6rgK2z/IYG6tqsqomJyYm5lOGJGkW8wr3JCv6Vl8D7L6S5hrgzCTPTHIEsBa4eWElSpL21v5zdUhyGXACcGiSbcB7gBOSrKM35XIf8BaAqtqS5Arge8CTwLlVtWs8pUuSZjNnuFfVWQOaL9lD/wuACxZSlCRpYfyEqiQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCc4Z7k0iQ7k9ze13ZIkuuS3N3dH9y1J8lFSbYmuS3JseMsXpI02DBn7p8CTpnRdh5wfVWtBa7v1gFOBdZ2tw3AxaMpU5K0N+YM96r6KvCjGc3rgU3d8ibg9L72T1fPN4GDkqwYVbGSpOHMd8798KraAdDdH9a1rwTu7+u3rWt7miQbkkwlmZqenp5nGZKkQUb9D9UMaKtBHatqY1VNVtXkxMTEiMuQpH3bfMP9wd3TLd39zq59G7C6r98qYPv8y5Mkzcd8w/0a4Oxu+Wzg6r72N3ZXzRwPPLJ7+kaStHj2n6tDksuAE4BDk2wD3gO8H7giyTnAD4Azuu7XAqcBW4HHgTeNoWZJ0hzmDPeqOmuWTScN6FvAuQstSpK0MH5CVZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNWj/hQxOch/wKLALeLKqJpMcAnwWWAPcB7yuqh5eWJmSpL0xijP3P6iqdVU12a2fB1xfVWuB67t1SdIiGse0zHpgU7e8CTh9DM8hSdqDhYZ7AV9KckuSDV3b4VW1A6C7P2zQwCQbkkwlmZqenl5gGZKkfguacwdeWlXbkxwGXJfkzmEHVtVGYCPA5ORkLbAOSVKfBZ25V9X27n4ncBVwHPBgkhUA3f3OhRYpSdo78w73JM9J8tzdy8ArgduBa4Czu25nA1cvtEhJ0t5ZyLTM4cBVSXY/zj9X1ReTfAu4Isk5wA+AMxZepiRpb8w73KvqHuDoAe0/BE5aSFGSpIXxE6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDRpbuCc5JcldSbYmOW9czyNJerqxhHuS/YCPAqcCRwFnJTlqHM8lSXq6cZ25Hwdsrap7qup/gcuB9WN6LknSDOMK95XA/X3r27o2SdIi2H9Mj5sBbfWUDskGYEO3+liSu8ZUyygdCjw0ygcc9ELtQ3w9R8fXcrSWy+v5q7NtGFe4bwNW962vArb3d6iqjcDGMT3/WCSZqqrJpa6jFb6eo+NrOVotvJ7jmpb5FrA2yRFJngGcCVwzpueSJM0wljP3qnoyyVuBfwP2Ay6tqi3jeC5J0tONa1qGqroWuHZcj79EltU00jLg6zk6vpajtexfz1TV3L0kScuKXz8gSQ0y3DVySdYkuX1A+98lOXmOsecn+cvxVSf1JHlsqWsYJ8N9gVrfQUapqv62qr681HW0bLYDa7fNg+s+xHDvkx5fk9HYL8knkmxJ8qUkz07yqSSvBUhyWpI7k3w9yUVJvtA39qgkNya5J8mfL1H9/y+Mcp/04DpY9xp/MMntSb6b5PVd+8eSvLpbvirJpd3yOUnet5Q1D6PJIEvygSR/1rd+fpJ3JHlnkm8luS3Je7tta5LckeRjwLeBv0lyYd/YNyf5yBDP2eQOsgBrgY9W1YuAHwN/vHtDkmcBHwdOraqXARMzxh4JvIredxS9J8kBi1Py+CzyPvm0A2s3zoPrYH8ErAOOBk4GPphkBfBV4OVdn5X0vgQR4GXA1xa7yL3VZLjT+6Ky1/etvw6Yphc4x9H7Rf5Okld0238T+HRVHQN8CHh1X6C8CfjkEM/Z5A6yAPdW1eZu+RZgTd+2I4F7qurebv2yGWP/paqeqKqHgJ3A4WOtdHEs5j4564EV9s2D6xxeBlxWVbuq6kHg34GX0Ht/vjy9b7T9HvBg957+PeA/lqzaITUZ7lX1HeCwJM9PcjTwMPDbwCuB79A7GzqS3psA4PtV9c1u7E+BrwB/mORI4ICq+u4QT9vkDrIAT/Qt7+Kpn6mY62s29jR2WVrkfXJPB1bYNw+uezJwf6yqB4CDgVPonaR9jd5B+bGqenTxypufZf+m2YMrgdcCv0LvrGkN8A9V9fH+TknWAD+dMfYfgXcDdzLcWTvsYQdJ0r+DHMIy2kHG5E7g15Ksqar7eOoZbcsWa5+ceXB89ozt+9zBdQ5fBd6SZBO99+crgHd2274BvB04EXgevd/hlUtR5N5q+Zd2OfAJet/u9vvAbwF/n+QzVfVYkpXAzwYNrKqbkqwGjqV3djWMJneQcaiq/+7mn7+Y5CHg5qWuaZEs9j45m3314Dqbq+j9JX0rvW+v/auq+q9u29eAV1bV1iTfp/feXhbTqc2Ge1VtSfJc4IGq2gHsSPJC4BtJAB4D/oTemckgVwDrqurhIZ+yyR1kPrrAeHHf+ocGdLuhqo5M75fxUWCq63v+jMd68YCxy9IS7JOz1bGvHlyfoqoO7O6L3onYOwf0uQS4pFv+GfCcxaxxIfz6gVl0Vw9cWFXXL3UtLUryF8DZwDPozTm/uaoeX9qq/n8b5T6Z5MDur4XdB9e7q+rCucZp+TDcZ0hyEL0zmVur6oylrkcaxz7pwbV9hvsQkjwPGHS2dFJV/XCx65HcJzUXw12SGtTkde6StK8z3CWpQYa7JDXIcJekBhnuktSg/wOpvEW69r5xnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.qcut(df.Fare, 4, labels=['very_low','low','high','very_high']).value_counts().plot(kind='bar',color='c',rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fare_Bin']=pd.qcut(df.Fare, 4 , labels=['very_low','low','high','very_high'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>Fare_Bin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Mr</td>\n",
       "      <td>very_low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>very_high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>Miss</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113803</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>very_high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>Mr</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0   NaN        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0   NaN        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket Title   Fare_Bin  \n",
       "PassengerId                                                              \n",
       "1              male      1         0         A/5 21171    Mr   very_low  \n",
       "2            female      1         1          PC 17599   Mrs  very_high  \n",
       "3            female      0         1  STON/O2. 3101282  Miss        low  \n",
       "4            female      1         1            113803   Mrs  very_high  \n",
       "5              male      0         0            373450    Mr        low  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature: Age Stats ( Adult or Child)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AgeState']=np.where(df['Age']>=18,'Adult','Child')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adult    1147\n",
       "Child     162\n",
       "Name: AgeState, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeState'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>AgeState</th>\n",
       "      <th>Adult</th>\n",
       "      <th>Child</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>495</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AgeState  Adult  Child\n",
       "Survived              \n",
       "0           495     54\n",
       "1           279     63"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[df.Survived!=-888].Survived, df[df.Survived!=-888].AgeState)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature:FamilySize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['FamilySize']=df.Parch+df.SibSp +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b77d2c708>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAUhElEQVR4nO3df6xf9X3f8ecrOIRAQ8wPw6htatJYJFFUfvSWuWGrXEymQFtMpbBRdcVCXt1trElKp5ZE1eikTSJSFhLUidWDtCZNSYCQ4mUsDXFCq0mF5PJj/IhT2SWpfWMX34SfCUkI6Xt/fD/3cGNf21+Mz/drXz8f0lfnnM/5nO/3fQS6L5/P+ZWqQpIkgNeMuwBJ0qHDUJAkdQwFSVLHUJAkdQwFSVJnwbgLeDVOPvnkWrZs2bjLkKTDygMPPPCtqlo017rDOhSWLVvG5OTkuMuQpMNKkr/f2zqHjyRJHUNBktQxFCRJnV5DIcnvJHk8yWNJbk1yTJIzktyfZEuSTyU5uvV9XVve2tYv67M2SdKeeguFJIuB9wATVfV24CjgcuCDwPVVtRx4GljbNlkLPF1Vbwaub/0kSSPU9/DRAuD1SRYAxwI7gQuAO9r6DcClbX51W6atX5UkPdcnSZqlt1Coqm8CHwK2MQiDZ4EHgGeq6qXWbQpY3OYXA9vbti+1/ift/r1J1iWZTDI5PT3dV/mSdETqc/joBAb/+j8D+EngOOCiObrOPLt7rqOCPZ7rXVXrq2qiqiYWLZrz3gtJ0gHqc/joQuDrVTVdVT8E7gTeASxsw0kAS4AdbX4KWArQ1r8ReKrH+iRJu+nzjuZtwIokxwLfA1YBk8CXgHcDnwTWAHe1/hvb8t+09V+sHt8AlHvv7eur96tWrhzbb0vSvvR5TuF+BieMHwQebb+1Hvh94OokWxmcM7i5bXIzcFJrvxq4pq/aJElz6/XZR1V1LXDtbs1PAOfN0ff7wGV91iNJ2jfvaJYkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdXoLhSRnJnl41ue5JO9LcmKSe5JsadMTWv8kuSHJ1iSPJDm3r9okSXPr8x3Nf1tVZ1fV2cDPAi8An2Hw7uVNVbUc2MTL72K+CFjePuuAG/uqTZI0t1ENH60C/q6q/h5YDWxo7RuAS9v8auCWGrgPWJjktBHVJ0lidKFwOXBrmz+1qnYCtOkprX0xsH3WNlOt7cckWZdkMsnk9PR0jyVL0pGn91BIcjRwCXD7/rrO0VZ7NFStr6qJqppYtGjRwShRktSM4kjhIuDBqnqyLT85MyzUprta+xSwdNZ2S4AdI6hPktSMIhR+jZeHjgA2Amva/BrgrlntV7SrkFYAz84MM0mSRmNBn1+e5FjgncBvzWq+DrgtyVpgG3BZa78buBjYyuBKpSv7rE2StKdeQ6GqXgBO2q3t2wyuRtq9bwFX9VmPJGnfvKNZktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktTpNRSSLExyR5KvJdmc5OeTnJjkniRb2vSE1jdJbkiyNckjSc7tszZJ0p76PlL4KPC5qnoLcBawGbgG2FRVy4FNbRngImB5+6wDbuy5NknSbnoLhSTHA78A3AxQVS9W1TPAamBD67YBuLTNrwZuqYH7gIVJTuurPknSnvo8UngTMA38SZKHktyU5Djg1KraCdCmp7T+i4Hts7afam2SpBHpMxQWAOcCN1bVOcB3eXmoaC6Zo6326JSsSzKZZHJ6evrgVCpJAvoNhSlgqqrub8t3MAiJJ2eGhdp016z+S2dtvwTYsfuXVtX6qpqoqolFixb1VrwkHYl6C4Wq+gdge5IzW9Mq4KvARmBNa1sD3NXmNwJXtKuQVgDPzgwzSZJGY0HP3//bwCeSHA08AVzJIIhuS7IW2AZc1vreDVwMbAVeaH0lSSPUayhU1cPAxByrVs3Rt4Cr+qxHkrRv3tEsSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkTq+hkOQbSR5N8nCSydZ2YpJ7kmxp0xNae5LckGRrkkeSnNtnbZKkPQ0VCkne/ip+4xer6uyqmnlX8zXApqpaDmxqywAXAcvbZx1w46v4TUnSARj2SOF/JPlykn+fZOGr/M3VwIY2vwG4dFb7LTVwH7AwyWmv8rckSa/AUKFQVf8M+HVgKTCZ5M+TvHOYTYHPJ3kgybrWdmpV7WzfuxM4pbUvBrbP2naqtf2YJOuSTCaZnJ6eHqZ8SdKQFgzbsaq2JPkDYBK4ATgnSYAPVNWde9ns/KrakeQU4J4kX9vHT2Sun52jjvXAeoCJiYk91kuSDtyw5xR+Jsn1wGbgAuBXquqtbf76vW1XVTvadBfwGeA84MmZYaE23dW6TzE4EpmxBNjxivZGkvSqDHtO4Y+AB4GzquqqqnoQuj/6fzDXBkmOS/KGmXngXwCPARuBNa3bGuCuNr8RuKJdhbQCeHZmmEmSNBrDDh9dDHyvqn4EkOQ1wDFV9UJVfXwv25wKfGYwwsQC4M+r6nNJvgLclmQtsA24rPW/u/3OVuAF4MoD2SFJ0oEbNhS+AFwIfKctHwt8HnjH3jaoqieAs+Zo/zawao72Aq4ash5JUg+GHT46pqpmAoE2f2w/JUmSxmXYUPju7DuMk/ws8L1+SpIkjcuww0fvA25PMnM10GnAv+qnJEnSuAwVClX1lSRvAc5kcD/B16rqh71WJkkauaFvXgN+DljWtjknCVV1Sy9VSZLGYqhQSPJx4KeBh4EfteYCDAVJmkeGPVKYAN7WLhuVJM1Tw1599BjwT/osRJI0fsMeKZwMfDXJl4EfzDRW1SW9VCVJGothQ+EP+yxCknRoGPaS1L9K8lPA8qr6QpJjgaP6LU2SNGrDPjr7N4E7gD9uTYuBv+irKEnSeAx7ovkq4HzgORi8cIeX35gmSZonhg2FH1TVizMLSRYwx1vRJEmHt2FD4a+SfAB4fXs38+3A/+qvLEnSOAwbCtcA08CjwG8xeCHOnG9ckyQdvoa9+ugfgf/ZPpKkeWrYq4++nuSJ3T9DbntUkoeSfLYtn5Hk/iRbknwqydGt/XVteWtbv+xAd0qSdGCGHT6aYPCU1J8D/jlwA/BnQ277XmDzrOUPAtdX1XLgaWBta18LPF1Vbwaub/0kSSM0VChU1bdnfb5ZVR8BLtjfdkmWAL8E3NSW07a7o3XZAFza5le3Zdr6Va2/JGlEhn109rmzFl/D4MjhDUNs+hHg92b1PQl4pqpeastTDG6Eo023A1TVS0mebf2/tVst64B1AKeffvow5UuShjTss4/+26z5l4BvAP9yXxsk+WVgV1U9kGTlTPMcXWuIdS83VK0H1gNMTEx4r4QkHUTDXn30iwfw3ecDlyS5GDgGOJ7BkcPCJAva0cISYOa9z1PAUmCq3Rz3RuCpA/hdSdIBGnb46Op9ra+qD8/R9n7g/W37lcB/rKpfT3I78G7gk8Aa4K62yca2/Ddt/Rd9qY8kjdYrufro3zEY918M/FvgbQzOFQxzbmG23weuTrKVwTmDm1v7zcBJrf1qBjfMSZJG6JW8ZOfcqnoeIMkfArdX1b8ZZuOquhe4t80/AZw3R5/vA5cNWY8kqQfDHimcDrw4a/lFYNlBr0aSNFbDHil8HPhyks8wuCLoV4FbeqtKkjQWw1599F+T/B8GdzMDXFlVD/VXliRpHIYdPgI4Fniuqj7K4LLRM3qqSZI0JsM+EO9aBlcNvb81vZbhn30kSTpMDHuk8KvAJcB3AapqB6/8UlRJ0iFu2FB4sd1IVgBJjuuvJEnSuAwbCrcl+WMGj6j4TeAL+MIdSZp3hr366EPt3czPAWcC/6mq7um1MknSyO03FJIcBfxlVV0IGASSNI/td/ioqn4EvJDkjSOoR5I0RsPe0fx94NEk99CuQAKoqvf0UpUkaSyGDYX/3T6SpHlsn6GQ5PSq2lZVG/bVT5I0P+zvnMJfzMwk+XTPtUiSxmx/oTD7vclv6rMQSdL47S8Uai/zkqR5aH+hcFaS55I8D/xMm38uyfNJntvXhkmOSfLlJP8vyeNJ/nNrPyPJ/Um2JPlUkqNb++va8ta2ftnB2EFJ0vD2GQpVdVRVHV9Vb6iqBW1+Zvn4/Xz3D4ALquos4GzgXUlWAB8Erq+q5cDTwNrWfy3wdFW9Gbi+9ZMkjdAreZ/CK1ID32mLr22fAi4A7mjtG4BL2/zqtkxbvyrJ7HMakqSe9RYKMHhERpKHgV0MHpHxd8AzVfVS6zIFLG7zi4HtAG39s8BJc3znuiSTSSanp6f7LF+Sjji9hkJV/aiqzgaWAOcBb52rW5vOdVSwx8ntqlpfVRNVNbFo0aKDV6wkqd9QmFFVzwD3AisYPH575qa5JcCONj8FLAVo698IPDWK+iRJA72FQpJFSRa2+dcDFwKbgS8B727d1gB3tfmNbZm2/ovtxT6SpBEZ9tlHB+I0YEN79PZrgNuq6rNJvgp8Msl/AR4Cbm79bwY+nmQrgyOEy3usTZI0h95CoaoeAc6Zo/0JBucXdm//PnBZX/VIkvZvJOcUJEmHB0NBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJnT4fiKe9yL33juV3a+XKsfyupMOHRwqSpI6hIEnqGAqSpI6hIEnqGAqSpE6f72hemuRLSTYneTzJe1v7iUnuSbKlTU9o7UlyQ5KtSR5Jcm5ftUmS5tbnkcJLwO9W1VuBFcBVSd4GXANsqqrlwKa2DHARsLx91gE39libJGkOvYVCVe2sqgfb/PPAZmAxsBrY0LptAC5t86uBW2rgPmBhktP6qk+StKeRnFNIsgw4B7gfOLWqdsIgOIBTWrfFwPZZm021tt2/a12SySST09PTfZYtSUec3kMhyU8AnwbeV1XP7avrHG21R0PV+qqaqKqJRYsWHawyJUn0HApJXssgED5RVXe25idnhoXadFdrnwKWztp8CbCjz/okST+uz6uPAtwMbK6qD89atRFY0+bXAHfNar+iXYW0Anh2ZphJkjQafT4Q73zgN4BHkzzc2j4AXAfclmQtsA24rK27G7gY2Aq8AFzZY22SpDn0FgpV9X+Z+zwBwKo5+hdwVV/1SJL2zzuaJUkdQ0GS1DEUJEkdQ0GS1DEUJEkd39F8BBnXu6HB90NLhwuPFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJnT7f0fyxJLuSPDar7cQk9yTZ0qYntPYkuSHJ1iSPJDm3r7okSXvX55HCnwLv2q3tGmBTVS0HNrVlgIuA5e2zDrixx7okSXvRWyhU1V8DT+3WvBrY0OY3AJfOar+lBu4DFiY5ra/aJElzG/U5hVOraidAm57S2hcD22f1m2ptkqQROlRONGeOtpqzY7IuyWSSyenp6Z7LkqQjy6hD4cmZYaE23dXap4Cls/otAXbM9QVVtb6qJqpqYtGiRb0WK0lHmlGHwkZgTZtfA9w1q/2KdhXSCuDZmWEmSdLo9PY6ziS3AiuBk5NMAdcC1wG3JVkLbAMua93vBi4GtgIvAFf2VZckae96C4Wq+rW9rFo1R98CruqrFknScA6VE82SpEOAoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6vT2mAtpttx771h+t1auHMvvSocrjxQkSR1DQZLUMRQkSR3PKUg9GNc5FPA8il4dQ0Hz2jj/OEuHI4ePJEkdjxSkecbLf/VqHFKhkORdwEeBo4Cbquq6MZckSXs1H88dHTKhkOQo4L8D7wSmgK8k2VhVXx1vZZKGMR//QB6JDqVzCucBW6vqiap6EfgksHrMNUnSEeWQOVIAFgPbZy1PAf90905J1gHr2uJ3kvztCGo72E4GvjXuIkbsSNvnI21/YYz7nHH86MDhus8/tbcVh1IozLWPtUdD1Xpgff/l9CfJZFVNjLuOUTrS9vlI219wn+eLQ2n4aApYOmt5CbBjTLVI0hHpUAqFrwDLk5yR5GjgcmDjmGuSpCPKITN8VFUvJfkPwF8yuCT1Y1X1+JjL6sthPfx1gI60fT7S9hfc53khVXsM20uSjlCH0vCRJGnMDAVJUsdQGJEkS5N8KcnmJI8nee+4axqVJEcleSjJZ8ddyygkWZjkjiRfa/+9f37cNfUtye+0/68fS3JrkmPGXdPBluRjSXYleWxW24lJ7kmypU1PGGeNB4OhMDovAb9bVW8FVgBXJXnbmGsalfcCm8ddxAh9FPhcVb0FOIt5vu9JFgPvASaq6u0MLhS5fLxV9eJPgXft1nYNsKmqlgOb2vJhzVAYkaraWVUPtvnnGfyhWDzeqvqXZAnwS8BN465lFJIcD/wCcDNAVb1YVc+Mt6qRWAC8PskC4Fjm4T1GVfXXwFO7Na8GNrT5DcClIy2qB4bCGCRZBpwD3D/eSkbiI8DvAf847kJG5E3ANPAnbcjspiTHjbuoPlXVN4EPAduAncCzVfX58VY1MqdW1U4Y/MMPOGXM9bxqhsKIJfkJ4NPA+6rquXHX06ckvwzsqqoHxl3LCC0AzgVurKpzgO8yD4YU9qWNo68GzgB+Ejguyb8eb1U6UIbCCCV5LYNA+ERV3TnuekbgfOCSJN9g8NTbC5L82XhL6t0UMFVVM0eBdzAIifnsQuDrVTVdVT8E7gTeMeaaRuXJJKcBtOmuMdfzqhkKI5IkDMaZN1fVh8ddzyhU1furaklVLWNw4vGLVTWv/wVZVf8AbE9yZmtaBcz3d4JsA1YkObb9f76KeX5yfZaNwJo2vwa4a4y1HBSHzGMujgDnA78BPJrk4db2gaq6e4w1qR+/DXyiPcPrCeDKMdfTq6q6P8kdwIMMrrJ7iPn4+IfkVmAlcHKSKeBa4DrgtiRrGYTjZeOr8ODwMReSpI7DR5KkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkzv8HobSu+fFhrMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['FamilySize'].plot(kind='hist', color='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                         4\n",
       "Cabin                                      G6\n",
       "Embarked                                    S\n",
       "Fare                                     16.7\n",
       "Name          Sandstrom, Miss. Marguerite Rut\n",
       "Parch                                       1\n",
       "Pclass                                      3\n",
       "Sex                                    female\n",
       "SibSp                                       1\n",
       "Survived                                    1\n",
       "Ticket                                PP 9549\n",
       "Title                                    Miss\n",
       "Fare_Bin                                 high\n",
       "AgeState                                Child\n",
       "FamilySize                                  3\n",
       "Name: 11, dtype: object"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.FamilySize.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Name</th>\n",
       "      <th>Survived</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>325</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>793</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>847</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>864</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1080</td>\n",
       "      <td>Sage, Miss. Ada</td>\n",
       "      <td>-888</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1234</td>\n",
       "      <td>Sage, Mr. John George</td>\n",
       "      <td>-888</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1252</td>\n",
       "      <td>Sage, Master. William Henry</td>\n",
       "      <td>-888</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1257</td>\n",
       "      <td>Sage, Mrs. John (Annie Bullen)</td>\n",
       "      <td>-888</td>\n",
       "      <td>11</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Name  Survived  FamilySize    Ticket\n",
       "PassengerId                                                                   \n",
       "160                 Sage, Master. Thomas Henry         0          11  CA. 2343\n",
       "181               Sage, Miss. Constance Gladys         0          11  CA. 2343\n",
       "202                        Sage, Mr. Frederick         0          11  CA. 2343\n",
       "325                   Sage, Mr. George John Jr         0          11  CA. 2343\n",
       "793                    Sage, Miss. Stella Anna         0          11  CA. 2343\n",
       "847                   Sage, Mr. Douglas Bullen         0          11  CA. 2343\n",
       "864          Sage, Miss. Dorothy Edith \"Dolly\"         0          11  CA. 2343\n",
       "1080                           Sage, Miss. Ada      -888          11  CA. 2343\n",
       "1234                     Sage, Mr. John George      -888          11  CA. 2343\n",
       "1252               Sage, Master. William Henry      -888          11  CA. 2343\n",
       "1257            Sage, Mrs. John (Annie Bullen)      -888          11  CA. 2343"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.FamilySize==df.FamilySize.max(),['Name','Survived','FamilySize','Ticket']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>FamilySize</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>374</td>\n",
       "      <td>72</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>89</td>\n",
       "      <td>59</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FamilySize   1   2   3   4   5   6   7   8   11\n",
       "Survived                                       \n",
       "0           374  72  43   8  12  19   8   6   7\n",
       "1           163  89  59  21   3   3   4   0   0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[df.Survived!=-888].Survived, df[df.Survived!=-888].FamilySize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature:IsMother"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsMother']=np.where(((df.Sex=='female') & (df.Parch>0) & (df.Age>18) & (df.Title !='Miss')),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>IsMother</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>533</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>303</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IsMother    0   1\n",
       "Survived         \n",
       "0         533  16\n",
       "1         303  39"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[df.Survived!=-888].Survived, df[df.Survived!=-888].IsMother)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1        NaN\n",
       "2        C85\n",
       "3        NaN\n",
       "4       C123\n",
       "5        NaN\n",
       "        ... \n",
       "1305     NaN\n",
       "1306    C105\n",
       "1307     NaN\n",
       "1308     NaN\n",
       "1309     NaN\n",
       "Name: Cabin, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
       "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
       "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
       "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
       "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
       "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148', 'B45', 'B36', 'A21', 'D34', 'A9', 'C31', 'B61', 'C53',\n",
       "       'D43', 'C130', 'C132', 'C55 C57', 'C116', 'F', 'A29', 'C6', 'C28',\n",
       "       'C51', 'C97', 'D22', 'B10', 'E45', 'E52', 'A11', 'B11', 'C80',\n",
       "       'C89', 'F E46', 'B26', 'F E57', 'A18', 'E60', 'E39 E41',\n",
       "       'B52 B54 B56', 'C39', 'B24', 'D40', 'D38', 'C105'], dtype=object)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>Fare_Bin</th>\n",
       "      <th>AgeState</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsMother</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>340</td>\n",
       "      <td>45.0</td>\n",
       "      <td>T</td>\n",
       "      <td>S</td>\n",
       "      <td>35.5</td>\n",
       "      <td>Blackwell, Mr. Stephen Weart</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113784</td>\n",
       "      <td>Mr</td>\n",
       "      <td>very_high</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked  Fare                          Name  Parch  \\\n",
       "PassengerId                                                                   \n",
       "340          45.0     T        S  35.5  Blackwell, Mr. Stephen Weart      0   \n",
       "\n",
       "             Pclass   Sex  SibSp  Survived  Ticket Title   Fare_Bin AgeState  \\\n",
       "PassengerId                                                                    \n",
       "340               1  male      0         0  113784    Mr  very_high    Adult   \n",
       "\n",
       "             FamilySize  IsMother  \n",
       "PassengerId                        \n",
       "340                   1         0  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Cabin=='T']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Cabin=='T', 'Cabin']=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44', 'A34',\n",
       "       'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14', 'B37',\n",
       "       'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38', 'B39',\n",
       "       'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68', 'B41',\n",
       "       'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48', 'E58',\n",
       "       'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148', 'B45', 'B36', 'A21', 'D34', 'A9', 'C31', 'B61', 'C53',\n",
       "       'D43', 'C130', 'C132', 'C55 C57', 'C116', 'F', 'A29', 'C6', 'C28',\n",
       "       'C51', 'C97', 'D22', 'B10', 'E45', 'E52', 'A11', 'B11', 'C80',\n",
       "       'C89', 'F E46', 'B26', 'F E57', 'A18', 'E60', 'E39 E41',\n",
       "       'B52 B54 B56', 'C39', 'B24', 'D40', 'D38', 'C105'], dtype=object)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_deck(cabin):\n",
    "    return np.where(pd.notnull(cabin),str(cabin)[0].upper(),'Z')\n",
    "df['Deck']=df['Cabin'].map(lambda x:get_deck(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Z    1015\n",
       "C      94\n",
       "B      65\n",
       "D      46\n",
       "E      41\n",
       "A      22\n",
       "F      21\n",
       "G       5\n",
       "Name: Deck, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Deck.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>Deck</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Deck      A   B   C   D   E  F  G    Z\n",
       "Survived                              \n",
       "0         8  12  24   8   8  5  2  482\n",
       "1         7  35  35  25  24  8  2  206"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[df.Survived!=-888].Survived,df[df.Survived!=-888].Deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 17 columns):\n",
      "Age           1309 non-null float64\n",
      "Cabin         294 non-null object\n",
      "Embarked      1309 non-null object\n",
      "Fare          1309 non-null float64\n",
      "Name          1309 non-null object\n",
      "Parch         1309 non-null int64\n",
      "Pclass        1309 non-null int64\n",
      "Sex           1309 non-null object\n",
      "SibSp         1309 non-null int64\n",
      "Survived      1309 non-null int64\n",
      "Ticket        1309 non-null object\n",
      "Title         1309 non-null object\n",
      "Fare_Bin      1309 non-null category\n",
      "AgeState      1309 non-null object\n",
      "FamilySize    1309 non-null int64\n",
      "IsMother      1309 non-null int32\n",
      "Deck          1309 non-null object\n",
      "dtypes: category(1), float64(2), int32(1), int64(5), object(8)\n",
      "memory usage: 210.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsMale']=np.where(df.Sex=='male',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,columns=['Deck','Pclass','Title','Fare_Bin','Embarked','AgeState'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 39 columns):\n",
      "Age                   1309 non-null float64\n",
      "Cabin                 294 non-null object\n",
      "Fare                  1309 non-null float64\n",
      "Name                  1309 non-null object\n",
      "Parch                 1309 non-null int64\n",
      "Sex                   1309 non-null object\n",
      "SibSp                 1309 non-null int64\n",
      "Survived              1309 non-null int64\n",
      "Ticket                1309 non-null object\n",
      "FamilySize            1309 non-null int64\n",
      "IsMother              1309 non-null int32\n",
      "IsMale                1309 non-null int32\n",
      "Deck_A                1309 non-null uint8\n",
      "Deck_B                1309 non-null uint8\n",
      "Deck_C                1309 non-null uint8\n",
      "Deck_D                1309 non-null uint8\n",
      "Deck_E                1309 non-null uint8\n",
      "Deck_F                1309 non-null uint8\n",
      "Deck_G                1309 non-null uint8\n",
      "Deck_Z                1309 non-null uint8\n",
      "Pclass_1              1309 non-null uint8\n",
      "Pclass_2              1309 non-null uint8\n",
      "Pclass_3              1309 non-null uint8\n",
      "Title_Lady            1309 non-null uint8\n",
      "Title_Master          1309 non-null uint8\n",
      "Title_Miss            1309 non-null uint8\n",
      "Title_Mr              1309 non-null uint8\n",
      "Title_Mrs             1309 non-null uint8\n",
      "Title_Officer         1309 non-null uint8\n",
      "Title_Sir             1309 non-null uint8\n",
      "Fare_Bin_very_low     1309 non-null uint8\n",
      "Fare_Bin_low          1309 non-null uint8\n",
      "Fare_Bin_high         1309 non-null uint8\n",
      "Fare_Bin_very_high    1309 non-null uint8\n",
      "Embarked_C            1309 non-null uint8\n",
      "Embarked_Q            1309 non-null uint8\n",
      "Embarked_S            1309 non-null uint8\n",
      "AgeState_Adult        1309 non-null uint8\n",
      "AgeState_Child        1309 non-null uint8\n",
      "dtypes: float64(2), int32(2), int64(4), object(4), uint8(27)\n",
      "memory usage: 197.2+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop and Reorder Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Cabin','Name','Ticket','Parch','SibSp','Sex'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=[column for column in df.columns if column != 'Survived']\n",
    "columns=['Survived']+columns\n",
    "df=df[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 1 to 1309\n",
      "Data columns (total 33 columns):\n",
      "Survived              1309 non-null int64\n",
      "Age                   1309 non-null float64\n",
      "Fare                  1309 non-null float64\n",
      "FamilySize            1309 non-null int64\n",
      "IsMother              1309 non-null int32\n",
      "IsMale                1309 non-null int32\n",
      "Deck_A                1309 non-null uint8\n",
      "Deck_B                1309 non-null uint8\n",
      "Deck_C                1309 non-null uint8\n",
      "Deck_D                1309 non-null uint8\n",
      "Deck_E                1309 non-null uint8\n",
      "Deck_F                1309 non-null uint8\n",
      "Deck_G                1309 non-null uint8\n",
      "Deck_Z                1309 non-null uint8\n",
      "Pclass_1              1309 non-null uint8\n",
      "Pclass_2              1309 non-null uint8\n",
      "Pclass_3              1309 non-null uint8\n",
      "Title_Lady            1309 non-null uint8\n",
      "Title_Master          1309 non-null uint8\n",
      "Title_Miss            1309 non-null uint8\n",
      "Title_Mr              1309 non-null uint8\n",
      "Title_Mrs             1309 non-null uint8\n",
      "Title_Officer         1309 non-null uint8\n",
      "Title_Sir             1309 non-null uint8\n",
      "Fare_Bin_very_low     1309 non-null uint8\n",
      "Fare_Bin_low          1309 non-null uint8\n",
      "Fare_Bin_high         1309 non-null uint8\n",
      "Fare_Bin_very_high    1309 non-null uint8\n",
      "Embarked_C            1309 non-null uint8\n",
      "Embarked_Q            1309 non-null uint8\n",
      "Embarked_S            1309 non-null uint8\n",
      "AgeState_Adult        1309 non-null uint8\n",
      "AgeState_Child        1309 non-null uint8\n",
      "dtypes: float64(2), int32(2), int64(2), uint8(27)\n",
      "memory usage: 135.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using linear regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop('Survived', axis=1)\n",
    "\n",
    "#Taking the labels (Survived)\n",
    "Y = df['Survived']\n",
    "\n",
    "# Spliting into 80% for training set and 20% for testing set so we can see our accuracy\n",
    "\n",
    "X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020909068866897806"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.89373951e-01,  2.49660345e-01, -8.43462495e-01, -3.09257743e+01,\n",
       "       -2.48033515e+01, -1.82872652e+01,  1.88759076e+01,  8.00362417e+00,\n",
       "        4.11205691e+01,  8.07010099e+01,  5.35233315e-01, -6.17221054e+01,\n",
       "       -6.92269734e+01, -5.25011866e+01,  7.99688467e+00,  4.45043020e+01,\n",
       "        2.87708779e+02, -1.26071618e+02, -1.03598187e+02, -6.15477054e+01,\n",
       "       -1.59307241e+02,  8.13439969e+01,  8.14719748e+01, -1.82726684e+01,\n",
       "        1.31453662e+01,  1.28241637e+01, -7.69686141e+00, -3.88531280e+01,\n",
       "       -3.42679008e+00,  4.22799181e+01, -3.30147245e+01,  3.30147245e+01])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = linear_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02564049530206547"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = linear_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177451.92937322392"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linear_model_mse = mean_squared_error(y_predict, y_test)\n",
    "linear_model_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421.25043545760747"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(linear_model_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.2, copy_X=True, fit_intercept=True, max_iter=1000, normalize=True,\n",
       "      positive=False, precompute=False, random_state=None, selection='cyclic',\n",
       "      tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lasso demo\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso_model = Lasso(alpha=0.2, normalize=True)\n",
    "lasso_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015175485453136184"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=lasso_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.008832939590533773"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = lasso_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "417.78458326060314"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_nodel_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(lasso_nodel_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "      normalize=True, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ridge demo\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge_model = Ridge(alpha=0.001, normalize=True)\n",
    "ridge_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020909003441713092"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = ridge_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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skv69GcCL52gs5fHkfwFX/g4Q1YAlq4DDTwUu+2Z+I/jlPwAP/xB4x8/yv73lM8B9/5H+I13Qk9v7i0fGyEHA0sOAerqQkq7YOKZ2iYXxnrXAIc/pX795LfC99wDjW4FmupHVBkUbA0PAxHagPZXv4/Q3ABd/Pv/Z994DbL0n/X0duODjwLHn9L8fewr4zGoxnsUrxWKf3i0EIpA+l0OBI88ALv2Gv7Ly478EHv9F/xlN7wGmdgBxJ3/dm74LnPByfRuP/wz45puBP39Qr4QDwL3fBH712X4/zQnxLjqN4rVDo8CyI8SzMPW55wngihcC/+MWYNXz9Nfc+W/AD97b//eqk4A/uU1/LQB84/eBQ04ALvi7/mdJAvzzamDi6eIcoWDZkeK9UBSX7/wx8MB3gcHFop+X/k/gjDfpr+22gasuAya22dtcvAK47GpgaLH++weuEesnI2taE2Ludmb087XXfwf4p1OA6V3F5xKnStf0bjE///ROYMXx/d9uuh34/nv1a2fRwQAiaSzbxBw54w+A11yhH0scA998I7DlLjF3kxh403eAE17Rv2bb/cC//hbw3nuAg47St/PI9cANfyn6bE3mvzvpQuDSr+c/u+lvgSNOB07+XWksXeCbbwLGUhGcdIUMyZTQd/1SyLBen9cBV12qH4+K+jAwehiAKL92BpeIzwdTJ0qSKkyTO0T/gFCel6wSytvULogXHgnFe+Rg8R5n9opro7pwXA0tEW00U6OlPiTeUa3enyOnvBZ47Zfy4/zB+4D1N4gxxh2cC+D2EQDrgL8YAXBlet1brwOOPbf/uyd+CfzbhX1Zv3il+FvFsecC/+3v85/d9n+FQrrmj/Kff+F84PRLgXP/tP9ZYxz4h5OEfF60HDj4WOB1V+bn6GM/A7723/Nt1QaErF28Ar05evxL8vICEOtp3TX9f5/3XuC03+//O1s7mQzPsGgFsPgQ8R6md4n38N67gOXH9a+595vANe/oj2VwkXjO2XzN5sgZbwZe9v58+9d/CHjiZlgxMAS85nPAoSfZr7PhnquAX3xCjKs9DZx8EfD6r5mvv+KFwPjm/r9f+xXglN/r/3tyJ3D1ZcCex4VMAfRzZOUJ4rcmRfbadwH3/0f/3y96B5rH/U8AKTOYJMCnTwVe9gHgzDf3r2tNi2f+qo8BBz+r/3mmm1BQHwKWHi7mwcs/nP/uaxcDT/5SOFZGDwde8dfAyRf2v1dl/eAI8Hv/mp+vOx8BvvM2IXcaY+kzqov1OrhIrOOWwRDMMLhY9P/SDwCrL9Nf02kCX38dsOOhVKalG4dNNxleKt5hc5+Yy392b77N//wz4K6vAgMjov8hnVFs0U0AsXaWHwv8/teAg4/Rj33zWuDOrwAXXWGeI7f9X+CubK4mQlZMbhM640s/APzWX+p/Z8LWu4EvvUrc87Ij+p8/8Uvgh38h9ApVvp7ye8Ar/ybfzs8+Bjz8o/64ZsbEuLTPYjlw0DHAa78MrDxRP65Hfwxc807gzx/K6wa3fFroBO9U5MSvPitkT4az35XXTZIE+M/3AadfBjzr7P7n40+LNRVnxnsNePdtwKrn6sc1z7HQjEHdLM/F5URR9A4A7wCAZz3rWZrL5xHGnxb/P+sPhQDacKMQdrL3cGyjMJpUPPSfwMRW4Jh0cg6PCmEzergQPECqOO0VgnZye59FiiLgmBeJjeD+b4nvZGPwqVuBnQ8BZ71VCJ/BxeKaiVRJe+4RYlMeHgUQiUU2sbU4xvu/Lcaz6iTg0euAjbfkjcFMoTvlEuE5m9kjBO/o4eLf07uEMffodUK58fUurvuuUO4OP038+4jTRdtLDxPCaWYP8NO/sRsdezeKjaYxZjYG1/9YbObP/g3x76F041l6uMQEpgb7xHbgti+IDdJkDI5vEQJm32azMTiePu8L/0kIuK336q+T76M+lP8sSYTAPu4lwKEnp84EjQDWYWyjuO+ZPUK5dmF8i9gsT7oQuPtrQqE2GYNTO4ENPwEOOzWvoKjtPfELYN8m8zPacCOw89G+4TS4SLyXB78nfm9CtyXm3vEvA1Y+t7h2nvVi4fG8/z/EHJYVl42/EmtnzduEYdZbO9v6bAkg5sjSw8S7G9esnQytSeCRHwFHv0gYZnf8a19uZBh7Sox5YpvZGHz852IOvPCPxTMYWQYgEgqCrv+7vgacsDVvDDb2ibEc+nxg+fHiWRz9QrFp3/N1MbdlYzBr95V/I9azCe1pMfaJbQCS/tpJuv3PZYbiyDOE/FmySjACk9uEEThyEDB6hDBopveIz2f2iuuWHi7kx9RO0V57Sjz/pYcJb3Mm3+KO6P+hH+ifywPXiD5Oez2w9DDcs20G37xjEy445XBcv24b3vfiZTjsrn8qypQJSdZ322JcSZK/Zvv9wmGiGoP3/4eQ6aoxuFezNzT2iXt77qvFHFv3HeEskOdoNpbf+rAw0Lqt/v1nRvO2+8S9qsbgQz8Qz/XoFwlH2fqf5I3BTkPIhJMvAp7zW+IdTu8Rz2N6l1DmmpPAum8LJVo2BrM1efa7heLdnhHvZzSV1xPbhEx/+D+LxuAD3xVGZCbrVbSnxBrYelc5Y3D9DWJsZ71VrAXb2s3u6cTfFs/i+sv7zz7DrkeAzXcAz/sdsT+NHCTan9wmnluSALvXi3fxe/9qZvDHtwpn3znvEcbq+Nb8OYNJImSlOl/2PC7e6QsuzsvabP7+xofs8r09I8b6wLXivajG4PgW4NAXAM9+GXDH/xPvQDYGJ3f0Zf2ig8W+uO2+/HzdereYw6ddKozioVFhOGVO6aWHpTLtIBRVxCSVEduFUbbhRrMxOLFN7CnHvQRY81ZhhDXGxOcze8RaGT2ir5tMPC3afs5hwNP3irmlYt8WYPRIYQRNbu+zSCpU3QTor53tDwAPXgvseNBsDD7+M+Dufwde/Umzc/ThH4o5cOx54t/DqYG+9ivueazD2FNAtynuSzYGN94i5vWL3tnXRye3i333oR8UjcEHvycMwKPOEv8+/DSx5pce3mcCk1g8i12PCp316XvNxuDYRvHemhP5Z6GTl0Bel37iF8KYlHWTuAPceaUYj2wMTu0UetqaPxL7/K1XiHnJxuB+wWYA8mo4GkBuFidJ8kUAXwSANWvWzO8Ejsyzfc57xGJ+6lYx6WXEcf+63G9jIUAu+0Z4/0/cLBZWrLSfjeFVH6WFNd73zWIbWTvPv0goHf9nZVH5yX6z+g15pkPGouXA5tuLz4WCJAae8wrgos/qvx/bJIxB3fPttdHNj9XUz7Ij6e/izq/Y7yfry3ZN0gUQCUG0a4NgjmxIupr3nP77+JcVlSsX7vgS8MM/p7+XuAuseLaYU4/d5Li39LsXvR046y36a9Z9F/j2Wx3tJMIQUN/L1rsJzxZiTp73Xv01j/9cKOnq3OmtnY/Zw3YzbL7DMf/S9l5wMfD81whjUL2+N18c7QwtAV798fznj1wnlDnd9eozytbvWX8IvPid0j3cKYxB07M4/Q3A0lXmsc1HbLtfz+wniXD6vOIjAIBH79iEq359H55//Cm46t51+IPjDhXGYEGOp8/m3D8V60CHH/6FUPoLfcZF2QnY1/TJvyuM5nXfMY/ltN83O1u+/17g0ev1YznyTLGm/vkMzRxJ2z7mxUKh1mHDT4UxWJgv6b9f/r/NRs+u9XpHThILg+uif9b/bu9G4DOnhe0jaj+jRwAXfEwoptO7LNcmABLxvM54kzAGTXvtOe8Gjjtf387NnwJu+j/uPWPZUeKZ3/ElIInz1URN+1jWpmlvPvW1eUexCWNPCaNFN67DTxFOhfu+pZkv6b9f/A7gqDXA588xX/ObH8w7D3zx6I9p+87qN5oNRhN++jdClhfa7AIHHS32vVBsvVsYg669DnDf36EnF/fDddfY9w4TTPtONgbVqTXxNLBJE72UxIIkeN2V7j53PyZ0VpL+pJEvseZ3SZxG5X0D+Jxl/plk3QmvFM7VW6+w64nzHAuqgAyAOwCcGEXR8VEUDQG4FMD353hM4cgmTq3eDwfRbe7aCdwVoRJlkP3epFzqwphM7egWZzZGUz/Zv233YXouFLieURbzb2s7NgiC3DVdvzygqOYwLgkGqNxnzdFedr3pPYfkClGenQz5XUQ1/ZwujKvsu+vq7831/Cn9R4b+k4C143I0ZO2Z7pnksOjqx1Srm+UL9d5qhjVKeY7zFTXDe1GeY6ZwZ0dLdGCR44BD1hn61K1d0+e6PSVE7pruP4777zOq2/u3tS1fK7ftGldU08tilwz2lVcmyO/f9IzkMWXXldlrKWNPivtBnxlM7Aqyrm1vOWaQqUmcl/u2uWicF55jsY2R4ngL6Ue3FgB/3UDbNkEHojoEdWuLoj+Y2gOK+0ds2GtM+37soctmIbAh+pORWJF1E40ua3q22b3Y1vcCwoJiBpMk6URR9B4APwZQB/DlJEk07qgFAlkQ2jaL2RIyLuWSukBrNZH8riIbYxllsfdcAjy6rmdEWcBUZtDHMDcZzxl6BqhLubcoZbrry75nGb7CT34Xzk2ZqDi7+jc5A2p1fW5t73cEpbRmWq9x/nsXaq65oFMoiZuVeo1uTKZ34TI0cm24nD0LzecIh3LXv5+smmj/aImsCE2gkWRyqpkMUxPTk1OuDfPFJRuNipPFGKKsHdd8sTmnTIqry/FXlbJWMIYJEQYUR07ZscsKdfrumm1xfcvGDJr2Gl9HDmW96OYLRb6V2adk+BjvIW0D6fyQ5q+vbqBDVXpK3BV5s7r2K2UGDWvRJlN85pmuT+q4TM42m2Pd6TCxyNoFhAVlDAJAkiQ/AvAj54ULAbLwsW0WLo9bKIzKpadCq1vkWYiMzWAheaj3BzMYEHKgXuPDrrk8cd7MoGOTA/ResUo2P+J7keerc1MmzD8yM2jYlOYNM1izV9skKZSx/nO1HZNhbJIvVMXV5dE/4JjB/v1kzGB2tES7FDNokAsmj3bcNTsGajULM0h0dpi8+DZjiBJtYJsvrj3NaHTE5eUFBTlj2CPCoMxe680M1hVmMEYSd0Q2ncnQKu0QNswXF+uSc17McoSBj/Ee0navDen3JgPMB9S9DnDfn88e4ILRwWDYd03rJfbQZalrwTQuEjNomH+2yKoDgBlcgC7bAwilmcGSr88kfH2FosvjR71GhzKLzKUgmJQlGeQQPI+NyuWJ8w35mFNm0CNnMJtvrvFS5h+lf9vmV7b/mqH/LESGWr7aNZacQmmYrySHhWEt2Dz6lTGDC9AYNCmOiqLT7MSIImDxkPisE5dlBg3KSmHtps42krc6UO66FCebFz2UGXQp/EQj3dxnSc99wRimyBGHsxcoL+vi4n6QGYNJAnQ7FgVZHof6eSXMoGW+UBRqivOCAh/jPaRtuY0MvrqBDtS9Tte/DJtztFJm0GDcWZlBj7QKXZ+5cVly/EzGqE1PNYXD5tZ3CdJinoCNwblEL0dC9voTJ3AlQsaiXPootFbWz2IAUGL0y3h0ZS9uaNtlQvBs/VbCDErhN4Cb4TR5pUM8oUE5gwG5Nsb+iXkUptBISv9WBc2Qu+DtGCCyxJFkDBrDWCr0Cidd++YnozeugHDE+YooIsndRruLkYE6BuviGbRj07wgrDWfnEGXEh/ZlGuKs8PkxXcYQyRD0zJfXLKIGL5bQFXKWo5F8GEGIwBRuJNAvtY0rlw4ZoxGu399u9PpX6f+Tte2rwFmDd+16QAVOS8o8DHevdt26FJlUJWeYgzfdOTw29rTjcvIDFrk25wzg5Y5SnGYMDPIKIVEnkwmYWIJEapCOGZtqePyVmgdiqAujCQmCN9SzKDjGc17ZtCl3GcCjHgfJiMiZB5R+pQhC3xyuE7JeWH1UBL6D2GsfdcldS7IoeRBzKBBWfbKGTSEtJnGVZVHfy6gY+k099/sxBgerGGwLozAVjatTOGTLueUkRn0DO+zpR6QnB0UZlDnRScamvK1vd8SwsUo4Yg6VJYz2M3vaS75I/etMx5Jso4ga2WjI13TGTMIAO1Ou3+d+jt5rOrnXkXkdHLEkR5AUagpc4qC2cwZtOlSpfU0yv5OSBUwRYfUHDn8xvYM+47JCV9pzmBgNVFAr4fa9FSXrLWlcCwgsDE4l+N3ceIAACAASURBVCBVCswob6X0cxXMoC3UzZfpciV925jBkPwSCqoIHeqVbLacUhLEDNr6JFQwVau0Ua435mvsD2bQEYohoypvuckAqhkqEspjBexzx7R2fHN5dWvHNBbTfCXNF5NXuK6f2zbngTpfTPOvzPyaa2jzm4qKe8YMDqTMYCfJwkQNz8IZmhkX34fOMDeWO9fNF5PcdcxvV0iVNdojQO7KMsL22xDnaGU5g2o4JmXtSoydMceyLDOo7AfS0RIA0M7+JucMEvZmdYxaZtA1X6Q1NefMYAnnVVX7ga3tSphBk5EWwgwadFMrM2gIvfdhoLPfOMelvgvD8Rsq2091tuZkLUEHm+dYgLv0AQQfr1hBQMbllSybsuDLDNrClQB92JUXAxSwyFwhGpUJWc93Qd2UqCGQpPvQMMylPKGezKBL4KrXyn2E9m/b/MoaoyZPsDcz6AjR0TGDVM8lZVzavK/Ez3ngVOIW4DZjZTH691NkBi3hwwCRAdIoMUZGR7MvZON3zRfXWLRe/Bi9EGkd01WmIAolpM7kyHHJYF95ZYKLRVCvBWDNlSYZz0RZpzjbmm2JGWy39W2Yqon6hkxamWRLeoAsU5xOpQqMKp/35QOTnuKqW+DVNmHPdO0BxuOFQpztBj3FFo5qqpbsw0BnvzGOy6Qzm2Sm1L9ujKb71B4twcYgIwQ6rxi1bDzFi+qCUVnwbNsm5HMhNSFe0UCPS1ZggRTqRwgZdHplPZaSi5nyjf+nCsgQBdUEX+GnJmlT2NjSrK7BqeEb3qWDLTTSay5Qx2LJASMdRWJ4FloFNfOgEp0HNkfWQgwRBciKu5oz2CpVQMakDHctjI7J0LMo1xSDrfdOLREpNQ2rTHLkWOYLhRnUOS/kytXWPksqa/L69j0iRxuC5vMuHHuGEoUjh4l2spxBU/8he7MM3VzI2skxgyqLJCvUlvnvMxYTTMZIr58KmEGtYVRRziBl7LYIJpNe53JOm+CrmxrD4B16mtpG1of3uAy6nNy/LfTdGCnCYaKMssgxg67iELMgZKxJz8TiMQBNcXKFiLjG6LvIfBT6ssxgEBtU1sunGFfO67thCqoJJkeCCfJ8JRdeoLC6jk3BtPmV7T8y9O+7Ll1jyW04njIid43BSLVtflTngY3pKavAzRVsuSPSc8yYwYGakJedUsyg5TlSQ5d0e0qI3KW8U1tESDAz6DIGy/ZZ1hiU1rdvuLutOIVtv6WGiarMoBQm2ukYmEHftW6CSabnnJaaAjoUhbpMoTN1jGUjUmxty21kqEIGUnQgXweyjGBm0DB3TFFrxqNzPPZMyrPoMXlUYqXoSNH/zuIwqSryYA7BxuBcgpIPZNv0K2MGLYuD2o4rpEp7jY9X1HORkRR6Q4U3GSRF2/N5UYuGUEMgqQynyStcihkkvpeCh5iyKZecF8HMIEVZrmjzd43FR6F0eUu1BWQCNz+1Dd31C5oZpBkdjXYXwwM1DA64mEGP8EmdsmJauzYlvkz4rs2QtBUE8Vq7mvniWjvWPvdzmKivHCt9FIeLGZTDUWOFGTQp7ib9gjBfc2M0GFqFsFqb88Iy/4HyssTXePdtW24jQxUysKoIJptzNDQNR+5bHgs1CiW7njzPCPu+LzMo7422KLcQWbuAwMbgXEIWPkavv2dctg+MYWeeCq1OmKgC3HrNLBSQoW4g1A2iSmbQ1acpBMzU55wwg56MbUGJJObJlenfygyW7N/E4oQ4BkjMIEEBD3FY2BgwkywiM4MVhLLPFaz5Tf17anZijAzWMZjOx3ZWQIbAKhZQOTNoUa4pclE7Fumd2hwJIWuXcvi0bh8hrdfIHZFAgcsYto2r6ueljqu3H4j7lHMG3cxgQF5pboy69aKE77qig8o4Lyiowglpa1tuI0MlTnvKXlciVcB1/qKxPYJxlesnXbu6gjM+8wyw79+2cQF2p6VNvoTI2gUENgbnEj3hE9mFCaCfoLMVfuDNDOoKCSibnO2akPwSF6iGjnODMIQc5K4JMAAqzRkkFhgwerY8QoIzhDCDobk2of3L7Gnut0Q2OIRp8DWAXHksqofep9qZeg01X8S5+SnPJTKwYc8AZrDZjjE8UMNAWkCm2bXlDEb2tWZlBokOAFLYHWHvoBiSwcygjXV0FZAJ7DP7vjQzGCtr0SMHrTST6ljfhZzBbm+69XMGS6512xhNbdjCaqtyXlDgNN492VAZRl3KM4dc2zZxr9P1L8MYHeLYj43tGfJNbcyg6XofBjr7jXFchjltLARTdKQUxqf7nby+mRlklIIswG1hBtrPCV5UF0zKh7dC6/D4ua6ZjVyP/coM+hbcceUMGoSWOi4qM2jyGsbEZ6SDr/DT5LQYQfKWE9lQXRtUpSAkp6oKR4ranjwWHatZihn02fwM88XGaC3ESqKAnrHVOAkanS6GB+u9nME+M6j5LdUAK+S6xB5OQkLqQSlmUHqnWgPAI/Q/ZL6E5gxm31fCDEr3T5JjtudVkazLMYNiXM1OjKVDAwCATjc7dN4j3Ng3VURlfRLd/VsYSNNeX4Xj29S/aSy+qBnWmg/r5Wq7rJ5iM9IqZwYNe416fVa5mvqMKAy/kxm0zPUg56hF1i4gLNCd+gCBegYPYPZmzEb4Qa/PkmcY6jykBWZQp1wRlO7QXA8yM1gr3r8MU7W13DVdeHn/qGykM+RDYtpsYzSeu1PGE+rJDMoCn3r/lTCDRDZM139ILquvAeQai6pQZkpXbryG6p+FcemehU65djgP1HZMipDvuphPqNWLcsHCDEZRhMF6hE431stDisJjPIy9azZMjWvawgwmifu9UAxJbYhxtnYpDGigkRqyj2Tfl1XWVBaBZFzIERHqXluRrIuV/SDNGVw6IozBbsdgDNpYFN89TW2fwoxqq4mWNExNcDneyoSjGtdLXE14K+De6wC709zmHC2VM+jLDMrGYIAOQtafDI4HnQPNqqe6nKORWwdbAFigO/UBAnVjBSzejFlQtCjeX2o7xg3aEhpYlVdUB7V/E6h5BK5NxPc4AZKXzxGCJBtX8lgL1xo2ijKe0F5YDFH4yXOKzAwSwjRnY/OjhIn2FNqSjhTyWLJ3bQsTJc4XGTZvPTXvzebIWrBhohq5oFkvzU4XI4Pi3wO1GjpxYvYuUxxT2bXqb6khT/IYTQWyKGOx7Q02mV4m7NFUkl79rSsc0YTKmMFAp5Y1naKkrNM425qdLpYOp8ygqYCML7tjgo71Ufd363yJzKxPVbnHs5kzaKv0XBkzWEVqCbGIGAVGosLwvnQyJZbePxXUyB4vFtympzrai+rm97+AwMbgXEJNxgcsillJz50OVXnitIqTagzqlM6KvKI6UI1BqrfQtelX6tkiGqCycWW73hjmUIIZNHn3TSgoUZRcG4oxZmvHwIDoQi1lUJwUthwR37lACRkO8VwWxmVI6jc5cqjrdbbDu+YClDxo9JlBABioR2h1YvMzpTBXgF6Wms5ncznYtEwywXllzAON88o94RkVYJsvFGbQxoba4MoVpqBMuDvRwVAASdapzrYYzbbEDPbCRE1rvaQjR8v6KPdGYXW1BmNFTiWn420WImUqjeAijD3EIahj+Cnw1U217HGAAU4N9yWHicqsukW+2KLfQvXUeQQ2BucSOWbQQDPbNv2yQsYYRuSp0OqEiTZM1HGNbYy+9Ds5TLQCw6xqNojE9CjGle16Z5hDCWaQKvzk+eosvEBhF4hFc0xn65X1EBvzbQmKts9YVNbDmtPgmFPGDTrJGxu+zoNnDDNYNIYbEjM4VK+hk1XE1CkOLmPFFj7pHbrkMNio+Ys5xU2pDmkyNOXfa9s2OSEpzKCGJaPKsarCRG1rUYaPMURxPLnkhGJQNdoSM9h1FZDRhET76gBA/vlqjWGHDmCKVAgpcqaCer5tiJO9qhxybduUvc7AeskwMXbBBWRMxpVpr9HcR1CYqKvommVc8vfy9TY9lSJrQyPY5hHYGJxLyAqCrbKn6fOyQsbmzfLKFyCEC1mvoYTIeC4yqoIQGnKgXuMrzEJKI+f6lAWY4xkZlftsI65w8zNBdXz4eNRD+zcpNFSlgJIzGMIAqe1Q5oJVoSzhsNA9R1/ngfGoAIIBNF9BCH3vxgna3STHDHa6ifkdUZnB3LtICywYHQCmNe1goyhyUR0Lpe0ya5ey7+j2LKrjr5Iw0Ti/FgHz+i1UArY8r7LHKyWqQisKyIwWcgZ99AtPHUBth8T6EeRbVREGvmG9vm0DegdaJWOvIoLJ8E5D10XPACVGhOjWS6y8fwqoRdd04caAwdlgmaMUWcvMIKMUtOyOaSLKC6hEOIMMGzPoy3S5woUo1+hACZHQYT4zgy5PHOnMIMLc6V1LyC/yhY/woyhFvuOi9G8zgGaTGfTKtyXOBYp33cUcmAxj9bchzoNQo2O+wmroiPtvpnlYwwPiHgdqNbR6BWQCFFodu+Jau8YCLhZPtxczqHES5I4KqDD0P6E8I53RQZRjoblRMnIOXJfc1T2vABlMkXU5ZrCOJEmriQ4rxqApOqds5JHVSLfMFzIzWIEccYVDUnUGU9uAnlWqoohWZakluj0gcF1YmUHiXpMEPHPXeH2YwczZFpKGkcsZdDiGFgDYGJxLyJ5QW5iB+nkZJV6GqQKSr0JLyYWwXmPziu4PZjAg5EC9pmzBHRkkwa4IMNv1LuW+yrAYHShKUW5cBGfHrDKDhJzFqs7opJbJlhPcTZtSZcxggPMgNBxxvoIQjpgd6j0yKK4ddDKDLtbLUoTD11udhdWZwl1nnRkMWDv7hRksqazlnHCGY0RM49I6ciqQdVn4rkahXTo8CICQM1hWjumUYZ3sMvVvixqpjBl0ON4o+oitbbmNDL5RViZQ81OpqSUyXHuQrT25b3ksVmZQXrsBbGyo/qSTmQX22iLTjCHONWYGGSUhe0KdzKDF4xYKY6hbwEZgouQpzGBZr6gOVPaUukFUygwGerYKfXp6qE2x8CEbrQ9jqxqdZGbQplAS+jdufsT+rQqtof8QjzplLsghwVTDIHeNKV/Eoly78ntk6NjWhcwM6phf5f6bHfF8MmZwMJczqJGHVANM5zlXi8gYvdWa+aJV1qhGlyU3z3ZGZQirTpkvNufFfmMGFSeckxmkPC+KrHEYnRqFNgsTjbseCnL2uW/qg9qOOi5rdJBlb6iygAzFoAraDy3OwcpCXCtMLZGhy/2lwMjAGd6XLdqg0uOYLOGggMHBRWDvbbKOq4kySiFRkvEBjcKeKgCzwQwai9aEbARKEYqCV1AjcLLryzJAOlC9fM5iJpScQc/n5fRsUZgeuQKW4T2q7YVUczXBixnUOQZKhuuQmEGDAeQK6aA8FxuTPxtezpyiZ3iPrtxDW1K/Tr54MYN1aItQLNBzBmc62dmNZmOo0Rb/zpjBgXoN7W6ilymk4igWNk4dC6XcefZ/rbLmKmZjYSmt+YgUVj1TCgOKllDCEU1w5RpRkGMGHQ6pghNMMy+yNUI5l9Eor7J+Mja4hiS9NjMGzYfOm9Y64SxK7Rg189WVv5p9l10b4rygoIr0BBN0Id7Zv/eLIetILVGLP8lwGZomyI6q3FgM78sWbeC9ZwbobDonJ2V/dVVutjHfCwgLc6c+UCAvGtsxD4CiCJTwYKmoItdHtykWGA2Lx2VWmEHiMyKHXzg2kSqZwd47T8zX6DzUxkIGJu9vidxTn2qilJBh2/Wh/RsNIKISZ81lzc5wq4AZVB0ptrHY1lGIw0KnxITMF+24kmpk1Bzgu3dvE3/oQpqMzGCEdjfWKyuUMFEbM6iOxXQkAHW+UFi0wlh0+YiO/nUwOlIMyqr2txaFzoRKmEGP8HyqMeQct0PWFZxtfaU0yxnsMYPUELhgHcDitKbMlyr0ERNmlRnU3b+Sj1YGZSOYrFEdoWGiniHGVmZwFvQnnXxR+/fZXwufE/L5FxDYGJxLyIvGtLFQ4pzLwOiJ8/HU2CqJZcauQXGWf28an9o2BRQPNUAQLBSWLsCLWskBspkQcikLJmGmeLZ84GIjZWhDHQmePasxRujftBG7SnVTlUttaCRBoVXbyMaqHYtPToNjHls36Dh/rW5MtjVlqhS4AJnBvVMtbBxriH9YlNsCM1hLcwZN74gSsp5d2+tT817ksbgcbFpPN2Es1nBMW0gfxZGTOVI0v3WGr2rWPVXWu4wBCuR15HIqUY1n6rxwMpD9cUXp/rJoqI56LULXFCZq2t982ThbPpgPk6x1pFRlUNXtDtaqI2Wq1tPKRDD1xqJhoEOdJLboBKvjUXOMkZf+5NAfjOGr2RqwyA6dnmrcDwnOjgWEhbdTH0iQNwLTxkKZwGWgFdCeCi3FKxjsFdUIEAp8FHoSM1hhmCg1NJUa/+8ymG15IfLvfRASJiobr5RnXrbcuilMUReOp+ufErKm9Yp6nInlOjqFpCw5vMK9celCd0LCRA0hQGWdSvMEv9ywC93EYpj1qomqzGAtZQYNuWEhsk7HgMnjchUk0YasUsaiMUBU511UK8plarSBab6EsGTk9eqQuxTI65sqd3P5cAFhhE4GUsNupJ8ND9QxWI8Qx44w0cqYQR1jK8kudb4U9ARLKG1ZONm1EnpVmfBlavtlIphs+lCoEWPaJ4z7ro008NSfXHudaVzy9/LfIc5WSqXvBQQ2BucSFHZHNxGrDBM1hjR5KLS2WHAb/U7yikb9MfnAR6Evm5jty4A4j5ZwCHbA4KF2GBSmIhQhnkuf8F1KKJAMygZB6d+k0OjC8Xz7z9oJYYB8xqJVwA2bknVOGby1WiWGGIKotjNb4V37Gb94ZCe66dbYbLf7XyhOCpUZ7BmDocygjrE2hokSDXZtARkC0xIa9keNNjDNlyDGkijHqlDWcg5czwgD7fOKac/K1o+OGcyMwcEaBus1xMZD5y1r3dfBCRicB9m4InP/OefFLMkR8vm2AWpxmfBlCshhogGpD8HMoEFPMYaJ6uRbQKqKa7xOZlCz15HCRE3MoKX4zAICG4NzCQq7o5vAZcL7VIQqLmob8rgADRsUqCyWDhMlKGBlPG7ZNb4GQIhnq9CnJIRs15vCzvYbM+gpNCnjcuVJZt/ZDCCqR9+EyORICVg7Lq8/RQF3OiwshrHW0HCExajtHADMYJIkuHn9zt64907OSF/m719lBgfqETqx6WgJAgOkmwu69yJ/7jLYjXKXmL+oOyDaprhTZUowM2hxXlCM7dJhot38WgToEQbG8yep78LhvFJ0iQgxhgdqGJKNQYqCnH0eEu5uM9KtYcWWvaGyg9tdTsgSxlsZxpqCsqklTtkd4CQxMnCOMFGdfPNOs7G9xyyyypCWpHVwWULfbbJWLv7EBWQYwchVhKz3P5Oh89xVKWR03roqNgJVodYJnLgCr6gJPgo9yeMWoGiH9kliBrv5TdZ2vZFdqCIsxiNnMDfXLUVTKM6OMsygs/oqcYMyKnchHvWSCiVgfxcmpdMW3uTFDOrCEQkG0DzDQ09PYOdEEycetgwAsEc2BpX7z5jB4RwzaMkZLF1ARpMq4GQGA410rRffwzFBCffUhiYGGEZl2EhfVM4M+hjArpzBvC5RR5yGidYQdy25gbp78DXArEdLOOZi9l029hDnBQVVOCFNsFVlni2nvQxXaolTdgesCyszSNxrkoBn7mL4vZhBpX8tw2uRtfLaZWaQEQxZQQhiBivyOFXGDFpCJEzhHyFhQRRQn5Fzg3CE4IVUDHN54lwhH4CBVXYoC3Lb8udBCfOOXDcZqsD39aiH9m8ygKj9h7DKVThS1PbksZjyFG1t2MZlZQbVfixsu25cC5AZ/MWjOwEAq489BACwd7LR/9LADI5I1UQ7tmqi1LP9qmAGZyMig6K4UxVq7XzxCV8NiHAoq6yp5fl9c4/Lhg+7ZERPvorr6ykzODgQIYldzGCAYZ4bo0WOuAy97Lvs2tmSI7PJDOocOWX210L7JSOYrLI7kDE3MoOG92VlBn32zNCcQY0up9tf1Wtssla+zyqcTXMINgbnErIRYTxaYraZQY2XxZvpyvL6LBv0bOVLmEBWSojFXKgV4yigevlc18gVsChjNP0dkiPh8150BWRsvyWzC4TNnZq7oBsvKd80QKFV25D7VKFbR8ZqZy7DmMiShjCDugpvVXn09yNufnQnTj5iGY44eAkAYI9sDCrhiCozOFDLcgYNFemoBpiRGdR8bpoLsyF3ddUxC3nIxGiD0PlSOmewhLJmYhHIlYBDi/k4ZG2BGRT/j5BgJM0Z7JrOGTSyKIQ54hpjNi9yhp6h/5zBWFIfMcHpNA3IX8ugdYgTzlH2ab9MBJMtUkq3p1DQM640RYFMDkNATxpUmTPoqv6pq2aqOFLIzlF5jTAzyAiGvGhMDEGsmcBVlywuG6Mfmi9A8oo6BLgJXgpCCZaOGo4qw+nlcxig2bhKh4lW4AmlCD9jKAbRo24bg5MZtHgonWGihP7LGkA1jTGWa0+Xe2tS6IjzRYa2VL9p81OUzlw7hvW9gIzByWYHazfuwUufuxKji4YBmJjBtLiMwgwO1CN7mCiV9crl6WneC9B/X7oy/HJbpvkSEpGhyjqtckfcm4KfUYncrNLMoGp0UY1BqfqoLlWibHSM7p2jHyY6VK8h6R0tYZgvZdk46nxxMoO6gkeeYzGBuk9WtR9WGSZKDXENubdQJ4nJCWncdzWGVhAzGPgsss9deqr6W6vDRGYGa0XDeAFh4ezUByLkRWMSVJQ45zIweuJKegV13sqQkDrK4eI6qP2bUJmQ9XxeZbx8QF6JcBpXBoWyzDzyeS8GZSUopEUdQxAzSHxeQaFunooLOdTMUh2SdBSJT1J/Nl+UvE4b62NioBZQmOitj+1Gu5vgZc9dhcHBQQDAWI4ZzK+XZkf8O2MGh+o1dLLnXJWs0zG28t8u9tY0X0LYqMIxJxblLsQw82JPdQYoJWcwgAHJQGERbNeHVsqkRlIo+0EWJjo0UEPsmi8h48qN0cL62Ay9HntoMxgryj0mv6+Kcugr19MoOgMxr1SGjuGnwMqYUZlB4tpV2wnRn7RRdkr/tnNMday2PFc4TJQRDHnRaD1LiX4Cqx7HMqjCE2cau/ydjkUJ9VBTQA3RIJdsLmm4yChrgGbfFTzUDmUB0BuGIRutyysuo2DQUD3qFOXOVIRGUTJk6MLxcr/1cCSULZriHIuO6SEqdIVxUZP6DWHFNuUmNDdtHuGX63di8VAda45d0Rv32LS5mmijLZ7N8EAaJlq3HTpPYYAsIUqAXtHWsrdRf28wMoOE4kjZuNU+beHe1LVjmi9k9lS3H7p+qymW5gNvp5ZqDJmcR9SQXUd4ozKumlRAJtGxInKbpXUAgmPAJrtkg3m2nEoUJjdUXtnWblURXDZjjZrOYpLdtt+aYGPMbLn6Woe0Z36qtYCMI0xUJztsjnVjSL6yv1VRrXgOwcbgXELLDGo88dm1vc8rFDJVeOK0CqWiFGgZSB8P9SyFiZY1zEK8f+TQVMe4fMMu5bblz0M2Wp/wXZMSWdZDa3t3LsOF1D/BkaBlgAJYdaOzQVlHJtYJsL8L07hsm5/pc92aCg2Bm0d4YtcUTjxsFEMDtd7c2zclh4nmFYdmp4uhgRqi1PAaqNXQ6hWQCZgXNnah8DkxRCuUjdKFL+uUe9c1xvYN8yWEJfORF2WYQd1alPtXQTGGfNhQ0/pOlPuXmcHBWnrovElxNym6oTqAjRnU6QCK88LkNK7C8U1hWENDOm3RUZUYstTUEqJjQoZPMThdmzpHwqwfOu+pPxmJFY2eKo8L8JC1zAwyQpFjdyybnOnzKoRMqOKitgHkFyglXMnHQx0aJlr6aAlHCF6IYe4qWtPbuIk5YE6Pp2lOBQjiDD7vhaJEyqAySjZW18ZQVJozqM5pTwPIxQz6eNdt78Lk6bcpMYXPbd5lk9GxcLaYXZMtrFo6JP6RPpfx6Wb/AiXUqtmOMTLQv7+hgZrEDJaIgtApK3L/8ueucFQjG0V1wlmcAZRccWP7hvlCPnMvIMKhrLJmlGPEvSHYSeCQtbHST3p9DTGG6jU7M2jaayh7s2uMFNaPolBXFWFAMd5Ddaoya4GCshFMVtntmMcm+IYY22SKlzPd8Sx043KF21OYwRBZu4CwcHbqAxGyB8UVIrQ/hUwVZwwV2CCTkKdu/oFeq8qYQUeIjq8BYOvTmxkkMm1y2/LnIV7XKHJ7K9U+bXNdBnVTDmYGI3f/8nhNCM2DVdsA3F5/WdEzHQbvmlMmRk/tP0TuUBS9eY7dk00cskQUjsney/i0hhmM+szg8GD//gZqUZozGMgY61hi536g5HWqXnljTjiVjbI4A8qwIab5ElLl1IuNLKGs6aqDAua1q3teLnZBB9ceqCrU6f9H6kCtFokCMqZ9jKrouqBVotV8rDoK81WrUAc4UiiYzTBRrTEckA9nQtkIJptz1BWpY4I1l46416iODApC9CdThIUpakknX1x6MjODjGAkDq+YK0SoKgE5GxuBzuOi3fypHurAeHaKB7xMyKZNyBr7dHm2HCEfQH7j8soZLOmVk0EVfoacFnu4DoUZtPRvNVyIz4s0d3TKXUmPeq69/FpPagbnja0N21mYQREJpiIEJeXIHCKOE+yeamHlaMoMps9qqtFCq6NEB6T332jHvXxBABhID51PTPOCaoC5cjYB+z4hv2et3CU4+yih/1pHAjHaIHS+lMnNqowZVHOfiXuDztAJLeajG5ciXxeJGkgYrEuGt24u6Nr2NcBsRrqNSS4wg4FhzRTMJjOozWUtub+q7ZdiBi1jcc1jE+aMGXQ8Cy0zaJCXXsygK2dQs74XENgYnEuoG4G6ce+PnEFdYnKwQmvzuOg2f0pRhVlmBl1FBVweN6rRKaMqZlANMXaFiAB6RTM4aZ7oaTd4rq3jnU1mkNI/EMYqV+FIUdsDgKiGfdNt/OShXZhutpVr1nWiTQAAIABJREFUXKHMlvux5XEUPrfcW2ho5DzBvpk2unEiMYNi3DUk2D2VhooqMqXZ6WJEYgYHa4JxTrTnyVEMMEsRDrn/wufKmpbfkVa5jsMiMnRhf+q4ylT2pMyX0sxgFTmDyv1TZZDu/EnKXushI+T/j9TFfBwcCGEGCXNEO0bN/lJT9il1vjjDmivKPZ7NnMGeQRVw5ia1/TIRTBTnaKiOVZjThvelLTIUoIM49ac4/3+1T5sOZJMvWgefFFXlcvLPc7AxOJdw0cwuz0ZICWQVVXjitAtIYS+1XlFCP1EEIArwWhHZ05qLGdQIltz3AYY5NU/RdY1vdU717xBDVoaLVVX79Kl+SmYGHfccxAwS504VBhDF6x/VgCjC9okGWnGEVls1BqkOC88Kb0BxvpjuLTQfap5g16Qw+FaOpsZgep91xNg10RKfKetFZQYH078T6MJECQxQSM6g7hr5PQfnqelYSkXWWQ1GgoET4kixhiMSjO1KmEHl/p17gyT3QvZap4zQ7LUQYaIAMFiPJGNQo9DqPq9CB6DOF1d5/qrkyKwyg4ScyTJwRjA5HIIkZtDzaAkT22x6X1Zm0Id8IOpPxjNaLfLKN2ewwGozM8gIgbpoVIPJyOgEGCAmmMI3fZmu7HcZdN5KXXhbWQbIBGr4ZlXhF75e1CQ2C9/eBm0rJS1t1mXDRMvkSVDeCyUUQ72esjnY+idtfjYlTqpwZ0JNxwB5KlGUsaTtTTY76ELy8mdwzRdSyCwxZ9A0z7W5aUk1Mmo/YNekMPhWLskKyPSLcGSGoosZHEiZwVgr6whz2skMyu8oMVyjzL/gPDXLWGxOnTKOnCQpxww612vAPpIbn7KOqE4l2RgK2WudecV6Z9uiQTFHhuo1JDaFVte2dyEsy3ypWZ6XVgcqqY+YUJUTUgerDlSFnubYa10RTDZ9KLRIn8lpbXpfNueRr/5kY/i1YaKuKDuVvdbo4doICwervYDAxuBcQl00NmZQpxTMVix6sFdQR7/LxqBOcaYo/Q6DTQdqiIYz/MKVjxXwLpyJ7A4DNPuuYFwZ2jOGmpWcRzpjSAejEmUyXggsiqt/2/uPCM+L8ky0+bYEhVY7Fsv8Stubbnb1xqBrvtjmqI1pKfxtMe5CqwXPE2ShoIcsLTKDOzNjUFFumyozWJeZwZAoCJ2yHJA/qOYMhjgsbGOxOXWoa8c0X6jhq7rnQmLyyxiDhPuXoTOGQvZaKgPZG5cSJlqv2RVa7efEvTlDKDNYCGvWPSPifuACJYKmbM7gXDODxnvLzt0l7gEUWEOMPR2PXvqmQ+/QjcvIEprCRDXyxRUFVFa+zDEWxk59oELdCNQN0pUvMlux6LPhFSzj8XOFcupAFcRkZjAgHNEE3RleMlxevuw7NXclmBkMnEdU4afLnVHHJaMKJ4Ht3mqE50UyRg2sS0iujW0upO1NtTqIdSGIrvlikxe6zY9qaKjtlHUqzSF2TaRhosrREvVIZgbza72h5gymxmAczMZZ2AWfz9WcwZDqjJQzwUxVpMnMoGa+hLBkVIWybBiXWh0yiBkMeReOVIlYuf/0/yN1YQDkCsjI9yH/djaig4zVZy2h56ZqyVXoOpTc+rI5gyHhy9T2q4hg0jnnXPPYBNvh7lYGsmSqStDREo75rzhSaGGizAwyqoJOEBrDtWYpTFQbruMpFClewTLKom6DcIH6jEJCDnTf+woz+bcqnPH/gR5qoKhEpvloQaAKP4qHWIaPkyAkTNTFxpViBn096oS5kI53utVBNzEUBLG2YVkLvkfamO5tNsO79gN2T7VQi4CDF+eriS4eQD9nsKfcifWiMoMDKROjNdi92DhTqoDpvSjXF/YUhxKjg85JoSqUOoa/TOh/THlGJXKzXLLeBdXBRI3IsDKDCU1e2GRdgRkU/x9O/zk0IIWJytcDOdZjx0QDl33x18L54R0dpFH0KSF4Oof4bBWiokTkBKdM2JjBigzZSsJEZ5kZtFWurspgdukdWmbQNP/1jhS9rNUc41PId+WcQUYIVOVK3SxmK7xPhm5h+Sq0tljwKpjBEPqdzAw6vEzOEDxlw6PAWRDAU7BTPdRy29n1ZRwKZGbQ5CF2G0DB/VM2v7L96xjrUI+6bS6kG85ks4suNNVvnQ4Li7ywbn6azw9UZnCyhRVLhlBP8/6ycS9fNNBnBhWZUmQGJWOwwAwSjCRtCCTxvcifk3KwqAVkLIqTicmcTWawTG5W2TCugnFHiciQco9Dz58E7GM3MYMD4p9D9Ui/N8t/J108sGUctz6+Gw9uHfeXY7YIAxvrQjmrrbIwURczSFgXxrY1joHKnfYUPYXomFDbltugQsvAWYw7W/Vh37lmZQY1oc8meUlxrFOjMFx5nfMcbAzOJWIHzTxb4X0yIk34SfBGYBEKUR0FzwqVgQxZZFSPkzNn0KVoE41OtU/5typ8Qz68mEFlsy4zh6ie9t58zZQiCjNH9ZaHbH4EpYCkoAUyQD5jkdqbbnaQoIZIp0S72pD7kuHKGVSVG9O9acMRS+Tg7GfsmmxiZZYvCPSe1fJFNXMBGZUZrGVhooFKf/DREsrnNgdjbyxUo8ui3JtYOnLxJyUflbIfaJ0XRIWyrLJWcMJRjAuHoVNFFIQhl3E4Hd5gvYYaTDlTfdkx3hBViqdbXX8DzGqkW8Jqq3JeUOB0QpZwXtkY68rSeUx7neHdyrAZpq4CRSbYGDhb5Wrd9VUWkNEZxpUwg5rPOWeQUQnk4wGAIs2cM5x0C64qj1NAgYFcG4RYcEryuHWMgV6r0sxgiRA8Y59UZtBRMKAQfuMYo9pmmYR5wIMZJAhcGWR2wfLuSh0t4aHQ5p6tJUTG2AaFGRTXTLVEARlAKbzjKh5AMYzLMoM6lnQBMYO7J5s4JMsXBHrv/6CROnZOGJjBdldbQCZOaigUR6IYOjqWWGf0ZO2Z/pbfkTbyg2J0GfIBAQczSHXwqXudh/Ou0CdRoayaGXRGGCjz31hEjboHOtZ35mxLn0MWJjo4UEMdhvki7TWTzQ4AYKbdob/H3vgsrI9tvlAU6qrkSFXpCTr08jo1z7kSPc221xlktwzbHuCaxybo9BSr0WmRb5Uyg5ZxqZ8bHeuO9ZK16XJkLCCwMTiXcNHMLg9xVQKSGvNtawPQeKiiwgYV5EUOWWReCoLNy1RC0TbBlWviNEB1rCuKCqh6vdpm2fAbqqfdWIXP8kwryxm0eSgtz58aJqpj1Mp61HNj6cuIqfRoiVrSRaereachDgtd/qTRMLQ8l9DjFOYJdk22+gfOA737PHikbjlaItaHiYZGW+jeBeW92EKXQtkoSui/kekJcOTY1mvud1mfAUUoyiprRhaBKMfKpErYZK2BsewZg3XFGNTNqbiLyYYwBqeaXf+1W9V80bE+ZYw0GQbH21SzgzhOyhudpsiu2dDTZJhkhAxS+KYvM6jRjShGp5YZ9NkzHXqHrkKuiyUMZQYLzh42BhkhcHnFnN6MqpjBkgqtaQGpG2H2ea8vH69ooNeKpCBYhKCTpQt5XkQmj2qAeoWJBhjjJlDfiym8qjQzaOmfEhZDVeKs/Vu8jBS4DHkp1HI6rSZaQ4J9M9LB877zRUZN03/IfNEoK3Hcxb/ftrnHOMxn7FbDRNPnctBIDXun22h3Y8jhiHGcoNlRC8iIv7uhBWSczKBhP3DJ3aqPufBhekwIdaTMac4gIb9IhlocRpsqUYWsM4SJ1rNzBiNESIrXy2NPuphIjcGZVjc3riRJxGeu8WX3o/Yz75jB/v03O12c94mb8K21m1A6rF0de9pPXIWaTdnrgJLOUc+1oXNak5hBjeyqkhnUha+aIikKupRPQbVEM3c9Dep5BDYG5xJarxhl8w8wQExQlYUQhVa7gJQwE1MoKVlxCPRazfah89R+ZDiNN41nS9tnZlw5So/bvFxl5hC1VLsxvMpiYJOMMUv/thAd3/AuE2oVOFKcx1z02bip9JzBOmKMycag73yRYQvv0v1tPXQ+33+SdLGv2cXmvdP638wTzLS6mGp182Gi6bNaNiL+v2eqJSk6td7z71UfBTBYsxWQIcg67bEJBiXeFj7qCk30KWajNdjyRXaKcyTAuKEqhdqKlEQZHLKPyFD7cTGDuv0dUPb4CmSdIQx/WDpawswMJr3PMqfNdKubm6/XrduGF330xrwDSoUXM2iRKdoiOyWNtAya/neMNzE23cbD2ybs8o0CAzP48R+vD29Tbtu116l/666xHi8U6HDX7R3WfVczF331J+N9Jv3vfJxq1oq3pr+7mjBRNgYZIXBV0qJO4DIo5D0Rvay5NgwebTVcyXUNdYwUUO/DFXKgE3i674PYIIfx5hP/bw0jIbIIvqCGXRWMV1fhBeKmTAmdsW1+tv6pzKC2elzAXLCGiQrlO2MG64gxNt0q9hvisLAxLbrPTetJ8y6iJEYXtf7RDPMUWRjoKh0zmFbh2DnRTMOCakAUYcdEAwBw6LL+bwZTlrCbaOYlxUjyDVEyfZ7LQ9fITlWJ0Y7Fkktas6xjarSByfHpfEaEcdl+W4YZVNe3b3VK47mMJWWdakin/x+SCsjUXQVkEqmATLuTW+sbd09jotnBo9snzOOzsT6256V1XqiOlJJGWq/t4vm22Tretq9BfxfG9hUjJb3PjWON8Dbltl3F0gC7YyJrR0UwM6jZd9SzOGVkzz8kIiDXji1k2uD0MMlLY8qNQ/fOPq855u4CAhuDcwkvZlC3+VVVpSpgY1XbkH8LFBXqMl5RmyfIBGqFudLMYIDx7GLGXAaozuggh5Gonq2yYTE+zKDqUbcZQJQCLjYPIWHzK9t/zZT3FDIXbAqluGay2QFqojLg2HQ7f42rDdO4tJufJaTGdG/qu0gS1JAghlSNc55i95QwVnXM4EHDQoFRz17LisrIBuRArWTOYI8ltjhvKJ/b9hTqWHThy+qa0rDagg1OsHH3lLv9kHAxK2NJMQZLeO6NTJdNjrj2QKJDzibf4/z9Z2GJvUPnB5Rqorr9Po57OYMzrW5ub55uic+txmAoM1gwmDUyvayRZhnj9nGxjreNNypwjta0a3SyZUgB8G3bFfkB+DmQM7gYbhO0zKBGNyn0o1m73mGinoaxSV6aopaqkrULCGwMziW0XlxHbD8QZoCYYMwZLMl0GZlBC81ubJ9YqEQGmRkMYNRy14jP20lA5TWXgelzbEJIgnnZHAlyARlViXQxc8RN2dY/5Wy9sv1H9fLrksQMZkpZF0uGh1FHjL3TmpxBY0EcywZdGTOoL37VTRaAMZiO75AcMyie1WhKr+yabEEOI9uRKpGHLhvp/SSrJtpNaoV3kSRd7JqyhNllUOeU0StNZPtDc7AClftWq41dUx384N6tueYmmx2862t3Ysd4ypIU5kveoLnm7s34gy/dVhyXzXNPyg8voayVlWMmZZjMDDrWdzpn24lwSmTM4FA9IjCDcT5MVNqbp5rimvXbJ83j67FuOmZQYZJtekKo84KAB56eKvS/bZ+Yj9vHG7R1YUMhZ1D8PdGsIHTQpgOZZLcMy9705N5GsR0KtMygZS3aZIr30RKO+zT1o16j7o1O56inrF1AmBNjMIqiT0ZR9HAURfdFUXRNFEUHS999MIqiDVEUPRJF0aukzy9IP9sQRdHlczHuyqEaQ6YKa9m16ueVxNHrvVlBTJeae5htTt0YX751Y/9z+ZqyB+6aQGVPyyZmp9d85AcP0cfm8sSRmUFVEDmUBbnt7PPSYTGE96IKfN9cm5D+rR5KRwEZHyeFbtPw8nI6cgalDWeq2cHSRUOoRQnGppr5a1xtmMblFZpocR4oz6LREmxbFzXsnOfGYGasrswxg6kxKDOD0rvI7mnVqBQmKheQUd5F0u3gP+7aiid2ORizQh6qx36Qfe7KYyExg5o8ZIIxNNNqIUYNM+38/T/89Diuf2Ab7ty4N23DlJ4gnvcdT+7FL9fvQkNpx+y8kCpXG++ppLJmCpMtwwySZZ2FGVJCbNvdzBiUcgYjBzOYKAVkpDmSMYMbdtiMQbMcaXRTBze5gIyOGSyv6/zLL54o9L89DRPdMdFEUtboVHSpOP17piN0oFIg6indrqFYl2Vv+vRPNxTaIaGXq25xAMgwkQaGcRlBWQuFfhzzv8AMEqMzmBksjZ8AOCVJktMAPArggwAQRdHzAVwK4AUALgDwuSiK6lEU1QH8C4BXA3g+gMvSaxc21Hj5QgLyfmAG1RDMUgqtPgTxwa3jWL9zpv957xqPMNEQrxV1kwX0XlfTu8hdIz5fv2sa3ZgYDuLyKOuqdOnGpToSSJ5DS/K+L8g5g6rApeTalJwXVg8loX9qARntuvT0cgIWr39fRky3ulg8LAyW8RnJwJKKQOjbsG3QuqIlAc4DRYnbskcojvGCyBlMw0Q1R0sM1yMsGkzPGpTexY7xJhYP1bF0eKD3k4H0aIluEhXkRZIk6KKGJ3ZZFOqsX4pH27imE42TqJv/HgltjhZSCJS9QaPcNVptxIgw08rff2YcTmSVZQ35VVnbWbGS3hmP8v0Axd/6pBuYKve6YMovskUnqDlFcju6a0ygsCFp+9mjH0qbLR46r1nrcb+AzFQrf87gVFpJdP0OzzDR9Dn3CqhQIohmkRkczxg6qf+M4e/GCdrtTjmdStGlZppiDseIeoZ2qbYJ+3u7bejHsjeNNx37hwkSq1wYi5UZ1BWQ8SQfKMygUX+2hL57nbur6E/UtJl5ijkxBpMkuSFJkmzW/hrA0enfrwFwdZIkzSRJngCwAcCL0v82JEnyeJIkLQBXp9cubLhoZtfEroQZrOs3h7IHzkr3tnbj3vSwbAR6RQMWmY+CAOjbJ1XpEvfT6kYi74ACp0e5uGlpv8+FiVryYWyx86U8oURPOyVJW72+MmaQ6KFU+w/J4wlZlyRmUFwz2exgYEAYH/umpLnW25RDDGMfZtAyX5QNOjMGk6g+78NEd002sWSojkVD8nrqK7crR4f6BWTSd7FzspljBQFgsCYxg4qyUoeoBLt574x9MKpzzuUQ1F2jygWd4RiyvtS9QaPcNVttLTOYHU0wlRmDRkeKaHM8NQazAh+5+wEKv20nEf7fLx933w8QrrCp0Sa9tWM637VbVBbV/quIglDWdysWTonBlBkcsh46399rJrICMsrREtk72z7eNFcUtbA+T+3JQoN1xoDGYNY6L8rpOt046efuSf1vl/bsdqftjiSyQXlH081+dET2bKtqOwfp83bH0I9hD0iSBNPt9LmE5gxSiYrKmEHbs6DIS8ue7Zs2Ic+XVNZ24wRJqMNpDjEfcgb/CMB16d9HAdgkfbc5/cz0eQFRFL0jiqK1URSt3blz5ywMtyLohFzBE+vwBFfCDOpzfYKKYBgMvTs37umftRNijFBz02T4MoM64UIJE00/76LmLpqQgZJrYvte9/5DBWRZTyjlvfiez+XFDHrkVcq/0/R/3f1P4/Gdk3RjVO2/VIi1ZaOP6mLTbnUxMCiMwfFpTZioz3zJoMtfDZkvyvzbnBqDq5YtmvfG4O7JFlYqhp2suBxx0CI8vW8md/87xhs4VPlNnhnsP4tGK2UIEoIxGMIM2thbo8MihBlU1pRGuWu22uiihplWnqHIjMOsSInLkTLe6BsgORjCEdtJDb941LHfuyICXKCwCDJi5V3o+pccDFaQmEExrjTFLxcmaiwgIzGDvTDRZifnEJtqdnoRuMZQUY2hl8SivYmMqqRUU3XNuUBMNjo9h3Sz3Y9U2D7ewNHLFwFANcygdP/TjZR1RK0iZtBdNKXTcTGD+ftrdmJ0kmKVVRJ0TkhbZJl2/gfosjaG31b8TPd32WqiOR1MOOR/8uB2nPy/r7cXXJqHmDVjMIqiG6MoWqf57zXSNX8JoAPg69lHmqYSy+fFD5Pki0mSrEmSZM2qVavK3sbsQSfkTCwdYGAGK3h9Ru+vp6dG/i3Qo9CTJMHaJ/ciTgVOoykXvqiAATLB53gCQL/RmhQxGennCWp4ajfxPDUnM5UJWdNB5Jr3Ty4gExBeZQK1Op/KKFDuPyRnT9unzUMpKS5Jgvd98x585ZYn6Qqaev9BzKCDrUjfUbMToxsnGBwYBKAYgyHzJYOOGTeG1FjWlKKsbN0rHCMrF4IxONXEIUuG8h9Kyu3Ryxdhy96Z3HrZOdnEoaMjuZ9kOYMd1HLKykNbRZ6cYAYdMkKtRGp8L0S238HA2cdiYCktRwW020LpNjGDkzlmUNe2wgyq0Raa+RrHMeIkwpOUCqaA/17S64jAIshQIwwoYZImWM9UzefkZczgUJQdOl9DDZrQuNzYEzQ74u8s31cOT3/OqqUAgPUmBVdzb51OB51EMoS0TmNdqJ1OWS+n64w32j2H9O6JvkNmx3gTpx99cDredgX7YX/sGTMYJ7XesR1VtZ2DNC+6JmOwF46Zf47TrW7vuSTeDndNOosrsqySSDTLnkkKpddcQz1nUG1TE9m3ZWwGjXacP6poAWDWjMEkSV6RJMkpmv++BwBRFL0FwIUA3pj0OdXNAI6RmjkawFbL5wsXukVgytGQrwcqZgYr8MSZhHxNeMJ3TDRx3KHLAACb90gbdhVeURN8Qv0AAzNoEAKaa7qo4ak9RGOQygz6GDohhXCoxrgJVGPQpETZwlpJ746uIMnoaljq6VYXzU6MvdMtuoJWMykuAWvHETI7nSrTg2mY6MSMpCQ754uNGdQpqAFhxYqykhmDS0aGsXuyhZiaT1shmp0urrv/aWfIzq6JVr6SKJBjMY5evhjbxhvodvvzYue4Jky0xwzmFYp1T+0BACxfOiKMShtMxpvUXuFvG3vryM2zj8VRXEwzd1rtLEzUlTOohgPmGYXMGNxuyhmUxtVM+9yydwatjkUeueSuC97h7sq7oBRQMcGWKqEotFlqXBYmOjgQWQ6d73+eXdNMmexsb55qdfC8w0YxMljDeiMzWHQMZCzxVEtigwG380JmfULCCDXYN9Puyf3dk0J2TjU7mGh28Pwjl6Fei9Dpdsvth8rancmFiVbADBLSQDpdxx6gPMfplsSYtjwMVuPh7hZmMPs8NFKh14alAJxPaGhuvGroO0Hu6tZ33MXWsRksHqrj4MWD7nuZR5iTMNEoii4A8L8AXJQkiaxBfx/ApVEUDUdRdDyAEwHcDuAOACdGUXR8FEVDEEVmvr+/x10ptMygocKafL38eSUHsVbJDBYXytqNQhF68bMFS5vlEsnXeI+RAiq7VJIZbLWFAO2iho1UYzC95yTu4p9/ur7IKEqhO1ro5g4xjKS4Ee/HAjJqeJVtvJUdOl8MKnj/d9YV+t+TnjU3Nt32U9C0IScBXk5rmGitl7cz0DMGNcygM4zMwgySciQszgPlXWwbE3N68cgQOnFizjWaRfzkwe34H1+/C/dt3me9bvdUEytVY1B6LkcvX4Q4SZW7Wh0zrS4mmp2CMTjQqyaaP1z5gS2CGTxy+RJamCg1RKn3uUW5jmoAkqJyTWajLI5CTYhxuyOU7kYrPxczZ8Zkw5AzKBk0SZL0mJQdhTDRoiOn1RZ9xgmwyca8usI6XVDXkW+4exlm0JYqoRhMzW4+Z7BYQEY/d+qIsXR4AM10T+sxg80ulg4P4IRDl5qNwex6jWNg0soMqs4LRdE3MFq+EMygeC57J6XjJAAccdAIDh0dTp091eUMZgVkKjEG1b1GhvS5sZqowSE43er2jMGJaY8IDl3eqTwWIzOocTD5GuC2o7mMofSO+V9w8FBkrYEZ3DuDIw9ehMhV3XieYa5yBq8AMArgJ1EU3RNF0RcAIEmSBwB8C8CDAK4H8CdJknTTYjPvAfBjAA8B+FZ67cKFlt0xhJ3J18ufV1FAphCuk/TH4tOGPK7s71oda5/ci9HhAZz2rEMAAJv3TBSucUI9f5ECH4Ue0HuZdLHwCqYbaSgIImwiG4Pi2W4Zm8I//uRRfP/eLfnvY43HTTeuwtxxXA+UF8QyqEa6SYmketRD+jcovUmS4LaNY4X+9063+v8PZQbLsOq2arW1es+7PjQovI2TM63+9xlsuUumcXltfhbngfQukiTBtrGMGRThl3MRKpqdIfaIJXejGyfYM9XKHysB5BT3LKdoutkColr/wHkDM9hJ8vN7XWoMLls8gt1TrV6pfi0KBWQcDsHC54rBrspmnzlqDBPVM/ztboxuVyiX00pVw+yIiMmmIWdQMmhm2l200+MIigVksvvpj6vV6rM+1rzt0gVkFNbDyQw63gUg9rWyxyvFeVnXyozB9DiJobpSQEYt4JJ1gQSHjg6j2c4bb1OtDhYP13HioaPYYMuDUvSXVhoyPNnsCHZeW03VZDAre2DJKKjxmT4DNjYpHDJZPuphy0Zw2LIRYUiV0akUB0cjCxOtpIAMbX/vdvz2ADlMdHyGWABPbk/p3/m+dDLF95nb1h2JGbREShgKVBnbLzCDMbbuE8bgQsNcVRM9IUmSY5IkWZ3+9y7pu48mSfKcJEmelyTJddLnP0qS5Lnpdx+di3FXCp0HxRYiVLZQhQku7y+1DXlc2d9RHXdu3IvVzzoYi4aEEpuFj/Wvma0wUQ+FHghmBrNQkKWLRrCRmjOYCpyNaanuHWoolJMZ1IRikHMGlXe9P4+WIHvUiRsE5Z6Vdp7e18B4o5jrkGcGifmmUc2wLn2YQUdRix4zKL4fSgvINJotcXaV8j5bnRh3pmx8cVyaZ+rFDFrWlOTxHZtu9xShpSPCYJqLswazPo15ThDGf5ygyAxK7+WY5YsBSMZgyiyoBWSyaqI9YzDuYt9MG0/tEv0vWyyuN4WKTjVThdWgrJMZQ9kjrcpmn1BmcgEZ0ea2fQ3UECNOar0cwQwFZlBlCKT9cHymb0hSmcFMoX1il0UGawxJL6jrmxLhIDtPTI4XRUbtmWrh8u/ch1f84y+UHEuHMZAxg70w0fT/qjFoYQZXjQ73885CAvGoAAAgAElEQVTSwlVTzQ6WDAlmcOu+htmwUcYo3kuEdjdBsxPr779gMCvysCLHt5wzuHcqO1tQ/P+wZcM4fNkI4m6nJDOYN4ZnWn1HcWVhohqneFaoB4C4Bx0Me9N0q9N7LlMzHkcAGRk4x/vSyRRfPdamP7jGpV5DCH0nO0dTXXrL3hkcxcYggwxdbLUpeRrQG4ZVMIMqG1lGoVU2uS5qeGT7BNYcu6I31q175TBRYs6azStmApkZtFTSMgkECVkoyAmHH4R9M23smyZ4ANN7fmp3agyqCk/Wl5P1Kgoi/fWmmPcAr5wMMjMYkmtD9Zab8ij0BtAj2yd6HmI5Yb7ADJKZk1lwpMhI53HGJg2mzGCERBiuytr93j1bcMnnb80fbm6TF7r5bzNAbJt8Go741J5p1FJWYukiYQBlZ/ntT2QMni20bXd2xqDKDEqs1+EHjaAWpZ7+Wr23XlVmsFaLUIvSaqLpb9dt2dcr3nHQElFwxhQq+ukbH8X2iTb6py6BppQY1vSO8QY++3PloO2eMUgIYdKyd9Lh7opS9nRqDA4M1NEw5AzmjBvDeV9ZSPHyxYNFZjCKCoZkxkABLmawZJhogUWgyDF5f+/Pi/w1qTxKEnzjtqfwW//wc1x9xyZs2DGJ+7MQZxIzmOZ+pf8c6IWJRuZzBqXnWEeMw5aNIMqujWpodmLECbBkeAAnHiqKyDy20/CMFXncavcNjclmR8+6GA3mAOdF1m8n7hXDyTAu5QzuU8JEBTM4jPKHzufvv9GT2QPVHC0BaPe7hpTr140dBWSU+5uRwkQnGx4OO5MzyvW+dLnClTKDmig39VpdiKu6pqmkjELmJHEXu6daOOrgfHGxhQA2BucKukWjhmy6PBtV5AzOYgGZmU6CJAHWHLe8d59Pj031CzpUwQCZ4FOpNLte14bubwkZM/i8ww8CAFoRmfSeN+3KmEFF4THF46vfUw88pRab8IUtT1HXv03gyqBuyrY8GsM8fmRb3xjM8j0BYM9U/4ytmMqYugpsUOB6Fuk7ynIGszDROmLsm2kpazfusdP3bR6jj6uQ+2jb/CxtAEASY9Pe6R4T0TMGVfbbgQ9fuw7/+3vr0C1ReCYzQNdvNxuDWfhq7sB5IHc/g/UaDl82Ihw/Ub3HOKrVRAGRNyiXar9n01jvWSzvGYN6GbFtvIlWHGGqIRnOFMbWsKYf3T5ZPGjbR77r9qNCPmK/7a1jM6gjxvDQYKGaaEOtJmpxpGT5giceOoq90+2CYq/+NstTPPaQxXjSFp1RuoCMyiJEACKz3FWfl6OAzH9t2IUPXXM/nnfYKL7xxy8GADywdV//tyZZq+gSzU4WJpoVkFHDRPVzqoYYhy0b7l9b68udJcN1nHjYKACYS+Yr+3T2XoD0rEJKzqQxrJmu63z42nX4439bm/tsvNE3TPdNZ8ZgE4uH6lg6PIDDDhoB4m6f1Q+Bsh9kBVkWDw9XwAyaI0imJXmRGAvI6PeAqVa3l0s52QhkBrXr2PAcK2UGXTqbB0toZQYtbRaKdYnvj1rOzCCDCt2iMVVYAzSTsEbz7rpQaQGZ/Bin2kC9FmH1MQf3Q1ia7T5L4MUA0TbwLWMzwriy5TfJKBkmmpXhfu4Rojz1xj2EswbT57UpLaZjDBM19atT6Mhhooqh6VAKkyTBljFD0QsqY6syCr5V+DSYanbw2O4GEhcbqjKD2yZ6SkFW+hsA9k71/+52iLkjlTCDjmeRKotqmGg9ilNmMD9Xtu4T7+p+uWgKZYMmhcVY5oukrGzaM9NTKBcPD6Fei7xyBndONPHvt23EV2/diD//1j3odMPC+jJmcMvYTP+wcwXZuFaNmo6WEPd/9PLForBGygzWImCFehwFRH5WP0w0xr2bxnDcCmEELhkZxlC9hs2G9TTZEMrz2KTkHDIqJW72dtdks3++qxptEBKR4VDct+6bQQ0xRoYGzWGiPWZQdaT012tWSfQ5KRO1U5WPiqzrdDqIEeGUow4i5gwGGoO+crfADNqNocdTxu2KN5yJc09YiUNHh/Hg1vH0txZZq4yrkTpQBqSjJeqRW0nOmME6+nUDsve2eGgAz1qxGEMDNfNZg8oY2xIzONHo6PfagoNBZQb99ZFHtk8Uxjg+08aiYeFIm2q00OrE2D7eEExoFOHwZSOoQziwg6HsB81UN1g8MlRNARlAOwdk51Hs6RydkaqJeoWJxia5ZH5fm/dOo51oZIpvEbuQs6Gpxqtv2oTqHEvl2JEHsTHIoEK3aIws3aAmtKQEoyOjUmYwP8aJVoyTjxjFkuGB3gKuIRYHe2d9VcwM/snX78JbvnyHMBLSZ9SNE/x/37wH16/bVvyBRsg+sm0CF3z65l5uAWqDRq9sFqJx0pHCGPRhBsenmxgZrGHHRLPPlmYlm2tpWWJbYZuCV8oSMpm1V2ARzCLgzo178Nov3IrzPn4Tfnjf08VmbRXOZBg95DSPuu5ogOvXbcOtj+91n6ukbDQPb5tAPa3IOSOF1+yZ7m+E5BLjhWIftJCm2x7fjZ89vKPfhvxbFek8zsJEh9Pc2xpi7M0qnwI9GfH0mJiz92/Zl2/DNq5CUr95vuya7vQVerWN9Jqn9kxj+SLxjGu1Gg5ZMuRlDP74gW1IEuDSFx6D792zFX/+rXuxY7yB+zfvw08f2o6tJueEgl2TzZ7BZlJge2GiBWYwY30yY3CR8PRHdeyYaGDl0mHUa0Vn3EA9yjGD924ewylHLAEA1Op1HLV8kTFMdKopijlkzAWAdC1kskAxnoxrum8M9o9RUfJkQ+SuQ3HfOjaDoVqCgQHBDMrHiTgPnZdKvGdhollYYsFZpszXdlu8l2evFNVa2wbnQUaSVsYM6u4jd71SHEZVNHuyXny+Y6KBei3qnXn5giOX4YHMGLQanTFkZ1tTKSAzWK8hQoJuNJDvP7unWj/aYNXocD+ktNYvXLVkqI56LcJzVi015+BqjPSuHCbqxQwGzNcUOyea2DPVyu0b4402Fqf5yzXEeHrfDHaMN3t5v4cvG0EtSjBTxmYr5EyKxhaPDJc/Z9DitJ5KwztbSR0whYkSqolONX2qiWZHNw2SHaIf+9FD2DPdwc7x6fz1FeQMduNE5Mrn9kN5nknjtUVHGfN6dTI4KczdKIkBJMwMMjygWzQmlq4+VPSiVpEvmPWZKz3uH6OvE/JJEmOqleCMY5an12TGYILHs3wmqlFrM3QUPL5zEg89PY4d+6Z7ff7nfVtxzd1b8HfXPVQMO9MIlu/evRkPb5voFX5AfcjCDAohv2p0EVYuHaIdPJ/ecw0xznn2IWh14n7p/ew+60OFcfWg9VA7Qiaz9gg5YN04wXuvuhuXfP5WPLVnGs9asRgf+9FDvYqAAPDl/3oC1z+4A12KYlUQuJoc09z1/RDfJ3dN4bS/vgH3bBrLXZKxHkZPqGYet7sxHtsxiTOOWQEAmGnomcGYWlWuEF7ZV2htuOJnG/CJ6x/Oj88YApakSpnoZ3hQvMc6YoxNt/LzJe7i6ZQZfGDreF8Zp5z9pLJO9aIzotXp4PYnx/DZn67XtwEAcReb907jyGWDvc9XLh32yhm8bt3TePbKJfi73zsV/+uCk/D9e7fiRR/7KX73iv/C2/5tLc7/xE1461dux/Xrtv3/7L1ntCTHdSb4RWRWZvl63nf3awN0A40GSBAeNABBI5A6okRSbmTIkTQiNRqttBK1Gkmj3bM7nBVXo5E7MiNHGVISKXFEiYciQQJ08JZwjfb+eV+vfFW6/REmIyOz6lUDmIWwp++fBupVpYmMjLj3ft/9btcegp4fYKPWxu37mIpxt7rB9VobBiUoZRJ6QinO3cxgBo7jIOBqomPF5IbCJqVweDC4ut3ASqWNw5MsqAExMNMjGKy2GcJVUYPBwEcnSHhfur7T4d6wlhQMXhYyqO9H0eDmk49eCq8FwFK5BdsAiGCBKD3/RDDY8Xg9V7ckpIIMXjXOg8GkxvNabzVCKPYM5+D5QeL4btTa+LUvnIiOAYBvX9rCkf/jK7iw3gejI6l36U6MjJ6oV3SNWq20MZK3QHmS4fBUCWfWamzd7dlTNXqeFj+8QAYNSmDCh0e6OLT8XbeNAINZK6SJkpCRkLVZIHlVr/YS2nxxFZporRsyGPhaucMrEDwCSxyu1dpou75ENQGmJpqz2ftC4WNhq4mVKkMGAWC8lAaFj8YrCQa7IIP5VxUZjM+BBg80fWLC35FKrAvIePD5+tJ8OTTRJN9UvV7Fzq3V4YLikdNrIYLfLztMtYSxuP/YCj7wR4+FmhT6dfnK9cYEjNT5l1TXm7wfdnu/TRLIefV6sivB4Gtll4MMGqn4JHw1kUH1XC+Do5/EZ/dcF05AsGsoEzmPbQYvAxnsEegoVm05qPBF99xqBaAGXM/H7z5wGnnbxMWNBr76koYOJiwsArWRBdVGquv5RV2AYTAaTX/IIBsvAz7edjXrvyiz3+ozB3pTIfrOUHvh8XplxbgdXdjGF55fxI/evgff+sW78OvvP4KFchN/8cgFAMDzc2UWHHp9NqrdodYofr2hQ/uF5xdRbbk4sVSJfGWz3oEHGhGBiR4jHjBf3Kij4/m448AIvIBIiq84Xp47PJ7n9vd+KaIp8j7V++tilZYbFdJQr1c33wMIQb3tghAmRgAwh6asIoNGCkHgYWm7haGchVrbDfte7nRdSXTXhExos92BD4ovPL8YbyKvIYOTJUt+PlKw+0YGN+sdPH5uE/cemQAhBD9113786Y/ehP/zuw7jT37kTfjcR2/HT999AMeWKvjop5/B3z891/U4fgC8ac8gUgbB6dVkNOOlxQp2D2WlA95tXGYGs6Dw4QQEq9U2RnX1UW6WQSRN9PwqQ2evHmVqpKAsGFzoUjNYazPFw3qzI5untzoOHJ+J0riuJiwj3+lkUYP1ageB1vPwssTH9P1IWS826x380YNcnIavnQvlJmwagBrsO2rdoEobrbXc7olPasg1/EBXZJDE9hpQA3tH2DhfSKCKLldaUlhFHa8Hjq2g2nZxn74vKNbseKxNSdJ7xBOVv/mVk/jbJy5Ff6g7ul1RLyrvU3UiD08V4fkBTi5Xe6/vmj8g4g61TtAgSjAYqwNlnxcsgqxlhJRStWbQYsff2wt91ZFBz0WQiAz2KFWIoaf9ranCKi1XvjubSoKv0nKQS4esivlyE8vbLYwXQ2TQgI+G8wpookpSre168Hn9Xi5tvXIBmR77RIMjej4xY37Kt06t4a8fu9DVr2t0XAQkXjaxo0V804T3WHteQRDgwkYddsqEAR//9i+fYmJ7/fqAqiX4D6IOu9JUfLZEYEXz5bqylrTf9sOs4mM7VUghZbz+QqvX3xX//8VeKTL4aojHAAmZuMvIHEeOE7120QhYbm78eNNFmykdBgGAoL/z9IkMLnKK3Jv2DKJcb6HpAv/03CLOrdfxGx+8HrPDWfzxg+eiaIIWyM5vNXCKC07UBXWwBzIosn+gFLuHsv21l+D3PJg1cGiyCEBRFFWfuXJdEbvs1hL+ZSGDQmXtg2+aQdYyceeBEbzjmjH8wTfO4OJGHf/LZ57FWMEGpUZ/wWA//bYi1+vJDN2XObV3ox7dqDbrLDDZsU5SeU9OLLOA4I4Dw6wxdju89q1GB/tGc/ynXn/JENLNcek9p6stJ6xhE3TEHeqO6m0POcuUqItFA5Sbnch8IQFDXe45NAZAoYrulF1Pqt9KSB602uydXtpu4akLWvsKfl2ex5vuFi35+Uje6ltA5v5jy/D8APdeNyk/e+e14/jQHbN41+EJ3DQ7hF9410E88ktvx/RABg+eWk88jgg+J0pp7BvJ40yCiMxWvYNHzqzj3Ycnki9GeaemBzMw4KPtEYYMJojHAFEBmTJH+IZznJ5HDMwMZrFe68Rq6gBGEyW8fvMkn6vzm0zwyAOVvRsB9IX2b9RVZPBlONdJfW/5e/G14yvyPmvcAVvabsEyAEo5DVsNBpX/ZsqSXQIjwtREc5aBsQJTcY23l9AQKM8FoQb2DLP3Nwnlq7c9GZio9yTm8dePr3Ydht//xmm867e/pTRjjzqAjVYbf/jNM/j8s/PRH+6UBNPWi9VqO9Ku5PAUEyV7abGyA000GRkkyhiZRKGJ6j4Gf9eLtoGMZYQ0UYWenuOJshF+fVv1hMBB26c915XrVVc1Ud2h1sfoMpXT1frSSDDYdJDjNFGD+Di2WEHb9aV/krNNmAheWTCo1EyWG06oqJx+FQRkejBIhKJ5wEEDNVD/9OMX8XtfO9M18dvoeLAtto68Kshgl+e1Wm2j5fiwUyncvm8A85tN/OLnno/7Bv1Ygv8gRL06wh8xrHjSQ3yu09N3quv1vctCBmcGkhOF/9rtSjD4WlmiIiRFRA632wRWNuVXbHqfsy50gh0tpiTmwg+YCp/8O4CpYooVyveJosjv9FH0v1BmgdjPv/NqpA1gre7i9752Goenirj3ugn8+Fv24bm5Mp66sKUcO/ryy1ouALUWpyfpC55ioYNgYPdwDkvbTZmZ7Hk/APYMpOXmLxVF1WcOJAfBSQ7djsigCC6TUQTVRCZezVL/8nuuQcvx8F2//wjmNhv43R98I4YLGThun8igfq3qfSRdLzFwcaOO4xwR3KjFg0EPO1Cn1HOB1YIalODwVAkBaPjswNRE940wZzLw++w3pbdl6LO+pdrS6u52EoegzCnLMQ4eAKBkUw0ZFBSoAG87OArLoDgqgsGdrisJGdTmn+P56DgOpgZzyKQM/PPzi9ox2HWtlGtw/QAT+RAZHOU00W6UTtW+9OIydg9lcXiq2PN7pkFx0+wgnrqwmXhctTH8VeN5nEpABr/y0jJcP8B3Xj8Z+5u8J37/M4MZEARoewHWa+1YW4nwuoikiQohmMGMIY8nGtgniTLVWi4yaQsUAZ6bY2vU/EYNATHgg2JhU2vLk5QwUpFBlSYaY368jCSc4lB+5aUViVKfW6ug3nax3XRgEQUZ7IRzvNnxUEiz7zOUqHuQWmk6KGZSMCjBaMGWySlp2l7Dgg4TwzlLMkB0q7WV1gI8SG85Hp6f20Y6RfH0xU1Gu06wixsNVFouTi6Vw/PLMaI4v1aBHyT0j+xaD5e8165WWhhVkgy7hjIopE2mKNorIepHHVpRcaC+0wZ8uEk1g8q7nrMospapqYlydMviwSCvZ9STc/I+1GDQ8+RciD7znR3qOK25P38kEgwqz7PacqWy8YBt4NuX2Ps1puxxJvVR67zCYJCP7VajAwM+AlAU0uarEAx2b4ElFM1hMIqveq7Vahub9TZPcMbf+WbHgy1613acvtZoAD2Cq+SEqEjQGKaJkayJf3/3fnz12Aoqjfbl+7IJCPMaTxi11WCwH2ClL2Sw11ob/+30QFxY7PVgV4LB18qSgq5uCms6tN1vrV0/pgtYvIyCbQCxzcpxHHggMWRwqmjh0mYDjmiOusNCsFhu4vxmq2swpprYjK8ay2PPoI1K28elzQZ+/p1XgxCCD944g6GchT958Gz4I+3l//qJVewZzsKkJJRa1ikHislMFDWweyjLnIIdBC7EfrNr0JabkaRCqXQG5boillgzaKCnIEtSZksvgOa2WmmBEEghAwDYP5rHD9+2B9tNBz/z9qtw8+wQRotZBLxGrKcFWvKiH2SQGhIVzNsmNutRdGCDI4OkaxAVz1CeWK5idjiLdMqAT6jcOIIgwFajg6mBDCyTIrgcARn1PvpEBmstlzdiVp7jTshghyGD4tiltBHtM2iEIhC7h7I4NFkIg8GdHKokOqA2/569VAaBj8nBHN557Ti+9OJSNOnBj73I58KkVjPY8XxJ/1Pt75+aw0//7bdxcaOO7YaDR86sS4roTnbT7BBWq23MbcbfN6kSmrdx1VgB81tNiXII++ILS5gd7hF4KmvaZCkDgzBn0Q/QtWYwRSkcXyCDbaQMglxK9OWjshmx/s60XQ8dj2XO0ybw7FwZbdfDcrkOwzABakR7tHZ9p8PAYL3aCdVEX04Nlr4fKYmJh06v4X1v3A0AOLdSkbWqJg1ADSHQFK0ZFAE0o4nqwjbh+7rddFDklL6xQrqLgEx4Xb7ngRgGCCG8vUQcGay2QjGTYwssEHhhfhsdz8eP3bkXfsAodUkmAowX5zbD83MLiIHzPNGwXGlF6ZM7BTrKOu54PjbqnQgySAjBtZNcRKZXqYS2vjbF1xTH3iQBXJI0X1RkkNFEQ2SQyncma7NrF4JMenKO3Uf0Gl3PBaUGKFGoweKc6vl7BcyX6Y+sKXT0zVoyMjiYMaRK64QaDCJA7ZXSRPnYlhsOKAIE1EAhzQSVugkb9X1sIHEOyNYShgUKRX8AwFqlBT/gqucJ73y940lRMgQeKv0q6Oh+il4qofl1IkGTMhmV9Udu2wPbpDi7uv3qIoMiwctR0mOLFVbj3q3kKoZM74QMinU0Kv4EQK61U132hn/tdiUYfK2sL5qo4uj1Qe97WRYrau/PoY0fJ5oVZHLfCk1U8KlLFlw/wBxvuL7TeT7z1By+PV9hdVw72Hy5CcugGMnb2DVgIyAUN+wawNs5bS5jGfiR2/bggeOrOCOQAsUpaXY8PHp2A28/NIZSJqUssqkoYquYUAwDMbBnmNWs9JQ3B3Bxiy1cMyUbedtE1jIUmmjUuVef+0K5id++/1Qo2hJDlbsXj4ciFDsjzKvVNkbyNkyN9/6/fcdB/MG/uRE/8/YDAIDxgRwofDx6ZqPn/XanYvRA9QgLBo9Ml3DVeD6Wid5qdOBBa6asHwOIjNHJ5SoOTTDHPyBUUnwrLReeH2AoZ2Egk4LfRaBpebuFn/r0M2Gwr787fVCaHM/fWVlRNV5TU2+7zCGjIhikGk00rIeZLGVw3XQJRxe2WaZ3R2SQJmx+UQriQ6fXYMDHWDGH971hCuWGg4fPKM6zoCZyNGxIQcNGeNuGpLrBv3nyEv7lhSW867cfxM999lm4foD3XNcFqdPs5lkmThWjrCJ04Ec4MhgEoXQ/u842Hj27jvdeP9k98FScW8ukyBhAlWdyutUMpkwClweDlUYLwzmbK8xB0kSBeON5gcBQw8SATfH8XBmPnt2A73lIpRg9eKvWwkZNSRol1RXz+RIEAaOJyjYXcQRuR0sqWyAGHjy1hrbr455r2XO6uF7FHL8fkwQw+FzQawbFmEVpovF9p9JypKDPWMGOB4NK4svxfAS+B8qPNzuSS6SJ1rg4DxAGg2Le/Pib92I4Z+HrJ5Kpoms11kpEIrPKe+QGBJVmGzfMlOAHbI0Ix0tf97rTRMW7oScZDk+VcGK5An/HmsHwecqci+IsG/DhQd/rgwjqIWoGqdJaQghXCWRwmD/DjXoC7TsxSDeRs83kZy7+uy9k8OXTRD0/QLXtIp9h9zmYoXB5zfO4Mt4s2fMKAjbl/suNDtubCA0R8VeCDvZg08jadyPF+8+GSU4RJDXbnS7IoCtFySgCrCc91yTzNT9lh4TohY06TEpgmiYQeBjO23j/jdNY2KyH7IV+LaFmUDx36Y9xAOWLLyziv91/KlQOT9LfSEQG9YSJhgwmgDkCVZ4uXkEGr9jlWNJL001ARm9t0Ccy+PSFzUiWKNFimbjL4+hHjqNSdzwXxDCRsYRTyP6dKLDF4+yK0ky3h81vNeAHFF63ZqqKLWw1MTmQBqUEKRpg/1gJf/RDN0acvR+9fQ8ARnNSrwuBh8fOraPt+rj74BhK2RTqgn6hyxEr5ghRB44MAsDcDiIyZ9eYwzDD6QTM4RE0UTH+cWTwzx86j9/92mmcXt6OXjvQk2pYbbYxt9WK19h1mUes/1Lc2c1aJt57/aQMEkvZNFIkwMNnkuu2pPVDxYh830fNCfD8XBn3HpngrQk0mmiN0URp4EdUTiPnVM7V6Li4tNnA1bxxckAMuXGI+pehnIXBLKeRaOPi+wF+4R+ew5ePLuMRcb8vAxlUHYKIiExXFTiODLZdZBVksGhTbNW11hIAMgZDdK+bKqHSYve843URY8fN78FTa0gbAayUibdcNYpSJoV/fm4xegwAdV4/lrMEGsaQQSDeeL7j+ji+VMH73ziNe64ZwzdOrmF6IIPrZ0rJ16nZ1WMFFNImnr4YDwbXa21kUgZyliFbFKgiMl8+ugw/AL7z+qnuJ9Cc24wJVLmz2FtNlL0flXobw3krMhfHCjZSBokFg2JeEGqgaFOcXavjH7+9AMsIYJkpGIYJCj981xThj6R3ervpwPGCV7m1BAs0v/rSCgayKdy0dwQAo2bdf4ytpybxQc1oMBgEQRQZTGozoCAKlaaLIm9NMlZMx9VEleey3XRA4TP0FMDscDZR4KSWgAw+eX4TV43lMZy3cdfBMXzz5FpiT8u1ahvvPjwBk4RBvbCGy+p3P/K2/QA0VoiebOux14pkoF6LeniqiJbDlLl3Yg8Ia3s+C3xVmijx4UKjiWrK1TmLxGiiDS5clU6x+xjuiQyG86Xj+gAP0gsiGExEBneoJ79cZLDahmVQmJRImqh4twoZNraldHgsdbwpOPKvi2P1a8r9bzUcNo7UkMHgK6KK9tgzhaI5Ma1IMFhusDUAYPXe3WsGQ1GyxOeaZEF039mpVOLiRgO7hrIg1JTz/sfu3AsEHrZblxmAJyGDfG+RpR+8tYRQlN2OtAnr4QMlidsl1WcrNc7Cyvw+pJL268yuBIOvlfWFDHaBtvtoFr5Ra+P7/vgx/NWjF3pfxyvMxIXHiQYjnusySoAwfr0zJQtjBRv/9cvH+zrP/FaTCSj0gQwulpuShgXfQzZtYWog2u9lOG9jKGeFm7aSCfr6iVVkLQO37hvCQAwZjC/CQRCwHlf8PsYKNtIpuqOIzKk19vdRLiwRoULF6Be+PNdXjzHa5HMXN6LXLv47YaOotBw8e3EDTkBZMNiXgEx3gQzVCDVg0QCPnFnvvYHGFtxoD7ek71/YZIv3vddNYjhnR0o8yYoAACAASURBVGiibddDte2ilGG9oRaTaKraPBaiQAcnCvJzEQwKp2EwZ2Egm0oclz9/+Dwe4QjovAj2u9K+ui+raq2g/O+e9UDMoWx0PKZ2SsOawe1mnCY6WWLS9EemWUB1dKGys0NFE4RCFNRps97BCwvbsA12DMukeM+RSdx/bCWkXoqgm78zOZMHg4SGwaDmaJxaqaLj+rj70Bj+8IfehL/9iVvxh1ryppdRSnDTnsFoDTC3tWobIwULhBDMjuRgUoLTiojMv7ywhH2jORwS8yHxBNF3Km1CIm2j+eT3I2UQOHwot5u8z6EyFyklmB7IxGiiVS4CQQ0DBZud44svLGKqaIFQA4ZpImsCD53mwWBPuXMajnXXOrU+1veE/SggFA8cX8E9h8ZhmiEa/YXnFkEJYCCQgZkQyWnxARHBYGIDcmWMtnnNIMASZRv1TjS4UxDbMq/NEtTUPcM5uH4Q60VZa4eqiRfWqqi1XTxzcQs372VtZu65ZgzbTQfPai1sWo6HasvFddMl7Btme4kIKtuuh7oTYNeALedRpG4wJlvfba+lcv0f02pRD08zJsNWy+uxXkb9gbbjxxJ/kZpBfb0yhJoohUEJbEMggyE9XbyTJV7LudlVQCYapBNqIJ82tdYSvai0r0zQbq3K6nkHc5akiYoef/ksG9sSf7+KaSVZLcYooP2jY7op67ioGSScJqpex8s+NtATGaSmDUp82ZpFRdRbnU7ivtRQaKIG/JB5sJPpDKY+kME9w9mIn3jVeAEThRQ2m15YMtGPaWPheL7cw50IMujL/aksgsGdWHaERGo/5Xli9ykSJuGYlnmwP1m6QhO9YpdjXZHBl6GAlGCPn9uEH+yMUsUUvvpwaJOPoyODfjQY5Iu8bRD82YdukkX8Qmyhmy1sNVk/uX6QQTUY7KG4OjWQDp0F/p3Ad/GNE2u488AIbNNAKZOKFGYnZuQcH4F0rggIIUxRdIcxP73K/k75b0eLdpj91gRBxHM/tlTB/FYTedvE0fl47UoS1dD3A/z8Z59Hx3FgGAZzYvpABpnEeR8LGqFIUVa/d7JbI2J2IfFAhPYIgAIf59abODRRwN6RHIbyVqSJcLnBezsWuRjHVgItV+utd3KZ1YhI559QdBxWMC+RwSxHBrU6gpcWt/EbXzmBdx8ex1QpLSlxsXpb/u+x5e40YdUhkCgh7SEgw9/1esdF1jLk8yrYlAleaPNFZCWvnsgjZRCmKLpTABBz+oNIoPHImXUEAWBRyHH5rhum0Oh4YXAikMF2GyYlkD4WUZBBzdEQaqcCCbzjwAhu2DWQfI1d7KbZIZxZrcXUDddqYfuHlEGxdyQnEwKr1RaeOL+B77x+qnfgqY1L2gjrQroKyCh9BmtN1pxeZ1vMDGa70kQNw5A1hkEATJdsgFIQYmCyZOOh02vsPUiiLgHynRZjPSiC1stIWEjT96PAR8sNUGm5eNfhcfnMJ4uslcl4MQ0SeGEwyB0zgRCKBFNimwHlfa20wppBUWYQmTuK0y1qs0wuVLKXi0Bd0BJy1ZaLVCrFT+XhM09eQq3t4lYeDL7lqhGYlOBrmqqoKkR0eLIAPyB45CxLCn3t+Cpcn2B2KC2TjpEgNFYP113MRzBDdMR5/2gelklRbrq9E0YqMuj6scQfRQCnCzIY8HddoPlhjatgJITHppRgMGsl00SV+bLddGSQnpfIYALqEviA+g7GkgQJvR172FqtjZGCjeGcJQMEgZQV0+x9Ecig3guOBAE8EKxsv8xgMJKkcGDRAIQYKL6ayGBCuUqLBzzUTIEikPcr2UbiOwn+UKPjSjVRAz7Wk4L8JOvipyQ9ryAIcHGjgdnhXGzfnx1Kw/GBL6gsk51M23c3ah15Wpmc5z6zoDlXGu3wc1/b6/Rx0UtuIok30bs37r9v8WJdwX57vdmVYPC1siQ6Jnc+1mttPHx6XUGJ9Akc0k88P8DPfuZZ/PnD5yOHf/wc27CWdYqNbq9azWDUcfJ9F1bKjP6dH//6mQH83999GADw+eeWu6JKrudjudLi/eR6L6Qd18dqtY3pQTUYTL6HyVIGS2VBG2DfWdiqYaHclPWFA1kr7LvTpbWEyH76oHJD2zuSw9luTXn5PZ3gwaA45ngiMhh19L760gooYXV7ErHUKUi+h5cWt/GJL5/Af/qnF/Hhv3wKDxxfwcGxLGwrxR2E3giz6/nYqLcjqnZdjYY9qR5JoIp+/tl5/M4Dp2LB1S//44twAxLpEej7Af70wXP48F88ibbjYKHSke0FhnMWHC+QAiSCyjJWYrTciOS+PGB0Hp9YriKTCqm8bFPyUGu7Mss9lLMwmEtFMu2O5+NnP/MchnIWPvH+6zEzlA0TLDElXvbvxz73YldlwmoSTZQko7rymISi3nZZ3Y6oGbQJ6h0vVHMVwSCvz7NNA1ePcxGZHgrBv/S5F7BSc2Lry9lNNh+PLZRx/7EVhgiQ8J2SPeAq0feo3mqjmEmFwj7UwFDOAiXxYPCF+W2UMqnwmbwMu3mWOfPPXIyig+vVjgxCAdbA/NRKFd86tYaP/cMLnCK6Q22iFgzZlNEuC3YUUVAtZVI4PhvnaiOODAJMmVQXmaoJZJCaMImP/aM5GJRgsmix50YopooWVipt1vR7h3YxYqxHxHv8ctb3hFrSuhMgnaJ461Wjcv3ZN8TOMVlKA77H6oIQCsiI7PxQLhWKiXShA3qgqLbcCDIIMLYCAHz9xApqji+va4tL+BucmirqtvW6wVrbhc2DQZP4+NOHzgEI508hncIte4fw9RMrkd+tKsHg3uE0fELwa/98FN/xOw/i5z77HAg1MZpPIZ1iSY84TVTb38Fo+zf+5/vx/NyG/Hy10gYhiMxZgCUyDk0UsNnwYlTyTz9+Eb/+peNxZND14GvohgE/DAa1uRBwxDDPg8Cs2LY5Mij6rwobyVsxOuHHv3gMF7da8piVlggGDeTTKVS71QwqAXMQBHh2vhq9RrmO98cWWK20MJq3MZi15LouEnDFrAWAoMiRwVgwCA8+aMxnajke/uyhczH06olzG/jIp56G54dIqpyX9Q4yJjhNlM07tdfg83Nl/PnD5/Ffv3ICv/r5F6WgTVfT9xrFRO27qdFE1frJtpMsINPoeMjYombQj4ju9LQufkr4vMK9ZqPeQa3thsigcg/DWcZ6uO9o9z6fMZNjwd4H9T47ajDoe5KdEAkGE+qghZ1YrsAFxdePL+F9v/8wvvsPHsFGtYULW53ofSewbUQwKJMprzO7Egy+VpZUyM8zS7//9TP40U8+ocjkJkDb/Hd/8uA5/PNzi/jTB89FgqrHeDCo02Vidhkc/fmtRszpihyH/9b3A/i+ByuViv6d/REAcM/VwwCAFxareORscs3Z0nYLnh/EEa3E7zYRBAhpoV3EUQBgeiCjIIM8GNxgAZyg10WRQR4gaFk5kf1UF5PDUyWcW693bTL71WMrKLeiYz1WtNHosMBEp+4IR/6rx1Zw054hfODGGdgK6iKNZ8v/4Btn8McPnsWXXlzGxY06PvK2fazvDe0PGVznWbZ+kUEa+Ng3mksMBv/+qXn8zgOnUW225PN/9tIW/u7JS3B8ItX5giDA//6Fo/gvXzqOxXITJnzcfmAUH71rHwCwuitAUljEJj9aZI7f0mZC8K0FQC/Ob+PgREE2FyfUhIEAG7UOthSaaCljgQYeAj4uZ9dqOLNaw8fedRCDOQu7BrOYExQ/maGMPk8noKxWL8G61gz2EoegBhptjwnI8OvKc4emymv0RHZ/QqlXODJdwosL22EiRXsffD/AfS8to9ph76sw13OxVGP//5UXF/CF5xfx5gMjIEryQAh8yJpkPs7NlsMy4QoaaVCCoZyVgAyWcf1MqW9aaJJdP1OCZVA8pdUNrmntHw6MFXBps4EPffJJvDBfxs+94ypZP9rVtGDIMliwMtrj3UhRAkdmqV2tZpCN0fRABmvVdqTWtSaQQa6298E37cIP3rILFuWKv9SQGecHT60lU5eAEBmUAjrsHZEN6/uswbq00cB2248du+4At+4dVmrBKfYMs/GYGsgAEWSQ/VbcZ9ZSxERidEAROLLBkwIyfKxXKy3U2i7+188+j/V6uE4KmqjJx2I0b8M2aYyGW2u5SPPaqNnBNFYqbUwPZCJlBHcfHMOplVqkRlEig3kbJnwQYoASgqmBDD50+x6MlbKg/B6m9SA/hgyy/17ZrmOz3sEz50O6/2q1jaGsldis+vBUEZtND4G2Rnz2qTn803MLEX8AYMhggCgCQ+GjE2jIIP9X0EeFE5sRSwilaAjhKsWGclZM0Ovbl7aYuBI/plrLmbcN1FpKzVoXmt5j5zbwB988F73Gy0xOi7Yvgk0CQCpkFtMpFpzxtTOCwgYBSMASuwva3Hng+Ao+/i/H8dWXoomCTz1+EV95aSWca1rNYMZk151UM/jRTz+D//zFY/jv3zqHv3niEv7Ht7UelYrV2i7+6on56Hgo1pbIIAsGRfArEhlDOYv5kokCMh7SVogMq4jv2bVa9zZZXfwUcX2Png/XYyGoNzuci4EGxPeRMlM7KrBHTJtHa7Xwfe24Ck008GQ/32pToYnquhx8XQ6CAD/+l0+j4xEsbtWRT5sopE0EvouFqrZ+JszLzYbmQ7/O7Eow+FpZQgZFvCjPXNyCHwAr23yRSUIGiYEX57fxW/efxGQpjeVKC8/Ps3qHtWobZ1ZrsE3WILpn75jL4Oj/xn0n8e//5pkuxwk3n/V6GzTwQ8niHufxQVkT+gQTVCrbSoV0zC4majVmJE00HugImyylUW27bNHkC+Rahf1+N88slzKpaM8aIEbTEYphgeJki2Dy+FIybfKTD5/H1FAucjzZa7ASZlbVjNvcZgPHlyp41+Fx5GwT10ywa/QRTySUGw7etHsQ3/61d+Jbv3g3fvnea0B8D5Sa8AIS34g1R1z09OqnZlBsfnfuH8ET5zdjCO8il5s/u1KJJC8KaaaO+OS5Ndx/bAWfuO8EPv34JXzkrfvwlZ97Kwz4eMPuYdg82z+cY+MjNndB/8lxdbjF7R7IIKc6PTdXxu37h5XhojAIk3PfrDuwDIqcZWAwmwKFL+u+Lqyzd/CaSVa7s2sog5UKd+S79Oj0ERcIEVZVGt2HyGAv2XgfAaGod1yWoefnLKSiwWA7YGM1UQiVzA5OFLDddMIWKdr7cGathu2mAzegsnE4wJoPe/x43/emKfzEm/fio2/bH0F4LZMikzJiwWCjzZBBPQAaydtYq4YOZMthjdXF+/JyLZ0ycN10EU8rdYOO52Or0YkEg+97wxQ+cOMM/vCHbsQTv3IPfu4dV+98cO25WDSADxqr61LNNAg6HrtnCh8jOTuW+BNOqOpQiySBYbB19Kfu2o+Pf/eR0FkmjD56zWQRf/HIBVZDHRM1CBMg67UOKAGGCmw9bLW1zPYOzvVvfvUkji3VImte4HvoeCEVUxxnIp/CeNFm74jvS2RQBIECIcykVDGRZMpkjb94glon0JuVaht/98QlbDcdtFzI5IVIyJmchUIIwUA2FRNOq3dcWFw18ZpJhmrfwimiwq7mFHKV5i/UGMcKtgx0v/Gxu/DJD9+MX33vtaxHG7+W6YF0tGZQZ14IZJCjFJfWK/LztWqrK/X4yPQAu2elbr7j+ji5XMVW3WFBonKeltMNGUxmAblg+3SWb9dZWe8r6OlRZHA4b8dqBpe3W3B8ApdfY6Xp8PpRA3nbZDRonVYvroGPy4On1hXBI71Vwc7BoMvbc4wKmqiODGaYAFeK+HjHNWN484ER5TrYNRmGGfNHhArx146HwaDr+SwpA4WSHGk630HaZPVnYTDIrqPleFjabuFn3n4Apz9+L0YLdqztjWrPXNzCo+e2ouPBzfcDdBylz6BaM1hpI2sxlXPHcRPf+UbHQ8YSyHDIulnebuHdv/0g/vaJi8kXpYkP6e/xL37uJVzi4yL20D3D2bi2QeDDNI3LCwY1urVI2KQMAk9HBvkaJPZJhgwmz7+5zSYWyk2YpokfvmUGf/MTt+FTP34rRrImMmnFrwQSmX0bzeh79XqzK8Hga2VBfDKBUASBj2O80fZaRWSczOgE9tli/7OffRbDORuf/cnbYVKC+15iULugiL7j2nE0Ol5if6/wnHrtRigrrduxpQrWqu2QFqGaIkKxWmnDgA/LSkIGo44zod0XApFxmyhlQQK/Z5+eeX4MSRPtIo4ChOjhUrkl73+t0sBgNiVrVQayqVBVrUvPv0RkkBf8yx5vij0/V8bTF7fw/TczRdMwGFR6DeqSzYGHr/Dn+q5rJwAA108xp+XosoKI8UTCdjOUZZcWeCDUgAsaoWYiiI+RCAb7QwbZOQ9OFNDoeJGCdd8PsFRuwTIolst1OAHBhfU67ntpmfUYslIYz6fwU59+Bn/8rXP44dt24z/ee4iLv0f7H4reVkIUY5M7aILestxVQIYAhODJ85tw/QB37g83f2Kwflqb9Q626h0M5lIghNXDUPjgQI3MaookwS7eGmCh3IxlKAUC54F2rdVVs8P1PgVkPFD4AUNWxPMq8PqeGncsm54IBsNnL+iXoZJa9Fk/ybO3Pqis4QWARpsptQbEwHTJwn/6zmtxZKYUe6dKGcXp5p83Wx3m/GhBx0jejiCDJ5arcLygb+XQXnbz7BBemC/L4IPVl0Ypd/tH8/hv33cD3nNkUiYZdjStvsWkAXyQnhTqlEFlH1EDPpu7GmV5IMvmrVrnKGiiQnpdmnBWeLLnE+8/gtVqC2uVhkSDY9lqamCjziiqIuvfEImtpH0nwU6v1tDyEBHucl0XThD2ShTHoQjw9V+4Cx956z4gYNRAgxLp4Ip/M1aCmIgWmFTa7PrEGjacs0AIE236k4fOwTIoPFApmiGEOgQaKX6rB4PVlos0r406NM7eC0ERFTbD9w713V2rMvome469a5+nBxgyGKh1RRHmT0gTBYBLogURF5AZKybPqxt2leCBhuguuPiS56Pj+XDdqFLkVsOJITAUPjqSJirqR9m/jkQG2bWmzbBmsNHxkNMo0cMayu/5AVaqbfggsn5tu8npu4aJvJ3qkgCIzsWHTq/JBKfrifnaPzIo3vvRAqOJMkXdMDgqZlIyGPmzD92M9984E/6YX1Mpa+OcFgyK4PCbp0K12WfnytKvkq2kFFXmctNBxgg0mij7vvB39o3mQClr5yFUL5NssdxEgOSm8/WOG7ZXMiyYWs3gWMHGaN5m9MkEZkjT8ZC1UwChyKWofK7fPLkK1w/w4kIX+uoOyKAHii8fXZLjQwmrlY7tdYGHVCqFasvtX2BH23eFEu/MYDZUdzeiaqL1lsbyUu+Dzz/hNxuGqQWsHjKZDP+6DpoodNiGQA8vUx31X4ldCQZfK+sqIOPJYGt1uxlxBMLf+liruzi/Xsdvff8N2D2cxR0HRnDf0WUEQYDHz20gb5t45zXjALTeR7rpCl9dMnEtx8P59Tr8AJJWFzElE7m83YIBX26+kfvUKHWFjI3FcvL1zW81QQgwPpCFAb8n5XWxzL47WdoZGZwaYJvu4nZTvszr1Wakdmkgmwr7LSWp9iEMBokyVmOFNMYKdmIw+BePnEfBNvFdvFmzShMFeDCYoNL11WMrODRRkAHJoXGWmX/ghELN5EXPlVaoxCctCEAMAz4oXC86j/QxEgGdXk+RaNwR2iWcKCUoW6+10fF8fPjOWRAE2Gp6+LOHzyFFKT58xywINXDPoRHsGc7iB27ehf/ru65jdMGE+Tei9bbarHdASBgMrnRDBvkxHjmzDsugeNOeQflnwzCletpmo8OEYxA+97bHNt8LGw0M5yyZJNiltg/REhwCXfNAuyODKk1UFZDpUTPY4X3rcgpNdCTPrufCOksINPn1jivBoLjWigj0tGf91IVNjBZsEEolWgEw5bmMzVQse9GKI043/7zR4eIf2nMcyUcdyBc5i+HIzOUJxiTZTbNDcDzWigSIin68ItMd6sDHaDGLOxWEWbeUQWWfQYqA0US1AGxAp9iC0UQJEY5IQl0vv5Ybdg3gV95zDVptB+c3Bdongo8QGVzjNZNCHEIigwp62M18P8D59Ro80NC5ApsXHogMmsIx8pGzTdZyJvBACEONBSIosvMZy1DERLQkpHiH2uxexBpmGhTDORt/9+QlrFXb+Om7D8ADlRT+csNBigaRNTgpGKy1XRkYv3GmiH/3lr1475FozagIctV3d63axnDOkvcW21MU53Z6IIO264eIb6xmMIrmLyp9C1cr7a6I88HxAgg1IsjgS4vh/sJQH3Zs3+eCWJoIhgEfTqDt9XxuO5w+muU00ZysGaS8v6mGDOYsVFuupBCKBLEHKmu2KpwmapomSwC03ZDJkqBcvlFr46XFCmaGGGp7ZqUSjiEfo51sVaH0itKCcsNBpcXaY+RFa56kxBu/llIuHUcG1+uwDIpyI1Sb/caJVZiUwDapRL7UxEC50YFtEoAw9WXbpKxuEuH8Ej1Hs5YpBaSSbLHcDBHThIS06qcwZNCV4zFWSGOkYLPacm3uNiV9m60v2VSoEvuNk0xI6VQ3YTjJYEpuLeGB4ku8DvDCRgPTgxlYJo3vdb4nhQYXuuyZMdP23bVaG8W0iVImhY4bva4GDzBlLSGNB3piXB4/v8Hq2w2tbMP3kM+yZ7VRa0TOLd67Ssth7V/UsXid2ZVg8LWypEWOGCAIAAR484ERbFQbbLLpan++h0rbxzuvGccdHO34jsMTuLjRwInlKh47t4Fb9g7JTVvQ9RKtK000OjXOrNZkkJrUPFq9xuVKC5T40lmP3KeWxS7l7BhHX9j8VhMTxTRK2TQofMxtdr+Pha0mxgo2W3CA5Cwut4jyG8+WbVSb0nkGgIGMFWbc9F463ERdhH6e66ZLOLoYDQaXt1v44gtL+L6bdyGfjtK7kmii57iAx5dfXMDTFzbxrsMT8lhpg23aDxxfC7PQ3HHfbiQggz7r9+SDhGpb/HP92lcrLRAS9pPqaUIQY4Bdv1qnI7Kft8wOYapoYbPh4R+ensf7b5xmGXBiIGsCD/z82/CJD1wva/mS5p9ABkVxuwjeKGWbyEalGUerlUX+0bMbuHHPQET0g4pgkCOD4hyDOVZ3IRgfFzfqMggHGE0UAFMU1RxaQTX2AxoJjFWrtlykDIK8bUrnIEkJVprvweHBBUMGufhBPoWrxvJ44izbtOsu/zynBIPc2ah0QQafvrCFW2aHkLZSMpANggDtjoNCxk5cd9RjFDNmHBlsd1gw2AUZFPP1hfltDOcsTJX6SDrsYDfPDoKQsE5aUPt0MY7LNj0Y9j3ctHcEP3DL7q4/MSmRwTslPqM4a2MxyOeamlSrtZhAENHrRwW6pFzLh++YRd4ieHGpHn6HX5+47vVaGyN5WwZAzcugiS5uN9Hi7QnU9aLdceCBhuyLLmMEaiCdMmI1g5lUDzERgQx2BE00nMfjRRuVlos37h7AD966Cz4o65uGULUxnqSIsmFqLVeWLaQN4Fffey1K2eg6mU6x9kA6MijnUVIduuLcTgvWgHBq9eCRr2kyaSRq7MEQmW7BoGlQDObTkbreowpiw1Afdp5Ky4HrB7FEDqsZTE7Idjh9VAxHRqWJtuPI4BAPtETgsMT9Cx8UHY5AbzcdpEgg+wwCQN3RqJ8KlVz0z/xezpp5UYjrXEYrFPHeC2RQXGOl6aBgm2yP6ZZ4458N5hm621J6ZJ5fq+E9RyYiarPfOLmGm2YHsXckpyCDLGEQBAHKDQdpA3K+FNIpSRMV80v4Z1nLkMq7SbagBIMdN5rkqDRdhcHEfBaxJq/zNhujeRuu68o6eGECNctaBkANXjPYQcf1ZRul06vVZIE/+eyi7ATR83Agl8Hzc2XMbzVwcaPO6gWB+J4S+FJbYkd9C2F6zSC/z0zKYCi5cl1tx8Ew39MDYkSovOy6Q//tiXObuGV2KCEJ6qOQ4/oEW1HxP/HbXgH768WuBIOvlSUtcnxiHRzN4i1XjaDW6iCgRqx2xfNctFzgEK9jAoB3HR4HIcBfP3YR59bquG3fECZ50NMNGXx+royP/Y+X2P/sICBzcjnMEK1XE5BB5QVarTBk0FYFZGLIIN/0s+keyGAD0wMZFLNpGPAjohxBEEQaBEfaSojzdMl+jxXSMChhiw+/rnKtJZXoAJaZVhdZAHjy7Bo++qlnZOBRaTowSBQZBFgweGa1JpWsAOBTj1+AHwT48B2zMUeolEnBMilDNPhnT8+xDea/f+MUAgDvORIGg2IMFyqOlE4GMRAErP+eqLdRvy9aS6hUo27I4Ejelo3le5oQxCix8VGDdfFMpwcz2DuUhhMQtF0fP/GWffy3bL7ExEMS5p9lsroLkXHfrHcwmE2Fhd++F5HRlsehBjbrHRxbqkQoouzSWY/EjVqHBZfcQR/IsJpBAdpJSWxu44U0LIOyXoNa7cI6p3Xvnyj1oIk6KKRTvI5GbTrfHRkUwWBeQQYReHjPkUmcXGKZ6porvhOOZ8ZiCocVUS+hPOuFMquPuHl2EBnbQqvjYLvpsB6ZvodiLo2ImmbAklRdnW7+eavtMJqoljk9PM2aZ3/6iUsAWFuJI69QPEbYQNbCkemSFDES4im9avv6sgRK005OqWlQtPnzMuBzAZlkZFC0SAEYTZTVhCYHV6oTRQjBUMaAYXbv8cWCQQsZgQx2OtHv9mgtcZbXSOnrRbvjwIdGE9WdKz5GWcuQDrVwOmXNYERMJIoMSpqoEqiJ5/jTdx3AaN5GQAzpdJabHaRIELmfYiYlqYHCam0XaVurb0qwmcFMFBlUhYi6IoM8GOTjIsseNGEXqbjb7MAyqNxfao4P1w96ztfhQhbwPYnGvbiwLVU+1XowQaXXkwqRYFCbL22BDPIgUG4flNUM5mLIYJSpscT9Cy+gMljZbjLEFtSQv691PEDtL6vM14dP3bpA+gAAIABJREFUr6OUSeHILkbdFeJi+nz98otLuPm/PBARXxK2prz3Ipm5We9E2TLdEm+BCAbTCALIXsHs9y6unxmQarNL200cX6rg7oNjmB3O4bwIBvk6Xm27cP2A9Wvkz6WYNiWtdH6riZRBZHlI1jJ6IoNL5ZZEVefWo2JplZYToYmqaqKrPEgaKdig8MNghZugb2c5Ypo1WYLqyfObqLVdvOOaMbQcP5bYXNpuKshgNLEtSpt+9M69AID7ji7j4mYjquKtrW8iSdN33aDGZlvjCGjWMmTNqriuVsfBVeN5HgzS+Pm5nzi/1cBCuYlb9w0lJkGLPBhcEcrlGvX/wnojjny/zuxKMPhaWcKmLCbTTbuLODJTgiHaFmi1K02eoRXy7gDLgt88O4TPPsWcrdv3jWCsYIOQcLHW7a8evYBqR6NsdMkcq33k+kEGTRKAqg6HRCCjUPpALoOVaiuxHnB+q4mZwQxyaQsmiS5KH/+X43jv7z0ss1YL5Wa0wby+EStmUIKJYprVDPKFJQi8GE1Urxl86NQy7ntpWdJItpss+0e081w3VYQfAMd5b7u26+HvnpzDO64ZZ+ijFhgTQjBWsFm9Hv8sZbEN909++EY88ktvx6GJMPAXc8cHDVsYUAOB5yEIEKeJ+h6oYTKaqBoMJiCDK5VW/040v++0wTZgFRkUWb6pgQzyFkEpl8YP3LwrnLM7bMr6/BvJ2zIY3Kh1mENCQqGOGMWEB7qP8Z5gdxyIBoMgBmwT2Ky3GTIoaaIhMth2PSxuNyNJAkoJpgczbC5qDu0GVzV7w+5hzG81E4Wbqi0XhXRInQrHIom6FACBL5EmtWYQvof3Xj8pHYGqw8aCaOfcPZQJKaDK+/gUrxe8ee8QMjbLKL8wX8azc1ugCDCQs6PXldCeIuJ082M7Lm8LoPWbet8N03jb1aP4+BeP4fm5Mk6tVHH9KxSPUe3OAyN49lIZtbb7KiKDOqWpO9tAmKU0nU8bAcu6a2i3CHTU9iP1tod82kwIrnjChhrhmIJRUHePFOAGFPU2f778WgNCJTJo2zxDLgQq+kAGRWucADR0rgC0OR1xKKcxPhKCV0YTZZ+rdLScbbB5r/fc4/OrImiiSkLr9v3DeMtVI7jnmjEQQpBKmWEw2HBg0mivMJ0m6vtBhCbai8a1ayiL+bJCd68qwWBSHbpy/wIxDZHBIMb8AVgvzjfsGpD7y3aL1473oOaPFjMwwERjXM/H8aUK7uB0ZccNx1OgdURN5AAgCGTQF+717O9tn11Xhv9Z/BsQymsGdQEZoe4skEG27hmmCdcJg0GLsGAoz59lXSDC2jMPCMVDp9dx54FhWft5YbWK7YYTS5p/+9IW1qrtmFosEAaDI3k7gl5Wmm6INHdLvPHPhvLsGZ7nQZfY6/eO5vD2Q0xt9lOPMVGVuw+NYXYkh7nNBksQ8z2tXGdjYFGENd5pU5YIiCS3QcW6bkQSx7otbjcxO8r2/7OrUcZRRegWAICRAuGlIo2Oi1rbxVjRxmjegoEArrYd6chgxiQIAuAfn51HyiD48B0soBP9WQEwIbZf/zpOcd9G11MQ7Ji7Dk3i2skiPvPUHMoNR0MGtZpB04Rl0P6DwZiaKEcGLSP0b/h1eZ6Lq8cLIAh4C7A4MglC8cQ5th/etm84RrFG4IOaXJ9AiDrG+hhX4RON3fQ6syvB4GtlCZvyJuem3bi7iCPTLBh0AxKbwE0u8HBgNB855L3XTcAPgIJt4tqpIlIGxWjexlLCS7bddPAvLy6F2YwdkMHjSxVJkUsMBpXNZ6XShkm0jVCnBUlaBsvE6eil6DE4M5gF5RvEPG//EAQB7ju6jJO8b5gQK4nQl4LejtvUQDoiAmLA12iiqUjGDQBW+UInBH5EMKg7VtdxJ1fUDT5wbBWb9Q7+za27tbEIF5yxgo3ValsKgkyPsGOMF1LRIBeQc8EDDdEFQiWNKF4zyJHBQEcG4wHzSqXdX70gEAmGZgYzEWRwodxE3jaZU+d72D1cwCc+cL3yWxpxbsNrSha4GMpZsrXEVoPTOvn5KYL4RhL4AKV45Ow6cpYRFyqhFGmDYK3WRrnphMhgNgWT+Gg6DOkMAkSQQQDhvWoiGBtVtlFMD+bQdn0ZlKhWazNVUFY7pbxriYExGx+Rr8lpyOBVY3nMcFS2IuIK7Ti7hrJMTVR7zk9d2ETBNnFooohcmjkLz10q49lLZZgkQD6TZg5mjILYxelW3qNiRECGB+yU4De/9wYU0iY+/BdPwg9enXpBYW85MALXD/DEuQ2sVdvI9+gF2LfFHIf+kMEO7zM4kDYS62BtkyFnKjJYbXMEJilzncAOQeBhz2gBHijOLHMHkb87HZ+g5fgYztvI8AAojgz2CAbXaihlUsikLXjKeuE4LkzTjKK56hgp6HHaMtCQaqLs37RlIG+nGAqiM0UCERh5oASR3nY/+db9+NSP3yrPa6dMSV8tNxy212iIda3tSuZIXQjY9IkMLpZbcD0fQRBICpq8xliD6vD+S5kUCrYZrkX6fBHIYKuDPcNZKfYkWg31SsKNFrMgCPDc3BbOrNXQdn289epRALxtCD+PWCOpERUiooEnFYf1PbjtC+EY9rFtinWHwPODWGsJFXUDgOXtJmyTImtbcn+pNF2JDAqaaLXlRueLYFQ0XCxXWnjLVaNyjAh81nJKW0fE2C4ksInWqm3ZA3RI0kTbHBk0w+N0S7wBGOZonRCREf/uG8nhHVyD4c8eOo/pgQyuGstjdjgLxwt4yQl7dwX921boyypNlCW5Q18jZ5lyjuomfJuDk2ytvLAWreGrtFQBGeazVJqO7Ms5VkhjlCODbqAjgxyxt9j6IvpL3nd0GTfPDuENu9k51brBh7iC6uIWDxA1pfX1qhDyy+K910/iDE8syYRqQu9SQg1M6kq8vUwGXewdXa2ENFG5XvHrMuBj91BWKkFDS5KIBM/j5zYwkE3h4Hgh/p3Ak8Hl6naDJXm1JMWJ5QqG84p44evQrgSDr5UlbMoLZfYCv3GmiEI6hcGMwV5gbXI22x34oNg3GnVS383rym7ZOySzTpMDmcTG8194bgFtN5QB1zflGDK4XMXNe4ZgGVRSUSKmvOQrlRZMpUG1/HvkPDwYzGnUGm7LFdZjcGYwI4+zsBlm68T3//qxC1jjYiUzMZpod4dnspRhGU0aOrF7FKe/lIkjg2s8KySK97ebDlNe0xyryVIaQzlLBoOffXoOU6U02+yAuCMEtmivVtt4/BxbbGfHuKPcI4sZCQapIZVCk2oGDcOEBxpRB0ysGay2+1MS5ecUx9Ez6gypTTMHLulZ7FC7oX9/OBc2Ot6sc1qneHbEjweDPjvno2fWceu+4Xj/LmIgbQQ4t1ZHEABDHK0R/WIbbiBrQVRkEGABVgQZ5Ne8xZHBKZ5USBKRYTRRHgy2lEAqSYGMz4+Ol4QM+iCE4LZZNk/Wm1o9jrjWwSwarU6sXuSpC5u4cc8gDEpgminkLJb1fW6ujIzJaLSRACQheRVxupVnUUgQkAFYLc9vfu8NTO0QeFWURIXduGcQtslqj9ZrnVcuHgN0Qb16b5mmQaQS7UBaW/OUsRvMWnIcAKDWYnVNscy5YDgkOFGlLKu9Pb0cFduo8dqskbwlqZGyTU4/yOBaDftHc0hbVqROzXEdpExtbVHHSHnmmRRFSyCDCk1Uiol0aS1RafsoZlI96cN2KgXHdeF4PsqNDttrNAEZIBRrEgi8DAZ7IYODWXh+gOVKC5Wmi47nYzSvBoNJyGD4vKYGFJqpPl/4b5ttNj9nh9hxt5pRVekkK2RsGMTHc3Pbsl7wtn1sP3a9sAZznQdo1IjOXQpfIoBxZJBdY4qweZPhvoOo8Yshg5wmKpLCi9stTJbSsK2URJK3mw73AahEBmtdkMGza2y83nxgRI5RLkVY6wZtHRFjm5TgVim9gzJgdVBpOiEyuAMjxU6lMFawZTuJ8+t1pAyC6YEMZkdy2DeaQ8fzcdfBURBCpM9wcaMh310RDFpGmBCPIoPNiAhTpgcyuF5nvs0IbxFzYT0aDErdAgCgKYaABYFENscKNkbyNm+XFH2nmhIZZPuKSAY0Oh7uPjiGvG1ieiATKRF68gJD0DZ50Kcjgxv8c2qYuPe6sLRlVrSjSQq0iBHt/byTKX5HvcPaR4wWbEYT1ZBBAz6ylomSzVSIu9Hanzi/iZtnh1hdacJaK2oQHcdhc1CblyeXq5ge5ODMFZroFbssSxDKuMSDwb18kxjNmYwipk3OVsdByjSRTkU3pqmBDH7lPYfwU3ftl59NFtOJL9lnn57DtZNFmXHqpSa6We9gtdrGNZNFDOfjzaPl9/0wGDTQHzI4xDdA/Rojilt8Q13cYgu0qA367jdM4Zun1iQVMIIMJlF6FJsayGBpuymRUYsGmFAQMdOgUl1NZJlETdixRbYZl5sO0kYQcxAIIUxEZqGChXITD51ewwffNCMD9BhFCkxRdLXSwqNnWIH66ABviN1j4/JBUW4qPeQEMpjWkUEm8e2DRjL9epDG+jS1e0rnR280dC5mBjNY4hl1gD1PWVuURK/ruiknqx0O51mjY98PsNVgReHi/ANpmkAT9eCB4MJGQ9KpIkYN2EZIcRLOg7imhhPI/lF7NGRw12AW5YaDhuDdBB4cz5ctHCb5ppBUN8hooqlQVRGIInCqycy9qAcM+wyK79/Cg8G6pwUe3HYPZUFE8Ty3cqODUys13DzL1VUJRcGiePriFo4tVphToAtXJawLwumutEJkgsLnNNHkoOOug2P4D3cfwBt2DfSPQPdh6ZSBW/YO4ZEz61irtjCS70MAaSd7GcigZVCJ5BbT2nseU7zUaKK2GU+SqDTRWK2LAWoYKNebOLFckdda46p2IwUbGUETFUIwCf2xdDu3Vsf+0TyydopTz9nxXNdFKqXVI6tIi7KnZRQBmabjIWUQpAwqUaKWuBUtObjd8uPrl2a2xRCQC+t11Dse22tIfF4K1Fqo9mZt4bh2d9YEYjO32ZTNrKM0UT2pFH13pwcVp7YLMhj4zHndPcjmv2AEjfVIwhFqwISP5+a2cHRhG1nLwN6RPIZyHL3l5xEiW4aGDJLAR0tXE+XzqeFH9+Y0Rwa3miJYiM6VYsaESYmCDLYwUUqzVlK+j2rL4cEg8wEEyluTyGD0/GfWm9g7kuMlFGytOzyZw0On18NWSCIhzNf5JJ9GRXFTBqsz36y3UW25IVtGD0aEKe/o3pGcpIeeX6tj91BW1tDfc2gMAHD3Qfbv7AibLxc26vIdFfMupdCXWTDooNnxsF5rR4LBnN0dGRS198M8GFzcrKHths9Vp4kCLAASiNwoDwaZmmw0GKzLmkHGOMkor/ZdB1ni+urxvEQGHc/HMxdZv8NN0ehdqxmUQSIxsG80j0O8d6csweki4DLF27L0ZYrfodaJZiwzTHbz66LwkbMNFG3KWHYJCb6OT3Bps4FbRd/RpLWWjy2Fj5cWK5G1rtnxcH6jjimuhHsFGbxil2cJCMilLfaCibqfkVwKbkDQcBGZYJ2OE23orthPvnU/blL6J00OpGON548usOziD9yyC+Ml9pJ6WrZObUZ+gmeeD04UYv3CpPFFvuV42GowYZUoMpiclRzM7RQMZuSCWm60UWu7eOj0OmYGM/iP914DSgh+6/5TAIDpAQXBScriKjY1kIbjBdjiXslIzgyDNW55LRgUC+CxxQqCgPXzsQ0kogXXTRVxaqWKv3mc1Rd87027tPGK0lXGCkwx76V5ttgSvZmranwz80BDdGEnZNDUkEFBdVCCrvUa69N0+cigj12DWbg8ow6w5ynprUliPjvUbuhjOpyzsdXooNx04PkBo4ny808WrURkUNTa3anXCwIAoYzGw03WQfHzNzkyWEibTKxGsZAuHfYVmttsyOc5NciCx2RkkNUM5mxFTrxb03kh8MD/lOV0HvU6BU3UET3EtOPMDGXC4nluokG77LNGDeQtwhrQ+0FIb4pk8ZORQYA73fz4kibag474sXcfxD/99J3x+32F9uYDIzi1UsOpldqrgwzqVOYkmqBmJqWSjlUS9LqEBMdgLhVFBgVNNAkZpAm1LtyJMkwTJgnw+W8vyDGvchGW0bwNyxQ1g1qfwS73UWk5WK22sW80z5E0H+WGg3rbReB7UvkvHCPlehWKd9YyZTDY6HgycSlQopZIpGiBSbnlxtcvzdIWq+sVMv/dkEEZDApkMB2ltCWZeLfntxphq4KeAjLRd3dadWr1hKSscQ4wWrCxiweDJ1fqKKbjyd2I8eOcW6visbMbODxVhEEJBnMWW9P53zfqbZQyqagiYhAwARlfm498PjW1zy2uVi2CQV1AhhDCafthMDhVyiCTMkHAWBoVEQwSKoPBqug1qJ3/7HoTb7lqJHKf100VsFBuYlX2WjbQ6LiybnwxQQchUt8JxibZbOjIYJfEm7K+7RtVgsH1OvaOhOU4P3zbHnz/TbvwZn6944U0bJMyFglnwYj+oSmiBoOsl94CZ8+oJSmZlIGW4yf2bxZ+kUAGA9/HKaW/cKXlSOEflRp5eiVEBnO2iRQN5H4orKnWDBLKSl7A5rCo7b96ooBza3W4HguCGh3WAmer3oycE4GPrXonVC3mz/Ejb9uH73njdDi3E5Q6QZgo1Wq1LQWSepoCLKhthDKp0AdSx4IJVxG2LsdqFn0pWnXbPp40jiRBo6VCKRIwZpiyv51erSIIINuiXKkZvGKXZ5rAQrnRwUotumEP55gC5EbDlRPM8wN0XCcsht/BJkvpWOP5zz41B9ukeN8N05jkmdD1bU0yV9n0BE3g0ESBITQ9aKLi5aS8ZkuajgyKjSeVwkg+7syLAvHJgbS8FoNngx87u4G3XDWCiVIa7z48LlVGRf9AeZ4elK4p3o9whSujDufi45mz+O95zWLge7h6PI8NjpRWmk6kLkC166ZLcP0Af/7wedy5fySy+LPxijp3kiIkFx+NvqsaHzsfBNtChELZZIsZM/Z90+TIoBc6CAAizopoON+LrhS9hxCNEhn1+a0mGh0XWw1HCQZ719pErzUZURrKWfD8kP7CagbZMScLqRgyOL9ZQ6XtY7xoszqA2LUbUBPeQopcjGHdYf2RZodzMcqaaNmwWg+pd+fW6jJDm7WZml2SyEGV0wFFprjnWEhkkP2vrClTrpPwHlOOFIeIbqa7BlmPTlVJ7qkLm7AMiht2cVYAochb4T2yWh9t4+yBDG43Q2l7iqAnMvg/00TQv1nvvHLxGKALTbT3/ZgGkWyDoq2pyyljMZCxIgIygj4cqx8VAUg3ZJAa2D2YxuefXZAJPSEKNpK35fXKPlsJjBTVBD1u/2gO2TQLuua40h6FH09CqkimHx47rQjItBwPGREM8sBA6rtogcF2O4ivX5oJwaNnL7FgkCK6vnQLBrN90EQnSxkQwlrHrOmqtN0EZJR3bnowg+2mw86pB49KwmQ0b2MXb8lzbLnWUzyG/TbcA0+uVHF4ilGsh0UwyI+9UeswgZdI0MX+dQPKkkIa2t9wo6yCtMHWlM26ghxpNswFvQSldqKURtpm8+XiRgPVtgtDCMiI1hJCOEibL00PeONusRaxc+0fDpEwMXbqGr+U0C5rTQsGh3IW1qotprAt5tSOSUgD+0by2Kx3sFnv4PxGPVKOs2c4h//ng9fL4IZSgj3DWcYikTRRB4QAKU1AptHxZE/CKDLI6cMJCqkyGCyyPYfCxwsLZfn3StOFzJ0oaNiZtRpMSuS+ljYQCwYbGk3UMoB0iuLth8bknnf1WAEdz8eFjYYUHbtz/wjKEhkMk5CnVqohSsmf4/e8cQa//f1vCE+axLagBqYHM4naEYmmaBUIFXFBEw17Lpp8LALkbBMFiyGDTkCi5/c9bLU8FNImrhHq/ElJUI4MjuRMVv6jrKMnuH+8a6Qgj/l6tCvB4Gtl2qb87KVyTMxlMG0gAMF63ZWfzW02QAM/lMnewUQTdvGStRwP//TcAu69bgKlbAoTAyybISiYSU7fyeUqhnKWpBz0ookKZIhC3wi1rKSCADGKQHQRmN9qYrxowzaNSF3fF19YQrXtSsfvR26bBcDU5woqvWinmkEeOC5XmcMwmos7IFI0j/PFKfFxDy8if2lxmymmGUh0EK/jm3Xb9fF9N++K/V3flEY5GjczYEXO2b2WjCBrmSG6oAkZ6N+nhomAEAUBjjvrIhh8OTWDYnOb22yEbSUGlILqHip8EeuCKAkFO5HxVJHBiWIKC+UmvnVqDfcfW8HP/N2zePzsGgg18Nc/dmvYw1A7v5WEDPJxqTsMGdTrBYEwq7sqkje+h/Pr9chGODOUjfXFDAKmaihbS3Q4Ba/bWEhkkIASwDZpHGHnvzswkVxjOllKwyQ+PEU84PHzm7h+phRma4mBtMEy1LuHWPDIAhC1ZjAe0ESRwZAmylpL7ExHfLXt2smifI6jr0Yw2E3Zs4elDAIfbL4V7Oh6rianBrIpWe8bBAHqHS8UCEoKQLsihhQHRlhW/enzjD4vkEFVZEn0f9tJQEYoie4fyyPLnfu5zSYWtpqsXZCl7TsRAZlwTclYNEITFQGFCAwa4ha1DHy56e2IDKZSJlIkwHMcGTS0Xq9daaLpHmwLbpZJMVlMY36rEaIO+XR4rTFkMOrcyvYSm0q7AWE0fEdGC7ZE9Vsu2VnBmYaBJAAc4SJlgzkLvhcyLzbq7ZBCr+21smZK2wMagmLOn4EAtDc4Mpi343vjcM7CRr2N9RprOD85kEGGz5fjXGBNILa5GE00en4PVCZnxRiN5U0QErYqADUwzwOjiWK8HVWzw1o66MHgJU7177dmEITRRAFWjtJxffn/3WzPcA4X1kOaaLnBeq0SZb4I30SwrFQBmQyvyWwkUEWFEFuOz92CRaQWAcCQQZk7UWminB0h9r60ETJMhMnWEnzdIYGPv//I7fjYuw/K7xzkNM9TK1U8cX4Te0dyuGFXCVXRrkjpwXxqpQpKxLrfJbRISrDxmkFxvztaAjI4VkgjYxnhHkxDWmfGYswXDxS1ji/neRAEWNmuY6ni4N2HJ0JmWNckKMF4IYWji5UIs+/EUhXpFMUYZ9ldQQav2OWZXoC6Ug2z93wysfYMJnM6+eQ8s1oDhR9mOXewSd7UWTSef/j0OqotFx940wyAkNK2VK5Fzq1ueseXqzg4XgAhhEn81zpx2Xz+Aomgk+hqnjFkMDxPUvHw/FYjXDAVR/Nzz8yBEMi+cbftG8LV4/mwQFnYDll8sficWWWbxUg2IRhMxekXb+c1A09f2ILjBV2RwV1DGRTTJkqZFN517Xj8AjTnTtQr3rFvMHLOrogRNTCYtRQBGQoSMCU+veAfvgdCKAJiIBA00QQHdVVZWPsyJTCZGmAZ9fmtplzQZQ1nVyequ2hKXECGbfKnV1VkkH1n9wBDvz/0ySfx7/76adx3dAnXTeYwWszKzSx+7ZQhYNwkMqhQ7ea3mjElUfbdFHKWgVVBEw08nFuvISfQNWrwfmVRZLDR8ZjaL6eJen6AluN3Hwu+abU9hgoSQrq+R99z097o+HEzDYqCTeHyAKXacnB0YRu3q3WUlDkC33PjNL7njdNaAKKhPrQLAqM4q1Fk8P+7LYZSIutD/+cJyOwUDFK5jofIYHxOD2RTKDcdBEEgKWJ5O9UDGYyriYq6zumSheGchc8/cwkAa88gepd2Rwa7BIMcUdg9lEU+w4Qn5rYamC+zYDDGSOkqIBMig0k0UYkMavN4q+ntWDNIqIm0CZzkjjVFNEgvasFglSODuT6QQYA56vNbTazV2rAMGqJKfSS1xJq3uMVFNxISogYPBsVhPfQRDGqImVCsHs5xkR9BExVtd2g86AtAEt/pegwZZP+72VD60Gk2nLewWe/ImuvJYhpWKgWDBLKmXjwXy6SwTRoXkOH/+gENWST8Pi3KWA2iVQGIIWn3t+wdwmI52rpHJKjVJNBQzsIST3BGawaT9tQwebWXI4FfP8Hq9/ftEAzuHcnh4mYDAVjy6JlLW5gdzkbmS4HP++PLVVgmjVxnjidKGgm9BhfLTUyW0rKX8d7hNF78f9l78yjLkrM+8Bf3vj33rKysvauqq7qr91arN6nVkrolJLWE2CQQGJDFZiwhNBoPi4+ZOWYMx5jRHAbbBwaPWWzgmPE5YwYMDPaMEEgIbUjCWtDW6n2pqq6sJSv3l/nevfNHRNwbETf2+1KVWeR3jk6rXr53I+69Ed/2+31fCMHg1XWRJloGg8v9gbSm2mmO/tCADDZL/XLX0WkpGXNq/zgIAb56fhmffoYeyn5kulc+w6KchSLWvQYqzcokqdA0GTIYEgwKjKSF5T4aCcF0t8mQQZnWSRvIpBhr0iTdymYO5ENkWY6f+5Mv49ziKvZNdPHPv+OO8vqavcPZGQfGG1hY7uPyCptnkuJrLy3hzIEJpAprZ7fJXjB4rUQxygvL/eKMHVFZNpoNvLS8hZwHgwsrdIF3PINB5eD5jzy+gF6LNlsA6KHvAHCed4JUMvpZluPx88u45RB1qufGW9gcZlhaV7JYbANxdImoAQAhgHjgrOA4HGbBoKjcX1wUOm6xuUy2aSfTOw5PFQ0/CCH4zXfdL1MRACcyOMWUx6efo4Zrtlf9btlAplSyvJ30J56iTWt4+2xVCCH4h689hZ960xl9LYhilM4cmMDPfstteOzW/dKYxoCJpJjqNkuqGcvsTXabVSSMfZ+QlGaR+Wfsd1wuLG2AEPg33xACk1YjwcHJDp6/siadMcj/XnWiHN1EDcggL2YXkcE33bYf/+ndr8Tvv+ch/PGPP0zPZZwfKwyoae5F97xmWh5DwJ73+oBSsm/QIIOEEByb7Um07icXVrGPJxRIimMzPby4uC7VgfBucuPsnEEAWO5vVahmhbB3tDEQAnwTwm5ZL5PtBAPWMfDTz1zGMMsqgrkfAAAgAElEQVTxyhuFYJBRyn7hO+7EP3rDzTI1sXLOoB0ZTEmG8Vb10PlvlDzMGAMjoYlqG8i4uomWweAETw5o1vRMj9Kel/sDugbAAiWSymwAnlSrzKVsLJMiww88dAJ/zToRL/Wzcg9zZHCgJoHMweDxfT000wStJj1m5YUra3jhyhoSkqOtrRmsosddVjNIg91hsb8KmqjQfEmc19X+sHo0jiokQTsF+NYiSrOyOsggwA6ev7xW0A6J0MreldTiHa3P8kSQSDFnc2ynOX0OGaf7J26aKPvt3Ucn0G2mOMUClpleC3k2LFhFl1Y5TbQa9A35OWsK2r/Cg0H2eYvRRC+u8SC6ulZmx1q4vLJZdPU8yAKWdpLTBhvgiK3QQKWvIoPs/kmCgyxpLdqUU/vHiqMKkKR4kR3WfvexafQHWdHABkC1vhMUNeUuRXFupTMJmVBqfULwF1+jweDJ/S5ksIfNQYZl1n31Sy8u0mS7sF74+F85t4Sj013JRnPUfE3TUfTs4ga1o+y5HJ/p4Gvnl4smMkvrDBnkDb+AIiASn0VLiwwO0UoT2hzHgJh2WymOz/bw/3zhLK6ub+GBk7M4MtMV6JgCMnh+BXO9hsPuyr0SODLI379XR1EFGZwbpwhotykgg2nJ5hprNVgwmGCpnyHPhviZP/gi/t3HnsGB8SZuOzJDGWhcDHXQICn2j9PrPrVQJnu+em6ZJp2FHgq7UfaCwWslilFeWO5jvKNkLvMheu0W1ocoGn88cWEFzQTVFt8GKQ6eZ8HWhx+/gIdO7SsXP1Mg5xdX5bHZ589dXsP61rDoCsWdrIurClWUKZOXljbQ4UFUpU5M13mOBoNrm8MC5RoMM5xb3CiDQXYdTqtRG4Icm+3hlHLmoqvZAyEEh6Y6+MJZijTtsyCDOcsy9RoEU90mbjs8iS+8QLNzrQSywRfkvY+exve/4rhhAjIFLUkIfvBVJ0t0ydpAhiGDYxRdoBdIQXJDVp1380xSZJnZKbzAFGtDPYbBJEWGjr7TYyyjfnZxHQkBDkjnc/kig3n5d0H42VZPXKjWDDZJjvtOzOLe4zO48+gUdaxclD6SFsigdIi26DihesYgl6MzPZxfKql3T4nBYJLg6EwXW8O8qGkAUNQITnSaRVdFeuaaqYEMfT4bQ5RnfRlqb23rZaKdFJ3kPvHkJbTSBC8/PlN+oVKPlpcBSCVDWq4N7rQvCTWDY01CHR3DeZHbLW++8xDe+YrjeODGWfeXXRKDDCYEGaPkFnWYtuY7a1tFI6HxdgrehKIck+kxNWGg0Eff+crjhb66ujEsg2HmjG9ucRS7GtSL8tTCKm7kupSkaJC8oIm2kxxEpX8lQvAqoMe8RrA/yLC+WaWJrlaQQdrxdmMrc9JEkaQQ45NECdI7TYpGLak1gx1BH1nk6GwP55c2cHZxHXMiYqfWwbN7FdfI3HgbrTTBWR1NlFMG20l55A6ornEjg3Tc97/uFH7nhx8odPTseKvoFDnMclxZ26S6Uqp7KhuO5aIzzuujeV6XzYc31rq0ypAjDTI4N97Gcn+AZ4t6/W6hUzm6IyK24+2GcOi8PP54p1Ue/SOgPqfnx6XulC9cWcPh6W6BIp0T6ssWNMHgPkGvl8hgYrA7pb5qNRLcMEs7Ro+3G07KObcRi7zhThP4tpdxhgXTBayT7dMXV+Wu5yifr44mWjRiY8/xxD5qV7j/sbwxQDslZfIOlFFGn0WZYGglNBjcGpb3vr45KJOgJsQUwE0HJvAkqyV+4OQsjkx3haCLPuM8G+DxC8u094ILGdQ0kOk0U+yfaPudNSgkRMXjRCSaqNhAppWi0yiDwSwb4D9++nn82COncGiyBaL1UxXQgj3fuTEKInzyCZp4W9wY4tLqJm45OCkxpXaj7AWD10oUB+HC8gbGukKhOvtvp91Cr91CzhCdJy6slJkgD2mm1NCcu7qBZy6t4fnL63gtO6wWQKGsLvAGMgodjBfHnjlIi2uLYHBZCQaZMnlpqY/DE63yM8135HFSHGH1e9yIvLTcxyDLS5oou85RVnBfdB6zie44A0UOT3eLFucz3ep3OY1nE/RvBybo+Ve3H54qEJ+mgSbqFFfNnI0mygIdqQkFoYpQ60gxZ4kkabGOTDWDTqdEFAWl4hn1FxfXcXCyUwaVglEsf+vo6qZ8nyPB565uYKyVUrRVRcnU6ziQQW40Z8TmQWz8rAgGq8ggQGnA51jzobX+Ji6u9DHbTYt9yesKxbpBTlfjNFGAoRYqNZBLpkMGFeqhx3qhxfO0gcMnnrqEe26YltFqNSuccQqixigKz7TTTNFuJBIyWHTgvQYNZAAaZP38t9/hpBp6iY7S5KwZLJHBcQcyCABX1jYL5KqgiWrpoCJ1qTzcnQcj070W3noHpbC/uLRZBjFsvWxtuZHBwTDDM5dWy8QaQx15A5lWCk1SR1gjYs1gk467tjnE2mbZQIbT5da2FGRQ2K8FimMSkhT1vg2eeFDmRY/uKIPBTjMpE6geyGCWA3/74pIcCPB9Ic1F3jtJQnD3sSn8ly+8WP69+CN7Bk15XQyRuGnN7DoHJ1plF2DQgCcF7RR5ZY12g9433lYQOEGnic44++/KljwfHh+4kEEA+NLZJbQaCe24nMjU+0QoFRlrN1jNYFWnTPaEe5eQwfHynEuS4EV2XBEPBkUUaWFZOQYEAvUfkLuJ2uwue7+8TvDkXLWBmCq8rpwzRb759gPUDks1g6wJXS7XCwJmZHBjiwYaR6Y7xXO59UAPnWaCP/xvdH1RZDAvdQSAKXakjWjLm+zQdbH53+rmsKCoGmspgaIB26GpDo7O0Odf1AYyu7O4soHFtS3s66XVPSKK9gw/+n3v4yUEG3hhqV/cZ6/V0NJEx1oNkHwIkjTwzOUN5MMhXn3THH7ijWf0fqImkcKR1xbJ8V33HsUnnngJQHkc3C0HJ0qfxaFfdqrsBYPXSjJhkYFmtsbUzGU+BCEpbj44BZIPcXZxHU9eWKGc/oCM+8EpevD8Rxjt4bU3z5d/ZMpqdaNPjSdTYFmW44Nffgm/8hdfR0LoeTMAMMcCvcrB80yZnF/awMFJQfFqviPdI0mLIyG4cn+BZRuPKHUEp+e6mGg3cK+IapjEg9J1eKpbOP3iMQNcONCzOqDjz4/TD27jXadgpok6xVXInlocF6ZAxSYUfA7cEFS/TzNbRetltv62coKMBbYvLfXDzn5TUCqeUX/20pqc/dQ50a5gWPl+M6X3CwhnAtqUb+Zw3EmKJKfGUHQaRAety7KVOjk20yvQja+epY0sZnplgCo21OHCaaIT7YbQbn2rmi3lwmmiQ8EhM9XeFuulGhiPM4rMl84u4Utnl8oW2lxMAYiUxde/l6luE1fXBGTQEgDtOkmUAEytg9ZIIyVFMDjWlJFztWYQABbXtkqaaHG0hAaN1NZvyp+/7Z5DAIDlfo65MZkmOhi4u4k+f2UdW8O8oCCCJEhBa2efv7xOAzDPBB9HHNa3htjYKmsGxyrIYIkq8uNP3DTRsvnTdK8Jokk2icHg8saABtpqIsUgvFvwitKQxFeP/fRjt+Dictn4pJw3rVsaV2pJO60Wbj88Cauo+57JTI92Vu0Py3P/9o235L0rHEWUa4L3ftGJmAWD7NkurA5ACNBpVNc8R92+9OJVWtNGSIEkF1NWkMHlyqHz9L/TY4LNEdCVU/PjQkOQtDisnTd/k4PBPhJS1pYXz4FJUffpsrtsfDEYdMmhqS5aaYJPPUPRuu++9zD9g6ZmECiPL+FiQgY58kmRQbpmuinBm24/iD/+/NmiaU6b+4M8GGS2Qjy3sklyDJFIzf/WN4cKMqhHzG9mrLAHTs6CEIJuK8VUSy6heYF1fZ3tpnYdqbU1PNnvefC8SBMVkUENTTQlGTpNqseTNMWF1QEaJMO/+p57aMMYnZ9o1LV07/zQwyeRs2fFj4M7c3BiDxnck0jJZWdpYbmPCRUZZHStWw7PoEEy/JuPPInl/oBy+gNqcQ5P0YPnP/L4Ak7Ojcl1UMLZR08trADZEBlJ8bpf+jD+we98BldWt/CB77y7UFhc2VY6iiYp8izDUwurODxpQwbVGqSkOBKCZ4V4N7LioFJ2nXe+4hj+9P2vtp/HxMWD0nV4ultp2iNKlxVm8wL7ecYXFw13k9RABrVNQ1Skx1wzKDah4HOY1iCc/PtJkiJXHIFf/rMn8JZ//VF8/MmLuLDcD0QG5WfHM+pffPFqWS/I70mrcO21G6rwbPQ+xdE1o6euDGWG4/vGynUmXCsDbRluygqf3D9WdI38tb+g51yKhpAnMsSzBiWaaEehiVqQwfWayOBYi2CYJ/j9z76APIfcPAawBCBiFl9P+yycbjavgm3toCPuCtG05/dBBnmCqehWWyQ4yrU0LSCDJU20YXaWtIF5In2fd0TOQASaKJ3v1kChiWp0o9hJlF8/QYbNQYaLK329rpOCi/IZcR29vjmUuok20wSdZoK1LeVe8ow24IBHMCig+ioCw0VFBic6DWHvOGiiQiJLCgZ1NkWzd+8/MYs33ELZK/yYDy5DJJWEyX/6sYdxet7Q6EocB6iMtY/RRDczUtjkfWNtLWJLg0FB77LxyzNK6ec8h3FxbYBeM9V2Y+aB1lMXV4smdUhSNFDeLxGO/JjoNCrdRHNWsjAlBYNlgu/0/jIY3BhSH+noTA/7xlpoNRKJJnr26gZmx9rSWcGzQmDobiAj+2MhwWCaEByb7eIqoxndc4y9SyHQEDud+yKDUu19Utq6t738KJY2BvjDz1F0sMMRe/adaZYQFlHtZpIhQ4IFwW9b2xyUFGCVni4IT36LdeazXNEzu/PsJao7ZgR2jFZMtgb0aLAXld4ROjnPgIgsG+KSQhMtu5nS+Y01SEHJbjRKHTDbE5LtVj9V1LU0qDy+bwwPHKfP5PEL69g/0aZovCFhs1tkLxi8ViIEQxtb9BzACeWsM+5Ec2X5f37qGQC8Ti0EGaSb7BNPXZIpokCxgBPQJhh5nmEroxSCX/nee/CRn3oE38k6jwLUIU8IcEkNBkmCpTXaaprTlfR1YmrGJcHsWAvtRoKzi+vIshy/+8lncceRybKtf5EVQ/W8PpN4ULoOTXcKh163gXkykbEBC2eLHrFB35V64LG3kESfifNFBkmC6W7ZhII7mtNtzZZmCo8kjSKjxdfY1X6GJy6s4Ht//VO4uNJ3NzIQRUUGmRO1OcjkYFBH2fQ0yqLMMeNe1PjZlK+OmiqNTxX77/7wA/gnb7m1Mn6GRHusBJdXn57Dz3wz7UD2I686jt/+oQcw3SmRwU4zxfxEG88LHUU5HVCiifIGMtq1UDaz6bWFzLbwt7Jm0LxexpoUrfqTL5xFu5GUZ3pxUdeiFIAoCKRi6NVgsEDDrgdkUMtksJvMRlIeLaFtQsBERAZX1AYyyFHUzmoDcxUZlMchSSrV/QHAYIsljdhvX1isHg/0xAILBudKmig/xxIAmjpdJ41frhHuZG5syTRRgAa9K5ucJqpBBl0UX0K7Bk52GjSo1jh0YjC42h+UgTYbyyaHpjpFULFfrRnU2TTN3n3/604BAP78a5eKz7aGGbKcYLyCGHu4YU5kMC8ogJVzBgWdlmuOliiCQX72L0tiDHNSOXCei4jA8eOrQBJ6tiDoESsiBW+83aC1m8K8llmJw8y4YCuSUr/NjLUKqvW5JfrdI9NdEEJoglsIBj/3/CLuPCKjq7M93mQOtKkVYEEG5WQPP1vwRkfzGC4n9o0VSSCSC2tboYkCcrIBKOvBV5VgsOjKLSCDyIZ4+PQc5ifa+PcfewYA0G4AxbmwEGiigi1vMGRwYVkMBsskjZGdAuD0/Dj+r3e/UvYDedKZ2Z2vn7uKG/ePuVlrqq0TntGR6S76gwyXVjXnWDN55uIqHvtXHwMAfOXsFWR5uUd7rGYwEwLjbmGPMuyb6OHvPci6bot7wJbgMrAw3nI77RD/sacuF/009pDBPQmSYZbjhStr2BqU9D6+QScKmqi4UMtNzil+WrqORQ5PdbGxlWFjK8NrzyjBILt2K8nx5MIKnr24jEFO8JNvvBlvvetwpZlImhB6mKtCE81Jgiur67jl4ARee9NscW+SGFr2EkLY8RIb+MjjC3hyYRU/8vCNJSpjqw0ziQcyeGS6pInqrt1J6fgvXKX3up/VlhFCcNvhKRCC8ky2UDEapWqXrup3qJHlDqVI05vS1dsw5DBJhecv0CF/8e134SfecDOme0287NhU2D0IczwmZDzlYFDnRDlqBjXPlAeBM17IoCMZwJ7/vvG2fI4Wb7aQJ8bmMQDtGvmqm2jS48ETUzTJoqy5Y7M96XiJZSEYHC+CQR9kMC9rOwqnUHHALeulleTISYLVzSHuPa50TgPovix0jkCHNNFlBCmcbvY5T7jS3xGYmivtCtFSIO0ms9koawZ1uo7LdFcMBhVkEJADPGNgrkdvf+m7X44333GwvAdQlGZjKyu++/p/+VH8zXNXivnkeY4/+txZnDkwgSn+EhmVmkuDaBgp0jMqg14e/K1vUWSwIxxcPt5uYHVLQU3zITJWm+1uIJOAZHQt3zQ/7kYGNwYlBVd8fgZppEmBdlVqBnVJLY3tuGGG/u7Tz17F11jd/aWVTWRIyi7VIei5Ye4zvSZSZNgYoKSJVhrICMggquulDAbp5xyNy5AYg8FZgYJZdAIlZWv/qS4/Z4/Vz3bEBjJ0HE6lndHSROl15pnNfZHZYB5IHZoqKYWXVzfxxIUV3HdCbhrF5zjRbpTopinxpuzvB0/uw89+y214420Htfevys0HJ5Ckyt4VguFOM0WL+VKVYJAFqusKTfTs4joIAS3dEN5/mhB8xz1H8DXWWbs43or7AJwmKiQyGsgwBJEYXVIwaGIqMbn/xKzkC/IeCzk7z++pC0t44MSsh91VbJ2QKOY+g62JzOdfWMSANej6g88+L90nP3SeHqHCk5PlOI00xfykcNwVYEEGNbpW+Pym/fQ6wzwpg0HVNu8y2QsGv8Hyl19fwMP/y1/gJX6UA0kL6H6SK8VMWai8PXOLUMUWGIBwZd1qJHjFSU29EGg93NdfWsFnn7mInKR4+8uPqpcpRHfw/PnlLQwHA7znkVNlZswLGWRZoRlaPPwbf/UUDk528JY7DwlzDCzMFRssWOTE3BgyC3Wo0wCynOCZK/Re58ZLw/j6W+Zxz7Fpdp5ixDZyHq1gQQYLmmhJNePdIrU1g0zhJmmDOl1ZLjkIR6a7eN/rb8Ln/ukb8bpbNGciGu9BzlaLGfWjEk3UUaQtiuWwck5N2ueNDLoylOYM8VvuPoLvuu+Y+fe68ZX6g2MzXbmBzMYWCDsHclxtIGOpGVwflLVWFaeweF7m9ULyYXFszSvVekF+zUrBvCUAEaREBnkwKCDtuxkVBBRk0JykEKWZJEKCyRxI0/MfG0oDGYHKKNmAxKw7NZSmiW552DS/Hj17bKv47jBP8Kt//kQxn088dQlfPreEH3r4RHkzQiAJ0PobX2Sw26LjrvQH2Bxk6DVL3TneEYJB4Z6ygibqaiBDn8Wv//378Avffrs0Vy5SzWB/QFHXAGeNO+zVmkGV7m5P5KRpA//+408DoDTHIZLSQQ1Bzw26rpEmaCa0ZvDSCq2bm+7pj5bIQBgyKL+vzYImWk0U9lr6uU20G0Vwc1igiZI8Q7uRUFqmoAPG2uLREnT8i+wMwdkJERmU1//cOP09DwZ5Lfrh6W5xrMVnn6VJjfuVYHCslbJzIoXkgmcSMmXdvbuG+1flvY+exo8+crM0d3W9THQaaCtnDAIoEierytkPZxfXMT/Rls4L5dd+m+CftVOh+zOAiTbd++LxOgkyJElDQQZFmqghOW0QTkXlDbU3NjdpMB5qdzMBGZypNgZS5Wvnl4ujKx65aRan9o8VZ252mjQZkZPyWRRlC4UvrdpsA9rvQAaJ0KH3Vt5DwrMmeafKXjD4DRZen3R1lVEckhQXllgw2NUhg2mxyH7ooRN4692HtQXzNuE1eQ+enK0qN3btQxMt/PlXX8LyWp+eL2U5XmDfeEsKBvM8x+MLa2inwDffechs5CzO5eGpLr58bgkfe+IS3vXQCaoAizn6ZXQL8TSyR6a7+NP3PyL/RpBOg1IrnmXB4D7hLMJ3PXQC//ePvcqdCTOJs4GM7dB56pTNCFQz3hV10tRAhhCkKaVRrG4OSgchT/zPFdTdA1AYdzGjXqGJOrrwSd8FtNQpHgQWtSA2xNgTGawIey5vuP0wTs+PV/+uXgNAhdLH5NhsD+eurhdnQi33Bxhv0Sx1p5kgTQilCDrmsjaAgAwaUCfHekkbLBhU6wUBOSssZsg19UXqnprsNrG0vlU48mXNoOad7zYRAy1PnSI2kHEF0tNjNGBZ6W8hZWtCrA0q/lsJzMUOdwZKk3gPoM7gan8oJYE+9NULxQHhv/nRp7FvrEVb4nNh850fayJNiD4JKSa1hPF5zeBlxiDhwSGg0ESzIa6sbuKzz1zE8maGA5NtuaGTTtizaKRJ2T2QVNfl8sYAwyzHSn8rCBkESpbDvKtm0JHIuenQJP7sKxeQZTkWVjYoMsgPCbfouopYdF2D5FgfABdXNzHTa9GEnMbWZnlC0VdlvQyJjAyWddNJWausToeQgqlxUKCJknyIIzNdSvUVdPBEu4HNQUaTr2zcyysMGRRposo74sf1fOHsMtKE4OAkty8dnF/awGCY4TPPXEYrTXDXUZnVwuco0Y4jyhN8ZLzdwP4JO+o00Wng6Ey3UofO9/76lhoMbpR2VHn/Zw5OFL0LirIhZhtefmwSb7nzoOxDZbRmTmz8V20gExAMMmRwYY3u4wQZQwYdQIVq6/LSl/U5eP5r55dxfI7e98OnZvChn3ikqMFsNxI0QGsjS6aKsNcElp38jixsh0pyVKbk//Sbb8M338WAC8+a5J0qQdaaEOJHoN4To1DOO3B1jQWDAjJYFFKLDoiQ8XnfoyfxL952p3vDKXJspoeEAK+7Zb76R7ZpDkw0keXA/FgDzaY9Mzs33pZaFH/8yUu4tDbEVIcdYFpsIIUiJtUxyAjQ4ekuNgcZus0U3/vADdo5+iOD3Mi6KWo3H5wEoC+eboC2Y372Mn1XurMIef1esLiMkrOBTNldc3F9CwxcwKSuZpAZ5SRtCE5hmdmKPqBbRTFQZtR5AqL4+whqBveN85pBpVutCWF0ZSitDWx8svVKJlDZl6f2jyPLgecuUadneWNQ1I4QQtjZW8NiLnmey8Xz3HHPiZLd1mQui/WiKb7Ph2g1m5jqNnHX0enq37W1can8jAz1TVPdJpb7Ayz16d+7DYECF5Mk2UliODbBJs2UaJBBPdo93W0VDWTGWinryKgGkiJlV6mz80FvCQ8Gc0rT4zSnA5MYbzfwqx9+Ak8urOBDX72A73/Fcbk5F1vfx2fbODjZodlwa4focnyOOFxhdWFyzWATyywYfPbiMl7zv/4Fvv7SElrNJv7oxx8uz5wziaZOUtX1nGq6vLFV0kQDGCan58cpiuPqJuqoQbvt8AwWlvv43AuLBTLYjUEGLbquQXKsD2ngXXTQlJ4RqwGs1AzSv0+P9+RrC91He5pjJbjwscQGMsiG+K57j+Gtdx2S9D5nQmQCTfUSQ6mmusIzVmz9PmbjPvz4Zem4okNTtFnZheU+Pv3MZdx5dErbWG52rCUjzc4kZA2dpWWKlNc7PN2lXSc1MtZiNFpBzi6u4zAPtDU+0DvuO0YDyRSSn/jozfvwv3/fvfIA+RDtZqM4ggMA1rbUoyX8g5ipDv3dhVX6m9luSrukuphSqt0V/KepbhPj7QZ++YOP47F/+Zd4529+qkB9uXz1/DJOH2RBv4LwE0LQTtka4w3Nmoo90iKDOrRfU+8tfU5//+qbD5RlF6F+6g4TBx+DCiHkIQC/AWAcwA2EkLsB/MM8z39sOyd3PUqnmeLARAfLawxZYzWDCQEmekypqs6lTskEZLDmJzv4k/e9ujgeQhKOzLEOoPccmwQ5Z7+2ShP99Y8+he9sNNxnjBkoRUBJEfiu+46WNSvKHL2RwdBOhobAhORDZCShqFuDdqbSjhWTTXQig+ajAriR5TTRxbVNrA8YTdTSQCZNG0iRMTSqRICcNTomSaqZsJNzY/ja+WWpe5reiUoMgYv53c2qyKBmfOk6tvdCUvOzBTwdNJ3xL58/P6/tyYUV3HRgAssbW5SuxmS83aB1hA26/r77//gkbjs8if/5W2+X7itDIidydAGAreFQnmH/RBf/9UdfLWeLpesp+zJJqYM9FHSReM9M+No5t7yFaYi0nMh9sZPEUA9nE/GcQW1HOkH40TDLY4Nyv+gy10YE0EIf5SLQRFf6AyAfYsCoTa+/9QDtUL0xQKuR4PtfcVy+GTaXd7z8EC5uNoGPO+jewvg8+OPNILoCwjTRaeAsa5bxoS+fQzM9ijffMo+psx1M+TSw0jRH0dFEATDkdVDuO0863LseOoHX33pADjB0jqMjqXXL4Rk0EoIPfvkljLVSDJFgkl8yKvFU1VkpybE+yHFptV82dtEgthkSiswpwfvMxBjQR8U2Dy3IIFDq40NCzSDyId7zyCmq2z9UlmqMs/U9RIJGgQyyw+lTcb3K63+WsXFevLqBB0+WNFCebHz64iq++OJV/NDDJ7Vz/KnHzqAtJhfEtSNKSDMfk2hRp/LefvV7X67tzArQTpjrQgOZPM/x4uI6Xn8rb8ZXff/vfMVxvObm/eh+9I9RdBZWviNcEJ1WUypbWOsPy32ZJMDA3LhFFV6Ocp4FgzftZ523Xclx/vzzHEUHU/bcCCH4F2+7E5986hIuLPfxsScu4nc/8UxxlNjSxhZeXFzH9z1wBHhcf5+tJKdrnCODKk1URdiNCR5FvxCGtlt1baCfusPEd+X/MoA3AbgEAHmefx7Aa7ZrUte73DDbw8p6H1ZVmb0AACAASURBVGANFhaWWVtkrhR19SKAvEADM1i3HZ7UUz/ZYr7ryDj+63//ahyaaDodnrnxNjtMeICr61v4q69fxLG5iYJHbaaJ6ilFAHDf8RnccWQS/+DVNxrn6F2YG0r5sGR3M5Q1QESrZMPfRTE3WzBiPXSejjklNKFYH9DAasKCDHKa6IpAF+u1W0YD5RSN8nv/62/Gb/3A/dV7qtQaOWo3NO/u1kMTGGulZVLDpnxdCZPEMb6Xg6Yaf9lZ5p3onlxYBcCRwTJIpsggrRnM8yE++9wV/IdPPYtzV6nBzlggdvrAZNkdks/NiAzqHdMkTcuuf6rorsfrLnQBiCB8DfLulB2JJrrLg0Et6mU3mVIwWAno5H0202thcW2zpDECZnTB0HzLiQxKNNEBsuEAWZ7g2EwPP/zwSbQbCf7y8QV8x8uOVM/UZL/9znsO492vPeVGxoTxuypNVAiqxtoplvtUX3317BV89/3HaBLLt/TBs5YVoMjR1jCXn69H5r7TTKs0cV2Cw4SosM967RYevHEWH/zyS1hY7iNnZzfS60UknrQMlgxrW7RBzayEDMrPaMhtmWKD902OyfNh/82RaA+c5zI3TuvZpO7OBvvOn/8wL/XuFRYM6mjN3DZOtpKiYYh4di2nT/6Xvz2HrWGO+4/L9YJcHj0zj4dOz8nXNzFJ1LmEiqMebWasZUy8jrUatHyDyeXVTfTFrtyqrQGQJIQefaFSIA3JibFuG2evrmNja4jBMMPmMJO7iQYEMQwYxBfOUdt2ek4AMlw0UUD2ZYVn/i13H8Y//4478et//z48emYen3r6csGWeZw1Yrrl0FQ5liKtNGfIIL1mAQqbgBUta8mga0kqv1vxfoBdjwx6p0HyPH9e+Wh33vEOkKOzXaxs9AvHYmGZnZWiKvwQaDtWisxxjlsOTlYUmE44PeTi8iY+/LULGGQ5jsyMa1A/m+MgZ+JOzI3hT973av3REcHIYIBDD1izuzlJrWcRRiMgxmCIPRfnofMpmmmCcdaEYr2giSqBnWDkGo0mEmS0YQW77lgnsl6QXVOd48GpDu65YaZ6T4EZdd36Pj0/gS/93GM4zrt82pSvT4bS5hR41fFoasOENTfWbuDQVKc4v60474zJeIe3WycYDoYYZjm2hjn+3ceeAQD87QuXAQCvOaN0tNM0gbAjgw4DrTV+SZDT/QJr896RMrG7vGbQA4FS5eYDE/jvXn9G/o2hfpKfE7raH5ZOd8VZEmmiGkdbymJr1i67HkcGV/tbGCLB0Zku5sbb+HuMkq9FVnRnWqrvVFqLeTGvDqsRvLymqxlsYoWdv5cgx/c9eIN7v6pj2jL0KNcl70xY7DuT3vUR3Xs06pEyefKGWw/giQsr+OtnrijsmAAGi0XXpSTD+iDHxZU+5iyBGQ0Gy/EH7OzJ/VPj5f2xeXGb17Mgg9/6ssN492vUrt/sWBSFvsuDwQFIsV6urGwIv2OirDmSl0eOiOfzcTTyT794HgAK9MgpxuA90GfQXltB7wLWdK+dSucM8tq++QkBdeXXVKVAvSx+Sk6DwTwHnr20hjVWn1gGg4Z1bBCeGP+rp2iAduOcQDV2JWH59xyN/h44OYtzVzeKs3q/yoLBM4emjPNtJ7TPA59DV7VH6jtyJbgkXevQO5a63t0gvtb6eUYVzQkhLULITwL4yjbO67qWG2Z7WOtvIWcLaYEf9q1zLnWb3LXhQkSLOtqXBe+GdXG1j//vSy/RQzcnOk4D7eNc6ueotOJ2STAyaKKOUENUaQihfCcqMDeNWSA9jqMl2JjTvSaurm0VhzhPtJS5CM85TWnrZUoXo9+vFQz6Buk6JNtRa+O3LhzIYEghu/g7IC5br3GWT+0fx5Ps/LZlXrvEROywNxxSx+zGuTH83qeew9X1LXzoy+cAAPednJOuKdfyeRxF4tIXmuMJrAGIIJzS/cIidV74cSzXBTKoo8869nqaEPyjN9ws729Dgm2618LV9S1cXd8qaHQVOiDXLybdqavrFNcge18pobXCy+t9ZCCFY/2PH7sF//m9r9LXMhXrW+j8qd6HjjpFCFppgoSUxx10mzJNtD+k6+S2Q+N0LiHrxdTgQZAiGGTNKMbFbryOQ62NYkIRHAyDb7qNdmj+yrklkESTYAhqIFMdK0GGYU6wtDEo6qp19U1FN1H2+SJrYndgqqegHkN6BAVgRQYfPTOP/+GNZ4Q5io6+bN85TXeYExpsZjmW1somepVrCHPP2TXEDtUTnSYmOg1cXt3ETfPj5XFDLnF28R4FTVTYj57X67XkYJDvm5meUh9vs3U2BlU2xASrzXz64grW+jwYFBDzkCQJe14bGd3HvMzIHxkcCvpK//0Hb6Ro718/TZOitPykQbvXGux3kweDbJxOQ7FHDjZPMR9vFgaRfwdc98jguwG8F8ARAC8AeBn7955EyA2zPdYCV0EGVc43D8y01KERZd11qKNDgfGGI2cX1/Hhr13AG247QNv9Ogy0j3PpNUeXhNYM2qg+JCkMozF4iwnMTchYgQy2yutr50XHnO41cWVtE2usxfN4JRgsHcRGo4GUlLVDADDRDThkXncPgJu+q1tTLmTQx4g6jJ89Q+l4/j77Sx1fYwhP7R/DkwuryPMcyxtbEk10QqCJZiwY/GffdjtW+gP8yp9/HZ95+iIAoNVUa2h1GUp7zaDTQBuRQTvaz53u5zhNlP95lAmrayVaJkOITrEn76a7TeQ5cO7qOibUo0NUGpMxW03sCTZCkIMUNNHV9X6BDAKUDnn3MU1TIX593Vyk+6wGHSC0GU6v1cCVomaw/N14u1FQ7x86OW19RlrxsCO8achZNRj0pIlqxVVfJIowr6MzPdzG2s9r7WRNXZcISN7smIYmKiGD5Xq5skqfzfxUT04qZCUaZ0MGqxMR7KSBJjrIaZ3YwnJfn2DhTZSKNVc66+r5fLy5inq+oFVcScA6CSxdp2fP6/VaDSkYXGSIOu8L4GXrHAEjDwafuriKNUZJ7UU2kEE+xBApMnakVdHV16dxG5+zIxly8/wEprpNfOrpSwBoMHjmwARFog37uJkAWU7oOoeKDArPyOZLS3tHTY6qiRxdIuM6DgbzPL+Y5/n35Xl+IM/z+TzPvz/P80vbPbnrVY7N9pAiQ4YUeZ5jYUWliSrOpRaBGBUyqCxgDwXGaaL/+XNnsbo5xBtvO2Aw0JYAYFQIkE5Cs3y2bKGLJhqLgDiRKRfSQ+c002tRqhk7t6ud5NXvsvEajUbhFObs8/FeZCdRfg+mOYriomKo3xWvbR3f9l48AiCHExc8vmZfnpofx0p/gIXlPpY3BpjsiMhgSim7JEWeDTHRbuDh03N4zc378esffRqZN8JOgIS3hzc5C5a9YCqMD6DjPceOX2k3LCjSbhNLjbP7t4qu0zyLGdYV9+LKZonAqJlrMaOttDUvbIMHI4OeMzjA6gY9+PzQtGejFt1cTPepjN9ppiVNVOom2ih06o37hHb8Icggb0JhQGwryOAoaKJaZNCUVJL12BsYOkjEvRak68y6NkEZDBbHBGnWyxCM5cKuschomgenxyqJj4yNN+Z5zl5ljsq9cZruIKd76uzV9bJ2UluHWT6jJEnRa6W46YCMXvMmMvef8KSI8rGsScgaOquGn0aRwbJm8ArL7s6MGRpLiWIqJxIly9BqNjE/0cbTC6tF4CkfLREQDDKgQnuMjrU8Q7NGDM8oSQjuPzGLv2Z1g185v1QyGAz2u8WQQX5kY5GcDPGlTUlQXTmFtt71Og4GCSG/TQiZFv49Qwj5re2b1vUtNxTBYILFtS1sDXNKE1UzS0W9SHzGySk+2V9FeDD451+9gPF2g55d5oEiaDMr24IMBmb5LIEJSVI7TXTUyKBKE9UpaOH9T3U5TZT/Lat+FwBIgmajUdQOrfWpkzZRq2bQ8lxEcSlc9bv8787xbcbPIwDycOKCxtfsyxvnaD3OV84voz/IJJroeLtZNpDJhjg+Rzuyvfu1NwIAbt6vnDHFRYc62Y7Z8KXuZJmFFmOn4z23SB3Lon/R9dBNtDYyKDZJ0NQMdsu9N95WnL4KMqgJzNUMuWHtEpKinQKr/QHWNjaRk7Rsh24TKYvv57iL43db1L4BkA4vH++UwaA3oqCOCWgRKC7dZopmSoqawe1FBvXOtzgvHgwmSSMuwWDRtYQ1OgOg0ETlcXKlgQyniRbIoHCkVYEMtkOQQXG9yPfGn/8WQwbPLq4jAUscORIMjUYDX/pnb6o0ODrEaKPqYfNW2VZk0G0PTKIig/xIluLMTZut5frFo2zi5NwYnr64WpxpWHSLDU2SsKCveoyOwx/SIoPm77/ixlk8c2kNn3/hKpY3BriFB4MmZJDkGOQJ1nkwyGmiWVayXfj4xX047CtQPt+KDhZ++3fknMG78jxf5P/I8/wKgHu2Z0rXv+wfb6OZ5BjkpDhjUN9AZigvYImLPiJHSzum/drtRorJTgPDLMdrz+ynjoXUstpAtSMEVUpRvXoJrTjoBxUxBmb0OXPKgY1KGixGaqoSDDqoqTM9elbZypZw8Ln6XYCiA2kZDC4xR6A4ziRGfGkRWoXrrrWpNb4zAHLUbHohg+q+zDXIIG1287nnqPqsNJDZHFAacp7hBGuM88ob9+E9j5zC99x7RD8XNQDgWUtxLuo9+RjoPCt/r9JiDHu600zRaiRYZ8mIItufZ/A553NHiwX1CvqtYS1OC0foSMgV/43YYCHRvAsbfVSZSxEM9vsRQdfQrNMtz0hEA8UjGl55ah9+5DWn5d+E6FFxrRvmRQjBVLdpqBmMDAZN9UX8b6Io87r98CQ+8Pa7MN5tRyKDZl1HoKGJahI5RTdRdo2r7HirVrNZDeoLZDAgGNSuF/pZr5WCEGArozTVc4sbZSJAS9OT623Vg9oB4C13HML33H+sQh+1ijEJaa9f85IK6uTvp/VaKdaEcwYX1zbRaSby0Sa2QNaJDA4BQnDjfhoM8jMNYw+dR54jSRv4wDtYGJDJ78soxTMyo/qiPMCOE/mdTzwDADhzcLL8jeZZNJMcw5wU5y53UuFsVq4v+b+L+dr8VGEdm3Rw5d4i9cs1Fl8vNiGEFFg8IWQWnmcU7klVkoRgsk1bJl9YYsHgeLu6mcV6EYBlQgMz1M7JqGP6KbA5lqV7I8t4ejlOsc6VjS+vE0dhckUsSrbTauJt97Lzt7Soi0P5mcRITeVzbwAgzkBnutekZ2ltKkG2OD+gUGYpybGyMcBVVrw/WSsYVBSrToyIgqlpT8C7s43vSpgk5ncuXdsmKqWPGVxRDk520Gul+PwLPBgUj5ZIkedAPyMgeUbbhIM6sv/4sVtw60F+hIbFWaroCNMatVF3BEdTzHhq6aPV63B0cAAxIeSfFd+xokW9PE2misxo1mJRDwS6FgDIuk4N+nQIoIk+qsylk9Jutuv9LUpV9BHRNpjuX5swYMGgEEiIyOBkp4n38Y6r4vP11tfmRiWiTHabBdpSBNu1kEFNgsO075TnRQjBO+4/hkajIdtaPieX2HRdNixpovycQc3aney1sbpVjsttQIUCJyCDtgYyFdEig+X9T3aaWOpnyHNKEy3OgHMmu/R77uGb5vCLb79LGygaxWV36vRiUP0UF2VSkLFWirWtYXGMwpW1rRIVFK9vs3Ue9vDk3BgurW7ipSX67mOPlkBGWVOP3sI6XasAhkmI0NDFwwe87dAkxlop/uTztJlaSRPVv8cmyTFAUnRXl486SmSElXe9da0//pmkgzW6NtRP3WHiu/J/CcDHCSE/Twj5eQAfB/CB7ZvW9S80GCRYYLz9+cmOGaVLNMZvVG3bK5kSPwU2N9ZGIyF45Aw/FFXnOKlOicd3tHMMLMwNpXxYKIuNRgPvfd3N5vFddEST+NBVHIglQB3xLAcWVgflfNT5AYUya7CawSWWFZ6oUzPoQ981vQvbvem+Hzp+5kIGHeOHOGgWBIgQglP7x/H55zXIIKMGXlwbIEWJDBZicsCl2gWPehFXYCY5/WIAoqmR0FyHB4OVM8x2O01UNO7bgAzOiMhgW9M1UKSDmuo3tZ9XA7Z2muPq+hb6m1tIRkjH1NdV0vG7zXIeEsIhXkf8bQgFF3A6lOKZbhMiDTeWxqV7jyb6nmleUvAckXhSx8lzEORIkhSNhBSNc3SJ11sOT2NxY4icjbvMbACIQoETkcEQmqi4XjT39oOvOoHzy1u4utrH2cV17BtrVL5TzMeXdhgqTkZKDb+qggz6z73baiDPgQ3WFXxxbbMaDBptFg90bMEgfacnWdnCl84uARCQ32BkUBnTV+/rbI1l/TfSBPeemMXmMMPhqU65pw3zbSQZBjnBBvtTWbagSZqGNDrk91opc0rk3/Fr70LxbSDzOwDeDuAlABcAvC3P89/dzold7zLRSrCVoUQGJzTIYCXjo2TuRyG6s3E8FNib7jiIH3johLw5i2sYHGoNdSWsa6PnJgulfNgMRF1H2yTGYEhQMlZaSNlABhCCQVuGOkmRsEYSPBicHkkDGRsyKDi06m9tRtknwLZmQh3r2OkUeGSbPffOqf00GwsICIXw/xdWNpEgw4k5JRg0OpSqs+SoF3Gh/Vr9wtefu8aX64D8ukMGRTpioN6VHAo9MjvRaRbLrNJARkW9uH6V6FVKwsiSPGglwJMXVkCQIUk9HXzTXEz3qSKDLABsNRKkibr/lQRfAIri61DydZkmBB0emJoYGT6i29+mRKUxCZZo9lSNUgl2jXaziX3jLfnMPyXovO3wDDYGwMbmFoZZjpX1fjm+apvZffZCGshISeuqfX/f627C7HgXl5bX8N+eW8RsEbhaqMexzBvjHF12t8ZY/Nl7oJqq8P3Pm8hcXt0sm8dwMSKDTNfa6sbZ2uXsEx4MdsVzBkOSJDrWGv+vF0106L3+H2RUUen4G4P/0CQ5hnmCpT6dT0kTVXw5KcFlW3/CPnYlR317KOxQsb4FQsgk++8sgPMAfg/AfwBwnn1WSwghP0kIyQkhc+zfhBDyrwkhTxBCvkAIebnw3XcRQr7O/veuumNfa5loJxggwdcvrKDbTGnXLnVjZZqs/ygyWKJEtkP+4YdP4n96623lB1yZ2LjgknMZQ5Hx3GQxlC6LArU72pGZSxf/H7AbLuGcQYCd3wRUEU4FRUhIjtX+AMvMEZj8RiGD2sSApWavbpLA4IBXf2t4XjHZesPeObV/vPj/kwpNFABeWqHI4Ek1GLQ64IrxtVFUnNQdHe0uURBI83MpgkGSoDjD7XpABnUoXVCCSXhHmt+lCSmeXXkous5ZUmyDqd25JdPdTnOcvUrrtNLU9x48KF1aB4khgy1LQCHWDgFh68XToeTPdrzdEIIkQxLIR3T7W2UHcPF9XiHsFaC6v9m9tFtNzI4Julxcf+w3tx+dQYYEK+t9ShOUutKKCaYMrUYD77jvKE7Pl7rLKQ7ENk0IXnZ8H5okx4XlPma7ifw7ae5KsmtU4sPIiRUVmQsIZHnihNOaF9e2JBp5MTcbU8iYmChrj2+Y7SEhwJdZMCjRRIORQTG48nxfPgkmRcpgcFK+jsbWNQjtJnqFdZApVI/qyzkTXBo/VdvES+fj7s5g0JUi/D0AbwXwWQBiz3rC/n1j7MCEkGMA3gDgOeHjNwO4if3vQQC/BuBBFnj+LID72LifJYT8EWtksytlrEWQ5Qn+5tkrmJ9sU2OlHq6ea7L+o8hgqaJmd2MbovDfGylFhoyL89oKAuOSGEqXsTlMWlXy0nciERBb7UIiKGjHd7jBKLt6qc6CUjPIGsjwYLDRULKPofcA2JWfDxVD+n7MujAF1bYASDRiwveCahYJINZ1mpBBwaHS0URfWtpCQnLMdBV17LuP6iYstNnSVH5HluRNGQxuY0b/WojkuPDOhwHolUft0HS3icW1rZImKr4LCQE0BewpKg6KZr3w40dTZLRuzUck2p9hH2upU/Q7nBraVSmi4hxj1osYGHkkKcQOvrVoorYGMhW9a3M0I2ytSdex53fTwSm85ehBYV7VIP3g9BiebjawtrGKC4vK0Q4kLdd4PkSaNvCB77zbb26VOZrfS7vZwPxEC41NgvkJZc0X1xETHJH21STbigwKOthUj2YQTsflweCVtU2JRk6vb+khkDTMyVHh3lqNBMdme3j20hoAYW8mBn/EJLrgis/FFxnM+dj2Z3T3sWm87Z4j+Ja7D8nX0TyLtAgGOTLI/lDx5Sy+tIltofvc9ttdJlZNlOf5WwlNqb02z/Mbhf+dzPM8OhBk8ssAfhpykPltAH4np/JJANOEkEMA3gTgg3meX2YB4AcBPFZz/Gsq402CIRI8dXGVNo8BqptZ5YI7jF+0qPUFMQpR66w4Mn7i71zzE3/jkjqULuk6zFhb6Rf+Cl8SGzLGxzNRmgSFWyCDprMQRaVFEiQ5DQYLitBICuYt78WocC1orO77oeM7AyDDOw1dOx6UJhEZlI+WoP//4hqlBhFbIC+KWtOgFsar4mugddREFwURKjK4TRn9ayGS41JDp1jWIk/mFDRR8V1okUH1cw/0lqRosfNHU2RoxNBEjUiXGNzI43PUQRsMahMpEYGRB31ZTMDUctZsyKC6d31KJUJsrUnXsef30Ol5vO/1N8nzUmwtSVJM9dpY62/i+ctrZTDIkw1Zzb0rrRfD/ZMUnTTHh3/qETx0cqYcX72OcMzF6BPfhgSrbi4h4klf1glH0Vc3B8gyWt+rbSATw2BSdDdnoHSbKZJEpBUH7Atu60LLjERf1pOF00wT/G/f/TLcfnhKuI7ef2iQDDkILrMjbbjeK9Z04ctZfGljfbYmOeqb5N4F4tzxOW1v9AejHJQQ8q0AXszz/PPKn44AeF749wvsM9Pnu1Z6zdKBL87PUTdzwQUXHYHAejgfqWQrI67tg16qzmWIY8Xn5iMxyKBRySb28UNqXUQx1qwJz9+kWASFyw2G8SxEUeESWjO4siEEg6PKhJpkW5FBy/ghGUpRgumAqtNfXQvH91FqDqB2E6VOqjGQt+4jTebcY71oJTE41xqHUnedya7QnCNmf+9U0QZDvsigHx2QJ3MmtMigjOoXc8nlPe1Tq91mtTO9JvHvJqoNhnX1NfrxeRDYNdWdxdoDT6oZDwbHKshgpLOm29+OIM3n2AQvCdVXBhbO9FgXyIb40FcvlEc7qHs9mu3iv16OzvTQJOaAsfZcTPKNQgYD9QVv5LK+OcTSxhayHHqaqM3WeSCDQBkMSvTt0CQJ3wsVhN9ldwVbY0LXfMSEDIIig5fW6N+KWyx8aY9EkonB5oMMiqUSu0x8vdhPEkLuD7kwIeTPCCF/q/nftwH4HwH8U93PNJ/lls914/4oIeQzhJDPLCwshEz5GypNkoMwRTE/YUEGkxQSfdSSoY+WSiauZlbQhyITlBUNLMyNyuJbKKDOA18jlJkrywfIWX9pXqUTMcmy3pXDX8Xv8mslNBhc7W9hdWOTfT6iTKhJfBSu9P2AZEctZNDkxAVmiEWanMEQdpopjs320G4kaDXKvTWuvjtvh1Ku7wlZL1ox0QGle3M36tAilrtZpOcSo1Pcuo4ncyrnDIrogloqIO1pc81eOZcETULN5XiTRKBRwrrQOj/68bs2migQbw9069WSpJBooiZGho9okUFHIkcbDMXYwEB9pUvkkASzE10kyPDBL72EibaACo2EHSSsFyOSLK4X06Hz4pqOZN6YZDu7idZgcPHAbLU/wBWGalVoot7IoJ2yfCNHBsVgMBgZZPq9gvA73peY7KpzTJrhWaSEnrnJ2TYSMqh2P7XuHcXuFQkT1R5a9vcuE9++wY8CeDch5BkAq6DBWZ7n+V2mH+R5/k26zwkhdwI4CeDzrKj7KIC/IYQ8AIr4HRO+fhTAWfb5I8rnHzaM+28B/FsAuO+++3ZuiJ4Nwbu6OZFB3QLeNmRwFFlBQ32N1FQhYBwfBEqU4PoeC2XRRr8AUKvG0pjl4zRRQ5AqBOyNNMFEp4Fh34EMCg7l+uYAy+jTVFCtTKhHLadPhlz6vkHJWsfXbPM89w+ARAlFgIjgXFocl1P7x4uDfrlwauDQVe/pRNgd6yXP7Pej1S+Jcm9mZ0nb7nvUTty1EB+apO23DvowIKJXQkAPyJlr0QZY6zrNiRfeTLPXhL++0iEdruBGGL/DaaJeyGBEzaDIlLGsS7GDb70GMpaaQVOQpg2GWCIuhFVi0rU2HQFUAvluq4lOCmxuZphqp8A6yr1e2wfQoD5aCqiaPLB0ms4C1oWP2HQk/3us1Gg4xYPB9a0hrqzR9aE9WkJr63g9nGONFDRRWrYw1qqRJBH3a2UfW9Y0EfYL/17IOZHidTTvMUWGDAkurtL5JGoTL21STdPpuJJIYc9XpMMWwbDht7tMfIPBN49qwDzPvwhgnv+bBZj35Xl+kRDyRwB+nBDyH0EbyFzN8/wcIeT/BfALwsH3bwTwT0Y1p2sieVnIv7+CDAq0G3UBe/Ksg6RSL1AnKygqQgelqG5W1CQxDn0M/UL8Tqh4IYOOeTGZ6bXKYNBYL1AaixQZBoMB0EK9deRD37VliJGzoE1QqCHUERdN1DdDqf5OvLZLEsVwGAzhO195HE+8tE/6rN1I0UoTM6prcsBNiIopwHahdCb9QkjVKGqeqfZ4meuhgYyPc2v8rfqO9M//FTfO4smFFbQbgmPFfyMG4FKti4Le6ihNyn00WYa81whBBkWU0nRtJYgQvtPzQgaFZIM3MqiblzkYnKggg5HOmm4fmXSQTzAUi4aqcwLs81LWS68JYB2Y6iTAhpBIKuZVow6ez8mo9/2SF3KyKyJYMImtczj/e6x40pd10mOB2Wp/iEUeDI6pNYO20hKBPeCiie7XIYOBQYzJT3ElOKSAWVh7oWJowJciwzBPcImhq1Zgxbb+AEXX8uSoI2FyvSKDhJAOgHcDOA3giwB+M8/zHOQf6gAAIABJREFUge03NeVPAbwFwBMA1gD8IADkeX6ZHXb/afa9n8vz/PI2zmP7JRsWhfzzEx36mUo54Ztcp2RGTp2IQOyka3Djk1sch6R6b0HX9txkwQ69KTDTBOPSOGEdwyRxUVOLeZmQwXLM6V4T2RXBWVS/y6/FrkvDjwAEziQ+9F1b7Qj/rdjQIiTZ4aLv+hglk+EMydh71Es8emYej56Zr3w+3mkg21DOp+Ji20e6MU3r2LWnTfpFzfjq5gLgwCRNZqWjSCrtJBHRmNBabXHvWhykx+44hMfuULrk8d9Ie1eHOiiIjiUw4DTRTpOEBbS6uVTuU1kj7DtdFzIormPXUTDaeVnoiCiPcal2E41w1oT2/PJcXIwMXTAUYWtdDa8q70X4vmIDuk2qb6Y6gm1TKXC1OoqLAagFSR51OYnvHK000TrJUR2q7/cce8I5g5dX6W+q3URtCWTBTzSWitC/H5rsoN1I5JpBU5BsEtXuBDeQEXRiVDJdP98UlCa6sDqkyW41weFD/dfqPfZ8XesyVr/sAHEhg78NYAvAR0HRwdsAvH+UE8jz/ITw/3MA7zV877cA/NYox76mkmdoNk3IoKAsvxHIoErLiApuPM6kMtHbXBKNDAZkmo3oktiy2US/GJ0ykzNulnkJz26618IihOevfpfPMdEEg3U6PvoE6ab1KiU+BDUUmuywGXfbNUy1PnXGj9g7Y+0UrawJ5DBn/XX7SKW/AOb14jLQUtAjODE+WXwAdx2dxu+/5yG0/rCpOLo1ji3ZCVIj009R1QiU1DSm7XNdfYtyzRZrINNtIE7v2uprOMKvOMDWoyUAmXYVaw8s72Wqp6GJxmbuXSiCdydghUkQjAx6Jq/EPS0m5JIU3RRoNRLs6wnripA42yyK73oRAwfxd+J1dA2yRiGiLRdt3yga80mBRh50vZ50ziBFBisNZMRgRBT+jJyJiYT9h+DWQ5NlrwrbtU0i1aoHvC/PRI5TDHY/YTTRzUxzTJsIrNjGNzEipCSsIWFyvSKDAG7L8/xOACCE/CaAv97+Kf0dkXyIqV4HP/nGm3HrIXaYpqrws6HiCCiw9agkNkhTrwH4U4qiDKFnCWgUpcvAxVezSaKE0lErY+qQQSH7bER6ZOdyutvEFZ+aEvabFBlSYjDWIeITpDtrWjydG9scYpBB0/gxDWRq7J3xdhMzSRdYCZiL6lDVRQa1tLvUkMXX39+9x2e2N6N/LcSUIfb9bS1dp6CRRsRQdMTM62VfN8Wvfd/LMfXZBMgCkmSVMQ1BhyYAcHYTjX5GfrVZ+8ZamOk1i+6JxW9ttHaTmNZ/qG1IlPcVkyTQzksXpEO7XlLk+KuffhRzH/8o8JygO+ruXZ/94oUMus9ujRbJxxLmNgpkUOsD+emLRkqbi61tDtEfDJEmpGgOJ83dlkAOoCz/xrvuQ1N8NyZ/xCSirVNLJazIoKAviNDAKFQMti5h3UQrHbqLZ+RJfee/VXWt6purEvocd5C4gsEt/n/yPB+QUXK3/65LNkSj0cCPv048G0hx6PmGq1GY7CWjpoh4I4O+xt+Aepkk9Bm5AgrT+HUMiA/qZ0K9FATqhtkelia7wAY0wZXgoLHrUmQwLHOpvweD8RHFiihofhsajNmQQa8MpeF5RSGD4Y7Lex45hdPPHQA+q5uLyaFKgAFvQpEFr5eKiPSiSgCioD6u68Ts750qWqQjIkkRQwfUIDrlXMRstUddZ5KA5BnefOch4DMBAYgPpUqcrzJ+13bOIP9e1DPySDyCIpOf+plvQjMV/JZYZ80HRRDFRvHmfxtFqYQLsTSsl/nJTpns5H+rXSrC14KtVERAzC1nEW5b7bH4vlKBuVAnsculJurVa6VY2xxgkOWY7jZR8betPkNqZjBp5jI33pa/wxkGag2/SVQGk4SYedpdz0PnjdcxIoMpciilFyp6qto66SKGvaMi1rq1YmJ87QJxBYN3E0KW2P8nALrs37yb6OS2zu56Ft2mkRahUI9WI+PkJaNuIGNFNCLGCaWJBgcUCYoOb9J1hqUB1XHq6wTmtgCUKxkr0lO+/x9/3Wms37xOSd1G2mPpUKbI5AOHY8VkfESxZYjFv1e+HxLIq8bPI6BzZtpDnP54J+pb7z4MDGdoMBiS9Zf2Udh6qQixGD/d57brXJfIoCUYsv02hj4svgs1Kw1Uk20VpIVUnTn1vTQU+plJxP1t2peScyfvHeuh87p5xVAmHXtdPMqFfk9IpISID4ogik9CVNy7LgllMpgCExOToTKvGJtmqFNU55WL6xVV3TSKuZjEhp7VDTp9Gi5ZZKzVwNrmEGubg+L8Ufn6GlsHlPrFVfrgS9/0mbP4vEz2SCfiOiY1fFmDrSP5EDlpVzt083Xk40sbkUEFATUig9dhMJjn+Qh34Z5IolNykvNRRXS2FxmMyNCq1+C/HzVFxgeBEiU0y2cNzDQKj0stZNCgNCQla6A0KWun00zRGe+Wf1O/y6/F1lGKHJ30G4UMupwoldZqcGhtc6hr/EQJ3V+xNUDauUQ4lBHrpSKJYe+G6p3K/h5hwupaiI9za/yt8o4Sz8bd4rvQBuZKqYBPQBXNyBDevxEZNASvEGoGrUdLjKquMoK+GyI+CJwoNkczxtbG0FH5333WyyjQOG1iwBEMa9vzq89ohLXHtiRkXZ/Khx1lkS5DBq+sblWPlQACkEFPOyKKqOt85szfHb+ur37RrZFoZLBq6whbu0Px7N6i+VPi9460ulaTSNHNO5aGvgNkl1vrXSy6bL3WsCayIajTtMQkI8kKGpwVUSSKyAiyoiYZlYMgGkVdwJgFjiOKMctnMNDSvDQK1xpcQTIWCTJMthP5dzFCDF0wRQl1okLXn9j4oDKmR4ay9tESohPlONtwVHPxcejU6/gGxqrxKz73OLszlgK3U0VyXGLoy0pW2ut3mho8Keuv6FcfRyx2LqaAQr02H1tZr+6aQWW9hNRjAijYM7p5mcSU+HPJqOjusbbWpGttjWr4vIwOrcG+hdhmUSyJgXJeqVtHqHPcNcigLknhf82xVoq1TXrOYKV5DL++jU3kXIse7BDfREmFYuz5vkSbUst/MtAxsyFIkgIgyED06599z6zTNLq26NzsSJjE6pcdILvcWu9i0SKDBCgWsC4rXDObYpKKgo7J1HhkkWtnRQMUlW58k/hQNnWOdmDHMElcWT7AqvCqwZWhdkVDNUvFYLBWjQRxZ8KMa0Hp9lV8P3D9Jbr34vH+Q1vCG6+jOuM16m2N3WptCLvHenEFZlIiR1wvgbSn7aR3XQvxcW6Nv02UdRGh6zSofpW6lAJiXacrWx09F4/6GuUZHZnp4rHbD+LBk7P661dQosCAOQaBiUUGbXsRqO5daz1cxLpwJYy83ovq0Ar6SgrMIwMjn+SJqmt8khejTCqJdY2i5Hn9cXz2i0W6rRRrfRoMVo6V4NfXJvuyMjHE/63+HfC0AQE+lpRI8HxfhU2JoN6r1zEkPgm7lxyJA1gx+SYa/cKfb5GENQS91ytNdE+2UUyOIzeQmi6QUoe5USODMYideg1AzvhY6W0BGT+TwTVJlIPgQFR0AWMoAimKMQA1ZNykcTUBky3jyf/O6VtpjvFWjU5e6rg25WdzikzzDUIGNeP7JEwSj+flI5KBiN07hloP47NL9EZJ+yw8zsLUGr9Ufkc+aGuso7tTRes4RLANotA4A0qrBuZqptuVrY7Su0Pz/UvPKJc+azdS/Jt33mu+vrpeQgLtyrxCEn8RNC7T+nfqXU3wyJ9TTC2piUpuswfqehEbuPDxR4HG6VAX2/My0pqFus5R65HtpIn67BeLjLUaOL+0gStrW5hVD5zn17clkInl3sT56SQ04Z4Ny9rjkGST9P6Je1626+jmmgnBIP+OtP4F1M+HtST+lgi6w3SfsfplB8geMnitxGS4+QLONAtYQgZHnC2re64PMWwg9Tuig+TtWBmcZZMEOwgeCJwOyYuoCyjEGOgJz8WKWFoUmPTdalJhbqyJmS7LA9XNhrpoETanyDTfUGTQSN/1CIBMzytkbcYgQD5zMTrgovHN7OvFByXVOnGaAMR1b+K8rgdksA4CJdGHAyizpuMctIF5Knw/M4+jJg+CO1jaHCedbfINcJT1EtNZMzjxZ2BkuMTFcPB1wCv6omaphAsZVN+dSV9VbHMNH8BGAZTWi8MHAsICZq85GnyJWFaH9toR9GVQZPDy6iY2B5meJko0+h0oda0LPY6xhyZRE+W+70t9/655Ga9jeBZ5BpJS3ybn+kXDjrKOb9IvPonfWP2yA2QPGbxWYto0BTKoWcAqbD0qGUkTDNGJsVCKYoJOUzdPkwQfD2CiHChUiLr1ZaJYlJkfMqhpO83/pn6X/51d95ffcSfmnjsLvIj6htaEqurGV38H1DfKWvpujRqJPHTtiJn22L1jyfrrGixIWfwhkPAMrWa9eCF6BlqM6ty57k2d125HBnX02RAqo4i0hNIBrQig5vMCaXHVYEUk4awJPt3a8Wz+FLtedMmLmPcSIi4UwbveN9LWGnWEo+5Jt150Dm1skCqKD2JbQQYNDnXduRjnaEpCjiB5VSd5BIoMvrS0AQB6mqjOBwFK/VKn9KFYx55nOUuJ8gD9Ir3/GqUqFt8oEZFBVXer+lI3vhEZJO77dCXHd7DsBYPXSkyLqchmVJ34KFqMj8Q6C+o1ADelKDbjF9KlqQ6lS70O0RhLLrWQwQTgtT6i8yRmyE2UA53j5KIrCSjC0ak20JApXdHiUn4+VAz1+yHGQYfqhhi/Sp3eEAjpZhpbG6adiy4wdmXOHevFKyts2Luqc+dEBsXAeMQZ/WshpiDZ67ceWWTT7wBzRlu0DSIyaHtH0ZRVD0qVunZC1n/segmlL4ticqhd4hPciGLTe7WQQdP+ttB31fWi01cm+miI+ARDEpJtWC/SXEYQpEnj25KQI7CFQLSf1m2lyJhZ1iODBBhqbC1/RmLPCVFCkMEQ9lWBKgckOMRnJKLSoWLyjbJhEQwW89IxKbwSFureUe2uQ9fuMtmjiV4rMW0abiBFJV+HFuMjUu1GIE1PvAb/vY1SFBt0hmyyGEqXln7hqseqEZiLz0u9pk7JSvPSrB2TsyBSJn3Q21Bx0SJ8FK76/WCH0vReIpDBUOdjFJSm0LmYEJVYKrM2W5oqesfj3urs750oJvqs729jWBDiu5Ay2jonRmMbQtaLS4hhLtr5eqLH0m/rUiZjENtIZy00qWXTezG21uSsh9B3icahlRxncb1GojWA4r9YnpfVB6qZYDPJtiKD9fy0sXb5XXMDGc3aFd+Xzpepc+6uSYz1po73pdMpsYkHw7NIUj6vpMoe8HlHJmCjwshx6NpdJrvcWu9iMWbFklJRAv7ZjDoiLuBYh9aXIhKToVbn6JJROQgVJasGWjUCKlv3T9FAa6mkmrVjq4Xgf/dBb0MlGhn0uP/Y8b0CIMP4oc7HSFF1Dd3VmH3UZM516yUIGTRQaoKQwcj9vRNFclwi6Igxz0JE2oyBuWVPh6wXl0iOu0cjqDqJnFjK5KgSfy4JPVrCp1Qi6J4NpRI+iGWxXkg1kBaRwazm3g1aLw5kMOYZ+YgxCRmZBBdFlzwKuGavVRL1ZkwNZLRJa9EGaHyZOslRk0isqQD9Iq6/zLB2fcTyLJKU90NI3etfN76KcPNSDTXxq9W1AQy2HSZ7weC1EldWTOeUiYXJI0cGR5UVFBWhpo4hpsW5OkeXhFK6LJSD2krWJEa6iqNO0afFu/R9i0Opq0cLldrIYM1gTDd+SABUeb6Bzoe0d+rSqzznIiEqrvUSEBjzJiT8+5LjpqlTrVxnBIHxThIVxQAQREeMQuPEdyFQuRPlXfAxVEqTab1IzWwigq5Cp2vqVwHm3AWu/9hnpFuvQfTdCGfN9P5tiRzdvOqwcLS6zsMe5IKul95prrdvsWicFkm21WNl+vcm3ucogjTT+KKEULlNok1S+F+zJ5zHOR2MDGqCei4+/lAoMig1LuMIHD/c3WZ3FZqwa17G65ifRZoqz0JEIMVAz+anFnNU7CtQ+uHaetdI/bIDZJdb610s1qyYmhXW0Pu2DRkcRVbQ4IDWGcfU8VMnwU1AzJSDUhHosrKBjogoPkYphJpqCy7530ORHh9xKT/b8Qji/Ir55mHrQje+z/tPLM8rFBmM6YjoPRdNAFZBVGzrxeeweI0TIwUgQ7/9WsfR3YniQ5O0/VbMIsegcYUTI9BBVeZFpa7T4FxnmvUSMxdbFr0OMhiSSKnDlAmxI6I4HUedntbUHieR7wLQ6zoTuqIG8qId478T9dVIfQBbjaniy7iodiGdeH3Eer7tCFgygB11sogUDHZNR0tobG2l6Zx6hqKHPQxFBnWJcq/GbSMqVTEhcFmGlCGDpDIvxQfySaznyn2Kn9uYfbtQ9oLBayWmFt+uBSw6CKOSOohdcQ1N5lproPIysxLrOLgklKIRS7+IqAsoxAcZs1IgLRlX0/clFCHS4KtiQlW5+GSI1fmGGGUtfdfDEBPL+EEOmlr3VGMt+FJWTYiKbr34ZF919CYxCWU7ILoyr6z8zW6niYqZ4Dp0xChkUAzME/3nOrTfxjYBwvSuqi/4mKKoayeo+RNbL6H2QLrn0MRfgB0RxeTcW5N6pncRaWu1+9vl0Cq6vkLT1CSScge6YxIt6uJwtG0+EP/eSH0dD7sbfW3DHvUUThMdbzfQapiSOgamkIjSxTBlTAi3SVS7I+kIX1vjYZts1zE010sbDfk7Wn1po4kqOlV8tuLnLl27y2QvGLxWYjKchYHUUYGUzP2oJLa+RRQVRRA/U79jo4jYru+bcQlVxEbKgUIR8K3X8BEfJ0KLenkoMNP3VXrVKDKuLuVnoqjY5huEDAZQp9TfmcYPamzExvc53D10LkaDo9D+bOvFZy9o9YsmAHEigyNIKu0kEc+Qi0IGWYbeh2LLxYTGSaiDJlFo29NqYOod0HogcBXHKXTvRlA96ziUIXZEFB+kSxRbIifW1lqRQZN+NVHdlD0tOtd5ZADm8158khexiZTQOYoyiuRo0c0zYk2jRAZnxjQUUUBeO1zUfUk069vLBhjWsUlUP8VXR2qptDE20+B3ZGXNIOFBqk6P2qi8qq4Vny0bwytRu8tkLxi8VmJaTGo7XFPjj1Fm3QtutQfn2yQqiqCjyFScy1CKjOcmC3UQTJSDUTjaxjEFY62OGUNNLZ6thSIiKbMR0fhctAifzLUooQ6lDT31QsMszz9k/IiGAdW5eDqUaubcul58DLTG6U9SRe94PJdKUmmXm5daCFRkgs3kLLk+tzkolfUSUF+nG1MUH6THeH3F1tU9fsP3t6NEBk163JbIiQ10rPtbtQek/LsOGVTXi7pe69bBe68X03oVqPej9HW2ExkEBHsQmDxCiQzO6I6V4NdyMYW0AaOHbTKVbZhEV6vusxdNya5QMfkdeYZjs+P4nvuPUYRQ1d3q+teNX0lYCM9W/NyFau8y2eXWeheLKysmKnlT5n5UwhfwqGiPtnsD7IbAJC46oihRlC4d/3xYGlVdwFgrABAccOmagpIJoaa6aKIkrReMm8Sl/IwKd1TInC1b6lm7IEpUtj7CoVWvAWgcSltNjYjARawX9Xp8/Myid1z3tp0Z/WshdRyX2PMnjcigGrCzZJsPeltZLwEBbTGmifofiB5X5hXzbGu8Fx3VzkdCG3eZkickBXipRPBZu5H2QNIRgtMvBYkjoLurySM+Z/UedONL3xnBXJxz9NS1oVLYg/B6OI4Mas8Y5NdyIoOaICmUvukjoj3gYwYhgxGJHPU6WjbXELPjHfzi2+8Cqcwrqa5/3fim83VVfejStbtM9oLBayW+yKCUobfU49WRgprKN01Eh0mfA6p9AkaTmNA7nURRuvSKxYq61AoADJk40/k9xZxMRtZx4GwF6RlxJtQkMcFrjEPpM6bP+FFJihFkOUPmImXxXevFx0Ab6ICh60VFoEaZ0b8Wok3CeerG2GdRjJnrM9o6eh+ACqVJuqZhvbhElyRQdR3xGN8ksUnIOrVZHMUIFSftUYcMGii7/O+hia+Q/e1cL0MNulNz7/q8F1OnRlHE7rejrj22IoMjcIdr2AN+zqD2jEHAEejxgMUjYNRe2/BcTKJDlYORwZpsmspeUEo1dPrFJ5Fk2jtqIOvqhLvLZC8YvFZiUnKcIiEu1JqFyU4p6p5qbM7tRgZDNlmwg2CgHKiKIEbJmsTHKIUiPdrASHindYJxkziRQVeGWBPsBjuUnnWVPuNHZetHkOXUzsXiUEqZc9t68chQS/QXjwDEdp3rFhlk9XghtX/FswhY0+K7EAMQk+70QftN68UlRDcXNamjIk0Rwc03GhmMcdZ8UARRbIkc/vfQuu1ayKBjvYh6NHbviokMU/CsdjM1tuffJj0yqvNtTVKxB/7vt+tDE3XZOh0yVSc5ahI1weAb3OmaDI0KGVTH1+kXMWnu201UvJ74uQnVjkk27QDZCwavlZiUHF/A4gaWCpMNSraO6MaMuQZQzTiKImVWAp1uFwIlSmhQ64UM6gKtET0v9ZoSdcek/D356iLaK6EIIzKyLlqEySha779mtjwkANI6cSHjJ9XkTaiEopQm1Cl0vYjXA6r3UQQmnt0eC0covHnCjhT1ucSgXkAgMsh0vehcqYG5GET4JNhi56JjpFSQHiVgjEnkBCOD4nvJw34bYkdE8UERRDEhB+L7imJBeJYqqIkc13oRrx2Lxqn3Jn6mm5cRGVQSKbulmyhQJpZjkMGCJmpqIKNDBpV9qW2oFpIcDWBfqUnIEDqqWpIQKtagV0lwqONUgkQL2i8hjWoix7F2d5k0rvUE/s6KNSumoYOq2ZdRUyds2V/fawD2jKdEEQl0ul0IlCgx3emcStaWiapZbC9dUzHQIU1rXAGr5CCMqkaC2A2I04nSODdBDqVmfK8MpWH8YPryCPal7Vm43rNrvfjMS0fdSYT6Cl9KW+EIbYOOuhZSQTEiHfdgXafQrtTAXNy7voyMGNRHx0ipID11ghu+XvhZmIFBqm1eJgmxI6I4G2FpbIOJ+QNEBs+WunVrR8Rcv150lDr+tyibJjrLBr1fCUYdSc1sREGaOr6vrg0VHTXRU6a6TbzzFcfxTbcesFzbZOuEQMf1HZ2YEFOTqBRjb2RQeP9qHWuIaPeCsuZM9qjwdw3PRdJ7yn0CZYBpZPbtBYN7EiImRcgXsDbLoTgIo5KRIoMWulBt58pzk4VSNHwooLrxR4IGWYKR0Cyfi0biU9cZKi7lZ8wQG2omTfSLkPFzj/dvdAoCn0uSlkYDiM9y8rFFMdYlpEDRhMKxXnyc5QqKoMmE+iQPKkml6wUZtGSCjb8V6/QidZ34HCuBueKgWJFBATEORSldzSFqI4NDv/0q/U6tcdV0rjZJbObetI9CkaY6wbN1f5v0q2W9SIihgDrFUiZDkUFb8iI2keISce2IYtK1oaKjJvpOjRD8/LffYb+20R8Q/MQYpoxpHZtETUj5stYkm6Kg1SFi2wvGoE/x5ZwUaw5aEPn3Nl27i5HBXW6td7HYqJS2BZwbFnAd4VmWWjWDKurnyorG1EuEUBgCnS9ADsxMlANRIjqGFWLspqnJuEljWt6/tclNKiNQeebvQNnEpfxMBsJWuxH67kz3bM1QEvm70viBKI6NcuJ7DUBPATM59/zv4gHRsQkL1fglqfI5N36Oe+N6pE5SaSeJqfOij0hoXGRtmLR3LbVefIw8tyMtMUcHFXPxqa8JRFfU9RJcyxjxXsRESogY6ZgmPeZCBjPz/jaJbX+b3otay2miPkvIYOC8uEj2naO9NsTSkryITaSEzFGU2HuuXD8p9yL/96hEG+gp798nYNReOxQZVBIJvnpfff9AfDLdZeu4Dtb6crl5/IquVeyhLTka4qfuMNkLBq+VWLNimqCvwn8esYKMLHour2HYQKL4ZLFN4jrPTpSYjCsgb+JKMO5BSwgRK71IQYPVv4tzFsVGI5KoZhFZaZO4kMGYBjIjQwYjMqGx49cJgEyJAR+EXQzedM/CZ17q3hUdRPFz173VyIrvSAkNhkURO//FJKdMh8ubkEFbYKTSpWI6WJocSikwCwx6Y9dLHYaDSe+6xGQbRfquKEYKpJpgqFkqYUQsHUiyWrcvOtexaJy6Rvmc1XvgY1iPzolMXnjPUcfIGYE7PAp7YBJroCfo7JAEsnhtwD+QkRgpAftYtSmuedmuY/KNXPNKEvv4Jl1bQQY1yfQQP3WHyV4weC3EpuQqdFCFxrEd9Tg1ip7La3gEeqHOpfpbXwpDMF1J44xX6Bc21C0meDYEIxJ1R5f9sqC3LgVpchDqiAsZDD2fKxSZEylF4jUATzSsZoa4cNy3IzHguY+4UQpdL8X1xHWRyeuv+NxjvWynI3QtZBT6KoY9oEUGDXMREW5jAKJeL4KRYXScVJ1SEwH1/R0Q+V4MiReX+KAIotgCc/73qCSBIxgo5hW4Xjgap7bnDxEVsQU0wbPHetlOPWJk5IzSHnpSJkOFWGxd8X4jk9ahNFEpSEr8dV2i2BrXvIzXYXpJTMKopRompgpJ7OMb946SyPH1wXaJ7AWD10JsSo7Xd+gyPlKQOAKKXzGmIQANvQbgzvgBgtMZaMS9s1aBxkznjFcUiEdWLkRsNXPSOzcpfx1iZMkKkqRqrEeGDFreixNR0BnlQAQmihZjCcaDnGVHltHrGra5uPZRjfWiXq9i/ALXiw7R2s1SobQFJinyyCRBJTmXuLPVLkZGbLLPWTPowQgxSWwipa4dAfxtCRcfFEEU70ROzVKJ0Pb4RkdXuY86Ns2aEPZYL6Og3pvEloQciT0cgT0wXlt3jrCGwVQHGfQ9MF2lT4a8r5GwaQTbxEUd36T3XOOb9IuJkaP+dg8Z3BNvsW2aguesKPntpGAV166BOvpQqirOZQSmapjBAAAgAElEQVSlyEeCkUGNM26q2RSljjLzMUohmWD+mSm4ko6WiHCiTOKiRYQ6UVEOpXoNnwxloBPnGv9aIoPFetG8i5CssLp31SyuExncRkfoWojUhCM06GDfHW7R/9ZF41Q6pqgjAIcDXsP5ctUDifOKYWTEoKeV9R9oR4Bwh80HgRPFK5ETmrS07W8Hfde1Xvjnw81yrFCpnDlp012W9ZKkAPJtCqgM72vUTJnt9NNEGVXSOgQZVJltofpF8jcDmj9J19DUOGpRUs28XMGryU+tJHI8fbBdInvB4LUQFzKY67IZ25lxSvVjhl4DsBuCOhSZkE02Cgdh25FBTWaLX7N455pMoO39myiTIm2CjxH6jEziei9OJ8py/7HjByGDutqRiPFrNROq41AOqzpCvYZrXj7IoI/jGpoh3ukiPudgtgG7d+5chyYYeADKf1t550qHOxcyCADZIHwuRDMX7bUt45skdr0kyrqsm/jzkWBk0DAvcfxR6DrjvFRb61gvBaq3JX8vVERfwhjo8Xk5muzUnYtOjEnISGqsKqNAvVzXFsWEhokSSt90icogC0X4ebLLhK75iDaBr4yvghxix1UfZFDVL15rN4DBtsNkl1vrXSq2TcMXsErvKjbQNlCw+Gau49BWUCef4vlQx8FzkwVTDTWBgVbJGig6UcX2BuWra9ksihUZNFAmtcpsRJlQXS2lKD7Oivr9oGAslhbjQdP1kUTdlzUaL1SaUDgcSm3Cok69SK43fkUm1JHBVY3sbg8G6zrugIAMRqzpXHi/UgdZi4NiWy8xczE5VOq1Y9HTGMe5WP+R9F0gAhn0qC9Sv29i/vDxQwMQna7zrXuqrBdWb1VBBiPWiCiF/+II9Gw02SKRMqg3F51Yk5Aj0FeV/TJqP81h62onRz32RcEgU+tNPf3Hgv1W45mHJPALeyQGiRabrdbVmpKjRv99LxjcE1+xIoNCZo3/m39XzL5sKzIYUzMoUqoMjlMt50oT6JgkpgkI4Ec5UMcRvxMiVrpKZJbPZCwqyiwii28SrthNYkQGTcFYYPc43fheTVNGlCEmala0xt7RoguGjmVAFXXSvX+fIFVNElTQAs/9qjpCu50mWkG9IoKOGGSQB/VisslUa2WiNEnX485NBNLim0UvzpetE2j6BoPsfmL0mCkYcEkxR2U/ivcvivFdiA5lRCBrSvbYEEsRxajYYIUCF7NeRfFGBi0OdWUuI3RTjXbHELwHX18IdIBtCGSVY1FU/Z6k+qSi+B2dhCRJTAicbxJQTHZFrzPNe3Qhg5Xg1eSbGBIWFQaNAfDYo4nuibfYNqfJoVLrK0aZcSKkvgIToXXfeomYNuQ+EoMuAfImNikWUeoEAEa6iuLohVBTtd/XIT3ZiI2f5b2Y0FNbMBxTX6VeA7CvAWtXuZDxR0DfDu1IqFK6bEeReB04TACQUr8QYf3xefnsKZKAOiuWNbqbRELjQpNXfH/zACxE1ynIoJYmmpbfLeboQKNi6sGkJKSmvqdSVxm6d/JwPUpIvEMZQocTxbS/Q7tT+tTVmcRGEzU6tJl5vYgObWWNRO5dEXXRJbJ8ShXqJFKc8xsRI8UknCZoSh7UEdF+c1F1bSxTxrSOdWJipPgmdURfdpQ0UdXWkRTa8wQLZNJgG026tsJIMPi4obplh8heMHgtxLZp1MxSJcvBN+IIX5167RjlK6EIHvUSMQjMdiGDOmTGh35RKwCwBEM6So86psnQao2ckv0dJU3URYvwyVyr8w117kzGz3Yd6/g1HfdQMc3FhbAPlRow3bvwruMQ7kN1EG3Gzzav3Y4MVlC3CJ0S8ywqaFxiDsx99nRlvYQmWyzJgIrjFHlt8VpBv42wI0B49t6IIrBEim9wEYq2i6Ld36ZkmwNJNjm6MWtEGjd0vdjWa0TzJZ/5Af7Be6iMwh6YROunKAFNEphALq6t8TVMog2uFH1lE9HfjH0+Ot9ITSqZzhMkDn/X5Kcmyp4yNUncQwb3xFtsm8Z0QLCoZEedca9jlMVrAH4Zv9hajyBkMILSJWXc1OefmBGokQYAmfzOxXHEeZkMrZb+ImQN+WcjK5h30CJMHcOsyGDNJIEPTdE4fmSSYjsSAy6HSqVRWRvIeBhoVb9IDqXHc6nTNGUnio/j6vptDNVN5yyZ6HUmp986l4hkiw/1P5gmqtq60MAoIgANocOJYtvfJvrmqJ9XSIOo0PVSZ71K445gvWwrMmhJAo7EHo7AHphEpClyqSCDGj/JZy46H8gkleAqgVwq4ZE0tPmJPqJlc2l8Nl3QV2HzKHMwJbjURIpp7e4hg3viLT5UP3XDxRo/HxkFMuiDIvg4LiYJQgYDHTdb0GVDXUYSAIgBqHLgr47S4lw7GqdEd71RrSOX8nNmiDXPtG6SwIaeusaPytZHOrSVuXhmq4vMuULpslKZPQy0ui5CUZ/KvHa5eVHps1HIYATtToeumJJtQUhLzFwSu76ug3TpENCg39ZAbEeFDBZzUXWQYb0U72sAIKI+Wru/HfRdn/XCf1+XJjoSZLDG3vGZH6BPAo6MKVPTlzKJGIxwUfeOrbeBFRkM2Bfq9UID4JEig7pnoc5Lg556JSwU/aImR42/Veo6d4nscmu9S8W2aSrZDBEZjDB+PsIXcKaMGXwN1DcExusn/hssOEPto2Q1Br9OAKAtgNYoWUAe15qhNlAmddcbdSbUJD6JAZ/vh4zvRYsJrNNzjR/j0IrXAKqBvMuhHCoNQbROaYCBrhi/wPVimtduFslxCKj/qfMs+LuQkEFD0GWiNI1qLtzu2LrnucY3SR3HOdahNO01l4Q27jIlT+rQMU37O8TWmtaLSs2M3buF/2Jylj3YKUUw+I08WmIYp7tVMQUgo5DY3gYhyKAPqqXaulBqrKhT6iQdAL9nEepLm3Stuqdc+26XyV4weC3EtmlEp4z/G4BEqRm1k6U6C3UOnN1uSpGPjMJBqCiWyMJsk1gPTdVkcU3zEsUUGKlUoLpZOVFqI4OeGXXr+BGIrdiEQpToTrQRZ7hVruHrUBoy5yNDBiPXSx0EaqcK31MxxyYA8chgpnGWdahLhZERsF58hK8pk7Nc6M48nG1Qh1LnQixNYmIEuER1KKW5pJpEjkPvRTfz8UyemWytab1U5hW5d6Wkkm29OJpwACgbZI0yoDIkA0ZqDwMok6HXBjzQMF05i+NwdxNiqhMdNTUIGUxKnfINQQZNvnTg3lE/1+oC3uk4UL/sANkLBq+FWJFB7nyoGy6pn00xidqdMDoryOeeewQAgRQZFwIlSkygWcwL8v9XFYso/397bx97XXaVhz373Hdm7PH4a+yMbfwBNpiAzYcxg2MRkRBC+EoTN21IbZHgRm0dRaZKokoRaaRiWkWKon6oqVIkN1iBqikhhbRWZepClDZqK4KdFAEOchlhCFO72GQA4xjPeN7f6R/3nvvus89ae6+Pve/H712PNHrfue89e+97zj5rr7WeZ69dcxBakGS2qEyg+mgJZg9iV2awVkBGUExo9X2jQ1m2kfehuVabIT5KdxyOSy5H3IxF4FDmeyRKiYo0Qz0t9mUmFj+h3eHGdc1YBUOGoMO6T48KQI+M4U3xzCFTZFiei5QZ9NwjSyJlaoyLg1UmqlVktAJzzzEf5bhEzCAxX6gjJ7r4ALVAL08a39QDxqutJpoHIB1toCdpLbXdGmYwTyTc3L237pyNGWS29pDMoMBPLe3L8fOZH7smqL4w3ILV+gpRkxcuE7iW5RixZxDoeOBsayE0VNhrMVA55lnv8AB6+YXk/B4O0swWsHbumxnqilOyyQr3WvwqwSCbISZkssv/qxiCRLcByBZAb4a4PCDZXHiBGssN/5yB7ftKOgvCOUrZl5WkS2B3eknNLgkt57Z2HeA/dD7P5lMSPAl767K70zagoNqeK/1z6HKPmHeEg1XGVXuPSLvbCJ6tv3lzhhyTVM3XNG6+3BBrg9sHyFiflrPM2ZTjWBab2vF4htq603MPfa/1ddV2bWtJIYHMIfltGvl0ub5uCIxWX1nAbH22x+eYJz6LAHzphwwSJX5qYV8k+6NDJhpQoXYGTWsCc1plD0oZUZesoCTjpzAEGmZQyy6RgRlj8HJ4ZKLSAHQzrlqGekdLJkmmRxkwc2jJd7lnwcpEtcEYEaRLz+KkZKLaZEu3d4djF2rOfRF0VQsONX4TZV9WgYaAgSkPN78VMtHpXjBsYr2WZ2SwdWWfi31l3+lW8sAwR7mxLCjniDaRA8B0FuNqXM7EnwRVeb6CjfE8C46BrFXunmeQewPLBMdmXMa1YSWrbtj9lkzvpAVkOidHewWXq7YFfsrEJYQFa2HZNgfWT1mSTYq1pqdMlBoXteVK6qeW9mXKPg9mMNAFTYc+CwZJZrB3MNgpK9hynI4sirGognSfh9ZxE2/MprT4sD2PnKVbsDFatXExGa3aQlCek9NzwzwHbr5ymUhtsqMmE239PjKLaugf6FeFL4e0IAi1p+HYhjBhscrWEvNPMl/Kcd0KmSjBxkngKYLBOUurzwkHRVxARpko0+yvsSThPOuBmsk3Zu5ba7aUGSznhdfWidgNZr5Qlau97652vohkzR39HTYJ2Wn7TSt54kH+7BZQEkjLb5sIX4MDlSgH5O9xj3tEbqEhgr7cl6aCxGqCi0uYVN6RYAYDKlSzjNmLAtzLnuaL3ygj45V3SReCXgshB6tciZRfHO4/JYf0VAwjA715/W9k9qvCelFjLDNYKwlah3nUku9yGbTaouxmBoVBOplpNxaQ6fLuCCWr3KIkyZZySIR9kWTxa+O6Fcxg7rhYGGOrrSOcFapoikS61MPuNo9KsATM5biszKDF1muZwZrdJZJK3HxxB8BC1md1LAoxX+4W+zS7JYQ7zBfPuyMZH8AkTXvKRJXvggQkM1gEQGZmkPCBOGwC0OVYEuHzsiZyyjYAxjcqZKJUkl0iqy/fHcm7q7mPF4YIBs8BETNYOHHHiT3QyHjPB2tVePNk/DTVRM2SrgZLxzFQLmawkeXLP6fGVbbZkl32zly2gnSJs1J+X+3cFUVT5so9Kq+VsnHV/tHh3aHGwjmUjDTVwwxO2btLtSeZLyMz+ueCVdLksnWMQ0mNJXc+tPNFNRaOgRL0X2vbPC5jUouy9RJo5fliCaQhAC7Hxb2XtflS9t/r3VXPl85+gmR8wPZ5dV0PDbJyCarMYPZ8yd8mVHVIfCwqAAXkz8taCXjVhuBe5EHn0q+k/7zSuOTdycEVKLoCRDB4DrT2H5BHSzSMrAcbZ8ErEeEyfuXLpHG6iWCMQ48Kc6RhYRgo09lyApmDmhmksoJFxTarc8uBcoRy1IIrNng1nOdGZUul++RyaN+vbk6UZs9gw6GTzpeyzeO7S7SnYgZv4dESWglkD1tXOnFUIkfyjFwBSEP6L2Em2bY992i5F8Z9impm8C7Aleen1qbmVgnDWqsJOgFmvnCJpOJzj3yvOl+WfaK1gLHTWLjxATb2TIJWwSUPju9aLWnNJFjFzKDgvaACUED+vHqwp+S6X9lalX8u6Z+yL5L6AFblwQUggsFzoCYvPDplxAvXc69X2SfQ4cDZxhg9EhlKisPBzAxWAjMy0HIsIJScgDOyM2X8mfsrYgZv9E4Uh1aQXlsUe9xTSSDPXkv0r5addZI0acbCvUfV+SJYoMt3d5XFl+w76WRHLgl5dtliU3odOn8cS+HEcJKmXmM5FsRg3svNPuQT3iOL022VcUkYuBxiibchAF6NS5BsW82Xov+J+dy8rjUY2ym7/2zAWI6lo7/DnS/bWykzghkk1zqm0GAOjapDwpizzKDweS33iDtaRAKJ/5QH5vnnEl+aSpqX+2prtjZkogERSto6x3Le1ybL0Xmv16pPR4a2bGdUhpoychzMDkIloDg6aEUpY8+9WtrI28v/jarwVWWViYC5vBdDmMHKc9E6UV0CeeFeTm2mvdZ/L3mVZCxcJjbPuudtALIFerP4ZQubqJpoJ4XBJSGv7uxivZT2aHHi8zmcS9DyveSA0u5agq5K0LGMy5rIMTODN/VxUdDI4XJIGLgc+fmuVP+mZ8GpB2rJtmW+NOSgm3E51EGSQK/2Th2f0aC9x+XzmmdAe+4xh97ra9k2MCZpbWIGi6Shhhk8zkvrekn4RmWivGQG83eg9YxW9oX5nZyyrxzXleAWrNZXiFq2/pg14YKRToxO2SeAfgfOMgvhcaOxtcS5MNvS4+wprmJWWWTGU4J70x6R2Sq/U927Ish4chI0K6iiNTmqBnfC9twsq8SXKv4jyVA6945s3h3PnsEyMGWO/2g5dJZ7QdmXZV/nceFsyHd7OZSXhMVx0DLpnn16VGXXpc3NM1rmX+UMQc/xJ7lDxc0h8z0q57FRvupN/ElQs/VkIoe5F561VlIgbDWuJYFccWg3icdO6qBmoFdhdUcyg0t7Fhspajt7X0b5abWxL3Y8h+QYKc1et/Jwee17vCI2rOslte4TPvPN3XvjXQXMFT91ab8MWCVJipCJBlSoGZ88EwsUWQ5nNoVDSfOb2a7GfoHjyzT40HmrQ08yg4uRpVi6E2je2XFVEgk5KKmZlOmRgOozR+1ZcBnaHs8uTYLghZC4mquJGuZ02Y40MOUCAOpeSGWinH3RbPYvx3UbZKIr1ssQdJhtHdEnpQ4pbWrVAXfYXcn+Git7ahqXUSljZQZrga5EkVH23/Voica4qvOlZAy9PkA2X6q2q5HUzMcywt+pMUrethcF0Sg/reanpN1W7SJN/LbW8rw9IPNNF99IaPfztcY6zyRrXT7/82tafurynTJgPSYpindndd0yv0MmGpBA4tBTWeGWXMeKct+TJ1sjkitZDhlu7E3LYZUrVTdmM8bHbMxqe92KBZpauLiN9xtmkJCa9dzgTslnc1Qz6hPz25zOnTRhwu0d0c5LoMNhzUkuAdu8r6WjJ0weUP2Xe+M0sqde+ycvCdbKdx6bmleO3shEi7FsbCpT4MQ8lrzPGjO2BB2K4k/dxmVJHimdtZbCQSp39/xmVkpeYzda86V0dDspHG6YuSCR1XtkzZoxLpDaSFHbE53I6YEykM7/PmVrgHUfPjWPKVAMHKA4WiLzZXsm08lxzVmyKQ+YG2saZV8kDChXoOgKEMHgOVDNimWZu1VWOA8SB8gmgH4HZzcPP3aUOJfAKumqyS84WYL1WZDGrOyTygQWkoeyTUpGVLILPWUsLedKwigcv2vI0HJyW8lvI5lU5ab2Xo4Ll/WvZs6X92g5C5NKWCj2Ty7vLmd3JG2sxnULgkFNMFxeB/Q9zoEai8R2u8fS2A9oDcy63CMtk6+Qw+WoBsOUfJNLiDrWWq2snZwvxVYN1tF1OOnVhHD5+xUy+F7gkpBd1sN8fe3spx2ZwUog6ymOQ/kPFDaJcqUMvYeUtuYblT7b3c+vx8Ql23JQ9kWy1luVBxeAswWDKaV/P6X00ZTSR1JKfyP7/K+klJ44/Nu3Zp9/2+GzJ1JK33ueUXdC63gAYD+BuQx970xZmdnpshAI9gJ4JTIcRhWQAbaLyEiZAyU5qBUE4WSim3nUUSbacq6ajIKXGST6lzKDbKbd0H+vd0cyFu49qs4XwQJdYwYlBRY87/elwipp6mHrNvt9iWSbZP5txmJhKWtBh1Gy2e0eOW29BJJgOEdrq0S3Q+cFybbVfGH67/XutubLUs1z1HwVjXFXX+s9sCaPJKCKk3RlBoXB4CYAVc6dlp8oAbXWsUFqxZeuFl8qmUGFrb1CZvDOOTpNKf0hAG8D8FXzPD+dUnrs8PkbALwdwBsBfAGAn0opfenhsr8F4I8AeBLAh1JK75/n+Z+ffvQdUNOo51n/VTYjm5yj5AfdmMHWQmg8Y+moxW9IkawZ6pqR5QLGkTIHScBYtkntF+DmUVdmkDF+GieqFuhq+pcW9tFm2mv992YG5xmsJJzLnFMSFel+mImxL2mnk/9Q47pmWCVNXltHOZQrlq58RoOYwZb0f/mOiRl0zBer022t9idh4HJw88VjL6itEmL5bikHZQrIjGYGl7Zrv7/XWFpjXNCVGey8vuY4MoPFWrf0C/iZQcl7wSWtxQVklns0dWYGC/8hH5fWByLfHYUK4wqZwbMEgwD+PIC/Ps/z0wAwz/MnD5+/DcCPHD7/WErpCQBvOfzbE/M8/zIApJR+5PDd6wwGq3sGswlHMjoDMk4ex6VspzZGl0RmcXQFwaA1Q10zslwmypNBBbA5qiLviyxa05g7TWYwd+467Rksx5hDYnDz7+ZtWvuXsgWag95b/fc+WkKkHmCkXuV8kWaFqXd3mnSL/Gpct2AXgjUJ53L6GWeFC7qmXX3+ecZyTBI0CqjcPAsRe9xrXFbGlpLaSdCdGXQkCcTjIuYLZ6/KwNx8/ptAgpem+nwtx9g7qNok3iq2VouzMYNZsE9tfRCtAYT/QGETgCqf17LuJ08yXcCS5uPS+tKUDS5/5y1jBs+1Wn8pgG9IKf2TlNL/nlL6usPnrwTwa9n3njx8xn2+QUrpXSmlD6eUPvypT31qwNA7oOb05pS7de+OFiXNb31BWxlij0RG85J1ZQbLPYMlA9Uzs8UYWRUzSDglF8sMTv5F2cPYlgundH8d1X+XQ+epwLi2p4Y7dL4MjAVjmph5cXT0IVvkqXFdM47OnXIvqUc+PO3uMcMbZpBwNFvsrdfutsqwTztbNd3NuJT311IV2eqsNfcXCauJetZaTdB5HFcxXzh71c0HWOZLJXkyNWzK0veoqsRsErKDO3xMUowo9Mesdfm/sUlrSXKU8B8olPdLa+tS9u4OYQbLxAe3Z7D27hD2pbRXNVabK6h3wRjGDKaUfgrAy4l/+quHfl8M4K0Avg7Aj6aUXgeAonxm0EErebfneX4vgPcCwOOPP36ZT6TG7hxf5kLnnFImkxxgHJc+8/+3tHNzF+y5Nh5mMM8E7R6of9e8Z7BxZs3S/wKPZFd0aOpiWIR7wNisYKMIhQct2ZVkk/bxuwZmkMuWigMgZ1W58t3xzAfxc2beI7bIkSQwnmj7Mu3kzvomc3qLmEHzfjgL65WyALRM5BCB2YoZ7G13d1kWnbG7aWeb/15m8OZzeltPOcsSVPcXTfcCl+P3meB5s9Zq928WWyVEewazsXPMZFcfoMHYpoZNKYOL4cygwe5zWK2vne1fNWlNJQSzBLZ424SGGWxIjzkcE4+TY71U3Iu7z2ClJssTfNV3+u5aWZWKd6Q2d0Mmeg/zPH8z928ppT8P4MfneZ4B/ExK6QbAS7Fn/F6dffVVAD5++Dv3+fWhds7gagIzWeFWMKTFRqNvlYjkWcFaxm8wM6gNmDWSg5LJ8mQTW4sSafAq7BXHDOZjzB3KHovsUXbFHS1xF5gYM7M4N8fvVt4LTf9iWQwXjFoc2g4l2UmZqMCh3Dh0eTuCA4eXa4+LXz5fGpKu2rh6O3HnwMLSmdUGDgbo5i5wh8poU8fFCLLV1oBNkkW3BpqrcSmTQC05IncdoGcGW8zos0+vP2MLqDj3SQLbYLCZbMvObuXmgkQCJ8HyXGo2uCU9H51UWhLrC7oeOt+B9WLbZta6pV/gnu0uf5/keUqZQdZPkSYNp3sJCvN6yfxOblxsUq2SsLCcxRkyUTX+RwDfBACHAjEPAvgNAO8H8PaU0kMppdcCeD2AnwHwIQCvTym9NqX0IPZFZt5/lpH3QMuhB/aym80EXrJ8vY3MskA5D85ulVXOf1verwSUo8tBKotYQEpABYGZl11rMWNVKQTDALRKnOd99lr8yjHmqLGnXDDsde7KfZK1a90FbJZS7Z2q8C2oSsmZ95ViuKVBTP7uaiSItXHdBpmopIAKdx1gt3VsARnq86nej8vuCn5/2tna7naPBq0jOapM17S1f81984a1lpOBt9iN/NpjNc+if88cySF5LmpZ8wCGbVgBmdyO9h63wE9JxPxWrQGWoyXKudNaJwQJpuZYmbVuNa7sXSvtaM1PXb6z2YZRMNbVRO31BYPnKiDzPgDvSyn9AoBnALzzwBJ+JKX0o9gXhnkWwLvneT/bU0rfA+CDAHYA3jfP80fOM/QOaDn0wIEZZOR9vTNOG+mIsf2WBHEjJ7BIOYWZKxPrWMkWUpko7767TTBUZvkWw1KOK4E+XHpHBIMli5D6VmlrGT9R5hrrNkwyzYI9FWdCK5lFTf/dC8hU7gWXOecKDkklQpR9WWXxBYt8Pq7bwAymCZifMTBQTlvH7d88Fk1RsLdeOWaTGdzZK5WOHBd3HWBgBhvB8GpdkCR7Lcxg9n4v6qCWfJeaf9R86fXu9pgvo+3IyWSiA8YN1JVCEzG/NUoZ09ES5XvcsHXLPUqTb70EGDXNcu5uNq6VHZ0gKiBT/p5jIuV2FpA5SzA4z/MzAP40829/DcBfIz7/AIAPDB7aadAqAgIw5XBvBjGDPReCyn4B10JIBEYcuhSQKYwsGTD2YAYr+8Qo1qsl15IUkOlZ7bFl/DROlIkZpBY/ITNc7vUx7Vns/O4cx2JwKDnmQDKmibEvmvmyOSrgXMKTjvAyUD0Pep8m4Nlntp+vntEAu3vcXyNxnE6USOHukbTPnsygJ5HTgxmUyHdb86XXu9tjvvRKsLXGuMBSOIxDD9aLAxnoFcSChxmk/AcKXABa+qwcWn6iBBo1l+loCcW7s7rOaF8uALdgtb5CiBaLz6+dr5Mwg96DsxuSopQAJJukTrPXw8wMVowsFzD2ZAY5IyuVppbtLd9fMT27vtUeW8ZPE7x2YwaFz3/jxHmYwc6HzlfVA0Wf1vmyanOi7YtmvnDjumaYGahCUmRivagCMsRYWs/I81xWfSokVdK2V+NSSkxPygy2CrU4KwFLYFkPqGdOSuA6vbs95ovn3RGPkUrCdnCH82d0EmZwYdjSuv9N0lqoDpH6VwA2qqnycHcOEul5sw3mXuT/xvrSUum78N0prxeeeOEAACAASURBVANkDOuFIYLBc8DEDE73HITuG6od2cqynVYZequkSJNx0QZpFOvI6c/LgNHzLMpgiNP/izPBu+39KSWT1iw+hyYzKKh2l38X0N1TLpC3LH5dmEHHhnjpWIYxg4R90cyX0Rn9c8Aq+/LKASk2khtLi731HO4uklRNtvmvZRTKa1sMFIVjARWls1YNbhp2fNW/w15Y1oMmu1E69D3k7hoJXi0YHMUMMknInuvh3WfsawEHjhks/UTAyAwS/gOF8n6ZmEEi2aVBNYHfGJekf/bdadi6K64mGsHgOVBdLBids1WuJMHGWWgc6s62Iwj0rMEItWePg7XYg8SwWIxsrd9aRpnMBCuCK2ArmbQG4xyoQDpHK0NMLsqWghCGvZwaNq7Vv8WhFY1F4CxZ58uqTca+aOZLr6TSJSGXNFmSFD0PVOeSba19nZ6xTMxYVu0r9pVy49LOFStjSzEnEmgUGapEjtPWWeSY1Hwpx2V20lP7uaz2rdfY02vcM5jXfOg9biKRsbERXNJaEaS1wCWtpe/xKpHjkCMDsq09Fl+ae3fyz2trczCDARFqGvWc2t4wg4NLFpd9WtppSX68kiKJsaoFINyYluvyNvJ/Yxko5/2qVgbjitZU7i2wXSw2TE9H+U1LvqtxoiyLMrn4aTbMe5nBZYF+1nc/TQ4lU+1MOl/KNin7ImH72XHdguXlyCLMuufrlQPOBOs1Zc9C8057nsvCWFePCjDalHxcFmaQOnOxBc12gxymPYOUxLvYKuG1dWI5ZmO+lDJR67q2JDJqdn9q2JSjHNJZ2ZQDm4TssR5m97G7nyZY6zxJ64XVbeGmmN/a9zifI25msFJzIT+vspz/i02rvtPE/Jsm2dy9wgIyt2C1vkJUzxBbnEvqbJS7Y7ToRwPiNGDTDs2zZtIE04G7Gpmo2kFgAr3831jWxSMTbSxKnMGrBVf52Knvt8540qKVCWsxCtSZSV7GWHr+47Qr+jcEo6uF0DkXpGdfcUwPOV+Ec5SzLynJg4jR8q5zIOU2zZC8MskBGZlomuhn0WJvU7amLN+XQppFt54VCBiZwVy+OmgdyVE9oLpMas33PufGYHkWnK2TyHc3cmPujNIeh843HO08GK0yg4OSSqUip+c5g72UIrW2a/adXAOEZ82WWxU4lOt0yuaORFWW7/e13nMu8Qncs7WJeRbHuf5s3U9tJVKo52u1LxeACAbPAWlxiNKAA34GgkK+AdjFdE3tzGJrIWDbJowchx4V5iTVRN0y0TIAYDJb0iyf5Psrx63j4scZv5qzxgXD7mcnlcUkpn8D0+Bd/BfndkGtmE3p3Nfmi/RdWLLCZAEZYfLAE3RcKnKbZkpeWW3dvLX1q/0qinfaw/ockwSt/TWO4ObGkEixykStmftW0FXbb06NoWcBmSq7QTwXqv+yaIvZSc/mS83uX9TREobCYWzbjjndbJtZ68ptIACREBYGg9Jkez6ePHjXMJCuAjKCtY4jOSRKOM7W5nM3mMGAG1IJGJXx8QZsFHpJG1aOUyVg6bUQcrBW/tOeM6gtXlBiU3hgySgXma2NLKSSzdqMcV6PMU0wHXjMoWX8Wk4UxcZ6nTup/IRzCiz993h31DLRQsZC3Qvpu7Ds9Smfl0ZWvBoXcxbmtcEsaz/8dulBzGWfy7Wlc0ONpfVOe5iWoxyztb/G2DZw+J1KV8RakdCauW/JRDXVKSXrJHkd835X+yHmH9V/LzZu5egL5otovnb2d0o5pGWvOoeUzeneQSwb6BGBjlUmajp0frF1Upnowgz2LiBTsqRLgqOwL/l4JQkubj2ssdraAlUXgAgGzwFptbFVRiKX1AzQ0C9tu4KbRsZv+dxVQKaxiM8zgFn3O1j9eebQsqyb41mUwQhXpUtTSvw49uz7LNPTYbFqOVctRoFiY0/FDJb9ew+d7ykZFqkHyrPClPOlbJMtpW0oIHMbWEHAYa+YZyS6lrH13Fha7G0p3+2d7NCwx2TbxgIy0r2sVJ+mAjKVoEuz99h6vyzrQXO+7NZ/nuTQ+aluUzzzVTRGJgnZcz30rgcUJIEeKSVVrAGqAjKEHFPMDBoSOWUbQFsFRY1L4u96bW0wgwERxMwgNbFHMIOZRMQb3DSZQSbj0oI0o2vZDK4xLD0LyLQCADIT3AiulrEfv184MRqmR4KWc9XKEJPMoFM+bGUGLRnibsxgIzGQI98YD9jny6pNJuNpYgYH2KhzwWuvvNeKmcFBTMuqCAPnOBmZLs98Gb2OlDAVkKk5mh5mMGe1hPJddr4UewZHnzW8tF2TXp+EGVQE7xqsfKkB4wbqgd5RpmpkBqXKq3w82vd4lTBwyJEBghmkgr6KL60tdChltWPPYECE6n4gQTZjlJHpygzWJCKODHWTGWxIdMi2DxXeqoVXBAGjFmZmsHJvgXpg1J0ZJBafHBonyrRnkFr8hPLdUi5kKiDTac+gyaHkmEHjgcNSFqHWBnDLmEFm70jzuuxeeK4tE1KWZ7QZS2f2zmpTvMyg6fcQtl4CTVXkJjPYYMY4WNaDi2QGtfO1sy3hkpDdmcHe42bWOpIZNKpDpDUZlu/nf0pVa12ZQcExG12ZwRarLfRTLxARDJ4DtcXiqGcmDso8fj5IfuBtWxLomSVFxH44CtYsHxWYjGYGN1XNGCMrLVrDMoOFgey5yLaMX9OJUuy1kfY/34WsqpnSiav17w2A1A4l5dAREhVNVph6LzXzZaQjdC5Y7VV+LzzXrhJSE/OMGnbX81wkBZKsNsWTSPEktUq7K4GKGWzIzZNxTmnfb818KaWZ1vc3TQf5bmWrRmu+rMYyYO/xUGZwYEKMev4iZlCqDhEWkGGZQeF7nHYAVSBLA3bdz+Y5Ny51gkuxHlLs7ZUggsFzoNyPliPP7EzFJFw+HyGbOPbpaHuatgFNiVyvb5EUtTbmWrN8m2whd36PUKIjQclMcRuzxXtEuMWiMJCWoItDSxbRcla8i7KHsd0U8LEwg9m742YGhdVEl8/L94hyBCz7Rcp5L50v+fzrva/5XEjG9yV3VtKkc2gTY4+5sUzMs2u1J4FkbeDmTgurcSnni3UdWb5vOlpCmNSSJHJca6B0PVDMl5JRsr6/U3YvrPOllz/CgVrrubFo0Ws9qLVdq1sgCZI4lPeFw6a4nvJ55QGTmRkUyGFLG7zpv+FLUe9oa+5Sa/CV4Jas2FeG1plF1N8pmrsXuD5d7VQ221v6ktLvluMBAIYZ7GRkOWw2sjMlm63MYHk8Rt52+bkVrQ3TlmqiqnkhyJay15ZOnKOaaD4WC0q2ojWPqWfKzRfJuKjy5HnbtbGQ192SpcVqdynbIQVnI7mxtN5pq80tv88FtOZ75Fh3qKqB4muFTm+Om4ragGUGme9b11vS1gn2kJd/p+bLIp/VjmkzRoENaNmJXjaVQ5roJGyPvkomvyck6iROPilaA4RJko2fonwX8/eiq//E3IvaGLV+asvWUWquK8EtWbGvDBKHvvy7Z/FrYZU1cbQtcQqsDmNrb9oCy/EAwDYw2BgWinU51aHzFcYyR/l9juk5fr/j4lc7dL5aQKY4BqMco6X/moO0upYLRg3SLcD37nCJAa5NKtPJzRfRvRAEgK12PEHHpaJHIKVOTOV9Mo4T+05X1Cbl30VjEQQu1rXJs6Z5AsmShZegeog6sd+8Ni7r8yC3DQjWg/LvnGNcyv4skKwvLZviSaRIMJXbMxq2VoORfhqV7BMdOm9cDzmUhda0yY1eCWmKkWcTmYqArvycs7vU820ppS4YEQyeAy1px/HvjNHszQx6FtYcEsfJagikzKBV8rHZv8ecWdP90PlKRpmUBSnOGaQWud4Oe0u+O9/wiywXDFv2V20yoYLfxmb0Df0Dvvu5GYtAbl3+nZ0vQkfgOBYBS0i2MdBGnQue31Q+F2+fkgCQlC7lrI9Bjkn1mcOaYHLdW+e13QvIaA6dNzIjlK2rFpDh3uPGeuBla6g+c4gCxg6BKYfNumvcWkJhZCCbiOe/OVqio1KGA7dPEZA9r14J6c2aOfNzW0usaBnG8rMoIBMQQXI8AFDJ1vc2MsqXmW2HeWlyWJ1nacbFcjzA8v2LOVpit/5zUzCgcW+PMlFikesuE20E6ZoqfJZ9NFrp1KZ/hRNX6197HTmWCktc4vh5tveYZA6E+7Ek0tDW7+sVGF8SPM5dydhqryuv1Wa9ybEMYOCsNsXL7h2vddp6CXofLZFfK4V1PSj7bLEk3jWN6ieHar4OsCMtRY4HIxNiE/H8y2TfMWDMf5/ieCEJY86dbQjI3sVRzCCn5ir71Mw/7bXBDAZUkBQBAfgX7lKZwZGOg5gZNGb5qMCE1OJXAkYtWsEImwlu3NsjM0gscr0zl+6jJai9G84kgXWPhKeAjPY6ciyKYy6ozDnJDCqywmU75d+DGbRdqy6OInC0tOytlWnxOE7Nth2JlItjBgvnuzYuc0KU2qog9SUE86UHG9drvljfHQmGMoOd1gMKC8Nfq21ABozSNUAoE90wg8p3cZSahqvzUPap9lMVtlbqp14gIhg8B8TZPEFmowe6ZWokEhGjpr4lR1zQ7WiJG/rF30hJOxqzDTNIsF6t6pzLd/I/WcPdc8+ghRlk9ux5kwTSqmZl/56jJbTXUe1YHEqKxZPOF6q9TZuK+WJ9ty8ZLgaKeEbqPgVOzEim5WTMoGdfpdPWSyBR89wUSbiTMYPKoKslgfNK96h+clwEM+gsHMZhdEJsIgLZJjOoUIeIjpZgzjbM+6+hp78pLSAj2W+dw8oqBjMYUEG6Z9BDxWsQzCDDDBLBrcXIsn2WwUixKKW0/46mlPjyHYAuptM7c1kzfpKAptuewUtgBj1OFPGca2PpzgxK9looFvpgBu/dx16sV49Mt/U3ALyts+5D7sG6mq4VMiA5qnvzCrvb2qpgZgaV77d2vljnK9un4/f3GAsHlhns4FeN9NOA/dir21m8ewYtzKB2z2AndRLJDCqSHrX+tUFl+VlUEw2IUMvmSSoXjSpZDMB1wOtQSdFhXEMPna9VpnIYWQ6bojUMk1cGjM0MdckMKrNiGtSCdElA4z3viWMGLYuf92iJrsxgy6FcsvjE+ySdL6v2lAFIq50RGf1z4CzMoDIwl6wN1HyRQKJImYxrkycx5bk27XCsXCyFRpGhYQYtBXeOQadRjtqaU941jepz9Z3Mx2j5QUOYQaI+QG0sGoxmBqVsWM2X4SBmBhlpKnBiZpAI6nsrKWrfp2wdVWjwShDB4DlQyzJK2MDeBpLr09OOtax0q23p0RKWqnmWPYNDjpYonMFNJliYoaYynr2ZwaVtSr4ryZC3fr+lf00ANN/ccwwtGeKerLqJGSQCB+l8odrL21nGRX3eaue2yEQlwVDrWhcbZ3To2bF4Csg07E6t/9Z1HmbwFAVkNIqM5hmhxuC5tHXWoJN7ppNxvubgggR2XI2zK0/KDHboa/ixGDtj0lpou8XVRB0J5knw/CXY+A/M1h6gMv+5s0AF7wv1fKVHoF0gbsmKfWWYZ6EB5zISnY1MqzS5uB2t4+DIinKwBoNSyYGlaiWHVmZrGZf60Pmbe9/NPwcKY9ZRFkMtIq3gKk3rDL2FGaT61x6nsIzBIlPtdT83LHFjHlOZ8+O9KO6pVDJL/V1rG0bKu84BCYvB4cgMahNTgvdV4vSs2rQygwLn3ro2ed4dj1JGKofLITnS58gMNhg7KwNX2rrW+XgaaahnXDkkc1GTvPAECxy0tlbbNvX3XigDtvI4hc0cme9d12xbmCQp11dtcNeL2Nj4D+XWHom9dCRSau93FJAJiFAtIMMYE0+GWgJrFnvVhtJxsDj94qMlDE6P9pzBLkdLVPoECONvOGfQo+9vgcpELmgFd1yG1uJQbgrIKFis2h7LZhvZGW7uvTbzNjBtvUeUjbAUkJHYmvteJmoMpKzy0rJPdm1Q2N1eY8lhTfCdSyYqlcPlkMjzyyrO7P0yJkxKW9dKXnGJnJZz61W7UH2uxiWwKSPtCKtI6bEeDkzaA3sbUGP9lv7LrSIi2134QBw4NjLvv4ZhMlHGZyv7VPupgnbKa4MZDIgg3vStzEh40MP4ijbmWiUyFQYqR7cCMkz2y3J+D9vn1HbcqcIirXtbykQl0mMrakF6M6CZ6EXZmyS4EQbp2r0+rTF4EwOrsQgC6fw6gJaodD1aQhJgL47uLVlaOHusudYjgRTtXVE44L3GksMamK0SKR5m0GnrJdAc6WOpBCzBxkYoZKIilqTDu9trvvRITnOgEqy1sWjQextGiUQEspQtqG0VqbUtKiDDnG2Y919Dt60VhP8gSaSJ5p/A95Ymua8Et2TFvjJ4y0FfLDM4MOM3mhkkZaIU61Iyg84s6spxJ+SNpWERyUTLzLHDcLdQk++ehBkk+hcfLVFca2aVl3fHKRPVjIV7jzTzZdVeMIMkXAyUUZopCQC1z8hrd4EKA+WwKV3G5bT1EkiOg9oEaY37ZbUzWhuRX1v+nWQGPQ56p/lifXckOBkzOMC9nog1k3q2JmZQ+F64C8h0UidR/oPIXjoUbFJb2zoC7QIRweA5YCoH7dgjIcGpJSJeiQwHFzNYkYBSrIvU0a722WIGBQFj3t4y9uW7Sxt5e+X3PXAxg+Wi7NgzaGIGSyfOmCEewgwK9lvm1+XtSOfLqr2Gg1h+p9XOiIz+OeAJdLy2DuCDdG5t0M6X5lgUjnut/1b7I8ZVu1Zb7U+jyLAmclqwqgfKsbQSP8MTwhIF0UA7QhWLWz734mKYQaFkuWzbdLSEcqtEN2awcS96KCmq11aSHcEMBkQIZnAgM2g8QFYqOeh5tMTGcSeYRjJgFGaoT3q0hKWADJHZA3TJDpKxFcp32Uz7iRxa0Vha75FjvlDtlW0GM3jv76dKEgxlBrV7BhWOe61/tv3O90h0rbBQRg6NIkO8Z9BoZ45y1B7MIMGSuNY07XxprGVXxwx23pNfYioSGdyZf2Zm0LBnMG9fW7naK0mWbO0p+9TMv8212T5wthKuwb5cACIYPAeq7I5EqzyCGTQWO1i1IcyalN+VQM0MGhZay9ESrgBAsmewMNCaEufknkHlZu8WvMwgsK1+ailCYTnyo+zfvGewg6RJ7VAy75FmvlDtATJmqtXOrWEGe7BexuuA/pluF0spWLMsigzTuBwOZWl3JbAcLdE9MOdshFKOyQVaPSoB95ovI/cec0nInuthr/ZKpNQ4TqGsG6BQu0jfC+pd0LzHXZnBstI6Y697MYOSqsxlMcIrQQSD50AtW89WQxqccUrMAmFpA2hLRKxOw9BD52tGtgy0ZgCVI0IkSDtsSyMTC7R6z2AluOrODFaCdGlAs9nsrhjXIlExnauklHex7XQIgKxZf898odor/65mBjsklS4J3H3RXNuNGczfY6ZSZG9FhkSR4rpHxvnicSi1MlFpQZiNfLMRpKnHrZWjcr4EY6+O4+pUQMYzX3r4IxwWdk1auVmD3usr1X5trePUQWJmUBAMUslWjX3pdY8ouW8riJP23woqa+MOmWhAjNrxABKnbJSBLPtUt6HZu+LMinIwHzovNbLlsQ0eJrUhTV3alx5nwVUTlRhIK6gjNxa0FqLFod2cm2Vwkiwy0eU7lv0VVDs9WPXNsSBKhp26F1rpDscSatq5LcGgx+56jxCo/l2b6TaORaP2qPXPoQdjaQkkNTIu6btYO9Kn7D//U4rS1mr2DFJr2cah78AMagI9agzltSPsyPE+zus/r2HP4EYmWh6nwM1Fyfl/wiBmnn2JBM+7W7ZTO4O5Nf9r/bfsbu3Zau3LheCWrNhXBmkBGQnN3Qu5FtrbBtD+fVbJ1bCjJaat5IAyCN79Zas+ywCUcNwpxrJlwI5ZQSK46mWI8/HlfeZoOVFcFtMSjFkKyHD7K9Rzs8e7k9ZjaDp6TJ/lvRDLRJnD1bXJgx5Ss0sCJ5nVXGu1dYCMpZW80167C/C/35NgMt8jh1JGywxKizlt2JjG961JJ22RqfzaWv/dmUFuT9XA5IUEPRQpbNsMe98LiVjrKHtt+W1pkgUxZG0DxfPqtVWFXOsEajpOVZGjKbGuvCNa+3IhiGDwHDAVkOnsxJfoKXWrtWOWThULLgePTLTGLm2ysh0WkI0xExwtUWO9WHZJ4FBaUS6sOaSZa8+h70s7PY6WWDLEZ5WJCh1KTm5d3gvpWZgSFYKmnRFZ8XPAk+n32jqg4tAIkoZUm70kqzk8gVlvKa30Wk3mXirHVB86b02IKvcmlmPhHPce726v+TLSjpS+RI/E7oLRMtGJWOsoe11uFRHb7nm9dYUCWdtAYV+6yUQJ30iSSNP4qdy1tbkSBWQCYkiOBwD4DP0QI2N0FlZtSBYCo+NcCzpyWI8HaOnPU1pnznrsM9g47sRe0lb2q2xv+U7+p+dMoBZK+WyOZkCzW3/PysxNu2zxUzx/baa91U6Pd0ddQMYxX6j2yja186VHYHxJ8DguXltXXttcGxLPRkicGM1YcriYwc73SHTt1F5Hcmj3Pl/80RJL/yW70yOppQj0qDH0HAsHap3sFXT2LtBWIu3Wa22Z7DsmJizM4HJfBOorjlX2VK7WglJNqexlpX/2WoGfLJXbXhgiGDwH7oujJVp7AZwLIQdrlo+SGlK6+J7Skqkw7JyRreniy/aW7+R/alkEDarMoNSJyjLqaeId2toYagFw7bq8f3MBmQ6Spl4OpWa+UO2VbWrny61jBh3OnURSVLuu7LOZra6MrwtLqQw6RO071wPAmDzqyQxaEznGAHhjryTyXcF8ORkzmL0XLSnpCBUUtU72CjqHM4OtpDXjA4hst0J95Zk7XZnB0n+SKCkE/XttbTCDARGq7A6zR2U4M3iqhaCTRIaDec9gkc2h2Nv8JR/GDJZ9FpIDUYnzQq7EZsU6zKPaAqJ1oqR7/agxWPYdap24VjtnYQYd84Vqr2xTzQwa3+9LxUUxg421oZWt7jmWHB6b4l0PauOqXatiBpVy92HMYGFrpXLUcixsIqnDu6uZLyPmqwRDmcHO62uJVAQapZpoKn+b5mgJqY/FbGfJ+6+h1z1qng19qDRe9qnxU5d+ymuDGQx0QS0TtcgRgStkBgfuBajJEXN0O1qCkGzmmSirHHXVp8BxlwSMeXvLd/I/2axYp9efM35SZyVflC3zj2QGFYFLt6MlOjhRcyF3NTGDwvlCtVe2qXW6bx0zyARjoms779M7CzMocZwcgdlIxrLW5xBmUFnl08wMKoNOYD13L4UZHDFfJSifl3RftQTDmcHCvouZQYV886qYwYb/RDHMp2AGo4BMQIRWJoqSnXmcEgmOL42jAlavhYC8rtikz8HFDDYkmyQz6AkApkLSRyxKpeMi2W9aC4xGZC4550p7Phd1fpG2f82+w4tiBo1Z/9p80ZyF2azCVtmPtmqnQ1LpkpAcdrc7M8h8LspWG6XMkmqqkirSbPud75Ho2qldJCNHs2onF6S1tkoY5cNa9UB+7ar/MpHUQ+6umC/V+UowOr1AJSGHMIMD/LSy4ueGDeuxZ1CgvrrEaqLc1p5yXB7pu2juTm3S4gIRweA50Dr7KxHGelX6fYSR6eDQShwna1nt0shxMBchaRjZpc2NkXWyQa3D0jcBY40ZLIMr4miHEZlLriCDmhmsBLrS/j0b5lvBa6udHiXZy/1A2j01+XyxBMbl37Wl/62O7qWCuy+aa9W2LjF/ZwJ2ibyvxzEX3LXcdgYJzPeIuRfSazUyrtZ7tEnktM4ltD4Lwl4CBmaQ6b+7D8CNS/D7e9hUtm1CQdPrGIjRzCCZtCbev7KaqCU5yoE7AkvaT6/K+Juif8zWnk2fCj+1/PvSXsvWhkw0IEJLltCcwCOMTIdMnEomql0Ii2qeHMzHA5SGhQnMpJXcJKAyW9QCvRkXVzGQywo2nEgv0o7OtLcCmjIAmm9siY68f83zl9wvUf8dM+qrwHhqP+vafDk6i44FWsvcWJM9lwoX62V0+tUyUUE/Xjaq7DMHl7SUoPc9kl6rkYm2gi4tG2NmaZnklcihJeYLVYm41p4E3LEoq3EJHOqRSaWJWnd6JUaZ5E0vlBLEeaafbbmdRbUeCrbiUIny/M9qPwxLp0UZGJP+E7EeSdhrr62NAjIBEVrl3klqe3DGqft+gdZCYGGAisCIgtmhL40sk2XyVp4s+8zP9aEWpWlXVByVyEQrC8EpZaLSzPWQAjKaTGh+v4RyyLJ/oM+7k0vAqlIU5j3K54uFJS3b1P62WycTdbBeEqe3dl3Zp0smanwuqgSf0aabxuVwKCXrSA5tVeThR0uU73djrS3HwspEO7y7mv1YI+arBNR2iiEy0RFjbyiYlqS5SSkjDAarR0sI+ul1jyjfiKrzUPapTXBZbG0wgwERWkUdKBnFcGbwVAuBMRO8tCk+WsKZLabY2xXrYtxfVvYJ1AOAkrEUyURLiQiXFeuVDbXKRMvgtfFesP0Tz0WUCS2cAmuJ8Z7vjnQstX0/loI4bCZU+ds87/clwstAea4rr+VYF8kz6j2WHJ75b50vp2QGL+ZoiYKBbI6r4T9sEkkd3t1e86VHcrrV9lUeLVEEGqSCKZvfmq0PUpkoV9tg6buFXveIVHNxc5qzqY13eumnvLY1d4MZDIhgyfpfBTMokFS5mcGRBWQaRjbPRFn3l636JBZ3kulpGDyuvWYBmU6vP2f8pM6KmxncYcsMSha/cq+Po3+gjxOVs5RmZtCxf7JsU2sXbh0z6LC7Xjlgee1ZmEGF43Q2ZtBp61tQHy1hrATcQrlVQlVAhkgCXjQz2EF6z7a9PC+hrdVgODNYrLWcz2DZ+lDOYw5cALr03UJXZrBxLyh1hihhkSVGVvU6hLY2qokGRLBk/U/GDDo13AtGOA7DmcFGoRaSGewRAOT7xCp9WqpzLm3k7S3j7rlp3sUMZvf0vmUGBYmBHJx8czVfNMxgAhrtVgAAIABJREFUi0UQzvORGf1zwJM86c4MNtYDy3xRjWWA9N98jxx7OUtb34K0OqhWvum1dVY5ai2RRH1fA9F8EQR6J2cGO+6fP/Yzas9gIzm8YgYN6hDT0RIK+9JLnUQm8AVz2iN9l6yHwQwGxDAxg4JNrx50XwgaAYBpIWTkiDnMzKBQcmAxsmyfVDBCsZHKTHCt2tyIRZZlBrXBqzFDa2YGBcG4tH9pn+KxENnXHNyitJovhgOH87Hkfw9m0M5eWRlFgA96qLXBMl+aYxH8fs+e2bMwg4J1JIe2KvJSyIKtBNxpq4RVjsr1f1HM4EA7QilorooZXJIOM19nYC4TE4o1QMQMChMMFIYxg8zWnrJPj58qsXVRTTQghriaKDNpR5ZbvoSFgIOIGTTKN6WSg5PvGdxhmwluZajLwEipedeCk0Voz+eqVUqV9t8q617r35oh7vnuuPcMZvfCcuAw4Jsvt5oZNNgUz3XltS5m0DgWSTVVrjqlBL3vkQSSqtQ5LHsGRwU6q/fbKEfl+u9RCVi1VaSWvBi495hkBjvZqxF78nNMeXEYJtmXkpMZNFQTPc41xboL+IiN0u/gtvaUfUr6Z98dwVqfppCJBoSYb+pOLzXhPCXOJdC8zK02au14HGfJS+aqJpozg0w1UUvVSrbPonoXV7JZWh2SCq7KMfaofLnpl3GuxE5U5feL+u+0+Ln3DA5ODOSoOXTePYPkfBHO85FO3DnAHbkhulbg9Lb6ZANAwR4wcizaoEvw+5Nj/vfYV6mu/sskrzho7S5V4r7sH7C9I+T73XouAgc5/9zloAuCIc1RKEOYQSoJ2SsYTH2CarZ9Sp1EMLymPYMFY8qh655Bj79Z+B3SIy88fqrE1oVMNCBG09FrTOCR0gmPATuOt1Ke/9iPlQFqGSoFM1SOS8UM9iggsyxKlWBkmrBheloGrKwmSjmUPZ31fIw5ms5KEQx7ZJomZpAIxq0OmrRP6Viog33JPivzxXIvAHqxFMtEHSzRJcKT6bfOCy7oawWJovkyQibqDG4A/Xrgei6CdSSH9BD5XJ7dYg6WcWiheb+5s4O5+dLFjgkcbU3yYqQKSmprre0PGbtgrVuth/O965ptF/OYQ3m24dInNZZaP9Jx1drZMINcgoNLqjUSPKVdkvxOrX25ENySFfvKIJaAMS/NKC163veoNtwyUYGEoTUGru384HSKvU0TVmcCAr5nsalmSSxKmgB0+XyzWBALdE9nnTN+zSp8RSby5AVkyv6Nhw9bGSCqjVwCprURSztDjpYQ/raQiWbXGp1+VibKJNsk7NpIOaZn7TiHTFSyjuSQyjGlSS23TFT4frM2gpkvPRUOtXZEfsJAdm2jSOl46DzQx5fiMBFrHSUDbrGHXNtAmzWntlNoEhySBJMEpJpLkOCQJJJqBdpq1y3/prEvF4IIBs+Biz5awknbA6dZCDl0PVqiXCwFEg1tn0tfAL0o5ZKDZnCVAKR6YDTCWedkEdq9Nic/WqLo/6wy0TIxIFQPVOeLRibaUCGImcGBjtA54NnfYr0XnLPEtSdiWjrIMbX7azTtW1lX07WCdSSHeO9zLncfJBPVvN/a+dIzIVxrZ6SsWQJSJtpTKTMwIUYGesTztdQ2KO8LB7K2gcK+eBI5ZTvHd4EpptPypc3vTkOFEcygDCmlN6WUfjql9LMppQ+nlN5y+DyllP5mSumJlNLPpZTenF3zzpTSLx3+e+c5xt0NpgIyTGajFzx7+Y5tCLMm+Xc1KDXiFMzMoFYmagw6V31SwYiT6VkFRhQzOGCRNTOD5aJ8YwuuV9IZDTMoCMal/Uv7FI/F6FCu5ovhwOF8LPnfpb/ttjKDp0wSUDLdWnsStt8agIgcJ4fa4CzMoGAdyaE9L9WayJHAxAwK50t3ZrC1H0syXwfYEep82yHM4AA/bSLWOpIZrGwVqbWdX8OhWkBGsu46EjmrdnZr34Hqn/SlD0nzWv/Nd0fog10RzsUM/g0A3z/P85sA/EeH/weAbwfw+sN/7wLwAwCQUnoUwPcB+H0A3gLg+1JKLz71oLvBwgzmG5OHMoMdFoKzMoNG+aaIGRRk5bR9Ln0tbZJMj/D8qqXNDTMocCg94GQRluqnbmZQ8fwlwbio/w6SJi1LWctcauZL2V7ZZjCD+z9PmSTgnBUXM2gdCzMvtP2fY1y1a7seLSGw4zm8CYabTI4qGddJmUGJBE8zXwftuwPGFJAB+qwHHFYFZJhk36riqIUZFBTpY+eOQo4K+IiN/F2oFdMp+8z/v8X2W21tMINizABecPj7CwF8/PD3twH44XmPnwbwopTSKwB8K4CfnOf5qXmefxPATwL4tlMPuhuaWf+GXnmU/ADoxAyeQCLDwbNnsGVkT8IMVvpUM4PEGIcwg4wswlL91OR0E4ufZFEi5V0ndGjJsTgdyvxemPYMGvajrdoZ6AidA57f47V1Zb8eJ97MwAmKPXjmf5d9lQa286qPlhBuVdDOF898Ldug+m31T45lgB2hkpBDmMFBY29VCk2EDyBaA4QFZLjtLEvfLXgSOat2BEGvde+f19ZeITN450z9/kUAH0wp/afYB6Rff/j8lQB+Lfvek4fPuM83SCm9C3tWEa95zWv6jroXxIsFIeO4+fyVM4OdJDIc5rvYOLTatlnDYjSytT6BejCSS5okAWja1SWTI5x1zvhZjpZwM4OKIF3rxLXauRhm0JEV9izyq3ZuyXZ0l+PutHV4lg56LPK+HgHIUGbQuB8zTT5bL4GaGTRsA5FCsx5o50vPrSK1dkbP1xY2SUhjFWsOIwNZSaC3ChgVSpnyvnCoMoOKdVc6rlo7rXtRW9fuVvqv+eD5v5PXTm129QIxLBhMKf0UgJcT//RXAfxhAH9pnucfSyn9KQA/COCbAVBWfa58vv1wnt8L4L0A8Pjjj5PfOTusZePzBbA3erQ9sqrd0r7lQFQJ8sCken6PUKIjweLE3FQWJTUzSBS5IZnBjnOIc66kzop3Iz8ZyEuYwaJ/NzPYo5iQtpqoc75s2uNkNsLfNtIROgd6s3uaa0tn5RzMIHAvA98MOk6YSPH+HuDwjgmeq7SaqDaR47V15j2DzHzxHHlRtlFrRzJflrVxKDPoVIRwGBrICtiwRPgAGvmmaM9gGSQp5k43ZpBa94mgD6isa9y7w/ngkrm7i2AwxzzP38z9W0rphwH8hcP//n0Af/vw9ycBvDr76quwl5A+CeAbi8//t05DPT2kErARxppDjyMHJOfreM7g4c6zy2E9MygPDGqSg2efuddPfp0Fy/OtVaHLs0ySPsnFgtoz2DMYZJ6L9FzE/Ewks0y0uEeSdnqdN9XlfK5EjKXCeLBniCnnS34dwC9+0t828oytc8Dze3pfy7UneaePzrVVnv95fj66ghvjPfL+HuDwfgiuP75HjbNzxe+uY63VvN/a+dJdJsqNS9DPiHXq2D/1vDq6wiNt4EQlpCVJa0NylAN3BJa2H+n3a+2UZzBzzOBmrjeeEfcMj+1V3m9tgaoLwblW7I8D+IOHv38TgF86/P39AL77UFX0rQB+e57nTwD4IIBvSSm9+FA45lsOn10f5hkAcWhnjtYEHiIT7RBonkquVINV6pcvEKyRpYxPDzYoZwaJBVp6tMTyb+WBs9QCfQqZKNV/eR1QFJCxOqvFczHJRB2H3kv7FI9Fyi4Qh+Jq5suxPca2mGWit4QZ9NqrnteyjI4gMPKw1+Is+oC2e18HyJ3eBWKFg5Bp8u5D9R4twc2XHu/uivVh5oNkvoxm14BCkdJ5Pcz76YlErHXU1pJWxVGubUCQcKdkogrfVLIPWYIlMJ7ne74G5bNR42r5QZ5tE1oZ+oXgXHsG/z0A/2VK6Q6Az+Gwxw/ABwB8B4AnAHwWwJ8FgHmen0op/ScAPnT43n88z/NTpx1yJ0iLgADECzfYyLTG1aMNr3TKsrlZ2jawb7+WZTpLARlFAErp6CnpTm9ZDLWAiM/nyn7/9KChf4INFTGDRf+twk61/qV9qsZitBGWhIVHgihp51rhtVfmawmH+Vwy0da1HptyDpmoVA634OIKyGRScsm4pPOlZ1LL+/tHBlRUErLrejg4ad/aD5gIH0CzVcCyFUf7vBZfqsuaecP7GlabyV4neLYSP/UCcZZgcJ7n/wPA1xKfzwDezVzzPgDvGzy08RAdD9DI0g8xMj0XgoEZ6mHMYOaM186sOcvREooAtAxY07Rmj0Y467l8NofWibqIoyVO7PRbx8I6dJNuvmza4+Q0wt820hE6Bzy/pzcb581WU9eKxtK4B575b72/Pe5tN2awUyJHAvL9biTbpPPFc0+PbRyORfH+/qHMYPG8hjGDg8auKiBjYQYr7wWnbLMkDe825NTNNg7ztJrAbyleWu+OMLgsrw1mMNBEF2ZwoIEczgx2kshwsLI7Z2UGM0kHlYlSHy1Rqc7ZY29oCc74iZ2o7Pebs+UlMyjZu1D0fyuOltjp5oukPWkbq3ZuyZ7BLuxepwQDd29vBTOonC8XzQw25OZuZlD4fnNyzFoiKf93K6ZdR2ZwgK+zSUIa1UQcRgayZKBHPN/WMVlk21lwxaEWgC59S5B2wOQssrJK4DO+RndmUDh3W+zqBSKCwVNDyu4AfFbi4pnBkQuhQc8ubRtoSw5uhBIdUZ/F3gXu/B4VM1hIJkdIgUpwQbrUWenKDCqC9LJ/NzPYofBCfqC0iRlUzhdJe4D8t410hM6B3uye51rPvs7eY9H233tcPX6PtOKf9HB3qdzcG8h69wz2SvxwSLsOzOBAhQGVhOyZvLpIZlCTHK0xg1wAqnxek8CXk7QBtBP4gJ7h89raK2QGb0n69opw8cygs7oTMDBDLajS5D5aoiE5sBhZSZ9Lm2RlMEWWrwyMRi343BhzaOVVszFDSzKDigBIej5Yq52uiYGWQ8lk8S+GGbwlweBIRk17bfMZGeaLaixKOaKqbeW1PX6PmhnkCqKUiRypxNu5VcJ9tIRBAifB1TGDRp+Bw2hmEIeCKezRErt2kRm2bdTfi67MYIekA2BkBo3Sd6mtvcKjJSIYPDVExwNwGb2oJjq+mmjDsJxjz+CxYpaQVa4FV8OYQepoCeX5XGevJuroX9qnaiwGhzK/F6pqosxeH8sin1937RjJqFWvJWy9R97nDUDKvcdk/2dgTz33tns1UanE+8zVRGuJJOr7WqSdMDFxW6uJOt6HFpZx1pLWqyIzBnWIiBl0zp1p6pN0AA73gvE1tOx48zqJrRWQFheICAZPDdHxAJyxXj53bLpt9ulhBpcXTFAcx3omVSvjcmM8K04qOejJDG7OO6rs8ZtvbNVEy7kieUZa5IxpjmaBA2pRtsyLtA6Aa33W+rcGo54zz6xjqdmIkjmQjmvaEfNF+b6OPGPrHPD8Ho98mLL1zbOvDPNFOhZLwTNp2/mfPcfV6lOavW+el3pIpEiTSh4bvDreSFpNVDhfeh07JJ0vou8M9HW8SchW+0OCweU82rv8WpcHI5o14HhfKu8F195xrgmfl/XdLdsAGlt7jDaTtbUCWxcy0YAIIoe+MoFHya96St1E+yWsDFCLGbwxZr9zwyI4Z1DDunDYsEGVw1xr46qNUSuPsIAL0ucbAIlfIDZ7NzwyUcuh8x37l/bZayyso0fMUen7kIhsrXovSCeH8lLg2g/nDCR7SZ5W11rG0sjie4IbL0vmTfxJILEp065ud3N4g2f1ofPC+aJ16DlI58u5ZaJeu89huEwUhT9AJa0N62F5Xyhw7WkTCS32WNSG0GejxtXyVbWJlPLaYAYDTagKyBDGepT8qqfUbdhCKMi4DD9awqDFb/a5yEApWWeWxRTLRCvVOUdIWDjjJ62IWat+qu1fkwnV7vVptXMWmSg1Rw0SoeV7rIMoDSiXcd2SpUVSKp+91mnrWMkTl5V3OuCasWj7b47LksAzrocSOVwOrTy/tffYc78273ct2cY4vJ6kggQ95stQmWjx/FvBuxYjA1lSJko839ZZhGTbhTqFQi0AXfqWgEp2aSFVc1Gft/q3ykuP/3bY13lFuCUr9hVBVUDm2pjBwRk/bm9ajq4FZIj7bzGybJ95oNcwsnPF4JVtXlIBGUlioFb9VNu/qoAMIc10MYMemWiWGADsDuXKKVUmLKgF0rLIU+O6ZngZKDPrxSVyDM+o91i0/Z9jXK0+xQVklIW7pDJRr63zHD9D9d/r3e0xX0YqDMrnbz2OisNJmMFKcjjttslRDTMoKiDDyTGFv9mayCnbANp1HgDal7bMP42tvTJ2MILBU8PFDE4nYAYdU0JVyMCyEE4nYAZvKoaF0OJ3YQYbWb6lP3UBGYoZHOCsu5nBLKPuTRJoCsikhNX+CmuGeBgzaNinNYwZVLRBjeua4WWgerJxVvbWzVIOkv571wMXMyg9WkKgNsgVGeIgzblVwqMeoPrv9e6K58sgP6GF8vl3LyAzYI1dQDKDxPPNf1uNPabavsoCMlpm0Chl1tjaK9s3GOcMnhreoyWGMYMXJBGpXTvsaImMpTtVARlJoLcKGLVHS1B7BgcssrkjlEN6Vt4qeDU6lBZmsLzWmiHuwqof5IjqrD/l0B0kKtqzMKkFMphBPwNlupZxVkj2dvSewcbv96wdZ2EGM1svgVaRIVVEnI0ZbO0Z7OCki36/M2C0gjrftut6KPh9VhyZwUrSuiwgo7XdJztawimjXDGDla091OdWPzWYwUA3iKqJVrISo/bidF0ITiCR4WBld6SSg+HMIGPMbioVs8o2a8HVEGZwog1f86y8wmia9wwSjK20HU2mvda/ps8eY2llLlcJBsV+P+98CWZwfR3Ql42zsrcjGTiPTRlxj6R9DtszaAzSJMhVENJkm3S+dGMGO8yX41gG+DtUErI3Mzg6aV87TmHa2X6bhDFvMoPC59XyE6VtAPv7wG7tqSQ+TO+OQv0WzGCgimMwWKHta3rlYUam10Igzbg4JTIcrOzOSnJQObPGcn6PpM9alm/pT8oq1wqyDGMGieciDWi67hlUsmEaeVetDU2f1bF0yvpL50vZpoV1WrUxcK/PuWCVNI1g4ygnSpOtHsHAeea/u5qo47l0ZQY7JHIkyLdKSJNt0vnima9lO97fP5QZLJ5/a3+2FtbkkQRHZrChJroKZtB5MHu+1jX3DBL3qOe7Q47rug6eD2bw1JAsLJwD1uNsFg4nWwg8Epmp/YJ1qSZaYwZ7VhPNjAYrTdVWE52KrCC30ftSq4k6kwRHZlBYHr0LM9jJcVmNxZj1186Xsk32/ChlNdER54OdC9R9EV3XuZooN5ZzM4PasvLkuIwMn/X3AAOZwcZZt70SolI5qnS+LO/saGZQMl9GHIF0bJtIQl4lM1hhwyyVUlXVRJ37TXvco1UCn/OfmHfNmrCQzN2QiQZE0BSQKR2qaWDGybOgl+1INtdaiw00g0HjofPkmTXEAdzHjdnz+joLVn0y7R0zgcZzBkmHsnNSgQvSW8+izKDNs9GhLH4zoFgA0/paz9zx3tM03ZsH0nvnnS9lm+zip2gjH8dtQJqMwYrH1jHvaCKOuZD047W7o4KbEfeoeV32jkggsfUbuyt5Fh1sXXVMzLEo3D3nHGf1GDvMl142tdb26j52TF6NTNongX2fdrY1TcJoscFgoj+v9dVjvVzG1DpnUEuscL9HNHeV9uVCEMHgqeEqIDMNzDg5MrSrdhpZQZdcaRpYQCZjqWos3XCZKLNAq2SijT14vWUsZploUTTFIxNdiqa4Csh4D53v4ESJJWCMQ6mdL2WbXlnxSHnXueCVI/aUQJJSXsH8G3m4u2f+e9cDl0xU6Kxp5flimag1IaqQAGrmS0+Fg2i+DPITWqAUKbdOJpr/NuE8O6lMtOEnitoQ3AtW7tlirxPtb2ts7ZUxg7Fn8NRwHS2x8zucHHoWwRgmV2KCjhzmAjKZREJVQMbDDAoc91XAKCw+lEtEqPH1lrHkjlAOSXC1CV4dDmUuF5H+vo3s6sQO7aqdCXIJWMOhk86Xss0ee0Hy624DzHJEj61j2EjrMxopx/TMf+96cDKZaKM8f54obMrjnYG5NOhc+tjMl0YiqYeTLvr9zoDRivL5d5eJnoAZrCWtp8n220r5LIVuR0vs4D6UfbXuV+o8UJ9LfCCvrb2yAjLBDJ4a0uMBAPqFG84M9lgIJBIR60I4mhls6M97LiAkM8gYs9W+wsb9PTkzONGGTxJclffU69y5j5Zw9N/j3XEXkKHmi2KB7rEXhBrXNePimUHB/BvJwHFyRFHb3nvkTB5JIGXgTlJAJrcRwmSb1HHvmdTy/n6P9LiF8nzZq2IG87WOSfalne23SRjzJjMolYk2/ERpGwCaZ0NTn0v699raK2MGIxg8NUTHAzATbhppZHotBA3HqZdEhoOV3ZFKDpb9fT0WEDUzKC1kkGWOqTEOYQaJ56J1osxHSxAZQhUz6Dx8uOe74z5aIpOomI6WcDqIt3bPoNFxB/qycZQTo2IGjb/DEnSI2nbMFy9jq2EGJSyCNEjzBuYaJYNmvnjm66ZP5+8frTBYntc8AzDuVefQe33NcWQGGz7DNTCDPZIOQKOYTiXxIWIGHbb2ypjBkImeGq49gycwMieTiBid7tY+jy7MYENysGSiujGDjQNkAcWewakdXFkLYnDggnSxE9UIXiX9L9fPzKJQ7d/LDHZ8d7wOpSTBUGtz097C+ij3nYySSZ0DZgbKu0+PSuQY93WOYCnLMbiYQSPD500eSXCxzKBkXIr5cklbRUYrDJbn1aMqeImhzCCx1lFqDsuxGZK9brWzDZe+JUg7+I+WoO6FcFzWBJdm7l4ZMxjB4KmhOnSeyEpcPDPYQSLCXjsBz3wG+NvfzH/nk78IfOHXG9o+jOcn/jJfPW55Hj/4R4DffrKD839o7//6r4B/9sOHPhhj9mP/LvD076zHSra5A37zY/t79KmPAi974/Y7Q5jBZ7fP5V8+ATzyssa1E/Dz/wPw5If2/+9x7n7ojwG/8+u6NqYJ+H8+uB/70592MoPOAGjaAR/9if1YnvlMvb2W1Ovv/lvAZ59af7cFboHUzJfRGf1zwMtA9WS9rOyteywGSZW0bfO4nIztT/xl4DkvbH//t/6FLBj+2D/ev7uf+WT9fnkD888+te/nXz4BPPzS9rik8+VkCWHJfB2sMEg74Of+HvCr/+e6vx44BTP4438O+Py/utdf2f/Tn97Pkad+GXjwEV37//d/C/zyP6L//enPHL4nrNrJYVFZebD09T//JeDu5+n+a4mP5jtN2BfJWn8MUp17Ik+M3Xve855zj2EY3vve977nXe9617mHscbnfhv4rV8Ffu93AI88Rn/nOS8AHnk58NpvWH9+57nAY18OfMGb+o/r4ZcAz34O+Mrv9LFG6c5+3I++jv73h54P/O5vAV/1p2QLcY4Hngt85v8Ddg8Cdx6i/3vhq4CvfgcdBNXw0Av2hvPOc/bjesVXA2/+buDOg1n/zwM+/fF9/488BnzZHwVe9426fnLceQ7wOx8HdneAB5+3f7Zf+28Dz31RMa5f2f+2hx/dB7pf+Z28MZruAJ/9jf33X/AF+/v8iq9efydNwBd/E/DiL7SPPccDDwOf/gSwe2D9LJ7/CuCN/wbw6q/jr33ms8D87P77L/kS4GvfuR+3Bg8+sg/O7zwIPO+l+3frS/6w7Nq7z+zn/Z2HgBe/dv/MtffluY/u2/mKP+l7d579HHD36cO9+OL9vXjRq+nvPvzo/jl/zXft34sFDz6yd17vPAQ87yX75/xlfxSi0um7B4BXvwX4Pb+3+IcEfOm3y57Lwy/dOyJf/Y79XLwN2N0BXvP1wEu/RHfdwy8Bfvc3gTd919qOSPDAc4DH3rC19dMO+KJv2M+PBWkH3Hwe+PI/zq8pDz8KfO639mPZPaAfy8u+YmtHSnzptwIvfKWu7ec+uk8kfcW/qXfIdw8Cr6LmawO5refWkfy/Rx4DvvxfA173B/k2b57dO+d3HgJe/EXAm/8MvwY+54XA7z4FvOkdwEMKRx3Yt/+ZX9/Pp6N9fQv//TTtbeGLXpO18VzgmX+1v+cPP3rv84dfchjXd+37seLOQ8AXfA3wsjfwY7p5FnjDH+OThfm7s1O+OxI885l9MHLnOcBLX79fd5//8j5tP/jwfi171eN92svx0POB3/yV/T1+7ouBV/8+4Kvevl537jxnP0d2D+5/0xv/BPCat8raf/rThzaYd+Gh5wMv/4r9/Xrw4XvXPfwogAS84W3CtebBwxxR+mk5Hno+8NTHDvfiRcAr3wx8zZ9e27eHXgA87zHgdX9gfe0DDwOPfdl+DBzSBHxx8e488PA+Kf+Vf3J//yl89qm9n/qGP7735S8I3//93/+J97znPe+l/i3NVxa9avD444/PH/7wh889jEAgEAgEAoFAIBA4C1JK/3SeZzJLcYs2dgQCgUAgEAgEAoFAQIoIBgOBQCAQCAQCgUDgPkQEg4FAIBAIBAKBQCBwHyKCwUAgEAgEAoFAIBC4DxHBYCAQCAQCgUAgEAjch4hgMBAIBAKBQCAQCATuQ0QwGAgEAoFAIBAIBAL3ISIYDAQCgUAgEAgEAoH7EBEMBgKBQCAQCAQCgcB9iAgGA4FAIBAIBAKBQOA+RASDgUAgEAgEAoFAIHAfIoLBQCAQCAQCgUAgELgPEcFgIBAIBAKBQCAQCNyHSPM8n3sMw5BS+hSAXz33OAi8FMBvnHsQgatHzKNAD8Q8CvRAzKNAD8Q8CngRc4jGF87z/Huof7jVweClIqX04XmeHz/3OALXjZhHgR6IeRTogZhHgR6IeRTwIuaQHiETDQQCgUAgEAgEAoH7EBEMBgKBQCAQCAQCgcB9iAgGz4P3nnsAgVuBmEeBHoh5FOiBmEeBHoh5FPAi5pASsWcwEAgEAoFAIBAIBO5DBDMYCAQCgUAgEAgEAvchIhhUXC7CAAAERklEQVQ8MVJK35ZS+mhK6YmU0veeezyB60FK6VdSSj+fUvrZlNKHD589mlL6yZTSLx3+fPG5xxm4LKSU3pdS+mRK6Reyz8h5k/b4mwf79HMppTefb+SBSwEzh96TUvp/D/boZ1NK35H92185zKGPppS+9TyjDlwaUkqvTin9o5TSL6aUPpJS+guHz8MeBcSozKOwSUZEMHhCpJR2AP4WgG8H8AYA70gpveG8owpcGf7QPM9vysomfy+AfzjP8+sB/MPD/wcCOf4OgG8rPuPmzbcDeP3hv3cB+IETjTFw2fg72M4hAPgvDvboTfM8fwAADmva2wG88XDNf31Y+wKBZwH8B/M8fzmAtwJ492G+hD0KaMDNIyBskgkRDJ4WbwHwxDzPvzzP8zMAfgTA2848psB1420Afujw9x8C8K+fcSyBC8Q8z/8YwFPFx9y8eRuAH573+GkAL0opveI0Iw1cKpg5xOFtAH5knuen53n+GIAnsF/7Avc55nn+xDzP/+zw998B8IsAXomwRwEFKvOIQ9ikBiIYPC1eCeDXsv9/EvUJHAjkmAH8rymlf5pSetfhs5fN8/wJYG8gATx2ttEFrgncvAkbFdDgew7yvfdlEvWYQ4EmUkpfBOBrAPwThD0KGFHMIyBskgkRDJ4WifgsyrkGpPj98zy/GXvpzLtTSn/g3AMK3DqEjQpI8QMAvhjAmwB8AsB/dvg85lCgipTSIwB+DMBfnOf507WvEp/FXAoAIOdR2CQjIhg8LZ4E8Ors/18F4ONnGkvgyjDP88cPf34SwD/AXubw64ts5vDnJ883wsAVgZs3YaMCIszz/OvzPN+d5/kGwH+De7KrmEMBFimlB7B34P+7eZ5//PBx2KOACtQ8CptkRwSDp8WHALw+pfTalNKD2G9off+ZxxS4AqSUnpdSev7ydwDfAuAXsJ8/7zx87Z0A/qfzjDBwZeDmzfsBfPehit9bAfz2It8KBHIUe7f+BPb2CNjPobenlB5KKb0W++IfP3Pq8QUuDymlBOAHAfziPM//efZPYY8CYnDzKGySHXfOPYD7CfM8P5tS+h4AHwSwA/C+eZ4/cuZhBa4DLwPwD/Y2EHcA/N15nv+XlNKHAPxoSunfAfAvAHznGccYuECklP57AN8I4KUppScBfB+Avw563nwAwHdgv8H+swD+7MkHHLg4MHPoG1NKb8JebvUrAP4cAMzz/JGU0o8C+OfYV/179zzPd88x7sDF4fcD+DMAfj6l9LOHz/5DhD0K6MDNo3eETbIhzXPIZgOBQCAQCAQCgUDgfkPIRAOBQCAQCAQCgUDgPkQEg4FAIBAIBAKBQCBwHyKCwUAgEAgEAoFAIBC4DxHBYCAQCAQCgUAgEAjch4hgMBAIBAKBQCAQCATuQ0QwGAgEAoFAIBAIBAL3ISIYDAQCgUAgEAgEAoH7EBEMBgKBQCAQCAQCgcB9iP8f4lGyI6BFx7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.025710975121473467"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = ridge_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421.264908923075"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(ridge_model_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_test_df=pd.read_csv('titanic_test.csv', quotechar='\"',index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Kelly, Mr. James'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-224-21ee764c93be>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_predict_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mridge_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitanic_test_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0my_predict_test\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    219\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m         \"\"\"\n\u001b[1;32m--> 221\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    202\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"coef_\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 204\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'coo'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    205\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[0;32m    206\u001b[0m                                dense_output=True) + self.intercept_\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    534\u001b[0m         \u001b[1;31m# make sure we actually converted to numeric:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"O\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 536\u001b[1;33m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    537\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    538\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Kelly, Mr. James'"
     ]
    }
   ],
   "source": [
    "y_predict_test = ridge_model.predict(titanic_test_df)\n",
    "y_predict_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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