Exploratory Data Analysis with Titanic Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis with Titanic dataset\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('D:/Jamil Moughal/C# Video Tutorials/Big Data/Datasets/Titanic_train.csv')\n",
    "test_data=pd.read_csv('D:/Jamil Moughal/C# Video Tutorials/Big Data/Datasets/Titanic_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Initial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Mr</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
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       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male  33.0      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked Initial  \n",
       "0      0         A/5 21171   7.2500   NaN        S      Mr  \n",
       "1      0          PC 17599  71.2833   C85        C     Mrs  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S    Miss  \n",
       "3      0            113803  53.1000  C123        S     Mrs  \n",
       "4      0            373450   8.0500   NaN        S      Mr  \n",
       "5      0            330877   8.4583   NaN        Q      Mr  \n",
       "6      0             17463  51.8625   E46        S      Mr  \n",
       "7      1            349909  21.0750   NaN        S  Master  \n",
       "8      2            347742  11.1333   NaN        S     Mrs  \n",
       "9      0            237736  30.0708   NaN        C     Mrs  "
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     "execution_count": 63,
     "metadata": {},
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   "source": [
    "train_data.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
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       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8HHgjcAqwA1gKvD0zH633vQfYBpwMnAjcB3w6M19Xbz8O+C7wmrqGm4FXAccA92TmDfV+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\npCL8PzVYvqdRPrkYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a540c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot('Survived',data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph it is clear that not many persons survived.\n",
    "Out of 891 persons in training dataset only 350, 38.4% of total training dataset survived. We will get more insight of data by exploring more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll explore features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that 233 female survived out of 344. And out of 577 male 109 survived. The survival ratio of female is much greater than that of male. It can be seen clearly in following graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a5559208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()\n",
    "sb.countplot('Sex',hue='Survived',data=train_data,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Sex' is very interesting feature. Isn't it? Let's explore more features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BGWCwu68sblUNh3pYkTCz7wBTgFbFrkUK4kpgnbuXA72B/yhyPVIYfQDcvSdw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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aabae630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot('Pclass', hue='Survived', data=train_data)\n",
    "plt.title('Pclass: Sruvived vs Dead')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow.... That looks amazing. It is usually said that Money can't buy Everything, But it is clearly seen that pasangers of Class 1 are given high priority while Rescue. There are greater number of passangers in Class 3 than Class 1 and Class 2 but very few, almost 25% in Class 3 survived. In Class 2, survivail and non-survival rate is 49% and 51% approx.\n",
    "While in Class 1 almost 68% people survived. So money and status matters here.\n",
    "\n",
    "Let's dive in again into data to check more interesting observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row0_col0 {\n",
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       "            background-color:  #008066;\n",
       "        }    #T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col1 {\n",
       "            background-color:  #008066;\n",
       "        }    #T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col2 {\n",
       "            background-color:  #008066;\n",
       "        }    #T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col3 {\n",
       "            background-color:  #008066;\n",
       "        }</style>  \n",
       "<table id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"index_name level0\" >Pclass</th> \n",
       "        <th class=\"col_heading level0 col0\" >1</th> \n",
       "        <th class=\"col_heading level0 col1\" >2</th> \n",
       "        <th class=\"col_heading level0 col2\" >3</th> \n",
       "        <th class=\"col_heading level0 col3\" >All</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Sex</th> \n",
       "        <th class=\"index_name level1\" >Survived</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level0 row0\" rowspan=2>female</th> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level1 row0\" >0</th> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row0_col0\" class=\"data row0 col0\" >3</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row0_col1\" class=\"data row0 col1\" >6</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row0_col2\" class=\"data row0 col2\" >72</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row0_col3\" class=\"data row0 col3\" >81</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level1 row1\" >1</th> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row1_col0\" class=\"data row1 col0\" >91</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row1_col1\" class=\"data row1 col1\" >70</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row1_col2\" class=\"data row1 col2\" >72</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row1_col3\" class=\"data row1 col3\" >233</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level0 row2\" rowspan=2>male</th> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level1 row2\" >0</th> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row2_col0\" class=\"data row2 col0\" >77</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row2_col1\" class=\"data row2 col1\" >91</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row2_col2\" class=\"data row2 col2\" >300</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row2_col3\" class=\"data row2 col3\" >468</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level1 row3\" >1</th> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row3_col0\" class=\"data row3 col0\" >45</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row3_col1\" class=\"data row3 col1\" >17</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row3_col2\" class=\"data row3 col2\" >47</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row3_col3\" class=\"data row3 col3\" >109</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level0 row4\" >All</th> \n",
       "        <th id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8\" class=\"row_heading level1 row4\" ></th> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col0\" class=\"data row4 col0\" >216</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col1\" class=\"data row4 col1\" >184</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col2\" class=\"data row4 col2\" >491</td> \n",
       "        <td id=\"T_3f240ed0_22ce_11e8_bd97_485ab6c270e8row4_col3\" class=\"data row4 col3\" >891</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24aabb97b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([train_data.Sex,train_data.Survived],train_data.Pclass,margins=True).style.background_gradient(cmap='summer_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XYBqJ/4oYzY0U33MthFoyWJnIBxKsQiQQG74roWPPsG3j/W4ZRU/elaGKRL6QYBXCRvjg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\n3gTGk3siEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aab67550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot('Pclass', 'Survived', hue='Sex', data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use FactorPlot and CrossTab here because with these plots categorical variables can easily be visualized. Looking at FactorPlot and CrossTab, it is clear that women survival rate in Class 1 is about 95-96%, as only 3 out of 94 women died. So, it is now more clear that irrespective of Class, women are given first priority during Rescue. Because survival rate for men in even Class 1 is also very low.\n",
    "From this conclusion, PClass is also a important feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oldest person Survived was of: 80.0\n",
      "Youngest person Survived was of: 0.42\n",
      "Average person Survived was of: 29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "print('Oldest person Survived was of:',train_data['Age'].max())\n",
    "print('Youngest person Survived was of:',train_data['Age'].min())\n",
    "print('Average person Survived was of:',train_data['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISN4qOTdTZ5PsT8UmEREpoqOPPppbb7016jBEJAwqNomIxF4YuZk6m2Q/Guwo\nIlJZZs2aFXUIIlIl1NkkIjK6asjN1Nkk+0ur2CQiIiIiY6Bik4iIoGKTDMNPqtgkIiIiIoXzk5U7\nf0RERPKnYpPsT9voRERERGQMtI1ORERAxSYZho6sFREREZExSSbwfT/qKEREJGIqNsn+tI1ORERE\nRMbC93WysYiIqNgk+1P7s4iIiIiMleY2iYiIik2yH22jExEREZExU5e8iEjVU7FJ9pfQapSIiIiI\njI06m0RERMUm2Y+v0+hEREREZIw0kkFERFRskv2lkvieF3UUIiIiIlKOVGwSEal6KjZVuYdsy/AP\nqLtJRERERMbA18wmEZGqp2JTFct6Plc9vXPYx9T+LCIiIiJjosNmRESqnopNVaylL01nMjv8g+ps\nEhEREZEx0PxPERFRsamKNfWmcj7ma0VKRERERMZAHfIiIqJiUxVr7kvnfExJgoiIiIiMSTr3gqaI\niFQHFZuq2N6eERIBtT+LiIiIyAh83x/+ARWbRESqnopNVay+K3dByVeSICIiIiIjWNfUO+z7NbNJ\nRERUbKpiu0coNpFSsUlEREREcluys3P4B9K5RzWIiEh1mBR1ABKdkYpNflorUiIiY/XktnYe3dzG\nyUcdxAUnH8WEmpqoQxIRKbqWHPM//YyKTSIi1U7Fpirl+T47OkcYAq4VKRGRMVnX1MvX59bhA3Pq\nWplQU8MFJx8VdVgiIkWX9nLMbFKxSUSk6mkbXZXa050ikfFyP0FJgojImNxT28jgP7/+8nxDZLGI\niJRSOpsjl8xmwg1ERERiR8WmKrWlrX/Ex9X+LCIyNra17yVvjzgfT0SkjCVyFJuUR4qIiIpNVWpz\n+8jFJjJakRIRGYuRmkZFRCpJMpNjG102G24gIiISO5rZVKXW5ziqdh9PSYKIyFhk/Rx/fInEkDFm\nMvAX4DVAFvgSkAFuA3ygFrjUWqsyquwn5+875ZEiIlUv1GKTEpr4WNc8crHJ14qUiMiY5JxhIhJP\n/wxMsta+yxjzIeCXwGTgR9bahcaY64HzgJlRBinlRXmkiIiEvY1uX0ID/A8uofkNLqGZAdTgEhop\noc5Ehl2jzRDx9ceSiMhY5DydSSSeNgGTjDETgEOBNHAasCh4fC7wwYhik3KlYpOISNULexvdcAnN\nO3hpQnMWWj0rqRcae0Z/klbmJYZW7u1m/pY2jj1sGp868UgmTaiJOiSR/aSzKjZJWenBdZxvBI4A\nPgq811pv5kGnAAAgAElEQVQ78IPcDRyW78Xq6uqKHZ+UoWQiwR79LIiIVLzp06fnfCzsYpMSmhh4\nrO7F4eDHP3YFFw167I7XHgBAR0cH3fr6Sozs6snyraVdDMwiXb+rgYunHxhtUCLDSKT3P2BBr1fF\nMVJCI2P2LeBRa+1lxphjgCeBKYMePwToyPdi+h5Vj0TGA1YN+9jUKVP0syAiUuXCLjYpoYmBLWvX\nj/qcww8/jJfr6ysxMnfZbjJ+17635+1O84t/1s+oxE9mwQrcGMIX6fVKYqwd12kO0Iab17TaGHOm\ntXYhcA6wIKLYJMZa+9K5H9RBCSIiVS/smU3tQGfw75ckNMH7zgEWhxxTVelNZ6kd7SQ6kRjaPWTO\nWL/Ol5eYSmkbspSX3wKnGmMW4xYBLwcuBX5mjHkWtyh4f4TxSUy1jFhs0u9BEZFqF3Zn02+BW4OE\nZgouoVkB3GSMmQJsQAlNSS3b3UVGw2ulDCVVXJIyMXRk00SNFpMYs9b2AJ8a5qEzwo5FyktzXyrq\nEEREJMZCLTYpoYnekp2doz8JoCbspjeRkSXULSJlauok/T4Vkcqzp1vFJhERyU0ZcBXxfZ8lO/Mc\niaVTviRm+tI6RlnK0wEqNolIBdrTnRz9SSIiUrWUAVcR29pPfb6rUOpskpjpT6uzScqTOptEpBLt\nVWeTiIiMQBlwFZm/pS3v59ZMnFjCSEQKp84mKVcHTNLvUxGpPDs6E7kf1KKliEjV0ytBlfB9v6Bi\nExP0x5HES686m6RMHTBZL7UiUlk83x+l2KRxDCIi1U4ZcJWwrf1s6xghKRhKnU0SM70pdTZJedLM\nJhGpNA09KVJDj94cTMUmEZGqpwy4Sjxomwv7gImhHlQoMqr+zEs7m5TGSrk4UJ1NIlJhtrT1Rx2C\niIjEnDLgKpDOeszZ1FrQx9RMUrFJ4u2Qqeq+k/hJZ/ff7qkB4SJSaTaNVmyaoN97IiLVTq8EVeCp\nHZ209WcK+yB1NkmM+P7+rfqHTtXPqMTP0A48gBr14YlIhalr7Rv5CRoQLiJS9fRKUAXuXddU+Aep\ns0liZLg/4KdO1K8viZ9+DbIXkSpgRy02qcguIlLt9NdahdvW3s/TuzoL/rganUYnMdKR2L8zT3ms\nxFFfWoPsRaSyJTLeqDObavQiLSJS9VRsqnB3146hqwnU2SSxMlyxSSSO+tTZJCIVblNrHyMdRAdo\nRUhERFRsqmTdyQyzNhZ4Ct0AzWySGGnrT0cdgkhe1NkkIpVuXXPv6E9SrUlEpOqp2FTB7l3XRO8Y\nV9lrJmobncRHe6ED7kUios4mEal0LzT25PEsVZtERKqdik0VKpHxuP35xrFfQDObJEZa1dkkZUKd\nTSJS6dY25tPZpGKTiEi1U7GpQj24sWV8f6BP0I+GxEdLn4pNUh6GOzlRRKRSdCYybOtIRB2GiIiU\nAQ3mqUCprMfNq/eM7yI1KjZJfAwUm45NtfEvHWtI10zkmYPfHXFUIvvrV2eTiFSw/LbQAf5oE8RF\nRKTSqdhUgf6+oZk93anxXWSC2p8lPpr70kz0Pf6w+37+Keva90/ftBd4V7SBiQyhziYRqWSrG/Is\nNqFik4hItVP7SoVJZDxuWDnOriaRmGnuTXNMun1foQnguN69+Em18ku8JFRsEpEKtrqhO78neio2\niYhUOxWbKsy965po6i3CfBtPfzBJfDT3pZgwXEt+RrOcJF5UbBKRSpXx/PyGgwO+8kgRkaqnbXQV\npCuZ4YYV9cW5mJIEiYm+dJauZJYjhnnMz2R0uLLESjKr350iUpk2tvTmv1XY0/w6kWrgtTWTmj8T\nr7ONSeZkJr/3HGp00JQEVGyqIDeu3ENnskgv7io2SUw09Y4wfyybCS8QkTwkM64D74hMDzN6ttAy\n6WBq/MMjjkpEZPxW7813XhOQVbFJpBr0//m3eLu3AZDd+DxMO4Ap73h/xFFJXKjYVCHqu5Lcubax\naNfztT1JYqJxpG2hGRWbJF5SWY9Dsglu3XU3R2bcH2ZPHdABZ78+4shERMYn/+HgaDFIpAr4mfS+\nQtOAbN16ULFJAupxqxC/XbqLVLaIwxhT4zzNTqRIGkY4WVFFUYmbVNbnbX079xWaAN69bUmEEYmI\njJ/v+6zam+dwcMBP6/VZpOINM0/V7+6MIBCJKxWbKsDy+i7mbm4r6jX9lE75knhoHGkbnYpNEjMZ\nz+fwbP9L3jfR17ZkESlv9d0pmvsKeM1Na9FSpBr5PSo2yYtUbCpzGc/nV0t2FP/CqWTxrykyBg09\nI3Q2aeVUYiad9UjWaIe6iFSW1QV0NQH4KjaJVCWvsz3qECRGVGwqc/eta2JTa//oTyyQ31NYUiFS\nKg09IxQ+lcxKzGQ8n9SEifu93x+m1VxEpFwUNK8JIJXQ7z2RatTXg5/UDhlxVGwqYy19aX63bHdJ\nru33qtgk8bB3xM4mFZskXjK+j0fN/g9oWK6IlLE1hRabPE8LQiJVymtrjjoEiQkVm8rYNc/spDtV\nmqNl/d6uklxXpFAjbaMjre2eEi/ZXOOZ9EeXiJSpnlSWura+gj/OTxS/815E4s9vbYo6BIkJDZYo\nU8vqu5i9qbVk19d+W4mD3nSWrmTugqqvUxMlZrI5to34qSQ1BxwUcjQiozPGfA74XPDmNODNwHuA\nawEfqAUutdZq0n2VWtvYgzeWHXGJPjj08KLHIyLx5jXvjToEiYlQi01KaIojlfX4xaLtJb2Hr/ZH\niYGG7lGKSRpkLzHj5ZpRosKoxJS19jbgNgBjzP8BtwI/Bn5krV1ojLkeOA+YGVWMEq2C5zUF/N6x\nfZyIlDevaU/UIUhMhLqNzlp7m7X2TGvtmcBK4Ou8mNDMAGpwCY2M4KaVe9jaUdrBa35vt4a7SeT2\njjQcHPBT+hmVeMm1+u+rMCoxZ4x5K3CitfZG4DRgUfDQXOCDkQUmkXu+cWxFI69HIxlEqlG2oTQz\nhaX8RLKNblBCc6kx5ie8NKE5C62e5bSlrZ8bV4XTmui1NjHx6FeHci+R4Yw4rwlABVGJmVynL6l4\nL2XgcuBnwb9rrLUDP8zdwGH5XqSurq7YcUmEPN9n9Z6xFY0at26mf1rePzoiUm4yaV4+3Lt376DO\nWpig8dDVYPr06Tkfi2pmU1ESmmrj+T4/XbSNzJg2zo/hfo31KjZJpEY6iQ40fFTiJ+dvZ3U2SYwZ\nYw4HjLV2QfCuweMMDgE68r3WSEmnlJ8tbf30ZvL+9r/EPx00jan6eRCpWH46xXB9jxMyKY77h0OY\ncOTRocck8RJ6samYCU21rZ7N25Vk1d7CTwMZq5ba1fQcfERo9xMZatOe3hEf72puYleV/R6QeEsk\nhi8q7dm+leTEA0KOpvKokFEy7wWeGPT2amPMmdbahcA5wIJhP0oq3li30AH4OmxGpGpld25RsUki\n6WwqWkJTTUlnQ0+KOxa+EOo9D0v08Ioq+hpL/PSu3wjk7m46ZMokjtTPqMTIlNW1w77/Ff/4D0zW\nz6rElwG2Dnr7O8BNxpgpwAbg/kiiksitGeNwcAC/s62IkYhI3HQlMjkHQGe3b2LyW2eEGo/ETxTF\nJiU0BfJ9n/9ZtJ3edLiH9Hm7t+H7PjU1NaHeV2TAqAPC+8Pr9BPJR67D6LTlU+LMWnv1kLc3AWdE\nFI7EyHg6m7wOFZtEKtnfNjTz6RyPZbdtCjUWiafQi01KaAo3d3Mbi3aMbb/8ePhdHfitTdQccVTo\n9xbxfX/UAeH6A17ixs8xtclP6mdVRMpLVzLD5rax/+7yO1qLGI2IxM2mltyLvt7eXfi93dQcdEiI\nEUncaER8zLX3p/nV4h2R3T+7dWNk95bq1tqfIZUdeRi+3z/yTCeRsOXsbFIXnoiUmfF0NQH43Z06\niVOkgnWnsrkf9H0ym4YfLSDVQ8WmmLvy6Z20JzKR3T+zZUNk95bqtqd79NO7/D4VmyRecpZH1YUn\nImVmPPOaBnitjUWIRETiaMRiE5C14c4blvhRsSnGntrRwexN0bYgZzetxc+1VC9SQnu7R95CB0Ci\nD98Ld5aZyEhydjZpG52IlJlVe7vHfQ2vpakIkYhIHHUlR26IyGxYozy9yqnYFFO9qSw/W7Q96jDw\nO9rwdkcfh1Sf+jw6m/B90PYkiZFcxXltoxORcpLOerzQOP7uYa95bxGiEZE46hhl943f1YG3a+uI\nz5HKpmJTTP126a5RhyOHJbNuZdQhSBXa3ZVHsQnwe8e/8ipSLDnX77SNTkTKyIaWPhKZ8XckeI31\nRYhGROLG9326kyNvowPIvLA8hGgkrlRsiqFVe7u5pzY+bceZ2hVRhyBVKK/OJsDvU7FJ4sPL1dmU\nUGeTiJSPYmyhA/AadhflOiISLz2jzGsakH5+qUayVDEVm2ImlfX4ycJtuYfMRsCr30FWK1MSsvo8\nO5u87q4SRyKSPy/XzCZ1NolIGVmxp0jFpsZ6zWwRqUAtfem8nue3NuHt3FLiaCSuVGyKmZtX7WVr\ne/yOic0891TUIUgVyXo+u/LeRqdik8RHNke1ScUmESkXnu+zskidTaSS+G3NxbmWiMRGvsUmgPTK\nJSWMROJMxaYY2drez40r90QdxrDSK5doZUpCs7cnRSZXi8gQvjqbJEZybaMj2a82chEpC5ta++nK\nYxZLvrK7txXtWiISD429+c8Wzqx6Bj+Tf3FKKoeKTTHh+T4/W7SddJ5/YIfN72wju2lt1GFIldje\nkX8XiN/dUcJIRAqTc56u50E6Hoc+iIiMZHl9/os4LU/fzUXb+vf9Nxxvl4pNIpWmsTf/4pHf202m\nVgdOVSMVm2Ji1saWou2PL5XU4kejDkGqRCFbSf0uFZskPjIjdIBqK52IlIPn9hS3Y1idTSKVp7HA\nU9PTSxeUKBKJMxWbYqAjkebXz+6KOoxRZdevxmveG3UYUgW2tRfQ2aRik8RIdqTm1FR+c8hERKKS\n9fyiL35md23VKAaRCrM7z9mqA7Kb1uI1N5QoGokrFZti4LdLd9ORyEQdRl5SS+ZHHYJUgbq2/ItN\nXld7CSMRKUw6O0JnUzJ+hz+IiAxmW/uKOq8JgP5eLVaKVJj67gIX0Hyf1DOPlSYYiS0VmyL2fEMP\n968vn1M60ssW4vf2RB2GVDDf9wsqNvmd7Rq8LLEx4mB7dTaJSMwt212aQzey2zaV5LoiEj7f96kv\nsLMJIL1skRbeqoyKTRHKej6/WLw96jAKk0yQWjwv6iikgu3pTtGTKmBVNZOGPhVAJXqe74+4jc7X\ngHARibllBQwHL4S3XcUmkUrR2JumP+eJKCPo7yW9/KniBySxpWJThGZubGZ9c1/UYRQs9dQ8/ET5\nxS3loba5t+CP8TraShCJSGFSIw5sQqfRiUispbNeyQ6ryWzZUJLrikj4Cjk1eqjUorma4VZFVGyK\nSFcyw7VLd0cdxtj095JarNlNUhrrmgovNvntLSWIRKQwqRHmNYE6m0Qk3mqbesfWrZAHv6URT6/V\nIhWhkFOjh/JbGsjUrixiNBJnKjZF5I/L62kvk6Hgw0ktmIOvrUtSAi80Fv5z5XW0liASkcKM2tmU\nLfLQXRGRIirVFroB2bp1Jb2+iIRjcwGzVYeTenK25q1WCRWbIrCjI8HdtU1RhzE+/b2knpwddRRS\nYdJZj7Vj6Gzy2spnyL5UrtE6m8iW7wKDiFS+UhebMio2iVSETa3jG6fi7agjq621VUHFpgj8dumu\nkU8sKhOpp+bhdWpWjhTPxpY+EmNo4fdVbJIYGHUbnYpNIhJTyYzHmobSdqxn7Vp1M4iUOa/AU6Nz\nST02qwjRSNyp2BSylXu7eWxre9RhFEc6RWreA1FHIRVk+RgHk3qtZd4pKBVh1G10+iNLRGLq+cae\n0X+HjZPf3YFXv72k9xCR0trZmdh3avSxj1/FRdv69/1XiOymtWR3bC5FiBIjKjaFyPd9fvPMrqjD\nKKr0sgVk63dEHYZUiKW7x9bCr210EgejbqNTsUlEYuq5Em+hG5DZsCaU+4hIaRTzJPXk/JlFu5bE\nk4pNIVq0o4M1Yxh+HGu+T3LW7WqLlnFLZT1W7R3jkct9PRpYL5FLjtYVoKN+RSSmxtpZXKjMehWb\nRMrZWA7yySW7fhXZXVuLdj2JHxWbQuL5Ptct2x11GCWR3byezNrlUYchZW7lnu5xHbnsNTcUMRqR\nwqVG+/mtqQknEBGRAiQzXlH/gByJt6MOr7sjlHuJSPGtLvJst9T8vxf1ehIvKjaFZO7mNja1jn+Y\nWlwlH/wrfioZdRhSxhbtGF/y6bWo2CTRSo62jW6CXnJFJH7CmNe0j++TqV0Zzr1EpKj601k2thRv\nGx1ApnYl2Z1binpNiQ9lviHIej5/Wl4fdRgl5bc1k3r8wajDkDLl+/74i01Ne4sUjcjYjLqNTp1N\nIhJDK0LaQjcg84K64UXK0ZqGnpKcqJ6cd3/RrynxoGJTCB7d0sa2jkTUYZRc6snZeM36g18Kt7mt\nn52d4+uM85r2FCkakbEZbRtdzcRJIUUiIpK/lWOdlzhG2bpa/P7eUO8pIuO3rEQHCWQ3rCG7bVNJ\nri3RUrGpxDzf5/oVVfJHcDZD4oHbNCxcCvb4tvZxX8NrrOzuQYm/UbfRqdgkIjGTynqsKfIMllFl\ns2ReeC7ce4rIuI311Oh8JB+5V39DViBlviX22NZ2trRX7qymobL2BTKrn2Xyqe+KOhQpI49tKUKx\nqbkBP5ulZuLEIkQkUrjkaAPC9bMpMWWMuQw4F5gC/BFYBNwG+EAtcKm1VscpVqANzX0kxnE4x1il\nVz7D5NPfF/p9RWRs2vrT1DaVriMxu3k92U21TDInl+weEj51NpWQ7/vcsqpKupoGSc78C36vjqGX\n/Gxr78e2FmHYYCaN39o4/uuIjNFoM5tqJk0OKRKR/BljzgTeBbwbOAM4BvgN8CNr7QygBjgvsgCl\npFaFvIVuQHbzOrzO8S80iUg4Fu/opNR9R+puqjyhdzZV0+rZ0t1drGsu7sT+cuD3dJGcfRfT/u3L\nUYciZWDu5raiXSu7ZxcTjjy6aNcTKcSo2+gmTwknEJHCfBhYC8wEDgW+B3wJl58BzAXOCh6XChP2\nvKZ9fJ/M6meYcuZHorm/iBRkvAf55MPbuYXM2uVMftPbS34vCUeoxaYhq2cHAt/lxdWzhcaY63Gr\nZxWR0NyyunqHZaeXLWDSW9/DpONPiDoUiTHf95lb11q063l7d8KbTy/a9UQKMdo2upopU0OKRKQg\nRwDHAh8FXgs8BEyw1g4sL3cDh+V7sbq6uqIHKKXh+T4r6jsju3/P4vm0Hj1dJ3WKxFwy67Noe+mL\nTQA9M++gZeqhMEGjB8rF9OnTcz4WdmdT1ayebWjp5dkSDlErB4l7b+Kg7/2v/sCSnDa09LG1iCc1\nent2Fu1aIoUade7JZG2jk1hqBTZaa1OANcYkcFvpBhwC5P1XxkhJp8TL5rY+utPh/AE5nMltTbxu\n6gQmHnt8ZDGIyOjmb2kjkQ3nd8WkjhaObd/LlHdoplslCLvYVDWrZ/9XqyNd/ZYGGu6+iZ53fTjq\nUCSm7rDF3Waa3LGZ3TH+vSCVral15N/7O/Y2kO3PhBRNZVIhoySWAN8wxvwGeAVwEPCEMeZMa+1C\n4BxgQYTxSYms2BPRFrpB0ksXqNgkEnPzijjyIh+pefcz+dR3qWGhAoRdbKqK1bPm3hSLn3g+6jBi\n4eDnn+HI953DxFcfF3UoEjMZz+eZp9cU9ZoTuzs4/uhXUHPQwUW9rkg+pu7YgnuZG95rphsmHP6P\n4QUkkgdr7RxjzHuB53AHx1wKbANuMsZMATYA90cYopRILIpNq59h6nkXUjPtgKhDEZFhdCYyPLkt\n3GH+fmcbqcWPMvUD54Z6Xym+sItNVbF6dndtExlPk/QB8H0S99zAgd/+FTWTQp9HLzG2dHcnLX3p\nol83W7+dSa8/qejXFRlN/2gzm6ZqhU7iyVr7/4Z59xmhByKh8X2f5TEoNpFMkF65hCnv/lDUkYjI\nMB7Z3Eo6gr9rU48/yJR3vI+agw4J/d5SPBPCvJm1dg6wGrd6Nhu3evYd4GfGmGdxJ9SV9epZKuvx\nt/VNUYcRK97eXaTm/z3qMCRmZtviDQYfLLtzS0muKzKa0YpNTJkWTiAiIqPY1pEoyYLPWKQXP6rj\nzkViyPd9HljfHM3NE30kH58Vzb2laEJvNan01bN5m9to00yO/aSeeJBJJ7+Nice8NupQJAZ601me\nKFFLrrdra0muKzKavnQ294OTp1AzUSeriEg8PFcfn0NsvMZ6spvXMWm6upJF4mR1Qw8bWoo7X7UQ\n6cXzmfKeDzPhZUdGFoOMT6idTdXgrrWNUYcQT55H4u4/4WdUiBN4fEv76F0gY5Tdubkk1xUZTW8q\n98+0hlyKSJwsi1GxCSD91LyoQxCRISL/uzabIfnIvdHGIOOiYlMRrW3sYW2TTqHLxW2neyDqMCQG\n5mxqKdm1/Y42vI5wT80QAehJjVBMn6rhtyISD1nPZ+nueBWbMutW4TXtiToMEQnUdyWZvyX6fDqz\n6hmNyChjKjYV0X3rNKtpNKknHtIvjCrX3JdiaYlXVNXdJFHoTuXeRqfh4CISF+uae+lKjrDtNwq+\nT2rBw1FHISKBW1bvJRuTUWrJ2XdprluZUrGpSLqSGR7ZHH31N/Y8j8Rdf8JPp6KORCIyr66NUh9q\nkd22qbQ3EBnC8326R/rjTcPBRSQmluzsjDqEYaVXLMbr7og6DJGq19iT4u8bIhoMPozs5vVk16+O\nOgwZAxWbiuQh20KiRDNoKo3XWE9qXlkfOijjMHdzaU6hG0zFJglbZyLDSDVUzWwSkbhYtCOmBZ1M\nWrObRGLg+hX1pEu9Mlyg5Oy78LMx68iUUanYVAS+73PfuvhUf8tBasEcstvrog5DQranO8nzjaWf\na+bt3oqfSpb8PiIDWkc7hXTKlHACEREZQXNvitoYzxdNLZ6P39sTdRgiVWtbez8PxKiraYDXWE96\n+VNRhyEFUrGpCNY09LClvT/qMMqL79N/159UEKgyoQ0azGY1G0xC1dafHvHxmskqNolI9GLb1TQg\n2U9qsbqbRKJy7dLdsZnVNFRq3t/0t2OZUbGpCP62Pn7V33LgN+8l+bCOs6wmj21tD+1e2S0bQruX\nSFPvKHPoJk0OJxARkRE8HuLr8FilnpqHn+iLOgyRqvP0zk4e3xbf3xF+ZzspbbUtKyo2jVN3MsOj\nMTgWslylF88jo6JAVWjuS/F8Q3it8dnN60O7l8ie7pGLTepsEpGodSQyPLu7tKfBFkV/r/6gFAlZ\nMuPxi8Xbow5jVKknHsLv7Y46DMmTik3j9HBdqwaDj4fvk7jnBvxkIupIpMQWbe8YcYBysWV31OFn\nRt7aJFIse7pHaeueNCmcQEREcnhyWzuZmA39zSW14GH8Ps1uEgnLzav2sLOzDLaoJfpIPv5g1FFI\nnlRsGqc4HQtZrvyWRpKPaDtdpVu8I+SjltNpDaGX0NR3jZKgTZwYTiAiIjnMrSv9abBFk+gjteDh\nqKMQqQobWnq5cdXeqMPIW/rp+Xgd2llUDlRsGocNLb2sa9ae8mJIL36UzJaNUYchJZLOeiytD791\nP1u3LvR7SnXa2j5Kd2aNXm5FJDoNPany2EI3SOqpuXjdIS9UiVSZVNbjsse3lk3XIwDpNKnHZkYd\nheRB2e84zNzQEnUIlWNgO51OGKhIa5t66UllQ79vZlNt6PeU6tOTytIwyoDwmgl6uRWR6MzZ1BLq\nVvaiSCVJPT4r6ihEKtrvlu2mrq38TlVPL12A19IYdRgyCmW/Y5TKeszZpGJTMfktDSTn3R91GFIC\nK/ZEM8jP27lFJ9pIyW1tL78kTUSqh+/7PLixPHPW9NOP4bU2RR2GSEV6akcHf17TEHUYY+NlSaq7\nKfZUbBqjRds76EyG36lR6dILHya7a2vUYUiRFaPYNHHRjVy0rX/ff3nxsjqVTkqutql39CeVXUuB\niFSKlXu72dpRpgexZLMk5/4t6ihEKs7e7iSXPVHef3NlVizGay7TYlmVULFpjGaW6QpR7Pk+iXtu\nxM9moo5EiiTr+axpiO6I0oxdG9m9pTqsbcrnxCRVm0QkGvetK+/DbDKrniZbvyPqMEQqRirr8d35\nW+hIlPnfW55HUlttY03FpjFo7UuzZGdH1GFULG/PDlILH4k6DCmSbR0JetNeZPfPbHwhsntLdVjb\nOHpnk59VJ6yIhK+tP838LWV+apPvk5x9V9RRiFQE3/f52aLtrGnMZ6Es/jIrluC1lXdBvZKp2DQG\ncze3ktUidUml5v8dr13dY5WgNq+uj9LxWxo0QFBKprkvxbZ8tqdk0qUPRkRkiL+tayZdTqdM5ZC1\nL5DZ+HzUYYiUvdtfaGRWJe3Q8bKkFsyJOgrJQcWmMXjIVtD/QeMqlSQ5646oo5Ai2NAS/YBuJahS\nKsvyPUo8U+at6iJSdlJZj7trK2exJfnQnfhedJ3SIuVu4fZ2rnlmZ9RhFF166QK8bu06iiMVmwq0\ntb2fdc3R//FcDTIvPEdmw5qow5Bx2hyD41QzG1RsktLIt9jkp8p0OK+IlK1HN7fR3Fc5XZXe3l2k\nn1sUdRgiZemFxh6+O38LFdDouL9MmvSSx6KOQoahYlOB5ta1Rh1CVUnM/Au+OgLKWhyKTdm6Wvx0\nKuowpMJ4vs/Tuzrzeq6fVLFJRMLj+z63PV95pzSl5t6n36ciBdrRmeArD2+iP1O5nYHppx/DTynX\njxsVmwrg+z7zNpf5kMUy4zc3kH7m8ajDkDHqTmZoicOqajpFdvP6qKOQClPb1Etjb54/38noi64i\nUj2e2dXFxhhsYy82v6uD1JOzow5DpGw09ab4j9mW9nI/eW4Ufm836RWLow5DhlCxqQC2tZ+t+QyC\nlaJKzX8Av3/0054kfnZ1JaMOYZ/M+tVRhyAV5slt7Xk/1+/T7zARCc8tq/dGHULJpBbMwevQTgOR\n0eEhVCwAACAASURBVLT3p/niQzZW+XgppZ+ej+9X4j7B8qViUwHK/ujYMuX39pB8bFbUYcgY7OqM\nz4tbZv1qvQBJ0fi+z2NbCyk2VcYRwyISf2sbe1hWn+fhBeUonSL58L1RRyESaz2pLP8xZxNb2qun\ns9rbs5PsNht1GDKIik0FeKKAVWwprvTiR/E6VewrN3t74lNs8tua8Rp2Rx2GVIi1Tb1sL6DT1e/t\nUbFTREJx/co9UYdQcpkVi8nu2hp1GCKx1JvO8p9zLOuaq6+rOr1kftQhyCAqNuVpR0ciFoOOq1Ym\nTerJOVFHIQVq6InXoL7MupVRhyAVYvamlsI+IJOGhF5DRKS0NrT0snB7dRwBnpx5u4r4IkP0pbN8\n5eFNrG6ozo7qzAvL8XoquLOzzKjYlCd1NUUv/ezjeF3VkUBVisbemBWbaldFHYJUgFTWY25d4Z2W\nXpdeR0SktG5YUfldTQOy2yyZ55+LOgyR2OhPZ7n0kU2s2NMddSjRyWbIaFB4bEyKOoBysXinihyR\nS6dJLZjNtPMuijoSyVMsTqIbxNu5Ga+rgwmHHh51KFLGntzWPqZTXfyudjjqlSWISGR8jDGrgIGl\n4G3AL4HbAB+oBS611lbumdkVwrb0FTRLrhIkZ9/FpBPfQs3kKVGHIhKpvqDQ9Fx9FReaAulnn2Ty\nGf9MTU1N1KFUPXU25aEvnWXV3upsRYyb9LNP4vdX3lG+lSpuxSZ8X1vpZNzuqW0a08f57To9SeLH\nGDMNqLHWnhn893ngN8CPrLUzgBrgvEiDlLz8cUV91CGEzm9rIvXUvKjDEInUwNY5FZocr2kP3o66\nqMMQIuhsKsfVs+X13WQ87QmPhWSC9HMLmXLGP0cdieShNW7FJiCzdgVT3vmBqMOQMrW1vZ/lo7Sn\n73z2AQb3X97x2gMA8NoLnPMkEo5TgAONMfNxeeHlwGnAouDxucBZwMxowpN8bGjp5fEq62oakHps\nFpPf/l4mHKKuZak+vUGhqaq3zg0j/dxTTHzN66MOo+qFWmwavHo26H0P4VbPFhpjrsetnsUqoXl2\nd2fUIcggqcWPMnnG2dRMUGNenCUzHr3pWNWNAchuqsVP9FEz7cCoQ5Ey9NcXGsf8sV5bcxEjESma\nPuAa4GZgOq64VGOtHVhl6wYOy/didXVaTY7CVWuquAM/2U/zPbfQdea5UUciEqq+jM/PV3ezoSMb\ndSixk1y5hF0nvRO0xbbkpk+fnvOxsDubynL1bE2VTvOPK7+1iez61Uw66bSoQ5ERjGWmTSiyGTLr\n1zD51HdFHYmUmY5Emgft2LuT/NaxF6pESmgTsDkoLm0yxrTicrMBhwB5D64cKemU0ljb2MNzzeuj\nDiNSB25YyREf+RcmHn1s1KGIhKI7meE/5lgVmnKYkErymv52Jp/wnqhDqWphF5vKbvUsmfVZ39xb\n8vtIYdqfnEPH1EOjDkNGsLUrpsUmoOOZJ+k45J+iDkPKzH1b+0lkxt6tl9q7m3p1fYyLChkl8QXg\nZOArxpijgUOB+caYM621C4FzgAURxiej+N1zu6MOIXq+T3LWXzngvy7XUGCpeJ2JDF+eY6lt0t+o\nI0kvW8Tk01RsilLYxaayWz1bubebrK+T6OJm2o46jnv5kUw4JO/apISscWcHrn4cP9N21nH8q4+h\nZuq0qEORMpHIeDy6ZM24rjGxr4fjj3mltnBK3NwC3GaMWYKbn/kFoAW4yRgzBdgA3B9hfDKCFXu6\neGZX1+hPrALZulqy61Yy6aS3Rh2KSMl0JDJ8afZG1jfrwKTRZDevw2trZsI/aoE5KmEXm8pu9Wyd\nKsbx5GXJrHyaKWdqUHhctffHt7OJdIrMhjVMfvM7oo5EysQD65toLcLPtNe0l4mvPq4IEYkUh7U2\nBVwwzENnhB2LFMb3fa5dqq6mwRIP3slBb3gzNZNCPwNJpOQ6Ehm++NBGNrSo0JQX3ye9bCFTz/nX\nqCOpWmFPWL4FODxYPbsXV3z6BvAzY8yzwBRitnq2pb0/6hAkh/TyRaM/SSLT2h+/k+gGy6xZGnUI\nUiZSWY9bVzcU5Vpeg/4wFJHiWLyzk9WaK/oSfksD6cXzog5DpOg6EhkuUaGpYOllC/CzmmsVlVDL\n/uW4era5rbKKTdPn/2rExy/a9tLPd+DI7jjy9uwk21jPxKNeGXUoMozm3pgXm9avxk8mtJVORvWg\nbaGhN1WUa2UbdjO5KFcSkWrm+T7XLVPxejjJ+X9n0ltnaNSCVIyupNs6t1GFpoL5ne1k1q9m8sna\nXhsFnR0/At/32arOpljLrH426hAkh+a+eBebSKfI/H/27js8qjLtH/j3zDnTUklIJYHQwgm9g4IU\nG1aIiIpi73V11/quoq6udXdtu66s4mLBsqiIARQEBRQp0qTDIUAIhFASSM/UM+f3B+APNEDKzNyn\n3J/req+LEN+Zb9jJzDn3cz/3s2k1dQqmc341hLdXlYbt8UKlu8P2WIwx65q7/TDfeJ6M1wP/N59R\np2AsLGr9Ku6crfCMphYILP6WOoJlcbHpFOoCIVT7uO1Oz4Jrl0PTtNP/hyzq9teGpxMkkgJrllJH\nYDo3fUsZ9oXxtRwqLQ7bYzHGrCmghvBP7mo6pcDPC6GWFFHHYKxF6gMq7v5awfoDPEO4JdTCjVD5\n+osEF5tO4WCYtk2wyAkd2IvQvj3UMVgDSmt81BFOS92yDqFaPsWHNcwXDOGd1eHragIAraYKoWo+\n4ZQx1nxfbinHnmr9f8aS0jT4ZnzIC5LMsAJqCA9+ux1r9vFctnDw/zCHOoIlcbHpFHS/DYgBAILr\nV1BHYL/hV0M4YIRibUjlQeHspD7deAAHIzB7LMSr7YyxZqoPqHhr1V7qGIag7tyK4C/cwcyMJ6Rp\neGJBERbvrqKOYhrBVT8hdLiMOoblcLHpFMq52GQIwXU/U0dgv7G7yoeQQRYTA6sWU0dgOlTjC2Ly\nmn0ReWx1z86IPC5jzPymrjvA16dN4Jv5MTQvz19lxvLykt34uvAQdQxzCanwz59BncJyuNh0Ct5A\niDoCa4TQ/hKoB3iVT0+KDDRYP1S8nV8/7HfeX7cfld5gRB6bi02MseY47Angv7+Ed2uv2WlVFXyD\nyQzl4w0H8NH6A9QxTCmw4geEyvnfNpq42HQK/hAXm4yCT6XTl8LDxik2AUBwxQ/UEZiOlNX78cHa\n/RF7/NDuHTxHhDHWZG+vKkUdL4Q2mX/RN7yoxAxhcXElXvqJB1lHTCgE38yPqVNYChebTsGv8s2A\nUQR/WcY3bzqy8aCxhhkGVv4ITY1MFwsznkkrS+EJRu6GTqupglZRHrHHZ4yZT3GlF//bdJA6hjGF\nVPg+/y9fJzJd237Yg4fmbTfMGAqjCm5YiaCynjqGZXCx6RT4Q8k4QgdLEdq7izoGw5Hfm40HjXVE\nq1ZTheCmX6hjMB3YVenBF5sjf0On7toW8edgjJnHa8v3IMh3oc2m7tiC4MofqWMw1qC6gIo/fVvI\nnYtR4vvyA2hBnn0XDVxsOoUYu0gdgTVBYA2fOKIHuyq9OOQxXpdQYNl31BGYDry+vATRaGpVi7jY\nxBhrnDX7ajB/ZwV1DMPzzfwYWl0NdQzGTqBpGp5ZtAs7K7zUUSwjdLAU/rlfUMewBC42nYJb4n8e\nIwmuWsxboXRg+d5q6gjNom5dz0MDLW7d/tqo3dCpO5WoPA9jzNhCmoaXftpNHcMUtLoaeHleC9OZ\nL7aU8clzBPwLZiHI12IRx9WUU+DOJmPRaqoQ3LiaOoblLd1TRR2h2fxLubvJqjRNwyvL9kTt+UL7\ndkPzGGu7KWMs+mYq5dhUxu8V4RJc8QOCW9dRx2AMAFBS7cPLXEymoWnwfjoJmreeOompcbHpFJLc\nEnUE1kSBpd9TR7C0uoCKJbuNW2wKLF8IzcdtzFa0qLgSq/dFcXuFpnF3E2PslOr8Kl5bXkIdw3S8\n0ybzDSYjF9I0PLlwZ0QPJGGnppUfgPeTSdD4BPqI4WLTKWTEOagjsCZSt21AqGwfdQzL+nFXJXxG\nPsXRU4cADxC1HDWk4XWCG7rgto1Rf07GmHFMWrUX5fU8xDbctMpD8M38hDoGs7gvNpdhxV6eIUYt\nuGEV/N99RR3DtLjYdAppsQ7YBOoUrKn8C2ZTR7AsM+w59/84l1c4LGb2tkPYftgT9edVC7nYxBhr\n2PbD9Zi6nucIRkpg2fd8/DkjU+UN4vXl0du6z07NP/cLBDasoo5hSlxsOgXJJiAtlrubjCaw8geE\nKg9Tx7Cc/bV+/FBcSR2jxbSyfQhu5A8cq/CrIby5kmabSmjfHoSqjf87wxgLL03T8NcfixEMGbhT\n2AC8n0zi0+kYiUmr9qLKp1LHYMdoGrxT/4ngzq3USUyHi02n0SnJTR2BNZWqwr/oa+oUljN980GY\n5brY//1MaJpJfhh2Sp9vOojSGj/Z86tb1pI9N2NMnwqUcqwq5SJIpGnVlUfmN/HnPYui4kovPt14\nkDoG+61AAJ7Jf4daWkydxFROW2ySZTlOluV/y7L8vSzLybIsvy3Lclw0wulBl9ZcbDKiwNLvuLsp\niuoDqqk+OEO7d0At3EQdg0WYNxjCO2toZ7wFN/9C+vzMmKx+bWZmZXV+vLyET6eKluCGlQj8vJA6\nBrOQyWtKuWtRr7z18Lz9EkJl+6mTmEZjOpv+CaASQDoAL4AEAO9EMpSeyK1jqCOw5gj44ZvzGXUK\ny/h8cxkqvEHqGGHlnzedVztNbtqmg+TDd4PKBmhBHgDMmszS12ZmdWz7XDVvr4kq34wPoR7YSx2D\nWUBpjQ+zthl/vqmZadWVqP/3swgdLKWOYgqNKTb1VRTlCQABRVHqAVwLoE9kY+lHXgoXm4wquPJH\nqHuKqGOYnieg4r1fzHcCoLpjK9Ttm6ljsAjxBFT8l7irCQDg80BVNlCnYMZj6Wszs5q7/TC+L6qg\njmE9fh+8770GzeelTsJM7oN1+7mryQC0qgrUv/lXqPtpZnqaSWOKTb9dXhEBWOaopo5JbsQ7ROoY\nrDk0Db6CqdydEmHvrd2PMpMezeyf+wW/fkzqyy1lOOTRx+s2sO5n6gjMeCx9bWZG+2v9ePbHXdQx\nLCt0YC+8n/H8JhY5noCKgq3l1DFYI2k1lfD8+69Q9+6ijmJojSk2/SjL8ssA3LIsXwDgSwCW2dws\n2gT0zeAxCEal7tiC4KqfqGOY1oFaP6aYsKvpGHXnVqhb11HHYGEWUEN4b61+9uMHN6yCFqAbUs4M\nydLXZmYT0jQ8/v1O3j5HLLhmKQJLv6OOwUxq/s4K1Pj5d9xItNpq1P/rWQS3baSOYliNKTY9BqAW\nQBWA5wGsB/BIJEPpTb/MeOoIrAW8X32IUE0VdQxT+tuS3fAEzb2Y7pv9KbSQuX9Gq/m68BD21eqo\nuOOtR3DjauoUzFgsf21mJh+s3Y+f91ZTx2AAfDM+gFq0jToGM6HpW8qoI7Dm8HngeeclBFYvoU5i\nSNLp/gNFUQIA/nr0/yxpYBYXmwytvha+GR/AfcP91ElM5fudFZi7w/wn/oVKdyO4egnsA4dRR2Fh\noGka3tdRV9MxgZ8Xwd73TOoYzCD42sw81u6vwes/81wQ3VBVeKa8gpgHn4ctKYU6DTOJA7V+rCqt\noY7BmktV4f3oTYQqD8FxzmgIgkCdyDBOW2ySZbkIwPEbmDUA9QA2AnhQURTz7qE5qkdaHBKcIrc3\nG1jwl2UI9hsCqccA6iimUOUNWmq2hO+baZB6D4LgcFJHYS30895qFB72UMf4HXXbBoQOl8GWnEod\nhRkAX5uZw6H6AB78djsPDNYZrbYannf/gZj7/wLB6aKOw0zgOx78bwr+2Z8idLAUritvhSDZqeMY\nQmO20X0FYAGAcQDGAvgawCoAK2CRY3Ylm4AzshOoY7AW8k6bzNvpwkDTNDy9qIj8yPho0ioPwb9w\nNnUMFgZT1x+gjtAwTYN/8VzqFMw4LH9tZnTBkIaH52/HgTrrfJYaSai0GN5PJvE2ehYW8y2wE8Aq\ngit+gOet5xGq5a3PjXHaziYAwxRFOb4d5H5ZllcoinKzLMs3RyqY3gxtm4h5O7gqbWRabTV8n02G\n65aHuP2xBT7fXIb5O633u+BfMAv2wSNha9WaOgprpt1VXvywq5I6xkkFli2E84JxEFwx1FGY/oX9\n2kyW5TQAqwGcDyAI4H0c6ZjaCOBeRVH4rjuMXlu2Byv28rYaPQuuXwH/N9PgvPQa6ijMwGp8QazZ\nx7/rZqIWKah/bSLctz4EsU0OdRxda0xnU4Isy78OLZJlOQFA7NEvLXPHPiKnFXUEFgbBjasR+JkP\n7GmureX1eOmnYuoYNPw++GZ+TJ2CtcAXm8ug680qPg8CyxZQp2DGENZrM1mW7QDeBnBsj+mrACYq\nijLs6OPltywuO97nmw/i/XX6mx3Hfs///Uz4l8ynjsEMbHlJNVRdX3yw5tAOl6H+jacRWLOUOoqu\nNabYNAXAz7IsPyPL8l8BLAMwWZblPwDYEtF0OpIa60Cv9NjT/4dM93wzPkSojMdZNFWFJ4D752yD\nz8KfmMFfliGobKCOwZrBr4bw1Vb9nwTj/34mNK/+Zkox3Qn3tdk/APwHQOnRr/sD+OHon+cAOK+F\nedlRy0uq8NyPFl20MSjf9Pf4xFDWbIt38wgP0/L74J36L3hnfgxN5dnODTltsUlRlJcA/AlAIgAX\njhyxew2AJQBubc6TyrKcJsvyHlmW82RZ7izL8k+yLC+WZXmSLMuNKYCROKd9EnUEFg5+HzxT34QW\nDFInMYyAGsKD87Zjb42Ojosn4p3+HrQgz9gwmgVFFTjk0f/vvFZXA/+Pc6hjMJ0L57WZLMs3AShT\nFOXb4/5aUBTl2MpCzdHnYS2047AHf5zLA8ENR9Pg+fCfUIu3UydhBqNpGpbu4WKT2QUWzobn7Zd4\njlMDGjOzCTgycLIfgHsB3Abgn4qirGnOE56iVXuRLMv/wZFW7RnNeexIO6dDEh9PaxKhPTvhn/s5\n78NvBE3T8OJPu3m2xFFa2T74F8yCc9Tl1FFYE0zfov+upmP8C2fDfua5sMXz/T07pXBdm90CQJNl\n+TwAfQB8CCDtuO/HA2j0sLPCwsJmRDC/Mk8I/7eyGjV+LjQZUsCP2v+8iENjb4OalEKdhhlEaZ2K\nfbW8UGsFauFGVL/8KCovuAqB9LbUcaIqNzf3pN87ZbFJlmUZR1bOrgOwC0dWz9oritKSEu2xVu0/\nH/36t63ao6DTYlOnZDc6Jrmws8JLHYWFgX/BLIhyL0i53amj6Nq7v+zDtE0HqWPoin/eDEi9B0NM\nz6KOwhphX40Py/YYaLXJ64Fv5sdwX3sPdRKmQ+G+NlMUZfhxj70IwF0A/i7L8khFURYBuAhAo4cd\nnuqi06oOewL404wtOOTjQpOR2bz1SJvzMWLufwa2JD4shJ3eqo0HABjo+oO1iFhbhdZfTYFz7I2w\nDzmPD6TCKbbRybL8DYAfAfgBjFQUpQeAmpYUmszQqn1+x2TqCCxcNA3eT96C5qmjTqJbs7eV4/Xl\n3M33O2oQvmmT+UhkgyhQyvU9GLwBwVWLEdy+mToG05lIXJudxEMAnpFleRkAB4Avwvz4llHnV3HX\n7G0oquSFSjPQKg/B858XeLsMa5QlPK/JelQVvi+mwPvJJGh+H3UacqfqbOoDYA2OHHl7rCe6pdfr\nhm/VzrPrf+YHazyt8jDKPngTVefylqjfWlUWwIvraqlj6JZapGBvwafw9BhEHYWdgqZp+HyDMW8K\naj6ehPKr7gYkO3UU3eCumYhcm/1KUZSRx305IlyPa1X1ARV3f70Nm8rMs6iVO++FU37/+qITDziY\n2sEdyTgkQgdL4Zn8N8Tc/QQEl/l+PhYefjWE5SXGvP5gLRdctRj1pcVw3/Qn2FIzqOOQOVWxqR2A\nsQDuAfC6LMtfA2jRO6oZWrU7axrabVmP3VVcqTQLt7IWSUPPgb3nQOoourFybzX+vkHho1pPI/Hn\n79BmxCjYklOpo7CTWF1ag/2eRq9h6IpUWY62W1bAdflN1FGYfoT92oxFhieg4t5vtmH1Pp53aEah\n3TvgmfIK3Lc/CsHuoI7DdGjNvhp4gtwBb2Wh0t2oe/UJuK+9G1KPAdRxSJx0G52iKEFFUT5XFOVs\nAAMA7APglmW5UJblu8KYwVCt2oIgYBRvpTMd32fvIlTDra4AsOFALe79Zht8XGk6PZ8X3mnvQNP4\n30qvZmw1zmDwhgQWf4vglrXUMZhORPHajLWALxjC/XMK+WANk1MLN8HzwRvQVN71wH5v/s4K6ghM\nD7z18Pz3Ffi+/p8lx2+ctNh0PEVRNiuKcj+ANgD+DuCOlj6xoigjFUXZqijKNkVRRiiKcqaiKLco\niqK29LEj7YLOXGwyG622Gr5Zn1DHILe5rA53zFZQF7Dem2Fzqds2IrBsAXUM1oC6gIq52w9Tx2gx\n7yeTEKo0/s/BwisS12as5TwBFX+YU4ilvH3GEtRNa+D95D+WvIlkJxfSNHzHxSZ2HP93BfC88zK0\nOmstQjSq2HSMoij1iqK8oyhKv0gFMoKuKTFom+CkjsHCLLjyRwR3bqWOQWZreT1um7kV1T7d13t1\nxzfzI4QOG7uDxoy+3nbIFC3sWm01PO+9Cs3Pxyez3+NrM/2oOzqjacke7pS2kuCaJfB9MYW7nNmv\nVpXWoLw+QB2D6YyqrEfdq09A3buLOkrUNKnYxI4QBAHnd+LuJjPyfTEFmmq9YotSXo9bZ25FFRea\nmsfnhfdTXtnUmy82H6SOEDah3Tvg/Wwy38wwplM1viDumKVgZam1Vq3ZEYFl38M/+3/UMZhOTN/M\nC5CsYdrhMtS/8RQCq3+ijhIVXGxqplEdk6gjsAgI7duDwE/zqGNE1ZbyOtwycysqvTxzoCXU7Zst\n99rRs00H67CprJ46RlgFV/8E//cF1DEYY79R6Q3g1pkK1u7nE1ytzL9gJnzf8Xu01VV6A5i3k7e+\ns1MIBOD96N/wzfrU9AvVXGxqph5psciMM/7pE6IoIicnBz179kROTg5EUaSORM7/3VeW2a6yuawO\ntxRwoSlcfLM/RehgKXUMBuCj9fupI0SE/+tp8C/nGWGM6cWBWj9umLEVm8rqqKMwHfB//T/4l8yn\njsEIfbapDH4+ZIc1gn/BTHimvALN66GOEjESdQCjEgQB53VMwtT1B6ijNFtqairGjx+P/Px8SJKE\nYDCIgoICTJs2DWVl1m3/1GqrEVixCI6zRlFHiaj1B2px52yFZzSFU8APz8dvIeb+ZyBw4ZbMgVo/\nvjHBYPCT8X32LoSYONh7DaKOwpilFVd5cfvMrdhbY40FKtY4vunvQXC5Ye9/FnUUFmV1fhXvr91H\nHSNicue9cMrvX190YtFkagd3JOOYgrppDerfeAru2x6BrXUadZyw486mFjjPwFvpRFHE+PHjMW7c\nOEjSkZqjJEkYN24cxo8fb/kOJ/+CWaY+yvaXfTU8DDxCQrt3wL9gJnUMS/t4wwEEQyZeVdQ0eD/8\nF4Jb11EnYcyylPJ6XP/lFi40sd/TNHg/+Q+/R1vQpxsP8PxT1mSh/SWof/1JqLt3UEcJOy42tUCf\njHgkuYzZHJadnY38/PwGv5efn4/s7OwoJ9IXraIcwV+WUceIiFWl1bhjtoK6gLn3CFPyz51uqZMm\n9KTKG8Rnm8wzGPyk1CA87/4DwU1rqJMwZjmrS2tw41dbcMjDp02xkwip8Lz3milvHlnDyusDeHeN\nebuaWGRptdWo//dfEdz8C3WUsOJiUwtINgEj27eijtEsCQkJv3Y0/ZYkSUhISIhyIv0x4ykBy0uq\ncdfsbajnQlNkhVR4P5kELWje7ji9en/tPtT4LbKqqAbhee9VBDasok7CmGUsKKrAbbO2Wud9hjWf\n3wfP5L8hVGbOGYLsRK8s3c3vC6xl/D54/vsP+Jd+T50kbLjY1EJntzfmVrrq6moET3IjHAwGUV1d\nHeVE+qMWboLmMc9pVkt2V+GerxV4glxoioZQ6W74502njmEph+oDhp6j1yyqCu/7ryNg0k5MxvRk\n+uYyPDC3kIf/skbTaqtR/85LCNVUUUdhEbRybzVmbjtEHYOZQSgE3+fvwr9gFnWSsOBiUwud0TYB\ndptAHaPJSkpKUFDQ8PGsBQUFKCkpiXIiHVJVBLespU4RFouLK3HfnG3w8QVyVPm/n8kt9FH0zupS\naxZTQyq8U/8F/49zqZMwZkqapmHy6lI8tagIZh4HxyJDKz8Az5RXoQV526UZVXmD+PP3O6ljMJPx\nzfoEvjmfQ9OM/aFjzIFDOhJrFzEwKx5L9xirE0hVVUybNg0AGjyNTlW5DRQAghtXwd5vCHWMFllc\nXIk/zClEwGBXyKY48SIUgvd/byPmwRcgnGTbKguPwkP1+HSjxbqajqdp8M34AFpNJRwXj4cgGG8R\nhDE9CmkaXv5pNz7aYOH3F9ZioV3b4P38v3BdfSe/P5uIpml4alER9tXyQQEs/PzzvoTm98E55lrD\nvm/w3U8YjMhpZbhiEwCUlZVh0qRJmDVrFhISElBdXY2SkhIuNB1H3VNEHaFFFu+uxP1zjVdoMpPQ\nvj3wf18A5wXjqKOYlqZpeOGnYnDjHuD/rgChqgq4rrqdC5yMtZBfDeHx73dizvbD1FGYCQRX/IBA\nm3ZwjLiYOgoLk083HsR3OyuoYzATCyz6GhAEOEdPMGTBia9Ew2Bo20TqCM2mqiqKi4upY+iWdrgM\nmqpCEEXqKE22ZHcV7p/DsyX0wD9/BqRegyBmtqWOYkrf7jiMFXtrqGPoRnDlj/BUlMN9058gxMZR\nx2HMkOr8Ku6fW4jlJcZbTGT65Sv4CLb0bEh5vaijsBZasrsKL/3E91As8gILZ0NwueEcdTl1lCbj\nmU1h0L6VC5lxDuoYLBJCKrSKcuoUTbZybzX+MGcbF5r0QlXh+/y/0EIWnCcUYRWeAF5YzBd7aNMk\nKAAAIABJREFUv6Vu34y6N55CqIyPYWasqQ7VB3BzwRYuNLHw0zR4P3oToepK6iSsBbYfrseD87Zz\nRzWLGv+cz+H/4RvqGE3GxaYwEAQBQ9sZt7uJnVqo3FhzGtbtr8U93/AwcL1RixQEV/5IHcN0nl9c\njEOehk/WtDqtbB/qXn8Kwe2bqaMwZhh7q324fsZmbCozz2m0TF+0uhp4P53EC1AGtb/Wj7u/3oZa\nP48dYdHl+2oqAmuWUsdoEi42hckZWQnUEViEaH4fdYRGU8rrcdfXCuoDfAGjR75ZH0Or4+1e4TJ/\nx2GepXI69bXwTHoB/qXfUSdhTPe2HarHtV9uRnGVcT73mTGpW9cj8NM86hisicrq/bh15laU1vBA\ncEbD++l/oO4qpI7RaFxsCpOBXGwyLcFup47QKHurfbhztoJqH6+06JVWVwvft9OpY5jCgVo//vLD\nLuoYxhA6so3T+8UUaCp3gTHWkLX7a3DjV1tQVs/H07Po8M36BGrpbuoYrJEqPAHcPlPBrkovdRRm\nZcEAPFNeQcggY1642BQmKTF25Cbr8Oh11nJ2/c/jqvAEcMdshS+SDSCw5DuEDvIcnZZQQxoe+24H\nKr1cOGmKwJL58PznRYRqeQ4NY8dbuqcKt83kxRoWZcEAvJ+/C03jsQd6d6g+gFtnKig87KGOwhi0\nmip4/vsKtKD+7/u42BRGg7i7yZT0fny4LxjCfd8U8kqLUYRU+GZ/Qp3C0CavKcXKUt6O2Bzq9s2o\nf20i1FIeqs4YAMzbcRh3f70NniBvP2fRF9pViODa5dQx2Cnsr/Xjxq+2QDnEc9yYfoT27oJv5sfU\nMU6Li01h1D8znjoCiwAhMZk6wklpmoanFxVh7YFa6iisCYIbVkEt2kYdw5BWlVbjrZV7qWMYmna4\nDPVvPI3A2p+pozBG6qutZXho3nYEQ9xZwuj4Zn0CLcAzgPRod5UX18/YjCJe0GU6FFj8LQIbVlLH\nOCUuNoVRvzZx1BFYuLljIbRqTZ3ipN5dsw+zth2ijsGawTd/BnUEwymr8+PBb3fwUcPh4PfB+8Hr\n8H0zjU9EYpb02aaDeGJBEbjOxKhpFeWGPNLc7DaX1eG6L7fwMHCma97/vY1QVQV1jJPiYlMYpcY4\nkJPopI7Bwkhs0xaCIFDHaNBPuyvxxs8l1DFYM6lb1kLdU0QdwzACaggPztuOQx797083Ev/8r+B9\n7zVoXp5Dwazjo/X78QwfMMB0xL9gtqFOPza7xbsrccOMLXzNwfSvvg6+L9+nTnFSXGwKs368lc5U\nbJntqCM06ECtH//33U7wgqyx+b/7ijqCYby6fA/W7OPtopEQ3LgK9f98GqFDB6mjMBZxU37Zhxd/\n4hPAmM546hBct4I6BQMwY0sZ7uU5bsxAgutXILBen9vpuNgUZn0zuNhkJmLHPOoIvxMMaXhk/g5U\n8ElchhfcuAqhysPUMXRv9rZyfLjuAHUMUwvt24P61yYiuGMLdRTGIub9tfvwyrI91DEYa1Bg+QLq\nCJamaRr+taIEExcW8XZ9Zji+6VN02aXOxaYw65vJc5tMw2aDlNeLOsXvvLd2H1bv45O4TCEUQmDF\nIuoUuralrA5PL9pFHcMStLoaeCY9j8DKH6mjMBZ2U9ftx9+XcqGJ6Ze6cyvUA3wABgVfMIRHv9uB\n/6wqpY7CWLNo1ZXwL5xNHeN39H2muwF1aOVCK5eESu46MTyxgwzBHUsd4wQ7Dnvw7xV8IWImgeUL\n4TjvMgg2rv3/VoUngPvnFsKr81b23HkvnPL71xeduNI0tYM7knFaRlXh/WQSQuUH4LjwCt3OrGOs\nKT7deAAvLeGtc0z/gmuWQrzoSuoYllLhCeAPcwrxy37eqs+Mzb9wNuxnngtbK/2cpM7FpjATBAG9\n0+PwQ3EldRTWQlK3vtQRTqCGNDy5cCcCfHSOqWgV5VCLtkHqpL8tm5SCIQ0PzdvBp8AQ8c/7EqHy\n/XBdfScEu4M6DosgWZZFAJMByAA0AHcB8AJ4/+jXGwHcqyiKvqu+J/HV1jI892MxdQzGGkXdvYM6\ngqXsrPDgnq+3YU81D2dnJhDwwz/3c7iuvpM6ya94KT0CeCudOUh9zqCOcIKZ28qx7kAddQwWAcFN\nq6kj6M4ry3bj573V1DEsLbhmKTzvvAzNW08dhUXWaABQFGUogIkAngfwKoCJiqIMAyAAyKeL13wL\niirw1EI+9ZMZR6ikCJrGi4rRsLykGtd+uZkLTcxUAisXI3S4jDrGr7jYFAF9M7jYZHRip66wJadS\nx/iVJ6Dinz+XUMdgEaJuWkMdQVdmKjwQXC/U7ZtR/+/nEKqpoo7CIkRRlK8A3HH0yxwAlQD6A/jh\n6N/NAXAeQbQWWVVajYfmbedBv8xQtNpqaFV8cEikfbmlDHfOVlDtU6mjMBZeIVVXs5t4G10EdE+N\nhWQTEOTtToZlHzicOsIJPlx3AAfrAtQxWISEDpYidLhMVwVOKpvL6vCXRdyJoCehkiLU/+sviLnr\ncX6NmpSiKEFZlj8AMBbAFQDOVxTl2EVMDYDExj5WYWFhBBI2za6aIB5fVQM/30dGnCiKyM7ORkJC\nAqqrq1FSUgJV5X/4lihZuQy+9jJ1DFPSNA2f7PDi8yIvdRTGIsa/bAH2du6DUEx0GmByc3NP+r2o\nFpvMPhfgGLddRLfUGKznLU/G5HBC6j2YOsWvvMEQpq7fTx2DRZi6e6flb+QrPAHcP6cQPm5F0B2t\nbD/q//kXxNz3FGwp6dRxWAQoinKjLMuPAfgZwPFT7ONxpNupUU510RkNZfV+vPTFZtTzOS0Rl5qa\nivHjxyM/Px+SJCEYDKKgoADTpk1DWZl+tnEYTWZqCuzEv0dm5FdDeHphEWZyoYmZnKAGkV1WDOd5\nl1FHifo2OtPOBfitfhnx1BFYM9n7DoHg0s9pUbO3laOCTzc0vdBea3fzBEMaHp6/A/tqeSC4XmlV\nh1H/1nO6mgXAWk6W5etlWf7z0S/rAYQArJJleeTRv7sIwGKKbE3lDYbwh28KsZ/fRyJOFEWMHz8e\n48aNgyQdWbuWJAnjxo3D+PHjIYoicUID41NAw67GF8SdsxXM3HaIOgpjURFYtgBaiL5/J6rFJrPO\nBWgIDwk3LvtQ/bwENU3DR+t5do0VqCXWLja9vnwPlpfwQHC90yrKUf/vvyJUUU4dhYXPlwD6yrL8\nI4BvAfwRwL0AnpFleRkAB4AvCPM1iqZpeOL7ndhwkLvKoyE7Oxv5+Q2vD+fn5yM7OzvKiUzExiN1\nw6m8PoCbCrZixd4a6iiMRY12uAyqsp46RvRnNpltLsDJJPnpK4ms6QKpbbDTqwI6eW0V16ooPOyh\njqELZp8L4dtfir06ed1F27IDfry3nm8QjUI7XIaq15/GoXG3Q3PHRvW5qbdomZGiKHUArmrgWyOi\nnaUlpqzdj7k7eLBytCQkJPza0fRbkiQhISEhyonMQ+DOprAprfHhtplbUVzFJ84x6wms/BFS1z6k\nGUgGhJtlLsDp5G7YwIUCg4k/91Ik6+h1tWBVKQDu9rDCXAjJ79H9e1ok7Kr04M1Fm6hjsCaSqg8j\nc+GXiLl7IgSHgzoOs7jVpTV4Y/ke6hiWUl1djWAw2GDBKRgMorqar12aS2jVmjqCKeys8OC2mVtx\ngA/YYRYV3LQGmt8HweEkyxDVPk0zzQVojIFZPLfJUNyxkPoOoU5xggW7KqgjkLPMXIj6OmiqtWZz\n1QdU/HHudtQFuBPUiEK7CuH9dJIuZgIw6zpUH8DD87eDzxWIrpKSEhQUFDT4vYKCApSUlEQ5kUk4\nnLBltqNOYXi7Kj24uYALTczi/D4EN68ljRDtTcGmmAvQWIPacAuxkdgHjSCt/P6WXw1ha3k9dQxy\nlpoL4bXOCSmapuGvP+7i7k+DC65dDv+306ljMIvSNA1//n4nDvINZdSpqopp06Zh+vTpCAaPLJQE\ng0FMnz4d06ZNM9U292gS23aEYJZFNCK7q7y4pWAryuv5fSHSRFFETk4OevbsiZycHPMsAJtIcMNK\n0ueP6jY6s8wFaKxBWQmwCUCIV9sMwaGjweAAsLPCiyC/eKw1F+IkP6cZzdhajpkKnwpjBv75MyB2\n7goptwd1FGYxM7aWY8meKuoYllVWVoZJkyZh1qxZpp2nGG1ie+ttpw+n0hofdzRFiRVGXJiBum0D\ntFAIAtHBA3zcQQQluiT0TIvu8FTWPGKXnrClZlLHOMH2w9zVBPz/uRANMd1cCMlOnSAqlPJ6PPfj\nLuoYLFw0Dd6P34JWxyf9sOg5WOfH35bspo5heaqqori4GBs2bEBxcTEXmlpI5KJ9s9X4grj7623Y\nX+unjmJ6lhlxYQJabTVC++g+K7nYFGFD2jb6cD1GyD7kXOoIv1Phsdb8npOxzFwIQbBE63xdQMVD\n87bDxwNWTEWrqoB32jvQNP7flUWepml49oddqPFzYYOZh5CaCTG3O3UMQwqGNDw4bzu289b8qLDU\niAsTUAvpDuLhYlOEDWvXijoCOw0hPhFSj/7UMX7Hq/LQXcA6cyGscvrMC4uLUVRpndlUVhLcsArq\n5l+oYzALWFZSjYW7Gn14MWOG4DhrFNlWF6N78adiLN1jok53nbPUiAsTUPfsJHtu6wwIIdIzPRat\n3XYc8vDeYb2yDx4JQdTfr0KAOz9+ZYW5ELaUDOoIETd7Wzm+2lpOHYNFkLdgKmLlXhAsNH+MRZem\naXh9uUk6Whk7xumCfdBw6hSGNG/HYfxv40HqGJZybMRFQwUn0424MAF1TxHZc3P5PMJsgoCR7bm7\nSc/sg8+mjtCgeIf5t1Q1hdnnQthS0qkjRFRxlRfP/LCLOgaLMK1sPwI/fUsdg5nY/J0V2FRWRx2D\nsbCyDx4JwRVDHcNwDtb58ZdFdDfSVmWZERcmoZXtg+al2WLKxaYoOKcDF5v0SuzcTbc3+ZnxDuoI\nLIpsGebd3x5QQ3hs/g7UB3hrqBX4v/sKWoAHtLLw0zQNb63cSx2DsbAS4hLgHDWOOobhaJqGiQuK\nUOUz1+KjEVhlxIWZhCpodhZwn3sUDGmbiHiHqItBloWjHj/h69x5L5zw9dQO7mjGIWcfNII6wkll\nxjmpI7AoEjt3o44QMe+sLsWGg9yJYBVaXS2CvyzT9fsrM6YNB+tQyAOAmck4R0+AEBtHHcNwFhRV\nYsmeKuoYlmWFERdmolUeAjLbRv15ubMpChyiDed2TKKOwX7L6YbUaxB1ipPqnOyGS+JfUSsQYuNM\n29m0bn8t3l5dSh2DRZl/8bd8Mh0LuwKFZ74xcxE75UEayLOamkoNaXjjZ96qRc3sIy7MJFR5iOR5\n+U42Si7ubI2TpozE3mcwBKeLOsZJOSUbBrSJp47BokDs1M2UJ9B4Air+/P0O8Kx76wmVFCG0dxd1\nDGYifjWEbwppLpYZiwhRhHPcLRAEgTqJ4czaVo4dFdzlyFij+WnGG5jv7kanBmcnoLWbdy3qiTRg\nGHWE0xrSNpE6AosCqfdg6ggR8eaKvSiu8lHHYESCW9ZRR2AmsuFgHap5NgszEedlN0Ak2NZiBh+s\n208dgTFjCdF8fnKxKUokm4Axcgp1DHaUkJQCsWMedYzTuqBTMkRe8DI3hxNS937UKcJu3f5afLie\nLwatTFXWU0dgJrL9EHcxMPOQ+p8F+9DzqWMY0vbDHmzj9wPGmkQL0RzSw8WmKBrXNZU6AjvK3v8s\nQ2xbyohz4PyOydQxWARJPQboejtnc/jVECYu3IkQb5+zNLVoGzSflzoGM4ntFfXUERgLC1tmW7iu\nuo23zzXTnO28nZaxpqK619D/3baJdEhyo38mz+DRA/tA/W+hO+baXunUEVgE2c8YSR0h7D7ZcAA7\nK7jIYHkhFaGyfdQpmEkU8XsKMwOXG+6b/wTBwScON9fi4krqCIwZjq0VzfxoLjZF2VXdubuJmi2n\nM2xpbahjNFrfjDgMaZtAHYNFgK1NO4idu1PHCKvy+gAmrbL26XOiKCInJwc9e/ZETk4ORFGkjkQm\ndOggdQRmEgFulWRGJ0pw3/IQbKmZ1EkMK6RpvJjFWDMIrWh2yvDE6igb1SkZ/1i6B2X1AeoolmU3\nwGDw4wmCgP8bmoPLP9uIIF9sm4pjxMWma6N/Y/ke1PqtO8Q3NTUV48ePR35+PiRJQjAYREFBAaZN\nm4aysjLqeFGnHbbez8wiI5kPWWFGJghwXXcfpFxzLTBF2/5aPzxBmtkzjBmWIMDWmmanDHc2RZlD\ntGFCT94WRUYUIfU5gzpFk3VKduNaft2YipCYBKnvmdQxwmpXpQcztpZTxyAjiiLGjx+PcePGQZKO\n3BhLkoRx48Zh/Pjxluxw0upqqCMwk0h226kjMNZsznE3w97HnCfPRlNJNZ9wy1hT2bLaQ3DH0Dw3\nybNa3JXdUuHkI8ZISD0HwhZnzC1p9w3KQsdW5hokbWWO88dCsDuoY4TVB2v3w8q9d9nZ2cjPz2/w\ne/n5+cjOzo5yIh0Q+DKDhUd6rLneL5l1OC4YBwefPBcWfPfEWNOJhB2VfBVIIMltx7huPLuJgv2M\ns6kjNFuMXcTfR3WGgwuVhickp8I+2LivxYYcqg+gQLFuVxMAJCQk/NrR9FuSJCEhwZiF7hYxwKmf\nzBjOapdIHYGxJrOffSkcF4yjjmEaLok/UxhrKqlLT7Ln5t9YIjf3yYRk46JBNAlJKRBze1DHaJG8\nlBg8MqQddQzWQs4LxkE4SVHCqD7bfBA+1cp9TUB1dTWCwWCD3wsGg6iuro5yIh0w2euc0emWGouu\nKTTbABhrDvvZl8I5eoLpZjNSctuttx2dsZYQklMhdqG7/+ViE5E28U7kyynUMSzFfuY5EEywyn5N\njzRcyZ1xhmXL7gDJYEPqG2NBUQV1BHIlJSUoKCho8HsFBQUoKSmJciJ6tuQ06gjMRLgrnBkFF5oi\nIzvByYv1jDWB46xRpPe/xr/zNrDb+mWCd0RFiWSH/cxzqVOEhSAImDi8PYbzlgJDcl1+kymKnsc7\n7Algc1k9dQxyqqpi2rRpmD59+q8dTsFgENOnT8e0adOgqtY7pc+WmkEdgZnIJbmtkeDkzgamb1xo\nihyXZIPcmjscGWsUhxP2wSNJI3B/O6F2iS6M65qKzzbz0dCRZu9/lmEHgzdEsgn4xwWdcdNXW0x7\nk1846vETvs6d98IJX0/t4I5mnLCQ+p8FsUMX6hhht7zEgtvDTqKsrAyTJk3CrFmzkJCQgOrqapSU\nlFiy0ARwsYmFV4JTwlPD2+Ph+TuoozDWIMeoy+G48AouNEVQ34w4bCqro47BmO45zhkNISaONIO5\nltcN6K4BWXwyXRTYh19IHSHsYu0iJo+WeYXHKFwxcI6eQJ0iItYfqKWOoCuqqqK4uBgbNmxAcXGx\ndQtN6VkQ3LHUMZjJXJTbGlfxdjqmQ84x18J50ZVcaIqwYTnc2c/Y6QipmXCcO4Y6BhebqKXHOXBt\nL175jSQxrzfENuYcqt3KZceUfC44GYFzzATYEpOoY0REfSBEHYHpkCjTnX7CzO2xs3LQJdl43a3M\npAQBzitvhePsS6mTWMKQtonIindQx2BM11xX3AJBslPH4GKTHtzWNxOJPIMgYpwXXkEdIaKOFZy6\npXLBSa/ETnmwDz6bOkbEBFQuNrHfk+Re1BGYSbkkG16/MBdpsfQX0szibDa4rr0HjiHnUSexDJsg\n4IpufPgEYydjH3oeJMIT6I7HM5t0INEl4d6BWXjhp93UUUxH7NoHYk5n6hgR18plx3v5XfHA3EKe\nn6M3djtcV91uuqHgx/OrGnUEpjeuGIidu1GnYI0gy7IdwBQA7QE4ATwHYDOA9wFoADYCuFdRFF1V\nlXNaufDx5d1w52wFOyu81HEsw4zzFJtNssN1w/2w9xxAncRyLu+aindWl8IT1NXbEmPkxE5d4bzs\nRuoYvzLv3Y/BXNU9DR1buahjmI7Zu5qOF+cQMemSLrgktzV1FHYc58XjYUtrQx0jovh0KPZb9gFn\nQXA4qWOwxrkOwCFFUYYBuBDAmwBeBTDx6N8JAPIJ851Um3gnpo7thv6Z8dRRmNU4nHDf8SgXmoik\nxNhxa79M6hiM6YqQnArXTX+EIOmnn4iLTTphF214dKg55wpRkfqeCbFdJ+oYUeUQbXjpvI64uQ/P\nAdMDsYMM+/CLqGNE3KAs85z0yMLDfsY51BFY430O4MmjfxYABAH0B/DD0b+bA0C3e4RauSRMHi1j\nVCdzzsRjOhQTh5h7JkLK1cc2Fau6qXcG0nkrLWNHxMTCfevDujt9XT9lL4ZhOa0wsn0rLNpVSR3F\n+BxOOMdcS52ChE0Q8PCQduiU7MYzi3YhEOItTiTsDriuvtPU2+eOObNtAgQc2W/DmK19F4hZOdQx\nWCMpilILALIsxwP4AsBEAP9QFOXYr3QNgEYf/1RYWBj2jI1xVwcNHewx+KCwHl5rHgDJokCNiUPF\n6BsR9GsA0Wud/X/XdXTglQ0B6hiMkVLdsai49EYE63wk70u5ubkn/V5Ui01GnQsQTX8+KwfL9lTB\nxzNQWsRx3mWwtbL2drKxeanISXThj3MLccgTpI5jOc4x18KWZo0W71YuO3qkxWLDwTrqKEwHrLR9\n2SxkWW4LYAaAtxRF+USW5b8d9+14AI1eBTvVRWekyV2AcQN8eHpREZbx/EIWZkJSChLufgKtUrl7\nXC86d9aw2bMDc7Yfpo7CGAkhoRXi734CrTKyqaM0KNpL7oadCxAt2QlO3NbP3PNdIk1ISYdj5MXU\nMXShX2Y8/ndFd3Tnk+qiSszrBfvQ86ljRNVV3flkGHbk5EVRJyegsMaRZTkdwDwAjymKMuXoX/8i\ny/LIo3++CMBiimzNkZXgxOTRMp4Z2R5xDp4nx8JDSElHzH1PwcaFJl0RBAFPDm+PjFgHdRTGok5I\nSkHMfU9B1GmhCYh+scnQcwGi5da+mWibwINVm0UQ4Bp/BwQ7f+gcc2yA6riuqdRRrCEm9sj2OUGg\nThJVl+WloFd6LHUMRsxx0XjLvfZN4HEASQCelGV5kSzLi3BkK90zsiwvA+DAke11hiEcPRr9q/E9\ncEGnZOo4zOBsaW2OFJqS+TpKjxJdEl44ryNs/NHDLETs3A0xDz4PW6q+d1FEdRudWeYCRMNtne14\neo2POobh1PU6E/s1O++jb8D12UAGYvCOUo+AZTeqRl7FWZdg/8FDwMFD1FGi7qb2Njx8AOCXlzV5\nuvTG/pAY0fdfyi1aZqUoygMAHmjgWyOinSXcMuOdePWCzth4sBZvLC/BUt5ax5rIltkW7rufgC2+\n0bcnjMDgrAQ8OqQdXlqymzoKYxFnH34hnGOuhSDqf/x21BOaZS5ApOXmAqtqd2DWNuvdsDaXLT0L\n6dfeiQzuajqp3FxgZI86PPjtduyp5mJmuEn9z0K7i8ZSxyCTC2CNpxgfrT9AHYVFW0wsUq67G2l8\nQ8Z0qEdaHCaPycPykiq8uqwEm8p4vhw7PVtGNtz3TNTd6U6sYdf1Sse2Q/X4cms5dRTGIkOyw3Xl\nrbAPMs5aUFS30ZltLkCkPTKkHRKdPG+gUWwiXNfew9vnGqFbaiw+v7I7HxMdZkKr1nCNu4k6BrkH\nz2iLQVnx1DFYlDlHX8sr/0z3zshOxLQruuG1CzqjQysXdRymY7a0NnDf8wQXmgxEEAQ8OaI9+mfy\nNQgzH1tWe8Q89IKhCk1A9Gc2mW4uQCS1jrHjkSHtqGMYgnPsDRDbdqSOYRjxTgmvjuqMJ4blwM6b\n3MPCNeEuCG6eWeSUbHjz4i7okx5HHYVFiditL+yDR1LHYKxRBEHAqE7JKLi6J16/oDPPmmO/I6Rk\nHCk0xbeijsKayCHa8K+LctEl2U0dhbHwEAQ4zr8MMX/8q64HgZ9MtGc2mXYuQKRclpeCWdsO4ee9\nPGfgZKT+Z1nu5K9wEAQBE3qmo29GHB6atx3FVbytrrnswy+ElMsncB0Taxcx6dIuuLlgK7aW11PH\nYREkJLSC65q7eCg4MxzRJuD8Tsk4r2MS1uyrxZS1+7BoV6MnOTCTEpJSEHPPRNgSebC8USW6JLw9\nWsZ1X27G3ho/dRxTKRz1+Alf58574YSvp3bgIl84Ca3T4JpwD6SOMnWUZot2ZxNrIkEQ8PTI9nCK\nfCHfEFubdnBddRvf6LRA19RYfH5lD4yRW1NHMSRbWhs4L7mGOobuJDglTB4tIy8lhjoKixRBgOva\ne3mbCTM0QRDQv008/n1xF8y8uifGdU3ljl+riomF+87/gy2Jr4eMLi3WgXdGy2jttlNHYazpbCIc\n54xB7KN/M3ShCeBikyHkJLpw7yDjtc1FXEws3Dc/CMHhpE5ieLEOES+e2wkvntsRMXZ+W2g0QYBr\nwt0QHDwrrCHJbjs+GtsVl+TyhbsZOUZdDqkLd/Qx8+iU7MazZ3fA/Ot74+4BbZASwzeqliHZ4b7l\nYYjpWdRJWJi0b+XGlHwZyW79n9jF2DFiBxkxD78I5+hrTHGPy799BnFj7wzMKTyELbwl5QjJDvet\nD8OWkk6dxFTGyCnonR6HR+Zvx6Yyfq2djn3kJRBzOlPH0DW3XcTL53VE97RYvLJ0N1SNOlH4WbGt\nXOo1CI5Rl1PHYCwiUmMduG9QNu7s3wbfF1Xg040Hsaq0hjoWiyDXhLshdcqjjsHCrHNyDN4dnYeb\nC7agyqdSx2Hs5GJi4Rw9AfZBIyHYzLPwb56fxOQkm4Bnz+4A3k13hGvC3ZA68kVBJOS0cuGjy7vh\n5j4Z1FF0TUjNgPPCK6ljGIIgCLixdwYmj8lDkovXOIzO1qbdkY4+E10MMdYQu2jDhZ1b44PLuuKr\n8T1wdY807v41IcclV8Pe90zqGCxC5JQY/HdMHhL4hG+mRzYb7GeNQuyfX4XjjHNMd21lrp/G5Lql\nxuKG3lwAcI6ewBcFEeYQbXh4SDu8fWkXtOb24wa5xt/J2+eaaHBWAqZf1QPD2yVSR2GtEK7LAAAg\nAElEQVTNJMQnwn3LQxCcfGw8s5bc1jF4cnh7LLyxLyYOz0FnPu3KFMTu/eA4dwx1DBZhXVNjMSU/\nD614wYvpiCj3QszDL8E17mbTzr/kYpPB3DswC9kJxt+/2Vz2s0bBfval1DEs46x2rTB9fA+cmW3O\nN8Dmsg8eye32zZQe58Bbl3TBc+d0QLyDVxkNxeGE+/ZHYWudRp2EMTJxDhHX9EjHV+N74OPLu+Ky\nvBS4JL6cNiIhKQXuCXfzITMW0TUlFu/l5/EMJ0ZOSM2E+7ZH4L7z/yBmtqWOE1H86WgwbruIp4a3\np45BQho0As6xN/JFQZSlxhw50eOPZ2TzNk4AiImD41I+fa4lBEHA2LxUfHV1TwzjLidjsNngvumP\nENt2pE7CmC4IgoA+GfF4/pyOWHhjHzwxLAddWnO3k2GIItw3PgAhJo46CYuiLq1j8F5+Hp9Sx0gI\ncQlwjrsZsY/9DVL3fpa4p+VikwENbZdoudOdpN6D4brqdtPtYzUKmyDg9n5t8OHYrsiIs/bWMefo\na0zb6hptGXEOTLqkC547uwMSeZaCrrnG3wGpax/qGIzpUoJTwoSe6fjyqh74dFw3jOuaCjd3O+ma\n48Ir+YAPi+qcHIMPLstDWiwXnFiUOJxwXDAOsU+8DsdZoyCI1umu409Cg3psaDvLDLoTu/WF67r7\nIIjW+Hn1rE9GPKZf1QMj27eijkLC1iYH9kEjqWOYiiAIGNs1FV9P6IVxXVOp47AGOC+7HvZBI6hj\nMKZ7giCgV3ocnj27Axbd1BdPDs+B3DqGOhb7DVt6FhwjL6GOwQh1SHLjg8u6ItPiC6gswmw22Iee\nh9gnXofzwisguKzX/crFJoNqHWPHn84w9x5PABC79ID7xj9CkKxTAda7Vi4Jb16Ui0eGtIVkM3/7\n5/GcY67l7roISXLb8ezZHfDR2K68FUVHHKMuh2PExdQxGDOcOIeIq3ukY/pV3bnbSWecV9zC15UM\n7RJd+HBsV7S18CxcFjlSnzMQ+9g/4LriVtgSrLlID3CxydCu6JaKnmmx1DEiRuzSE+5bH+ETv3RI\nEATc1CcT7+Vbpw1ZzOsNSe5JHcP0+mbG4/Mre+Cxoe34iHFi9mEXwHHhFdQxGDO047udFt7Yh7ud\niEkDhkHq3I06BtOJNvFOvH9ZV7RvxSessvAQ83oh5sEX4L7xAdjSMqnjkOMreQOzCQKeHN4eZmwu\nEeVecN/6MBeadK7f0cLAwDbx1FEizsk33VEj2QTc0DsD30zohdFdrDWfTi+kQSPgvOwGSwyvZCxa\n4p3SCd1Ol+WlwMEnb0SPTYTz4vHUKZjOZMQ58H5+HjomccGJNZ8tpzPc90xEzJ1/hti2A3Uc3eBi\nk8F1T4vFVd3NdQy1mNcL7lse4kKTQaTE2PHumDzc3CeDOkrEiHIvHiRKIDXWgZfO64QPL+OtddEk\n9T0TrvF38JZRxiLkWLfT8+d0xIIb+uBPZ2Tz7JgokPqeCVsSL2Cw30uNdeD9/K7onMzXGqxpbGlt\n4Lr5QcQ88Cyk3O7UcXSHryRN4L6BWaYZFi527cOFJgOSbAIeHtIOfzu/E5wmXKV1nH8ZdQRL69/m\nSAfd48NyTPNep1dit35wXXsPF5oYi5Iktx239WuDb6/rjX9dlIszs/m000hxnM1DwdnJtY6xY8qY\nPHRK4oITOz0hMQnO8bcj5tG/wd5rIHeCnwRfTZpAktuOewZkUcdoManHALhveRCCnQtNRnVJbmt8\nOLYr0k00x8mW1R5ixzzqGJYn2QRc2zMds6/phXw5hTqOKYmdu8F94wOWOpKXMb0QbQLO6ZCEd8fk\nYebVPXFNjzQeKB5GYm53iFntqWMwnWsdY8eUfC44sVNwx8Jx6TWIffw1OM44h09LPw3+FDOJq3uk\noaOBh9tJvQfDddMDECTzFCmsqkdaHKZd2d00w+vtQ87l1QodaR1jxwvndsR7+XmGfs/TG1vbjjwn\njzGd6JTsxsTh7fHdDb1x36AsJLm4ANxSUr8h1BGYQaTE2PHffJmHhrMTiSLsIy5G3BOvw3nuGAgO\nPsWwMbjYZBJ20YY/ndmWOkazSP2GwnX9H3g13URSYxyYkp+Hs9sb/KhPhxP2fkOpU7AGDMpKwPTx\nPfDA4GxTbt2MJlt6Ftx3PAbBxSu5jOlJK5cddw/Iwvzre+OJYTnI5iPam03q2oc6AjOQ1BgH3h0t\no008L8AwQOo5ELGP/R2uy66HEBtHHcdQuNhkIme3b4U+6cb6BZAGDj8yH4RbEE0nxi7ijQtzcXUP\n4w6wl3oO4BtwHXOINtzRvw0Kru7Jc06aSUhMgvvO/4Mtjv/9GNMrt13EhJ7p+HpCL/z9/E7IS4mh\njmQotuwOsCUmU8dgBpMZ78SUMXlIM9FoCNY0tqz2cN/7JNy3PAhbaiZ1HEPiYpOJCIKAP56RTR2j\n0eyDz4br6jt5EK2JiTYBE4fl4P5BxnldHk/qMZA6AmuEtokuTB4t48nhOTzjpCmcbrhvfwy2JJ6B\nxZgRSDYBF+e2xhdXdse/LsrluTKNJMk9qSMwg2qb6MK7o/OQyIeTWEtM3JHh3w8+D6lzN+o0hsZX\n5SYzMCsBQ9rqf4Xafua5cF51GxeaLEAQBNw5oA3+b2g76ihNI9khde1NnYI1kiAIuLpHOr4c3wP9\nMo3V4UnCJsJ98x8hZuVQJ2GMNZEgHBkmPmN8Dzx3TgdkxPFWn1OxpRtzwYvpQ6dkNyZdIvNilkVI\ng0Yg9s+vHBn+zfepLcb/giZ0V399n0xnH3o+nFfeyr/AFnN97ww8O7IDjDJdR+zQBYKTh0MaTbtE\nF97P74pHhrSF3WaUV1v0OfOvgyT3oo7BGGsB0SZgbF4qvpnQC48MacvdFydhS82gjsAMrndGHF67\nsDMkvq4wLVtGNtx/eBrua+7i0QJhxHf7JtS/TTz6Z8ZTx2iQ/cxz4bz8Jj7dy6LGdUvF0yPbU8do\nFLF9LnUE1kyiTcBNfTIxdWxXZMTyiv9vSQOGwT7sAuoYjLEwcUo23NQnE99e1xs39E4H3w+fiGet\nsHAY1q4V/jKiPXUMFm42GxwXXoGYh1+E1DGPOo3pcLHJpO7or78PVmnQCDivuIU7mizuym5peNQA\nW+rEHC42GV3P9Dh8dmV3DMrSZ/Gdgi2rPVxX3sYFf8ZMKN4p4bGhOfj48m7okszznAAAosinR7Gw\nGds1FXf0b0Mdg4WJLa0NYh54Fs4LxvGp6BHCd/0mNbRtInJ1dKEh9R8K1/g7uNDEAAA39s7APQP0\n/WFt41k2ptA6xo7Jo/Nwcx/eRgHJDtf190FwcLcXY2bWKz0O067sjj8MyuLtxHwDycLs/kFZuDiX\nTzc0OvvQ8xHz0AsQ23WijmJqfOdvUoIgYELPdOoYAACp1yC4rrmbC03sBPcMzMLoLq2pYzRMlCAk\nJFGnYGEi2QQ8PKQd/jKyvWFmhkWC8+KrIKbre6YfYyw8HKINdw3IwvSreqB7aix1HDp87cnCTBAE\nPHd2R/RMs/DvlZE5nHDd8iBcV9wCweGkTmN6/A5sYpd0aY14B+2wSLFTV7iuuxeCyEMr2YkEQcCz\nZ3dA3wz9tbcLySlcHDWhK7ul4YVzO1pynomtfS7sIy6mjsF0TJblwbIsLzr6586yLP8ky/JiWZYn\nybLMb4gG1SnZjalju2KMrNPFnUjjz3IWAU7Jhn9elIvUGDt1FNYEQlIKYh54BvaeA6mjWAa/A5tY\nrF1Efl4K2fPb2uTAfevDEOy8ZYM1zCEe+bBOi9XXh7WNu5pMa4ycgr+f38lyJ8q48q/nAio7KVmW\nHwXwLoBjR3C+CmCioijDAAgA8qmysZZzSja8cE5HPDa0HURrvfUBPi80TaNOwUwoLdaBf16UC4fl\nfqmMSewgI+ZPz0Fsw2MyoomvPE3uMpmm2CQkp8J9x2MQ3DEkz8+MI9ltx9/O66SvbhOn6/T/DTOs\nCzu3xovndqSOETVSzwF8uiI7nR0ALj/u6/4Afjj65zkAzot6IhZWgiDght4ZeGe0jASnhbrNVRWo\nr6NOwUyqV3ocJg5rTx2DnYaY1wvuux+HLT6ROorl8NQ8k8tLiUHnZDe2H/ZE70mdbrhvfxS2RO4O\nYY0zMCsBd/Vvg7dWlVJHAQAIXGwyvYtzW2PboXpMXrOPOkrEOS66ijoC0zlFUabLstz+uL8SFEU5\n1g5SA6DRV+iFhYXhjMbCrDWAib1j8NTqGnhV6jTRUbRxPdTkVOoYzKR62YHzsxyYv9dPHYU1wN+m\nPQ4PGwPsKqaOYlq5uSdf0CQpNsmyPBjAy4qijJRluTOA9wFoADYCuFdRlBBFLjMSBAGju7TGa8tL\novac7uvugZiRHbXnY+Zw54AsLNxViS3l9dRRAJuFVn0t7A+DsqGU1+PH3VXUUSJG7CBDzGxLHYMZ\nz/HXYfEAKhv7/3iqi06mD7kAUjOrcddsBT7V/FvM2iUnQuLXJYuglzuEUDpjCzaVcRednthyOiP5\nrsfR2qWfE9qtJurb6HguQPRd2Dl6QyEdF10JqceAqD0fMw/JJuAvI9vrYzudGqROwKJAtAl4+fxO\nyIgz71w5aeAw6gjMmH6RZXnk0T9fBGAxYRYWAYOyEvDaBbmWmF8XKttPHYGZnFOy4dULOltri6rO\nCXEJcN/6EAQuNJGimNnEcwGiLDvBidzkyP+iST0HwnHeZRF/HmZePdLiMKFHOnUMIBigTsCiJMEp\n4fGzTDosUhBg73MGdQpmTA8BeEaW5WUAHAC+IM7DImBE+1a4b1AWdYyICx3UxxZ9Zm7ZCU48f451\n5kHqneuau2CLb0Udw/Kivo2O5wLQ6J2oofBw5B5fjU3AgYHnQduxI3JPwixhVHII00XAQzhLoq6y\nAiX8/mIZbTUN/VMkrC43V0dbICkN20vMdZPFW7QiR1GUXQDOOPrnbQBGkAZiUXFLn0z8sKsSv+yv\npY4SMaEDe6kjMIs4p0MSbu6TgffWcjcdJftZoyB160sdg0EfA8J5LkAUjI2vwRdFWyL2+HHX34dW\ncs+IPT6zlls9e/HmCrqLQ7caQAq/v1jK8+leXPrpBgRD5plfEpPbDcn8OmaMnYJoE/DCuR1x+bSN\n8ATNOTKVi00smh4YnI01+2qw7gDPbyLhcsN54ZXUKdhRFNvofovnAkRB99RYxNgj8z+3ffiFkLjQ\nxMLohl4ZiHfQ7XvXaqvJnpvRaJvowoWdkqljhJWND2pgjDVCu0QXbuuXSR0jYrSKcmgevvFn0WEX\nbfjHKJ7fRMUx/CIIsXHUMdhReig28VyAKLCLNvTNiA/74wop6XBeck3YH5dZW6xDxLiudMcUa7XV\n0FSLnAnNfnVTnwzqCGElxMRSR2CMGcS1PdNJF3kiTS3dTR2BWUibeCdePJfnN0Wd3QHH8IuoU7Dj\nkBSbFEXZpSjKr3MBFEUZoSjKmYqi3KIoCt/hRcjArPAXm1z510NwmPckJ0ZnQs90upPpNA1aVQSH\nnDFd6poai4Ftwv8+ScbNxSbGWOPEOyVc30sHB3REyP9r786j4zrrNI8/d60qraXNsiRrsyy/lpfY\njpV4j+3YTuItdnZIAtnTCXsgJ0BPaOjT0D3QwwBJT5iBHmA6zdDLQM9h6BkGyOnTTQNDT0/3sA4v\nISydhRAcspDFAUeaP0oJjiPJJelWvXXrfj/n+CR1VXXvoyOp6t7ffd/fO/HgT1xHQMbsHCr1b0L1\nhGPrGNVUY2phZBOqZP3iZP/4ghWnKVh1eqL7BF7Q15LTpiUtzo4/8YufOzs23DnfdLqOkBjP5yMe\nQPkuX9Ot0NldnsqaeIhiE6rvjRuXaF3C11+YWbj2TNcRcBLORDNkRWeCd7n9QLkjr5bn1edJCWrD\ngdEOZ8eefPRnzo4Nd84ebqubi63JY8+6jgAgRdoKkTY7vMlTSc8//IDrCMigKPD1/nOWqS1fC2ty\n1TnPUzjGCnS1hmJThjTFgQZbc4nsKxzfpqC7L5F9ATPZs7Td2YX/xNFHnBwXbhXzoTb21cfF1uSx\nZ1xHAJAy+x3e5KmkiYcf0ORk/aw2ivRY3BTrfXtHVB+3sWqX390nr9DgOgZOQrEpY8YSGt0Un30o\nkf0As2mKA53e42b48cTPf+rkuHDvrMFW1xESMfnEY64jAEiZXUNFBfV4VfzcMU0+dtR1CmTUlv5W\nve5MbtJXkj8w4joCpkGxKWOG2/IL3ke4ZpxRTaiabQNFJ8edOPqwk+PCPZe9wpI0wVRQAHPUnAu1\nsqs+FxeY+NmDriMgw27c0Ksdg27OabMg6B10HQHToNiUMUPFhRebop0HE0gClGdjBVZRLMfE0UcY\ncp9RI20FdRQi1zEWbOIoxSYAc3dGnUwlPtnEY4+6joAM8z1P792zVMMJXIvh5fxFPa4jYBoUmzJm\nuFhY0Ou9rh4Fw8sTSgOc2vKOBsUuxvQ/96wmn/5l9Y8L5zzP03ivmyJnkiYeeYiCKYA5G+9J//vf\ndCaf+IXrCMi45lyoO/aNqjHiEjxpfhfFplrEb3rG9C+wQXg0vo0V6FBVceBrZZIrKc7BJCNDMuu0\n7jqYRnLsWU0+zsUVgLlZtagO3v+mMfE4I5vg3tK2Ag3DkxaE8tq7XKfANCg2ZUxzHCyomh6Nb08w\nDVAe0+lmdYmJX/zcyXHh3mndbhrTJ23i4ftdRwCQMp0Nkbob0z+V+GSTT7JoAmrDzqE23bql33WM\nuuF3dsvzKWvUIn4qGeN5nhY3zW90kz8wIp+qMRwYSaCx/XxwYppdK7saFfrpv+848fADriMASKG6\nbBJ+/LjrBMCLrlq7WBev5LoqCT4LV9Usik0ZtLgpntfrwhVrE04ClMfZyCb6O2RWPvS1rH1hPe5q\nwcQjD7mOACCFXH3uVtTEhOsEwIs8z9Pt2we1pb8+G/JXE8Wm2kWxKYM6G+Y3NDowaxJOApTndEfN\nSieffNzJcVEbVtdB35KJR37qOgKAFFreUX/FpkmKTagxUeDrg+eOaqwei7tV5PcvdR0BM6DYlEHt\nhXDuL8oVFAwuSz4MUAbfUVP6yWPPOjkuasOqOphGMnH0YdcRAKSQ6Uj/yM6XYXVO1KDGONCHDy5X\nX/P8Zp5ACgYoNtUqik0Z1FGY+8imYHBEXjCPIhWQZs9RbMqyNXWwIt3kU7/UJBdYAOaovyWvfFhf\nlwlek5tR0sCpdDXE+sgho475DAjIOK/YLr+13XUMzKC+PkVQluI83siCgZEKJAFq2+SxY64jwKHR\n9ob0X2xNPC89+7TrFABSJvA9jdZB37oT+cUO1xGAGQ0VC/oPh4ya48B1lFQJx9a7joBZpPwsGvPR\nEs+92OT3U2xCFjEiJMtC39PKrvT3UZh8+peuIwBIoRV11kfGa+t0HQGY1Vhno+46sFyFtN/oqqJw\n1emuI2AW/CZnUEtu7hXzoH+4AkmAGsfU0cxb293kOsLCeXzUA5i7eis2MbIJaXB6T7Pu3DeqXOCm\nX2mqxDkFo6tcp8AsOAPNoObcHC+gc3l5fEAjg+hThg29ddDjI5zfCqQAsq0eFkk4ETdOkRab+1t1\nx75RxRScZhWt3yIvzrmOgVlQbMqgpjnOBfYX9cpztBoY8IJPf/hO3T1cePFfVeT4AMu69YvrodhE\n0RTA3JnOBoV+fZz/ea3t8rp6XMcAyrZtoKgPnTeqqE7+Bish2rbXdQScAsWmDGqK5/Zj9xf1VigJ\nUNv8Vkb0ZV0xH2p5mpvkRrG8hjqYCgig6uLAr5updMHoKm6cInXOGiwywmkG/tCogiWMVqx1FJsy\nqCGa68gm7gQhm7zWNtcRUAM297e6jjBvfnefPJ+PegDzs64e+tZJCunrgpQ6a7CoP9q3nB5OJ8nt\nOeI6AsrAGWgGxYE/pyGZfntXBdMAtctrY2QTpC39La4jzJvf0+86AoAUO70e+tYFoYKVLI+O9No6\n0KoPHzSsUjclGFnB33RK8BubUQ1R+T96r31RBZMAtSvoHXQdATVgvLcltXcUg4ER1xEApNiGnvQX\nm8J1G+U3pfemASBJG/ta9NFDRs1z7L1bj3KHrmBabEpQbMqouUylY2QTMskPGBUCSVI+9LVpSQov\nVDxP4ZozXKcAkGKdDZGGinnXMRYk2kITYdSH9T3N+tjhFWrLZ3fhj3B8u4LBZa5joEwUmzKq7GKT\nH8hrKVY2DFCD/J4l8qLYdQzUiN3D7a4jzFkwbOTTdwzAAqWy2D7F7x1QMLzcdQwgMSu7GvWJI2Na\n1Bi5jlJ1XlOL8kde5ToG5iC7ZdGMK5Q5jc5rbaO5LDIpWDrmOgJqyK7hovy/lSYmXScpX7h+s+sI\nSDFjjC/pLklrJT0n6Xpr7Q/cpoILm5e06M++/YjrGPMS79jPdBvUnWXtBd19wUpd/9nv6f4nn3OW\n4yd7btPf3Xdn1Y6Xu/haeY3pn9qbJVQRMqrcBnN+kQbJyKaQxoM4QXsh0niKGuV6TS2KzjjLdQyk\n2xFJeWvtZklvk/R+x3ngyBm9LZppXZnOra/U3cOFF//VEr93UOH4dtcxgIpY0pLT3ReMaXl7bf3d\nVUq4frOitRtdx8AcUWzKqHIbhHvF9E0dARYszilYxsgmvNTB0U7XEcoW7TwgL5fuPitwbpukz0uS\ntfZ/SRp3GweutOZDrVnU6DrGnOXOv4LR+ahrXY2xPnFkTOu6m1xHqSivc7Hyl17vOgbmgWl0GVUI\ny+vZ5LVSbEL2hCvWyguzNxces9s70qZ3f/nH+tXztT2XzmtsUrztHNcxkH4tkp444fHzxpjQWnt8\nthfde++9lU0FJ1Y2Pq9vuA4xB8cGRvWwn5f4fUQGvG1VqH99PNT/fXTWt+dUmvQDHd11gX56/4Ou\no2AGo6OjM36tJopN9AWovhdGNv1g79v1tR98cMbn+RSbkEHRmTtcR0ANasmF2jlU1Bfue8x1lFnF\nB1/JqCYk4UlJJ84d9U9VaJJmP+lEeh1ueUqf+uF3XccoTxCq/fLfUld3n+skQNV8fHRCb//SD/X5\n+37hOkqi8hdepeEttAVIq1oZW0pfgCordzU6j5WMkDFeS1HBirWuY6BGHTFdriPMKli2UtHGXa5j\noD58RdJ+STLGbJL0Lbdx4NKqRY1qL9TEPepTivddooBCEzImDny9b++ILllZ2+cpcxFt3KVoyx7X\nMbAAtVJsoi9AlZW/Gh0jm5At0fhZ8oLyirHInq0Dreo+xXLDA5svctMwN4qUv/QGVl5CUv5K0jFj\nzFclfUDSLY7zwCHf87Stv9V1jFPyh5Yr3nXQdQzAicD39M4dQ7p2fY/rKAvmD44qd/E1nNOkXK3c\nophXXwDMX7kjm3wahCNL/EARvW4wi9D3dHhFlz7yfx5yHeVlcoeukN+12HUM1Alr7YSkm1znQO3Y\nPljUZ7//qOsYM4tzKlx+M03BkWme5+ktm/vVEgf64NcfcB1nXryWogrXvIn+qXWgVopN8+oLQBPK\n+Xv68WNlPe++R45Kjz5e4TTAqYWP/kyVXgvs2WWr9fDRX0hH62u+O5K1ofC86wgv8+yy1Xq4e7ju\nm+HSDwhwZ0t/q3xPmqjRNRJyh6+k4A5MuWFDrxrjQO/58k9cR5mbKFLhulvpG1wnaqXY9BVJhyT9\nxVz6AnDSOX9Dx38u2R/N+hyvsVmjK1j+HbXh+aa8nqnwMToOX65FvQMVPgrSblTSlp98T1+9/0nX\nUSRJ/qJedV33Zi3KV3HKHoDMKeZDrVnUqG/87GnXUV4m3LBN0ebdrmMANeXyNd0qhL7e8Tc/Uo3W\niF8m/4qbF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aaf42978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "sb.violinplot('Pclass','Age',hue='Survived',data=train_data,split=True,ax=ax[0])\n",
    "ax[0].set_title('PClass and Age vs Survived')\n",
    "ax[0].set_yticks(range(0,110,10))\n",
    "sb.violinplot(\"Sex\",\"Age\", hue=\"Survived\", data=train_data,split=True,ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n",
    "ax[1].set_yticks(range(0,110,10))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above violen plots, following observations are clear,\n",
    "1) The no of children is increasing from Class 1 to 3, the number of children in Class 3 is greater than other two. \n",
    "2) Survival rate of children, for age 10 and below is good irrespective of Class\n",
    "3) Survival rate between age 20-30 is well and is quite better for women.\n",
    "\n",
    "Now, in Age feature we have 177 null values filled with NaN. We have to deal with it. But we can't enter mean of age in every NaN column, because our average/mean is 29 and we cannot put 29 for a child or some olde man. So we have to discover something better. \n",
    "Let's do something more interesting with dataset by exploring more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is, if I look at 'Name' feature, It looks interesting. Let's check it...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Initial']=0\n",
    "for i in train_data:\n",
    "    train_data['Initial']=train_data.Name.str.extract('([A-Za-z]+)\\.') #extracting Name initials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "        <th class=\"index_name level0\" >Initial</th> \n",
       "        <th class=\"col_heading level0 col0\" >Capt</th> \n",
       "        <th class=\"col_heading level0 col1\" >Col</th> \n",
       "        <th class=\"col_heading level0 col2\" >Countess</th> \n",
       "        <th class=\"col_heading level0 col3\" >Don</th> \n",
       "        <th class=\"col_heading level0 col4\" >Dr</th> \n",
       "        <th class=\"col_heading level0 col5\" >Jonkheer</th> \n",
       "        <th class=\"col_heading level0 col6\" >Lady</th> \n",
       "        <th class=\"col_heading level0 col7\" >Major</th> \n",
       "        <th class=\"col_heading level0 col8\" >Master</th> \n",
       "        <th class=\"col_heading level0 col9\" >Miss</th> \n",
       "        <th class=\"col_heading level0 col10\" >Mlle</th> \n",
       "        <th class=\"col_heading level0 col11\" >Mme</th> \n",
       "        <th class=\"col_heading level0 col12\" >Mr</th> \n",
       "        <th class=\"col_heading level0 col13\" >Mrs</th> \n",
       "        <th class=\"col_heading level0 col14\" >Ms</th> \n",
       "        <th class=\"col_heading level0 col15\" >Rev</th> \n",
       "        <th class=\"col_heading level0 col16\" >Sir</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Sex</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8\" class=\"row_heading level0 row0\" >female</th> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col0\" class=\"data row0 col0\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col1\" class=\"data row0 col1\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col2\" class=\"data row0 col2\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col3\" class=\"data row0 col3\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col4\" class=\"data row0 col4\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col5\" class=\"data row0 col5\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col6\" class=\"data row0 col6\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col7\" class=\"data row0 col7\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col8\" class=\"data row0 col8\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col9\" class=\"data row0 col9\" >182</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col10\" class=\"data row0 col10\" >2</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col11\" class=\"data row0 col11\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col12\" class=\"data row0 col12\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col13\" class=\"data row0 col13\" >125</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col14\" class=\"data row0 col14\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col15\" class=\"data row0 col15\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row0_col16\" class=\"data row0 col16\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8\" class=\"row_heading level0 row1\" >male</th> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col0\" class=\"data row1 col0\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col1\" class=\"data row1 col1\" >2</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col2\" class=\"data row1 col2\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col3\" class=\"data row1 col3\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col4\" class=\"data row1 col4\" >6</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col5\" class=\"data row1 col5\" >1</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col6\" class=\"data row1 col6\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col7\" class=\"data row1 col7\" >2</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col8\" class=\"data row1 col8\" >40</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col9\" class=\"data row1 col9\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col10\" class=\"data row1 col10\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col11\" class=\"data row1 col11\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col12\" class=\"data row1 col12\" >517</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col13\" class=\"data row1 col13\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col14\" class=\"data row1 col14\" >0</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col15\" class=\"data row1 col15\" >6</td> \n",
       "        <td id=\"T_78a771d0_2357_11e8_b267_485ab6c270e8row1_col16\" class=\"data row1 col16\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24a5578470>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data.Initial,train_data.Sex).T.style.background_gradient(cmap='summer_r')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many names which are not relevant like Mr, Mrs etc. So I will replace them with some relevant names,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['Initial'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','Countess',\n",
    "                               'Jonkheer','Col','Rev','Capt','Sir','Don'],['Miss',\n",
    "                                'Miss','Miss','Mr','Mr','Mrs','Mrs','Other','Other','Other','Mr','Mr','Mr'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial\n",
       "Master     4.574167\n",
       "Miss      21.860000\n",
       "Mr        32.739609\n",
       "Mrs       35.981818\n",
       "Other     45.888889\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby('Initial')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Mr'),'Age']=33\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Mrs'),'Age']=36\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Master'),'Age']=5\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Miss'),'Age']=22\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Other'),'Age']=46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Age.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pl1LemeSHkvxMxnucnN/H4GuyfGn8n9Va37vt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\nAABN/j+Uzc9JjfKAkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b0274e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,20))\n",
    "train_data[train_data['Survived']==0].Age.plot.hist(ax=ax[0],bins=20,edgecolor='black',color='red')\n",
    "ax[0].set_title('Survived = 0')\n",
    "x1=list(range(0,85,5))\n",
    "ax[0].set_xticks(x1)\n",
    "train_data[train_data['Survived']==1].Age.plot.hist(ax=ax[1],bins=20,edgecolor='black',color='green')\n",
    "x2=list(range(0,85,5))\n",
    "ax[1].set_xticks(x2)\n",
    "ax[1].set_title('Survived = 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plots, I found the following observations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) First priority during Rescue is given to children and women, as the persons<5 are save by large numbers\n",
    "(2) The oldest saved passanger is of 80\n",
    "(3) The most deaths were between 30-40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s33uuCYbgioW1LDynkv0LVEIQEZEvstKhn1J9fX3qBykiKaO4uDim5anKLf1b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\nNl13vE8LRufJshg0AwAAAACAzHOEz6MVZ1fqpjd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b0269898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot('Pclass','Survived',col='Initial',data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above FactorPlots it is Clearly seen that women and children were saved irrespective of PClass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore some more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: SibSip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SibSip feature indicates that whether a person is alone or with his family. Siblings=brother,sister, etc\n",
    "and Spouse= husband,wife"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background-color:  #f9fc66;\n",
       "        }    #T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row4_col1 {\n",
       "            background-color:  #fcfe66;\n",
       "        }    #T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row5_col0 {\n",
       "            background-color:  #ffff66;\n",
       "        }    #T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row5_col1 {\n",
       "            background-color:  #ffff66;\n",
       "        }    #T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row6_col0 {\n",
       "            background-color:  #fefe66;\n",
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       "        }</style>  \n",
       "<table id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"index_name level0\" >Survived</th> \n",
       "        <th class=\"col_heading level0 col0\" >0</th> \n",
       "        <th class=\"col_heading level0 col1\" >1</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >SibSp</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row0_col0\" class=\"data row0 col0\" >398</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row0_col1\" class=\"data row0 col1\" >210</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row1_col0\" class=\"data row1 col0\" >97</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row1_col1\" class=\"data row1 col1\" >112</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row2_col0\" class=\"data row2 col0\" >15</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row2_col1\" class=\"data row2 col1\" >13</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row3_col0\" class=\"data row3 col0\" >12</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row3_col1\" class=\"data row3 col1\" >4</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row4_col0\" class=\"data row4 col0\" >15</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row4_col1\" class=\"data row4 col1\" >3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row5_col0\" class=\"data row5 col0\" >5</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row5_col1\" class=\"data row5 col1\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8\" class=\"row_heading level0 row6\" >8</th> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_dd00d0ec_23bb_11e8_9099_485ab6c270e8row6_col1\" class=\"data row6 col1\" >0</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24b03ef748>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([train_data.SibSp],train_data.Survived).style.background_gradient('summer_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fITnhWhpzUum0M3el0SqEcBCZq8kxxua1dM81znVAVYzxyEEKTZIkSRoQc1FJGr6cJSJJ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\nJEmSJKVpr9ZKLnn1tJRjugeTvPXWNtZ0D+aoKik/DBYlSZI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b03f5668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,8))\n",
    "sb.barplot('SibSp','Survived', data=train_data,ax=ax[0])\n",
    "ax[0].set_title('SipSp vs Survived in BarPlot')\n",
    "sb.factorplot('SibSp','Survived', data=train_data,ax=ax[1])\n",
    "ax[1].set_title('SibSp vs Survived in FactorPlot')\n",
    "plt.close(2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <th class=\"col_heading level0 col2\" >3</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >SibSp</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row0_col0\" class=\"data row0 col0\" >137</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row0_col1\" class=\"data row0 col1\" >120</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row0_col2\" class=\"data row0 col2\" >351</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row1_col0\" class=\"data row1 col0\" >71</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row1_col1\" class=\"data row1 col1\" >55</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row1_col2\" class=\"data row1 col2\" >83</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row2_col0\" class=\"data row2 col0\" >5</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row2_col1\" class=\"data row2 col1\" >8</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row2_col2\" class=\"data row2 col2\" >15</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row3_col0\" class=\"data row3 col0\" >3</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row3_col1\" class=\"data row3 col1\" >1</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row3_col2\" class=\"data row3 col2\" >12</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row4_col0\" class=\"data row4 col0\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row4_col1\" class=\"data row4 col1\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row4_col2\" class=\"data row4 col2\" >18</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row5_col0\" class=\"data row5 col0\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row5_col1\" class=\"data row5 col1\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row5_col2\" class=\"data row5 col2\" >5</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8\" class=\"row_heading level0 row6\" >8</th> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row6_col0\" class=\"data row6 col0\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row6_col1\" class=\"data row6 col1\" >0</td> \n",
       "        <td id=\"T_ca9a9e54_23bc_11e8_aff5_485ab6c270e8row6_col2\" class=\"data row6 col2\" >7</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24b0604ac8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data.SibSp,train_data.Pclass).style.background_gradient('summer_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many interesting facts with this feature. Barplot and FactorPlot shows that if a passanger is alone in ship with no siblings, survival rate is 34.5%. The graph decreases as no of siblings increase. This is interesting because, If I have a family onboard, I will save them instead of saving myself. But there's something wrong, the survival rate for families with 5-8 members is 0%. Is this because of PClass?\n",
    "Yes this is PClass,  The crosstab shows that Person with SibSp>3 were all in Pclass3. It is imminent that all the large families in Pclass3(>3) died."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That are some interesting facts we have observed with Titanic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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