Titanic_projectv1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train = pd.read_csv('train.csv')\n",
    "titanic_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's see what is in the data?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <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",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are  there any missing values in our Dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gilinski, Mr. Eliezer</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                   Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                    891   891   \n",
       "unique          NaN         NaN         NaN                    891     2   \n",
       "top             NaN         NaN         NaN  Gilinski, Mr. Eliezer  male   \n",
       "freq            NaN         NaN         NaN                      1   577   \n",
       "mean     446.000000    0.383838    2.308642                    NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                    NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                    NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                    NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                    NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                    NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                    NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare        Cabin  \\\n",
       "count   714.000000  891.000000  891.000000     891  891.000000          204   \n",
       "unique         NaN         NaN         NaN     681         NaN          147   \n",
       "top            NaN         NaN         NaN  347082         NaN  C23 C25 C27   \n",
       "freq           NaN         NaN         NaN       7         NaN            4   \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208          NaN   \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429          NaN   \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000          NaN   \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400          NaN   \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200          NaN   \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000          NaN   \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200          NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  It seems Age , Cabin and Embarked variables have some missing values. Let's check how many missing values are present in these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### In our Dataset Cabin variable has huge missing values which makes this variable inappropriate to include in our Dataset.      Instead of it Age and Embarked variables have less missing values that can be handled and included in our Dataset.      Let's handle missing value in these two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[titanic_train['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Our Embarked variable has missing value in which class is first class and fare is 80$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### We have to find out that which embarked is related to the first class passenger whose fare is 80$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211bcd6b50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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myiuv3O3clClTsnLlyn2fGAAAAACAA9ag23eMGDEiu3btyu/+7u/m8ccfT0tLy17/ckIAAAAAAEhewZPS73znO3PppZfm5ptvztlnn51HHnkkRxxxRInZAAAAAAAYYgaN0vPnz8+73/3uHHnkkbnjjjvy0EMP5bTTTisxGwAAAAAAQ8wet+/41Kc+NfD1q1/96iTJG97whrz3ve/NuHHj6j8ZAAAAAABDzh6j9Jo1awa+njNnTpFhAAAAAAAY2vYYpWu12sDXL730UpFhAAAAAAAY2vYYpX9ZpVKp9xwAAAAAABwE9hilhWgAAAAAAPa31j298KMf/SjHH398kmTnzp0DX9dqtVQqlXz/+98vMyEAAAAAAEPGHqP0N7/5zZJzAAAAAABwENhjlD7qqKNKzgEAAAAAwEHgFf2iQwAAAAAA2B9EaQAAAAAAihGlAQAAAAAoZtAoXa1Wc+mll+bUU0/NT3/601x00UXZsmVLidkAAAAAABhiBo3SN9xwQ6ZNm5YRI0Zk7NixOfbYY7Nw4cISswEAAAAAMMQMGqWfffbZnHXWWWlpacnw4cNzzTXXZNOmTSVmAwAAAABgiBk0SlcqlfT39w8c79ixY7djAAAAAAB4pVoHe0NnZ2euvvrqbN++PUuXLs2yZcsyY8aMErMBAAAAADDEDBql58+fn69+9avp7+9PT09Pzj777MydO7fEbAAAAAAADDGDRukkOeOMM3LGGWfUexYAAAAAAIa4QaP0G9/4xv+zh/Shhx6a17/+9fnYxz6WY445pm7DAQAAAAAwtAwapS+44IIcdthhOf/889PS0pJly5blySefTEdHRz784Q9nyZIlJeYEAAAAAGAIaBnsDQ888EAuv/zyjB07NmPGjMmFF16YJ554Iqecckq2bdtWYkYAAAAAAIaIQaP0Cy+8kC1btgwcb9myJb29vUmSvr6++k0GAAAAAMCQM+j2HZdccknOPPPMnHTSSenv78/999+fD37wg7n99ttz/PHHl5gRAAAAAIAhYtAoPXv27EyePDnf+c530tramssuuyxHH310nnnmmYwfP77EjAAAAAAADBGDRukk6e3tzZve9KbUarX85Cc/yYMPPpizzjqr3rMBAAAAADDEDBqlr7/++qxZsya9vb1pb2/PU089lbe85S2iNAAAAAAAe23QX3R4//3359vf/nZOOeWUfOYzn8mdd96ZQw89tMRsAAAAAAAMMYNG6ba2towaNSqvec1r8u///u858cQT85Of/KTEbAAAAAAADDGDRunhw4fnoYceyjHHHJP77rsv27dvz/PPP19iNgAAAAAAhphBo/Q111yTpUuXpqOjI0888UTe+ta35t3vfneJ2QAAAAAAGGIG/UWH69aty8c//vEkyd13353t27dnzJgxdR8MAAAAAIChZ9Anpb/0pS/tdry3Qfrmm2/OBz7wgSRJT09Purq60tnZmcWLF+/VdQAAAAAAOPAN+qT07/3e72XhwoX5wz/8w4waNWrgfGdn56AXv//++7N8+fK84x3vyM6dO3PddddlyZIlmTBhQubNm5e1a9emo6PjN/sJAAAAAAA4YAwapZ977rk899xz2bhx48C5SqUyaJR+7rnnsnjx4syfPz9PPPFEHn300UyaNCkTJ05MknR1dWX16tWiNAAAAADAQWTQKL1kyZJ9uvDf/M3f5KqrrsqmTZuSJFu2bElbW9vA6+3t7dm8efM+XRsAAAAAgAPToFG6Wq3m+uuvz8aNG/NP//RPufbaa7No0aLdAvOvWrZsWSZMmJApU6bknnvuSZL09/enUqkMvKdWq+12/EqNGzd6r78HgH3T1uYX2wKU4H4LAPvGGgoHpkGj9A033JBp06blC1/4Qg4//PAce+yxuf766/OZz3xmj9+zatWqVKvVnH766fn5z3+e559/Ps8++2yGDRs28J5qtZr29va9Hnjr1h3p76/t9ffRfCwc0Pyq1e2NHgFgyGtrG+N+CwD7wBoKzaulpfKyDxe3DHaBZ599NmeddVZaWloyfPjwXHPNNQNbcuzJnXfeme7u7qxYsSJXXHFF3vnOd+Yf/uEfsn79+mzcuDF9fX3p7u7O1KlT9/4nAgAAAADggDXok9KVSiX9/f0Dxzt27Njt+JUaMWJEFi1alAULFqS3tzcdHR2ZPn36Xl8HAAAAAIAD16BRurOzM1dffXW2b9+epUuXZtmyZZkxY8Yr/oDZs2dn9uzZSZIpU6Zk5cqV+z4tAAAAAAAHtEGj9Pz58/PVr341/f396enpydlnn525c+eWmA0AAAAAgCFm0Ci9dOnSnHbaaTnjjDNKzAMAAAAAwBA26C86fOCBBzJt2rRcd911+cEPflBiJgAAAAAAhqhBn5RevHhxfv7zn6e7uzsf+chHsnPnzsydOzfvfe97S8wHAAAAAMAQMuiT0kkyduzYnH322Zk3b15GjRqVz372s/WeCwAAAACAIWjQJ6Ufe+yxfOUrX8nq1avzhje8IZdcckne+c53lpgNAAAAAIAhZtAnpf/8z/88RxxxRJYtW5a/+7u/S29vb84555wSswEAAAAAMMQM+qT0mjVrsn379nz5y1/OF7/4xTz//PM577zzSswGAAAAAMAQ87JR+sknn8xdd92VFStW5KijjsrOnTuzZs2ajBkzptR8AAAAAAAMIXvcvuPSSy/Neeedl9bW1tx1113p7u7OYYcdJkgDAAAAALDP9hilH3vssbzxjW/M6173ukyaNClJUqlUig0GAAAAAMDQs8co/S//8i8588wz093dnZNOOilXXHFFent7S84GAAAAAMAQs8co3drampkzZ2bJkiW555570t7ent7e3nR2duZLX/pSyRkBAAAAABgi9hilf9lrX/vaLFy4MPfdd18uuuii3H333fWeCwAAAACAIegVRen/NXLkyJx99tlZvnx5veYBAAAAAGAI26soDQAAAAAAvwlRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAACimrlH6E5/4RGbOnJlZs2blzjvvTJL09PSkq6srnZ2dWbx4cT0/HgAAAACAJtNarws/+OCD+d73vpeVK1fmpZdeysyZMzNlypRcd911WbJkSSZMmJB58+Zl7dq16ejoqNcYAAAAAAA0kbo9Kf1Hf/RHueuuu9La2pqtW7emr68v27Zty6RJkzJx4sS0tramq6srq1evrtcIAAAAAAA0mbo9KZ0kw4cPzyc/+cl8/vOfz/Tp07Nly5a0tbUNvN7e3p7Nmzfv1TXHjRu9v8cEYA/a2sY0egSAg4L7LQDsG2soHJjqGqWT5Iorrsgll1yS+fPnZ8OGDalUKgOv1Wq13Y5fia1bd6S/v7a/x6QBLBzQ/KrV7Y0eAWDIa2sb434LAPvAGgrNq6Wl8rIPF9dt+47//M//zOOPP54kGTlyZDo7O/PAAw+kWq0OvKdaraa9vb1eIwAAAAAA0GTqFqWfeeaZLFy4MLt27cquXbvy7W9/O+ecc07Wr1+fjRs3pq+vL93d3Zk6dWq9RgAAAAAAoMnUbfuOjo6OPProoznjjDMybNiwdHZ2ZtasWXnVq16VBQsWpLe3Nx0dHZk+fXq9RgAAAAAAoMlUarXaAbVBsz2lh462tjHpev+KRo8B7MHXPn66/dkACrAfJgDsG2soNK+G7SkNAAAAAAC/SpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKCY1npe/Pbbb8/Xv/71JElHR0euvfba9PT05Kabbkpvb29mzJiRq666qp4jAAAMOWMOH5lDR9T1f+MorK1tTKNHYD/a2ftStm97odFjAAA0rbr9aaanpyff/e53s3z58lQqlVx88cXp7u7ObbfdliVLlmTChAmZN29e1q5dm46OjnqNAQAw5Bw6ojVd71/R6DGAPfjax0/P9kYPAQDQxOq2fUdbW1s+8IEP5JBDDsnw4cNzzDHHZMOGDZk0aVImTpyY1tbWdHV1ZfXq1fUaAQAAAACAJlO3J6Vf97rXDXy9YcOGfP3rX895552Xtra2gfPt7e3ZvHnzXl133LjR+21GAF6ev04OAPvGGgpQhvstHJjqvhnhj3/848ybNy/XXntthg0blg0bNgy8VqvVUqlU9up6W7fuSH9/bT9PSSNYOKD5Vav+8jE0I2soND9rKED9tbWNcb+FJtXSUnnZh4vrtn1HkjzyyCP50z/907z//e/PmWeemfHjx6darQ68Xq1W097eXs8RAAAAAABoInWL0ps2bcpll12W2267LbNmzUqSTJ48OevXr8/GjRvT19eX7u7uTJ06tV4jAAAAAADQZOq2fcfnPve59Pb2ZtGiRQPnzjnnnCxatCgLFixIb29vOjo6Mn369HqNAAAAAABAk6lblF64cGEWLlz4a19buXJlvT4WAAAAAIAmVtc9pQEAAAAA4JeJ0gAAAAAAFCNKAwAAAABQjCgNAAAAAEAxojQAAAAAAMWI0gAAAAAAFCNKAwAAAABQjCgNAAAAAEAxojQAAAAAAMWI0gAAAAAAFCNKAwAAAABQjCgNAAAAAEAxojQAAAAAAMWI0gAAAAAAFCNKAwAAAABQjCgNAAAAAEAxojQAAAAAAMWI0gAAAAAAFCNKAwAAAABQjCgNAAAAAEAxojQAAAAAAMW0NnoAAAAAgBLGHD4yh46QQoaStrYxjR6B/Whn70vZvu2FRo9BAe7EAAAAwEHh0BGt6Xr/ikaPAezB1z5+erY3egiKsH0HAAAAAADFiNIAAAAAABQjSgMAAAAAUIwoDQAAAABAMaI0AAAAAADFiNIAAAAAABQjSgMAAAAAUIwoDQAAAABAMaI0AAAAAADFiNIAAAAAABQjSgMAAAAAUExdo/SOHTty2mmn5ZlnnkmS9PT0pKurK52dnVm8eHE9PxoAAAAAgCZUtyj9wx/+MOeee242bNiQJNm5c2euu+663HHHHVm1alXWrVuXtWvX1uvjAQAAAABoQnWL0nfffXc+9KEPpb29PUny6KOPZtKkSZk4cWJaW1vT1dWV1atX1+vjAQAAAABoQq31uvBHP/rR3Y63bNmStra2geP29vZs3rx5r687btzo33g2AF6ZtrYxjR4BAA5I1lAA2DfW0IND3aL0r+rv70+lUhk4rtVqux2/Ulu37kh/f21/jkaDuMlA86tWtzd6BODXsIZC87OGQnOyhkLzs4YODS0tlZd9uLiuv+jwl40fPz7VanXguFqtDmztAQAAAADAwaFYlJ48eXLWr1+fjRs3pq+vL93d3Zk6dWqpjwcAAAAAoAkU275jxIgRWbRoURYsWJDe3t50dHRk+vTppT4eAAAAAIAmUPcovWbNmoGvp0yZkpUrV9b7IwEAAAAAaFLFtu8AAAAAAABRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAC1XI2wAAAcLSURBVAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYkRpAAAAAACKEaUBAAAAAChGlAYAAAAAoBhRGgAAAACAYhoSpb/2ta9l5syZ6ezszBe/+MVGjAAAAAAAQAO0lv7AzZs3Z/HixbnnnntyyCGH5JxzzsmJJ56Y1772taVHAQAAAACgsOJRuqenJ29961vzW7/1W0mSU089NatXr87ll1/+ir6/paVSz/EorP2IkY0eAXgZ7rnQvKyh0NysodC8rKHQ3KyhQ8Ng/xyLR+ktW7akra1t4Li9vT2PPvroK/7+I444rB5j0SCfW9jZ6BGAlzFu3OhGjwDsgTUUmps1FJqXNRSamzX04FB8T+n+/v5UKv+/lNdqtd2OAQAAAAAYuopH6fHjx6darQ4cV6vVtLe3lx4DAAAAAIAGKB6l3/a2t+X+++/Pz372s7zwwgv5xje+kalTp5YeAwAAAACABii+p/SRRx6Zq666KhdccEFefPHFzJkzJ29+85tLjwEAAAAAQANUarVardFDAAAAAABwcCi+fQcAAAAAAAcvURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAr61re+lSVLluSpp57a7fyXv/zlBk0EAAeGDRs2ZPPmzUmSZcuW5SMf+UhWrVrV4KmAfVGp1Wq1Rg8BAAAHg9tuuy3r1q3LMccck9WrV+faa6/N6aefniQ588wzs3z58gZPCADN6R//8R+zZMmS9Pf3561vfWs2bdqUU045JWvWrMnxxx+fyy67rNEjAnuhtdEDAAem//qv/3rZ11/96lcXmgQADhxr167N8uXL09ramvPPPz8XXnhhDjnkkMyYMSOeFQGAPfvKV76SVatW5ac//WlOO+20fO9738uIESMyd+7czJkzR5SGA4woDeyTefPmZcOGDWlvb/8/f4iuVCr59re/3aDJAKB51Wq1VCqVJMnRRx+dT3/603nf+96XV73qVQPnAYD/q7+/P4ccckiOOuqoXHjhhRkxYsTAa319fQ2cDNgXtu8A9smOHTvynve8Jx/60Ifylre8pdHjAMAB4fbbb09PT08+8IEP5M1vfnOS5JFHHsnll1+eXbt25ZFHHmnwhADQnD7xiU/kwQcfzF133ZVhw4YlSZ544oksXLgw73jHO3L55Zc3eEJgb4jSwD579NFHs2zZstx4442NHgUADhj3339/2tvbc8wxxwyc27RpUz7/+c/n+uuvb+BkANDcHnrooZxwwgkDx08++WSefvrpdHR0NHAqYF+I0gAAAAAAFNPS6AEAAAAAADh4iNIAAAAAABTT2ugBAADgQPbMM8/klFNOyetf//qBc7VaLRdccEHmzJnza7/nnnvuyT//8z/n05/+dKkxAQCgaYjSAADwGzr00EOzYsWKgePNmzfntNNOy5ve9KYce+yxDZwMAACajygNAAD72ZFHHplJkyZlw4YNWbt2bZYvX57W1tZMmjQpixYt2u29P/jBD3Lrrbdm165dqVaredvb3paPfexjeemll3LjjTfm+9//foYPH57f+Z3fyU033ZQRI0b82vOHHXZYg35aAADYO6I0AADsZ//6r/+ap556Ki+88ELuueee3H333Rk7dmxuuummfOELX8iRRx458N677rorV1xxRU488cT84he/yLve9a6sW7cuO3fuzIMPPphVq1alUqnk1ltvzY9+9KP09/f/2vPHH398A39iAAB45URpAAD4De3cuTOnn356kqSvry9HHHFEbr311nznO9/J9OnTM3bs2CTJBz/4wST/s6f0/1q0aFHuu+++/P3f/32efPLJ9Pb25vnnn8+xxx6bYcOGZe7cuTnppJNy6qmn5s1vfnO2bdv2a88DAMCBQpQGAIDf0K/uKf2/enp6UqlUBo63bduWbdu27fae8847L7//+7+fP/7jP86MGTPywx/+MLVaLYcffnhWrFiR73//+/ne976XK6+8MhdddFH+5E/+ZI/nAQDgQCBKAwBAnbztbW/LLbfckosvvjijR4/Opz71qdRqtbzhDW9I8j+R+t/+7d/y2c9+NmPHjs0DDzyQp556Kv39/bn33nvz+c9/PnfeeWdOOOGE1Gq1rFu3bo/nAQDgQCFKAwBAnXR0dOQ//uM/cu655yZJXvva1+bGG2/MN77xjSTJ4YcfnksvvTRnnnlmRo0alSOPPDLHH398Nm7cmLlz5+a+++7LaaedllGjRmXs2LG58cYbM2HChF97HgAADhSVWq1Wa/QQAAAAAAAcHFoaPQAAAAAAAAcPURoAAAAAgGJEaQAAAAAAihGlAQAAAAAoRpQGAAAAAKAYURoAAAAAgGJEaQAAAAAAihGlAQAAAAAo5v8BfQZg1TH3h1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.set_ylabel('Average Fare')\n",
    "titanic_train.groupby('Pclass').mean()['Fare'].plot(kind='bar',figsize=(25,7), ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211c17cf40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Embarked', y='Fare', hue='Pclass', data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  We can notice in above mentioned plot that 'C' Embarked is appropriate for 1st class and median fare 80$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### That's why we can place the C embarked in our missing data. Let's do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['Embarked'] = titanic_train.Embarked.fillna('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  Now we can see that Embarked variable doesn't have any missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Now we do some Data visualization of our dataset to get some insights from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002211BCC8610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C538DC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C571250>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002211C59E6D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C5CAB50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C5F6070>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002211C602040>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C65A940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002211C67AE50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train.hist(bins=10, figsize=(25,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211cb7d4c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=titanic_train,row='Survived',col='Sex',margin_titles=True)\n",
    "g.map(plt.hist,'Age', color='purple')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211c5a9a60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_train,row='Survived', col='Pclass',margin_titles=True)\n",
    "g.map(plt.scatter,'Age','Fare',edgecolor='w').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211c0ad5e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k1wohRFOzYsUKj8ufeOIJn9v0WwHI3r17+fbbb9m0aRMfffQRq1ev5ujRo8ybN4+VK1eyZcsWDh8+zM6dOwGYO3cuCxcuZNu2bSiKQmpqqr9CE0II0YguXbqk/nfmzBm2bNmiPg7GV3679Bk4cCDvvvsuOp2OnJwcHA4HhYWFdOjQgbi4OACSkpJIS0ujU6dOWCwW+vTpA0BycjLLly9n0qRJ/gpPCCFEI0lJSXF7rSgK9913X73a9Gtpvl6vZ/ny5YwePZqEhARyc3MxmUzqz81mMzk5OdWWm0wmcnJy/BmaEEKIAKHRaLj55pvr1YbfB6WefPJJZs6cyeOPP052drbbvFuKoqDRaHA6nR6X10VMTKTXn5lMUXUPvAEFcnwSm+/8EV+wHueBHBsEdnyBHBvUP76zZ8+yfv160tPT69WO35LZ8ePHsdlsdOvWjbCwMIYPH05aWhparVZdJy8vD7PZTJs2bcjLy1OX5+fnYzab67S9goJinE6l2nKTKYq8vCLf34ifBXJ8EpvvrhafryeAYDzOAzk2COz4Ajk2qDm+mo5xu93Ojh07WLt2LUeOHGHEiBGsXLmyXrH4rZvx9OnTpKSkYLPZsNlsbN++nYkTJ5KVlcXJkydxOBxs3ryZxMREYmNjMRqNambeuHEjiYmJ/gpNCCFEIxo6dCj/+Mc/mDBhAjt37mTBggV07969Xm3W6cqssLCQ6OjoWq07ZMgQDh48yNixY9FqtQwfPpzRo0fTsmVLZs+ejdVqZciQIYwYMQKAJUuWkJKSQnFxMT169GDq1Kl1fzdCCCEC3syZM1m7di1vvfUWubm5jBkzhmbNmtWrTY2iKNX7LKo4ceIETzzxBEVFRaxbt45p06axYsUKOnbsWK+NX0vB2P0CgR2fxOY76Wb8WSDHBoEdXyDHBr53M7rs37+ftWvXsmPHDhISEli6dKnPsdSqm/HFF19k/vz5xMTE0Lp1ayZPnszChQt93qgQQggRHx/Pyy+/zBdffMGgQYPq1VatuhkvXbrEbbfdxmuvvQbAww8/LDc1CyGE8Im3GUAAPvzwQyZOnFjnNms9Zma1WtVy+by8PJxOZ503JoQQQly6dKnaMteIV1GRb92qtUpmDz30EI8++igFBQX8+c9/5tNPP+Wxxx7zaYPXO4NBi1avIzxMT2lZucfxD+G7qvvXUW7HZnM0dlhCiEqqzgACsGvXLqCiOMQXtUpm48eP54YbbuCrr77Cbrfzpz/9idtuu82nDV7PDAYtDk0Ir67ax5GsArrfGMPcKf0wGLRywr0GPO3fZyb3w2BA9q8QASgnJ4ePPvqI9evXExYW5v9HwDzyyCP87//+LwMGDPB5QwK0eh2vrtrHoeP5ABw6ns9rq9OZN20AyMm23jzt3yXvyf4VItDs2LGD1NRUDh8+zIgRI3j99dfp0aNHvdqsVTIrKiqitLSU8PDwem3sehcepudIVoHbsiNZBYSH6SkrsTZSVE2H7F8hgsN//dd/0a1bN/7xj3/QtWvXa9JmrZJZWFgYw4YNo0uXLm4J7c0337wmQVwvSsvK6X5jjHrlAND9xhhKy8obMaqmQ/avEMFh586dbNiwgaeeeoqoqCiSk5MZM2YMUVG+z/NYq2T2wAMP+LwB8TNHuZ1nJvdjyXvpbmNmjnK723pSxOAbT/v3mcnV968QonG1bNmSRx99lEcffZTvv/+e1NRUli5dyt69e31us1bJbNy4cW6vFUXh5MmTPm/0emWzOTAYYN60AWqiCg/VU1BQrK4jRQy+87R/5YuAEIHLNX3h4sWLfS7Jd6nVDCAffvghffv2pVu3bnTr1o3u3bvz8MMP12vD1yubzUFZiZWC/GLKSqyEhLg/6kar17HkvXQOHc/H4VTUIgat3u9P62kSqu5fSWRCBJ6zZ8/y0EMPMXDgQPr168cjjzxCaWlpvdqsVTL729/+xjvvvMOQIUPYsGEDTz75JHfddVe9Niw8q6mIQQghmoIXXniBxMREMjIy6NKlC9OnT+dPf/pTvdqsVTJr3rw5t9xyC926daOgoIBf//rX7Nu3r14bFp65ihgqkyIGIURTcubMGX79619jMBhQFIWhQ4dy7ty5erVZq2Sm0+m4fPkyHTp04ODBgwA4HNJ94w+uIoZeHVuhDdHQq2MrKWIQQjQpdrudwsJC9bXNZsNur985rlYDMRMmTGDWrFm8+eabjB07ls8//zygHv/SlEgRgxCiqRszZgwTJkzggw8+oKysjJkzZzJ69Oh6tVnr0vxRo0YRHh7OmjVrOHToELfffnu9Niy8s9kccKWQQQghmprf/OY3dOjQAaPRyIgRI+jWrZv6oGZf1djNuGDBAvXfFosFgNatW3PXXXcRGhparw0LIYS4fnXu3Jk1a9bQvHlzbrjhhnq3V2MyO3z4sPrvRx99tN4bE0IIIT7++GOmT59OZmYmR48e5dFHH+X999+vV5s1djO6ni9T9d9CCCGEr958803Wr19P69atAbhw4QIPPfQQkyZN8rnNWt+J63owp2g8Ms2VEKIpiIyMVBMZVExvFR0dXa82a0xmTqeTy5cvoygKDodD/bdL8+bN67VxUXsyzZUQoqno3bs3r7zyCsnJyTgcDtasWcPNN9+sDm317Nmzzm3WmMyOHTvGrbfeqiawQYMGqT/TaDRkZmbWeYPCN/KsLiFEU7Fz504AvvjiC7fl3333HYqisGPHjjq3WWMyO3r0aJ0brGzFihVs3boVgCFDhvDss8+ye/duXnnlFaxWKyNHjmTOnDkAZGZmMn/+fEpKSujfvz/PP/88Op3MR+giz+oSQjQV27dvv+Zt1moGEF/s3r2bb775hg0bNvDxxx/zf//3f2zevJl58+axcuVKtmzZwuHDh9UMPXfuXBYuXMi2bdtQFIXU1FR/hRaUZJorIYTwzm/JzGQy8dxzz2EwGNDr9XTs2JHs7Gw6dOhAXFwcOp2OpKQk0tLSOHPmDBaLhT59+gCQnJxMWlqav0ILSjLNlRBCeOe3frybb75Z/Xd2djZbt25l8uTJmEwmdbnZbCYnJ4fc3Fy35SaTiZycnDptLyYm0uvPTCbfn17aEGobn9OpsGDGIIxGLVarA6NBW+0RMo0VW2MI5NjAP/EF63EeyLFBYMcXyLFB4MTn90GpH3/8kVmzZvHss8+i1WrJzs5Wf6YoChqNBqfT6Vb671peFwUFxTid1e+FM5miyMur30Pf/MmX+IqvrF5c82r1Fsj7LpBjg6vH5+sJIBiP80CODQI7vkCODWqOr7bH+PHjx+nYsaP6f1/5rZsRID09nWnTpvHb3/6WcePG0aZNG/Ly8tSf5+XlYTabqy3Pz8/HbDb7MzQhhBAB4Le//a3b/33lt2R27tw5fvOb37BkyRJ1NuRbbrmFrKwsTp48icPhYPPmzSQmJhIbG4vRaCQ9PR2AjRs3kpiY6K/QhBBCBAhXL1x9J+bwWzfj//zP/2C1Wlm0aJG6bOLEiSxatIjZs2djtVoZMmSIOlPykiVLSElJobi4mB49ejB16lR/hSaEECJAXKupEv2WzFJSUkhJSfH4s02bNlVb1rVrV9atW+evcIQQQjRhfh0zE0IIIWpyreb9lWQmhBCi0biGlOo7tCTJTAghRKMZN26c2/99JclMCCFE0JNkJoQQIuhJMhNCCBH05BkrQp5g3YQE42cZjDGL+iktLeUvf/kLO3bs4OzZs2g0Gtq2bctdd93F7NmzCQ0NrXObksyuczU9wVoEl2B8Gnkwxizq749//CMRERG8/vrrmM1mFEXh3LlzpKamMn/+fP785z/XuU3pZrzOafU6lryXzqHj+TicivoEa61evucEm2D8LIMxZlF/R44c4Q9/+ANdu3alZcuWxMTE0LNnT1544QUyMzN9alOS2XWupidYi+ASjJ9lMMYs6s/pdHL+/Plqy8+dO+fz9Fby9ec653qC9aHj+eoy1xOsI8OlrzGY1PRZBqpgjFnU3//7f/+P8ePHc/vtt2M2m9FoNOTk5LBr1y4WLlzoU5tyZRYADAYtYRFGYlpFEhZhxGDQNti25QnWTUcwfpbBGLOovzFjxvD+++/TvXt37HY7VquVLl26sHbtWu666y6f2pQrs0bmdCqNOgBuszkwGGDetAFSTRbkgvGzDMaYxbURFxfHlClTrll7cmXWyKw2R6MPgNtsDspKrBTkF1NWYpUTSRALxs8yGGMWgUeSWSMzGrUyAC6EEPUkyayRWa0Out8Y47ZMBsCFEKJuJJk1MqNBKwPgQghRT1IA0shCQjRoFacMgAshRD1IMgsANpsDrgyCC3cyb58QojYkmTWQ6/GkXN/3LPNGCiFqS8bMGoDrpPzyqn0kP/sJL6/ah0MT0qA3Rzc0T+/ZGRJCRFRord+3zNsnhKgtvyaz4uJixowZw+nTpwHYvXs3SUlJDB8+nKVLl6rrZWZmkpyczD333MP8+fOx25tW8cP1eFL29J5fW53OpSJrrRO5zNsnhKgtvyWzAwcO8NBDD5GdnQ2AxWJh3rx5rFy5ki1btnD48GF27twJwNy5c1m4cCHbtm1DURRSU1P9FVajuB5Pyt7ec+uYiFoncte8fZXJbQtCCE/8lsxSU1P5wx/+gNlsBuDgwYN06NCBuLg4dDodSUlJpKWlcebMGSwWC3369AEgOTmZtLQ0f4XVKK7Hk7K393w6p6jWiVzm7RNC1Jbf+rleeuklt9e5ubmYTCb1tdlsJicnp9pyk8lETk6Ov8JqFK6T8pL30t0KGSpOyk2zmsHTe37ywT6s3ppZ60Qu8/YJIWqrwQZtnE4nGo1Gfa0oChqNxuvyuoqJifT6M5Mpqs7tXWtOp8KCGYMwGrVYrQ6MBi0hIRXvMxDi88ZTbE6ngtXm8Pheqq6XMmMgoUYd5/NLeG9rJhcLrcyd0o/wUH2dHzFTsf7PvxPI+w38E1+gH+feBHJsENjxBXJsEDjxNVgya9OmDXl5eerrvLw8zGZzteX5+flq12RdFBQU43RWf6ibyRRFXl6Rb0H7QfGVUIqvvA60+CrzFJurSrHqVaZWcXq9Yiq3aomOMPD0w/3Uq6uCgmKP69YntkBytfh8PQEEy3FeWSDHBoEdXyDHBjXH19BJrsFK82+55RaysrI4efIkDoeDzZs3k5iYSGxsLEajkfT0dAA2btxIYmJiQ4UVlBrz+WfeKjN1Br3XOGRWdCGEvzXYlZnRaGTRokXMnj0bq9XKkCFDGDFiBABLliwhJSWF4uJievTowdSpUxsqrIBU083GNd1IXNskUZ+bmT1VKbaMNqLRQFR0GGWWcuy2mtu7Hm8gF0L4l9+T2Y4dO9R/JyQksGnTpmrrdO3alXXr1vk7lIBjMGgps9iJaRWpntSBGpOVVq/j1VX71MfMu66M5k0bALVICPVNhlUfc58YH8vUUd158e29tWrP2/YjovSEGnWS3IQQPpEZQBpI1a7BsHA9Gr2Oi0UWFAUKS2xo9Dp0Bh2ffZvNrHG9+OjVJGaN68Vn32ar92XV5p61mroh63sDd9Vy+Yfv6cqyD/fXuj1v279UZL1uZkcRQlx7TXcKigDi6Wpk7uR+aDROVqw9oC57amI8Mc1DGdovjuVrMtxK2sNCdZSVWKtdGYH7PWtXu/KqKRnWZqJjV7n8/OkDCTPqQEOd2qvpZurKya22V5r1JV2eQjQNcmXWADxO7fReOqUWu9uyZR/ux2J1sHxNhtvy5WsysFgrTrCVr4yGxMfy1nO/5MVfD0aj+fnEXNOV17W4gdtmc2C3lXOp2Mr5/JI6tVfTzdQuDTU7yvU4Z6YQTZUkswZQ09VI1WVhoTqP64aFViQjm82BVnGSMmMgj97XkxVrD5D87Ce89E7Fifhq3ZDXalYNVxzNo4zMnVL79jxt/6mJ8aRuP6au01Czo1yPc2YK0VTJX20D8NY1mFNQ4rZe9xtjKLPYa+xGhJ8LQV5bnV6tEGT+9IE1/v61nFXDZnNgszkIC9dXdDuG6iiz2FGcDspKPRd/aPU6IkN1ldYvx6HAxUIr2hBNldlRfFeb7sP6drkKIQKHJLMG4Glqp7mT+6HVhdCrYyu3sS0NSg1TX1UwGLReT8ShRu1Vf/9aPgzUYNBic8CS/61azb5JuyIAACAASURBVKh1Sx7exvK0ihO4tlNW1bZi82rjj0KI4KFRFKX6dAJBKNBnRnBdKYSF6rBYHepVTIhGg9GorXRFU16r+8wsVjsr1h5wOxH36tiKedMG4Ci316qo4WpXL7XZd2ERRl6udKtA5ThcydJg0KIz6Akz6jiVU0Tq9mN8vf9MtfXqoqbYahOTK666zmZyLeJz/dwXgX6cexLIsUFgxxfIsUFgzQAiV2YNxGZzYAAu253uV2hT+mGz2txOnjVdObnuM2sRbeTJB/u4VT3OnzEQFNfNy3ZKS62UlXq+yqjL/WY1Jb3KV4iJ8bFM+GVn2reOwmqzq4UUDk0Ir76z1606E2DXgbOEh+lrnXxrq7bdh1W7XMssdkKvfLEwUPub0IUQja/JJzOnUyEswujzibLqiRzFCVcKLSq3V5sxGk83PL+2um5l6K4TtePKt/NZ43rRvnUUdruDEou92lVGWLjeY0Kr7c3XV0t6rq66FtFGpozs5pZcn5ncD6M+hFffcd/O8jUZzBrXi4uF1ooxs3rOaFJVXboPXV8yLlX5klHfGK4HTfW2hqb6vpq6Jl3NaDBouVxi9bn02lPpts0Bm7854dZeWLi+ViXe1+IhnWWWn0vbv95/hieWfMmCN3djdygeK/M0IZ7fa21juVrFn2s88OF7ula7pWDJe+mEhXreTlzrqIoxQo3mmlcU1rViU6oa666p3tbQVN/X9aBJJ7PKFX++nKS8neQSerWrljBqczKs6z1enmby0Gg0PDUxvlpp+9VK+qu2W2axs/7VJFY8M4zE+FivsVwt6blK9Nu0ivC4nsVq9/iey6x2tIqTUKPnuOtzn5krpnnTBrB+cRLzpg2ocRzsenwSeH011S8ATfV9XQ+a9Cd0tfGcqxVFgOfZLdq3jnJ7HRaqo2W0kRXPDKN96yhO5xSxdvsxwsP0bl0V3qoaPV0xeOveizRoWbnugNq9eDqniHe3HOHX99/isWutzGL33G6VMaw4cyTDb72hWiy16bJzdTdOvLszCb3aqXHtOXQWwGN1pd1W7tZNWduKQtfn41Qquo+9dQHVpWJTqhrrrind1lB5KALqNqONCBzaP/7xj39s7CCuhbIyG1XrMjUhGo5mX6TrDS2YMrIbK9cd5K9rM8jMvkjCLbHotRocjp9/ST3Rv/s9K9ZmMCS+PVlnC8m9WKqu0/OmVnSOa86W3dnq69t6t6PbDS3567oD/HVtBj+dL2LWuN5Yyx08vGArR09e2Z5Og0YbQp+bTcy4tyc9bmxJWKgOxel0iwPAEGrg1Xe/59DxfBQFci+Wcvz0ZQb3asdn3/3Eqk+P8OHn/2bL7myiI4zc2b89A7q34fjpyxRcLqPnTRVda3qthvLKY2Ae2s0+W8hj9/UCh8MtMUREGCkrtZJwS2y1dnE40GpDMIQaaNYsDKdToWNcCxwOJ5HhBsqsdnp0agVOJzidDOkXx+RR3bi1Zxu37YRo8Np+tX1S5fM5evIig/vEEhZmQINSbf3aqksMtRURYaS01Fbjz33h6TivzfauxmDQqp8lGg0hGmp+7xoNR09erPa3cWvPNtjL3b9chIUZIERT+7YbkMGgxWJ3XvVv3tP7qvM+80F9P1d/qyk+X49xXzXp0vyIqFCKS8uxO5zVytgfvqcLSXd0JCz055natXqdW0l3Ynws08f04L/f/8HtquKzb7P58PNjlYoctLz0zt5qpeBPPtiHmS9/ob6eP32gx/U8lafHtIok+dlP1EIPAG2IhvWLkyguK6e4xEabmAhKLOWEXyn3/+Rfx6tdGd2b2BGnU1GvYGpqt6iwzG3gOzxUT0FBsccBccCtrP1/Uu7G4VBY9uF+t7kmI8P1lBRZavzsvN224KosdMXureT+99MGoACKjwP1BoMWjV5HcYmN1jER5BSUEBlh8Lk9aLzS/KsVL3j7LKu+/2ZRRlAUwkK9t1Ob2xoMBi3OkBBeW33tb3/wVeV9UGaxu/1NJsbH8sio7m7Hsbf35a/bOiqT0vzaa9LdjKFGHSvXHeDph/u5dR0kxscytF8cL1Xqaps7uR+RlboLY82RWKx2wsP0/H7aAMIMOspsdiLC9CTd0ZHxd3VWT7RhoUaPXROmFuFur72Na3nqwvDW9WWx2rHb3ScofvLBPpiah/Hh58f457Z/q+trQzSMv6sLC97crVbneWu3zGJHcyWZJ/Rqw9B+caCB8MhQNChUPX9WrYYMM+p48e2fTwquuSZTZgy86ufk7baFJx/sw4nTl+jd2UxMdJi6v6ruv4hQPfmXyogM1/t0Iqn6JQZ+/pLREJMdXyuupFxYYiMsVE9hiY3ICIN6m4G3rutwow6LrdJN+XotDqfCohoqTGs7k0x9H1l0rVXdRxpNxfP4XL7efwYNkDJjoPpIoooKZh0x0WFuX3w9va+UGQPR6nVSAdkImnQyKy0r50KhlVPni9xO4BN+2VmtvAPUiX9TZgxk6qju7Pj+J7eZ6yfe3Znht97g9VuYpwQx8e7OlJaV8/Fr96pXSbWZqgoq/uBCQjT8ftoAiqpeLSioA9Su2JevyeD3jwzw2PbpnCK3P7QQjYbnpg2odmVXWGLDqNeq00yVlJXz6Tcn2HPovHo1ejq3mEn3dKVNq4o5JV2JP651FBabnYRebdzG8tZuP0aoUUfJVb5YhoXr0YRoiTLqmDWul3pT9fI1GcybPpCXr3zpWPnsnR7H5U7lFPHWhkPMnz6QiEgjToVq943VdHJpKuM/eqOe4tLyak9icCX5mk7AtvLqT3BoEW2s8UkGtRmXrMu+9eWqsq4Jw9s+UqhIZAAXCq04nQpFhWXoDDrCwoyczy/hzfUHuVBo5ZnJ/Yjy8r6MBp3bl0dJaA2nSY+ZhWhgcJ9Yvko/xYN3dyH7bCEFl8v41dherFib4bZ+weUyHh7ZjZdX7WPskE6sXHdQHVeaNa43f0nNqDZ+NaRfHPZyR7Uxl0nDuzD81htYvPp7dQxt6qjuGPRa7r61A8P6tqeoxEZ0RMUkvZXHZlzfnrfsyiKudRTL12Tw17UZZJ0tJKFnWyLDDR5jnzKyGz07xqjv0TXuExaqZdroHiT0akuoUYvDoWC12Xn9wwxWrM3g3ycv0qtjKy5cKsNo0KljB/8+eZGJw7ugKAqpX/zIg3d3offNJv6SeoAVazMYPqgDvTuZ+Ou6itd6XQjD+v+Cv6RmuI0bakM02Gze51kMC9djc6Bu96fzRfxqbC8uF1v54Wguk0d04/U1+1EUaNcqoto2pozqzveZ5/nsu5NMHtGNYoudrbuzWPDWHo5mV7y3z747yU1xLaqNkarqMP5TW40xZlZud7L4SvWu6zjNOlvIHX1i0aAQGWms8biv+nuPjOqujg0XXC5j8qhulNV1/KaW+7am8VCdToMx1IBdwe3nnsa9r8Zg1HncR4/d25Otu7PVvxutRsGuaNTtZZ0tZObYXhw7dZEd+06R0KstR7Orv6/Occ1Z9ekRt/NDfciYWe016dJ8m81Bswgj9yZ2pFXzMH7/yADWv5pEmZdycYvVrlYrVv7WVfU1/Pzt0lUVWbkUPOmOjh4f+VJcVk7ys5+wYu0BHr2vJykzBtIswuj27c1VGpzQqx3LPthfrY0SL+X9p3KK+Cr9FL9/ZIDbQz1Ly+wk/+4TXnpnL3kXyyixlPPnf/5Q7REz7VtHeyxJHto3jiNZBZhbhrvdR1Zud7o9lDOhV7tqv7/sw/04rzIk6+m2huVrMpjwy84V7yv358u63p1MHmPs3cmk7oOqt04sX5OhxuatvPpaPUmgsXm7zSHMqMOhCXG7R9Gl8nFf9fcqV+3WprozLFxPeGQoMa0iCY8Mrbj/stxeq6cqeHxM0up0yqx2bA64VGT1eJuNzqCv0z1g3vZRm1YRfPRqEk+MvwWdLgRNSIjX47LyHKiV39eTD/ZRn/4gt3Y0vCbdzQgQEqJBUWDhW3vU7pU1L47iqYnx1YoVNGjUrrnKXXZVX0PFH/f5/BJCjbqfuxOudLnEtIr0+AfTslkot93Sjgm/7EyzCCNWmwOLzU5Mq0i1bz48TE/LaCOmFmH86fHBnK40l2HFH4iOuZP78VqVsaWv0k8xtF8cb244qHaXuMbMXNs0twj32h3oaTyvZbQRrVZT8QXAYncbWzC1CK9Vwg8L1VNa7L0byts4YlzrKJ55uB+ffZd91W3EtY7iyQf7sHprpsdbJ1y/563b8Fo+SaAxuZJV1ePU1Q2bMmNgtdsknpoYj9Xm8Nh9m1NQ4vFJBp5mxdGEaAk1aim12EGBy8VWIiMMaHUQFWa86r51HfeVb285+J88Qg06XnpnL396fLD3+yivPH3Bbrv6Z+ZtH+VeKHUr1nrx1563F9cmiol3d8ZiddAs0qB2y5/PL2H11kz1b09u7Wh4TbqbESoudbXaim9Ori64jrHNaNc6yq1EPjxcTwgKA3u2ZecP7t2SzSMNTBnV3a10+8kH+/Be2lF27DtVrTshRBuCXhfCrHG9+dW43tx+SzuaRxq4KbY5iX3bU2axs3P/aWLNkRSVlhMRZuDCZQuR4QbsdoW+Xc0UFtvUEvdBPdpQUGjhtt5tubFdM6LCDST0asvDI7oyuFc7osMNdGzfnKNZBW4FID1vakWvjjEM7t2OsFAdel0IdrtC95ticDgU3vzoAN8cOMuscb3RaDRu3Sauqq7Fq9MrunWyLzJrXG8uFVs5eb6I229px0/ni9T1fzkgjhNnqpc0J/Rqi06nQW/Q06xZGJqQECIjjITotERHh1JmsdMpthnfHDjr9nuDe7Wl3OGkf/c2arfsTe2aeSybHtSjDW9/8n98vf+Mx1snOsc156fzRST0aluthN9VXh0ZWdFNZymzYbWU17vEujG6GctKrQy+JZb/VDlOP/js3/xwNJeHR3aj3Fqu3iYxuHdb/rHxMKWWco/dtwZDCFNHdSehV1v1doqq3YEGfQg3xDZn6+4szC3C+fM/f1C75fp3bU2oUY9Br+XypVIcdgch2hAiI42g0aDXh6jHhdXqqHZ7y8ThXYgM0/PXdQe4rbf78QYVn233G1sy9Y/bOOrldpvKDAYtWp2WQT3auO2jpx6K590tmZw8X9ELUHC5jPsSO5LpoRux+40t6XJDS/7veD7/9d87OZp9kYE92xASomHHvlN1vrXjauX90s1Ye026NB8qykOLS21s/uaE2zdPi7Wc9q2jqzyDq9xjmbjr22eIVovRoHO7WnKVtRfkFwMVB6cx1ECptWKexJbRRrVoosxix2jQYrVVKj83aDmdV8yeg2e559YbCDNWVE1WLmX+/bQB6EJCsJS7L3ddkd01sAMGfQg6bQihRh02mwOHU3Ercb9YaMVo0LoVlERFVCTLHft+YuyQTmrMrmILT7Pyz58+EKNei7XcoRaKfJV+isG926HThlBSVl7tlgGHUr0yrurtDUezClj8Xjrdb4zhuWkDsNkcbrdEzJ3SD22IBovNwbIP9tfYVuXXrn1054BfsGPfTwy/9Qa1cMdTefXcyf3Qa1Hns/S16KAxSvMvXy5Fo9ehASJC9Vd9QoHrVodZ43rx1oZD1T7rJ8bfQqhR51ZuHh5p5KVKc22ueGYYb204xKxxvdhz6Gy1q7ukOzoSatRiszmrHb9zp1Tc1mLQXTmePDxV4fePDOCV/93nde7PXQfO8LePD6sxe7rNpeKpDTrCQvWczy8h/WgO/bq2Vv8mP/nXcbcvgb06tiJlxkAsNke16trVWzO5WGhl1rhePLHkS7f1nU6lTsdJbcr7pTS/9q6LK7OyUis3xbVw++Z596AO4HBQXGSl3GbHXl7xXC2HQ8Fe7qCsrJxym52yUhv2cgf2cic6vZYX/vEdqz49on6LqzyY7To4FRQWr05X/wBdRRMGXQjtW0f9PIidfZFenVrxTcYZhvaLY/u+n+gU11y9qfmOPrH8amwvWkSHYi13eLzZeeyQTqxYe4DE+FheemcfGcdy6XpDS1577+crql4dW7Hj+5+4sV0zdnz/Ewvf2qN+c9ZqNfyiTTR6nZatu7MYO6QTM8f2IirCc6HJ5BHdKCyt2Nd/XZvB59/9xMThXWgWYaxINB/udyss+ezbbOLMUfz7p4tknytUi2fGDunEp7uy1Nfj7+rMwyO60TmuOTqthv9+/we39/qfU5fpcVMM7287yiOjuvOrsb1I6NWWiDA9Hds3Z/LIbiT0aoteq6FzhxZMHtmNQT3b0CzSSKvmYRj1Wr764bTblbSnG8j/c/oyd/SNIwQFrTakWlFCbYsOGuPKzO5UeGXVPv7vRAGd2jdn5bqD/HA01+uVgqtwqXXLcI+f9aP39aTcWu72+KGICPcikl+N681f12bw2H09MbcMVycm+Ol8EQ/e3YVmEQbyLpVRZrWrY8CVP9PenVrhUBS1aKdqAZCrsGnnD2cICYGpo7oxeWQ3bv5Fc3amn+K2W2K5fKW3wFORStUryayzhSQP68R7aUfZsiubAd1b84s20dUKpxS7gxAUhg34BZOuHJcffPZvvt5/Ri0i+/Dzf6v76uGR3Si8XKaeL2pzZe9tYoTKPT1yZVZ7AVUA8sknnzBq1CiGDx/OP//5z2vWbl3n6vPmaoUCrkHsyHADR7IK3G4B8FYk4SpQWL4mg4Te7dRS5sT4WPWbaPKznxDhZcJedTwoVM+h4/mM/2Vnt8KMqkUQQ/vFuRVohBl1LF+TQViojg8/P8YTS75k7NxN6u0MlbnGX15bXVGIMnlkN1pEG1nyXjrWcofX97bsw/1M+GXnanFXfh0WqmPs3E08seRLWkSFen2vX+8/Q+r2Y+ReLOWld/bywHObeemdvVwqsmK3lVNSbEVRIOWN3UxasJVxz37CrEXbee29dHXw3jUw77Vs3KhDq9cF3Tx9rvfz9f4zrN6ayaxxvfjo1aSKe588HO+uvwtvBVGlZeXVipPO55e4resaT7ZY7dUmml6+JgOLzcHyNRm0jvE8d2frmAiPRTtqAVBOEau3ZvL7RwbwywG/4OVV+7hv7iaeeO1L/rnt3+q6lWOuzNNnWLmQo0V0qLqv1r+axPzpP+8rm82BosCCN3fzxJIv3cbDSiptx9fxMZkT9NoKmGSWk5PD0qVLef/99/n4449Zs2YN//nPf65Z+7YrxRkF+cWUlVh9Gty/WlJ0HZyuyXVrWxXpWh5njlLvRauaCE/leE4urpOJq+rvatswt3S/kTvUWHGjeNWJh1O3H6s2obGrWst1Eqp8UvBWyOHabtXKuNM5RW6vS8rK1e2Ueqm6c/1O1X1TNcl4O0m0bx3lduLxNvHzqdwidV7NYDrZVH4/lZ+o4HQqXo93m82B3VZeq2rO8DA97287ypMP9lHX3XPobMWN1172leu4OF3D8evpy43rqQp7Dp1l14GzRITpMRq8H2M1xVzTsXA6p0jdV8m/+4SwUJ3bvgo1at3er+vvIDxUV+/K17pOPC5qFjDJbPfu3dx66600b96c8PBw7rnnHtLS0ho7rGpqSorqwanAUxPjySnw/C22ssoJqdRqp9zhqJj4t0pSSt1+zOMfletksufg2Vptw2K1uy2/WGhh6qjuvPTOXpJ/9wlvbTjElJHdiDNHEhqqY/70gay/UurvqtaqehJyzSBS03ZdlXGuP/49h866vdZrQ1i/uKI0+sv0U9Xea+XfqbpvwD3JeDtJ5BSUuJ14HOV25noor95z8CylZeVBd7Lx9RaD2vZcuCYhqHzVlxjfvmIGkRpud+l+Y4zX4zd1+zGPX27KrHaOZhUwtF8c6xcnUWIpr/b35FrXarO7XVFVjdnbsfDUxHi1lN61vOpnW2ax81X6KfX9zhrXi6/ST2GxOerVywNN55aQQBEwY2Zff/01BoOBhIQEAE6ePMmJEye48847a/X7/pqAtS5cYxCHj+fTrnUUGg3cfks7tXLKW1XkNxln1JuqP/3mBN8cOEvvm038u9LNpifPFxEdrmfGvT2ZMqpifCg6wkA7UyShRh2/aB3NiTOXyTp7mVnjepNVaQzAtY2HhnfF4VT45F8nfh5HAZZWGcvIPlvIjKSebNl1gg5toykqtbmNv7gq5KLCDfS4sSWJfduTf6mUXw74hcf39tA9XYkON/DwiG7cHNecs3lFJPRqp74PrUahpNiKw+5Aq9OS+sWP/HjqIo/e25NH7+t5pSIyhDYtI5hxb09KLeVu+wbcxy49TRw8d0o/IsP0KPafJzl2OBSMRi139I1j8oiKcZhvMs4w/NYbwOFAcTp9noC4McbMioos6LUar5M610QdK65hzMe1X3fsO8XqrZkcPl5AYt/2OO12tDotfbuY3Y67pybGo9Fo6NfVzM4fzvDjqYv8+v5bmJbUg+43tuSfaUe5eGVGjZ0/nOJI1gW3CbKbR4ex9IP9ZBzLpXenVoSH6unftbXbNp6e1BdNiAanl4ILb8dCVJieEG0IX3z3U42fbYiGauPtU0Z1V8fbazs+5m2fX+3zkjGz2guYasY33ngDq9XKU089BUBqaiqHDx/mhRdeaOTI6sbpVLDaHOj1IVhtDvXem4hQPWVWD9WMRi0WqwOdVoM2JEStErRaK+5Bq3w/2dwp/QjVV5TYV64mDDPoKLc7UVDUakanUvHvkrJywow6ci+UEhGmx1ruoFWzMDWWkBCNx4mHP3o1ift/9wnrFyehKIpa2Xk+v4T3tx3lQqFVrUaz2R1EhxuxlbtXURoNWs7kFqvffqtWo82d3I9mkUZCQjTV9p/RqMVqdagxVl5uszkos1bfN80ifm7LWzs1fWae1q1LO9eDmj6fEkt5tWpZXUgIBkOI2wTSVf8GPFX4arUh1bZl0IdQbneqx7bFaidEo8Ggr/kzqc0xVdNnK8dAcAiYkew2bdrw/fffq6/z8vIwm821/v2rzSbeWOy2itJurhz7GiD/UhkH/5NH704m4q6MFZSX2ym8Ug7u+p2oiJ9vynTdPlBcVFat7RCtBofNiaPc7jZDfbn1ys9DNDSLNKIN0RAVYcBitVNiKWflRwfUgXZPczp2vzGGU+crbridN20ARYVlREcYePrhfurtCk6nQnSE0a0cubS4ouy78o3qAHHmSPX9uNYvKCj2uN+Kr3xkVX/qWh4TE1ntRlxPbXlrpy7brGs70Hiz5jcUT/vDYNASHWFAo4HoCAOOcjvFNgcmUxSlxSWUXlnZdT19tddVt1XZ1eb7rG3MAGGhURQXlV31s63rMXAtNPb562oCqTQ/YJLZ4MGD+ctf/sKFCxcICwvjs88+409/+lNjh1VvlWcGMRi0XC538NaGQ25XFCFO9z73qhO4lnr567naRK+efu4qVXbdqxVnjqw2K4Tr3izX/139+N6252n7nh5EOvzWG7DbyikorvmRMLUREqKhrMQaVBMBN3V1eSCqENdawHQzQkVp/ltvvUV5eTkPPPAAM2fOrPXvBso31qupehOu65lhjRlDxSMuQtxmmrdYHbWacb4u27mWU0QF2udaVVO/MquLQI4NAju+QI4N5MrMq6SkJJKSkho7DL+q+u01MtzQ6DG4VL0SjAiLqte3bPmmLoRoKAFTmi+EEEL4SpKZEEKIoBdQ3Yz1UVOpbKCX0QZyfBKb7/wRX7Ae54EcGwR2fIEcGwROfAFVACKEEEL4QroZhRBCBD1JZkIIIYKeJDMhhBBBT5KZEEKIoCfJTAghRNCTZCaEECLoSTITQggR9CSZCSGECHqSzIQQQgQ9SWZCCCGCniQzIYQQQa/JTDTs7aGFLVqEc/Fi1QexB45Ajk9i893V4rvWD+cM5P0RyLFBYMcXyLFBzfE19MM5m/yVmU6nbewQahTI8Ulsvmvo+AJ5fwRybBDY8QVybBBY8TX5ZCaEEKLpk2QmhBAi6EkyE0IIEfSaTAGIuDqDQYtWryM8TE+ZxU6oUUuZxY6j3I7N5mjs8IS4Jiof56Vl5XJ8XyckmV0nDAYtDk0Ir67ax5GsArrfGMOTD/bhq/RTDL/1BgwG5A9eBD1Px/kzk/vJ8X0dkG7G64RWr2PJe+kcOp6Pw6lw6Hg+y9dkkNCrHUveS0erl+81Ivh5Os7l+L4+SDK7ToSH6TmSVeC27EhWAe1bR3Ekq4DwMH0jRSbEtePtOJfju+nz69eVKVOmcOHCBXS6is288MILlJSU8Morr2C1Whk5ciRz5swBIDMzk/nz51NSUkL//v15/vnn1d8T9VdaVk73G2M4dDxfXdb9xhhO5xTR/cYYSsvKGzE6Ia4Nb8e5HN9Nn9+uzBRFITs7m40bN6r/denShXnz5rFy5Uq2bNnC4cOH2blzJwBz585l4cKFbNu2DUVRSE1N9Vdo1yVHuZ1nJvejV8dWaEM09OrYiicf7MOeQ2d5ZnI/HOX2xg5RiHrzdJzL8X198Nulz4kTJwCYMWMGly5dYsKECXTu3JkOHToQFxcHQFJSEmlpaXTq1AmLxUKfPn0ASE5OZvny5UyaNMlf4V13bDYHBgPMmzbArZpxzO03SbWXaDKqHudSzXj98FsyKywsJCEhgQULFlBeXs7UqVN57LHHMJlM6jpms5mcnBxyc3PdlptMJnJycuq0vZiYSK8/a+g5wuqqMeKLuDKGEBluAAxe1wvkfRfIsYF/4gvW47yxYrva8e0i+853gRKf35JZfHw88fHx6usHHniA5cuX069fP3WZoihoNBqcTicajaba8rrwNgGryRRFXl6RD++gYQRyfBKb764W37WeaDiQ90cgxwaBHV8gxwY1x9dkJhr+/vvv2bNnj/paURRiY2PJy8tTl+Xl5WE2m2nTpo3b8vz8fMxms79CE0II0cT4LZkVFRWxePFirFYrxcXFbNiwgaeffpqsrCxOnjyJw+Fg8+bNJCYmEhsbi9FoJD09HYCNGzeSmJjor9CEEEI0MX7rZhw2bBgHDhxg7NixOJ1OJk2aRHx8PIsWLWL27NlYrVaGDBnCiBEjx0Uc9wAAIABJREFUAFiyZAkpKSkUFxfTo0cPpk6d6q/QhBBCNDEaRVGqd8AHoWAcS4DAjk9i852Mmf0skGODwI4vkGOD62TMTAghhGgoksyEEEIEPUlmQgghgp4kMyGEEEFPkpkQQoigJ8lMCCFE0JNkJoQQIuhJMhNCCBH0JJkJIYQIepLMhBBCBD1JZkIIIYKeJDMhhBBBT5KZEEKIoCfJTAghRNCTZCaEECLoSTITQggR9CSZCSGECHqSzIQQQgQ9SWZCCCGCniQzIYQQQU+SmRBCiKDn92T26quv8txzzwGwe/dukpKSGD58OEuXLlXXyczMJDk5mXvuuYf58+djt9v9HZYQQogmxK/JbM+ePWzYsAEAi8XCvHnzWLlyJVu2bOHw4cPs3LkTgLlz57Jw4UK2bduGoiikpqb6MywhhBBNjN+S2aVLl1i6dCmPP/44AAcPHqRDhw7ExcWh0+lISkoiLS2NM2fOYLFY6NOnDwDJycmkpaX5KywhhBBNkN+S2cKFC5kzZw7R0dEA5ObmYjKZ1J+bzWZycnKqLTeZTOTk5PgrLCGEEE2Qzh+Nrl27lrZt25KQkMD69esBcDqdaDQadR1FUdBoNF6X11VMTKTXn5lMUXVuryEFcnwSm+/8EV+wHueBHBsEdnyBHBsETnx+SWZbtmwhLy+P++67j8uXL1NaWsqZM2fQarXqOnl5eZjNZtq0aUNeXp66PD8/H7PZXOdtFhQU43Qq1ZabTFHk5RX59kYaQCDHJ7H57mrx+XoCCMbjPJBjg8COL5Bjg5rja+gk55dk9s4776j/Xr9+PXv37uX5559n+PDhnDx5kvbt27N582buv/9+YmNjMRqNpKen069fPzZu3EhiYqI/whJCCNFE+SWZeWI0Glm0aBGzZ8/GarUyZMgQRowYAcCSJUtISUmhuLiYHj16MHXq1IYKSwghRBPg92SWnJxMcnIyAAkJCWzatKnaOl27dmXdunX+DkUIIUQTJTOACCGECHqSzIQQQgQ9SWZCCCGCniQzIYQQQU+SmRBCiKAnyUwIIUTQk2QmhBAi6EkyE0II0eCys7P54Ycf1NeFhYXYbDaf25NkJoQQokHt3buX8ePHs3//fgBWrVrF4MGDSUxMJCMjw6c2G2w6KyGEEAJg+fLlvPXWW/Tt25fS0lKWLVvGqlWriIyM5OWXX+bdd9+tc5uSzIQQQjSonJwc+vbtC8C+ffswmUz0798fgMuXL/vUpnQzCiGEaFBGo1H99759+4iPj1dfO51On9qUZCaEEKJBRUZGcu7cOZxOJ1988QWDBg0CYNOmTbRt29anNqWbUQghRIOaPXs2EyZMIDIyEo1Gw+jRo/nyyy9ZtmwZf/vb33xqU5KZEEKIBnXbbbexevVqjh07xuDBgwkNDeWOO+5g+/btaDQan9qUZCaEEKLBORwOWrRowZEjR9RlAwcO9Lk9SWZCCCEa1OLFi1mzZg3t2rVzW/7JJ5/43KYkMyGEEA1q69atfP7557Rs2fKatSnVjEIIIRqU2WymRYsW17RNuTITQgjRoIYOHcr8+fMZPXo0er1eXS5jZkIIIYLGli1bADh06JDbchkzE0IIETTqk7S88Wsye/3119m2bRsajYYHHniA6dOns3v3bl555RWsVisjR45kzpw5AGRmZjJ//nxKSkro378/zz//PDqd5FohhGhqVqxY4XH5E0884XObfisA2bt3L99++y2bNm3io48+YvXq1Rw9epR58+axcuVKtmzZwuHDh9m5cycAc+fOZeHChWzbtg1FUUhNTfVXaEIIIRrRpUuX1P/OnDnDli1b1MfB+Mpvlz4DBw7k3XffRafTkZOTg8PhoLCwkA4dOhAXFwdAUlISaWlpdOrUCYvFQp8+fQBITk5m+fLlTJo0yV/hCSGEaCQpKSlurxVF4b777qtXm37tx9Pr9Sxfvpy3336bESNGkJubi8lkUn9uNpvJycmpttxkMpGTk1OnbcXERHr9mckUVffgG1Agxyex+c4f8QXrcR7IsUFgxxfIscG1iU+j0XDzzTfXqw2/D0o9+eSTzJw5k8cff5zs7Gy3ebcURUGj0eB0Oj0ur4uCgmKcTqXacpMpiry8It/fgJ8FcnwSm++uFp+vJ4BgPM4DOTYI7PgCOTaoOb7aHuNnz55l/fr1pKen1ysWvyWz48ePY7PZ6NatG2FhYQwfPpy0tDS0Wq26Tl5eHmazmTZt2pCXl6cuz8/Px2w2+ys0IYQQjchut7Njxw7Wrl3LkSNHGDFiBCtXrqxXm34rADl9+jQpKSnYbDZsNhvbt29n4sSJZGVlcfLkSRwOB5s3byYxMZHY2FiMRqOamTdu3EhiYqK/QhNCCNGIhg4dyj/+8Q8mTJjAzp07WbBgAd27d69Xm3W6MissLCQ6OrpW6w4ZMoSDBw8yduxYtFotw4cPZ/To0bRs2ZLZs2djtVoZMmQII0aMAGDJkiWkpKRQXFxMjx49mDp1at3fjRBCiIA3c+ZM1q5dy1tvvUVubi5jxoyhWbNm9WpToyhK9Q74Kk6cOMETTzxBUVER69atY9q0af+/vTMPjKLK9v+3u7qru9MJW0iChAwggkGIIcYAAV4C6rBnhKgsEhCQeeiIDDwBGUBnnqACZsTn8Bz1N47OAEoCsghPECSCYGCEGLYRZJEgRAwhLFl6r6rfH00VvVR1eu/qzv38k3R1Vd3TVefWuXXuOedi9erV6NatW0CNB5NonEsA5C0fkc1/yJzZHeQsGyBv+eQsGxD4nFllZSU2bNiAsrIy5ObmYtWqVX7L4pWbcdmyZVi8eDESExORkpKCoqIivPzyy343SiAQCARCVlYWXnvtNXz55Zfo169fQOfyys148+ZNDBw4EG+88QYAYNKkSSSpmUAgEAh+IVUBBADWr1+PCRMm+HxOr+fMzGazEC5fW1sLlmV9boxAIBAIhJs3b7pt42e8Ghr8c6t6ZcwmTpyIp59+GnV1dfjzn/+M//u//8OMGTP8apBAIMQmNE2BUqsQp1PDYLSCsdpgsTBgWQ46vUbYDo4FFErotCqYzAx0WpXT/oTYx7UCCAB88803AOzBIf7glTF74okn0KVLF+zduxc2mw1Lly7FwIED/WqQQCDEHjRNgVEoseKjw/j+Qh3u65qIeUXZ0MUpcavJjDfWVDhtP32hFnd3aoO3S446badpEIPWgqipqcGnn36KTZs2QafTBVRN36sAkKeeego5OTmYP38+XnzxRWLICASCE5RaheK1FThx/hoYlsOJ89dQvLYCCiWFN9a4b8+4Jwlvlxx1206pyUoZLYGysjI888wzeOyxx3D9+nX8z//8T8DLwnilOQ0NDTAYDIiLiwuoMQKBEJvE6dT4/kKd07bvL9RBp1WJbtfHie8fp1PD2GQOubyEyPL73/8ePXv2xN/+9jekp6cH5ZxeGTOdTochQ4bg3nvvdTJo7777blCEIBAI0Y3BaMV9XRNx4vw1Ydt9XRNhNNlEtzcZxPc3GK1hlZsQGfbt24fNmzdjzpw5SEhIQGFhIUaPHo2EBP+LFnuVNL1582bR7WPHjvW74WATjcmkgLzlI7L5T0tLmubnzIrXOs+N0RRgZSEyZ1YnOmdGcWzY58yCce2kgl/kIFsoCUah4SNHjqC0tBR79+7Ft99+67csXhkzVziOw8WLF9GlSxe/Gw42cu3kzSFn+Yhs/tPSjBkg/UBPTIyHwWSVbTRjoNdOypAHwzDL4b56IlBj1tjYCIqioNPp0NDQENCbmVduxvXr12PlypUwGo3Ctnbt2gmhlAQCgWCxMICFcZvzUioVMDaZ3bbznw2NYRMxJFBqFVZ8dFhwmfLBLIum5gAkMlOUn3/+GS+88AKOHTsGAMjJycHKlSsDMmZeRTO+//77+PDDD5Gfn4/Nmzdj9uzZeOSRR/xulEAgEGIFqeCXOJ06QhLJn1deeQV5eXk4evQo7r33XkybNg1Lly4N6JxeGbM2bdogMzMTPXv2RF1dHZ599lkcPnw4oIYJBAIhFuCDXxwhwSyeqa6uxrPPPguapsFxHAYPHowrV64EdE6vjJlKpcKtW7fQuXNnHD9+HADAMOT1mUAgEBirDfOKspHRrT0opQIZ3dpjXlE2GKst0qLJFpvNhvr6euGzxWKBzRbY9fJqzmzcuHGYOXMm3n33XYwZMwa7d++W1fIvBAIh8oQqoi9YNCefv/JbLAxoGlg0NUe2v11ujB49GuPGjcMnn3wCo9GI3/72txg1alRA5/TKmD3++OMYOXIk4uLiUFJSghMnTmDQoEEBNUwgEGIHqXJWNB1pyew0J5+n7701aGLBLwRxnnvuOXTu3BkajQbDhw9Hz549hYWa/cWjm/Gll14S/jeZTACAlJQUPPLII9BqtQE1TCAQYgepclZyKU/VnHxylz8W6dGjB0pKStCmTZugpHl5NGYnT54U/n/66acDboxAIMQmco/oa04+ucsfa2zZsgXTpk3DqVOncPr0aTz99NP4+OOPAzqnx2GHYz61H7nVBAKhhSBVzspgtCI+LvK+xubk8/Q9Ifi8++672LRpE1JSUgAA169fx8SJE/Hkk0/6fU6vohkBCAtzEgiElg1NU9DpNUhsHw+dXmOfbwphRJ9Ye77SnHyO3+dnpeK9hQ9j2bMDoFDAr/YInomPjxcMGWAvwtGqVauAzunxzYxlWdy6dQscx4FhGOF/njZt2gTUOIFAiC6kAiUojgXFsUGP6As0MIOnuYhD/vsl0/vCbGXcakmSddaCy/3334/XX38dhYWFYBgGJSUl6N69uzC11bt3b5/P6bE2Y3p6OhQKhaiLUaFQ4NSpUz43GCrkXLPOE3KWj8jmP7Fam1Gn1+A1h9JNAJDRrT0WTc2RjOQLRDZ/2vMVR/nC0Z6/sskRf2szPvzww5LfcRyHsrIyn2Xx+GZ2+vRpn0/oyOrVq7Fjxw4AQH5+PhYsWIDy8nK8/vrrMJvNGDFiBObOnQsAOHXqFBYvXoympiY8+OCD+O///m+oVCSSiECQE54CJULxsI/19loqe/bsCfo5vZ4z85Xy8nIcOHAAmzdvxpYtW/Dvf/8b27dvx6JFi/DOO+/g888/x8mTJ7Fv3z4AwPz58/Hyyy/jiy++AMdxKC0tDZVoBALBT8JduinW2yMEj5AZs6SkJCxcuBA0TUOtVqNbt26oqqpC586dkZaWBpVKhYKCAuzcuRPV1dUwmUzo06cPAKCwsBA7d+4MlWgEAsFPwl26KdbbIwSPkPnxunfvLvxfVVWFHTt2oKioCElJScL25ORk1NTU4OrVq07bk5KSUFNT41N7iYnxkt/5Oz8RLuQsH5HNf0Ihnxz0nGU5vDS9HzQaCmYzAw1NQan0HO0ciGz+tOcrjvKFoz1/ZZMjcpEv5JNSZ8+excyZM7FgwQJQFIWqqirhO47joFAowLKsU+g/v90XIj0x7i9ylo/I5j+xGgDiSOPt5ppbjixYsnnbnq9IyReq9nwhmvXcWx0/f/48unXrJvz1l5C5GQGgoqICU6dOxQsvvICxY8eiQ4cOqK2tFb6vra1FcnKy2/Zr164hOTk5lKIRCAQCQQa88MILTn/9JWTG7MqVK3juuedQXFwsVEPOzMzEhQsXcPHiRTAMg+3btyMvLw+pqanQaDSoqKgAAGzduhV5eXmhEo1AIBAIMoH3wgVamCNkbsYPPvgAZrMZy5cvF7ZNmDABy5cvx/PPPw+z2Yz8/HyhUnJxcTGWLFmCxsZG9OrVC1OmTAmVaAQCgUCQCcEqlRgyY7ZkyRIsWbJE9LvPPvvMbVt6ejo2btwYKnEIBAKBEMOEdM6MQCAQCARPBKvuLzFmBAKBQIgY/JRSoFNLpF4UgUCIKmiaAqVWBbWgsZzaa2mMHTvW6a+/EGNGIBCihmBV0ZdrewT/IW5GAoEQNVBqFYrXVuDE+WtgWA4nzl9D8doKUOrQjMvD3R7Bf4gxIxAIUYOnqva+wi/6yXKc5KKfwWzPG1kCWYC0pRPzxoxlOaIkBIIMCMYDO1hV7Xn34WsfHUbhgm147aPDYBRKN5nCUUXfW1linePHj4Nh/HfdxrQxo2kKt5rMLV5JCIRIE6wHdrCq2nvrPgxHFX3iyrRTUlKCNWvW+H18TF8tSq3CCodVY3klWTQ1ByCTtwRC2AhWX7RYGNA0sGhqTkDRhd4uwhms9vyVJVaprq522zZu3Dg899xz6N+/P9LT030+Z0wbM7JqLIEgD4LZFy0WBrAwAfVh3n3IG1dA2n0YjPY8YTSJy2I0WaHX0SFpM9IUFBRIro4yY8YMHDhwwOdzxrQx80VhCQRC6JBbX+Tdh8VrK5xC7iOxCKdCocCcCVl4a32lIMucCVlBq4whR7777rugnzOmjRljtWH+5Gy8sSbyCtuSIUmnhHAaD17fdFoVTGYGOq3KTe8sFga6OCUWT+sLnVYFo8kGrYaC0WQDjfDmkGk1Kryz8Rhmjs1Ap5QEXK5pwD8//x7/NSk7bDLEAjFtzCwWBomJupD6uwmeIUmnBCA8c0/AHX3bceBHDM5Ow9slR0X1jqYpWBhg1zfnPe4XDgxGK67XmzGr+CthW0a39jAYrYiPi003YyiI6WhGAFAqFTA2mVF3rRHGJjN5gIYZEqlF4LHcnncKZV/k9S03oyPeLjkqqXfe7hcOwhEx2RIgTxRCSCFBOIRwwutbp5QEj3rn7X7hIFxvrbFOzL+ZESJLOJJOCQQeXt8u1zR41Dtv9wsX4XhrjXXIm5mPxEIwQzh/g5yixgiBI3f95/Vt16EqzC/KhtFsQ0qiHjV1TYjX04LeOe43e3wftzkzop/RBzFmPhALwQzh/g3EhRI7RIP+8/r2m7xuMFsYrN5wzElWymW/0YPuhk6rEqIaiX5GL8TN6AP+BDPIrYBoJAIyiAslNgiW7gTaJ5o73mJhwLIc3pCQlT8+oZUOAKCAAoZGE9HPKIe8mfmAr8EMchzJkoAMgr80V3bJmzfvQPuEt8d7kvWmjXU6fv7kbHuoPjFiUQ15M/MBX4MZ5BiWTgIyCP4iqTsmm9cFhAPpEzRNQUWr0SZeg5ljMzAws6Pk8VKyGk02t/bfWENSRWKBkBqzxsZGjB49GpcvXwYAlJeXo6CgAEOHDsWqVauE/U6dOoXCwkIMGzYMixcvhs0mz8lXX/NB5FhAlOS0EPxFTHfmF2Vj+/7zXhsnf/sE/0b26offovDFbXhv8wlMHtETeVmposdL6blWQ8muTxKCQ8iGI8eOHcOSJUtQVVUFADCZTFi0aBHWrFmDu+66CzNnzsS+ffuQn5+P+fPnY9myZejTpw8WLVqE0tJSPPnkk6ESzW98DWaQWz06gARkEPxHTHd0WhXW7z7jtJ8nt7W/fUKs6v7bJUcxc2wGbtSb3Y6X0nOjySa7PkkIDiF7MystLcUf//hHJCcnA7AvvNa5c2ekpaVBpVKhoKAAO3fuRHV1NUwmE/r06QMAKCwsxM6dO0MlVsD4Eswg17cgEpBB8BdX3eGNgyOejIO/fULqjS4tJUHyeDE9F327nBz5PkkInJC9mb366qtOn69evYqkpCThc3JyMmpqaty2JyUloaamJlRihRXyFkSIdXzNI/S3T0i90RnNNlAc63WfEms/TqtGXV2j9z/aC+SejxeLhG3Wk2VZpyUN+LVspLb7SmJivOR3SUkJPp8vFNiLhroXDpWSj2U5mC0MNBoKZjMDDU1BqQz+shCe2gnk2oVafrncVylCIZ8c9ZxlObw0vZ9wn2m1EhYri4RW9s8sy0nKJtUnXGEYVtRoamkKCo3KSc/49r3RO76QbzCvHctyuNVkdouYTEzU+aX/LVHP/SFsxqxDhw6ora0VPtfW1iI5Odlt+7Vr1wTXpC/U1TWCZTm37UlJCaitbfBP6DAgJR8/4e3aeX0ZhXqDp3Zat47z+9qFWv5ova+O3/uDnPTc8e2DYVk01FsAAEaL832fPzkbSjaw+67Ta7DrUJXTMim7DlWh4D/uBsMBjU0WpNB6NJmssLIUlruE7nvSu2BfO51egzfWVDjN772xxr6qtq/pL9Gs5+E2cmELzc/MzMSFCxdw8eJFMAyD7du3Iy8vD6mpqdBoNKioqAAAbN26FXl5eSGXR27JzK6EK6w/VO3IMS2BEDz4wYprSL5ao/Y69N2XPhinU2P97jOYVfwVxsz/DLOKv8L63Weg1ahgMtmwesMxPPbiNrz58XcwmWxo20ojqXeu7YoNDgLBm4hNuT9/opGwPVk0Gg2WL1+O559/HmazGfn5+Rg+fDgAoLi4GEuWLEFjYyN69eqFKVOmhFQWOSYzuxKu5OZQpQ+ESn7+bYDlOOj0GjIXESHEogvtb0rdsPSZAbhc04DSPWfwdWW16H2X6oP6BDW0GveyUlJRiCYzg7fWVzrJ8db6Sswcm4GvK6sB3NE7xmqDWqOG2cI4uwCLgps0LTm/Z7IhsX08TGYbTC4yNPf8IXNwzRNyY1ZWVib8n5ubi88++8xtn/T0dGzcuDHUogiIdcTitXY3AGSiIGIdYsKvewgdIlgK7SlU2t+FAWnavmLvphUFuOTwUAs0BDoaBiEtBdfBSl5WKgZnp+HVD78V7s3s8fYI5bTkeDe9leqDs57IxO9e2uF2b7Uayq0g8OzxfaDTqkQHTZ1S7ri4eEPCKJSwmmx48+PvnF2AayuweFqO3zrkaGhMZhsUUIiucH/szFWsXFuBdxY8hNUbjnl8/jgO2vQJWjfjN3+y3fBbzVai+7dpkRVA5JjM7IprCPGkYfdiaP8u9qRRLyot+NtOoOkDUsmtk4bdG3BaAnFdygfXChvjHu7htsjl2yVHMb2gl6jeShmhlES96L01mmzYW3EJM8dm4NMVBZg5NgN7Ky7BYBKv9FFT1yTo8+zxfaDVUCheW4F2rbWi7eq0/vV9R3frm+sq0GiwYunf/4UPtp7ErCcysWllAWaP74Ndh6qQmpwAhuWQkqj3+PxxdeHebDCLum5vNpiD8gyIFVrkU0COycyuuIYQG002vPrht0F/mwx2+oBUcuviaX1hswQ2iiR1JeWDa0h+msQilzqNCsv+7q63i6f1xYKibGTckwR9nBpNBitOnKvF5ZoGp+P5e8tYbRjav4tTYMmcCVn46sglzJmYhbc+qRS2vzDpATAsh43LR8NoskGvU8NotqFdKw1MZil3pX+DLEd9Xz1viJPLc19lNTK6tcfMsRlYv/sMnnjkXgAQ1lCTev649iEp45eSqMdL75bLyqMUSVqkMYuWNbYsFga4nfiZ2D4+ZA9yx3YCRcrg6LQq1DWaAjp3NAxCWgpigy2xe6PViL+BaTUU0rsm4vV/OLuML1TfdDqev7eu7ZnMNryz8Rj2VVbj9MUbQpSj2WKDjeWw+8CPGJyd5uSWnDMhC2YLgzkTsvDW+kqn7f7iqO9Sq1Z3SkkQFgIFgNI9Z9xkcHz+uPYhKeN3uaaBDOYcoP70pz/9KdJCBAOj0QJOJChJr9fAYLA4bWMYDmpKgfzsNBSN7In+vTsADBMR37OjfDRNgdbSaN1aBygUUCrssgIAFAqcvngDV28YhGN7390e/Xt3gM0aGrnFrl2zhFBOpQLIzUzF+cu3UHfLiN53212iYJg710kmNHft9HqNX+f1Rc9DDcNwsFkZGA0WKMCJ3huO43C6yl0fBmR0xIp/HsGJ89fAccDVGwacv3wLYwbfgy37zoveW8f2VGoKu/71E67eMODiLw34vLwK//6xDoMyO4K12tDz7vaCa44//4Wf65GbcRdUlBIP3JuM6b/pjV5d20EfpwZjsfmsQzRNgeUUKBreE4MyO8JsZfBjdb3bb+3VtR3yHuiEfd9dwvcXriMhjsbgB9OQn5WKohEizx+XPtRotOKZsffjws/1wrWdPb4PPtn1AxLi6JA+A5rDk975q+P+ouA4sa4Rfcgp/8YXePmay8sKV96ZmGy+EGo5oyWqqyXkmbkidm8AgFUqnYIhZo/vg+R2cShcsA2Mw2+hlApsWlkAAF4vJSOlZ4nt4yXPz7Kc4H40WWzQ0iqfK4CItT+/KBsMy+HNj79zCtTQqCmA48BBAZ1WBaPJBo5lYDSIexTEzr1wag4ohf34X6414eMvTuN6vTnkz4DmkFOeGTFmEYaXT6fX4DUHPzkATBp2Lwr+oxu0GgomMyP8lVoRl3+Y6LQqYb/mOo43svmK40PNaLJBq7FHNwbT8ETLffX0vT/ISc/F9E1MLxMT42Gy2KChVULI/u8ey3SaAwaAjG7tsXhaXxgk3NGuxhIcCyiUogMbsf7Ez1+V7jmDcQ/3QFpKAoxmG3QacWPmaeAkdf6XpvcDy7HQaZ0Nuq8DPKm25TaYk5Mxa5FuRjnBy9e6tQ6rNxwVfkNeVioezeuGXYeqkNw2DsVrK3DsTC16/Kot4uNo2BgWLMMKrhF+NLej/IKw/+oNR3G66gb6ZXSEVkPBZmWd2vbo1nSQrbn9XKEoJSiVEmoVhbqbRvy/LSew618/ITczFWpKERSXYLTcV0/f+4Nc9FxS3y7ecLvP8fEamC0Mtuw9h7SUBAzL7QKVyh5pWOXiOmsdT8MoMgcqpGX884jQTr+Mjnb3nNkGJaVEfLwGUCig0aigVCrRr3cHnLvk7Pa8UH0Tg7PT8M7G40L/yO6ZDJXSWS/F2uN/F0UpoVJT+HW/zhiU2RGNRisu/tKAultGTBqRjvqbRhgNFtisdhcpraVFXar52WmS7kHepRqv1+DWTYOoq5U/fySRk5uRGLMII8jn4iefX/Qg3tl4HGPy78E7G4+jbSsNJo/oib+UHhM6oeNDg+8w/P6uHSfvgTRYLXcCXDx1Vr6D6PUa2GxMs/s54nreCz/X47djMnDm0g2UHb7ksQP7dd1kSswbs2b0zfE+6/UaWCxW3NO5HRiGRXwcDYPJhh+qruOxh7pj8sie6NerA36ouo7E1jpR/ZCweLM3AAAaJUlEQVQyCENyfgUrCyf97Nv7Lnz+zY/Y991lPP2b3nj60d7IzbgLlIJDh/YJbnNp5y65G5bm2lv+jyP43w1H8dMvDfjPMRm41WgWnb+iaUrS8BWN7AljM/csmvU83MasReaZyRHXfC8+1JmPkBLL43HMw+EjoKQiqnRa58BVb3O2fM3tEtv/7ZKjGPdwD9nl8hH8pzl9c73PSopyKjt14mytEM1YuGAbXv/HYaR3TbS7Dj2059oOAFH9zM3oiH2V1Zi5fA+W/LUcAGA0WCXz21zl9aW9t0uOYtKwdLeIaH5gt+zv3+IxlwVFSRRu8CHGTCZYLAwojsWiqTnYtLIAxtv5MHxYbnMPDT5snd/fEb4CgiPeJo77mmAutT8fnkw6cGzQnL6J3Wc+B4thOaQmJ4gaISjEH0muSdp8O1Kh/44VQBwHc1LncZXX1/Y6tNe7zYFJDezEDB8hcGLemLEsFzUFPR0XE7RZrJhXlI2DJ37G7PF9UFPX5LET8m92/P6uFT041tl1E2inljJKUvvX1DWRDhxG+EK27RL1iIvXBl3/GasNC6fmQK9TY9mzA/DewoeRn5UqWUFGq1GhXSsNVs8bgi1v/AZpHbx7o3NsT6xSjVGiAohj8rXjYM7xPPlZqXhv4cNY9uwAKBRwuja+tuc6WASkB3Ziho8QODEdzUjTFJRqFRqaLEhJ1KOmrgnxehqcFxFA4Yoa8hQN5BgtZrGwMFltbvXeHDuFL9GM3oTQJyUl4NYtg0+RWKIhy7fDk4NZR45EM0q3x9+DXYeq3BKHgxXK7ct9TkpKgNFkRYPBKiQKu9YnBOzRgJ6WSZEK/RfTz12HqrB+95k7NR4pCH2Apil7wWEr41V/aq692eP7YG/FJQzt38XpeKmIR1+WgolmPSeh+X4i1sn1CVo0OnQgPts/Pk6NpgbpahThzOnyRVmDHfLenMF2zIHzxbAHOhDw5vho7uT89/7gjTHjH6Izx2bgvc0nRNM9pMLovcWXB3VSUgKajBa8+uGd/fOyUvHUyPvcqmD408fE+oVjKovYYC4QQ0PTFFS0GjqNyqmQtuvxwXiORLOeh9uYxXw5K7HlIRZP6+ux8rzcquq7dlaO43Cr0Yy/fnoncdKxcry3xkSqjJXYMivGJrPXo8nmymN5ki/aK+PLYYkaqeAMscr2vl5bx3sn5Sbkq+Lz95dlOei0zvt/XVkNBYAl0/tCq1E5Dc5oiMsjpTcWCwMawE0b69FwOB7Pyysmf3N6brEwSGilc0vKdj0+2HVPCZ6JaWMmNVmr06rw5roKUUMAyKugrdjDffb4Pvi60h52bGVY7DpUhdGD7gYcKoW4rtekT1CDVlMwW+64HzW0/bPjyFXs+IVTcxBHq5wSQT0lcHrarrp9nl+uNeHdTcfd7oHcBhK+IAdD7Lj8TpNLLUvHiFjAuehvfIK22bd8x983szBDsmAvK6J/Gppy2/96vRkcB3AcB5bjoIAC9U0WxOtp8IsP8XpkNjMwWWyiy6Dw1TUStCr84akc7P3uEt7fclJYXy2hlQ5GkxUMB7x2+/h3Fjzkcc0xx6RssaRwg9GKCb/ugdyMjkhLSUCT0Yq42/1Kn6AVXK3e1j2VWzJ0NBLTeWYKpVK0Ltx9Xdshs0eSkPs0+ME0WB0VJ4x1EJvLIxHLd6n6uR5j8u/B26VH8WDPFHRNbY3WCRoYjVZodO77n7t8CwMzO8Jott3Jx6m6geyeKfj3+Wto2zpOSKp2bS+9S1v07pqIN1ySYrUaCjZO4ZZ/5u12qfwz1+RxAKI5OXLMv/ElOTYUeWauOYEatRLTftMbw/p1xoxHM6ChKZw8fw0Xf7njFqq7ZUTR8J6ovWHErn9dxN1pbaVzCB1+X6PRiv8ck+GU9DyvKBuUUonlIvqXl5WKvr06uNVu/Pf5a9DQKvx53XeCXjyYnoI4nRqsUomGJgv0OhpmK4Md5Rfw5eFLTvlh99/THlAosKP8Al567yB+uHgDE4beiz7d2+P+7klOxQMeTE/BDz/dQNWVetQ3WdzqHc4ryhbOwydl8/KJJYV37tgafyk9itUbjuKHizeQcU97+1IvSQlQa1SgHIoLeCo84CnnU6tVy07PHSF5ZmFCoQBemPSAU0TSnIlZWPfFaafcJ9e1jMQimeZPzoZSqQh7NKSnUHd+GYi3S47CZL496awV359SKkRDoTPusXd4hZISbW/cwz2cQqr54xRKSvR8vmwXyz/zNXpSTkR6nTzXUPBLVxthsTBCbteyv3+LKSPvQ15WqnDMfV0TcammAW+XHEVuRkfJHEKapqBUKrD0mQFYPW8IAGDNjlOYWZiBTSsLsHhaDlQqJXSSVfJV2HWoSliP7A9P5QhrfLnmT761vhIc4JSX9vpHhzE4O81Jdl7/+bwyR31L75Ioet5xD/cAYHdz/vPz77Fket/b8vfFrkNVWPfFD279Qyy/k4N7f+Kv4VvrK9HYZBGuo+v6ZK5rEZJ1+oJDTBszDU2BVlP4w+3crT9MzUGcRoWnRt2Hpc8MQFJbHSb8uofbWkZ8ztfiafbjZj2RiQ+2nsSyv38b9sXwpB7ufH4PvwwEn0fDr9fkur+Uy1Ufp/aYh+MpCTsY213zz4K9WGg4iZQh1sWp7W4unRozCzOEB/64h3sIqyo7PtAnDUt3WriydM8ZpwGSq/GVSv4FgPc2nYDBaAXHAcs/OoxLEnlnJrMN63efwazirzBm/meI06lx+WqjZIi+VqNyG0Txgx/H8/L6L5ZX5pgKsHreELRrpXHa73q93fV3q9EMnUaF9bvPuMnB9w/X7Z70mTey/HVszlhFehAUK8S06bdYWBhNNrdoRopS4LEXtwlzGkqFQuRY+/xN6e4fkJvREXOfzMblmgan+alA8DpQgGPd1l7jQ4Fnj++DNTtOCQ9MmqbAccCyZwc4VdaeMyFLcr2pJoNVNA+Hb4/PbxObWwjGdtf8s2ieNI/EOnm6ODUsDFD8j2+d9AOQHoh0aK/HphUFuFTTgDU7TgmRePwAydX4us5jtm2lActyeGFSNgwmG8AyiIvT4PsLdSjdcwazx/dxW0dMAYVw//OyUmG22DBl5H345Zq4fpnMNlHZ01ISQCkVwu/k9f9yTQPyslLvFBA22TDj0d5Y6RB6P2dCFm42mITjF0/vC44DWus1aHKYA+uUkoDLNQ04eOJnNBnEt0vpM38Na+qa0Epvn/lrLljG0zp98XE0CN4Rs6H5fC6J4yq3gD38dsn0vvjfjcecKmdzLONWgTs+QYvaG0anjjl7fB8ktdWhscHk14SttzkuPDq9BtsP/ChMNBtM9pI8NXUGp2UgaMpuX12XpGA5Dmo1hStXG5DUTu/2oD19oQ7pXRPd8nD432axMDCYbW7HadVKmKzu0WNickjKF0D+mb9pA6HGW3mCFZofF68VrT4/szADeq1aeDNzfNAbzFbYbKzTg14qVwqA03IqeVmpmDyip1OfmF+UDS2twtavzyE3oyNSk+OFJVaaTFZwALQqJZrMNnxx0J77pqKUePPj74Sao665cBzH4c/rvhMNnY/Tqd2WQTl9oQ73/Kqt04rTcyZk4R+ff4+vK6uF4/noSVe9XlCUjfSuiW56azRZoNPSXuk/fw2H9e9ib8NkgcXCSKYBzHoiE1qNSrLPUByL1q3jSGi+l8SsMdPpNYjTqUXXNPp0xWg3IyWWaKmjVVj693+JLlNhtjJehwHzkVFKihJkOXG21m3J+D73Jtt/g0MUldnCCEtnXLtpQI9f2RcTNJps0KgpmK13ohO37T/vNoIs+I9uMFmsaDTYkJoU77S/azSjq8xxWjUMJqtgTPnzVl9tQGaPZOE8rvk84Vi+wp+E7nASrjwzT+t2mcw2NBqsKDvyk/uqyxOz0CqOFqIfPeUsOg6oktrq8NnX4noGhb3ijl5nd3sqlQpQt4Mdrt0yIbltHMxWBlqagkKpcDKQ4x7ugU4pCTCZ7Xp86Wqjm5HjjVN657YY/ECa0A9uNhjRJkEnbtTHZmBW8VdO16XuWqPbIGD1vCFuOXl8Xxc776KpOU4pCEaTDVqawuXaRhw8/jOG9u8iDBDF8s34t8ob9Wa3czn2DVc9ktvgTU7GLGbdjHwYrZTLSyxEeebYDKz74gfh85LpfSX95Y4K7ho+LhWiTXEszBYG7VppRZeMp1UUXn7/4O2RZi3u7tRG6Mz8yNH1mNMX6rBybQU+XT7a7YE1e3wfaGm7wbt20yDaqXlEQ/onZ6OVnsb63Wew7osfAEAYmTvmKjmO6mmakgxH9jZM2VuiOYw/WEi5uy790oBOKQl4Z+MxzBx7P17/h/N1eusTe74lrwMGT2tTciyG9u+C4rUVeGVmrqieaWgK9U0Wt4GFXqtCo8kGDa1EvcEiVCVhWU6Q++vKasHVuezZAVi/+4xgnGeOzUCnlASYLTa8s/EYACDnvg5u/UCroSTnsByvCz8/7jrn5evccJxOjbomM2BhoFAo3Aze8XN1WDytLwCr4DpfMr2vMDDl3buUUuF0Lk99Qw6pH3JGVgEg27Ztw8iRIzF06FCsW7cuoHOZzAy2H/jRrU7h/KJsxElE/LlOIms1KsnJbE8TtlITvmqVEm+trxTe6ly/N1sZpyiqvRWXhOivzB7JktGIDMvB4GCgHSfMDSabEN3l+BvE5kVcz//GmgqYzIzTNRCr3t9cJFyoIBPnAMcybgEzfFDH5ZoGXK83S14n15UUJLn9VnHi/DWngaDj/TdbxHXaxnAoXlsBLa0Wog7fLjmKdV+cFq0h6hjA9HVlNWYVf4WX3rVXvb9eb5ZcPcJVT4E7c7JCJPPtuTvgziCAx1OB7uaCejwFPfFYLAxYlsNL75ZjVvFXguvTlwAhEvXoGdkYs5qaGqxatQoff/wxtmzZgpKSEpw7d87v8+m09uikNTtOCQZhZmEGWsXTaPK6OKlVNLKO/971eF4ppR4efERhc8rPR1ENzk7De5tP4LEXt0keo4+zP7j1Em3y23ValcfoQE8PPLGlaVz3k4qECyXRHMYfLIwGK/RaFRZP6ytE3vKj/tI9ZzBnQlazRaqbw1E3pPSsuShW/i+vJ19XVjv1zSXT+0J7O0p4zoQs53SaCVkAgHlF2ZL6p9Oq3I77rycfQJsEDT5dYb8uWq0KDGPXe9dBwMETP4v2dbHBgmv/kTJ4rsWHA43UJYM3z8jGpJeXl6N///5o06YNAGDYsGHYuXMnZs2a5df5+Acd78IAeB94DiilAvMnZ7sFYOw6VCVEOs0ryobNYgMF98g6q9k9wtBRKT1FJzlGEIpFFvL/u7pCmzuGD4l2czfd3m402bBpZYGkn92TzPzSNPzcgKcornAakkhED8oRk9ECpVqFG/Um6DQq3Kg3g1IqcKPeDK1WBbWEvnt7nRx1Q0rPpPSCP5b//rLD8Y7uxcXT+mLb/vPQ69TIz07DrCcyheLgWq0KSoUCOo0KBpO4nprMNui0KqfjNDQFBRRQKIDW8RqnSjdGgxW6ODUWT+srzP0qwLn1daPBPm3gKbqWN3iueui6UkWgkbqe+ihBRgEg7733HgwGA+bOnQsA2LBhA44fP46lS5f6dT6W5XCryezUgedPzkZrvQZKpQIsy9mDKzQUzGYGtFoJi5UVPmtuJ4l6Or/j8Y77i7XtGHF17qcbopFT3xyrxsETv2BeUTZa62k8tnC7MHfwn2N6Y2Bmqmg04sq1FZjw6x7CvIbYXFarOBoqlfSLeHPXy9N+QhRXbhe3/UONp/vQkuCvA00rhdJLjtcjkOvkeM/btdJgikuB4DkTs6DXqmAWiW7lo2U5joVCopL//MnZSNDRqDdYbrsiO2Bwdhr0twdPGpqCxcqAVlGw2txXj+D1FEDEdIFhWJgcSsVpaQoUFVzHl7d9tKUiG2P217/+FWazGXPmzAEAlJaW4uTJk3jllVe8Ol5qCRhaQ0OjocIe+SMWzehY542PJOSVn7pdXYTfV6Gk3CaV/zDlQacoQjWlgJXh7kQnukQ38lGGCnBoamw+6EIsmrGuzj0yINjV+/0hmquJ89/7gzdV80OB4z03m21gObjpmav+8TquUVMwWWzQ0ip7NOPt/V0r9+vi1FAoKafziulVsCP65KxLJJrRe2TjZuzQoQOOHDkifK6trUVycnJA57RYGLRurYqIokpF7vGf+aqPel0CDI3uy9HQtLsrM71rImwWK+pc9ucj0Qyunz1FqHkhs1TCput+vrZDiD7E9NlVz9z07/ZnfVICjDfMbvriqjd2F6C1Wf0NdlRsNNGSf3tzyMaYDRgwAH/5y19w/fp16HQ67Nq1y28XYywQzZUwCAQCIdzIxpilpKRg7ty5mDJlCqxWKx5//HHcf//9kRYropBRGIFAIHiHbIwZABQUFKCgoCDSYhAIBAIhypBNnhmBQCAQCP4iqzezQPAUmir3sFU5y0dk859QyBetei5n2QB5yydn2QD5yCeb0HwCgUAgEPyFuBkJBAKBEPUQY0YgEAiEqIcYMwKBQCBEPcSYEQgEAiHqIcaMQCAQCFEPMWYEAoFAiHqIMSMQCARC1EOMGYFAIBCiHmLMCAQCgRD1xLQx27ZtG0aOHImhQ4di3bp1kRYHq1evxqhRozBq1CisXLkSAFBeXo6CggIMHToUq1atirCEwIoVK7Bw4UIA8pKtrKwMhYWFGDFiBJYtWyYr+bZu3Src1xUrVoRVNrnpOED03F/krONAZPXcK7gY5ZdffuGGDBnC3bhxg2tqauIKCgq4s2fPRkyeb775hhs/fjxnNps5i8XCTZkyhdu2bRuXn5/P/fTTT5zVauWmT5/O7d27N2IylpeXc/369eNefPFFzmg0yka2n376iRs0aBB35coVzmKxcBMnTuT27t0rC/kMBgOXk5PD1dXVcVarlXv88ce5PXv2hEU2uek4xxE99xc56zjHRVbPvSVm38zKy8vRv39/tGnTBnFxcRg2bBh27twZMXmSkpKwcOFC0DQNtVqNbt26oaqqCp07d0ZaWhpUKhUKCgoiJuPNmzexatUqPPPMMwCA48ePy0a23bt3Y+TIkejQoQPUajVWrVoFnU4nC/kYhgHLsjAajbDZbLDZbIiPjw+LbHLTcYDoub/IWceByOq5t8RM1XxXrl69iqSkJOFzcnIyjh8/HjF5unfvLvxfVVWFHTt2oKioyE3GmpqaSIiHl19+GXPnzsWVK1cAiF+/SMl28eJFqNVqPPPMM7hy5QoGDx6M7t27y0K++Ph4/P73v8eIESOg0+mQk5MTtmsnNx0HiJ77i5x1HIisnntLzL6ZsSwLheLO0gQcxzl9jhRnz57F9OnTsWDBAqSlpclCxg0bNuCuu+5Cbm6usE1O149hGBw8eBCvvfYaSkpKcPz4cVy6dEkW8p0+fRqffvopvvrqK+zfvx9KpRJVVVVhkU1O98gVoue+IWcdByKr594Ss29mHTp0wJEjR4TPtbW1SE5OjqBEQEVFBWbPno1FixZh1KhR+Pbbb1FbWyt8HykZP//8c9TW1uLRRx/FrVu3YDAYUF1dDYqiIi4bALRv3x65ublo164dAOCRRx7Bzp07ZSHfgQMHkJubi8TERABAYWEhPvjgg7DIJkcdB4ie+4OcdRyIrJ57S8y+mQ0YMAAHDx7E9evXYTQasWvXLuTl5UVMnitXruC5555DcXExRo0aBQDIzMzEhQsXcPHiRTAMg+3bt0dExg8//BDbt2/H1q1bMXv2bDz00EP429/+JgvZAGDIkCE4cOAA6uvrwTAM9u/fj+HDh8tCvvT0dJSXl8NgMIDjOJSVlYXtvspNxwGi5/4iZx0HIqvn3hKzb2YpKSmYO3cupkyZAqvViscffxz3339/xOT54IMPYDabsXz5cmHbhAkTsHz5cjz//PMwm83Iz8/H8OHDIyajIxqNRjayZWZmYsaMGXjyySdhtVoxcOBATJw4EXfffXfE5Rs0aBC+//57FBYWQq1WIyMjA88//zwGDhwYctnkpuMA0XN/kbOOA5HVc28hK00TCAQCIeqJWTcjgUAgEFoOxJgRCAQCIeohxoxAIBAIUQ8xZgQCgUCIeogxIxAIBELUQ4xZC8dqtWLQoEGYMWNGpEUhEEIC0fGWATFmLZzdu3cjPT0dJ0+exPnz5yMtDoEQdIiOtwxInlkLZ/LkyRg5ciTOnj0Lm82GV155BQDw/vvvY+PGjdDr9XjwwQexZ88elJWVwWKxoLi4GIcPHwbDMLjvvvuwZMkSxMfHR/iXEAjiEB1vGZA3sxbMuXPnUFlZieHDh2PMmDHYunUrbty4gf3792PTpk3YuHEjNm3ahKamJuGY999/HxRFYdOmTfjss8+QnJyM4uLiCP4KAkEaouMth5gtZ0Vonk8++QRDhgxB27Zt0bZtW3Tq1AmlpaWora3F8OHD0apVKwDApEmTcOjQIQDA3r170dDQgPLycgD2+Qi++CiBIDeIjrcciDFroRgMBmzduhU0TeOhhx4CADQ2NmLt2rUYNWoUHL3PjpWxWZbFokWLkJ+fDwBoamqC2WwOr/AEghcQHW9ZEDdjC2Xbtm1o06YN9u/fj7KyMpSVleHLL7+EwWBAr169sGvXLjQ0NAAANm7cKBw3aNAgrFu3DhaLBSzL4qWXXsKbb74ZqZ9BIEhCdLxlQYxZC+WTTz7BtGnTnEakrVq1wuTJk/HRRx9h3LhxGD9+PAoLC9HQ0ACdTgcA+N3vfofU1FSMHTsWI0eOBMdxWLhwYaR+BoEgCdHxlgWJZiS4ceLECVRWVmLKlCkA7OtAHTt2DG+99VaEJSMQggPR8diDGDOCG42NjVi0aBF+/PFHKBQK3HXXXVi6dClSUlIiLRqBEBSIjscexJgRCAQCIeohc2YEAoFAiHqIMSMQCARC1EOMGYFAIBCiHmLMCAQCgRD1EGNGIBAIhKiHGDMCgUAgRD3/H+Er+Mo4N2kHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 440.8x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_train,row='Survived', col='Sex',margin_titles=True)\n",
    "g.map(plt.scatter,'Age','Fare',edgecolor='w').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211ca68460>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_train,row='Survived', col='Embarked',margin_titles=True)\n",
    "g.map(plt.scatter,'Age','Fare',edgecolor='w').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211d23af70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train['Died'] = 1- titanic_train['Survived']\n",
    "titanic_train.groupby('Sex').agg('sum')[['Survived','Died']].plot(kind='bar',figsize=(25,7),stacked=True,\n",
    "                                                                 color=('green','red'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211d4e8df0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train.groupby('Pclass').agg('sum')[['Survived','Died']].plot(kind='bar',figsize=(25,7),stacked=True,\n",
    "                                                                    color=('green','red'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211cba65b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train.groupby('Embarked').agg('sum')[['Survived','Died']].plot(kind='bar',figsize=(25,7),\n",
    "                                                                      stacked=True, color=('blue','yellow'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Survival by Age')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Survived', y='Age', data=titanic_train)\n",
    "ax = sns.stripplot(x='Survived',y='Age',data=titanic_train, jitter=True, edgecolor='gray')\n",
    "plt.title('Survival by Age', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Survival by Fare')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Survived', y='Fare', data=titanic_train)\n",
    "ax = sns.stripplot(x='Survived',y='Fare',data=titanic_train, jitter=True, edgecolor='gray')\n",
    "plt.title('Survival by Fare', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Now we'll check the relationship between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between Features')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4wPMjR45k1KhR+umYmBhcXFz0066urgQFBemnw8PDcXZ2ZsKECVy5coVKlSrx6aefPnVe0lETQgghhFEy5F2fgwYNomfPngWef7CaBpCbm4uiKPppnU6XbzonJ4eTJ0+ycuVKateuzYIFC5g9ezazZ89+qrykoyaEEEKI/7yHhziL4u7uzunTp/XTsbGxuLq66qddXFzw9PSkdu3aAHTp0oXRo0c/dV5yM4EQQgghjJKiKAZ7FFezZs04fvw48fHxZGRksHv3bv31aAD+/v7Ex8cTHBwMwP79+6lZs+ZTbwOpqAkhhBBCFJObmxtjxoxh4MCBaDQa+vTpg5+fH0OGDGH06NHUrl2b//u//2PSpElkZGTg7u7O3Llzn3p90lETQgghhHgCXbt2pWvXrvme++GHH/R/16lThw0bNjyXdUlHTQghhBBG6Um/iPbfSLaAEEIIIYSRkoqaEEIIIYyS/Ci7VNSEEEIIIYyWVNSEEEIIYZye4Gsz/q2koiaEEEIIYaSkovYP8cNXI7gUHM6CJdtLO5XHalHOkZF1vFGrFMIS05gWGEpajrbYcSoFxvhXollZR0wUhRXBEfwadheAgHJlmNqkCnfTs/TLeXtvEOmFLL+kNHd3ZEQtL8xMFMKS0plxuvD2FRVnbWrCpAa+eNpaolIUtt+KZsXVSABalC3D5Ia+RD/QvmEHLhisfS0rOPJ+PW/UJgohCWlMPhpKmkZbrBgbtQnTmlfB2z6vXVvCovn5YgSV7K2Y26qq/vUqRaGKozXv77/M3vA4g7TrQU3dHBle0wszlUJYcjqzzoQWun0fFbe9c2NiM+7vo9WhkeyOiMXL1pKP/StjZWKCDvj20k1OxiSWaHtK8nj7W7dKbrSu4MSYQ5cBeKN6Bdp73v+tQ0dzNVZqE1ptOF6ibQ0o78jout6Y/fXem3Ki4PvzcTFuVmas7FiXl7efITErB4CGbvaMqeeNqUohKyeXOaevcTEutUTb8rAW5RwZVSfvuApNTGPaiYL7sagYlQIf+FeiaVlHTFUKv1y5vw9bli94zhy8x7DnzGci5aTHd9QiIiLo2LEjPj4+KIqCRqPB1dWVWbNm4e7ubogcn6tFixYB5PuBVchr58CBA9m/f39ppFWkqpXLsWD6WzT09+FScHhpp/NYDuZqPmtchbf2nOd2aiaj6ngxqq4Xs09fK3Zcb5+yeNha0nfHn1iZmrKsfR2C41O5FJ9KHRc7VgRHsvTy7dJpn5kpkxr4MvRAELdTM3m3thfv1PZi3tlrxY4bVtOTmIwsxp8IxsJExZr29Tgbm8zF+BT8nGxZFRLJ8uAIg7fN0VzN9OZVGLDjPOEpmYyp78WY+l58fuJasWJG+XsSnZ7FBweuYGmqYnOP+vwZncT52BT6bD2rX8bYBt6EJqSVSifNwcyUifV9GX4wiIi0TEbU9GJETS++PF9w/xUV52FjSbImhzf+OFdg+R/Wqcz2WzFsvxWNr701iwNq03n7CbS6EmpPCR9vdmamvOvnRScvF87EJOmXt+xKBMuu5L1HbdQm/NK+LtNPhpZMI//iaK5mWtMqDNqV995739+L9+p6MfPUtWLHdPF25Z06nrhametfY6pSmBtQjRH7LhKckEbL8mWY0bwq3bf+WaLteZCDuZopTarw5p7z3E7JZHTdgvvxUTG9K5fFw+6hfZiQyqW4VPyc7VhxJZKfS+mcKZ5dsfqqrq6ubNmyhc2bN7N9+3aqVq36TN+yK4pv+MD2LFu7n43bA0s7lWJp6u7A5bhUbqdmArAhLIpOnq5PFPdCRSe2XY9Gq4MUTQ67bsXS2Stvnp+zHQ3d7FnT0Z8fX/TD3+Xxv8v2PDV2c+RKwv28N16LoqOHyxPFfXX+Ol8H3QDA2cIMM5WKNE3eJ/vaTnY0cHFgZVt/vn+hNnWdDde+ZuUduHQvlfCUvJzXXY3ipUquxY6ZdfI6X5y6DoCzZV67UrLzf2qv52pHey9nph0PK+nmFKqRa95+iUjLy3/TjSjaVyy4/x4VV6uMLbk6Hd8E1GZ5G3/erFpRfyI1UcBWnff518rUhGxtbom2p6SPt3YeztzLyGbB2RtF5jDGvxLHohI4FpXwvJuXvw1lHbgYd/+9tz4kis7ersWOcbE0o01FJ0bsu5jvNTm5Otr9epLghDQAKthY6CtthtK0rAOX4lK5/Vfe/wuNopNXwbYVFdO6ghNbr93fh7vD7+/DOs52NHS3Z20nf35q60c9A58zn5miGO5hpJ5q6LNx48Z89dVX/P777yxdupTMzEyys7OZOXMm9erVY+nSpWzatAmVSoWfnx/Tpk0jODiYyZMnk5OTg7m5ObNmzcLLy4tDhw7x9ddfk5OTQ4UKFZg+fTqOjo60adOGbt26ceTIETIyMpgzZw61atUiJCSEcePGodVqadCgAYcOHWLPnj3cu3ePyZMnc/fuXRRF4cMPP6RZs2YsWrSIc+fOERUVxeuvv56vHZcvX2bixIkAVKtW7dm3ZgkYM3kZAC+29CvdRIrJzco8X4k9Jj0LGzNTrE1N8pXxHxXnXsg8XwdrAJKyNOy8Fcu+2/eo62zHly1r0O/3M8RkZBugdXl5PzgsGZORhY268PY9Kk6rgykNq9CmgjMHI+O4lZKR175sDbvDY9kfGUcdJzvmNavO63vPGqR97tb5t3t0Wha2ZqZYq030Q0ePi9HqYHZAVdp5ObPv1j1uJqfnW8eHDb35+szNAsNVhuJqZU7MA0OWsX/tFytTk3xDQY+KM1EpnI5J5NtLNzFVKcxrWpO0HC3rr93hy/PX+LpFbV6pXA5HczWfnbpaYtU0KPnj7e/hs67eBTt/AN52VrxQwYnu204913YVxt3anOi0B9576YW/P4uKic3I5oNDVwpddo5ORxkLNes6++Ngrubjw8El25iHFDhf/J33A/vxUTEPz4t+8JyZreH3m3+dM13s+KplDV7dYbhzpnh2Tzz6q9Fo2LVrF3Xr1mXt2rV89913bN26lbfffpslS5ag1Wr5/vvv+fXXX9m4cSMajYbo6GiWL1/Om2++ycaNG+nbty/nzp0jPj6eL7/8kp9++onNmzfTokULvvjiC/26HBwc2LBhA6+++irff/89AOPGjeO9995jy5YtVKxYEa027008Y8YMevfuzcaNG/n222+ZPHkyqal51xhkZ2ezY8cO+vfvn68tn3zyCWPHjmXTpk1UqFDhqTeiuK+oH7bV6nTFjnt4jqIo5P71+o+OXGHf7XsAnLuXTNC9ZBq7Oz5b0k9AUaCw/7sF2/f4uCmnQuiw9QR2ZqYMruEBwLjjweyPzBsSPB+XTFBcCo1cHZ5X+o+kQkFXSNK5DzxZnJhxh6/SYs1x7M3VjKjjoX++rostZczVbL8e+1zzfhIqeGz+j4vbdjOa+UHXydTmkqrRsi4skpblnDBTKUxrVI0ZZ0LoufMU7x66wEd1K+NqaVYibYGSP94ep3/VcqwLuUOqATreCkqhx1Ruru6JYooSn6mh3caTDNh1jmlN864hNRRVEXlrHz72iohRPXS+UVD0rx17+IFzZmzeObNJWcOdM5+ZVNSKV1GLiYmhe/fuQF6nx8/Pjw8//BBTU1P279/PjRs3OHnyJCqVChMTE/z9/enTpw8vvvgib775Jm5ubrRq1Ypp06Zx+PBh2rRpQ+vWrTl06BBRUVEMHDgQgNzcXOzt7fXrDQgIAMDX15fdu3eTmJhIZGQkrVq1AqB379788ssvABw7dozr16/z9ddfA5CTk8Pt23lj8n5+BatR8fHxxMTE0Lx5cwB69erFr7/++uRbUDC8ticty5cBwFptQlji/SqKi6U5SVkaMh8aArqbnkktJ9tC4+6mZ+HywD83Z0szotOzsVGb8LJvuXzXpylAjq5kh5eG1vAgoNxf7TM15VpyWv68swu2Lzo9i1plbAuNa+zmwLWkdO5lZpOhzWX37Vhal3fGRm1Cb5+y+a5PU5S8T/uGEJWWSW2X+zm7WuXtk4yc3GLFNCvnQGhCOrEZ2WTk5LLjRgztPJ31sR29Xdh6LabQfzYl6e3qHrRwz9t/VmpTrj+w/5wtzEkuZP/dzciixgP778G4DhVdCEtK49oD1cKcXB2V7KyxMFFx7G7eEOClhBRuJKdTw9GWmIzndz2eoY63x1Ep0KaiM6/vOvvY2OfhbnomtZ0Lee890NbixDzMRm1CI3cH9t/O20fB8WlcTUyjsoOVvtJd0u6mZ1LrwbwL2Y+Pinl4H7pYmhHz1zmzr2+5fNenKUBObsmeM8Xz9UTXqG3ZsoXff/+dOXPmoFar6dOnDxERETRs2JABAwbo47/55humTJmCTqfj7bff5uTJk3Ts2JFNmzbh5+fHsmXL+Oyzz9BqtdSrV0+/7A0bNug7WgDm5nkXfP79adDExARdEf+0cnNzWb58uX5Z69evp0qVKgBYWFgUiFcUJd+yTExMirMpRCG+u3CL/jvP0n/nWd7YfZ7azrZUtMnb5n18y3IwsuA/qRNRiUXGHYyIp1slN0yUvJNoBw8XDkTEkZ6jpa9vWdpUcAKgqqM1NZ1sOX6nZK+NWXI5nAF7zzFg7zkG/3GeWmXu592rkjuH78QXeE1gdGKRcW0rOPN2jYoAqFUKbSs4czo2kXSNlj4+ZWldPq99VRysqeFow/G7Jdu+vx27k0gdF1s8bPNyfqVqWfY/dMH/o2I6erkwoq6Hvl0dvFwIjLp/x2MDd3tORJXsHZCF+fFKOG/8cY43/jjH0APnqeloSwXrvPx7ertzOKrg/jsZnVhkXCU7a96u7okKMFOp6F2pHPsiY4lIy8Da1FTfQS9vbYG3nSWhSWkFlv8sDHW8PU5le2tSsnOIemCosSQdv5OIn/P9997LvmUL5FmcmIdpdTqmNvGl7l/XbvnYW+FtZ8WFuJQSaEXhjkclUtvJlop/5d3btywHH27bI2IORMTT3eeBfejpwh9/nzOrlKVNxfznzJK+nvC5UhnwYaSe+us5bt68iaIoDB8+HJ1Ox8cff4xWqyU+Pp7XXnuNDRs24O/vz927d7l69SqrV6+mS5cuvPrqq/j4+DBr1izee+89Jk2axI0bN/D29uabb74hOjqa2bNnF7pOW1tbKlasyMGDB2nVqhXbtm3Tz2vSpAmrV6/mnXfeISwsjNdee419+/YVmb+joyPlypXjwIEDvPDCC/z2229PuynEAxKyNEw9EcLcFtVRq1REpGYw+UQIANXL2PBpI1/67zz7yLgNYXeoYGvBmk71UKtUbAyL4kxs3h1nHxy+zMf1fRhW2xOtTsf4o8EkZhvuwt+ELA3TT4cyq0l1TFUKkWmZTD2Zl3c1Rxsm1q/MgL3nHhm3MOgG4+pVZnU7fwAO3oljXegddMBHx64wtm4lhtTwQKvTMSnwKkkGal98poZJR0KY3zpvn9xOyWD84RBqOtkwtbkvfbaeLTIGYN7p60xu6sum7vUA2Bcex8rLd/TL97C15M5fF7OXlsRsDTPPhPJ54+qo/9ov00//tf8cbBjnX5k3/jj3yLifg8P5oI4Pv7xYD1OVwh+R99h2MxqACYFXeN+vEmYqFVqdjjlnw4hMK7k2l/Tx9igetpbcKcG2PSw+S8Pk4yF80fKvNqRkMPFYCDXK2PBZE19e2XG2yJhHycjJ5f2DV/ioQSVMFQVNbi7jjwQTU4yq4vOSkKVhSmAI8x7YP58eD6F6GRsmN/al3+9ni4wB2BB6h4o2FqztVA+1iYpfQ6P0d+mOOXSZTxr4MPyvc+a4I8EGv1lCPBtFV1SJ6i9FfW2FVqvlo48+4tKlSyiKQosWLdi7dy8HDhxg2bJlrFu3DktLS7y9vZk+fTrh4eFMnDiR3Nxc1Go1kyZNws/Pj/3797Nw4UJyc3Nxc3Nj3rx5+psJfvnlFypUqEBgYCCLFy9mxYoVXLt2jQkTJpCdnU3VqlUJCgpix44dREdHM3nyZO7cyfvHMHbsWFq1alXg6zgenA4NDWX8+PHk5ORQt25dDh069ERfz2Hp0a/4W/ofJiN8DfXXHC7tNErEn/0CaLzhSGmnUWIC+7Sg1rJ/574DuPhGAM03/Xv339GeLf61xx7kHX91Vv4723f+9QDqrf53tg3gTP8Ag6/TN25VDp8AACAASURBVOB7g60r9PAwg63rSTy2o2ZsFi9eTN++fXF1dWX37t1s27ZN3/kyNOmo/TNJR+2fTTpq/2zSUfvnko5a6fjH/TJBuXLleOuttzA1NcXOzo4ZM2aUdkpCCCGEECXiH9dR69WrF7169SrtNIQQQghR0oz3WzMMxojvcxBCCCGE+G/7x1XUhBBCCPEfoZKSmlTUhBBCCCGMlFTUhBBCCGGcjPinnQxFKmpCCCGEEEZKKmpCCCGEME5SUJOKmhBCCCGEsZKKmhBCCCGMk9z1KRU1IYQQQghjJRU1IYQQQhgnuetTKmpCCCGEEMZKKmpCCCGEME5SUJOKmhBCCCGEsZKKmhBCCCGMk9z1KRU1IYQQQghjJRU1IYQQQhgnKahJRU0IIYQQwlhJR00IIYQQwkjJ0KcQQgghjJJOvvAWRafT6Uo7CSGEEEKIh1XuvNRg6wrb8abB1vUkpKL2DOqvOVzaKZSYP/sFYOnRr7TTKBEZ4Wv4/Oze0k6jxEzyb0vLbUdLO40Sc6hrcxpvOFLaaZSYwD4t8P3+UGmnUWJCh7XEe+y20k6jRNz4oisQUtpplKAqhl+lfD2HXKMmhBBCCGGspKImhBBCCOMkBTWpqAkhhBBCGCupqAkhhBDCOMldn1JRE0IIIYQwVlJRE0IIIYRxkrs+paImhBBCCGGspKImhBBCCOMkBTWpqAkhhBBCGCupqAkhhBDCOMldn1JRE0IIIYQwVlJRE0IIIYRxkoqaVNSEEEIIIZ7Etm3b6Ny5M+3bt2fVqlVFxh04cIA2bdo807qkoiaEEEIIUUzR0dHMnz+fjRs3YmZmxquvvkrjxo2pXLlyvrh79+4xZ86cZ16fVNSEEEIIYZxUhnskJycTERFR4JGcnJwvpWPHjtGkSRMcHBywsrKiQ4cO7Ny5s0DqkyZNYuTIkc+8CaSiJoQQQoj/vOXLl7N48eICz48cOZJRo0bpp2NiYnBxcdFPu7q6EhQUlO81v/zyCzVq1KBOnTrPnJd01IQQQghhnAx4M8GgQYPo2bNngeft7OzyTefm5qI8kJdOp8s3HRISwu7du1m2bBl379595rykoyaEEEKI/zw7O7sCnbLCuLu7c/r0af10bGwsrq6u+umdO3cSGxtL79690Wg0xMTE0L9/f1avXv1Ueck1akIIIYQwTooBH8XUrFkzjh8/Tnx8PBkZGezevZuWLVvq548ePZpdu3axZcsWlixZgqur61N30kA6akIIIYQQxebm5saYMWMYOHAgPXr0oEuXLvj5+TFkyBAuXLjw3NcnQ59CCCGEMEo6lXF+4W3Xrl3p2rVrvud++OGHAnEVKlRg//79z7QuqagJIYQQQhgpqaiVkhblHBlZxxu1SiEsMY1pgaGk5WiLHadSYIx/JZqVdcREUVgRHMGvYXl3lwSUK8PUJlW4m56lX87be4NIL2T5xuKHr0ZwKTicBUu2l3YqTyzizEXOrt1CriYHB4/yNB32GmZWlgXignceIGTvYUDB1s2ZJkNfw9LeNl/MgS+XYOVoT6O3XjFQ9gU1cXVkWHVP1CoV15LTmHM+rND3TlFx0+pXpby1hT6urJUF5+OS+e7KTSbXq6J/3kRRqGRnzaRTVzh0N94gbQNo7u7IiFpemJkohCWlM+N04cdeUXHWpiZMauCLp60lKkVh+61oVlyNBKBF2TJMbuhL9APH3rADFwx27L3gUYYPG3lhZqLialwaEw6GkKrRFitGpcBnzSvTqJw9AAfC45lz4gYAtV1smNjMBytTE1SKwpLzt9kaGmOQNhWldXVXPu5UHTNTFcFRyXyy/jypWTn5YnrUK8/QF3zQ6SBDo2Xq5otciEgC4PVmnrzSyBMLtYqLEUl8sv482drc0mjKM9HpdIwbt4AqVTwZPLhXaafz/MlPSBm2orZz50569epFt27d6Nq1Kz/++OMzL3PNmjWsWbPmmZczYMAAAgMDn3k5xeFgruazxlX46PBlem//k4jUTEbV9XqiuN4+ZfGwtaTvjj8ZsOsc/auWp2YZGwDquNixIjiS/jvP6h/G2kmrWrkcv6+ZRM/OjUo7laeSmZzCse9W0GrMELrP/wxbV2fOrtlSIC7uejiXf9tHx2lj6fbFJOzcXTm/flu+mEtb9xATfM1QqRfK3syU8XUr8+npYF7/4wxR6ZkMq+75RHGT/7zK4EPnGXzoPPPOXyNVk8P8C9e4lZqhf37wofOcik1kT0SsQTtpDmamTGrgy/gTV+i76wyRaZm8U9vrieKG1fQkJiOL/nvO8sa+c/SqVJZaZfI63H5OtqwKiWTA3nP6h6GOvTIWama/UIWRuy/TYd1pbqdkMraxd7Fjevi64e1gxUv/+5OuG87QqKwDHSs5A7C4XQ2+Pn2Lbr+e4e3fLzChaSU87SwK5GAoZazNmPtKXUb8cpoX5/5BeHw6H79UPV9MJRdrxnepwaAfAnlp/iEW7w3l20ENAehQy51Bzb15/fvjtP/iAOZqE95qWak0mvJMrl27zaBBk9i162hppyJKkME6atHR0cyZM4effvqJrVu3snbtWnbs2MG+ffueabn9+vWjX79+zylLw2jq7sDluFRup2YCsCEsik6erk8U90JFJ7Zdj0argxRNDrtuxdLZK2+en7MdDd3sWdPRnx9f9MPf5fG3G5eW4QPbs2ztfjZuN0wn+Xm7E3QFZx9P7Mrmbfsq7QK4ceQUOp0uX5xTJQ96LJiCmZUl2mwN6QmJmNtY6+ffvRRC5PnLVGnbwqD5P6yRiyPBialEpOW95zbfvEu78i5PFWeqKEzw92XRpRvEZGbnm+dXxo5WZZ348oJhO6aN3Ry5knD/mNp4LYqOHgXb96i4r85f5+ugvEqTs4UZZioVaZq8Sk5tJzsauDiwsq0/379Qm7rOhjv2WlRw5EJMCreS83JefekO3Sq7FjtGpShYmaowM1FhplJQmyhka3MxM1FY9Gc4xyITAbiblk18hgZ3G3ODte1hAVVcCLqdyM17aQCsPHaT7v7l88Vk5eQy7n/niU3Jq25eiEjExdYctYlCrwYV+fHgdZIyNOh0MOnXIDb9GWHwdjyrVau28/LL7ejYsXlpp1JyjPCuT0Mz2NBnQkICGo2GzMy8E4S1tTWzZ8/G3NycNm3a8Msvv1ChQgUCAwNZvHgxK1asYMCAAdjb2xMaGkrXrl1JSEjg008/BWD27Nm4u7uTkpICgL29Pbdu3Sow/+WXX2batGmEhoai1WoZMmQIXbp0ITs7m4kTJ3Lx4kXKly9PQkKCoTYFblbm+YYlY9KzsDEzxdrUJN8QzKPi3AuZ5+uQ948/KUvDzlux7Lt9j7rOdnzZsgb9fj9DTEb+f5bGYMzkZQC82NKvdBN5SulxiVg5OeqnrZwc0GRkosnILDD8qTI1IfzUeU4sWYXK1JQ6L3fJW0Z8IqeXb6DN+HcJ3XvEoPk/zNXSLN/7JDYzCxu1KVamJvkqQ8WJe8nDjXuZ2RwupGI2ooYXPwSHG7zS62Zlnm9YMiYjL+/Cjr1HxWl1MKVhFdpUcOZgZBy3UjIASMrWsDs8lv2RcdRxsmNes+q8vvesQY49dxtzotLu53w3LQtbc1Ns1Cb64c9HxWwMuUsnH2eOvN4YE0XhaEQC+2/l7bsNV+9/aecr1d2xVptwLjqlxNtUlLIOlkQlZuin7yZlYmepxsbcVD/8GZmQQWTC/ZhJXWuy7/JdNFod3i7WONmYseztxrjZWXDqRhyztl8xeDue1eTJwwE4evRsKWciSpLBKmrVqlXjxRdfpG3btvTp04d58+aRm5uLp2fBYZUHVa1alV27dtG/f3/27NmDVqtFp9Oxe/duXnrpJX1cly5dCp3/7bffUrNmTTZu3MiqVav47rvvuH37NitWrADg999/Z9KkSYSHh5do+x+kFDHmrn2oCvOouIfnKIpC7l+v/+jIFfbdvgfAuXvJBN1LprG7I+L50+XmFvpBTFEVfmh5NKxD3x/mUqfPS+ybtRitRsPhRUupP7A3Vo72JZtsMSgo6NAVeD734fdmMeL6VirHL6G3C8TUcrTFwcyUvZGxzyHjJ6MoFJJ1Ycfe4+OmnAqhw9YT2JmZMriGBwDjjgezPzIOgPNxyQTFpdDI1eF5pf9IKgV0hST9YM6PihlV35P4DA1NfzlBwKpA7M3VvOWXv0o1tG5FRtf3ZNjOS2SV4vVcxWnr3yzNTPi/AfXxdLbmk/XnATBVKbSo4sLIFX/SbeEh7K3MGNuxWkmnLZ6GSjHcw0gZ9GaCqVOn8s4773DkyBGOHDlC3759+eKLLx75Gj+/vEpLmTJlqFatGoGBgajVary9vfP91lZR848dO0ZmZia//vorAOnp6YSGhnLy5EleeSXvgm0vLy/8/f1LqNV5htf2pGX5MgBYq00IS0zXz3OxNCcpS0PmQye+u+mZ1HKyLTTubnoWLpZm+nnOlmZEp2djozbhZd9yLL18/x+kAuTo/nkXyRqrc+t/I+LPvN9102Rk4lCxnH5eenwiZtZWqC3yDwsl340hMzEZ12qVAfBp3ZTAH9cQdz2c1Oh7/Lki7/2ZkZiMLleHVpND02GvGaQ9b1X1oLlbXkfe2tSU6ylp+nnOFuYkZxd8b0ZnZFHD0abIOF87a0wUhXNx+X/MGKBNOWd2RcQW2hEqCUNreBBQ7q9jz9SUa8n32+diaU5SYe1Lz9Jfd/ZwXGM3B64lpXMvM5sMbS67b8fSurwzNmoTevuUZXnw/SE0RYGcwnoUJeBOahZ1XO/n7GZtTmKmhoyc3GLFtPd2ZtrRMDS5OjTZWjaFRNOxkjM/B0ViplKY07oqPo5W9N18jsjULErTncQM6nrc//Dpbm9BYno2Gdn5K7TlHCz58a2GhEWn0u/bY2T9tS1ikrPYdSFKX33bfCaC0e2qYOwWLlzJ/v0nAWjTphHvvfd6KWckDMFgHbUDBw6Qnp5O586d6d27N71792b9+vVs2LABQH9NT05O/rt2LCzuX7DavXt3duzYgVqtLvD9JUXNz83NZd68edSsWROAe/fuYW9vz/r16/NdR2RqWrKb4rsLt/juwi0AHM3VrOtcj4o2FtxOzaSPb1kO/vUp/EEnohIZ41+p0LiDEfF0q+TGocg4LE1N6ODhwsxTeXfd9fUty63kdPZHxFHV0ZqaTrZMORFSou37L6nbtwt1++YNW2YkpfDbxzNIjorBrqwrIXuPULFBwWHcjIRkjixaykuzx2NhZ8ONI6dwqFgO16o+9P5mhj7u/P+2k5WSatC7Pn++Gs7PV/Mqyg5mapa9UJcK1hZEpGXS3dOdI4UMXZ6KTeTdml5FxtV1suPMvaRC11fHyY4FF6+XTGMKseRyOEsu57XP0VzN6nb++mOqVyV3Dt8p2L7A6ETe8/MuNK5tBWdal9cx+8w11CqFthWcCYxJJF2jpY9PWcJTMvgjMo4qDtbUcLRh2inDHHtHbicwvkneRf63kjPpV6Ms+27FFTvm0r1UOldyIfBOEqYqhRe9nPTDm1+0qYalqYpXNp/L1/ErLYdDYpnYtSZeztbcvJdG/yae7LmU/zcVrc1NWDOiKb+ejuDrPfn3we9Bd3ipTjnWBoaTlZNL+5ruBN1ONGQTnsp7773+3+ucyV2fhuuoWVhYMH36dPz8/KhQoQI6nY4rV65QvXp14uLiCAsLo2LFio+8ueDFF1/k66+/RqfTMXbs2GLNb9KkCWvWrOHzzz8nJiaGHj16sHbtWpo2bcq2bdto3bo1UVFRnDlzpsTa/rCELA1TT4Qwt0V11CoVEakZTP6rI1W9jA2fNvKl/86zj4zbEHaHCrYWrOlUD7VKxcawKM7E5v1j/ODwZT6u78Ow2p5odTrGHw0mMTunyHzE07O0t6XZ8Nc5NP9HtDk52Lq50PzdgQDEXbvF8SWr6DJnAm7VK1OrRwd2T1uAykSFpaM9rcYOLeXsC0rM1jD7XBjT6ldDrVKITM9kxtlQAKra2/BxHR8GHzr/yDiACtaW3M3ILHQdFawt811faUgJWRqmnw5lVpPqmKoUItMymXoy75iq5mjDxPqVGbD33CPjFgbdYFy9yqxul1eFP3gnjnWhd9ABHx27wti6lRhSwwOtTsekwKskGejYi8/UMO7AVRa1r4GZSkV4cgYf/XGVWs42zGxVhW6/nikyBmDmsWt81qIyO/s2IFen43hkIj+cv01dN1s6+bhwPTGddd3r6tc3N/AGRyIMd23vg+JSs/lo3Tm+GVgftYmKW3HpfLjmLLUr2DP75Tq8NP8QA5t7U97Rig613OlQy13/2te+P86KYzextzJj25iWmCgKFyOTmLEhqFTaIsTjKLqHb08rQZs2beKnn35Co9EAEBAQwMcff8zx48eZPn069vb2tGjRgjNnzuhvJhg5ciSNGzfWL2P8+PFkZ2fz5ZdfArBo0SIARo0aVej81NRUpkyZQnBwMFqtlqFDh9KzZ080Gg1Tp07l1KlTlC9fnrS0ND744IN863qc+msOP5ftYoz+7BeApcc/627a4soIX8PnZ/eWdholZpJ/W1pu+/fern+oa3Mabyjdmy5KUmCfFvh+f6i00ygxocNa4j122+MD/4FufNEV+DePXhh+eNhn4DqDrevaL6X3/ZWPYtBr1Hr27EnPnj0LPN+qVStatWpV4Pm/L/h/0KxZs/JN/91BK2q+jY1NodfBqdVqPv/882LlLYQQQohSICOf8hNSQgghhBDGSn5CSgghhBDGyYi/NsNQpKImhBBCCGGkpKImhBBCCOMkFTWpqAkhhBBCGCupqAkhhBDCKOmkoCYVNSGEEEIIYyUVNSGEEEIYJ7lGTSpqQgghhBDGSipqQgghhDBO8qPsUlETQgghhDBWUlETQgghhHGSa9SkoiaEEEIIYaykoiaEEEII4yTlJNkEQgghhBDGSjpqQgghhBBGSoY+hRBCCGGc5Os5pKImhBBCCGGspKImhBBCCOMkX88hFTUhhBBCCGMlFTUhhBBCGCWdXKMmFTUhhBBCCGOl6HQ6XWknIYQQQgjxMO8xWwy2rhvzuxtsXU9Chj6fQeMNR0o7hRIT2KcFn5/dW9pplIhJ/m2x9OhX2mmUmIzwNcy/uKe00ygxY2q1o/J3h0o7jRITNrwldVYeLu00Ssz51wM4HrO9tNMoEU1dX8LnzfWlnUaJuba0b2mn8J8kHTUhhBBCGCe561OuURNCCCGEMFZSURNCCCGEcZK7PqWiJoQQQghhrKSiJoQQQgjjJNeoSUVNCCGEEMJYSUVNCCGEEMZJCmpSURNCCCGEMFbSURNCCCGEMFIy9CmEEEIIo6STmwmkoiaEEEIIYaykoiaEEEII4yQVNamoCSGEEEIYK6moCSGEEMI4yU9ISUVNCCGEEMJYSUVNCCGEEMZJykmyCYQQQgghnsS2bdvo3Lkz7du3Z9WqVQXm7927l+7du9OtWzfeeecdkpKSnnpd0lETQgghhHFSFMM9iik6Opr58+ezevVqNm/ezLp16wgLC9PPT01NZcqUKSxZsoStW7dStWpVFi1a9NSbQDpqQgghhPjPS05OJiIiosAjOTk5X9yxY8do0qQJDg4OWFlZ0aFDB3bu3Kmfr9Fo+Oyzz3BzcwOgatWqREVFPXVeco2aEEIIIYyTAb9Hbfny5SxevLjA8yNHjmTUqFH66ZiYGFxcXPTTrq6uBAUF6acdHR1p164dAJmZmSxZsoQBAwY8dV7SURNCCCHEf96gQYPo2bNngeft7OzyTefm5qI8MFSq0+nyTf8tJSWFd999l2rVqhW63OKSjpoQQgghjJMBK2p2dnYFOmWFcXd35/Tp0/rp2NhYXF1d88XExMQwePBgmjRpwoQJE54pL+molZLm7o6MqOWFmYlCWFI6M06HkpajLXactakJkxr44mlriUpR2H4rmhVXIwFoUbYMkxv6Ep2epV/OsAMXSC9k+YYQceYiZ9duIVeTg4NHeZoOew0zK8sCccE7DxCy9zCgYOvmTJOhr2Fpb5sv5sCXS7BytKfRW68YKPvn44evRnApOJwFS7aXdipP5NafFwlcuRVtTg5OnuV54Z3+he67izsOcmnXYVAU7N2daTWiP5b2tuye9yNJd2P1cSkxcZStUZlO44cbshl6L3iUYWxjL8xMVFyNS2P8gRBSNdpixagU+KxFZRqXtQfgQHg8s0/cAKBJOXs+aVIJU5VCljaXaUevERSTYvD2BZR3ZHRdb8xMFEIS0phyIpS0h9r3uBg3KzNWdqzLy9vPkJiVA0AleysmN66MpakJAAvP3uBYVKLhGlaEc8cus+H77eRocqjgU47B417B0tqiQNyaxVs49cd5rO2sACjr4co7UwcCMLLLpzi62OtjO/VrTbP29Q3TgEd4wa8sH/WpjZmpiuCIJMb/fIrUzJx8MQNerEz/1j6gg1uxqUxcepq4lKx8Md+MbEZ0YgZTV541ZPr/as2aNWPRokXEx8djaWnJ7t27mT59un6+Vqtl+PDhdOrUiXfeeeeZ12eUHbWIiAg6duyIj48PiqKg0WhwdXVl1qxZuLu7F4jfuHEjJ0+eZPbs2aWQ7ZNzMDNlUgNfhh4I4nZqJu/W9uKd2l7MO3ut2HHDanoSk5HF+BPBWJioWNO+Hmdjk7kYn4Kfky2rQiJZHhxRSi28LzM5hWPfraDj1A+xK+vKmVWbObtmC40Hv5ovLu56OJd/20eXuRMws7LkzxUbOb9+G02G9NfHXNq6h5jga3g1rWfoZjy1qpXLsWD6WzT09+FScHhpp/NEMpJS+GPxSnrM+ACHcq6cWLGZEyu30nJo/k5y7LVwzm3dx8tfjsfc2pLjyzdycs1vtBrej/Yfva2Piwm7xe55PxIwpHQ62WUs1MxpXYW+m89xKymTjxp781ETbz47HFasmB5V3KjkYEXn//2JSlFY36MunSo5s/dmHAvbVefN3y5wOS6N1h5l+KJNVdqvPf2IbJ4/R3M105pWYdCu84SnZPK+vxfv1fVi5qlrxY7p4u3KO3U8cbUyz7fsCY182Hwtms3XoqnmaM2P7fxo9b/jaHUGbWI+yQmp/DRrLRO/GYV7RRfWf7uN/333GwM/7FMgNuziTUZMGYBvbe98z0eFx2Bta8X0pWMNlXaxlLE1Z+7ghvSduZ+b0al8/LIfH73sx2crzuhjank68nbHqrw0eTepGRrGv1KHMb1qMWn5n/qYoZ2q0qCKM9tP3i6NZjwXOiP8ZQI3NzfGjBnDwIED0Wg09OnTBz8/P4YMGcLo0aO5e/culy9fRqvVsmvXLgBq1arFjBkznmp9RnvXp6urK1u2bGHz5s1s376dqlWrMnfu3NJO67lo7ObIlYRUbqdmArDxWhQdPVyeKO6r89f5Oijv07yzhRlmKhVpmrxPW7Wd7Gjg4sDKtv58/0Jt6jo/vpRbUu4EXcHZxxO7snll4SrtArhx5BQ6Xf4zvFMlD3osmIKZlSXabA3pCYmY21jr59+9FELk+ctUadvCoPk/q+ED27Ns7X42bg8s7VSe2O3zwbhW9sShXN6+q9EhgLDDBfedi48H/RZ/hrm1JTnZGtLikrCwtc4Xo9Xk8MeiFTR/qw82zo4Ga8ODWlR0JCgmhVtJecfT6st36FbZtdgxJoqCpakKMxMVZioF9V/VM02ujuYrArkclwZARTsLEjM1BmxZnqZlHbgYl0p4Sl7u60Oi6OztWuwYF0sz2lR0YsS+iwWWbaIo2Jnlfa63UpuQrc0tyaYUy8VTV/GuVhH3innnxNY9mnN8z5kC709Ndg63QiPZsfoPJg2ay6JJS4mLTgAg7MJNVCYKM99dxKRB89iydBe5RtC2FjXdCLoRz83oVABW7Q+jexOPfDEXbyXw4rgdpGZoMDNV4eZgSUJqtn5+46outKztzpo/rhs09/+Krl278ttvv7Fr1y6GDBkCwA8//EDt2rVp164dwcHBbNmyRf942k4aGGlFrTCNGzfmq6++4tixY8yePRudTke5cuX48ssv88X9/vvvLF26lMzMTLKzs5k5cyb16tVj6dKlbNq0CZVKhZ+fH9OmTSM4OJjJkyeTk5ODubk5s2bNwsvLq8Tb4mZlnm9YMiYjCxu1KdamJvmGPx8Xp9XBlIZVaFPBmYORcdxKyQAgKVvD7vBY9kfGUcfJjnnNqvP63rPEZNw/iA0lPS4RK6f7/5itnBzQZGSiycgsMISmMjUh/NR5TixZhcrUlDovd8lbRnwip5dvoM34dwnde8Sg+T+rMZOXAfBiS7/STeQppN1LwMbZQT9t4+RAdnrh+87E1IQbgec5+O1qVGpTur36Ur75wfuOY+Voj3fjOgbJvTBlrc2JSr1/PN1NzcLW3BQbtYl++PNRMb9evUunSs4cHdAYE0XhSEQC+2/FA5CTq8PJUs2WPvUoY6Fm9J4rhm0c4G5tTnTa/dyj07OwNTPFWm2iH9p8VExsRjYfHCo875knw/ihrR+vVytPGQs1nxwJLtVqGkB8TCJl3O6/P8u42JORlklmela+4c/Ee0nUqFeZ3kM6Ud7bnd/X/MHC8T8z9acP0Gq11Khfhb7DXyInJ5f5H/+AhbUFHfq2Ko0m6ZUtY0VUfIZ++m5CBrZWZthYmOYb/szR6mjnX46ZbzYkO0fLgs15nWxXBws+7e/Pm18dot8LPgbPXzxfRltRe5BGo2HXrl3UqlWLsWPHMmfOHLZt20aVKlXYtGmTPi43N5e1a9fy3XffsXXrVt5++22WLFmCVqvl+++/59dff2Xjxo1oNBqio6NZvnw5b775Jhs3bqRv376cO3fOIO1RFCjsHKd96JNgceKmnAqhw9YT2JmZMrhG3ieucceD2R8ZB8D5uGSC4lJo5OpQyJJKni43l8IK14qq8LeeR8M69P1hLnX6vMS+WYvRajQcXrSU+gN7Y+VoX+hrRMnIq0wU3HtF7TvvxnV4Y9kcGvTtzPbpx31/iAAAIABJREFU/4cu935lIui3/dTr06GkUi0WVTGOp0fFjKrvSXymhibLT9BiZSAO5moG+5XXx8RlaGixIpCXN51jTusqeNkXvJavJCkoheaem6t7opiHmakU5gZUZ/LxENpvOsmbe4KY1NgXNyuzZ0/6GehydSiFvD9VD1187lLOiQ/mDaVCpbIoikKnfq2JibzHvah4XujWlAFjemFu+f/s3Xd0VNX2wPHvtPTeG+lAQEroSC/SpCliAQXxWQDbkx+CNFGKoIDtoegTURQBUYqgICK9Kb23EEpCeu8zySQzvz/GFwgJEJQp6P6sNWsx9+47s09ucubcfc4d7HF2daTXo505vPOEpZpwQ0qFolplEKCihvP065EUWr28lv/8cJov/68TdmolH4xsy1vfHiXzj8rwXU1pwYeNstmKWkZGBgMHDgSgrKyMJk2aMHToUM6ePUuDBg0AGDt2LGBaowagVCr5+OOP2bp1K5cuXWL//v0olUpUKhXNmjVj8ODBdO/enaeeegp/f386d+7M9OnT2bVrF926daNr165ma89zDUPpGOQFgLNazYWC4sp9vo725Jfp0V1Xck8vKaWRl2uNcW38PbiQX0KWrgxthYFNVzLpGuyDi0bFQ1GBVdanKRRQXsMfvbkc/e4nkg6ZvlNGr9XhUSeocl9JTh52zk5oHKqugSlIy0CXV4BfTDQAUV3vZd/ny8m+mEhRehaHlqwCQJtXgNFgpEJfzr0jH7dQi/45Diz/icsHTR9UZSU6vMOunrvi7HzsXaqfu/zUTEryCghsYLpyj+l2L7s++5bS4hIcXF3IungFQ4WBoHvqWq4hNUgpKqWp39W/J39ne/J0erTlhlrF9Ir0YfruePQGI/qyClbHpdM70ocVZ9O4N8iDXy+bLo5OZRVxNruY+l7OXM6/WhUxt7QSHY19rubu52RPfqke7TX9Sm1irhft4YyDSsnOZFP18ERWIRfyi2ns40Z6YpYZWnJjqz//mSN7TgGgK9YREhVYuS83Kx9nV0fsHav+fl6JTyExPoX2vVte3Wg0VYH3bDxIaHQQdaKDqmy3tpScYppGeVU+9/d0JK+oFG3Z1RmXMD8XfNwdOHTedA6+33WJGU82p3G4J6F+Lkx6zFS99nV3QKlUYK9RMelLy66bFHeGzQ7U/rdG7Vpnz56t8l0lhYWFFBdfHfAUFxczePBgBgwYQKtWrahfv37l/8G1YMECjh49ys6dO3nmmWeYN28evXv3plmzZmzbto3Fixezfft2Zs6caZb2fHY6kc9OmxaTe9prWNajGXVcHLhSpGNQZAC7UnKqHbMvPY9/N4moMe6+EB+6Bht5+/AFNEoF94X4sC8jjxJ9BYOjAkks1LItOZt6Hs409HRh+oE4s7SrJrGP9CP2EdO0pTa/kJ/Gv0VBagZugX7Ebd5NnZbVpwG1uQXsnv8lfd+eiIObC5d2H8CjThB+9aN4aMHVuf1j36+ntLDorrvr827Rakg/Wg25eu6+GzOLvJQMPIL8OL1pF+GtGlc7piQ3n83vL2bwuxNwdHPh/K4DeNUJwsHVBYCU0/EEN65X4/cMWdKuK7lMvDeSMHcHEvJ1DG0YyOY/Ble1iTmVWUSfKF9+T8lHrVTQPcybo+mFGAxG3u5aj+yfT3E4rYC6nk5EejhxLKOgpjTM5reUPMY2jyTU1YHEQh0P1w1ke1L2bcdc70qhFhc7NU19XDmWVUiIiwNR7k6czSkyZ3NqNOiZPgx6pg8ABbmFTHlyLmlXMgmo48u2H/bSrEOjascolAqWfriGek0i8A3yZusPewmJCsTLz4PkS6kc3HGcl2aOoLy8nM2rd3NvD+vfrLT7ZDqTHo0l3N+Fy+lFDO0axeYjKVVifN0d+GBUW/q/sYncojIG3htKXFIBh+Kz6TD2p8q4lwfeg6er3d1716cN3kxgaTY7UKtJREQE2dnZxMfHEx0dzeeffw5AWFgYAJcvX0ahUDBq1CiMRiPjx4+noqKCnJwcHn/8cVauXEmzZs1IS0vj3LlzLFu2jH79+vHYY48RFRXF7NmzLdKO3FI9Mw6eZ3bbBqiVCpKLdUzbbxpIxXi6MLlFNMM2H71p3IfHLzGheTTLejQDYEdKNivOp2AExu09w6uxkTzbMJQKo5Ep+86RX1Z+o3TMytHdlXajnmDn+59TUV6Oq78v7V8w3RaffSGB3z5bSr93JuHfIJpGD/Ri0/QPUKqUOHq60/nV56ySszBxdHelywtP8Ou8RVSUl+MW4EO3l0znLiM+gR2fLOPhdycS2DCa5g/1Yt3UD1GqlDh5utPrtWcrXyc/NQNXP29rNaNSjk7Pa9vP8VGPhmhUShILtIzbeo5Gvi7M6lyPASsP3zAG4K29F3ijQzS/PNoSg9HI3uQ8Fh69gt5gZPTG00xpF4VaqaCswsCYzWdJK7bsmtCcUj1Tf4tjXqcGaJRKkgq1TN4bR0MvF95oW5dHNxy5YczNFOor+L8dpxnfKgp7pZIKo5Hpv8eTVGTdaTU3T1eenvgYH7++mPLyCvyCfHh2yhAALp29whfvrGDGl68SEhnIE688yAcTFmEwGPDy9WD0m6ZviR/4VC++eX81U56cS0VFBa26NKVz/7bWbBYA2YWlvPbFfj56vh0atZLEjCJe/Xw/jcM9mfVUS/q/8SsHz2ex4KczLHutK+UGAxl5OkbN32Pt1IUZKIw1TYRbWVJSEsOHD2fr1q3V9v3vazj0ej2hoaHMmTOHX375hf379/PWW28xbtw4Tp06hUKhoEOHDmzevJnt27ezePFiVqxYgaOjIxEREcyYMYPExEQmT56MwWBAo9EwZcoUmjSp/aLvNivvroXtt2Pf4A7MPLLZ2mmYxZRm9+EYOsTaaZiNNnE575/81dppmM2YRj2I/nSntdMwm/hRnWj6zS5rp2E2x57oyG8Zd9f3CdbWvX59iXrqO2unYTYXvnzE4u8Z9s4Wi71XwmvdLfZet8MmK2ohISE1DtIAWrduXbkm7X8GDRrEoEGDAHjvvfeq7JsyZQoAI0aMYMSIEVX2xcTEsGrVqjuUtRBCCCHEnWWTAzUhhBBCCEv+F1K2yoZvSBVCCCGE+GeTipoQQgghbJMU1KSiJoQQQghhq6SiJoQQQgibZJQ1alJRE0IIIYSwVVJRE0IIIYRtkv+ZQCpqQgghhBC2SipqQgghhLBNskZNKmpCCCGEELZKBmpCCCGEEDZKpj6FEEIIYZtk5lMqakIIIYQQtkoqakIIIYSwSUopJ0lFTQghhBDCVklFTQghhBA2Sb7vVipqQgghhBA2SypqQgghhLBJUlGTipoQQgghhM2SipoQQgghbJJCSmpSURNCCCGEsFVSURNCCCGETZKCGiiMRqPR2kkIIYQQQlwv+tOdFnuv+FGdLPZet0Mqan9Bo8W7rJ2C2Zwc0ZFOP+6xdhpmsbN/e94/+au10zCbMY164Bg6xNppmI02cTmTD26xdhpm81bL7gzdvsPaaZjNsi6difrEch++lnRhdCfq/vfv2TaA8yMtP5CRipqsURNCCCGEsFlSURNCCCGETVJIOUkqakIIIYQQtkoGakIIIYQQNkqmPoUQQghhk+RmAqmoCSGEEELYLKmoCSGEEMImKaWiJhU1IYQQQghbJRU1IYQQQtgkWaMmFTUhhBBCCJslFTUhhBBC2CSpqElFTQghhBDCZklFTQghhBA2SSElNamoCSGEEELYKqmoCSGEEMImyX/KLhU1IYQQQgibJRU1IYQQQtgkWaImFTUhhBBCCJslFTUhhBBC2CSpqElFTQghhBDCZslATQghhBDCRslATQghhBA2SaGw3ON2/Pjjj9x///307NmTpUuXVtt/5swZBg0aRK9evZg8eTLl5eV/+mcga9SsoFOIJ680j0CjUhCXW8zUPecp1lfUKsZFo2J6+3pEuDuiVChYG5/OFyeTiHR3Yk7n+pXHKxUK6nk688rW02xOzLZo+9r6eTKyQRgapZILBcW8cyyekvKKWsdNb1GfYGeHyrhAJweOZRfw6ZnLTG1er3K7SqEg0s2ZKQfOsDMtxyJtq0nCoZPs+2YdFeXleIcF0+X5odg5OVaLO7lhB6d+2QUKBe4BPnQePRRHd1c2zf2c/LTMyrjCjGwCG0bTZ+IoSzbjL1n43mhOnU3kg8/WWzuV25J65AQnV6ylorwc9zrBtHz2CTQ1nLv4Tdu5uHkXKMDZz5cWzzyOg7tr5f6S7By2vjGXHrMnY+/qYskm3FT+ieMkr1mDobwcx+Bgwoc/icqxevsAjEYjlxcvxjE4mICePQEoLy4mcelSSpKuoLSzw6dde/y6dbNkE6roEurFuLbh2KmUnM0uZuK2OIqu6ztvFONur2ZGp2ga+LhQoq9g1dl0vj6ZAoC7vZo3OkQT7emEg1rJgsOJ/BCXYfG2jW1tyvtcdjGTdtTctppi5vdoQJjb1fMa4urA/tR85u27xHvdYyq3KxUK6ns788KmU2y6ZNnPhb+T9PR03n//fVavXo2dnR2PPfYYbdq0ITo6ujJm3LhxzJw5k9jYWCZNmsR3333H0KFD/9T7/e0qanFxcdSvX59ffvnF2qnUyNNew4z29Xhl22n6rzlEUqGOMS3Cax3zUrMw0ktKeXDtYR776QiPxgTS1NeVi/klDF53pPKxNzmX9RczLD5Ic7dTMzE2mtcPnuWJbYdJLdExskHYbcVNPXSOp3ce4+mdx5h77AJF+nLeP3GBhCJt5fandx7jQGYevyZlWnWQps0vZNtH39Bz3DMMmT8VN39vfv9mXbW4zAuJHF23hQdmjeXRDybjHujL/uU/AdBz3DM8/O5EHn53Ip1HmwZ5HZ991NJN+VPqRwfx8/IpPHh/a2uncttKCwo5+NkS2r7yHL3nvYmznw8nVvxQLS73UiJx6zfT9c1X6fnO67gE+HJq5Y+V+xN2/c72Ge+jy823ZPq3pC8s5PJXXxE5chSNps/A3seX5DWra4zVpqZy/v33yDt8qMr2K999h9LBnnvenEbMhInknzpJ3vHjlki/Gi8HDXO61eOFX07TY/lBrhToGNc2otYxU9pHUaw30OvbgwxefZTOoV50DfMCYE63+qQVlzJg5WGG/3icqR2iCHC2s2jb3u5Sjxc3nabXioNcKdTxapvqbbtRzEu/nmHAqsMMWHWYyTvjKCgr583d8cTnlVRuH7DqMLuTcvnxfMZdNUhTKiz3KCgoICkpqdqjoKCgSk579+6lbdu2eHh44OTkRK9evdi4cWPl/uTkZHQ6HbGxsQAMGjSoyv7b/hn86SNt1KpVq+jduzcrVqywdio1ahfswamsIhILdQCsOJdK30i/WsfM3n+ReQcuAuDjaIedUklhWdWrruZ+bvQM92H6b/Hmbk41rX09OZtXRFKxKfcfLqfRI9j3T8WpFQomNavL/FOXyNCVVdnXxMuNzoHevHvigplaUjtXjp3FLzoMjyDT+WnYqyPxuw5gNBqrxPlGhTLkozewd3akvExPcXY+Dq7OVWIq9OVsm7+E9v8ajIuPp8Xa8FeMGt6Txd9uZfX6fdZO5balnziDZ2QYrgGmcxd1XycS91Q/d54RofR+dxoaJ0cqyvTocvOxczGdO21uHskHj9HxtRctnv+tFJw+jVNYGA7+/gD4du5M9r591doHkLl9Gz4dOuDZokWV7SWJCXi3aYtCqUSpVuPeqHG1wZyldKjjyfGMQi7nm/qMpadSGFjXr9YxjXxd+CEuHYMR9AYj2xKy6RPpg7u9mg4hHvznYAIAacVlDFp1lLzSPz9VddttC/HkREYhCQWmvJedSmFAtN9tx2iUCuZ0rc9bey+QVlxaZV/LADd6R/owddd5M7bk7vbVV1/RvXv3ao+vvvqqSlxGRga+vlc/r/z8/EhPT7/hfl9f3yr7b9ffaupTr9fz448/snTpUh577DESExMJDQ1l3759zJw5E5VKRWxsLBcuXGDJkiUkJCTw5ptvkpeXh4ODA6+//joNGzY0a44BzvaklVz9A0ovLsXVTo2zRlU5/XmrmAojvN2xPj3CfdiSkMXlgpIq7zG2VQT/OXy52nSqJfg52pGhvTqoytSV4qJR46RWVZn+rE1c31B/snRl7KqhYja6YTgLzybWOKVqScVZubj4eFQ+d/H2oKxEh16rqzb9qVKruLTvGDs+WYZSo2bAY32r7D+75TecPN2JaNPUIrnfCWOmLgage6cm1k3kTyjJzsXJ6+qA2NHLg3KtjnKtrtr0p1KtIvngUQ4tXIpSo6bz4H6mYzw9aDdmpEXzri19bg52Xl6Vz+08PTHodBh0umrTn6FDTFMyBadPV9nuHBFB9r7fcYmOwqAvJ+/IYRQqlfmTr0Ggiz2pRVf7xbSiUlzt1bhoVJVThDeLOZpeyAP1/DmUVoCdUkHvKF/0FQbC3B3JKCnj6SYhdA71xE6l5PNjSVzO11qsbQEu9qReM7BKK67ettrEPBwTQHpxGb9erl4xe61tJO8duFxtOtXWWfLrOZ588kkefPDBatvd3NyqPDcYDFX+s3ij0Vjl+a32366/VUVtx44dBAUFERERwX333ceKFSvQ6/WMHz+euXPn8sMPP6BWXx2bvvbaa4wbN441a9YwY8YMxowZY/YclSio4YIWwzUbaxMzYdc5Oiz/DXd7DaObhlZuj/V1xctew/qLmdVfwAIUKDBSPXnDdQ2qTdwjkUF8ff5KtZhGnq542KnZnGydNl7LVJ2o/geoUNb8pxXRpikjFr9Dy0fuZ/2MjzEaDJX7jv+0leaDe5krVXEdo9FY46fAjc5dcMtYBvx3Lg0H9WX32/OrnDtbZDTU/LvJDdpXk5DBDwMKTs+cyYVPFuDaoCEKlXWu75UKaugxoOLavvMmMbP2XsCIkR8fbs6nfe5h95Vc9AYjGqWCUDdHivTlPPLDMf796xkmt4ukkY/l1hoqFdTY51dr2y1iRjQOZsHhxGoxzfzd8HLQ8ON5y667u9u4ubkREhJS7XH9QC0gIIDMzKufP5mZmfj5+d1wf1ZWVpX9t+tvVVFbtWoV/fqZrnTvv/9+Xn31VXr16oW3tzcxMaYFlYMHD+att96iuLiYkydPMnHixMrjS0pKyM3NxdPTfNNOqcU6GvteXYTs52RPfqkebbmhVjHtgjw4n1tCprYMbbmBDZcy6BHmUxnbO8KXdRcyauyszOVf9UNp72/6mTmr1VwsLK7c5+NgT0GZHl1F1Q+1dG0pDT1dbhhX180ZlULB0eyqawMAugX58EtSpkXbeK0Dy3/i8sETAJSV6PAOC6rcV5ydj72LExoH+yrH5KdmUpJXQGCDKABiut3Lrs++pbS4BAdXF7IuXsFQYSDonrqWa8g/0KmVP5JyyHTuyrVa3OoEV+7T5uShcXZCfd25K0rLQJdfgE9900LhiC7tOPzFcsqKS2zqxgGAlHVryTt2DIAKnQ7H4KvtK8vLQ+XkhMre/kaHV2PQaQl56CHUzqap3tQNG7D3q76UwRJSCktp6ne1X/R3tidPV7XvvFlMoIs97/x2ifw/pjRHN69DQr6W9GJTZX/lWdPUVEKBjoNpBTTxd+VkVpElmkZKUS3adouYht7OqJUK9qdWXyvZN8qXNefTrdZn/hW2+IW37dq1Y/78+eTk5ODo6MimTZuYMWNG5f7g4GDs7e05dOgQLVq0YO3atXTq1OlPv9/fpqKWnZ3Nrl27+OKLL+jWrRtTpkyhoKCAnTt3YqjhytdgMGBnZ8fatWsrH99//z0eHh41vPqdszclj6a+roS6mu5qfLR+IFuvW/B/s5je4b6MjjVV0DRKBb3CfdmXmld5bMsAd36/5rklfHEusXKB/6jdx2no6UrIH3dtDgwLYHcNU5cHMvNuGhfr7cbhrJoXZzf1duNQlmXbeK1WQ/pVLv4f9ParpMddJi/FdKV6etMuwls1rnZMSW4+m9/7Em2BqeM/v+sAXnWCcPjjgz7ldDzBjev9pfK4uLV7Bvenx+xJ9Jg9ia7TxpMTf4nCNNO5u7hlF0Etqk/havMK2Df/C0oLTecucc9+3OsE2dwgDSBowEAavj6Vhq9PJea1CRRfvIjuj7UxWTt34NE09rZeL3PHTlLWrQVAX1BA1p7deLVuc8fzro3dSbk083cj3N3UZwy9J5DN103x3Sxm6D2BvNLKdMOSt6OGRxoEsO58BkmFOk5mFjKovn/lvub+bpzIKLRU09h9JZdYPzfC3Ex5D2kYyJaE7NuKaR3kwW/JNfeLrQPdb7hP3D5/f3/GjBnD8OHDeeCBB+jXrx9NmjTh2Wef5cQJ04XgvHnzmD17Nr1796akpIThw4f/6ff721TU1q5dS9u2bfn8888rt82fP5/du3dTUFDAuXPnqF+/Pj/+aLpby9XVlfDwcNauXcvAgQPZs2cPU6dOZfPmzWbNM0enZ8ruON7v2gCNUsmVQi0Td8Vxj7cL09rXZfC6IzeMAZh78CJT763LmoHNAdiSmM03p1MqXz/U1ZGUIp1Z23AzeWV63j4az/QWMWiUCpJLdLx1xLR4tb67C+ObRvH0zmM3jQMIcXYkTVtzO0KcHaus4bMmR3dXurzwBL/OW0RFeTluAT50e8n0B5kRn8COT5bx8LsTCWwYTfOHerFu6ocoVUqcPN3p9dqzla+Tn5qBq5+3tZrxj+Tg7krLkcP4/cOFGMrLcfbzpfXoJwHIuZjAoYVL6TF7Er4x0cQ80JsdM99HoVTh6Olus+vSrqVxcyP8yRFc/Oy/GMvLsff1JfypfwFQfPkyCUu+puHrU2/6GgF9+nDpi0WcmvYmGI0E9R+Ac3i4+ZOvQbZWz2vbzvFRz4ZoVEoS87W8uvUcjX1dmNWlHv2/P3zDGIBPD1/h3e71+flR0w0TH+xP4ESmafA9auNppnWMZug9gSgVCj46lFi5zxJydHombD/H/J4NsVMqSSzQMm7bORr5uDCrcz0GrDp8w5j/CXN3JLmw5n7RtM96nwt/hUJpmxev/fv3p3///lW2LVy4sPLfMTExrFy58o68l8JY0y1Ad6H+/fszZswYul3zHT85OTl07dqVRYsWMXPmTJRKJRERERQUFLBw4UIuXLhQeTOBRqPhzTffpEmT2i+KbrR4lzmaYhNOjuhIpx/3WDsNs9jZvz3vn/zV2mmYzZhGPXAMHWLtNMxGm7icyQe3WDsNs3mrZXeGbt9h7TTMZlmXzkR9stPaaZjFhdGdqPvfv2fbAM6P/PPTd39Wq+92W+y9DjzSwWLvdTv+NhW1/1XKruXl5cWRI0eYN28ey5Ytw8nJiS+//LLyNtmoqCiWLFli6VSFEEIIUQuyGuRvNFC7EaVSiYeHB4MHD0aj0RAcHMxbb71l7bSEEEIIIW7pbz9QA3juued47rnnrJ2GEEIIIW6DVNT+Rnd9CiGEEEL83fwjKmpCCCGEuPtIRU0qakIIIYQQNksGakIIIYQQNkqmPoUQQghhk2z0+24tSipqQgghhBA2SipqQgghhLBJcjOBVNSEEEIIIWyWVNSEEEIIYZMUUk6SipoQQgghhK2SipoQQgghbJKsUZOKmhBCCCGEzZKKmhBCCCFskkJKalJRE0IIIYSwVVJRE0IIIYRNkoKaVNSEEEIIIWyWVNSEEEIIYZOkoiYVNSGEEEIImyUVNSGEEELYJKmogcJoNBqtnYQQQgghxPW6bthjsffadn97i73X7ZCKmhBCCCFsklIqajJQ+yvar9lt7RTMZs+DHWiz8u/Zvn2DOxD96U5rp2E28aM6MfngFmunYTZvteyOY+gQa6dhNtrE5UQPWmLtNMwmfvUwwuZutXYaZpEwrhuNFu+ydhpmc3JER2un8I8kNxMIIYQQQtgoqagJIYQQwibJ1KdU1IQQQgghbJZU1IQQQghhk5QK+WIKqagJIYQQQtgoqagJIYQQwibJGjWpqAkhhBBC2CypqAkhhBDCJkk1SX4GQgghhBA2SypqQgghhLBJctenVNSEEEIIIWyWVNSEEEIIYZPkrk+pqAkhhBBC2CypqAkhhBDCJkk1SX4GQgghhBA2SypqQgghhLBJskZNKmpCCCGEEDZLBmpCCCGEEDZKpj6FEEIIYZMU8oW3UlETQgghhLBVUlETQgghhE2SmwmkoiaEEEIIYbOkomYl9/p7MuqecOyUCuILSph9+Dwl5RW3Fbf+/jZkaksrY5edT2ZTUibhro6MbxaNk0qFEfjk1GX2Z+RZqmm0D/BkdKNw7FQK4vNLeOvgeYpraNuN4pzVKqa0rEuYqyNKhYL1CeksOZcMQIdAL6a2qkt6ydV2j9x+osafnTl0CfXi1Tbh2KmUnMsuZuL2OIr0FbWKUSrgjQ7RtAl0B2B7Yg5v/34JgLZB7rzWNhK1UkFphYHpey5wPKPQIm26mdQjJzi5Yi0V5eW41wmm5bNPoHFyrBYXv2k7FzfvAgU4+/nS4pnHcXB3rdxfkp3D1jfm0mP2ZOxdXSzZhL9s4XujOXU2kQ8+W2/tVG5LlxbBvPp4M+w0Ss4l5DHx498o0uprjL2vdR3m/bs9sY9/W2V7oLcT37/dh/7/9xO5haU1Hmst3SK9Gd8pCjuVgrOZxYzfeIaispr7gXf7NOBcVhGfHbhSuW1YbDCPNQnCQa3kRHoh4zeeoazCeuuhOoV48krzCDQqBXG5xUzdc57i6/qWG8W4aFRMb1+PCHdTn7k2Pp0vTiYB0DnEi1kd65FafPX8Dd9w3GJ95l8l1aS75GewceNGBg0axIABA+jfvz+ff/45AM8++yzp6emsXr2aCRMm1Hjsvn37ePTRRxkwYAB9+/Zlzpw5VFRY9xfUw07N5BZ1mbzvDEM2HyalWMfoe8JvKy7UxZECfTkjth2tfGxKygRgbNNo1idkMGLbUWYdPs+M1jGoLFQ+9rBTM6VlXSb+foZHfjlMcrGO5xvX3LYbxY28J4wMbSlDfz3CiC1r/eraAAAgAElEQVRHGRQZSCMv04d+E29XlsYlM2zz0cqHpTocLwcN73StxwubTtPz24MkFugY1zai1jEP1PMn0sOJ+78/RL+Vh2kd5EGfSB80SgUf9mjA5B1x9F95mI8PJTKvW32LtOlmSgsKOfjZEtq+8hy9572Js58PJ1b8UC0u91Iices30/XNV+n5zuu4BPhyauWPlfsTdv3O9hnvo8vNt2T6f1n96CB+Xj6FB+9vbe1UbpuXmz3vvNiOF+buoOdL60hML2TcsGY1xoYFujLxyeZc30U80CWSZTN7EeDtZP6Eb5OXo4a5vRsw6ocTdFu0j8Q8LRM6RVWLi/ZyYvkjzbi/nl+V7b3r+jKieQhDvzvCfV/sw0Gt5OkWoZZKvxpPew0z2tfjlW2n6b/mEEmFOsa0CK91zEvNwkgvKeXBtYd57KcjPBoTSFNfU5/ZzM+NxSeTGbzuSOXjbhmkCRObH6ilp6fzzjvvsGjRItatW8e3337Lhg0b2LJlCwsXLsTf3/+Gx5aVlTF27FjmzZvHunXrWLNmDRcvXmTp0qUWbEF1rf08OZNbRFKxDoA1l1LpWcf3tuIaebliMBpZ0LExX3VrxlP161SeTJUCXDWmYqmTWkVZhcH8jfpDG39TzleKTDmvvpBK79DqbbtZ3HvHLvKf46ZKk4+DHXZKJcX6cgAae7vR0teDb+5rxn+7NCbWx80SzQKgQx1PjmcUkpBvynnZ6RQGRPvVOkalUOCoVmKnUmKnVKD5o3qmNxhpv2Qfp7OLAajj5kCerubKhyWlnziDZ2QYrgGm/KPu60TingMYjVWrDp4RofR+dxoaJ0cqyvTocvOxc3EGQJubR/LBY3R87UWL5/9XjRrek8XfbmX1+n3WTuW2dYgN4nh8Fgmppqrsso1xDOgYUS3OwU7Fu//uwKzFh6ps9/N0pEfrOjw1fbNF8r1dncK9OJ5WwOU8LQDfHE1mYMOAanHDm4Xw7YkU1sdlVNk+6J4AFh5IJF9XjhGYtOkcq0+nWiL1GrUL9uBUVhGJhaZ+Y8W5VPpG+tU6Zvb+i8w7cBEAH0dTn1n4R3WxqZ8brQPdWTWgGV/1aUILf8v1mXeCUmG02MNW2fzUZ25uLnq9Hp3O9Mvp7OzM22+/jb29Pd26dePrr78GICEhgccff5z8/Hy6dOnC2LFj0Wq1FBUVodWa/pjt7OyYPHkyxcWmD8Rhw4YRExPDwYMHKS0tZdKkSXTo0MHsbfJzsifjminLTG0pLho1TmpVlSudm8WplAoOZuTxyanLqJUK5t57D8XlFXx3IYV3j13gPx0a82h0EJ72Gt44cA5LVfT9neyrTEtm/JGzs1pVZfrzVnEVRnizVT26hfiwIzmbhELTOcwv07MpMZOtydk09XZjbrsGPLH5CBnaMrO3LdDZntSiqzmnFZXiaq/GRaOqnP68Wcyqc2n0ifRhz7A2qBQKdiflsjUhB4BygxFvRw1rBzfHy0HDy7+eMXt7bqUkOxcnL8/K545eHpRrdZRrddWmP5VqFckHj3Jo4VKUGjWdB/czHePpQbsxIy2a950yZupiALp3amLdRP6EQG8nUrNKKp+nZZfg6myHi6OmyvTnzFFt+XZTHGcv51Y5PiNXywtzdlgs39sV6OpAyjVTsamFpbjZq3GxU1WZ/py6JQ6AjuFeVY6P9HTimJMdXw1uir+LPQeS8pi1I94yydcgwNmetGv6w/TiUlzt1DhrVJXTn7eKqTDC2x3r0yPchy0JWVwuMJ3//FI96y9m8mtCFs383JjfrSEPrTtMeon5+8x/mpSUFMaNG0d2djYRERHMmzcPZ2fnKjEZGRlMnDiRrKwslEol48eP5957773p69p8RS0mJobu3btz3333MXjwYObOnYvBYCAsLKxKXFJSEvPnz2f16tUcOnSILVu24O7uzsiRIxk0aBD9+/dn5syZpKenExMTU3lcUVERa9as4d1332XChAmUlZn/l1cJGGsYOBmu23izuB8vp/P+8YvoKgwU6StYEZ9MpyBv7JQKpreO4a3DcTy48QAv7DzBuNho/BztzNKW6ykUUNOYsOK6htQm7s0DcfRa9ztudmqebmialpjw21m2JmcDcCy7gOPZhbT287hT6d+UshY53yzmpRZh5Oj0tP3qdzp8sw8Pew1PNwmujMnW6umwZB8PrznKO13rEe5efS2YJRmNRtOJuo5CWXO3EdwylgH/nUvDQX3Z/fZ8jAbLVXJFVUqlolrlE6DCcHXb473rUW4wsHLrBUumdkeY/s5qaF9NHWYN1CoFHcO9eGHdSfp/fQB3Bw3jOlSfOrUUJYpbfibUJmbCrnN0WP4b7vYaRjc19ZmvbDvDrwlZABzJKOBoZgH3BnlWfyEbpVRY7vFXTZs2jaFDh7Jx40YaNWrEggULqsXMmTOHbt26sXbtWt59911effXVWy7HsvmBGpgav3XrVoYMGUJKSgqPPPIImzZtqhLTrVs3vLy8sLOzo0+fPuzfvx+A0aNHs3PnTkaOHElxcTHPPvssixcvrjzukUceAaBBgwb4+vpy7tw5s7ThmQahLO4ay+KusfQLD8DnmoGTj4M9BWV6dNdNUaZpS28Y16uOL1FuVdeOlBuMRLo546BSsjfNdIV8KreQSwUlNPR0xVyeaxjKkvtiWXJfLAPDA/C9JmdfR3vya2hbeknpDePa+Hvg42Dap60wsOlKJvU9XHDRqHgyJqTK6ygUUF7LzvmvSikqxd/pas7+zvbk6fRoyw21iukV6cPKs2noDUaKyipYHZdOm2APXOxU9Aj3rjzmVFYRZ7OLqe9V9UrMEk6t/JFfJ87i14mzuLxtD9pr1pVpc/LQODuhdrCvckxRWgZZ565WIyK6tKM4K4ey4hKEdaRkFuPvdbV/8Pd2Iq+wFG1peeW2QV2jaBLtw7p3+7JoSjcc7FSse7cvfp7WvUCojZRCHf7OV38PA1ztydPq0eprd3GQXlTKxrhMisoq0BuM/HA6jeZB1psSTC3W4XtNv+HnZE9+adW+5WYx7YI8KvtTbbmBDZcyaODtgqudimcb16nyXgqgXC6ialRQUEBSUlK1R0FBwS2P1ev1HDhwgF69egEwaNAgNm7cWC2uR48e9OtnmnEICwujtLSUkpKb95U2P1Dbvn07GzZswN/fn4ceeoj333+fKVOmsHLlyipxavXVWVyDwYBarebo0aMsXboULy8v+vXrx+zZs/noo4/4/vvvK2NVKlW148zh8zOJlYv+n9t+jHs8XQlxdgDgwYgAdqXmVDtmf3reDeMi3Zx5pkEYSsBOqeShyCC2JGeSVKzFWa2uXHwf7OxAhJsj5/OLzdIugM9OJ1Yu7H962zEaeblSx8WU86DIAHalVG/bvvS8G8bdF+LDMw1NnYtGqeC+EB8OZuZRoq9gcFQgXYNNg5p6Hs409HTht7Tcaq9vDruu5BLr70aYuynnoQ0D2Xw5u9YxpzKL6BNlWoenViroHubN0fRCDAYjb3etR/MA0wdFXU8nIj2cOJZx687hTrtncH96zJ5Ej9mT6DptPDnxlyhMM63vubhlF0Etqk8DavMK2Df/C0oLiwBI3LMf9zpBd93dnX8nu46lElvPh7BAUz8wtGc9Nl9zxyPAQ6/9zP2v/MiAset5euZWdGUVDBi7noxcrTVSvi07L+fQLMidcA/ToPLxpkFsis+q9fE/n8ukb4wf9mrTR2DPaF+Op1nvLuu9KXk09XUl1NXUbzxaP5Ctidm1jukd7svoWFMFTaNU0Cvcl32peRTrK3gsJpD7wkx9ZoyXM418XNmdbJk+805QWvDx1Vdf0b1792qPr7766pZ55ubm4uLiUjmG8PX1JT09vVpcr169cHc33fm/aNEiGjRogKvrzQspNr9GzcHBgRkzZtCkSRNCQkIwGo2cOXOGBg0aEB9/9Sp+x44d/Pvf/8be3p4NGzbw8ssv4+7uzkcffUSLFi0qpztPnTpFgwYNKo/bsGEDTZo04cSJExQUFFCvXj2ztymvTM+sw+eZ2aYBGqWC5GIdMw6a1lLEeLgwoVk0I7YdvWncF2cT+b+mUXzdvTlqpYJtyVn8eNn0SzFp3xleaRKJnVJJhdHIO0fiSf7jhgRzyy3VM+PgeWa3bYD6j5yn7f+jbZ4uTG4RzbDNR28a9+HxS0xoHs2yHqa71HakZLPifApGYNzeM7waG8mzDUOpMBqZsu8c+WXlN0rnjsrR6Xlt+zk+6tEQjUpJYoGWcVvP0cjXhVmd6zFg5eEbxgC8tfcCb3SI5pdHW2IwGtmbnMfCo1fQG4yM3niaKe2iUCsVlFUYGLP5LGnF1l1D4uDuSsuRw/j9w4UYystx9vOl9egnAci5mMChhUvpMXsSvjHRxDzQmx0z30ehVOHo6X7Xrkv7u8jJ1/HaR3v5aFwnNGoViWmFjPvPHhpFeTHr+XsZMPbu+qqR62WX6Bn38xk+GdgIO5WShDwtYzacprG/K+/0juH+rw7c9Pivjybh7qhm/bBWKJVwMr2ImZvOWyj76nJ0eqbsjuP9rg3QKJVcKdQycVcc93i7MK19XQavO3LDGIC5By8y9d66rBnYHIAtidl8c9rUZ7689TQT20TxQmwYFUYjr+44S16pZfrMu82TTz7Jgw8+WG27m1vVauvPP//M7Nmzq2wLCwtDcd1SkeufX2vx4sWsWLGCb7755pZ5KYw1LWSwMWvWrGHRokXo9aZFsB07dmT8+PH07t2br7/+mv3797Nx40YKCgooKCigX79+PP/884BpAPfhhx9SVFSEQqGgSZMmvP7667i5uTFs2DDc3NxISUkB4I033iA2NrbWebVfs/vON9ZG7HmwA21W/j3bt29wB6I/3WntNMwmflQnJh/cYu00zOatlt1xDB1i7TTMRpu4nOhBS6ydhtnErx5G2Nyt1k7DLBLGdaPR4l3WTsNsTo7oaPH3HLHTcje1LO7U+U8fq9fradOmDQcOHEClUpGamsoTTzzBli3V++I5c+awY8cOFi1aREBA9buVr2fzFTWABx98sMZR7tatpj/2kJAQBg0aVOOxnTt3pnPnG//whw8fTps2be5MokIIIYT4x9FoNLRs2ZINGzbQv39/fvjhBzp16lQtbvHixezbt4/ly5dXq9TdyF0xUBNCCCHEP48tf7/Z9d544w0mTJjAJ598QmBgIO+99x4Ay5cvJyMjg5dffpmPP/4YFxcXhg0bVnncZ599dtPvhP1HD9SWLPn7Ti8IIYQQwnKCg4NrHFcMGXJ1qcaBAzdfP1kTm7/rUwghhBDin+ofXVETQgghhO26E19Ee7eTipoQQgghhI2SipoQQgghbJJUk+RnIIQQQghhs6SiJoQQQgibdDd9PYe5SEVNCCGEEMJGSUVNCCGEEDZJ7vqUipoQQgghhM2SipoQQgghbJJU1KSiJoQQQghhs6SiJoQQQgibJNUk+RkIIYQQQtgsqagJIYQQwibJ96hJRU0IIYQQwmZJRU0IIYQQNknu+pSKmhBCCCGEzZKBmhBCCCGEjZKpTyGEEELYJKkmgcJoNMotFUIIIYSwOa/u22qx95rXppvF3ut2SEXtL2ixfJe1UzCbQ0M6Uve/O62dhlmcH9mJpt/8fc/dsSc6MnT7DmunYTbLunQmetASa6dhNvGrh+EYOsTaaZiNNnE54VN/tnYaZnF5eh86/7TH2mmYzY5+7S3+nnIzgVQVhRBCCCFsllTUhBBCCGGTFPKFt1JRE0IIIYSwVVJRE0IIIYRNkjVqUlETQgghhLBZUlETQgghhE2SapL8DIQQQgghbJZU1IQQQghhk5Ry16dU1IQQQgghbJVU1IQQQghhk+SuT6moCSGEEELYLKmoCSGEEMImSUVNKmpCCCGEEDZLBmpCCCGEEDZKpj6FEEIIYZNU1k7ABkhFTQghhBDCRklFTQghhBA2Sb7wVipqQgghhBA2SypqQgghhLBJ8vUcUlETQgghhLBZUlETQgghhE2SippU1IQQQgghbJZU1IQQQghhk1RSUZOBmqV0CPLkxaYRaJQK4vOKmb7vPMXlFbWOUypgTLNI2gV6olIoWHI2iVXxaVWOHRDpT9cQb8bsPA3AiAYh9Azzrdzvaa/BSaOi88rfzNrWLqFejG0djp1KybnsYibtiKNIX1GrGKUC3mgfTesgdwC2J+bwzu+XAGjs68LkdlE4qVUoFQo+O3aFdeczzNqW63UM9uTl2AjsVAricot58/fzFF/XtlvF+DvZ8U3vWB5ef5i80nIAIt2dmNomGke16esdPzxyib2peZZr2A3knzhO8po1GMrLcQwOJnz4k6gcHWuMNRqNXF68GMfgYAJ69gSgvLiYxKVLKUm6gtLODp927fHr1s2STbihLi2CefXxZthplJxLyGPix79RpNXXGHtf6zrM+3d7Yh//tsr2QG8nvn+7D/3/7ydyC0stkfYdt/C90Zw6m8gHn623diq3pWs9X8bfVw87tZKzaYW8tvYkRX/8Pf3PA02CGNkhAqMRtPoK3txwmhMpBdirlczodw9Ng91RKOBoUj6v/3SK0nKDlVpj0tbPk+diwtAolVwsKOad4/GU1PA5caM4JfBKo0iaepv6z98zcvnkzGUAYtxdePGeCBxUSlQKBcsuJPNrcqYFWyf+rLti6jMpKYlGjRoxcOBAHnjgAfr27ctTTz1FWlrarQ++ifnz5zN//vw7lOWNedhreKNNPcbtOs1D6w+RVKTjpdjw24p7KCqQUFdHHtlwiGG/HGVo/WDu8XIBwM1OzcSW0bzaPJJrLz4Wn0li6MYjDN14hOe2HEdbXsHEPWfN2lYvBw1vd6nHi5tO02vFQa4U6ni1TUStYx6o60+EhxN9vz9E/5WHaR3oQe9IHwA+6tGQ/xxMYMCqwzzz8wkm3RtJmJuDWdtzLU97DdPvrcfYnacZuO4QyUU6/n3debxVTL8IP77s2RQ/J/sqx01qHcUPF9J5dMMR3vgtjjkdG1j9SlJfWMjlr74icuQoGk2fgb2PL8lrVtcYq01N5fz775F3+FCV7Ve++w6lgz33vDmNmAkTyT91krzjxy2R/k15udnzzovteGHuDnq+tI7E9ELGDWtWY2xYoCsTn2zO9afjgS6RLJvZiwBvJ/MnbAb1o4P4efkUHry/tbVTuW1eTnbMfaAxo789Qvf/7OJKrpbXetSrEhPp7cykXvUZ/vVB7v9kD/N3XODTx5oD8GLnKFRKBb0X7Kb3x7txUCt5vlOUNZpSyd1OzYSm0bx+6CzDth8mpUTHyJiw24rrGeJHHRdHntpxhH/tPEqstxtdAr0BmN4yhi/jEnlm1zHG7z/NCw3DCXa2XP/5ZykVlnvYqrtioAbg5+fH2rVr+eGHH1i/fj3169dnzpw51k6rVu4N8OB0dhFXinQArIxPpU+Y323FdanjzY8X06kwQqG+nF8SMrk/3LSvR6gPWdoyPjhy6YY5jGkWyd7UXPam5t7p5lXRIcSTExmFJBSY2rDsVAoDov1qHaNUKHBSK7FTKbFTKtCoFJRVGLBTKZh/KJG9yaYqU1pxGTlaPQEuVQc85nRvoAcns4tILDTl/V1cKvdH+NU6xtfRjm51vBm95WS111YpFLjZmQrcThoVZRXWvbIHKDh9GqewMBz8/QHw7dyZ7H37MBqrfwFl5vZt+HTogGeLFlW2lyQm4N2mLQqlEqVajXujxtUGc9bQITaI4/FZJKQWArBsYxwDOkZUi3OwU/Huvzswa3HVnP08HenRug5PTd9skXzNYdTwniz+diur1++zdiq3rWO0D8dT8rmcUwLANwcSGdgkqEpMWYWB19aeJLPIVOk8kZKPr4s9GpWC/Zdz+WhHPEYjGIxwKq2AEHfrDlpa+XpyNq+I5GJT37E2IY37gn1vK06pAAeVCo3S1H+qlUpT/6lUsDgukUNZ+QBk6srIKyvHz8Fy/af48+7aqc82bdrw3nvv8fPPP/Pll1+i0+koKytj1qxZNG/enGHDhuHu7s758+f54IMPiI+P55NPPkGhUNC4cWNmzJgBwPHjx3nsscdIT09n0KBBvPTSS3c8V38ne9JKrk6LZJSU4mKnxlmtqjL9ebO4gBr21fVwBqicAu0fUX3wBxDh5kSXEG8G/njgjrarJgEu9qQWX80zrbgUV3s1LhpV5fTnzWJWx6XRJ8qH3U+0QaVQsCcpl60JOQCsPHe1gvpogwCcNSqOpheavU3/E+BsT/o1eaeXlOJqp8ZZo6qc2rxZTKa2jP/beabG1561P56F9zXhiZhgvBw0vLb7LBVW/kJufW4Odl5elc/tPD0x6HQYdLpq05+hQ4YCpsHdtZwjIsje9zsu0VEY9OXkHTmMQmX9/70v0NuJ1KySyudp2SW4Otvh4qipMv05c1Rbvt0Ux9nLVS9wMnK1vDBnh8XyNYcxUxcD0L1TE+sm8icEuTuQmq+rfJ5aoMPNQYOLvbpy+jMpT0tSnrYyZkrvGDafy0BfYWTXhazK7cHuDvyrbTgT11W/gLIkPwc7MnRllc8zdaW4aNQ4qVVVpj9vFrfxSgZdAn1YdV8rVEoFBzJz2Zth+t3dcOXqMpH+of44qVWcyrVc//lnyf9McBdV1K6l1+v55ZdfiI2N5dtvv+XTTz9l3bp1PPPMM3z22WeVcfXr1+eXX37By8uL2bNn88UXX7B+/XoqKirYscPUyWZnZ/P111+zatUqFi1aRFFR0R3PV6GouaZacV1l4mZx1+9RKBQYaqhs1GRo/SBWxKVUWydmDkoF1JTWtW29WcxLLcLI0eq59+vf6bh0H+72Gv7VJLhK3HOxdXi5RRgjN56i1IKVJwUKavqJGwzG24q5np1SwZyODZj6Wxw91+znqV+PM6VNXfyd7P560n+B0WCEar95gLL23UbI4IcBBadnzuTCJwtwbdAQhcr614dKpaLGymDFNefp8d71KDcYWLn1giVTE7WguFEfUsPfmaNGxcePxBLu5cyEtSeq7GsU6MZ3T7flq/0JbI2z7not5Q0adX0/f7O4EfVCyS/T88Cv+xm8+QBuGg2PRFatNA6NCuapeqFM3H+aMoP1K/fi1qzfY9ZSRkYGAwcOBKCsrIwmTZowduxY1Go1W7du5dKlS+zfvx/lNR8iTZqYrhSPHDlC8+bNCQgIAGDu3LkAnDlzho4dO2JnZ4eXlxeenp7k5+fj4uLyl/Md1TiMTsGmaoSzRkV83tWrd19He/JL9eiuG2Skleho5O1aY1xaSSm+jlc/uH0c7UgvKeNWlAroVseHJ3458lebVCspRaU09bvaBn9ne/J0erTXLNK9WUzPCB+m74lHbzCiL6tgTVw6vSN9+OJ4MnZKBe90rU+UpxOP/HCU5CLLLt5OK9HR2Odq3n5OpvOjveY81ibmetEezjiolOxMNlUOT2QVciG/mMY+bqQnZt3wOHNIWbeWvGPHAKjQ6XAMvjpILsvLQ+XkhMq+9tMlBp2WkIceQu1sqv6mbtiAvV/16RxLS8kspmldn8rn/t5O5BWWor1mMfqgrlE42qtZ925f7NRKHOxUrHu3L8/M3EpGrramlxUWkpKno1mwR+XzAFd78krK0F53MRrk7sCix1sQn1nEY1/uq3KzQP9Ggczo15Cp60+z7kSqxXK/1r/qhdLO3xMAZ7Wai4XFlft8HOwpKKv+OZGuLaWBh0uNcR0DvPjPqUuUG42Ul1ewMSmDzoHefHcxBY1SwcSmdQlzdeL5PcdJ094dN7/Y8toxS7lrBmr/W6N2reLiYh566CEGDBhAq1atqF+/PkuXLq3c7+BgWnOgVqurVKtycnIq/61WX/0RKBQ1X2X/GZ+eSODTEwmAaYH5ivubU8fFgStFOgbXDWRHcna1Y35PzWNMs8ga43Yk5TAg0p+dydk4qlX0CvVl1oH4W+YR7e5MYVl5lalGc9p9JZeJbU2L/BMKdAxpGMiWhOxax5zKKuL+SF/2peSjViroHu5dOb05r1sMjmolj/5wtMrAz1J+S8ljbPNIQl0dSCzU8XDdQLYnZd92zPWuFGpxsVPT1MeVY1mFhLg4EOXuxNmcO1/dvZWgAQMJGmC6INIXFHB6+jR06ek4+PuTtXMHHk1jb+v1MnfspEKnJXTIUPQFBWTt2U3ks8+ZI/XbsutYKhNHtCAs0JWE1EKG9qzH5gNXqsQ89NrPlf8O9nVmwwf9GTD27roz8u9q14UsJveOIdzLics5JTzeKpRfz1a9A9zZTsW3T7Vh1dFkPtxeta/sXt+PN+5vwLCvD3AipcCSqVfxRVwiX8QlAuBhp+HLzrEEOzuQXKxjQFgAe9Jzqh1zIDOP5/+4EeD6uPMFxXQN9OZIdj4qhYL2/l6c/mN6c0psPexVSl7Yc7za4E/YtrtmoFaTy5cvo1AoGDVqFEajkfHjx1NRUX16r3HjxkybNo3MzEx8fX2ZNWsWbdq0sVieuaV6pv0ex5wODdAolSQVaZn6exwADbxceL11XYZuPHLTuJXxKYS4OrC8T3M0SiWr41M5nJl/y/cOdXUkpVh3y7g7JUenZ8L2c8zv2RA7pZLEAi3jtp2jkY8LszrXY8CqwzeMAZi19wJvdIhm4yMtMRiN/Jacx8JjV4j1d6VPlC8X80pYMfDqYGHOvkvsTjLvDRKVbSvVM/W3OOZ1+uP8FGqZvDeOhl4uvNG2Lo9uOHLDmJsp1FfwfztOM75VFPZKJRVGI9N/jyepyHLnrSYaNzfCnxzBxc/+i7G8HHtfX8Kf+hcAxZcvk7Dkaxq+PvWmrxHQpw+XvljEqWlvgtFIUP8BOIeHmz/5W8jJ1/HaR3v5aFwnNGoViWmFjPvPHhpFeTHr+XtlQGbjsovLGLfmBJ881gyNSklCTgn/t/o4jYPceGdgY+7/ZA9Ptgkj2MORXg386dXAv/LYoYv3M6lXfRQKBe8MbFy5/WBiLlPXn67p7Swir0zP28fimd4iBo1CQVn5CSYAACAASURBVHKJjllHzwNQ392FcU2ieGbXsZvGfXTqEq80iuTrLs0wGOFwVh7LLyRzj4crXYJ8SCzS8lG7q23+79kEDmRa/2uAxM0pjHeqhGRGSUlJDB8+nK1bt1bZXlFRwbhx4zh16hQKhYIOHTqwefNmtm/fzrBhw3jxxRcrB2QbN25kwYIFGAwGYmNjmTZtGgsWLACovIGgW7dufP3114SEhNQqrxbLd93BVtqWQ0M6Uve/O62dhlmcH9mJpt/8fc/dsSc6MnT73b3Q/WaWdelM9KAl1k7DbOJXD8MxdIi10zAbbeJywqf+fOvAu9Dl6X3o/NMea6dhNjv6tbf4ey44vcli7/V8w55/6fiUlBTGjRtHdnY2ERERzJs3D+c/ln1cr6ioiAceeIC33nrrloWju6KiFhISUm2QBqBSqXjvvfeqbJsyZQoAS5ZU7ch79+5N7969q2y7/g7Pmt5DCCGEEOJWpk2bxtChQ+nbty8ff/wxCxYsYNy4cTXGzpgxg4KC2k2735V3fQohhBDi78+SX3hbUFBAUlJStUdtBlT6/2/vzuNjOts/jn9mspNFQhKCLChBrOWxU7QPsZaqWqrq6fKrFtUHRS3VpkVpVUv3RVHVzRJalFpq62NPbSWoLUQSJCJ7MjO/P1JDJLaSmdF+317zeplzrnPOdc+cM3Pnus85k5vLtm3baNeuHQDdu3dnxYoVRcYuW7aMkiVLUq1atZt6De6KipqIiIhIcZo9ezYzZ84sNH3QoEE3vMdqcnIynp6e1gsU/f39SUhIKBR3+vRpZs+ezezZs3nqqaduKi911ERERMQh2fKGt/3796dbt26Fpnt7exd4vnz5ciZNmlRgWkhISKF7oV793Gw2M2bMGMaNG2e9K8XNUEdNRERE/vG8vb0LdcqKEhkZSWRkZIFpubm5NGrUCJPJhJOTE0lJSQQEFPy1oD/++IM//viDMWPGAHDixAnGjh1LVFQUjRs3vub21FETERERh+R0l9zw1sXFhQYNGrBs2TI6d+7M4sWLadmyZYGYKlWqWH8VCSh0d4pr0cUEIiIiIrfp5Zdf5ttvv6VDhw5s376doUOHAjB//nzeeeedv7xeVdRERETEId1NPyFVvnz5QrcGA+jdu+j7IhYVWxRV1EREREQclCpqIiIi4pDupopacVFFTURERMRBqaImIiIiDkkVNVXURERERByWKmoiIiLikJxs+MsEjkoVNREREREHpY6aiIiIiIPS0KeIiIg4JFWT9BqIiIiIOCxV1ERERMQh6fYcqqiJiIiIOCxV1ERERMQhqaKmipqIiIiIwzJYLBbdTU5EREQczqJjy222rW6hkTbb1q3Q0OdtqPPlBnunUGx+e7QFYcOX2juNYnH0zc78mvijvdMoNk0COlL5g/X2TqPYHBnYkpCpa+ydRrE5PqINoeNt9+Vka8dejcQjuLe90ygWmSfmM+TXtfZOo9i826S1vVP4R1JHTURERBySzlHTOWoiIiIiDksVNREREXFIqqipoiYiIiLisFRRExEREYekipoqaiIiIiIOSxU1ERERcUhOqqipoiYiIiLiqNRRExEREXFQGvoUERERh2Q06FcuVVETERERcVCqqImIiIhDUjVJr4GIiIiIw1JFTURERBySbniripqIiIiIw1JFTURERBySbniripqIiIiIw1JFTURERByS7qOmipqIiIiIw1JFTURERBySrvpURU1ERETEYamiJiIiIg5JFTV11OyiRXlfhtQNw9XJQGxyOhP+d4j0XNMtxQSWcOXL9nV5+MedpGTnAdAw0IcX6ofhbDSQnWfmje1H2HsuzaZtu1rr6gG8GFkdV2cjB+JTGfntb6T9me8lD9Yvz9P3VcZigcxcE68s3sueuAsAPNo0hEf+FYK7i5G9cRcY+e1v5JjM9mjKNcVs3s/3H/1IXm4eFSoH8cSoR/Ao6V4obv7MaLat/Y2S3iUAKBccwLOvPAbAoE7j8PX3scZG9m5N03/fa5sGXOG+YD9GNA7F1cnIgXPpjF4bS9pV++a1YnzcnIlqWYXqZTzJyDWx4EACc/aeBsDHzZmXm1ehim8J3J2NvL/zBItjE23eviu1qVSaF1tWxtXJwIGkdF5c8TtpOaYiY9+KrM7Bs2l8vO2kdVq/uuXpVTsId2cjexIu8uKK38kxOc6Jz62r+vPi/VXzj70zFxkZvbfwsVc7iP9rHmY99iYs28+e06m4ORuJ6lSTOuV9MBggJu4C437YR3aeYx17N+OTaQPZd+AE0z/+0d6p3LKkmD3Efr8Yc14eXhXKE/FEP5w9PArFnd68haPLV2LAgJObK+F9H8EnLASAE6vXEbd+E+acXLxDg4n4Tz+MLi62borchrt66DMuLo6IiAi6du1a4BEfH2/v1K7J182FV5tUZdj6/XRdsoNTaVk8Xzf0lmI6hQUw6991CCjhZp3mbDQwpUU4r/7vED1/3MUne0/yerNqNmpV0fxKujLlkboMnLOdtlPWcuJ8Bi92rF4gppJ/SUZ3qkH/T7bQ8e31zPz5EB/0bwhAu4iy9G8WxqMf/cq/31yHm4sT/2lZyR5NuabU5DQ+m/Q1g157nMlfjSYgyI/vPvyhyNjDe48xcEI/omYNJ2rWcGsnLf5EIiW9SlinR80abpdOmp+7C1PaVOW5n/bzwPztnEzNYkTjsJuOGdusMum5Ztp9vZ0eC2NoFexH6xA/AKa0qcaZ9Gy6fL+Tx5buZnzzypQt6WrzNlrb4eHC1PbVeWbxHtp8toUTKZmMalm5UFwVvxLM71mPDlUDCkxvf48/j9evQJ9vd3H/51twdzbyxL3Btkr/hvxKuDL1wVoM/HoXbd/dwMnkTEY+ULVATKXSJXmpXTUem7OdDh9sYsYvR/iwV30ABrWqjJPRQPv3N9L+vY24Oxt5tojXx5FVqxLE8vlj6dbhX/ZO5S/JSb3I3s/mUHfQ07SY/AoeAWWI/W5Robj0+DMc/GYBDYYNoWnUWCp1jiRmxocAJGzfxYmf19FgxFCavT4eU04ux35abeum3BajDR+OypFzuykBAQFER0cXeJQrV87eaV1Tk3Kl2HsujRMXswD4NjaeDmEBNx3j7+FKm4qlGbh6b4Fl8swWHliwlQPJ6QBU8HS3VtrspUVVf3afTOHY2fycvtx8jK71yheIyc4zM+q730i6mA3AnrgU/L3ccHEy0L1BRT795Q8uZOZiscDYBbtZtCPO5u24nr3bDhIWXpGyFf0BaP1gM35dtROLpWBlJTcnj+OHTrHsq7WM7T+FGWNncS4hGYDDe45hdDIw8bkZjO0/lehZP2G2Q9WweUVfdide5NiF/P1u3r7TdL0n4KZjIvw9WRybgNkCuWYLa4+fI7JSGXzcnGleoRTvbj8OwJn0HLoviLHr/tky1I/dZ1I5lpIJwJcxp+hao2yhuMfqVeDrPaf58arqX/eaZflk2wkuZOVhAV5aeZCF+x3nD8QWVcqw+/QFjp3PAODLbSfoWjuoQEyOyczI6L0kpf157J2+gL9n/rG39VgyM385jMUCZgvsO5NKBZ/CVWJH9sxj/+aLr9ew8Mct9k7lLzm7dz/eYSGULBsIQHDrlsT/urXQZ4vR2ZmIAf1wK5VfkfcOCyH7QirmvDxOb/ofoe3vx9WzJAajkZr9+xDUrJHN2yK352859BkbG0tUVBQZGRmcP3+ep59+mt69ezNjxgxiYmKIj4/n0UcfpVmzZkyYMIGUlBTc3d0ZN24cNWrUKNbcypZ0IyE92/o8ISMbL1dnSro4WYc2rxeTlJnDf9f/XuS68ywW/Nxd+KZDPUq5ufDihgPF2pYbKVfKg/g/vwgBzlzIwtvDBU83Z+sQzKnkTE4lX44Z27kmq/efIddkIcy/JKU9XfniyUYEeruz7eg5Jv1YdNvt5XxiCn6BpazP/fx9yEzPIisju8DwZ8rZC9SoX4WHnoqkfFhZls9fyzujP+eVz/6LyWSixr1V6flMR/LyzLz94ie4l3SnXc9WNm1LOU834tMu73dn0rLxcnPG08XJOvx5vZiYhIs8WDWQHWdScTUaaF/Zn1yTmRAfDxIzcniidgVaBfvi6mTk09/iOHYhs1AOtlLOy53TFy+3I/5iNt5uzni6OhUY/hy/OhaAFqF+BZav5FuC30q4MrtHHQI93dgWl8LEXw7bJvmbEOTjTvyfnWmA+NQsvN0LHntxKZnEXXF8jm0fzs8HE8k1Wdhw5Kx1enkfd/7TOJTRSwr+cejoXhj/BQBtW9a2byJ/Udb5ZNz9fK3P3fx8ycvMwpSVVWD408O/DB7+ZQCwWCwcnP89AfVqY3R2Jj0hAZ/UULa/+S7ZKRfwrVqFqo90t3lb5Pbc9R21xMREunbtan3euXNnEhISePbZZ2nSpAknT56kS5cu9O7dG4CcnByWLVsGQK9evRg/fjw1atTg8OHDPPfcc/z000/Fmq8BA0WdxWI2W24p5lrOZ+XywMKthPuV5JO2tXh0RQbHL9rnC9FoAEsRKZuKmOjh6sSbj9SlXCkP+n/yPyB/OLd5VX+enrWN7DwTb/aqx/D24UQt2Vfcqd80i9mCgcJnuxqvOgPWP6g0/536tPV5ZO/WLJm9irPx57mvSxPrdDeg3SOt+Pn7DTbvqBkNFLnfXfl+XS9m4uYjjG5aiaUP1ycpI4eNJ5OpX9YbF6OBYG8P0nLz6Ln4N0K83fn6wTocS8lk71n7nEOZ347CLSlq3yyKs5OBFqF+PLloN9l5Zt7qUIMRzSvz6tpDdzrVv8RwrWOviM8QDxcn3uxWiyAfD/rP3VZgXkQ5bz7qXZ/ZW4+zJjapuNKVolgsGAxFnElvLHogLC87m72fzCbrfDL3DhucvwqTibP7fqf+8wMxuriw55MvOPR9NNX79izOzO+ool6Cf5q7vqN2aejzSiaTiQ0bNvDRRx8RGxtLRkaGdV7t2vl/XaWnp7N3715Gjx5tnZeRkUFycjK+vr4UlzMZWdQq43U5/xJuXMjOJfOKoa6bibmap4sT/ypbijUnzwFw4Hw6B1PSqVKqhN06aqdTMqkbfPm1LOvjTkpGDplXnbAdVMqDT//TkMMJafT+YLP1hOXE1Gx+2hNvrQAs3hnHkKvOs7GHhZ8uZ9em/M5iVnoWFSpfHmpPPnuBkl4euHm4FVjm5OHTnDh8mmbtG1yeaAEnZyc2rdhOcJUgKlYJKjDd1k5fzKZOwOX9LrCkGylZuWRecQL59WLKebrxxq9HufDn+zWwfkWOX8gkIT0HgO8PJABwPDWL7WdSqR3oZbeO2umLWdQt5219XtbLjZTMXDJzb27IOSEtmxWxSdbq2+L9ZxjSJLQ4Uv1LTqdkUa/85UpvWS+3/GPvqgtDgnzc+azvvRxOSqPXrC0FLhboHFGOqE41GP/jfpbscZxh3b+zQwuXkLRrNwB5WVl4Vrg8XJ2dnIJzyRI4u7kVWi7z3Hl2Tn8Pz3LlaDjqBZxc88//dCtVisB761krcEFNGnFkyd13UcU/3V1/jlpRhg4dyqpVq6hcuTJDhw4tMM/dPX84ymw24+rqWuDctu+++45SpUoVtco75tfTKdQu40WwV34eD99TjnVx52455momi4VXGt9DXf/8L5/KPiUI8y7BnnMXi6EVN2dDbBL1QnwJLVMSgD6NQ1i170yBmJJuTswf2IQVe84wZN7OAl8Uy3efpmOdINyc83fTf9csy+6TKbZrwDV0fzLSetL/uI+e58i+45w5mV9tWLt4M/WaRxRaxmA0MO+dRSSdzn8f1yzeTIXK5fALKMWpo/Es/GwFZpOZnOwcfl64kX+1qWvTNgFsjEumXqA3oX+ei9SnZjl+PnbupmP61CzH0Ib5V5qV9nChZ/WyLDmUSNzFLPYmXaR7tUDrvPqB3uxJtN++uf7YeeoF+RBaKv8LrG+dIFYePnuDpS5bfjCJjuEBl/fNKv7sPmO/9lxtw5Gz1K1YilC//CuM+zYMZtWBgufZlXR14usBjVixP4HB3/1W4NhrWy2AlztUp9+cbeqk2dA93bvQNGosTaPG0mjci1w4cpT0M/l/4Jxcu56AenUKLZOXmcW2ydMIvLcedZ590tpJAwhsUJ8z23ZgysnBYrGQsDMG7z+vBr1bGGz4cFR3fUWtKJs2bWL58uUEBgYyb948IL/KdiUvLy9CQ0OJjo6ma9eubNq0ifHjx/Pzzz8Xa27ns3MZ/2ssb7asjovRSNzFTMZsjqWGnycvN76HR5btumbM9WTmmRn6y++MaFAJZ4OBXLOZ0RsPkJiRU6ztuZ5zaTmM+CaG9x+7FxcnI8fPZTBs/i5qVfBh8sN16Pj2eh5rFkZ53xK0iyhLu4jLJ3P3/ehX5m4+hk8JV5a+0BIng4G9py7w+ve77daeonj7evHE6F68N+4L8vJMBASV4amx+cPsRw+c5PM3viFq1nAqVCrHo0O7MX3UZ5jNZvz8SzFwQj8Aug5ox5dvL2Rs/6mYTCYa3leHVp0b27wt5zJzGbn2IDP/XQMXJyMnLmQyfM1Bavl7MvG+qnT+buc1YwA+3HmSt9pWY/kj+VesTt96nD1J+RWzZ1bs55UWVehTsxxGg4GZO05Y59nDuYxcRiz/nQ+6RuDqZOR4SiYvLNtPrUAv3mgfTofZ2667/JyYOHw8nPmxX0OMRtibkMZrKx1j2BPgXHoOIxbt4YNe9fKPvfMZ/HfhbmoFefNG11p0+GAT/RuFUL6UB+2qB9KueqB12T5fbOWldtUwGAy80bWWdfr2E8mM/3G/PZrzj+Tm7U3EE48R897HWPJMlAjwJ+KpxwG4cPQ4+z6fS9OosZxYvY7Ms+dI3BFD4o4Y6/INRg4luG0rctPT+XXCRCxmC94hFQnv3cNOLZK/ymC5+hKSu0hcXByPPfYYa9asKTB91qxZfPnll7i5uREeHk5MTAyzZs1iyZIlAAwenD9+f+TIEevFBC4uLkyYMME6NHoz6ny54c41xsH89mgLwoYvtXcaxeLom535NfHvW/5vEtCRyh+st3caxebIwJaETF1z48C71PERbQgdv9zeaRSbY69G4hHc295pFIvME/MZ8utae6dRbN5t0trm29x+1naf1Q3KdLTZtm7FXV1Rq1ChQqFOGsCAAQMYMGBAoemXOmiXVK5cmblz5xZbfiIiIiK3467uqImIiMjf19/yRPpbpNdARERExEGpoiYiIiIOyWC4a0+jv2NUURMRERG5TadPn6Zv3760b9+egQMHkp6eXigmJyeH1157jQcffJCOHTuycePGG65XHTURERFxSHfTfdReeeUV+vTpw4oVK4iIiOD9998vFPPpp5+SnJzMokWLmD59OqNHjy70+61XU0dNRERE/vFSU1OJi4sr9EhNTb3hsrm5uWzbto127doB0L17d1asWFEobvny5Tz11FMYDAbuueceZs2adcOOms5RExEREYdky9/6nD17NjNnziw0fdCgQYVu73W15ORkPD09cXbO71b5+/uTkJBQKO748eNs27aNV199FZPJxAsvvECVKlWuu2511EREROQfr3///nTr1q3QdG9v7wLPly9fzqRJkwpMCwkJwXBVr/Lq55D/K0lnzpxh3rx5HDx4kCeffJLly5fj5eVVKPYSddRERETEIdnyNzi9vb0LdcqKEhkZSWRkZIFpubm5NGrUCJPJhJOTE0lJSQQEBBRatkyZMnTs2BGDwUB4eDhly5bl6NGj1/1VJJ2jJiIiInIbXFxcaNCgAcuWLQNg8eLFtGzZslBc69atrTEnT54kPj6esLCw665bHTURERGR2/Tyyy/z7bff0qFDB7Zv387QoUMBmD9/Pu+88w4Aw4cPJzExkY4dO/LMM8/w2muvXXfYEzT0KSIiIg7KaMuxz9tUvnz5In8/vHfv3tb/e3p6MmXKlFtarypqIiIiIg5KFTURERFxSHdRQa3YqKImIiIi4qBUURMRERGHZMsb3joqVdREREREHJQqaiIiIuKQVFBTRU1ERETEYamiJiIiIg5JFTVV1EREREQclipqIiIi4pDupl8mKC4Gi8VisXcSIiIiIleLvfCDzbZV1aeTzbZ1K1RRuw31v9pg7xSKzc4+LYBYe6dRTKpSecC39k6i2ByZ1ZN7Plpv7zSKzaH/a0nEF3/fY2/v4y1o9cMme6dRbH7p1Iwhv661dxrF4t0mrfEI7n3jwLtU5on5Nt+mCmo6R01ERETEYamiJiIiIg7JYNDZWaqoiYiIiDgoVdRERETEIekcNVXURERERByWOmoiIiIiDkpDnyIiIuKQDBr7VEVNRERExFGpoiYiIiIOSdUkvQYiIiIiDksVNREREXFIOkdNFTURERERh6WKmoiIiDgkFdRUURMRERFxWKqoiYiIiEPSOWqqqImIiIg4LFXURERExCGpoKaKmoiIiIjDUkVNREREHJJRJTVV1EREREQclSpqIiIi4pBUUFNFTURERMRhqaJmB82DfBlcJwwXJwOHUtJ59X+HSM8z3VSM0QD/rVeJJuV8cTYamPN7HAsOnwGgZXk/XmlclTMZ2db1PLFqNxlXrdsRWSwWRo2aTtWqITzxRHd7p3PT7qtdjhE9auHqbORA3AVGf76NtKy8AjH92lahT+vKYIHjSWmMmbWdcxezC8S8P6gpCSmZvPLlLlumX8h9wX4M+1cork5GDp5L56VfYknLNd1UzIwHqhPi7WGNq+Dlztb4C7y55SjT2oZbpxsNBqqVLslzK/ex8ug5m7UNoGUFX4bWzz+uYpPTGb/pEOlXte9aMZ4uTrzarCphPh4YDQaiDyfw+d44AFpV8GNii6rEp19+Xx9bZp9jr3GAL0+Hh+BiNPJHajpv7D5cZB7XijMCQyMqUae0DwD/S0zmg9+PARDu48mgmmG4OxlxMhj46sgpVp1KsmHrCkqK2UPs94sx5+XhVaE8EU/0w9nDo1Dc6c1bOLp8JQYMOLm5Et73EXzCQgA4sXodces3Yc7JxTs0mIj/9MPo4mLrpvxln0wbyL4DJ5j+8Y/2TkWKyV1dUYuLiyMiIoKuXbvStWtX2rVrx+jRozl79ix79uxhzJgxt7S+atWqFVOml5Vyc2FC46oM37if7j/s4FRaFoPrht50zENVyhHs7UHPZTt4dEUMfaqVp2ZpTwBql/Fm7u+n6L18l/VxN3TSjhw5Sf/+Y/npp032TuWW+Hm5MeWJhjz33mYeeGkFJ5PSGfFw7QIxESG+PNm+Gg+/vobIcT9xPCGNF7pHFIh5OrIaDaqWsWXqRfJzd2HyfVUZtHI/7b7ZzsmLWQxvFHbTMYNX/U6XBTvpsmAnY9bHkpqTx4SNhzmckmGd3mXBTjbGJbP0UKLNO2m+bi5ENavK0LX76bxoB3EXs3jh3tCbjhlcL4SEjGy6Re+k1w+7eCS8HHX8vQCoF+DNF3tP0WPJLuvDHseej6szo+pUYdyOA/Rbt5PTGVn8X3jILcX9u0IAFT09GPDLLv6zPoa6pb25r1xpAF5tEM6s2BM8ueE3Xty6n+dqhFK+pLtN23hJTupF9n42h7qDnqbF5FfwCChD7HeLCsWlx5/h4DcLaDBsCE2jxlKpcyQxMz4EIGH7Lk78vI4GI4bS7PXxmHJyOfbTals35S+pViWI5fPH0q3Dv+ydSrEyGCw2eziqu7qjBhAQEEB0dDTR0dGsWLGCMmXKMGTIEGrVqsXrr79u7/QKaVKuFPvOpXHyYhYA3x2KJzI04KZjWlcozZIjCZgscDE3j5Unkujw57w6ZbxpWNaHryPr8dn9tanv723Dlv118+b9yMMPP0D79s3sncotaV4zkN1Hz3MsIQ2AeWsO07VxcIGYvceTaTtqGWmZubg6Gwks5UFyWo51fqNq/rSsVZb5a/+wae5FaV7Blz2JFzmemr/ffbXvNF2qBNxyjIvRwJTW1Xh98xHOpBesHDYo6037SmUYv+FQMbakaE3Ll2Lf2TRO/HlcfXMwno6VAm46ZtLWP3hzW/77VMbDFVejkYs5+Z2xOgHe/KucDwu61GN2ZG3uDbTPsdfQ35cDKWmcSs/PP/r4Ge4v739LcUYDuDs54WI04mo04Gw0kmMy42o08EXsCXacvQBAUlYOKTl5BLi72ah1BZ3dux/vsBBKlg0EILh1S+J/3YrFUvAL1+jsTMSAfriVyq8QeoeFkH0hFXNeHqc3/Y/Q9vfj6lkSg9FIzf59CGrWyOZt+SueeezffPH1Ghb+uMXeqUgx+1sNfRoMBgYPHkyzZs2YM2cOq1atYu7cuRw/fpwJEyaQkpKCu7s748aNo0aNGsTFxTFixAgyMjKoU6eOTXIMLOFGwhVDk4kZ2Xi5OlPS2ck6/Hm9mKvnJWRkc0+pkgBcyMll+bEkVp88S11/b6a1rEGvZTtJzLzcMXBE48c/A8CmTfYd9rtV5fxKEH8+0/r8THImXiVc8XR3LjD8mWey8EC9ICYOaEhOnonpi/cCEFDKnXF96jFg2np631fZ5vlfraynW4GhuzPp2Xi5OePp4mQd/ryZmIfDy5KQnsOqY4UrZiMbV2LatmOFhlNtoWxJtwKnBSSk/3lcuThZhz9vFGOywOQW1XggtAyrj5/lWGoGABeyc/nxjyRWHT9LvQBvZrSpwUNLdpKQYdtjL8DdlcSsy9tMysrG08WZEs5OBSp814tbcTKR+8qVYcH9DXEyGtiWlMzmxGQAlp1MtC7TOTiQEs5O7Eu+aIOWFZZ1Phl3P1/rczc/X/IyszBlZRUY/vTwL4OHf37F2mKxcHD+9wTUq43R2Zn0hAR8UkPZ/ua7ZKdcwLdqFao+cnecevHC+C8AaNuy9vUD73K6mOBvUFG7mqurKyEhIZQpc3koaeTIkYwYMYJFixYRFRXFCy+8AEBUVBTdu3cnOjqa+vXr2yQ/IwaKKrCarvgr8HoxRgMF5hkwWJcdvuF3Vp88C0BMUiq7z6bSuJxvEWuSz7o5MAAAGq5JREFUO8FoMBT66x3AZC48bdWu0zQcEs27i/cz678tcXU2Mv3/GvP61zEkXciyRbo3ZDRAEc0puG/eRMzjtcrz/s4ThWLqBXrj5+7C0kOJhebZghFDkbmbrz72bhAzasNBms//FR83FwbWya+gDl37O6uO5x97uxJTiUlKpUmQ7Y89o6HoN8h8dZXpOnGPVw3mQk4uD67aSo+ft+Ht4kLPSkEF4vpULs+AqsGM3rqfHLP5zjbiZlksGIr6IUhj0V9rednZ/PbeJ2QkJFFzQL/8VZhMnN33O3Wfe4omE0aTm57Ooe+jizNrkVv2t6qoXWIwGHB3zz9vIj09nb179zJ69Gjr/IyMDJKTk9m6dStvvfUWAF26dGHs2LHFntuZjCwiynhZnwd4uHEhO5csk/mmYs5kZOPv4Wqd5+/hSmJGDp4uTvS8J4jP95+0zjMAefb6EL2Od975kjVrtgLQps2/eP75R+2c0V9z+nw6dSr7WZ8H+nqQkpZNZs7lykVIgCdlfNzZcSj/S/y7DUeJ6l+fWqG+BAd48lKv/Equv487RqMBNxcnXpq13bYN+dPptGzqBFze7wJLupGSlUtmnvmmY2qULomz0cDW+AuF1t+xsj+LDiUU+UeILcSnZ1HL/4rjqkT+cXVl+64X0zSoFIeSM0jKzCEzz8yyo4k8EFIGL1cnelUL4pM99jn2/lM1mKaB+Z3Cks7O/HEx3TqvjLsbqTkFP18AEjKzqV7Ks8i4FmX9eHffUfIsFvLyTKyIS6RVudJ8+8dpXIwGRte5hxCvEjy7aTdnMgsObRe3QwuXkLRrNwB5WVl4VrjcgcxOTsG5ZAmc3QoPxWaeO8/O6e/hWa4cDUe9gJNr/meoW6lSBN5bz1qBC2rSiCNLdFK+I9GPsv8NK2o5OTkcPXqUc+fyh13MZjOurq7W89iio6P57rvvKFWqFIC1ImIwGDBe4y+xO+nX+BRqlfaiold+R/Khe8rxS9y5m45ZF3eerpUDcTKAp4sT7UL8WRt3jow8Ez2rlqNNxfyTfqv5lqRmaS82xycXe5tu1fPPP0p09LtER79713bSADbuTaBepdKEBuZ/4fVpXZmfd50uEOPv4847zzTG1zP/i6Frk2Bi41LZcfgczYf9QOeXV9H55VV8tfYPftx60m6dNICNJ5OpG+BNiHf+fte7RjlWHz93SzH/CirFr6dSilz/v8r5XHOeLWw+nUIdfy+C/zyuHqlWjjUnzt10TPtQfwbWza+guRgNtAv1Z0t8Cum5JnqFl+P+kPxjL9yvJBFlvNh4yjbH3ud/ntz/5IbfGLhpNzV8vawn+HcJKcumhPOFltmWlHLNuEOp6bT+8+IBJ4OBZoF+7P9zeHNs3aqUcHbiOTt00gDu6d6FplFjaRo1lkbjXuTCkaOkn0kA4OTa9QTUK3wKS15mFtsmTyPw3nrUefZJaycNILBBfc5s24EpJweLxULCzhi8wwpffCFiT3+riprZbGbGjBnUqVOH4OD8D1QvLy9CQ0OJjo6ma9eubNq0ifHjx/Pzzz/TtGlTlixZQt++fVm5ciXZ2cX/wZOcncuELbFMbV4dF6ORuLRMxv0aS3U/T8Y3uofey3ddMwbg+0OnqejpzteR9XFxMrLgUDw7E/OrFy+s38/IBpV5plYIJouFURsPkJKdd7105Dacu5jNyM+3MvPZprg4GzmRmMbwT7dSK9SXiQMa0PnlVWw/dJb3f/idr0a2Js9sJjEli2dmOObVreezchm17iAz/l0DV6ORE6mZjFh7kIgynkxsVZUuC3ZeM+aSEB8PTl0s+jjKn2e/Yd7zWbmM3RjL263zj6uTFzMZvSGWmqU9eaXZPfRYsuuaMQBTt//B+Cb3sKhr/mkSq0+c48v9p7EAQ9bsZ3SjyjxXN//YG/6LfY69lJxcJv92mFfvDcfFYOBURhYTY/Iv3Kjm48mI2pV5csNv142bue8oQyMqMee+epgtsPNsCvOPnKJmKS/uCyrDibRMZjatZd3mRweOsy3J9h1wN29vIp54jJj3PsaSZ6JEgD8RTz0OwIWjx9n3+VyaRo3lxOp1ZJ49R+KOGBJ3xFiXbzByKMFtW5Gbns6vEyZiMVvwDqlIeO8eNm+LXJsKamCwFHWSzV0iLi6O9u3bU7ly/onYZrOZ6tWrM2bMGA4cOMDMmTOZO3cuR44csV5M4OLiwoQJE6hduzYJCQmMGDGClJQUIiIiWLFiBTt37rzp7df/akNxNc3udvZpAcTaO41iUpXKA761dxLF5sisntzz0Xp7p1FsDv1fSyK++Psee3sfb0GrHxyzM38n/NKpGUN+XWvvNIrFu01a4xHc295pFJvME/Ntvs2krCU225a/exebbetW3NUVtQoVKrB3794i5zVq1IhGjfIvs65cuTJz584tFBMYGMicOXOszydOnFg8iYqIiMgt+9udn/UX6DUQERERcVB3dUVNRERE/r501acqaiIiIiIOSxU1ERERcVAqqamiJiIiIuKgVFETERERh2RQRU0VNRERERFHpY6aiIiIyG06ffo0ffv2pX379gwcOJD09PRCMTk5OQwbNozOnTvTtWtXNm/efMP1qqMmIiIiDslgMNrscbteeeUV+vTpw4oVK4iIiOD9998vFBMdHY3ZbGbp0qVMmTKFUaNG3XC96qiJiIjIP15qaipxcXGFHqmpqTdcNjc3l23bttGuXTsAunfvzooVKwrFmc1mMjMzMZlMZGZm4u7ufsN162ICERERcVC2u5hg9uzZzJw5s9D0QYMGMXjw4Osum5ycjKenJ87O+d0qf39/EhISCsV169aNRYsW0aJFC1JTU5k2bdoN81JHTURERP7x+vfvT7du3QpN9/b2LvB8+fLlTJo0qcC0kJAQDFf9jMLVzwFmzpxJ3bp1mT9/PseOHePxxx+nZs2alC9f/pp5qaMmIiIiDsmWt+fw9vYu1CkrSmRkJJGRkQWm5ebm0qhRI0wmE05OTiQlJREQEFBo2dWrV/P2229jMBgICwujTp067N69+7odNZ2jJiIiInIbXFxcaNCgAcuWLQNg8eLFtGzZslBceHg4P//8MwDnz59n7969VK9e/brrVkdNREREHJTBho/b8/LLL/Ptt9/SoUMHtm/fztChQwGYP38+77zzDgCjR49mz549dOzYkf79+/Pf//6X0NDQ665XQ58iIiIit6l8+fLMnTu30PTevXtb/1+mTBk++OCDW1qvOmoiIiLikO7E/c3udnoFRERERByUKmoiIiLioPSj7KqoiYiIiDgoVdRERETEIdnyPmqOShU1EREREQdlsFgsFnsnISIiInK1tNw1NtuWp0sbm23rVqiiJiIiIuKg1FETERERcVC6mEBEREQclOpJegVEREREHJQqaiIiIuKQDAbdnkMVNREREREHpYqaiIiIOChV1FRRExEREXFQqqiJiIiIQ9JPSKmiJiIiIuKwVFG7RXFxcbRv357KlStjMBjIzc0lICCASZMmUbZsWXund8tmzJgBwODBgwtMj4uL47HHHmPNmqJ/vmPFihV8/PHH5OXlYbFY6Nq1K08++eRt5TJ//nwAevfufVvr6devH4MGDaJRo0a3tZ6r3ep7v3DhQrZu3crkyZPvaB72EhsbS+fOnXn33Xdp166dvdP5S6613z711FO89tprbNq06Zrv2ZYtW5g2bRqZmZmYTCZatWrFsGHDcHJyskNLilZcn0/X+pywpyvbeqUPP/yQcuXK2SmrO+PqtmVlZVG/fn2GDRtGfHw8X3/9Na+//vpNr69atWocPHiwuNItZqonqaP2FwQEBBAdHW19PnnyZKZMmcK0adPsmJXtJCQk8MYbb7Bw4UJ8fX1JT0+nX79+hIWF0bZt27+83tvtoNnCP/m9X7BgAe3bt+ebb765Kztq19tvP/nkk+sum5OTw7Bhw5g/fz4VK1YkJyeHIUOGMG/ePB577DEbteDm/JP20avb+ndyZdssFgvTpk1jyJAhfPXVV9SqVcvO2YktqaN2BzRq1Ihp06axfPlyZs2aRVZWFjk5OUycOJH69esza9YsFi1ahNFopHbt2rz66qscOHCA8ePHk5eXh5ubG5MmTSI0NJT169fz7rvvkpeXR4UKFYiKisLX15c2bdrQpUsXNm7cSGZmJm+88QYRERHExsYyatQoTCYTDRo0YP369axatYqzZ88yfvx4zpw5g8FgYNiwYTRt2pQZM2YQExNDfHw8jz76aIF27N+/nzFjxgAQHh5+zfYmJyeTm5tLVlYWACVLlmTy5Mm4ubnRpk0b5syZQ4UKFdiyZQszZ85k7ty59OvXDx8fHw4dOkTnzp1JTk5m3LhxQP4XSdmyZbl48SIAPj4+HD9+vND8hx9+mFdffZVDhw5hMpl46qmn6NSpEzk5OYwZM4a9e/dSvnx5kpOT7/h7fC2X3vvNmzczefJkLBYLQUFBvPXWWwXi7sS+YU+5ubksXbqUefPm0atXL06cOEFwcDBbtmzhtddew8nJibp163LkyBHmzp3L8ePHmTBhAikpKbi7uzNu3Dhq1Khh1zbczH4LcPz4cfr27cuFCxe47777GDZsGJmZmaSlpZGZmQmAq6srY8aMIT09Hciv4oaHh7N9+3ays7N56aWXaN68uX0aepUbfT5deWxOnz6dw4cP88EHH2AwGKhVqxZRUVEA7N69m169epGQkED37t0dqrp2pdjYWKKiosjIyOD8+fM8/fTT9O7du9BnX7NmzRxuH70Wg8HA4MGDadasGXPmzGHVqlXXPc7i4uIYMWIEGRkZ1KlTx97p3xado6aa4m3Lzc3lp59+om7dunz99dd8+OGHLFmyhCeffJKPP/4Yk8nERx99xIIFC1i4cCG5ubkkJCQwe/ZsBgwYwMKFC+nZsycxMTGcP3+et956i88++4zFixfTvHlz3nzzTeu2SpUqxffff0+vXr346KOPABg1ahTPP/880dHRVKxYEZPJBMDrr7/OQw89xMKFC/nggw8YP348aWlpQH51YNmyZfTp06dAW0aOHMnw4cNZtGgRFSpUuGabw8PDadu2Lffffz89evRg6tSpmM1mQkJCrvtaVatWjZ9++ok+ffqwatUqTCYTFouFlStX0rFjR2tcp06dipz/wQcfULNmTRYuXMi8efP48MMPOXnyJHPnzgXyO0Njx47lxIkTt/AO/nWX3vuIiAiGDx/OG2+8wdKlS6latSqLFi2yxpnN5tveN+ztl19+ISgoiLCwMO6//36++eYbcnNzefHFF5k6dSqLFy/G2fny330jR45kxIgRLFq0iKioKF544QU7Zp/vZvfbuLg4ZsyYwcKFC9mxYwerV6/Gx8eH//u//6N79+507tyZ1157jYSEhAJ/0KSlpbFo0SLeeustRo0aRU5Ojq2bWMiNPp8uuXRs+vn5MWnSJD7//HN+/PFHTCYTv/zyCwDnzp1jzpw5LFiwgM8++8z6eWJPiYmJdO3a1fr49NNP+e6773j22WdZsGABc+bMYcqUKdb4Kz/7HHEfvR5XV1dCQkIoU6aMddq12hAVFUX37t2Jjo6mfv369kpZ7hBV1P6CSx8OkH/g165dm2HDhuHs7MyaNWs4evQoW7duxWg04uTkRL169ejRowdt27ZlwIABBAYG0qpVK1599VU2bNhAmzZtaN26NevXryc+Pt46lGI2m/Hx8bFut0WLFgDcc889rFy5kpSUFE6dOkWrVq0AeOihh6xVgc2bN/PHH3/w7rvvApCXl8fJkycBqF27dqE2nT9/nsTERJo1awZA9+7dWbBgwTVfg1deeYVnn32WjRs3snHjRnr27FmgU1mUS9v18/MjPDycLVu24OLiQlhYGP7+/ta4a83fvHkzWVlZ1rwyMjI4dOgQW7du5ZFHHgEgNDSUevXqXTeP21HUe9+nTx8OHDhA9erVARg2bBiQf44agNFo5L333rutfcPeFixYQKdOnQDo0KEDw4cPp127dpQuXdraWenRowevv/466enp7N27l9GjR1uXz8jIIDk5GV9fX7vkf8nN7Ldt2rTBz88PgMjISLZu3cr999/PwIEDeeSRR9i8eTObNm3iqaee4vnnn+fxxx8HoGfPngBUr14df39/Dh48aJchqlv5fLrk0rG5a9cu6tevbz2fberUqQD8/vvvtGjRAldXV/z8/PD19eXChQt4enrauHUFFTX0aTKZ2LBhAx999BGxsbFkZGRY511qpyPvo9djMBhwd3cHrt+GrVu3Wqv6Xbp0YezYsXbJ907QLxOoo/aXFPXhkJ6ezkMPPUSXLl1o2LAh1apVY968eQC8//77xMTEsH79ep588knefPNN2rdvT7169Vi7di1ffPEF69at47777qN+/fp8+OGHAGRnZ1uHVgDc3NyAyzuuk5MTFoulyBzNZjOzZ8+mVKlSQP6Hd+nSpfn555+tB/qVDAZDgXVd7wTpdevWkZGRQYcOHXjooYd46KGH+Pbbb/n+++8BrOvJy8srsNyV2+3atSvLli3DxcWFzp07F9pGUfPNZjNTp06lZs2aAJw9exYfHx++/fbbArlfWdm504p67w8cOFDgw+TixYsF3rf09HR69OhxW/vGa6+9VmxtupFz586xYcMG9u3bx5w5c7BYLKSmprJ+/XrMZnOheLPZjKura4HX6cyZM9Z90V5utN9ecuX+YzabcXZ2JiYmhn379tG3b186depkfUycONHaUbvymLm0nD3c6ucTXD42nZ2dC+zL58+ft/7/yvZc/XnhSIYOHYq3tzetW7emQ4cO/PDDD9Z5l9rpqPvo9eTk5HD06FHOnTsH3LgNl94fg8FQoFMudx+9e3fIsWPHMBgMPPPMMzRq1Mg6dHf+/Hk6dOhA1apVef7552nWrBkHDx5k6NCh7Nmzh169evH888+zf/9+6tSpQ0xMDEePHgXyv8SvLNtfzcvLi4oVK1qHJpYuXWqd17hxY7766isADh8+TOfOna3n1xTF19eXoKAg1q1bB1Dgw+1q7u7uvPXWW8TFxQH5Hwi///471atXx9fXl8OHDwOwevXqa66jbdu2bNu2jU2bNvHAAw/c1PzGjRtbrwxNTEykS5cuxMfH06RJE5YuXYrZbObUqVPs3LnzmtstDmFhYZw7d87a7k8//dSaJ9yZfcOeoqOjady4MevXr2fNmjWsXbuWZ555ho0bN5Kammq9muzS/ufl5UVoaKj1C2TTpk307dvXbvlfcr399kq//PILqampZGdns2zZMpo2bYqPjw8zZ87kwIED1rh9+/YVWHbZsmUA7Nmzh9TUVKpWrWqDVt2ca+2DV6tVqxYxMTEkJSUBMHHixOsex45o06ZNDBkyhPvvv5/169cDFGqro+6j12I2m5kxYwZ16tQhODgYuH4bmjZtypIlSwBYuXIl2dnZ9kn8jjDY8OGYVFG7Q8LDw6levTqRkZEYDAaaN2/Ojh078PPz45FHHqFHjx54eHgQFhbGQw89RMOGDRkzZgzvvfceLi4uTJgwAX9/fyZOnMjQoUMxm80EBgZahx6uZcqUKbz00ktMnz6datWqWf9iHDt2LOPHj7dWo6ZMmXLDYYqpU6cyevRopk+fTt26da8Z17hxYwYNGsQzzzxDbm4ukD8s+9xzz1G/fn2ioqKYOXPmdU+mdnd3p379+uTk5FCyZMmbmj9o0CAmTJhAp06dMJlMjBgxguDgYPr06cOhQ4eIjIykfPnyNv+CdHNzY+rUqbz44ovk5uYSHBzMlClT+Omnn4A7s2/Y06JFiwqdv9O3b18+/fRTPvvsM0aOHInRaCQsLMy6/02dOpUJEybw6aef4uLiwttvv233IYzr7bdX/pFTqVIlnn76aVJTU+nUqZN1P548eTIvvfQSaWlpGAwGateuzfjx463LnTx5km7dugHw9ttvO9RtO661D14tMDCQMWPG8MQTT2A2m6lbty7du3fn/ffft0PWf83gwYPp06cPbm5uhIeHU758eWvn/EqOuI9e6cohbLPZTPXq1Zk2bVqBPxau1Ybx48czYsQIvvnmGyIiIor8jJW7h8HiqPVruSkzZ86kZ8+eBAQEsHLlSpYuXWq955FIcTKbzbz55psMGjSIEiVKMGvWLBISEhg1apS9U7O54rp3n8g/XZbpfzbblrtTY5tt61aoonaXCwoK4j//+Q/Ozs54e3vf0k0QRW6H0WikVKlS9OjRAxcXF8qXL6/9T0TuKIPO0FJFTURERBxTtmmrzbbl5vQvm23rVqiiJiIiIg7Kcc4btBfVFEVEREQclCpqIiIi4pAc6Upce1FFTURERMRBqaImIiIiDkoVNVXURERERByUKmoiIiLikHQfNVXURERERByWKmoiIiLioHSOmipqIiIiIg5KFTURERFxSAZV1FRRExEREXFUqqiJiIiIQ9IvE6iiJiIiIuKw1FETERERcVAa+hQREREHpXqSXgERERERB6WKmoiIiDgk3Z5DFTURERERh6WKmoiIiDgoVdRUURMRERFxUKqoiYiIiEPSDW9VURMRERFxWKqoiYiIiINSPUmvgIiIiMgdMn36dGbMmFHkvJycHEaMGEFkZCTdunXjyJEjN1yfOmoiIiLikAw2/He7Ll68yEsvvcSsWbOuGTN37lw8PDxYvnw5L730EqNHj77hejX0KSIiIv94qamppKamFpru7e2Nt7f3DZdfvXo1oaGhDBgw4Jox69at4/nnnwegYcOGnD9/ntOnTxMUFHTNZdRRExEREQdV1WZbmj17BjNnziw0fdCgQQwePPiGyz/44IMA1xz2BEhMTMTf39/63N/fnzNnzqijJiIiInI9/fv3p1u3boWmX11NW758OZMmTSowrVKlSnzxxRc33IbFYilwyxGLxYLReP2z0NRRExERkX+8mx3ijIyMJDIy8i9tIzAwkMTERIKDgwE4e/YsAQEB111GFxOIiIiI2ECrVq2Ijo4GYPv27bi5uV132BPUURMREREpNvPnz+edd94BoF+/fuTk5NCxY0def/11pkyZcsPlDRaLxVLcSYqIiIjIrVNFTURERMRBqaMmIiIi4qDUURMRERFxUOqoiYiIiDgoddREREREHJQ6aiIiIiIOSh01EREREQf1/7jI6O7xvOe2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = titanic_train.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr, vmax=0.8,linewidths=0.01, annot=True,square=True, cmap='YlGnBu', linecolor='white')\n",
    "plt.title('Correlation between Features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Now we'll look the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>91</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baimbrigge, Mr. Charles Robert</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.627188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.907576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId      Pclass                            Name   Sex  \\\n",
       "count    418.000000  418.000000                             418   418   \n",
       "unique          NaN         NaN                             418     2   \n",
       "top             NaN         NaN  Baimbrigge, Mr. Charles Robert  male   \n",
       "freq            NaN         NaN                               1   266   \n",
       "mean    1100.500000    2.265550                             NaN   NaN   \n",
       "std      120.810458    0.841838                             NaN   NaN   \n",
       "min      892.000000    1.000000                             NaN   NaN   \n",
       "25%      996.250000    1.000000                             NaN   NaN   \n",
       "50%     1100.500000    3.000000                             NaN   NaN   \n",
       "75%     1204.750000    3.000000                             NaN   NaN   \n",
       "max     1309.000000    3.000000                             NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch    Ticket        Fare  \\\n",
       "count   332.000000  418.000000  418.000000       418  417.000000   \n",
       "unique         NaN         NaN         NaN       363         NaN   \n",
       "top            NaN         NaN         NaN  PC 17608         NaN   \n",
       "freq           NaN         NaN         NaN         5         NaN   \n",
       "mean     30.272590    0.447368    0.392344       NaN   35.627188   \n",
       "std      14.181209    0.896760    0.981429       NaN   55.907576   \n",
       "min       0.170000    0.000000    0.000000       NaN    0.000000   \n",
       "25%      21.000000    0.000000    0.000000       NaN    7.895800   \n",
       "50%      27.000000    0.000000    0.000000       NaN   14.454200   \n",
       "75%      39.000000    1.000000    0.000000       NaN   31.500000   \n",
       "max      76.000000    8.000000    9.000000       NaN  512.329200   \n",
       "\n",
       "                  Cabin Embarked  \n",
       "count                91      418  \n",
       "unique               76        3  \n",
       "top     B57 B59 B63 B66        S  \n",
       "freq                  3      270  \n",
       "mean                NaN      NaN  \n",
       "std                 NaN      NaN  \n",
       "min                 NaN      NaN  \n",
       "25%                 NaN      NaN  \n",
       "50%                 NaN      NaN  \n",
       "75%                 NaN      NaN  \n",
       "max                 NaN      NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test.describe(include='all')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### There are some missing values present in Age,Cabin and Fare features.Let's handle these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_test['Age'] = titanic_test['Age'].fillna(titanic_test.Age.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n",
       "152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n",
       "\n",
       "     Fare Cabin Embarked  \n",
       "152   NaN   NaN        S  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test[titanic_test.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_test['Fare']= titanic_test['Fare'].fillna(titanic_test[(titanic_test['Pclass']==3) & \n",
    "                                                    (titanic_test['Embarked'] =='S')]['Fare'].median())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Now we are creating some variables by using other features to get some useful information about our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Deck , where exactly were  passengers on the ship?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['Deck'] = titanic_train['Cabin'].str[0]\n",
    "titanic_test['Deck'] = titanic_test['Cabin'].str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['Deck'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'B', 'E', 'A', 'C', 'D', 'F', 'G'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test['Deck'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yusuf\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "C:\\Users\\Yusuf\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211d780490>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1152x180 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survived',col ='Deck', kind='count', data = titanic_train[titanic_train.Deck.notnull()],\n",
    "                size=2.5, aspect=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['Deck'] = titanic_train['Deck'].fillna('Z')\n",
    "titanic_test['Deck'] = titanic_test['Deck'].fillna('Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Z', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['Deck'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train = titanic_train.assign(Deck = titanic_train.Deck.astype(object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yusuf\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:723: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211f2817f0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_train, col='Pclass',row='Survived')\n",
    "g.map(sns.boxplot, 'Deck', 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### How big was there family?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['FamilySize'] = titanic_train['SibSp']+titanic_train['Parch']+1\n",
    "titanic_test['FamilySize'] = titanic_test['SibSp']+titanic_test['Parch']+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  5,  3,  7,  6,  4,  8, 11], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['FamilySize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  5,  4,  6,  8,  7, 11], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_test['FamilySize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2211d5bb8b0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD8CAYAAACSCdTiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVcElEQVR4nO3df2zU9eHH8deVs4cRDMruVlcIJmjShUTLcIOy5CqLu1bLWTgxYQjdxlcDCXYRSZXRDiKW2LGaTmNY/EN0otksCLZ29TDKxkbazdksNCzVmEE76MhxlE160jvau8/3D+dNBvQ+Xe+Xb5+PhKT3/nz6eb8+R3ndp5/73AeHZVmWAABGKsh1AABA5lDyAGAwSh4ADEbJA4DBKHkAMBglDwAGc+Y6wH/75z8/USIx+as6Z86cpqGhSBoSpQ+Z7CGTffmYi0z2pCtTQYFDN9xw3VWX513JJxJWWkr+s23lGzLZQyb78jEXmezJRiZO1wCAwSh5ADAYJQ8ABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMlncfhkpl+vXXaqrLXmy3e/q4y6OxMQ2fH0lHLADIS1+4kp/qcsq/qS0t23rz6WoNp2VLAJCfOF0DAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMBglDwAGs/WJ1zVr1ujcuXNyOj9dffv27frkk0/01FNPKRaL6e6779bGjRslSX19faqvr9cnn3yiO+64Q0888UTy+wAA2ZWyfS3LUn9/v377298myzoajaqyslJ79uzRTTfdpHXr1unw4cMqLy9XXV2dGhsbVVpaqi1btqi1tVWrVq3K+I4AAC6X8nTN8ePHJUlr167Vvffeq1deeUW9vb2aM2eOZs+eLafTKb/fr2AwqMHBQUWjUZWWlkqSAoGAgsFgZvcAAHBVKY/kz58/r7KyMv3kJz/R6Oioampq9OCDD8rtdifX8Xg8CoVCOnPmzCXjbrdboVBoQoFmzpw2ofUnK9WdKr/o89lBJnvyMZOUn7nIZE82MqUs+fnz52v+/PnJxytWrNCzzz6rBQsWJMcsy5LD4VAikZDD4bhsfCKGhiJKJKyrLk/3kxIOZ+8+lG739KzOZweZ7MnHTFJ+5iKTPenKVFDgGPfgOOXpmvfff1/d3d3Jx5Zlqbi4WOFwODkWDofl8XhUVFR0yfjZs2fl8Xj+1+wAgElKWfLDw8PauXOnYrGYIpGIDhw4oEcffVQnTpzQwMCA4vG4Ojo65PV6VVxcLJfLpZ6eHklSW1ubvF5vxncCAHBlKU/XLFmyREePHtWyZcuUSCS0atUqzZ8/X01NTaqtrVUsFlN5ebkqKyslSc3NzWpoaFAkEtG8efNUU1OT8Z0AAFyZrQvYH3nkET3yyCOXjJWVlam9vf2ydUtKSrRv3770pAMATAqfeAUAg1HyAGAwSh4ADEbJA4DBKHkAMBglDwAGo+QBwGCUPAAYjJIHAINR8gBgMEoeAAxGyQOAwSh5ADAYJQ8ABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMRskDgMEoeQAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMJjtkv/pT3+qzZs3S5K6urrk9/vl8/nU0tKSXKevr0+BQEAVFRWqr6/X2NhY+hMDAGyzVfLd3d06cOCAJCkajWrLli3atWuXOjs7dezYMR0+fFiSVFdXp61bt+rgwYOyLEutra2ZSw4ASCllyf/rX/9SS0uL1q9fL0nq7e3VnDlzNHv2bDmdTvn9fgWDQQ0ODioajaq0tFSSFAgEFAwGM5seADCulCW/detWbdy4Uddff70k6cyZM3K73cnlHo9HoVDosnG3261QKJSByAAAu5zjLdy7d69uuukmlZWVaf/+/ZKkRCIhh8ORXMeyLDkcjquOT9TMmdMm/D2T4XZPN3o+O8hkTz5mkvIzF5nsyUamcUu+s7NT4XBY1dXV+vjjj3XhwgUNDg5qypQpyXXC4bA8Ho+KiooUDoeT42fPnpXH45lwoKGhiBIJ66rL0/2khMPDad3eeNzu6Vmdzw4y2ZOPmaT8zEUme9KVqaDAMe7B8bgl/+KLLya/3r9/v9577z098cQT8vl8GhgY0KxZs9TR0aH77rtPxcXFcrlc6unp0YIFC9TW1iav1zvpHQAA/O/GLfkrcblcampqUm1trWKxmMrLy1VZWSlJam5uVkNDgyKRiObNm6eampq0BwYA2Ge75AOBgAKBgCSprKxM7e3tl61TUlKiffv2pS8dAGBS+MQrABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMBglDwAGo+QBwGCUPAAYjJIHAINR8gBgMEoeAAxGyQOAwSh5ADAYJQ8ABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMRskDgMEoeQAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMJitkn/mmWd0zz33qKqqSi+++KIkqaurS36/Xz6fTy0tLcl1+/r6FAgEVFFRofr6eo2NjWUmOQAgpZQl/9577+mPf/yj2tvb9frrr2vPnj364IMPtGXLFu3atUudnZ06duyYDh8+LEmqq6vT1q1bdfDgQVmWpdbW1ozvBADgylKW/Le+9S29/PLLcjqdGhoaUjwe1/nz5zVnzhzNnj1bTqdTfr9fwWBQg4ODikajKi0tlSQFAgEFg8GM7wQA4Mqcdla65ppr9Oyzz2r37t2qrKzUmTNn5Ha7k8s9Ho9CodBl4263W6FQaEKBZs6cNqH1J8vtnm70fHaQyZ58zCTlZy4y2ZONTLZKXpJ+9KMf6aGHHtL69evV398vh8ORXGZZlhwOhxKJxBXHJ2JoKKJEwrrq8nQ/KeHwcFq3Nx63e3pW57ODTPbkYyYpP3ORyZ50ZSoocIx7cJzydM3f/vY39fX1SZKuvfZa+Xw+/elPf1I4HE6uEw6H5fF4VFRUdMn42bNn5fF4JpMfADAJKUv+1KlTamho0MWLF3Xx4kW9++67WrlypU6cOKGBgQHF43F1dHTI6/WquLhYLpdLPT09kqS2tjZ5vd6M7wQA4MpSnq4pLy9Xb2+vli1bpilTpsjn86mqqko33nijamtrFYvFVF5ersrKSklSc3OzGhoaFIlENG/ePNXU1GR8JwAAV2brnHxtba1qa2svGSsrK1N7e/tl65aUlGjfvn3pSQcAmBQ+8QoABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMRskDgMEoeQAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMBglDwAGo+QBwGCUPAAYjJIHAINR8gBgMEoeAAxGyQOAwSh5ADAYJQ8ABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMZqvkn3vuOVVVVamqqko7d+6UJHV1dcnv98vn86mlpSW5bl9fnwKBgCoqKlRfX6+xsbHMJAcApJSy5Lu6unTkyBEdOHBAb7zxhv7617+qo6NDW7Zs0a5du9TZ2aljx47p8OHDkqS6ujpt3bpVBw8elGVZam1tzfhOAACuLGXJu91ubd68WYWFhbrmmms0d+5c9ff3a86cOZo9e7acTqf8fr+CwaAGBwcVjUZVWloqSQoEAgoGgxnfCQDAlTlTrXDrrbcmv+7v79dbb72l1atXy+12J8c9Ho9CoZDOnDlzybjb7VYoFJpQoJkzp01o/clyu6cbPZ8dZLInHzNJ+ZmLTPZkI1PKkv/MRx99pHXr1umxxx7TlClT1N/fn1xmWZYcDocSiYQcDsdl4xMxNBRRImFddXm6n5RweDit2xuP2z09q/PZQSZ78jGTlJ+5yGRPujIVFDjGPTi29cZrT0+PfvCDH2jTpk1avny5ioqKFA6Hk8vD4bA8Hs9l42fPnpXH45lEfADAZKQs+dOnT2vDhg1qbm5WVVWVJOn222/XiRMnNDAwoHg8ro6ODnm9XhUXF8vlcqmnp0eS1NbWJq/Xm9k9AABcVcrTNS+88IJisZiampqSYytXrlRTU5Nqa2sVi8VUXl6uyspKSVJzc7MaGhoUiUQ0b9481dTUZC49AGBcKUu+oaFBDQ0NV1zW3t5+2VhJSYn27ds3+WQAgEnjE68AYDBKHgAMRskDgMEoeQAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAw23ehxPimX3+tprpSP52p7qIZjY1p+PxIumIB+JKj5NNkqssp/6a2SW/nzaerlV83RAXwRcbpGgAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADEbJA4DBKHkAMBglDwAGo+QBwGCUPAAYjJIHAINR8gBgMEoeAAxGyQOAwSh5ADAYJQ8ABrNV8pFIREuXLtWpU6ckSV1dXfL7/fL5fGppaUmu19fXp0AgoIqKCtXX12tsbCwzqQEAtqQs+aNHj+p73/ue+vv7JUnRaFRbtmzRrl271NnZqWPHjunw4cOSpLq6Om3dulUHDx6UZVlqbW3NaHgAwPhSlnxra6u2bdsmj8cjSert7dWcOXM0e/ZsOZ1O+f1+BYNBDQ4OKhqNqrS0VJIUCAQUDAYzmx4AMC5nqhV27NhxyeMzZ87I7XYnH3s8HoVCocvG3W63QqHQhAPNnDltwt8zGW739KzOZ0e6Ml0cjavwmimTns/udtLJ5L+XdMvHXGSyJxuZUpb8f0skEnI4HMnHlmXJ4XBcdXyihoYiSiSsqy5P95MSDg+nZTvpzJXOTP5NbZPezptPV6ctkx1u9/SszmdHPmaS8jMXmexJV6aCAse4B8cTvrqmqKhI4XA4+TgcDsvj8Vw2fvbs2eQpHgBAbky45G+//XadOHFCAwMDisfj6ujokNfrVXFxsVwul3p6eiRJbW1t8nq9aQ8MALBvwqdrXC6XmpqaVFtbq1gspvLyclVWVkqSmpub1dDQoEgkonnz5qmmpibtgQEA9tku+UOHDiW/LisrU3t7+2XrlJSUaN++felJBgCYND7xCgAGo+QBwGCUPAAYjJIHAINR8gBgMEoeAAxGyQOAwSh5ADAYJQ8ABqPkAcBglDwAGIySBwCDUfIAYDBKHgAMRskDgMEoeQAwGCUPAAaj5AHAYJQ8ABiMkgcAg1HyAGAwSh4ADObMdQB8uUy//lpNdaX+sXO7p6dcJxob0/D5kXTEAoxFySOrprqc8m9qS8u23ny6WsNp2RJgLkoeX3r8dgGTUfL40uO3C5iMN14BwGAcyQN5yO4pJCn1aSROIX25UfJAHsrXU0jpev+CF57soeQB2JauFx/eu8iejJT8m2++qV/84hcaGxvT97//fT3wwAOZmAYA+O0ihbSXfCgUUktLi/bv36/CwkKtXLlSCxcu1C233JLuqQAgL3+7yKfLctNe8l1dXVq0aJFmzJghSaqoqFAwGNTDDz9s6/sLChwp1/HccO2kMk50PrvSlYtM9qUrF5nsM/lnKl2Zprqc+r/Gt9OyrRcafPpknFypMjssy7LSkuTfnn/+eV24cEEbN26UJO3du1e9vb168skn0zkNAMCGtF8nn0gk5HD855XFsqxLHgMAsiftJV9UVKRwOJx8HA6H5fF40j0NAMCGtJf84sWL1d3drXPnzmlkZERvv/22vF5vuqcBANiQ9jdev/rVr2rjxo2qqanR6OioVqxYodtuuy3d0wAAbEj7G68AgPzBDcoAwGCUPAAYjJIHAINR8gBgMEoeAAxGyWfIO++8oz179ujvf//7JeOvvfZajhJ9qr+/X6FQSNKnt5xobGxUZ2dnTjN9XlNTU64jSJL+8Ic/6Pz585KkN954Q9u3b9frr7+eszy9vb3Jr7u7u9XU1KTm5mYdPXo0Z5kaGxv18ccf52z+L5ru7m795S9/kSTt3r1b69ev13PPPaeLFy9mdF4uocyA5uZmHTt2THPnzlUwGNRjjz2m6upqSdLy5ct14MCBnOR66aWXtGfPHiUSCS1atEinT5/Wd7/7XR06dEjf+MY3tGHDhqzm+fGPf3zZ2KFDh/Sd73xHkvTUU09lNc9nduzYob6+PrW0tOjVV19Vb2+v7rrrLv3+97/XrFmz1NDQkPVMn/3cvPrqq/r1r3+t++67T5J04MAB3X///Vq9enXWM91xxx2aOXOmNm3aJJ/Pl/X5v0h27typ999/X2NjY5o1a5YcDocCgYAOHTqkeDyuxsbGzE1uGWBwcHDcP9m2dOlSa3R01LIsyzpx4oS1ZMkSq7Oz07Isy6qurs56ns/nikaj1qlTp6zS0lIrGo1almVZsVjM8vv9Wc/T1NRkLVq0yHrppZes/fv3W/v377fuvPPO5Ne5cs8991hjY2OWZVnWsmXLrFgsZlmWZY2NjVmVlZU5ybRs2TLLsizr3nvvtc6dO5ccHx4etioqKnKSqbq62vroo4+sBx54wFqxYoX1m9/8xhoZGclJlny3dOlSKx6PWyMjI9bChQutixcvWpZlWYlEIuP/9oz4n6HWrVun/v5+eTweWf/1i4nD4dC7776b1TzW527KdvPNN+v555/XD3/4Q9144405vVlbIpFQYWGhiouLtXbtWrlcruSyeDye9TyPP/64vF6vfv7zn+vRRx/VwoUL9ctf/lLLly/PepbPmzp1qoaGhuTxeFRUVKQLFy6osLBQIyMjcjpz809mbGxMiURCM2bMUGFhYXK8sLBQBQW5OevqcDh0yy236JVXXlFXV5dee+017dixQzfffLOKior09NNP5yRXPrIsS8PDw7pw4YJGRkYUiUR0ww03KBqNanR0NKNzG1Hyv/rVr7Rq1Spt27ZNCxYsyHUcVVZWas2aNdq8ebNuu+023XrrrXrmmWf08MMPZ/z823h8Pp9Wr16tl19+WbW1tZKkDz74QA0NDbr77rtzkqmsrExf//rXtW3bNv3ud7/LyYvNf9uwYYNWrFihqqoqzZo1S2vWrFFZWZmOHDmiBx98MCeZZsyYoTvvvFOS9OSTT6qpqUnd3d362c9+psrKypxk+vwB1eLFi7V48WKNjo7qww8/1MmTJ3OSSfr0PZTxLFu2LEtJ/uOhhx6Sz+eTZVmqq6vT2rVrVVZWpu7u7uSpt0wx5px8b2+v9u7dmzf3re/u7pbH49HcuXOTY6dPn9bu3btVX1+fs1x//vOf9c1vfjP5+Pjx4zp58qTKy8tzlukze/fu1VtvvaXdu3fnOopOnjypd955RwMDA4rH4/rKV76iJUuW5Pw+TMePH9f58+dVWlqqnp4eDQ8PJ8s/2/bu3av7778/J3OP5/HHH9fbb7991Re/XL3XE41GFY/Hdd111+nDDz/UkSNHVFJSom9/+9sZndeYkgeAz6xfv1533XWXVqxYkesoOccllACMs337di7v/DeO5AEY5R//+Me4y7/2ta9lKUl+oOQBGMXv9+fV1Xa5RskDMEokEsmrq+1yjXPyAIwybdo0NTY2pryU8suCI3kAMBhH8gBgMEoeAAxGyQOAwSh5ADDY/wO4lkt4stKczAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train['FamilySize'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train.loc[titanic_train['FamilySize']==1, 'Fsize'] = ' single'\n",
    "titanic_train.loc[(titanic_train['FamilySize']>1) & (titanic_train['FamilySize']<5), 'Fsize'] = 'small'\n",
    "titanic_train.loc[titanic_train['FamilySize']>4,'Fsize'] = 'large'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_test.loc[titanic_test['FamilySize']==1, 'Fsize'] = ' single'\n",
    "titanic_test.loc[(titanic_test['FamilySize']>1) & (titanic_test['FamilySize']<5), 'Fsize'] = 'small'\n",
    "titanic_test.loc[titanic_test['FamilySize']>4,'Fsize'] = 'large'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['small', ' single', 'large'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['Fsize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " single    537\n",
       "small      292\n",
       "large       62\n",
       "Name: Fsize, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['Fsize'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yusuf\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211d5bb7c0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Fsize', y='Survived', data=titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['NameLength'] = titanic_train['Name'].apply(lambda x: len(x))\n",
    "titanic_test['NameLength'] = titanic_test['Name'].apply(lambda x : len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23, 51, 22, 44, 24, 16, 30, 49, 35, 31, 27, 36, 32, 20, 28, 55, 21,\n",
       "       29, 57, 19, 46, 34, 56, 40, 17, 25, 45, 26, 50, 13, 15, 33, 18, 52,\n",
       "       47, 38, 37, 41, 14, 42, 39, 82, 48, 54, 43, 67, 65, 53, 61, 12],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['NameLength'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    64\n",
       "25    55\n",
       "27    50\n",
       "18    50\n",
       "26    49\n",
       "28    43\n",
       "24    43\n",
       "17    42\n",
       "21    40\n",
       "23    39\n",
       "20    39\n",
       "22    38\n",
       "30    37\n",
       "29    32\n",
       "31    30\n",
       "16    26\n",
       "32    23\n",
       "33    22\n",
       "15    15\n",
       "47    11\n",
       "37    10\n",
       "38     9\n",
       "36     9\n",
       "39     9\n",
       "45     9\n",
       "44     8\n",
       "41     8\n",
       "34     7\n",
       "46     7\n",
       "40     7\n",
       "51     7\n",
       "35     6\n",
       "43     5\n",
       "42     5\n",
       "49     5\n",
       "50     4\n",
       "52     4\n",
       "56     3\n",
       "14     3\n",
       "48     3\n",
       "13     2\n",
       "12     2\n",
       "53     2\n",
       "55     2\n",
       "57     2\n",
       "67     1\n",
       "54     1\n",
       "61     1\n",
       "65     1\n",
       "82     1\n",
       "Name: NameLength, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['NameLength'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0, 20, 40,57,85]\n",
    "group_names = ['short', 'ok', 'good', 'long']\n",
    "titanic_train['NL'] = pd.cut(titanic_train['NameLength'], bins,labels = group_names)\n",
    "titanic_test['NL'] = pd.cut(titanic_test['NameLength'], bins,labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ok, good, short, long]\n",
       "Categories (4, object): [short < ok < good < long]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['NL'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ok       558\n",
       "short    243\n",
       "good      86\n",
       "long       4\n",
       "Name: NL, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['NL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yusuf\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:723: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2211f679fa0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=titanic_train, row='Embarked', col='Pclass')\n",
    "g.map(sns.barplot,'NL','Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= [titanic_train,titanic_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = {'Mr.':1,'Mrs.':2,'Miss':3, 'Master':4, 'Rare':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n",
    "                                            'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle','Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms',\"Miss\")\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs.')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <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>Died</th>\n",
       "      <th>Deck</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>NameLength</th>\n",
       "      <th>NL</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>23</td>\n",
       "      <td>ok</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>51</td>\n",
       "      <td>good</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>22</td>\n",
       "      <td>ok</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>44</td>\n",
       "      <td>good</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <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",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>24</td>\n",
       "      <td>ok</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Died Deck  FamilySize  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S     1    Z           2   \n",
       "1      0          PC 17599  71.2833   C85        C     0    C           2   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S     0    Z           1   \n",
       "3      0            113803  53.1000  C123        S     0    C           2   \n",
       "4      0            373450   8.0500   NaN        S     1    Z           1   \n",
       "\n",
       "     Fsize  NameLength    NL Title  \n",
       "0    small          23    ok    Mr  \n",
       "1    small          51  good   Mrs  \n",
       "2   single          22    ok  Miss  \n",
       "3    small          44  good   Mrs  \n",
       "4   single          24    ok    Mr  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  5,  3,  7,  6,  4,  8, 11], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['FamilySize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "886        211536\n",
       "887        112053\n",
       "888    W./C. 6607\n",
       "889        111369\n",
       "890        370376\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['Ticket'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['TicketNumber'] = titanic_train['Ticket'].str.extract('(\\d{2,})', expand = True)\n",
    "titanic_train['TicketNumber'] = titanic_train['TicketNumber'].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_test['TicketNumber'] = titanic_test['Ticket'].str.extract('(\\d{2,})', expand = True)\n",
    "titanic_test['TicketNumber'] = titanic_test['TicketNumber'].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <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>Died</th>\n",
       "      <th>Deck</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>NameLength</th>\n",
       "      <th>NL</th>\n",
       "      <th>Title</th>\n",
       "      <th>TicketNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>19</td>\n",
       "      <td>short</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>ok</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>31</td>\n",
       "      <td>ok</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>19</td>\n",
       "      <td>short</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>773</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mack, Mrs. (Mary)</td>\n",
       "      <td>female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O./P.P. 3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>E77</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>17</td>\n",
       "      <td>short</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mudd, Mr. Thomas Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O./P.P. 3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Z</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>24</td>\n",
       "      <td>ok</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "179          180         0       3              Leonard, Mr. Lionel    male   \n",
       "271          272         1       3     Tornquist, Mr. William Henry    male   \n",
       "302          303         0       3  Johnson, Mr. William Cahoone Jr    male   \n",
       "597          598         0       3              Johnson, Mr. Alfred    male   \n",
       "772          773         0       2                Mack, Mrs. (Mary)  female   \n",
       "841          842         0       2         Mudd, Mr. Thomas Charles    male   \n",
       "\n",
       "      Age  SibSp  Parch       Ticket  Fare Cabin Embarked  Died Deck  \\\n",
       "179  36.0      0      0         LINE   0.0   NaN        S     1    Z   \n",
       "271  25.0      0      0         LINE   0.0   NaN        S     0    Z   \n",
       "302  19.0      0      0         LINE   0.0   NaN        S     1    Z   \n",
       "597  49.0      0      0         LINE   0.0   NaN        S     1    Z   \n",
       "772  57.0      0      0  S.O./P.P. 3  10.5   E77        S     1    E   \n",
       "841  16.0      0      0  S.O./P.P. 3  10.5   NaN        S     1    Z   \n",
       "\n",
       "     FamilySize    Fsize  NameLength     NL Title  TicketNumber  \n",
       "179           1   single          19  short    Mr           NaN  \n",
       "271           1   single          28     ok    Mr           NaN  \n",
       "302           1   single          31     ok    Mr           NaN  \n",
       "597           1   single          19  short    Mr           NaN  \n",
       "772           1   single          17  short   Mrs           NaN  \n",
       "841           1   single          24     ok    Mr           NaN  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[titanic_train['TicketNumber'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train['TicketNumber'] = titanic_train['TicketNumber'].fillna(titanic_train['TicketNumber'].median())\n",
    "titanic_test['TicketNumber'] = titanic_test['TicketNumber'].fillna(titanic_test['TicketNumber'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train['TicketNumber'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <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>Died</th>\n",
       "      <th>Deck</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>NameLength</th>\n",
       "      <th>NL</th>\n",
       "      <th>Title</th>\n",
       "      <th>TicketNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3101282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>113803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</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",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>373450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked  Died  Deck  FamilySize  Fsize  \\\n",
       "0         A/5 21171   7.2500   NaN         2     1     8           2      2   \n",
       "1          PC 17599  71.2833   C85         0     0     2           2      2   \n",
       "2  STON/O2. 3101282   7.9250   NaN         2     0     8           1      0   \n",
       "3            113803  53.1000  C123         2     0     2           2      2   \n",
       "4            373450   8.0500   NaN         2     1     8           1      0   \n",
       "\n",
       "   NameLength  NL  Title  TicketNumber  \n",
       "0          23   2      2       21171.0  \n",
       "1          51   0      3       17599.0  \n",
       "2          22   2      1     3101282.0  \n",
       "3          44   0      3      113803.0  \n",
       "4          24   2      2      373450.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelEnc=LabelEncoder()\n",
    "\n",
    "cat_vars=['Embarked','Sex',\"Title\",\"Fsize\",\"NL\",'Deck']\n",
    "for col in cat_vars:\n",
    "    titanic_train[col]=labelEnc.fit_transform(titanic_train[col])\n",
    "    titanic_test[col]=labelEnc.fit_transform(titanic_test[col])\n",
    "\n",
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [titanic_train, titanic_test]\n",
    "\n",
    "for dataset in data:\n",
    "    mean = titanic_train[\"Age\"].mean()\n",
    "    std = titanic_test[\"Age\"].std()\n",
    "    is_null = dataset[\"Age\"].isnull().sum()\n",
    "    # compute random numbers between the mean, std and is_null\n",
    "    rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n",
    "    # fill NaN values in Age column with random values generated\n",
    "    age_slice = dataset[\"Age\"].copy()\n",
    "    age_slice[np.isnan(age_slice)] = rand_age\n",
    "    dataset[\"Age\"] = age_slice\n",
    "    dataset[\"Age\"] = titanic_train[\"Age\"].astype(int)\n",
    "titanic_train[\"Age\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.plotting_context(\"notebook\",font_scale=1.5):\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.distplot(titanic_train[\"Age\"].dropna(),\n",
    "                 bins=80,\n",
    "                 kde=False,\n",
    "                 color=\"tomato\")\n",
    "    plt.title(\"Age Distribution\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlim((15,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scale = preprocessing.StandardScaler().fit(titanic_train[['Age','Fare']])\n",
    "titanic_train[['Age','Fare']] = std_scale.transform(titanic_train[['Age','Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scale = preprocessing.StandardScaler().fit(titanic_test[['Age','Fare']])\n",
    "titanic_test[['Age','Fare']] = std_scale.transform(titanic_test[['Age','Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    -0.005007\n",
       "Survived        1.000000\n",
       "Pclass         -0.338481\n",
       "Sex            -0.543351\n",
       "Age            -0.058263\n",
       "SibSp          -0.035322\n",
       "Parch           0.081629\n",
       "Fare            0.257307\n",
       "Embarked       -0.174199\n",
       "Died           -1.000000\n",
       "Deck           -0.301116\n",
       "FamilySize      0.016639\n",
       "Fsize           0.249714\n",
       "NameLength      0.332350\n",
       "NL             -0.312234\n",
       "Title          -0.064970\n",
       "TicketNumber   -0.096161\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.corr()['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Age\",\"SibSp\", \"Parch\", \"Fare\",\n",
    "              \"Embarked\",\"NL\", \"Fsize\", \"Title\",\"Deck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = RepeatedKFold(titanic_train.shape[0], shuffle=True, random_state=1)\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf.get_n_splits(titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train,test in kf.split(titanic_train):\n",
    "    train_predictors =titanic_train[predictors].iloc[train,:]\n",
    "    train_target = titanic_train[target].iloc[train]\n",
    "    lm.fit(train_predictors, train_target)\n",
    "    test_prediction = lm.predict(titanic_train[predictors].iloc[test,:])\n",
    "    predictions.append(test_prediction)                          \n",
    "                                 \n",
    "                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = np.concatenate(predictions,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions[predictions > 0.5] = 1\n",
    "predictions[predictions < 0.5] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = predictions.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5409652076318743"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy =sum(titanic_train['Survived']==predictions)/len(titanic_train['Survived'])\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import ShuffleSplit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Fare\", \"Embarked\",\"Deck\",\"Age\",\n",
    "              \"Fsize\", \"NL\",\"Title\",\"Parch\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = ShuffleSplit(n_splits=10 , test_size=0.3 , random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(lr, titanic_train[predictors], titanic_train['Survived'], \n",
    "                           cv=cv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8014925373134328\n"
     ]
    }
   ],
   "source": [
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Age\",\n",
    "              \"Fare\",\"NL\",\"NameLength\", \"Fsize\", \"Title\",\"Deck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10, min_samples_leaf=1, min_samples_split=2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(titanic_train.shape[0],random_state=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = cross_val_predict(rf, titanic_train[predictors], titanic_train['Survived'], cv=kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.Series(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(rf, titanic_train[predictors],titanic_train['Survived'], cv=kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.813692480359147\n"
     ]
    }
   ],
   "source": [
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Age\",\n",
    "              \"Fare\",\"NL\",\"NameLength\", \"Fsize\", \"Title\",\"Deck\",\"TicketNumber\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50, max_depth=9,\n",
    "                            min_samples_split=6,min_samples_leaf=4,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=9, min_samples_leaf=4, min_samples_split=6,\n",
       "                       n_estimators=50, random_state=1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(titanic_train[predictors], titanic_train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(titanic_train.shape[0], random_state=1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = cross_val_predict(rf, titanic_train[predictors],titanic_train['Survived'], cv=kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.Series(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(rf, titanic_train[predictors], titanic_train['Survived'], cv=kf )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8316498316498316\n"
     ]
    }
   ],
   "source": [
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Important Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 10.0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1wiEdolrKyspCz5498fjjj2PgwIGmbg5RnbGHT0SkEuzhExGpBAOfiEglGuRZOgaDAYWFhZXODSciouoJIaDX62FjY1PlhWwNMvALCwulc4yJiKhuOnbsWOVpuQ0y8Muv8uzYsWOd7lBYXzIyMuDs7Kyauqasrba6pqzNff7n1y4pKcHZs2ervVK+QQZ++TCOpaWl0aX1SlJbXVPWVltdU9bmPqujdnVD4TxoS0SkEgx8IiKVYOATEakEA5+ISCUY+EREKsHAJyJSCQY+EZFK1Crwk5KS4O3tDU9PT+lJMhXt27cPfn5+8PX1xdixY6VHg8XFxcHDwwN+fn7w8/NDdHR0/baeiIhqrcYLr3Q6HaKjo7Fr1y5YWlpi2LBh6NGjB9q3bw8AuH37NiIiIhAbGwsHBwd8+OGHWLlyJcLCwpCRkYGZM2fCx8dH9h2pjqGkBI3qeLWui4uL7DWIiJRWY+AfOnQIbm5uaNKkCQDAy8sLe/bswfjx4wHce6B2eHg4HBwcANx7/FxSUhIAID09HX/++SfWrVsHR0dHzJ07F4899phc+1KlRpaWOOgXKGsN94RYWbdPRFQfahzSyc3NhZ2dnTRtb28PnU4nTT/++OMYMGAAAKC4uBjr169H//79Adx71ujYsWORmJiIFi1aIDIysr7bT0REtVRjD99gMBjdl0EIUeV9Gm7duoVx48bByckJAQEBAIDVq1dLy0ePHi19MNRWRkZGndavSl2HZ/6qtLS0BrWdh6m22uqasjb3WT21q1Jj4Ddv3hypqanSdF5eHuzt7Y3Wyc3NRXBwMNzc3DB79mwA9z4AYmNj8dprrwG490FhZmZWp8Y5Ozub9MZHdVEfHyxpaWmKfUA1lNpqq2vK2tznf37tu3fvPrCjXOOQTq9evZCSkoL8/HwUFRUhOTkZvXv3lpaXlZVhzJgxGDRoEObMmSP1/q2trfHxxx/j+PHjAIAtW7bUuYdPRET1p8YevoODA6ZMmYKRI0dCr9djyJAh6NKlC0JCQjBx4kRcuXIFmZmZKCsrw969ewHc65kvWrQIy5cvR0REBIqLi/Hkk09i6dKlsu8QERFVrVb3w9dqtdBqtUbzNmzYAADo3LkzTp8+XeXrXF1dERcX9zebSERE9YFX2hIRqQQDn4hIJRj4REQqwcAnIlIJBj4RkUow8ImIVIKBT0SkEgx8IiKVYOATEakEA5+ISCUY+EREKsHAJyJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglGPhERCrBwCciUgkGPhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqQQDn4hIJRj4REQqwcAnIlIJBj4RkUow8ImIVIKBT0SkErUK/KSkJHh7e8PT0xNbt26ttHzfvn3w8/ODr68vxo4di4KCAgBATk4Ohg8fjoEDB+Ltt99GYWFh/baeiIhqrcbA1+l0iI6OxrZt2xAfH4+YmBhkZWVJy2/fvo2IiAisX78eiYmJcHR0xMqVKwEA8+fPR1BQEPbs2QNnZ2esWbNGvj0hIqIHqjHwDx06BDc3NzRp0gTW1tbw8vLCnj17pOV6vR7h4eFwcHAAADg6OuLy5cvQ6/U4evQovLy8AACDBw82eh0RESmrxsDPzc2FnZ2dNG1vbw+dTidNP/744xgwYAAAoLi4GOvXr0f//v1x/fp12NrawtzcHABgZ2dn9DoiIlKWeU0rGAwGaDQaaVoIYTRd7tatWxg3bhycnJwQEBAAnU5Xab2qXvcgGRkZdVq/Ki4uLn97G7WRlpbWoLbzMNVWW11T1uY+q6d2VWoM/ObNmyM1NVWazsvLg729vdE6ubm5CA4OhpubG2bPng0AaNq0KW7duoWysjKYmZlV+bqaODs7w8rKqk6vMZX6+GBJS0tT7AOqodRWW11T1uY+//Nr371794Ed5RqHdHr16oWUlBTk5+ejqKgIycnJ6N27t7S8rKwMY8aMwaBBgzBnzhypF29hYQFXV1fs3r0bABAfH2/0OiIiUlaNPXwHBwdMmTIFI0eOhF6vx5AhQ9ClSxeEhIRg4sSJuHLlCjIzM1FWVoa9e/cCuNczX7RoEcLDwzFz5kysXbsWLVq0wAcffCD7DhERUdVqDHwA0Gq10Gq1RvM2bNgAAOjcuTNOnz5d5etatWqFzZs3/80mEhFRfeCVtkREKsHAJyJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglGPhERCrBwCciUgkGPhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqQQDn4hIJRj4REQqwcAnIlIJBj4RkUow8ImIVIKBT0SkEgx8IiKVYOATEakEA5+ISCUY+EREKsHAJyJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglahX4SUlJ8Pb2hqenJ7Zu3VrtejNmzMCuXbuk6bi4OHh4eMDPzw9+fn6Ijo7++y0mIqK/xLymFXQ6HaKjo7Fr1y5YWlpi2LBh6NGjB9q3b2+0Tnh4OFJSUuDm5ibNz8jIwMyZM+Hj4yNP64mIqNZq7OEfOnQIbm5uaNKkCaytreHl5YU9e/YYrZOUlIR+/fph0KBBRvPT09MRFxcHrVaLadOmoaCgoH5bT0REtVZj4Ofm5sLOzk6atre3h06nM1pn9OjRePnllyu91s7ODmPHjkViYiJatGiByMjIemjyw8NQUlKn9V1cXGTdPhGpW41DOgaDARqNRpoWQhhNP8jq1aul/48ePRoDBgyoU+MyMjLqtH5V6hqif1VaWlqVtQ/6BcpW0z0htsq6f1V9bot1G2Zt7rN6alelxsBv3rw5UlNTpem8vDzY29vXuOFbt24hNjYWr732GoB7HxRmZmZ1apyzszOsrKzq9BpTUeqDRa66aWlpJtkHtdU1ZW3u8z+/9t27dx/YUa5xSKdXr15ISUlBfn4+ioqKkJycjN69e9dY2NraGh9//DGOHz8OANiyZUude/hERFR/auzhOzg4YMqUKRg5ciT0ej2GDBmCLl26ICQkBBMnTkTnzp2rfJ2ZmRmWL1+OiIgIFBcX48knn8TSpUvrfQeIiKh2agx8ANBqtdBqtUbzNmzYUGm9xYsXG027uroiLi7ubzSPiIjqC6+0JSJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglGPhERCrBwCciUgkGPhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqQQDn4hIJRj4REQqwcAnIlIJBj4RkUow8ImIVIKB/w9kKCmp82tcXFxkr0FEplWrh5jTw6WRpSUO+gXKWsM9IVbW7RNR/WMPn4hIJRj4REQqwcAnIlIJBj4RkUow8Kle1fXsHZ4dRKQcnqVD9UruM4R4dhDRX8cePhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqUStAj8pKQne3t7w9PTE1q1bq11vxowZ2LVrlzSdk5OD4cOHY+DAgXj77bdRWFj491tMRER/SY2Br9PpEB0djW3btiE+Ph4xMTHIysqqtM6YMWOwd+9eo/nz589HUFAQ9uzZA2dnZ6xZs6Z+W09ERLVWY+AfOnQIbm5uaNKkCaytreHl5YU9e/YYrZOUlIR+/fph0KBB0jy9Xo+jR4/Cy8sLADB48OBKryMiIuXUeOFVbm4u7OzspGl7e3ucOHHCaJ3Ro0cDANLS0qR5169fh62tLczN75Wws7ODTqerl0YTEVHd1Rj4BoMBGo1GmhZCGE1Xp6r1avO6ijIyMuq0flXqeun+X1Xxw07J2qaqa8raVdU15XYeptrcZ/XUrkqNgd+8eXOkpqZK03l5ebC3t69xw02bNsWtW7dQVlYGMzOzWr+uImdnZ1hZWdXpNaaiVMg2lLqmrF0fddPS0kzWflPV5j7/82vfvXv3gR3lGsfwe/XqhZSUFOTn56OoqAjJycno3bt3jYUtLCzg6uqK3bt3AwDi4+Nr9ToiIpJHjYHv4OCAKVOmYOTIkfD394ePjw+6dOmCkJAQpKenP/C14eHh2LFjB7y9vZGamorJkyfXW8OJiKhuanW3TK1WC61WazRvw4YNldZbvHix0XSrVq2wefPmv9E8IiKqL7zSlohIJRj49I8g94NX/koNooaGD0ChfwS5H7wC8OEr9PBjD5+ISCUY+EREKsHAJyJSCQY+EZFKMPCJiFSCgU9EpBIMfKK/Se5rAHj+P9UXnodP9DfJfQ0Az/+n+sIePhGRSjDwiYhUgoFPRKQSDHyihxQPFlNd8aAt0UOKB4uprtjDJyJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglGPhERCrBwCciUgkGPhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqQQDn4hIJRj4REQqUavAT0pKgre3Nzw9PbF169ZKy0+dOoXBgwfDy8sLc+bMQWlpKQAgLi4OHh4e8PPzg5+fH6Kjo+u39UREVGs1PvFKp9MhOjoau3btgqWlJYYNG4YePXqgffv20jrTp0/HwoUL0bVrV8yePRs7duxAUFAQMjIyMHPmTPj4+Mi6E0REVLMae/iHDh2Cm5sbmjRpAmtra3h5eWHPnj3S8uzsbBQXF6Nr164AgMGDB0vL09PTERcXB61Wi2nTpqGgoECm3SAioprUGPi5ubmws7OTpu3t7aHT6apdbmdnJy23s7PD2LFjkZiYiBYtWiAyMrI+205ERHVQ45COwWCARqORpoUQRtMPWr569Wpp/ujRozFgwIA6NS4jI6NO61fFxcXlb2+jNtLS0kxS21R1TVmb+2zaug1hWw9DXVPXrkqNgd+8eXOkpqZK03l5ebC3tzdanpeXJ01fvXoV9vb2uHXrFmJjY/Haa68BuPdBYGZmVqfGOTs7w8rKqk6vMRWlAqeh1DVlbe7zw1c3LS3NJPtgqrqmqn337t0HdpRrHNLp1asXUlJSkJ+fj6KiIiQnJ6N3797S8latWsHKykr6JEtISEDv3r1hbW2Njz/+GMePHwcAbNmypc49fCJqmAwlJXVav67BV9ftU+3U2MN3cHDAlClTMHLkSOj1egwZMgRdunRBSEgIJk6ciM6dOyMqKgphYWG4ffs2OnXqhJEjR8LMzAzLly9HREQEiouL8eSTT2Lp0qVK7BMRyayRpSUO+gXKtn33hNgq5xtKStDI0rLW2/krHzR12f7DpsbABwCtVgutVms0b8OGDdL/nZycsHPnzkqvc3V1RVxc3N9sIhHRPab6oPmn4JW2REQqwcAnIlIJBj4RkUow8ImIVIKBT0SkEgx8IiKVYOATEakEA5+ISCUY+EREKsHAJyJSCQY+EZFKMPCJiFSCgU9EpBIMfCIilWDgExGpBAOfiEglGPhERCrBwCciUgkGPhGRSjDwiYhUgoFPRKQSDHwiIpVg4BMRqQQDn4hIJRj4REQ1MJSU1Pk1Li4usteoK3PZKxARPeQaWVrioF+grDXcE2Jl3T7AHj4RkWow8ImIVIKBT0SkEgx8IiKVYOATEakEA5+ISCVqFfhJSUnw9vaGp6cntm7dWmn5qVOnMHjwYHh5eWHOnDkoLS0FAOTk5GD48OEYOHAg3n77bRQWFtZv64mIqNZqDHydTofo6Ghs27YN8fHxiImJQVZWltE606dPx7x587B3714IIbBjxw4AwPz58xEUFIQ9e/bA2dkZa9askWcviIioRjUG/qFDh+Dm5oYmTZrA2toaXl5e2LNnj7Q8OzsbxcXF6Nq1KwBg8ODB2LNnD/R6PY4ePQovLy+j+UREZBo1Xmmbm5sLOzs7adre3h4nTpyodrmdnR10Oh2uX78OW1tbmJubG82vDSEEAKCkni411jz2WL1spzp37941SW1T1TVlbe5zw6hrytr/xLo11a6t8swsz9D71Rj4BoMBGo1GmhZCGE1Xt/z+9QBUmq6OXq8HAJw9e7ZW69fEatK4etlOdTIyMkxS21R1TVmb+9ww6pqy9j+xbk2160qv16Nx48aV5tcY+M2bN0dqaqo0nZeXB3t7e6PleXl50vTVq1dhb2+Ppk2b4tatWygrK4OZmVml1z2IjY0NOnbsCAsLi1p/SBARqZ0QAnq9HjY2NlUurzHwe/XqhZUrVyI/Px+PPDOejTcAABbRSURBVPIIkpOTsWDBAml5q1atYGVlhbS0NLi4uCAhIQG9e/eGhYUFXF1dsXv3bmi1WsTHx6N37961anSjRo3w6KOP1nIXiYioXFU9+3IaUd1gTwVJSUlYt24d9Ho9hgwZgpCQEISEhGDixIno3LkzTp8+jbCwMNy+fRudOnXCe++9B0tLS2RnZ2PmzJm4du0aWrRogQ8++ACPyTwORkREVatV4BMR0cOPV9oSEakEA5+ISCUY+EREKsHAJyJSCQY+EZFKMPCJFFJQUGDqJqgWT0a8h4EP4IsvvjCaLioqQmRkpGL1TRkESteOjo5WtF5Fer0e+/fvR3x8vNE/uZ06dQoDBw6En58fdDodBgwYgJMnT8peFwAuXLiAxMRECCEwd+5cBAYGIj09XZHawL1reKKjo1FUVKTIzxoAtm3bZjR9+vRpDB06VJHa94uIiDBJ3eow8AHs27cPb775Jq5du4ZffvkF/v7+aNRI/h+NKYPAVLV/+OEHk/W2Jk2ahFWrVuHw4cM4cuSI9E9uCxcuxOrVq9GkSRM4ODggIiIC4eHhstcFgFmzZsFgMOC7777Dn3/+iVmzZmHRokWK1I6KisL+/fuRnJyMsrIyxMbGYvHixbLX/eqrr7B582bcvXsXS5cuRUhICIYPHy573aokJiaapG61BAkhhNiyZYtwdXUVHh4e4sSJE4rUDAoKEllZWcLPz08IIcSBAwdEYGDgP7r2iBEjhKenp5gyZYqYOXOm9E8JXl5eitS5X0BAgBBCSD9rIYTQarWK1C5/T2fPni1iYmKM2iM3Pz8/YTAYpP3W6/Vi0KBBstctKioSwcHBwsPDQ4SFhYkbN27IXrM6Xbt2NVntqtR4Lx01OHz4MDZv3oyXXnoJf/zxB9auXYvw8HA4ODjIWreoqAhPP/20NO3u7o4lS5bIWtPUtQMCAmSvUZ02bdogJycHLVu2VLRukyZNcPr0aelGgImJiYrdYsTMzAx79+7Fjz/+iEmTJmHfvn2KfHsFINUp3++SkhJZa1ccMvL09MSpU6dgbW2NH374AQDg7+8vW+3qNLSbPzLwAcyePRvvvvsu3NzcAABbt27FkCFD8PPPP8ta15RBYKraAQEBuHTpErKysuDh4YHLly/jiSeekLXmiBEjoNFokJ+fD61WCycnJ5iZmUnLP//8c1nrR0REIDQ0FOfOnYOrqyvatm2LZcuWyVqzXGRkJD777DPMmzcP9vb2+Prrr7Fw4UJFag8cOBCTJ09GQUEBPvvsMyQmJuKll16Srd79w3O9e/fGzZs3pflyBX7579f9hBD1co/7+sR76QAoLCysdDvRS5cuoXXr1rLWvXDhAkJDQ5Geno7GjRtLQdCuXTtZ61ZXOyoqCk899ZSsdXfv3o21a9eiuLgY27dvh6+vL2bMmAE/Pz/Zav7yyy8PXN69e3fZald0584dGAwG2NraKlKvXG5uLuzt7ZGamoozZ84gMDDwgXdUrE8///wzDh06BIPBADc3N/Tp00eRupmZmXjmmWdw69YtZGRkoGfPnrLViouLqxT45bGq0WhM8s2iOgx83HtMY1hYGLKzs7FlyxZMmzYN7777ruyBX84UQbB9+3YMGzZM8doBAQHYvHkzXn31VcTHxyM3Nxevv/46vv76a9lrL1iwAHPnzjWaFxoaKvtQ1v09QI1Gg8aNG6Ndu3YYM2aMrN+swsPDodfr8cYbbyA4OBju7u4oKSlBVFSUbDXLLVy4EOPHj0eTJk2kedOnT5f9283777+PkydP4tNPP0Vubi6mTp2K7t27Y8KECbLU69u3b6V5Go0GeXl50Ov1OHXqlCx1/woO6QCYN28egoOD8f7778POzg4+Pj4IDQ3F1q1bZalX3VfAcnIPMQDAli1bMGzYMFhbW8teq6JGjRoZfbjY29vLPqY8Z84cXLx4ERkZGTh37pw0v6ysDDdv3pS1NgC0b98e5ubmCAwMBHDvLJIrV67AwcEBc+bMwapVq2SrnZ6ejtjYWKxatQpDhgzBhAkTpHbILT4+HgcPHkR0dDScnJwAAFlZWbLX/eGHH5CQkADg3u/Xxo0bERAQIFvgf//990bThYWFWLJkCQ4cOGD07JCGgIEP4Pr16/Dw8EBUVBQ0Gg2GDh0qW9gDkO0Xry6aN2+OkSNH4tlnn4WVlZU0f/z48bLW7dChA7Zs2YLS0lKcOnUK27Ztk8JALm+//Tays7OxaNEio/0zMzMzOnAtl+PHj2PXrl3StJOTEwIDAxEVFSX7uellZWXSaZnz589HUVERioqKZK1ZrnXr1oiMjMSECRMwadIk+Pj4KFK3tLQUxcXF0jBt+SNTlZCSkoKwsDC4u7sjMTFR8eG7mjDwce8JMVeuXJF63ampqbC0tJStXvmYcXVDDEqMKXft2lX2GlWZN28e1q5dCysrK8yePRtubm4IDQ2VtWbr1q3RunVrrF27ttLQisFgkLU2cC9wzp07hw4dOgC496xmg8GA4uJi2cPI398fHh4e6NatG5599ll4e3srdhGSRqNBly5dsGXLFowbNw4nT56Eubn8kTNs2DAMHjxYGmr56aefZD8P/86dO1i8eLHUq3d3d5e13l/FMXzc+9obFhaGCxcuoE2bNigoKMCHH36IZ599VpZ6FYcYnJ2dpfnlQwxJSUmy1H0QIQQuXbok+xkzwL0APH/+PCwsLPDkk08anTEjp4CAAJw7dw4dO3aEEALnzp2DnZ0dzMzMsGDBAtkO7B05cgShoaFo1qwZhBAoKCjAsmXL8N133+Gxxx7Dm2++KUvdcgaDQRo2y8/PR9OmTWWtV87f31/6BlNSUoJZs2Zh9+7dioxpp6en4+jRozA3N4erqyueeeYZ2WpV7NWHhoZW+zzZhkD1gf/DDz+gffv2aN68OdavX48jR47gueeew/jx42FhYSFLzUuXLklDDGFhYdL88iGGige55BITE4MlS5YYfb1v3bo1vv32W1nr/vLLL5g+fTqaNWsGg8GAO3fu4P3330fnzp1lrQsAY8aMwfjx46UP2TNnzmDVqlWYPXs2xo8fj9jYWNlql5aWIjMzEz/99BMOHDiAM2fO4NixY7LVK/fbb79h3bp1uHPnDoQQMBgMyMnJqTTuLIfS0tJKPfpjx47hueeek712UlISsrKy8NZbbyE5OVnWM2WcnJxgbm4Oe3t7o2+QQghoNBp89913stWuK1UP6XzyySfYvXs3lixZgvPnz2PDhg2YM2cOTp06haVLl2LOnDmy1LWyskKPHj3w0UcfVVp2584dRQJ/3bp1SEhIwPLlyzFlyhTs378fv/76q+x1Fy9ejPXr18PR0RHAvZ7Y/PnzsXPnTtlrZ2dnG32jcnR0xIULF9CiRQtZh3YuXryIHTt2IDY2Fjdv3sSYMWOwZs0a2epVNHv2bAQHByMuLg4jRoxAcnKyrL1dAJg7dy4WLFiA119/vcqTE+Q+KSEqKgpXrlzByZMnERISgtjYWJw+fRozZ86UpV5DCvSaqDrwExISEBMTg0ceeQRRUVHo27cvXn75ZQgh4O3tLVvdAQMG4LfffsOrr74KjUZjdG8ZpXoEzZo1wxNPPAFHR0ecPXsWw4cPr3QTOTkIIaSwB4DOnTujrKxM9roA8MQTTyAqKgp+fn4wGAz46quv0LZtWxw7dkyWM4W+/fZbbN++HSdPnsSAAQOwbNkyzJ07V/YD4xVZWloiMDAQ2dnZ+Ne//oWlS5dCq9XKWvOVV14BYLqTEw4cOIC4uDgEBATA1tYWGzduhK+vr2yB36pVK1m2KwdVB75Go8EjjzwC4N44a1BQkDRfTk8++SSAyqdzKemRRx7B4cOH4ejoiH379qFz584oLi6Wrd7Ro0cBAO3atcO8efMwZMgQmJubIykpSZHhHABYunQpVq1ahalTp8LMzAw9e/bEu+++i++//x7z58+v93oTJkzAoEGDEBMTg7Zt2wJQ/lJ7Kysr3LhxA0899RSOHz+Onj17yv4BW/4tqmvXrvj999/h5OSEpKQkZGZmIiQkRNbaACp9eMt9S4eHiaoD38zMDDdv3sSdO3dw6tQp6ch6dna2rGcTmPL+GjqdDg4ODpg7dy527tyJ0NBQ7Ny5E4MGDZK157lixQqj6YoX3yj187C1ta2yl+fr6ytLvcTEROzatQtBQUFo1aoVXnrpJcW+zZR77bXXMGXKFKxcuRIvv/wykpKSjIa15DR9+nS0bt0aJSUlWLlyJfz8/DBr1iysW7dO1rrlt3S4efMmPvvsMyQkJCh2SmhDp+qDtnv27MHSpUtRWlqKvn37IiIiArt370Z0dDTGjRsn24EeZ2fnKm/MpsRBnoCAAMTFxQEAPv30U7zxxhuy1Wpodu3ahSVLlkgXW5X/vOU+a6S0tBQ//vgjdu3ahZ9++gm9evXC8OHD8cILL8hat1z5ft65cwd//vkn/u///k+RD9nAwEDExsZi2bJl0tlI5fPkdPbsWWRmZuLzzz9Hq1atEBgYiBdffFHWmg8LVffwBw4ciOeeew7Xr1+XLv6xsbHBwoUL0aNHD9nqtm3bFuvXr5dt+w9S8fM9KSlJ8cBPTU3Fpk2bKj14RYmri9esWYPNmzejY8eOsteqyNzcHP3790f//v2Rn5+P+Ph4vP/++7IG/qxZsx64/L333pOtdrmysjLk5+dj3759WLlyJfLy8mS9mdi1a9cwceJEZGVloW3btjA3N8fhw4dRXFwMFxcXPProo7LVflioOvABwMHBwai3rUSvy8LCwmQHeu4/bUxpM2fOxPjx4xW/RTFw7zJ7pcP+fk2bNsUbb7wh+wetUjeEq0r5sGFwcDCGDh2Kvn37omPHjvDy8sKkSZNkq/v+++/DxcUFn332mXRKtV6vx4oVK7Bo0SJFHr7S0Kk+8E2hW7dupm4CANMcS3BwcDDZ3QM7deqEiRMnwt3d3eh2Eg3pbob1pfy5A7dv30ZCQgKGDx8OnU6H7du3y36h15gxYxAXFwetVovc3FwEBwcDuHenVDkvsjt27Bi++eYbo3kWFhZ45513ZL0b68OEgW8C8+bNM1ntc+fOoV+/fgDu9cTK/6/URSIjRozAtGnT4ObmZnRgXInQvX37NmxsbPDbb78Zzf8nBn65adOmSafB2tjYwGAwYMaMGVi5cqVsNSt+c/zqq6+kwJf7iuqKH+IVaTQanqXz/zHwVWbv3r0mrR8bG4u7d+8iLS3NaL4SoVs+bl1QUKDYg2ZMLScnR7rAz9bWFlOmTJG9t2uqYcMHfWNtaE+eMhUGvsqY+iKRq1evSmcJKe306dOYPHkyiouLERMTg1dffRXLly9Hp06dTNIeJWg0Gpw5c0bq5Z8/f16RG5hVrK+Uit9eKxJCIC8vT7F2NGSqPi2TlBceHo4XX3wRvXv3VuymaeWGDx+OyMhITJ061ehe7Urc1sFUUlJSMG3aNOnEhOvXr2PZsmVwdXWVrWbF047LD+AC8g8bZmdnP3C5qTs7DQF7+KSo7777DjExMUbzlDgXHjDtQ+OVptPpsHTpUpw7dw4vvPAChg0bBktLS7Rr107WW38Dphs2ZKDXjIFPijpw4IDJapvyofFKmz17Njp27AitVou9e/fiiy++UOTce4DB25BxSIcUVd3j/JS4oZgpHxqvNB8fH3z11VcA7p2L7u/vr8hzg6lhYw+fTEav1+Pnn3+W7UEz92vTpg2++OILowe3X7lyRZHaSqv4LAcLCwvZnu1ADxcGPinq/p78uHHjFL+9Q8UHt3t7eyvyHABT42mJBDDwycQKCwuRk5Njsvr/1BHN+09RLL/IriE+hYmUw8AnRfXt21fqbZY/33X06NEma88/tedr6gvsqGFi4JOiNm/eLP1fo9HgX//6F2xtbWWtWf7wlfuVP+P1n4hnylBVGPikiOpCt/ximf/85z+y1b7/4SsVKfW0LaKGgKdlkiJGjBhRaZ5Go5FuZHbixAmlm0SkOgx8MokrV65gzpw5uHHjBhYvXowOHTrIXjM7OxthYWHIzs7G1q1bMXXqVLz77rto3bq17LWJGgLeM5QUt3PnTgwePBjdu3fHjh07FAl74N5tqYODg2FtbY1///vf8PHxQWhoqCK1iRoCBj4pRqfTITg4GF988QU2bdqEt956S9EbqF2/fh0eHh4A7g0nDR06FLdv31asPpGpMfBJETt37kRAQAC6d++OL7/8UrFefUWNGzfGlStXpFMxU1NTZb+RGFFDwjF8UkT5Q+KByg/IUOpumenp6QgLC8OFCxfQpk0bFBQUYPny5ejatavstYkaAgY+qYper8eff/6JsrIyRW4VTNSQ8Dx8UtSECRMqPU911KhR2LRpk+y1f//9d+zYsQMFBQVG85W6bTCRqTHwSRHjx49HZmYmcnNzje7xUlpaihYtWijWBm9vb+lxf0RqwyEdUsTt27dx48YNLFq0CGFhYdJ8c3NzNGvWTJHnrA4bNgzbt2+XvQ5RQ8XAJ8WlpaXh7NmzCAwMxPHjx2W9rUJFMTExyMnJgZubm9EHjFL1iUyNQzqkqE2bNmHfvn3Izc3FwIEDMW/ePAwZMgTBwcGy1z527Bh+/fVXo/vfazQafP7557LXJmoIGPikqLi4OOzYsQNDhw7F448/jp07d+Lll19WJPBPnjyJ5ORk2esQNVS88IoU1ahRI6NTIa2srBS72rZDhw44ffq0IrWIGiL28ElR3bt3x5IlS1BUVIR9+/YhJiYGPXr0UKT277//joCAANjZ2cHCwoJPfyLV4UFbUpTBYMCOHTtw6NAhGAwGuLm54b///a8ivfzye+/fjw8LIbVg4JOiTpw4gS5dukjTRUVF+PDDDzFz5kzZa5eUlGD//v0oLCwEAJSVleHSpUuYNGmS7LWJGgKO4ZOipk+fjmPHjgEA9u/fj5deegk3b95UpPY777yDzz//HNHR0fj5558RHR2N8+fPK1KbqCFgD58U9ccff2DChAl44okncOnSJYSHh8PV1VWR2gMGDEBycjIWLVqEwMBA2NraYvLkyYiNjVWkPpGpsYdPisjJyUFOTg6srKwQERGB9PR0jBs3Di1btkROTo4ibWjWrBk0Gg2eeuopnDlzBk888QT0er0itYkaAp6lQ4p49dVXodFoUP6F0tLSEkuXLgUAxc6U6dChAxYsWID//ve/mDZtGnJzc8EvuKQmHNIhxen1elhYWECv16OkpAQ2Njay1iv/BlFWVgadToeWLVviu+++Q0pKCqZMmWKSh7EQmQIDnxT1zTffYM2aNUhKSsKFCxcwYsQIzJ07F/3795etZt++faWHrpT/ums0GuTm5qK0tFSRh68QNQQMfFKUVqvFxo0b8e9//xsAcO3aNbzxxhtISEhQrA2FhYVYsmQJDhw4gAULFsDd3V2x2kSmxIO2pCi9Xi+FPXDvQKqSfY6UlBT4+voCABITExn2pCo8aEuKcnFxwTvvvAOtVguNRoPdu3cr8kzZO3fuYPHixezVk6pxSIcUVVJSgs2bN+Po0aMwNzeHq6srgoKCZH22bEpKCsLCwuDu7o7Q0FDZDxITNVQMfFJEXl4e7Ozsqj3nvmXLlrLVdnJygrm5Oezt7aWDtwB48zRSHQY+KaJr16747bffjM6YAZQJ3epumlaON08jteAYPiniySefBAB8//33itdmoBPdw7N0SBEVe/VEZBoc0iFFODs7w8HBodJ8jqMTKYdDOqSItm3bYv369aZuBpGqMfBJERYWFhxLJzIxjuGTIrp162bqJhCpHsfwiYhUgj18IiKVYOATEakEA5+ISCUY+EREKsHAJyJSif8HGgxDUmTbMV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std = np.std([rf.feature_importances_ for tree in rf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "sorted_important_features=[]\n",
    "for i in indices:\n",
    "    sorted_important_features.append(predictors[i])\n",
    "#predictors=titanic.columns\n",
    "plt.figure()\n",
    "plt.title(\"Feature Importances By Random Forest Model\")\n",
    "plt.bar(range(np.size(predictors)), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(np.size(predictors)), sorted_important_features, rotation='vertical')\n",
    "\n",
    "plt.xlim([-1, np.size(predictors)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectKBest,f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Age\",\n",
    "              \"Fare\",\"NL\", \"Fsize\",\"NameLength\",\"Deck\",\"Embarked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=5)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(titanic_train[predictors],titanic_train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = -np.log(selector.pvalues_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.argsort(scores)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_important_features = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in indices:\n",
    "    sorted_important_features.append(predictors[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 9.0)"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Features Important by SelectKBest')\n",
    "plt.bar(range(np.size(predictors)),scores[indices],\n",
    "        color='seagreen',align='center')\n",
    "plt.xticks(range(np.size(predictors)),sorted_important_features, rotation='vertical')\n",
    "plt.xlim([-1,np.size(predictors)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8026119402985075\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\",\"NL\",\n",
    "              \"Fsize\", \"Title\",\"Deck\"]\n",
    "\n",
    "# Initialize our algorithm\n",
    "lr = LogisticRegression(random_state=1)\n",
    "# Compute the accuracy score for all the cross validation folds.  \n",
    "cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=50)\n",
    "scores = cross_val_score(lr, titanic_train[predictors], titanic_train[\"Survived\"],cv=cv)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8257462686567164\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\",\"NL\",\n",
    "              \"Fsize\", \"Title\",\"Deck\",\"TicketNumber\"]\n",
    "adb=AdaBoostClassifier()\n",
    "adb.fit(titanic_train[predictors],titanic_train[\"Survived\"])\n",
    "cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=50)\n",
    "scores = cross_val_score(adb, titanic_train[predictors], titanic_train[\"Survived\"],cv=cv)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}