titanic_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CazISR8X_HUG"
   },
   "source": [
    "# A feladat - Túlélés a Titanicon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vgC61-ah_WIz"
   },
   "source": [
    "## Az adatok beimportálása"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UrxyEKGn_ez7"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('DA_titanic_train.csv')\n",
    "dataset_pred = pd.read_csv('DA_titanic_test.csv')\n",
    "final_test = dataset_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>C85</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.9250</td>\n",
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       "      <th>3</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",
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       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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            5         0       3   \n",
       "4           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3                           Allen, Mr. William Henry    male  35.0      0   \n",
       "4                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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            373450   8.0500   NaN        S  \n",
       "4      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
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       "      <th>0</th>\n",
       "      <td>4</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",
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       "      <td>Moran, Mr. James</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
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       "   PassengerId  Pclass                                               Name  \\\n",
       "0            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "1            6       3                                   Moran, Mr. James   \n",
       "2            7       1                            McCarthy, Mr. Timothy J   \n",
       "3            8       3                     Palsson, Master. Gosta Leonard   \n",
       "4            9       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "\n",
       "      Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "0  female  35.0      1      0  113803  53.1000  C123        S  \n",
       "1    male   NaN      0      0  330877   8.4583   NaN        Q  \n",
       "2    male  54.0      0      0   17463  51.8625   E46        S  \n",
       "3    male   2.0      3      1  349909  21.0750   NaN        S  \n",
       "4  female  27.0      0      2  347742  11.1333   NaN        S  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A megoldás során a két adattábla ugyanúgy kerül előkészítésre. A dependent variable csak a DA_titanic_train.csv-ben található meg, ezért az előkészítés után ez az adattábla kerül felbontásra (train/test) a legjobb modell megtalálásának érdekében."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 624 entries, 0 to 623\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  624 non-null    int64  \n",
      " 1   Survived     624 non-null    int64  \n",
      " 2   Pclass       624 non-null    int64  \n",
      " 3   Name         624 non-null    object \n",
      " 4   Sex          624 non-null    object \n",
      " 5   Age          494 non-null    float64\n",
      " 6   SibSp        624 non-null    int64  \n",
      " 7   Parch        624 non-null    int64  \n",
      " 8   Ticket       624 non-null    object \n",
      " 9   Fare         624 non-null    float64\n",
      " 10  Cabin        128 non-null    object \n",
      " 11  Embarked     623 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 58.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 267 entries, 0 to 266\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  267 non-null    int64  \n",
      " 1   Pclass       267 non-null    int64  \n",
      " 2   Name         267 non-null    object \n",
      " 3   Sex          267 non-null    object \n",
      " 4   Age          220 non-null    float64\n",
      " 5   SibSp        267 non-null    int64  \n",
      " 6   Parch        267 non-null    int64  \n",
      " 7   Ticket       267 non-null    object \n",
      " 8   Fare         267 non-null    float64\n",
      " 9   Cabin        76 non-null     object \n",
      " 10  Embarked     266 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 23.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>count</th>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>459.128205</td>\n",
       "      <td>0.383013</td>\n",
       "      <td>2.328526</td>\n",
       "      <td>29.658745</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>30.523470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.314092</td>\n",
       "      <td>0.486511</td>\n",
       "      <td>0.822684</td>\n",
       "      <td>14.770773</td>\n",
       "      <td>1.071871</td>\n",
       "      <td>0.775559</td>\n",
       "      <td>48.186064</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>0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>241.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>460.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.931250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>683.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</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>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   624.000000  624.000000  624.000000  494.000000  624.000000   \n",
       "mean    459.128205    0.383013    2.328526   29.658745    0.519231   \n",
       "std     257.314092    0.486511    0.822684   14.770773    1.071871   \n",
       "min       1.000000    0.000000    1.000000    0.670000    0.000000   \n",
       "25%     241.750000    0.000000    2.000000   20.000000    0.000000   \n",
       "50%     460.500000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     683.250000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  624.000000  624.000000  \n",
       "mean     0.354167   30.523470  \n",
       "std      0.775559   48.186064  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.895800  \n",
       "50%      0.000000   13.931250  \n",
       "75%      0.000000   30.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>415.318352</td>\n",
       "      <td>2.262172</td>\n",
       "      <td>29.789773</td>\n",
       "      <td>0.531835</td>\n",
       "      <td>0.445693</td>\n",
       "      <td>36.132224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>255.301178</td>\n",
       "      <td>0.866346</td>\n",
       "      <td>13.995008</td>\n",
       "      <td>1.173773</td>\n",
       "      <td>0.871354</td>\n",
       "      <td>52.935439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>174.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>412.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>629.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   267.000000  267.000000  220.000000  267.000000  267.000000  267.000000\n",
       "mean    415.318352    2.262172   29.789773    0.531835    0.445693   36.132224\n",
       "std     255.301178    0.866346   13.995008    1.173773    0.871354   52.935439\n",
       "min       4.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%     174.500000    1.000000   21.000000    0.000000    0.000000    8.050000\n",
       "50%     412.000000    3.000000   29.000000    0.000000    0.000000   16.100000\n",
       "75%     629.500000    3.000000   39.000000    1.000000    1.000000   36.125000\n",
       "max     890.000000    3.000000   70.000000    8.000000    5.000000  512.329200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Az elemzéshez fel nem használt értékek eltávolítása\n",
    "dataset = dataset.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1)\n",
    "dataset_pred = dataset_pred.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Az oszlopok rendezése a könnyebb átláthatóság érdekében\n",
    "dataset = dataset[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Survived\n",
       "0       3    male  22.0      1      0   7.2500        S         0\n",
       "1       1  female  38.0      1      0  71.2833        C         1\n",
       "2       3  female  26.0      0      0   7.9250        S         1\n",
       "3       3    male  35.0      0      0   8.0500        S         0\n",
       "4       2  female  14.0      1      0  30.0708        C         1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       1  female  35.0      1      0  53.1000        S\n",
       "1       3    male   NaN      0      0   8.4583        Q\n",
       "2       1    male  54.0      0      0  51.8625        S\n",
       "3       3    male   2.0      3      1  21.0750        S\n",
       "4       3  female  27.0      0      2  11.1333        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A hiányzó adatok kezelése"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Sex           0\n",
       "Age         130\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      1\n",
       "Survived      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Sex          0\n",
       "Age         47\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Fare         0\n",
       "Embarked     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_pred.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "Survived    0\n",
      "dtype: int64\n",
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "\n",
    "age_mean = dataset['Age'].mean()\n",
    "age_std = dataset['Age'].std()\n",
    "\n",
    "for ds in (dataset, dataset_pred):\n",
    "    for i in range(len(ds['Age'])):\n",
    "        if type(ds['Age'][i]) != int:\n",
    "            ds.iloc[i, 2] = randint(round(age_mean - age_std), round(age_mean + age_std))\n",
    "    for i in range(len(ds['Embarked'])):\n",
    "        if type(ds['Embarked'][i]) != str:\n",
    "            ds.iloc[i, 6] = ds['Embarked'].mode()[0]\n",
    "    print(ds.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Az adattábláról"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dependent variable megléte miatt ez a rész a DA_titanic_train.csv alapján készült"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.328526</td>\n",
       "      <td>29.608974</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>30.523470</td>\n",
       "      <td>0.383013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.822684</td>\n",
       "      <td>8.996574</td>\n",
       "      <td>1.071871</td>\n",
       "      <td>0.775559</td>\n",
       "      <td>48.186064</td>\n",
       "      <td>0.486511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.931250</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Age       SibSp       Parch        Fare    Survived\n",
       "count  624.000000  624.000000  624.000000  624.000000  624.000000  624.000000\n",
       "mean     2.328526   29.608974    0.519231    0.354167   30.523470    0.383013\n",
       "std      0.822684    8.996574    1.071871    0.775559   48.186064    0.486511\n",
       "min      1.000000   15.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      2.000000   21.000000    0.000000    0.000000    7.895800    0.000000\n",
       "50%      3.000000   30.000000    0.000000    0.000000   13.931250    0.000000\n",
       "75%      3.000000   38.000000    1.000000    0.000000   30.000000    1.000000\n",
       "max      3.000000   44.000000    8.000000    6.000000  512.329200    1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Új változó létrehozása az elemzéshez\n",
    "\n",
    "isAlone = []\n",
    "\n",
    "for i in range(len(dataset)):\n",
    "    if dataset.iloc[i, 2] > 0 and dataset.iloc[i, 3] > 0:\n",
    "        isAlone.append(0)\n",
    "    else:\n",
    "        isAlone.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003288</td>\n",
       "      <td>0.088388</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>-0.535236</td>\n",
       "      <td>-0.375041</td>\n",
       "      <td>0.051772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.003288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032177</td>\n",
       "      <td>0.018270</td>\n",
       "      <td>-0.059708</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>-0.016760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.088388</td>\n",
       "      <td>-0.032177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434930</td>\n",
       "      <td>0.165351</td>\n",
       "      <td>-0.018764</td>\n",
       "      <td>-0.708491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.018606</td>\n",
       "      <td>0.018270</td>\n",
       "      <td>0.434930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209408</td>\n",
       "      <td>0.095096</td>\n",
       "      <td>-0.366164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>-0.535236</td>\n",
       "      <td>-0.059708</td>\n",
       "      <td>0.165351</td>\n",
       "      <td>0.209408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285250</td>\n",
       "      <td>-0.187943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.375041</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>-0.018764</td>\n",
       "      <td>0.095096</td>\n",
       "      <td>0.285250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.132843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsAlone</th>\n",
       "      <td>0.051772</td>\n",
       "      <td>-0.016760</td>\n",
       "      <td>-0.708491</td>\n",
       "      <td>-0.366164</td>\n",
       "      <td>-0.187943</td>\n",
       "      <td>-0.132843</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pclass       Age     SibSp     Parch      Fare  Survived   IsAlone\n",
       "Pclass    1.000000  0.003288  0.088388  0.018606 -0.535236 -0.375041  0.051772\n",
       "Age       0.003288  1.000000 -0.032177  0.018270 -0.059708  0.029872 -0.016760\n",
       "SibSp     0.088388 -0.032177  1.000000  0.434930  0.165351 -0.018764 -0.708491\n",
       "Parch     0.018606  0.018270  0.434930  1.000000  0.209408  0.095096 -0.366164\n",
       "Fare     -0.535236 -0.059708  0.165351  0.209408  1.000000  0.285250 -0.187943\n",
       "Survived -0.375041  0.029872 -0.018764  0.095096  0.285250  1.000000 -0.132843\n",
       "IsAlone   0.051772 -0.016760 -0.708491 -0.366164 -0.187943 -0.132843  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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P3/k/8vLyAEhMOMriBfO5+LLRFa5Tm6UkJRAe2aD4dnhkFCknSdomvv48d1w1jLhD+xl56dUAjLj0ag4f3MfYq4fzyB1Xc8vdj2Kx1N4Pqey0ZAJCT+zXASHl9/2c9GQCQ0q8PkIiyPmnjQjzPxjH7FfvZ9fiORVuY8+yP2h0dnfnB1+TRJx/qaUc3Vt/BApFpBXwKdACqLguq5zGGIdalb9LpMp1z7vsZq5/7Sta9xzM5j9/Kb6/Qct2jH5xMpc//S7r5kynID+vWjG7TvkOlasIVdRnEfKyMzmyeQUjx33CRS9MpSAvl4Or/wJg75LZdLn0di587nO6jLqdNdPec0Hsp8+xXaGCv1EVbz4DbnqIrX//woyX7yM/NweLl4eMHlfY1/LNbr/7Qb74bhYffj6NY8eOMe2rTwH48J3/4457HqrVlYaTMRW9uE/yPN/z2PNM/u43GjdrwdK//wBg/eplND+rLVO+m8vrk7/l0w9eIzsr05Uhn6aqn+uK/ya2/4Y//DoXPfke59/zIjsW/srRXZtLNdv02zQsVistzh3srICVizmauBQZYwqAy4B3jDEPAdEnaywiY0VktYisLkja4ow466TA0AgyU058k8pKTSIwJLxcm5LfkrNSkwioH+7QugCteg5i39ol5e4PjW6Gt68fqYf3O6Enp88/xFZJ+kdOejJ+ZYZ1/EPCySnZJi0Zv+AwEnauJzC8Ab5B9bFYvWjcuQ/J+7cBcGDVnzTqbJvj0PicfqQe3FkDvak+W/Wk9PMZUNW+kFa+TVkhDZsy8sFXuOzp9znrvIEER570Ze12M3/4lrE3XMnYG64kPCKKxIT44mWJCUcJj4gqt054RCQigo+PDyMuupTtW20fWju3beWlZx7n2kuHs/CvP3jv9ZdZvGB+jfWlun77eTqP/vsaHv33NYSFR5KceLR4WXJiAmHhESdd12q10mfQMJYvsvXvr99m0bO/bVglunFToho24vCh/a7uwikLCIkgu0TlMDstCf/64eXaZKWVeH2UaPPPa8CvXghNu/Qm+cCO4nZ7ls/j8OZV9L350SqT/FpPh4rKyReRa4CbgP/Z7zvpTC5jzBRjTA9jTA+viLNPN8Y6K6p5G9IT4jiWGE9hQT67Vy0gpkuvUm1iuvRi5/L5GGM4umcbPv6BBIaEVbpu+tHDxesfWL+ckIZNADiWGE9RYSEAGclHSYuPJSi8AbVBaNPWZCbGkZUcT1FBPrHrFtLo7PNKtYk+uycHVv2JMYbk/dvx9g/Av34YAaGRJO/fTkFeLsYYEnZuoF5UUwD8g8NI2mP7MEvctZGgyEY13jdHRDZvw7GEODKSbM/n3tXl94VmXXqxy74vJOy17QsBVczZyTmWBtjmAK2bPY12Ay50VRdO26VXXsOUr35gylc/0Hfg+fw+exbGGLZu3kBgUBDhEZHl1vln3osxhiUL/6RFS9uRJl/P+I1vZs7lm5lzGTD4Au5/7Gn6DRxSo/2pjhGjRvPG5G95Y/K3nNt3EAv++BVjDDu3biIgMIjQ8NJ9N8Zw5PCh4utrli+kcbPmAERENWTT2pUApKUmE3foAA2iG9dof6ojPKYNGQmHybTv+/vXLKRJp56l2jTp3JN9K2yv/cR924v3/YLjueTn2uY6FhzP5ci2tYRE2ya0x21ZzdY/fmDQv5/Fy8evxvulTp2jdeFbgDuBl40x+0SkBfBf14V1+qZOuJn+3VsTERLE7t/GM37SbKbOXObusKrFYrXS79q7mP3OMxhTSNu+wwhrHMPWv38FoMOgi2jW6VwOblrFtKdvxcvHj0E3P1TpugArfvqctPhYRISg8CgGXH8fAPG7t7B+znQsVi/EIvS77h7869WvOLgaZrFaOeeKO1k8+TlMURHNew4lODqGvUtsY9Yt+46kYYcexG9bzdyXx2L18aXHmAcACItpS5MufZn/5oNYLFZCGrekRZ8RAHS7+l7bIdZFhVi8fOg2+l639bEyFquVPmPuYs67z2CKCmnTdxihjWLYtsC2L7QfeBFNO57LoU2rmP6MbV8YcNNDxev/+cmrHNmxkdzMY3zzxPV0v/gG2vYbzp5Vf7P1b9t3keZd+9Cmj2ccEtyzT39WLF3IDVdeiJ+fH48981Lxsv88dBePPPUCEZFRvPLck6SnpWAMnNW6LQ898awbo3aObj37sW7lEu67cRQ+vn7c89jzxcteeep+7nx4HCFh4Ux87bniIaCYlq2544H/AHDl9Xcw8fXnePh22/ye6++4n+D6oTXeD0dZrFbOHX0X8yeOwxQVcVbvCwhpFMPORbMBaNP/QhqffS5xW1bz8/O32w6Hvt627+dkpLJgyssAmMJCmp87kEZn9wBg5fRJFBXkM//9pwGIaNGOntfUzte/Qzy9YlQNUuHYYGUriIQCTY0xGx1p79/13upt4Azy8rsPuzsEt0rKLHB3CG4VGuAh80Vc5JouTdwdglulZNaW+WE17+cd8VU3OsONG9qqRjMJ/2GvO/2zNuf3x2plNuToUUV/i0iwiIQBG4DPReQt14amlFJKKVWao3Nc6htjjgGXA58bY7oDQ10XllJKKaUcpodDl+MlItHAaE5MzlVKKaWUqlGODsK/CMwFFhtjVolIS2CX68JSSimllMNq8eHLzuZQ4mKM+R74vsTtvcAVrgpKKaWUUtVQi4d2nM2hxEVE/IDbgLOB4gPejTH6i1RKKaWUqjGO1pa+AhoCw4EFQBMgw1VBKaWUUqoa9My55bQyxowDsowxU4GLgE6uC0sppZRSqjxHJ+fm2/9PE5GOQDzQ3CURKaWUUqp6dI5LOVPsZ8wdB8wCggDPP3e2UkoppTyKo0cVfWK/ugBo6bpwlFJKKVVttXhOirNVmriISKU/tmOM0dP+K6WUUu5WhxKXqnpar4qLUkoppeooERkhIjtEZLeIPFnB8voi8ouIbBCRLSJyy+lus9KKizHmhdPdgFJKKaVczA2Tc0XECkwELgBigVUiMssYs7VEs3uArcaYi0UkEtghIl8bY07559Md/XXoqSISUuJ2qIh8dqobVUoppZTHOw/YbYzZa09EpgGjyrQxQD0REWwH9qQABaezUUePKupsjEkrjsKYVBHpejobVkoppZSTuGeOS2PgUInbsUDPMm0+wHY0chy2KSZXG2OKTmejjvbUYj8cGgARCcPxpEcppZRSriTi9IuIjBWR1SUuY8tutYJITJnbw4H1QCPgHOADEQk+na46mny8CSwTke/tQY0GXj6dDSullFKq9jLGTAGmVNIkFmha4nYTbJWVkm4BXjXGGGC3iOwD2gErTzUuR8/j8qWIrAbOx5ZhXV5m8o1SSiml3MU9Q0WrgNYi0gI4DIwBri3T5iAwBFgkIg2AtsDe09loVedx8QPuBFoBm4BJxpjTmlSjlFJKKc9njCkQkXuBuYAV+MwYs0VE7rQvnwSMB74QkU3YCh9PGGOSTme7VVVcpmL7naJFwEigPfDg6WxQKaWUUk7mpt8qMsbMBmaXuW9SietxwDBnbrOqxKWDMaYTgIh8ymmMSSmllFLKNaQO/chiVYNi//wqNDpEpJRSSil3q6ri0kVEjtmvC+Bvvy2AMcac1iFNSimllDp9daniUtUp/601FYhSSimlVFX0JHJKKaWUp6s7BReHz5yrlFJKKeV2Lq+4vPzuw67eRK319ANvuTsEt3pz4qPuDsGtsvJO6+c4PN7cXfHuDsGtEjPzq250hgrx11kGNU3nuCillFLKY9SlxEWHipRSSinlMbTiopRSSnk4rbgopZRSStVCWnFRSimlPFxdqrho4qKUUkp5urqTt+hQkVJKKaU8h1ZclFJKKQ9Xl4aKtOKilFJKKY+hFRellFLKw9WliosmLkoppZSHq0uJiw4VKaWUUspjaMVFKaWU8nBacVFKKaWUqoW04qKUUkp5urpTcNGKi1JKKaU8h1ZclFJKKQ9Xl+a4aOKilFJKebi6lLjoUJFSSimlPIZWXJRSSikPpxUXpZRSSqlaSCsuSimllKerOwUXTVyUUkopT6dDRUoppZRStZBWXJRSSikPV5cqLrU+cTm4eTVLp03CFBXRrv8Iuo4cXWq5MYal0yZxcNMqvHx8GXTLI0TGtKp03VUzv2T/+mWIWPAPrs+gWx4hMCSchH07WPjle7bHxdDj4uto0a1vzXbYSSY9dx0jB3QkMSWDHle94u5wnMIYw4JvPmL/xpV4+fgx7LZHiGreuly79MR45kx6hdzMDKJiWjF87ONYvbzZs3Ypy2Z8iYhgsVoZcM2dNG7TkYzkBH7/5HWy0lMREToOvJCuwy5zQw8rd2jzapZPt+3PbfuNoMuI8q+FZd9NInbzKqw+vgy8+REimtleCwunvsXBTSvxrxfCFc9NKl4n+dAeFn/9PoX5+VgsVvpcew9RLdrWaL9OhTGG+V99yN4NK/H29WXk2MdoWMG+kJZwhF8mvkJO1jEaNG/Nv+58AquXNwAHt23gz/9+SGFhIf5BwVz7zFs13Y1TErtlNSumT8aYItr0HU7n4eX3gxXTJxO7xfae2O/Gh4lo1oqC/DzmvPk4hQX5mKJCmnftR9eLr3dTL6rHGMOibz7igP19fshtjxAVU/75PpYYz9xJE8jNyiAyphUX3PEYVi9vUo8cYt5nb5J4YA+9Lr+JbiOuBCD1yCHmTppQvH56Yjw9L72Bc2rh61+dUKsTl6KiQpZ8M5GLHnqFwNAIfnr5AZp36Uloo5jiNoc2ryI9IY4xL39Kwt7tLP76Ay576p1K1+0y/ArOvfRGADbN/5k1v3zDgBvuI7RRDJc/8x4Wq5WstBR+ePFuYrr0wmK1uutPcMq++mU5k75bwCfjb3R3KE6zf+Mq0o4e5qZXPyd+73b+/Op9xox7r1y7Jd9/Qtdhl9O25yDmT32XLQt/o/P5F9O0Q1dadu2NiJB4aC9zPnyZGyd8isVqpf/VY4lq3pq8nGy+feFemp3djfDGMRVE4R5FRYUs/XYiIx+07c8/T3iAZp1LvxZiN6/iWEIcV43/lMR921ny9QeM+s87ALTufQEdBl/Cgs/fKPW4K3/8lG7/uo6mHc/l0KaVrPzpU/71yGs12bVTsnfDSlKPHuaON77gyJ5t/PH5e9zwwvvl2i347hN6jLic9r0HM/fzd9j49290HXoxuVmZ/PHFe1z12ASCI6LISk91Qy+qr6iokOXTPmT4/S8TEBrBL68+SLPOvQiJblbcJnbLao4lHOaKFz4hcd8Oln37ARc/8Q5WL29GPDgBbz9/igoL+PWNR2l8dg+iWrZzY48cc2DTKtKOxnH9hM84unc7C778gKvGvVuu3dLvP6XLsMto03MQf335HlsXzaXT4H/hG1iPAdfexd61y0q1D41uypgXPgRsf9svHr6elt361EifnK0uVVxq9RyXhH07CY5sRHBkNFYvb1qdO5D965eXarN//XLa9BqCiNDgrPYcz84kKy2l0nV9/AOL1y84nss/z7e3r19xklKYn4d48DTtJWv3kJKe7e4wnGrvumW07zMUESH6rPYcz84iKy25VBtjDIe2baB1j/4AdOh7AXvsb1Y+fv7FL+6C47n888QHhoQXV258/AMIi25KZlpSTXXLIYn7dhIcdWJ/btljIAc2lH4tHNiwnNb210JUy/bk5WSSnZ4CQHSbTvgG1Cv/wCLk5dj2k7ycbALrh7u8L86we+0yzu5n2xcatepAbnYmmRXsCwe3rqfteQMA6NhvGLvWLgFg27I/adOjH8ERUQAE1g+t2Q6coqT9O6kX2Yh6xfvBAA5uKP1hfHDDcloV7wftyMvOIjs9BRHB288fgKLCAooKC/GUz7p965bRro+tTw2L3+fLP9+x2zfQyv7ab9dnKHvXLgUgIDiEBi3aVvolNHbreoKjogmOaOC6jriQiDj9UlvV6opLdloSQWGRxbcDQyNI2LejVJus1GQCwyJKtclOS6py3ZUzvmDnsvn4+Ady8aOvFt9/dO92FnzxNhkpCZx/66MeWW05U2WWeU6DQiPITE0mMOTEh21u5jF8AwKLn7eg0AiySiQhu9csYekPn5GdkcaoB8eX28axpHgSDu6hYS37FpqdlkRgaOn9ObHsayGtzGshJIKs1CQC6oed9HF7jf43v737DCt//ARjDBc//qbzg3eBjNQkgsOiim/XC4sgIyWJoBL7Qk7mMXwDgor3hXphEWSm2D7sUuJjKSoo4NuXHyEvN4fuwy+jY78LarYTpyA7LZnA0BPPcUAF+0FF+0p2mm0/KCoq5JcJD3AsMY52A/9FZIvatZ+fTGZqcunXflhk1a/9sMhyyU1ldq1cQJueg5wWs3KdKisuItJARD4VkTn22x1E5DbXhwbGONSq/F0iVa573mU3c/1rX9G652A2//lL8f0NWrZj9IuTufzpd1k3ZzoF+XnVilm5UEXPaZlvBabCJ/5Em1bd+3LjhE+5+L7nWTZjaqlWebk5/PrBeAZecye+JapytYFjL4Xyrar61rRtwa/0Gj2Wa179il5XjWXRl++cUnw1zpG+VrQv2JsUFRYSv38XVzzyElc9PoGlM/9LypFYFwTqXBXt3459M7a1sVisjHr6A0a/8iVJ+3eSeni/cwN0mVN8vh2smhcW5LNv/fLiao1HEhdcailHhoq+AOYCjey3dwIPVraCiIwVkdUisnrZrG9PObjA0AgyUxKLb2elJpXKsP9pk5WSVKpNQP1wh9YFaNVzEPvs5eOSQqOb4e3r50Ev7DPThvmz+PrZu/j62bsIDAkr9ZxmpiYRFFK6muBfrz7Hs7MoKiwsblPR8964bSfSE46Qk5EOQGFBAb9+MJ62vc+nVY9+LuzRqbFVT0rvzwFVvRbSyrcpa9eyeTTvapuA3qJ7fxL376i0vTut/eNnvnj633zx9L8JCg3nWEpC8bKMlCSCQkv31bYvZBbvCyXb1AuLpEXnc/Hx8yegXn2atu1MwsE9NdeZUxQYaqui/SO7gopagAP7im9AEA1bdyJ26xrXBnwaNs6fxbTn7mbac3cTGBJe+rWfkkhgmde+X9nXfgVtTubAptVExrQiwEOGDOs6RxKXCGPMdKAIwBhTABRWtoIxZooxpocxpkfvS6455eCimrchPSGOY4nxFBbks3vVAmK69CrVJqZLL3Yun48xhqN7tuHjH0hgSFil66YfPVy8/oH1ywlp2ASwzUgvfpNLPkpafCxB4Z453nmm6DLkEq578SOue/EjzurWh21L52GM4ciebfj6B5RLSkSEJu26sGv1IgC2LvmDlt16A5B29HDxN9aE/bsoLCjALygYYwzzPn+LsEZN6Tb8iprtoIMim7fhWEIcGUm2/Xnv6vKvhWZderHL/lpI2Gt7LVQ2TAQQEBLOkZ2bAIjbvp7gqMYu68Pp6nbBKG5+eTI3vzyZ1t37smWxbV+I270V34DAUsNEYNsXmrXvwo6VCwHYvPh3WtsnXrbu1pvYHZsoKiwk/3guR/ZsJ7xRs3LbrG0iYsruBwtp2rnMftC5J7uL94PtxftBbkY6x7MzASjIO86R7euL3/tqo85DLmHMCx8y5oUPadm1N9uX2voUv2cbPgGBFb72G7frzG77a3/70nm07NrboW3tWvE3rc8b5Owu1Cid41JaloiEY6/ViUgvIN2lUdlZrFb6XXsXs995BmMKadt3GGGNY9j6968AdBh0Ec06ncvBTauY9vStePn4MejmhypdF2DFT5+TFh+LiBAUHsWA6+8DIH73FtbPmY7F6oVYhH7X3YN/vfo10VWnmzrhZvp3b01ESBC7fxvP+EmzmTpzWdUr1mLNO5/H/o2rmPrELXj5+HLBbY8UL5v51jMMveUhgkLD6XfVbcyZ9ArLfvqCyGatOLv/cAB2r17MtqXzsFi98PLxZeRdTyEiHN65me1L5xPepAVfP3sXAH2uuIUWXc5zSz8rYrFa6TPmLua8+wymqJA2fYcR2iiGbQtsr4X2Ay+yHxm0iunP2F4LA256qHj9Pz95lSM7NpKbeYxvnrie7hffQNt+w+l/w/0s+24ypqgQq5cP/a+/311drJaWXc5j7/oVfPzoTbbn8o5Hi5f98PpTDL/9YeqFRjBwzB3Mmvgyi374ggYxZ9Fp4AgAwhvH0KLzuXz+1FhELHQeNJLIpi3c1R2HWaxWeo25i9/ffwZTVETrPrb9YPtC237QbsBFNOl4LrGbV/Hjs7dh9fGl/422/SA7PYVFU9/EmCJMkaFF9/407dTTnd1xWEzn8ziwcRVfPXmr7XDoWx8uXvbL2+MYfPODBIWG0+fK25g7eQIrZkwlotlZdLC/9rPSU5j+4v3k5WQjImz4YybXvTQZH/9A8o/ncnDLWgbd6Bn7vgKpeE5AiQYi3YD3gY7AZiASuNIYs9GRDby1cK9Dw/Nnoqcf8IzzQrjKmxMfrbrRGSwrr8jdIbhVWECtnvvvcomZ+e4OwW0CfWv1Aas14r6+LWq0ZNHk7plO/6yN/fDSWll2qfKdxRizVkQGAm2xTdfZYYypu69IpZRSqpapzUM7zlZl4iIil5e5q42IpAObjDEJFa2jlFJKqTOfiIwA3gWswCfGmFcraDMIeAfwBpKMMQNPZ5uO1HJvA3oDf9lvDwKWY0tgXjTGfHU6ASillFLqNLmh4CIiVmAicAEQC6wSkVnGmK0l2oQAHwIjjDEHRSSqwgerBkcGIouA9saYK4wxVwAdgONAT+CJ0w1AKaWUUh7pPGC3MWavMSYPmAaMKtPmWuAnY8xBAGeM1DiSuDQ3xhwtcTsBaGOMSQF0rotSSinlZm46HLoxcKjE7Vj7fSW1AUJF5G8RWSMip/0Deo4MFS0Skf8B39tvXwEsFJFAIO10A1BKKaXU6XHF5FwRGQuMLXHXFGPMlJJNKlit7NFNXkB3YAjgDywTkeXGmJ2nGpcjics9wOXAP6cTXQlEG2OygMGnumGllFJK1V72JGVKJU1igaYlbjcB4ipok2TPGbJEZCHQBdtZ+E9JlUNFxnailz3YhoUuw5Y1bTvVDSqllFLKudw0VLQKaC0iLUTEBxgDzCrT5megv4h4iUgAtvmxp5VDnLTiIiJt7EFcAyQD32E7YZ1WWZRSSqk6zhhTICL3Yvs9QyvwmTFmi4jcaV8+yRizTUR+AzZiO9jnE2PM5tPZbmVDRduBRcDFxpjdACLyUCXtlVJKKeUG7joBnTFmNjC7zH2Tytx+HXjdWdusbKjoCiAe+EtEPhaRIdTqH7pWSiml6ihxwaWWOmniYoyZYYy5GmgH/A08BDQQkY9EZFgNxaeUUkopVcyRyblZxpivjTH/wjZjeD3wpKsDU0oppZRj3DQ51y2q9ROexpgUY8xkY8z5rgpIKaWUUupk6vbvziullFJngNpcIXG2alVclFJKKaXcSSsuSimllIerQwUXTVyUUkopT6dDRUoppZRStZBWXJRSSikPV4cKLlpxUUoppZTn0IqLUkop5eHq0hwXTVyUUkopD1eH8hYdKlJKKaWU59CKi1JKKeXhLJa6U3LRiotSSimlPIZWXJRSSikPV5fmuLg8cUnKLHD1JmqtNyc+6u4Q3OqRe95wdwhu1Xn0Ve4Owa26tYl0dwhu1aGhv7tDcJs7erZwdwh1Tl06qkiHipRSSinlMXSoSCmllPJwdajgohUXpZRSSnkOrbgopZRSHk7nuCillFJK1UJacVFKKaU8XF2quGjiopRSSnm4OpS36FCRUkoppTyHVlyUUkopD1eXhoq04qKUUkopj6EVF6WUUsrD1aGCiyYuSimllKfToSKllFJKqVpIKy5KKaWUh6tDBRetuCillFLKc2jFRSmllPJwdWmOiyYuSimllIerQ3mLDhUppZRSynNoxUUppZTycHVpqEgrLkoppZTyGFpxUUoppTxcHSq4aMVFKaWUUp5DKy5KKaWUh6tLc1w0cVFKKaU8XB3KW3SoSCmllFKnRkRGiMgOEdktIk9W0u5cESkUkStPd5tacVFKKaU8nDuGikTECkwELgBigVUiMssYs7WCdv8HzHXGdj0qcYnftoYNMz7GmCJa9LyAtkOvKrXcGMOGGVOI37YGq7cvPa55gNCmrQDY9fdM9i3/HREhOLo5Pa55AKu3D2mH97Lu+w8pzM9DLFa6XnkXYTFt3NG9KhljWPDNR+zfuBIvHz+G3fYIUc1bl2uXnhjPnEmvkJuZQVRMK4aPfRyrlzd71i5l2YwvEREsVisDrrmTxm06kpGcwO+fvE5WeioiQseBF9J12GVu6KHzTHruOkYO6EhiSgY9rnrF3eE4Xa+WoTw8tBUWizBr/RG+XH6ownbto+vx6Y1deWbmVv7ckYSPVZh0/Tn4WC1YLcKfOxL5eNGBGo7+9HVoEMjoLg0REZbsS+X3ncmlljeo58ON3RvRNMSPWVsSmbfLtrxBkA+39WxS3C4i0Jv/bU3kz90pNRp/dRljWPTNRxzYtAovH1+G3PYIUTHlX/vHEuOZO2kCuVkZRMa04oI7HsPq5U3qkUPM++xNEg/sodflN9FtxIkvvRv+mMmWhXPAGDoMGMk5tfy1b4zh/ya8zOKFC/Dz92P8y6/SvsPZ5drdfMO1ZGdlAZCSkkzHTp155/0P2bd3D88+8xTbtm7hvgce4qZbbqvpLpxJzgN2G2P2AojINGAUsLVMu/uAH4FznbFRj0lcTFEh63+cRL87xxMQEs6fbz9MdMeeBDdsVtwmftsaMhPjGP7UZFIO7GDdDx9x/kNvkpOWzO5FvzDsiQ+x+viy/ItXObRuIc3PG8qmWZ/TfvgYGrbvwZGtq9n0y+cMvHeCG3t6cvs3riLt6GFuevVz4vdu58+v3mfMuPfKtVvy/Sd0HXY5bXsOYv7Ud9my8Dc6n38xTTt0pWXX3ogIiYf2MufDl7lxwqdYrFb6Xz2WqOatycvJ5tsX7qXZ2d0Ibxzjhl46x1e/LGfSdwv4ZPyN7g7F6SwCjw1rzX3TNpJw7Dhf3NyNRbuS2ZecXa7dvYNasGLfiQ/lvELDPd9sICe/CKtFmHLDOSzbk8LmuIya7sYpE2DMOdG8t/gAqdn5PHl+SzYeySA+I6+4TXZeIdM3xNOlUb1S6x7NzOOV+XuLH2fCRW1Y7wF9P7BpFWlH47h+wmcc3budBV9+wFXj3i3Xbun3n9Jl2GW06TmIv758j62L5tJp8L/wDazHgGvvYu/aZaXaJ8fuZ8vCOVz1zLtYvbyZ9dbTNO9yHiENGtdU16pt8aKFHDywn1/m/M6mjRt46cXn+Xra9+XaffHVN8XXH37gPgafPwSA4PohPPGfp/nrz/k1FXKNcNMcl8ZAyW9NsUDPkg1EpDFwGXA+TkpcHJ7jIiINReQSEblYRBo6Y+PVkXJwF4ER0QRFNMTi5U2TrgOI27yiVJsjm5cTc+75iAjhzduRn5NFTrrtTdsUFVGYn0dRYSGF+cfxDw77p2Pk5+YAUJCbhX/9sBrtV3XsXbeM9n2GIiJEn9We49lZZKWV/qZpjOHQtg207tEfgA59L2CP/c3Kx8+/uJxYcDy3eE8PDAkvrtz4+AcQFt2UzLSkmuqWSyxZu4eU9OyqG3qgDo2CiU3NIS4tl4Iiwx/bEhjQJrxcu9E9GvPXjiRSsvJL3Z+TXwSAl0XwsgimRqJ2nuZh/iRm5ZGUlU+hgdWx6eUSlIzjhRxIzaWw6OSP0y4qkKTMPFKy80/eqJbYt24Z7foMQURoeFZ7jmdnVvjaj92+gVb21367PkPZu3YpAAHBITRo0RaL1VpqndQjB2nYsh3evn5YrFYat+1UvE5t9def87n4kksRETp3OYeMjGMkJiactH1WViYrVy5n8JChAISHh9OxU2e8vDzme7tDRMQVl7EisrrEZWzZzVYQStm3lHeAJ4wxhc7qq0PPnIjcDjwL/Ikt0PdF5EVjzGfOCqQqOWnJBIREFN/2rx9OysGdpdukJ+Nfsk1IOLnpyYQ2a03rQZcx+8VbsXr70KBtVxq06wZAl8vuYPGkZ9k06zOMKWLQ/a/XTIdOQWZaEkFhkcW3g0IjyExNJjDkxIdWbuYxfAMCi9+ggkIjyCqRhOxes4SlP3xGdkYaox4cX24bx5LiSTi4h4Yt27mwJ+p0RAX5cPTY8eLbCRnHObtRcKk2kUE+DGwTwT3fbODp6LalllkEpt7SnSah/vyw5jBbPKDiUFKIvxepJZKN1JwCWoT5V/txejQNZlVsujNDc5nM1OTSr/2wyKpf+2GR5ZKbssIaN2fZT1PJyTyGl7cP+zetIqp57Rwq/0dCwlEaNDzx3blBg4YkHD1KZGRUhe3/nDePnj17ExQUVFMhnjGMMVOAKZU0iQWalrjdBIgr06YHMM3+pTkCuFBECowxM081LkdTzseArsaYZAARCQeWAhUmLvasbCzAiHtfpOvIq081vhLKfy+UssleRV8dRcjLzuTI5hWMHPcJ3v6BLP/iVQ6u/otmPQazd8lsulx6O4279CV23SLWTHuPAXe/5IR4XeAk/SvVxFTYqPhaq+59adW9L4d3bGLZjKlc/tj/FS/Ly83h1w/GM/CaO/H1D3RS0MrpKviOU/Zpf2hoKyb+tZeiCnaHIgM3fLaGIF8rr13RkZYRAexN8pzqVIVf8apZNrIKdI6ux8zNJ/+mXrtU8P5Xdmygitd+RcIaNaP7yKuY9cZ/8PbzJ6JpSywWa6XruF0F/axsYuqc2f/j8iuuOunyM4WbzuOyCmgtIi2Aw8AY4NqSDYwxLf65LiJfAP87naQFHE9cYoGSX8syKD2uVUrJLO2p2TudUon2D4kgu0TlICc9Gb8ywzr+IeHklGyTloxfcBgJO9cTGN4A36D6ADTu3Ifk/dto1mMwB1b9SZfLbNWvxuf0Y8137zsjXKfZMH8WmxfMAaBBizZkpiQWL8tMTSIopMzfoF59jmdnUVRYiMVqJTM1qdS3sn80btuJ9IQj5GSk41+vPoUFBfz6wXja9j6fVj36ubZT6rQkZOTRINi3+HZUPV+SMo+XatM+OojxozoAEBLgTZ+zwigoMizcdeIbeObxQtYcTKN3yzCPSlxScwoIDfAuvh3q70V6bvWGe85uGMTBtFwyjjuteu10G+fPYuvC3wCIKvvaT0kksMxr36/sa7+CNhXpMGAEHQaMAGDZj58TFBpRxRo1b9o3X/PTD9MBOLtjJ47GxxcvO3o0nsioiqstaWmpbN60ibffm1gjcdY1xpgCEbkX29FCVuAzY8wWEbnTvnySK7braOJyGFghIj9jS/1HAStF5GF7cG+5IriSQpu2JjMxjqzkePzrhxO7biHnXf9oqTbRZ/dkz+L/0aTrAFIO7MDbPwD/+mEEhEaSvH87BXm5WL19Sdi5ofhoI//gMJL2bCayVScSd20kKLKRq7tSLV2GXEKXIZcAsG/DCjbMn0WbnoOI37sdX/+AckmJiNCkXRd2rV5E256D2LrkD1p26w1A2tHD1I9qhIiQsH8XhQUF+AUFY4xh3udvEdaoKd2GX1HjfVTVsy3uGE1D/Ymu70dixnEuaB/FuFnbSrW57KOVxdfHXdSWJbuTWbgrmRB/bwqKisg8Xoivl4Xzmofy5fKDNd2F03IgNYeoIB/CA7xJy8mnR5P6fLbycLUe49ym9Vl9qHYPE3Uecgmd7a/9/RtWsHH+L7TuOYije7fjExBY4Wu/cbvO7F69iDY9B7F96Txadu1d5Xayj6UREBxCRnICe9Ys4cqn33ZJf07HmGuvY8y11wGwcMHfTPvmv4y48CI2bdxAUFC9kw4T/T73NwYMHISvr2+Fy88k7joBnTFmNjC7zH0VJizGmJudsU1HE5c99ss/frb/X6+Cti5hsVo554o7WTz5OUxREc17DiU4Ooa9S2zViJZ9R9KwQw/it61m7stjsfr40mPMAwCExbSlSZe+zH/zQSwWKyGNW9Kij+0bRrer77UdYl1UiMXLh26j762pLlVb887nsX/jKqY+cQtePr5ccNsjxctmvvUMQ295iKDQcPpddRtzJr3Csp++ILJZK87uPxyA3asXs23pPCxWL7x8fBl511OICId3bmb70vmEN2nB18/eBUCfK26hRZfz3NJPZ5g64Wb6d29NREgQu38bz/hJs5k6c1nVK3qAQgNv/LGb98Z0wiLCLxvj2ZeUzWVdowGYse7ISdeNCPLh2X+1xWIRLCLM35bIklp+KHBZRQamrY/nvn7NsIiwdH8aRzKO079FKACL9qUS7GvlyfNb4udtwRg4v1UYL/6xh9yCIrytQruoQL5ee/K/U20T0/k8DmxcxVdP3mo7HPrWh4uX/fL2OAbf/CBBoeH0ufI25k6ewIoZU4lodhYd7K/9rPQUpr94P3k52YgIG/6YyXUvTcbHP5A5E8eTm5mBxWpl4PX34BdYY2/rp6T/gIEsXriAf428AD8/f1586cTpDu658w6ee/EloqIaADB3zmxuve2OUusnJSZyzdVXkJWZicVi4b9fTWXGrNkePwemLp3yXyqeE1HJCiKhQJpxcEVnDRV5oiYhPu4Owa0euecNd4fgVp1Hn/nj6pXp1iay6kZnsA4Nqz9h+ExxR88WVTc6w/l5VTHByMkGvbPU6Z+1fz/Yp1ZmQ5UeDi0iz4pIO/t1XxH5E1vl5aiIDK2JAJVSSilVORHnX2qrqs7jcjWww379Jnv7SGAgcOadjlQppZRStVpVc1zySgwJDQe+tZ9EZpuInFln71FKKaU8VF2a41JVxeW4iHQUkUhgMPB7iWUBrgtLKaWUUo6qS0NFVVVNHgB+wDY89LYxZh+AiFwIrHNxbEoppZRSpVSauBhjVgDlzv1e0XHbSimllHIPS20ukTiZQz+yKCLhIvKeiKwVkTUi8q79tP9KKaWUUjXG0V+HngYkAlcAV9qvf+eqoJRSSinlOJ3jUl6YMabkTwm/JCKXuiAepZRSSqmTcrTi8peIjBERi/0yGvjVlYEppZRSyjEi4vRLbVVpxUVEMrD9qKIADwNf2RdZgUzgOZdGp5RSSqkqWWpvnuF0VR1VVLt/bUsppZRSdUpVFZd2xpjtItKtouXGmLWuCUsppZRSjqrNQzvOVtXk3IeBscCbJe4r+QuU5zs9IqWUUkqpk6gqcflERBoaYwYDiMhN2A6J3g8879rQlFJKKeWIOlRwqfKooklAHoCIDAAmAFOBdGCKa0NTSimllCPEBf9qq6oqLlZjTIr9+tXAFGPMj8CPIrLepZEppZRSSpVRZeIiIl7GmAJgCLb5Lo6uq5RSSqkaoIdDn/AtsEBEkoAcYBGAiLTCNlyklFJKKVVjqjqPy8siMh+IBn43xvxzRJEFuM/VwSmllFKqano4dAnGmOUV3LfTNeEopZRSqrrqUN7i8G8VKaWUUkq5nU6wVUoppTycpQ6VXLTiopRSSimPoRUXpZRSysPVoYKLVlyUUkop5TlcXnEJDai7RZ2svCJ3h+BWnUdf5e4Q3Grj9O/dHYJbtX98bNWNzmCH0/PdHYLb7IjLcHcIbtelWb0a3Z4eDq2UUkopj1GH8hYdKlJKKaWU59CKi1JKKeXh9HBopZRSSqlaSCsuSimllIerO/UWTVyUUkopj1eXjirSoSKllFJKeQytuCillFIezlJ3Ci5acVFKKaWU59CKi1JKKeXh6tIcF01clFJKKQ9Xh/IWHSpSSimllOfQiotSSinl4erSUJFWXJRSSil1SkRkhIjsEJHdIvJkBcuvE5GN9stSEelyutvUiotSSinl4dxxOLSIWIGJwAVALLBKRGYZY7aWaLYPGGiMSRWRkcAUoOfpbFcrLkoppZQ6FecBu40xe40xecA0YFTJBsaYpcaYVPvN5UCT092oVlyUUkopD+emOS6NgUMlbsdSeTXlNmDO6W5UExellFLKw7kibRGRscDYEndNMcZMqWKz5iSPNRhb4tLvdOPSxEUppZRS5diTlCmVNIkFmpa43QSIK9tIRDoDnwAjjTHJpxuXJi5KKaWUh7O4Z6hoFdBaRFoAh4ExwLUlG4hIM+An4AZjzE5nbFQTF6WUUkpVmzGmQETuBeYCVuAzY8wWEbnTvnwS8CwQDnxon4dTYIzpcTrb1cRFKaWU8nDuOv+cMWY2MLvMfZNKXL8duN2Z29TERSmllPJweuZcpZRSSqlaSCsuSimllIerQwUXrbgopZRSynN4VMXl0ObVLJ8+CVNURNt+I+gyYnSp5cYYln03idjNq7D6+DLw5keIaNYKgIVT3+LgppX41wvhiueK5w2RfGgvS75+n/zjuQSFRzH4tsfx8Q+s0X45yjX938Pir9+nMD8fi8VKn2vvIapF2xrt16no1TKUh4e2wmIRZq0/wpfLD1XYrn10PT69sSvPzNzKnzuS8LEKk64/Bx+rBatF+HNHIh8vOlDD0bvWpOeuY+SAjiSmZNDjqlfcHY5LdIoO4tpujbEILNyTwq/bEkst7x0TwoUdIgHIzS/iy9WHOZSWC8CtPZtwTqNgjuUW8Mwcpxyd6XJHt61h44yPMaaImJ4X0HboVaWWG2PYOGMKR7etwertS/drHiCkqe21v3vBLPYvnwvG0Lz3cFoNtJ2Rfdtv37B/+Vx8A+sD0OGiG2nY4bQO9qgRxhg+//AN1q1cgq+vH3c/9jwtW7cr1+6jN19k785tGGOIbtKMex57Hj//AGZN/5JF838DoKiogNiD+/n0+z8ICq5f011xKjcdDu0WHlNxKSoqZOm3Exl+33iueH4ye1b9TWpc6Q+c2M2rOJYQx1XjP6X/9fez5OsPipe17n0BI+5/qdzjLvrqHc69/BaueO4jmnftw8bff3R5X06Fq/q/8sdP6fav67h83ES6X3I9K3/61OV9OV0WgceGtebB6ZsYM2UVwzpE0SI8oMJ29w5qwYp9KcX35RUa7vlmA9d/tobrP1tDr5ZhdGxUrybDd7mvflnOqHsmujsMlxGBG7o35q2/9/HU7J30jAmhUbBvqTaJWXlMmLeXcXN2MWtLAjefe+LnURbvTeXNv/fVdNinzBQVsuHHSfQZ+zxDn5hI7LqFHIs/WKrN0W1ryEqM44KnJtN19D2s/+EjAI4dOcD+5XMZ9NCbnP/Y+8RvWUVm4onzg7UaOIrzH3uP8x97zyOSFoB1K5cQf/gQ730xg7EPPs0n702osN1Ndz7M65O/5Y0p04iIashvP08H4JLRN/L65G94ffI3XHPrvXTo3M3jkxawvS6cfamtPCZxSdy3k+CoRgRHRmP18qZlj4Ec2LC8VJsDG5bTutcQRISolu3Jy8kkO932oRXdphO+AeU/oNKPxtKwdScAGrfvxv51i13fmVPgqv4jQl5ONgB5OdkE1g93eV9OV4dGwcSm5hCXlktBkeGPbQkMaFM+7tE9GvPXjiRSsvJL3Z+TXwSAl0XwskjF56f2YEvW7iElPdvdYbhMy7AAjmbmkZiVR2GRYcXBNLo2CS7VZndSNtn5hQDsScomLMC7eNnOxCyy8gpqNObTkXJwF4ER0QRGNMTi5U2TrgM4snlFqTZHNi+n6bnnIyKENW9Hfk4WuekpZBw9RFhMW7x8/LBYrUS06kjcxmVu6olzrF62gAFDL0REaNOhE1mZGaQmJ5VrFxAYBNgqNHnHj1f4WEv+mkvfwcNdGq9yPocSFxHxFZFrReQpEXn2n4urgyspOy2JwNDI4tuBoRFkp5U+c3BWWjKBYREn2oREkJVafocuKbRRcw7aE4B9axaRlVJ5e3dxVf97jf43K3/8lG+fvIEVP35Cj8tudmrcrhAV5MPRYyfeiBIyjhNZr/Q37sggHwa2ieCndeXOPo1F4Ktbu/PbA31YuS+VLXEZLo9ZOU9ogDcp2SeS0dTsfEL9vU/afsBZoWw84rnPcW5aMv4hJ17X/vXDyU0v/drPSS/TJiScnPRk6kXHkLR3C8ezjlGQl0v81tXkpJ14T9i76Ffmv3Yfa759l7zsTNd3xglSkhKJiGpYfDs8ogEpSQkVtv3w9RcYO3o4cYf2M/LSMaWWHc/NZf3qZfTqd75L460pIuL0S23laMXlZ2w/VV0AZJW4VEhExorIahFZvfyXb08/Sk7yq03lGpVvVdUff8BND7H171+Y8fJ95OfmYPGqndN+XNX/bQt+pdfosVzz6lf0umosi75855Tiq1EVdKls1x8a2oqJf+2lqII/XJGBGz5bw8UfLOPsRsG0jCg/zKRqr+q8nbaLCmRAyzCmrz/isnhcr6JXvzjQRAhu0JQ251/Bko/GsXTy89Rv1AKx2N72W/QdybBnpnD+o+/iFxzKpp9r/zAx2CooZZ3sfe7ux55j8rQ5NG7WgqV//15q2ZrlC2l7dpczYpiornH0U7qJMWaEow9a8oeZXv97r1Mq8bbqwYkJeFmpSQSElB4eCAyNKFUxyUor36askIZNGfmgbQJj+tFYDm1e6Yxwnc5V/d+1bB69r74TgBbd+7Poq3ecF7SLJGTk0aDEnIaoer4kZZYuBbePDmL8qA4AhAR40+esMAqKDAt3nfimmnm8kDUH0+jdMoy9SWfu0MqZJiU7v9TQT2iAN6k5+eXaNQnx49bzmvDmgn1k5RXWZIhO5RcSUapKkpOejF/9sFJt/EPCS7dJS8Y/2Namea9hNO81DIAtv36Jv3042K9eaHH75r2Hs+zjF13Wh9P128/TmT97JgBnte1AUkJ88bLkpKOEhkeeZE2wWK30GXgBs77/isEjLim+f8nfv9PvDBom8ph5H07gaF+Xikgnl0ZShcjmbTiWEEdGUjyFBfnsXb2AmC69SrVp1qUXu5bPxxhDwt5t+PgHElDmBV5WzrE0AExREetmT6PdgAtd1YXT4qr+B4SEc2TnJgDitq8nOKqxy/rgLNvijtE01J/o+n54WYQL2keVSkgALvtoJZd9tILLPlrBn9sTeX3uLhbuSibE35sgXysAvl4Wzmseyv4UTVo8yb6UbBrU8yEi0BurRejZLIR1scdKtQkL8Oa+fjFMWX6Ioxl5borUOUKbtiYzMY6s5HiKCvKJXbeQ6LPPK9Um+uyeHFr1J8YYUvZvx9s/oDi5OZ6RBkB2agJxG5fSpNtAAHLTT0xaP7JxGcHRMTXToVMwYtTo4gm15/UdxMJ5szHGsHPrJgICgwgNjyjV3hhD/OFDxddXL19Eo6bNi5dnZ2WydeNaevQeWJPdUE5SacVFRDZhK0J6AbeIyF7gOLY6pTHGdHZ9iDYWq5U+Y+5izrvPYIoKadN3GKGNYti24FcA2g+8iKYdz+XQplVMf+ZWvHz8GHDTQ8Xr//nJqxzZsZHczGN888T1dL/4Btr2G86eVX+z9e//AdC8ax/a9BlWU12qFlf1v/8N97Psu8mYokKsXj70v/5+d3XRYYUG3vhjN++N6YRFhF82xrMvKZvLukYDMGPdyYcFIoJ8ePZfbbFYBIsI87clsmR3yknbe6KpE26mf/fWRIQEsfu38YyfNJupMz17QmZJRQb+uzqORwe1xCKwaG8qcceOM7iV7YP6r90pjOrYgCBfKzf2sCXihUWGF37fDcCdfZrRLiqQIF8v3hrVjpmbjrJwb6rb+lMVi9VKlyvuZMnk56CoiJieQwmOjmHfkjmAbcinQYcexG9bzR8vj8Xq40u3MQ8Ur7/i8wnkZWcgVitdrrgLnwDbpNXNv3xOetw+QAgIi6LrVfe4o3vV1vW8vqxdsYT7b7oUH18/7n70ueJlE566n38/PI6QsHAmvvYc2dlZgCGmZRtuv//J4nYrF/9Fl+498fP3d0MPXKM2z0lxNqlovLB4oUilKbgxpsoTYDhrqEh5nh+WVXxulbpi4/Tv3R2CW139+Fh3h+BWDev7uTsEt7mmY7S7Q3C7Ls3q1Wgm8eDP253+WfvOqHa1MhuqdKjIGHPAnpxEAyklbqcADStbVymllFLK2Ryd4/IRUPJYuSz7fUoppZRyM4s4/1JbOZq4iCkxpmSMKcLDfi5AKaWUUp7P0cRlr4jcLyLe9ssDwF5XBqaUUkopx+gJ6Mq7E+gDHAZigZ5A3Z55p5RSStUSdWmoqMrhHhGxAm8ZY8ZU1VYppZRSypWqTFyMMYUiEikiPsYYzz6Tk1JKKXUGqsUjO07n6ATb/cASEZlFid8oMsa85YqglFJKKaUq4mjiEme/WIB6rgtHKaWUUtVlqUMlF4cSF2PMC64ORCmllFKnpi79yKJDiYuIRAKPA2cDxeexNsac76K4lFJKKaXKcTRJ+xrYDrQAXsA252WVi2JSSimlVDWIOP9SWzmauIQbYz4F8o0xC4wxtwK9XBiXUkoppVQ5jk7Ozbf/f0RELsI2UbeJa0JSSimlVHXo5NzyXhKR+sAjwPtAMPCQy6JSSimllKpApYmLiPhhO91/K6Ax8KkxZnBNBKaUUkopx9ShgkuVFZep2IaJFgEjgQ7AA64OSimllFKOq82/LeRsVSUuHYwxnQBE5FNgpetDUkoppZSqWFWJyz+TcjHGFNTmn7lWSiml6iqdnHtCFxE5Zr8ugL/9tgDGGBPs0uiUUkoppUqoNHExxlhrKhCllFJKnZo6VHBx+HBopZRSStVSdWlybl36XSallFJKeTituCillFIeTqg7JRetuCillFLKY2jFRSmllPJwdWmOi8sTl2u61N3fYpy7K97dIbhVtzaR7g7Brdo/PtbdIbjVd69NcXcIbjXy3lvcHYLbHI4JcXcIbteFejW6vbqUuOhQkVJKKaU8hg4VKaWUUh6uLp3ZXisuSimllDolIjJCRHaIyG4RebKC5SIi79mXbxSRbqe7Ta24KKWUUh7OHXNcRMQKTAQuAGKBVSIyyxiztUSzkUBr+6Un8JH9/1OmFRellFJKnYrzgN3GmL3GmDxgGjCqTJtRwJfGZjkQIiLRp7NRTVyUUkopDyfiiouMFZHVJS5lD5VsDBwqcTvWfl9121SLDhUppZRSHs7igsm5xpgpQGXnNahoo+YU2lSLVlyUUkopdSpigaYlbjcB4k6hTbVo4qKUUkp5OIs4/+KAVUBrEWkhIj7AGGBWmTazgBvtRxf1AtKNMUdOp686VKSUUkqpajPGFIjIvcBcwAp8ZozZIiJ32pdPAmYDFwK7gWzgtE8prYmLUkop5eHcdf45Y8xsbMlJyfsmlbhugHucuU1NXJRSSikPZ6lwDuyZSee4KKWUUspjaMVFKaWU8nB16KeKtOKilFJKKc+hFRellFLKw7njt4rcRRMXpZRSysO54sy5tZUOFSmllFLKY2jFRSmllPJwdajgohUXpZRSSnkOrbgopZRSHk7nuCillFJK1UJacVFKKaU8XB0quGjiopRSSnm6ujR8Upf6qpRSSikPpxUXpZRSysNJHRor8ujExRjDxLdeZcWyRfj6+vH4uJdo065DuXb/9+LTbFy3hsCgIAAeH/cSrdq0K16+fetm7rv9Op556XUGnj+sxuI/HcYY5n/1IXs3rMTb15eRYx+jYfPW5dqlJRzhl4mvkJN1jAbNW/OvO5/A6uUNwMFtG/jzvx9SWFiIf1Aw1z7zVk1345R1aBDI6C4NERGW7Evl953JpZY3qOfDjd0b0TTEj1lbEpm3y7a8QZAPt/VsUtwuItCb/21N5M/dKTUa/+nqFB3Etd0aYxFYuCeFX7clllreOyaECztEApCbX8SXqw9zKC0XgFt7NuGcRsEcyy3gmTk7azx2V5v03HWMHNCRxJQMelz1irvDcbqujYO5tVdTLBaYtyOJGRuPllo+4KwwLu3cALA991OWHmR/Sg4AF50dxQVtIwDbuv/bklCzwTuBMYYZn77LtrXL8fb15Zp7n6LpWW3LtVs0+0cW/u97kuIPM/6LXwgKDgEgOzODaR9MIOnoYby9fRlzz5NEx7Ss4V6o0+HRicvKZYuIPXSAL7//lW1bNvLuay8x8bNvKmw79r6HK0xKCgsL+Xji2/To2cfV4TrV3g0rST16mDve+IIje7bxx+fvccML75drt+C7T+gx4nLa9x7M3M/fYePfv9F16MXkZmXyxxfvcdVjEwiOiCIrPdUNvTg1Aow5J5r3Fh8gNTufJ89vycYjGcRn5BW3yc4rZPqGeLo0qldq3aOZebwyf2/x40y4qA3r4zJqMPrTJwI3dG/M63/tIyUnn+eGtWLd4WPEHTte3CYxK48J8/aSnV9Ip+h63HxuE8b/sRuAxXtTmb8zmTt6NXVXF1zqq1+WM+m7BXwy/kZ3h+J0FoE7+jTjhd92kpyVz2uXtGPVwXRi7UkpwNGM44z7dSdZeYV0bRLMnX1jePKX7TQL9eOCthE8/vM2CooM44a3Zs2hdI6U2G88wba1y0k8EstTE7/lwM6t/DDlTR76vynl2rVo14mze/Thg3H3l7p/3o9f0qhFa2598hWOxh7gx4/f4u4X3q2p8F2m7tRbPHyOy5KFfzHswksQETp07EJmZgbJSYlVr1jCzO+/of/goYSEhrkoStfYvXYZZ/cbiojQqFUHcrMzyUwrXXUwxnBw63ranjcAgI79hrFr7RIAti37kzY9+hEcEQVAYP3Qmu3AaWge5k9iVh5JWfkUGlgdm14uQck4XsiB1FwKi07+OO2iAknKzCMlO9/FETtXy7AAjmbmkZiVR2GRYcXBNLo2CS7VZndSNtn5hQDsScomLMC7eNnOxCyy8gpqNOaatGTtHlLSs90dhku0igzkyLFcjmbkUVBkWLw3lfOahZRqsyMhi6w823O/MyGL8EDbc9+4vh87E7LIKzQUGdgan0HPmBA8zeaVizl30AhEhOZtzyYnK5P0lKRy7Zq0bENYVHS5++MP7adN5+4ANGgSQ0pCPBlpnlVxrYhFxOmX2srhxEVE+onILfbrkSLSwnVhOSYpMYHIqIbFtyOjGpCUWHHp87NJ73P7dZfz4Tv/R16e7Zt5YsJRFi+Yz8WXja6ReJ0pIzWJ4LCo4tv1wiLIKPPizck8hm9AEBartbhNZootuUmJjyU3K4NvX36EqePuZvPiP2ou+NMU4u9FaolkIzWngBB/70rWqFiPpsGsik13Zmg1IjTAu1SylZqdT2gl/R9wVigbj3hWVUlVLDzAm+SsE899cnYeYYEnf+6HtolgXewxAA6m5tKhYRBBvlZ8rEK3pvWJCPRxeczOlp6SSEjEife+kPDIChOXk2ncvBUbly8A4MCuraQmHiUtuXpfeJV7OZS4iMhzwBPAf+x3eQP/raT9WBFZLSKrv/7ik9OP8mSMqWDb5ZvdfveDfPHdLD78fBrHjh1j2lefAvDhO//HHfc8hNX+we5RKuy7VNnmn3piUWEh8ft3ccUjL3HV4xNYOvO/pByJdUGgzlfR94CKuloZq0Dn6Hqstb+pe5LqfA9qFxXIgJZhTF9/xGXxKDc7yb7fMTqIIW3D+XKV7XV9OD2XGRvjeX5EG8aNaM3+5BwKq/vCqQWMI+99lRhy+fXkZGbw+sO3sGj2jzRu0RqLxQM/A8oQF1xqK0fnuFwGdAXWAhhj4kSk3skaG2OmAFMAYlPznPrKmPnDt8z++UcA2rbvSGJCfPGyxISjhJfIxP8RHmGbpOjj48OIiy5l+jdfALBz21ZeeuZxANLTU1m5bDFWq5V+A4c4M2SnWfvHz2z8ezYADVu25VjKiepSRkoSQaHhpdr716vP8exMigoLsVitpdrUC4vEv159fPz88fHzp2nbziQc3ENYdBNqu9ScAkJLDH2E+nuRnlu94Z6zGwZxMC2XjOOFzg7P5VKy80sN/YQGeJOaU77/TUL8uPW8Jry5YF/x0IHybMnZ+cVDPwDhAT4VDnXGhPpzd7/mjJ+7i8wS+/j8ncnMt09kv657I5I9ZJh08ZyfWPbHLwA0a9WOtKQT731pyYkEl3nvq4xfQCDX3PcUYEuCxt85mvAG5YeUVO3laOKSZ4wxImIARCTQhTFV6tIrr+HSK68BYPmShcz8/hsGXzCSbVs2EhgUVJyklJSclEh4RCTGGJYs/JMWLVsB8PWM34rb/N+LT9Or38Bam7QAdLtgFN0uGAXAnvUrWPvHz7TvNZgje7bhGxBIUEjpF6+I0Kx9F3asXEj73oPZvPh3WnezTUJu3a03f3z5AUWFhRQW5HNkz3Z6jLi8xvt0Kg6k5hAV5EN4gDdpOfn0aFKfz1YertZjnNu0PqsPed4wEcC+lGwa1PMhItCb1JwCejYLYdLSg6XahAV4c1+/GKYsP8TREpOWlWfbnZhFdLAfUUG2hKVfy1De/ntfqTYRgd48PrQl7y7YV27ibX0/L9JzC4gI9KZn81D+88v2mgz/lPUbeTn9Rtren7asXsriOT/Rtd8QDuzcin9AEPXDIhx+rJysDLx9/PDy9mb5vF84q0MX/ALc9pHmNLV4SorTOZq4TBeRyUCIiNwB3Ap87LqwHNOzT39WLF3IDVdeiJ+fH48981Lxsv88dBePPPUCEZFRvPLck6SnpWAMnNW6LQ898awbo3aOll3OY+/6FXz86E14+fgy8o5Hi5f98PpTDL/9YeqFRjBwzB3Mmvgyi374ggYxZ9Fp4AgAwhvH0KLzuXz+1FhELHQeNJLIpm6ftuSQIgPT1sdzX79mWERYuj+NIxnH6d/CNsF40b5Ugn2tPHl+S/y8LRgD57cK48U/9pBbUIS3VWgXFcjXaz1z+KTIwH9Xx/HooJZYBBbtTSXu2HEGt7JNMP9rdwqjOjYgyNfKjT0aA1BYZHjhd9tRRXf2aUa7qECCfL14a1Q7Zm46ysK9nnNUWVWmTriZ/t1bExESxO7fxjN+0mymzlzm7rCcosjAJ8sO8uyI1lhEmL8ziUNpuQxrZ/vg/n17EqO7NqKerxdj+zQDbM/947NsCcpjQ1pSz9eLwiLDx0sPemQlrkP33mxbu5yX7x6Dj68fY+79T/GyKS89xtV3P0H9sAgW/voDf874hoy0FF5/6Gbad+vFmHue5GjsAb5+72UsFgsNmjRnzD1PurE3zlOXzuMiFY0Xlmpg+2s0AdoBw7ANfc01xjg0m9PZQ0WeZO6u+KobncFWH8p0dwhulXMGH7njiO9eK3+Ial0y8t5b3B2C29x+hh5qXx0Xnh1Vo5nEt+sOO/2z9pqujWtlNlRlxcU+RDTTGNMd8JxDT5RSSqk6wqPPbVJNjvZ1uYic69JIlFJKKaWq4Ogcl8HAv0XkAJCFbbjIGGM6uywypZRSSjmkLs1xcTRxGenSKJRSSimlHOBQ4mKMOQAgIlGAn0sjUkoppVS11J16i4OJi4hcArwJNAISgBhgG3C260JTSimllCPq0lCRo5NzxwO9gJ3GmBbAEGCJy6JSSimllKqAo4lLvjEmGbCIiMUY8xdwjuvCUkoppZSjLC641FaOTs5NE5EgYCHwtYgkAHX77FpKKaWUqnGVJlUi0sx+dRSQDTwE/AbsAS52bWhKKaWUcoSIOP1SW1VVcZkJdDPGZInIj8aYK4Cprg9LKaWUUo6qvWmG81U1jFXyb9HSlYEopZRSSlWlqoqLOcl1pZRSStUStXhkx+mqSly6iMgxbJUXf/t1OHHK/2CXRqeUUkopVUKliYsxxlpTgSillFLq1Fjq0CwXRw+HVkoppVQtVZeGimrzOWaUUkoppUrRiotSSinl4aQODRVpxUUppZRSTiUiYSLyh4jssv8fWkGbpiLyl4hsE5EtIvKAI4+tiYtSSinl4UScfzlNTwLzjTGtgfn222UVAI8YY9pj+yHne0SkQ1UPrImLUkop5eEsiNMvp2kUJ860PxW4tGwDY8wRY8xa+/UMYBvQuOq+KqWUUkqVISJjRWR1icvYaqzewBhzBGwJChBVxbaaA12BFVU9sE7OVUoppTycKw6HNsZMAaacfJsyD2hYwaKnq7MdEQkCfgQeNMYcq6q9Ji5KKaWUqjZjzNCTLRORoyISbYw5IiLRQMJJ2nljS1q+Nsb85Mh2dahIKaWU8nC1cHLuLOAm+/WbgJ/LxywCfApsM8a85egDa+KilFJKKWd7FbhARHYBF9hvIyKNRGS2vU1f4AbgfBFZb79cWNUDu3yoKCUzz9WbqLUSM/PdHYJbdWjo7+4Q3Opwet1+/kfee4u7Q3CrOR987u4Q3Oaa7k+5O4Q6p7adgM4YkwwMqeD+OOBC+/XFUP3AdY6LUkop5eEstStvcSkdKlJKKaWUx9CKi1JKKeXhattQkStpxUUppZRSHkMrLkoppZSHc8UJ6GorTVyUUkopD6dDRUoppZRStZBWXJRSSikPp4dDK6WUUkrVQlpxUUoppTxcXZrjoomLUkop5eHq0lFFOlSklFJKKY+hFRellFLKw9WhgotWXJRSSinlObTiopRSSnk4Sx2a5KIVF6WUUkp5DK24KKWUUh6u7tRbNHFRSimlPF8dylx0qEgppZRSHkMrLkoppZSHq0tnztWKi1JKKaU8hlZclFJKKQ9Xh46G1sRFKaWU8nR1KG/RoSKllFJKeQ6tuCillFKerg6VXLTiopRSSimP4dEVF2MMn098nbUrl+Dr68c9jz9Py9bty7X78I0X2btzK8YYopvEcM/jz+PvH0BWZgbvvzqOpIR4CgsLueSqGxg84hI39KT6YresZsX0yRhTRJu+w+k8fHSp5cYYVkyfTOyWVXj5+NLvxoeJaNaKgvw85rz5OIUF+ZiiQpp37UfXi693Uy+qxxjDom8+4sAmW5+G3PYIUTGty7U7lhjP3EkTyM3KIDKmFRfc8RhWL29Sjxxi3mdvknhgD70uv4luI64sXmfDHzPZsnAOGEOHASM5Z9hlNdk1hxzdtoaNMz7GmCJiel5A26FXlVpujGHjjCkc3bYGq7cv3a95gJCmrQDYvWAW+5fPBWNo3ns4rQaOAmDbb9+wf/lcfAPrA9Dhohtp2KFHzXbsFHRtHMytvZpiscC8HUnM2Hi01PIBZ4VxaecGAOTmFzFl6UH2p+QAcNHZUVzQNgKwrfu/LQk1G7yLTXruOkYO6EhiSgY9rnrF3eE4nTGGX794n53rVuDt68cVdz1Bo5ZtyrVb/tsMls7+gZSjcfzn45kEBtv28W2rFjNv+ueICBarlQtvupfm7TrVdDecTg+H9hDrVi7hyOFDvD91Jv9+6Bk+fndChe1uvuth3pgyjTc//o6IqIb8NvM7AObO+p4mMS15Y8o0nn9zClMnv01+fn5NduGUFBUVsnzahwy790Uue3YSe1ctIO3IwVJtYres5ljCYa544RP6XHs/y779AACrlzcjHpzApc9MZNTTHxC7dTUJe7e7oxvVdmDTKtKOxnH9hM8YfNMDLPjygwrbLf3+U7oMu4wbXv0M38Agti6aC4BvYD0GXHsXXYdfUap9cux+tiycw1XPvMuYFz5i/4YVpB097PL+VIcpKmTDj5PoM/Z5hj4xkdh1CzkWX/o5P7ptDVmJcVzw1GS6jr6H9T98BMCxIwfYv3wugx56k/Mfe5/4LavITIwrXq/VwFGc/9h7nP/Yex6RtFgE7ujTjJd+38UDP26lf8swmoT4lWpzNOM4437dycMztvH9+iPc2TcGgGahflzQNoLHf97GwzO20r1pfaKDfd3RDZf56pfljLpnorvDcJmd61eQHH+Yh979L5fe8QizPn27wnbN2nbklmfeJCSyQan7W3bqzr2vfcK9r33C5Xc+zszJr9dE2C4n4vxLbeXRicuqpQsYeMFFiAhtOnQiKzOT1OTEcu0CAoMAW6aedzwXsT8jAuRkZ2GMITcnm6B6wVit1prswilJ2r+TepGNqBcZjdXLm5Y9BnBww7JSbQ5uWE6rXkMQEaJatiMvO4vs9BREBG8/fwCKCgsoKiys1TtoSfvWLaNdH1ufGp7VnuPZmWSlJZdqY4whdvsGWvXoD0C7PkPZu3YpAAHBITRo0RZLmec49chBGrZsh7evHxarlcZtOxWvU1ukHNxFYEQ0gRENsXh506TrAI5sXlGqzZHNy2l67vmICGHN25Gfk0VuegoZRw8RFtMWLx9b/yJadSRu47KTbKn2axUZyJFjuRzNyKOgyLB4byrnNQsp1WZHQhZZeYUA7EzIIjzQG4DG9f3YmZBFXqGhyMDW+Ax6xoRwJlmydg8p6dnuDsNltq1awjkDhiEiNG3TgdysLDJSk8u1a9SiNaFRDcvd7+vnX/wZkHc8t05VKs4UHp24pCQlEF4imw6PjCIlqXziAjDx9ee546phxB3az8hLrwZgxKVXc/jgPsZePZxH7riaW+5+FIul9v9JstOSCQyNKL4dEBpR7gM8Oy2JwNDI4tuBoRFkpyUBtorNzy/fy7ePX0uj9l2JbNGuZgI/TZmpyQSFnehTUFgkmWXesHIzj+EbEFicnASFRZb725QV1rg5h3duJifzGPnHc9m/aRUZKRXvR+6Sm5aMf8iJ59y/fji56aX7lZNepk1IODnpydSLjiFp7xaOZx2jIC+X+K2rybHvCwB7F/3K/NfuY82375KXnen6zpym8ABvkrNOVEaTs/MIsycmFRnaJoJ1sccAOJiaS4eGQQT5WvGxCt2a1ici0MflMSvnyUhNon54VPHt4PAIjqUkVbJGeVtXLuKdh27kq1f/w2V3Pe7sEN1CXHCprSqd4yIiGYA52XJjTLDTI6oGYyoI7STlg3see57CwkI+++A1lv79B4NHXML61ctoflZbnntjMvFxsYx/4m7ad+paXKGprSrqtzhUNrG1sVisjHr6A45nZ/Ln5JdIPbyf0MbNnRukSzjQ74r2iSpegmGNmtF95FXMeuM/ePv5E9G0JRZLbau8OdCvCpsIwQ2a0ub8K1jy0Ti8fP2p36gFYk/QW/QdSbthVwPC1jn/ZdPPn9L9mgecHbzrneRdqmN0EEPahvPU/3YAcDg9lxkb43l+RBtyCgrZn5xDYYX7jKqtKn7fr95jdDivPx3O68++rRuY991n3DruTecEp2pEpYmLMaYegIi8CMQDX2HbRa4D6p1sPREZC4wFGDfhXa687lZnxctvP09n3uwZALRq04HkxBOT8pITEwgLjzjZqlitVvoMGsas6V8yeMQl/PXbLC675hZEhOjGTYlq2IjDh/bTul1Hp8XrCoGhEWSlnviGkZ2aRED9sFJtAkIiyEo9UTXISk0iICS8VBvfgCAatu5E7NY1tTZx2Th/FlsX/gZAVIs2ZJaohGSmJBIYUrrffvXqczw7i6LCQixWa4VtKtJhwAg6DBgBwLIfPyco9OT7kTv4hUSUqpLkpCfjV+Y59w8JL90mLRn/YFub5r2G0bzXMAC2/Pol/vVt+4JfvdDi9s17D2fZxy+6rA/OkpydXzz0AxAe4ENKdvm5aTGh/tzdrznj5+4i83hh8f3zdyYzf6etWnVd90YkV7Cuql2Wz53B6vm/AtD4rHakJ5+YUH0sOYngU3y9tujQhR8/jCPrWHrx5F2PVZtLJE7m6LjIcGPMh8aYDGPMMWPMR8AVJ2tsjJlijOlhjOnhzKQFYMSo0bwx+VvemPwt5/YdxII/fsUYw86tmwgIDCI0PLJUe2MMRw4fKr6+ZvlCGjdrDkBEVEM2rV0JQFpqMnGHDtAgurFT43WFiJg2HEuIIyMpnsKCfPauXkjTzr1KtWnWuSe7l8/HGEPC3u34+AcSUD+M3Ix0jtuHAwryjnNk+3pCGjZxRzcc0nnIJYx54UPGvPAhLbv2ZvtSW5/i92zDJyCQwDLJmIjQuF1ndq9eBMD2pfNo2bV3ldvJPpYGQEZyAnvWLKF1z0HO7sppCW3amszEOLKS4ykqyCd23UKizz6vVJvos3tyaNWfGGNI2b8db/+A4uTmeEYaANmpCcRtXEqTbgMByE1PKV7/yMZlBEfH1EyHTsPuxCyig/2ICvLByyL0axnKqoNppdpEBHrz+NCWvLtgH0eOHS+1rL6fV3Gbns1DWbQnBVW79Rp+WfGE2g7n9mX9wt8xxnBo51Z8AwKpFxpe9YPYJccfLq7axO3dSWFBAQH13Dp4oKrJ0cOhC0XkOmAatqLsNUBh5au4Xree/Vi3cgn33TgKH18/7nns+eJlrzx1P3c+PI6QsHAmvvYc2Vm2D+uYlq2544H/AHDl9Xcw8fXnePh226HE199xP8H1Q8ttp7axWK30GnMXv7//DKaoiNZ9hhHaKIbtC23fSNoNuIgmHc8ldvMqfnz2Nqw+vvS/8SEAstNTWDT1TYwpwhQZWnTvT9NOPd3ZHYfFdD6PAxtX8dWTt9oOh7714eJlv7w9jsE3P0hQaDh9rryNuZMnsGLGVCKanUWH/sMByEpPYfqL95OXk42IsOGPmVz30mR8/AOZM3E8uZkZWKxWBl5/D36BJy0ouoXFaqXLFXeyZPJzUFRETM+hBEfHsG/JHMA25NOgQw/it63mj5fHYvXxpduYE0M+Kz6fQF52BmK10uWKu/AJsA2Hbv7lc9Lj9gFCQFgUXa+6xx3dq5YiA58sO8izI1pjEWH+ziQOpeUyrJ3tW/fv25MY3bUR9Xy9GNunGQCFRYbHZ9mOnntsSEvq+XpRWGT4eOnB4km8Z4qpE26mf/fWRIQEsfu38YyfNJupMz13MnZZbbr2Yue6Fbz1wPX4+Phy+V1PFC/7csKTXPrvRwkOi2DZnB9ZNGsamWkpfPD4bbQ5pyeX3fkYW1YsZP3CuVisXnj7+HL1g886ONReu9WlScZS4Xhh2UYizYF3gb7YEpclwIPGmP1VrbvxUGadHUCeveto1Y3OYIG+tX+isysdTq/bQxA7j2S4OwS3mvPB5+4OwW2+/Pwpd4fgdled06hGM4n1BzOc/ll7TrN6tTIbcqjiYk9QRrk2FKWUUkqpyjn0lVhE2ojIfBHZbL/dWUSecW1oSimllHJEXToc2tFa/sfAf4B8AGPMRmCMq4JSSimllKqIo5NzA4wxK8tMYCpwQTxKKaWUqq7aXCJxMkcTlyQROQv7aZ5E5ErgiMuiUkoppZTD6tJRRY4mLvcAU4B2InIY2IftJHRKKaWUUjXG0cTlgDFmqIgEAhZjTN0+zlEppZSqRc6AU9E4zNHJuftEZArQC6j9v8KmlFJKKbcRkTAR+UNEdtn/P+nZXUXEKiLrROR/jjy2o4lLW2AetiGjfSLygYj0c3BdpZRSSrlQLTwc+klgvjGmNTDffvtkHgC2OfrADiUuxpgcY8x0Y8zlQFcgGFjg6EaUUkop5UK1L3MZBUy1X58KXFph2CJNgIuATxx9YIfPyS4iA0XkQ2At4AeMdnRdpZRSSnkWERkrIqtLXMZWY/UGxpgjAPb/o07S7h3gcaDI0Qd2aHKuiOwD1gPTgceMMVmObkAppZRSruWKw6GNMVOwHVFc8TZF5gENK1j0tCOPLyL/AhKMMWtEZJCjcTl6VFEXY8wxRx9UKaWUUmc2Y8zQky0TkaMiEm2MOSIi0UBCBc36ApeIyIXYRnKCReS/xpjrK9tupYmLiDxujHkNeFlEyv3ypDHm/srWV0oppZTr1cLDoWcBNwGv2v//uWwDY8x/sP2cEPaKy6NVJS1QdcXln1m+qx2PVSmllFJ13KvAdBG5DTgIXAUgIo2AT4wxF57qA1eauBhjfrFf3WiMWXeqG1FKKaWU69S2gosxJhkYUsH9cUC5pMUY8zfwtyOP7ehRRW+JyHYRGS8iZzu4jlJKKaVqQu07HNplHD2Py2BgEJAITBGRTSLyjCsDU0oppZQqy+HzuBhj4o0x7wF3Yjs0+llXBaWUUkopx4kL/tVWDiUuItJeRJ4Xkc3AB8BSoIlLI1NKKaWUKsPR87h8DnwLDLNPrFFKKaVULVELD4d2mSoTFxGxAnuMMe/WQDxKKaWUqqY6lLdUPVRkjCkEwkXEpwbiUUoppZQ6KUeHig4AS0RkFlD8O0XGmLdcEpVSSimlHFeHSi6OJi5x9osFqOe6cJRSSimlTs6hxMUY84KrA1FKKaXUqanNhy87mxhT7rcTyzcS+Quo6EcWz69q3fHzdle9gTNUiL/V3SG41R09W7g7BLfaEZfh7hDc6nBGjrtDcKus/AJ3h+A2N97yirtDcLucdR/UaCaxOyHH6Z+1raL8a2U25OhQ0aMlrvsBVwB191WplFJKKbdwdKhoTZm7lojIAhfEo5RSSqlqqpWlERdxKHERkbASNy1AD6ChSyJSSimllDoJR4eK1nBijksBsB+4zRUBKaWUUqqa6lDJpdLERUTOBQ4ZY1rYb9+EbX7LfmCry6NTSimllCqhqjPnTgbyAERkADABmAqkA1NcG5pSSimlHFGXfh26qqEiqzEmxX79amCKMeZH4EcRWe/SyJRSSinlkLr0I4tVVVysIvJPcjME+LPEMkfnxyillFJKOUVVyce3wAIRSQJygEUAItIK23CRUkoppdysDhVcKk9cjDEvi8h8IBr43Zw4za4FuM/VwSmllFJKlVTlcI8xZnkF9+10TThKKaWUqrY6VHLReSpKKaWUh6vNRwE5W1WTc5VSSimlag2tuCillFIeTg+HVkoppZSqhbTiopRSSnm4OlRw0cRFKaWU8nQ6VKSUUkopVQtpxUUppZTyeHWn5KIVF6WUUkp5DK24KKWUUh5O57gopZRSStVCWnFRSimlPFwdKrho4qKUUkp5Oh0qUkoppZSqhbTiopRSSnm4uvTr0B6VuMRtWc2qH6Zgiopo1XcYHYeNLrXcGMPq7ydzeMtqvHx86X3DQ4Q3awXAjHG34O3nj4gFsVq58Il3AVjz06cc3rwSi9WLepHR9L7+QXwCgmq8b44wxrDom484sGkVXj6+DLntEaJiWpdrdywxnrmTJpCblUFkTCsuuOMxrF7epB45xLzP3iTxwB56XX4T3UZcCUDqkUPMnTSheP30xHh6XnoD5wy7rMb6Vl3GGP5vwsssXrgAP38/xr/8Ku07nF2u3c03XEt2VhYAKSnJdOzUmXfe/5B9e/fw7DNPsW3rFu574CFuuuW2mu7CaTHG8PmHb7Bu5RJ8ff24+7Hnadm6Xbl2H735Int3bsMYQ3STZtzz2PP4+Qcwa/qXLJr/GwBFRQXEHtzPp9//QVBw/ZruyikxxjDj03fZtnY53r6+XHPvUzQ9q225dotm/8jC/31PUvxhxn/xC0HBIQBkZ2Yw7YMJJB09jLe3L2PueZLomJY13ItTY4zh1y/eZ+e6FXj7+nHFXU/QqGWbcu2W/zaDpbN/IOVoHP/5eCaB9ud226rFzJv+OSKCxWrlwpvupXm7TjXdDZeZ9Nx1jBzQkcSUDHpc9Yq7w1Eu4DGJS1FRISunf8SQ+14iICSCOa89RJNOvQiJblbcJm7LajIS4xj1/Mck7d/BymkTGfn428XLhz4wAb+g0m/M0e270nXUzVisVtbO/IzNv0+n26W31li/quPAplWkHY3j+gmfcXTvdhZ8+QFXjXu3XLul339Kl2GX0abnIP768j22LppLp8H/wjewHgOuvYu9a5eVah8a3ZQxL3wI2P7OXzx8PS279amRPp2qxYsWcvDAfn6Z8zubNm7gpRef5+tp35dr98VX3xRff/iB+xh8/hAAguuH8MR/nuavP+fXVMhOtW7lEuIPH+K9L2awa9tmPnlvAq+8P7Vcu5vufJiAQFsiPnXSW/z283QuHXMzl4y+kUtG3wjA6mUL+fWnbzwmaQHYtnY5iUdieWritxzYuZUfprzJQ/83pVy7Fu06cXaPPnww7v5S98/78UsatWjNrU++wtHYA/z48Vvc/UL511JttHP9CpLjD/PQu/8ldtc2Zn36Nne+/FG5ds3adqRtt958+uKDpe5v2ak79/boi4gQf2AP0955gQff/rKGone9r35ZzqTvFvDJ+BvdHUrNqjsFF8+Z45K8fyf1IhtRLyIaq5c3zbsPIHbj8lJtDm1cToue5yMiRLZoR15OFtnpKZU+bqP23bBYrQBENG9Hdmqyy/pwuvatW0a7PkMQERqe1Z7j2ZlkpZWO1xhD7PYNtOrRH4B2fYayd+1SAAKCQ2jQom1xfysSu3U9wVHRBEc0cF1HnOCvP+dz8SWXIiJ07nIOGRnHSExMOGn7rKxMVq5czuAhQwEIDw+nY6fOeHl5TO5eyuplCxgw9EJEhDYdOpGVmUFqclK5dv8kLcYY8o4fr/Cxlvw1l76Dh7s0XmfbvHIx5w4agYjQvO3Z5GRlkp5Svv9NWrYhLCq63P3xh/bTpnN3ABo0iSElIZ6MtMrfK2qLbauWcM6AYYgITdt0IDcri4wK3rcatWhNaFTDcvf7+vkj9pmcecdzz7ghhiVr95CSnu3uMGqcuOBSWzmUuIhIAxH5VETm2G93EJEara1npyUTEBpRfDsgJILsMh/aOenJBIZEFt8ODIkg5582Isz/YByzX72fXYvnVLiNPcv+oNHZ3Z0fvJNkpiYTFHaif0FhkWSWecPKzTyGb0BgcXISFBZZLrmpzK6VC2jTc5BT4nWlhISjNGh44k25QYOGJBw9etL2f86bR8+evQkKqp3DgNWVkpRIRIkPpfCIBqQkVZy4ffj6C4wdPZy4Q/sZeemYUsuO5+ayfvUyevU736XxOlt6SiIhEVHFt0PCIytMXE6mcfNWbFy+AIADu7aSmniUtOREp8fpChmpSdQPP9H34PAIjlWj7wBbVy7inYdu5KtX/8Nldz3u7BCVcilHKy5fAHOBRvbbO4EHXRBPJUy5e8oe/mVM+Tb/pI3DH36di558j/PveZEdC3/l6K7NpZpt+m0aFquVFucOdlbALlDR36DcH6GC9RzLnQsL8tm3fnlxtaZWq6Cf5f4WJcyZ/T9GXniRKyOqURXt6yfr/92PPcfkaXNo3KwFS//+vdSyNcsX0vbsLh41TATV639Fhlx+PTmZGbz+8C0smv0jjVu0xmI5eSWyNqnsfc5RHc7rz4Nvf8m1j45n3nefOScw5VYizr+cXjwSJiJ/iMgu+/+hJ2kXIiI/iMh2EdkmIr2remxH6+QRxpjpIvIfAGNMgYgUVhLwWGAswCUPjqfHRWNO1tRhASERZKee+FaRnZaEf/3wcm2y0k58a8oq0SYgxPa/X70QmnbpTfKBHTRo3RGAPcvncXjzKobe/3K13vxqwsb5s9i60DaJMqpFGzJTTvQvMyWRwJCwUu396tXneHYWRYWFWKzWCtuczIFNq4mMaUVA/Qr3L7eb9s3X/PTDdADO7tiJo/HxxcuOHo0nMiqqwvXS0lLZvGkTb783sUbidJXffp7O/NkzATirbQeSEk70PznpKKHhkSdZEyxWK30GXsCs779i8IhLiu9f8vfv9POQYaLFc35i2R+/ANCsVTvSSlSY0pITCQ4NP9mq5fgFBHLNfU8BtkRg/J2jCW9Qfkiptlg+dwar5/8KQOOz2pGefKLvx5KTCC5Rja6OFh268OOHcWQdSy+evKuUkzwJzDfGvCoiT9pvP1FBu3eB34wxV4qIDxBQ1QM7WnHJEpFw7F/5RaQXkH6yxsaYKcaYHsaYHs5IWgDCY9qQkXCYzKR4Cgvy2b9mIU069SzVpknnnuxb8SfGGBL3bcfHP5CA+mEUHM8lP9c25llwPJcj29YSEh0D2Cb0bv3jBwb9+1m8fPycEqszdR5yCWNe+JAxL3xIy6692b50PsYY4vdswycgkMCQ0m/WIkLjdp3ZvXoRANuXzqNl1yoTWAB2rfib1ucNcnYXnGbMtdcx/aefmf7TzwweMpRfZs3EGMPGDesJCqpHZGTFicvvc39jwMBB+Pr61nDEzjVi1Ghen/wNr0/+hvP6DmLhvNkYY9i5dRMBgUGEhpf+8DLGEH/4UPH11csX0ahp8+Ll2VmZbN24lh69B9ZkN05Zv5GX89hbn/PYW5/T8bz+rPr7N4wx7N+xBf+AIOqHOf7hnZOVQUF+PgDL5/3CWR264BcQ6KrQT1uv4Zdx72ufcO9rn9Dh3L6sX/g7xhgO7dyKb0Ag9aqRtCXHHy6u2sTt3UlhQQEB9YJdFbqqIeKCf6dpFPDPEQNTgUvLxSwSDAwAPgUwxuQZY9KqemBHKy4PA7OAs0RkCRAJXOnguk5hsVo5d/RdzJ84DlNUxFm9LyCkUQw7F80GoE3/C2l89rnEbVnNz8/fbjsc+vqHAMjJSGXBlJcBMIWFND93II3O7gHAyumTKCrIZ/77TwMQ0aIdPa+5tya75rCYzudxYOMqvnryVtvh0Lc+XLzsl7fHMfjmBwkKDafPlbcxd/IEVsyYSkSzs+jQ3/aNOis9hekv3k9eTjYiwoY/ZnLdS5Px8Q8k/3guB7esZdCN959s87VK/wEDWbxwAf8aeQF+fv68+NKJwx7vufMOnnvxJaKibBOM586Zza233VFq/aTERK65+gqyMjOxWCz896upzJg122PmwHQ9ry9rVyzh/psuxcfXj7sffa542YSn7uffD48jJCycia89R3Z2FmCIadmG2+9/srjdysV/0aV7T/z8/d3Qg9PToXtvtq1dzst3j8HH148x9/6neNmUlx7j6rufoH5YBAt//YE/Z3xDRloKrz90M+279WLMPU9yNPYAX7/3MhaLhQZNmjPmnicr2Vrt0qZrL3auW8FbD1yPj48vl9914kvslxOe5NJ/P0pwWATL5vzIolnTyExL4YPHb6PNOT257M7H2LJiIesXzsVi9cLbx5erH3y21lWaT8fUCTfTv3trIkKC2P3beMZPms3UmcuqXtHT1b6nsIEx5giAMeaIiFT0zbIlkAh8LiJdgDXAA8aYrMoeWCocL62ooYgX0Bbbn2eHMSbfkfXGz9vt2AbOQCH+njFm7ip39Gzh7hDcakdchrtDcKvDGTnuDsGtsvIL3B2C29x4i54/JWfdBzWaSiRmFjj9szaqnve/sU/7sJtijCk+74CIzAPKH7oGTwNTjTEhJdqmGmNKzUMQkR7AcqCvMWaFiLwLHDPGjKssruocC3oe0Ny+TjcRwRhz5hz8r5RSSnkoV2RJ9iSl/AmSTiwfetJ4RI6KSLS92hINVHTYYywQa4xZYb/9A7a5MJVy9HDor4A3gH7AufZLD0fWVUoppVSdMwu4yX79JuDnsg2MMfHAIRH557TXQ4CtVT2woxWXHkAH4+i4klJKKaVqTC2cpvQqMN1+zreDwFUAItII+MQYc6G93X3A1/YjivYCt1T1wI4mLpuxjWMdqWbgSimllKpjjDHJ2CooZe+PAy4scXs91RzBcfg8LsBWEVkJFJ833BhzyclXUUoppVRNONN+uqEyjiYuz7syCKWUUkqdulo4VOQyDiUuxpgFItIA26RcgJXGmJP/op1SSimllAs4elTRaGAltsk1o4EVIlKjJ6BTSimllHJ0qOhp4Nx/qiwiEgnMw3bMtVJKKaVUjXA0cbGUGRpKxvHfOVJKKaWUC+kcl/J+E5G5wLf221cDs10TklJKKaWqQ48qKsMY85iIXAH0xXZm4SnGmBkujUwppZRSqgyHf6vIGPMj8KMLY1FKKaXUKdChIjsRyQAqOs2/AMYYE+ySqJRSSimlKlBp4mKMqVdTgSillFLq1NShgkv1jwwSkUARuU5EfnVFQEoppZSqJnHBpZZy9AR0PiJyqYhMx/ZDi0OBSS6NTCmllFKqjKrmuFwAXAMMB/4CvgLOM8ZU+bPTSimllKoZejj0CXOBRUA/Y8w+ABF51+VRKaWUUkpVoKrEpTswBpgnInuBaYDV5VEppZRSymF16XDoSue4GGPWGWOeMMacBTwPdAV8RGSOiIytiQCVUkoppf7h8FFFxpglxph7gcbA20Bvl0WllFJKKYfVoYOKHD6qqK+IBNpvXguMwFaBUUoppZS71aHMxdGKy0dAtoh0AR4HDgBfuiwqpZRSSqkKOJq4FBhjDDAKeNcY8y6gZ9VVSimlagFxwb/aytEfWcwQkf8A1wMDRMQKeLsuLKWUUkqp8hytuFwNHAduM8bEY5ug+7rLolJKKaWUw0Scf6mtxDYCdOYSkbHGmCnujsNdtP91t/91ue+g/df+1+3+n8kqTVxEJAOoqIEAxhgT7KrAnEVEVhtjerg7DnfR/tfd/tflvoP2X/tft/t/Jqt0josxRifgKqWUUqrWcPgEdEoppZRS7lYXEpe6Psap/a+76nLfQfuv/VdnpDN+cq5SSimlzhx1oeKilFJKqTOExyQuIlIoIutFZLOIfC8iAZW0fV5EHq3J+NxFRC4TESMi7dwdi6uJyNMiskVENtr3hZ4i8omIdLAvzzzJer1EZIV9nW0i8nyNBu4k1XkNOPh4zUVks7Piq0kl/hb/XJq7O6bTVdH+7YTHvEREnnRSfBW+vlzBkW2JSFf7e9/w6q6rPJujZ86tDXKMMecAiMjXwJ3AW26NqHa4BlgMjOEM/uFLEekN/AvoZow5LiIRgI8x5nYHVp8KjDbGbLCf9bmtK2N1oVN6DYiIlzGmwMWx1bTiv4WjRESwDY8XuSakU3ey/dvBdU/6/BpjZgGznBdprfLPe981wFw3x6JqkMdUXMpYBLQCEJEb7d9QNojIV2UbisgdIrLKvvzHf76lishV9m+uG0Rkof2+s0Vkpf3bzkYRaV2jvaomEQkC+gK3YUtcEBGLiHxo/+b2PxGZLSJX2pd1F5EFIrJGROaKSLQbw6+uaCDJGHMcwBiTZIyJE5G/RaT4XA0i8qaIrBWR+SISab87CjhiX6/QGLPV3vZ5EflKRP4UkV0ickcN9+l0LAJaicjF9mrSOhGZJyINoLhvU0Tkd+BLEfn/9s4/2KqqiuOfr0BBPXooECEyvHIcmmCMEfrlD3o0Df8wE06ahKgx5Yw1TmNN5T9MJmI5DtVEOjQO6ZiE+QBFGGpSB3xChmAojwcN2A8wtawoxWCcyufqj7Vu73C59/GQ94Pz3vrM3Dnr7rP3uWftvfbe66y9773jJK0Le2+TdGFcZ4ikFWEvj0oa0W8anQKSGqLNn5HULmlupDdFlG058AwwUdI3YkzYLWlx/975/6ln3wfDiUHSDEmtIVe373ZJUyoXi34xXdJCSXdKaoxrnRHn3yHpBUnDJJ0r6ZcxLmxVRG8lvVfStqirJX1cHxU9xkvaos5I4yWRLuByYCEwW9LwGmUlaWmUa5c0L9Kbo37WStonaVVcr+xj5ODBzErxAo7EcSiwHvgSMAXYD4yJc2fF8Wbg6yGPLlzjVuDLIbcDE0IeFcc7gAUhvw0Y0d96n6BOrgLuDvnXwAV4Z/4F7pS+B3gl0oZFnrGRfx5wT3/rcBK6NgC7gOeA5cDHI70VmBGyFdrvJuDOgvwKsA64DhhesJM2YAQwBngBOLu/de2iDmr1gTPp3GR/LfC9gm47KzYMtABfCXkI0Ag0AW8A0yJ9NXBVf+vZzbroCHvYFe06FHhXnBsD/B7/ocwm4E3go3FuNv5tE0Uf2QjMPA30qWffB+kc32YArXXa96vA4pDHA8+FvLDQD9YDs0KeB/w45E3AeSF/BNgc8gbgmpCvr9hfH9v614BFBbsdGfLFwKaQ7wc+XaPsZcBjUW4c8Keom2bgMHBO2MC2uF6px8jB9CrTUtEISbtC3grcjU9Ca83sEICZ/bNGuamSbgVG4YNDJaT4JHCvpNXAQ5G2DVgk6RzgITP7XW8o0oPMB34Q8gPxfhiwxjwc/rKkx+P8ZGAq8Fg8XAwhohBlwMyOSJoOXALMAlp0/Nr9m/gEDfBTol3N7Bb50sps4Eq8npoj33ozex14Perqw8DDvajKqVCrD0zG62I87mwfKOTfELoBfAK4BjzqBByWdCZwwMwq19yJT/Rl4JilIknDgO9ImonbwQR8sgJ43syeCnl2vJ6N9w3AecCWvrjpenTTvqsptu9qfJL+FnAFsKZG/hZ8Mn4cj9Aul0dtLwTWqPPPad4ex4vwyR9gJXD7yerVAzwN3BPt+3DBVufjYx5xvJrOcbzCxcDPwt7/KukJ4EPAa8AOM3sRIPpUE/AqJR4jBxNlclyOW9OO8N6Jvs99L3Cp+f6GhcSEZWZflG9+mwPskjTNzO6XtD3SHpF0rZlt7lk1egZJo/HJaKokwzuZ4U+fNYsAe83sY310iz1ODECtQKukduBzJypSKPsH4EeSVgB/j/o7Jk+d96cTtfrAHcD3zWyDpGaO3ed0tBvX/HdB7sCjT2VkATAWmG5m/5V0EKgsHxTrQcBtZnZXH9/fCalj32/QuaRfvRxytFD2JUn/kHQ+7pxcV+MjNgC3SToLmA5sBt4JvFptV8Xbemva9AxmtiWc0TnASklLgVW4Q/UpSYvwNh0taaSZ/atQvKu/Cay2+6EMgDFysFDWPS4VNgFXVCah6JDVjAT+Eh77gkqipHPNbLuZ3QQcwte+3wf80cx+iHfy83tdg7fO5cB9ZjbJzJrMbCL+tH0IuEy+12UcnZGF/cBY+SZAYm17Sq0Ln45Imqxj9xxNA56vynYGXi/gkZVfRdk5lTVs/Om6A3+6ApgraXjYUDP+hFcmGoGXQu7KkduELy0haYik0/5/xk6SRuBv4bTMAibVyfcI8PmINCBpgqR399VN1qML+z6IOxnQGf2oxwPAjUCjmbVXnzSzI8AOYBmw0Xy/12vAAUmfifuQpA9GkSeJvXMUxs6+RNIkvF1X4BHGC4BPAm1mNjHGvknAg8ClVcW3APPC3scCM3H961HqMXIwUWrHxcz2At8GnpDURu1vWHwT2I6HUfcV0pfGhq09uIG34U8qeyJ0+H7gvl68/VNlPsdHVx4EzgZeBPYAd+G6Hzaz/+CT+u1RV7vwEHFZaAB+Ium3knYDH+D4b1EdBaZI2olHo26J9KuB/dGuK/F9MB1xbgfwc+ApYImZ/blXteh5bsbD/Ftxp7UeNwCz4kl+J74/bCCxCpgh6Tf4JLuvViYzexTfE7Et6mIt/nDT39Sz78XAsmjfji7Kg+vyWXzZqB4t+N64lkLaAuALMS7sBeZG+g3A9ZKexh3D/qAZj4g/iztuy6g/9l1ZlbYO2I2P7ZuBG83s5XofNADGyEFD/nLuAERSQ6yZj8Yn5ou66rCDFfnvuRwxs+/2970kSZIk3aNMe1yS7rNR0ih8s+aSdFqSJEmSgUJGXJIkSZIkKQ2l3uOSJEmSJMngIh2XJEmSJElKQzouSZIkSZKUhnRckiRJkiQpDem4JEmSJElSGtJxSZIkSZKkNPwPlTto215CiXkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Korrelációs mátrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "dataset['IsAlone'] = isAlone\n",
    "\n",
    "corr_df = dataset.corr()\n",
    "display(corr_df)\n",
    "plt.figure(figsize = (10, 8))\n",
    "plt.title('Korrelációs mátrix')\n",
    "sns.heatmap(corr_df, annot = True, cmap = 'Blues')\n",
    "plt.show()\n",
    "\n",
    "dataset = dataset.drop(['IsAlone'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.676056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.459259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.233429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.676056\n",
       "2       0.459259\n",
       "3       0.233429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Túlélők aránya osztályonként\n",
    "\n",
    "dataset[['Pclass', 'Survived']].groupby(['Pclass'], as_index = True).mean().plot(kind = 'bar', figsize = (10, 8), legend = False)\n",
    "plt.title('Túlélők aránya osztályonként', fontsize = 20)\n",
    "plt.xlabel('Osztály', fontsize = 15)\n",
    "plt.ylabel('Túlélők aránya', fontsize = 15)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()\n",
    "\n",
    "display(dataset[['Pclass', 'Survived']].groupby(['Pclass']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WZOQkfbyWaD2JcpbftymJ6/45wjXnIuLZlGV5CfCCzLylR7F3UH4EdsiuOz5EudbfoMazTHuuh2NwA/CIiFi6u7U6ypmDq1K6CnT7POWQ+lezdyf98WzLX6Rc4/AtwPER8aLMvK1HuSXR2Q77/Q6t1BVT57M/qk/5XsOXZBoyc1FEnAI8LyI2qq3Ce1GS86O7indifGpXi9pkOIxy/ctXRcQ3KAnjpZQ/ORMV06Moh3W7ddafG7qe52Tm8T3Kaxy8lEa7XAc8KnrfB3CzMdZxAeVfyTP71DNWZ9fnraP3KeXb1edeZ/vcQWl+/x5lh/atPnX08zjg/B6J2UMoh1kG9RjK4ag/9kjMHk9puZkQmXkVpdVvi3po9hWUvjM/7yq6PmX7+3mPxGztOn6sHlefe93toF8iOyFq7P8HrBURG/Qo0m89OYRyduYRmfnxfvXXOo+jrFO7Zv87XMyuz6f3GNfvUhF313n0+qc+Gcu0c5i4X8vA2ZR1Ypse47ap091vOUbEuylnvv28PvdyIeXMzU3qGZfdRtqWMzPfSvm+dgROqC2eE+G6+vyA7S8iHkfXH9Ist4q7iLKuze5RX699Q2c/tmX0vuzHSPuTefV5r4jYhHKyxU/rNt7UWed6ni06wb5BOYrwZkrr/MrA//bogjCemB6wfkfE+pTvaWHed/u/qfzcM47JWbv8kfIv8nXNgVGuwbPVWCrIzMWUszTXBD5fT7O+nyjXLtt4lHoWUQ4pzqarf09EbEE53f86yg9nr+nvoiQm36rP3xkgWVwIbBARj27MMyitFSPG3UfnzgjPbcZQT8v/MhO/HXy5Pn+H0ppzeI+Ww4X1eetmclD7Dn2VwVq1O3Vt2xxYd6ifHKCeJXUEJfn9dNdnWZVydl2nTGf4O4H/oJxFt0+/Suv0J1JaUHbPcnHZfjrf8c5ddexEWVd7uaY+r9tj3ML6vG1XfeNZptdRT2bpM76zjD4RESs05rkC5VA5lBMiOsNfQrkUw1+Al9Vt/wHqYdKjKOviR5rjIuKxlBbcuygn+fSUmf9JOTS8HXBS9Lnm1YAuoLRszaktyZ2Ylqe0BvZyJGV7/UQ0OtRGxDr06IeYmZdQ+vA9jvtasTvT7ET/xB3KZSFupPStnVuHzetR7uuUP8QfiojNu0dGuXbdtiPMZ8wy8wZKi/MmwMfo0Zd1AmJ6R5R7r95bFvg0Zbl/vVHuR5Tt7m0RsUuviiJiy+a6PBWi3Mt1o7puT1se1myXL1ASsy9HxHMph3GeSrl0xU8ot0EZi4/W6fahXC/rFErT9+qUvmhbUc5s6u4I2m0fyvWBPh0RO1I6cXauc3YP8LruVp+mLNfn2YvSd+yNwLERsXuOfg2bgynN92dHxA8oPxxbURKzH1NaXMYsM6+KiKMohwD+HBEnUtb9OZQWhT8xgbejyszfRsS5lO/gLhqJSaPMFRFxDKXD8TkR8XNKErIDZXmdw32dmUfTuYbRu2oH4bMpCcBulM72/ZKBifIZSlI0Bzi3Lt8VKOvJ6sCnMvM3cO8hxs9SkpS/APvHA09aOSfLLX0+QvlR/ROwVW2J7HZI/Sd/KOXM0KNq0nIxZX15AeUw1Kt6THtyjfHYGvNtwD8z85tMwjLNzJsj4g+U60UdBfyd8uN6fGb+OTOPjog5lOtonRcRP6QspxdRLuT63cw8qlHltyg/mGfUOLtnuTAz59XX+1FaOPaNcnHQU7nvOmcPo5wZePEo8X8gIm6nHLb/RUTslOO4PVY9seZ/KAn82RFxHGW73IFydmh3H00oyeiLKNvN4xvbzcspl6B4EfcdLu14G2U/dmhNIjrXOXspJcGY02MaMvO2iPgeZb16KyWZP6FHuWsiYnfKH9XTI+Jkyhng91DWky0p/bOW6GLQPRxK2Z+uBfy4JqATGdNvKfuk71AOXT6fsi87i7L8O/O4q25rJ1FaVH9H2W/dSvmdeAZlOa9J45p7U2At4G+UM8tnT+F8J9awTxediQ/6XEqjjtuaspO5lfKv7QRKc/qBjPE6Z3V4UC40ezJwLaUP16WUs5M+AKzTKDubPqf4U1b0L1NW9Dsplzv4IfCMHmXn0vu09KAknknZkJcfwzKaS9nQb6nzPA548qDLoTF+eUrS+g9K8nMJ5WKpj6DHJRVYgtP8u8Z3TiP/3ghlVqCcEbagK6ZH9opplOW1DqV1pHNSwXmUi+cuNdqy6aqn77pQx/eMi7KT/wClD91tlLP7fkPpvN+r/pEe82rZeWMoO7tR9xbct7535j+n33dJOUz4ccqhsru6l9Ogy7TfutlV5nGUxO8ayg/l/bYXSrL1VsofoVvr4yxKgvGQrrpGWzbd8a1MafX7B+Uw8fWU1vFel+7ouczquPfWcX9ihGvSjWVboewb9qO0wNxJOQP7U5Rto+d09XN8npK83UFpgXs3pY9gUhL27mk2orSEXU/Zp/ye0k/vPXWaF/WJb+vG8vzCKJ9zNqWPXmcfc2ON7Zvd9XPfuj27Rz19l32jzNm1zK6TENP6dXleUKe5lHJY++F95rE6pWX3r3V9vbnO7/uUVsfmtQ8PpM82wij7nhGWU/d63qmn5zo3XR5RP4ykCRTlFjJ7US6+e/KQw5Ee9CLiTZQzsffJUe520JjmKMph743yvrt9tFqUy9dcRvkTsl4OdrKVpgn7nEkTrPZ/2ZPStH7KKMUlDaDZF7UxbB3K4dHFdJ25WPtXPeBM1dp1ZA/KyUfTIjGr3kLpP3ioidmDl33OpAkSEa+kXNxxT8o1sD6YNk1LE+0H9cSesyiHKWdT+gKuQLnOW/f9aJcBLomIUymH6hZTrmu4A+VQ6tumJuwlF+VG6G+hdDN5E+XadIeOOJGmNQ9rShMkIuZTLnlwCeUWPocMNSDpQSgi3krpT7sB5WSAmyl9sL6Ymcf2KD+L0mdqe8pt6Vag9GP9NXBQZp49NZEvuXrpkIspfezOAv4je9+0XA8SJmeSJEktYp8zSZKkFnnQ9DlbddVVc/bs2cMOQ5IkaVRnnXXW1Zm5Wq9xD5rkbPbs2Zx5Zq8b3UuSJLVLRPyz3zgPa0qSJLWIyZkkSVKLmJxJkiS1iMmZJElSi5icSZIktYjJmSRJUouYnEmSJLWIyZkkSVKLmJxJkiS1iMmZJElSi5icSZIktYjJmSRJUouYnEmSJLWIyZkkSVKLmJxJkiS1iMmZJElSi5icSZIktYjJmSRJUouYnEmSJLXIUsMOQA9Os/c7YdghjGjhQbsOOwRJknqy5UySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrEG59LLdTmG8d703hJmly2nEmSJLWIyZkkSVKLmJxJkiS1iMmZJElSi5icSZIktYjJmSRJUouYnEmSJLWIyZkkSVKLmJxJkiS1iMmZJElSi5icSZIktYjJmSRJUouYnEmSJLXIUJKziNgpIi6MiAURsV+P8dtGxA0RcU59HDCMOCVJkqbaUlM9w4iYBXwJ2AFYBJwREcdn5vldRU/LzN2mOj5JkqRhGkbL2ebAgsy8KDPvBI4B5gwhDkmSpNYZRnK2FnBJ4/2iOqzblhFxbkT8NCKeODWhSZIkDdeUH9YEosew7Hr/J+AxmXlzROwC/BDY4AEVRewN7A2w7rrrTnCYkiRJU28YLWeLgHUa79cGLmsWyMwbM/Pm+vpEYOmIWLW7osw8PDM3y8zNVltttcmMWZIkaUoMIzk7A9ggItaLiGWAPYHjmwUiYo2IiPp6c0qc10x5pJIkSVNsyg9rZubiiNgXOAmYBRyRmedFxD51/GHA7sBbImIxcBuwZ2Z2H/qUJEl60BlGn7POocoTu4Yd1nj9ReCLUx2XJEnSsHmHAEmSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWqRoSRnEbFTRFwYEQsiYr8Ryj0jIu6OiN2nMj5JkqRhmfLkLCJmAV8CdgY2Bl4RERv3KfdJ4KSpjVCSJGl4htFytjmwIDMvysw7gWOAOT3K/QfwA+DKqQxOkiRpmIaRnK0FXNJ4v6gOu1dErAW8GDhsCuOSJEkaumEkZ9FjWHa9PwR4f2bePWJFEXtHxJkRceZVV101UfFJkiQNzVJDmOciYJ3G+7WBy7rKbAYcExEAqwK7RMTizPxhs1BmHg4cDrDZZpt1J3iSJEnTzjCSszOADSJiPeBSYE/glc0Cmble53VEzAN+0p2YSZIkPRhNeXKWmYsjYl/KWZizgCMy87yI2KeOt5+ZJEmasYbRckZmngic2DWsZ1KWmXOnIiZJkqQ28A4BkiRJLWJyJkmS1CImZ5IkSS1iciZJktQiJmeSJEktYnImSZLUIiZnkiRJLWJyJkmS1CImZ5IkSS1iciZJktQiJmeSJEktYnImSZLUIiZnkiRJLWJyJkmS1CImZ5IkSS1iciZJktQiJmeSJEktYnImSZLUIiZnkiRJLWJyJkmS1CImZ5IkSS1iciZJktQiJmeSJEktYnImSZLUIiZnkiRJLWJyJkmS1CImZ5IkSS1iciZJktQiJmeSJEktYnImSZLUIksNOkFELAWsCyzXPS4zz5+IoCRJkmaqMSdnEbE08HlgL2DZPsVmTURQkiRJM9UghzUPAHYD3gAEsC/wOuBkYCHwgokOTpIkaaYZJDl7OXAg8N36/o+ZeWRm7gj8BpgzwbFJkiTNOIMkZ+sAf8/Mu4HbgVUa444CXjqRgUmSJM1EgyRnlwMr19cXA9s0xj12ogKSJEmayQY5W3M+8Gzgx8BXgc9ExOOAO4A9gG9PeHSSJEkzzCDJ2f7AqgCZeUhEBLA7sDzwBeAjEx+eJEnSzDLm5CwzrwCuaLw/GDh4MoKSJEmaqcbc5ywivh8Ru0SEdxWQJEmaJIMkWqtR+pstioiDImKjSYpJkiRpxhrksOZzImJ9YC7wGuC9EfEH4AjgO5l50+SEOByz9zth2CGMaOFBuw47BEmSNAkGOkSZmRdl5gGZuR6wI7CA0u/s8oj4RkRsO/EhSpIkzRzj6T92OnAqcCGwArA9cEpEnBMRm05EcJIkSTPNwMlZRDwnIr5OOXPzs8AfgWdk5jrAk4BrgCMnNEpJkqQZYsx9ziLig5T+ZrOB04C3At/LzNs7ZTLz/FrutIkNU5IkaWYY5CK0+wDfAI7IzAUjlLsAeP24opIkSZqhBknO1q03PR9RZl5LSeIkSZI0oEEupXFvYhYRSwHL9Chz6wTFJUmSNCMNcoeAh0fEFyPiMuB24KYeD0mSJI3DIIc1vwLsBnwNOB+4c1IikiRJmsEGSc6eD/xnZn5tsoKRJEma6Qa5ztktwKLJCkSSJEmDJWefBd4aEeO5q4AkSZJGMMhhzbWApwIXRsSpwPVd4zMz3z9RgUmSJM1EgyRnuwP31Gl26DE+AZMzSZKkcRjkOmfrTWYgkiRJWoIbn0uSJGnyDHJYk4gIYCtgQ2C57vGZeegExSVJkjQj9U3OIuIR9T6ZnfePAk4GNqb0L4s6KhuTmZxJkiSNw0iHNc+KiKc23n8WuJFy1uadwJOA9YEDgQWU1jRJkiSNw0jJ2ReBkyPilfX9c4DPZObl1NayzFyYmR8FvoWtZpIkSePWNznLzM8CWwP/UQetDFxdX18LPKpR/LfAsyYhPkmSpBllxLM1M/MCygkAABdTDmlCufH5axtFX0RJ2CRJkjQOo15KIzPvqS9PpNz8HOAg4JUR8a+IWAi8DfjCpEQoSZI0gwxyEdr9Gq9PiYhnAi8FVgR+kZknTkJ8kiRJM8qYkrOIWBZ4D/CTzDwXIDPPBs6exNgkSZJmnDHdISAz7wD2p5wUMG4RsVNEXBgRCyJivx7j50TEnyPinIg4MyK2noj5SpIktd0gt2/6A/D08c4wImYBXwJ2plzQ9hURsXFXsZOBp2bmJsDrga+Nd76SJEnTwSC3b3ofcHRE3Ek5OeDf3P/uAGTmrWOoZ3NgQWZeBBARxwBzKGeAduq5uVF+xe75SJIkPVgN2nL2WODzwD8odwu4qesxFmsBlzTeL+K+S3TcKyJeHBEXACdQWs8kSZIe9AZpOXs9E9OCFT2GPaDezDwOOC4itgE+CjzvARVF7A3sDbDuuutOQGiSJEnDNcilNOZN0DwXAes03q8NXDbCfH8dEY+NiFUz8+qucYcDhwNsttlmHvqUJEnT3iCHNSfKGcAGEbFeRCwD7Akc3ywQEY+LiKivnwYsA1wz5ZFKkiRNsUEOaxIRewBvAjYElusen5mrj1ZHZi6OiH2Bk4BZwBGZeV5E7FPHH0a5uO1rI+Iu4DZgj8y0ZUySJD3ojTk5i4hXAkcA84Dt6+uHAC8ErgeOHGtd9W4CJ3YNO6zx+pPAJ8danyRJ0oPFIIc130vpmP+2+v7QzHw9sB5wNTCWy2hIkiRpBIMkZxsAv83Mu4G7gYcDZOZNlFaufSc+PEmSpJllkOTsBmDZ+vpS4AmNcQE8cqKCkiRJmqkGOSHgTOAplI78xwMHRMRi4E7gAMpFaiVJkjQOgyRnnwAeU18fUF8fSjnj8gzqxWAlSZK05Aa5CO3pwOn19fXAnIhYFlg2M2+cnPAkSZJmloGuc9YtM+8A7pigWCRJkma8YdwhQJIkSX2YnEmSJLWIyZkkSVKLTFhyFhHj6r8mSZKkAZKziPjYCOOWp1z7TJIkSeMwSMvZ2yNi/+6BEfFQ4GfAxhMWlSRJ0gw1yKHIFwInRMStmXkwQESsAvwcWBnYZuLDkyRJmlkGuQjt/Ih4CfDDiLgN+CHwSyCBZ2fmFZMToiRJ0swx0AkBmXkSsAdwCHAWcCuwjYmZJEnSxBix5SwidukxeDFwNPAC4HPAlhEBQGaeONEBStIgZu93wrBDGNHCg3YddgiSWm60w5o/oRy2jD7jj268TspN0CVJkrSERkvO1puSKCRJkgSMkpxl5j+nKhBJkiSNkJxFxDqZeUnXsKWAlwJbA48ArgVOA47NzMWTGagkSdJMMNLZmudFxHsiYhZARKwOnAl8G9gVWL8+HwOcERGrTXawkiRJD3YjJWdbAs8ETq/vPwc8EtgiM9fPzC0zc31gizr8c5MaqSRJ0gwwUp+ztwFvAVav73cB9s3MM5qFMvOMiPgv4AuTE6IkSdLMMVLL2bLAeZRDl533N/UpexOwzATGJUmSNCP1Tc4y8w3ApsDD6qDTgfdHxIrNcvX9+7nv8KckSZKW0GiX0rgU+GB9+27gVOCSiPg58G/KIc/nUy5Su+3khSlJkjQzjPnempl5DrABcDiwGrADJTk7DNggM8+djAAlSZJmktHuEHA/mXk1sN8kxSJJkjTjjbnlTJIkSZNvxJaziDiDckPzMcnMzccdkSRJ0gw22mHN8xggOZMkSdL4jHa25twpikOSJEksQZ+zKNaJiGd1X/NMkiRJ4zNQchYRbwUuBf4JnAY8vg4/NiLeOeHRSZIkzTB9k7OIOCwilm68fy/l5uZfBbanXHi2Yz6wxyTFKEmSNGOM1HK2CfCriFijvn8bcEBmfojSatZ0IbDhxIcnSZI0s4yUnD0L+Anwh/p+DeCsPmXvAZabwLgkSZJmpJFufH5PZn4c2K0OWgA8p0/xbYDzJzg2SZKkGWfU2zdl5l/qy0OAQyPiTuD7ddjqEfEG4F3AmyYlQkmSpBlkzPfWzMyvRcQqwAHAh+vgE4FbgQMz8+hJiE+SJGlGGfTG55+OiMOALYFVgWuB32fmDZMRnCRJ0kwzUHIGkJk3AT+fhFgkSZJmvNFufL7LIJVl5onjC0eSJGlmG63l7CeUG5/HKOWo5WaNOyJJkqQZbLTkbL0piUKSJEnAKMlZZv5zqgKRJEnSACcERMTGo5XJTC9EK0mSNA6DnK35V0q/spHY50ySJGkc+iZnEXEc8JrMvLkO2q5HsUcAO9bHOyY+PEmSpJllpJazBM6MiBdn5t8y81d9yh0XER8DXk45u1OSJElLaKQbn78E+Cjw0zHUcyowZ6KCkiRJmqn6JmcAmXkUsMUY6tkVuH4iApIkSZrJRrtDwDbAn+rr7/YosgywEbAB8IEJj06SJGmGGe1szVMpNzn/I7A6Dzxb83bgNOBd3rpJkiRp/EZLzu69bVNmbju5oUiSJGnEPmeSJEmaWmO5CO0uEbHRWCrLzCPHGY8kSdKMNpbk7IAx1pWAyZkkSdI4jCU52w44c7IDkSRJ0tiSs9sy85ZJj0SSJEmeECBJktQmJmeSJEktMuJhzcw0eZMkSZpCJl+SJEktYnImSZLUIiZnkiRJLWJyJkmS1CJDSc4iYqeIuDAiFkTEfj3Gvyoi/lwfv4uIpw4jTkmSpKk25clZRMwCvgTsDGwMvCIiNu4qdjHwnMx8CvBR4PCpjVKSJGk4htFytjmwIDMvysw7gWOAOc0Cmfm7zLyuvj0dWHuKY5QkSRqKYSRnawGXNN4vqsP6eQPw00mNSJIkqSXGcm/NiRY9hmXPghHbUZKzrfuM3xvYG2DdddedqPgkSZKGZhgtZ4uAdRrv1wYu6y4UEU8BvgbMycxrelWUmYdn5maZudlqq602KcFKkiRNpWEkZ2cAG0TEehGxDLAncHyzQESsCxwLvCYz/z6EGCVJkoZiyg9rZubiiNgXOAmYBRyRmedFxD51/GHAAcAjgUMjAmBxZm421bFKkiRNtWH0OSMzTwRO7Bp2WOP1G4E3TnVckiRJw+YdAiRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFlhp2AJKk9pi93wnDDmFECw/addghSJPOljNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYxOZMkSWoRkzNJkqQWMTmTJElqEZMzSZKkFjE5kyRJahGTM0mSpBYZSnIWETtFxIURsSAi9usxfqOI+H1E3BER7xlGjJIkScOw1FTPMCJmAV8CdgAWAWdExPGZeX6j2LXA24EXTXV8kiRJwzSMlrPNgQWZeVFm3gkcA8xpFsjMKzPzDOCuIcQnSZI0NMNIztYCLmm8X1SHDSwi9o6IMyPizKuuumpCgpMkSRqmYSRn0WNYLklFmXl4Zm6WmZutttpq4wxLkiRp+IaRnC0C1mm8Xxu4bAhxSJIktc4wkrMzgA0iYr2IWAbYEzh+CHFIkiS1zpSfrZmZiyNiX+AkYBZwRGaeFxH71PGHRcQawJnAw4F7IuKdwMaZeeNUxytJkjSVpjw5A8jME4ETu4Yd1nh9BeVwpyRJ0oziHQIkSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrE5EySJKlFTM4kSZJaZCjJWUTsFBEXRsSCiNivx/iIiM/X8X+OiKcNI05JkqSpNuXJWUTMAr4E7AxsDLwiIjbuKrYzsEF97A18eUqDlCRJGpJhtJxtDizIzIsy807gGGBOV5k5wJFZnA6sHBFrTnWgkiRJU20YydlawCWN94vqsEHLSJIkPegsNYR5Ro9huQRliIi9KYc9AW6OiAvHGdtkWhW4eqIqi09OVE3ThstvybnsxsflNz4uv/GZ0OU3w7R92T2m34hhJGeLgHUa79cGLluCMmTm4cDhEx3gZIiIMzNzs2HHMV25/Jacy258XH7j4/IbH5ffkpvOy24YhzXPADaIiPUiYhlgT+D4rjLHA6+tZ20+E7ghMy+f6kAlSZKm2pS3nGXm4ojYFzgJmAUckZnnRcQ+dfxhwInALsAC4FbgdVMdpyRJ0jAM47AmmXkiJQFrDjus8TqBt011XJNsWhx+bTGX35Jz2Y2Py298XH7j4/JbctN22UXJgyRJktQG3r5JkiSpRUzOJtlot6rSyCLiiIi4MiL+OuxYppuIWCciTo2Iv0XEeRHxjmHHNJ1ExHIR8ceIOLcuvw8PO6bpJiJmRcTZEfGTYccy3UTEwoj4S0ScExFnDjue6SYi/rNut3+NiG9HxHLDjmkQJmeTaIy3qtLI5gE7DTuIaWox8O7MfALwTOBtrn8DuQPYPjOfCmwC7FTPHtfYvQP427CDmMa2y8xNpuvlIIYlItYC3g5slplPopx8uOdwoxqMydnkGsutqjSCzPw1cO2w45iOMvPyzPxTfX0T5UfSO22MUb193M317dL1YSfdMYqItYFdga8NOxbNSEsBy0fEUsAK9LhWapuZnE0ub0OlVoiI2cCmwB+GHMq0Ug/LnQNcCfwiM11+Y3cI8D7gniHHMV0l8POIOKveDUdjlJmXAp8B/gVcTrlW6s+HG9VgTM4m15huQyVNpoh4KPAD4J2ZeeOw45lOMvPuzNyEcpeSzSPiSUMOaVqIiN2AKzPzrGHHMo1tlZlPo3SLeVtEbDPsgKaLiFiFcpRqPeDRwIoR8erhRjUYk7PJNabbUEmTJSKWpiRmR2XmscOOZ7rKzOuB+dj/cay2Al4YEQsp3Tm2j4hvDTek6SUzL6vPVwLHUbrJaGyeB1ycmVdl5l3AscCzhhzTQEzOJtdYblUlTYqICOB/gb9l5ueGHc90ExGrRcTK9fXylB3+BUMNaprIzP/KzLUzczZlv3dKZk6rlothiogVI+JhndfAjoBnrI/dv4BnRsQKdT/4XKbZiSkmZ5MoMxcDnVtV/Q34bmaeN9yoppeI+Dbwe+DxEbEoIt4w7Jimka2A11BaLc6pj12GHdQ0siZwakT8mfJH6xeZ6SUhNBUeBfwmIs4F/gickJk/G3JM00btG/p94E/AXyi5zrS6W4B3CJAkSWoRW84kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMzqQJFBEPjYiLIuIDw45lSUTEF+oVyVt5k+CIeHdE/DMiVh12LFOp7d9L20TEWhFxXUS8ctixSEvC5EytFxE5hse245zH3hHxogkI93PAOcAnJqAuACJiXkScOcayh0bEd+rrhRHxmQHmsx3wcuDFmXn7kkW7ZCJimYg4MCI2GaHMRsD+lPiunsLYDoyIKZtfj/kP7XuZSBExJyL+FhF31ovTjqeu3ep2P7tPkSOAeZl5dNd0A20TY4xlx4h450TWKS017ACkMdiy8Xp54BTgY8AJjeHnj3Mee1Mu8vjDJa0gInYGng1snsO7Rs0uwAGDTlQvePlVYM/M/NeERzW6ZYAPAQspye39RMQs4BvAOzo3c58JWvC9TIj6/R0J/BR4E3DLJM7rLcCywHsnax5ddgR2p9xLVJoQJmdqvcw8vfO63icS4P+aw9sgM39K+fEZVb2t0j2ZefdEzb/e93EdYOCLVWbmTcDjJiqWiVaX0xZjLR8Ry2fmbZMY0pRo+/cygDWBhwNHZ+ZvJnNGmfll4MuTOQ9psnlYUw8KEfHGiDgvIu6ofZLe1zX+iRHxs4i4NiJuqYdX3lbHzQeeDuzVOEw6NyK+ERF/7DGvfSPitk6iGBEPiYj9ImJBnf/fI2KvrmnmR8T36+HT/wNup9yQd9TYe8x/mYg4NiL+FRHNH+5dgTPqvfia5f+z3l3huog4pnNLojpuxYj4YkRcGBG3RsTFEfGliHh4o8yBfQ4lL2yUOSgi/hIRN9d5HRURa3TF8cLab+qWGssfIuI5dfRN9fnrjfpnD1D3woj4bER8MCIWATfW4X2/9zp+14j4RURcGRE3RsTpEbHjKMs/ovQBuy4i+iaMEfGG+r3eFhFXR8SvIuKJddy8Pst0/li/l1ou6/f72Yi4ps7nPXXcXlH6P14fEUdEo79aRKxZh11U4/t7RHwsym3mRvrsA08XEXOBS+rbH9WYD6zjxrLtRF0Hr4yImyLiSEqi1z2f5SLiUxFxSa3r3BjhjhgRMbvPd3C/bhJRDseeGRG3R8QVdR5L13EHAu8GHtOYdl5j2q3r935r/X6+GvW2TJ1lU6d5cl0Pb4mICyLiJX2/BM0Itpxp2ouI9wIfBz5FuTn104GPRsStmfnFWux4yn0RXw3cATye+3bwb6XcHPwi4KN12P8B/wZOjIj1M/OixixfTrmdys31/ReAvYCPUG4XsgNwRERc03W7n62AxwLvB24Fbhhj7M3PulyNdSPg2Zn5z8boXbn/od5OrH+mHLZdm9In7uP1MwOsSDkEdABwBaXlbX/ge8Dza5mvcf/WuBUpN7P+e2PY6rXey4DVKD9Yp0TEkzPz7oh4LOV2Kv9DOdy0XP2sj6jTb88DD1dfXp8fDXwSWASsCrynWXcjhlcC59XP1tm3jfS9A6wH/Bj4DHAPsDPw04jYJjN/27UsiYiHAF8BXgRsn5lnd5ep5bYBDqMs19/XeW4JrFSLfLSO71gD+Db3LdOxfC8d76Yss1cAuwGfjojVgWcAbwfWBQ6udR9Up1kVuBZ4F3AdsCFwIOW7e3OvzzSO6U4AXkK5+fR7gN9SvksY27bz9rocPg6cVuv6VI/5fJ9yc/APUbbflwPHR8RmmXlOj/KXc/8uE1CW5W7UZDIiXk75Xr4CfICy/X6C0rDxHsq2sQFl/X1xreOqOu1WwMmUrhK7A4+kLP9V6vumoym3F/o08B/AMXW/swjNTJnpw8e0eQAPBRKYW98/HLgZ+FBXuY9QftRmUX5QEnjyCPWeSelA3By2FHA1sF9j2FqUH/Hd6/vH1fd7dU17JKUVq/N+PnAbsEZj2Kix1/fzanwrAL+kJBtrdU2zCrAYeFpj2ELKj9RSjWGHAFeMsoy3rMtr3T7jv0NJwtbsM35WXU4JbFOH7Q5cM9bvdZT41mzW3fislwPLNYaN+r131fuQ+p2fBBzRGH5gXQ9mAd+s380TR6nrPcBZY5zv0sBvKH0eVxzke6nvT+36DJdTEqeHN4Z/F/jDCHUvRUlubweWGUvcg0wHzK6x7tYYNuq2U5f5ZcCXu8r8otY3u75/bn3/nK5yvwa+17WefKZPjLsBd3fiAQL4J/D1rnKvp2zLj6zvPwMs7FHfac3vpg7bvsb5pPp+bn3/+kaZR1K25X3G+j34ePA9PKyp6W5LSivD9yJiqc6D0grzKEpr0bWUf8KHRcQetVVhVFluXH8ssEdj8MsonZk7rTvPpfzAHNc1/5OBTaJ0hO44KzOvGDD2jhUprVePovwAXdoV7vOBK4HulpxT6+foOB9YvXkYKiJ2j4jfRMS/I+J2SiIJpVXkfiLi/ZQWgt0z8/LG8J0j4ncRcQPlh2VRVx1/AVaKcqh4x4hYsbvufiJi+3rI59KIuA24uE98J+f9z2Yc9XuPiLVrTJfWuO+idPDurnsWpbVwW0pSeN4oYZ8DbBoRB0fENqMcLvw88CTK2Zj3dpQf4Hs5ufMiM++hLJ+zMvPGRpkFlIS5U3dExDsj4vy6TO8CjqK01q3bL9Alna6PsWw761CS8R91TXts1/vnUZLm3/aoa7PRAomIDYFvAYdl5jfq4A3rZ/puj+1zOcp31q++FSjbd/e0v6Ess6d3TfLzzovMvIayLa+NZiyTM013netdnUfZ6XUep9bh69QfrB0pO+8jgCsi4rSI2HQM9R9D+aHo/CDuARyf93U2X5Xyw31D1/znUVoV1mzU9e9BY2+UfTTwLODYzOyuB8ohzRMzs/ss0eu73t9JaRFYBiAiXkA5VPZHSuL5dOCFtez9rqkVpS/Wx4H/zMzfNYY/g3L4cBHwGsqP0jObdWTmhcAcYH3gRODqiDg6Ilbr8Vma83waJSm9HHgt5VBd58e2+5pf91suo33v9RDl8ZTlegCwXa3/pz3qXoFyyPOUzPw7o8jMXwKvA7ahJFVXR7nMyf2S0oh4A+Vw4Gsy8x+N4WP+Xuj9Hfca1pzuncBngeMo38vmQKcv3kjXUlvS6XoZy7bT6Vt4Zde03e9XrWXv6nocyP23oweofcB+SNkO39lVJ5T1tVln58/BSPWuUj/boV3T3kFpKe2e9vqu993fl2YY+5xpuru2Pu/GA5MfgAsBMvMC4KW1I++zKX2YToiIteuPeD/zKT/ue0TpiLwF97+G2bWUFpetKK0A3Zo/It2J05hir/5B6a81LyKuyHJGGnBvkrETpV/ZoPYEfpeZ72rU16uFaX1K35tvZeaXuka/mNLPZo9OchgRj+muIzNPoCzzlSjJ5CGUPkd7jhDfSynLcK9G3ev1KfuAy5eM9L1TDqttCuycmff2qYuI5XvUfRMlMT8hIi7PzP1GiLkz728A36gJ6Eso/b5uBPar89kC+BLw0cz8cdfkY/pexuFllMN9+zfq33gSp+tlLNtO5zeq+7N3v78WuJTSF3DMIiIol2hZBXhuZt7VVSeU7apX38KLewzruJ6yPh5ISe66XTZInJp5TM403f2e0v/j0fXHf0R153tKRHyO0gl3ZcpOuOc/1cy8JyK+T/lhvp3y49rsHH8K5R/ySpn5i0mO/ZtRzhD9YkTclJnfqqO2oPRf++WA84dyuLT7h/HVzTe1tec4Sv+bfXrUsTxwV1er3av6zTAzbwCOjnKmZqdD9p31ufs7WJFyyZFm3a9mQH2+904SdkenXE0qt6KcRNFdx8kR8TLg2Lr8/3uM874K+EqUM/A2rvNZg3Jixy8pP+DdRv1exml5Gp+76vudTcB0vYy67UTEJZQ/R3O4/3bXfTbjyZTO/DfXhHys/h/lz9H2zcP01YWUhG92Zn51hDoesO/IzFsi4nTg8Zn5kQHikQCTM01zmXl9lNPZ/6f+sP6acrh+Q2C7zHxxRDyF0mn3O5QzMlehnDF5bmZ2/h1fADw/Ip4PXANcXPt+UKfbF/hP4LjM7CQSZOaFEXEY5eyqT1E67i8HPBHYMDPfOJ7Ye0zz5ZqgfT0ibs7MH1JaoX6d5ZpYg/oFJdn7ICVZ3I3Sr6rpYEpS8RrgqaWxAYA7spyt+AvgnRFxCOXMx2fxwATvzZRE7GeUVoMNKK0wR9bPdWdEXAy8PCL+SkmE/1zrfkdEfIFy6Gmr7rr7Ge17j4hbKIdiP1s//8OAD1N+kHvKzB9HxGuAoyLixsz8Qp95f5hyJup8yskEmwLPobaa1c/9MOCLwBaNZXpjZp7P2L6X8fgF8PaI+APlpJFXMbbrqS3pdA8wlm0ny5m+nwI+E+UuDadRWlOf0COuk4BfRMQnKYcoHw5sQjlJ5L+65x8RW1O+768DiyPimY3R52fmjRHxbuCbUS5h8lNKIrY+pYVu98y8lbLveFSUS4b8Fbg6MxcC7wNOjoh7KGeS3kTpw7YrsP9YDo9rBhvm2Qg+fAz6oM9ZfZQf7LMoLVHXAX8A3lXHrU45y+4iyo/+FZRDdM2z3tantGLc0F0/pY/Wv+rw5/eIKSh9Vc6jtCpcBfwKeG2jzHzg+30+U9/Y6/h5wJld03ykfpYdKIdc3tmj3oV0nZnGfWeHPbS+n0VJYK6ktAr+gNISd++ZdTX27PFY2Kj3fZTO97fU5bhBLbNvHb8l5SSKy2rcF1MOMS7bqGNHSkJ2O/c/E2/Eukf4rGP53p9B6dd1G+XQ8dzu5U09W7Or7jdQzuyb273c6/jdKK05V9V5X0hJzKIRb69lOn+s30std7/l0G9d6/4MlO3o65RW42spl4TYjcaZhCNsf0sy3ezu2AfYdoJy6ZGrKAnOUZQzRO9dR2q5ZSnJ1gJKEnUF5c/Arr3WE+7bFno9tm1MszMlKbylfhfnUC75slQdv1xdJlfWaec1pt2ixnBjnf58yuVsVuq1PY60PvuYWY/OjkLSNBQRa1FafzbMRodySdL0ZXImSZLUIl5KQ5IkqUVMziRJklrE5EySJKlFTM4kSZJaxORMkiSpRUzOJEmSWsTkTJIkqUVMziRJklrk/wPmz7YE88WJyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.338824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.541096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "SibSp          \n",
       "0      0.338824\n",
       "1      0.541096\n",
       "2      0.523810\n",
       "3      0.272727\n",
       "4      0.142857\n",
       "5      0.000000\n",
       "8      0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.343035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.489362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "0      0.343035\n",
       "1      0.546512\n",
       "2      0.489362\n",
       "3      0.750000\n",
       "4      0.000000\n",
       "5      0.333333\n",
       "6      0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsAlone</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.477387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.338824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived\n",
       "IsAlone          \n",
       "0        0.477387\n",
       "1        0.338824"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Túlélők aránya a hozzátartozók függvényében - 3 változat\n",
    "\n",
    "#1\n",
    "dataset[['SibSp', 'Survived']].groupby(['SibSp'], as_index = True).mean().plot(kind = 'bar', figsize = (10, 8), legend = False)\n",
    "plt.title('Túlélők aránya a hozzátartozók függvényében I.', fontsize = 20)\n",
    "plt.xlabel('Testvérek/házastársak száma a fedélzeten', fontsize = 15)\n",
    "plt.ylabel('Túlélők aránya', fontsize = 15)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()\n",
    "\n",
    "display(dataset[['SibSp', 'Survived']].groupby(['SibSp']).mean())\n",
    "\n",
    "#2\n",
    "dataset[['Parch', 'Survived']].groupby(['Parch'], as_index = True).mean().plot(kind = 'bar', figsize = (10, 8), legend = False)\n",
    "plt.title('Túlélők aránya a hozzátartozók függvényében II.', fontsize = 20)\n",
    "plt.xlabel('Szülők/gyerekek száma a fedélzeten', fontsize = 15)\n",
    "plt.ylabel('Túlélők aránya', fontsize = 15)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()\n",
    "\n",
    "display(dataset[['Parch', 'Survived']].groupby(['Parch']).mean())\n",
    "\n",
    "#3\n",
    "\n",
    "dataset['IsAlone'] = isAlone\n",
    "\n",
    "dataset[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index = True).mean().plot(kind = 'bar', figsize = (10, 8), legend = False)\n",
    "plt.title('Túlélők aránya a hozzátartozók függvényében III.', fontsize = 20)\n",
    "plt.xlabel('Hozzátartozók a fedélzeten', fontsize = 15)\n",
    "plt.ylabel('Túlélők aránya', fontsize = 15)\n",
    "plt.xticks([0, 1], ['Egyedül utazók', 'Nem egyedül utazók'], rotation = 0)\n",
    "plt.show()\n",
    "\n",
    "display(dataset[['IsAlone', 'Survived']].groupby(['IsAlone']).mean())\n",
    "\n",
    "dataset = dataset.drop(['IsAlone'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.179293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.736842\n",
       "1    male  0.179293"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Túlélők aránya nemek szerint\n",
    "\n",
    "dataset[['Sex', 'Survived']].groupby(['Sex'], as_index = True).mean().plot(kind = 'bar', figsize = (10, 8), legend = False)\n",
    "plt.title('Túlélők aránya nemek szerint', fontsize = 20)\n",
    "plt.xlabel('Nem', fontsize = 15)\n",
    "plt.ylabel('Túlélők aránya', fontsize = 15)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()\n",
    "\n",
    "display(dataset[['Sex', 'Survived']].groupby(['Sex'], as_index = False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VadrvE7s_lS9"
   },
   "source": [
    "## Kategorikus adatok kezelése"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values\n",
    "X_pred = dataset_pred.iloc[:, :].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in (X, X_pred):\n",
    "    for row in x:\n",
    "        if row[1] == 'male':\n",
    "            row[1] = 0\n",
    "        else:\n",
    "            row[1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(), [6])], remainder = 'passthrough')\n",
    "X = np.array(ct.fit_transform(X))\n",
    "X_pred = np.array(ct.fit_transform(X_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in X:\n",
    "#     print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A dataset szétválasztása Training set-re és Test set-re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k-McZVsQBINc"
   },
   "source": [
    "## Klasszifikációs modellek betanítása és értékelése a Training set-en"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modellek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "models = {\n",
    "    'Logisztikus regresszió': LogisticRegression(random_state = 0, max_iter = 2000),\n",
    "    'KNN': KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2),\n",
    "    'SVM': SVC(kernel = 'linear', random_state = 0),\n",
    "    'Kernel SVM': SVC(kernel = 'rbf', random_state = 0),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "    'Decision Tree': DecisionTreeClassifier(random_state = 0),\n",
    "    'Random Forest': RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0),\n",
    "    'XGBoost': XGBClassifier(random_state = 0, eval_metric = ['logloss'])\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Betanítás és eredmények"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 757,
     "status": "ok",
     "timestamp": 1586353664008,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "ywPjx0L1BMiD",
    "outputId": "099836bc-4d85-4b4f-a488-093faf02e8cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mLogisztikus regresszió\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[55 14]\n",
      " [18 38]]\n",
      "\n",
      "Találati arány: 0.744\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 80.17 %\n",
      "Szórás: 3.58 %\n",
      "\n",
      "\u001b[1mKNN\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[53 16]\n",
      " [32 24]]\n",
      "\n",
      "Találati arány: 0.616\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 70.53 %\n",
      "Szórás: 7.76 %\n",
      "\n",
      "\u001b[1mSVM\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[62  7]\n",
      " [16 40]]\n",
      "\n",
      "Találati arány: 0.816\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 79.56 %\n",
      "Szórás: 5.57 %\n",
      "\n",
      "\u001b[1mKernel SVM\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[64  5]\n",
      " [44 12]]\n",
      "\n",
      "Találati arány: 0.608\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 70.93 %\n",
      "Szórás: 3.55 %\n",
      "\n",
      "\u001b[1mNaive Bayes\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[59 10]\n",
      " [27 29]]\n",
      "\n",
      "Találati arány: 0.704\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 76.75 %\n",
      "Szórás: 5.16 %\n",
      "\n",
      "\u001b[1mDecision Tree\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56 13]\n",
      " [18 38]]\n",
      "\n",
      "Találati arány: 0.752\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 76.54 %\n",
      "Szórás: 3.59 %\n",
      "\n",
      "\u001b[1mRandom Forest\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[58 11]\n",
      " [20 36]]\n",
      "\n",
      "Találati arány: 0.752\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 77.55 %\n",
      "Szórás: 2.20 %\n",
      "\n",
      "\u001b[1mXGBoost\u001b[0m\n",
      "\n",
      "Confusion Matrix:\n",
      "[[59 10]\n",
      " [20 36]]\n",
      "\n",
      "Találati arány: 0.76\n",
      "\n",
      "k-Fold:\n",
      "Pontosság: 78.74 %\n",
      "Szórás: 5.15 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "data = []\n",
    "for model in models.keys(): \n",
    "    \n",
    "    print('\\033[1m' + model + '\\033[0m')\n",
    "    print()\n",
    "    \n",
    "    # Betanítás\n",
    "    classifier = models[model]\n",
    "    if model == 'XGBoost':\n",
    "        classifier.fit(X_train, y_train, eval_metric = ['logloss'])\n",
    "    else:\n",
    "        classifier.fit(X_train, y_train)\n",
    "    \n",
    "    # Az eredmények előrejelzése\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    \n",
    "    # Értékelés: Confusion Matrix és k-Fold Cross validáció\n",
    "    print('Confusion Matrix:')\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print()\n",
    "    print('Találati arány:', accuracy_score(y_test, y_pred))\n",
    "    print()\n",
    "    \n",
    "    accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "    print('k-Fold:')\n",
    "    print('Pontosság: {:.2f} %'.format(accuracies.mean() * 100))\n",
    "    print('Szórás: {:.2f} %'.format(accuracies.std() * 100))\n",
    "    print()\n",
    "    data.append([model, accuracies.mean() * 100, accuracies.std() * 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Pontosság</th>\n",
       "      <th>Szórás</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logisztikus regresszió</th>\n",
       "      <td>80.171429</td>\n",
       "      <td>3.582725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>70.526531</td>\n",
       "      <td>7.762627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>79.559184</td>\n",
       "      <td>5.569770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel SVM</th>\n",
       "      <td>70.934694</td>\n",
       "      <td>3.553902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>76.751020</td>\n",
       "      <td>5.163328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>76.538776</td>\n",
       "      <td>3.589136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>77.551020</td>\n",
       "      <td>2.195068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>78.742857</td>\n",
       "      <td>5.154273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Pontosság    Szórás\n",
       "Modell                                     \n",
       "Logisztikus regresszió  80.171429  3.582725\n",
       "KNN                     70.526531  7.762627\n",
       "SVM                     79.559184  5.569770\n",
       "Kernel SVM              70.934694  3.553902\n",
       "Naive Bayes             76.751020  5.163328\n",
       "Decision Tree           76.538776  3.589136\n",
       "Random Forest           77.551020  2.195068\n",
       "XGBoost                 78.742857  5.154273"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(data, columns = ['Modell', 'Pontosság', 'Szórás']).set_index('Modell')\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A táblázat eredményei alapján, és mivel az egyszerűbb modell a preferált, a megoldások a logisztikus regressziós modell segítségével kerülnek elkészítésre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Megoldás"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dataset_pred (DA_titanic_test.csv) kiegészítése az elhalálozás valószínűségét megadó oszloppal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A modell betanítása a DA_titanic_train.csv teljes tartalmán\n",
    "classifier = LogisticRegression(random_state = 0, max_iter = 2000)\n",
    "classifier.fit(X, y)\n",
    "\n",
    "# Az eredmények, és azok valószínűségének előrejelzése a DA_titanic_test.csv tartalma alapján\n",
    "y_pred = classifier.predict(X_pred)\n",
    "y_pred_prob = classifier.predict_proba(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "NotSurvivedProb = [i[0] for i in y_pred_prob]\n",
    "# NotSurvivedProb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test['NotSurvivedProb'] = NotSurvivedProb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "      <th>NotSurvivedProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</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>0.092730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0.869373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0.594802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.960364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.434561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "1            6       3                                   Moran, Mr. James   \n",
       "2            7       1                            McCarthy, Mr. Timothy J   \n",
       "3            8       3                     Palsson, Master. Gosta Leonard   \n",
       "4            9       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "\n",
       "      Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked  NotSurvivedProb  \n",
       "0  female  35.0      1      0  113803  53.1000  C123        S         0.092730  \n",
       "1    male   NaN      0      0  330877   8.4583   NaN        Q         0.869373  \n",
       "2    male  54.0      0      0   17463  51.8625   E46        S         0.594802  \n",
       "3    male   2.0      3      1  349909  21.0750   NaN        S         0.960364  \n",
       "4  female  27.0      0      2  347742  11.1333   NaN        S         0.434561  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(final_test.head())\n",
    "final_test.to_csv('result.csv', index_label = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NotSurvivedProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.092730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.869373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.594802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.960364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.434561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NotSurvivedProb\n",
       "0         0.092730\n",
       "1         0.869373\n",
       "2         0.594802\n",
       "3         0.960364\n",
       "4         0.434561"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result = pd.DataFrame(NotSurvivedProb, columns = ['NotSurvivedProb'])\n",
    "final_result.head()\n",
    "# final_result.to_csv('final_result.csv', index_label = False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Megoldás - kiegészített"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A megoldásban található dataset_pred kiegészítése a Survived oszlop prediktált értékével (0/1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "SurvivedPred = [i for i in y_pred]\n",
    "# SurvivedPred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test['SurvivedPred'] = SurvivedPred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>NotSurvivedProb</th>\n",
       "      <th>SurvivedPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
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       "      <td>S</td>\n",
       "      <td>0.911597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>886</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0.260751</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.779474</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>0.460238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                           Name  \\\n",
       "262          880       1  Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)   \n",
       "263          882       3                             Markun, Mr. Johann   \n",
       "264          886       3           Rice, Mrs. William (Margaret Norton)   \n",
       "265          887       2                          Montvila, Rev. Juozas   \n",
       "266          890       1                          Behr, Mr. Karl Howell   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \\\n",
       "262  female  56.0      0      1   11767  83.1583   C50        C   \n",
       "263    male  33.0      0      0  349257   7.8958   NaN        S   \n",
       "264  female  39.0      0      5  382652  29.1250   NaN        Q   \n",
       "265    male  27.0      0      0  211536  13.0000   NaN        S   \n",
       "266    male  26.0      0      0  111369  30.0000  C148        C   \n",
       "\n",
       "     NotSurvivedProb  SurvivedPred  \n",
       "262         0.053919             1  \n",
       "263         0.911597             0  \n",
       "264         0.260751             1  \n",
       "265         0.779474             0  \n",
       "266         0.460238             1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(final_test.tail())\n",
    "final_test.to_csv('result_2.csv', index_label = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>NotSurvivedProb</th>\n",
       "      <th>SurvivedPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0.053919</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0.911597</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0.260751</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>0.779474</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0.460238</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "     NotSurvivedProb  SurvivedPred\n",
       "262         0.053919           1.0\n",
       "263         0.911597           0.0\n",
       "264         0.260751           1.0\n",
       "265         0.779474           0.0\n",
       "266         0.460238           1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result_2 = pd.DataFrame([NotSurvivedProb, SurvivedPred], index = ['NotSurvivedProb', 'SurvivedPred']).T\n",
    "final_result_2.tail()\n",
    "# final_result_2.to_csv('final_result_2.csv', index_label = False)"
   ]
  }
 ],
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