titanic-info.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
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  {
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   },
   "source": [
    "**Reading Dataset.**"
   ]
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   "source": [
    "# Importing dataset into variable named train_df, test_df\n",
    "train_df = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test_df= pd.read_csv('/kaggle/input/titanic/test.csv')"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d771d445",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.107634Z",
     "iopub.status.busy": "2023-10-25T14:46:17.106427Z",
     "iopub.status.idle": "2023-10-25T14:46:17.127588Z",
     "shell.execute_reply": "2023-10-25T14:46:17.126294Z"
    },
    "papermill": {
     "duration": 0.039642,
     "end_time": "2023-10-25T14:46:17.130201",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.090559",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                          Name     Sex   Age  SibSp  \\\n",
       "413         1305       3            Spector, Mr. Woolf    male   NaN      0   \n",
       "414         1306       1  Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "415         1307       3  Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "416         1308       3           Ware, Mr. Frederick    male   NaN      0   \n",
       "417         1309       3      Peter, Master. Michael J    male   NaN      1   \n",
       "\n",
       "     Parch              Ticket      Fare Cabin Embarked  \n",
       "413      0           A.5. 3236    8.0500   NaN        S  \n",
       "414      0            PC 17758  108.9000  C105        C  \n",
       "415      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416      0              359309    8.0500   NaN        S  \n",
       "417      1                2668   22.3583   NaN        C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8c3ac51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.161897Z",
     "iopub.status.busy": "2023-10-25T14:46:17.161138Z",
     "iopub.status.idle": "2023-10-25T14:46:17.203477Z",
     "shell.execute_reply": "2023-10-25T14:46:17.202350Z"
    },
    "papermill": {
     "duration": 0.061796,
     "end_time": "2023-10-25T14:46:17.206575",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.144779",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</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>0.420000</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>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</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",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    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  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68b4951f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.240086Z",
     "iopub.status.busy": "2023-10-25T14:46:17.238479Z",
     "iopub.status.idle": "2023-10-25T14:46:17.251790Z",
     "shell.execute_reply": "2023-10-25T14:46:17.250129Z"
    },
    "papermill": {
     "duration": 0.033342,
     "end_time": "2023-10-25T14:46:17.254775",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.221433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41514bbc",
   "metadata": {
    "papermill": {
     "duration": 0.014506,
     "end_time": "2023-10-25T14:46:17.284830",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.270324",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now lets look at the columns available in this dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6aa1916",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.317378Z",
     "iopub.status.busy": "2023-10-25T14:46:17.316867Z",
     "iopub.status.idle": "2023-10-25T14:46:17.325284Z",
     "shell.execute_reply": "2023-10-25T14:46:17.324209Z"
    },
    "papermill": {
     "duration": 0.027999,
     "end_time": "2023-10-25T14:46:17.327820",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.299821",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b35c975d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.360224Z",
     "iopub.status.busy": "2023-10-25T14:46:17.359449Z",
     "iopub.status.idle": "2023-10-25T14:46:17.392398Z",
     "shell.execute_reply": "2023-10-25T14:46:17.390775Z"
    },
    "papermill": {
     "duration": 0.052696,
     "end_time": "2023-10-25T14:46:17.395690",
     "exception": false,
     "start_time": "2023-10-25T14:46:17.342994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9607fa",
   "metadata": {
    "papermill": {
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   },
   "source": [
    "There are some null values in some columns hence proceed to the next stage of exploration which is data cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418c57fb",
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   "source": [
    "**Data Cleaning.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc110a69",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "Drop columns that will not be needed in this analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34a10c0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.518958Z",
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   },
   "outputs": [],
   "source": [
    "train_df.drop(columns=['Ticket','Name','PassengerId'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88b135c0",
   "metadata": {
    "execution": {
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    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2         1       3  female  26.0      0      0   7.9250   NaN        S\n",
       "3         1       1  female  35.0      1      0  53.1000  C123        S\n",
       "4         0       3    male  35.0      0      0   8.0500   NaN        S"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "285bc3da",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.interpolate(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbdd41c8",
   "metadata": {
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   },
   "source": [
    "Check Which columns have null values in them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b34ea93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.790819Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age           0\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "The coulmn 'Embarked' still have 2 data null values it is beacause the interpolate method is designed for numerical data and the column 'Embarked' is with characters so we can use bfill method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4cb12b9",
   "metadata": {
    "execution": {
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   },
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   "source": [
    "train_df.bfill(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57942f5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:17.920257Z",
     "iopub.status.busy": "2023-10-25T14:46:17.919785Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Cabin       1\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "299084e0",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "False    891\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Age.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97c7cad9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.018997Z",
     "iopub.status.busy": "2023-10-25T14:46:18.018538Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "False    890\n",
       "True       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Cabin.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e908aac5",
   "metadata": {
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     "end_time": "2023-10-25T14:46:18.066197",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is a huge number of data missing values in the cabin column,hence the need to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9adbc757",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.100070Z",
     "iopub.status.busy": "2023-10-25T14:46:18.099643Z",
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   },
   "outputs": [],
   "source": [
    "train_df.drop(\"Cabin\", axis =1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fdd60504",
   "metadata": {
    "execution": {
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       "      <td>1</td>\n",
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      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S\n",
       "5         0       3    male  44.5      0      0   8.4583        Q\n",
       "6         0       1    male  54.0      0      0  51.8625        S\n",
       "7         0       3    male   2.0      3      1  21.0750        S\n",
       "8         1       3  female  27.0      0      2  11.1333        S\n",
       "9         1       2  female  14.0      1      0  30.0708        C"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef1d789",
   "metadata": {
    "papermill": {
     "duration": 0.016673,
     "end_time": "2023-10-25T14:46:18.198375",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.181702",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets now explore different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c074d261",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.234922Z",
     "iopub.status.busy": "2023-10-25T14:46:18.234524Z",
     "iopub.status.idle": "2023-10-25T14:46:18.242642Z",
     "shell.execute_reply": "2023-10-25T14:46:18.241373Z"
    },
    "papermill": {
     "duration": 0.030152,
     "end_time": "2023-10-25T14:46:18.245451",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.215299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "classes = train_df.Pclass.unique()\n",
    "print(sorted(classes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52401764",
   "metadata": {
    "papermill": {
     "duration": 0.01647,
     "end_time": "2023-10-25T14:46:18.279580",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.263110",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are three different classes,lets have a deep dive at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "746356a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.315417Z",
     "iopub.status.busy": "2023-10-25T14:46:18.314977Z",
     "iopub.status.idle": "2023-10-25T14:46:18.324959Z",
     "shell.execute_reply": "2023-10-25T14:46:18.323749Z"
    },
    "papermill": {
     "duration": 0.031353,
     "end_time": "2023-10-25T14:46:18.327763",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.296410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('Pclass').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0aa57fab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.364070Z",
     "iopub.status.busy": "2023-10-25T14:46:18.363635Z",
     "iopub.status.idle": "2023-10-25T14:46:18.374849Z",
     "shell.execute_reply": "2023-10-25T14:46:18.373401Z"
    },
    "papermill": {
     "duration": 0.032492,
     "end_time": "2023-10-25T14:46:18.377724",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.345232",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('Sex').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bd3f5fb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.415379Z",
     "iopub.status.busy": "2023-10-25T14:46:18.414933Z",
     "iopub.status.idle": "2023-10-25T14:46:18.426365Z",
     "shell.execute_reply": "2023-10-25T14:46:18.425103Z"
    },
    "papermill": {
     "duration": 0.033307,
     "end_time": "2023-10-25T14:46:18.428990",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.395683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    549\n",
       "1    342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('Survived').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d22dd2fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.465858Z",
     "iopub.status.busy": "2023-10-25T14:46:18.465461Z",
     "iopub.status.idle": "2023-10-25T14:46:18.477592Z",
     "shell.execute_reply": "2023-10-25T14:46:18.476458Z"
    },
    "papermill": {
     "duration": 0.03396,
     "end_time": "2023-10-25T14:46:18.480322",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.446362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Sex     Pclass\n",
       "0         female  1           3\n",
       "                  2           6\n",
       "                  3          72\n",
       "          male    1          77\n",
       "                  2          91\n",
       "                  3         300\n",
       "1         female  1          91\n",
       "                  2          70\n",
       "                  3          72\n",
       "          male    1          45\n",
       "                  2          17\n",
       "                  3          47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby(['Survived','Sex','Pclass']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e5ffd206",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.518572Z",
     "iopub.status.busy": "2023-10-25T14:46:18.518143Z",
     "iopub.status.idle": "2023-10-25T14:46:18.848013Z",
     "shell.execute_reply": "2023-10-25T14:46:18.846115Z"
    },
    "papermill": {
     "duration": 0.353782,
     "end_time": "2023-10-25T14:46:18.852961",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.499179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "sns.countplot(x= \"Survived\", hue=\"Sex\", order=[1, 0], data = train_df).set(xticklabels=[\"Survived\", \"Perished\"])\n",
    "plt.xlabel(\"Survival Comparison\")\n",
    "plt.ylabel(\"Number of Passengers\")\n",
    "plt.title(\"Comparison\")\n",
    "plt.legend(labels=[\"Male\", \"Female\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49498a91",
   "metadata": {
    "papermill": {
     "duration": 0.017924,
     "end_time": "2023-10-25T14:46:18.890412",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.872488",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now lets look at each class per Survivors and Perished."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5e90bd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.929516Z",
     "iopub.status.busy": "2023-10-25T14:46:18.929058Z",
     "iopub.status.idle": "2023-10-25T14:46:18.942004Z",
     "shell.execute_reply": "2023-10-25T14:46:18.940379Z"
    },
    "papermill": {
     "duration": 0.036308,
     "end_time": "2023-10-25T14:46:18.945391",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.909083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Pclass\n",
       "0         1          80\n",
       "          2          97\n",
       "          3         372\n",
       "1         1         136\n",
       "          2          87\n",
       "          3         119\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby(['Survived','Pclass']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f27b341",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:18.984784Z",
     "iopub.status.busy": "2023-10-25T14:46:18.984317Z",
     "iopub.status.idle": "2023-10-25T14:46:19.382417Z",
     "shell.execute_reply": "2023-10-25T14:46:19.380717Z"
    },
    "papermill": {
     "duration": 0.420956,
     "end_time": "2023-10-25T14:46:19.385437",
     "exception": false,
     "start_time": "2023-10-25T14:46:18.964481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j6elp6RL/E44cOYKbmxtFixa1dikiIiI5Is+HomPHjpGcnGyy0PpBaWlpRERE8Oyzz1KwYEEAGjdujK2tLZs3bzb2i42NZe/evTRt2jRX6n6SHD58mPDwcN58801rlyIiIpJj8tSaouDgYKpXr463tzcFCxbk9OnThIWF4e3tTWBgIJcuXSIkJIR27drx9NNPExsby1dffcWJEyeYPXu2cZzSpUvz4osv8tFHH2Fra4ubmxuff/45Tk5OvPLKK1Y8w/wnJiaG4cOH4+/vT48ePaxdjoiISI7JU6HIx8eH8PBwFi5ciMFgoFy5cnTt2pXevXvj4OBAkSJFKFq0KPPnz+fGjRvY29tTvXp1QkNDadKkiclYY8eOpUiRIkyfPp3bt29Tq1YtFi9erKdZZ0NcXBx9+/bF1dWV2bNna4G1iIg80fJUKOrXrx/9+vV7aLurqyvz58/P0lgODg6MHj2a0aNHW6q8/5S7d+/Sv39/4uPjWb16tcKkiIg88fJUKJK8ISUlhWHDhhEdHc3KlSuNr1cRERF5kikU5aJCJcx7GnZuH2/SpEns2LGDkJAQEhIS+OWXX4xtVatWxcHBwUIVioiI5B0KRbnEkJZm1tOlLXHc7L7mY9++fQBMmzYtQ9u2bdseeiegiIhIfqZQlEus8d4zc4+7ffv2HKhEREQkb9PtRCIiIiIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgLoidaSiV27dhEaGkpUVBQJCQm4ubkRGBhIcHAwTk5O1i5PREQkRygU5ZK0tDRsrfCqD3OOe+vWLXx8fOjevTuurq5ERkYye/ZsIiMj+eKLL3KoUhEREetSKMoltra2fL5rGZdjr+baMcu6uNE/oEe2P9epUyeTbX9/fxwcHBg3bhxXr17Fzc3NUiWKiIjkGQpFuehy7FXO3bho7TLM4urqCkBycrJ1CxEREckhCkXyUKmpqaSkpBAVFcXcuXNp0aIF7u7u1i5LREQkRygUyUM1b96cq1fvX+5r0qQJ06dPt3JFIiIiOUe35MtDLVy4kFWrVjF58mSio6MZMGAAqamp1i5LREQkR2imSB6qcuXKAPj5+VGjRg06derEli1baNOmjZUrExERsTzNFEmWeHt7Y29vz/nz561dioiISI5QKJIsOXbsGMnJyVpoLSIiTyxdPpMMgoODqV69Ot7e3hQsWJDTp08TFhaGt7c3gYGB1i5PREQkRygU5aKyLrn70ENzj+fj40N4eDgLFy7EYDBQrlw5unbtSu/evXFwcLBwlSIiInmDQlEuSUtLM+vp0pY4bnZf89GvXz/69euXQxWJiIjkTVpTlEus8d4zax5XREQkv9HfmCIiIiIoFImIiIgACkUiIiIigEKRiIiICKBQZHEGg8HaJVjNf/ncRUQk/1MoshB7e3sA7ty5Y+VKrCf93NO/CxERkfxEzymyEDs7O1xdXbl27RoAhQsXxsbGxspV5Q6DwcCdO3e4du0arq6u2NnZWbskERGRbMtToWjXrl2EhoYSFRVFQkICbm5uBAYGEhwcjJOTk7Hf9u3bmTlzJmfPnqVs2bL069ePF154wWSspKQkZsyYwbp167h9+zZ+fn6MGzcOT0/PHKu/dOnSAMZg9F/j6upq/A5ERETymzwVim7duoWPjw/du3fH1dWVyMhIZs+eTWRkJF988QUAhw8fJjg4mBdffJExY8bw008/8d5771GkSBHatGljHGvy5MmEh4cTEhKCm5sbCxYsoFevXmzYsMEkYFmSjY0NZcqUoVSpUiQnJ+fIMfIqe3t7zRCJiEi+lqdCUadOnUy2/f39cXBwYNy4cVy9ehU3Nzfmz5+Pj48P77//PgD169fnwoULzJo1yxiKYmJi+Oabb5gwYQIvvvgiADVq1KB58+asWrWKvn375uh52NnZKSCIiIjkM3l+obWrqysAycnJJCUlcfDgQZMZIYCgoCDOnDnDxYsXAdi7dy9paWkm/VxdXWnUqBG7d+/OtdpFREQk/8hTM0XpUlNTSUlJISoqirlz59KiRQvc3d2JiooiOTk5w7qgihUrAhAdHY27uzvR0dGUKFECFxeXDP2++eabRx67ZcuWD227cuUKZcqUMfOsREREJC/Lk6GoefPmXL16FYAmTZowffp0AGJjYwFwdnY26Z++nd4eFxeX6bohZ2dnYx8RERGRB+XJULRw4UISExOJiopi/vz5DBgwgMWLF+fKsbdt2/bQtkfNIomIiEj+lidDUeXKlQHw8/OjRo0adOrUiS1btlCpUiUA4uPjTfrHxcUBGC+XOTs7k5CQkGHcuLi4DJfURERERCAfLLT29vbG3t6e8+fPU6FCBezt7YmOjjbpk76dvtbI09OT69evZ7hUFh0dnaPPKRIREZH8K8+HomPHjpGcnIy7uzsODg74+/uzadMmkz7h4eFUrFgRd3d3ABo3boytrS2bN2829omNjWXv3r00bdo0V+sXERGR/CFPXT4LDg6mevXqeHt7U7BgQU6fPk1YWBje3t4EBgYC8NZbb9GjRw8mTpxI27ZtOXjwIOvXr2fGjBnGcUqXLs2LL77IRx99hK2tLW5ubnz++ec4OTnxyiuvWOv0REREJA/LU6HIx8eH8PBwFi5ciMFgoFy5cnTt2pXevXvj4OAAQJ06dZg9ezYzZ87km2++oWzZskyePJm2bduajDV27FiKFCnC9OnTuX37NrVq1WLx4sU59jRrERERyd9sDAaDwdpF5Bfpd5896g41ERGR7Jiw7mPO3bho7TKs5ukS7kzqOCpHj5HVv7/NmilKSEggPj7e5EGGV69eZdWqVSQlJdG6dWt8fHzMGVpERETEKswKRePHj+fixYusWbMGuB+SXn75ZWJiYrC1tWXZsmUsWrQIf39/ixYrIiIiklPMuvvsyJEjNGvWzLj9ww8/cO3aNVatWsWhQ4fw9vZm/vz5lqpRREREJMeZFYr+/vtv3NzcjNvbt2+ndu3a+Pr6UrRoUTp37szp06ctVqSIiIhITjMrFDk7O3P9+nUA7t69y5EjR2jUqJGx3c7Ojrt371qmQhEREZFcYNaaIj8/P7788ks8PT3Zs2cP9+7dM3kv2J9//mkykyQiIiKS15k1U/T2229ToEABBg8ezJo1a+jVqxfPPvssAKmpqURERFC3bl2LFioiIiKSk8yaKXrmmWeIiIjgzJkzFC1a1Ph6DYDExETGjRtnfKmriIiISH6Q7VCUmJjIqFGjaNWqFR07dszQXrRoUeMrOURERETyi2xfPitUqBD79+/XQmoRERF5opi1pqh27docPXrU0rWIiIiIWI1ZoWj8+PEcOXKEGTNmEBMTY+maRERERHKdWQutO3bsSGpqKgsXLmThwoXY2dkZ32KfzsbGhiNHjlikSBEREZGcZlYoat26NTY2NpauRURERMRqzApF06ZNs3QdIiIiIlZl1poiERERkSeN2aHo8uXLjB8/ntatW1O3bl3+97//AXDz5k0mT57MyZMnLVakiIiISE4zKxRFRUXx/PPPs3HjRtzd3UlISCAlJQWA4sWLc+TIEVasWGHRQkVERERykllrij7++GOcnJxYs2YNAA0bNjRpDwgIYOPGjY9fnYiIiEguMWum6H//+x+vvvoqxYsXz/QutLJly3L16tXHLk5EREQkt5gVigwGAwULFnxo+82bNzM8t0hEREQkLzMrFFWtWpVdu3Zl2paSksKGDRuoWbPmYxUmIiIikpvMCkX9+vVjz549TJgwgcjISABu3LjB/v37efPNN4mOjqZfv34WLVREREQkJ5m10DogIICpU6cyZcoU42LrUaNGYTAYKFq0KB9++CF169a1aKEiIiIiOcmsUATQuXNnWrVqxb59+zh37hxpaWlUqFCBxo0bU7RoUUvWKCIiIpLjzA5FAIULF+a5556zVC0iIiIiVmNWKLp8+fIj221sbHB0dKRYsWJ6cayIiIjkC2aFohYtWmQp7Dg6OlK7dm0GDhxI7dq1zTmUiIiISK4wKxR98MEHLF++nCtXrtChQweefvppAM6dO8ePP/5IuXLl6NKlC+fOnWPdunX07NmTRYsWUb9+fYsWLyIiImIpZoWia9eukZyczJYtW3B2djZpCw4Oplu3bty9e5f33nuPgQMH8sILLzB37lyFIhEREcmzzHpO0apVq+jatWuGQATg6upK165dWblyJQDFihWjS5cunDhx4vEqFREREclBZoWiW7dukZiY+ND2O3fucPPmTeN2yZIlzTmMiIiISK4xKxTVqFGDZcuW8fvvv2doO336NCtWrMDHx8e478yZM7i5uZlfpYiIiEgOM2tN0dixY+nZsyfPP/88vr6+Jgutf/nlF4oWLcp7770HwL179zh06BCtW7e2XNUiIiIiFmZWKKpcuTLr1q0jNDSUPXv28OuvvwJQtmxZunXrRp8+fShdujRw/7b877//Pkvjbty4kXXr1vHbb78RFxfH008/Tffu3XnhhReMjwDo3r07hw4dyvDZ8PBwKlasaNyOj49n6tSpbN26leTkZJo0acLYsWMpVaqUOacsIiIiTzizn2jt5ubG2LFjLVkLS5YsoVy5coSEhFCsWDH279/PuHHjiImJITg42NivVq1ajB492uSz7u7uJtvDhg0jKiqKiRMn4ujoyMyZM+nbty/ffvstBQo81oO8RURE5AmUp9LB/PnzKV68uHG7QYMG3Lp1i8WLFzNw4EBsbe8vgXJ2dsbX1/eh4xw9epS9e/cSFhZG48aNAfDw8CAoKIjNmzcTFBSUo+chIiIi+Y/ZoejMmTN8++23XLx4kdjYWAwGg0m7jY0NS5cuzdaYDwaidFWqVGHNmjXcuXMnyy+a3b17N87OzjRq1Mi4z9PTkypVqrB7926FIhEREcnArFD0/fffM2bMGAoUKICHh0emzyv6Z0gy15EjR3BzczMJRIcOHcLX15fU1FRq1qzJ0KFDqVu3rrE9OjoaDw+PDK8i8fT0JDo62iJ1iYiIyJPFrFA0Z84cqlSpQmhoaKazO5Zy+PBhwsPDTdYP1a1bl06dOvHMM89w7do1wsLCeOONN1i+fDl+fn4AxMXF4eTklGE8FxeXf32IZMuWLR/aduXKFcqUKWPm2YiIiEheZvZrPt58880cDUQxMTEMHz4cf39/evToYdw/ZMgQk37NmjWjffv2zJs3j9DQ0ByrR0RERJ5sZoUib29vrl27ZulajOLi4ujbty+urq7Mnj3buMA6M4ULFyYgIIBNmzYZ9zk7OxMTE5Ohb2xsLC4uLo889rZt2x7a9qhZJBEREcnfzHqidUhICN988w0///yzpevh7t279O/fn/j4eBYtWpTpZbB/4+npydmzZzOsazp79iyenp6WKlVERESeIGbNFIWGhuLk5MRrr71GpUqVKFOmTIbZHBsbG+bPn5+tcVNSUhg2bBjR0dGsXLkyS68GuXPnDjt37qRGjRrGfU2bNmXevHkcOHCAhg0bAvcD0cmTJ+nTp0+2ahIREZH/BrNC0R9//AFAmTJluH37NlFRURn6/PPOr6yYNGkSO3bsICQkhISEBH755RdjW9WqVTl+/DiLFi3iueeeo1y5cly7do3Fixfz119/8dlnnxn7+vn50bhxY8aMGcPo0aNxdHRkxowZeHt706pVq+yfsIiIiDzxzApF27dvt3QdAOzbtw+AadOmZWjbtm0bJUuWJDk5mRkzZnDr1i0KFSqEn58fkyZNMnkBLcDMmTOZOnUq48ePJyUlhcaNGzN27Fg9zVpEREQylacSQlbCVlhYWJbGcnJyYsqUKUyZMuVxyxIREZH/ALNDUWpqKhERERw8eJAbN24wZMgQvL29iY+P58CBA9SqVYunnnrKkrWKiIiI5BizQlFcXBx9+vTh+PHjFC5cmMTERF5//XXg/i3ykydPpnPnzowYMcKixYqIiIjkFLNuyf/kk0+IjIwkLCyMrVu3mtz6bmdnR+vWrdm1a5fFihQRERHJaWaFom3bttG9e3caNWqU6V1mzzzzDJcuXXrs4kRERERyi1mhKD4+Hnd394e2p6SkkJqaanZRIiIiIrnNrFBUoUIFfvvtt4e279u3j4oVK5pdlIiIiEhuMysUvfjii3z77beEh4cb1xPZ2NiQlJTEjBkz2LNnDy+//LJFCxURERHJSWbdfdazZ0+ioqIYMWIEzs7OAIwcOZJbt26RkpLCyy+/TNeuXS1aqIiIiEhOMisU2djYGG+737RpE+fOnSMtLY0KFSrQtm1b6tata+k6RURERHLUYz3Ruk6dOtSpU8dStYiIiIhYjcVe85GYmMiGDRtISkoiICCAcuXKWWpoERERkRxnVigaM2YMx48fZ/369QAkJSXx0ksvERkZCdx/79jSpUupWrWq5SoVERERyUFm3X128OBBnnvuOeP2+vXriYyM5JNPPmH9+vU89dRTzJkzx2JFioiIiOQ0s0LR9evXTS6Pbd26lerVq9O+fXsqVarESy+9xPHjxy1WpIiIiEhOMysUFSpUiPj4eOD+06sPHTpE48aNje1FihQxtouIiIjkB2atKapWrRpr1qzB39+f7du3c/v2bVq0aGFsP3/+PCVKlLBYkSIiIiI5zaxQNGzYMPr06cMLL7yAwWCgdevW+Pj4GNu3bNlCrVq1LFakiIiISE4zKxTVqFGDjRs38vPPP+Ps7Ey9evWMbXFxcXTr1s1kn4iIiEheZ/ZziooXL05gYGCG/c7OzvTs2fOxihIRERHJbWaFosuXL3P58mWTp1mfPn2aL774gqSkJNq3b59pYBIRERHJq8wKRZMnT+bOnTssWbIEuH+Lfo8ePUhOTqZIkSJs2rSJzz77jFatWlmyVhEREZEcY9Yt+cePH6dhw4bG7e+//567d+/yww8/sHv3bho0aMAXX3xhsSJFREREcppZoSg2NtbklvudO3dSt25dKlSogK2tLc899xzR0dEWK1JEREQkp5kViooXL87ly5eB+3eb/fLLLzRp0sTYnpqaSkpKimUqFBEREckFZq0patiwIcuXL6do0aIcPHgQg8FAy5Ytje1RUVGUKVPGYkWKiIiI5DSzQtHbb7/N2bNn+fDDD7G3t+edd96hfPnyACQlJbFx40Y6dOhg0UJFREREcpJZoeipp55i1apVxMfH4+joiIODg7EtLS2NpUuXUrp0aYsVKSIiIpLTzH54I4CTk1OGfQULFqRy5cqPM6yIiIhIrnusUBQTE8PJkyeJj4/HYDBkaO/cufPjDC8iIiKSa8wKRffu3WP06NFs3ryZtLQ0bGxsjKHIxsbG2E+hSERERPILs27J//TTT9myZQvDhg1j+fLlGAwGpk2bxhdffEHTpk2pXLkyP/zwg6VrFREREckxZoWiTZs20aVLF/r160elSpUAcHNzo2HDhnz++ec4OTmxcuVKixYqIiIikpPMCkU3btzAx8cHuL+wGiAxMdHY3rp1a7Zs2WKB8kRERERyh1mh6KmnnuLvv/8GoFChQri4uHD27Flje0JCAvfu3bNMhSIiIiK5wKyF1j4+Pvz888/G7ebNmxMWFkbJkiVJS0tjyZIl+Pr6WqpGERERkRxn1kxR9+7dcXd3JykpCYChQ4fi5OTEO++8Q0hICE5OTrz33nvZHnfjxo289dZbNG3aFF9fXzp16sQ333yT4Xb/r7/+mtatW1OjRg06duzIjh07MowVHx/PmDFjqFevHn5+fgwZMoRr166Zc7oiIiLyH2DWTFGdOnWoU6eOcbtMmTJs3LiRP/74A1tbWzw9PSlQIPtDL1myhHLlyhESEkKxYsXYv38/48aNIyYmhuDgYAA2bNjAuHHjGDBgAPXr1yc8PJzg4GBWrlxpMjs1bNgwoqKimDhxIo6OjsycOZO+ffvy7bffmlWbiIiIPNmylQ727NnD0qVLuXjxIsWKFaNNmzb07NkTAFtb28d+kvX8+fMpXry4cbtBgwbcunWLxYsXM3DgQGxtbZk1axbt2rVj2LBhANSvX58//viDuXPnEhoaCsDRo0fZu3cvYWFhNG7cGAAPDw+CgoLYvHkzQUFBj1WniIiIPHmyfPns0KFD9OvXj7179xIbG8vx48eZNm0aH330kcWKeTAQpatSpQoJCQncuXOHCxcu8Oeff9K2bVuTPkFBQRw4cMB4OW/37t04OzvTqFEjYx9PT0+qVKnC7t27LVaviIiIPDmyPFP0+eefU6JECb744gu8vLyIjY1l6NChfPnllwwZMsR4a76lHTlyBDc3N4oWLcqRI0eA+7M+D6pYsSLJyclcuHCBihUrEh0djYeHh8nTteF+MIqOjn7k8Vq2bPnQtitXrlCmTBkzz0RERETysizPFP3xxx9069YNLy8vAFxcXBgxYgR3794lMjIyR4o7fPgw4eHhvPnmmwDExsYC4OzsbNIvfTu9PS4uLtOX1bq4uBj7iIiIiDwoyzNF169fx93d3WRf+vbt27ctWxX3XzY7fPhw/P396dGjh8XHf5ht27Y9tO1Rs0giIiKSv2V5pshgMGS4HJW+/c9b5h9XXFwcffv2xdXVldmzZ2Nre79MFxcX4P7t9v/s/2C7s7MzCQkJGcaNjY019hERERF5ULbuPvv+++85duyYcfvevXvY2NiwcuXKTGdYxo4dm+2C7t69S//+/YmPj2f16tUml8E8PT0BiI6ONv4+fdve3p7y5csb+x04cCBDkDt79qzx8p+IiIjIg7IVivbt28e+ffsy7N+6dWuGfTY2NtkORSkpKQwbNozo6GhWrlyJm5ubSXv58uV55plniIiIIDAw0Lg/PDycBg0a4ODgAEDTpk2ZN28eBw4coGHDhsD9QHTy5En69OmTrZpERETkvyHLoej06dM5WQcAkyZNYseOHYSEhJCQkMAvv/xibKtatSoODg4MHjyYkSNHUqFCBfz9/QkPD+f48eOsWLHC2NfPz4/GjRszZswYRo8ejaOjIzNmzMDb25tWrVrl+HmIiIhI/pOnHu2cPgs1bdq0DG3btm3D3d2d9u3bk5iYSGhoKAsXLsTDw4M5c+bg5+dn0n/mzJlMnTqV8ePHk5KSQuPGjRk7dqyeZi0iIiKZylMJYfv27Vnq17VrV7p27frIPk5OTkyZMoUpU6ZYojQRERF5wpn1QlgRERGRJ41CkYiIiAgKRSIiIiJAFkPRsmXLOHv2bE7XIiIiImI1WQpFU6dO5cSJE8btKlWq8OOPP+ZYUSIiIiK5LUuhyNnZmRs3bhi3Lf1aDxERERFry9It+f7+/syePZtTp04ZX7vxz1d+ZMac13yIiIiIWEOWQtGECROYMmUK+/bt48aNG9jY2Dz0lR/pzHnNh4iIiIi1ZCkUlShRgunTpxu3K1euzMcff0yHDh1yrDARERGR3GTWLflTp07N8FoNERERkfzMrNd8PP/888bfR0VFcenSJQDKlStHpUqVLFOZiIiISC4y+91nW7duZdq0acZAlM7d3Z2QkBBatmz52MWJiIiI5BazQtGuXbsYMmQIZcuWZfjw4VSsWBGAM2fOsGbNGgYPHsyCBQto2rSpRYsVERERySlmhaJ58+bh7e3NypUrKVy4sHF/y5Ytef311+nWrRtz585VKBIREZF8w6yF1r///judO3c2CUTpChcuzPPPP8/vv//+2MWJiIiI5BazQpGjoyOxsbEPbY+NjcXR0dHsokRERERym1mhyN/fn2XLlnH06NEMbceOHWP58uU0aNDgsYsTERERyS1mrSkaNWoUr7zyCt26dcPHxwcPDw8Azp49y/HjxylRogQjR460aKEiIiIiOcmsmaLy5cuzbt06unfvTmxsLOHh4YSHhxMbG0uPHj344YcfcHd3t3StIiIiIjnG7OcUlShRgjFjxjBmzBhL1iMiIiJiFWbNFImIiIg8aRSKRERERFAoEhEREQEUikREREQAhSIRERERwIxQlJiYSJcuXfjqq69yoh4RERERq8h2KCpUqBAXL17ExsYmJ+oRERERsQqzLp81adKEvXv3WroWEREREasxKxQNHDiQP//8k1GjRnH48GGuXr3KrVu3MvwSERERyS/MeqJ1u3btAIiKimL9+vUP7Xfq1CnzqhIRERHJZWaFokGDBmlNkYiIiDxRzApFgwcPtnQdIiIiIlZlkecUxcfHk5qaaomhRERERKzC7FD066+/0rt3b2rWrIm/vz+HDh0C4ObNm7z11lscPHjQYkWKiIiI5DSzQtHPP/9Mt27dOHfuHB07diQtLc3YVrx4cRISEli9enW2xz137hzjx4+nU6dOVK1alfbt22fo0717d7y9vTP8OnPmjEm/+Ph4xowZQ7169fDz82PIkCFcu3Yt+ycrIiIi/wlmrSmaMWMGFStWZM2aNSQkJPD111+btPv7+/Pdd99le9zIyEh27dpFzZo1SUtLw2AwZNqvVq1ajB492mSfu7u7yfawYcOIiopi4sSJODo6MnPmTPr27cu3335LgQJmnbaIiIg8wcxKB7/++isjRozAwcEh07vQ3NzcuH79erbHbdGiBYGBgQCEhIRw4sSJTPs5Ozvj6+v70HGOHj3K3r17CQsLo3HjxgB4eHgQFBTE5s2bCQoKynZtIiIi8mQz6/JZgQIFTC6Z/dPVq1cpXLhw9ouxtcz7aXfv3o2zszONGjUy7vP09KRKlSrs3r3bIscQERGRJ4tZM0U1a9Zk06ZN9OrVK0PbnTt3WLt2LXXr1n3c2h7q0KFD+Pr6kpqaSs2aNRk6dKjJ8aKjo/Hw8Mgwi+Xp6Ul0dPQjx27ZsuVD265cuUKZMmUer3gRERHJk8yamhkyZAgnTpygX79+xpmX33//na+//pouXbpw8+ZNBg4caNFC09WtW5f33nuPRYsW8eGHH5KYmMgbb7zB0aNHjX3i4uJwcnLK8FkXFxdiY2NzpC4RERHJ38yeKVq4cCETJ040LnieNm0aABUqVGDhwoVUrlzZclU+YMiQISbbzZo1o3379sybN4/Q0NDHHn/btm0PbXvULJKIiIjkb2bfhtWgQQM2bdrEyZMnOXfuHAaDgfLly1O9evVcfQVI4cKFCQgIYNOmTcZ9zs7OxMTEZOgbGxuLi4tLrtUmIiIi+cdj35tetWpVqlataolaLMbT05MDBw5gMBhMAtrZs2fx8vKyYmUiIiKSV5l9u1dSUhIrVqygb9++BAUFERQURN++fVmxYgX37t2zZI2PdOfOHXbu3EmNGjWM+5o2bUpsbCwHDhww7jt79iwnT56kadOmuVabiIiI5B9mzRTFxMTwxhtvcPbsWUqWLMnTTz8NwOnTp9mzZw8rVqxgyZIllC5dOlvjJiYmsmvXLgAuXbpEQkICERERANSrV4/o6GgWLVrEc889R7ly5bh27RqLFy/mr7/+4rPPPjOO4+fnR+PGjRkzZgyjR4/G0dGRGTNm4O3tTatWrcw5ZREREXnCmRWKJk2axOXLl5k5cyZt2rQxadu4cSMhISFMmjSJ+fPnZ2vcGzduMHToUJN96dvLli2jdOnSJCcnM2PGDG7dukWhQoXw8/Nj0qRJ+Pj4mHxu5syZTJ06lfHjx5OSkkLjxo0ZO3asnmYtIiIimTIrIfz000/06tUrQyACaNu2LSdPnmTFihXZHtfd3Z3ff//9kX3CwsKyNJaTkxNTpkxhypQp2a5DRERE/nvMWlNUpEgRihcv/tD2p556iiJFiphdlIiIiEhuMysUdenShe+++47ExMQMbbdv32bt2rW88MILj12ciIiISG7J0uWzzZs3m2xXqVKFnTt30rZtWzp37mxcaP3nn3/yww8/4OLigre3t+WrFREREckhWQpFQ4YMwcbGBoPBAGDy+wULFmToHxMTw9tvv6230YuIiEi+kaVQtGzZspyuQ0RERMSqshSK6tWrl9N1iIiIiFiV2U+0lpyTlmawdgl5gr4HERHJTWY/yfDw4cN8++23XLx4kdjYWOMao3Q2NjasW7fusQv8L7K1tWHuV/u4dC3W2qVYTblSLgx6tZG1yxARkf8Qs0LR4sWL+eijj3B0dMTDw0Nvns8Bl67F8uelv61dhoiIyH+GWaEoLCyMWrVqsWDBApycnCxdk4iIiEiuM2tNUWJiIh06dFAgEhERkSeGWaHI39+fP/74w9K1iIiIiFiNWaFo3LhxHDhwgLCwMG7dumXhkkRERERyn1lrisqUKcPLL7/MRx99xCeffIKjoyO2tqb5ysbGhiNHjlikSBEREZGcZlYo+uyzz1iwYAFubm5Ur15da4tEREQk3zMrFK1atYqAgADmzZuXYYZIREREJD8yK9EkJyfTrFkzBSIRERF5YpiVapo1a8bhw4ctXYuIiIiI1ZgVioKDgzlz5gwTJ07kxIkT3Lx5k1u3bmX4JSIiIpJfmLWmqE2bNgCcOnWK1atXP7TfqVOnzKtKREREJJeZFYoGDRqEjY2NpWsRERERsRqzQtHgwYMtXYeIiIiIVen2MZE8Li0tzdol5An6HkQkp5k1UzRnzpx/7WNjY8OgQYPMGV5EHmBra8vnu5ZxOfaqtUuxmrIubvQP6GHtMkTkCWfxUGRjY4PBYFAoErGgy7FXOXfjorXLEBF5opkVik6fPp1hX1paGpcuXeLLL7/kf//7H6GhoY9dnIiIiEhusdiaIltbW8qXL8/o0aN5+umnmTx5sqWGFhEREclxObLQum7duuzatSsnhhYRERHJETkSik6cOKH3oomIiEi+Ytaaou+//z7T/XFxcRw+fJjNmzfTtWvXx6lLREREJFeZFYpCQkIe2lasWDH69eunO89EREQkXzErFG3bti3DPhsbG5ydnSlatOhjFyUiIiKS28wKReXKlbN0HSIiIiJWpdXQIiIiImRjpqhDhw7ZGtjGxoZ169Zl6zPnzp0jLCyMY8eOERkZiaenJ+vXr8/Q7+uvv2bRokVcvnwZDw8Phg8fTvPmzU36xMfHM3XqVLZu3UpycjJNmjRh7NixlCpVKls1iYiIyH9DlkORq6trlvpdv36ds2fPYmNjk+1iIiMj2bVrFzVr1iQtLQ2DwZChz4YNGxg3bhwDBgygfv36hIeHExwczMqVK/H19TX2GzZsGFFRUUycOBFHR0dmzpxJ3759+fbbbylQwKyrhiIiIvIEy3I6WL58+SPb//rrL0JDQ1m9ejV2dnZ07Ngx28W0aNGCwMBA4P4dbidOnMjQZ9asWbRr145hw4YBUL9+ff744w/mzp1rfLXI0aNH2bt3L2FhYTRu3BgADw8PgoKC2Lx5M0FBQdmuTURERJ5sj72m6Pr160yZMoXnnnuOL7/8kqCgIDZu3MjUqVOzX8y/PPDxwoUL/Pnnn7Rt29Zkf1BQEAcOHCApKQmA3bt34+zsTKNGjYx9PD09qVKlCrt37852XSIiIvLkM/s6UvrM0Jo1a0hJSaFDhw4MHDiQ8uXLW7I+E9HR0cD9WZ8HVaxYkeTkZC5cuEDFihWJjo7Gw8MjwyU8T09P4xgiIiIiD8p2KPrrr79YuHAhX3/9NSkpKXTs2JG33norR8NQutjYWACcnZ1N9qdvp7fHxcXh5OSU4fMuLi6ZXpJ7UMuWLR/aduXKFcqUKZOtmkVERCR/yHIounbtmjEMpaam0qlTJwYMGJArYUhEREQkp2U5FD333HMkJSVRpUoV+vfvj7u7O3Fxcfz2228P/Uy1atUsUmQ6FxcX4P7t9iVLljTuj4uLM2l3dnYmJiYmw+djY2ONfR4ms6d1p3vULJKIiIjkb1kORffu3QPg5MmTxju/HsZgMGBjY8OpU6ceq7h/8vT0BO6vLUr/ffq2vb29cdbK09OTAwcOGOtId/bsWby8vCxak4iIZJ8hLQ2bf7m5RiS3ZTkUmXM3maWVL1+eZ555hoiICOOt+wDh4eE0aNAABwcHAJo2bcq8efM4cOAADRs2BO4HopMnT9KnTx+r1C4iIv+Pja0tZ9eHknjjirVLsRoXj+qUa9rF2mXIA7Icip5//vmcrAOAxMREdu3aBcClS5dISEggIiICgHr16lG8eHEGDx7MyJEjqVChAv7+/oSHh3P8+HFWrFhhHMfPz4/GjRszZswYRo8ejaOjIzNmzMDb25tWrVrl+HmIiMi/S7xxhcSr561dhtUULF7a2iXIP+SpRzvfuHGDoUOHmuxL3162bBn+/v60b9+exMREQkNDWbhwIR4eHsyZMwc/Pz+Tz82cOZOpU6cyfvx4UlJSaNy4MWPHjtXTrEVERCRTeSohuLu78/vvv/9rv65du9K1a9dH9nFycmLKlClMmTLFUuWJiIjIE0yr3ERERERQKBIREREBFIpEREREAIUiEREREUChSERERARQKJI8ysWpIIa0NGuXISIi/yF56pZ8kXRFCjroibfoibciIrlJoUjyND3xVk+8FRHJLbp8JiIiIoJCkYiIiAigUCQiIiICKBSJiIiIAApFIiK5Ki3NYO0SROQhdPeZiEgusrW1Ye5X+7h0LdbapVhNTe+yvNzG19pliGSgUCQikssuXYvlz0t/W7sMqylb0tnaJYhkSpfPRERERFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERIB+GorVr1+Lt7Z3h1yeffGLS7+uvv6Z169bUqFGDjh07smPHDitVLCIiIvlBAWsXYK5Fixbh5ORk3HZzczP+fsOGDYwbN44BAwZQv359wsPDCQ4OZuXKlfj6+lqhWhEREcnr8m0oqlatGsWLF8+0bdasWbRr145hw4YBUL9+ff744w/mzp1LaGhoLlYpIiIi+UW+u3z2by5cuMCff/5J27ZtTfYHBQVx4MABkpKSrFSZiIiI5GX5dqaoffv2/P3335QtW5aXXnqJPn36YGdnR3R0NAAeHh4m/StWrEhycjIXLlygYsWKDx23ZcuWD227cuUKZcqUscwJiIiISJ6S70JRyZIlGTx4MDVr1sTGxobt27czc+ZMrl69yvjx44mNjQXA2dnZ5HPp2+ntIiIiIg/Kd6GoSZMmNGnSxLjduHFjHB0dWbp0KQMGDHjs8bdt2/bQtkfNIomIiEj+9kSsKWrbti2pqamcOnUKFxcXAOLj4036xMXFARjbRURERB70RISiB3l6egIY1xali46Oxt7envLly1ujLBEREcnjnohQFB4ejp2dHVWrVqV8+fI888wzREREZOjToEEDHBwcrFSliIiI5GX5bk1R79698ff3x9vbG7i/BmjNmjX06NGDkiVLAjB48GBGjhxJhQoV8Pf3Jzw8nOPHj7NixQprli4iIiJ5WL4LRR4eHnz77bfExMSQlpbGM888w5gxY+jevbuxT/v27UlMTCQ0NJSFCxfi4eHBnDlz8PPzs2LlIiIikpflu1A0duzYLPXr2rUrXbt2zeFqRERE5EnxRKwpEhEREXlcCkUiIiIiKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICPOGh6MyZM7zxxhv4+vrSqFEjPvroI5KSkqxdloiIiORBBaxdQE6JjY2lZ8+ePPPMM8yePZurV68ybdo07t69y/jx461dnoiIiOQxT2woWrVqFbdv32bOnDm4uroCkJqayqRJk+jfvz9ubm7WLVBERETylCf28tnu3btp0KCBMRABtG3blrS0NPbt22e9wkRERCRPsjEYDAZrF5ETGjRowAsvvMDIkSNN9jdp0oROnTpl2J+uZcuWDx3z4sWL2NnZUaZMGYvWmpm4hLukpKXl+HHyKkf7AhQp5EDKnXgMaanWLsdqbAvYY1ewCPF3E0j5D38PBWztcCpY1NplWIx+vvXzDfr5TpcbP99XrlzBzs6OX3/99dG15GgVVhQXF4ezs3OG/S4uLsTGxpo1po2NDQUK5M5X5ly0YK4cJ68rUNjJase+cuUKQK6E4H/zJAUC0c93Ov1836ef75xXoEABHBwc/r1fLtSSr2zbts3aJUgekT5rqP8nRJ48+vmWzDyxa4qcnZ2Jj4/PsD82NhYXFxcrVCQiIiJ52RMbijw9PYmOjjbZFx8fz19//YWnp6eVqhIREZG86okNRU2bNmX//v3ExcUZ90VERGBra0ujRo2sWJmIiIjkRU9sKHrllVcoUqQIgwYNYu/evXz77bd89NFHvPLKK3pGkYiIiGTwxIYiFxcXli5dip2dHYMGDWL69Om8+OKLhISEWLs0ERERyYOe6LvPKlasyJIlS6xdhoiIiOQDT+zDG0VERESy44m9fCYiIiKSHQpFIiIiIigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkedy6det48cUXqV27NrVq1aJt27a899573LhxI1fraNGiBe+//36uHe+DDz6gRYsWuXY8EWuYPXs23t7exl/169enR48eHD582CLje3t7ExYWluX+3bt3p3///hY5dmbi4uLw9vZm7dq1OXYMeTxP9HOKJH8LDQ1l+vTp9OrViyFDhmAwGIiMjOTHH3/k2rVrlChRItdqmTNnDs7Ozrl2PJH/ioIFC7J06VIAYmJimDdvHr169WLt2rV4eXk91tirV6+mbNmylihT/iMUiiTPWr58Oc8//7zJU8gDAgLo06cPaWlpjz3+3bt3KViwYJb6Vq1a9bGPJyIZ2dra4uvra9z28fGhRYsWrFq1ivHjx5s1ZvrP9oPjimSFLp9JnhUXF0epUqUybbO1/X//62Y2Rb5kyRK8vb2N2wcPHsTb25udO3cyZMgQatWqxdChQwkJCaF9+/YZxt+xYwfe3t5ER0cDppfP1q5dS9WqVbl+/brJZ27dukX16tVZtWqVcd/Ro0fp0aMHvr6+1K5dm7fffjvDpb+rV68yYMAAatasSZMmTQgNDc3K1yPyRCpbtizFixfn4sWLwP2ftw4dOlCjRg2aNGnCjBkzSE1NNfZfu3Yt3t7eHD16lDfeeANfX18++ugjIOOfDUeOHOG1116jdu3a+Pn50aFDB7777rsMNURERNC6dWv8/Pzo0aMH58+fN2lPSkri008/pXnz5lSvXp22bdvy448/ZhhnzZo1tGjRgpo1a9KzZ0/OnTtnke9Ico5miiTPqlatGqtWrcLd3Z1mzZpRsmTJxx5z3LhxdOzYkblz52Jra0tSUhLfffcdf/zxh8lU/fr166lWrRqenp4ZxnjuueeYMGECERERvP7668b9mzdvBqBNmzbA/UDUvXt3AgICmDFjBomJicycOZOBAweyevVq4+cGDhzI1atXmThxIk5OToSGhnLlyhUKFNCPp/z3JCQkcOvWLUqVKsXixYv5+OOP6dmzJyEhIZw5c8YYikaOHGnyubfffpuXX36Z/v37U6hQoUzH7d+/P7Vr1+bTTz/FwcGBqKgo4uLiTPqdOnWKmzdvMnLkSFJTU5k2bRqjRo0y+ZkdOnQoP//8M4MGDaJixYrs2rWLUaNG4ezsTEBAAHD/H1bjxo2jS5cuBAUF8dtvvzF06NAc+MbEkvSnruRZEyZMIDg4mLFjxwLg7u5O8+bN6dWrF+7u7maN2aJFC0aNGmXcTklJoXjx4mzYsMEYihITE9m+fTvBwcGZjuHk5ERAQADr1683CUXr16+nUaNGuLq6AjB9+nSqV6/OnDlzsLGxAcDLy4v27duza9cuAgIC2L17NydOnGDJkiU0aNAAAH9/fwICAozjiDzpUlJSgPtrij788ENSU1Np1qwZo0aNok+fPowYMQKARo0aYW9vz7Rp0+jduzfFihUzjvHKK6/Qr1+/hx7j7NmzxMfHM2LECOMscvrP3IPi4+P5/vvvKV68OAB37tzh3XffJSYmhtKlS/PTTz+xfft2wsLCaNy4sbGuv/76i9mzZxtD0fz586lTpw5Tp04FoEmTJty7d4958+Y97tclOUiXzyTP8vLyYv369SxcuJAePXrg5OTE8uXL6dixI6dOnTJrzGbNmplsFyhQgDZt2hAeHm7ct2PHDhITE2nXrt1Dx2nXrh2//PILly9fBuDatWv873//M34mMTGRn3/+mTZt2pCamkpKSgopKSk888wzlClThl9//RWA48eP4+TkZPKHs5OTEw0bNjTr/ETymzt37lCtWjWqVatGy5YtOXjwIOPHj8fR0ZE7d+7Qpk0b489PSkoKDRs25O7du0RGRpqM88+f7X+qUKECRYsWZeLEiYSHh3Pz5s1M+1WuXNkYiAAqVaoE3A9sAPv27cPV1ZX69etnqOvUqVOkpqaSmprKb7/9xnPPPWcyduvWrbP79Ugu00yR5GkODg4EBAQY//W1Z88e+vfvz9y5c5kzZ062x8vsjrV27drx5Zdfcvz4cXx8fNiwYQN16tShdOnSDx2nefPmFCpUiA0bNtC3b182btyIo6MjgYGBwP31UKmpqUydOtX4L8UHXblyBbgfph78A/hRdYo8iQoWLMiKFSuwsbGhWLFilClTBltbW9atWwfA888/n+nn0n+G0j311FOPPI6LiwuLFy9m1qxZvPPOO6SmplKnTh3Gjh1rsv7wn3eZ2tvbA3Dv3j0A/v77b27dukW1atUyPc5ff/2FnZ2dcRY6OzWK9SkUSb7SpEkTKleuzJkzZ4z7HBwcSE5ONun3z3UC6dIvYz2odu3alClThg0bNuDh4cHu3bsZM2bMI+soWLAggYGBhIeH07dvX8LDw2nevDmFCxcG7s/22NjY0L9/f2NQelD6tH+pUqUy/Rdrbj+HScRabG1tqVGjRob9Li4uwP3HYWT2DxRzLqH7+PiwaNEi7t69y8GDB/nwww8ZNGgQW7duzfIYLi4uFC9enIULF2baXrx4cezs7ChQoECGn+1/3pwheY9CkeRZ169fz/Avq7t373LlyhXjlDZA6dKlTUISwP79+7N8HBsbG4KCgli/fj3PPvssaWlpWZrmbt++Pf369WPPnj388ssv9O3b19hWuHBhfH19iY6OzvQP/HQ1atQgPj6eAwcOGC+hxcfHs3//fq0pkv80Pz8/ChUqRExMTIbLUI+rYMGCBAQEcP78eT744APu3buHo6Njlj7bsGFDFi1ahL29PZUrV35ov6pVq7JlyxZ69epl3Ldp06bHLV1ymEKR5FkdOnSgefPmNG7cmFKlSnH16lVWrFjB33//Tc+ePY39WrduzdKlS6lRowYeHh6sW7eOq1evZutY7du3JywsjM8++4xGjRpleknrnxo2bIirqytjxozB2dmZpk2bmrS/88479OzZk2HDhtGuXTucnZ2JiYlh//79dOnSBX9/f5o2bUq1atUYNWoUI0eOxMnJiYULF1K0aNFs1S/ypHF2dmbIkCF8/PHHxMTEUK9ePezs7Lhw4QLbtm1j9uzZmd5l9jA7d+7km2++ITAwkLJly3L9+nVWrFhBrVq1shyI4P6i6ubNm9OnTx/69OmDt7c3iYmJREVFce7cOT744AMABgwYwMCBA3n33XeNd5/98MMP2f4eJHcpFEmeFRwczI4dO5g2bRo3b96kWLFieHt7s2TJEurXr2/sN3DgQG7cuMHcuXOxsbHh5ZdfpkePHkybNi3Lx6patSoeHh6cPXs2w62+D2Nvb0/r1q1ZvXo1L774Ig4ODibttWrV4ssvv2T27Nm8++67JCcnU7p0aerXr8/TTz8N3J+lmjdvHhMmTGD8+PE4OzvTvXt3rl+/zrZt27Jcv8iT6M0338TNzY3FixezYsUKChQoQIUKFWjWrJlxrU9WVahQAVtbW2bOnMmNGzdwdXWlcePGxjvbsmPWrFksXLiQr776ikuXLuHk5MSzzz5Lly5djH1atmzJpEmTWLBgARs2bKBmzZrMnDmTrl27Zvt4kntsDAaDwdpFiIiIiFibbskXERERQaFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRsbCQkBBatGiRo8fw9vZm9uzZOXqMJ0X37t3p3r27tcsQyRf0mg+RfOz3339n7ty5/Prrr1y/fh1XV1cqVapEixYt9Bfh/y8hIYElS5awefNmLly4QGpqKhUqVCAgIIAePXrg5uZm7RJFJI/Qaz5E8qmff/6ZHj16ULZsWTp37kzJkiW5cuUKx44d4/z582zZssUqdSUnJ2MwGDK8C86SvL29CQ4OZvDgwY/sd+HCBXr16sWVK1do06YNtWvXxt7ent9//53169fj6ur6xL+5PCkpCSBH/3uIPCk0UySSTy1YsAAnJye++eYbnJ2dTdpu3LhhsePcuXOHwoULZ7l/dl/UmVNSUlIIDg7mxo0bLFu2jDp16pi0Dx8+nNDQUCtVl/MSExMpVKiQwpBINmhNkUg+df78eSpVqpQhEAGUKFHC+PuLFy/i7e3N2rVrM/T759qc2bNn4+3tTVRUFG+//TZ169alW7duhIWF4e3tzaVLlzKMMX36dKpXr05sbCxguqYoOTmZevXq8e6772b4XEJCAjVq1ODDDz8E7s9ofPbZZ3Tp0oXatWvj6+tLt27d+Omnn7L5zdy3efNmTp8+zYABAzIEIoCiRYsyfPhwk30bN26kS5cu+Pj44O/vz8iRI7l69apJn5CQEPz8/Lh8+TL9+/fHz8+PJk2asHLlSuD+Jc0ePXrg6+tL8+bN+fHHH00+v3btWry9vfnf//7H+PHj8ff3p1atWrzzzjvG7zDd1q1b6devH40bN6Z69eoEBgYyd+5cUlNTTfp1796d9u3bc+LECV577TVq1qzJp59+amz756XU5cuX065dO2rWrEndunXp0qVLhjpPnjxJnz59qFWrFn5+fvTs2ZNffvkl03M5cuQIU6dOpX79+vj6+jJo0CBu3ryZ2X8WkTxNoUgknypXrhy//fYbf/zxh8XHHjp0KImJiQwfPpyuXbvStm1bbGxs2LhxY4a+GzdupFGjRri4uGRos7e3JzAwkK1btxov46RL3xcUFATcD0lff/019erVY+TIkQQHB3Pz5k369OnDqVOnsn0O27ZtA6BTp05Z6r927VqGDRuGra0tI0aM4KWXXmLLli28+uqrxMXFmfRNTU2lb9++lC5dmpEjR1KuXDnef/991q5dS58+fahevTojR46kSJEijB49mgsXLmQ43vvvv8+ZM2cIDg6mc+fO/PjjjwwaNIgHVzR89913FC5cmDfeeIP33nuPatWqMWvWLD755JMM4926dYu+fftSpUoVxowZg7+/f6bnuWbNGiZPnkzFihUZM2YMgwcPpkqVKhw7dszYJzIyktdee43Tp0/Tp08f3nrrLS5evEj37t1N+qWbPHkyp0+fJjg4mFdffZUdO3bw/vvvZ+l7F8lLdPlMJJ9688036du3L507d8bHx4fatWvToEED/P39H/sSVuXKlZk+fbrJPl9fX8LDw+nTp49x3/Hjx7lw4QLBwcEPHSsoKIhvv/2Wffv20bx5c+P+8PBwypcvT40aNQBwcXFh+/btJpd7XnrpJdq2bcvy5cuZMmVKts4hOjoaJycnypQp8699k5OT+eSTT/Dy8mLlypU4OjoCULt2bfr378+SJUsYMmSIsf+9e/fo2LEj/fv3B6BDhw40adKEMWPG8OmnnxqDXsOGDWnbti3ff/99hvVP9vb2LFmyxPjfqmzZsnz88cds376dli1bAvdn4QoWLGj8zKuvvsr48eP56quvGD58uMl39ddffzFp0iReeeWVR57rzp07efbZZ5k1a9ZD+8ycOZPk5GS++uorypcvD0Dnzp1p06YNH3/8MStWrDDp7+rqyhdffIGNjQ0AaWlpLF++nPj4eJycnB5Zj0heopkikXyqUaNGrFq1ihYtWnD69GkWLVpE7969adq0qXGWxFyZ/cXatm1bfvvtN86fP2/ct3HjRhwcHAgMDHzoWPXr16dYsWKEh4cb98XGxrJ//35jeACws7Mz/iWflpbGrVu3SElJoXr16pw8eTLb55CQkECRIkWy1PfEiRPcuHGDV1991RiIAJo1a4anpyc7d+7M8JmuXbsaf+/s7IyHhweFChWibdu2xv2enp44OztnOlP08ssvm4TXV199lQIFCrBr1y7jvgcDUUJCAjdv3qROnTokJiYSHR1tMp6DgwNdunT513N1dnYmJiaG48ePZ9qemprKvn37CAwMNAYigFKlStG+fXuOHDlCQkKCyWdeeuklYyACqFOnDqmpqZlebhXJyxSKRPIxHx8f5syZw6FDh/j666/p378/t2/fZujQoURFRZk9rru7e4Z9bdq0wdbW1hhuDAYDERERNG3alKJFiz50rAIFCtCqVSu2bdtmvIS2efNmkpOTTUIR3L9c1KFDB+OangYNGrBz507i4+OzfQ5Fixbl9u3bWep7+fJlADw8PDK0eXp6GtvTOTo6Urx4cZN9Tk5OlC5d2iQcpO//5+U3gKefftpku0iRIpQsWdIkSERGRjJo0CBq165tnAkcNWoUQIbvxM3NLUuLqvv27UvhwoXp2rUrrVq1YtKkSRw5csTYfvPmTRITEzP9LipWrEhaWhpXrlwx2V+2bFmT7fR1bpmdt0heplAk8gRwcHDAx8eHESNGMHHiRJKTk4mIiADI8Jd0un8u1n3Qg7Ml6dzc3KhTp45xXdEvv/zC5cuXMwSbzLRr147bt2+ze/duACIiIvD09KRy5crGPj/88AMhISFUqFCByZMns2jRIhYvXkz9+vUx58khnp6exMfHZ/gL3BLs7Oyytd+c+uPi4nj99dc5ffo0Q4YMYcGCBSxevJiRI0cC92fTHvTgrNKjVKxYkYiICGbMmEHt2rXZvHkz3bp1e+TltH9ja5v5XyV64ovkNwpFIk+Y6tWrA3Dt2jUA4wLof/6r/Z+zH1nRtm1bTp8+TXR0NOHh4RQqVMhkndDD1K1bl5IlSxIeHs7Nmzf56aefMoSpTZs2Ub58eebMmUPnzp1p0qQJDRs25N69e9muEzDWtW7dun/tmz7Tcfbs2QxtZ8+ezTATYgnnzp0z2b59+zZ//fUX5cqVA+DQoUPcunWLadOm0bNnT5o3b07Dhg0zXdCeXYULFyYoKIipU6eyY8cOmjVrxoIFC7h37x7FixenUKFCmX4X0dHR2NraZmmdlkh+pFAkkk/99NNPmf5LPH1NiqenJ3D/MlKxYsU4fPiwSb8vv/wy28ds3bo1dnZ2bNiwgYiICJo1a5alZxjZ2trSpk0bduzYwbp160hJSckQitJnWR48p2PHjmW4DTw7tXp5ebFgwQKOHj2aoT0hIYEZM2YA94NkiRIlWLVqlcldcrt27eLMmTM0a9bMrBoeZfXq1SQnJxu3v/rqK1JSUmjatCnw/2ZfHvw+kpKSzPrv9qC///7bZNvBwYGKFStiMBhITk7Gzs6ORo0asW3bNi5evGjsd/36ddavX0/t2rUfeblUJD/T3Wci+dTkyZNJTEzkueeew9PTk+TkZH7++Wc2btxIuXLlTBbddu3alYULF/Lee+9RvXp1Dh8+nOlMwL8pUaIE/v7+LF68mNu3b2fp0lm69LvIZs2ahZeXFxUrVjRpb9asGZs3b2bQoEE0a9aMixcvsmrVKipVqsSdO3eyXau9vT1z5szhjTfe4PXXX6dNmzbUqlULe3t7IiMjWb9+Pc7OzgwfPhx7e3tGjhzJu+++y+uvv067du2MD30sV64cvXr1yvbx/01ycjK9evWibdu2nD17li+//JLatWsb7zzz8/PDxcWFkJAQunfvjo2NDT/88MNjX5Lq3bs3Tz31FLVq1aJEiRJER0ezYsUKAgICjGFn2LBh7N+/n27dutGtWzfs7OxYvXo1SUlJxjVNIk8ihSKRfOqdd94hIiKCXbt2GWcdypYtS7du3XjrrbdMHuqY/jC9TZs2sXHjRpo2bcqiRYto0KBBto8bFBTE/v37KVKkCAEBAVn+XK1atShTpgxXrlzJNEx16dKF69evs3r1avbu3UulSpX4+OOPiYiI4NChQ9muE+4vZv7+++9ZsmQJW7ZsYdu2baSlpfH000/TtWtXk4cadunShYIFCxIaGsonn3xC4cKFCQwMZNSoUZk+IPNxjR8/nh9//JFZs2aRnJxMu3btGDt2rHENWLFixViwYAEffvghM2fOxNnZmY4dO9KgQQN69+5t9nFffvllfvzxRxYvXsydO3coXbo03bt3Z+DAgcY+zz77LCtXrmT69Ol8/vnnGAwGfHx8+Pjjj6lZs+Zjn7tIXqV3n4mI5KK1a9fy7rvv8s033xif0SQieYPWFImIiIigUCQiIiICKBSJiIiIAFpTJCIiIgJopkhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhEREREA/j+FJRG32rDQBwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "sns.countplot(x= \"Survived\", hue=\"Pclass\", order=[1, 0], data = train_df).set(xticklabels=[\"Survived\", \"Perished\"])\n",
    "plt.xlabel(\"Survival Comparison\")\n",
    "plt.ylabel(\"Number of Passengers\")\n",
    "plt.title(\"Comparison\")\n",
    "plt.legend(labels=[\"1\", \"2\",\"3\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "495c1ec0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:19.429864Z",
     "iopub.status.busy": "2023-10-25T14:46:19.427834Z",
     "iopub.status.idle": "2023-10-25T14:46:19.877146Z",
     "shell.execute_reply": "2023-10-25T14:46:19.875811Z"
    },
    "papermill": {
     "duration": 0.474939,
     "end_time": "2023-10-25T14:46:19.879901",
     "exception": false,
     "start_time": "2023-10-25T14:46:19.404962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of Male Survivors')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QVFSEDh06YPv27Xj99def+lw/Pz/s2bMH69atw1dffYWCggI4OjrCx8cHw4cPf+Jzhw8fjrp16+LMmTNISEhAfn4+GjZsiG7dumHq1Knw8vLSjA0ICEBERAS2b9+O//znP/D29saGDRuwbNmyavcbGhqKzz77DCqVqsJRmiexsbFBYGAgoqKi8NJLL1V6T6MlS5bAxcUFBw4cwNGjR/Hcc89h6tSpOt/VOzAwEBkZGYiMjNTsk126dMHMmTNRv359nWslqm0yQZ/rKonIJBw9ehTh4eH4+uuv+SmbiKgSnMNDZGYKCwu1HpeWlmLnzp2ws7NDmzZtjFQVEZFp4yktIjPz4YcforCwEO3bt4darcaRI0dw4cIFvPXWW/xiRyKiKvCUFpGZiYqKwvbt23Hjxg0UFRWhefPmGDlyJMaMGWPs0oiITBYDDxEREUke5/AQERGR5DHwEBERkeRx0jKATp06Qa1Ww9HR0dilEBERkY6ysrJgbW2Nc+fOPXUsj/Dg7+/kKSkpMdj2BEFAUVHRE7/U0NyxR/Mn9f4A9igFUu8PYI/6KCkpQVFRkU5jeYQHf39rMwDEx8cbZHsFBQVISkqCp6enzt/EbG7Yo/mTen8Ae5QCqfcHsEd9BAUF6TyWR3iIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIieUTKZDHK5HDKZzNilEBHVuDrGLoDoWVZWJsDCwjiBQy6Xw8vLq9rPM2bNRERiMfAQGZGFhQwrdp1HemaesUvRiYtTfcwd3dHYZRARVRsDD5GRpWfmISUjx9hlEBFJGufwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkmdR3aR0/fhybN2/GtWvXkJ+fDycnJwQHB2PGjBmoX78+ACAiIgIHDhyo8NzNmzejR48etV0yERERmQGTCjzZ2dnw8fHB2LFj4eDggOTkZKxevRrJycnYtm2bZpyrqytWrFih9Vx3d/faLpeIiIjMhEkFnkGDBmk99vPzg7W1NRYuXIjMzEw4OTkBAOrWrQtfX18jVEhERETmyOTn8Dg4OAAAiouLjVsIERERmS2TOsJTrrS0FCUlJbh27RrWrl2LwMBAuLi4aNbfuHEDHTt2RFFRETw8PPDGG28gODj4idsMCgqqct2dO3fQpEkTFBQUGKR+lUql9bcUsUf9yWQyyOXyGtl2TVOpVBAEwdhlPBX3U/Mn9f4A9qgPQRAgk8l0GmuSgad3797IzMwEAAQEBOCTTz7RrPP09ETbtm3RqlUr5OXlYffu3QgPD8eqVavQv39/0a+pVquRlJSkd+2PS0tLM+j2TBF7FE8ul8PLy6tGtl3Trl+/bla/nLmfmj+p9wewRzHUajVsbGx0GisTTPBj2tWrV6FSqXDt2jWsX78eLi4u2L59OywtLSuMLSsrw4gRI5Cfnw+lUinq9YKCgiAIAg4dOqRv6QD+TrBpaWlo0aKF2X6Cfxr2qL/yIzyzP/0BKRk5Bt9+TXB3boDP3uplVkd4uJ+aN6n3B7BHfYSFhUEmkyE+Pv6pY03yCE/r1q0BAO3bt0fbtm0xaNAgxMXFVXoEx8LCAn379sV///tfFBYWom7duqJeUyaTwdbWVq+6/0kulxt8m6aGPT6bzO2X8rPwHkq9R6n3B7BHMXQ9nQWYwaRlhUIBKysr3Lx509ilEBERkZky+cBz8eJFFBcXa01aflxZWRliY2Px4osvij66Q0RERNJmUqe0ZsyYAW9vbygUCtStWxdXr17F1q1boVAoEBwcjIyMDERERGDAgAFo3rw5cnJysHv3bly5cgWrV682dvlERERkokwq8Pj4+ECpVGLTpk0QBAHOzs4YOnQoJk2aBGtra9SrVw92dnZYv3497t+/DysrK3h7e2Pz5s0ICAgwdvlERERkokwq8EyZMgVTpkypcr2DgwPWr19fixURERGRFJj8HB4iIiIifTHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeSZVOA5fvw4xowZg65du8Lb2xtBQUH4+OOPkZeXpzUuISEBL7/8Mtq2bYt+/fohMjLSSBUTERGROahj7AIel52dDR8fH4wdOxYODg5ITk7G6tWrkZycjG3btgEAzp07hxkzZuC1117D/PnzcebMGbz33nuoV68e+vfvb+QOiIiIyBSZVOAZNGiQ1mM/Pz9YW1tj4cKFyMzMhJOTE9avXw8fHx/83//9HwCga9euuHXrFj7//HMGHiIiIqqUSZ3SqoyDgwMAoLi4GGq1GomJiRWCTWhoKFJSUpCenm6EComIiMjUmdQRnnKlpaUoKSnBtWvXsHbtWgQGBsLFxQXXrl1DcXExWrZsqTXe3d0dAJCamgoXF5dKtxkUFFTl6925cwdNmjRBQUGBQepXqVRaf0sRe9SfTCaDXC6vkW3XNJVKBUEQjF3GU3E/NX9S7w9gj/oQBAEymUynsSYZeHr37o3MzEwAQEBAAD755BMAQE5ODgDA3t5ea3z54/L1YqjVaiQlJYl+fmXS0tIMuj1TxB7Fk8vl8PLyqpFt17Tr16+b1S9n7qfmT+r9AexRDLVaDRsbG53GmmTg2bRpE1QqFa5du4b169dj2rRp2L59u17bjI+Pr3JdUFAQBEGAp6enXq9RTqVSIS0tDS1atDDbT/BPwx71p+unElPk5uZmNkd4uJ+aN6n3B7BHfVhbW+s81iQDT+vWrQEA7du3R9u2bTFo0CDExcWhVatWAFDhMvXc3FwAQIMGDUS/pkwmg62trejnV0Yulxt8m6aGPT6bzO2X8rPwHkq9R6n3B7BHMarzwdHkJy0rFApYWVnh5s2baNasGaysrJCamqo1pvzxP+f2EBEREQFmEHguXryI4uJiuLi4wNraGn5+fjh8+LDWGKVSCXd39yonLBMREdGzzaROac2YMQPe3t5QKBSoW7curl69iq1bt0KhUCA4OBgAMH36dIwbNw4ffPABQkJCkJiYiEOHDmHlypVGrp6IiIhMlUkFHh8fHyiVSmzatAmCIMDZ2RlDhw7FpEmTNBOTOnXqhNWrV+Ozzz7Dt99+i6ZNm2LJkiUICQkxcvVERERkqkwq8EyZMgVTpkx56rigoKAn3leHiIiI6HEmP4eHiIiISF8MPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQa707JKpUJ0dDTUajV69uwJZ2dnQ22aiIiISC+iAs/8+fNx6dIlHDp0CACgVqsxbNgwJCcnAwDq16+PL774Al5eXoarlIiIiEgkUae0EhMT0adPH83jQ4cOITk5GStWrMChQ4fw3HPPYc2aNQYrkoiIiEgfogLPX3/9pXXK6ujRo/D29kZYWBhatWqFYcOG4dKlSwYrkoiIiEgfogKPXC5HXl4eAKCkpAQ///wzunfvrllfr149zXoiIiIiYxM1h6dNmzbYu3cv/Pz8kJCQgEePHiEwMFCz/ubNm2jcuLHBiiQiIiLSh6jAM3v2bEyaNAlDhgyBIAjo168ffHx8NOvj4uLQoUMHgxVJREREpA9Rgadt27aIjY3FL7/8Ant7e3Tp0kWzLjc3F6NGjdJaRkRERGRM1Q48hYWFWLlyJfz8/BAcHFxhvb29PcaPH2+Q4oiIiIgModqTluvWrYs9e/bg/v37NVEPERERkcGJukqrTZs2+PPPPw1dCxEREVGNEBV45s+fD6VSiX379qGkpMTQNREREREZlKhJyxEREZDJZFi0aBGWLFkCJycn2NjYaI2RyWQ4ePCgQYokIiIi0oeowOPg4AAHBwe4ubkZuh4iIiIigxMVeHbu3GnoOoiIiIhqjKg5PERERETmRNQRHgAoLS3FwYMH8cMPP+D27dsAgKZNm6J3794YOHAgLC0tDVYkERERkT5EBZ68vDxMmjQJly9fRr169eDq6goA+Omnn3DkyBHs3r0bW7duhZ2dnUGLJSIiIhJDVOBZuXIlfvvtNyxYsADDhg2DlZUVAKC4uBj79u3DRx99hJUrV2LhwoUGLZaIiIhIDFFzeOLi4jBy5EiMHj1aE3YAwMrKCqNGjcLIkSNx+PBhgxVJREREpA9RgSc7O/uJl6S7ubkhJydHdFFEREREhiQq8DRv3hwJCQlVrk9ISECzZs1EF0VERERkSKICz8iRI3Hq1ClMnjwZP/74I9LT05Geno6TJ09iypQp+OmnnzB69GhD10pEREQkiqhJy6NHj8aDBw+wadMm/Pjjj9obrFMH4eHhGDVqlEEKJCIiItKX6PvwzJw5E6NHj8bp06eRkZEBAHB2doa/vz8aNWpksAKJiIiI9CUq8AiCAJlMhkaNGmHAgAEGKyYmJgYHDx7Eb7/9htzcXDRv3hxjx47FkCFDIJPJAABjx47Fzz//XOG5SqUS7u7uBquFiIiIpENU4AkICED//v0REhKCjh07GqyYHTt2wNnZGREREWjYsCF++uknLFy4EHfv3sWMGTM04zp06IB58+ZpPdfFxcVgdRAREZG0iAo8Xbp0QWRkJHbt2gUnJyeEhIQgJCQEPj4+ehWzfv16rdNh/v7+yM7Oxvbt2/HGG2/AwuLvOdb29vbw9fXV67WIiIjo2SHqKq1PP/0Up0+fxqeffgofHx/s3r0bw4cPR3BwMD799FMkJSWJKqayuT+enp7Iz89HQUGBqG0SERERiZ60XLduXc2RnYKCAiQkJECpVGLHjh3YvHkzmjdvjtjYWL0LPH/+PJycnLS+l+vnn3+Gr68vSktL0a5dO7z55pvo3LnzE7cTFBRU5bo7d+6gSZMmBgtVKpVK628pYo/6k8lkkMvlNbLtmqZSqSAIgrHLeCrup+ZP6v0B7FEf5XOKdSE68DzO1tYWYWFh6N27Nw4cOICVK1fixo0bem/33LlzUCqVWvN1OnfujEGDBqFFixa4d+8etm7digkTJmDnzp1o37696NdSq9Wij0xVJS0tzaDbM0XsUTy5XA4vL68a2XZNu379uln9cuZ+av6k3h/AHsVQq9WwsbHRaazegUelUiEhIQExMTE4efIk1Go1mjVrhpCQEL22e/fuXcyZMwd+fn4YN26cZvmsWbO0xvXq1QthYWFYt24dNm/eXOX24uPjq1wXFBQEQRDg6empV83lVCoV0tLS0KJFC7P9BP807FF/un4qMUVubm5mc4SH+6l5k3p/AHvUh7W1tc5jRQWeoqIi/PDDD1AqlThx4gRUKhWcnZ0xduxYhIaG6v2pNTc3F5MnT4aDgwNWr16tmaxcGVtbW/Ts2VPvLyuVyWSwtbXVaxv/JJfLDb5NU8Men03m9kv5WXgPpd6j1PsD2KMY1fngKCrwdO3aFYWFhXj++ecxbNgwhIaGol27dmI2VUFhYSGmTp2KvLw87NmzB/Xr1zfIdomIiOjZJSrwDB48GCEhIejUqZNBiykpKcHs2bORmpqqueT9aQoKCvDDDz+gbdu2Bq2FiIiIpENU4Fm4cKGh6wAALF68GMeOHUNERATy8/Px66+/atZ5eXnh0qVL2LJlC/r06QNnZ2fcu3cP27dvR1ZWFlatWlUjNREREZH50ynwnD17FgA0l36XP36ap10q/k+nTp0CACxdurTCuvj4eDg6OqK4uBgrV65EdnY25HI52rdvj8WLF+t900MiIiKSLp0Cz9ixYyGTyXDx4kVYW1trHlel/Lr46l7mnZCQ8NQxW7durdY2iYiIiHQKPF9++SWA/13+Vf6YiIiIyBzoFHi6dOnyxMdEREREpkzUd2nt2rULDx48MHQtRERERDVCVOD58MMP0aNHD0yYMAH79u1Ddna2gcsiIiIiMhxRgScmJgbTpk1DVlYWFi5ciO7du2Py5Mn47rvvkJ+fb+gaiYiIiPQi6j48bm5umDFjBmbMmIHk5GRER0cjNjYWERERsLa2Rvfu3TFgwAAMGDDA0PUSERERVZuoIzyPe/HFFzF79mzExsbiu+++w/jx45GYmIh33nnHEPURERER6U3vwFPu6tWriImJweHDh/Ho0aNqfYMpERERUU0SdUqr3LVr16BUKhETE4O0tDTUqVMH3bt3x8yZMxEYGGioGomIiIj0IirwrF27FrGxsbh27RosLS3RtWtXTJkyBcHBwfx2cyIiIjI5ogLPunXr0LlzZ4wdOxZ9+vRBw4YNDV0XERERkcFUO/AUFxdj9+7dcHR0xAsvvFATNREREREZVLUnLVtYWGDkyJE4evRoTdRDREREZHDVDjyWlpZo2rQpioqKaqIeIiIiIoMTdVn6mDFjsHfvXn6lBBEREZkFUZOWy8rKYG1tjT59+qBfv35wdnZG3bp1tcbIZDL861//MkSNRERERHoRFXiWLVum+e9vv/220jEMPERERGQqRAWe+Ph4Q9dBREREVGNEBR5nZ2dD10FERERUYwz2XVpEREREpkrUEZ7AwEDIZLInjpHJZLxXDxEREZkEUYGnS5cuFQJPaWkpbt++jV9++QUvvvgivLy8DFIgERERkb5EBZ6lS5dWue7q1auYNGkSBg4cKLooIiIiIkMy+Bye1q1bY/jw4VixYoWhN01EREQkSo1MWm7cuDGuXbtWE5smIiIiqjaDB56HDx8iMjISTZo0MfSmiYiIiEQRNYdn3LhxlS7Py8tDamoqiouLsXz5cr0KIyIiIjIUUYFHEIQKy2QyGVxcXODv748hQ4bA3d1d7+KIiIiIDEFU4Nm5c6eh6yAiIiKqMbzTMhEREUmezoEnKysLZ8+exaNHj7SWFxcXY9WqVQgODka7du3w6quv8stFiYiIyKToHHg2bdqEN998E1ZWVlrLly1bhg0bNiA3NxetWrXC9evXMWvWLJw9e9bgxRIRERGJoXPgOXv2LHr37g1ra2vNsgcPHuDrr7+Gu7s7jh49isjISERHR6Nhw4bYtm1bjRRMREREVF06B547d+7gxRdf1Fp27NgxlJWVYeLEibC3twcAODs7Y8iQIbh06ZJhKyUiIiISSefAo1arYWtrq7Xs3LlzkMlk8Pf311ru6uqKnJwcw1RIREREpCedA4+LiwuSkpK0liUmJqJp06Z44YUXtJYXFBTAwcGh2sXExMRg+vTp6NGjB3x9fTFo0CB8++23Fe77s2/fPvTr1w9t27bFyy+/jGPHjlX7tYiIiOjZoXPg6dOnD7777jsolUrcuXMH69evx+3btxESElJh7MWLF+Hi4lLtYnbs2AG5XI6IiAisX78ePXr0wMKFC7F27VrNmOjoaCxcuBAhISHYvHkzfH19MWPGDPz666/Vfj0iIiJ6Nuh848HXX38dx44dw1tvvQWZTAZBEODm5oZp06ZpjXv48CESEhIwadKkahezfv16NGrUSPPY398f2dnZ2L59O9544w1YWFjg888/x4ABAzB79mwAQNeuXfHnn39i7dq12Lx5c7Vfk4iIiKRP58Bja2uLffv2IS4uDrdu3YKzszOCg4NhY2OjNS4zMxMzZ85Ev379ql3M42GnnKenJ/bu3YuCggI8fPgQaWlpeOedd7TGhIaGYvny5VCr1VpXkREREREB1fxqiTp16lR6CutxrVu3RuvWrfUq6nHnz5+Hk5MT7OzscP78eQCAm5ub1hh3d3cUFxfj1q1bVX6HV1BQUJWvcefOHTRp0gQFBQUGqVmlUmn9LUXsUX8ymQxyubxGtl3TVCpVpd+pZ2q4n5o/qfcHsEd9CIIAmUym01hR36VVW86dOwelUol58+YBgObKr/JL4MuVP9bnyjC1Wl1hUra+0tLSDLo9U8QexZPL5fDy8qqRbde069evm9UvZ+6n5k/q/QHsUQy1Wl3hTFNVTDbw3L17F3PmzIGfnx/GjRun9/ae9HUXQUFBEAQBnp6eer8O8HeCTUtLQ4sWLcz2E/zTsEf96fqpxBS5ubmZzREe7qfmTer9AexRH9WZxmKSgSc3NxeTJ0+Gg4MDVq9eDQuLvy8ma9CgAQAgLy8Pjo6OWuMfXy+GTCarcJ8hfcnlcoNv09Swx2eTuf1SfhbeQ6n3KPX+APYoRnU+OJrct6UXFhZi6tSpyMvLw5YtW1C/fn3NupYtWwIAUlNTtZ6TmpoKKysruLq61mqtREREZB50Cjxffvklrl+/XtO1oKSkBLNnz0Zqaiq2bNkCJycnrfWurq5o0aIFYmNjtZYrlUr4+/vzCi0iIiKqlE6ntD7++GM0bNhQc3WUp6cnli9fjoEDBxq0mMWLF+PYsWOIiIhAfn6+1s0Evby8YG1tjZkzZ2Lu3Llo1qwZ/Pz8oFQqcenSJXz11VcGrYWIiIikQ6fAY29vj/v372se19RkxVOnTgEAli5dWmFdfHw8XFxcEBYWBpVKhc2bN2PTpk1wc3PDmjVr0L59+xqpiYiIiMyfToHHz88Pq1evRlJSkmZOzXfffYeLFy8+8XkLFiyoVjEJCQk6jRs6dCiGDh1arW0TERHRs0unwPP+++/jP//5D06dOoX79+9DJpPh1KlTmiMylZHJZNUOPEREREQ1QafA07hxY3zyySeax61bt8Z///tfg8/hISIiIqoJoi5L//jjjzlnhoiIiMyGqBsPvvrqq5r/vnbtGjIyMgAAzs7OaNWqlWEqIyIiIjIQ0XdaPnr0KJYuXaoJO+VcXFwQERHxxC/rJCIiIqpNogLP8ePHMWvWLDRt2hRz5szRfEN5SkoK9u7di5kzZ2LDhg3o0aOHQYslIiIiEkNU4Fm3bh0UCgV27dql9Z0YQUFBGDNmDEaNGoW1a9cy8BAREZFJEDVp+Y8//sArr7xS6ReA2dra4tVXX8Uff/yhd3FEREREhiAq8NjY2CAnJ6fK9Tk5ObCxsRFdFBEREZEhiQo8fn5++PLLL3HhwoUK6y5evIidO3fC399f7+KIiIiIDEHUHJ533nkHI0aMwKhRo+Dj46P5UtHr16/j0qVLaNy4MebOnWvQQomIiIjEEnWEx9XVFQcPHsTYsWORk5MDpVIJpVKJnJwcjBs3Dt9//z1cXFwMXavZKiurmS9brUnmWDMREVFVRN+Hp3Hjxpg/fz7mz59vyHokycJChhW7ziM9M8/YpejExak+5o7uaOwyiIiIDEZ04KHqSc/MQ0pG1RO9iYiIqOaIOqVFREREZE4YeIiIiEjyGHiIiIhI8hh4iIiISPKqHXhUKhUGDx6M3bt310Q9RERERAZX7cAjl8uRnp4OmUxWE/UQERERGZyoU1oBAQH48ccfDV0LERERUY0QFXjeeOMNpKWl4Z133sG5c+eQmZmJ7OzsCn+IiIiITIGoGw8OGDAAAHDt2jUcOnSoynFJSUniqiIiIiIyIFGBJzw8nHN4iIiIyGyICjwzZ840dB1ERERENcYg9+HJy8tDaWmpITZFREREZHCiA8/ly5cxadIktGvXDn5+fvj5558BAA8ePMD06dORmJhosCKJiIiI9CEq8Pzyyy8YNWoUbty4gZdffhllZWWadY0aNUJ+fj727NljsCKJiIiI9CEq8KxcuRLu7u5QKpWYM2dOhfV+fn64ePGi3sURERERGYKowHP58mUMHjwY1tbWlV6t5eTkhL/++kvv4oiIiIgMQVTgqVOnjtZprH/KzMyEra2t6KKIiIiIDElU4GnXrh0OHz5c6bqCggLs378fnTt31qswIiIiIkMRFXhmzZqFK1euYMqUKThx4gQA4I8//sC+ffswePBgPHjwAG+88YZBCyUiIiISS/QRnk2bNuHGjRuYN28eAGDp0qVYuHAhysrKsGnTJrRu3dqghRIRERGJJepOywDg7++Pw4cP4/fff8eNGzcgCAJcXV3h7e0t+msnbty4ga1bt+LixYtITk5Gy5YtK3xX19ixYzX3/HmcUqmEu7u7qNclIiIiaRMdeMp5eXnBy8vLELUgOTkZx48fR7t27VBWVgZBECod16FDB82RpXIuLi4GqYGIiIikR3TgUavV2Lt3L44fP46MjAwAgLOzM3r27ImhQ4fCxsam2tsMDAxEcHAwACAiIgJXrlypdJy9vT18fX3Flk5ERETPGFGB5+7du5gwYQKuX78OR0dHNG/eHABw9epVnDx5El999RV27NiBJk2aVGu7FhYG+WovIiIiIi2iAs/ixYtx+/ZtfPbZZ+jfv7/WupiYGERERGDx4sVYv369QYr8p59//hm+vr4oLS1Fu3bt8Oabb/IyeCIiIqqSqMBz5swZ/Otf/6oQdgAgJCQEv//+O7766iu9i6tM586dMWjQILRo0QL37t3D1q1bMWHCBOzcuRPt27ev8nlBQUFVrrtz5w6aNGmCgoICg9SoUqk0f8tkMsjlcoNst7apVKoq51E93qNU1XSPUt03TAn3U/Mn9f4A9qgPQRB0vlBKVOCpV68eGjVqVOX65557DvXq1ROz6aeaNWuW1uNevXohLCwM69atw+bNm0VvV61WIykpSd/ytKSlpUEulxtsUndtu379+lN3zrS0tNopxohqqkep7xumhPup+ZN6fwB7FEOtVus8Z1hU4Bk8eDAOHDiAYcOGVfiE+ujRI+zfvx9DhgwRs+lqs7W1Rc+ePau883O5+Pj4KtcFBQVBEAR4enoapCaVSoW0tDS0aNHCrL9iw83N7YlHeMp7NNejFE9T0z2KvX2DKXjSvmFKuJ+aP6n3B7BHfVhbW+s8VqfAc+TIEa3Hnp6e+OGHHxASEoJXXnlFM2k5LS0N33//PRo0aACFQlGNko1PJpMZPJzI5XKz3nl1qV0ul5t1qNPFs9BjdZnbfv0svIdS71Hq/QHsUYzqfHDUKfDMmjULMplM84nu8f/esGFDhfF3797F22+/jdDQUJ0LEaugoAA//PAD2rZtW+OvRUREROZJp8Dz5Zdf1nQdAP4+5HX8+HEAQEZGBvLz8xEbGwsA6NKlC1JTU7Flyxb06dMHzs7OuHfvHrZv346srCysWrWqVmokIiIi86NT4OnSpUtN1wEAuH//Pt58802tZeWPv/zySzRp0gTFxcVYuXIlsrOzIZfL0b59eyxevBg+Pj61UiMRERGZH72/WsKQXFxc8McffzxxzNatW2upGiIiIpIK0YHn3LlziIyMRHp6OnJycipcsSGTyXDw4EG9CyQiIiLSl6jAs337dixfvhw2NjZwc3NDgwYNDF0XkdGV3xjQnC8ff9bxPSSicqICz9atW9GhQwds2LAB9evXN3RNRKKUlQmwsDDcP2zmfGPAmuJQ38bgP+eaVP4elpaVGbsUIjIyUYFHpVJh4MCBDDtkUiwsZFix6zzSM/OMXYpOOrR+HuNCzStQ2cmtzO7n7OJUH3NHdzR2GURkZKICj5+fH/78809D10Kkt/TMPKRk5Bi7DJ24PG9n7BJEM6efMxERAFiIedLChQtx+vRpbN26FdnZ2QYuiYiIiMiwRB3heeGFFzB8+HAsX74cK1asgI2NDSwstLOTTCbD+fPnDVIkERERkT5EBZ5Vq1Zhw4YNcHJygre3N+fyEBERkUkTFXi++eYb9OzZE+vWratwZIeIiIjI1IhKK8XFxejVqxfDDhEREZkFUYmlV69eOHfunKFrISIiIqoRogLPjBkzkJKSgg8++ABXrlzBgwcPkJ2dXeEPERERkSkQNYenf//+AICkpCTs2bOnynFJSUniqiIiIiIyIFGBJzw8nN9NQ0RERGZDVOCZOXOmoesgIiIiqjG8zIqIiIgkT9QRnjVr1jx1jEwmQ3h4uJjNExERERmUwQOPTCaDIAgMPERERGQyRAWeq1evVlhWVlaGjIwMfP311zh79iw2b96sd3FEREREhmCwOTwWFhZwdXXFvHnz0Lx5cyxZssRQmyYiIiLSS41MWu7cuTOOHz9eE5smIiIiqrYaCTxXrlzh92wRERGRyRA1h+e7776rdHlubi7OnTuHI0eOYOjQofrURURERGQwogJPRERElesaNmyIKVOm8AotIiIiMhmiAk98fHyFZTKZDPb29rCzs9O7KCIiIiJDEhV4nJ2dDV0HERERUY3hzGIiIiKSPJ2P8AwcOLBaG5bJZDh48GC1CyIiIiIyNJ0Dj4ODg07j/vrrL1y/fh0ymUxsTUREREQGpXPg2blz5xPXZ2VlYfPmzdizZw8sLS3x8ssv610cERERkSGImrT8uL/++gubNm3C3r17UVJSgoEDB2L69Olo1qyZIeojIiIi0pvowFN+ROfxoPPGG2/A1dXVkPURERER6a3agScrKwubNm3Cvn37UFJSgpdffhnTp09n0CEiIiKTpXPguXfvnibolJaWYtCgQZg2bRqDDhEREZk8nQNPnz59oFar4enpialTp8LFxQW5ubn47bffqnxOmzZtDFIkERERkT50DjxFRUUAgN9//x2zZ89+4lhBECCTyZCUlFStYm7cuIGtW7fi4sWLSE5ORsuWLXHo0KEK4/bt24ctW7bg9u3bcHNzw5w5c9C7d+9qvRYRERE9O3QOPB9//HFN1gEASE5OxvHjx9GuXTuUlZVBEIQKY6Kjo7Fw4UJMmzYNXbt2hVKpxIwZM7Br1y74+vrWeI1ERERkfnQOPK+++mpN1gEACAwMRHBwMIC/v5H9ypUrFcZ8/vnnGDBggOYoU9euXfHnn39i7dq12Lx5c43XSERERObHpL5Ly8LiyeXcunULaWlpCAkJ0VoeGhqK06dPQ61W12R5REREZKb0vvFgbUpNTQUAuLm5aS13d3dHcXExbt26BXd390qfGxQUVOV279y5gyZNmqCgoMAgdapUKs3fMpkMcrncINutbSqVqtLTiuXrHv/b2Mz550y1o7CwsMr92VTpUq+p/b9oaFLvD2CP+iifM6wLswo8OTk5AAB7e3ut5eWPy9eLoVarqz3J+mnS0tIgl8vh5eVl0O3WluvXrz9150xLS6udYp7CnH/OVLMc6tugrExA3bp1jV1KtZSUlOL3339DcXGxTuNN5f/FmiL1/gD2KIZarYaNjY1OY80q8OgjPj6+ynVBQUEQBAGenp4GeS2VSoW0tDS0aNECtra2BtmmMbi5uT3xCE95j6ZwZIVfVktVsZNbwcJChhW7ziM9M8/Y5ejExak+5o7uiBdffPGpR3lM7f9FQ5N6fwB71Ie1tbXOY80q8DRo0AAAkJeXB0dHR83y3NxcrfViyGQyg4cTuVxu1juvLrXL5XKzDnX07EjPzENKhvijwMZQnd8fUv9/Uer9AexRjOp82DWpSctP07JlSwD/m8tTLjU1FVZWVrzrMxEREVXKrAKPq6srWrRogdjYWK3lSqUS/v7+1Tq0RURERM8OkzqlpVKpcPz4cQBARkYG8vPzNeGmS5cuaNSoEWbOnIm5c+eiWbNm8PPzg1KpxKVLl/DVV18Zs3QiIiIyYSYVeO7fv48333xTa1n54y+//BJ+fn4ICwuDSqXC5s2bsWnTJri5uWHNmjVo3769MUomIiIiM2BSgcfFxQV//PHHU8cNHToUQ4cOrYWKiIiISArMag4PERERkRgMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDwkikwmg1wuh0wmM3YpRERET1XH2AWQ6XGob4OyMgEWFlWHGblcDi8vr1qsioiISDwGHqrATm4FCwsZVuw6j/TMPGOXo5MOrZ/HuFAGMCIiqhwDD1UpPTMPKRk5xi5DJy7P2xm7BCIiMmGcw0NERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RkxngTUCLd8LJ0IiITo8vNP8uZ0k1Ada2ZyBgYeIiITIw53vzTxak+5o7uaOwyiKrEwENEZKLM6eafRKaOc3iIiIhI8swu8Ozfvx8KhaLCnxUrVhi7NCIiIjJRZntKa8uWLahfv77msZOTkxGrISIiIlNmtoGnTZs2aNSokbHLICIiIjNgdqe0iIiIiKrLbANPWFgYPD09ERQUhI0bN6K0tNTYJREREZGJMrtTWo6Ojpg5cybatWsHmUyGhIQEfPbZZ8jMzMSiRYuqfF5QUFCV6+7cuYMmTZqgoKDAIDWqVCrN3+V3QSUiehaoVCoIgmDQ7T3+txSxR/EEQdD5LuNmF3gCAgIQEBCgedy9e3fY2Njgiy++wLRp0/D888+L2q5arUZSUpKhygQApKWlmdRdUImIatr169dr5B/utLQ0g2/T1LDH6lOr1bCxsdFprNkFnsqEhIRg27ZtSEpKqjLwxMfHV/n8oKAgCIIAT09Pg9SjUqmQlpaGFi1awNbW1iDbJCIyB25ubgY/wlP++1SqR8vZo3jW1tY6j5VE4DEEmUxm8HAil8slu/MSEVWmpn7nyeVyyX+AZI/VV50vzTXbScuPUyqVsLS05KkjIiIiqpTZHeGZNGkS/Pz8oFAoAPx9qmrv3r0YN24cHB0djVwdERERmSKzCzxubm6IjIzE3bt3UVZWhhYtWmD+/PkYO3assUsjIiIiE2V2gWfBggXGLoGIiIjMjCTm8BARERE9CQMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxERmSyZTAa5XA6ZTGbsUsjM1TF2AUREZP4c6tugrEyAhYVhg4lcLoeXl5dBt/m4mqiZTBMDDxER6c1ObgULCxlW7DqP9Mw8Y5ejExen+pg7uqOxy6BawsBDREQGk56Zh5SMHGOXQVQB5/AQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERFRjZLJZLCysjJqDQw8REREZqSsTDB2CdX29x2z2xj1K0J440EiIiIzYm53tAb+d1fr4mLj1cDAQ0REZGZ4R+vq4yktIiIikjyzDDwpKSmYMGECfH190a1bNyxfvhxqtdrYZREREZGJMrtTWjk5ORg/fjxatGiB1atXIzMzE0uXLkVhYSEWLVpk7PKIiIjIBJld4Pnmm2/w6NEjrFmzBg4ODgCA0tJSLF68GFOnToWTk5NxCyQiIiKTY3antE6cOAF/f39N2AGAkJAQlJWV4dSpU8YrjIiIiEyWTBAEs7qg39/fH0OGDMHcuXO1lgcEBGDQoEEVlpcLCgqqcpvp6emwtLREkyZNDFKjIAgoLi6GlZUVZDIZZDIZcvKLUFJqHj9qGytL2NlaseYaxpprB2uuHeZYcx1LGRrY2cDY/wwKgoDS0lJYWlrqdJ8ac/s3Bfjfz7qsrMyg9+K5e/cuLC0tcfny5afXYLBXrSW5ubmwt7evsLxBgwbIyRF3iZ5MJkOdOnUM9ibcvXsXAPDCCy/8rz47G4NsuzY9qeY7d+4A0O7RFBjy51xbPRpr39CnP3PZn+/cuQM1ADvbF8ym5sfpUrOp/b9o6J9zbfRnzJvhAZX/m/E05rY/37lzBwV5hn8f69SpA2tra93GGvSVTVh8fHytvVb50aTafM3axh7Nn9T7A9ijFEi9P4A91hazm8Njb2+PvLyKd5fMyclBgwYNjFARERERmTqzCzwtW7ZEamqq1rK8vDxkZWWhZcuWRqqKiIiITJnZBZ4ePXrgp59+Qm5urmZZbGwsLCws0K1bNyNWRkRERKbK7ALPiBEjUK9ePYSHh+PHH39EZGQkli9fjhEjRvAePERERFQpsws8DRo0wBdffAFLS0uEh4fjk08+wWuvvYaIiAhjl0ZEREQmyiyv0nJ3d8eOHTuMXQYRERGZCbO78SARERFRdZndKS0iIiKi6mLgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4DGglJQUTJgwAb6+vujWrRuWL18OtVpt7LJEu3HjBhYtWoRBgwbBy8sLYWFhlY7bt28f+vXrh7Zt2+Lll1/GsWPHarlScWJiYjB9+nT06NEDvr6+GDRoEL799lv888JFc+0PAI4fP44xY8aga9eu8Pb2RlBQED7++OMK30eXkJCAl19+GW3btkW/fv0QGRlppIr18+jRI/To0QMKhQKXL1/WWmeu7+P+/fuhUCgq/FmxYoXWOHPt73EHDhzAK6+8grZt28LPzw+vv/46CgsLNevNeT8dO3Zspe+jQqFAdHS0Zpw5v4/x8fEYOnQo2rdvj+7du+PNN9/ErVu3KowzWo8CGUR2drbQrVs3YfTo0cKJEyeEffv2CR07dhQWL15s7NJEi4uLE3r06CHMnDlTCAsLEwYMGFBhzKFDhwSFQiGsXLlSOH36tLBw4ULBy8tLuHDhQu0XXE3Dhg0T5syZI0RHRws//fSTsGLFCqF169bC6tWrNWPMuT9BEITvvvtOWLZsmRAbGyucOXNG2Llzp9ClSxdhwoQJmjFnz54VPD09hYULFwqnT58WVq5cKSgUCiEmJsaIlYuzfPly4aWXXhI8PDyES5cuaZab8/sYGRkpeHh4CCdOnBAuXLig+XP79m3NGHPur9y6deuE9u3bCxs3bhQSExOF2NhY4f333xfy8/MFQTD//TQ5OVnr/btw4YIwe/ZswcvLS7h//74gCOb9Pp45c0Zo3bq1EBERIZw6dUqIjo4W+vbtKwQHBwsqlUozzpg9MvAYyIYNGwRfX1/h4cOHmmXffPON4OnpKdy9e9d4hemhtLRU89/z5s2rNPD07dtXeOutt7SWDR8+XHj99ddrvD59lf+SedyCBQuEDh06aHo35/6qsmfPHsHDw0OzX06cOFEYPny41pi33npLCAkJMUZ5ol27dk3w9fUVdu/eXSHwmPP7WB54Kttfy5lzf4IgCCkpKYKXl5fwww8/VDlGKvvp4wIDA4XJkydrHpvz+7hw4UIhMDBQKCsr0yw7ffq04OHhIZw9e1azzJg98pSWgZw4cQL+/v5wcHDQLAsJCUFZWRlOnTplvML0YGHx5N3j1q1bSEtLQ0hIiNby0NBQnD592uRP5zVq1KjCMk9PT+Tn56OgoMDs+6tK+T5aXFwMtVqNxMRE9O/fX2tMaGgoUlJSkJ6eboQKxVmyZAlGjBgBNzc3reVSfR/LSaG//fv3w8XFBT179qx0vZT203K//PIL0tPTMXDgQADm/z6WlJSgXr16kMlkmmX169cHAM00AWP3yMBjIKmpqWjZsqXWMnt7ezg6OiI1NdVIVdWs8r7++Q+Mu7s7iouLKz13a+rOnz8PJycn2NnZSaq/0tJSFBUV4bfffsPatWsRGBgIFxcX3Lx5E8XFxRX2XXd3dwAwm303NjYWf/75J8LDwyusk8r7GBYWBk9PTwQFBWHjxo0oLS0FII3+Ll68CA8PD6xbtw7+/v7w9vbGiBEjcPHiRQCQzH76uEOHDsHW1hZBQUEAzP99HDx4MFJSUrBr1y7k5eXh1q1b+PTTT+Hl5YUOHToAMH6PZvldWqYoNzcX9vb2FZY3aNAAOTk5Rqio5pX39c++yx+bW9/nzp2DUqnEvHnzAEirv969eyMzMxMAEBAQgE8++QSANHpUqVRYunQp5syZAzs7uwrrzb1HR0dHzJw5E+3atYNMJkNCQgI+++wzZGZmYtGiRWbfHwBkZWXhypUr+PPPP/H+++9DLpdjw4YNmDhxIo4cOSKJHh9XUlKCmJgYBAYGwtbWFoD576edOnXCmjVr8Pbbb+P//u//APx9xHzLli2wtLQEYPweGXiIANy9exdz5syBn58fxo0bZ+xyDG7Tpk1QqVS4du0a1q9fj2nTpmH79u3GLssg1q9fj8aNG2PIkCHGLqVGBAQEICAgQPO4e/fusLGxwRdffIFp06YZsTLDEQQBBQUFWLVqFVq3bg0AaNeuHQIDA/HVV1+he/fuRq7QsE6dOoUHDx5UeeWrOfrll1/w73//G8OGDUOvXr2QnZ2NdevWYcqUKfj6669Rt25dY5fIU1qGYm9vX+FSX+DvxNqgQQMjVFTzyvv6Z9+5ubla601dbm4uJk+eDAcHB6xevVozd0kq/QFA69at0b59ewwdOhTr1q1DYmIi4uLizL7HjIwMbNu2DbNmzUJeXh5yc3NRUFAAACgoKMCjR4/MvsfKhISEoLS0FElJSZLoz97eHg4ODpqwA/w918zLywvXrl2TRI+PO3ToEBwcHLSCnLn3uGTJEnTt2hURERHo2rUr+vfvj02bNuH333/H999/D8D4PTLwGEjLli0rnEfOy8tDVlZWhfPOUlHe1z/7Tk1NhZWVFVxdXY1RVrUUFhZi6tSpyMvLw5YtWzST7ABp9FcZhUIBKysr3Lx5E82aNYOVlVWlPQIw+X03PT0dxcXFmDJlCjp37ozOnTtrjnqMGzcOEyZMkOz7WE4K/bVq1arKdUVFRWa/nz6usLAQR48eRf/+/WFlZaVZbu7vY0pKilZgBYAmTZqgYcOGuHnzJgDj98jAYyA9evTATz/9pEmqwN8TKS0sLNCtWzcjVlZzXF1d0aJFC8TGxmotVyqV8Pf3h7W1tZEq001JSQlmz56N1NRUbNmyBU5OTlrrzb2/qly8eBHFxcVwcXGBtbU1/Pz8cPjwYa0xSqUS7u7ucHFxMVKVuvH09MSXX36p9efdd98FACxevBjvv/++JN9HpVIJS0tLeHl5SaK/3r17Izs7G0lJSZplDx8+xG+//YY2bdqY/X76uISEBBQUFGiuzipn7u9j06ZN8fvvv2sty8jIwMOHD+Hs7AzA+D1yDo+BjBgxAjt37kR4eDimTp2KzMxMLF++HCNGjKjwD6m5UKlUOH78OIC/d9z8/HzNjtqlSxc0atQIM2fOxNy5c9GsWTP4+flBqVTi0qVL+Oqrr4xZuk4WL16MY8eOISIiAvn5+fj1118167y8vGBtbW3W/QHAjBkz4O3tDYVCgbp16+Lq1avYunUrFAoFgoODAQDTp0/HuHHj8MEHHyAkJASJiYk4dOgQVq5caeTqn87e3h5+fn6VrmvTpg3atGkDAGb9Pk6aNAl+fn5QKBQA/r6b7d69ezFu3Dg4OjoCMO/+ACA4OBht27bFrFmzMGfOHNjY2GDTpk2wtrbGqFGjAJj3fvq4qKgoNG3aFB07dqywzpzfxxEjRuA///kPlixZgsDAQGRnZ2vm1z1+GbpRe6zxO/08Q65duyaMHz9e8PHxEfz9/YWlS5cKRUVFxi5LtFu3bgkeHh6V/jlz5oxm3N69e4U+ffoIbdq0EcLCwoSEhAQjVq273r17V9nfrVu3NOPMtT9BEISNGzcKgwYNEtq3by/4+voKAwYMED777DMhLy9Pa9zRo0eFsLAwoU2bNkKfPn2Effv2Gali/Z05c6bCjQcFwXzfxw8//FDo27ev4OPjI3h7ewthYWHCF198oXWDN0Ew3/7K3b9/X5g7d67QsWNHwcfHR5g4caKQnJysNcbc99Ps7GyhTZs2wvLly6scY67vY1lZmfD1118LAwcOFHx9fYVu3boJ4eHhwrVr1yqMNVaPMkH4xxcHEREREUkM5/AQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQkdnZtWsXFAoFhg4dauxSiMhMMPAQkdmJioqCs7MzLl26hBs3bhi7HCIyAww8RGRWbt26hQsXLuDdd99Fo0aNEBUVZeySiMgMMPAQkVmJiopCgwYN0LNnT/Tr16/SwPPw4UO888476NChAzp16oR58+bh6tWrUCgU2L9/v9bYlJQUzJo1C126dEHbtm0xePBgxMfH11Y7RFRLGHiIyKxERUWhT58+sLa2RlhYGNLS0nDp0iXN+rKyMkyfPh3R0dF49dVXMWfOHGRlZWHevHkVtpWcnIzhw4cjJSUFkydPRkREBGxtbREeHo64uLjabIuIalgdYxdARKSrK1euIDU1FQsXLgQAdOzYEU2aNEFUVBR8fHwAAEePHsWFCxcwf/58jB8/HgAwcuRITJgwocL2PvroI7zwwguIjIyEtbU1AGDUqFEYOXIkVqxYgT59+tRSZ0RU03iEh4jMRlRUFJ577jn4+fkBAGQyGUJDQ6FUKlFaWgoAOHnyJKysrDBs2DDN8ywsLDB69GitbWVnZ+PMmTMICQlBfn4+Hjx4gAcPHuDhw4fo3r070tLSkJmZWXvNEVGN4hEeIjILpaWliI6Ohp+fH9LT0zXLfXx8sG3bNpw+fRrdu3fH7du34ejoCLlcrvX8Zs2aaT2+efMmBEHAqlWrsGrVqkpf8/79+3BycjJ8M0RU6xh4iMgsnDlzBllZWYiOjkZ0dHSF9VFRUejevbvO2ysrKwMATJw4EQEBAZWO+WdIIiLzxcBDRGYhKioKjRs3xqJFiyqsi4uLQ1xcHBYvXoymTZsiMTERKpVK6yjPzZs3tZ7j6uoKALCyssJLL71Us8UTkdFxDg8RmbzCwkIcOXIEvXr1Qv/+/Sv8GT16NB49eoSEhAR0794dxcXF2Lt3r+b5ZWVl2LVrl9Y2GzdujC5dumDPnj24d+9ehdd88OBBjfdFRLWHR3iIyOQlJCTg0aNHCAwMrHS9r68vGjVqhIMHD2Lt2rXw8fHBsmXLcPPmTbRs2RIJCQnIyckB8PdE53Lvv/8+Ro0ahYEDB2LYsGFwdXXFX3/9hV9//RV3797FwYMHa6U/Iqp5DDxEZPIOHjwIGxsbdOvWrdL1FhYW6NWrF6KiopCbm4uNGzfio48+woEDB2BhYYE+ffogPDwcI0eOhI2NjeZ5rVq1QmRkJNasWYMDBw4gOzsbjRo1gpeXF8LDw2urPSKqBTJBEARjF0FEVNOOHj2K8PBwfP311+jYsaOxyyGiWsY5PEQkOYWFhVqPS0tLsXPnTtjZ2aFNmzZGqoqIjImntIhIcj788EMUFhaiffv2UKvVOHLkCC5cuIC33noLdevWNXZ5RGQEPKVFRJITFRWF7du348aNGygqKkLz5s0xcuRIjBkzxtilEZGRMPAQERGR5HEODxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJ3v8D8iYsVzk3A9UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[(train_df.Sex == 'male') & (train_df.Survived == 1)].Age.hist()\n",
    "plt.ylabel('Number of Survivors')\n",
    "plt.xlabel('Age')\n",
    "plt.title('Age of Male Survivors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2c6863d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-25T14:46:19.919708Z",
     "iopub.status.busy": "2023-10-25T14:46:19.919314Z",
     "iopub.status.idle": "2023-10-25T14:46:20.340816Z",
     "shell.execute_reply": "2023-10-25T14:46:20.339462Z"
    },
    "papermill": {
     "duration": 0.444986,
     "end_time": "2023-10-25T14:46:20.343693",
     "exception": false,
     "start_time": "2023-10-25T14:46:19.898707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of Female Survivors')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2bMDixYvh7u5e4rIDgwcPxtatWxEVFYUrV67AwcEB+/fvx++//47p06eXO0+o2JgxY/DGG2+gTZs2aNSoEVJSUrBx40Z06dJFq/2JdI2Bh0jH9uzZA0tLS3To0KHU7WZmZujatStiY2Px5MkTNGzYEN9//z2+/vpr7Nq1C2ZmZggJCUFERASGDh2qcei/efPm2LFjB5YuXYpdu3bh6dOnsLW1hZeXFyIiIsqty8zMDCtWrMDixYuhUCiwc+dOODg44F//+hc++OADnY7B64+7bNkyrFu3Dj///DMOHjwIuVwOR0dHhIeH6+wUkrm5OVauXInZs2fj+++/h6WlJUJCQjB8+HCtrgI9btw4ODs7Y926dVi2bBmAl/OLOnToUOENOP/1r38hPj4eR44cwdatW1FQUIAmTZpg2LBhmDBhgsY1fCIiIvD48WPs378fcXFx6Ny5M1atWlXmpO7yhIWF4cSJE6hbty66dOmi9X5t2rTBW2+9hQcPHpQalOrUqYMNGzZg/vz52LVrF3Jzc+Hi4oI5c+ZohLfyDB48GLGxsVi7di3y8vLQuHFjhIeHS+5GtGQ6ZEJpx2GJyOAOHTqEiIgIbN68md9sISKqIs7hITICr9/6oLCwEBs2bEC9evXQsmVLA1VFRCQdPKVFZAS++uor5Ofno3Xr1lCpVDhw4ADOnz+PTz/9tNSv/BIRUeXwlBaRESie63Dnzh08f/4czZo1w9ChQzFixAhDl0ZEJAkMPERERCR5nMNDREREksfAQ0RERJLHScsA3n33XahUKtjZ2Rm6FCIiItLSw4cPYWFhUeLq5aXhER4Az58/x4sXL6rUhyAIeP78eamXl6eXOEbl4/hUjGNUPo5P+Tg+FTO1MXrx4gWeP3+uVVse4QHw5ptvAgASEhJE95GXl4fk5GR4enpW6s7KNQnHqHwcn4pxjMrH8Skfx6dipjZGQUFBWrflER4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIyGTKZDHK5HDKZzNClEJGJqW3oAojItBQVCTAzM0zgkMvl8PLyqvR+hqyZiIwDAw8RVYqZmQzzN51DWmaOoUvRiqN9fUwd/o6hyyAiA2PgIaJKS8vMwa30LEOXQUSkNc7hISIiIskz2sDz7NkzdO7cGR4eHrh06ZLGtu3bt6NHjx7w8fFB3759cfjwYQNVSURERKbAaAPP8uXLUVhYWGL9vn37MGPGDISGhiImJgZ+fn6IjIzEhQsXqr9IIiIiMglGGXhu3bqFzZs3Y+LEiSW2LV68GL169cLkyZPRvn17fPnll/Dx8cGyZcsMUCkRERGZAqMMPLNnz8aQIUPg4uKisf7evXtITU1FaGioxvqwsDCcPHkSKpWqOsskIiIiE2F039KKj4/H9evXsWTJEly5ckVjW0pKCgCUCEJubm4oKCjAvXv34ObmVmq/QUFBZT7mgwcP0LhxY+Tl5YmuW6lUavykkjhG5TOF8Sm+8J8pUiqVEATB0GXolSm8hgyJ41MxUxsjQRC0vhCpUQUepVKJuXPnYsqUKahXr16J7VlZL78Ga21trbG+eLl4uxgqlQrJycmi9y+Wmppa5T6kjmNUPmMeH7EX/jMGt2/fNpkP8aoy5teQMeD4VMxUxkilUsHS0lKrtkYVeFasWIFGjRphwIABOu87ISGhzG1BQUEQBAGenp6i+1cqlUhNTYWzs7PJ/gWsbxyj8pnC+JjyLR1cXFxqxBEeY38NGRLHp2KmNkYWFhZatzWawJOeno41a9Zg2bJlyMl5eQXX4lNMeXl5ePbsGRo0aAAAyMnJgZ2dnXrf7OxsAFBvF0Mmk8HKykr0/sXkcrlO+pEyjlH5OD76YQof3rrC11D5OD4VM5UxqswfYUYTeNLS0lBQUIBx48aV2DZy5Ei0atUKCxYsAPByLo+rq6t6e0pKCszNzeHk5FRt9RIREZHpMJrA4+npifXr12usS05Oxpw5czBr1iz4+PjAyckJzs7OiI+PR3BwsLqdQqFAQEBApQ5tERERUc1hNIHH2toa/v7+pW5r2bIlWrZsCQCYOHEipk6diqZNm8Lf3x8KhQJJSUnYuHFjdZZLREREJsRoAo+2evfuDaVSiZiYGERHR8PFxQVLly5F69atDV0aERERGSmjDjz+/v64du1aifUDBw7EwIEDDVARERERmSKjvNIyERERkS4x8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQGQmZTAa5XF6pu/8SEZF2jPpKy0RSV1QkwMzsZcCRy+Xw8vIycEXSY1PfUmOcTYUp1kxkzBh4iAzIzEyG+ZvOIS0zx9ClaKVNizcxMsy0Qlk9ubnJjbOjfX1MHf6OocsgkhQGHiIDS8vMwa30LEOXoRXHN+sZugTRTGmciUj3OIeHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSvtqELeNWRI0cQExODmzdvIjc3F/b29ggODkZkZCTq168PAIiKisKuXbtK7BsTE4POnTtXd8lERERkAowq8Dx9+hS+vr4IDw+HjY0Nbty4gSVLluDGjRtYs2aNup2TkxPmz5+vsa+bm1t1l0tEREQmwqgCT79+/TSW/f39YWFhgRkzZiAzMxP29vYAgDp16sDPz88AFRIREZEpMvo5PDY2NgCAgoICwxZCREREJssoA09hYSGeP3+OK1euYNmyZQgMDISjo6N6+507d/DOO+/A29sb/fv3x6FDhwxYLRERERk7ozqlVaxbt27IzMwEAHTq1AkLFixQb/P09ISPjw+aN2+OnJwcbNmyBREREVi0aBF69uxZZp9BQUFlbnvw4AEaN26MvLw80TUrlUqNn1QSx0iTTCaDXC43dBlkxJRKJQRBqFT7V3+SJo5PxUxtjARBgEwm06qtUQae6OhoKJVK3Lx5EytWrMD48eOxdu1a1KpVC6NGjdJoGxgYiCFDhmDx4sXlBp6KqFQqJCcnV7V0pKamVrkPqeMYvSSXy+Hl5WXoMsiI3b59W9QvHr7HysfxqZipjJFKpYKlpaVWbY0y8LRo0QIA0Lp1a/j4+KBfv344ePBgqYHGzMwM3bt3x7fffov8/HzUqVOn1D4TEhLKfLygoCAIggBPT0/RNSuVSqSmpsLZ2Zl/tZeBY6RJ279KqOZycXGp9BEevsfKxvGpmKmNkYWFhdZtjTLwvMrDwwPm5ua4e/euXh9HJpPBysqqyv3I5XKd9CNlHCMi7Yj9hcP3WPk4PhUzlTGqzB+ORjlp+VUXL15EQUGBxqTlVxUVFSE+Ph5vv/12mUd3iIiIqGYzqiM8kZGR8Pb2hoeHB+rUqYOrV69i9erV8PDwQHBwMNLT0xEVFYVevXqhWbNmyMrKwpYtW3D58mUsWbLE0OUTERGRkTKqwOPr6wuFQoHo6GgIggAHBwcMHDgQY8aMgYWFBerWrYt69ephxYoVePToEczNzeHt7Y2YmBh06tTJ0OUTERGRkTKqwDNu3DiMGzeuzO02NjZYsWJFNVZEREREUmD0c3iIiIiIqoqBhySjqEj7r+8SEVHNYlSntIiqwsxMhvmbziEtM8fQpWilTYs3MTKMFx4kIqoODDwkKWmZObiVnmXoMrTi+GY9Q5dARFRj8JQWERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJXm1ddaRUKrFv3z6oVCp06dIFDg4OuuqaiIiIqEpEBZ7p06cjKSkJe/fuBQCoVCoMGjQIN27cAADUr18fP/zwA7y8vHRXKREREZFIok5pnT59GiEhIerlvXv34saNG5g/fz727t2LN954A0uXLtVZkURERERVISrw/PXXXxqnrA4dOgRvb2/07t0bzZs3x6BBg5CUlKSzIomIiIiqQlTgkcvlyMnJAQC8ePECv/32Gzp27KjeXrduXfV2IiIiIkMTFXhatmyJbdu24Y8//sDKlSvx7NkzBAYGqrffvXsXjRo1qnS/R44cwYgRI9C+fXt4e3sjKCgIc+bMKRGeEhMT0bdvX/j4+KBHjx7YsWOHmKdBRERENYSoScuTJ0/GmDFjMGDAAAiCgB49esDX11e9/eDBg2jTpk2l+3369Cl8fX0RHh4OGxsb3LhxA0uWLMGNGzewZs0aAMDZs2cRGRmJ999/H9OnT8epU6fw73//G3Xr1kXPnj3FPB0iIiKSOFGBx8fHB/Hx8fj9999hbW2Ndu3aqbdlZ2dj2LBhGuu01a9fP41lf39/WFhYYMaMGcjMzIS9vT1WrFgBX19ffPnllwCA9u3b4969e1i8eDEDDxEREZWq0qe08vPzMWfOHFy4cAHBwcElgo21tTVGjRoFT09PnRRoY2MDACgoKIBKpcLp06dLBJuwsDDcunULaWlpOnlMIiIikpZKB546depg69atePTokT7qAQAUFhbi+fPnuHLlCpYtW4bAwEA4Ojri7t27KCgogKurq0Z7Nzc3AEBKSoreaiIiIiLTJeqUVsuWLXH9+nVd16LWrVs3ZGZmAgA6deqEBQsWAACysrIAvDyK9Kri5eLtpQkKCipz24MHD9C4cWPk5eWJrlmpVGr8pJL0OUYymQxyuVzn/RIZklKphCAIlWr/6k/SxPGpmKmNkSAIkMlkWrUVfaXlcePGwd3dHe+99x5q19bZHSoAANHR0VAqlbh58yZWrFiB8ePHY+3atTp9jNepVCokJydXuZ/U1NSqFyNx+hgjuVzOK3uT5Ny+fVvULx5+DpWP41MxUxkjlUoFS0tLrdqKSipRUVGQyWSYOXMmZs+eDXt7+xIPKJPJsGfPHjHdo0WLFgCA1q1bw8fHB/369cPBgwfRvHlzACjxNfXs7GwAQIMGDcrsMyEhocxtQUFBEAShSvOOlEolUlNT4ezszCMNZdDnGGmb8IlMiYuLS6WP8PBzqGwcn4qZ2hhZWFho3VZU4LGxsYGNjQ1cXFzE7F4pHh4eMDc3x927dxEYGAhzc3OkpKSgU6dO6jbFc3den9tTGTKZDFZWVlWuVy6X66QfKeMYEWlH7C8cvsfKx/GpmKmMUWX+2BUVeDZs2CBmN1EuXryIgoICODo6wsLCAv7+/ti/fz9GjRqlbqNQKODm5gZHR8dqq4uIiIhMh24n31RRZGQkvL294eHhgTp16uDq1atYvXo1PDw8EBwcDACYMGECRo4cif/+978IDQ3F6dOnsXfvXixcuNDA1RMREZGxEh14CgsLsWfPHvzyyy+4f/8+AKBJkybo1q0b+vTpg1q1alW6T19fXygUCkRHR0MQBDg4OGDgwIEYM2aM+jzdu+++iyVLluC7777DTz/9hCZNmmD27NkIDQ0V+1SIiIhI4kQFnpycHIwZMwaXLl1C3bp14eTkBAA4ceIEDhw4gC1btmD16tWoV69epfodN24cxo0bV2G7oKCgcr9mTkRERPQqUYFn4cKFuHLlCv7zn/9g0KBBMDc3B/Dyasjbt2/H119/jYULF2LGjBk6LZaIiIhIDFF3Sz948CCGDh2K4cOHq8MOAJibm2PYsGEYOnQo9u/fr7MiiYiIiKpCVOB5+vRpuV9Jd3FxKfeqx0RERETVSVTgadasGRITE8vcnpiYiKZNm4ouioiIiEiXRAWeoUOH4vjx4xg7dix+/fVXpKWlIS0tDceOHcO4ceNw4sQJDB8+XNe1EhEREYkiatLy8OHD8fjxY0RHR+PXX3/V7LB2bURERGDYsGE6KZCIiIioqkRfh2fixIkYPnw4Tp48ifT0dACAg4MDAgICYGtrq7MCiYiIiKpKVOApvh27ra0tevXqpeuaiIiIiHRK1ByeTp06Yfbs2Th37pyu6yEiIiLSOVFHeNq1a4cdO3Zg06ZNsLe3R2hoKEJDQ+Hr66vr+oiIiIiqTFTg+T//5/8gPz8fhw8fRlxcHLZs2YJ169bBwcEBYWFhCA0Nhaenp65rJSIiIhJF9KTlOnXqqI/s5OXlITExEQqFAuvWrUNMTAyaNWuG+Ph4XdZKREREJIqoOTyvs7KyQu/evfHtt9/iX//6F6ysrHDnzh1ddE1ERERUZaKP8BRTKpVITExEXFwcjh07BpVKhaZNmyI0NFQX9RERERFVmajA8/z5c/zyyy9QKBQ4evQolEolHBwcEB4ejrCwMHh5eem6TiIiIiLRRAWe9u3bIz8/H2+++SYGDRqEsLAwtGrVSte1EREREemEqMDTv39/hIaG4t1339V1PUREREQ6JyrwzJgxQ9d1EBEREemNVoHnzJkzAIC2bdtqLFekuD0RERGRIWkVeMLDwyGTyXDx4kVYWFiol8tSfK+t5ORknRVKREREJJZWgWf9+vUAAAsLC41lIiIiIlOgVeBp165ductERERExkzUlZY3bdqEx48f67oWIiIiIr0QFXi++uordO7cGaNHj8b27dvx9OlTHZdFREREpDuiAk9cXBzGjx+Phw8fYsaMGejYsSPGjh2L3bt3Izc3V9c1EhEREVWJqOvwuLi4IDIyEpGRkbhx4wb27duH+Ph4REVFwcLCAh07dkSvXr3Qq1cvXddLREREVGlVvlv622+/jcmTJyM+Ph67d+/GqFGjcPr0aXz22We6qI+IiIioyqoceIpdvXoVcXFx2L9/P549e6b+CjsRERGRoYk6pVXs5s2bUCgUiIuLQ2pqKmrXro2OHTti4sSJCAwM1FWNRERERFUiKvAsW7YM8fHxuHnzJmrVqoX27dtj3LhxCA4ORv369XVdIxEREVGViAo8y5cvR9u2bREeHo6QkBA0bNhQJ8XExcVhz549uHLlCrKzs9GsWTOEh4djwIAB6ltZhIeH47fffiuxr0KhgJubm07qICIiImmpdOApKCjAli1bYGdnh7feekunxaxbtw4ODg6IiopCw4YNceLECcyYMQMZGRmIjIxUt2vTpg2mTZumsa+jo6NOayEiIiLpqHTgMTMzw9ChQxEVFYXw8HCdFrNixQrY2tqqlwMCAvD06VOsXbsWH3/8MczMXs6xtra2hp+fn04fm4iIiKSr0t/SqlWrFpo0aYLnz5/rvJhXw04xT09P5ObmIi8vT+ePR0RERDWDqK+ljxgxAtu2bauWW0qcO3cO9vb2qFevnnrdb7/9Bj8/P/j4+GDEiBE4c+aM3usgIiIi0yVq0nJRUREsLCwQEhKCHj16wMHBAXXq1NFoI5PJ8I9//KNKxZ09exYKhUJjvk7btm3Rr18/ODs7488//8Tq1asxevRobNiwAa1bty6zr6CgoDK3PXjwAI0bN67SUSSlUqnxk0rS5xjJZDLI5XKd90tkSEqlEoIgVKr9qz9JE8enYqY2RoIgqL/UVBFRgeebb75R//dPP/1UapuqBp6MjAxMmTIF/v7+GDlypHr9pEmTNNp17doVvXv3xvLlyxETEyP68VQqFZKTk0XvXyw1NbXKfUidPsZILpfDy8tL5/0SGdLt27dF/eLh51D5OD4VM5UxUqlUsLS01KqtqMCTkJAgZjetZWdnY+zYsbCxscGSJUvUk5VLY2VlhS5dumD//v3l9llezUFBQRAEAZ6enqJrViqVSE1NhbOzM480lEGfY6RtwicyJS4uLpU+wsPPobJxfCpmamNUmbs6iAo8Dg4OYnbTSn5+Pj766CPk5ORg69at1XYhQ5lMBisrqyr3I5fLddKPlHGMiLQj9hcO32Pl4/hUzFTGqDJ/7Fbp1hK69uLFC0yePBkpKSnYtGkT7O3tK9wnLy8Pv/zyC3x8fKqhQiIiIjJFogJPYGBghalKJpPh0KFDlep31qxZOHz4MKKiopCbm4sLFy6ot3l5eSEpKQmrVq1CSEgIHBwc8Oeff2Lt2rV4+PAhFi1aJOapEBERUQ0gKvC0a9euROApLCzE/fv38fvvv+Ptt98WNYH0+PHjAIC5c+eW2JaQkAA7OzsUFBRg4cKFePr0KeRyOVq3bo1Zs2bB19dXzFMhIiKiGkBU4CktkBS7evUqxowZgz59+lS638TExArbrF69utL9EhERUc0m6sKD5WnRogUGDx6M+fPn67prIiIiIlF0HngAoFGjRrh586Y+uiYiIiKqNJ0HnidPnmDHjh1o3LixrrsmIiIiEkXUHJ5Xr3z8qpycHKSkpKCgoADz5s2rUmFEREREuiIq8JR25U+ZTAZHR0cEBARgwIABcHNzq3JxRERERLogKvBs2LBB13UQERER6Y1eJi0TERERGROtA8/Dhw9x5swZPHv2TGN9QUEBFi1ahODgYLRq1Qrvvfee3m8uSkRERFQZWgee6OhofPLJJzA3N9dY/80332DlypXIzs5G8+bNcfv2bUyaNAlnzpzRebFEREREYmgdeM6cOYNu3bpp3Ir98ePH2Lx5M9zc3HDo0CHs2LED+/btQ8OGDbFmzRq9FExERKWTyWSQy+WVuoM0UU2hdeB58OAB3n77bY11hw8fRlFRET744ANYW1sDABwcHDBgwAAkJSXptlIiohrCpr4liopKfhu2InK5HF5eXpDL5XqoqmJiaiaqLlp/S0ulUsHKykpj3dmzZyGTyRAQEKCx3snJCVlZWbqpkIiohqknN4eZmQzzN51DWmaOocvRiqN9fUwd/o6hyyAqk9aBx9HREcnJyRrrTp8+jSZNmuCtt97SWJ+XlwcbGxudFEhEVFOlZebgVjr/eCTSBa1PaYWEhGD37t1QKBR48OABVqxYgfv37yM0NLRE24sXL8LR0VGnhRIRERGJpfURng8//BCHDx/Gp59+CplMBkEQ4OLigvHjx2u0e/LkCRITEzFmzBidF0tEREQkhtaBx8rKCtu3b8fBgwdx7949ODg4IDg4GJaWlhrtMjMzMXHiRPTo0UPnxRIRERGJUalbS9SuXbvUU1ivatGiBVq0aFGlooiIiIh0ibeWICIiIslj4CEiIiLJY+AhIiIiyWPgISIiIsnTKvCsX78et2/f1nctRERERHqhVeCZM2cOLl++rF729PREbGys3ooiIiIi0iWtAo+1tTUePXqkXhYE3iCOiIiITIdW1+Hx9/fHkiVLkJycjPr16wMAdu/ejYsXL5a733/+85+qV0hERERURVoFni+++AL/+9//cPz4cTx69AgymQzHjx/H8ePHy9xHJpMx8BAREZFR0CrwNGrUCAsWLFAvt2jRAt9++y369Omjt8KIiIiIdEXU19LnzJmD1q1b67oWIiIiIr2o1L20ir333nvq/7558ybS09MBAA4ODmjevLluKiMiIiLSEVGBBwAOHTqEuXPnqsNOMUdHR0RFRSEoKKjKxRERERHpgqjAc+TIEUyaNAlNmjTBlClT4ObmBgC4desWtm3bhokTJ2LlypXo3LlzpfqNi4vDnj17cOXKFWRnZ6NZs2YIDw/HgAEDIJPJ1O22b9+OVatW4f79+3BxccGUKVPQrVs3MU+FiIiIagBRgWf58uXw8PDApk2bYGVlpV4fFBSEESNGYNiwYVi2bFmlA8+6devg4OCAqKgoNGzYECdOnMCMGTOQkZGByMhIAMC+ffswY8YMjB8/Hu3bt4dCoUBkZCQ2bdoEPz8/MU+HiIiIJE5U4Ll27RqmTJmiEXaKWVlZ4b333sPChQsr3e+KFStga2urXg4ICMDTp0+xdu1afPzxxzAzM8PixYvRq1cvTJ48GQDQvn17XL9+HcuWLUNMTIyYp0NEREQSJ+pbWpaWlsjKyipze1ZWFiwtLSvd76thp5inpydyc3ORl5eHe/fuITU1FaGhoRptwsLCcPLkSahUqko/JhEREUmfqMDj7++P9evX4/z58yW2Xbx4ERs2bEBAQECViwOAc+fOwd7eHvXq1UNKSgoAwMXFRaONm5sbCgoKcO/ePZ08JhEREUmLqFNan332GYYMGYJhw4bB19dXHUBu376NpKQkNGrUCFOnTq1ycWfPnoVCocC0adMAQH1UydraWqNd8XJ5R53K+9bYgwcP0LhxY+Tl5YmuValUavykkvQ5RjKZDHK5XOf9ElHlKJVKo73fIj+nK2ZqYyQIgsaXmsojKvA4OTlhz549+P7773H06FEoFAoAQJMmTTBy5EiMGzcOjRo1EtO1WkZGBqZMmQJ/f3+MHDmySn1pQ6VSITk5ucr9pKamVr0YidPHGMnlcnh5eem8XyKqnNu3bxv9L0t+TlfMVMZIpVJpPYVG9HV4GjVqhOnTp2P69OliuyhTdnY2xo4dCxsbGyxZsgRmZi/PvDVo0AAAkJOTAzs7O432r24vTUJCQpnbgoKCIAgCPD09RdesVCqRmpoKZ2dnHmkogz7HSNuET0T65eLiYtRHePg5XT5TGyMLCwut24oOPPqSn5+Pjz76CDk5Odi6dav67uwA4OrqCgBISUlR/3fxsrm5OZycnEQ/rkwmK/VbZ5Ull8t10o+UcYyIpMsUfknyM6hipjJGlfljV9SkZX158eIFJk+ejJSUFKxatQr29vYa252cnODs7Iz4+HiN9QqFAgEBAZVKekRERFRzGNURnlmzZuHw4cOIiopCbm4uLly4oN7m5eUFCwsLTJw4EVOnTkXTpk3h7+8PhUKBpKQkbNy40XCFExERkVEzqsBz/PhxAMDcuXNLbEtISICjoyN69+4NpVKJmJgYREdHw8XFBUuXLuXd24mIiKhMRhV4EhMTtWo3cOBADBw4UM/VEBERkVRUeg6PUqlE//79sWXLFn3UQ0RERKRzlQ48crkcaWlp/BowERERmQxR39Lq1KkTfv31V13XQkRERKQXogLPxx9/jNTUVHz22Wc4e/YsMjMz8fTp0xL/iIiIiIyBqEnLvXr1AgDcvHkTe/fuLbOdLm7VQERERFRVogJPREQE5/AQERGRyRAVeCZOnKjrOoiIiIj0Rie3lsjJyUFhYaEuuiIiIiLSOdGB59KlSxgzZgxatWoFf39//PbbbwCAx48fY8KECTh9+rTOiiQiIiKqClGB5/fff8ewYcNw584d9O3bF0VFRepttra2yM3NxdatW3VWJBEREVFViAo8CxcuhJubGxQKBaZMmVJiu7+/Py5evFjl4oiIiIh0QVTguXTpEvr37w8LC4tSv61lb2+Pv/76q8rFEREREemCqMBTu3ZtjdNYr8vMzISVlZXoooiIiIh0SVTgadWqFfbv31/qtry8POzcuRNt27atUmFEREREuiIq8EyaNAmXL1/GuHHjcPToUQDAtWvXsH37dvTv3x+PHz/Gxx9/rNNCiYiIiMQSfYQnOjoad+7cwbRp0wAAc+fOxYwZM1BUVITo6Gi0aNFCp4USERERiSXqSssAEBAQgP379+OPP/7AnTt3IAgCnJyc4O3tzdtOEBERkVERHXiKeXl5wcvLSxe1EBER6Y1MJoNcLucf5TWU6MCjUqmwbds2HDlyBOnp6QAABwcHdOnSBQMHDoSlpaXOijR1RUUCzMxM6w1mijUTkeHY1Lc0+s8NuVxe4g90Y6+ZdEdU4MnIyMDo0aNx+/Zt2NnZoVmzZgCAq1ev4tixY9i4cSPWrVuHxo0b67RYU2VmJsP8TeeQlplj6FK04mhfH1OHv2PoMojIhNSTm/OzjoyaqMAza9Ys3L9/H9999x169uypsS0uLg5RUVGYNWsWVqxYoZMipSAtMwe30rMMXQYRkV7xs46MlajAc+rUKfzjH/8oEXYAIDQ0FH/88Qc2btxY5eKIiIiIdEHU19Lr1q0LW1vbMre/8cYbqFu3ruiiiIiIiHRJVODp378/du3aBaVSWWLbs2fPsHPnTgwYMKDKxRERERHpglantA4cOKCx7OnpiV9++QWhoaH4+9//rp60nJqaip9//hkNGjSAh4eH7qslIiIiEkGrwDNp0iTIZDIIggAAGv+9cuXKEu0zMjLwz3/+E2FhYToslYiIiEgcrQLP+vXr9V0HERERkd5oFXjatWun7zqIiIiI9EbUpGUiIiIiUyL61hJnz57Fjh07kJaWhqysLPWcnmIymQx79uypcoFEREREVSUq8Kxduxbz5s2DpaUlXFxc0KBBA50Uc+fOHaxevRoXL17EjRs34Orqir1792q0CQ8Px2+//VZiX4VCATc3N53UQURERNIiKvCsXr0abdq0wcqVK1G/fn2dFXPjxg0cOXIErVq1QlFRUYmjRsXatGmDadOmaaxzdHTUWR1EREQkLaICj1KpRJ8+fXQadgAgMDAQwcHBAICoqChcvny51HbW1tbw8/PT6WMTERGRdImatOzv74/r16/ruhaYmXEONREREemeqCM8M2bMwAcffIDVq1djwIABsLGx0XFZ5fvtt9/g5+eHwsJCtGrVCp988gnatm1b7j5BQUFlbnvw4AEaN26MvLw80TUV32bj9dttyGQyyOVy0f0aklKpLPO0otj+Xv2pS6Y8zkRkWLr+rDNl+vyc1gdBECCTybRqKyrwvPXWWxg8eDDmzZuH+fPnw9LSssTRGZlMhnPnzonpvlxt27ZFv3794OzsjD///BOrV6/G6NGjsWHDBrRu3Vp0vyqVCsnJyVWuLzU1VWNZLpfDy8uryv0awu3bt/Xyon99jHTBlMeZiAxLX591pkwfn9P6oFKpYGlpqVVbUYFn0aJFWLlyJezt7eHt7a3zuTzlmTRpksZy165d0bt3byxfvhwxMTFl7peQkFDmtqCgIAiCAE9PT9F1KZVKpKamwtnZWeNIg7bJ0xi5uLjo/AhPaWOkC6Y8zkRkWLr+rDNl+vyc1gcLCwut24oKPD/++CO6dOmC5cuXG3zejZWVFbp06YL9+/dXqR+ZTAYrK6sq1yOXy3XSjzHQ14tdSmNERKbPFH6xVzdT+ZyuzB+7otJKQUEBunbtavCwQ0RERKQNUYmla9euOHv2rK5rESUvLw+//PILfHx8DF0KERERGSlRp7QiIyMxZcoU/Pe//8X777+PJk2alHq0p7Lf3lIqlThy5AgAID09Hbm5uYiPjwfw8gamKSkpWLVqFUJCQuDg4IA///wTa9euxcOHD7Fo0SIxT4WIiIhqAFGBp2fPngCA5ORkbN26tcx2lf3W06NHj/DJJ59orCteXr9+PRo3boyCggIsXLgQT58+hVwuR+vWrTFr1iz4+vpW8lkQERFRTSEq8EREROjlWzGOjo64du1auW1Wr16t88clIiIiaRMVeCZOnKjrOoiIiIj0hl+zIiIiIskTdYRn6dKlFbaRyWSIiIgQ0z0RERGRTuk88MhkMvW9LRh4iIiIyBiICjxXr14tsa6oqAjp6enYvHkzzpw5U+5tHoiIiIiqk87m8JiZmcHJyQnTpk1Ds2bNMHv2bF11TURERFQlepm03LZtW/UFBImIiIgMTS+B5/Lly7zPFhERERkNUXN4du/eXer67OxsnD17FgcOHMDAgQOrUhcRERGRzogKPFFRUWVua9iwIcaNG8dvaBEREZHREBV4EhISSqyTyWSwtrZGvXr1qlwUERERkS6JCjwODg66roOIiIhIbzizmIiIiCRP6yM8ffr0qVTHMpkMe/bsqXRBRERERLqmdeCxsbHRqt1ff/2F27dvQyaTia2JiIiISKe0DjwbNmwod/vDhw8RExODrVu3olatWujbt2+ViyMiIiLSBVGTll/1119/ITo6Gtu2bcOLFy/Qp08fTJgwAU2bNtVFfURERERVJjrwFB/ReTXofPzxx3ByctJlfURERERVVunA8/DhQ0RHR2P79u148eIF+vbtiwkTJjDoEBERkdHSOvD8+eef6qBTWFiIfv36Yfz48Qw6REREZPS0DjwhISFQqVTw9PTERx99BEdHR2RnZ+PKlStl7tOyZUudFElERERUFVoHnufPnwMA/vjjD0yePLnctoIgQCaTITk5uUrFEREREemC1oFnzpw5+qyDiIiISG+0DjzvvfeePusgIiIi0hveS4uIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCTPqALPnTt3MHPmTPTr1w9eXl7o3bt3qe22b9+OHj16wMfHB3379sXhw4eruVIiIiIyJUYVeG7cuIEjR46gWbNmcHNzK7XNvn37MGPGDISGhiImJgZ+fn6IjIzEhQsXqrdYIiIiMhmVvlu6PgUGBiI4OBgAEBUVhcuXL5dos3jxYvTq1Ut9e4v27dvj+vXrWLZsGWJiYqqzXCIiIjIRRnWEx8ys/HLu3buH1NRUhIaGaqwPCwvDyZMnoVKp9FkeERERmSijOsJTkZSUFACAi4uLxno3NzcUFBTg3r17ZZ4KCwoKKrPfBw8eoHHjxsjLyxNdm1Kp1PhZTCaTQS6Xi+7XkJRKJQRB0Gl/r/7UJVMeZyIyLF1/1pkyfX5O60Pxzcq1YVKBJysrCwBgbW2tsb54uXi7GCqVSid3d09NTdVYlsvl8PLyqnK/hnD79m29vOhfHyNdMOVxJiLD0tdnnSnTx+e0PqhUKlhaWmrV1qQCT1UkJCSUuS0oKAiCIMDT01N0/0qlEqmpqXB2dtY40qBt8jRGLi4uOj/CU9oY6YIpjzMRGZauP+tMmT4/p/XBwsJC67YmFXgaNGgAAMjJyYGdnZ16fXZ2tsZ2MWQyGaysrKpWIF4eadBFP8ZAXy92KY0REZk+U/jFXt1M5XO6Mn/sGtWk5Yq4uroC+P9zeYqlpKTA3NwcTk5OhiiLiIiIjJxJBR4nJyc4OzsjPj5eY71CoUBAQEClDm0RERFRzWFUp7SUSiWOHDkCAEhPT0dubq463LRr1w62traYOHEipk6diqZNm8Lf3x8KhQJJSUnYuHGjIUsnIiIiI2ZUgefRo0f45JNPNNYVL69fvx7+/v7o3bs3lEolYmJiEB0dDRcXFyxduhStW7c2RMlERERkAowq8Dg6OuLatWsVths4cCAGDhxYDRURERGRFJjUHB4iIiLSn+KLuErxUh9GdYSHiIioutjUt0RRkQAzM9P65V5YJKCWnmrW50VcDT3WDDxERFQj1ZObw8xMhvmbziEtM8fQ5WilTYs3MTLMy6RqBgBH+/qYOvwdg9bAwENERDVaWmYObqWLvzVRdXJ8sx4A06rZWHAODxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw9VG5lMBrlcDplMZuhSiIiohqlt6ALI+NjUt0RRkQAzM90GE7lcDi8vL532SUREpA0GHiqhntwcZmYyzN90DmmZOYYuRyttWryJkWEMU0REVDoGHipTWmYObqVnGboMrTi+Wc/QJRARkRHjHB4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8kws8O3fuhIeHR4l/8+fPN3RpREREZKRM9tYSq1atQv369dXL9vb2BqyGiIiIjJnJBp6WLVvC1tbW0GUQERGRCTC5U1pERERElWWyR3h69+6NJ0+eoEmTJhg0aBA+/PBD1KpVq8z2QUFBZW578OABGjdujLy8PNH1KJVKjZ/FZDIZ5HK56H6JiIikQqlUQhAEnfUnCAJkMplWbU0u8NjZ2WHixIlo1aoVZDIZEhMT8d133yEzMxMzZ84U3a9KpUJycnKV60tNTdVYlsvl8PLyqnK/REREpu727dslDgxUhUqlgqWlpVZtTS7wdOrUCZ06dVIvd+zYEZaWlvjhhx8wfvx4vPnmm6Xul5CQUGafQUFBEAQBnp6eoutSKpVITU2Fs7OzxhEdbZMnERGR1Lm4uOj0CI+FhYXWbU0u8JQmNDQUa9asQXJycpmBpyIymQxWVlZVrkUul+ukHyIiIqnR9RSPyhxU4KRlIiIikjxJBB6FQoFatWpxrgwRERGVyuROaY0ZMwb+/v7w8PAA8HJuzrZt2zBy5EjY2dkZuDoiIiIyRiYXeFxcXLBjxw5kZGSgqKgIzs7OmD59OsLDww1dGhERERkpkws8//nPfwxdAhEREZkYSczhISIiIioPAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSZ5JBp5bt25h9OjR8PPzQ4cOHTBv3jyoVCpDl0VERERGqrahC6isrKwsjBo1Cs7OzliyZAkyMzMxd+5c5OfnY+bMmYYuj4iIiIyQyQWeH3/8Ec+ePcPSpUthY2MDACgsLMSsWbPw0Ucfwd7e3rAFEhERkdExuVNaR48eRUBAgDrsAEBoaCiKiopw/PhxwxVGRERERksmCIJg6CIqIyAgAAMGDMDUqVM11nfq1An9+vUrsb5YUFBQmX2mpaWhVq1aaNy4sei6BEFAQUEBzM3NIZPJNLbJZDJk5T7Hi0LTGGpL81qoZ2XOmvWMNVcP1lw9WHP1MMWaAaB2LRka1LOEriNHRkYGatWqhUuXLlVcg04fuRpkZ2fD2tq6xPoGDRogKytLVJ8ymQy1a9cuEVQqIyMjAwDw1ltvlbq9QT1L0X0biq5rfvDgAYCyx0gXTHmcq2N8dMVQ41yVMTLl14a2jOE1ZMzjXNb4GHPNZdFXzfp+DVXl92xpateuDQsLC+3a6vSRjVhCQoJe+y8+gqTvxzFlHKPycXwqxjEqH8enfByfikl5jExuDo+1tTVycnJKrM/KykKDBg0MUBEREREZO5MLPK6urkhJSdFYl5OTg4cPH8LV1dVAVREREZExM7nA07lzZ5w4cQLZ2dnqdfHx8TAzM0OHDh0MWBkREREZK5MLPEOGDEHdunURERGBX3/9FTt27MC8efMwZMgQXoOHiIiISmVygadBgwb44YcfUKtWLURERGDBggV4//33ERUVZejSiIiIyEiZ5Le03NzcsG7dOkOXQURERCbC5C48SERERFRZJndKi4iIiKiyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4qujWrVsYPXo0/Pz80KFDB8ybNw8qlcrQZRnMnTt3MHPmTPTr1w9eXl7o3bt3qe22b9+OHj16wMfHB3379sXhw4eruVLDiIuLw4QJE9C5c2f4+fmhX79++Omnn/D6lyVr6vgcOXIEI0aMQPv27eHt7Y2goCDMmTOnxP3zEhMT0bdvX/j4+KBHjx7YsWOHgSo2rGfPnqFz587w8PDApUuXNLbV1NfQzp074eHhUeLf/PnzNdrV1PEptmvXLvz973+Hj48P/P398eGHHyI/P1+9XYrvMZO8Do+xyMrKwqhRo+Ds7IwlS5YgMzMTc+fORX5+PmbOnGno8gzixo0bOHLkCFq1aoWioqISv8gBYN++fZgxYwbGjx+P9u3bQ6FQIDIyEps2bYKfn1/1F12N1q1bBwcHB0RFRaFhw4Y4ceIEZsyYgYyMDERGRgKo2ePz9OlT+Pr6Ijw8HDY2Nrhx4waWLFmCGzduYM2aNQCAs2fPIjIyEu+//z6mT5+OU6dO4d///jfq1q2Lnj17GvgZVK/ly5ejsLCwxPqa/BoqtmrVKtSvX1+9/OqV+Gv6+KxYsQIxMTEYP348/Pz88OTJE5w8eVL9WpLse0wg0VauXCn4+fkJT548Ua/78ccfBU9PTyEjI8NwhRlQYWGh+r+nTZsm9OrVq0Sb7t27C59++qnGusGDBwsffvih3usztEePHpVY95///Edo06aNeuxq8viUZuvWrYK7u7v6PfXBBx8IgwcP1mjz6aefCqGhoYYoz2Bu3rwp+Pn5CVu2bBHc3d2FpKQk9baa/BrasWOH4O7uXup7rVhNHp9bt24JXl5ewi+//FJmG6m+x3hKqwqOHj2KgIAA2NjYqNeFhoaiqKgIx48fN1xhBmRmVv5L6t69e0hNTUVoaKjG+rCwMJw8eVLypwNtbW1LrPP09ERubi7y8vJq/PiUpvj9VVBQAJVKhdOnT5f4KzMsLAy3bt1CWlqaASo0jNmzZ2PIkCFwcXHRWM/XUPlq+vjs3LkTjo6O6NKlS6nbpfweY+CpgpSUFLi6umqss7a2hp2dHVJSUgxUlXErHpfXP6Td3NxQUFCAe/fuGaIsgzp37hzs7e1Rr149js//U1hYiOfPn+PKlStYtmwZAgMD4ejoiLt376KgoKDE+87NzQ0Aasz7Lj4+HtevX0dERESJbXwNvdS7d294enoiKCgI33//vfp0TU0fn4sXL8Ld3R3Lly9HQEAAvL29MWTIEFy8eBEAJP0e4xyeKsjOzoa1tXWJ9Q0aNEBWVpYBKjJ+xePy+rgVL9e0cTt79iwUCgWmTZsGgONTrFu3bsjMzAQAdOrUCQsWLADA8QEApVKJuXPnYsqUKahXr16J7TV9jOzs7DBx4kS0atUKMpkMiYmJ+O6775CZmYmZM2fW+PF5+PAhLl++jOvXr+OLL76AXC7HypUr8cEHH+DAgQOSHh8GHiIDycjIwJQpU+Dv74+RI0cauhyjEh0dDaVSiZs3b2LFihUYP3481q5da+iyjMKKFSvQqFEjDBgwwNClGKVOnTqhU6dO6uWOHTvC0tISP/zwA8aPH2/AyoyDIAjIy8vDokWL0KJFCwBAq1atEBgYiI0bN6Jjx44GrlB/eEqrCqytrUt8XRZ4mYAbNGhggIqMX/G4vD5u2dnZGtulLjs7G2PHjoWNjQ2WLFminvvE8XmpRYsWaN26NQYOHIjly5fj9OnTOHjwYI0fn/T0dKxZswaTJk1CTk4OsrOzkZeXBwDIy8vDs2fPavwYlSY0NBSFhYVITk6u8eNjbW0NGxsbddgBXs6T8/Lyws2bNyU9Pgw8VeDq6lrifGZOTg4ePnxY4vwnvVQ8Lq+PW0pKCszNzeHk5GSIsqpVfn4+PvroI+Tk5JT46izHpyQPDw+Ym5vj7t27aNq0KczNzUsdHwCSf9+lpaWhoKAA48aNQ9u2bdG2bVv1UYuRI0di9OjRfA1VoKaPT/Pmzcvc9vz5c0m/xxh4qqBz5844ceKEOvkCLycTmpmZoUOHDgaszHg5OTnB2dkZ8fHxGusVCgUCAgJgYWFhoMqqx4sXLzB58mSkpKRg1apVGtcGATg+pbl48SIKCgrg6OgICwsL+Pv7Y//+/RptFAoF3Nzc4OjoaKAqq4enpyfWr1+v8e/zzz8HAMyaNQtffPEFX0OlUCgUqFWrFry8vGr8+HTr1g1Pnz5FcnKyet2TJ09w5coVtGzZUtLvMc7hqYIhQ4Zgw4YNiIiIwEcffYTMzEzMmzcPQ4YMKfGLrKZQKpU4cuQIgJeH33Nzc9UfLO3atYOtrS0mTpyIqVOnomnTpvD394dCoUBSUhI2btxoyNKrxaxZs3D48GFERUUhNzcXFy5cUG/z8vKChYVFjR6fyMhIeHt7w8PDA3Xq1MHVq1exevVqeHh4IDg4GAAwYcIEjBw5Ev/9738RGhqK06dPY+/evVi4cKGBq9c/a2tr+Pv7l7qtZcuWaNmyJQDU6NfQmDFj4O/vDw8PDwBAQkICtm3bhpEjR8LOzg5AzR6f4OBg+Pj4YNKkSZgyZQosLS0RHR0NCwsLDBs2DIB032MyQSjlUriktVu3buGrr77C+fPnUbduXfTr1w9TpkyR/F8JZUlLS0NQUFCp29avX6/+sN6+fTtiYmJw//59uLi44NNPP0W3bt2qs1SDCAwMRHp6eqnbEhIS1H891dTxiY6OhkKhwN27dyEIAhwcHBASEoIxY8ZofCMpISEB3333HW7fvo0mTZpg3LhxeP/99w1YueGcPn0aI0eOxE8//QQfHx/1+pr6Gpo9ezaOHTuGjIwMFBUVwdnZGQMHDkR4eDhkMpm6XU0dHwB4/Pgx5syZg8OHD6OgoADvvvsuPv/8c43TXVJ8jzHwEBERkeRxDg8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RmZxNmzbBw8MDAwcONHQpRGQiGHiIyOTExsbCwcEBSUlJuHPnjqHLISITwMBDRCbl3r17OH/+PD7//HPY2toiNjbW0CURkQlg4CEikxIbG4sGDRqgS5cu6NGjR6mB58mTJ/jss8/Qpk0bvPvuu5g2bRquXr0KDw8P7Ny5U6PtrVu3MGnSJLRr1w4+Pj7o378/EhISquvpEFE1YeAhIpMSGxuLkJAQWFhYoHfv3khNTUVSUpJ6e1FRESZMmIB9+/bhvffew5QpU/Dw4UNMmzatRF83btzA4MGDcevWLYwdOxZRUVGwsrJCREQEDh48WJ1Pi4j0rLahCyAi0tbly5eRkpKCGTNmAADeeecdNG7cGLGxsfD19QUAHDp0COfPn8f06dMxatQoAMDQoUMxevToEv19/fXXeOutt7Bjxw5YWFgAAIYNG4ahQ4di/vz5CAkJqaZnRkT6xiM8RGQyYmNj8cYbb8Df3x8AIJPJEBYWBoVCgcLCQgDAsWPHYG5ujkGDBqn3MzMzw/DhwzX6evr0KU6dOoXQ0FDk5ubi8ePHePz4MZ48eYKOHTsiNTUVmZmZ1ffkiEiveISHiExCYWEh9u3bB39/f6SlpanX+/r6Ys2aNTh58iQ6duyI+/fvw87ODnK5XGP/pk2baizfvXsXgiBg0aJFWLRoUamP+ejRI9jb2+v+yRBRtWPgISKTcOrUKTx8+BD79u3Dvn37SmyPjY1Fx44dte6vqKgIAPDBBx+gU6dOpbZ5PSQRkeli4CEikxAbG4tGjRph5syZJbYdPHgQBw8exKxZs9CkSROcPn0aSqVS4yjP3bt3NfZxcnICAJibm+Nvf/ubfosnIoPjHB4iMnr5+fk4cOAAunbtip49e5b4N3z4cDx79gyJiYno2LEjCgoKsG3bNvX+RUVF2LRpk0afjRo1Qrt27bB161b8+eefJR7z8ePHen9eRFR9eISHiIxeYmIinj17hsDAwFK3+/n5wdbWFnv27MGyZcvg6+uLb775Bnfv3oWrqysSExORlZUF4OVE52JffPEFhg0bhj59+mDQoEFwcnLCX3/9hQsXLiAjIwN79uypludHRPrHwENERm/Pnj2wtLREhw4dSt1uZmaGrl27IjY2FtnZ2fj+++/x9ddfY9euXTAzM0NISAgiIiIwdOhQWFpaqvdr3rw5duzYgaVLl2LXrl14+vQpbG1t4eXlhYiIiOp6ekRUDWSCIAiGLoKISN8OHTqEiIgIbN68Ge+8846hyyGiasY5PEQkOfn5+RrLhYWF2LBhA+rVq4eWLVsaqCoiMiSe0iIiyfnqq6+Qn5+P1q1bQ6VS4cCBAzh//jw+/fRT1KlTx9DlEZEB8JQWEUlObGws1q5dizt37uD58+do1qwZhg4dihEjRhi6NCIyEAYeIiIikjzO4SEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyfu/BB2J7z4IlBEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[(train_df.Sex == 'female') & (train_df.Survived == 1)].Age.hist()\n",
    "plt.ylabel('Number of Survivors')\n",
    "plt.xlabel('Age')\n",
    "plt.title('Age of Female Survivors')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 10.626224,
   "end_time": "2023-10-25T14:46:21.085572",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-10-25T14:46:10.459348",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}