Titanic_Case_Study.ipynb

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    "kaggle": {
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      "dataSources": [
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          "databundleVersionId": 26502,
          "sourceType": "competition"
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      "language": "python",
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    "colab": {
      "name": "Titanic Case Study",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Isha307/Python-for-Data-Analysis/blob/main/Titanic_Case_Study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Link: https://www.kaggle.com/c/titanic**"
      ],
      "metadata": {
        "id": "1vwhTxJT4d28"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "metadata": {
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        "execution": {
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      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "/kaggle/input/titanic/train.csv\n/kaggle/input/titanic/test.csv\n/kaggle/input/titanic/gender_submission.csv\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "df1 = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
        "df2 = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
        "df3 = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-06-03T05:31:42.483262Z",
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        "outputId": "ec5eaf17-5f47-468f-fc3f-f097147ce9ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.info()"
      ],
      "metadata": {
        "execution": {
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        "outputId": "4431258c-a560-414f-9d7f-11ec162a09f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 2 columns):\n #   Column       Non-Null Count  Dtype\n---  ------       --------------  -----\n 0   PassengerId  418 non-null    int64\n 1   Survived     418 non-null    int64\ndtypes: int64(2)\nmemory usage: 6.7 KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Display Top 5 Rows of The Dataset\n",
        "df1.head()"
      ],
      "metadata": {
        "execution": {
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        "outputId": "fda563eb-2c98-47aa-9ab8-c001337bcb67"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 48,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Check the Last 3 Rows of The Dataset\n",
        "df1.tail(3)"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-06-03T05:31:42.557888Z",
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        "outputId": "8a94173e-4528-4be2-cd5e-7416200c8d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 49,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     PassengerId  Survived  Pclass                                      Name  \\\n888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n889          890         1       1                     Behr, Mr. Karl Howell   \n890          891         0       3                       Dooley, Mr. Patrick   \n\n        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n889    male  26.0      0      0      111369  30.00  C148        C  \n890    male  32.0      0      0      370376   7.75   NaN        Q  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>888</th>\n      <td>889</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2</td>\n      <td>W./C. 6607</td>\n      <td>23.45</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>890</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Behr, Mr. Karl Howell</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>111369</td>\n      <td>30.00</td>\n      <td>C148</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>891</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Dooley, Mr. Patrick</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>370376</td>\n      <td>7.75</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Find Shape of Our Dataset (Number of Rows & Number of Columns)\n",
        "df1.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.597231Z",
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      "outputs": [
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(891, 12)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement\n",
        "df1.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.608972Z",
          "iopub.execute_input": "2024-06-03T05:31:42.610349Z",
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      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Get Overall Statistics About The Dataframe\n",
        "df1.describe().T"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-06-03T05:31:42.629281Z",
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        "outputId": "f5fa1821-f662-460c-b3e6-3d827df029ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             count        mean         std   min       25%       50%    75%  \\\nPassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \nSurvived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \nPclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \nAge          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \nSibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \nParch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \nFare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n\n                  max  \nPassengerId  891.0000  \nSurvived       1.0000  \nPclass         3.0000  \nAge           80.0000  \nSibSp          8.0000  \nParch          6.0000  \nFare         512.3292  ",
            "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PassengerId</th>\n      <td>891.0</td>\n      <td>446.000000</td>\n      <td>257.353842</td>\n      <td>1.00</td>\n      <td>223.5000</td>\n      <td>446.0000</td>\n      <td>668.5</td>\n      <td>891.0000</td>\n    </tr>\n    <tr>\n      <th>Survived</th>\n      <td>891.0</td>\n      <td>0.383838</td>\n      <td>0.486592</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>1.0</td>\n      <td>1.0000</td>\n    </tr>\n    <tr>\n      <th>Pclass</th>\n      <td>891.0</td>\n      <td>2.308642</td>\n      <td>0.836071</td>\n      <td>1.00</td>\n      <td>2.0000</td>\n      <td>3.0000</td>\n      <td>3.0</td>\n      <td>3.0000</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>714.0</td>\n      <td>29.699118</td>\n      <td>14.526497</td>\n      <td>0.42</td>\n      <td>20.1250</td>\n      <td>28.0000</td>\n      <td>38.0</td>\n      <td>80.0000</td>\n    </tr>\n    <tr>\n      <th>SibSp</th>\n      <td>891.0</td>\n      <td>0.523008</td>\n      <td>1.102743</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>1.0</td>\n      <td>8.0000</td>\n    </tr>\n    <tr>\n      <th>Parch</th>\n      <td>891.0</td>\n      <td>0.381594</td>\n      <td>0.806057</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>6.0000</td>\n    </tr>\n    <tr>\n      <th>Fare</th>\n      <td>891.0</td>\n      <td>32.204208</td>\n      <td>49.693429</td>\n      <td>0.00</td>\n      <td>7.9104</td>\n      <td>14.4542</td>\n      <td>31.0</td>\n      <td>512.3292</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Data Filtering\n",
        "df1.columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.677228Z",
          "iopub.execute_input": "2024-06-03T05:31:42.677673Z",
          "iopub.status.idle": "2024-06-03T05:31:42.686819Z",
          "shell.execute_reply.started": "2024-06-03T05:31:42.677638Z",
          "shell.execute_reply": "2024-06-03T05:31:42.685473Z"
        },
        "trusted": true,
        "id": "q_csJeqA4d3R",
        "outputId": "68b36705-31ca-4481-af5c-7724b4c0164a"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n      dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(df1.Sex=='male')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.692089Z",
          "iopub.execute_input": "2024-06-03T05:31:42.692622Z",
          "iopub.status.idle": "2024-06-03T05:31:42.703726Z",
          "shell.execute_reply.started": "2024-06-03T05:31:42.692577Z",
          "shell.execute_reply": "2024-06-03T05:31:42.701753Z"
        },
        "trusted": true,
        "id": "TKx2DB_54d3S",
        "outputId": "959326b1-f1e2-42dc-f257-abbfd93a9a0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "577"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7.Check Null Values In The Dataset\n",
        "df1.isnull().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.705701Z",
          "iopub.execute_input": "2024-06-03T05:31:42.706198Z",
          "iopub.status.idle": "2024-06-03T05:31:42.718988Z",
          "shell.execute_reply.started": "2024-06-03T05:31:42.706162Z",
          "shell.execute_reply": "2024-06-03T05:31:42.717544Z"
        },
        "trusted": true,
        "id": "7nJ5x6qk4d3U",
        "outputId": "05899690-0361-4775-b1d5-aad53c55477d"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 55,
          "output_type": "execute_result",
          "data": {
            "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df1.isnull())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:42.720671Z",
          "iopub.execute_input": "2024-06-03T05:31:42.721056Z",
          "iopub.status.idle": "2024-06-03T05:31:43.384972Z",
          "shell.execute_reply.started": "2024-06-03T05:31:42.721026Z",
          "shell.execute_reply": "2024-06-03T05:31:43.383741Z"
        },
        "trusted": true,
        "id": "JqhEi8_b4d3V",
        "outputId": "8d014d49-c866-4f3f-f554-5d8aa2cf994a"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 56,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: >"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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/xtnZucTjc+fOfecXQ0REpDUMJPV6xem8v/jiC3z88cclHk9LS1O4/+eff2LIkCHo06ePQvnMmTMRHBwsv29hwciSVP5xDwSRHtOzJQzR03m/Go76999/R8eOHeHi4qJQbmFhUWroaqLyil/epGv4ntQPy5cvx4IFC5Ceno4mTZpg6dKlaNmyZanXR0VFITo6GqmpqahUqRI++eQTREZGwtTU9K3bVOsmyoyMDPzxxx+IjY0t8djcuXMxa9YsODk5oX///ggJCYGhIfd0EhGJibNiGqSlsEobN25EaGgoYmJi4OnpiaioKPj4+ODy5cslsl8DwPr16zFlyhSsWrUKrVu3xpUrVzB48GBIJBIsXrz4rdtV6zd2bGwsLCwsSix1jBs3Ds2bN4etrS2OHTuGsLAwpKWlldpxpvMmIiKdp6UljMWLFyM4OFiecyomJgZ//PEHVq1ahSlTppS4/tixY2jTpg369+8PoGhf4ueff44TJ06o1K5aBxCrVq1CQEBAiSmR4vDXAODm5gZjY2MMHz4ckZGRJZJmAUznTURUVnrz618XiDSAUPajWVlSSQDIz89HUlISwsLC5GVSqRTe3t5ITExUWn/r1q2xdu1a/PXXX2jZsiVu3LiBnTt3YuDAgSr1U20DiMOHD+Py5cvYuHHjG6/19PREQUEBbt26JU+69bKwsDCFQQcA2NjVE62vRGLhdDHpGr4nyx9lP5rDw8MRERFR4tqHDx+isLAQVapUUSivUqUKUlJSlNbfv39/PHz4EG3btoUgCCgoKMCIESPwv//9T6V+qm0AsXLlSri7u6NJkyZvvDY5ORlSqVTpWg3AdN5UfvCDkkiPiXQEU9mPZmWzD2V14MABzJkzB//3f/8HT09PXLt2DePHj8esWbMwbdq0t65H9HTeAPD48WNs2rQJixYtKvH8xMREnDhxAh07doSFhQUSExMREhKCAQMGwMbGRtXuEBER6QRBJs4mytKWK5SpVKkSDAwMkJGRoVCekZFR6knHadOmYeDAgRg6dCgAoHHjxsjNzcWwYcPw9ddfQyp9uxBRKgeSOnXqFJo1a4ZmzZoBKNrP0KxZM0yfPl1+TVxcHARBwOeff17i+SYmJoiLi8OHH36Ihg0bYvbs2QgJCcGKFStU7QoREZFeMzY2hru7O/bu3Ssvk8lk2Lt3L7y8vJQ+59mzZyUGCQYGBgCKDii8LZVnIDp06PDGBoYNG4Zhw4Ypfax58+Y4fvy4qs0SERHpNi2dwggNDcWgQYPg4eGBli1bIioqCrm5ufJTGYGBgahWrRoiIyMBAP7+/li8eDGaNWsmX8KYNm0a/P395QOJt8HAC0RERGLQUhjqvn374sGDB5g+fTrS09PRtGlTxMfHyzdWpqamKsw4TJ06FRKJBFOnTsW9e/dQuXJl+Pv7Y/bs2Sq1KxFUma/QIYbG1bTdBSIiKicK8u+pvY1n0WNFqcds5FJR6lE3zkAQiYhH5kjX8D2pQSJtoiwvVBpAREZG4rfffkNKSgoqVKiA1q1bY968eQqxG1asWIH169fj9OnTePLkCR49egRra2uFerKysjB27Fhs374dUqkUffr0wXfffQdzc3NRXhSRtujNByURlcRkWqU7ePAgRo8ejRYtWqCgoAD/+9//0LVrV1y6dAkVK1YEULS709fXF76+vgqRsV4WEBCAtLQ0JCQk4MWLFwgKCsKwYcOwfv36d39FRFrEX3tEpC/eaQ/EgwcPYG9vj4MHD6J9+/YKjx04cAAdO3YsMQPx999/o0GDBjh58iQ8PDwAAPHx8fDz88Pdu3fh6Oj4Vm1zDwQR0ZtxUFtEI3sgvhshSj1m42NEqUfdVI4D8bKcnBwAgK2t7Vs/JzExEdbW1vLBAwB4e3tDKpWqnMiDiIhIZwiCOLdyosybKGUyGSZMmIA2bdqgUaNGb/289PT0EiGrDQ0NYWtri/T0dKXPYTZOIqKy0YVf/3qDeyDezujRo3HhwgUcOXJEzP4oxWycRERlwyUMUpcyLWGMGTMGO3bswP79+1G9enWVnuvg4IDMzEyFsoKCAmRlZZUatzssLAw5OTkKN4nUoixdJyIiUg+ZIM6tnFBpBkIQBIwdOxZbtmzBgQMHUKtWLZUb9PLyQnZ2NpKSkuDu7g4A2LdvH2QyGTw9PZU+h9k4iYhI52kpEqW2qDSAGD16NNavX4/ff/8dFhYW8j0LVlZWqFChAoCiPQ7p6enyjJ3nz5+HhYUFnJycYGtri/r168PX1xfBwcGIiYnBixcvMGbMGPTr1++tT2AQEdHb4fIBqYtKxzhL+9W/evVqDB48GAAQERFRYr/Cq9dkZWVhzJgxCoGklixZolIgKR7jJCJ6M+6BKKKRY5zzgkSpx2zyalHqUTfmwiASET+sSdfwPVlEEwOI3MhBotRTMSxWlHrUjbkwiESkCx+URESawAEEERGRGMrRCQoxcABBRPQe46yYBvEUBhERvS+4B4LURfR03sUEQYCfnx/i4+OxZcsW9OrVS/6YstMcGzZsQL9+/VR/BUQ6hB/WRHqMSxile5t03sWioqJeG+xp9erV8PX1ld9/OWMnUXnFL28iPcZcGKWLj49XuL9mzRrY29sjKSlJIZ13cnIyFi1ahFOnTqFq1apK67K2ti41dDVRecUZCCI9xhmIt6csnfezZ8/Qv39/LF++/LUDhNGjR2Po0KFwcXHBiBEjEBQUxPDUVO7xy5uI9IXo6bxDQkLQunVr9OzZs9Tnzpw5E506dYKZmRl2796NUaNG4enTpxg3bpzS65nOm4iobDio1SCewng7ytJ5b9u2Dfv27cOZM2de+9xp06bJ/9ysWTPk5uZiwYIFpQ4gmM6biKhsuKymQXq2hCFqOu99+/bh+vXrsLa2hqGhIQwNi8Ynffr0QYcOHUqtz9PTE3fv3i0xy1CM6byJiIh0i6jpvKdMmYKhQ4cqlDVu3Bjffvst/P39S603OTkZNjY2JVJ2F2M6byKistGbX/86QOApjNK9KZ23g4OD0o2TTk5O8sHG9u3bkZGRgVatWsHU1BQJCQmYM2cOJk6cKMLLISKil3EJQ4P0bAlDpQFEdHQ0AJRYjng5VfebGBkZYfny5QgJCYEgCHB1dcXixYsRHBysSleIiOgt6M2XN2mcyksYqnr1Ob6+vgoBpIiISH04A6FBnIEgIqL3hd58eesCHuMkorLirz3SNXxPkrpwAEEkIn5QEukxLmGU7m2ycaanp2PSpElISEjAkydPULduXXz99dfo06eP/JqsrCyMHTsW27dvh1QqRZ8+ffDdd9/B3NxcvFdGREQc1GqQwAFE6d4mG2dgYCCys7Oxbds2VKpUCevXr8dnn32GU6dOoVmzZgCAgIAApKWlISEhAS9evEBQUBCGDRuG9evXi/8KiYj0GJcwNEjPBhASoSxHK/6/Bw8ewN7eHgcPHpRn4zQ3N0d0dDQGDhwov87Ozg7z5s3D0KFD8ffff6NBgwY4efIkPDw8ABRl+fTz88Pdu3fh6Oj4Vm0bGlcra7eJ1IYf1qRr+J4sUpB/T+1tPBn3kSj1WCzZIUo96iZ6Ns7WrVtj48aN6N69O6ytrfHLL7/g+fPn8tgRiYmJsLa2lg8eAMDb2xtSqRQnTpxA796936VLRFqlCx+URKQljET5dkrLxvnLL7+gb9++sLOzg6GhIczMzLBlyxa4uroCKNojYW9vr9gJQ0PY2trKI1u+itk4iYhI5+nZEkaZkmkB/2XjjIuLUyifNm0asrOzsWfPHpw6dQqhoaH47LPPcP78+TJ3MjIyElZWVgo3QfakzPURERHRuynTDERxNs5Dhw4pZOO8fv06li1bhgsXLqBhw4YAgCZNmuDw4cNYvnw5YmJi4ODggMzMTIX6CgoKkJWVpTSPBlCUjTM0NFShzMauXlm6TkREpB56NgMhajbOZ8+eAQCkUsWJDQMDA8j+/9qQl5cXsrOzkZSUBHd3dwBFacBlMhk8PT2VtstsnEREpOve4UxCuSRqNs569erB1dUVw4cPx8KFC2FnZ4etW7ciISEBO3YU7SqtX78+fH19ERwcjJiYGLx48QJjxoxBv3793voEBhEREWmXSnsgoqOjkZOTgw4dOqBq1ary28aNGwEUZdrcuXMnKleuDH9/f7i5ueGnn35CbGws/Pz85PWsW7cO9erVQ+fOneHn54e2bdtixYoV4r4yIiIiTZIJ4tzKiXeKA6FNjANBRPRmjANRRBNxIB4P6SJKPZYrE0SpR92YC4NIRPywJiJ9wQEEEdF7jANKzWEuDCIiem9wVkyDOIAgIqL3hd58eesC/YpkrdoAIjo6GtHR0bh16xYAoGHDhpg+fTq6desGAFixYgXWr1+P06dP48mTJ3j06BGsra0V6nB2dsbt27cVyiIjIzFlypSyvwoiHcEPayLSFyoNIKpXr465c+eiTp06EAQBsbGx6NmzJ86cOYOGDRvi2bNn8PX1ha+vL8LCwkqtZ+bMmQgODpbft7CwKPsrICIi0gHcA/Ea/v7+Cvdnz56N6OhoHD9+HA0bNsSECRMAAAcOHHhtPRYWFqWGrSYiIiqXOIB4O4WFhdi0aRNyc3Ph5eWl0nPnzp2LWbNmwcnJCf3790dISAgMDbkdg4hIbNxESeqi8rf2+fPn4eXlhefPn8Pc3BxbtmxBgwYN3vr548aNQ/PmzWFra4tjx44hLCwMaWlpWLx4canPYTpvIiLSedxE+Xp169ZFcnIycnJysHnzZgwaNAgHDx5860HEy1k13dzcYGxsjOHDhyMyMrJEwqxikZGRmDFjhkKZRGoOiYGlqt0nIiJSC33bA/HOoay9vb1Ru3ZtfP/99/KyAwcOoGPHjkpPYbzq4sWLaNSoEVJSUlC3bl2l1yibgbCxq8cZCCIieiuaCGX96NMOotRjs+mAKPWo2ztvPJDJZCW+3FWRnJwMqVQKe3v7Uq9hOm8iorLhHggN4hJG6cLCwtCtWzc4OTnhyZMnWL9+PQ4cOIBdu3YBANLT05Geno5r164BKNovYWFhAScnJ9ja2iIxMREnTpxAx44dYWFhgcTERISEhGDAgAGwsbER/9URaRg/rIn0l74tYag0gMjMzERgYCDS0tJgZWUFNzc37Nq1C126FGUgi4mJUdir0L59ewDA6tWrMXjwYJiYmCAuLg4RERHIy8tDrVq1EBISorAvgqg845c36Rq+J0ldmM6biIjee5rYA5HV80NR6rH9/aAo9agbgy8QERGJQOAeCCIqK+6BIF3D96QGcQBBRGWlNx+UVG7wPUnqIlXl4ujoaLi5ucHS0hKWlpbw8vLCn3/+KX+8Q4cOkEgkCrcRI0Yo1JGamoru3bvDzMwM9vb2mDRpEgoKCsR5NURERFoiyMS5lReiZuMEgODgYMycOVP+HDMzM/mfCwsL0b17dzg4OODYsWNIS0tDYGAgjIyMMGfOHJFeEhERkRaUoy9/MbzzKQxbW1ssWLAAQ4YMQYcOHdC0aVNERUUpvfbPP//ERx99hPv376NKlSoAio5+Tp48GQ8ePICxsfFbt8tTGEREb8Y9EEU0cQrjoY84pzAq7SofpzBUWsJ4WWFhIeLi4kpk41y3bh0qVaqERo0aISwsDM+ePZM/lpiYiMaNG8sHDwDg4+ODx48f4+LFi2XtChERkdbp2xKGygOI8+fPw9zcHCYmJhgxYoRCNs7+/ftj7dq12L9/P8LCwvDzzz9jwIAB8uemp6crDB4AyO+np6eX2mZeXh4eP36scCun4SuIiOg9pc0BxPLly+Hs7AxTU1N4enrir7/+eu312dnZGD16NKpWrQoTExN88MEH2Llzp0ptipqNc9iwYfLrGjdujKpVq6Jz5864fv06ateurWpTcszGSeUFp4tJ1/D98P7buHEjQkNDERMTA09PT0RFRcHHxweXL19WmmcqPz8fXbp0gb29PTZv3oxq1arh9u3bb0x++Sq1ZOMslpubC3Nzc8THx8PHxwfTp0/Htm3bkJycLL/m5s2bcHFxwenTp9GsWTOlbTAbJxFR2XBQW0QTeyAyOoqzB6LKftX2QHh6eqJFixZYtmwZgKIklzVq1MDYsWMxZcqUEtfHxMRgwYIFSElJgZGRUZn7qdZsnMUDhapVqwIAvLy8MHv2bGRmZspHRQkJCbC0tJQvgyjDbJxERGWjC1/eekMQ53tJ2Y9mZd+DQNFsQlJSEsLCwuRlUqkU3t7eSExMVFr/tm3b4OXlhdGjR+P3339H5cqV0b9/f0yePBkGBgZv3U/RsnFev34d69evh5+fH+zs7HDu3DmEhISgffv2cHNzAwB07doVDRo0wMCBAzF//nykp6dj6tSpGD16tNK/GKLyhr/2SNfwPVn+KFu2Dw8PR0RERIlrHz58iMLCQqX7C1NSUpTWf+PGDezbtw8BAQHYuXMnrl27hlGjRuHFixcIDw9/636Klo3zzp072LNnD6KiopCbm4saNWqgT58+mDp1qvz5BgYG2LFjB0aOHAkvLy9UrFgRgwYNUogbQVSe8YOSSH+JdYIiLCysRJZqMX9ky2Qy2NvbY8WKFTAwMIC7uzvu3buHBQsWqG8AsXLlylIfq1GjBg4efPO6Tc2aNVXe6UlERKTrBJk4SxilLVcoU6lSJRgYGCAjI0OhPCMjAw4ODkqfU7VqVRgZGSksV9SvXx/p6enIz89/65hMZY4DQURERP/RxjFOY2NjuLu7Y+/evfIymUyGvXv3KsRoelmbNm1w7do1yGT/NXblyhVUrVpVtYCOqnWViF6H681EpGmhoaEYNGgQPDw80LJlS/lWgqCgIABAYGAgqlWrhsjISADAyJEjsWzZMowfPx5jx47F1atXMWfOHIwbN06ldjmAIBIRv7xJ1/A9qTmCSKcwVNW3b188ePAA06dPR3p6Opo2bYr4+Hj5xsrU1FRIpf8tONSoUQO7du1CSEgI3NzcUK1aNYwfPx6TJ09Wqd13jgOhLcyFQUT0ZpwVK6KJOBB3PTuJUk/1E/tEqUfdVJqBiI6ORnR0NG7dugUAaNiwIaZPn45u3boBKApHPWnSJCQkJODJkyeoW7cuvv76a/Tp00deh7OzM27fvq1Qb2RkpNJgF0TlDT+siUhfiJrOOzAwENnZ2di2bRsqVaqE9evX47PPPsOpU6cUokzOnDkTwcHB8vsWFhbivSIiLeKXN+kavic1R6xTGOWFSgMIf39/hfuzZ89GdHQ0jh8/joYNG+LYsWOIjo5Gy5YtAQBTp07Ft99+i6SkJIUBhIWFRanHS4iISDycFdOc8rkhoOxETefdunVrbNy4EVlZWZDJZIiLi8Pz58/RoUMHhefOnTsXdnZ2aNasGRYsWICCgoJ3ehFERESkWSqfwjh//jy8vLzw/PlzmJubK6Tz/uWXX9C3b1/Y2dnB0NAQZmZm2LJlC1xdXeXPHzduHJo3bw5bW1scO3YMYWFhSEtLw+LFi0ttU1lccEEQmA+DdA5/7RHpLy5hvMHr0nlPmzYN2dnZ2LNnDypVqoStW7fis88+w+HDh9G4cWMAUAjP6ebmBmNjYwwfPhyRkZGlRt5iOm8qL/jlTaS/9G0AIVo676+++gqurq64cOECGjZsqPC4q6srYmJilD7/4sWLaNSoEVJSUlC3bl2l1zCdNxFR2XBWrIgmjnHeatpFlHqckxNEqUfdREvn/ezZMwBQCFYBFCXQejlc5quSk5MhlUrl6b2VYTpvIiLSdfq2iVK0dN716tWDq6srhg8fjoULF8LOzg5bt25FQkICduzYAQBITEzEiRMn0LFjR1hYWCAxMREhISEYMGAAbGxs1PICiYj0mS78+tcX+raEIVo6bwDYuXMnpkyZAn9/fzx9+hSurq6IjY2Fn58fgKKZhLi4OERERCAvLw+1atVCSEhIibSlREQkDi5haI62QllrC0NZExG9xziAKKKJPRDXG/mIUk/tC7tEqUfdmEyLiIhIBKqm4i7vOIAgIiISgUzPljDKHImSiIiI9Nc7zUDMnTsXYWFhGD9+PKKiopCVlYXw8HDs3r0bqampqFy5Mnr16oVZs2bByspK/rzU1FSMHDkS+/fvh7m5OQYNGoTIyEgYGnJChMo3rjcT6S9920RZ5m/skydP4vvvv4ebm5u87P79+7h//z4WLlyIBg0a4Pbt2xgxYgTu37+PzZs3AyjKodG9e3c4ODjg2LFjSEtLQ2BgIIyMjDBnzpx3f0VERERaoG/HOMu0hPH06VMEBATghx9+UIjf0KhRI/z666/w9/dH7dq10alTJ8yePRvbt2+XJ8zavXs3Ll26hLVr16Jp06bo1q0bZs2aheXLlyM/P1+cV0VERERqVaYZiNGjR6N79+7w9vbGN99889prc3JyYGlpKV+eSExMROPGjVGlShX5NT4+Phg5ciQuXryokPabqLzh8gGR/iqfQRHKTuUBRFxcHE6fPo2TJ0++8dqHDx9i1qxZGDZsmLwsPT1dYfAAQH4/PT1daT3MxklERLqOSxivcefOHYwfPx7r1q2Dqanpa699/PgxunfvjgYNGiAiIuJd+ojIyEhYWVkp3ATZk3eqk4iISEwyQSLKrbxQaQCRlJSEzMxMNG/eHIaGhjA0NMTBgwexZMkSGBoaorCwEADw5MkT+Pr6wsLCAlu2bIGRkZG8DgcHB2RkZCjUW3zfwcFBabthYWHIyclRuEmkFiq9UCIiIhKPSksYnTt3xvnz5xXKgoKCUK9ePUyePBkGBgZ4/PgxfHx8YGJigm3btpWYqfDy8sLs2bORmZkpz8CZkJAAS0tLNGjQQGm7zMZJ5QWPcRLpLx7jfA0LCws0atRIoaxixYqws7NDo0aN8PjxY3Tt2hXPnj3D2rVr8fjxYzx+/BgAULlyZRgYGKBr165o0KABBg4ciPnz5yM9PR1Tp07F6NGjSwwSiIiIygtuonwHp0+fxokTJwAArq6uCo/dvHkTzs7OMDAwwI4dOzBy5Eh4eXmhYsWKGDRoEGbOnClmV4i0gr/+SdfwPUnqwmycRETvMS6rFdFENs7kmj1Eqafp7W2i1KNujB1NRPQe04Uvb32hb3sgmEyLiIiIVMYZCCKi9xiXMDSnfG4IKDsOIIiIiERQnoJAiUHUdN4A0KFDBxw8eFDhuuHDhyMmJkZ+X1kMhw0bNqBfv37v0h0iInqFvvz6J80TNZ13seDgYIVjmWZmZiWuWb16NXx9feX3ra2ty9oVIiIqBZcwNEffNlGWaQDxcjpvZdk4zczMSg1LXcza2vqN1xAREZUX+raEUaY4EIMGDYKtrS2+/fZbdOjQAU2bNlVYwrh48SIEQYCDgwP8/f0xbdo0hVkIiUQCR0dH5OXlwcXFBSNGjEBQUJBK4akZB4KIiN6WJuJAHHf8WJR6Wt3/TZR61E30dN79+/dHzZo14ejoiHPnzmHy5Mm4fPkyfvvtv7+QmTNnolOnTjAzM8Pu3bsxatQoPH36FOPGjVNaJ9N5U3nB6WLSNXxPkrqoNIAoTuedkJBQajrvYcOGyf/cuHFjVK1aFZ07d8b169dRu3ZtAMC0adPk1zRr1gy5ublYsGBBqQOIyMhIzJgxQ6FMIjWHxMBSle4TEekdfnlrDpcwXmPr1q3o3bs3DAwM5GWFhYWQSCSQSqXIy8tTeAwAcnNzYW5ujvj4ePj4+Cit948//sBHH32E58+fK02opWwGwsauHmcgSOfw1x7pGr4ni2hiCeOowyei1NMmfbMo9aib6Om8X5WcnAwAqFq1aqn1Jicnw8bGptRsnEznTeWFLnxQEr2M70lSF1HTeV+/fh3r16+Hn58f7OzscO7cOYSEhKB9+/by457bt29HRkYGWrVqBVNTUyQkJGDOnDmYOHGieK+KSEv4a490Dd+TmiPTdgc0TNRIlMbGxtizZw+ioqKQm5uLGjVqoE+fPpg6dar8GiMjIyxfvhwhISEQBAGurq5YvHgxgoODxewKERGRRgnQr5lxpvMmInqPcQaiiCb2QBxy+FSUetqnbxKlHnVjLgwiIiIRyMrlz/Gy4wCCiIhIBDI9W8KQarsDREREVP680wBi7ty5kEgkmDBhgrzs+vXr6N27NypXrgxLS0t89tlnyMjIUHheVlYWAgICYGlpCWtrawwZMgRPnz59l64QERFplQCJKLfyQtRsnLm5uejatSuaNGmCffv2ASiKOunv74/jx49DKi0arwQEBCAtLQ0JCQl48eIFgoKCMGzYMKxfv/4dXw6RdnHDGpH+4jHOt1BaNs6jR4/i1q1bOHPmDCwti8JMx8bGwsbGBvv27YO3tzf+/vtvxMfH4+TJk/Dw8AAALF26FH5+fli4cCEcHR1FeFlE2sEvbyL9VZ5mD8RQpgHE6NGj0b17d3h7eysMIPLy8iCRSBSiRpqamkIqleLIkSPw9vZGYmIirK2t5YMHAPD29oZUKsWJEyfQu3fvd3g5RET0Mg5qSV1EzcbZqlUrVKxYEZMnT8acOXMgCAKmTJmCwsJCpKWlAQDS09Nhb2+v2AlDQ9ja2iI9PV1pm8zGSURUNlxW0xx9W8JQaRNlcTbOdevWKc3GWblyZWzatAnbt2+Hubk5rKyskJ2djebNm8v3P5RFZGQkrKysFG6C7EmZ6yMiIhKbTKRbeaHSDERSUhIyMzPRvHlzeVlhYSEOHTqEZcuWIS8vD127dsX169fx8OFDGBoawtraGg4ODnBxcQEAODg4IDMzU6HegoICZGVlwcHBQWm7YWFhCA0NVSizsaunSteJiIhIRGrLxlmpUiUAwL59+5CZmYkePXoAALy8vJCdnY2kpCS4u7vLr5HJZPD09FTaLrNxUnnB6WIi/cVNlK/xpmycALB69WrUr18flStXRmJiIsaPH4+QkBDUrVsXAFC/fn34+voiODgYMTExePHiBcaMGYN+/frxBAaVe/zyJl3D96TmyPRr/CB+KOvLly8jLCwMWVlZcHZ2xtdff42QkBCFa9atW4cxY8agc+fOkEql6NOnD5YsWSJ2V4iI9B5nxUhdmI2TiOg9xgFEEU1k4/zdob8o9fRMLx9BFZlMi4joPaYLX976olz+Gn8HHEAQiYi/9kjX8D1J6sIBBJGI+EFJpL/KUwwHMXAAQUT0HuOgVnNkehZeQKXwkBEREZBIJAq3evWKAjplZWVh7NixqFu3LipUqAAnJyeMGzcOOTk5CnW8+nyJRIK4uDjxXhEREZEWCCLdyguVZyAaNmyIPXv2/FeBYVEV9+/fx/3797Fw4UI0aNAAt2/fxogRI3D//n1s3rxZoY7Vq1fD19dXft/a2rqM3SfSLVxvJl3D9ySpi8oDCENDQ6Uhpxs1aoRff/1Vfr927dqYPXs2BgwYgIKCAvlAA4A8vDXR+4YflET6i3sg3uDq1atwdHSEqakpvLy8EBkZCScnJ6XX5uTkwNLSUmHwABSlAx86dChcXFwwYsQIBAUFMTQ1vRf4a49IfzES5Wt4enpizZo1qFu3LtLS0jBjxgy0a9cOFy5cgIWFhcK1Dx8+xKxZszBs2DCF8pkzZ6JTp04wMzPD7t27MWrUKDx9+hTjxo0rtV2m8yYiItIt7xSJMjs7GzVr1sTixYsxZMgQefnjx4/RpUsX2NraYtu2bTAyMiq1junTp2P16tW4c+dOqddERERgxowZih2XmkNqYFnWrhOpBWcgiHSTJiJRrnMcIEo9AffXqvyc5cuXY8GCBUhPT0eTJk2wdOlStGzZ8o3Pi4uLw+eff46ePXti69atKrX5Tsc4ra2t8cEHH+DatWvysidPnsDX1xcWFhbYsmXLawcPQNGsxqxZs5CXl1ci42YxpvOm8oJf3qRrOKjVHG2doNi4cSNCQ0MRExMDT09PREVFwcfHB5cvX4a9vX2pz7t16xYmTpyIdu3K9u+j0jHOVz19+hTXr19H1apVARTNPHTt2hXGxsbYtm0bTE1N31hHcnIybGxsSh08AEXpvC0tLRVuXL4gIiICFi9ejODgYAQFBaFBgwaIiYmBmZkZVq1aVepzCgsLERAQgBkzZsDFxaVM7ao0AzFx4kT4+/ujZs2auH//PsLDw2FgYIDPP/9cPnh49uwZ1q5di8ePH+Px48cAgMqVK8PAwADbt29HRkYGWrVqBVNTUyQkJGDOnDmYOHFimTpPRESkK8TaRKls35+JiYnSH9r5+flISkpCWFiYvEwqlcLb2xuJiYmltjFz5kzY29tjyJAhOHy4bLNUKg0g7t69i88//xz//PMPKleujLZt2+L48eOoXLkyDhw4gBMnTgAAXF1dFZ538+ZNODs7w8jICMuXL0dISAgEQYCrq6t85EREROLTl+UDXSDWMc7IyMgS+/7Cw8MRERFR4tqHDx+isLAQVapUUSivUqUKUlJSlNZ/5MgRrFy5EsnJye/UT5UGEK+LGNmhQwe8aT+mr6+vQgApIiIiUqRs39/rlvlV8eTJEwwcOBA//PADKlWq9E51MRcGEdF7jJsoNUesTZSlLVcoU6lSJRgYGCAjI0OhPCMjQ2nAxuvXr+PWrVvw9/eXl8lkRXMnhoaGuHz5MmrXrv1WbXMAQUT0HtOXL29doI1AUsbGxnB3d8fevXvRq1evon7IZNi7dy/GjBlT4vp69erh/PnzCmVTp07FkydP8N1336FGjRpv3TYHEERE7zHOQGiOtkJZh4aGYtCgQfDw8EDLli0RFRWF3NxcBAUFAQACAwNRrVo1REZGwtTUFI0aNVJ4fnE+qlfL30S0bJzFEhMT0alTJ1SsWBGWlpZo3749/v33X/njWVlZCAgIgKWlJaytrTFkyBA8ffpUpU4TERFRkb59+2LhwoWYPn06mjZtiuTkZMTHx8s3VqampiItLU30dkXLxgkUDR58fX0RFhaGpUuXwtDQEGfPnoVU+t84JSAgAGlpaUhISMCLFy8QFBSEYcOGYf369e/4UoiIiLRHm8m0xowZo3TJAgAOHDjw2ueuWbOmTG2Klo0TAEJCQjBu3DhMmTJFXla3bl35n//++2/Ex8fj5MmT8PDwAAAsXboUfn5+WLhwIRwdHVXtDhERkU4Q9Cy+oWjZODMzM3HixAkEBASgdevWuH79OurVq4fZs2ejbdu2AIpmKKytreWDBwDw9vaGVCrFiRMn0Lt3b/FeGRER6c3+A9I8lfZAFGfjjI+PR3R0NG7evIl27drhyZMnuHHjBoCifRLBwcGIj49H8+bN0blzZ1y9ehUAkJ6eXiIut6GhIWxtbZGenl5qu3l5efLIlsW3d8gBRkREJDqZSLfyQqUZiG7dusn/7ObmBk9PT9SsWRO//PIL6tevDwAYPny4fOdns2bNsHfvXqxatQqRkZFl7qSyqFwSqTkkzMZJOoY73knX8D2pOeXpy18M75RM6+VsnMUJtRo0aKBwTf369ZGamgoAcHBwQGZmpsLjBQUFyMrKKnVfBVAUlSsnJ0fhJpFavEvXiYiI6B28UxyI4mycAwcOhLOzMxwdHXH58mWFa65cuSKfufDy8kJ2djaSkpLg7u4OANi3bx9kMhk8PT1LbUdZVC5m4yRdpC+/tIioJH1bWBctG6dEIsGkSZMQHh6OJk2aoGnTpoiNjUVKSgo2b94MoGg2wtfXF8HBwYiJicGLFy8wZswY9OvXjycw6L3A6WIi/aWNSJTaJFo2TgCYMGECnj9/jpCQEGRlZaFJkyZISEhQiKu9bt06jBkzBp07d4ZUKkWfPn2wZMkScV8VkZbwy5uI9IVEKKfHGQyNq2m7C0QlcAaCdA3fk0UK8u+pvY1vnQaIUk9I6lpR6lE35sIgEpEufFASkXbo2ykMDiCIiN5jHNRqTrmczn8HHEAQEb3HuIRB6sIBBBERkQh4CuM1IiIiSkSErFu3LlJSUgAA169fx8SJE3HkyBHk5eXB19cXS5culacUBQBnZ2fcvn1boY7IyEiFBFxERCQO/vrXHH3bA6FyJMqGDRsiLS1Nfjty5AgAIDc3F127doVEIsG+fftw9OhR5Ofnw9/fHzKZ4l/rzJkzFeoYO3asOK+GiIiINEK0dN5Hjx7FrVu3cObMGVhaFuWoiI2NhY2NDfbt2wdvb2/5tRYWFq8NXU1UXnG9mXQN35Oaw02Ub1BaOu+8vDxIJBKFkNOmpqaQSqU4cuSIwgBi7ty5mDVrFpycnNC/f3+EhITA0JDbMaj805cPSiIqSaZnQwiVvrWL03nXrVsXaWlpmDFjBtq1a4cLFy6gVatWqFixIiZPnow5c+ZAEARMmTIFhYWFSEtLk9cxbtw4NG/eHLa2tjh27BjCwsKQlpaGxYsXl9puXl4e8vLyFMoEQWA+DCIiIi1RaQ9Et27d8Omnn8LNzQ0+Pj7YuXMnsrOz8csvv6By5crYtGkTtm/fDnNzc1hZWSE7OxvNmzeHVPpfM6GhoejQoQPc3NwwYsQILFq0CEuXLi0xQHhZZGQkrKysFG6C7EnZXzUREZHIZCLdyot3Wjd4OZ03AHTt2hXXr1/Hw4cPYWhoCGtrazg4OMDFxaXUOjw9PVFQUIBbt26hbt26Sq8JCwtDaGioQpmNXb136ToRkV7gsprm6NcChojpvF9WqVIlAEWpujMzM9GjR49S60hOToZUKoW9vX2p1zCdN5UX3LBGuobvSVIX0dJ5A8Dq1atRv359VK5cGYmJiRg/fjxCQkLkMwuJiYk4ceIEOnbsCAsLCyQmJiIkJAQDBgyAjY2N+K+OSMP4QUmkv8rT8oMYRE3nffnyZYSFhSErKwvOzs74+uuvERISIn++iYkJ4uLiEBERgby8PNSqVQshISEllieIiIjKG32LRMl03kRE9N7TRDrvqc79Rannm1vrRalH3Rh8gUhEXG8mXcP3JKkLBxBEIuIHJZH+KpfT+e+AAwgiIiIRcBPlG9y7dw+TJ0/Gn3/+iWfPnsHV1RWrV6+Gh4cHgKKMnXFxcbhz5w6MjY3h7u6O2bNnw9PTU15HVlYWxo4di+3bt0MqlaJPnz747rvvYG5uLt4rIyIizoqR2qgUifLRo0do06YNjIyM8Oeff+LSpUtYtGiRwhHMDz74AMuWLcP58+dx5MgRODs7o2vXrnjw4IH8moCAAFy8eBEJCQnYsWMHDh06hGHDhon3qoiIiDRMBkGUW3mh0imMKVOm4OjRozh8+O035Tx+/BhWVlbYs2cPOnfujL///hsNGjTAyZMn5bMW8fHx8PPzw927d+Ho6PhW9fIUBhHRm3ETZRFNnML4yvlzUeqZf2uDKPWom0ozENu2bYOHhwc+/fRT2Nvbo1mzZvjhhx9KvT4/Px8rVqyAlZUVmjRpAqAomJS1tbV88AAA3t7ekEqlOHHiRBlfBhEREWmSSgOIGzduIDo6GnXq1MGuXbswcuRIjBs3DrGxsQrX7dixA+bm5jA1NcW3336LhIQEeXjr9PT0EmGrDQ0NYWtri/T0dKXt5uXl4fHjxwq3chq+goiI3lNMpvUaMpkMHh4emDNnDgCgWbNmuHDhAmJiYjBo0CD5dR07dkRycjIePnyIH374AZ999hlOnDjx2nwXrxMZGYkZM2YolEmk5pAYWJapPiIifaELywf6ojztXxCDSgOIqlWrokGDBgpl9evXx6+//qpQVrFiRbi6usLV1RWtWrVCnTp1sHLlSoSFhcHBwQGZmZkK1xcUFCArKwsODg5K22U2TiovuN5MuobvSVIXlQYQbdq0weXLlxXKrly5gpo1a772eTKZDHl5eQAALy8vZGdnIykpCe7u7gCKsnbKZDKFo54vYzZOKi/4QUmkv/Rr/kHFAURISAhat26NOXPm4LPPPsNff/2FFStWYMWKFQCA3NxczJ49Gz169EDVqlXx8OFDLF++HPfu3cOnn34KoGjGwtfXF8HBwYiJicGLFy8wZswY9OvX761PYBAREema8rR/QQwqbaJs0aIFtmzZgg0bNqBRo0aYNWsWoqKiEBAQAAAwMDBASkoK+vTpgw8++AD+/v74559/cPjwYTRs2FBez7p161CvXj107twZfn5+aNu2rXwQQkREVB4JIv2vvGA2TiKi9xj3QBTRRByIcc59Ralnya2NotSjbsyFQUT0HtOFL299oW9LGBxAEBERiYDHOImozDhdTLqG70lSFw4giETED0oi/aVf8w8qnsIAitJ5DxgwAHZ2dqhQoQIaN26MU6dOKVzz999/o0ePHrCyskLFihXRokULpKamyh/v0KEDJBKJwm3EiBHv/mqIiIi0RN+ycao0A1Gczrtjx474888/UblyZVy9elUhnff169fRtm1bDBkyBDNmzIClpSUuXrwIU1NThbqCg4Mxc+ZM+X0zM7N3fClERESkKSoNIObNm4caNWpg9erV8rJatWopXPP111/Dz88P8+fPl5fVrl27RF1mZmalhq4mIiJxcFlNc/TtFIao6bxlMhn++OMPfPDBB/Dx8YG9vT08PT2xdevWEnWtW7cOlSpVQqNGjRAWFoZnz56984shIiLSFn0LJKXSDERxOu/Q0FD873//w8mTJzFu3DgYGxtj0KBByMzMxNOnTzF37lx88803mDdvHuLj4/Hxxx9j//79+PDDDwEA/fv3R82aNeHo6Ihz585h8uTJuHz5Mn777Tel7ebl5clzaRQTBIH5MIiI3oCnMEhdVIpEaWxsDA8PDxw7dkxeNm7cOJw8eRKJiYm4f/8+qlWrhs8//xzr16+XX9OjRw9UrFgRGzZsUFrvvn370LlzZ1y7dk3pckdERITSdN5SpvMmHcMPa9I1fE8W0UQkyi+cPxGlnlW3NotSj7qJms67UqVKMDQ0VHrNkSNHSq23OAtnaQMIpvMmIiobXfjy1hflaflBDKKm8zY2NkaLFi1UTvmdnJwMoGiAogzTeVN5wQ9rIv2lb5soRU3nDQCTJk1C37590b59e3Ts2BHx8fHYvn07Dhw4AKDomOf69evh5+cHOzs7nDt3DiEhIWjfvj3c3NxEfXFEmsbpYtI1fE+SuqicjXPHjh0ICwvD1atXUatWLYSGhiI4OFjhmlWrViEyMhJ3795F3bp1MWPGDPTs2RMAcOfOHQwYMAAXLlxAbm4uatSogd69e2Pq1KmwtHz7PQ3MxklE9GYcQBTRxB6IgTU/FqWen28rP1Cga5jOm4iI3nuaGEAMEGkAsbacDCBUDmVNRERExGRaRETvMS5haE55ymMhBg4giIiIRKBvxzhVWsJwdnYukUVTIpFg9OjRAIDnz59j9OjRsLOzg7m5Ofr06YOMjAyFOlJTU9G9e3eYmZnB3t4ekyZNQkFBgXiviIiIiNROpRmIkydPorCwUH7/woUL6NKlCz799FMARcc8//jjD2zatAlWVlYYM2YMPv74Yxw9ehQAUFhYiO7du8PBwQHHjh1DWloaAgMDYWRkhDlz5oj4soiIiDRL3+JAvNMpjAkTJmDHjh24evUqHj9+jMqVK2P9+vX45JOicJ4pKSmoX78+EhMT0apVK/z555/46KOPcP/+fVSpUgUAEBMTg8mTJ+PBgwcwNjZ+67Z5CoOI6M24B6KIJk5hfFqzpyj1bLr9uyj1qFuZT2Hk5+dj7dq1+OKLLyCRSJCUlIQXL17A29tbfk29evXg5OSExMREAEBiYiIaN24sHzwAgI+PDx4/foyLFy++w8sgIiIiTSrzJsqtW7ciOzsbgwcPBgCkp6fD2NgY1tbWCtdVqVIF6enp8mteHjwUP178WGmYjZOIqGx04de/vuAmyre0cuVKdOvWDY6OjmL2R6nIyEhYWVkp3ATZE7W3S0RE9LZkIt3KizLNQNy+fRt79uzBb7/9Fy3LwcEB+fn5yM7OVpiFyMjIgIODg/yav/76S6Gu4lMaxdcow2ycRERlwz0QmlNOAzuXWZlmIFavXg17e3t0795dXubu7g4jIyPs3btXXnb58mWkpqbCy8sLAODl5YXz588jMzNTfk1CQgIsLS1LpAB/mYmJCSwtLRVuXL4gIiIqsnz5cjg7O8PU1BSenp4lfqy/7IcffkC7du1gY2MDGxsbeHt7v/b60qg8gJDJZFi9ejUGDRoEQ8P/JjCsrKwwZMgQhIaGYv/+/UhKSkJQUBC8vLzQqlUrAEDXrl3RoEEDDBw4EGfPnsWuXbswdepUjB49ukS6biIiovJEBkGUm6o2btyI0NBQhIeH4/Tp02jSpAl8fHwUfqy/7MCBA/j888+xf/9+JCYmokaNGujatSvu3VPtpIrKxzh3794NHx8fXL58GR988IHCY8+fP8eXX36JDRs2IC8vDz4+Pvi///s/heWJ27dvY+TIkThw4AAqVqyIQYMGYe7cuQqDkbfBY5xERPS2NHGM09/pI1Hq2Xz11xIHB0xMTEr9oe3p6YkWLVpg2bJlAIp+6NeoUQNjx47FlClT3theYWEhbGxssGzZMgQGBr51P5mNk4iI3nvlaQDh/oUHZsyYoVAWHh6OiIiIEtfm5+fDzMwMmzdvRq9eveTlgwYNQnZ2Nn7//c0xJZ48eQJ7e3ts2rQJH3309q+BuTCIiIhEINYxTmUHB0qbfXj48CEKCwuVhkhISUl5q/YmT54MR0dHhThOb4MDCCIiIhGIlY3zdcsVYps7dy7i4uJw4MABmJqaqvRcDiCIiN5jPMb5fqtUqRIMDAxKJK58OYRCaRYuXIi5c+diz549cHNzU7ntMgeSIiIiov8IgiDKTRXGxsZwd3dXCKEgk8mwd+9eeQgFZebPn49Zs2YhPj4eHh4eZXq9Ks1AODs74/bt2yXKR40aheXLl2P48OHYs2cP7t+/D3Nzc7Ru3Rrz5s1DvXr/BX1SFr9hw4YN6NevXxm6T0REr8Nf/5qjrSiSoaGhGDRoEDw8PNCyZUtERUUhNzcXQUFBAIDAwEBUq1YNkZGRAIB58+Zh+vTpWL9+PZydneWpJMzNzWFubv7W7Yqaztvd3R0BAQFwcnJCVlYWIiIi0LVrV9y8eRMGBgby561evRq+vr7y+6/mzyAqrzhdTLqG78n3X9++ffHgwQNMnz4d6enpaNq0KeLj4+UbK1NTUyGV/rfgEB0djfz8fHnm7GKlnfQojWjpvJXNLJw7dw5NmjTBtWvXULt27aIGJRJs2bJF4bhJWfAYJxHRm3EAUUQTxzi71vB980VvYfedeFHqUbcyb6IsTucdGhqqdPCQm5uL1atXo1atWqhRo4bCY6NHj8bQoUPh4uKCESNGICgoiKGp6b3AD2si/SXWKYzyQrR03sX+7//+D1999RVyc3NRt25dJCQkwNjYWP74zJkz0alTJ5iZmWH37t0YNWoUnj59inHjxpXaFtN5U3nBL28i/VVO4zKWWZmXMHx8fGBsbIzt27crlOfk5CAzMxNpaWlYuHAh7t27h6NHj5Z6vnT69OlYvXo17ty5U2pbERERJaJySaTmkBpYlqXrRER6g7NiRTSxhNG5eldR6tl7d7co9ahbmQYQt2/fhouLC3777Tf07Nmz1Ovy8/NhY2ODH3/8EZ9//rnSa/744w989NFHeP78eamBM5TNQNjY1eMMBOkcfliTruF7sogmBhAdq3cRpZ79dxNEqUfdyrSEoSydtzLFZ1pf/fJ/WXJyMmxsbF4bdUtZVC4OHkgX6cIHJdHL+J7UHLFCWZcXKg8gSkvnfePGDWzcuBFdu3ZF5cqVcffuXcydOxcVKlSAn58fAGD79u3IyMhAq1atYGpqioSEBMyZMwcTJ04U7xUREZEcZyBIXVQeQOzZswepqan44osvFMpNTU1x+PBhREVF4dGjR6hSpQrat2+PY8eOwd7eHgBgZGSE5cuXIyQkBIIgwNXVFYsXL0ZwcLA4r4aIiEhLZNxEWT4wDgQR0ZtxBqKIJvZAtKvWWZR6Dt/b++aLdACTaRERvcd04cub3k8cQBARvcc4A6E5DCRFREREKtO3AYRK6bydnZ0hkUhK3EaPHo1bt24pfUwikWDTpk3yOlJTU9G9e3eYmZnB3t4ekyZNQkFBgegvjIiIiNRHtGycNWrUQFpamsL1K1aswIIFC9CtWzcAQGFhIbp37w4HBwccO3YMaWlpCAwMhJGREebMmSPCyyEiItKOcnomoczUmo2zWbNmaN68OVauXAkA+PPPP/HRRx/h/v378jSjMTExmDx5Mh48eKCQM+NNeAqDiIjeliZOYbR0/FCUev66f1CUetRNpSWMlxVn4/ziiy+UDh6SkpKQnJyMIUOGyMsSExPRuHFj+eABKMqp8fjxY1y8eLGsXSEiItI6QaT/lReiZ+MstnLlStSvXx+tW7eWl6WnpysMHgDI76enp5faFrNxUnnBHe+ka/ieJHUp8wBi5cqV6NatGxwdHUs89u+//2L9+vWYNm3aO3WuWGRkpNJsnBJm4yQdww9KIv2lb3sgyjSAuH37Nvbs2YPffvtN6eObN2/Gs2fPEBgYqFDu4OCAv/76S6EsIyND/lhpwsLCEBoaqlBmY1evLF0nUiv+2iPSX/p2jFMt2ThXrlyJHj16oHLlygrlXl5emD17NjIzM+X5MRISEmBpaYkGDRqU2h6zcVJ5wS9vItIXomXjLHbt2jUcOnQIO3fuLPFY165d0aBBAwwcOBDz589Heno6pk6ditGjR782nTcREZGu07clDJVPYZSWjbPYqlWrUL16dXTt2rXEYwYGBtixYwcMDAzg5eWFAQMGIDAwEDNnzlS950RERDpEBkGUW3nBbJxERPTe00QciCYOrd980Vs4m35MlHrUjbkwiIjeY9zYqznlKYaDGDiAICIiEoGsfE7olxkHEERE7zF9+fVPmscBBJGIOF1MuobvSc3hEsZrFBYWIiIiAmvXrkV6ejocHR0xePBgTJ06VR6XQRAEhIeH44cffkB2djbatGmD6Oho1KlTR16Ps7Mzbt++rVB3ZGQkpkyZIsJLItIeffmgJKKSuITxGvPmzUN0dDRiY2PRsGFDnDp1CkFBQbCyssK4ceMAAPPnz8eSJUsQGxuLWrVqYdq0afDx8cGlS5dgamoqr2vmzJkIDg6W37ewsBDpJREREWkeZyBe49ixY+jZs6c8AqWzszM2bNggD08tCAKioqIwdepU9OzZEwDw008/oUqVKti6dSv69esnr8vCwuK14auJiIhId6k0gGjdujVWrFiBK1eu4IMPPsDZs2dx5MgRLF68GABw8+ZNpKenw9vbW/4cKysreHp6IjExUWEAMXfuXMyaNQtOTk7o378/QkJClEa2JCKisuOymuZwCeM1pkyZgsePH6NevXowMDBAYWEhZs+ejYCAAAD/peRWlrL75XTd48aNQ/PmzWFra4tjx44hLCwMaWlp8oHIq5jOm4iobLiJUnO4hPEav/zyC9atW4f169ejYcOGSE5OxoQJE+Do6IhBgwa9dT0vZ9Z0c3ODsbExhg8fjsjISKU5MZjOm4iobPTly5s0T6VcGJMmTcKUKVPQr18/NG7cGAMHDkRISAgiIyMB/JeSuzhFd7GMjIzX7nfw9PREQUEBbt26pfTxsLAw5OTkKNwkUm66JCIi3SETBFFu5YVKMxDPnj2DVKo45jAwMIBMJgMA1KpVCw4ODti7dy+aNm0KAHj8+DFOnDiBkSNHllpvcnIypFKpPMX3q5jOm4iobLiEoTlcwngNf39/zJ49G05OTmjYsCHOnDmDxYsXyzNzSiQSTJgwAd988w3q1KkjP8bp6OiIXr16AQASExNx4sQJdOzYERYWFkhMTERISAgGDBgAGxsb0V8gERERiU+lAcTSpUsxbdo0jBo1CpmZmXB0dMTw4cMxffp0+TVfffUVcnNzMWzYMGRnZ6Nt27aIj4+Xx4AwMTFBXFwcIiIikJeXh1q1aiEkJERhXwQREVF5IwgybXdBo5jOm4joPcYljCKaSOdd085NlHpu/3NOlHrUTaVNlEREREQAk2kREb3XdOHXv74opxP6ZcYBBJGIOF1MuobvSc2R6dkpDJWWMAoLCzFt2jTUqlULFSpUQO3atTFr1qxSR10jRoyARCJBVFSUQnlWVhYCAgJgaWkJa2trDBkyBE+fPi3ziyAiItI2QRBEuZUXomfjLLZlyxYcP34cjo6OJeoJCAhAWloaEhIS8OLFCwQFBWHYsGFYv379u70aIi3Tl19aRESiZuMsdu/ePYwdOxa7du2SX1vs77//Rnx8PE6ePAkPDw8ARcdD/fz8sHDhQqUDDiIiIl1XnqJIikHUbJwAIJPJMHDgQEyaNAkNGzYsUUdiYiKsra3lgwcA8Pb2hlQqxYkTJ9C7d+93eDlERPQyzoppDiNRvsabsnECRcschoaGJZY0iqWnp5cIWW1oaAhbW1uFjJ0vYzZOIqKy4SZKUhdRs3EmJSXhu+++w+nTp0X9cmc2TiIi0nXlaQOkGETNxnn48GFkZmbCyckJhoaGMDQ0xO3bt/Hll1/C2dkZQFHGzszMTIV6CwoKkJWVVWrGTmbjJCIiXSeDIMqtvBA1G+fAgQPh7e2t8LiPjw8GDhyIoKAgAICXlxeys7ORlJQEd3d3AMC+ffsgk8ng6emptF1m4yQiItItombjtLOzg52dncJzjIyM4ODggLp16wIA6tevD19fXwQHByMmJgYvXrzAmDFj0K9fP57AICKickvfljBEz8b5NtatW4cxY8agc+fOkEql6NOnD5YsWaJSHURERLpE345xMhsnEdF7jKcwimgiG6etRR1R6sl6clWUetSNuTCIiIhEUE5/j5cZBxBERO8xXfj1ry/K0wkKMXAAQSQiTheTruF7UnM4A0FEZaYvH5RUfvA9Seqi0gCisLAQERERWLt2LdLT0+Ho6IjBgwdj6tSp8rgMpcVnmD9/PiZNmgSgKAnX7du3FR6PjIzElClTyvIaiIioFJyB0Bx9O4UhejrvtLQ0hef8+eefGDJkCPr06aNQPnPmTAQHB8vvW1gwsiQRkdj05ctbFzCZ1mu8TTrvV8NR//777+jYsSNcXFwUyi0sLEoNXU1EROLgDASpi+jpvF+WkZGBP/74A7GxsSUemzt3LmbNmgUnJyf0798fISEhMDTklgwiIjHxy1tzuITxGm+TzvtlsbGxsLCwwMcff6xQPm7cODRv3hy2trY4duwYwsLCkJaWVupAhOm8iYjKhjMQmsNTGK/xpnTer1q1ahUCAgJgamqqUB4aGir/s5ubG4yNjTF8+HBERkaWSJoFMJ03ERGRrlEplHWNGjUwZcoUjB49Wl72zTffYO3atUhJSVG49vDhw2jfvj2Sk5PRpEmT19Z78eJFNGrUCCkpKfKkWy9TNgNhY1ePMxBERPRWNBHK2sS0hij15D2/I0o96iZqOu+XrVy5Eu7u7m8cPABAcnIypFIp7O3tlT7OdN5ERGXDJQzN0bclDOmbL/lPcTrvP/74A7du3cKWLVuwePFi9O7dW+G6x48fY9OmTRg6dGiJOhITExEVFYWzZ8/ixo0bWLduHUJCQjBgwADY2Ni826shIiLSQ8uXL4ezszNMTU3h6empcDpSmU2bNqFevXowNTVF48aNsXPnTpXbVGkAsXTpUnzyyScYNWoU6tevj4kTJ2L48OGYNWuWwnVxcXEQBAGff/55iTpMTEwQFxeHDz/8EA0bNsTs2bMREhKCFStWqNx5IiIiXSEIgig3VW3cuBGhoaEIDw/H6dOn0aRJE/j4+CAzM1Pp9ceOHcPnn3+OIUOG4MyZM+jVqxd69eqFCxcuqNQu03kTiYjTxaRr+J4sook9EGJ9L+U+uVFi35+ypfxinp6eaNGiBZYtWwYAkMlkqFGjBsaOHas0wnPfvn2Rm5uLHTt2yMtatWqFpk2bIiYm5u07Kuip58+fC+Hh4cLz58/ZB/aBfWAf2Af2QWeEh4cLABRu4eHhSq/Ny8sTDAwMhC1btiiUBwYGCj169FD6nBo1agjffvutQtn06dMFNzc3lfqptwOInJwcAYCQk5PDPrAP7AP7wD6wDzrj+fPnQk5OjsKttAHUvXv3BADCsWPHFMonTZoktGzZUulzjIyMhPXr1yuULV++XLC3t1epnwz9SEREpENet1yhS1TaRElERES6o1KlSjAwMEBGRoZCeUZGRqn5phwcHFS6vjQcQBAREZVTxsbGcHd3x969e+VlMpkMe/fuhZeXl9LneHl5KVwPAAkJCaVeXxq9XcIwMTFBeHi4VqeJ2Af2gX1gH9gH3e+DrgsNDcWgQYPg4eGBli1bIioqCrm5uQgKCgIABAYGolq1aoiMjAQAjB8/Hh9++CEWLVqE7t27Iy4uDqdOnVI5nEK5PcZJRERERZYtW4YFCxYgPT0dTZs2xZIlS+Dp6QkA6NChA5ydnbFmzRr59Zs2bcLUqVNx69Yt1KlTB/Pnz4efn59KbXIAQURERCrjHggiIiJSGQcQREREpDIOIIiIiEhlHEAQERGRyjiAICK95+Lign/++adEeXZ2NlxcXLTQIyLdxwEE6a39+/eX+tj333+vsX7k5+fj8uXLKCgo0FibymRmZuLw4cM4fPhwqWmA31e3bt1CYWFhifK8vDzcu6f+LI5E5ZHeBpLSpI8//vitr/3tt9/U2JPSZWdnw9raWuPtXrt2DdevX0f79u1RoUIFCIIAiUSikbZ9fX0xbtw4zJkzB0ZGRgCAhw8fIigoCEeOHMHw4cPV2v6zZ88wduxYxMbGAgCuXLkCFxcXjB07FtWqVVOahlcdnjx5glGjRiEuLk7+JWpgYIC+ffti+fLlsLKy0kg/iuXn5yMzMxMymUyh3MnJSfS2tm3bJv/zrl27FF5rYWEh9u7dC2dnZ9HbfZPr169j9erVuH79Or777jvY29vjzz//hJOTExo2bKjWtgsLC7FmzRrs3btX6b/Dvn371No+lR/v/QAiNDT0ra9dvHixWvrw8oeSIAjYsmULrKys4OHhAQBISkpCdna2SgONdzFv3jw4Ozujb9++AIDPPvsMv/76KxwcHLBz5040adJE7X34559/0LdvX+zbtw8SiQRXr16Fi4sLhgwZAhsbGyxatEjtfdi/fz8CAwORkJCA9evX4+bNmxgyZAjq1q2L5ORktbcfFhaGs2fP4sCBA/D19ZWXe3t7IyIiQmMDiKFDh+LMmTPYsWOHPJRtYmIixo8fj+HDhyMuLk4j/bh69Sq++OILHDt2TKG8eFCpbIbgXfXq1QsAIJFIMGjQIIXHjIyM4OzsrJH34ssOHjyIbt26oU2bNjh06BBmz54Ne3t7nD17FitXrsTmzZvV2v748eOxZs0adO/eHY0aNdLYgB4Azp0799bXurm5qbEn9FZUyt1ZDnXo0EHhZmlpKZiZmQnNmjUTmjVrJlSsWFGwtLQUOnbsqJH+fPXVV8LQoUOFgoICeVlBQYEwbNgwYeLEiRrpg7Ozs3D06FFBEARh9+7dgrW1tbBr1y5hyJAhQpcuXTTSh4EDBwo+Pj7CnTt3BHNzc+H69euCIAhCfHy80KBBA430QRAE4cmTJ0JAQIBgYmIiGBkZCXPnzhVkMplG2nZychISExMFQRAU/g6uXr0qWFhYaKQPgiAIZmZmwuHDh0uUHzp0SDAzM9NYP1q3bi20b99e2Llzp3DmzBkhOTlZ4aZOzs7OwoMHD9Taxttq1aqVsGjRIkEQFN8XJ06cEKpVq6b29u3s7IQ//vhD7e0oI5FIBKlUKv//191I+977GYiX17kXL14MCwsLxMbGwsbGBgDw6NEjBAUFoV27dhrpz6pVq3DkyBEYGBjIywwMDBAaGorWrVtjwYIFau9Deno6atSoAQDYsWMHPvvsM3Tt2hXOzs7y0Kfqtnv3buzatQvVq1dXKK9Tpw5u376tkT4ARcsGp06dQvXq1XH//n1cvnwZz549Q8WKFdXe9oMHD2Bvb1+iPDc3V6O/+uzs7JQuU1hZWcn/O9GE5ORkJCUloV69ehprs9jNmzflf37+/DlMTU013odi58+fx/r160uU29vb4+HDh2pv39jYGK6urmpvR5mX/x3OnDmDiRMnYtKkSQozY4sWLcL8+fO10j9SpFebKBctWoTIyEiFD0UbGxt88803GpumLCgoQEpKSonylJSUEmuN6mJjY4M7d+4AAOLj4+Ht7Q2gaKpYHdPEyuTm5sLMzKxEeVZWlsaS5sydOxdeXl7o0qULLly4gL/++gtnzpyBm5sbEhMT1d6+h4cH/vjjD/n94kHDjz/+qHJWvHcxdepUhIaGIj09XV6Wnp6OSZMmYdq0aRrrR4MGDTTyBamMTCbDrFmzUK1aNZibm+PGjRsAgGnTpmHlypUa7Yu1tTXS0tJKlJ85cwbVqlVTe/tffvklvvvuOwhayHJQs2ZN+W3OnDlYsmQJhg8fDjc3N7i5uWH48OGIiorCrFmzNN43UkLbUyCaZG5uLuzfv79E+b59+wRzc3ON9CEkJESws7MTFi1aJBw+fFg4fPiwsHDhQqFSpUpCSEiIRvowevRooWbNmoK3t7dgZ2cnPHnyRBAEQdiwYYPQrFkzjfShW7duwtSpUwVBKPp3uXHjhlBYWCh8+umnQp8+fTTSBwcHB2Hnzp0KZfn5+cLEiRMFY2Njtbd/+PBhwdzcXBgxYoRgamoqjB8/XujSpYtQsWJF4dSpU2pvv1jTpk0Fc3NzwcjISKhdu7ZQu3ZtwcjISDA3N5cv9RXfxJaTkyO/7d27V/Dy8hL2798vPHz4UOGxnJwc0dt+2YwZMwQXFxdh7dq1QoUKFeTLBnFxcUKrVq3U2varvvzyS6Ft27ZCWlqaYGFhIVy9elU4cuSI4OLiIkRERKi9/V69eglWVlZCrVq1hI8++kjo3bu3wk1TTE1NhUuXLpUov3TpkmBqaqqxflDp9CqZVmBgIA4fPoxFixahZcuWAIATJ05g0qRJaNeunXw3vDrJZDIsXLgQ3333nfxXRtWqVTF+/Hh8+eWXCksb6vLixQt89913uHPnDgYPHoxmzZoBAL799ltYWFhg6NChau/DhQsX0LlzZzRv3hz79u1Djx49cPHiRWRlZeHo0aOoXbu22vvw8OFDVKpUSeljBw8exIcffqj2Ply/fh1z587F2bNn8fTpUzRv3hyTJ09G48aN1d52sRkzZrz1teHh4aK2LZVKFZZrBCWncAQ1bqIs5urqiu+//x6dO3eGhYUFzp49CxcXF6SkpMDLywuPHj1SW9uvys/Px+jRo7FmzRoUFhbC0NAQhYWF6N+/P9asWaP2z4jiFNClWb16tVrbL9a8eXM0atQIP/74I4yNjQEU/d0MHToUFy5cwOnTpzXSDyqdXg0gnj17hokTJ2LVqlV48eIFAMDQ0BBDhgzBggULNLLu/bLHjx8DACwtLTXarq7IycnBsmXLFL48R48ejapVq2qsD9nZ2di8eTOuX7+OSZMmwdbWFqdPn0aVKlU0Ml2s7w4ePPjW16pzQFehQgWkpKSgZs2aCgOIS5cuoWXLlnj69Kna2n6ZIAi4c+cOKleujIcPH+L8+fN4+vQpmjVrhjp16mikD7rir7/+gr+/PwRBkJ+4OHfuHCQSCbZv3y7/EUjao1cDiGK5ubm4fv06AKB27doaHzgUFBTgwIEDuH79Ovr37w8LCwvcv38flpaWMDc3V3v7sbGxqFSpErp37w4A+Oqrr7BixQo0aNAAGzZsQM2aNdXeB11w7tw5eHt7w8rKCrdu3cLly5fh4uKCqVOnIjU1FT/99JNa2y8eQL5KIpHAxMRE/qtLk54/f46NGzciNzcXXbp00ZsvLXd3d4SEhGDAgAEKA4iZM2ciISEBhw8f1kg/ZDIZTE1NcfHiRb35u3+d3NxcrFu3Tr5vrH79+ujfv7/GP7NJOb0cQGjT7du34evri9TUVOTl5cmDB40fPx55eXmIiYlRex/q1q2L6OhodOrUCYmJifD29sa3336LHTt2wNDQUGPBrJ4/f45z584pDVbTo0cPtbffuXNnuLu7Y/78+QpfGseOHUP//v1x69Yttbb/6vT9q6pXr47BgwcjPDwcUqn4+51DQ0Px4sULLF26FEDR9HDLli1x6dIlmJmZoaCgALt370br1q1Fb1uZ1atXw9zcHJ9++qlC+aZNm/Ds2bMScRrE9Pvvv2PQoEEICwvDzJkzMWPGDFy+fBk//fQTduzYgS5duqit7Vc1bNgQK1euRKtWrTTWZvPmzbF3717Y2NigWbNmr31fcumAir33xziBt48EqYkvzvHjx8PDwwNnz56FnZ2dvLx3794IDg5We/sAcOfOHfkxra1bt6JPnz4YNmwY2rRpgw4dOmikD/Hx8QgMDFS6617d693FTp06hRUrVpQor1atmsKJBHVZs2YNvv76awwePFg+HfvXX38hNjYWU6dOxYMHD7Bw4UKYmJjgf//7n+jt7969G3PmzJHfX7duHVJTU3H16lU4OTnhiy++wOzZsxVOiqhTZGSk0hDi9vb2GDZsmFoHED179sT27dsxc+ZMVKxYEdOnT0fz5s2xfft2jQ4egKLTQZMmTUJ0dDQaNWqkkTZ79uwpP/1UHFxLF/z888/4/vvvcePGDSQmJqJmzZr49ttv4eLigp49e2q7e3pPLwYQmg7F+zqHDx/GsWPHSkxPOzs7ayzmvrm5Of755x84OTlh9+7d8midpqam+PfffzXSh7Fjx+LTTz/F9OnTUaVKFY20+SoTExOlywhXrlxB5cqV1d5+bGwsFi1ahM8++0xe5u/vj8aNG+P777/H3r174eTkhNmzZ6tlAJGamooGDRrI7+/evRuffPKJfAlr/Pjx8PPzE73d1/WnVq1aJcpr1qyJ1NRUtbZ99+5dtGvXDgkJCSUeO378uEZnAwIDA/Hs2TM0adIExsbGqFChgsLjWVlZorf58uZYsTfKllV0dDSmT5+OCRMm4JtvvpH/qLCxsUFUVBQHEDpALwYQmto1/DZkMpnSX9d3796FhYWFRvrQpUsXDB06FM2aNcOVK1fkXxIXL17UWNz/jIwMhIaGam3wABQtk8ycORO//PILgKKZj9TUVEyePBl9+vRRe/vHjh1TumTVrFkzeRyKtm3bqu3LUyqVKpz1P378uELcB2tra42ePrC3t8e5c+dKvAdfna1Th65du+LIkSOwtbVVKD969Ci6d++O7Oxstbb/sqioKI219TqnTp3C33//DaAoRoe7u7tG21+6dCl++OEH9OrVC3PnzpWXe3h4YOLEiRrtC5VCK4dHtSA/P18wMDAQzp8/r9V+fPbZZ0JwcLAgCP/FP3jy5InQqVMnYfDgwRrpw6NHj4TRo0cLPXr0EP788095+fTp04VvvvlGI30ICgoSfvzxR420VZrs7GzB29tbsLa2FgwMDIQaNWoIhoaGQrt27YSnT5+qvf06deoIkydPLlE+efJk4YMPPhAEQRBOnjwpODo6qqX9l0MmX7hwQZBKpcKNGzfkjx84cECoWbOmWtpW5quvvhJq1qwp7Nu3TygoKBAKCgqEvXv3CjVr1hS+/PJLtbYdFBQkuLu7C48fP5aXHTx4ULC0tBQWL16s1rZ1zZ07d4S2bdsKEolEsLGxEWxsbASJRCK0adNGuHPnjsb6YWpqKty6dUsQBMWQ3leuXGEcCB2hV5soXVxcsGXLFo0kiyrN3bt34ePjA0EQcPXqVXh4eODq1auoVKkSDh06pDS08fvo2bNn+PTTT1G5cmU0btxYng2z2Lhx4zTWlyNHjuDcuXN4+vQp3N3d0blzZ420u23bNnz66aeoV68eWrRoAeC/X32//vorPvroI0RHR+Pq1atqSfS2ZcsW9OvXD23btsXFixfRokULbN++Xf745MmTcfPmTfkMjbrl5+dj4MCB2LRpEwwNiyZHZTIZAgMDERMTo9ZTKTKZDJ988gmysrKwa9cuHDt2DD169MA333yD8ePHq63dN3n+/Dny8/MVytR97NvX1xfZ2dmIjY1F3bp1AQCXL19GUFAQLC0tER8fr9b2izVo0ACRkZHo2bOnwibnpUuXYvXq1dzMqQP0agCxcuVK/Pbbb/j5559LTFVqUkFBAeLi4uRfWs2bN0dAQECJtU51e/bsGVJTU0t8QGkiy93KlSsxYsQImJqaws7OTmHXt0QikYcSVofExET8888/+Oijj+RlsbGxCA8Px7Nnz9CrVy8sXbpUIyG1b926hZiYGFy5cgVA0QmZ4cOH4+nTpxrZQLd3717s2LEDDg4OGDt2rEJ48RkzZuDDDz/UyMZa4aX4B3fv3kVycjIqVKiAxo0ba+xYcX5+Prp3745nz57h3LlziIyMxJgxYzTS9styc3MxefJk/PLLL/jnn39KPK7uDcYVKlTAsWPH5AHmiiUlJaFdu3Z49uyZWtsv9uOPPyIiIgKLFi3CkCFD8OOPP+L69euIjIzEjz/+iH79+mmkH/Qa2pz+0LTikL0mJibCBx98oPYwvcr8+++/GmnndTIzMwU/Pz+tZrmrUqWKMHv2bKGwsFAj7b3M19dXmDt3rvz+uXPnBCMjI2Ho0KHCokWLBAcHByE8PFzj/crJyRFiYmKEli1b6l22wcLCQsHIyEi4cuWKxto8e/ZsiduRI0eEGjVqCCNGjFAo16RRo0YJ9evXFzZv3ixUqFBBWLVqlTBr1iyhevXqwtq1a9Xefp06dYQTJ06UKD9x4oRQu3Zttbf/srVr1wqurq6CRCIRJBKJUK1aNa0vfdJ/9GoG4k0hezWx+9jS0hK9e/fGgAED0LlzZ7Wc73+TgIAA3L59G1FRUejQoQO2bNmCjIwMeVKx4gBT6mRra4uTJ09qJGT1q6pWrYrt27fDw8MDAPD111/j4MGDOHLkCICiuAPh4eG4dOmSRvpz6NAhrFy5Er/++iscHR3x8ccfo0+fPvJlDU149OgRVq5cKd80V79+fXzxxRcananTdPyD4jgcL38Evny/+M+aOlZczMnJCT/99BM6dOgAS0tLnD59Gq6urvj555+xYcMG7Ny5U63t//7775gzZw6WL18u/2/k1KlTGDt2LCZPnqyxY56PHz+WL9c8e/YMT58+lS/xXrt2TWsZQ+klWh2+6KHffvtN+OSTT4QKFSoIDg4Owvjx44WTJ09qtA8ODg7yXxgWFhbC5cuXBUEQhN9//11o06aNRvowYcIEYfbs2Rpp61UmJiZCamqq/H6bNm0UNo/evHlT7cnV0tLShMjISMHV1VWwt7cXxowZIxgaGgoXL15Ua7vKFG8WrFGjhjxhkpOTk2BpaSkcPHhQY/3Ytm2b0LZtW41tdL5169Zb3zSpYsWKwu3btwVBEIRq1arJ/1u9ceOGULFiRbW0aW1tLd8waWNjIxgbGwtSqVQwNjZW+LONjY1a2lembdu2wvPnz0uUp6SkCNWqVdNYP6h0enGM82Xazn3Qu3dv9O7dG0+ePMHmzZuxYcMGtGrVCi4uLhgwYACmT5+u9j7k5ubKR/I2NjZ48OABPvjgAzRu3FhjG5MKCwsxf/587Nq1C25ubiU2Uapj02CxKlWq4ObNm6hRowby8/Nx+vRphdmpJ0+elOiPmPz9/XHo0CF0794dUVFR8PX1hYGBgUaikCozevRo9O3bF9HR0fJETYWFhRg1ahRGjx6N8+fPa6Qfmo5/oKsh211cXHDz5k04OTmhXr16+OWXX9CyZUts374d1tbWamlTV46Ovszc3By9e/fGtm3b5Jtq//77b3Tq1Ekhdgppj14tYWg790FpLl26hICAAJw7d04jU6UtWrTAN998Ax8fH/To0QPW1taIjIzEkiVL5IMrdevYsWOpj0kkEuzbt09tbY8cORJnz57FvHnzsHXrVsTGxuL+/fvyXf7r1q1DVFQUTp48qZb2DQ0NMW7cOIwcOVIh34GRkRHOnj2rENxJEypUqIDk5GT5jvtily9fRtOmTTUWXOxN2XDVGYkyMjISVapUwRdffKFQvmrVKjx48ACTJ09WW9vFbty4AWdnZ3z33XcwMDDAuHHjsGfPHnlCqRcvXmDx4sVaPRWiSf/++y+8vb1RvXp1xMXF4eLFi+jcuTMCAgLU+gODVKDlGRCN6ty5szBp0iRBEBTPFR89elSj590FoWgz5caNG4WePXsKJiYmgpOTk9KYAOrw888/C6tXrxYEQRBOnTolVKpUSZBKpYKpqakQFxenkT5o04MHD4R27doJEolEsLCwEH777TeFxzt16iT873//U1v7iYmJwtChQwULCwuhZcuWwtKlS4UHDx5obQmjdevWwpYtW0qUb9myRfD09NR4f7ShZs2awtGjR0uUHz9+XHB2dtZIH6RSqZCRkSG//9lnnwnp6enCrVu3hF9//VXjmzkFoehzKicnR+GmSY8ePRKaNGkifPLJJ4K9vb0wceJEjbZPr6dXAwhLS0vh2rVrgiAoDiBu3bolmJiYaKQP8fHxQmBgoGBpaSnY2toKw4YN0+g6szK5ublCUlKS8ODBA632Q9Oys7OFgoKCEuX//POPkJeXp/b2nz59KqxcuVJo06aNYGRkJEilUiEqKkohmJG6vHzCIC4uTnBychIWLFggHD58WDh8+LCwYMECwdnZWWsDSk1/cZmYmCgE0Sp2/fp1jX02SCQShQHEy59RmvT06VNh9OjRQuXKlTV+SuvVf/OcnBwhJSVFqFGjhjBy5EitDWRIOb0aQFSuXFk4ffq0IAiK/3Hu3r1bqF69ukb6UKFCBeHTTz8Vtm7dKuTn52ukTV118uRJYdKkSULfvn3lm/eKb/omJSVFmDRpkuDg4CCYmpoK/v7+am1PIpEIUqlUfjyutJsmj5Nq84vL1dVV+Pnnn0uU//TTT0KtWrXU2nYxXRlAaPMYafF77tXby+9HTb8vqXR6tYlS27kPgKIcEJrKefGy4oRZb0MT64txcXEIDAyEj48Pdu/eja5du+LKlSvIyMhA79691d6+rqlbty7mz5+PyMhIbN++HatWrVJrezdv3lRr/WXx1VdfYf/+/YiOjsbAgQOxfPly3Lt3D99//71CLgR1CA4OxoQJE/DixQt06tQJQFGQra+++gpffvmlWtsuJpFISqTRfl1abXXZvn27/BhpUFAQ2rVrB1dXV9SsWRPr1q1DQECA2trev3+/2uom8enVJsqcnBx88sknOHXqFJ48eQJHR0ekp6fDy8sLO3fuRMWKFdXS7svnmZVlf3yZusLUvm7T4svUvYGxmJubG4YPH47Ro0fLw9TWqlULw4cPR9WqVd8Ys4PeP9qMfyAIAqZMmYIlS5bII7Oamppi8uTJGjkZBRTFpejWrZs8Aur27dvRqVOnEp9Lv/32m1r7YW5ujkuXLsHJyQnVq1fHb7/9hpYtW+LmzZto3Lgxnj59qtb2gaJovXPmzMEXX3yB6tWrq709Khu9GkAUezn3QfPmzeHt7a3W9gwMDJCWlgZ7e3t58JpXCVoIWKNNFStWlGf/tLOzw4EDB9C4cWP5Ma20tDRtd/G9tm3bNnTr1g1GRkbYtm3ba6/t0aOHRvqkC19cT58+xd9//40KFSqgTp06GglnXiwoKOitrlN3dmE3NzcsXboUH374Iby9vdG0aVMsXLgQS5Yswfz583H37l21tl/MwsIC58+f11iGYFKdXi1hFGvbti3atm2rsfb27dsnj+i3b98+rUxLviwnJweFhYUlogxmZWXB0NBQ7cl6gKL4E0+ePAEAVKtWDRcuXEDjxo2RnZ2tsVj7+qxXr15IT0+Hvb39ayMLanJQq434B68yNzfXaATQl6l7YPC2goKCcPbsWXz44YeYMmUK/P39sWzZMuTn5+Pbb7/VWD86deqEgwcPcgChw/RqBmLJkiVKyyUSCUxNTeHq6or27dvLg+m8r7p16wZ/f3+MGjVKoTwmJgbbtm1Te6hcAOjfvz88PDwQGhqKWbNmYenSpejZsycSEhLQvHlztU/Tku759ttvNRr/4OOPP8aaNWtgaWmJjz/++LXX6vP78fbt20hKSkKdOnXQuHFjjbUbExODGTNmICAgAO7u7iWWcjQ1M0al06sBRK1atfDgwQM8e/YMNjY2AIpyAJiZmcHc3ByZmZlwcXHB/v37UaNGDbX0oU6dOggICEBAQIBCECFNsrW1xdGjR1G/fn2F8pSUFLRp00ZpBkCxZWVl4fnz53B0dIRMJsP8+fNx7Ngx1KlTB1OnTpX/+5D6KMtK+tNPPyE8PBy5ubkay0oqk8mwYMECbNu2Dfn5+ejcuTPCw8ORmZmJpKQkuLq6qiVDbFBQEJYsWQILC4s3Lh/oyuyAOu3btw9jxozB8ePHS8xC5uTkoHXr1oiJiUG7du000p/X5QnSp+Venaal0x9asX79eqFDhw7yWBCCIAhXr14VOnXqJMTFxQl37twR2rRpI/Tp00dtfVi8eLHg4eEhSKVSwcPDQ4iKihLS0tLU1p4yZmZmwrlz50qUnzt3TqhQoYJG+0LaoywrqaGhocazks6cOVOQSqVC165dhZ49ewqmpqZCUFCQ2tsVBEGYMWOGkJubq5G2dJ2/v7+wePHiUh//7rvvhF69emmwR6Tr9GoA4eLiIpw5c6ZE+enTp+VnvY8ePSo4ODiovS+XL18Wpk+fLtSpU0cwNDQUunTpIsTGxqq9XUEQhA4dOghjxowpUT5q1Cihbdu2am27tHPeL98MDAzU2gcq4uDgoJDI7X//+59CMrVffvlFqF+/vtr74erqKsTExMjvJyQkCMbGxhpJ9f5q9Ed95uTkJFy6dKnUx//++2+hRo0aGuwR6Tq92kSZlpaGgoKCEuUFBQVIT08HADg6Oso396nTBx98gBkzZmDGjBk4fvw4Ro4ciaCgIAQGBqq97W+++Qbe3t44e/YsOnfuDKDozPvJkyexe/dutba9ZcuWUh9LTEzEkiVLIJPJ1NoHKvLo0SNUqVJFfv/gwYPo1q2b/H6LFi1w584dtfcjNTUVfn5+8vve3t6QSCS4f/++2o/wCfqzgvtGGRkZr00iZ2hoiAcPHmiwR0WJ/w4ePIjU1FT58dpi48aN02hfSAltj2A0yc/PT2jevLk8GqUgFM0+uLu7C927dxcEoSilcKNGjTTSnxMnTgjjx48XHBwcBDMzM6Fv374aaVcQBCE5OVno37+/0KBBA8Hd3V0ICgoSrly5orH2X5aSkiL06tVLMDAwEAIDAzWePllfOTk5ycOo5+XlCRUqVBD27Nkjf/zcuXMaSd8slUqFzMxMhTJzc3OloaXFJpFISrStr1xcXJTmRCn266+/aiwqpyAUfTY7ODgIlpaWgoGBgVC5cmVBIpEIFStW1Gg/qHR6NQOxcuVKDBw4EO7u7vKRdkFBATp37oyVK1cCKDrGtWjRIrX14cqVK1i3bh02bNiAmzdvolOnTpg3bx4+/vhjmJubq61doORmtU6dOuHHH38skTZZU+7fv4/w8HDExsbCx8cHycnJaNSokVb6oo/8/PwwZcoUeVZSMzMzhQ1y586dQ+3atdXeD0EQMHjwYIXNms+fP8eIESMUdt6r6yTEBx988Maj1WKnEtdFfn5+mDZtGnx9fWFqaqrw2L///ovw8HCFDbfqFhISAn9/f8TExMDKygrHjx+HkZERBgwYoDcZSXWdXp3CKJaSkoIrV64AKAoh/GoaY3WSSqVo0aIF+vfvj379+ilMIavbrFmzEBERAW9vb1SoUAG7du3C559/rvawya/KycnBnDlzsHTpUjRt2hTz5s3T2M5u+s/Dhw/x8ccf48iRIzA3N0dsbKxCGPHOnTujVatWmD17tlr7oc0ASlKpFFFRUbCysnrtdepMJa4rMjIy0Lx5cxgYGGDMmDHyz8WUlBQsX74chYWFOH36tMY+s6ytrXHixAnUrVsX1tbWSExMRP369XHixAkMGjQIKSkpGukHvYaWZ0D0SkFBgbBixQohKytLK+1rc7NasXnz5gm2trZCgwYNhK1bt2qsXSqdtrOSatOrCaz03a1bt4Ru3bqVSGDVrVs3jSwpvaxSpUryZdU6deoI8fHxgiAUbeY0MzPTaF9IOb2agSgsLMSaNWuwd+9eZGZmltisp4kcEKampvj7779Rq1Yttbf1KhMTE1y7dk0hxoWpqSmuXbumsXjzUqkUFSpUgLe392sDdulz4B7SnJfDzNN/Hj16hGvXrkEQBNSpU0crcVm6du2KwYMHo3///ggODsa5c+cwbtw4/Pzzz3j06BFOnDih8T6RIr3aAzF+/HisWbMG3bt3R6NGjbQSUrpRo0a4ceOGVgYQBQUFJdY2jYyM8OLFC431ITAwUOuhvImK6dHvJ5XY2NhoLaR3sTlz5shPxM2ePRuBgYEYOXIk6tSpo/FlV1JOr2YgKlWqhJ9++knhyJimxcfHIywsDLNmzVIanlWdeShezfYHKM/4x1//RET0Jno1gHB0dMSBAwfwwQcfaK0PL4dnffmXuKCBbJy6ku2PiOhtZWZm4vLlywCAevXqoXLlylruERXTqwHEokWLcOPGDSxbtkxr0+gHDx587eMffvihhnpCRKS7njx5glGjRiEuLk7+w8rAwAB9+/bF8uXL33hyhtRPrwYQvXv3xv79+2Fra4uGDRuWiLrGqXsiIt3Qt29fnDlzBkuXLoWXlxeAomi148ePR9OmTREXF6flHpJeDSB0IePeoUOHXvt4+/bt1d4HIiJdV7FiRezatQtt27ZVKD98+DB8fX2Rm5urpZ5RMb06haELa/sdOnQoUfbycgpT1BIRAXZ2dkqXKaysrLRyrJRKKj3h+nuqoKAAe/bswffffy8/InT//n08ffpUI+0/evRI4ZaZmYn4+Hi0aNFC7YmsiIjKi6lTpyI0NFSe6BAA0tPTMWnSJEybNk2LPaNierWEcfv2bfj6+iI1NRV5eXm4cuUKXFxcMH78eOTl5SEmJkZrfTt48CBCQ0ORlJSktT4QEWlTs2bNFGZkr169iry8PDg5OQEoytxqYmKCOnXq4PTp09rqJv1/erWEMX78eHh4eODs2bOws7OTl/fu3RvBwcFa7BlQpUoV+VElIiJ91KtXL213gVSgVwOIw4cP49ixYzA2NlYod3Z2xr179zTSh3PnzincFwQBaWlpmDt3Lpo2baqRPhAR6aLw8HBtd4FUoFcDCJlMpnST4t27d2FhYaGRPjRt2hQSiaRECN1WrVoxPCsRkRJPnz4tkbtInVF76e3o1R6Ivn37wsrKCitWrICFhQXOnTuHypUro2fPnnByctLIKY3bt28r3JdKpahcuXKJHBVERPrs5s2bGDNmDA4cOIDnz5/LyzURtZfejl4NIO7evQsfHx8IgoCrV6/Cw8MDV69eRaVKlXDo0CG1ZuRLTEzEP//8g48++khe9tNPPyE8PBy5ubno1asXli5dqpCngohIX7Vp0waCIGD8+PGoUqVKiejBjNqrfXo1gACKjnFu3LgRZ8+exdOnT9G8eXMEBASgQoUKam23W7du6NChAyZPngwAOH/+PJo3b47Bgwejfv36WLBgAYYPH46IiAi19oOIqDwwNzdHUlIS6tatq+2uUCn0bgChLVWrVsX27dvh4eEBAPj6669x8OBBHDlyBACwadMmhIeH49KlS9rsJhGRTujYsSO+/vpreHt7a7srVAq92kQZGxuLSpUqoXv37gCAr776CitWrECDBg2wYcMG1KxZU21tP3r0CFWqVJHfP3jwILp16ya/36JFC9y5c0dt7RMRlSc//vgjRowYgXv37qFRo0Ylche5ublpqWdUTK8iUc6ZM0e+VJGYmIhly5Zh/vz5qFSpEkJCQtTadpUqVXDz5k0AQH5+Pk6fPo1WrVrJH3/y5EmJ/0CIiPTVgwcPcP36dQQFBaFFixZo2rQpmjVrJv9/0j69moG4c+cOXF1dAQBbt27FJ598gmHDhqFNmzZKc1SIyc/PD1OmTMG8efOwdetWmJmZoV27dvLHz507h9q1a6u1D0RE5cUXX3yBZs2aYcOGDUo3UZL26dUAwtzcHP/88w+cnJywe/duhIaGAgBMTU3x77//qrXtWbNm4eOPP8aHH34Ic3NzxMbGKgS0WrVqFbp27arWPhARlRe3b9/Gtm3b5D/6SPfo1QCiS5cuGDp0KJo1a4YrV67Az88PAHDx4kU4Ozurte3io6I5OTkwNzeHgYGBwuObNm2Cubm5WvtARFRedOrUCWfPnuUAQofp1QBi+fLlmDp1Ku7cuYNff/1Vng8jKSkJn3/+uUb6oCw9LQDY2tpqpH0iovLA398fISEhOH/+PBo3blxij1iPHj201DMqxmOcRESkc6TS0vf4MxKlbtCrUxjx8fHyuAtA0YxE06ZN0b9/fzx69EiLPSMiopfJZLJSbxw86Aa9GkBMmjQJjx8/BlAUCfLLL7+En58fbt68Kd9QSURE2uPn54ecnBz5/blz5yI7O1t+/59//kGDBg200DN6lV4tYZibm+PChQtwdnZGREQELly4gM2bN+P06dPw8/NDenq6trtIRKTXDAwMkJaWJs9NZGlpieTkZLi4uAAAMjIy4OjoyFkIHaBXMxDGxsZ49uwZAGDPnj3yY5O2trbymQkiItKeV3/T6tFv3HJHr05htG3bFqGhoWjTpg3++usvbNy4EQBw5coVVK9eXcu9IyIiKj/0agZi2bJlMDQ0xObNmxEdHY1q1aoBAP7880/4+vpquXdERCSRSEpEnWQUSt2kV3sgiIhIt0mlUnTr1g0mJiYAgO3bt6NTp06oWLEiACAvLw/x8fHcA6ED9HYA8fz5c+Tn5yuUWVpaaqk3REQEAEFBQW913erVq9XcE3oTvRpA5ObmYvLkyfjll1/wzz//lHicI1oiIqK3o1d7IL766ivs27cP0dHRMDExwY8//ogZM2bA0dERP/30k7a7R0REVG7o1QyEk5MTfvrpJ3To0AGWlpY4ffo0XF1d8fPPP2PDhg3YuXOntrtIRERULujVDERWVpY8GImlpSWysrIAFB3vPHTokDa7RkREVK7o1QDCxcUFN2/eBADUq1cPv/zyC4CiXb7W1tZa7BkREVH5oldLGN9++y0MDAwwbtw47NmzB/7+/hAEAS9evMDixYsxfvx4bXeRiIioXNCLSJQymQwLFizAtm3bkJ+fj/v37yM8PBwpKSlISkqCq6sr3NzctN1NIiKickMvZiBmzZqFiIgIeHt7o0KFCti1axc+//xzrFq1SttdIyIiKpf0YgBRp04dTJw4EcOHDwdQlEire/fu+PfffyGV6tU2ECIiIlHoxQDCxMQE165dQ40aNeRlpqamuHbtGpNoERERlYFe/PwuKCiAqampQpmRkRFevHihpR4RERGVb3qxiVIQBAwePFienAUoyoUxYsQIeYIWAPjtt9+00T0iIqJyRy8GEIMGDSpRNmDAAC30hIiI6P2gF3sgiIiISFx6sQeCiIiIxMUBBBEREamMAwgiIiJSGQcQREREpDIOIIiIiEhlHEAQERGRyjiAICIiIpX9P0UhGNC8kFVcAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Drop the Column\n",
        "df1.drop(columns=['Cabin'], axis=1, inplace=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.386501Z",
          "iopub.execute_input": "2024-06-03T05:31:43.386848Z",
          "iopub.status.idle": "2024-06-03T05:31:43.394002Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.386818Z",
          "shell.execute_reply": "2024-06-03T05:31:43.392562Z"
        },
        "trusted": true,
        "id": "8jQwQCGP4d3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Age.mean()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.395688Z",
          "iopub.execute_input": "2024-06-03T05:31:43.396079Z",
          "iopub.status.idle": "2024-06-03T05:31:43.408621Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.396027Z",
          "shell.execute_reply": "2024-06-03T05:31:43.407523Z"
        },
        "trusted": true,
        "id": "XwIF7iqw4d3X",
        "outputId": "8e137636-21ee-4529-c108-4dd576e94500"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 58,
          "output_type": "execute_result",
          "data": {
            "text/plain": "29.69911764705882"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 9. Handle Missing Values\n",
        "#df1.isnull().sum()\n",
        "df1['Age'].fillna(30)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.412005Z",
          "iopub.execute_input": "2024-06-03T05:31:43.412426Z",
          "iopub.status.idle": "2024-06-03T05:31:43.42428Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.412393Z",
          "shell.execute_reply": "2024-06-03T05:31:43.422923Z"
        },
        "trusted": true,
        "id": "3sqNCDPF4d3X",
        "outputId": "bb2f7ee4-3962-49f2-dc8c-6766b99c5196"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 59,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0      22.0\n1      38.0\n2      26.0\n3      35.0\n4      35.0\n       ... \n886    27.0\n887    19.0\n888    30.0\n889    26.0\n890    32.0\nName: Age, Length: 891, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isnull().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.42604Z",
          "iopub.execute_input": "2024-06-03T05:31:43.42659Z",
          "iopub.status.idle": "2024-06-03T05:31:43.447163Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.426554Z",
          "shell.execute_reply": "2024-06-03T05:31:43.445791Z"
        },
        "trusted": true,
        "id": "0rzf5I0K4d3X",
        "outputId": "83455ab0-a01a-4e06-ec65-119c1cbb3703"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 60,
          "output_type": "execute_result",
          "data": {
            "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nEmbarked         2\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10. Categorical Data Encoding\n",
        "df1.Sex.unique()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.448744Z",
          "iopub.execute_input": "2024-06-03T05:31:43.449076Z",
          "iopub.status.idle": "2024-06-03T05:31:43.464578Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.449048Z",
          "shell.execute_reply": "2024-06-03T05:31:43.463112Z"
        },
        "trusted": true,
        "id": "y1pq0Ug54d3Z",
        "outputId": "41bc512d-694b-4a55-e84a-fd5ab7104f9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 61,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['male', 'female'], dtype=object)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['sex'] = df1.Sex.map({'male':1, 'female':0})"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T05:31:43.466029Z",
          "iopub.execute_input": "2024-06-03T05:31:43.467078Z",
          "iopub.status.idle": "2024-06-03T05:31:43.480935Z",
          "shell.execute_reply.started": "2024-06-03T05:31:43.467029Z",
          "shell.execute_reply": "2024-06-03T05:31:43.478893Z"
        },
        "trusted": true,
        "id": "1Ad7UUA54d3Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1= pd.get_dummies(df1, columns = ['Embarked'] )"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:10:06.747579Z",
          "iopub.execute_input": "2024-06-03T06:10:06.74807Z",
          "iopub.status.idle": "2024-06-03T06:10:06.760974Z",
          "shell.execute_reply.started": "2024-06-03T06:10:06.748034Z",
          "shell.execute_reply": "2024-06-03T06:10:06.759623Z"
        },
        "trusted": true,
        "id": "lrOsvzWF4d3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:10:29.394798Z",
          "iopub.execute_input": "2024-06-03T06:10:29.396244Z",
          "iopub.status.idle": "2024-06-03T06:10:29.404941Z",
          "shell.execute_reply.started": "2024-06-03T06:10:29.3962Z",
          "shell.execute_reply": "2024-06-03T06:10:29.403717Z"
        },
        "trusted": true,
        "id": "WP5Si4VM4d3b",
        "outputId": "edcd9885-f6fd-49e5-a68a-c3db7bfb14b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 68,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n       'Parch', 'Ticket', 'Fare', 'sex', 'Embarked_C', 'Embarked_Q',\n       'Embarked_S'],\n      dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Univariate Analysis**"
      ],
      "metadata": {
        "id": "TeHTFUaf4d3b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Categorical Data**"
      ],
      "metadata": {
        "id": "TB_zPStt4d3c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 11.  How many people survived and How many died?\n",
        "df1.Survived.value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:12:57.334402Z",
          "iopub.execute_input": "2024-06-03T06:12:57.3349Z",
          "iopub.status.idle": "2024-06-03T06:12:57.347235Z",
          "shell.execute_reply.started": "2024-06-03T06:12:57.334865Z",
          "shell.execute_reply": "2024-06-03T06:12:57.345817Z"
        },
        "trusted": true,
        "id": "jvox4SvY4d3c",
        "outputId": "7c130c46-5294-47db-9cf8-826418520883"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 70,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Survived\n0    549\n1    342\nName: count, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = df1['Survived'])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:17:17.754826Z",
          "iopub.execute_input": "2024-06-03T06:17:17.755849Z",
          "iopub.status.idle": "2024-06-03T06:17:17.95063Z",
          "shell.execute_reply.started": "2024-06-03T06:17:17.755807Z",
          "shell.execute_reply": "2024-06-03T06:17:17.949264Z"
        },
        "trusted": true,
        "id": "ugZDvYp24d3c",
        "outputId": "8138e4dc-f084-4d25-8972-d454f8bfb006"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 76,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='Survived', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 12. How many passengers were in first class, second class and third class\n",
        "df1.Pclass.value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:23:31.195064Z",
          "iopub.execute_input": "2024-06-03T06:23:31.195623Z",
          "iopub.status.idle": "2024-06-03T06:23:31.207517Z",
          "shell.execute_reply.started": "2024-06-03T06:23:31.19558Z",
          "shell.execute_reply": "2024-06-03T06:23:31.205422Z"
        },
        "trusted": true,
        "id": "AUUg9sNs4d3d",
        "outputId": "884eddf9-37c6-445f-d791-2d40ad8427b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 81,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Pclass\n3    491\n1    216\n2    184\nName: count, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = df1.Pclass)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:23:53.428271Z",
          "iopub.execute_input": "2024-06-03T06:23:53.4288Z",
          "iopub.status.idle": "2024-06-03T06:23:53.613635Z",
          "shell.execute_reply.started": "2024-06-03T06:23:53.428759Z",
          "shell.execute_reply": "2024-06-03T06:23:53.612175Z"
        },
        "trusted": true,
        "id": "7Oy6HOwG4d3e",
        "outputId": "ec9de4a0-40d1-44c2-e1cd-322fe8cf624b"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 83,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='Pclass', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 13. Number of male and female Passsengers\n",
        "df1.Sex.value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:24:49.875807Z",
          "iopub.execute_input": "2024-06-03T06:24:49.876214Z",
          "iopub.status.idle": "2024-06-03T06:24:49.886934Z",
          "shell.execute_reply.started": "2024-06-03T06:24:49.876185Z",
          "shell.execute_reply": "2024-06-03T06:24:49.885537Z"
        },
        "trusted": true,
        "id": "cWgQsHIF4d3e",
        "outputId": "baea477f-03b3-4bdb-c1b6-f94f76e18132"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 86,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Sex\nmale      577\nfemale    314\nName: count, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = df1.Sex)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:25:09.006917Z",
          "iopub.execute_input": "2024-06-03T06:25:09.007342Z",
          "iopub.status.idle": "2024-06-03T06:25:09.270561Z",
          "shell.execute_reply.started": "2024-06-03T06:25:09.007307Z",
          "shell.execute_reply": "2024-06-03T06:25:09.269269Z"
        },
        "trusted": true,
        "id": "zBANDDRn4d3f",
        "outputId": "bb519d25-b0c3-4a03-cb80-9f6a3e2ba818"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 87,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='Sex', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Continuous Data**"
      ],
      "metadata": {
        "id": "CnXWPAp94d3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df1['Age'])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:26:21.01707Z",
          "iopub.execute_input": "2024-06-03T06:26:21.017494Z",
          "iopub.status.idle": "2024-06-03T06:26:21.35374Z",
          "shell.execute_reply.started": "2024-06-03T06:26:21.01746Z",
          "shell.execute_reply": "2024-06-03T06:26:21.352377Z"
        },
        "trusted": true,
        "id": "3prUruFj4d3h",
        "outputId": "083b855c-9b50-4199-ce42-d963783cf905"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 89,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(array([ 54.,  46., 177., 169., 118.,  70.,  45.,  24.,   9.,   2.]),\n array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n        64.084, 72.042, 80.   ]),\n <BarContainer object of 10 artists>)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df1.Age)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:29:44.786314Z",
          "iopub.execute_input": "2024-06-03T06:29:44.786774Z",
          "iopub.status.idle": "2024-06-03T06:29:44.980772Z",
          "shell.execute_reply.started": "2024-06-03T06:29:44.78674Z",
          "shell.execute_reply": "2024-06-03T06:29:44.979377Z"
        },
        "trusted": true,
        "id": "yb0qc4FQ4d3i",
        "outputId": "1282d3bd-5a03-4a07-95cf-8db52e52c1b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 90,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: >"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bivariate Analysis**"
      ],
      "metadata": {
        "id": "RnsE3RQS4d3i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BarPlot is used to compare categories of Data**"
      ],
      "metadata": {
        "id": "33Wt4MVz4d3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Who Has Better Chance of Survival Male or Female?\n",
        "sns.barplot(x='Sex', y='Survived', data=df1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:45:29.084571Z",
          "iopub.execute_input": "2024-06-03T06:45:29.084993Z",
          "iopub.status.idle": "2024-06-03T06:45:29.904315Z",
          "shell.execute_reply.started": "2024-06-03T06:45:29.084963Z",
          "shell.execute_reply": "2024-06-03T06:45:29.903047Z"
        },
        "trusted": true,
        "id": "idg5tT1A4d3j",
        "outputId": "b1cb9e5a-dee4-4a81-f364-f79e796e026c"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 100,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='Sex', ylabel='Survived'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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OhZuvv/5a77777hHt7777rgKBwHEXBQAA0FmdevxCXl6e5s+frz179igtLU2S9Oqrr2r16tWhZ0IBAABYoVPh5u6771Z8fLzuuece7du3T5I0YsQI3XDDDfrVr37VpQUCAAB0RKfCjd1u14033qgbb7xRjY2NksRCYgAAEBY6/SV+X3/9tZ5//nn9+c9/ls1mkyTt3btXBw8e7LLiAAAAOqpTMzcff/yxLrroIlVXV8vv92v69OkaOHCg1qxZI7/fr5KSkq6uEwAAoF06NXOzbNkypaam6osvvlC/fv1C7XPmzJHH4+my4gAAADqqUzM3L730krZt26bIyMhW7UlJSfr000+7pDAAAIDO6NTMTSAQUEtLyxHtn3zyiQYOHHjcRQEAAHRWp8LNjBkzVFRUFNq32Ww6ePCgCgsLdfHFF3dVbQAAAB3WqdtS99xzj7KysjRmzBgdPnxYV111ld5//33FxcXpz3/+c1fXCAAA0G6dCjcnn3yy3njjDW3cuFFvvvmmDh48qPnz5+tnP/tZqwXGAAAAPa1T4ebw4cOKiorS1Vdf3dX1AAAAHJdOrbkZPny4cnNz9dxzz/GgTAAAEFY6FW4eeughHTp0SJdccokSEhJ03XXXafv27V1dGwAAQId1KtzMmTNHjz32mHw+n+6880698847OvfcczVq1CitXLmyq2sEAABot04/W0qSBg4cqLy8PG3ZskVvvvmmBgwYoDvuuKOragMAAOiw4wo3hw8f1qOPPqrZs2dr4sSJ+vzzz3XDDTd0VW0AAAAd1qlPSz377LPasGGDnnrqKfXp00eXXXaZtmzZoilTpnR1fQAAAB3SqXAzZ84c/fjHP9bDDz+siy++WH379u3qugAAADqlU+HG5/PxDCkAABCW2h1uGhsbFRMTI0kKBoNqbGw8at9v+gEAAPS0doebwYMHa9++fRo+fLgGDRokm812RJ9gMCibzdbmE8MBAAB6QrvDzf/+7/9qyJAhoZ/bCjcAAABWa3e4mTp1aujnadOmdUctAAAAx61T33Nz5pln6vbbb9f777/f1fUAAAAcl06Fm0WLFmnz5s0666yzNGnSJK1bt05er7fTRRQXFyspKUlRUVFKT09XRUVFu8Zt3LhRNptNs2fP7vS5AQCAWToVbq6//nq99tpr2rVrly6++GIVFxcrMTFRM2bM0MMPP9yhY23atEkul0uFhYWqqqrS+PHjlZWVpdra2mOO++ijj7R8+XKdf/75nbkEAABgqON6/MKoUaN0xx136L333tNLL72kuro65eXldegYa9eu1cKFC5WXl6cxY8aopKRE/fv3V2lp6VHHtLS06Gc/+5nuuOMOnXbaacc8vt/vV2NjY6sNAACY67jCjSRVVFTouuuu05w5c/Tee+9p7ty57R7b3NysyspKZWZmfluQ3a7MzEyVl5cfddzKlSs1fPhwzZ8//3vP4Xa7FRsbG9oSExPbXR8AAOh9OhVu3nvvPRUWFmrUqFH6wQ9+oF27dmnNmjXy+XzauHFju49TX1+vlpYWOZ3OVu1Op/Ooa3hefvllPfjgg1q/fn27zpGfn6+GhobQVlNT0+76AABA79Opxy98s5B48eLFuuKKK44IJ93lwIEDmjdvntavX6+4uLh2jXE4HHI4HN1cGQAACBcdDjctLS36wx/+oMsuu0yDBw8+rpPHxcUpIiJCPp+vVbvP51N8fPwR/ffs2aOPPvpIs2bNCrUFAgFJUp8+fbR7926dfvrpx1UTAADo3Tp8WyoiIkJLly7V/v37j/vkkZGRSklJkcfjCbUFAgF5PB5lZGQc0f+ss87Szp07tWPHjtD2k5/8RD/84Q+1Y8cO1tMAAIDO3ZYaO3asPvzwQ5166qnHXYDL5VJubq5SU1OVlpamoqIiNTU1hT51lZOTo4SEBLndbkVFRWns2LGtxg8aNChUEwAAQKfCza9//WstX75cq1atUkpKigYMGNDq9Y48FTw7O1t1dXUqKCiQ1+tVcnKyysrKQut4qqurZbcf94e6AADACcIWDAaDHR303bDx3Qdo9oangjc2Nio2NlYNDQ0dCmEA8I3qleOsLgEIO6cU7OzW43fk73enZm5eeOGFThUGAADQ3ToVbr77hHAAAIBw0qlw87e//e2Yr0+ZMqVTxQAAAByvToWbadOmHdH23bU34bzmBgAAmK1TH0P64osvWm21tbUqKyvTpEmTtGXLlq6uEQAAoN06NXMTGxt7RNv06dMVGRkpl8ulysrK4y4MAACgM7r0C2ScTqd2797dlYcEAADokE7N3Lz55put9oPBoPbt26fVq1crOTm5K+oCAADolE6Fm+TkZNlsNv3z9/+de+65Ki0t7ZLCAAAAOqNT4ebvf/97q3273a5hw4YpKiqqS4oCAADorA6tuSkvL9df//pXjRw5MrS9+OKLmjJlik455RRdc8018vv93VUrAADA9+pQuFm5cqXefvvt0P7OnTs1f/58ZWZmasWKFfqf//kfud3uLi8SAACgvToUbnbs2KELL7wwtL9x40alp6dr/fr1crlc+t3vfqdHH320y4sEAABorw6Fmy+++EJOpzO0/+KLL+pHP/pRaH/SpEmqqanpuuoAAAA6qEPhxul0hhYTNzc3q6qqSueee27o9QMHDqhv375dWyEAAEAHdCjcXHzxxVqxYoVeeukl5efnq3///jr//PNDr7/55ps6/fTTu7xIAACA9urQR8FXrVqln/70p5o6daqio6P10EMPKTIyMvR6aWmpZsyY0eVFAgAAtFeHwk1cXJz+9re/qaGhQdHR0YqIiGj1+mOPPabo6OguLRAAAKAjuuzBmZI0ZMiQ4yoGAADgeHXpgzMBAACsRrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYJSwCDfFxcVKSkpSVFSU0tPTVVFRcdS+Tz75pFJTUzVo0CANGDBAycnJeuSRR3qwWgAAEM4sDzebNm2Sy+VSYWGhqqqqNH78eGVlZam2trbN/kOGDNEtt9yi8vJyvfnmm8rLy1NeXp6effbZHq4cAACEI1swGAxaWUB6eromTZqk++67T5IUCASUmJiopUuXasWKFe06xsSJEzVz5kytWrXqe/s2NjYqNjZWDQ0NiomJOa7aAZyYqleOs7oEIOycUrCzW4/fkb/fls7cNDc3q7KyUpmZmaE2u92uzMxMlZeXf+/4YDAoj8ej3bt3a8qUKW328fv9amxsbLUBAABzWRpu6uvr1dLSIqfT2ard6XTK6/UedVxDQ4Oio6MVGRmpmTNn6t5779X06dPb7Ot2uxUbGxvaEhMTu/QaAABAeLF8zU1nDBw4UDt27NBrr72m3/zmN3K5XNq6dWubffPz89XQ0BDaampqerZYAADQo/pYefK4uDhFRETI5/O1avf5fIqPjz/qOLvdrjPOOEOSlJycrF27dsntdmvatGlH9HU4HHI4HF1aNwAACF+WztxERkYqJSVFHo8n1BYIBOTxeJSRkdHu4wQCAfn9/u4oEQAA9DKWztxIksvlUm5urlJTU5WWlqaioiI1NTUpLy9PkpSTk6OEhAS53W5J/1hDk5qaqtNPP11+v19PP/20HnnkEf3+97+38jIAAECYsDzcZGdnq66uTgUFBfJ6vUpOTlZZWVlokXF1dbXs9m8nmJqamrRo0SJ98skn6tevn8466yz96U9/UnZ2tlWXAAAAwojl33PT0/ieGwDHi++5AY7E99wAAAB0E8INAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwSh+rCwC6y7Jly1RXVydJGjZsmNatW2dxRQCAnkC4gbHq6urk8/msLgMA0MO4LQUAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYJSwCDfFxcVKSkpSVFSU0tPTVVFRcdS+69ev1/nnn6/Bgwdr8ODByszMPGZ/AABwYrE83GzatEkul0uFhYWqqqrS+PHjlZWVpdra2jb7b926VVdeeaVeeOEFlZeXKzExUTNmzNCnn37aw5UDAIBwZHm4Wbt2rRYuXKi8vDyNGTNGJSUl6t+/v0pLS9vs/5//+Z9atGiRkpOTddZZZ+mBBx5QIBCQx+Pp4coBAEA4sjTcNDc3q7KyUpmZmaE2u92uzMxMlZeXt+sYhw4d0ldffaUhQ4a0+brf71djY2OrDQAAmMvScFNfX6+WlhY5nc5W7U6nU16vt13HuOmmm3TSSSe1Ckjf5Xa7FRsbG9oSExOPu24AABC+LL8tdTxWr16tjRs36i9/+YuioqLa7JOfn6+GhobQVlNT08NVAgCAnmTpgzPj4uIUERFxxMMNfT6f4uPjjzn27rvv1urVq/X888/rnHPOOWo/h8Mhh8PRJfUCAIDwZ+nMTWRkpFJSUlotBv5mcXBGRsZRx/32t7/VqlWrVFZWptTU1J4oFQAA9BKWztxIksvlUm5urlJTU5WWlqaioiI1NTUpLy9PkpSTk6OEhAS53W5J0po1a1RQUKANGzYoKSkptDYnOjpa0dHRll0HAAAID5aHm+zsbNXV1amgoEBer1fJyckqKysLLTKurq6W3f7tBNPvf/97NTc367LLLmt1nMLCQt1+++09WfoxpdzwsNUlnPBivjgYmprc98VB/k3CQOVdOVaXAOAEYHm4kaQlS5ZoyZIlbb62devWVvsfffRR9xcEAAB6rV79aSkAAIB/RrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFHC4sGZQHcI9B3Q5s8AALMRbmCsg6N/ZHUJAAALcFsKAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAo1geboqLi5WUlKSoqCilp6eroqLiqH3ffvttXXrppUpKSpLNZlNRUVHPFQoAAHoFS8PNpk2b5HK5VFhYqKqqKo0fP15ZWVmqra1ts/+hQ4d02mmnafXq1YqPj+/hagEAQG9gabhZu3atFi5cqLy8PI0ZM0YlJSXq37+/SktL2+w/adIk3XXXXbriiivkcDjadQ6/36/GxsZWGwAAMJdl4aa5uVmVlZXKzMz8thi7XZmZmSovL++y87jdbsXGxoa2xMTELjs2AAAIP5aFm/r6erW0tMjpdLZqdzqd8nq9XXae/Px8NTQ0hLaampouOzYAAAg/fawuoLs5HI5238ICAAC9n2UzN3FxcYqIiJDP52vV7vP5WCwMAAA6zbJwExkZqZSUFHk8nlBbIBCQx+NRRkaGVWUBAIBeztLbUi6XS7m5uUpNTVVaWpqKiorU1NSkvLw8SVJOTo4SEhLkdrsl/WMR8jvvvBP6+dNPP9WOHTsUHR2tM844w7LrAAAA4cPScJOdna26ujoVFBTI6/UqOTlZZWVloUXG1dXVstu/nVzau3evJkyYENq/++67dffdd2vq1KnaunVrT5cPAADCkOULipcsWaIlS5a0+do/B5akpCQFg8EeqAoAAPRWlj9+AQAAoCsRbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAo4RFuCkuLlZSUpKioqKUnp6uioqKY/Z/7LHHdNZZZykqKkrjxo3T008/3UOVAgCAcGd5uNm0aZNcLpcKCwtVVVWl8ePHKysrS7W1tW3237Ztm6688krNnz9fr7/+umbPnq3Zs2frrbfe6uHKAQBAOLI83Kxdu1YLFy5UXl6exowZo5KSEvXv31+lpaVt9l+3bp0uuugi3XDDDTr77LO1atUqTZw4Uffdd18PVw4AAMJRHytP3tzcrMrKSuXn54fa7Ha7MjMzVV5e3uaY8vJyuVyuVm1ZWVl66qmn2uzv9/vl9/tD+w0NDZKkxsbG46z+2Fr8X3br8YHeqLvfdz3lwOEWq0sAwk53v7+/OX4wGPzevpaGm/r6erW0tMjpdLZqdzqdevfdd9sc4/V62+zv9Xrb7O92u3XHHXcc0Z6YmNjJqgF0Vuy9/2p1CQC6izu2R05z4MABxcYe+1yWhpuekJ+f32qmJxAI6PPPP9fQoUNls9ksrAw9obGxUYmJiaqpqVFMTIzV5QDoQry/TyzBYFAHDhzQSSed9L19LQ03cXFxioiIkM/na9Xu8/kUHx/f5pj4+PgO9Xc4HHI4HK3aBg0a1Pmi0SvFxMTwnx9gKN7fJ47vm7H5hqULiiMjI5WSkiKPxxNqCwQC8ng8ysjIaHNMRkZGq/6S9Nxzzx21PwAAOLFYflvK5XIpNzdXqampSktLU1FRkZqampSXlydJysnJUUJCgtxutyRp2bJlmjp1qu655x7NnDlTGzdu1Pbt23X//fdbeRkAACBMWB5usrOzVVdXp4KCAnm9XiUnJ6usrCy0aLi6ulp2+7cTTJMnT9aGDRt066236uabb9aZZ56pp556SmPHjrXqEhDGHA6HCgsLj7g1CaD34/2No7EF2/OZKgAAgF7C8i/xAwAA6EqEGwAAYBTCDQAAMArhBiekn//855o9e7bVZQAnjGAwqGuuuUZDhgyRzWbTjh07LKnjo48+svT86BmWf1oKAGC+srIy/fGPf9TWrVt12mmnKS4uzuqSYDDCDQCg2+3Zs0cjRozQ5MmTrS4FJwBuSyHsTZs2TUuXLtV1112nwYMHy+l0av369aEvexw4cKDOOOMMPfPMM5KklpYWzZ8/X6eeeqr69eun0aNHa926dcc8RyAQkNvtDo0ZP368Hn/88Z64PMB4P//5z7V06VJVV1fLZrMpKSnpe99zW7dulc1m07PPPqsJEyaoX79+uuCCC1RbW6tnnnlGZ599tmJiYnTVVVfp0KFDoXFlZWU677zzNGjQIA0dOlQ//vGPtWfPnmPW99Zbb+lHP/qRoqOj5XQ6NW/ePNXX13fb7wPdj3CDXuGhhx5SXFycKioqtHTpUl177bWaO3euJk+erKqqKs2YMUPz5s3ToUOHFAgEdPLJJ+uxxx7TO++8o4KCAt1888169NFHj3p8t9uthx9+WCUlJXr77bd1/fXX6+qrr9aLL77Yg1cJmGndunVauXKlTj75ZO3bt0+vvfZau99zt99+u+677z5t27ZNNTU1uvzyy1VUVKQNGzZo8+bN2rJli+69995Q/6amJrlcLm3fvl0ej0d2u11z5sxRIBBos7b9+/frggsu0IQJE7R9+3aVlZXJ5/Pp8ssv79bfCbpZEAhzU6dODZ533nmh/a+//jo4YMCA4Lx580Jt+/btC0oKlpeXt3mMxYsXBy+99NLQfm5ubvCSSy4JBoPB4OHDh4P9+/cPbtu2rdWY+fPnB6+88souvBLgxPVv//ZvwZEjRwaDwfa951544YWgpODzzz8fet3tdgclBffs2RNq+8UvfhHMyso66nnr6uqCkoI7d+4MBoPB4N///vegpODrr78eDAaDwVWrVgVnzJjRakxNTU1QUnD37t2dvl5YizU36BXOOeec0M8REREaOnSoxo0bF2r75nEdtbW1kqTi4mKVlpaqurpaX375pZqbm5WcnNzmsT/44AMdOnRI06dPb9Xe3NysCRMmdPGVAOjIe+67732n06n+/fvrtNNOa9VWUVER2n///fdVUFCgV199VfX19aEZm+rq6jYf0/PGG2/ohRdeUHR09BGv7dmzR6NGjercRcJShBv0Cn379m21b7PZWrXZbDZJ/1g7s3HjRi1fvlz33HOPMjIyNHDgQN1111169dVX2zz2wYMHJUmbN29WQkJCq9d4Zg3Q9Trynvvn93lb/xd895bTrFmzNHLkSK1fv14nnXSSAoGAxo4dq+bm5qPWMmvWLK1Zs+aI10aMGNGxC0PYINzAOK+88oomT56sRYsWhdqOtaBwzJgxcjgcqq6u1tSpU3uiROCE1l3vuc8++0y7d+/W+vXrdf7550uSXn755WOOmThxop544gklJSWpTx/+JJqCf0kY58wzz9TDDz+sZ599VqeeeqoeeeQRvfbaazr11FPb7D9w4EAtX75c119/vQKBgM477zw1NDTolVdeUUxMjHJzc3v4CgCzddd7bvDgwRo6dKjuv/9+jRgxQtXV1VqxYsUxxyxevFjr16/XlVdeqRtvvFFDhgzRBx98oI0bN+qBBx5QREREp2qBtQg3MM4vfvELvf7668rOzpbNZtOVV16pRYsWhT4q3pZVq1Zp2LBhcrvd+vDDDzVo0CBNnDhRN998cw9WDpw4uuM9Z7fbtXHjRv3yl7/U2LFjNXr0aP3ud7/TtGnTjjrmpJNO0iuvvKKbbrpJM2bMkN/v18iRI3XRRRfJbucDxb2VLRgMBq0uAgAAoKsQSwEAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AHqFuro6XXvttTrllFPkcDgUHx+vrKwsvfLKK1aXBiDM8GwpAL3CpZdequbmZj300EM67bTT5PP55PF49Nlnn1ldGoAww8wNgLC3f/9+vfTSS1qzZo1++MMfauTIkUpLS1N+fr5+8pOfhPosWLBAw4YNU0xMjC644AK98cYbkv4x6xMfH68777wzdMxt27YpMjJSHo/HkmsC0H0INwDCXnR0tKKjo/XUU0/J7/e32Wfu3Lmqra3VM888o8rKSk2cOFEXXnihPv/8cw0bNkylpaW6/fbbtX37dh04cEDz5s3TkiVLdOGFF/bw1QDobjwVHECv8MQTT2jhwoX68ssvNXHiRE2dOlVXXHGFzjnnHL388suaOXOmamtr5XA4QmPOOOMM3XjjjbrmmmskSYsXL9bzzz+v1NRU7dy5U6+99lqr/gDMQLgB0GscPnxYL730kv7v//5PzzzzjCoqKvTAAw+oqalJv/zlL9WvX79W/b/88kstX75ca9asCe2PHTtWNTU1qqys1Lhx46y4DADdjHADoNdasGCBnnvuOS1atEj33nuvtm7dekSfQYMGKS4uTpL01ltvadKkSfrqq6/0l7/8RbNmzerhigH0BD4tBaDXGjNmjJ566ilNnDhRXq9Xffr0UVJSUpt9m5ubdfXVVys7O1ujR4/WggULtHPnTg0fPrxniwbQ7Zi5ARD2PvvsM82dO1f/8i//onPOOUcDBw7U9u3btXTpUs2cOVMPPPCApkyZogMHDui3v/2tRo0apb1792rz5s2aM2eOUlNTdcMNN+jxxx/XG2+8oejoaE2dOlWxsbH661//avXlAehihBsAYc/v9+v222/Xli1btGfPHn311VdKTEzU3LlzdfPNN6tfv346cOCAbrnlFj3xxBOhj35PmTJFbrdbe/bs0fTp0/XCCy/ovPPOkyR99NFHGj9+vFavXq1rr73W4isE0JUINwAAwCh8zw0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjPL/AIW3uC0prCsGAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Which Passenger Class Has Better Chance of Survival (First, Second, Or Third Class)?\n",
        "sns.barplot(x='Pclass', y ='Survived', data=df1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-03T06:49:15.654585Z",
          "iopub.execute_input": "2024-06-03T06:49:15.655789Z",
          "iopub.status.idle": "2024-06-03T06:49:16.041907Z",
          "shell.execute_reply.started": "2024-06-03T06:49:15.655745Z",
          "shell.execute_reply": "2024-06-03T06:49:16.040542Z"
        },
        "trusted": true,
        "id": "rIfVXe2t4d3k",
        "outputId": "7e084f83-17e0-40a1-a18f-1daf1cb15745"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 107,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='Pclass', ylabel='Survived'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    }
  ]
}