Titanic_eda.ipynb

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        "<a href=\"https://colab.research.google.com/github/levi178u/IP_TASK/blob/main/Titanic_eda.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.7-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
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            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.26.4)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.1.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.13.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.2)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.4)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (9.0.0)\n",
            "Downloading catboost-1.2.7-cp310-cp310-manylinux2014_x86_64.whl (98.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: catboost\n",
            "Successfully installed catboost-1.2.7\n"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "import sklearn\n",
        "\n",
        "!pip install catboost\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n"
      ]
    },
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        "df_test =pd.read_csv(\"/content/test.csv\")\n",
        "df_train =pd.read_csv(\"/content/train.csv\")\n",
        "df_ss=pd.read_csv(\"/content/gender_submission.csv\")"
      ],
      "metadata": {
        "id": "220FWE_kmz4a"
      },
      "execution_count": 6,
      "outputs": []
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      "execution_count": 14,
      "outputs": [
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
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            }
          },
          "metadata": {},
          "execution_count": 14
        }
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    {
      "cell_type": "code",
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        "df_ss.head()"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         1\n",
              "2          894         0\n",
              "3          895         0\n",
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              "type": "dataframe",
              "variable_name": "df_ss",
              "summary": "{\n  \"name\": \"df_ss\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  </style>\n",
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              "        document.querySelector('#df-3df136f7-b371-4533-abab-632e04c475dd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3df136f7-b371-4533-abab-632e04c475dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0421e93-c75d-474e-a379-a0403598e854')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0421e93-c75d-474e-a379-a0403598e854 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training set shape :\",df_train.shape)\n",
        "print(\"Testing set shape :\",df_test.shape)\n",
        "print(\"Submission set shape :\",df_ss.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlNViBZb0UcI",
        "outputId": "144566da-6a51-4a6c-cfc1-a176a56d8bbb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape : (891, 12)\n",
            "Testing set shape : (418, 11)\n",
            "Submission set shape : (418, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###LET's start with EDA"
      ],
      "metadata": {
        "id": "xTNC3iNY0brp"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Data description\n",
        "\n",
        "# Here I will outline the definitions of the columns in the titanic dataset\n",
        "\n",
        "# Survived: 0 = Did not survive, 1 = Survived\n",
        "\n",
        "# Pclass: Ticket class where 1 = First class, 2 = Second class, 3 = Third class. This can also be seen as a proxy for socio-economic status.\n",
        "\n",
        "# Sex: Male or female\n",
        "\n",
        "# Age: Age in years, fractional if less than 1\n",
        "\n",
        "# SibSp: Number of siblings or spouses aboard the titanic\n",
        "\n",
        "# Parch: Number of parents or children aboard the titanic\n",
        "\n",
        "# Ticket: Passenger ticket number\n",
        "\n",
        "# Fare: Passenger fare\n",
        "\n",
        "# Cabin: Cabin number\n",
        "\n",
        "# Embarked: Point of embarkation where C = Cherbourg, Q = Queenstown, S = Southampton\n",
        "\n"
      ],
      "metadata": {
        "id": "pE0JBmZw1PfZ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nii-ecZz2hMC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ov4z6wd01WYS",
        "outputId": "e1a34030-911c-4316-bfcb-55ff02a57067"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "3nYEdGzc2jni",
        "outputId": "a79b7c8b-ccb7-497d-e484-2a750ba8767e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6aef647-3436-4160-9827-f40b127aec63\" class=\"colab-df-container\">\n",
              "    <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",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
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              "  </div>\n"
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            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we deleted or dropped out the duplicated data\n",
        "df_train.drop_duplicates()\n",
        "df_train.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYlUHB6v2nhd",
        "outputId": "674e5eb8-51ea-47ea-b930-9516aa65d195"
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      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 25
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    {
      "cell_type": "code",
      "source": [
        "# Now working on missing values\n",
        "\n",
        "df_train.isnull().sum().sort_values(ascending =False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
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        "id": "QWBYVmFz3dv9",
        "outputId": "368c7cb8-8785-408d-8d62-63d5fa51acf5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          687\n",
              "Age            177\n",
              "Embarked         2\n",
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
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        "df_test.isnull().sum().sort_values(ascending =False)"
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        "outputId": "a02bf85f-9c18-43ca-a717-d7a0a661d192"
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      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          327\n",
              "Age             86\n",
              "Fare             1\n",
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    {
      "cell_type": "code",
      "source": [
        "df_train.fillna(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ILAr0knF5u60",
        "outputId": "440e4cf9-d37d-4c37-d273-9c5aa2a7c955"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
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              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   0.0      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500     0        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250     0        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500     0        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
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              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500     0        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500     0        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
            ],
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <td>STON/O2. 3101282</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
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              "      <td>S</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
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              "    <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>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
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              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
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              "    <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>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.596074065915875,\n        \"min\": 0.0,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 148,\n        \"samples\": [\n          \"B101\",\n          \"A19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"C\",\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's visualize the missing data elements of the dataset\n",
        "\n",
        "import missingno as msno\n",
        "msno.bar(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "WqYpK0bb4bAN",
        "outputId": "fe0a0b24-e6f3-4163-fa71-908e1dac3494"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.matrix(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "my_SSlYT4vj5",
        "outputId": "d5b5bc2d-9635-4525-d0cf-48892efc62e4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dNscQJyn42Zr",
        "outputId": "07e148b2-649f-48ff-82e2-03f89db42967"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d9e5493-51f8-4eb7-8cfe-d628e6b02075\" class=\"colab-df-container\">\n",
              "    <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",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-86464ae5-0f6d-40a6-9977-32d6c9ca75e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429855,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "K3T0OirA49-l",
        "outputId": "dc7202d5-6d6c-4f9c-f547-c1384ddd5675"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
              "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
              "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
              "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
              "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
              "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
              "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
              "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
              "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-234634b0-0340-48b5-8fb6-6fae1ff12cad\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</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>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>417.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>2.265550</td>\n",
              "      <td>30.272590</td>\n",
              "      <td>0.447368</td>\n",
              "      <td>0.392344</td>\n",
              "      <td>35.627188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>120.810458</td>\n",
              "      <td>0.841838</td>\n",
              "      <td>14.181209</td>\n",
              "      <td>0.896760</td>\n",
              "      <td>0.981429</td>\n",
              "      <td>55.907576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>892.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.170000</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>996.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>27.000000</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>1204.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "        const element = document.querySelector('#df-234634b0-0340-48b5-8fb6-6fae1ff12cad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9763e143-513e-49cf-a986-bbabe35749e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 412.1232851470217,\n        \"min\": 120.81045760473994,\n        \"max\": 1309.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          418.0,\n          1100.5,\n          1204.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.0758997861715,\n        \"min\": 0.8418375519640503,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.2655502392344498,\n          3.0,\n          0.8418375519640503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.15868834351015,\n        \"min\": 0.17,\n        \"max\": 332.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.272590361445783,\n          27.0,\n          332.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.28745840271156,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          418.0,\n          0.4473684210526316,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.29373273558997,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3923444976076555,\n          9.0,\n          0.9814288785371691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206.20808392426468,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.627188489208635,\n          14.4542,\n          417.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Annalysis via Categotical variable :Sex,\n",
        "\n",
        "df_train['Sex'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "_FfrpHTd5GwX",
        "outputId": "291efd57-5bb2-4e8e-df72-6692cb8828ef"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>577</td>\n",
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              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean of survival by sex\n",
        "\n",
        "df_train[['Sex', 'Survived']].groupby('Sex', as_index =False).mean().sort_values(by = 'Survived', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SyNfHcy_OOcO",
        "outputId": "0b3bd7f9-335e-4f64-e462-ebbd35829914"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Sex  Survived\n",
              "0  female  0.742038\n",
              "1    male  0.188908"
            ],
            "text/html": [
              "\n",
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              "      <th>0</th>\n",
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              "      <td>0.742038</td>\n",
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              "      <th>1</th>\n",
              "      <td>male</td>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1074187b-42ab-4633-8fc5-5077fda79e2c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train[['Sex', 'Survived']]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.391122024068098,\n        \"min\": 0.18890814558058924,\n        \"max\": 0.7420382165605095,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.18890814558058924,\n          0.7420382165605095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = 'Sex', y = 'Survived', data = df_train)\n",
        "plt.ylabel('Survival Probability')\n",
        "plt.title('Survival Probability by Sex')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "x7mA2GR1OenG",
        "outputId": "3d4bd4bf-5777-4f4c-a1a8-249b98359af9"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Survival Probability by Sex')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Pclass'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "3Ybka_eJPPb4",
        "outputId": "fa89f0f9-53be-486d-aebc-36d188d4dc10"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>184</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "VwvTcOP-QBCC",
        "outputId": "cc55fa1a-385b-479d-e5ca-7a63f33f2d4e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Survived\n",
              "0       1  0.629630\n",
              "1       2  0.472826\n",
              "2       3  0.242363"
            ],
            "text/html": [
              "\n",
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              "      <th>Pclass</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.242363</td>\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "        document.querySelector('#df-a067678b-675d-4a96-ab94-0a57049327fe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a067678b-675d-4a96-ab94-0a57049327fe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<div id=\"df-7a84eb5b-615b-40eb-92ee-ceb75c2c27e4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a84eb5b-615b-40eb-92ee-ceb75c2c27e4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a84eb5b-615b-40eb-92ee-ceb75c2c27e4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train[['Pclass', 'Survived']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19479759330588634,\n        \"min\": 0.24236252545824846,\n        \"max\": 0.6296296296296297,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6296296296296297,\n          0.47282608695652173,\n          0.24236252545824846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = 'Pclass', y ='Survived', data = df_train )\n",
        "plt.ylabel('Survival Probability')\n",
        "plt.title('Survival Probability by Passenger Class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "zj2-0db4QEdt",
        "outputId": "976254ce-9c68-4afa-c24c-bde5dd2b7790"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Survival Probability by Passenger Class')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Rzsgfg9RdqF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pg = sns.catplot(x = 'Pclass', y = 'Survived', hue = 'Sex', data = df_train, kind = 'bar')\n",
        "pg.despine(left = True)\n",
        "plt.ylabel('Survival Probability')\n",
        "plt.title('Survival Probability by Sex and Passenger Class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "p0tDsOYRQOVO",
        "outputId": "b8fb3319-8daf-4cca-c0cb-0dc20bed2163"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Survival Probability by Sex and Passenger Class')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 598x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Embarked'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "aRKOrRECQzZZ",
        "outputId": "7f68aa9d-8502-468c-86f9-889482488894"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "S      644\n",
              "C      168\n",
              "Q       77\n",
              "NaN      2\n",
              "Name: count, dtype: int64"
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>644</td>\n",
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              "      <th>C</th>\n",
              "      <td>168</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[['Embarked', 'Survived']].groupby(['Embarked'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
      ],
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        "colab": {
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        },
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        "outputId": "a010119f-4be8-4c23-c0a0-70ca0b5af7e7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Embarked  Survived\n",
              "0        C  0.553571\n",
              "1        Q  0.389610\n",
              "2        S  0.336957"
            ],
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              "      const buttonEl =\n",
              "        document.querySelector('#df-113558e4-3ae7-4019-a4b6-c60bf7abb9f6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-113558e4-3ae7-4019-a4b6-c60bf7abb9f6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-93c1bd79-fee6-46b6-88f9-a77cd91b7078\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93c1bd79-fee6-46b6-88f9-a77cd91b7078')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-93c1bd79-fee6-46b6-88f9-a77cd91b7078 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train[['Embarked', 'Survived']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11297320475888119,\n        \"min\": 0.33695652173913043,\n        \"max\": 0.5535714285714286,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5535714285714286,\n          0.38961038961038963,\n          0.33695652173913043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.dropna(axis =1, how ='all')\n",
        "\n",
        "df_train.reset_index(inplace =True)\n",
        "df_train.set_index('PassengerId', inplace =True)"
      ],
      "metadata": {
        "id": "EixkNQ8jRuux"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(x='Pclass', col ='Embarked', data= df_train, kind= 'count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "Ye38HLBuSESq",
        "outputId": "fa2009b7-6931-43e9-be60-5752a4ee188a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff4ef254070>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1511.11x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figuring out the survival probabilities of all categorical variables\n",
        "\n",
        "plo= sns.FacetGrid(data =df_train, row = 'Survived', height = 2.2, aspect = 1.6)\n",
        "plo.map(sns.pointplot, 'Pclass','Sex', palette ='deep',data =df_train)\n",
        "plo.add_legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "dLn7we9rS_C2",
        "outputId": "0672eec9-812c-49e3-f12f-586eb106e197"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff4ed3f3be0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 363.111x440 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plo= sns.FacetGrid(df_train, row = 'Embarked', height = 2.2, aspect = 1.6)\n",
        "plo.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette ='deep',data =df_train)\n",
        "plo.add_legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "lr2tuUjLTpoV",
        "outputId": "8aa7abb0-af7c-4eb4-ec76-6cea8c7ceaee"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff4ed3b5570>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 450x660 with 3 Axes>"
            ],
            "image/png": 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2QM8gb73yf87fwnsrDyA7r2qbsxKRaTCxEZWjOpPfbaylmPFKNwzq6adXnnotFzO/3I/0W5VvhUNEpsHh/gZwuD8ZSxRFbPn9HDbtStErd3GQYe7EHmjV1MU8gRE9QthiIzIhQRDw4sC2eOP5jnhws+6cfBWivzqAf85XfS1KIjIOExtRLRgU6odZkd305sIVqkowd80h7Dtx3YyREVk+JjaiWhLawQcfvhoKe/n9TTRKNFp8+v0R7Nx/0YyREVk2JjaiWtS+lRsWvtkbjZxkujJRBFb9cgrf/5bMVUqIagETG1Eta+HjjE+mPI4m7vZ65Vv/OIflP/wDjUZrpsiILFO9SGwrVqyAn58f5HI5QkJCkJiYWG7dfv36QRCEMsdTTz2lqzN27Ngy5yMiIurioxAZ5NnIDosn90Hrh3YN2H34ChZu+BuqYq5SQmQqZk9sW7duRVRUFGJiYnDs2DF07NgR4eHhuHnzpsH6P//8M9LT03XH6dOnIZVK8cILL+jVi4iI0Ku3efPmuvg4ROVydpDho0m90Lmth1754TMZmLP6IPIL1GaKjMiymD2x/etf/8LEiRMxbtw4BAYGYtWqVbCzs8O6desM1m/UqBG8vLx0x++//w47O7syiU0mk+nVc3V1rYuPQ1QhW5kVZv9fCPp1bqpXnnTpDmat2I/buYVmiozIcpg1sanVahw9ehRhYWG6MolEgrCwMCQkJFTpHmvXrsWoUaNgb6///CI+Ph4eHh5o27YtJk2ahNu3b5s0diJjWVtJ8PaLnTGsbyu98isZeZj+5T5czcwzU2RElsGsie3WrVvQaDTw9PTUK/f09ERGRkal1ycmJuL06dOYMGGCXnlERAQ2btyIuLg4LF68GHv27MGgQYOg0Rh+jqFSqaBQKPQOotokkQgY/0x7jHs6UK88K7sQM5fvx9krd8wUGVHDZ/auyJpYu3YtOnTogO7du+uVjxo1Cs888ww6dOiAYcOG4ddff8Xff/+N+Ph4g/dZuHAhnJ2ddYevr28dRE8EDH+iNd5+sRMkDyxTklegxvurDuJIcqYZIyNquMya2Nzc3CCVSpGZqf8DnJmZCS8vrwqvVSqV2LJlC8aPH1/p+7Rs2RJubm64cOGCwfPR0dHIzc3VHVevXq36hyCqof5dm+GD/wuBzEaqK1OpNViw7jD+PJJmxsiIGiazJjYbGxt06dIFcXFxujKtVou4uDiEhoZWeO0PP/wAlUqF0aNHV/o+165dw+3bt+Ht7W3wvEwmg5OTk95BVJe6Bnhiwes94WhnrSvTaEV8vvk4fv7L8B9kRGSY2bsio6KisGbNGmzYsAHJycmYNGkSlEolxo0bBwAYM2YMoqOjy1y3du1aDBs2DI0bN9Yrz8/Px/Tp03Ho0CFcvnwZcXFxGDp0KPz9/REeHl4nn4nIGO2aN8LiyX3g5qK/Rc63v57B2v+ehlbLVUqIqsKq8iq1a+TIkcjKysKcOXOQkZGB4OBgxMbG6gaUpKWlQSLRz79nz57F/v37sXv37jL3k0qlOHnyJDZs2ICcnBz4+Phg4MCBmD9/PmQyWZn6RPWJr6cjPp3SBzFrEpCWcX905PY9qcjJV2HqyE6wkpr971Gieo37sRnA/djI3PIL1Phw7WEkX9YfHdm5rQdmRXaDrczsf5MS1Vv804+oHnKws8H813si5DH9QVTHzt7E7FUHkJuvMlNkRPUfExtRPSWzliI6shsGdG+mV34uLQczl+/DzTsFZoqMqH5jYiOqx6RSCaaMCMaIsDZ65dezlJj+5T5cTudiAkQPq/IztuHDh1f5pj///LPRAdUHfMZG9dGOfRex5j+n8OBPrL28dO3J9q3czBcYUT1T5RbbgytzODk5IS4uDkeOHNGdP3r0KOLi4uDs7FwrgRI96ob0aYnpo7vCSnp/lRJlUQnmfJ2AhFPpZoyMqH4xalTkzJkzcefOHaxatQpSaelqCRqNBm+88QacnJzw6aefmjzQusQWG9Vn/5zLwkfrE1GoKtGVSQRg0nMdERHqZ77AiOoJoxKbu7s79u/fj7Zt2+qVnz17Fj179mzwK+kzsVF9d+FaDuatOYSch0ZHvhzRDiPD2kAQhHKuJLJ8Rg0eKSkpQUpKSpnylJQUaLXc5p6otvk3dcEnU/rAq7GdXvm/Y1Ow6ueT0HCVEnqEGTXLc9y4cRg/fjxSU1N1K+sfPnwYixYt0i2FRUS1y9vNHp9M6YO5aw7h4vVcXfn/Dl5Gbr4aUS91ho21tII7EFkmo7oitVotlixZgmXLliE9vfShtbe3N6ZOnYp33nlH99ytoWJXJDUkBUXF+OjbRJy8cEuvvEMrN7w/rjvsba3LuZLIMtV4Sa17m3JaUgJgYqOGprhEg39tOob9/9zQK2/p44y5E3vA1UlupsiI6p7RE7RLSkrwxx9/YPPmzboH1Tdu3EB+fr7JgiOiqrG2kmL66K54ulcLvfKLN3Ix/ct9uHGLP5f06DCqxXblyhVEREQgLS0NKpUK586dQ8uWLTF16lSoVCqsWrWqNmKtM2yxUUMliiK2xZ3D97/pD+5ydrDB3Amh8Pd1MU9gRHXIqBbb1KlT0bVrV2RnZ8PW9v7eUc8++6zepqFEVLcEQcDIsLaY/EIwJA+M+M/NV+O9lftx/OxN8wVHVEeMSmz79u3D7NmzYWNjo1fu5+eH69evmyQwIjJeeI/miB7bHTZW93/EC1UafLj2EPYev2bGyIhqn1GJTavVQqPRlCm/du0aHB0daxwUEdVcj/be+PC1nnqjIks0Ij79/ij+uy/VjJER1S6jEtvAgQOxdOlS3WtBEJCfn4+YmBgMHjzYVLERUQ091rIxFr/ZG40eGhW5ZvtpbPxfErjPMFkiowaPXLt2DeHh4RBFEefPn0fXrl1x/vx5uLm5Ye/evfDw8KiNWOsMB4+Qpbl5pwBzvk7A9Sz90ZEDujfDm893hFTKHazIchg9j62kpARbtmzByZMnkZ+fj86dO+Pll1/WG0zSUDGxkSXKzVdh/trDOJuWrVfeLdATM17pCrmNUQsREdU7RiW2oqIiyOWWO+GTiY0sVZGqBIs2/o2jKfqjIwP8GuGD8SFwtLMp50qihsOo/gcPDw9ERkbi999/56LHRA2IXFa6MWn/rr565cmX72Dm8v3Iyi40U2REpmNUYtuwYQMKCgowdOhQNGnSBNOmTdPbdJSI6i8rqQTTRnXC8H7+euVXM/Mw48u9uJqZZ6bIiEyjRmtF5uXl4ccff8TmzZvx559/omXLlhg9ejTmzJljyhjrHLsi6VGxfc8FrP3vGb0yRztrzBnfA+38GpkpKqKaqfEiyPckJSXh5ZdfxsmTJw3OcWtImNjoUfLX0atYtuW43h5uNtZSzBzTFd0DvcwYGZFxajTGt6ioCNu2bcOwYcPQuXNn3LlzB9OnTzdVbERUB57o4osPxodAbnN/uyl1sQYffZuIPxLTzBgZkXGMarHt2rULmzZtwvbt22FlZYXnn38eL7/8Mh5//PHaiLHOscVGj6JzadmY980hKJRqvfLIpwLx3BP+ul08iOo7o1pszz77LAoLC7Fx40ZkZGRg9erVNUpqK1asgJ+fH+RyOUJCQpCYmFhu3fXr10MQBL3j4akHoihizpw58Pb2hq2tLcLCwnD+/Hmj4yN6FLRp5orFk3vDw1V/LuqGnUn45r+nodVylRJqGIxKbJmZmdi2bRuGDh0Ka+ua7c67detWREVFISYmBseOHUPHjh0RHh6OmzfLX4XcyckJ6enpuuPKlSt65z/55BN88cUXWLVqFQ4fPgx7e3uEh4ejqKioRrESWbqmHo74ZEof+Hnr91T8d+9FfLbpKIpLOL2H6r8qd0UqFApdt9y9XbPLU53uu5CQEHTr1g3Lly8HULrAsq+vL6ZMmYJZs2aVqb9+/XpMmzYNOTk5Bu8niiJ8fHzwzjvv4N133wUA5ObmwtPTE+vXr8eoUaMqjYldkfSoyy8sxoJ1h3Hm4m298uA27oiO7AY7ec3+oCWqTVVusbm6uupaUS4uLnB1dS1z3CuvKrVajaNHjyIsLOx+QBIJwsLCkJCQUO51+fn5aN68OXx9fTF06FCcOXN/uPKlS5eQkZGhd09nZ2eEhIRUeE8ius/B1hofvhqK0A7eeuUnzmXh/VUHkZOnMlNkRJWr8uJwf/75Jxo1aqT72hQPkm/dugWNRgNPT0+9ck9PT6SkpBi8pm3btli3bh2CgoKQm5uLJUuWoGfPnjhz5gyaNm2KjIwM3T0evue9cw9TqVRQqe7/oFbWIiV6FJQO+e+GlT/9g12H7nf3X7iagxnL9+HDV0Ph1djejBESGVblxNa3b1/d1/369auNWKokNDQUoaGhutc9e/ZEQEAAVq9ejfnz5xt1z4ULF2LevHmmCpHIYkglAt58viNcHeXY8vtZXXn6LSVmfLkP814NRQsfZzNGSFSWUYNHWrdujblz59Z4pKGbmxukUikyMzP1yjMzM+HlVbWJodbW1ujUqRMuXLgAALrrqnPP6Oho5Obm6o6rV69W96MQWSxBEPByRDtMei4ID3bUZOepMGvFfpy6cMt8wREZYFRie+ONN7Bz5060a9cO3bp1w7Jly8rt5quIjY0NunTpgri4OF2ZVqtFXFycXqusIhqNBqdOnYK3d+mzgBYtWsDLy0vvngqFAocPHy73njKZDE5OTnoHEekb3LMFZr7SDVYP7N1WUFSCOV8n4MDJG2aMjEhfjZbUOnfuHP79739j8+bNuHTpEp544gmMHj0aY8aMqfI9tm7disjISKxevRrdu3fH0qVLsW3bNqSkpMDT0xNjxoxBkyZNsHDhQgDAhx9+iB49esDf3x85OTn49NNPsX37dhw9ehSBgYEAgMWLF2PRokXYsGEDWrRogQ8++AAnT55EUlJSlbbbaUijIq9veB+avNKRa1LHxmgS+ZGZIyJLd+rCLSz49jAKikp0ZYIATBoehEE9W1T5PjO+3IfbuaW7CTR2tsUnU/qYPFZ6NNVoSa02bdpg3rx5OHfuHPbt24esrCyMGzeuWvcYOXIklixZgjlz5iA4OBgnTpxAbGysbvBHWloa0tPTdfWzs7MxceJEBAQEYPDgwVAoFDh48KAuqQHAjBkzMGXKFLz66qvo1q0b8vPzERsba5F7yGnybqMkNwsluVm6BEdUmzr4u2HhG73h6ijTlYki8NVPJ7FpVwqq+rfy7dxC3MwuPe4lOCJTqPEiyImJidi0aRO2bt0KhUKBIUOGYMuWLaaKzywaUostbfnrKMnNAgBYObuj2eRVZo6IHhUZt5WY83UC0m8p9cojQv3w+vAgSCUVj5wev2A3bt7d/83D1RZrZw+stVjp0WJUi+3cuXOIiYlBmzZt0KtXLyQnJ2Px4sXIzMxs8EmNiKrGq7E9PpncB/5N9UdFxiZcxuKNf0Nd3LB3+aCGy6jE1q5dO8TGxuLNN9/EtWvXsGvXLowZMwYODg6mjo+I6jEXRxk+mtQLwW3c9coTTqVjztcJyC8sNlNk9CirdmLTaDRYvXo1YmNjMXXq1DIToYno0WInL92Y9PFOTfTKz1y8jegV+/n8jOpctRObVCrFlClTyl2rkYgePdZWErzzUhc806elXvnldAVmfLkP17PyzRQZPYqM6ops3749Ll68aOpYiKgBk0gETBjaHmMGB+iV38wuxIwv9+FcWraZIqNHjVGJbcGCBXj33Xfx66+/Ij09HQqFQu8gokeTIAh44ck2mDoyGJIHRkUqlGq8v/IAjqWUvx0VkalUea3IBw0ePBgA8Mwzz+gthiyKIgRBgEbD0VBEj7Kw7s3h5CDD4o1HdKMji9QafLj2EKaN6oS+nZvq7e2WX1iMlCt30LaZK3fqphozah7bnj17Kjz/4ILJDRHnsRGZRvKlO/hw7aEyoyPdXGxxK6fsoBJ/XxdMG9UJzb3q988d1W81nqBtiZjYiEwnLUOBmK8TcCu3ajvY29taY/Hk3kxuZDSjuiL37t1b4fnHH3/cqGCoekRRhFhy/y9hTZESRdfPQebTmt05VG8083LCJ1Mex5yvD+DaTWWl9ZWFxVi25Tg+m/o4/z8moxjVYpNIyo45efB/wIb+jK0htNjUWWnI2rEcqvTUMudk3q3gPmQybNybmSEyIsOOnc1EzNeHqlx/yVt90LZ5o1qMiCyVUaMis7Oz9Y6bN28iNjYW3bp1w+7du00dIz1EnZWGGxtnG0xqAKBKT8WNjbOhzkqr48iIynfqQvUW6T50uvpbYREBRnZFOjuX3TF3wIABsLGxQVRUFI4ePVrjwMgwURSRtWM5tEUVd+loi5TI2rECPuMWsTuH6oXqLq/F5bjIWDXatuZhnp6eOHv2bOUVyWiqG+fLbamVqZt+AaobNdvlnMhUHGyta7U+0T1GtdhOnjyp91oURaSnp2PRokUIDg42RVxUjoJzidWuL2/SppaiIaq6kPZe+PHPqv+h1aO9Vy1GQ5bMqMQWHBwMQRDKbCjYo0cPrFu3ziSBkWGawspHlT1IlX4R2mIVJNayyisT1aK2zVzh7+uCC1dzKq3b2tcFbZq51n5QZJGMSmyXLl3Sey2RSODu7m6RO1TXN1Jb+2rVL7z0D64s/T/Yt+4G+4CesGvVCYIVu3io7gmCgGmjOmHm8v1QVvD8zN7WGlNHdeKzYTJatZ6xJSQk4Ndff0Xz5s11x549e/D444+jWbNmePXVV6FSqWorVgJg16Z7ta8R1UXIP7MPmT8uxuWl/4eb//0SBeePQtTw4TzVreZeTlg8uTf8fV0Mnm/t68LJ2VRj1ZrHNmjQIPTr1w8zZ84EAJw6dQqdO3fG2LFjERAQgE8//RSvvfYa5s6dW1vx1on6PI9NFEXc+HZmlQeQVEQit4ddmxA4BPaErV8HCFKjGvBE1SaKIiLn7UJ2XukfwnZyK3z4aijacK1IMoFqtdhOnDiBJ598Uvd6y5YtCAkJwZo1axAVFYUvvvgC27ZtM3mQdJ8gCHAfMhkSecVdkhKZPRoPHA+H9o9DsLE1WEdbpET+yT+RsWUBriybgKydK1Fw6R+I2oY9wZ7qP0EQYG11/9ePg6012jZvxKRGJlGtP9Gzs7P1dszes2cPBg0apHvdrVs3XL161XTRkUE27s3gM2ZBBSuP+MN9yJu6lUe0xSoUXjyB/KQDpV2QxWXX7NMW5iHvxB/IO/EHJHZOsG/XAw6BvSD3DYAgkdb6ZyIiMpVqJTZPT09cunQJvr6+UKvVOHbsGObNm6c7n5eXB2trDkyoCzbuzeAzbjHSlk2ARpkDABBkdvB+8YMya0VKrGWwbxsC+7Yh0BarUHDhGJRJB1Bw4SjEEnWZe2sLFMg7tht5x3ZDau8C+4BQ2Af0hNy3HQTBpFMfiYhMrlqJbfDgwZg1axYWL16M7du3w87ODn369NGdP3nyJFq1amXyIMkwQRD0RjhK5faVzlmTWMvgEBAKh4BQaNWFKDh/FPnJB1F44ZjBwSQaZQ4UR36D4shvkDo0gn1AKBwCe0HWpDWTHBHVS9VKbPPnz8fw4cPRt29fODg4YMOGDbCxsdGdX7duHQYOHGjyIKl2SGxs4fBYbzg81htaVQGU54+UtuQungA0JWXqa/LvQPH3Tij+3gmpkxscAnrCPqAnZD7+fDZCRPVGtRKbm5sb9u7di9zcXDg4OEAq1X/28sMPP8DBwcGkAVLdkMjs4Nj+cTi2fxyaIiUKziUiP+kgCi/9AxgYTKJR3ELu4f8i9/B/YeXiAfuAnnAI6AUbrxZMckRkViZbBBkAGjXiFhOWQCq3h2PQE3AMegKawjwozx6GMvkgCi+dAkRtmfolOTeRm7AduQnbYeXqVdqSC+wFG4/mTHJEVOfqxUOSFStWwM/PD3K5HCEhIUhMLH89xDVr1qBPnz5wdXWFq6srwsLCytQfO3Zs6fOnB46IiIja/hgWSWrrCKfgMHi/OAfNp34Dt0GvQe7XASjn+VpJdgZyDv6M69+8g2ur38KdPVu4fQ4R1Smzz8jdunUroqKisGrVKoSEhGDp0qUIDw/H2bNn4eHhUaZ+fHw8XnzxRfTs2RNyuRyLFy/GwIEDcebMGTRp0kRXLyIiAt9++63utUzGtRJrSmrvDKfOA+HUeSBK8nOgTDkEZfJBFKUlASg7z7/49g3k7P8BOft/gLW77/2WXOMmZW9ORGQiRu2gbUohISHo1q0bli9fDgDQarXw9fXFlClTMGvWrEqv12g0cHV1xfLlyzFmzBgApS22nJwcbN++3aiY6vPKIw9LW/46SnKzAABWzu5oNnlVncdQkncHypRDyE86ANW1lErr23j4wT6wJxwCesK6kXcdREj10fgFu3EzuxAA4OFqi7WzOfCMTMOsLTa1Wo2jR48iOjpaVyaRSBAWFoaEhIQq3aOgoADFxcVlnu/Fx8fDw8MDrq6u6N+/PxYsWIDGjRubNH4qZeXYCM7dBsO522CUKG4jP/kglMkHobp+zmB99c3LUN+8jOz4TbDxagmHwF6wDwiFtYunwfpkmRo72xr8mqimzJrYbt26BY1Go7eaCVA6ETwlpfK//AFg5syZ8PHxQVhYmK4sIiICw4cPR4sWLZCamor33nsPgwYNQkJCQpmRnACgUqn0Fm9WKBRGfiKycmoMl5AhcAkZguLcm1AmJ0CZdKDctS3VGRdxJ+Mi7vz5HWQ+re+OrgyFlbN7HUdOde2TKX0qr0RkBLM/Y6uJRYsWYcuWLYiPj9fbMmfUqFG6rzt06ICgoCC0atUK8fHxemtd3rNw4UK9FVQaEqljY4Nf1wfWzh5w6TEULj2Gojg7A8rkg8hPOgh15iWD9VU3zkN14zzuxG2ArElbOAT2hH27UFg5medzXd/wPjR5twGUfm+bRH5kljiIqHrMmtjc3NwglUqRmZmpV56ZmQkvr4p3z12yZAkWLVqEP/74A0FBQRXWbdmyJdzc3HDhwgWDiS06OhpRUVG61wqFAr6+vtX4JObTUH7ZWrt6waXncLj0HA717RtQJh+EMvkA1DcNj5hUXT8L1fWzuP37esh928E+oCfsA0Jh5VB3m09q8m7rnl8SUcNh1sRmY2ODLl26IC4uDsOGDQNQOngkLi4OkydPLve6Tz75BB999BF27dqFrl27Vvo+165dw+3bt+HtbXiggkwm46jJOmTT2Ac2vZ+Ha+/nob51Dcqkg8hPPoDiW9cM1BZRdDUZRVeTcXv3OsibB8IhoBfs2/WA1N7wfEoierSZvSsyKioKkZGR6Nq1K7p3746lS5dCqVRi3LhxAIAxY8agSZMmWLhwIQBg8eLFmDNnDjZt2gQ/Pz9kZGQAABwcHODg4ID8/HzMmzcPzz33HLy8vJCamooZM2bA398f4eHhZvucZJiNW1PYPD4CLn1eQHHWVeQnHYAy+QCK76QbqC2i6MoZFF05g1u7voGtX/vSllzbHpDaOdZ57ERUP5k9sY0cORJZWVmYM2cOMjIyEBwcjNjYWN2AkrS0NEgk9ycDr1y5Emq1Gs8//7zefWJiYjB37lxIpVKcPHkSGzZsQE5ODnx8fDBw4EDMnz+frbJ6TBAE2Hg0QyOPZnDtOwrqzMt3n8kdQElOZtkLRC0KL51E4aWTuBW7BrZ+QXAI7Am7Nt0hteWybkSPMrPPY6uPGtI8NksniiLU6am6KQSVPvOSWMGuZUfYB/aEfetulW7IWpH6MEeQiKrP7C02oooIggCZjz9kPv5o1P8VqG6chzLpAPKTE3QjFvVoS1Bw4SgKLhxFltQKdq06wSGgF+xad4VExrlSRI8CJjZqMARBgLxJG8ibtEGjsEiorp1DfvIBKJMToMnPLnuBpgQF5/5Gwbm/IVjZwLZVJzgE9oKdfxdIbORl6xORRWBiowZJECSQ+7aD3LcdGoeNRdG1FCiTDkKZkgCNMrdMfbFEjYKzh1Fw9jAEaxns/LvAPrAn7Fp1hsS67LNXURQhltzfeFVTpETR9XNldicnovqHz9gM4DO2hkvUalCUloT8pINQnj0EbUHFq8gINnLYt+4G+4CesG0VDImVDdRZacjasdzgaiky71ZwHzIZNu7NausjEFENMbEZwMRmGUStBoWXT0OZdADKs4ehLcqvsL4gs4O8WSCKLp+GWFxUbj2J3B4+YxYwudUQV3ah2sKuSLJYgkQKu5YdYdeyI9wGTUThpVPITz6IgrOHoVUVlKkvqgpQeP5IpffVFimRtWMFfMYtYrdkDXBlF6otTGz0SBCk1rDz7ww7/84QB72GgosnSpf1Ovc3RHVhte+nSr+AzB8/gbWLBwRrOSQ2cgjWstJ/beSQWMvu/iu//++981L+2PEZJtUmdkUawK7IR4e2RI3C1OOlK56kHAK0mtp/U4nVA4lQBsHa9u6/8gf+vXfeVpcQ9ZKjLmnKdMlTsLJpEEmBzzCptjGxGcDE9mi6+etXyP8nztxh1IBwP9HdS4APtyYNJs+H6lnfb3XeS6KCpOx2T8ZQZ6XhxsbZ0BYpy63DZ5hUU+wTIbrLyr6h/xEjQlQXQaMuf+CL0aRWeglQYl1ecnywXP81rGxw638rK0xqAJ9hUs0xsRHdZdemO3IO/lLl+u7D3oaNqxe0xSqIxUXQqosgFqvu/lsErVoFbXERRHXR3X/v19MWq+6XF6sgFqsqf0Nz0pRAW5gPFOajDjproUq/ANWN85A3aVMH70aWhomN6C6ZT2vIvFuVu9u3Xl1vfzgE9jJZi0IUtQ8kRf3k+GACvF9+9/W9xKkuL7kWAaLWJDHWtYJziUxsZBQmNqK7BEGA+5DJVXoG5D7kTZN2kwmCBIKNLSQ2pl3PUhTF0taWruV4P+mJd1uUeklULzkW3k2eDyTXB1qbYonapLE+TFNYcZclUXmY2IgeYOPeDD5jFlQwas8f7kPebDADGwRBAKysIbWyBmxNu2edqNVALFbfT4xlEuD95CmqVShIPQrVjQtVvr/U1vidGejRxsRG9BAb92bwGbcYacsmQKPMAVC6Kon3ix9wntUDBIkUgsy2yrsm2LYKxo310VW+v12b7saGRo84SeVViB49giBAsLLWvZbK7SFv0oZJrQbuPcOsUl1vf8h8WtdyRGSpmNiIqE7ce4ZZ2eavtfEMkx4tTGxEVGfuPcMsr+Um8/bn5GyqMT5jIyqH1LGxwa+pZvgMk2obExtRObiNSu0RBAFWrl6655hSx8acs0Ymw7UiDeBakUREDRefsRERkUVhYiMiIovCxEZERBaFiY2IiCwKExsREVmUepHYVqxYAT8/P8jlcoSEhCAxMbHC+j/88APatWsHuVyODh064H//+5/eeVEUMWfOHHh7e8PW1hZhYWE4f/58bX4EIiKqJ8ye2LZu3YqoqCjExMTg2LFj6NixI8LDw3Hz5k2D9Q8ePIgXX3wR48ePx/HjxzFs2DAMGzYMp0+f1tX55JNP8MUXX2DVqlU4fPgw7O3tER4ejqKiWthZmIiI6hWzz2MLCQlBt27dsHz5cgCAVquFr68vpkyZglmzZpWpP3LkSCiVSvz666+6sh49eiA4OBirVq2CKIrw8fHBO++8g3fffRcAkJubC09PT6xfvx6jRo2qNCbOYyMiarjM2mJTq9U4evQowsLCdGUSiQRhYWFISEgweE1CQoJefQAIDw/X1b906RIyMjL06jg7OyMkJKTcexIRkeUw65Jat27dgkajgaenp165p6cnUlJSDF6TkZFhsH5GRobu/L2y8uo8TKVSQaVS6V4rFIrqfRAiIqo3zP6MrT5YuHAhnJ2ddYevr6+5QyIiIiOZNbG5ublBKpUiMzNTrzwzMxNeXl4Gr/Hy8qqw/r1/q3PP6Oho5Obm6o6rV68a9XmIiMj8zJrYbGxs0KVLF8TFxenKtFot4uLiEBoaavCa0NBQvfoA8Pvvv+vqt2jRAl5eXnp1FAoFDh8+XO49ZTIZnJyc9A4iImqYzL5tTVRUFCIjI9G1a1d0794dS5cuhVKpxLhx4wAAY8aMQZMmTbBw4UIAwNSpU9G3b1989tlneOqpp7BlyxYcOXIEX3/9NYDS7TCmTZuGBQsWoHXr1mjRogU++OAD+Pj4YNiwYeb6mEREVEfMnthGjhyJrKwszJkzBxkZGQgODkZsbKxu8EdaWhokkvsNy549e2LTpk2YPXs23nvvPbRu3Rrbt29H+/btdXVmzJgBpVKJV199FTk5OejduzdiY2Mhl8vr/PMREVHdMvs8tvqI89iIiBous7fY6qN7uZ7D/onIEEdHRwiCYO4wqBxMbAbk5eUBAIf9E5FB7M2p39gVaYBWq8WNGzcazF9lCoUCvr6+uHr1Kn/YTIzf29rVUL+/DeV3w6OKLTYDJBIJmjZtau4wqo1TFWoPv7e1i99fMiWuPEJERBaFiY2IiCwKE5sFkMlkiImJgUwmM3coFoff29rF7y/VBg4eISIii8IWGxERWRQmNiIisihMbEREZFGY2IiIyKIwsRERkUVhYiMiIovCxEZERBaFiY2IiCwKExsREVkUJjYiIrIoTGxERGRRmNiIiMiiMLEREZFFYWKjBmnu3LkIDg6ulXvHx8dDEATk5OSY7J6XL1+GIAg4ceKEye5JRIYxsVGtGjt2LARBKHNERESYOzSLdOHCBYwbNw5NmzaFTCZDixYt8OKLL+LIkSPmDo2ozliZOwCyfBEREfj222/1yurrxpLFxcXmDsFoR44cwZNPPon27dtj9erVaNeuHfLy8vCf//wH77zzDvbs2WPuEInqBFtsVOtkMhm8vLz0DldXV915QRCwevVqPP3007Czs0NAQAASEhJw4cIF9OvXD/b29ujZsydSU1PL3Hv16tXw9fWFnZ0dRowYgdzcXN25v//+GwMGDICbmxucnZ3Rt29fHDt2TO96QRCwcuVKPPPMM7C3t8dHH31U5j0KCgowaNAg9OrVS9c9+c033yAgIAByuRzt2rXDV199pXdNYmIiOnXqBLlcjq5du+L48eM1+RZWShRFjB07Fq1bt8a+ffvw1FNPoVWrVggODkZMTAz+85//1Or7E9UrIlEtioyMFIcOHVphHQBikyZNxK1bt4pnz54Vhw0bJvr5+Yn9+/cXY2NjxaSkJLFHjx5iRESE7pqYmBjR3t5e7N+/v3j8+HFxz549or+/v/jSSy/p6sTFxYnfffedmJycLCYlJYnjx48XPT09RYVCoffeHh4e4rp168TU1FTxypUr4l9//SUCELOzs8Xs7GyxZ8+e4sCBA0WlUimKoih+//33ore3t/jTTz+JFy9eFH/66SexUaNG4vr160VRFMW8vDzR3d1dfOmll8TTp0+LO3bsEFu2bCkCEI8fP17u9+G1114T7e3tKzzKc+zYMRGAuGnTpgq/10SPAiY2qlWRkZGiVCot8wv6o48+0tUBIM6ePVv3OiEhQQQgrl27Vle2efNmUS6X617HxMSIUqlUvHbtmq7st99+EyUSiZienm4wFo1GIzo6Ooo7duzQe+9p06bp1buX2JKTk8WgoCDxueeeE1Uqle58q1atyiSQ+fPni6GhoaIoiuLq1avFxo0bi4WFhbrzK1eurDSxZWZmiufPn6/wKM/WrVtFAOKxY8fKrUP0qOAzNqp1TzzxBFauXKlX1qhRI73XQUFBuq89PT0BAB06dNArKyoqgkKhgJOTEwCgWbNmaNKkia5OaGgotFotzp49Cy8vL2RmZmL27NmIj4/HzZs3odFoUFBQgLS0NL337tq1q8G4BwwYgO7du2Pr1q2QSqUAAKVSidTUVIwfPx4TJ07U1S0pKYGzszMAIDk5GUFBQZDL5XqxVcbDwwMeHh6V1jNEFEWjriOyRExsVOvs7e3h7+9fYR1ra2vd14IglFum1Wqr/L6RkZG4ffs2li1bhubNm0MmkyE0NBRqtbpMfIY89dRT+Omnn5CUlKRLsvn5+QCANWvWICQkRK/+veRnrNdffx3ff/99hXXuvf/D2rRpAwBISUlBp06dahQHUUPHxEYNVlpaGm7cuAEfHx8AwKFDhyCRSNC2bVsAwIEDB/DVV19h8ODBAICrV6/i1q1bVb7/okWL4ODggCeffBLx8fEIDAyEp6cnfHx8cPHiRbz88ssGrwsICMB3332HoqIiXavt0KFDlb7fhx9+iHfffbfK8T0oODgYgYGB+OyzzzBy5EhIJPrjwnJycuDi4mLUvYkaGiY2qnUqlQoZGRl6ZVZWVnBzc6vRfeVyOSIjI7FkyRIoFAq89dZbGDFiBLy8vAAArVu3xnfffYeuXbtCoVBg+vTpsLW1rdZ7LFmyBBqNBv3790d8fDzatWuHefPm4a233oKzszMiIiKgUqlw5MgRZGdnIyoqCi+99BLef/99TJw4EdHR0bh8+TKWLFlS6XvVpCtSEAR8++23CAsLQ58+ffD++++jXbt2yM/Px44dO7B7924O96dHBof7U62LjY2Ft7e33tG7d+8a39ff3x/Dhw/H4MGDMXDgQAQFBekNu1+7di2ys7PRuXNnvPLKK3jrrbeMShyff/45RowYgf79++PcuXOYMGECvvnmG3z77bfo0KED+vbti/Xr16NFixYAAAcHB+zYsQOnTp1Cp06d8P7772Px4sU1/ryV6d69O44cOQJ/f39MnDgRAQEBeOaZZ3DmzBksXbq01t+fqL4QRD51JiIiC8IWGxERWRQmNiIisihMbEREZFGY2IiIyKIwsRERkUVhYiMiIovCxEZERBaFic0AURShUCi4sCwRUQPExGZAXl4enJ2dkZeXZ+5QiIiomrhWJJEBoijibFo2Dp/OQH5hMRxsrRHS3gttm7nqdhogovrJrC22vXv3YsiQIfDx8YEgCNi+fXul18THx6Nz586QyWTw9/fH+vXry9RZsWIF/Pz8IJfLERISgsTERNMHTxbrSoYCUcv2YvoX+/Djn+cRm3AZP/55HtO/2IeoZXtxJUNh7hCJqAJmTWxKpRIdO3bEihUrqlT/0qVLeOqpp/DEE0/gxIkTmDZtGiZMmIBdu3bp6mzduhVRUVGIiYnBsWPH0LFjR4SHh+PmzZu19THIglzJUGDm8v24cDXH4PkLV3Mwc/l+JjeieqzeLIIsCAJ++eUXDBs2rNw6M2fOxM6dO3H69Gld2ahRo5CTk4PY2FgAQEhICLp164bly5cDKN2Y0tfXF1OmTMGsWbOqFItCoYCzszNyc3N1uzXXR+wuMy1RFBG1bG+5Se1BrX1d8NnUx/l9JqqHGtQztoSEBISFhemVhYeHY9q0aQAAtVqNo0ePIjo6WndeIpEgLCwMCQkJdRlqrbuSocDSLcfL/BL+8c/z8Pd1wbRRndDcq/4mZVPSakWoizVQFWugUhv6t6Sccg2K1Bqo1CVQFWtwO7eoSkkNAM5fzcG5tGy0bd6odj8cEVVbg0psGRkZ8PT01Cvz9PSEQqFAYWEhsrOzodFoDNZJSUkp974qlQoqlUr3WqGo391M97rLlIXFBs/f6y5bPLm32ZNbiUZrIKmUlJuEHkw05SWjh/9VF2vM8tkOnc5gYiOqhxpUYqstCxcuxLx588wdRpWIooilW46Xm9TuURYWY9mW4+V2l4miCHXJ3aRTQaumqJIWj0pdmlgebiEV3b2vRlsverprRX4l/w2IyDwaVGLz8vJCZmamXllmZiacnJxga2sLqVQKqVRqsI6Xl1e5942OjkZUVJTutUKhgK+vr2mDN5GzadnV6i6b+q94SCXC3QSl38qpH09XG67TqbeQlV0Id1dbc4dCRA9oUIktNDQU//vf//TKfv/9d4SGhgIAbGxs0KVLF8TFxekGoWi1WsTFxWHy5Mnl3lcmk0Emk9Va3KZ0+HRGtepfulG/u1WrQxAAuY0UMmsr2NhIIbOWQvbQv3IbKWQ2VgbPlfev3MYKMhsprqQr8P6qg1WO59rNfEz6JA4vPNkaz/b1h421tBY/PRFVlVkTW35+Pi5cuKB7fenSJZw4cQKNGjVCs2bNEB0djevXr2Pjxo0AgNdffx3Lly/HjBkz8H//93/4888/sW3bNuzcuVN3j6ioKERGRqJr167o3r07li5dCqVSiXHjxtX556sN9bX7y0oqqTCByKuVbKzuXqNfbiWV1OooxA7+bvD3dalyixgAVGoNvv8tBX8kpmHCM+3R/TEvjpQkMjOzJrYjR47giSee0L2+1x0YGRmJ9evXIz09HWlpabrzLVq0wM6dO/H2229j2bJlaNq0Kb755huEh4fr6owcORJZWVmYM2cOMjIyEBwcjNjY2DIDShoqB1vral9TbhKxlkIuq0rCsdJ/beA+UmnDX51NEARMG9WpwoE5AGBtJYFWK+o9P8y4XYAF3yaiczsPTBzaHk09HOsiZCIyoN7MY6tP6vM8tpQrdzD9i31Vrv/plD5o58eRe9VR3lQKoHT+2tRRnWBjJcU3/zmNxKSyXcNSiYBnHm+FUQPawE5e/T9EiKhmmNgMqM+JjZOI64YoijiXlo1DD0x+79HeC20emvx+JDkT3/znFK5nKcvcw9VRhrFPP4Z+nZtCIuF/A6K6wsRmQH1ObEDl89gAwN7Wul7MY3sUFJdo8d+9qdj6x1kUqsrOqWvX3BWvDQ+Cf1OXug+O6BHExGZAfU9sQNW6y5jU6tbt3EKs35mE+KPXypwTBGBgSHO8MigAzg4NYwQuUUPFxGZAQ0hsQGl3WeS8XcjOK101xU5uhQ9fDS3TXUZ1K+nSbaz+5RQuXs8tc87e1hqjI9phUKifRQy4IaqP+JPVgAmCAGur+/8JHWyt0bZ5IyY1Mwts0Rj/mtYXbzzfEY52NnrnlIXFWP3LKUz7fA9Opd4yU4RElo2JrYFr7GwLD9fSo7EzV8CoL6QSAYNC/bA6+kk81asFHh47cjldgfe+OoBPvjuCrOxC8wRJZKHYFWlAQ+mKpIbj0o1crP7lFM5cvF3mnMxGytVLiEyIic0AJjaqDaIoYt+J61i34wxu5xaVOe/V2A4Th3ZAt0BPdicT1QATmwFMbFSbClUl+CHuHH6JT0WJRlvmfOd2Hnh1WAc0cXcwQ3REDR8TmwFMbFQXbtzKxzf/OY2/kzLLnLOSChj6eCuMCOPqJUTVxcRmABMb1aUjyZlYs/0Ubtwqu3pJI6f7q5ewe5KoapjYDGBio7pWXKLBf/ZexNbfz6JIXXb1kgC/Rnj12Q5cvYSoCpjYDGBiI3O5nVuI9b8mIf6Y4dVLwnv4YXREO65eQlQBJjYDmNjI3M5cvI2vfzmFizfKrl7icHf1kgiuXkJkEBObAUxsVB9otCJ2H7qM735LRl5B2QWv/byd8NqzHdC+lZsZoiOqv5jYDGBio/pEoVTj37HJiE24DK2Bn9bHg5tg3JDH4ObClWeIACY2g5jYqD66eD0XX28vf/WSEU+2wbC+rbh6CT3ymNgMYGKj+koURew9Xrp6yR1F2dVLvBvbY8LQ9ly9hB5pTGwGMLFRfVeoKsG2P85h+x7Dq5d0aeeBifV89ZIZX+7D7dzSBaAbO9vikyl9zBwRWQoOqSJqgGxlVoh8KhArpj+BrgGeZc4fTbmJyZ/+ifW/nkFBUfk7rZvT7dxC3MwuPe4lOCJTYGIjasB83B0QM6EH5owPgbebvd65Eo2In/66gEmL4xB/9CrYOUOPCiY2IgvQLdALK6Y/gTGDAyC30R88ckehwmebjmHm8v1IvZZjngCJ6hATG5GFsLaS4oUn22DVrCfRt1PTMueTL9/B20v34Ksf/4FCqTZDhER1g4mNyMI0drbFu6O7YNGbvdHCR3/wkygCvyVcxmsL/8DOA5egMTDwhKihY2IjslCPtWyMz9/uh0nPBcHRTn/rm/zCYqz6+STeXroHp1NvmSdAolrCxEZkwaQSAYN7tsCqWWEY1NMPkoemtl26oUD0Vwfw6fdHcCuHIxPJMtSLxLZixQr4+flBLpcjJCQEiYmJ5dbt168fBEEoczz11FO6OmPHji1zPiIioi4+ClG95GRvgzee64h/TeuLwBaNypzfe/w6Ji2Oww9x51BcUnbbHKKGxOyJbevWrYiKikJMTAyOHTuGjh07Ijw8HDdv3jRY/+eff0Z6erruOH36NKRSKV544QW9ehEREXr1Nm/eXBcfh6hea9XUBYve7I13Xu6CRk5yvXNFag02/i8Zb37yFxKTMswUIVHNmT2x/etf/8LEiRMxbtw4BAYGYtWqVbCzs8O6desM1m/UqBG8vLx0x++//w47O7syiU0mk+nVc3V1rYuPQ1TvCYKAfp2bYtWsJ/F8/9awkur3T6bfVmL+2sOY980h3MjKN1OURMYza2JTq9U4evQowsLCdGUSiQRhYWFISEio0j3Wrl2LUaNGwd5ef3JqfHw8PDw80LZtW0yaNAm3b5ddOJboUXZ/9ZL+BlcvOZKciTfvrl5SqCoxQ4RExjFrYrt16xY0Gg08PfV/qDw9PZGRUXlXSGJiIk6fPo0JEybolUdERGDjxo2Ii4vD4sWLsWfPHgwaNAgajeFnByqVCgqFQu8gelRUZfWS1xfFIf7YNa5eQg2ClbkDqIm1a9eiQ4cO6N69u175qFGjdF936NABQUFBaNWqFeLj4/Hkk0+Wuc/ChQsxb968Wo+XqD7rFuiF4Dbu2L4nFdv+OIci9f0/BO8oivDZv4/it4OX8NqzQWjZxNmMkRJVzKwtNjc3N0ilUmRmZuqVZ2ZmwsvLq8JrlUoltmzZgvHjx1f6Pi1btoSbmxsuXLhg8Hx0dDRyc3N1x9WrV6v+IYgsyL3VS1bOfBKPd2pS5nzSpTt4+/N4fPUTVy+h+susic3GxgZdunRBXFycrkyr1SIuLg6hoaEVXvvDDz9ApVJh9OjRlb7PtWvXcPv2bXh7exs8L5PJ4OTkpHcQPcrcXGwxfXRXg6uXaEXgt4OX8fqiP/C/g5egMbStN5EZmX1UZFRUFNasWYMNGzYgOTkZkyZNglKpxLhx4wAAY8aMQXR0dJnr1q5di2HDhqFx48Z65fn5+Zg+fToOHTqEy5cvIy4uDkOHDoW/vz/Cw8Pr5DMRWYrHWjbG59P64vXhQXCw1V+9JK+gGCt/Oom3P483uKs3kbmY/RnbyJEjkZWVhTlz5iAjIwPBwcGIjY3VDShJS0uDRKKff8+ePYv9+/dj9+7dZe4nlUpx8uRJbNiwATk5OfDx8cHAgQMxf/58yGSyOvlMRJZEKpXgqV4t0Ce4Cb7/LRmxhy7jwTEkl24oMGvFfjzeqQn+b8hjaOxsa75gicAdtA3iDtpE5Uu9loPVv5xC8uU7Zc7JbaQYEdYGw/q2grWV1MDV941fsBs3s0uX8fJwtcXa2QNrJV569Ji9K5KIGpZWTV2weHJvvPNSZzRy0u8F0a1e8ulf+Jurl5CZMLERUbUJgoB+XXyxcuaTeO4J/7Krl9xS4kOuXkJmwsRGREazk1tj7NOPYfn0/ujSzqPM+dLVS/7Chp1JequXiKKI4pL7e8HlFxYj5codTgAnk+AzNgP4jI2o+kRRxN/Jmfhm+2mk31aWOd/ISY5xQx5DC29HLN16Aheu5pSp4+/rgmmjOqG5F3/uyHhVTmzDhw+v8k1//vlnowOqD5jYiIynLtaUrl4Sdw4qddll7CQSAdoK5r7Z21pj8eTeTG5ktCp3RTo7O+sOJycnxMXF4ciRI7rzR48eRVxcHJydudQO0aPMxrp0ZOSqmU/i8eCyq5dUlNQAQFlYjGVbjrNbkoxmVFfkzJkzcefOHaxatQpSaemQXo1GgzfeeANOTk749NNPTR5oXWKLjch0TqXewte/nMLl9OotLr7krT5o27zspqhElTEqsbm7u2P//v1o27atXvnZs2fRs2fPBr9FDBMbkWlpNFrM++YQjp/LqvI1z/dvjcinAmsxKrJURo2KLCkpQUpKSpnylJQUaLVaA1cQ0aNMKpXAs7F95RUfkF9YXEvRkKUzakmtcePGYfz48UhNTdVtGXP48GEsWrRIt8YjEdGDHl5r0tT1ie4xKrEtWbIEXl5e+Oyzz5Ceng4A8Pb2xvTp0/HOO++YNEAisgwh7b3w45/nq1y/R/uKt64iKk+N57Hd223akp5F8RkbkemJooioZXsNzl97WGtfF3w29XEIglBpXaKHGb3ySElJCf744w9s3rxZ9z/fjRs3kJ/P5XOIqCxBEDBtVCfYV9LFaG9rjamjOjGpkdGMarFduXIFERERSEtLg0qlwrlz59CyZUtMnToVKpUKq1atqo1Y6wxbbES150qGAku3HDfYcmvt64KpXHmEasioFtvUqVPRtWtXZGdnw9b2/t5Lzz77rN5u2ERED2vu5YR/TX0cro73dwawk1thyVt98NnUx5nUqMaMGjyyb98+HDx4EDY2Nnrlfn5+uH79ukkCIyLLJQgCrK3u/13tYGvNydhkMka12LRaLTSasmvAXbt2DY6OjjUOioiIyFhGJbaBAwdi6dKluteCICA/Px8xMTEYPHiwqWIjIiKqNqO6Ij/77DOEh4cjMDAQRUVFeOmll3D+/Hm4ublh8+bNpo6RiIioyoxKbE2bNsU///yDLVu24OTJk8jPz8f48ePx8ssv6w0mISIiqmtGJbaioiLI5XKMHj3a1PEQERHViFHP2Dw8PBAZGYnff/+dix4TEVG9YlSLbcOGDdi0aROGDh0KZ2dnjBw5EqNHj0bXrl1NHR9V4vqG96HJK90mSOrYGE0iPzJzRERE5mVUi+3ZZ5/FDz/8gMzMTHz88cdISkpCjx490KZNG3z44YemjpEqoMm7jZLcLJTkZukSHBHRo8zotSIBwNHREePGjcPu3btx8uRJ2NvbY968eaaKjYiIqNpqlNiKioqwbds2DBs2DJ07d8adO3cwffp0U8VGRERUbUYltl27diEyMhKenp6YNGkSPD09sXv3bly5cgWLFi0ydYxERBZFFEUUXT+HO399j6z/rcadv75H0fVzqOEuYpW+56uvvopGjRpBEAScOHGi1t6rIpcvX6719zf6GVthYSE2btyIjIwMrF69Go8//rjRQaxYsQJ+fn6Qy+UICQlBYmJiuXXXr18PQRD0DrlcrldHFEXMmTMH3t7esLW1RVhYGM6fr/oGh0REtUWdlYYb387EjfXRyDn4C/KO70bOwV9wY300bnw7E+qstFp539jYWKxfvx6//vor0tPT0b59+1p5n/rAqMSWmZmJbdu2YejQobC2rtn27Vu3bkVUVBRiYmJw7NgxdOzYEeHh4bh582a51zg5OSE9PV13XLlyRe/8J598gi+++AKrVq3C4cOHYW9vj/DwcBQVFdUoViKimlBnpeHGxtlQpacaPK9KT8WNjbNrJbmlpqbC29sbPXv2hJeXF6ysjBoU3yBUObHd2ykbKG0RKRSKco/q+Ne//oWJEydi3LhxCAwMxKpVq2BnZ4d169aVe40gCPDy8tIdnp6eerEtXboUs2fPxtChQxEUFISNGzfixo0b2L59e7Vio0fb9Q3vI23560hb/jqub3jf3OFQAyeKIrJ2LIe2SFlhPW2RElk7Vpi0W3Ls2LGYMmUK0tLSIAgC/Pz8oNVqsXDhQrRo0QK2trbo2LEjfvzxR9018fHxEAQBu3btQqdOnWBra4v+/fvj5s2b+O233xAQEAAnJye89NJLKCgo0F0XGxuL3r17w8XFBY0bN8bTTz+N1FTDifye06dPY9CgQXBwcICnpydeeeUV3Lp1y+jPW+WU7erqivT0dHh4eMDFxcXg7raiKEIQBIMr/xuiVqtx9OhRREdH68okEgnCwsKQkJBQ7nX5+flo3rw5tFotOnfujI8//hiPPfYYAODSpUvIyMhAWFiYrr6zszNCQkKQkJCAUaNGlbmfSqWCSqXSva5ucibLdG8qBVFFHpxLWhGxpBgaZU6V7qlKv4C0ZRMgWFXcI1bVuavLli1Dq1at8PXXX+Pvv/+GVCrFwoUL8f3332PVqlVo3bo19u7di9GjR8Pd3R19+/bVXTt37lwsX74cdnZ2GDFiBEaMGAGZTIZNmzYhPz8fzz77LL788kvMnDkTAKBUKhEVFYWgoCDk5+djzpw5ePbZZ3HixAlIJGXbUjk5Oejfvz8mTJiAzz//HIWFhZg5cyZGjBiBP//8s0rfr4dVObH9+eefaNSoke5rU2zbfuvWLWg0Gr0WFwB4enoiJSXF4DVt27bFunXrEBQUhNzcXCxZsgQ9e/bEmTNn0LRpU2RkZOju8fA975172MKFCzlNgYiMUlt/AFU1CVaFs7MzHB0dIZVK4eXlBZVKhY8//hh//PEHQkNDAQAtW7bE/v37sXr1ar3EtmDBAvTq1QsAMH78eERHRyM1NRUtW7YEADz//PP466+/dIntueee03vvdevWwd3dHUlJSQaf6y1fvhydOnXCxx9/rHeNr68vzp07hzZt2lT781Y5sT34Qfv161ftNzKV0NBQ3X8IAOjZsycCAgKwevVqzJ8/36h7RkdHIyoqSvdaoVDA19e3xrESEdVHFy5cQEFBAQYMGKBXrlar0alTJ72yoKAg3deenp6ws7PTJbV7ZQ8O+Dt//jzmzJmDw4cP49atW7plF9PS0gwmtn/++Qd//fUXHBwcypxLTU2t3cT2oNatW+Pll1/Gyy+/jNatWxtzCwCAm5sbpFIpMjMz9cozMzPh5eVVpXtYW1ujU6dOuHDhAgDorsvMzIS3t7fePYODgw3eQyaTQSaTGTxHRFQRqWPjKtXTFOVDVBVW+b6CzBZSedlf9sa898Py8/MBADt37kSTJk30zj38u/DBAYKCIJQZMCgIgt6awUOGDEHz5s2xZs0a+Pj4QKvVon379lCr1eXGMmTIECxevLjMuQd/h1eHUYntjTfewKZNmzB//nx07twZo0ePxsiRI6ucjO6xsbFBly5dEBcXh2HDhgEo3Z07Li4OkydPrtI9NBoNTp06pdvgtEWLFvDy8kJcXJwukSkUChw+fBiTJk2qVnxERJWp6vqsRdfP4cb66Mor3uX94hzIm1S/tVIVgYGBkMlkSEtL0+uNq6nbt2/j7NmzWLNmDfr06QMA2L9/f4XXdO7cGT/99BP8/PxMNlLTqOH+b7/9Nv7++28kJydj8ODBWLFiBXx9fTFw4EBs3LixWveKiorCmjVrsGHDBiQnJ2PSpElQKpUYN24cAGDMmDF6g0s+/PBD7N69GxcvXsSxY8cwevRoXLlyBRMmTABQ+tfDtGnTsGDBAvz3v//FqVOnMGbMGPj4+OiSJxGZX2NnW3i4lh6NnS1/H0eZT2vIvFtVra63P2Q+xveGVcbR0RHvvvsu3n77bWzYsAGpqak4duwYvvzyS2zYsMHo+7q6uqJx48b4+uuvceHCBfz55596j3kMefPNN3Hnzh28+OKL+Pvvv5Gamopdu3Zh3LhxVR6I+LAapcc2bdpg3rx5mDdvHg4dOoRJkyZh3LhxGDNmTJXvMXLkSGRlZWHOnDnIyMhAcHAwYmNjdYM/0tLS9EbSZGdnY+LEicjIyICrqyu6dOmCgwcPIjAwUFdnxowZUCqVePXVV5GTk4PevXsjNja2zERuIjKfT6b0MXcIdUoQBLgPmYwbG2dXOORfIreH+5A3TTJAryLz58+Hu7s7Fi5ciIsXL8LFxQWdO3fGe++9Z/Q9JRIJtmzZgrfeegvt27dH27Zt8cUXX1Q4LsPHxwcHDhzAzJkzMXDgQKhUKjRv3hwREREGR1FWhSDWcLJEYmIiNm3ahK1bt0KhUGDIkCHYsmVLTW5pdgqFAs7OzsjNzYWTk5O5w6lQ2vLXdSOyrJzd0WzyKjNHZDn4vaXaoM5KQ9aO5QYnacu8/eE+5E3YuDczQ2SWw6gW27lz5/Dvf/8bmzdvxqVLl9C/f38sXrwYw4cPNziyhYiIStm4N4PPuMVQ3TiPgnOJ0BQqIbW1h12b7pD5tK71ltqjwKjE1q5dO3Tr1g1vvvkmRo0aVWbOGNUNURQhlhTrXmuKlCi6fo4/HET1nCAIkDdpU2uDQx511U5sGo0Gq1evxvPPPw9XV9faiImq4F53xoOTOEVVAW6sj4bMuxXch0xmdwYRPZKq/WROKpViypQpyMnJqYVwqCrMuZAqEVF9Z9SQk/bt2+PixYumjoWqwJwLqRIRNQRGJbYFCxbg3Xff1e3rU5PV/al6VDfOl9tSK1M3/QJUN7gPHRE9WowaPHJvlY9nnnlGb5BCdVf3p+orOFf+Jqzl1ecDaiJ6lBiV2P766y9Tx0FVpCmsuAvyYQWpx2Ef0As2nn4cKUlEjwSjEpsp1xaj6pHa2lervjrzMq6vfRfWjZvAIbA37B/rDZvGPrUUHRE9SsaOHYucnJx6t4mzUYlt7969FZ5//PHHjQqGKmfXpjtyDv5S7euKb19H9r6tyN63FTZeLeEQ2AsOj/WGlZNbLURJRBURRRFn07Jx+HQG8guL4WBrjZD2XmjbzJU9KyZgVGIztO7Xg/8x+Iyt9txbSLWqA0gMUWdcxJ2Mi7jz53eQ+wbAPrA3HAJCIbV3NmGkRBV7cOfpqu4EbQmuZCiwdMtxXLiao1f+45/n4e/rgmmjOqG5V/1eyq++M2pUZHZ2tt5x8+ZNxMbGolu3bti9e7epY6QH3FtIVSKvuEtSIreH18tz0XjAuApXCS+6mozbu9bgyrIJSN/8IfL++ROaSqYSEJnCvZ2nS3KzdAnO0l3JUGDm8v1lkto9F67mYOby/biSYfrR5f369cOUKVMwbdo0uLq6wtPTE2vWrNHtpuLo6Ah/f3/89ttvAEobKOPHj0eLFi1ga2uLtm3bYtmyZRW+h1arxcKFC3XXdOzYET/++KPJP0tljGqxOTuX/ct+wIABsLGxQVRUFI4ePVrjwKh8Nu7N4DNmQdUWUvXrAOfuT6M4OwP5SQegTNoP9U0DE7dFLQov/oPCi/8Av62GXavOcHisN+xad4XEmpuwEtWUKIpYuuU4lIXFFdZTFhZj2Zbj+Gzq4ybvltywYQNmzJiBxMREbN26FZMmTcIvv/yCZ599Fu+99x4+//xzvPLKK0hLS4O1tTWaNm2KH374AY0bN8bBgwfx6quvwtvbGyNGjDB4/4ULF+L777/HqlWr0Lp1a+zduxejR4+Gu7t7nY7NqPHq/g9KSUlB165ddbuzNlQNZXV/URSRtmyCblktQWYH7xc/qHStSHVWGvLP7Ef+mf0oyckstx4ACNZy2LfpBvvHesOuZUcIUusK61sSru5fuyzl+zvjy324nVv5ztjFJVpk56mqfF9XRxmsrSruVGvsbFvl7X/69esHjUaDffv2AShtkTk7O2P48OG6fTQzMjLg7e2NhIQE9OjRo8w9Jk+ejIyMDF0r7MHBIyqVCo0aNcIff/yB0NBQ3TUTJkxAQUEBNm3aVKU4TcGoFtvJkyf1XouiiPT0dCxatEi3azXVPkEQIFjdTzRSuX2V5qzZuDdDo34vwbXvi1Clp0J5Zh/ykw5Ck3+nTF2xuAj5Z/Yh/8w+SOQOsG/XAw6P9Ya8WSAEidSkn4eoIbqdW4ib2ZUntuqqThKsqqCgIN3XUqkUjRs3RocOHXRl9xa0v3nzJgBgxYoVWLduHdLS0lBYWAi1Wl3u7/gLFy6goKAAAwYM0CtXq9Xo1KmTiT9JxYxKbMHBwRAEocxyTT169MC6detMEhjVPkEQIPfxh9zHH42eHIOiqynIP7MfypQEaAvzytTXFuUj78QfyDvxB6T2LrAP7AmHx/pwNwGiBsLaWr/HRRAEvbJ7P8darRZbtmzBu+++i88++wyhoaFwdHTEp59+isOHDxu8972eup07d6JJkyZ652Syun2cYVRiu3Tpkt5riUQCd3d37lDdgAkSKWybPwbb5o/BLXw8Ci+dRH7SfijPHoaoLipTX6PMgeLv/0Hx9/9g5ewBh8d6wT6wN2w8mjPJ0SOlsbNtlerlFxajoKikyve1k1vBwbbirv+qvrcxDhw4gJ49e+KNN97QlaWmlj8aOzAwEDKZDGlpaWaf61ytxJaQkIDbt2/j6aef1pVt3LgRMTExUCqVGDZsGL788ss6z85kWoLUCnb+nWHn3xnaYhUKUo9BeWY/Cs4fhagp++C7JPcmcg7+gpyDv8DarSkcAnvD4bHesG7kbYboiepWVZ9xpVy5g+lf7KvyfT98NRRtmzcyNqwaa926NTZu3Ihdu3ahRYsW+O677/D333+jRYsWBus7Ojri3Xffxdtvvw2tVovevXsjNzcXBw4cgJOTEyIjI+ss9moltg8//BD9+vXTJbZTp05h/PjxGDt2LAICAvDpp5/Cx8cHc+fOrY1YyQwk1jI4tAuFQ7tQaFUFUJ5LRP6Z/aWjJ0VtmfrFt64he+8WZO/dApl3q9I5coG9YOXU2AzRE9UfbZu5wt/Xpdyh/g9q7euCNs3Mu9/la6+9huPHj2PkyJEQBAEvvvgi3njjDd10AEPmz58Pd3d3LFy4EBcvXoSLiws6d+6M9957rw4jr+aoSG9vb+zYsQNdu3YFALz//vvYs2cP9u/fDwD44YcfEBMTg6SkpNqJto40lFGRgPlGlmkKFFCmHEL+mf0oSksCUNH/RgLkzQLgENgL9u0azkRwSxm1V189it/fe/PYKhryb29rjcWTe3OSdg1Uq8WWnZ2tGzUDAHv27MGgQYN0r7t164arV6+aLjqqt6R2TnDqPBBOnQeiRHEb+ckHoTyzH6r0CwZqiyhKS0JRWhJu7VoL2xYdS5/JtQ2BRGZX57ETmUtzLycsntzb4MojQGlLbSpXHqmxaiU2T09PXLp0Cb6+vlCr1Th27BjmzZunO5+Xl1dm1A1ZPiunxnAJGQKXkCEovpOO/KQDyD+zD8W3rpWtLGpRePE4Ci8ex63/rYat/92J4P5dOBGcHgnNvZzwr6mP41xaNg49sFZkj/ZeaMO1Ik2iWolt8ODBmDVrFhYvXozt27fDzs4Offrcf3B68uRJtGrVyuRBUsNh3cgbrr2fh2vv56G+eaV0InjSfpTk3CxTV9QUo+DsYRScPQzBRg77Nt3hENgbti2DHqmJ4PToEQQBbZs3MuvgEEtWrcQ2f/58DB8+HH379oWDgwM2bNgAGxsb3fl169Zh4MCBJg+SGiYbj+Zo5NEcrv1egurG+dI5cskHocnPLlNXVBch//Re5J/eC4mtA+zbciI4ERmnWonNzc0Ne/fuRW5uLhwcHCCV6v/C+eGHH+Dg4GDSAKnhEwQB8iZtIG/SBo3DIlGUlnR3IvghaIvKLr+mLXxgIriDK+wDesLhsd6cCE5EVWKyRZABoFEjNqupYoJEClu/DrD16wC3iAkovHhvIngixGIDE8Hzs6H4eycUf++ElYuHbo6cjUdzM0RPRA2BUdvWmNqKFSvg5+cHuVyOkJAQJCYmllt3zZo16NOnD1xdXeHq6oqwsLAy9ceOHVu6juIDR0RERG1/DKomQWoNu9Zd4DF0Kpq/vQ4ew9+BXduQcp+vleTcRM7Bn3FtTRSufj0N2ft/RHF2Rh1HTUT1nVEtNlPaunUroqKisGrVKoSEhGDp0qUIDw/H2bNn4eHhUaZ+fHw8XnzxRfTs2RNyuRyLFy/GwIEDcebMGb31ySIiIvDtt9/qXnM1lPpNYi2DQ0BPOAT0hLZIeX8i+KWThieCZ11F9p7NyN6zGTJvf9g/1hsOAT05EbyBEEURYsn9uVyaIiWKrp9jdzOZhEm3rTFGSEgIunXrhuXLlwMoXXzT19cXU6ZMwaxZsyq9XqPRwNXVFcuXL8eYMWMA6G+lYAxO0K4/NMpcKFMSSieCX02upLYAebNAODzWG/btekBqV7P/dpb+vTUXdVZaBXsJtoL7kMmlewkSGcmsLTa1Wo2jR48iOjpaVyaRSBAWFoaEhIQq3aOgoADFxcVlnu/Fx8fDw8MDrq6u6N+/PxYsWIDGjQ3/Na9SqaBS3d8iQqEw/e61tUXq2Njg15ZCau8Mpy4RcOoSgRLFLeQnHUT+mf1QZxhajFVEUdoZFKWdwa1d38C2RVBpkmvTnRPB6wl1VhpubJwNbTm7tKvSU3Fj42z4jFnA5EZGM2tiu3XrFjQajd5qJkDpRPCUlJQq3WPmzJnw8fFBWFiYriwiIgLDhw9HixYtkJqaivfeew+DBg1CQkJCmZGcQOmurw9ONG9ImkR+ZO4Q6oyVkxtcejwDlx7PQH37BpRJB5CftN/wRHCtBoWpx1GYehy3rGxg59+5dLPUVp05EdxMRFFE1o7l5Sa1e7RFSmTtWAGfcYvYLUlGMfsztppYtGgRtmzZgvj4eL0tc0aNGqX7ukOHDggKCkKrVq0QHx+PJ598ssx9oqOjERUVpXutUCjg6+tbu8FTjdg09oFNnxfgcnciuDJpP/LPHEBJroGJ4CVqKFMOQZlyCIKNLezb3p0I3iIIgtTwjwCfAekr/X6ooVUVQKsqhKgqgFZdqHutfeC1eO+1rqz0taYwD6KqoErvp0q/ANWN81XaOJfoYWZNbG5ubpBKpcjMzNQrz8zMhJeXV4XXLlmyBIsWLcIff/yhtyusIS1btoSbmxsuXLhgMLHJZDIOLmmgBEGAzNMPMk8/uPZ7Garr50qnDyQdhEaZU6a+qC5E/qk9yD+1BxJbR9i3C707ETwAglA6SPjeM6AHrxdVBbixPrrBPQMSRRFicdH95KMqhFb9QPJRF0JbVACtukCvjqjWr69VFQJaTZ3GXnAukYmNjGLWxGZjY4MuXbogLi4Ow4YNA1A6eCQuLg6TJ08u97pPPvkEH330EXbt2qXbaaAi165dw+3bt+Htzf3BLJkgCJA3bQt507ZoHDYWRVfOID/pQAUTwfOQd3w38o7vhtShERwCe8LGpzVux35t9mdAoqiFqC4q0xoqfa2822oqLJOQ7r0WdS2mIoOjShsCTWHFXZZE5TH7qMitW7ciMjISq1evRvfu3bF06VJs27YNKSkp8PT0xJgxY9CkSRMsXLgQALB48WLMmTMHmzZtQq9evXT3cXBwgIODA/Lz8zFv3jw899xz8PLyQmpqKmbMmIG8vDycOnWqSi2zhjQqkionaopRkHoCyqQDUJ772+BE8OqSefsbfAYkajXQqovuJpa7iabIcLed+FAy0nXf3W0xWQrBxhYSmS3EkmJoC/OqfJ1Lz2fR6InRtRgZWSqzP2MbOXIksrKyMGfOHGRkZCA4OBixsbG6ASVpaWmQSO7PI1+5ciXUajWef/55vfvExMRg7ty5kEqlOHnyJDZs2ICcnBz4+Phg4MCBmD9/PrsbH1GC1Br2bbrBvk03aNVFKLhwFPln9qMg9RigKTHqnqr0C7jx7UwAwgOJqdAkSbN+ECCR2UKQ2UEis4XE5u6/MltIZPa6MkFXZgeJzd1/772W2UGwkeu6eIuun8ON9dGVvO99dm2619aHIwtn9hZbfcQW26NBU6REwdnDpRPBL59qsF12egTJA8nFFoKNfqLRT1J3E5OBOoK1TJeQTEUURdz4dqbB+WsPK69FTFQVTGwGMLE9ejTKXGT8+AlU16o2zcTkJFKDiUeXfB5uEd2tIzyYsGR2EKxs6nUyqGweGwBI5Pacx0Y1YvauSKL6QGrvDNtmAdVObILU2kDieTAxPdhqMtQ6Kk1cgtS6XickU7FxbwafMQsqWHnEH+5D3mRSoxphi80AttgeTdV9BuT9ynzYNgusxYgslyiKSFs2QTelQpDZwfvFDx7ZeYJkWvVidX+i+kDm0xoy76rtAC/z9ofcN6CWI7JcgiBAsLq/i4NUbg95kzZMamQSTGxEdwmCAPchkyGR21dYTyK3h/uQN/lLmKieYmIjesC9Z0Dltdxk3v4c2EBUzzGxET3Exr0ZfMYthtTeRVcmyOzgM3YhfMYtYlIjquc4KpLIgPKeARFR/ccWGxERWRQmNiIisihMbEREZFGY2IiIyKIwsRERkUVhYiMiIovC4f5EZBZSx8YGvyaqKSY2IjKLJpEfmTsEslDsiiQiIovCxEZERBaFiY2IiCwKExsREVkUJjYiIrIoTGxERGRRmNiIiMiiMLEREZFFYWIjIiKLwsRGREQWpV4kthUrVsDPzw9yuRwhISFITEyssP4PP/yAdu3aQS6Xo0OHDvjf//6nd14URcyZMwfe3t6wtbVFWFgYzp8/X5sfgYiI6gmzJ7atW7ciKioKMTExOHbsGDp27Ijw8HDcvHnTYP2DBw/ixRdfxPjx43H8+HEMGzYMw4YNw+nTp3V1PvnkE3zxxRdYtWoVDh8+DHt7e4SHh6OoqKiuPhYREZmJIIqiaM4AQkJC0K1bNyxfvhwAoNVq4evriylTpmDWrFll6o8cORJKpRK//vqrrqxHjx4IDg7GqlWrIIoifHx88M477+Ddd98FAOTm5sLT0xPr16/HqFGjKo1JoVDA2dkZubm5cHJyMtEnpYYmbfnrKMnNAgBYObuj2eRVZo6IiKrCrC02tVqNo0ePIiwsTFcmkUgQFhaGhIQEg9ckJCTo1QeA8PBwXf1Lly4hIyNDr46zszNCQkLKvadKpYJCodA7iIioYTJrYrt16xY0Gg08PT31yj09PZGRkWHwmoyMjArr3/u3OvdcuHAhnJ2ddYevr69Rn4eIiMzP7M/Y6oPo6Gjk5ubqjqtXr5o7JKoHpI6NYeXsDitnd26ESdSAmHWjUTc3N0ilUmRmZuqVZ2ZmwsvLy+A1Xl5eFda/929mZia8vb316gQHBxu8p0wmg0wmM/ZjkIXiRphEDZNZW2w2Njbo0qUL4uLidGVarRZxcXEIDQ01eE1oaKhefQD4/fffdfVbtGgBLy8vvToKhQKHDx8u955ERGQ5zNpiA4CoqChERkaia9eu6N69O5YuXQqlUolx48YBAMaMGYMmTZpg4cKFAICpU6eib9+++Oyzz/DUU09hy5YtOHLkCL7++msAgCAImDZtGhYsWIDWrVujRYsW+OCDD+Dj44Nhw4aZ62MSEVEdMXtiGzlyJLKysjBnzhxkZGQgODgYsbGxusEfaWlpkEjuNyx79uyJTZs2Yfbs2XjvvffQunVrbN++He3bt9fVmTFjBpRKJV599VXk5OSgd+/eiI2NhVwur/PPR0REdcvs89jqI85jIyJquMzeYquP7uV6zmcjIkMcHR0hCIK5w6ByMLEZkJeXBwCcz0ZEBrE3p35jV6QBWq0WN27caDB/lSkUCvj6+uLq1av8YTMxfm9rV0P9/jaU3w2PKrbYDJBIJGjatKm5w6g2JyenBvXLoSHh97Z28ftLpsSVR4iIyKIwsRERkUVhYrMAMpkMMTExXBasFvB7W7v4/aXawMEjRERkUdhiIyIii8LERkREFoWJjYiILAoTGxERWRQmNiIisihMbEREZFGY2IiIyKIwsRERkUVhYiMiIovCxEZERBaFiY2IiCwKExsREVkUJjYiIrIoTGzUIM2dOxfBwcG1cu/4+HgIgoCcnByT3fPy5csQBAEnTpww2T2JyDAmNqpVY8eOhSAIZY6IiAhzh2aRzpw5gxEjRsDd3R0ymQxt2rTBnDlzUFBQYO7QiOqMlbkDIMsXERGBb7/9Vq+svm4sWVxcbO4QjHbo0CGEhYUhLCwMO3fuhKenJxITE/HOO+8gLi4Of/31F2xsbMwdJlGtY4uNap1MJoOXl5fe4erqqjsvCAJWr16Np59+GnZ2dggICEBCQgIuXLiAfv36wd7eHj179kRqamqZe69evRq+vr6ws7PDiBEjkJubqzv3999/Y8CAAXBzc4OzszP69u2LY8eO6V0vCAJWrlyJZ555Bvb29vjoo4/KvEdBQQEGDRqEXr166bonv/nmGwQEBEAul6Ndu3b46quv9K5JTExEp06dIJfL0bVrVxw/frwm38JKiaKI8ePHIyAgAD///DO6d++O5s2b44UXXsCOHTuQkJCAzz//vFZjIKo3RKJaFBkZKQ4dOrTCOgDEJk2aiFu3bhXPnj0rDhs2TPTz8xP79+8vxsbGiklJSWKPHj3EiIgI3TUxMTGivb292L9/f/H48ePinj17RH9/f/Gll17S1YmLixO/++47MTk5WUxKShLHjx8venp6igqFQu+9PTw8xHXr1ompqanilStXxL/++ksEIGZnZ4vZ2dliz549xYEDB4pKpVIURVH8/vvvRW9vb/Gnn34SL168KP70009io0aNxPXr14uiKIp5eXmiu7u7+NJLL4mnT58Wd+zYIbZs2VIEIB4/frzc78Nrr70m2tvbV3iU59ixYyIAcdOmTQbPDxgwQOzYsWOF/x2ILAUTG9WqyMhIUSqVlvkF/dFHH+nqABBnz56te52QkCACENeuXasr27x5syiXy3WvY2JiRKlUKl67dk1X9ttvv4kSiURMT083GItGoxEdHR3FHTt26L33tGnT9OrdS2zJycliUFCQ+Nxzz4kqlUp3vlWrVmUSyPz588XQ0FBRFEVx9erVYuPGjcXCwkLd+ZUrV1aa2DIzM8Xz589XeJRny5YtFd7/rbfeEm1tbcu9nsiS8Bkb1bonnngCK1eu1Ctr1KiR3uugoCDd156engCADh066JUVFRVBoVDAyckJANCsWTM0adJEVyc0NBRarRZnz56Fl5cXMjMzMXv2bMTHx+PmzZvQaDQoKChAWlqa3nt37drVYNwDBgxA9+7dsXXrVkilUgCAUqlEamoqxo8fj4kTJ+rqlpSUwNnZGQCQnJyMoKAgyOVyvdgq4+HhAQ8Pj0rrVUQUxXLP8fkaPSqY2KjW2dvbw9/fv8I61tbWuq8FQSi3TKvVVvl9IyMjcfv2bSxbtgzNmzeHTCZDaGgo1Gp1mfgMeeqpp/DTTz8hKSlJl2Tz8/MBAGvWrEFISIhe/XvJz1ivv/46vv/++wrr3Hv/h7Vu3RpAaVLt1KlTmfPJyclo06ZNjeIjaiiY2KjBSktLw40bN+Dj4wOgdFSgRCJB27ZtAQAHDhzAV199hcGDBwMArl69ilu3blX5/osWLYKDgwOefPJJxMfHIzAwEJ6envDx8cHFixfx8ssvG7wuICAA3333HYqKinSttkOHDlX6fh9++CHefffdKsf3oE6dOqFdu3b4/PPPMWrUKEgk98eF/fPPP/jjjz+wfPlyo+5N1NAwsVGtU6lUyMjI0CuzsrKCm5tbje4rl8sRGRmJJUuWQKFQ4K233sKIESPg5eUFoLQV891336Fr165QKBSYPn06bG1tq/UeS5YsgUajQf/+/REfH4927dph3rx5eOutt+Ds7IyIiAioVCocOXIE2dnZiIqKwksvvYT3338fEydORHR0NC5fvowlS5ZU+l416YoUBAHffPMNBg4ciOeeew7R0dHw8vLC4cOH8c477yA8PByvvfaaUfcmamg43J9qXWxsLLy9vfWO3r171/i+/v7+GD58OAYPHoyBAwciKChIb9j92rVrkZ2djc6dO+OVV17BW2+9ZVTi+PzzzzFixAj0798f586dw4QJE/DNN9/g22+/RYcOHdC3b1+sX78eLVq0AAA4ODhgx44dOHXqFDp16oT3338fixcvrvHnrUyvXr1w6NAhSKVSDBo0CM2bN8eIESMwdOhQ7Nixo8ZdpUQNhSBW9LSZiBosrVaL8ePHY9euXdizZ4/uORyRpWNiI7JgWq0WX375JRwdHfF///d/5g6HqE4wsRERkUXhMzYiIrIoTGxERGRRmNiIiMiiMLEREZFFYWIzQBRFKBSKCtfdIyKi+omJzYC8vDw4OzsjLy/P3KEQEVE1cUmtBkwURahunEfBuURoCpWQ2trDrk13yHxa6xYNJuPwe0vUcHEemwEKhQLOzs7Izc3VbZFS36iz0pC1YzlU6WV3lZZ5t4L7kMmwcW9mhsgaPn5viRo2s3ZF7t27F0OGDIGPjw8EQcD27dsrvSY+Ph6dO3eGTCaDv78/1q9fX6bOihUr4OfnB7lcjpCQECQmJpo+eDNSZ6XhxsbZBn/xAoAqPRU3Ns6GOivN4HkqH7+3RA2fWRObUqlEx44dsWLFiirVv3TpEp566ik88cQTOHHiBKZNm4YJEyZg165dujpbt25FVFQUYmJicOzYMXTs2BHh4eG4efNmbX2MOiWKIrJ2LIe2SFlhPW2RElk7VnAATDXwe0tkGepNV6QgCPjll18wbNiwcuvMnDkTO3fuxOnTp3Vlo0aNQk5ODmJjYwEAISEh6Natm27vKa1WC19fX0yZMgWzZs2qUiz1uSuy6Po53FgfXeX6Nl4tIZHZ1WJElkOrKoA642KV6/uMXQh5E27eSVTfNKjBIwkJCQgLC9MrCw8Px7Rp0wAAarUaR48eRXT0/V/8EokEYWFhSEhIKPe+KpUKKpVK91qhUJg2cBMqOFe9btXq/KKm6ik4l8jERlQPNajh/hkZGfD09NQr8/T0hEKhQGFhIW7dugWNRmOwzsMbXT5o4cKFcHZ21h2+vr61Er8paAor7iajulOcYxnd20SWpkElttoSHR2N3Nxc3XH16lVzh1Quqa29uUOgu5RJB5D58xKoblwwdyhE9IAG1RXp5eWFzMxMvbLMzEw4OTnB1tYWUqkUUqnUYB0vL69y7yuTySCTyWolZlOza9MdOQd/qXJ978iPIW/CDSarouj6OaRveL9a1yiTE6BMToC8eXu4hA6DbctgznMjMrMG1WILDQ1FXFycXtnvv/+O0NBQAICNjQ26dOmiV0er1SIuLk5Xp6GT+bSGzLtV1ep6+0PepA0EQcKjCoe8Sdsqf28fVnTlNDK2LMD1b95B3um9EDUlRt2HiGrOrIktPz8fJ06cwIkTJwCUDuc/ceIE0tJK5whFR0djzJgxuvqvv/46Ll68iBkzZiAlJQVfffUVtm3bhrfffltXJyoqCmvWrMGGDRuQnJyMSZMmQalUYty4cXX62WqLIAhwHzIZEnnFXZISuT3ch7zJ1kM1VPV7K8js4BDUD4KNbZlz6ptXkPWfZbj61ZvITfwVWnVhbYVLROUw63D/+Ph4PPHEE2XKIyMjsX79eowdOxaXL19GfHy83jVvv/02kpKS0LRpU3zwwQcYO3as3vXLly/Hp59+ioyMDAQHB+OLL75ASEhIleOqz8P976l4dQx/uA95k6tjGKmq31tNkRJ5x3YjN/FXaJQ5Bu8lkTvAqUsEnLsNhtTeuZYjJyKgHs1jq08aQmIDuJ5hbarO91Zbokb+qb3IPfQfFN+5YfB+gpUNHIL6wSXkGVg38q6Lj0D0yGJiM6ChJDaqX0RRi4JzfyMnYTtU18+VU0uAfbsecA4dBrmPf53GR/SoYGIzgImNakIURaiupSDn4C8ouHC03HocSUlUO5jYDGBiI1NRZ6Uh59B/kX96H6A1PFLSxqMZnHsMg0NgLwjSBjUDh6heYmIzgImNTK1EcRu5f/8KxbHfIZYzUlLq5AaXkCFwDH4SEgMjLomoapjYDGBio9pS9ZGU4XDu9hRHUhIZgYnNACY2qm1iSTHyTu8pHUl5u5yRlFJrOHR8giMpiaqJic0AJjaqK6UjKY/cHUl5tpxaHElJVB1MbAYwsZE5FF1NRk7CdhScP1JuHXnzx+DSYxhsW3XiSEqicjCxGcDERuakzrp6dyTlXo6kJDICE5sBTGxUH5SOpNwJxbHdFjmS8vqG96HJuw0AkDo2RpPIj8wcEVkKJjYDmNioPtEWKaGo4khKp66DYeXgUqfxGStt+esoyc0CAFg5u6PZ5FVmjogsBfswiOo5idweLj2fhXP3p8sdSaktykfOgZ+Qe+i/cAh6Ai49hsC6kY+ZIiYyLyY2ogZCsLKGU3AYHDv2L3ckpagpRt7x3cg7/jvs24XAuccwbjRLjxwmNqIGRhAksG/bHfZtu6PoasrdkZR/P1RLhDLlEJQphziSkh45TGxEDZjctx28fGdBfesacg/9B3mnyo6kLLpyBhlXztwdSTkUDoG9OZKSLJpZd9AmItOwcWsK96ffRLM3v4Jz6DAIMrsyddQ305D13y+R9tWbyDm8A1oVd/cmy8TERmRBrJwao3H/V9B88io06v8KpA6uZepoFLdw54/1SFv+Gu7Eb0JJfk7dB0pUi9gfQWSBJHJ7uIQOg3O3p5B3+u7u3rev69XRFik5kpIsEhMbkQUrHUn5JBw7PoGC80eRk/ALVNc4kpIsGxMb0SNAECSwb9MN9m26VW0kZbPH4BI6FLatOnMkJTU4TGxEj5gqjaRMO4OMtDOwdm8Gl1COpKSGhYNHiB5RupGUk1eWO5KyOIsjKanhYWIjesRZOTaq3kjKv/7NkZRUr7FvgYgA6I+kzD+zDzkJ2w2PpDz4M3IP74BDUD84hzwDm8YcSUn1CxMbEekRrKzh2LE/HIL63R1JuR2qayl6dUpHUv6OvON/wK5td7iEDoO8SRszRUykj4mNiAwqM5Ly0HYUnCs7krLg7GEUnD3MkZRUb9SLZ2wrVqyAn58f5HI5QkJCkJiYWG7dfv36QRCEMsdTTz2lqzN27Ngy5yMiIurioxBZJLlvO3i9MAtNX1sGx45PApKyfxMXpZ1BxtaPcW1NFPJOxkPUFJshUqJ6kNi2bt2KqKgoxMTE4NixY+jYsSPCw8Nx8+ZNg/V//vlnpKen647Tp09DKpXihRde0KsXERGhV2/z5s118XGILFrpSMo3Kh9JueNLpK14EzmH/8uRlFTnzJ7Y/vWvf2HixIkYN24cAgMDsWrVKtjZ2WHdunUG6zdq1AheXl664/fff4ednV2ZxCaTyfTqubqWHelFRMYpO5KyUZk6mrzbuPPHhgdGUmbrzomiCLHkfotOU6RE0fVzEEWxTuInyyaIZvw/Sa1Ww87ODj/++COGDRumK4+MjEROTg7+85//VHqPDh06IDQ0FF9//bWubOzYsdi+fTtsbGzg6uqK/v37Y8GCBWjcuLHBe6hUKqhUKt1rhUIBX19f5ObmwsnJyfgPSPSIEEuKS0dSHvoPim9dM1hHkFrDIagf7Fp3Q86+rVClp5apI/NuBfchk2Hj3qy2QyYLZtYW261bt6DRaODp6alX7unpiYyMjEqvT0xMxOnTpzFhwgS98oiICGzcuBFxcXFYvHgx9uzZg0GDBkGj0Ri8z8KFC+Hs7Kw7fH19jf9QRI+geyMpm776OTxfmAW5b0CZOvdGUmZu+9hgUgMAVXoqbmycDXVWWm2HTBbMrC22GzduoEmTJjh48CBCQ0N15TNmzMCePXtw+PDhCq9/7bXXkJCQgJMnT1ZY7+LFi2jVqhX++OMPPPnkk2XOs8VGZHpF11KQk/AfFJwrfzBYeWTe/vAZt4ijK8koZm2xubm5QSqVIjMzU688MzMTXl5eFV6rVCqxZcsWjB8/vtL3admyJdzc3HDhwgWD52UyGZycnPQOIqoZedN28HphZulIyuAwQCKt8rWq9AtQ3Thfi9GRJTNrYrOxsUGXLl0QFxenK9NqtYiLi9NrwRnyww8/QKVSYfTo0ZW+z7Vr13D79m14e3vXOGYiqh4bt6Zwf2oSnDoNrNZ1xrT0iIB6MCoyKioKa9aswYYNG5CcnIxJkyZBqVRi3LhxAIAxY8YgOjq6zHVr167FsGHDygwIyc/Px/Tp03Ho0CFcvnwZcXFxGDp0KPz9/REeHl4nn4mIyhK1hp9xl0dTqKylSMjSmX3lkZEjRyIrKwtz5sxBRkYGgoODERsbqxtQkpaWBolEP/+ePXsW+/fvx+7du8vcTyqV4uTJk9iwYQNycnLg4+ODgQMHYv78+ZDJZHXymYioLKmtfa3WJ7rHrINH6iuFQgFnZ2cOHiEyoaLr53Bjfdnel/L4jF3I9SfJKGbviiSiR4PMpzVk3q2qVtfbHzKf1rUcEVkqJjYiqhOCIMB9yGRI5BV3MUrk9nAf8iaH+pPRmNiIqM7YuDeDz5gF5bbcZN7+8BmzgCuPUI1U+Rnb8OHDq3zTn3/+2eiA6gM+YyOqXaIoIm3ZBGiUOQAAQWYH7xc/gMynNVtqVGNVbrE9uOSUk5MT4uLicOTIEd35o0ePIi4uDs7OzrUSKBFZDkEQIFhZ615L5faQN2nDpEYmUeXh/t9++63u65kzZ2LEiBFYtWoVpNLS1QQ0Gg3eeOMNtnCIiMisjHrGtm7dOrz77ru6pAaUzh+Liooqd7sZIiKiumBUYispKUFKSkqZ8pSUFGi12hoHRUREZCyjVh4ZN24cxo8fj9TUVHTv3h0AcPjwYSxatEi3FBYREZE5GJXYlixZAi8vL3z22WdIT08HAHh7e2P69Ol45513TBogERFRdRiV2CQSCWbMmIEZM2ZAoVAAAAeNEBFRvWD0BO2SkhL88ccf2Lx5s26I7o0bN5Cfn2+y4IiIiKrLqBbblStXEBERgbS0NKhUKgwYMACOjo5YvHgxVCoVVq1aZeo4iYiIqsSoFtvUqVPRtWtXZGdnw9bWVlf+7LPP6m0aSkREVNeMarHt27cPBw8ehI2NjV65n58frl+/bpLAiIiIjGFUi02r1UKjKbsb7rVr1+Do6FjjoIiIiIxlVGIbOHAgli5dqnstCALy8/MRExODwYMHmyo2IiKiajOqK/Kzzz5DeHg4AgMDUVRUhJdeegnnz5+Hm5sbNm/ebOoYiYiIqsyoxNa0aVP8888/2LJlC06ePIn8/HyMHz8eL7/8st5gEiIiorpmVGIrKiqCXC7H6NGjTR0PERFRjRj1jM3DwwORkZH4/fffuegxERlF6tgYVs7usHJ2h9SxsbnDIQtS5R20H/TLL79g06ZN2LlzJ5ydnTFy5EiMHj0aXbt2rY0Y6xx30CYiariMSmz35OXl4ccff8TmzZvx559/omXLlhg9ejTmzJljyhjrHBMbEVHDVaPE9qCkpCS8/PLLOHnypME5bg0JExsRUcNl9CLIQOkgkm3btmHYsGHo3Lkz7ty5g+nTp5sqNiIiomozalTkrl27sGnTJmzfvh1WVlZ4/vnnsXv3bjz++OOmjo+IiKhajGqxPfvssygsLMTGjRuRkZGB1atX1yiprVixAn5+fpDL5QgJCUFiYmK5ddevXw9BEPQOuVyuV0cURcyZMwfe3t6wtbVFWFgYzp8/b3R8RETUcBjVYsvMzDTZmpBbt25FVFQUVq1ahZCQECxduhTh4eE4e/YsPDw8DF7j5OSEs2fP6l7f2w/unk8++QRffPEFNmzYgBYtWuCDDz5AeHg4kpKSyiRBIiKyLFUePKJQKHQDKe7tml2e6gy4CAkJQbdu3bB8+XIApQss+/r6YsqUKZg1a1aZ+uvXr8e0adOQk5Nj8H6iKMLHxwfvvPMO3n33XQBAbm4uPD09sX79eowaNarSmDh4hIio4apyV6Srqytu3rwJAHBxcYGrq2uZ4155VanVahw9ehRhYWH3A5JIEBYWhoSEhHKvy8/PR/PmzeHr64uhQ4fizJkzunOXLl1CRkaG3j2dnZ0REhJS7j1VKhUUCoXeQUREDVOVuyL//PNPNGrUSPf1w91/xrh16xY0Gg08PT31yj09PZGSkmLwmrZt22LdunUICgpCbm4ulixZgp49e+LMmTNo2rQpMjIydPd4+J73zj1s4cKFmDdvXo0/DxERmV+VE1vfvn11X/fr1682YqmS0NBQhIaG6l737NkTAQEBWL16NebPn2/UPaOjoxEVFaV7rVAo4OvrW+NYiYio7hk1KrJ169aYO3dujUcaurm5QSqVIjMzU688MzMTXl5eVbqHtbU1OnXqhAsXLgCA7rrq3FMmk8HJyUnvICKihsmoxPbGG29g586daNeuHbp164Zly5aV281XERsbG3Tp0gVxcXG6Mq1Wi7i4OL1WWUU0Gg1OnToFb29vAECLFi3g5eWld0+FQoHDhw9X+Z5ERNSAiTVw9uxZcc6cOWLr1q1FKysrccCAAeKGDRuqdY8tW7aIMplMXL9+vZiUlCS++uqroouLi5iRkSGKoii+8sor4qxZs3T1582bJ+7atUtMTU0Vjx49Ko4aNUqUy+XimTNndHUWLVokuri4iP/5z3/EkydPikOHDhVbtGghFhYWVimm3NxcEYCYm5tbrc9CRETmV6PE9qCEhAQxODhYlEgk1b72yy+/FJs1ayba2NiI3bt3Fw8dOqQ717dvXzEyMlL3etq0abq6np6e4uDBg8Vjx47p3U+r1YoffPCB6OnpKcpkMvHJJ58Uz549W+V4mNiIiBquGi+CnJiYiE2bNmHr1q1QKBQYMmQItmzZYprmpJlwHhsRUcNl1Moj586dw7///W9s3rwZly5dQv/+/bF48WIMHz4cDg4Opo6RiIioyoxKbPcGjbz55psYNWpUmTljRERE5lLtxKbRaLB69Wo8//zz1VplhIiIqC5Ue7i/VCrFlClTyl2rkYiIyJyMmsfWvn17XLx40dSxEBER1ZhRiW3BggV499138euvvyI9PZ0LCBMRUb1h1HB/ieR+PnxwMWRRFCEIAjQajWmiMxMO9yciariMGhX5119/mToOIiIik6jxBG1LxBYbEVHDZVSLbe/evRWef/zxx40KhoiIqKZq/IxNd6MHnrXxGRsREZmLUaMis7Oz9Y6bN28iNjYW3bp1w+7du00dIxERUZUZ1RXp7OxcpmzAgAGwsbFBVFQUjh49WuPAiIiIjGFUi608np6eOHv2rClvSUREVC1GtdhOnjyp91oURaSnp2PRokUIDg42RVxERERGMSqxBQcHQxAEPDzupEePHli3bp1JAiMiIjKGUYnt0qVLeq8lEgnc3d0hl8tNEhQREZGxqvWMLSEhAb/++iuaN2+uO/bs2YPHH38czZo1w6uvvgqVSlVbsRIREVWqWontww8/xJkzZ3SvT506hfHjxyMsLAyzZs3Cjh07sHDhQpMHSUREVFXVSmwnTpzAk08+qXu9ZcsWhISEYM2aNYiKisIXX3yBbdu2mTxIIiKiqqpWYsvOzoanp6fu9Z49ezBo0CDd627duuHq1aumi46IiKiaqpXYPD09dQNH1Go1jh07hh49eujO5+Xlwdra2rQREhERVUO1EtvgwYMxa9Ys7Nu3D9HR0bCzs0OfPn1050+ePIlWrVqZPEgiIqKqqtZw//nz52P48OHo27cvHBwcsGHDBtjY2OjOr1u3DgMHDjR5kERERFVl1Or+ubm5cHBwgFQq1Su/c+cOHBwc9JJdQ8TV/YmIGi6TLYIMAI0aNapRMERERDVl0kWQjbVixQr4+flBLpcjJCQEiYmJ5dZds2YN+vTpA1dXV7i6uiIsLKxM/bFjx0IQBL0jIiKitj8GERHVA2ZPbFu3bkVUVBRiYmJw7NgxdOzYEeHh4bh586bB+vHx8XjxxRfx119/ISEhAb6+vhg4cCCuX7+uVy8iIgLp6em6Y/PmzXXxcYiIyMyMesZmSiEhIejWrRuWL18OANBqtfD19cWUKVMwa9asSq/XaDRwdXXF8uXLMWbMGAClLbacnBxs377dqJj4jI2IqOEya4tNrVbj6NGjCAsL05VJJBKEhYUhISGhSvcoKChAcXFxmed78fHx8PDwQNu2bTFp0iTcvn273HuoVCooFAq9g4iIGiazJrZbt25Bo9HorWYClE4Ez8jIqNI9Zs6cCR8fH73kGBERgY0bNyIuLg6LFy/WrZCi0WgM3mPhwoVwdnbWHb6+vsZ/KCIiMiujRkXWF4sWLcKWLVsQHx+vt2XOqFGjdF936NABQUFBaNWqFeLj4/XWurwnOjoaUVFRutcKhYLJjYiogTJri83NzQ1SqRSZmZl65ZmZmfDy8qrw2iVLlmDRokXYvXs3goKCKqzbsmVLuLm54cKFCwbPy2QyODk56R1ERNQwmTWx2djYoEuXLoiLi9OVabVaxMXFITQ0tNzrPvnkE8yfPx+xsbHo2rVrpe9z7do13L59G97e3iaJm4iI6i+zD/ePiorCmjVrsGHDBiQnJ2PSpElQKpUYN24cAGDMmDGIjo7W1V+8eDE++OADrFu3Dn5+fsjIyEBGRgby8/MBAPn5+Zg+fToOHTqEy5cvIy4uDkOHDoW/vz/Cw8PN8hmJiKjumP0Z28iRI5GVlYU5c+YgIyMDwcHBiI2N1Q0oSUtLg0RyP/+uXLkSarUazz//vN59YmJiMHfuXEilUpw8eRIbNmxATk4OfHx8MHDgQMyfPx8ymaxOPxsREdU9s89jq484j42IqOEye1ckERGRKTGxERGRRTH7MzYiejTN+HIfbucWAgAaO9vikyl9KrmCqGqY2IjILG7nFuJmdqG5wyALxK5IIiKyKExsRERkUZjYiIjIojCxERGRRWFiIyIii8LERkREFoWJjYiILAoTGxERWRQmNiIisihMbERU50RRRHGJVvc6v7AYKVfugJuNkClw2xoDuG0NUe25kqHA0i3HceFqTplz/r4umDaqE5p78eeOjMcWGxHVmSsZCsxcvt9gUgOAC1dzMHP5flzJUNRtYGRRmNiIqE6Iooil/9/e3QdFVf97AH8vDwsLLE/ysDCyRFek1BACxM2HNCmyycm5U2E1I6QzjQQkkdeiCbVoBtTqp5hpMXeAqRiNGpTLIOrsT/BnoqMUjWT407kY3LFdjGeWR2HvH8DKKijKLmf38H7NOJ49+93lc47jvvl+z/d899Cv0PUM3LOdrmcAew/9ymFJemgMNiIyq1uDQ9A061B65n8n7Knd6WpjG/7d0Grewki0+LU1RDQler0ebZ190LZ0Q9PSDW2zDtqW7uHHzTr83daDoYfofJ2r1SAk0NP0BZPoMdiI6L66ewdGgmo4sLTNuuEQG/nTPzBo8p/ZdZ8hS6KJMNiICAO3hnCzrRvaZuNe1/B2Nzq7+6e9JheZ/bT/TBIHBhvRDDA0pEdrZ++YIcJuaFtuDxk2P+Rw4f24uzjA19MJvrOcYGcrwT8v/t+kX7t4gcL0BdGMwGAjEgldz4DhutbYa1zalm40tXSjf8wN0abiKLWFYpbzcHiNBJjCc/ixj6cTZA63P2L0ej0atF2TmkASHOCOuUoPk9dLMwODjchKDNwaRFNrD7QjvS3D9a6RbXNck7K1kcDbQwZfTyejABvddnWWQiKRTOq9JBIJUteF4/0vz9xzyr+zzB6b14VP+n2J7sSVR8ZhLSuP6PV6XGloxflaDbp6BuAis0f0AgVClB78UJgiIc7t6HChYZhw9HrXyDWv5o5emON/q4d8ZLjQ0xmKWcY9r1lujrC1Ne1dQfdaeSQ4wB2bufIITRGDbRzWEGxclsh8zHluu7r7jcJq7HZTa4/R+ommInOwG+llDYfX7e3h4UJH6fQP3Oj1esR/fBytnX0AACdHO3zylgpz+UsZmYBFDEXu378fu3fvhkajwcKFC7Fv3z4sWrRowvZFRUXIyMjA9evXERwcjJ07d+KFF14wPK/X67F9+3bk5uaira0NS5YswYEDBxAcHDwdh2N2o8sSTTScM7os0c7kpQy3BzTVc9s/MGi4vnXn9S5tsw663lsmr9nOVgJvD6e7hgtHH8ud7C0uLCQSCeztbvcEXWT2vGeNTEbwYDt8+DDS0tJw8OBBREdHY8+ePYiNjcWVK1fg4+NzV/uzZ8/itddeQ1ZWFl588UUUFhZi7dq1+OWXX7BgwQIAwK5du5CTk4OCggIEBQUhIyMDsbGxuHz5MhwdHaf7EE3qQZcl+nzzcov7ULNUD3Juswsu4D9XzBm+5jXmeldLR69ZavN0dRjubY30tBRjtme5yWBrw39jolGCD0VGR0cjKioKX375JQBgaGgIAQEBSElJwQcffHBX+7i4OOh0OpSWlhr2LV68GGFhYTh48CD0ej38/f3x3nvvYcuWLQCA9vZ2+Pr6Ij8/H+vWrbtvTZY8FFn3Zwv+K+dfk26/bKE/PFytO8ynS0tHL878dkOQn+3saHdHcDnBd9bt2YUO9raC1GVOGz89gabWHgCAj4cM//3RcwJXRGIhaI+tv78f1dXVSE9PN+yzsbFBTEwMqqqqxn1NVVUV0tLSjPbFxsbiyJEjAID6+npoNBrExMQYnndzc0N0dDSqqqrGDba+vj709fUZHnd0WO7K4udrNQ/U/l8CfVCTMTtbG/h6yu64xnV728VJKnSJRKIhaLD9/fffGBwchK+vr9F+X19f1NXVjfsajUYzbnuNRmN4fnTfRG3ulJWVhY8//vihjmG6cZkhyySRAJ6ujhNOi/d0dYQNhwuJpoXg19gsQXp6ulEvsKOjAwEBAQJWNDEuM2Q5wkO88dLy/4BiljN8PGSwtxPfcKE5zXKTjbtNNFWCBpuXlxdsbW2h1WqN9mu1WigU4y+no1Ao7tl+9G+tVgs/Pz+jNmFhYeO+p4ODAxwcHB72MKZV9AIFfvzn1Um3f/e1J/GIn2VdJ7RU12+04x+Hfp10+zdiH+NMvinYlbJM6BJIpAQNNqlUioiICKjVaqxduxbA8OQRtVqN5OTkcV+jUqmgVquRmppq2Hfy5EmoVCoAQFBQEBQKBdRqtSHIOjo6cP78eSQmJprzcKZFiNIDcwLcJ70s0cqI2ZwVOUlB/q74n5/rueQTkZUT/ItG09LSkJubi4KCAvzxxx9ITEyETqfDm2++CQBYv3690eSSzZs3o7y8HJ9//jnq6uqwY8cOXLx40RCEEokEqamp+PTTT1FSUoJLly5h/fr18Pf3N4SnNRtdlsj5PkOSXJbowfHcEomD4NfY4uLicPPmTWzbtg0ajQZhYWEoLy83TP5oaGiAjc3t/H3qqadQWFiIjz76CB9++CGCg4Nx5MgRwz1sALB161bodDq89dZbaGtrw9KlS1FeXm7197CNClS4YmfyUi5LZAY8t0TWT/D72CyRJd/HNpZer8e/G1pxbsx6hosXKLgskQnw3BJZLwbbONrb2+Hu7o7GxkaLDjYiEoZcLucvOBZM8KFIS9TZ2QkAFjvln4iEZemjOTMde2zjGBoawo0bN6zmt7LR++7YwzQ9nlvzstbzay2fDTMVe2zjsLGxwezZs4Uu44G5urpa1YeDNeG5NS+eXzIlwaf7ExERmRKDjYiIRIXBJgIODg7Yvn271SwLZk14bs2L55fMgZNHiIhIVNhjIyIiUWGwERGRqDDYiIhIVBhsVuz06dNYs2YN/P39IZFIcOTIEaFLEo2srCxERUVBLpfDx8cHa9euxZUrV4QuSzQOHDiA0NBQw/1rKpUKx44dE7osEgkGmxXT6XRYuHAh9u/fL3QpolNZWYmkpCScO3cOJ0+exMDAAJ577jnodDqhSxOF2bNnIzs7G9XV1bh48SKeeeYZvPTSS/j999+FLo1EgLMiRUIikaC4uFgU3zlniW7evAkfHx9UVlZi+fLlQpcjSp6enti9ezc2btwodClk5bikFtEktLe3Axj+8CXTGhwcRFFREXQ6HVQqldDlkAgw2IjuY2hoCKmpqViyZInRF9rS1Fy6dAkqlQq9vb1wcXFBcXEx5s2bJ3RZJAIMNqL7SEpKQm1tLc6cOSN0KaISEhKCmpoatLe348cff0R8fDwqKysZbjRlDDaie0hOTkZpaSlOnz5tld/4YMmkUinmzJkDAIiIiMCFCxewd+9efP311wJXRtaOwUY0Dr1ej5SUFBQXF6OiogJBQUFClyR6Q0ND6OvrE7oMEgEGmxXr6urCtWvXDI/r6+tRU1MDT09PKJVKASuzfklJSSgsLMTRo0chl8uh0WgAAG5ubpDJZAJXZ/3S09OxevVqKJVKdHZ2orCwEBUVFTh+/LjQpZEIcLq/FauoqMDKlSvv2h8fH4/8/PzpL0hEJvp25Ly8PCQkJExvMSK0ceNGqNVq/PXXX3Bzc0NoaCjef/99PPvss0KXRiLAYCMiIlHhyiNERCQqDDYiIhIVBhsREYkKg42IiESFwUZERKLCYCMiIlFhsBERkagw2IiISFQYbERjrFixAqmpqUKXQURTwGAj0UlISIBEIoFEIjGsIP/JJ5/g1q1bQpdGRNOAiyCTKD3//PPIy8tDX18fysrKkJSUBHt7e6SnpwtdGhGZGXtsJEoODg5QKBQIDAxEYmIiYmJiUFJSAgD4+eefsWLFCjg5OcHDwwOxsbFobW0d932+/fZbREZGQi6XQ6FQ4PXXX0dTU5Ph+dbWVrzxxhvw9vaGTCZDcHAw8vLyAAD9/f1ITk6Gn58fHB0dERgYiKysLPMfPNEMxx4bzQgymQzNzc2oqanBqlWrsGHDBuzduxd2dnY4deoUBgcHx33dwMAAMjMzERISgqamJqSlpSEhIQFlZWUAgIyMDFy+fBnHjh2Dl5cXrl27hp6eHgBATk4OSkpK8MMPP0CpVKKxsRGNjY3TdsxEMxWDjURNr9dDrVbj+PHjSElJwa5duxAZGYmvvvrK0Gb+/PkTvn7Dhg2G7UcffRQ5OTmIiopCV1cXXFxc0NDQgPDwcERGRgIAHnnkEUP7hoYGBAcHY+nSpZBIJAgMDDT9ARLRXTgUSaJUWloKFxcXODo6YvXq1YiLi8OOHTsMPbbJqq6uxpo1a6BUKiGXy/H0008DGA4tAEhMTMShQ4cQFhaGrVu34uzZs4bXJiQkoKamBiEhIXjnnXdw4sQJ0x4kEY2LwUaitHLlStTU1ODq1avo6elBQUEBnJ2dH+jbr3U6HWJjY+Hq6orvv/8eFy5cQHFxMYDh62cAsHr1avz555949913cePGDaxatQpbtmwBADz55JOor69HZmYmenp68Oqrr+Lll182/cESkREGG4mSs7Mz5syZA6VSCTu72yPuoaGhUKvVk3qPuro6NDc3Izs7G8uWLcNjjz1mNHFklLe3N+Lj4/Hdd99hz549+OabbwzPubq6Ii4uDrm5uTh8+DB++ukntLS0TP0AiWhCvMZGM0p6ejqeeOIJvP3229i0aROkUilOnTqFV155BV5eXkZtlUolpFIp9u3bh02bNqG2thaZmZlGbbZt24aIiAjMnz8ffX19KC0txeOPPw4A+OKLL+Dn54fw8HDY2NigqKgICoUC7u7u03W4RDMSe2w0o8ydOxcnTpzAb7/9hkWLFkGlUuHo0aNGvbpR3t7eyM/PR1FREebNm4fs7Gx89tlnRm2kUinS09MRGhqK5cuXw9bWFocOHQIAyOVyw2SVqKgoXL9+HWVlZbCx4X87InOS6PV6vdBFEBERmQp/dSQiIlFhsBERkagw2IiISFQYbEREJCoMNiIiEhUGGxERiQqDjYiIRIXBRkREosJgIyIiUWGwERGRqDDYiIhIVBhsREQkKv8PWvOTtQyjrSQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm= df_train.dropna(subset= ['Cabin','Embarked'])\n",
        "mm.fillna(\"0\", inplace=True)"
      ],
      "metadata": {
        "id": "ymrb7u7pUyqg"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "age= df_train.dropna(subset=['Age'])\n",
        "age=age['Age'].values\n",
        "np.median(age)\n",
        "sns.kdeplot(df_train['Age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "fPVAREsKWIj2",
        "outputId": "bf0c68d1-b3da-410f-d4c6-37ab2f468b4d"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fare= df_train['Fare'].values\n",
        "agee= df_train['Age'].values\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Fare\")\n",
        "\n",
        "plt.scatter(agee, fare)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "QijmHbqVXQx-",
        "outputId": "98a475f5-f07a-4763-c7dc-8a84e3ec004c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff4ed1850f0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x= pd.crosstab(df_train['Pclass'],df_train['Sex'])\n",
        "x.plot(kind='bar')\n",
        "sns.heatmap(x, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "JuXClistXgBg",
        "outputId": "648fbc2c-5d37-4a7f-f872-5512c7dfd8f6"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='Pclass'>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Pclass', y='Age', hue='Sex', data=df_train, palette ='coolwarm', order=[1,2,3], ci =0.95)\n",
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "OtwxoTwmY2sM",
        "outputId": "917bccd7-ef7f-41af-8d33-c27147831d28"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removal of outliers from the data\n",
        "\n",
        "from collections import Counter as Cnt\n",
        "\n",
        "def detect_outlier(df, n , fex):\n",
        "  out=[]\n",
        "  for i in fex:\n",
        "    if i == 'Survived':\n",
        "      continue\n",
        "\n",
        "    df[i] =pd.to_numeric(df[i])\n",
        "    Q1= np.percentile(df[i], 25)\n",
        "    Q3= np.percentile(df[i], 75)\n",
        "\n",
        "    IQR= Q3-Q1\n",
        "\n",
        "    out_step= 1.5*IQR\n",
        "    out_low= Q1-out_step\n",
        "    out_high= Q3+out_step\n",
        "\n",
        "    out_list= df[(df[i]<out_low) | (df[i]>out_high)].index\n",
        "    out.extend(out_list)\n",
        "\n",
        "\n",
        "  out= Cnt(out)\n",
        "  mout= []\n",
        "  mout =list(id for id,val in out.items() if val > n )\n",
        "  return mout\n",
        "\n",
        "\n",
        "drop = detect_outlier(df_train, 2, ['Age','SibSp', 'Parch', 'Fare'])\n",
        "print(\"Dropped indices :\", drop)\n",
        "df_train.drop(df_train.index[drop], inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OsvYggUZThs",
        "outputId": "1f56a201-06e0-472f-df68-4a831d2eeaa3"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dropped indices : [28, 89, 160, 181, 202, 325, 342, 793, 847, 864]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.loc[drop, :]"
      ],
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          "height": 394
        },
        "id": "U2qsrxith1Df",
        "outputId": "544117f9-e62a-453a-c0eb-b1cbc0edd0d5"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Survived  Pclass                               Name     Sex  \\\n",
              "PassengerId                                                                \n",
              "28                  0       1     Fortune, Mr. Charles Alexander    male   \n",
              "89                  1       1         Fortune, Miss. Mabel Helen  female   \n",
              "160                 0       3         Sage, Master. Thomas Henry    male   \n",
              "181                 0       3       Sage, Miss. Constance Gladys  female   \n",
              "202                 0       3                Sage, Mr. Frederick    male   \n",
              "325                 0       3           Sage, Mr. George John Jr    male   \n",
              "342                 1       1     Fortune, Miss. Alice Elizabeth  female   \n",
              "793                 0       3            Sage, Miss. Stella Anna  female   \n",
              "847                 0       3           Sage, Mr. Douglas Bullen    male   \n",
              "864                 0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
              "\n",
              "              Age  SibSp  Parch    Ticket    Fare        Cabin Embarked  \n",
              "PassengerId                                                              \n",
              "28           19.0      3      2     19950  263.00  C23 C25 C27        S  \n",
              "89           23.0      3      2     19950  263.00  C23 C25 C27        S  \n",
              "160           NaN      8      2  CA. 2343   69.55          NaN        S  \n",
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              "202           NaN      8      2  CA. 2343   69.55          NaN        S  \n",
              "325           NaN      8      2  CA. 2343   69.55          NaN        S  \n",
              "342          24.0      3      2     19950  263.00  C23 C25 C27        S  \n",
              "793           NaN      8      2  CA. 2343   69.55          NaN        S  \n",
              "847           NaN      8      2  CA. 2343   69.55          NaN        S  \n",
              "864           NaN      8      2  CA. 2343   69.55          NaN        S  "
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              "      spin 1s steps(1) infinite;\n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 325,\n        \"min\": 28,\n        \"max\": 864,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          847,\n          89,\n          325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Sage, Mr. Douglas Bullen\",\n          \"Fortune, Miss. Mabel Helen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6457513110645907,\n        \"min\": 19.0,\n        \"max\": 24.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"CA. 2343\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93.4452277183449,\n        \"min\": 69.55,\n        \"max\": 263.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          69.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"C23 C25 C27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train= df_train.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "uha9WTW8mYQn"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"{} rows\".format(len(df_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajeIPgz8mgqY",
        "outputId": "52c1b6b0-c4c3-442a-ce5d-09b2d65da6e0"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "871 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iIgfvRvmmo1y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}