Lab2_titanic_students_Mahmoud_Kassem.ipynb

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        "\n",
        "\n",
        "\n",
        "# Lab 2: Logistic regression - Titanic\n",
        "\n",
        "https://www.kaggle.com/c/titanic/data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "lU-B4cEo6-8B"
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      "source": [
        "import sys\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "uzt2psbh6-8D"
      },
      "outputs": [],
      "source": [
        "import sklearn as sk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'titanic'\n",
        "\n",
        "from urllib.request import urlretrieve\n",
        "urlretrieve(\"https://bianchi.wp.imt.fr/files/2023/12/titanic.zip\", filename + '.zip')\n",
        "\n",
        "import os\n",
        "zip_file = os.path.join(os.getcwd(), 'titanic.zip')\n",
        "\n",
        "import zipfile\n",
        "with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n",
        "    zip_ref.extractall(os.getcwd())"
      ],
      "metadata": {
        "id": "EXCH10vSGyuU"
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      "execution_count": 61,
      "outputs": []
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      "source": [
        "df = pd.read_csv(\"traintitanic.csv\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using head(), info(), describe() etc. inspect the dataset. Which features are numerical, which ones are categorical? Comment about missing data.\n",
        "\n",
        "df.head(40)\n",
        "df.describe()\n",
        "df.info()"
      ],
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      "outputs": [
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          "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"
          ]
        }
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "There are some missing data from different categories. For example there are only 714 in the age category, meaning that there are 177 missing entery in the age data, this can be fixed by replacing the nonexisitent records with the average of the rest. However, in the cabin column, there is only 204 entry, with more than 600 losses in the data. for this case, This feature has to be dropped due to the lack of data.\n",
        "\n",
        "And there are 7 numerical values and 5 categorical values in this dataset.\n",
        "The numerical are PassengerId, Survived, Pclass, Age, SibSp, Parch, Fare. The rest are categorical."
      ],
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          "text": [
            "<ipython-input-64-80b3c51c2758>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df.groupby(\"Pclass\").mean()\n"
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              "        PassengerId  Survived        Age     SibSp     Parch       Fare\n",
              "Pclass                                                                 \n",
              "1        461.597222  0.629630  38.233441  0.416667  0.356481  84.154687\n",
              "2        445.956522  0.472826  29.877630  0.402174  0.380435  20.662183\n",
              "3        439.154786  0.242363  25.140620  0.615071  0.393075  13.675550"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.507664514287285,\n        \"min\": 439.1547861507128,\n        \"max\": 461.59722222222223,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          461.59722222222223,\n          445.95652173913044,\n          439.1547861507128\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      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.629238349615627,\n        \"min\": 25.14061971830986,\n        \"max\": 38.233440860215055,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          38.233440860215055,\n          29.87763005780347,\n          25.14061971830986\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11895357771213794,\n        \"min\": 0.40217391304347827,\n        \"max\": 0.615071283095723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.4166666666666667,\n          0.40217391304347827,\n          0.615071283095723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018586089887066785,\n        \"min\": 0.35648148148148145,\n        \"max\": 0.39307535641547864,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35648148148148145,\n          0.3804347826086957,\n          0.39307535641547864\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.83173092143531,\n        \"min\": 13.675550101832993,\n        \"max\": 84.1546875,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          84.1546875,\n          20.662183152173913,\n          13.675550101832993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Using the df.groupby(\"Pclass\"), comment about the nature of the passengers as a function of the class ticket (first, second or third class)\n",
        "df.groupby(\"Pclass\").mean()"
      ]
    },
    {
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      "source": [
        "The mean of the survival rate of the first class is the highest. and the 3rd class is the lowest in the survival rate. Also the average of the first class is generally older than the other classes, followed by the second then the third."
      ],
      "metadata": {
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      "source": [
        "# Plot the histogram of Passenger's classes\n",
        "plt.hist(df[\"Pclass\"])"
      ],
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      "execution_count": 65,
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              "(array([216.,   0.,   0.,   0.,   0., 184.,   0.,   0.,   0., 491.]),\n",
              " array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),\n",
              " <BarContainer object of 10 artists>)"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "CgZL1nAn6-8N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9f7240a1-11fb-4d31-995d-b7e2f6eb3414"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-66-1ee60dfb211f>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df.groupby(\"Sex\").mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
              "Sex                                                                      \n",
              "female   431.028662  0.742038  2.159236  27.915709  0.694268  0.649682   \n",
              "male     454.147314  0.188908  2.389948  30.726645  0.429809  0.235702   \n",
              "\n",
              "             Fare  \n",
              "Sex                \n",
              "female  44.479818  \n",
              "male    25.523893  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3f6d9f6-08fd-4544-a618-fe012f39a88a\" 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",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>431.028662</td>\n",
              "      <td>0.742038</td>\n",
              "      <td>2.159236</td>\n",
              "      <td>27.915709</td>\n",
              "      <td>0.694268</td>\n",
              "      <td>0.649682</td>\n",
              "      <td>44.479818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>454.147314</td>\n",
              "      <td>0.188908</td>\n",
              "      <td>2.389948</td>\n",
              "      <td>30.726645</td>\n",
              "      <td>0.429809</td>\n",
              "      <td>0.235702</td>\n",
              "      <td>25.523893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3f6d9f6-08fd-4544-a618-fe012f39a88a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b3f6d9f6-08fd-4544-a618-fe012f39a88a 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-b3f6d9f6-08fd-4544-a618-fe012f39a88a');\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-6f9a01e2-b1b2-4818-8504-3ad6faf2cfb2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f9a01e2-b1b2-4818-8504-3ad6faf2cfb2')\"\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-6f9a01e2-b1b2-4818-8504-3ad6faf2cfb2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.34735508569995,\n        \"min\": 431.02866242038215,\n        \"max\": 454.1473136915078,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          454.1473136915078,\n          431.02866242038215\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      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16313825880403604,\n        \"min\": 2.159235668789809,\n        \"max\": 2.389948006932409,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.389948006932409,\n          2.159235668789809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9876317510589439,\n        \"min\": 27.915708812260537,\n        \"max\": 30.72664459161148,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          30.72664459161148,\n          27.915708812260537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18700015627599506,\n        \"min\": 0.42980935875216636,\n        \"max\": 0.6942675159235668,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.42980935875216636,\n          0.6942675159235668\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2927277981659793,\n        \"min\": 0.23570190641247835,\n        \"max\": 0.6496815286624203,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.23570190641247835,\n          0.6496815286624203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.403862701171406,\n        \"min\": 25.52389341421144,\n        \"max\": 44.47981783439491,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          25.52389341421144,\n          44.47981783439491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1100 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Comment about the impact of the sex of passengers\n",
        "plt.hist(df[\"Sex\"])\n",
        "df.groupby(\"Sex\").mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The females were most likely to survive than males, notably."
      ],
      "metadata": {
        "id": "9enaqqbavKpW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "173WtkBy6-8O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "c363abdc-99a1-453c-d1b5-ad29ef92128a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-67-751376c3c3a4>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df.groupby(\"Embarked\").mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
              "Embarked                                                                   \n",
              "C          445.357143  0.553571  1.886905  30.814769  0.386905  0.363095   \n",
              "Q          417.896104  0.389610  2.909091  28.089286  0.428571  0.168831   \n",
              "S          449.527950  0.336957  2.350932  29.445397  0.571429  0.413043   \n",
              "\n",
              "               Fare  \n",
              "Embarked             \n",
              "C         59.954144  \n",
              "Q         13.276030  \n",
              "S         27.079812  "
            ],
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              "      <th>PassengerId</th>\n",
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              "      <td>29.445397</td>\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-21b89adf-e549-4c03-8806-77e832645f86 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-21b89adf-e549-4c03-8806-77e832645f86');\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",
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              "\n",
              "\n",
              "<div id=\"df-778d36e6-3ffc-4246-b2c3-0a327b4e8b09\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-778d36e6-3ffc-4246-b2c3-0a327b4e8b09')\"\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",
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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",
              "    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-778d36e6-3ffc-4246-b2c3-0a327b4e8b09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.185643248657897,\n        \"min\": 417.8961038961039,\n        \"max\": 449.527950310559,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          445.35714285714283,\n          417.8961038961039,\n          449.527950310559\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      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5118149447142116,\n        \"min\": 1.8869047619047619,\n        \"max\": 2.909090909090909,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.8869047619047619,\n          2.909090909090909,\n          2.3509316770186337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3627471348529019,\n        \"min\": 28.089285714285715,\n        \"max\": 30.81476923076923,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30.81476923076923,\n          28.089285714285715,\n          29.44539711191336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09677578093854917,\n        \"min\": 0.3869047619047619,\n        \"max\": 0.5714285714285714,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3869047619047619,\n          0.42857142857142855,\n          0.5714285714285714\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.129017445448065,\n        \"min\": 0.16883116883116883,\n        \"max\": 0.41304347826086957,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3630952380952381,\n          0.16883116883116883,\n          0.41304347826086957\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.979548436857577,\n        \"min\": 13.27602987012987,\n        \"max\": 59.95414404761905,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          59.95414404761905,\n          13.27602987012987,\n          27.079811801242233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "# Comment about the impact of the embarkation city. How do you explain the discrepancies in survival rate?\n",
        "df.groupby(\"Embarked\").mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that people embarked from C were most likely to survive.However, the survival rate difference is not that big to have an impact on the model, the city itself maybe irrelevent."
      ],
      "metadata": {
        "id": "sq3pymIvvwV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the histogram of the number of men in each class. Do the same for women. Comment.\n",
        "# Filter male and female passengers\n",
        "male_passengers = df[df['Sex'] == 'male']\n",
        "female_passengers = df[df['Sex'] == 'female']\n",
        "\n",
        "# Plot histograms for male and female passengers\n",
        "plt.hist(male_passengers[\"Pclass\"], color=\"blue\",  label='Male')\n",
        "plt.hist(female_passengers[\"Pclass\"], color=\"red\",  label='Female')\n",
        "\n",
        "# Add legend\n",
        "plt.legend()\n",
        "\n",
        "# Add labels for x and y axes\n",
        "plt.xlabel('Pclass')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show"
      ],
      "metadata": {
        "id": "oPB8cvTk6WEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "59a82861-72f2-47f2-be2b-ba1a8756b482"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "block : bool, optional\n",
              "    Whether to wait for all figures to be closed before returning.\n",
              "\n",
              "    If `True` block and run the GUI main loop until all figure windows\n",
              "    are closed.\n",
              "\n",
              "    If `False` ensure that all figure windows are displayed and return\n",
              "    immediately.  In this case, you are responsible for ensuring\n",
              "    that the event loop is running to have responsive figures.\n",
              "\n",
              "    Defaults to True in non-interactive mode and to False in interactive\n",
              "    mode (see `.pyplot.isinteractive`).\n",
              "\n",
              "See Also\n",
              "--------\n",
              "ion : Enable interactive mode, which shows / updates the figure after\n",
              "      every plotting command, so that calling ``show()`` is not necessary.\n",
              "ioff : Disable interactive mode.\n",
              "savefig : Save the figure to an image file instead of showing it on screen.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "**Saving figures to file and showing a window at the same time**\n",
              "\n",
              "If you want an image file as well as a user interface window, use\n",
              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
              "limitation of command order does not apply if the show is non-blocking or\n",
              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
              "\n",
              "**Auto-show in jupyter notebooks**\n",
              "\n",
              "The jupyter backends (activated via ``%matplotlib inline``,\n",
              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
              "explicitly there.</pre>\n",
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          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1100 with 1 Axes>"
            ],
            "image/png": 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/b9vlcrmcq6++Mj//+U/y/PPPZ/ny19que/75v6Sh4b2rrfXZZ59+R+sCAAB6tyo6OwE9SBThTZVKpWy55Vbttj344B+SJCeddOIab7Nw4YJ2l//xxLZ9+vTJwIED222rq6vL8uXL2y5fddUPc+GF5+Xggz+V7bffIYMGDcqjjz6S//zPb7Tb740WL178jtYFAAAAogjvSH394CTJ8cf/a7beepvVrn/Xuzbs8H3cccftmThxco46anrbtqeemt/j6wIAAKC6iCK8I5tttnmGDx+R559/Lgce+IkuuY/XXns1ffr0abftllt+0ePrAgAAoLqIIrwjNTU1mT79+Jx++r/l1VeXZZddJmXAgAFZsOCvueeeu/L5z38xm266WYfuY8cdJ+Tqq3+Ua6/9cTbZZLPcfPPc/OUvf+nxdQEAAFBdRJHuNGZMVdzf7rvvkUGDBuZ737u07R0c7373/8mECf+UYcM26PDxP/OZaVm8eHFmz744SfKBD3wwxx13YmbMOL5H1wUAAEB1qSmXy+WeXkRv0tLSmhdffHm17StWLM8LL/w1G2zwf9KnT99215VKNRk2uH9q6rq/QZVXrsyLTa+mVKrJypWt3X7/vcFbza6S1NWVMnTo+nnppZfNspczy+pgjtXDLKuDOVYHc6weXTnLVcfefnvfPtPVxo9P7rsvaW5eltdeW/n2N6gQw4atn9ra0lrt650i3aC1tdwWJnrivltby1X9vdIAAACwLkSRbrIqTgAAAACVYe3eTwIAAABQZUQRAAAAoJBEEQAAAKCQRJFO5st8eh8zAwAAKCZRpJPU1tYmSZYvf62HV8I7tWpmtbXOOwwAAFAk/hXYSUql2gwYMDBLl76UJOnbt19qairna3BbW2vS0uIdEW9ULpezfPlrWbr0pQwYMDClkkYIAABQJKJIJ6qvH5YkbWGkkpRKpbS2tvb0MirSgAED22YHAABAcYginaimpiaDB2+QQYOGpqVlZU8vp01tbU0GD14vTU2veLfIP6itrfMOEQAAgIISRbpAqVRKqdS3p5fRpq6ulP79+2fZspasXOndIgAAAJA40SoAAABQUKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIFRVF7rzzzhx66KHZeeeds8022+SDH/xgzjrrrCxZsqTdfr/85S+z//77Z+zYsdl7771z7bXXrnas5cuX5xvf+EYmTpyYcePG5fDDD09jY2N3PRQAAACgwlVUFFm8eHG23XbbnH766ZkzZ04OP/zw/PSnP82XvvSltn1+//vfZ/r06Rk3blxmzZqVffbZJ//2b/+Wm266qd2xzjjjjFx99dU5/vjjc/7552f58uX5zGc+s1pgAQAAAIqprqcX8EYHHHBAu8sTJkxI3759c8opp2ThwoUZMWJELrroomy77bb56le/miTZeeed8+yzz+bb3/52PvShDyVJFixYkGuuuSZf+cpX8vGPfzxJMnbs2EyZMiU/+tGPMm3atO59YAAAAEDFqah3iqzJkCFDkiQrVqzI8uXL89vf/rYtfqwyderUPPnkk/nLX/6SJLnrrrvS2trabr8hQ4Zk4sSJmTdvXretHQAAAKhcFfVOkVVaWlqycuXKPPHEE7nwwguz++67Z+ONN84TTzyRFStWpKGhod3+o0aNSpI0NjZm4403TmNjYzbYYIMMHjx4tf2uueaaDq+vrq7iW1I7tbWldv+ldzLH6mGW1cEcq4dZVgdzrA7mWD26cpZ+PrpfqVTT6/4dvLYqMopMmTIlCxcuTJLsuuuuOfvss5MkTU1NSZL6+vp2+6+6vOr65ubmDBo0aLXj1tfXt+2zrkqlmgwdun6HjtFT6usH9PQS6ATmWD3MsjqYY/Uwy+pgjtXBHKuHWVaHgQP79/QSukxFRpFLLrkky5YtyxNPPJGLLrooRx11VC677LKeXlaSpLW1nObmV3p6Ge9IbW0p9fUD0ty8LC0trT29HNaROVYPs6wO5lg9zLI6mGN1MMfq0ZWzXHVsus/Spa9mxYqWnl7GWquvH7DW7yiqyCiy5ZZbJknGjx+fsWPH5oADDsitt96a9773vUmy2jfINDc3J0nbx2Xq6+uzdOnS1Y7b3Ny82kdq1sXKlb3zF3RLS2uvXTv/yxyrh1lWB3OsHmZZHcyxOphj9TDL6tDaWq7aOVb8h4JGjx6dPn365Jlnnsmmm26aPn36pLGxsd0+qy6vOtdIQ0ND/v73v6/2UZnGxsbVzkcCAAAAFFPFR5EHHnggK1asyMYbb5y+fftmwoQJufnmm9vtM3fu3IwaNSobb7xxkmTSpEkplUq55ZZb2vZpamrKXXfdlcmTJ3fr+gEAAIDKVFEfn5k+fXq22WabjB49Ov37989jjz2WOXPmZPTo0dljjz2SJF/4whfyqU99Kqeddlr22Wef/Pa3v80NN9yQc845p+047373u/Pxj3883/zmN1MqlTJixIhcfPHFGTRoUD75yU/21MMDAAAAKkhFRZFtt902c+fOzSWXXJJyuZyNNtooBx10UI444oj07ds3SbLDDjvk/PPPz7nnnptrrrkm73nPe3LGGWdkn332aXesk08+Oeuvv37OPvvsvPzyy9l+++1z2WWXrfFbaQAAAIDiqSmXy+WeXkRv0tLSmhdffLmnl/GO1NWVMnTo+nnppZer9uQ4RWCO1cMsq4M5Vg+zrA7mWB3MsXp05SxXHXv77ZP77+/UQ/MPxo9P7rsvaW5eltdeW9nTy1lrw4atv9bfPlPx5xQBAAAA6AqiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEh1Pb2AN/rFL36Rn//853n44YfT3NyczTbbLIcddlgOPPDA1NTUJEkOO+yw/O53v1vttnPnzs2oUaPaLi9ZsiRnnXVWbrvttqxYsSK77rprTj755AwfPrzbHg8AAABQuSoqilx++eXZaKONMnPmzAwdOjS//vWvc8opp2TBggWZPn16237bb799ZsyY0e62G2+8cbvLxx13XJ544omcdtpp6devX84999xMmzYt1157berqKuphAwAAAD2gourARRddlGHDhrVd3mWXXbJ48eJcdtllOfroo1Mqvf5pn/r6+owbN+5Nj3P//ffnrrvuypw5czJp0qQkyciRIzN16tTccsstmTp1apc+DgAAAKDyVdQ5Rd4YRFYZM2ZMli5dmldeeWWtjzNv3rzU19dn4sSJbdsaGhoyZsyYzJs3r1PWCgAAAPRuFfVOkTW59957M2LEiAwcOLBt2+9+97uMGzcuLS0t2W677fKlL30pO+64Y9v1jY2NGTlyZNt5SFZpaGhIY2Njh9dUV1dRLelt1daW2v2X3skcq4dZVgdzrB5mWR3MsTqYY/Xoyln6+eh+pVJNr/t38Nqq6Cjy+9//PnPnzm13/pAdd9wxBxxwQDbffPMsWrQoc+bMyeGHH57/+q//yvjx45Mkzc3NGTRo0GrHGzx4cB566KEOralUqsnQoet36Bg9pb5+QE8vgU5gjtXDLKuDOVYPs6wO5lgdzLF6mGV1GDiwf08voctUbBRZsGBBjj/++EyYMCGf+tSn2rYfe+yx7fb7wAc+kP322y/f+c53MmvWrC5fV2trOc3Na/9RnkpQW1tKff2ANDcvS0tLa08vh3VkjtXDLKuDOVYPs6wO5lgdzLF6dOUsVx2b7rN06atZsaKlp5ex1urrB6z1O4oqMoo0Nzdn2rRpGTJkSM4///y2E6yuyXrrrZfddtstN998c9u2+vr6LFiwYLV9m5qaMnjw4A6vb+XK3vkLuqWltdeunf9ljtXDLKuDOVYPs6wO5lgdzLF6mGV1aG0tV+0cK+5DQa+++mqOPPLILFmyJLNnz17jx2DeTkNDQ+bPn59yudxu+/z589PQ0NBZSwUAAAB6sYqKIitXrsxxxx2XxsbGzJ49OyNGjHjb27zyyiv51a9+lbFjx7Ztmzx5cpqamnLPPfe0bZs/f34eeeSRTJ48uUvWDgAAAPQuFfXxmdNPPz133HFHZs6cmaVLl+YPf/hD23VbbbVVHnzwwcyePTt77rlnNtpooyxatCiXXXZZ/va3v+W8885r23f8+PGZNGlSTjrppMyYMSP9+vXLOeeck9GjR2evvfbqgUcGAAAAVJqKiiJ33313kuTrX//6atfdfvvt2XDDDbNixYqcc845Wbx4cQYMGJDx48fn9NNPz7bbbttu/3PPPTdnnXVWTj311KxcuTKTJk3KySefnLq6inrIAAAAQA+pqELwy1/+8m33mTNnzloda9CgQTnzzDNz5plndnRZAAAAQBWqqHOKAAAAAHQXUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACikiooiv/jFL/KFL3whkydPzrhx43LAAQfkmmuuSblcbrff1Vdfnb333jtjx47N/vvvnzvuuGO1Yy1ZsiQnnXRSdtppp4wfPz7HHntsFi1a1F0PBQAAAKhwFRVFLr/88gwYMCAzZ87MRRddlMmTJ+eUU07JhRde2LbPjTfemFNOOSX77LNPZs2alXHjxmX69On5wx/+0O5Yxx13XO6+++6cdtpp+da3vpX58+dn2rRpWblyZTc/KgAAAKAS1fX0At7ooosuyrBhw9ou77LLLlm8eHEuu+yyHH300SmVSvn2t7+dfffdN8cdd1ySZOedd86f//znXHjhhZk1a1aS5P77789dd92VOXPmZNKkSUmSkSNHZurUqbnlllsyderUbn9sAAAAQGWpqHeKvDGIrDJmzJgsXbo0r7zySp599tk89dRT2WeffdrtM3Xq1Nxzzz1Zvnx5kmTevHmpr6/PxIkT2/ZpaGjImDFjMm/evK59EAAAAECvUFHvFFmTe++9NyNGjMjAgQNz7733Jnn9XR9vNGrUqKxYsSLPPvtsRo0alcbGxowcOTI1NTXt9mtoaEhjY2OH11RXV1Et6W3V1pba/ZfeyRyrh1lWB3OsHmZZHcyxOphj9ejKWfr56H6lUk2v+3fw2qroKPL73/8+c+fOzYwZM5IkTU1NSZL6+vp2+626vOr65ubmDBo0aLXjDR48OA899FCH1lQq1WTo0PU7dIyeUl8/oKeXQCcwx+phltXBHKuHWVYHc6wO5lg9zLI6DBzYv6eX0GUqNoosWLAgxx9/fCZMmJBPfepTPb2cNq2t5TQ3v9LTy3hHamtLqa8fkObmZWlpae3p5bCOzLF6mGV1MMfqYZbVwRyrgzlWj66c5apj032WLn01K1a09PQy1lp9/YC1fkdRh6LIokWLMnz48I4cYo2am5szbdq0DBkyJOeff35KpdcfzODBg5O8/nW7G264Ybv933h9fX19FixYsNpxm5qa2vbpiJUre+cv6JaW1l67dv6XOVYPs6wO5lg9zLI6mGN1MMfqYZbVobW1XLVz7NCHgj7wgQ/ks5/9bH7605/mlVc6590Tr776ao488sgsWbIks2fPbvcxmIaGhiRZ7bwgjY2N6dOnTzbZZJO2/ebPn59yudxuv/nz57cdAwAAACi2DkWRY489NosWLcrMmTMzceLEnHjiiZk3b15aW9etIK1cuTLHHXdcGhsbM3v27IwYMaLd9Ztsskk233zz3HTTTe22z507N7vsskv69u2bJJk8eXKamppyzz33tO0zf/78PPLII5k8efI6rQ0AAACoLh36+MxRRx2Vo446Ko888kiuv/763HjjjbnhhhuywQYbZN99982HP/zhjB07dq2Pd/rpp+eOO+7IzJkzs3Tp0vzhD39ou26rrbZK3759c8wxx+TEE0/MpptumgkTJmTu3Ll58MEH84Mf/KBt3/Hjx2fSpEk56aSTMmPGjPTr1y/nnHNORo8enb322qsjDxkAAACoEjXlf/yMSQeUy+X85je/yfXXX59bbrklL7/8ckaOHJn9998/+++/f97znve85e133333PPfcc2u87vbbb8/GG2+cJLn66qsza9asPP/88xk5cmROOOGETJkypd3+S5YsyVlnnZVbb701K1euzKRJk3LyySev9u6Td6qlpTUvvvhyh47R3erqShk6dP289NLLVfs5sCIwx+phltXBHKuHWVYHc6wO5lg9unKWq469/fbJ/fd36qH5B+PHJ/fdlzQ3L8trr63s6eWstWHD1l/rE612ahRJkuXLl+eOO+7IVVddlbvvvju1tbWpqalJa2tr9thjj5x88sldcnLW7iKK0FPMsXqYZXUwx+phltXBHKuDOVYPUaQ6FCGKdNpX8r7xHSJLly7NFltskRkzZuTDH/5wamtrc9111+Xiiy/Ov/7rv+byyy/vrLsFAAAAWCcdiiKPPfZYfv7zn+fGG2/MokWL8q53vSsf//jH85GPfCSjR49ut+8RRxyRfv365Rvf+EaHFgwAAADQGToURT7ykY+kf//++eAHP5iPfOQjmThxYkqlN3+Lynvf+96MGzeuI3cJAAAA0Ck6FEXOPPPM7L333ll//fXXav+dd945O++8c0fuEgAAAKBTdCiKfOxjH+usdQAAAAB0q7U7Heub+P73v58jjjjiTa//3Oc+lx/+8IcduQsAAACALtGhKHLNNddk1KhRb3r9e9/73lx11VUduQsAAACALtGhKPLss8++ZRRpaGjIM88805G7AAAAAOgSHYoiffr0yd/+9rc3vX7RokVv+W00AAAAAD2lQ8Viu+22y09+8pMsXbp0teuWLFmS6667Ltttt11H7gIAAACgS3To22emT5+eQw89NB/5yEfy6U9/Ou9973uTJI8//ni+973v5W9/+1vOPvvsTlkoAAAAQGfqUBTZbrvt8t3vfjennnpqvva1r6WmpiZJUi6Xs/HGG+eiiy7K+PHjO2WhAAAAAJ2pQ1EkSSZOnJhbb701jzzySNtJVTfddNNsvfXWbZEEAAAAoNJ0OIokSalUyjbbbJNtttmmMw4HAAAA0OU6JYo88cQTefbZZ9PU1LTG6z/ykY90xt0AAAAAdJoORZFnnnkm//Iv/5IHH3ww5XJ5jfvU1NSIIgAAAEDF6VAUOfXUU/PnP/85J510UnbYYYfU19d31roAAAAAulSHosh9992XI488MocddlhnrQcAAACgW5Q6cuOhQ4dm0KBBnbUWAAAAgG7ToSjyyU9+Mj//+c/T0tLSWesBAAAA6BYd+vjM5ptvntbW1hxwwAE58MAD8+53vzu1tbWr7bfXXnt15G4AAAAAOl2Hosjxxx/f9udvfOMba9ynpqYmjz76aEfuBgAAAKDTdSiKfP/73++sdQAAAAB0qw5FkZ122qmz1gEAAADQrToURVZZvnx5Hn744bzwwgvZfvvtM2zYsM44LAAAAECX6dC3zySvf4Rm0qRJOfjgg3PMMcfkT3/6U5LkxRdfzIQJE3LNNdd0eJEAAAAAna1DUeTaa6/NmWeemV133TVf+9rXUi6X264bNmxYdt5558ydO7fDiwQAAADobB2KIpdddlk++MEP5uyzz86UKVNWu37rrbfO448/3pG7AAAAAOgSHYoiTz/9dCZPnvym1w8ZMiSLFy/uyF0AAAAAdIkORZH6+vq89NJLb3r9E088kQ033LAjdwEAAADQJToURSZPnpyrrroqzc3Nq133+OOP5+qrr87uu+/ekbsAAAAA6BId+kre4447Lp/4xCey3377ZcqUKampqclPf/rTXHvttbnllluy4YYb5uijj+6stQIAAAB0mg69U2TEiBG57rrrsuuuu+YXv/hFyuVyfvazn+WOO+7Ivvvum6uuuirDhg3rrLUCAAAAdJoOvVMkSTbYYIN87Wtfy9e+9rW8+OKLaW1tzbBhw1Iqdai3AAAAAHSpDkeRN/KuEAAAAKC36FAUueCCC952n5qamnzxi1/syN0AAAAAdLouiyI1NTUpl8uiCAAAAFCROhRFHnvssdW2tba25rnnnssPf/jD/M///E9mzZrVkbsAAAAA6BKdfjbUUqmUTTbZJDNmzMhmm22WM844o7PvAgAAAKDDuvQrYnbcccfceeedXXkXAAAAAOukS6PIQw895Kt5AQAAgIrUoXOK/PSnP13j9ubm5vz+97/PLbfckoMOOqgjdwEAAADQJToURWbOnPmm1w0dOjSf//znffMMAAAAUJE6FEVuv/321bbV1NSkvr4+AwcO7MihAQAAALpUh6LIRhtt1FnrAAAAAOhWzoIKAAAAFFKH3imy5ZZbpqam5h3dpqamJo888khH7hYAAACgwzoURb74xS/mtttuyxNPPJFJkyZl5MiRSZLGxsbcfffded/73pc99tijUxYKAAAA0Jk6FEWGDx+eF154Iddff30aGhraXffkk0/m05/+dIYPH55PfOITHVokAAAAQGfr0DlF5syZk0MPPXS1IJIko0aNyiGHHJLZs2d35C4AAAAAukSHosiCBQtSV/fmbzapq6vLggULOnIXAAAAAF2iQ1Hkfe97X374wx9m4cKFq123YMGCXHnlldliiy06chcAAAAAXaJD5xT58pe/nM997nPZe++9s8cee2SzzTZLkjz11FO5/fbbUy6X881vfrNTFgoAAADQmToURXbYYYdcddVVOe+883Lbbbfl1VdfTZL0798/kyZNyjHHHJPRo0d3ykIBAAAAOlOHokiSbLHFFrnwwgvT2tqaF198MUkybNiwlEod+mQOAAAAQJfqcBRZpVQqpV+/fllvvfUEEQAAAKDidbhe/PGPf8wRRxyR7bbbLhMmTMjvfve7JMmLL76YL3zhC/ntb3/b4UUCAAAAdLYORZH77rsvBx98cJ5++unsv//+aW1tbbtu2LBhWbp0aX784x93eJEAAAAAna1DUeScc87JqFGjMnfu3Bx//PGrXT9hwoQ88MADHbkLAAAAgC7RoSjyxz/+MR/72MfSt2/f1NTUrHb9iBEj8ve//70jdwEAAADQJToURerq6tp9ZOYfLVy4MOutt15H7gIAAACgS3Qoimy33Xa5+eab13jdK6+8kuuuuy477rhjR+4CAAAAoEt0KIoce+yxeeihh/L5z38+8+bNS5L86U9/ytVXX52PfexjefHFF3P00Ud3ykIBAAAAOlOH3ylyySWX5Omnn86MGTOSJF//+tdzyimnpLW1NZdcckm23HLLTlkoAAAAQGeqW9cblsvlvPzyy9l+++1z880359FHH81TTz2VcrmcTTbZJNtss80aT74KAAAAUAnWOYqsWLEiO+20U44//vhMmzYtY8aMyZgxYzpzbQAAAABdZp0/PtO3b9+8613vSt++fTtzPQAAAADdokPnFPnoRz+an/3sZ1m+fHlnrQcAAACgW6zzx2eSZPTo0bn99tuz33775aMf/Wg22mij9O/ff7X99tprr47cDQAAAECn61AUOeGEE9r+fN55561xn5qamjz66KMduRsAAACATveOo8h//ud/ZurUqdlyyy3z/e9/vyvWBAAAANDl3nEUueSSS/K+970vW265ZXbaaae89NJL+ad/+qdceuml2WWXXbpijQAAAACdrkMnWl2lXC53xmEAAAAAuk2nRBEAAACA3kYUAQAAAAppnb595rnnnsvDDz+cJFmyZEmS5Omnn059ff0a9996663XcXkAAAAAXWOdosh555232lfwnn766avtVy6XfSUvAAAAUJHecRQ566yzumIdAAAAAN3qHUeRj370o12xDgAAAIBu5USrAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAhiSIAAABAIdX19ALe6Omnn86cOXPywAMP5PHHH09DQ0NuuOGGdvscdthh+d3vfrfabefOnZtRo0a1XV6yZEnOOuus3HbbbVmxYkV23XXXnHzyyRk+fHiXPw4AAACg8lVUFHn88cdz5513Zrvttktra2vK5fIa99t+++0zY8aMdts23njjdpePO+64PPHEEznttNPSr1+/nHvuuZk2bVquvfba1NVV1MMGAAAAekBF1YHdd989e+yxR5Jk5syZeeihh9a4X319fcaNG/emx7n//vtz1113Zc6cOZk0aVKSZOTIkZk6dWpuueWWTJ06tdPXDgAAAPQuFXVOkVKpc5Yzb9681NfXZ+LEiW3bGhoaMmbMmMybN69T7gMAAADo3Soqiqyt3/3udxk3blzGjh2bQw89NP/zP//T7vrGxsaMHDkyNTU17bY3NDSksbGxO5cKAAAAVKiK+vjM2thxxx1zwAEHZPPNN8+iRYsyZ86cHH744fmv//qvjB8/PknS3NycQYMGrXbbwYMHv+lHct6Jurre1ZJqa0vt/kvvZI7VwyyrgzlWD7OsDuZYHcyxenTlLP18dL9SqabX/Tt4bfW6KHLssce2u/yBD3wg++23X77zne9k1qxZXX7/pVJNhg5dv8vvpyvU1w/o6SXQCcyxephldTDH6mGW1cEcq4M5Vg+zrA4DB/bv6SV0mV4XRf7Reuutl9122y0333xz27b6+vosWLBgtX2bmpoyePDgDt1fa2s5zc2vdOgY3a22tpT6+gFpbl6WlpbWnl4O68gcq4dZVgdzrB5mWR3MsTqYY/XoylmuOjbdZ+nSV7NiRUtPL2Ot1dcPWOt3FPX6KLImDQ0Nueeee1Iul9udV2T+/PnZYostOnz8lSt75y/olpbWXrt2/pc5Vg+zrA7mWD3MsjqYY3Uwx+phltWhtbVctXPs9R8KeuWVV/KrX/0qY8eObds2efLkNDU15Z577mnbNn/+/DzyyCOZPHlyTywTAAAAqDAV9U6RZcuW5c4770ySPPfcc1m6dGluuummJMlOO+2UxsbGzJ49O3vuuWc22mijLFq0KJdddln+9re/5bzzzms7zvjx4zNp0qScdNJJmTFjRvr165dzzjkno0ePzl577dUjjw0AAACoLBUVRV544YV86Utfardt1eXvf//7efe7350VK1bknHPOyeLFizNgwICMHz8+p59+erbddtt2tzv33HNz1lln5dRTT83KlSszadKknHzyyamrq6iHDAAAAPSQiioEG2+8cf70pz+95T5z5sxZq2MNGjQoZ555Zs4888zOWBoAAABQZXr9OUUAAAAA1oUoAgAAABSSKAIAAAAUkigCAAAAFJIoAgAAABRSRX37DAAAAKyNLbfs6RVUvyL8HYsiAAAA9Bo1NTVJS0t++MPanl5KMbS0vP53XqVEEQAAAHqNUqkmqa1NDjkkefTRnl5OdRszJrniitTUrOjplXQZUQQAAIDe59FHk/vv7+lV0Ms50SoAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIdT29ALpPba0G1h1aW8tpbS339DIAAAB4G6JIAdTU1KSlJamvH9DTSymElSvLaWp6WRgBAACocKJIAZRKNamtTQ45JHn00Z5eTXUbMya54oqalEo1oggAAECFE0UK5NFHk/vv7+lVAAAAQGVwkgkAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkOp6egEAAJWgttb/VtQdWlvLaW0t9/QyACCJKAIAFFxNTU1aWpL6+gE9vZRCWLmynKaml4URACqCKAIAFFqpVJPa2uSQQ5JHH+3p1VS3MWOSK66oSalUI4oAUBFEEQCAvB5E7r+/p1cBAHQnH54FAAAACkkUAQAAAApJFAEAAAAKSRQBAAAACkkUAQAAAApJFAEAAAAKSRQBAAAACkkUAQAAAApJFAEAAAAKSRQBAAAACkkUAQAAAAqpoqLI008/nVNPPTUHHHBAttpqq+y3335r3O/qq6/O3nvvnbFjx2b//ffPHXfcsdo+S5YsyUknnZSddtop48ePz7HHHptFixZ19UMAAAAAeomKiiKPP/547rzzzmy22WYZNWrUGve58cYbc8opp2SfffbJrFmzMm7cuEyfPj1/+MMf2u133HHH5e67785pp52Wb33rW5k/f36mTZuWlStXdsMjAQAAACpdXU8v4I1233337LHHHkmSmTNn5qGHHlptn29/+9vZd999c9xxxyVJdt555/z5z3/OhRdemFmzZiVJ7r///tx1112ZM2dOJk2alCQZOXJkpk6dmltuuSVTp07tngcEAAAAVKyKeqdIqfTWy3n22Wfz1FNPZZ999mm3ferUqbnnnnuyfPnyJMm8efNSX1+fiRMntu3T0NCQMWPGZN68eZ2/cAAAAKDXqah3irydxsbGJK+/6+ONRo0alRUrVuTZZ5/NqFGj0tjYmJEjR6ampqbdfg0NDW3H6Ii6uopqSW+rVKp5+53oVLW1nf8zsuqYXXFsupdZVgdzrB5eJ7uf10nejDlWj66cpd/b3a9Uqul1/w5eW70qijQ1NSVJ6uvr221fdXnV9c3NzRk0aNBqtx88ePAaP5LzTpRKNRk6dP0OHYPqV18/oFcem+5lltXBHOGd8zrJ2zHH6mGW1WG99fplvfX69fQyukSviiKVoLW1nObmV3p6Ge9Inz61GTiwf08vo1Cam5elpaW1U49ZW1tKff2ALjk23cssq4M5Vg+vk93P6yRvxhyrR1fO0u/t7vfKK6/ltdd6z5eW1NcPWOt3KfWqKDJ48OAkr3/d7oYbbti2vbm5ud319fX1WbBgwWq3b2pqatunI1au7F2/oL39sPu1tLR22c9JVx6b7mWW1cEcez+vk93P6yRvxxyrR1fM0u/t7tfaWq7a52Sv+mlqaGhIktXOC9LY2Jg+ffpkk002adtv/vz5KZfL7fabP39+2zEAAACAYutVUWSTTTbJ5ptvnptuuqnd9rlz52aXXXZJ3759kySTJ09OU1NT7rnnnrZ95s+fn0ceeSSTJ0/u1jUDAAAAlamiPj6zbNmy3HnnnUmS5557LkuXLm0LIDvttFOGDRuWY445JieeeGI23XTTTJgwIXPnzs2DDz6YH/zgB23HGT9+fCZNmpSTTjopM2bMSL9+/XLOOedk9OjR2WuvvXrksQEAAACVpaKiyAsvvJAvfelL7batuvz9738/EyZMyH777Zdly5Zl1qxZueSSSzJy5MhccMEFGT9+fLvbnXvuuTnrrLNy6qmnZuXKlZk0aVJOPvnk1NVV1EMGAAAAekhFFYKNN944f/rTn952v4MOOigHHXTQW+4zaNCgnHnmmTnzzDM7a3kAAABAFelV5xQBAAAA6CyiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIFfXtM3StLbfs6RVUP3/HAAAAvYcoUgA1NTVJS0t++MPanl5KMbS0vP53DgAAQEUTRQqgpiZJbW1yyCHJo4/29HKq25gxyRVXpFQSRQAAACqdKFIkjz6a3H9/T68CAAAAKoITrQIAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhSSKAAAAAIUkigAAAACFJIoAAAAAhVTX0wsAAKgEW27Z0yuofv6OAag0oggAUGg1NTVJS0t++MPanl5KMbS0vP53DgAVQBQBAAqtpiZJbW1yyCHJo4/29HKq25gxyRVXpFQSRQCoDKIIAEDyehC5//6eXgUA0I2caBUAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAoJFEEAAAAKCRRBAAAACgkUQQAAAAopF4XRa677rqMHj16tf/71re+1W6/q6++OnvvvXfGjh2b/fffP3fccUcPrRgAAACoRHU9vYB1NXv27AwaNKjt8ogRI9r+fOONN+aUU07JUUcdlZ133jlz587N9OnTc8UVV2TcuHE9sFoAAACg0vTaKLL11ltn2LBha7zu29/+dvbdd98cd9xxSZKdd945f/7zn3PhhRdm1qxZ3bhKAAAAoFL1uo/PvJ1nn302Tz31VPbZZ59226dOnZp77rkny5cv76GVAQAAAJWk175TZL/99stLL72U97znPfnEJz6Rz33uc6mtrU1jY2OSZOTIke32HzVqVFasWJFnn302o0aN6tB919X1rpZUKtX09BIKp1Sq6fSfk9raUrv/0nuZZXUwx+rhdbL7eZ3kzZhj9ejKWfq93f264vd2peh1UWTDDTfMMccck+222y41NTX55S9/mXPPPTcLFy7MqaeemqampiRJfX19u9uturzq+nVVKtVk6ND1O3QMqt/Agf277Nj19QO67Nh0L7OsDuYI75zXSd6OOVYPs6wO663XL+ut16+nl9Elel0U2XXXXbPrrru2XZ40aVL69euX733veznqqKO6/P5bW8tpbn6ly++nM/XrV1e1P8CVaunSV7NiRUunHrO2tpT6+gFpbl6WlpbWTj023cssq4M5Vg+vk93P6yRvxhyrR1fOsk+f2i6Nq6zulVdey2uvrezpZay1+voBa/0upV4XRdZkn332yaWXXppHH300gwcPTpIsWbIkG264Yds+zc3NSdJ2fUesXNm7fkH36VPu6SUUTmtruct+TlpaWnvdzyBrZpbVwRx7P6+T3c/rJG/HHKtHV8zSx6u6X1f+3u5pVffT1NDQkCRt5xZZpbGxMX369Mkmm2zSE8sCAAAAKkxVRJG5c+emtrY2W221VTbZZJNsvvnmuemmm1bbZ5dddknfvn17aJUAAABAJel1H5854ogjMmHChIwePTpJcvvtt+eqq67Kpz71qbaPyxxzzDE58cQTs+mmm2bChAmZO3duHnzwwfzgBz/oyaUDAAAAFaTXRZGRI0fm2muvzYIFC9La2prNN988J510Ug477LC2ffbbb78sW7Yss2bNyiWXXJKRI0fmggsuyPjx43tw5QAAAEAl6XVR5OSTT16r/Q466KAcdNBBXbwaAAAAoLeqinOKAAAAALxToggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIVR1FnnzyyRx++OEZN25cJk6cmG9+85tZvnx5Ty8LAAAAqAB1Pb2ArtLU1JRPf/rT2XzzzXP++edn4cKF+frXv55XX301p556ak8vDwAAAOhhVRtFfvSjH+Xll1/OBRdckCFDhiRJWlpacvrpp+fII4/MiBEjenaBAAAAQI+q2o/PzJs3L7vssktbEEmSffbZJ62trbn77rt7bmEAAABARajad4o0NjbmwAMPbLetvr4+G264YRobG9f5uKVSTYYNW7+jy+tWNTU1r//hppsS51TpWn37JkkGDuyfgQPLnXzw1+c4ePB6STr72HQvs6wO5lg9vE52G6+TvC1zrB5dOUu/t7vN//97e731+mXAgL49vJi1VyrVrPW+VRtFmpubU19fv9r2wYMHp6mpaZ2PW1NTk9ratf8LrijDh/f0Cgrj9Sdh1/ycdOWx6V5mWR3MsYp4new2Xid5O+ZYPbp0ln5vd5tqfk5W7cdnAAAAAN5K1UaR+vr6LFmyZLXtTU1NGTx4cA+sCAAAAKgkVRtFGhoaVjt3yJIlS/K3v/0tDQ0NPbQqAAAAoFJUbRSZPHlyfv3rX6e5ublt20033ZRSqZSJEyf24MoAAACASlBTLper8rTOTU1N2XfffTNy5MgceeSRWbhwYb7+9a/nwx/+cE499dSeXh4AAADQw6o2iiTJk08+mX//93/P/fffn/XXXz8HHHBAjj/++PTt23u+SggAAADoGlUdRQAAAADeTNWeUwQAAADgrYgiAAAAQCGJIgAAAEAhiSIAAABAIYkiAAAAQCGJIgAAAEAh1fX0Alg7Tz/9dObMmZMHHnggjz/+eBoaGnLDDTe87e3K5XJmzZqVH/7wh3nxxRczZsyYfPnLX864cePa7bdw4cKcccYZueuuu9KnT5/sueee+fKXv5yBAwd20SMqrnWZ5aJFi3L55Zfn7rvvzjPPPJNBgwZlxx13zAknnJCNNtqobb/f/va3+dSnPrXa7adOnZpzzjmn0x9Lka3rc3L33XfPc889t9r2Bx98MP369Wu77DnZfdZllm/2XEuSkSNH5qabbnrL/TwnO98vfvGL/PznP8/DDz+c5ubmbLbZZjnssMNy4IEHpqam5k1v53WysqzLHL1GVqZ1fU56naws6zJHr5GV6c4778ysWbPyxBNPZOnSpRkxYkT22GOPTJ8+PYMGDXrL21599dWZPXt2nn/++YwcOTLHH398pkyZ0m6fJUuW5Kyzzsptt92WFStWZNddd83JJ5+c4cOHd+XD6hSiSC/x+OOP584778x2222X1tbWlMvltbrdrFmz8u1vfzsnnnhiRo8enSuuuCKf/exn87Of/SybbLJJkmTFihX53Oc+lyQ5++yz8+qrr+Yb3/hG/t//+3+5+OKLu+wxFdW6zPLhhx/OrbfemgMPPDDbbbddXnrppVx00UU56KCDcsMNN2TYsGHt9j/rrLPS0NDQdnno0KGd/jiKbl2fk0my995757Of/Wy7bX379m37s+dk91qXWW699db58Y9/3G7b0qVLM23atEyePHm1/T0nu97ll1+ejTbaKDNnzszQoUPz61//OqecckoWLFiQ6dOnv+ntvE5WlnWZo9fIyrSuz8nE62QlWZc5eo2sTIsXL862226bww47LEOGDMnjjz+e888/P48//nguvfTSN73djTfemFNOOSVHHXVUdt5558ydOzfTp0/PFVdc0e5/QDjuuOPyxBNP5LTTTku/fv1y7rnnZtq0abn22mtTV1fh2aFMr9DS0tL25xkzZpT33Xfft73Nq6++Wt5+++3LZ599dtu21157rTxlypTyV77ylbZt119/fXn06NHlJ598sm3bf//3f5e32GKL8gMPPNA5D4A26zLLpqam8ooVK9pt++tf/1oePXp0ec6cOW3bfvOb35S32GKL8oMPPth5C2aN1mWO5XK5PGXKlPLpp5/+lvt4TnavdZ3lP7r22mtXm5HnZPd54YUXVtt28sknl7fffvt2M34jr5OVZ13m6DWyMq3LLMtlr5OVZl3n+I+8RlamH//4x+UtttiivGDBgjfdZ6+99iqfcMIJ7bb93//7f8uf+9zn2i7fd9995S222KL83//9323bnnzyyfLo0aPLN954Y+cvvJM5p0gvUSq981Hdd999Wbp0afbZZ5+2bX379s2ee+6ZefPmtW2bN29eRo8e3a7QTpw4MUOGDMmdd97ZsYWzmnWZZX19/WqF9d3vfneGDRuWRYsWddbSeAfWZY5ry3Oye3XWLG+44YZsvvnm2XbbbTvleLwz//hugCQZM2ZMli5dmldeeWWNt/E6WXnWZY5eIyvTusxybXlOdp/OmqPXyMo0ZMiQJK+/+2pNnn322Tz11FPtXieT1z/idM8992T58uVJXn9O1tfXZ+LEiW37NDQ0ZMyYMe1eTyuVKFLFGhsbk6TdC0aSjBo1Ks8//3xeffXVtv3+cZ+ampqMHDmy7RhUnvnz5+eFF17IqFGjVrvu85//fMaMGZPJkyfnG9/4RtusqQzXX399ttlmm4wfPz7Tpk3Ln/70p3bXe072Pn//+9/zm9/8Jvvtt98ar/ec7Bn33ntvRowY8abnGPA62Tu83RzXxGtkZVrbWXqdrGzv9DnpNbKytLS05LXXXsvDDz+cCy+8MLvvvns23njjNe676vk0cuTIdttHjRqVFStW5Nlnn23bb+TIkaudZ6ahoaFXPCcr/MM9dERzc3P69u3b7qRUyev/i0q5XE5TU1P69++f5ubmNZ5cZ/DgwWlqauqu5fIOlMvlnHHGGRk+fHj23Xfftu2DBg3K5z73uey4447p169ffvOb3+TSSy9NY2Ojz9hWiN133z3bbrtt3vOe9+TZZ5/Nd7/73Rx88MH56U9/2nb+As/J3mfu3LlpaWlZ7f/h85zsOb///e8zd+7czJgx40338TpZ+dZmjv/Ia2RlWttZep2sbOvynPQaWVmmTJmShQsXJkl23XXXnH322W+676rnU319fbvtqy6vuv6tnpMPPfRQp6y7K4ki0Audf/75+c1vfpPZs2dnvfXWa9u+1VZbZauttmq7vMsuu2T48OH56le/mgcffNBbFivAySef3PbnHXbYIRMnTsw+++yTOXPm5LTTTuu5hdEh119/fbbeeuvV/pcUz8mesWDBghx//PGZMGHCm34DApVvXefoNbLyvJNZep2sXOv6nPQaWVkuueSSLFu2LE888UQuuuiiHHXUUbnssstSW1vb00vrMT4+U8Xq6+uzfPnyvPbaa+22Nzc3p6amJoMHD27bb+nSpavdvqmpqW0fKsdVV12VCy+8MKeffnp22WWXt91/1WcAe0OlLaLhw4fn/e9/fx5++OG2bZ6TvcszzzyTBx98MPvvv/9a7e852bWam5szbdq0DBkyJOeff/5bnjPG62TleidzfCOvkZVnXWe5itfJyrCuc/QaWXm23HLLjB8/PgcddFC+853v5Le//W1uvfXWNe676vm0ZMmSdtubm5vbXd/bn5OiSBVb9VnL+fPnt9ve2NiY97znPenfv3/bfv/4Wa9yuZz58+ev9nlNetatt96a0047Lccee2w+/vGP9/Ry6CKek73L9ddfn1KplKlTp/b0Ugrv1VdfzZFHHpklS5Zk9uzZa3wr7xt5naxM73SOq3iNrDzrOsu34znZvToyR6+RlW306NHp06dPnnnmmTVev+r59I/Pt8bGxvTp06ftI20NDQ2ZP39+yuVyu/16y3NSFKli22+/fQYOHJhf/OIXbdtWrFiRW265pd13hE+ePDmPPfZYnnrqqbZt99xzTxYvXpzddtutO5fMW/jtb3+bE044IQcddFC++MUvrvXtbrzxxiTJ2LFju2ppdMDChQtz7733tpuP52TvcuONN2annXbK8OHD13r/xHOys61cuTLHHXdcGhsbM3v27IwYMeJtb+N1svKsyxwTr5GVaF1n+Y+8Tvasjs7Ra2Rle+CBB7JixYo3PdHqJptsks033zw33XRTu+1z587NLrvskr59+yZ5/TnZ1NSUe+65p22f+fPn55FHHmn3elqpnFOkl1i2bFnbV4w999xzWbp0adsP50477ZRhw4bl05/+dJ5//vm2tz/169cvRx55ZM4///wMGzYsW2yxRa688sosXrw4RxxxRNux995771x88cU55phjcsIJJ2TZsmX55je/mQ984AM+y9cF1mWWTz75ZL74xS9m8803zwEHHJA//OEPbccbNmxYNt100yTJiSeemM022yxbbbVV2wmrLr/88uyxxx5eXDrZuszxhhtuyB133JHddtstw4cPz7PPPptLLrkktbW1Ofzww9uO7TnZvdZllqs88sgjefLJJ9vN7408J7vP6aefnjvuuCMzZ87M0qVL2/2e3GqrrdK3b1+vk73AuszRa2RlWpdZep2sPOsyx1W8RlaW6dOnZ5tttsno0aPTv3//PPbYY5kzZ05Gjx6dPfbYI0ly0kkn5ac//WkeeeSRttsdc8wxOfHEE7PppptmwoQJmTt3bh588MH84Ac/aNtn/PjxmTRpUk466aTMmDEj/fr1yznnnJPRo0dnr7326vbH+k6JIr3ECy+8kC996Uvttq26/P3vfz8TJkxIa2trWlpa2u0zbdq0lMvlXHrppXnxxRczZsyYzJkzp+2tTknSp0+fzJ49O2eccUZOOOGE1NXVZc8998xJJ53U9Q+sgNZllg888ECWLFmSJUuW5J//+Z/b3fajH/1ovv71rydJ3ve+9+X666/PpZdemhUrVmSjjTbKUUcdlc9//vNd/KiKZ13muPHGG2fRokU588wzs2TJkgwaNCg777xzjj32WM/JHrSuv1+T198W3Ldv3+y9995rPLbnZPe5++67k6Tt9+Eb3X777dl44429TvYC6zJHr5GVaV1m6XWy8qzr79bEa2Sl2XbbbTN37txccsklKZfL2WijjXLQQQfliCOOaHvHx5pmud9++2XZsmWZNWtWLrnkkowcOTIXXHBBxo8f326/c889N2eddVZOPfXUrFy5MpMmTcrJJ5+currKTw415X/84A8AAABAATinCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAAAAUEiiCAAAAFBIoggAAABQSKIIAFAIf/nLXzJ69Ohcd911Pb0UAKBC1PX0AgAA3sp1112XL3/5y22X+/btm/e85z2ZOHFijj766LzrXe/qwdUBAL2ZKAIA9ArHHntsNt544yxfvjz33ntvrrzyytx555254YYbMmDAgJ5eHgDQC4kiAECvMHny5IwdOzZJctBBB2XIkCG57LLLcvvtt2e//fbr4dUBAL2RKAIA9Eo777xzLrvssvzlL39JkjQ3N+eCCy7IbbfdlkWLFmXYsGHZeeedM3PmzAwbNmyNx3jsscdy+eWX53/+53+yaNGi1NfXZ/LkyfnXf/3XDB06tG2/pUuX5rzzzsvtt9+eRYsWZdCgQdlyyy1z4oknZuutt06SPPXUUzn77LNz3333pbm5OUOHDs373//+fPWrX82gQYO6/i8EAHjHRBEAoFd65plnkiRDhgzJyy+/nEMOOSRPPvlkDjzwwGy11VZ56aWX8stf/jILFy580yjy61//Os8++2w+9rGPZcMNN8zjjz+eq666Kk888USuuuqq1NTUJEm+8pWv5Oabb86hhx6aUaNGZfHixbn33nvz5JNPZuutt87y5ctzxBFHZPny5Tn00EPzrne9KwsXLsyvfvWrNDc3iyIAUKFEEQCgV1i6dGlefPHFLF++PPfdd18uvPDC9O/fP1OmTMmcOXPy5z//ORdccEH23HPPttscffTRKZfLb3rMgw8+OJ/97GfbbRs3blxOOOGE3Hvvvdlhhx2SJHfeeWc+8YlPZObMmW37TZs2re3PTz75ZP7yl7/kvPPOy4c+9KG27dOnT+/w4wYAuo4oAgD0Cp/5zGfaXd5oo43yrW99KyNGjMgtt9ySLbfcsl0QWWXVuz3WpH///m1/fu211/Lyyy9nu+22S5I8/PDDbVGkvr4+DzzwQBYuXJgRI0asdpyBAwcmSe66667stttuTvwKAL2EKAIA9AqnnnpqRo4cmdra2rzrXe/KyJEjUyqVkrz+UZq99trrHR9z8eLFueCCCzJ37ty88MIL7a5bsmRJ259PPPHEzJw5Mx/4wAey9dZbZ7fddstHPvKRbLLJJkmSTTbZJIcffnguu+yyXH/99dlhhx2y++67Z//99/fRGQCoYKIIANArbLvttm3fPtNZjjvuuNx///054ogjMmbMmKy33nppbW3N5z73uXYfu5k6dWp22GGH3Hrrrbn77rszZ86czJo1K+eff3522223JMnMmTPz0Y9+NLfffnvuvvvunHHGGbn44otz1VVX5d3vfnenrhsA6Bylnl4AAEBHbbrppnn88cff0W2amppyzz33ZNq0aTn22GOz5557ZuLEiW3v/vhHw4cPzyGHHJLvfOc7uf322zNkyJB897vfbbfP6NGjc/TRR+eKK67IFVdckYULF+bKK69c58cFAHQtUQQA6PX22muvPPbYY7n11ltXu+7NTrRaW1u7xu3f+9732l1uaWlp91GaJNlggw0yfPjwLF++PMnrJ4FduXJlu3222GKLlEqltn0AgMrj4zMAQK93xBFH5Oabb86XvvSlHHjggdl6663T1NSUX/7ylzn99NOz5ZZbrnabgQMHZscdd8zs2bOzYsWKjBgxInfffXf+8pe/tNvv5Zdfzm677Za99947W265ZdZbb738+te/zh//+Me2b6P5zW9+k69+9av50Ic+lM033zwtLS352c9+ltra2uy9997d8ncAALxzoggA0Outv/76ueKKK3L++efn1ltvzU9+8pNssMEG2WWXXdb4bTGrnH322fn3f//3/PCHP0y5XM7EiRMza9as7Lrrrm379O/fP//8z/+cu+++O7fcckvK5XI23XTTfOUrX8nBBx+c5PWPzUyaNCl33HFHFi5cmAEDBmT06NGZNWtWxo0b19UPHwBYRzXlN3tPKQAAAEAVc04RAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkEQRAAAAoJBEEQAAAKCQRBEAAACgkP4/zNuawo3p+4kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, in both male and female sexes, the greater amount is on the 3rd class, then the first then the second, so the sex doesn't make a difference for the choice of the class."
      ],
      "metadata": {
        "id": "AGFsmjVFlx6_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comment briefly the following code\n",
        "sns.relplot(data=df,x=\"Fare\", y=\"Age\",hue=\"Survived\")"
      ],
      "metadata": {
        "id": "RJnwP40j-kfQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "0939f6be-033c-426c-fc06-48dcdca74441"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x787d6cf33430>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 585.222x500 with 1 Axes>"
            ],
            "image/png": 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4TjUSQgghxMlCJvILIYQQos2QwEYIIYQQbYYENkIIIYRoMySwEUIIIUSbIYGNEEIIIdoMCWyEEEII0WZIYCOEEEKINkMCm+NAUQBVJWyCP2oSQ0HXpemFEEKI5tbqFuhraxRFIWLC0++vZsmaQkwLMlNcXHtOX7p3SEaRrQqEEEKIZiPdBi0sasHdTyxi0eraoAaguCLIff/9mh37atC0httGCCGEEOLoSGDTglRVYde+GvaW+hOWP/3eWmQ7JiGEEKL5SGDTgjRNZfXWUjxOnTSfE1WN750pKKrGMBsfilJVhQT7gDa7Q+slhBBCnKwkx6YFWViMGZhDp/Y+/MEo7dLdbNhRzuvzNmGYFh6XDSVBaGkpCqGoyY7CSlxOnZx0D3ZNwWoiCPquVLU296e8OkRJRYjsdDfJHjs6FpL2I4QQ4mQlgU1LURR2lQS45+nFBEKx+sMj+7bjlksH8/cXl3H22HycmophmPXlpqrw+rzNzFm0o/6Yw65x++VD6douCasZog5VVfBHTX7/5GKKygL1x/Pb+/jV1cOxKRLcCCGEODnJUFQLCUQM7n58YVxQA7B4TSFbd1dy0YTuTBqeGxfU6LrK8o3FcUENQDhicO+zSwnETJpD1IR7n1kaF9QAbNtbxUOvr8REhqaEEEKcnCSwaSHrtpcRaSQQ+WjxTiYNz0M9ZGgpFDN5fd7mhNeYpsXCb/dis2nHXLfqUJTdxTUJy1ZtLiEYlYxmIYQQJycZimohe0oSz4QC8AejmKaVIKxUKKkIomsKg3tmkeZzUlgaYNXmYiwLdu+rQWmGbOKaQLTJ8lDEwC0LCIojpOsqJqAqCkbMkGFMIcQJJYFNC+nVKbXRsnbpbjRVAeI/AVQFzh7XhYHdM1m8ei9F5QE6t0/i/NO78urcjfTvlhE3dHW00nyORst0TcHjlLeFODxVVQibFkvWFrFkTRE+r51pozuTnuSQhSeFECeMfIK1kLzsJDJTXRSXBxuUXTWtNw5NwTDi//jbNIXendP4w5OL6hfzW76hmA8WbOf2K4aRn5PULIGNy64xok82S9YWNSg7c1RnnDYNqxmeI9q2YMzijkcXUFYVqj/22Te7uOSMHpwxLFeCGyHECSHjDS3Eqav8/rrRDOyWUX/M57Fz08WD6NExJWGAEoqa/PPl5Rw6qzsSM3nq3TXN1sWvmBbXndufScPz0PevfOywaZx/ejfOO62rBDXisBRV5cWPNsQFNXVe/ngj/rDkaQkhTgzpsWkhlmXh1OCnMwYSiplEYyYuu4ZTVxvtdSmrDhMMxxKW7S314w/F8DmOPXkYQLNMrpjcnQvHdyMcMXDaNZw2FVOCGnEEIobJglV7Gi3/en0Rk4Z0INZMM/mEEOJISWDTgiwLFCy8dg2HLYJKGNNSCGuehMFN7DBBhdmMC/QBmIaFQwWHU9v/c9PPd2gGNisMQFR1Eo5Jh9/3lQVNrpodiRogywYIIU4ACWxamFONQNEmyua/SLR0N1pSGsmnXISzy1D8RnwSb2ayC11TiBkNPzCSvXY8rhPz69I0BbdRSeX8VylZvxAAT6/RJI+9mICW3CBXSLR9Nk1hQLcMVm0uSVg+rHc2sZgMRwkhjj/5yt2CbDoY27+m+M2/EC3dDYBRXUbZh4/hX/IWDjV+2MmuKVxxZu+E97rhggE4tRPz63Jb1RQ+dwf+tV+CaYBp4F/7JYXP/wa3WX1C6iROLNWCa87uiz3BsgCj+rUjxWM/AbUSQgjpsWlRDtNP0afPJyyrXvYh3qFTCeM7cNCyGDegPZ3a+3hl7gaKSgN0bp/MpZN7kOFzNMuMqO/Kpiv4V87HDDYMYMxAFf61X2DrP41oTHptvk8syyLZpfP3W07jjU83sXJTCUluG+ee2pUBXdNRZUaUEOIEkcCmBVkhP2Y40EihiVG5DyXFFzfbSbEs8rM83H7pEGKmha4pqBbNugHmd6GbIWo2L220PLhpCcl9JxJFvqF/31imhVtXuOrMXkQnmSiKgl1TTkgALoQQdSSwaUGKZgNAT8kmedQ56EnpWEYMRdPxr/0Kxe5MPIVbU4lEDKr8EXRNxevScds0oidgqwNL0VAdHgAUu5PkEWfj7NAdMxpBtdmJVhaTcIty8b1hGSa6AmBJvpUQ4oSTwKYFGXY37p4j8Q2aRMlHTxEr31tboOkkD52K7k2DQ2IVU1WY/80uXv54I+H9gUxmqovbLh9K+xTXcU/IjFg2kkZMJ1y4lezzb6Vi0TtUfPlafbmr21BSuwwlJG8lIYQQrYB81W5BYctJ2umXs+/tfx0IagCMGJVL3iW0fRXaQQnBNpvKxoIKnp29rj6oASguD3LXYwvxR45/j41pWqgZXciY9hMqFrxFaMfquPLg5m+omPsUTi3x+jtCCCHE8SSBTQsLFW7FDCXeSbviy1dxmAc2ywxGTV7+aGPi+0QMlqwrxOE4/j0jAdOBLaMjoYK1ics3LkWPNb7ppxBCCHG8SGDTgjRNJVK4rdFyo7oM9aCxKAuF3cWJgyCHXSMaNXE6beh686w+/F0YwaYCFwsr0nBPLCGEEOJ4k8CmhRgW1IQNrPS8Rs/RktKwlANBigLkZHrjzlEVuHRyT3522VD8oSgvfLSBnSV+wiZYyvFb2VV1eZssV+yu41QTIYQQonES2LQAfzDC5yv38JP751Fm74Dq9CQ8L3nMDELKgTKXTWXmpB5x5/zw7L7sKa7h3meW8L/PtvDmp5v5zaMLeOGj9RRVBDGPU3Bj2Dw4chMvHujqPoyYnvg1CiGEEMeTBDYtYF95kKffq81HeXB2Abazf4Wekn3gBFUn+ZQLsOUPjVvzIxo16NUplcvP7IVNV8lIceJ22pi/fHeDZ3y5Yg+7i/0EjlNCcci0kz79Jpx5feKOu7oMJnXStYQMmRUlhBDixGuVn0b/+9//ePbZZ9myZQtut5v+/fvz0EMP4XQ6AZg3bx7//Oc/2bZtGzk5OVx//fVceOGFJ7jWtXRdY+7SrfU/7yis5nevm1w9YRZ5KaBZMVIzM4mqbgJGw7hSNU3OGNaRsQNziMVMHn9ndYNz6sxfvguXQ2dQ1zQiLRzgWBbUmG58Z91CatSPFQ6iON0Yuodqw9aizxZCCCGOVKsLbP7973/z+OOPc8MNNzBo0CDKy8tZuHAhhlH7wf31118za9YsLrroIn7961+zaNEi7rjjDjweD2eeeeYJrj2ARWV1OO7I3lI/9722CQCHTePBn+WjNxGHmDETt64SUmDswBz8wShbdlXGnaNrCt06ppCV5iJsWNh0lVisZVZ81XUFmxlEASzdiWkaWB4XEcUtq8wKIYRoVVpVYLN161YeeughHnnkEU477bT641OmTKn/97///W8GDBjA73//ewBGjRpFQUEBDzzwQKsIbEzT4pQBOQmHjwAGds9AV1UwEwcEqqoQjFm89/kWlqwrxGXXOX1oR847rSsPvrKCSMwkr10SPzqnHwu+3cNfnv8Gl11n+th8BvfIRGvGPXoUBTxqkODaL6natorUMRfg37iUwKalKLod7+DJeLuPwG+6Eq+gLIQQQhxnrSqwefPNN+nYsWNcUHOwSCTC4sWLue222+KOT5s2jffee49du3bRsWPH41HVRpmmRY+8VHIyPOwpiZ8irWsql0/phWKZNBYHBKImtz/4Bf7QgQXv/jt7HX27pHPNOf14+r01/Oicfvz5v0vjznnkjVX075rOT2cMQmv07t+NRwlS8uo9RMsLaXfxryl66x+Ygar68vK5T2Nf8wVp596G33I2yzOFEEKIY9GqkodXrlxJjx49eOSRRxg9ejT9+vXjkksuYeXKlQDs3LmTaDRKly5d4q7r2rUrUNvj0xpkpLj43XWjmDq6M3a9tokHdMvg/llj8bn0Rns3FE3llbkb4wKWOmu2luJ26Jw5qhMfLd6R8Jxvt5RSWBZAaYaZUpqmEtmximjpbjw9huNfvyAuqKkT2bsZo3gbqnr8pp4LIYQQjWlVPTbFxcWsXr2ajRs3ctddd+FyuXj00Ue55ppr+Oijj6isrM0z8fl8cdfV/VxXfrR0/djjvLotEtx2jcsmdeeC07tiWWDTFHS1NgmXRoKAsGGxaHVho/detbmEK87sxY/v+6TRcz5fvptrpvc+5g0z7UqUytXzAXB26kfFV683eq7/23l4p/Qlahw+uKlrn4O3khAHSPscnrRR06R9mibt0/a1qsDGsiwCgQD/+te/6NWrFwADBw5kwoQJPP/884wdO7bFnq2qCqmpzbcWi89Xu2BdaWUQfzCKYan4PDbsukZFTZhw1MDl0EnzOdC12kX6SiqDaJoC0cT3dNo1dJva5DkOu4bXe+zDQmYkSJW2/+1hmihq4zOfFN2Ox+tAUY/87VTXPiIxaZ/DkzZqmrRP06R92q5WFdj4fD5SUlLqgxqAlJQU+vTpw+bNmznrrLMAqK6ujruuqqp2iCQ5Ofmon22aFlVVgaO+vo6mqfh8LsorA2zZU8Wjb35LUVkAh13jNz8cwbyvC/h8+W4M08Ll0Dn/9K5MHJqLTQVdVZg4LJd3v0y8DcPpQzpixYzDnlNefuz7NqmqgnfIFILbVxHY/DWe3qOpXPxOwnO9g86gsiqMZYUTlh+srn2qqoIyoyoBaZ/DkzZqmrRP005E+zTnl2ZxeK0qsOnWrRs7d+5MWBYOh8nLy8Nms7F161bGjRtXX1aXW3No7s131ZzTpfdVhPj9k4vr82lmTurBK3M3snpLaf05wXCMFz/cgGlanDkiDzNicPbYLixZW0RRWXyQNXlkJ1K8dqJHcE5zvQ5PVlec+QMJbluFb9g0AluWES3ZFXeOu/cY8LUnGv1uzzQMs8Wmp7cF0j6HJ23UNGmfpkn7tF2tKrAZP348b775JuvWraN379rl+8vLy1mzZg0/+MEPsNvtjBw5kg8//JCrr766/rrZs2fTtWvXEz4jqo4/GOWFOevrgxpVVejUzsd/Z69LeP7/5m/h9CEdsStgV+EP149m1ZYSvly5B7dDZ9qYfNqlulD33/BIzmmW12E6SZ7yE5JKd1C95nPSJ/2AWHUZ/rVf1U73HnomSmpHAoaj2Z4phBBCHItWFdhMmjSJ/v3789Of/pRbb70Vh8PBY489ht1u57LLLgPgJz/5CVdddRV33303U6dOZfHixbz33nv84x//OMG1PyAUibGpoKL+5yS3jZLKxne/DkcMguEYdqeOaVrowIiemQzvmYmiKFimiWWBuT9mOficYT0yURQwDQurBRaTCZhO1PReuE/vQQww03vi6TQMUAmbCqbR/M9UFGpft2W12Po4qtrw/omOCSGEOLm0qsBGVVUee+wx/vSnP/Hb3/6WaDTKsGHDeOGFF8jMzARg2LBhPPjgg/zzn//k9ddfJycnh3vuuYepU6ee4NofoGsq6clOaoK1Gb7BUAyf297o+YpSuyJxHZcaQQvXEKqpJOjOYdu+IKGoSbeOKbhsGsr+T97j1Y1qmhZhs27Gk8GBVQKaNwJQVYWoCSWVIfYU15Cd7iErxYVdra3DsVIUBbcSxPKXEivdjZ6ShZqcjYWCVVN7TEvORE3OJqR4MFogaBNCCNGyWlVgA5CWlsZf/vKXJs+ZOHEiEydOPE41+u6SvQ5mTOzBX1/4BoBIzCQUMchIcVJSEWpw/vDe2ThtKpgWHi1M9RcvELFsbEg/nYfeWYBx0If6tNGdueD0rs065NQaKErtisu/f3Ixe0sPJD9npri4+7pReGzqMQU3igJepZri1/5EtLR2VWjV4Sbrwtsp/egpoiUF9edq3hSyZv6WgC29WQIqIYQQx49M5G8hfTqncvbY/PqfX5m7gVkzBpHmi5+K3bVDMj86px+YVu2ieFuW4l+3gEivyfzrrU1xQQ3A7IXbWbejvM2twWAAf39pWVxQA1BcEeRPzy7lO+YmN+BUo5S9/0h9UAPgG3omFQvejAtqAIyaCopf+xMujn2WnBBCiOOr1fXYtBW6AueN68KZozuzbU8lDptGh0wP9/2/MRRXBCmtDJKT6SXFY0ejNq/DYfkpXfQ2zvxBvPttRaP3fn3eJnr9cARao2ecfIIRIy4v6WC79tXgD8fwOY7+FWtRP6GCtXHHHB17UrHgzYTnx6qKIVgBDlnrQgghTiYS2LQgxbJwaQr9OqXuP6CADrmZHnIzPcSiBlhWfaaKgkWsphxbp/4UFTfcMqFOWVUIy7SgDe1iEIo0vVJy8BgDGyvWcI0dK9bIKof7GYFqFGd7SSYWQoiTiAQ2x0HMgn0VQd77chtV/ggDumcwqEcmbocNt12tn+5kouNo3xWztIABuWNZujbx/Xp2SkXXlAPTpNoAr8uGpioNht6gNh5M9jSefH0kFIcHRbNhGQeCGUVRUHQ7ViyS8Bo9OZNQ22liIYT4XmhbiRqtkKkovLdgO7/+9wIWfLuX1VtLefHDDdz7zFJKq0Js2FVZv4FkGAcp468gvGcTQzs5SHI33MZAVeCyyb3qZ0a1FU6bypRRnROWnTa4Aw7bsb1VI6qbpOFnxR2rWfsVSYPPSHi+q/swDJv3mJ4phBDi+JPApoX5QzHemr+lwfGK6jAfLtzOmi2lhPdPKzZNC8ObQ9aMX6EtfYV7ruhJn/zU+mtyMjzcfd1oUty2Njc8YhkmF5zelRkTu+O01w45OWwa557ahSvO7I1yjL1TEUPFPXgqKeNmothr82YCm77G3XUIqaddiuJwA6BoNryDJ5My6UcEjcb3xxJCCNE6KVZLrOp2EjIMk7KyY99jSddVUlM99fs1zVlawIsfbkh4rqoq3H75UHweO+1TDsyW0jQVh+VHMSKEVTc1MQ3DBKdNw64pJ/X+Lwe3T6J1eDRNJRgziUQN7DYNp65iNuPrtengMPxYsTCKbieiebBQsRs1UHdMdRMxTkzMf7j2EdJGhyPt07QT0T6ZmUnH5TmiluTYtCBFUYg1scibZdUmAB+aV2JZFn7cRHGhWOCyq4RjJqZloWngtAIopomp2ggrzhOykJyqKjgIo1kRLEUlqrqJNJ7vfMQMw6zdWmJ/r01zBjUA0RhE8YDmqV1fMAZgxR9rOo9ZCCFEKyaBTQuKRg1G9Mnm1bkbE5YP6pHJpp0VTB6Zd+CgqlBcHeHJd1azfkc5Nl1l3MAcThvaEbfpR931JWUrPsKKBLFl5JI68WqM1M5EzOP3q7SrBnr1bso/eZpI4TYUmwPvoEkkDZtOjelqc8NkQgghTh6SY9PCkj12xg7MaXDc5dA5d1xXstNd2LXa5GFFUajwR/nlw1+yfkc5ANGYybxvdhGoKCfp62cJLHkbK1K771S0pIB9r9yDWrL5uC3Yp6oKevVuil64i0jhNgCsaJjqpe9T8tZfcasNV1YWQgghjhfpsWlBiqoQiZpMHd2ZIT2z+GDhdqoDEfp3zWDi8Fw0RWFYj6yDpnvDcx+sazA05bBrdEqKEt65OuFzyj95hvSZdxPAmbC8OTkIUT73KRLtExXZuwWrah+KN096bYQQQpwQEti0EFVV2FZUwx+eWkzMsOiY5eW0IR3p3N5HbnYSDk0F04zbkduw4NstpQ3u1T7dA/sazqyqEysvRDVCoLR8YKOZESJF2xstD21fhTYwn1hMElWEEEIcfxLYtJBA1OSvL3xTnzy8a18NL8xZD0B2mpt7fzwCb7gEKxZG86QSUV3EDB2v20ZFde0quaoC08bkM6pfezyBdVQ09jBVA1Vv7s22E7IUtclF7TRPMqZ01wghhDhBJMemhVTVRKgOJF6yv6gsQGVxMYXP/Jyi5+9kz+M3E135Psl2g+ljajfOVBT46czBVAei3PmfBZTZc0BLHId6eo0mqh6fPY2imhvvwAmJCxUVZ+eBJ/V0dCGEECc3CWxaSPQwH+6GcdBQjWlQueBNoluXMn5oR/p3TWdEn3Zs21PJ/GW7sCx44fO9OKfcXNs7cxBbWg7Jp15G2Dg+W2JGYgpJI87Fnp0fX6CoZJzzU8KqrNYrhBDixJGhqBaS5nOgayqxBAGOx6njsYIc2p9T8eVrZOQN4KcXDyIUNfj5g1/Wl329sQxVTefKC+5FK1qHM1qJq1Nf1NQO1Fhujss41H41pou083+BVVVIaNtKVG8qrvxBhFUPEVNiZSGEECeOBDYtxGnTuOLMnjzz/roGZTee2x1XyTdonfoRKlgHZm3vjVFdhq5puBQVVVXo3y2DwlI/OwurAViyvpQl60vp3N7HJWcMoU966v4k3aaDGptNQ1EgGj2QrKxpKpqmYJrWEa++qWlq/cJ5wbCClZSPPqQrlmVRHTNrp3UJIYQQJ5AENi1EsSzGDsghN9vHSx9toLDUT69OqfzgrN7sKSzjpaLupCT1ZMz5V2LbMBeHFcI74HT8K+cSqy5F6zSIq07rwKq9BpkpLp77YF19gLN9bxVupy1+OCsBU4HqYIzPF+4gEI4xZkAOHTM9eK1qIjvXEt61Dlt6Lt5uwwhpXmKGkvA+qqqgWBFUfwU1yxaihKpwdx+OnpmHPyYL8gkhhGg9ZK+o/Vpir6hYzERVFWJW7bYJiqLw+6cWs2tfTdw1f7iqL52jmyn78AkO7n3RU9thTPoZd7+0kZsuHswjr6+kuCJIZoqLP95wCnoTPTWWojBn8U5em7ep/liaz8n9V3Yh+NY9mMGD6qDpZM/8DWFfZw4dOdM0hUg4hLF5AeHPn41/rRm5ZM34NdXGkScuyz42TZP2OTxpo6ZJ+zRN9opq+yQhooWZpoVqWTh0ldc/3dQgqFEUSHFalH30JIcOKcXKC7GtfodRvdJ5+r01nD2uC107JPO760dhP8xvrtwfiQtqAC47LYfovEfjgxoAI0bxm3/BZQUa3CdsWBj+8gZBDUCspIDqJe9g1yU2FkII0TpIYHOchA2TT7/Z1eB4XnYSWtEGsBJ/c4hsWMhpfZLZWVjNgG4Z/OqqYbg0FdNsPJiw2TTmLtnZ4HiPbFuji+uZIT9mTcPFAQ3TwtyxvNFn1az8BJsZbLRcCCGEOJ4kx+Y4Mc3afZ8OZbdpEKlJcEXdhTFUpTaIicZMVMs67PwnRYGqQASbrnL6wGwGd/YQiVk4HDaa2snJioZQFOJyZkwTtHDj9bNiERQZzRRCCNFKSI/NcWLXFHrmpTY4vmtfDbTv0/h12flsL47gceokuW1H9KxYzGTyiFz+dV0fLnQvIW/J3+j+7SM49qwie8YvUOwJtl5QVPSU7AaJwHZdxeo4sNFnOfP6YKj2I6qXEEII0dIksDlONAWuPacvqho/8ygYjlEUceHsPKDhRYqKOupyXl9QyBVTe+O2H9kifKZp0SfDJPbW3YTXfIrhryRWUUTl/Oep+OoN0s+4psE1vqFTiajuBsd1TcFKykJv36Phg1SNlAlXE7YksBFCCNE6SGBznJimRWaSg7/MGkv/bumoCvg8di6d3IOOuTmkTv1/pJx2GarbB4qKPbcPzvN/y5yNFj88uy/De2UTix7ZxpIO3aRqwetY4QTJwHs2o7q8uPIHAAp6chbp036Ca/g5hM2GI5OWYeJISiVp2s04R1yA6vLu3zphAO2u+hMRZ2aT+T5CCCHE8SQ5NseRZVmkeezccvGg+mnVDl3BiJnUGE70vlPI6j0OVYWwqRO0HEzNtHDpCtHokU9L1M0QgY1LGy33r1tAyvRbSImGMFGJqG4CTUx7VCwLy56Ec9i5JA85AwULS7UTMGxYEtQIIYRoRSSwOY40TSFigj8UwzRrt1ZQVYW6fphYzCKGq34FXw0Tmw6OWA32aADFZiemuQmbtsMsiqeg2uwYkcSzlRSbk4hlP7D9gXn4oMk0LZxqBNUIY8aimLqFpdvQUDCM+MooioJTjaDFArW7gNs9hDUPsZgEQUIIIVqWBDbHiaopFFaGeeCVFewurp1llJ3m5kfn9qNLThKK0fBD36VFiK77gsKvXsfaH6S48geSOuV6avDS2NqKEdWFd+BEKhe8mbDcO2ACgWjsyOuuKnjMCkrfeYTw7g21x1xeHKMvpSKzH76UZFTr4HMrKX33EcK71tefm3L6FTg7DSFGgsRlIYQQoplIjs1x4o+Y/ObRBfVBDUBRWYA/PbuU4oowqhb/q9A1hdjmxZR/+lx9UAMQ3LaS4lf/iFttfO2YaAw8A8/Alt6hQZl3yBRMd8Z32gbBjZ+iF++uD2oAzGANwXmP46naxp7SINr++rvxU/TS3fVBTd25ZR88CkUb0bTE2zYIIYQQzUECm+NA01U+Xrwj4To2pmnxzhdbiB0SaThMPxVfvZbwftGyPVhV+1CaiBFqTBfpM35Dxnm34u4+DE+/U8m+4h7cIy4gaB75LCZVVYgVbcWoKU9Ybix+hVBlGRHDqj23eDtGdVnCc8s/ex49duzbVgghhBCNkaGo40FRWL8zcWAAsH1PFeGIiUs/EKkoRgTNnYxqdxGrKGpwTWTfDlRfZ4xDN3faz7LAb7nQ2g/C3WEgFirBmNlggePaHhSl0ftomnqgp0ZRsaW1ByBathcsk2jZXpJdKoZl4dQ0wrs3Nvo6Y+WFqOaRD4EJIYQQ35UENi3I0lTKqsJU+f1kp3lYvz1xcJOR6sKmq9TtFeVSwyixCN4+p6DYnNgzOlC1/OO4mU56ShaRIxhPMgyL2k3A4wMXS1EIxUzWbi4FC3rnp+GyqQ1WETZNCz0tB9/QKbi7DSNctA0AR3Y+/k1fE9iwiFDUIlVRME0LW1pOo3VR3T4s9cjW4hFCCCGOhgQ2LSRqwd9fXM6abaU47Rp3XTeK+csa7hUFcNYp+dg0Bcuw8GhhquY9TWDDogMnqBrpZ/wQRbPhX7cA1elFT+9I6CinWpuKwtyvC3j54/jelYsmdOfMkXmoBwU3hmHizR9AVekuCl/5Y9z5vuFnkXLWTeyodtdPW3fn9kHR7bWzoQ7hG3EOUc3Lke8FLoQQQnw3kmPTAqIxg3e+2MqabbWbSoYiBktWFzLrooH7e2ZqaarCpZN7kp/jwzJMdF0lvGlRfFADYBqUfvgkSQNOR/OmkX3JbwngPaq6KYpCUXmwQVAD8Pq8Tewu8cetjqwoCtGKIqqWvt/g/Kql76OqKr06p2Hszx8Kqh6yLrkT1emJO9fT7zScvcdhyLo3QgghWlCr6rF58803+dWvftXg+HXXXcdtt91W//Nrr73GE088wZ49e8jPz+fWW29l/Pjxx7OqTaqoDvPhoh1xx/43fwsTh+dy/6xxlFYFMQyL9hke9pbUsH1PFT06+LCbfkoWv9PIXS1CuzaQ/YM/44/Zj361X1XhrflbGi3+32db+OmMgdQNi9nVGFWN1glqlr6LZ8oswtQOMcUMBcubS/YP/oJZVYwZ8qOn5xDTPfgNG3qrescJIYRoa1rlx8wTTzxBUlJS/c/Z2dn1/37//fe58847ueGGGxg1ahSzZ89m1qxZvPDCCwwaNOgE1LYhw7QIRRpuf/DJ0gI27Cjnh9P70LVDMvf9dymbd1Xyo3P70bNjMoplYfgrG71vrKqEMC7Mo0zA1TSVGNC/WzplVSG27G74rIqaMIZpUpcJo1pGozOiAAx/BappAAdyZwwTanCDtxNKkkLIsqCR3SA0XSVqWOiqgmWa32ka+qEURcGhGmDFMFQHUVkQ8KjZdVDNCJZqI2Iox/R7EUKI46lVBjZ9+/YlLS0tYdkDDzzAWWedxS233ALAqFGj2LhxIw8//DCPP/74caxl4xw2jZwMD3tKDkxtdto1fnLhQKoDEd74dDOWBacN7siFE7qTkezENE1MxYazQw9CBWsT3zd/MOYRrBKciKEorN1exgcLd2CYJmMG5jBjYg8efn0F1YFo/Xn9uqRj01TM/bOkYoodR6d+RPbtSHhfZ14/Yqr90Nzkeo0tIogCgZjJ+/O3sHlXJe3TPZxzahdSvXaUo+iNcqkRqNhF9dL3MAOVOLsOJanvqfiVJNnL6juwqRaOWAXVC+cQLdyMntqepBFnE3WlEzFa5Z8LIYSIc1L9pSooKGD79u3cfvvtccenTZvG/fffTyQSwW4/8TtNp/qcXHN2X+55ekn9sRsuGMDsr7ax4aBp3+u2l5GXncRvrhmBZVkYmo3UU2ey94W7qRsKqqMlpVFi70CoLEj7VGeDbQyaYqDwj5eXs3bbgfVl1m8vJzc7iZ/OHMwf99fTYdeYdkrn+qAGIGpA0uAp1KyYixUNx91XsTnxDjqD6qPoQCooDnDX4wuJ7X8dmwoq+HzFbm6eOZhBXdO+0x5UTjVC6Jt3qFr6Xv2x8N4tVH/9PtlX/hG/miI9DkdA01RslVvZ+/IfwKztYgvv3YJ/7ZdkTL8JW+4QooYssCiEaN1aZfLw9OnT6d27NxMnTuQ///kPRu18ZbZu3QpAfn5+3Pldu3YlGo1SUFBw3OvamLx2ScyaMZCsVBcdMr0EQrG4oKbOzqJqlqwtQtNU7IafquUfk3XuzQdWDVZUXN2GkHn2TVQGDR54dQUJRrkapaoKm3ZXxAU1dQqKqtlcUEGf/DR65aXyp5+MwW1v+JYIqD7aXXEPjg496485OvSk3RV/IKD6jrwy+5VVhvjnK8vrg5qDPfrmKsLfIWgD0KPVcUFNHTPkp+LT53CosnbOkXBafkrfe7g+qDlY6Zz/4DAb7hYvhBCtTavqscnMzOSmm25i4MCBKIrCvHnz+Oc//0lRURG//e1vqayszQnx+eI/TOt+ris/Wrp+7HFe3dYC5dVh1m0r4/eXdAG7i0fe397oNXOX7GR032zMmlL8a78kUrQN35ApaEmpKIpKcOdait74C+nTfsWeEj/BiEGK+wh/dYrCR4t3Nlq88Nu9/OoHw3HoKjZVwbIsVL3ht/Kwux2p596GEguiAKbNTRgHimXxXZpN01TK/GGKyxNvCRGOGpRUBMnL9CQsT3S/0OrljZYHNy8jdVKYmH7ie/KORN37R9OO/3cOJeQnVlWcsMyKRTCrS9BTkhKWH08nso1OBtI+TZP2aftaVWAzbtw4xo0bV//z2LFjcTgcPPvss9xwww0t+mxVVUhNPbIP08aUVgYJRQzC5QE8ThtFZX6srWtRTBPT6tLodaZlYXfYUGuoX91Xddd+gBiBKsK71mOFA9QNT6nakdc1GI5h7h+HaZ/uYcaYduSl24hZCp+trWTDrhq8LhspSU1vTmmGgxjBGOgaqsOD5k7Cfcg5RiiAGaoBy0J1utFciT8Ey/wN17hpn+5h6imdyUp1k5zkwO1x4LAf2duz8dTmWpqmkppybL/b483nO/6r/YSLmv5Dryoc8/8jzelEtNHJRNqnadI+bVerCmwSmTp1Kk899RTr1q0jOTkZgOrqajIzM+vPqaqqAqgvPxqmaVFVdXRd7TEL1m4v49nZ6yguD+Jy6Jw5ujPnntYNS0/Hmv8Yk/oPYfWW0oTXTxyWi2IaKN50ss67ldCu9ZR88BhWJIiWlE7KqHNw9xrN5oBGu3Q3Tl2lvPzI9lxSVYXJIzqR4VGZOdiOseBJosU7sak65/U8BdelF6FiNXo/VVVwRCuo+PQ5Apu+BsvEkdOdtDOuJepth2GptedEyiif9yzBzcsBC0dub9LOuIaoKwvDOtADpGkqSW476clOSitDAIwZkMOYgTm88vEGdhRWo2sK4wZ14NLJPXHph5+R48wfBJ8+n7DM1WUgEezUHGF7nWiapuLzuaiqCja6zUVLcdpcaElpCff6UjQbSlLGEb/vWtKJbKOTgbRP005E+7SmLwTfB60+sDlYly61vR5bt26t/3fdzzabjdzc3GO6fyzBJpWHo+kqyzeV8OCrK+qPBcMx/vfZZnbszeLC0zqRmpFPz+QQXTsksWV3ddz1ORkeRvdrTyRsYNNs1Kz5gsDGA0nHRnUppR8/TdoZ1/DNjiCzLhqETVW+U117d06lt7uUmjf+QH1SshkjvO5zzKLNpF98J2Ez8bcXr+qn6IXfxk35Du/ZxN7nfkP7H95PRE/DZfopfO4OzOCBncvDBeso/O8dtPvhXwgr8QFnWoqbn148iN89uZjUJAfjBnXgz88trQ9gYobFp9/sYsOOcu7+0Sh0mo5sDLsP76AzqFnxcdxxxe4iZfxV+GM61qGbZLVyhmEe1fvxWAQ1N+nT/h/7Xr2XQzcVSz3jGkKK+7jXqSknoo1OJtI+TZP2abta/SDj7Nmz0TSNPn36kJubS+fOnZkzZ06Dc0aPHn1CZkSFoib/nZ14evayDftwulxU9JyOZkX5+dk5/L+zu9G1YzL5OT5+dE5f7v7RKGz7OzSUqD8uqDlYxRevcsWp7emQ7vrO3zLchIgufIlDZ1pB7U7hRunOuNWG6+i6SnjHqsTr2JgxKr96FafNIrB+QVxQU8eKRahe+i52Pf65iqLQuZ2Xf9xyKtec3Zc3P9ucsFdmT4mfgn3VCet2sKBpxzP6IjJn/ApnXh9smbn4RpxN+x/eT9Ce1viUcxHHMCxiqV1of81f8PQ9FVtGLq5uw2h35R9R84fLjCghxEmhVfXYXHvttYwcOZKePWtn33zyySe8+uqrXHXVVfVDTzfddBO33XYbeXl5jBw5ktmzZ7Nq1Sqefz7xUERLC0UMKmsa5ozU2VVUzaDu7THMdnjUCBMyTUYM7EQ0ZuHQVWKx2kXpVFUhuu+gvaQUFUW31U+xNkM1qLEglv7duzQ1K0qkuPEE4tC2Fdiy+mAeMhtGU8za4ac6qo6iqvX7QIV2riXFCFG1pfHk3dD2b3GPDAPxOTyKBUl2jW4dU9jYxM7nyzcU06NDcoO6HSpgOFDTe5F0VlcUyyCmOKg2aHRhQJFY1FSJaum4TvshHiOCpdkIGCqWtKMQ4iTRqgKb/Px83njjDQoLCzFNk86dO/PrX/+aK6+8sv6c6dOnEwwGefzxx3nsscfIz8/noYceYvDgwSekzjZdRVFI2OPQLt1N5xwfxZUh/MEoGSku7LqN4oogpgkZKU4cmoJlWliWheZOQktKI3XMRWjeVMyQH83jI7xnMxUL30LR7VSFDSqqw6T6HCQ7wRUqwQzWoCdnEtXchA5aRE1VwUUAAhVkTP0xmiuJwJZlVH09h7reG0Wz4e4+DC1Sil5ViupKwnImEcKNhYLqTcXRsRfJI84Cy8IyYqhODzVrviBSuA1L09E8KY22j+b2gaIm6iyqfT7gcdnwB6MJy9N8ziPucTFNixA6rextfVIKxxTAATJTXghxklEs6acHasdby8qOIjFSVXj0rdUsWVsUd7hDppfrz+vHw6+vZN9BU5tH9WvPuEE5/O3FZWiqwo/O6cuwnlkolkWSHsKs2EPpnCeIlu2pv8aVP5DkU87jqwI7D7y1of54j9xkbp3Wnug792CFg3gHjsd7ykz8hgNNVXAG91D8+p8x/BX7r1DwDjgdZ8eelMx+FFSd7PNvpWrlPIKbv6m/r57ansyLfkFAS8UdKydWtJmSj57aPzMLUHVSRp2Ns8sQwr5O2Kt2UvT8bxI2T8b5txHN7l+/+q+uq6Smeigv9xOLmWiaygdLdibclFNR4KHbxuPSvj9DIIe2j2hI2qhp0j5NOxHtk5l54pdJ+D5p9Tk2rZ1iWVx7dl9yMuKHiC6f2ot/vLQ8LqgBWLR6L2u2ljKqXzuiMZN/v/kt+ypDKErt7J+S2f+JC2oAgttWUr3yM3aVxt9rY0ElD39chG34hYBFzcp5hNZ8hk1XcFk1FL30u4OCGmrPWfUpRk05ztw+JPU/Df+mb+KCGoBY+V6KX/kjLgIoikXxe48cCGoAzBgVC/6HGQ5gmhamN5vkMTMatI1nwAS0dt2b3NLAMEwmDcujf9f0uOOqArdeOgRXgjV1hBBCiMZIn/0xsiywqwp3/2gUhWUBNhXUblVg0zUqasIJr/nk6wJunjmYBav2AvDaJ5uYdeEA8JcQqyhKeI1/7ZeMPfdM3vwi/vjqreVEJ/at/7lqybtk9zudyK51WJFQwntVfTOHrIt+iepws/uJnyU8J1ZVDDWl1Gxb0WCGTJ3KL18l+dxfEDTtOPpPoX3vMYS2r8QyDVydB2DYfQSMwyd0q5bJzRcPoqw6zOotpSS5bfTrmoFDa2SMTwghhGiEBDbNwLIsdCA33U3X9kn4fC7e/2prfbnTrtG/WwZ2XWPjznKKK2rXuhk7KIfi8iCFpX5MLGJVDdcPqWcaaGbiPJRg9EBqrhmqQVUsoqUHEpHtWZ2wpXfACFQR2rm2dgdxZxJGNAxN7BQeqyrBrCpptDxasQ/FigF2wqZOWE1B7zEeBaiJmY1ujJmIallkJtmZNLQDllXbkyNBjRBCiO9KAptmZFlW/VTsvOzaMdWLJnSne24K36wvIhg2mHlGDxx2DbdTxzJhUPdMBvfMxKYp6CnZjd5b0e2EsTU8roDHbtVP/tG8qZimgqN9N2xpOaRNvIpoyS7ChVtxZHcmZdS51GxcgqnoqDYFxeZosLllHT21HXpVh0brZM/qhKna4mYeHcuYtWUd2/VCCCGEBDYtJCPFxdVn9WFfWYA/Pbu0/vgXK3aT397Hdef1Y8G3e7AseOvzLfzh+tHkJadgy84nWrStwf1cg6bwv+UNe3RG981C37m0PrZIGTeTIC5c7bqRPuVa9r31T8zggUUBK5fOJvPcmzE0JzELfMOmUbnwfw3ua8/qjOVKwd1zFJVfvZ4w+EkZN5OAaafRKU9CCCHEcSbJwy3ANC1WbNhHn/w0Pli4vUH5tr1VLFlTxKAetWvzhCMGD762gt01GtFT/x/2zgMOnKzqOAadiWvINMKmSt1adaoCpw1qx1Uj3YS/eQfF5sR5yiWonYZgGCYRS6X881fjghoALJOS9x7GZviJxMA16EySRpwN2oEY19m5PxkX3E7QchJQk8i+7G701PYHquRKIuPcW4h628vid0IIIVoV6bFpARU1YTbtqmTz7sZ3G/9s+S4um9yT5Rtqd1POyfDy0eIdfLFiNxeOOZdhQy5FNcJEVQdvrapgxX9WcuOMgUwYlks0ZuK0aWhWlKgaIO3SP7C3Gl5dWsqF3VTSvSrR6mrCuzckfLYVixAr3YWS1gu/4cAx7AK8g6dghf0oNicx3U2Naduf6wIhdw7pM+9CifjBNMDpJax4iBkS1AghhGhdJLBpAZZVu+BeKNJ4vkg4YmC3afU/22wq/mCUQCjGc5/s4LlDznc7dfZVBOP2pKrzy6uGc99/a4e7zhvfs3bquNH0ympmJISqqYSiBrGYgkP3Ydi9tYWHrDJrGBYBnKA7UTSVaMwEpXblZENyYoQQQrQiEti0AK/Lhtdlo1fnJL5YsTvhOYN7ZLJu+4GcmR17qrhiam++XLmnkfOz2LCjYY5Nn/w0tu2p7RlyOXSS3DZiMQPd7UXzZWA0MqvJntWZR2avZfGaIhx2jSmjOjFpWC46VsLJSKqqEIiavPbxehatLsRuU5k8ohOTR+ahgwxJCSGEaBUkx6YFOOw6k0d1xm7T6Nzel6BcY8rozsxfdmBK9tRTOtMlx5fwfKddY/rYfOYvjw+SdE3hgvHd+GjxDgCuPqs3Dl2tHUKy+3CdenXC+nkGn8FHK8v5bNluguEYFdVhXvl4I/c+s5QYiRfEC8RMbn/wCz79ZhfBcIzKmgivzdvE755cTFRiGiGEEK2E9Ni0kCSnRreOydxwQX8WflvI/OW7CEcMhvXO5oLx3Xjz001YFnTtkMylU3qS3y6JqGFxxdRefLu5hPnLdxOOGAzukcmZozsTjZlcOrkn7325lepAlAHdMjh7bBfenr+FtGQnN108iE5ZXsz9082tmIE9tw+pl/yOwJcvESnahp6URtKo86lI7sGTjyxrUOdte6vYtreKHjm+uNWCFVXlrU834g81HN7ata+GTbsq6NcpBUNyboQQQpxgEti0kEjM4om3V5Oe7GLswBzGDMihOhDBNEzSvHaunNqby8/sha4q6NTOpNpXHuSPTy9hcI8sLp/SC5uusm57Gfc+s4RQxODPN45ldL/2YFnYbSqGYXH9+f3QFAVdocHWBVFDQ0/vgnf6/6FbMVA0AoqLX/7ts0bXvvt82W5656VgRg4k2kQMs8FeWAebv3w3/TunItO+hRBCnGgS2LQA07T4ctUeCvbVcMHYXPKTI1iRKkh3Ux4EZ6QMeyQEdjdRxYuFBljY9dqRwQFdUujTyUcoHKNzdi6nDsjm0bfXUR2MUl4dpmOWl5pQGI/ThlNXMQ2TxrZjikUNwE4EO1gQw8Rh1xP2vgC4XTrKIcNRiqLgOCjRucE1Dh1DUdA0tX6BwrZE18FpBLDCNSiajmnzEMLZ5B5YQgghTgwJbFpARXWYZev38Y/rB+Cf/19KNi5BdbjInvFL7Iv+R+HW5bUnqjqO/hOxDz0H7F5Skxz89Scj2F5Yzc8fWYQ/WLuFQl52Ev93yUCcDhv/enUF67eXA+CwaVwwvhsTh3ZEPcLkXYeucuboTrz4YeKp4GeMyCMajR1yjcLU0Z155v21Ca8Z3iebux9fxI/O7UeHdDeNRlknIacaxdy2jKJ5z2Lu3wjUlplHxjk3E7BlSHAjhBCtjCQPtwDDNPnx9K5Uf/wfQhsXAxYp4y6m7JP/EqoLagDMGOGVHxJZ9h6RSIRkl0qVP8KDb66rD2oAdhZV85v/LCIWM+qDGoBw1OCljzawcE0hun5kv0ojZjJ+SEe6dkhuUHbOuC6keh0NhqmMmMmYATn0yEttcM2EYbnsLfGzo7Caux9fhD9sNDjnZKVpCkrpVko/+Hd9UAMQLd5J0Qt34bZqTmDthBBCJCI9Ni1AUxV8aojS7atqD6ga9oxcyvZuTnh+5Nu5aD0nELal8tzchtspAPhDMdZuK6VbxxQ276qIK3vtk02M6J2NLfGEpgZ0LH551TB2FFYzf/kuPE4bE4fnkeq1N9rzo2Ny++VD2LWvhnnf7MJuUxneux07i6p44cP1ABimxXtfbeOyid3bxJCU3QpRMf/FhGVmqIZIwWq0TqPbxGsVQoi2QgKbFmBYFlbNgVWHvX1PxQw1/u3eMqJYkRAxE3bsrQIgM8VFSpKD4oogFdW1+zSt21FBfo6vQWBT5Y8QNUxsR9hrAwoOTaFHBx+9cvsCCtForMm1aCwLNCzyspNI9TkoKg3wr1eWEwzHD1vtLfZjqQoaykk/S0rDILJvZ6Pl4V3rcXQ5BaPtdFIJIcRJTwKbFqAqCpo7iaTh5xAZdD4b91TRx1Xd+AWKimp3oKm1C/edMbITFdVh9pUH6JDlxa5rPPv+Wjq387JsQ2mDy10OHV07fFCjqgou/Jilu4js244tvQN6ZmcCirfRWVINXxsUlQZYvKYw7rjHZeMHZ/Uh2etg3te7SEt20isvFYeuYJ2keSgmKnpKFrGKxDPCbJmdJMdGCCFaGQlsWoDPYydkplHc+xzuevBLqgNRfj2zBx1S2xErL2xwvqPHKIKahwyXnRmTuvP7J5fE5dhkpDi56eLBpCfZeGZ2w6Tfaad0xqU3PSNJURQ8ZiVFL92NUX1gBWPV6SX70rsIOrOOrIfFNLlwfLe4wMamq9x2+VCefX8t2/f3ONUd/80PR5CX6Tkpg5uI6iZ5zAxK33+oQZmi2XB1G0q1bCkhhBCtiiQPtwCbrlET0/jTs0upDtQGKP+ZsxPljFuxpeXEn9tpAPrIS1i8sYKiyhh//u83cUENQElFiBfmrMema2SkOOPKxg3KYerozofN83AqIUre/VdcUAO1uSL7Xr8PpxVo5Mp4lgXpSQ5uvGhg/fT0Uwd35LNvCuKCGoBozOSep5cQPkmHpGIxEz1vAL6R54Jy4H8V1eUl65LfEFSTTmDthBBCJCI9Ni2kKhCluDxY/3NpZYjfvLiFH03+CZ1TLJL1CJY7hQBuXpi/m69W7mZgtwzKqkIJ77dxZznhSIz7rhlARcxBTSBKZooLp01FMQ/fa6DFAkT2bklYZlSXQrASHK4jem2KZTGsRwb9f3Y6JRUhkr12bv3n/ITnRmMmW3ZV0rdTykk5bBMw7DiGnEP7QZMwKvah2p3gSSWE56TPIRJCiLZIApsWEghFSfM5GTMwB4/Lxs7CKpasKeT+1zehKnD/rLH85uGFhPZPj07x2hv01BwqHDFILV1JZtdxpHtstXkxCZJjVE0laljomgKmiWWBFYs0eW8zEgTHkb8+y7SwK5CT6iQQM4k18SFfVh1CURRO1pWJw4ZGmCRI3t9DY8KJeC2KAg7VRLGimIqdsHGE0+CEEOJ7RAKbFpKd5uGqab2Z900BFdVhenVK5bfXjuKFOevZtqcSt9NWH9QAVNREyEx1oygJYxUcdg2PQ8Xeviv+xoadFIWqUIy35m9g654qcjI8XDi+O2leO4rDg6LbGwlwFLSk9KN+rXZdJTvNTVFZ4uGsnnmpmEfQqyQa51Cj6P4iKhe+RayyCHu7bvhGnE3YlkLUlBFlIYSoI4FNC/AHI3y0eAdvfHpg3ZqdhdXMX76bX1w5jO17qyiq8De4zq6rnD6kI59+s6tB2QWnd8MV3oeVkgkJphermsLWvTXc8/Ti+oV/dxZWs2h1ITfPHMywHqkkn3IBFZ+/3OBaz4DxxDR3wvseCYem8sPpfbjvv183KOvVKZW0JEeTU8lF0+yqibltCYUfPl5/LFpcgH/N52TP/A1mSldZS0cIIfaTr3otoLw6HBfU1AlHDF75eCO9OqWha1p98m37DA93/HAEuqYwom87zj+9Kx5nbcyZmuTgqmm9cdg1It4cAkbi8aKIYfHAaysS7mbw6P9WEYiAo+8E0qZch+ZJAUB1ekgZN5OkMTMJGUcf4xqGSbcOyfzq6uG0S3cDtds9TDulMz+7bMgRb/cgErNbAcrmPt2wwDQoff8RnFbDIFkIIb6vjrnHZt++fZSVlZGXl4fb7W6OOp3UVFVh9ZaGa83U2bCznOpAhMwUFw/87HRM08Kmqdg1hcpglPuf+5ohPbP48QUDsOsa/lCUT5buZO22MnIyPHRv70vY+1ETjNUv5HeocMSgrDqMzedA7zqWzM6DUcwYlqoT0dz4E++H+d1et2XRq2Myf7huNFHDRFMVHPs36BRHT1HAqCgCI/EvKVZVDBE/6EeW+C2EEG3dUQc2c+fO5a9//Ss7duwA4KmnnmL06NGUlZVxzTXXMGvWLCZNmtRsFT2ZqEptUqfXZeOMkZ3o1al2j6WyqhBqLECfDANd86PZ7IQVL9GoUT/DRgG6tvcyqJ2FTghT0amuTmPjznKOJVVUxcSj+DFUL/6Yu/ZBFtAMQU0dwzDRqN1jCZCgprkc9hff8ITaROMYuhEEy8TUnLIjuRDie+GoApt58+Zx0003MWjQIKZPn85DDx1YwCwtLY3s7GzeeOON72VgY5oWfbqkkZPh4ccXDODNTzfz1vzNdG6XxK/Pz8X46hXKvqrdJdvRvivpk3+E6m1HOKrgcug8fstQHIXfUvH6PzFqKlB0O6f2P43xt5xNSE9qNFfF67KR5nMmnC7utGskhYooev1e3AMm4Bt1HjUxZ4K7iNbGskBLzkbRbFhGw1lzeko2lt29f6ZWLVWtXYyxfM5TBLetBCzsWZ1InXwdUW+OJBsLIdq0o/oL9/DDDzNs2DBeeuklLr/88gblgwYNYt26dcdcuZNVapKTn10+lL+98A0rNxUD8H/ndCL4v98TLlhbf1547xb2vnAXjkjtjt3JNgP77mWUfvgERk0FUDtNu3r5x4Tm/QefXjdtuiGHBjfPHISqxpcrCtw4vSus+B9WLIJ/2RzKP/g3Hr3p6d+i9QgrbtKmXNewQNVJP+tGwoon7rCbGopeuJPgthXUTUuP7NtB0Qu/xREppZG3kBBCtAlHFdhs2rSJqVOnNlqekZFBaWnjeSZtncdlY932Mqr8tcHD4O4Z6NsXYYUbToe2YhEqF7+Dx2GhRmuo+OrNhPcM7ViDEqigscEdw7DIy/Twz1tPY/LIPLp2TOa0ge3467X96FryGdHtKw/ca9tKCFU1cifR2kRNFSVvCO2uvg93r1Owt+uCd/AU2l/7V6JJuXEzonRdJbxtJYa/suGNLJOKL17BrsqunUKItuuohqJcLhfBYLDR8oKCAlJSUo62Tie9SNRg+YZ99T/3aO9C2bOm0fPDuzaQFA1ghQOYwcY3y4wW74S89g3WhlMUBcuysEwLr03lqjN7EaqpIvb160TeXUA02nB4KlqyCzUns03mXNT1SLSlyVhhUyfibId7wnUoZhRTtVMTg0MjXU0xCWxd1vh9dq0n2YwQRpKNhRBt01H12IwcOZK33nqLWKxh5mlxcTGvvvoqY8eOPebKnaw0TSXNdyCHpSJgoOyfYp3wfE8KihlBsdnj9iRKdJ65/9Na0xRiwL7qMJv2VFETMTH3f6IbMRPdDBP+9lOsBEENgOZObnNryzjUGF6qcJSux1m1Da/iR9fazmu0LAjHFEKmnUgjSd8mGpovo9F7aO5kzCbeY0IIcbI7qh6bW265hZkzZ3LRRRdx5plnoigKX375JYsWLeKVV17BsixuvPHG5q7rSUNTFaae0pmPl+wE4IvVxZxzyRmwcXHC893Dz6Fi2Tx8PYfi7jaEwKaGC92pTg8kt8OuqliWRbk/xu+fWhyXLDy0VxY/OX8AGCa6KwlH16GEtyS6lxctNRurDY1IuLUIwRUfULLobbBquzEUm5OMc29ByejxvUmYjUYNvAMnUf3NnITlvlHnEVFcNOjqEUKINuKo/tp36dKFF198kZSUFP71r39hWRZPPvkk//nPf+jRowcvvvgiHTt2bO66njRM06KiOsyVU3ujKuAPRllTasc79hIOnZprH3gmi4tchPPHUvrRM6SNvxxbZl7cOYrdRdaMXxGxp2AYJmHT4rePL2wwA+qb9ft4c/5mVE3BxEbS+KvR03Pj7+VwkzXzDoK0nTWHNE3F2LueqoX/qw9qAKxoiOI37sdhfL/yiaL2FNLO/HGD3j9Pv1Ox5Q2QVYqFEE365S9/yYQJE07Is3v27MmDDz54TPc46nVsunfvzjPPPENlZSU7duzAsixyc3NJS0s7pgrV8fv9TJ06laKiIl5//XX69+9fX/baa6/xxBNPsGfPHvLz87n11lsZP358szy3OZRXh3jotZX075rOndeOYkdhFe3aJzNvU3dGX/Qn1OJNtZtTZndn/oYaXn53K5dPyONUp4vi9x4h8+xZmCE/4cKt6MlZ2LI6E9CTMaMmiqKwt9hfn5h8qI+X7OSccV2wKxC1+UibcQexiiKiRduwp2Zhy+xESPFgtKHeGrsVpPyr1xMXWiaBNV9gG3we0WgbetFNCJs69s4jaH9dPyK7N2BFQzhye2PYffgN24munhDiEBs2bODhhx/m22+/paSkhJSUFLp168aECRO48sorT3T1TjrHvPJwcnIyAwYMaI66xHnkkUcwEnz6vv/++9x5553ccMMNjBo1itmzZzNr1ixeeOEFBg0a1Oz1+C50Xa3fWbusKsT85buZv3w3ORkefjC9L6/ML+DFT006ZHpRFYVdxRvqk3cLyqJo7mT8Gxax56mf4zz1apIGn4mi1K6hp8UMNLuKacK+8trZVR0yvfTKSyYcNVi2sYRAKEY0ZhKJmdhtKqZhEcSJltoZe0YXYjGTiGmdrJtsN0q1DGKVxY2WR0sL0JWmX7TNpqFiYqIQjZ78PRoRUyNCElrecEChxjCPei8wIUTLWbZsGVdddRU5OTnMmDGDzMxM9u7dy8qVK/nvf/97QgKbP/zhDyd1DuZRBTZvvfXWYc9xOBy0a9eOvn37Yrfbv9P9t2zZwosvvsgvfvEL7rrrrriyBx54gLPOOotbbrkFgFGjRrFx40YefvhhHn/88QR3a3mqqhAyLBas2svKzSVMH5tPxywvu/bVALCnxM/2vZXk5/jYVFBRf/xgfTs4iW4tBEDzJGN1GcXS9ftYsGovXrfOlEEZpAYL8HjcdMnJ449X9SY1tAvbzrlYbheXXXo6X++2ePWLPTj0+CEIw7ASBolthaHasGd1Jrwr8dpJjty+NLa5uKYquKwqgmsWEdq9AVt6R7z9Ties+9pEXk7titYn7x8oIdq6Rx99lKSkJF5//XV8Pl9cWXMtmxIIBL7Tlkc228nds3tUgc0vf/nL+oXiDo3qDj6uKAper5frr7+e665LsMBYI+655x4uueQS8vPz444XFBSwfft2br/99rjj06ZN4/777ycSiXznIOpYqapCVdjg1498RU2wdmVYj0Pnyqm9+dOzS+vP+3jJTq4+qw9/ff6bBvdIctvon2MjsnA7qDr62Xfwu2dXsbf0wOaGnyzdxQVjOjDJt4m8AZmUfPAg0bI91K9Fu+Zzhg88k95XTsFpUzFiJ3+vw5GKWHbSTr+Uvc//tkGZ6nDj7jaEqgTtoaoKzlAhhS/cdWD22KavqVz8DpkX/hwroxffo2YUQpwAO3fupFu3bg2CGoD09HQAdu3axcSJE/nTn/7EBRdcEHdOz549mTVrFjfddBMADz74IA899BDvv/8+//73v/n888/p0KEDZ599Nvfffz/z5s2jQ4cOcff429/+xtNPP81XX31FcnIyv/zlL1myZAnz5s0jGo0yZsyY+ucfrKamhtGjR3PFFVfwi1/8AoBIJMKjjz7Ku+++y969e0lPT6/vjDj48zkSifDXv/6Vd955h3A4zMiRI7n77ruPuT3hKJOH33rrLXr27MnIkSN58MEHefvtt3n77bd54IEHGDFiBL179+all17iwQcfpE+fPvz973/nxRdfPKJ7z5kzh40bNyacVbV161aABgFP165diUajFBQUHM3LOSYxCx55bWV9UAPgcuqgwDVn9yXJXRv5FpcHKSzx83+XDSHFe2CH7q4dkvnjj4air34XAM/oC3l3RU1cUFPnza92o3U/hZplHxAt29OgPLJyDnneyPcqqAHQVIjWVJAx9cdo3pT64/asTmSddwtGOJhwtV0nQUrffaDhlHjLpOSdf8mu2UKIFtehQwfWrFnDxo0bm/W+N998M8FgkFtvvZUZM2YwdepUFEXhgw8+aHDuBx98wJgxY0hOTm5QZrPZmDRpEnPnziUSic/trDs2bdo0AEzT5Cc/+QlPPfUU48eP584772TSpEk8++yz9aMsde644w6effZZxowZw2233YbNZuP6669vltd+VD02zz77LOnp6Tz55JNxx3v27MkZZ5zBj370I15//XX++Mc/MmHCBC677DJeeuklLrvssibvGwwGue+++7j11lvxer0Nyisra1dTPTSyrfu5rvxo6fp3j/Oqwwbrd5bHHeuRl8ajb6yiY1YS15/fH11T0TWV7Xur+Hz5Lv7vsiEkuXTUmn3Yy7aiLvgPyRN+iHfMJQT0FD75++eNPi8ajRFZ82Wj5f7V83GecjmxWOscetI0Ne6/zcFu+Sme+zS6L4O08Veg2Jwoqkq0rJDiDx7D1bk/rtOvJXZIwKeGA0RLdye8pxUJYtaUoicnNVs9j0RLtE9bI23UNGmfprW29rnmmmu47rrrOO+88xgwYABDhw5l9OjRjBw58piGhHr16sXf/va3uGODBg1i9uzZ/OhHP6o/tmrVKgoKCpg1a1aj95o2bRpvvPEGX331VdxEndmzZ5Obm1s/uefdd99lwYIFPPfccwwbNqz+vO7du3PXXXexbNkyhgwZwvr163nnnXe47LLL6tNNLr/8cn72s5+xYcOGo37NdY4qsJk7dy633nprwjJFUZgwYQL/+te/+OMf/4iqqkyZMoV//OMfh73vv//9b9LT07nwwguPplrHRFUVUlM9hz/xENWFtVOJM1NcTB7ZiQ5ZXrJS3eRle1m5uYRvt5TUn+u061w1rRepSU7suoJWUkRw8StonmQ+W13GhsIoV05L5/8uG0Kaz4WiWPhDMTRVwW7TCEdieF1RqmINN0OsY0WCJCUl3uDSjEUwA7UrG6tOD6r9u2+EaRkGhr+idta6ooJpgqKgeZJRVO2I7+PzNVz51gj5sSJBUFQ0dzKKdmT3i1WFsGJRwns2UbxnU8M6R0J4PQ4UNf4PWbio8YRjAMWMHtV7ojkkah8RT9qoadI+TWst7TNmzBhefvllHnvsMb788kuWL1/OE088QVpaGvfccw8TJ048qvtecsklDY5NnTqVe++9l507d5KXV7usyAcffIDdbm9y0+pRo0aRmprK7Nmz6wObyspKFixYwDXXXFN/3pw5c+jatStdunShrKws7nqAxYsXM2TIEObPnw/QIDH66quv5r333juq13uwowpsTNNk27ZtjZZv27YN86BsTbvdjsPhaPR8gN27d/PUU0/x8MMPU11d++EbCATq/+v3++u7yaqrq8nMzKy/tqqqNrhI1I12pEzToqqq4V5Oh+PQVc4/rSvdclN4a/4WtnxUQVqyk+ljunDqkFwefm0FpgU981K5aeYg/vfZZp6dvQ7TtBjVrx2XXPovHHuWsWZdmJmTevLGZ1v4ZGkBw3tnMX5YLq/O3ciGHeUkex2cMbIT7j5ZJHUfTmDdVwnr4+4zloqKQFzuk6IouMwqKhe9jX/152AauHuNJHnMxYTtqUe8rYKLAP5V84iW7iap3zgqF79LqGAdqsuLb9g03P3GEzzMUv2apuLzuaiqCtavp6IpJrZgMRWfPk9w+7eoTjdJQ87EM3DSEa23Y1PtuLoPx//tpwnLPf1Op6o61OB1Om1uVLcPM5BgnRtVQ/VlU15+fIejErWPiCdt1DRpn6adiPY53BekAQMG8NBDDxGJRFi/fj1z587lmWee4eabb+att97C6fzuX0ITrSV35plnct999zF79mxuuOEGLMtizpw5nHrqqQlHSerous7kyZN577336nNZP/roI6LRaP0wFMCOHTvYsmULo0ePTnifumTo3bt3o6pqfXBVp0uXLt/5dSas79FcNGHCBF566SU6derEjBkz6oOWcDjMq6++yssvvxy3Seby5csbvIBD7dq1i2g0mnCM7aqrrmLgwIH13Wpbt26Na4CtW7dis9nIzc1tcO13cehQxZFw2DR6dErlz/89sMJvSUWIZ95fy9hBOVwwvjuvz9vErIsHcddjCymtPJDP8cWKPazYWMz9s8YxwePnnqeXsrfUT8csL8P7tuP3Ty6u3++ovDrMq3M3smVXBddPuxJlyzdYkfjcEEeHXqgpHRqs1+JV/RS+8FuM6gMZ9v41XxLcsoJ2V99HtdX4G7qOWw1R+u4/iFWVkDHlWopev79+MTwzUEXF5y8T3L6KlGk3EzCbDmIBDMMkFqtdl8cZ2cfeZ38NZu0+AWawhsqvXie4dQVp596G32z6f+oYkDz6fIIbF2MestGovV0X1Iw8gpGGQ3NBzU36lOso/t/fGpQln3IhEcV1VO+J5lDXPqJx0kZNk/ZpWmtsH7vdzoABAxgwYACdO3fmV7/6FXPmzOH8889PeH5Ts10TdSZkZ2czbNgwPvjgA2644QZWrFjBnj17uO222w5bt7POOotXXnmFzz//nEmTJjFnzhy6dOlCr1696s8xTZMePXrwq1/9KuE92rVrd9jnNIejCmzuuOMOCgoKuOeee/jzn/9c33tSXFxMNBplwIAB3HHHHUBtsON0OvnhD3/Y5D179+7Nf//737hj69at409/+hO/+93v6N+/P7m5uXTu3Jk5c+bEdZvNnj2b0aNHH/cZUQDBqMGT76xOWPblij3c9//GEIvF+HpdYVxQU6c6EGXR6r2kJTvrE4anju7MKx9vTLiJ4zfr97F3bBc6zLgHZdV7BLcsQ7U5SRo6FWfP0dQY8UGApqmENi+LC2rqmKEaapZ/iG3ohTS1dp2iKFBdRHj3BlJPu4SKBW/GrfBbJ7xzLfhLUNwdjngDSocapfzT/9YHNQeL7N2MWbkXxdflsGsq+FUf7a6+j6rF7xDYtATV5sA7eDKu3uMaDYwMw0Jr15t2V9xDxRcvEdm3Az05k+QxM1AyuxE0j3xoTQghmlO/fv0A2LdvX/1oRN3oRJ09expOIjmcqVOn8rvf/Y6tW7cye/ZsXC7XES1wO3z4cDIzM5k9ezZDhgxh0aJF3HDDDXHn5OXlsX79ekaPHl0/QzqRDh06YJomO3fubNBJ0RyOKrBJSUnhpZde4uOPP+bLL79k9+7aBMyxY8cyduxYzjjjjPpfgMPh4J577jnsPX0+HyNHjkxY1rdvX/r27QvATTfdxG233UZeXh4jR45k9uzZrFq1iueff/5oXsoxC0UMSioSbzQJsLOomqmn5POvV1Y0eo4/FGXNtgPjkenJroRr3dTZsLOc59cW8purribzlJmgQER1UxNr+OGvE6Vmw8JG7xXY9DVpg88iSuO9IrquEtxcu2O0Lb0j4fkvN3puaOtytIF5R5y8rFsRQtsb3/k8uGkptpHdDrtqsGlCNT6cp1yOZ9QFgEJES9wmB4sYOlFPLknTbkE1o1iKRhhnm9z1XAjR+ixatIiRI0c2CATq8lC6dOmC1+slNTWVr7/+mh/84Af15xzpbOODTZkyhXvuuYf333+fOXPmcPrppx/RGjeqqnLmmWfyxhtvMGDAAGKxWNwwFNQGTfPnz+fVV19l5syZcWWhUAjTNHG73Zx66qn8/e9/57nnnotbq+7ZZ5/9zq8nkaNeeVhRFCZPnszkyZPrj0UiET755BNuvPFGvvjiC7799ttmqeTBpk+fTjAY5PHHH+exxx4jPz+fhx56iMGDBzf7s46ErqmoCkwakceIvu2IxkzsusbWPZW88/kWVFVh865KXI7Gm1pBweM8UK4ooCrQ2Gery6ETihiEDAVTcdWuv9bYyYqK6mj8Tas6nLVJwAddrqoKToIokRowYmi2NPyu/WPElgmaDkbi7aU1lwdHpBgsMOweQqbzML0tCorN0egu5Krz8MNkBwsbCtTl5RwmqKljKQoVER1/CFwODZe9tv1P4oU30TS1drp6uAZMA8WZREj10EonywnxvXXPPfcQDAY544wz6NKlC9FolGXLlvHBBx/QoUOH+nVrZsyYwWOPPcYdd9xBv379+Prrr5vMdW1Meno6I0eO5Omnn8bv9zcITpoydepUnnvuOR544AF69OhB165d48rPPfdcPvjgA+666676RGHDMNi6dStz5szhiSeeoH///vTu3Zvp06fz4osvUl1dzeDBg1m0aBE7duz4zq8nkWPeUsGyLBYuXMi7777Lxx9/TE1NDWlpaUyfPv2YKzdy5MiEU79mzJjBjBkzjvn+zcGhq/zqByNYsqaQPz2zFGN/gNGzUyq3XzEM07T47+x1XDqlJ9+s35fwHrntvAztnc385bU9X99uKWFo72yWri1qcK6mKnTM8tKrUyouu4ZxmJ6MiKHiHTot4Y7hAEnDphNRnNTt9qxp4AoWUfz234mV166ErDjctL/0t1TwIoHN3+DtfQo1qxNNSVewZ+Sy98nbwDKxt8sn45xb8avJjfaARFQX3kGTqF6aOBPe3XMUNS24x5Opqrz40Xo++2ZXfWw4sHsGN144EJtinZTBja5ZOPy7KHn7H/VbTagON6mTfogjdxBh8+ReVVSItuTnP/85c+bMYf78+bzyyitEo1FycnK47LLL+MlPflK/nMmNN95IWVkZH374IR988AGnnnoqTzzxRKOJuk2ZNm0aCxYswOPxcNpppx3xdUOGDKF9+/bs3bs3YUCkqioPP/wwzzzzDG+//TYff/wxLpeLjh07cuWVV8atQXfvvfeSmprKu+++yyeffMLIkSN57LHHvlN9GqNYR7khxOrVq3n33Xd5//33KSkpQVEUpk2bxhVXXMGgQYOaHF9rjQzDpKzsu8+A0XSVd77azhufbm5Q1jHLy1XTenPvM0u59ydjmLNoO58vj183ZUjPTH58wQA04J0vt/HOF1txOXR+efVwHnptBcXlwfpzFQV+fP4Atu6u5KIx7XC5vUSPoFfCpYXxL3gF/8p58ce7DiF58vX4D8rLSVJq2Pv07bXTrg+Sdf7/Ea0oouLzV8me8XNKP3qKaNneuHPSxl9BpHhnXNCj+TLIuuwP1Ji1s6V0XSU11UN5ub8+cS9JC1L8+p+IFu+Mu1/qxKvRuo9tsQ9iRVV5ce5GPl6ys0FZj9wUbr9iKNpxjmwStc93laRUs/fJn2HFGm6Umn3Z3YR8+Sf1UFtztFFbJu3TtBPRPpmZx3c9rO+779RjU1BQwDvvvMO7777Ljh07yM7O5uyzz2bAgAHceuutTJky5YQNCZ0owajJe18l7g7cta8Gj8vGJWf04JOvd3Dh+G5MOyWf+ct3YRoWYwfloGsqz89ZxzXT+nDO2M6cPqQjny3fxYqN+7jzmpHsLKxm5aZi0nwOBvfIwooEGJhkEHn7DyRffCdRDr/OStBw4Bk9k6RBk/Gv+RyMGO4+4yApC79xIHNe11VCW1Y1CGoAVLuTWHkRWRfeRnDnetLPvA4jUE1w6wo0byquvN7UrF3QoCfHqCrBqNiDktyt0SGpGtNF+oW/wiwtILBxMZrbh7vPWKI2X4v2LoRiJvO+Trxa9caCCvyhGD7HyZVAbLNpBL5dkDCoAaj4/GWSp/+MINJrI4Rom444sJk5cyarVq0iNTW1PvmobmXBnTsbfuP9vghHDMIJphLX2VxQwc7CajwuG2/N30JhaYAB3TLomOVlxcZi3vh0MzkZHqKGiQ6kuHRmnNYVu11HqdyFe/cH9MvwoSWlEl45h/CejUTqdrKORUA7fGCjqgqW3UNUd2MfdSlQO5vr0IlNqqoS3pNgWW9FxYyEqF4xl+pVn+HM7UW0eAexQDW6O4mkAePZ++LvwEzcDtHiAtTU7vs3ZGzIssBvuVDTe+IY1xvLonb4qYW/TAXDsfqhw0TKKoMkZ3tPquEoFZPQ3oaLFNaJluxCtWIggY0Qoo064sBm5cqVdOzYkV/+8pecfvrp6Poxp+e0CQ67hk1XiTbSpZmd5qZ9uoeKmjAFRTWcMiCZ9hkedu2rZvve2pljHTK96GrtKr6qquCPGCzbXMKuoiBdc88hPdnFus2F9OzeA1+7rTD/aRRNQ9HtTW7crKoKLgLESnYSKFiPmpyFu1M/QnpSwg9r0zSxZefDt5/FF1jmgVWKzRihHQemt0cAb5+xtQnA4cQLHNrScggdwdCHaVpEmggSm5vTrjWZpJ3ic55UQQ2AiYotsxM0klNlS2uHqWiy4bcQos064ujkzjvv5L333mPWrFkkJyczZcoUpk2b1ugU7e8Lp64yeWQe73+1vUFZVqqLnEwvdz22kPLqcP1xl0Pnt9eO5LNvdgFw0YTuKJaJoioUV0f47WMLCYYPzDpKSXJwyyWDue/l5XTL8XLttP/DUbyeiOapXZ0uAUUBt1VN8cu/q08gBajR7fjO+wVqZtcGHSyxmIm36xCUz15oMJQRLtqBI6c74QRbFoRLduMbfhaVX77WoEzzJKOld8RqhTkdTpvGKQM68OXKhvtFdWqXRJLLdtJNjYpGDZL6nkrlorcTrg2UPOZiwjiQyEYI0VYd8S5gl19+ef3aNVdffXX9fPpx48bxr3/9C0VRTrqE4eZgGibnn9aNUwfFbwOfm53EndeM5LkP1sUFNVA7BHLff5dy0YQe3HTxIDJ8DiwLIqbFH59ZEhfUAFRUh3lu9jrOHteFpRtKWVnhwzv8XCKNBDUATjVG+UePxwU1AFYsQtU7f0UNV6OqDX9fIdVL1iW/RfOkHDio6lhAxrk348jpEf+cTv3wDJiAq/8kvAMn1U4d309PySbr0rsIKidmv6XDMk2untaLYb2z4g536ZDMr64efuxTBk+QkOYj++Jfo7oOJCwqmo3UiT/ASj+5E4eFEOJwjnpWFByYGTV79myKi4vJyMhg/PjxTJgwgVNOOeWw+0O1Jkc7K6qOpSqEoiaVNWGS3HZMyyISM7n9gS8avea+G8eSneyo780oqYnw84ca37n7N9eM4J6nltAh08vd145Ea+Jbt5cq9j7200bL3ef8Ar1DH6wEeS+aBk4rAMEqLCOK5kklrLqJmipOJYIWrcEM1aC6kjB0DyHLjmWBQ4thMwKY/kpUuxPT4SWEO+6DtDXO2DAVhVDUoMofweOy4XHoaHDY1Y5bQnO1j6YqtevYhCqxjBiqJ5WI6iZitI4djY9Fa3wPtSbSPk2TWVFt3zF9Ke3Xrx/9+vXjF7/4BYsWLeKdd95h9uzZvPbaa7hcLpYvX95c9Wz1FNPCbddwpLjwehys2lyCaVr0zEtlWO9sVFXh2y0lrNxUjAIM6ZWNy6GBZeDRTaLoBMMxunVMYUTfbDRVZe22UpZt2Fc/GhLbP7W7OhDBtCw0BRRNrU08VhVUqA8iLKPxHcABjGA1ipW4y84wIKx5sDlVQCGoHdgzKaQ6iWp2tKR0tIOeBxA2dML4wFO77kJt8u+xBweapuJSQihGBFN34Y82PVPJoVmoZghL0YlgP2wPhWpZuHUVd/L+PCLLOukHagzTwo8b6hZntABZnE+Iw9J1BZtZOzM0qrrq/+6Kk0ez9Larqsopp5zCKaecwu9+9zs++eQT3n333ea49clBVagJG7z50QbW7ygnI8XFjAndyU73MLR3Fl+u3I1hWgzvk82F47uhqQrfrC/iz//9GrtNY9rQLAbne2ifns7o/u35YsVuojGTob2yOHtcFx55fSXVgSgZKU4UBQZ0y8BhU6kOxnhj/zPTfE4umtCdvCwvqmWh2N1onmQMf2XCKjuzO6MoCsYhH+GqquDBT2Dtl5Svng+KinfgRFw9T6EkqNc/LyPZxYUTupGbWfu8lpKkhTDKCihf+BaxqhIc7bqQfMr5xJxpBGPxb19dtXAalVR/9Q6hnWvQPCn4Rp+PmtmFgHH89xETQpw8FEXBqwYIrFtA+befAuDpPwFvr9H4TfcJ6cEVR+eYhqLakqNeoE9T2F0W4s7/LIibOvzHG07hyXfXsHV3fGCRmepi1oxB3P34wri81D6dUznntK7c92z8bJY0n5OfzhzEzt0ljOmbxeyv9zFpeB6VNWHueHRBg+nKF0/szpQRedg0UHcupfS9hxrU2dZ1OL5J1xGxGn7YJ6kB9r38O2IVB1Y9tqW1p2Lcz7jz2W8bPG/mGT2YPCwX5QjfRt+lG9itRQmvnkvFF6/EFygq7Wb+mmhGz/pZVKqq4Arupej5Oxv0VvmGnYVj2HmEToIVd2UY4fCkjZom7dO0xtrHqwUoeeUPDRYetaV3IGPGHdSYh99PqTEyFHV8nfwD7idYxIBH3lgZ94Gfm+2luCLYIKgBKC4PsnzDPgZ1z4w7vnZ7Of5gjIyU+M0oy6pCLFu/j1OyavBRw6mDO6BrCg+9vjLhGiyvzdtEOGYSi1loeYPIuOB29NT2AKhOD65RF5F6xrXE1Ib5T7quENiwIC6oAWDI+Tz83taEz3t17kbCLfTHUzcCVCSYaYVlUjLncZzGgZ1uHYQo+/CxhENwVV+/jx47+vwpIUTbpmkqoc1fNwhqAKKluwlvW46uy8flyUJ+U8coGDUa7MQ9sm97Plu2q9FrFq8pZMAhgQ3AN+uK6Nslo8HxL1fuIRzwE9q2in3lAcIJnlnHsmDL7kpUVSEY0zHa9Sd95l20u/4hMq/+C86h5xCynJgJkoZtZgj/6vkNjke97dld3Pjztu6pTDjD6lioqlK7xcKhqwjuF6sowgofCFY0I0SksPEt78O71qJp8nYXQjRksxL/7avj//ZTdDPxRr2i9TlZZ7S2Gok+Kk3LQlUUbLrK6UM6MqJvO9xOHY/TRiAYo7wmlHC1YlVTME2LKSNyuXRMOg5ioNvYWmKghgpA0Wqn1dN0EKHtDzIUBWxE0a0IlhFB0e0oRAgqtkbHixX1u28hYFMVnGqEqObAbtRANIyi24ipbkJHPQvHgsPV5eDlBQ6z1ICi6sjaLUKIxiiKiu7LxDd0Cra02l7uaNleKr+ZA4p22L8xIt6WLVu45557WL58OR6Ph3PPPZdbbrkFu73l8x0lsDlGTrtG1w7JbDlo2OnLFbu5/vz+nDUmn8+W7eK+Z2t3/fZ57Jx7alccdhWXXWfq6M58sHB7/XUj+rQj0xGho38tFa8+QGWwGlSd7N6jSB07A8tSaW94cOgqXTsms2VXw6EuVVXIz0kGwKvUUP7xMwQ3fQNYaEnppI6bgSenO0FbGjEj/n/UqOrCO+gMyj58PO64rXw7XTokJxxa01SFji4/0Y3LUe0uiue/WJuwrKi4e40i5bQrqLHc33mdO9MEe0ZH0HQwGi7YY8vIBbunfqaPoTpx5vUhtHNtgrspODr2orqRLR2EEN9vUcWJ75QLUbEo/+IVIkXbAbBn55Mx5VosVCI4aPF9XtqIyspKrr76ajp37syDDz5IUVER9913H6FQiN/+9rct/nzpmz9GugI3XjQQp/1A70JedhK5WUm8OncjX6zYXZ+bUuWP8NwH6wiFDeav2E3PTql0zPICcEq/bNqlOOhQs4ayuc9gBqtrb2bG8K/5kuL3HgZVwamr6ArMOuSZda47tx8OTcFNgH0v/4Hgpq+p66kwqkspmf0o0cJtuKyGOSexmImjyxDs7brEHTeXv82s6V0SP29qF2y7lqEoKiWz/31gFpZlEli3gOI3/oxbObou3IjuIWPytQ2OK7qdjGk3EFS89ceiqpO0CVehOhom+KWMvRC01p84LIQ4MQzDxJHWjn1v/aM+qAGIFG1j3//+gT01G8OQoOZIvfzyy/j9fh566CHGjRvHRRddxO23387LL79MUVHR4W9wjKTH5hiZpkWax8Y/bjmNed8UsGZrKZef2Ysqf4QNO8sTXvP251u47rz+vPbJJi6d3BOXFSIvxSLZF6PwrTcSXhPevRHTX47u9WEYJqnu2md+umwXq7eUkJnq5uyx+aR67KgKGKU7GyYB71ex6C1Sx1+BPbMnkVh8r43fdJF+3u1E92ygZuVcFFXFO3gKapaXf+5/jau3lpKV7OCsIWl4dnyBJyeX8s9fSfisaPEOrJpiFE/ud+61CUY13J2HkvOD+6hcOptY5T6cOd3xDppIWE+Om9HgMGoo/fR5ss7/GcFtKwnv2YTmScbTZyzh3RswaspQ3B1Oth0ShBDHgdsOlV/NbrCVDNSu1l759Rxcoy8h2PTyYK1OYamfjxbvoKg0QHa6m8kjO9EuveVXgv/8888ZPXo0KSkp9cemTp3KXXfdxVdffcUFF1zQos+XwKYZmKaFQ4Xpoztx1uhOGBYsXdd4VFodiGLXNQqKqumaqePa8jXB1Wtg4lWYocRJugCRou3Y07oQDJr1zzxrVB5njshFUxVMw8SyLDRdI1SwrtH7REt2oaCgGWEgfhaWZUGN5ULrMARvh/4oikLE1DCjFnYVLjglh0m+TVglOwnPXkQ0EkLNvw2juqzxeu/dgtqj01F94wkYdrC3wzvhGhQjgqE5qQqbDfbIUowI4R2rKdyxBlfn/tjbdcEM1VD8/iNY4QCqLwu1W6586xJCNKAaAcK7NzRaHt69Aa8RAI5+yvfx9snSnTzw6oq4BUrf/HQzN108iInD81r02Vu3buXCCy+MO+bz+cjMzGTr1sYneTQXCWyakREz0XUVf8TAaW+8aVVVqV0Iz2VDsXuIdBpNpOtkTJu/dq+lRmYCaUlpKKVb8bp8RDUPYVPH2N9rcfBUbNO0sKVkJbwH1E77RgFL1RtdjdYwTAzqhp4O3NswgZ0rCW1beeBky0LR7Qm/7QDovgwix9hVEggD2KGxqeWajqLZsIwowe2rCG5fdUgd0o+5DkKItslS7WjeVNi3I2G5lpQKmuOkSbEpLPU3CGqg9nPiwVdX0LdLeov23FRVVeHz+RocT05OprIy8aKxzUlybJqZqiqs3lqKw6aR5E6c1zFmQHuWrS/ijBF5rN1eTrXqY+GafXy8ugpPr1GJ7+vyYkvLoeiFu9j7+M2Elr6OW0scSBiGiaPTAFATB1dJAyeguFOIWN897yRi6iSNOi/umH/jYrx9xyY8X7E50bNafuPFqOrB0//0xHWwu9AzO8nmj0KIhIKGneSR5zRanjzibALGydMP8NHiHY3+vTNMi48WJw7g2goJbFqAaVq8MncjN18yGK8rPnjo1jGFmWf0pKwqRI+8VB54dTnl1WG6dUzm/UV7cJ5yCfb2XeOuUZ1esi/6BaYZoy5JpOabOcR2fdvo2ixBxUP2xb9C0eOn1rm6DMLdazSmK+2oPugty4KUjiSPvRj2Tzv3r1uEu8cIHLm9485V7C6yZv7muOzuHTYUfKMvxNHxkDo43GTP/A1BvI1cKYT4vjNNEzUlh5QxF0HcchoKKeNmoCS3P6m+GBWVBpouL2u6/Fj5fD6qq6sbHK+srCQ5OblFnw0yFNXsLMuiV+c0/vO/b3n+g/XceNFAQhGD8uoQHTK9RGMmsZhBz05p/P3FZVgWvDV/CxdN6Mao/jnc8PBK7rv6/5FtCxIt3o4tKQ09OYvKZR+iOdw4c3sTKqid0ly14E3SZvQlcEieDEDMVCClCznX/p1oyU4Mfzn27HxUl48wTsLH8O0jaNhw9JtM+95jiBRuARTU1A6kTf8pVrCKSNEONG8KeloHgoqH45XWUm04SZl+C0qwgsi+HejeFLS0DgQVb8JVk4UQok614cQ9YDId+o7dn2+j4OjQg5jmpuYk22suO73pXKDstJbNFerSpUuDXJrq6mqKi4vp0qVLI1c1HwlsmpmiKARDUS6c0I2S8iBrtpbg89qJGbXJXNNOyeePTy+luCJYf01pZRBNU+ndOZWdhVX83xOrGd03m0lD+9PdXkbFwrcIbP4ad/dhqO4De47EqktRGsnHgdrgpgovenY/bJpFzLSIGEqzfPMImzphJRm1w1AUBaKqVZsblNQB3ZuNiYo/ajaWLtRiAqYDHNlondsRMvf3MElujRDiCNRulmtH7zQagKqY2WgeYms2eWQn3vx0c8IvdJqqMHlkpxZ9/qmnnsqjjz4al2szZ84cVFVlzJgxLfpskMCm2Zmmhc/rYESfdmzbXUlOppf5y3dRUhGif9d07DYNr9sWF9h0y00hGjPYU+ynQ6aHH07riXPHQszlswlld8Y37EySBk/CikYom/dc/XWO9l0xFFuTC+q61RBm8Q5qln+EZVl4B05Ey+qK32zYy3M0HIRQqgqp+WYOZjiAu8dw1PQcQhu/xt33NGKuNMInYPNJQxbjE0IcpZN989B26R5uungQD766Ii640VSFn84c1OJTvi+55BKee+45brzxRn784x9TVFTE/fffzyWXXEJ2dnaLPhtkd+96R7u796EUTeXjrwvYvqeKnp1Sefq9+JVw3U6d2y4fykOvraSsKoSmKvz1p6dSHQxz79NLCUUMdE3lrst7kb7430SLd6DodrLOvZlYsJpwwXpqvv0MUGh35T0E3B0b3R7Bo4aomPNvQttXxh13dOhJ2jm3UmMcW3DjUiMElrxBzbIP447rKdlknPkjCl+9j9TxV6J2H0vErI2hZefhpkn7HJ60UdOkfZp2ItrnRO3uXb+OTVmA7LTjt44N1G6p8Ic//CFuS4Vbb731uGypIIHNfs0V2ARiJrP++hm/vXYkf3x6ScKuwB55qQzvnc1ny3bx4/P743XpvPDhBr5Zv6/+nDSfkz+ek0xk9l+A2mAhZdS56MmZFH/wGGmTr8XM6FYfMBxKVRVsRaso+d/fEpanT/t/GJ1GHdO6Lp5wIYXP/jJhWdKgSZihGvzrF9P++n9RQ213pPzRbZq0z+FJGzVN2qdp36fA5vtKZkU1I11XWbZhH+nJTorKAo0mrG7cWc6w3ln8+gfDQYG/v7Q8LqgBKKsKEXCk1/8cqyhC86YSqSwm88p7iWX1aTSoAbApsQY9KQerXv4hduvod6u12zX8axrfDbdm3QLc3YcBFuGCdbKzthBCiONCcmyaWSRqoqkKMcNEVWBo72zGDeyA3aaxo7CKjxbvoLQyxL7yICleB1X+CJ1zfBSW+okdmhfi9JFx1o1YRhTN7UNPzsQfivLOgkKmjWo6+UuxTCwjfv1ve/uu+AZORHX7MKNhNF2DxEvhALW9PjELDAs0TP5/e/cdJlV1P378fcv02dneYCm79L4U6aBgoxixoWjsvWCixkRj1Gji9xdjYjTW2I0mltgLiAUVFQSlN+m97LJ9ernl98fAwjCziwrLLrvn9Tw+MvecuffO2dmdz5z2SbNoSLpGVHYAYGqN7C+uxfZn525g4z5BEARBONJEYHMEaZrBoB55vPLxGjrme/j9pUNZuq6Cf72znGBYo3vHTK4+ox/L1ldQF4gA8OjrSxkzsD1/uGwYD726GG8gHgS47CrZkhfZaqd23kyi5ZuR7W7ShkyktCifcMzA1kgniCbbcPU9gciO+DbhWSdeAqZJzby30b2VqOl5ZI4xSOvYF5/mSD6BLLHHG+H5D1axZmsNDpvKqUMKmNjHgW3nElwDT0HtPRr/0s9SXt/ZbXB9pm1bxz74RSoDQRAE4SgQ4wNHWLrbyugB7QGTNz5bx4y5mwmG44mN1m2r4a8vfc+Ifu3o3iGTTxZsJRLT+ey7bTz73kquPqNf/XluOrsn1kAZe955kGj5ZgCMsJ+6b94gc8UruA6RMVvTDGydS7FktcPd73g0byXVn7+E7q2Ml9ftoeLDxwmu+AKnJTHokGWJCm+E2x//hjVb44k8QxGNd+fu4G8zywmld6TyrftR0/Owd+qbdG3J5iRt4Mn4V36Fe8CJaBYxviwIgiAcHSKwOcIsElx9Rl9MYN322qRyw4QXZ6xCliRWbKisP76zwk8oonHCoHb85dI+9EnzUjPn1ZTXiG1Zhi3mPeS9BEwnuefdhWfwRLyLUs+3qfv2XSxaYuJNzYTn319FqilCG3Z4qZDz0HzVxGrLyZw0nawJ12DJKUJJy8Y94EQKzvkd3qWfkT35BlwjzyPcDMu9BUEQhLZJDEU1gfQ0O6s3b26wfOOOOsJRjYJsF9vK9287vWJjJZf3C+H77AmkCVej1TacITy6ax1Kl4JGVzWZpolPd+DWKsHQUtfRouhBLzj2JyzTDJO122oaPO+SzX4m5HYktP57LMPOxywZRWanQUgYSKoNQ4vgPv4yopKdiBiCEgRBEI4iEdg0EU8DCTABrKqMRZW56fRiXvpiB0s3VAOQ6bYSXfsZ6SdfjZzdgdxf/hnZYkePhqn9+F9oVTuRHWmow86jNn8wobowbocFu0Xm4O4VWZaIGuALxajWc3Ge9SeklTOJrpufdD+SmnivEhI2q0IkmnrLzXSHglkXRnFlYJrxYS8NW7wwBuDYu2ngTw9q7EoMVQtghgNINie6xUXYsDbr5sFWxcBqBDFDXlAsYHMTllyHtVReEARBaBoisGkig3rmIX2wKuUH8vghHci0RPF+eDfXjb2M97MKmfHdbsaV5pPu+CW1896i5r0H2belsLWwC7ln/Zbqj5/GGH4J/5ixk/VvfwuAJMHY0vZcNLEn8t7gRpIlyurCPPDyIqq98bk4NovChSeeypARnYl++1r9vaiZhUj2tIRtw22qxMnHdeTDual7nQaXuIgs2UTWpOn4tSO333iaEqLmk2cIbVxcf8zeqS9Zk67HbzibJbhxyFGiP3xF2dev168yUzw55J75G8KOdiIHlSAIQgsj5tg0EYsic+XpfZGkxOMd89OYMKIjLjWGGYsQ/vRxftHPwRUTSsiWvNQtnEl49dccmCchunsjlW//Despv+b+d7ax/oC5O6YJc5bs5I3PNyDt3SsmrJnc/dS39UENQCSm89yszexI64eaEd/SWra7yDnl8qR7N3SDKWNLKC70JJVNP70r8upZZJ16JVH1yGXMtisxaj57LiGoAQhvXUn1jMewS0d/ybiiyBhla6j98j8JS+d1byXlr9yL00zOXisIgiA0rxbVYzNnzhyeeeYZNmzYgN/vJz8/n5NOOonp06eTlrZ/Zc3nn3/Oww8/zObNm2nXrh1XX301Z599djPeebKNO2pJd9t48FdjWbCqjDp/hN4l2RTluVm0Zg/OEhVHyQBCGxYjr5zB2B6jsNg6Ur3i85Tni9WWEwqZCXNyDjT7+22cMbYLLqvCV0t2Em1gR81Xvy7j9xOuxuPdhiWzgKrPX8bdbxxqzxMTduFUMbnjkuPYVRVg0ZpyMlwWhnVLxxnYgavnGURVd6MbBP5UqhYktH5hyrLwttVkagGQj26GXZsZpPrr11OWmbEw4S3LULuMEbu7CoIgtCAtKrCpra2lf//+XHTRRWRkZLB+/XoeffRR1q9fz/PPPw/AwoULmT59Oueccw533HEH8+fP5w9/+AMul4sJEyY08yvYTzNMNu3y8o9XFtGtQyYOu8qCVWXU+CJcelpvsNhQMwridat24IjUgR4DPXmSr6RacXQZSEVUx2FTCUWS62i6STiq4bYrrNve8MTfnRV+dCUP39LPiFXtBCBatgF775M5cE6MaYKCScccFyUndN1/PD0dv2YkTZ+xWOKb8WmaUZ+7SlVlJElC140GM4orioQsyxBpfEjLCPvBmdlonSNNxiBWtavB8ujuDdi6jT2KdyQIgnBs2Lp1K8899xzLli1j/fr1lJSU8OGHHx6Va7eowGbKlCkJj4cNG4bVauWuu+6ivLyc/Px8nnzySfr378+f/vQnAIYPH8727dt55JFHWlRg0yHfw8OvLcEwSVph9L/P1jG810hcvYZjSc/FklmIaerIsgKKekBwI5Ex6ixshV0JbVlBwbaPefCCUlZXyjw5Y3PC/A5VkbFbVUzDpHuHTOYt353yvtrnujH2bKwPagCsBV0xjNS9DqZpEos1HHTYZA1LzEvwhwUYYT+OrkNQMgpBCxNa/T2avxZHl4HImUUEzf1JN2VZwi0HiO1cT2TXWqTsIvLP/T21896u31TwQLL9yA17/VgGMpbsdgltdSBrYdcGAzZBEIS2bP369cyZM4cBAwZgGEaDyZqbQosKbFLJyMgAIBaLEY1GWbBgAbfeemtCnUmTJvHhhx+yY8cOioqKmuEukwVCseQUCXsFwxrBiEHkv/fg7H4cqiebmrJdbPNm07n3OCIrPgUg84TziVXvpnbuXw949vv07nIct009g//3+v4A4KShHbBbZDTNYFifAl79ZC2RFAHJBWMKMb/7X/1jyWLD2W0I3p8xnGKTY+jrv6Fy9r/rjxnBOuztulH16Qv1x3wLZ2DJ7UjuOb8nRDyzrC1SRdl/78YI7t+PR1Kt5J7+K2q1KNGy/ROX7R17o6uun7PI6rBEJCfpY6elTCQqWe3YOw/AJ4ahBEFooWK15fiWfEasthxLRj5pA0/CsneOZVMbP348J510EgC33347K1euPCrXhRY6eVjXdSKRCKtWreLxxx9n/PjxFBUVsW3bNmKxGCUlJQn1u3TpAsCmTZua43aTaJqOSePRqSyZYBoE1y5ADwfw5Q/mgTd+YE/HE7H1GovsykD15OBf/kXSc2Mbv6eDuZOiPDeSBCcMas8547ph7l1+bFcl/nTNCLLT9/eQ2CwKV57Wg/b+lfX74yhp2eRN+fUh7rRhlpiXmgOCGiSZtH7HU/Xpi8n3XLEN33fvY5FN9ICXyvf/mRDUQHxPncqPniJj2P6eO3vnfmRNnk7YPLrzayCe8V3O70HmuIuQ1P3XVz255J9/D0FJ7KgsCELL5Fv+BdufmE7tvLcJrJ5L7by32f7kjfhSfKY0BVluvvCiRfbYjBs3jvLy+IfvmDFjePDB+Dfmuro6ADyexNU6+x7vK/+5VPXwfxCKIlNRG8IXiOFxWetzPx2oINuJywwQ3Ps4VlfJrF1ZGIbJvf/9gbNGjWHiOecQmPdywxda9TF/uvhWwrIdp1WJR6gHvJHaZzm4//pR+IIxYpqByybhlCO4oj0xCn6DJKvoIR/VX76Cq/dobANO+0n7sqiqTPD7rxOO2QqKCW9fAw2ESv5ls0kbejp6LFKfJuJgRsiHkpZNwcV/QbY50C0uQthQlB99a0dUDDtqnxMp7DEcI1iHpFjA7iaquJEME1WWDn2Sn2BfFnSRDb1hoo0aJ9qncW2hfWK15VR8+ASYB/1NN3QqZjyJvWPvo9Zz0xxaZGDz9NNPEwqF2LBhA08++STXXnstL7zwwqGfeBhkWSIz03VEzuWv8PP+1xu49qz+/OOVxWgHBAwOm8r1Zw9AXT+r/phugC+k7f23yRtfb8fE5ORwoMFrGOEAmWlWVHfykux90oECQKurYNtj1+IDUq2pMoJePB4HkahGIKQhKxIZbtshX+eeQGIgKVlsGJFgA7XjPTKKDEYs9S7I9fX0KM5OvesfH5mfyuFKg+yC+kcp0oYeUR5P6ivoIT+mFkWy2lFszia+i5atoTYS4kT7NK41t49vyWfJQc0+ho5vyWdkjfvl0b2po6hFBjY9e/YEYODAgfTr148pU6bw6aef0rVrfHWOz5f48ez1xoc00tPTf/Y1DcPE6234Q/nHUhQZp10lw23no3lbuPPyoazcWMXuygAdCtLo0TETPewntP67+udYbBZG9sxg4Q/7Uyis3B7gxH4DYfvqlNdxdhlMSFfRahoOfvaxSir2jr3rs20fzN5rNNvLvbz95UaWrN2Dy2Hh9DElDOyWi6WBLzWSJOHsPixhqCxasZ20gSfDwo9SPsfWrjsaKqo9DdnmbCAIkpA9edT8iNfVGimKjMfjwOsNJfSgWYlgVm6hbu4baHUVWAuKyRh9LpozD61l/ho3mYbaSIgT7dO45mifI/Wl+ceKNZKOByBWt+co3UnzaPF/EXv06IHFYmHbtm2MHz8ei8XCpk2bGDNmTH2dfXNrDp5781Mdqf1IMjNdnH9KD37zz6+499n59OqcRZbHzuI1e5izaAd3n55NtDq+akm2OXG070GvUIy8TAd7akIArNlSQ+ik/ljdM9H9iauqJJsT9+AJ+KImDQ37HEiXLGSMu5iyl+5IiuJtHXpTpeZz68NfEd6bQqGqLsxjbyzjuN75XH16X5QGrmHN6YQlpwOxyu1AfBjJjIaxFXYhsntjYmVJJvPES4hgx+62kTHuQqpnPZ10zrTBE4jKjja/N4yuG/VtYFV0Iqu/oHbOK/XloQ01hDYsIfec2zBze6E3MFG9NTuwjYRkon0a15rb51DDTJb0vKN0J82jxQ8yLlu2jFgsRlFREVarlWHDhvHxx4mZqmfOnEmXLl1azIooXyCKM1rFAzeOZkC3XH7YUs33q8vp0t7DH3/ZA332kyDJOHoMJ33an9lOAaoqc++0Ek4ZUohFlZEk+HCpl+xpf8Le+3iQVZBkbF2Pw3HWPfjkhoegDmaaJlFHHgUX/z/sHeNDPJLVjmfo6aSd9htenPlDfVBzoO9Xl1PtiyTtnrxP0HSQO/UO0oZMRrLEh64C674nd8pNeIafgWSNd/Xa2vek4KL7iLoKME0TSVGwlBxH7lm/xZLVDgAlLYusU67CNfQMInqLj7ePKpsRpPar11KUmFTPegq7efg9jYIgtB5pA08CuYGJibISL2/FWtQnyPTp0+nbty89evTAbrezZs0annvuOXr06FG/bOy6667j4osv5p577mHixIksWLCADz/8kIceeqiZ736/qKYTXT8frf1Qitulc+rwTuiGyaIfylm82c+Ic/5IeXWAL9f7mPXEKkIRjS7t07n1glImDXNwwnHFWC0KXy/dxXVPLmd4rxGMnTQBSYI5G3189Owa/nZjDs6fMNk5Zsho9kI8k24ikxggEZEc+DWJRWsa7pacv3I3Z40tSbmXjWmCT3dgHTqV/CGTkQBNsuA1rKiDziS/9BQkTHQshLBhGCb7bjmKDTO/H1nn3o2MjoFMRHIREF3nCSQJtJqyBsfLdX8NRAJgab3zBQRB+GksGfnkTr6OihlPgnHA325ZIXfy9a164jC0sMCmf//+zJw5k6effhrTNGnfvj1Tp07liiuuwGqNL7cdMmQIjz76KA8//DBvvvkm7dq147777mPixInNfPcHkS1ISICJzaqQ5rDyizElmCasKPMz69tyVm+urq++cWcdn3y/k2ElDjyKD7fFSkmWxHdWlU8W7iaiywzvW0i3Ehc9uhRis4CLAFHFTUzbPwwhKzIRzSAU1bGqMnaLjGSamGY8OWZtzEokpmCzKDgsEkgmiiwnTHA+kNWaHPXLsoRmQiiqY5omDlR0xX3AeLVJTIMYB05uTR4q0XWDIPYDjoig5mCmSXzTxkZIzbisUhCElimt/zjsHXvH97Gp24MlPe+o7mMTCoWYM2cOADt37sTv9zNrVnzRzNChQ8nKymqya0vm0dwOsAXTdYPq6sOfsKqqMqYso9eWEbGm89rnmxg/pCNvfb6e5RsqAUh3W5l6YncqaoK891V8ftDAbllcO9aD/tVz9TvdWnKKUMZcwR4ll6+XlfPFou3ENAOLKnPq0CJ+0QOcoTLUkqGEDCuGJLFgdTmvfLKWQCiGJMFxvfK54vS+KDK8/eVGPlmwDU03sKoyE0Z05szju/CfWWuYvXB7ytfz8M3H47HtD25kRaLKF+WR/y1la1l8Endhjovp5wygMMsBh9iJV1VlMjNd1NQEWu349uFI1T5uKUDZ87dgxiJJ9S3Z7cmaehdBw55U1lqJ91DjRPs0rjnaJze37e15tWPHDk488cSUZS+99BLDhg1rsmuLwGavIxnYGJKMpmnc9cwCLprYixc+WEVFbSip7o3nlvLenI3srgrwyJU9ib51Z1KuKEmx4D7/L1z9+IqknYzHDyxgas5a0vIKoeso5q0s54m3lidd5+JJvdiy28tXS5JTA0wY2Ykzx3TlD0/NpbI2nFB21gldmDSiE/IBlw0bcPNDc5J2NVZkiYduOh63tfHeA/FHt3Gp2keVTdTylVS88w8O7PmSLDbyL7iHkKNdm0rtIN5DjRPt0zgR2LR+og+7CTjsKjsrg8Q0nUAoljKoAXjr8/WcMrwTY/rlIa3+JGUCTFOPEV3+KcN7J89i/3JpGXrnYdR+/RrhSIxXPk7OsQTQudDD10tT5zv6ZP42kEz+37WjuP7s/gzsnsvYge25/4ZRTBrROSGoUVSZz77bljJVg26YvD1ngxgWaQKaIWEU9KbwigdJGzwRR3F/0kefS+HlfyfsLGxTQY0gCMKhtKg5Nq2F025h7bZaCrPdbN7tTSrPz3IiSbC7KhBPe5Bnw9y6McWZ4sw96+jS7ji+WZF43DDBH5Nw+GuJxnRq/clDFZIEkahOXmZ8vsuemiAH9tEZhkmdP0qO28rwXnkM7ZmHLEnoup6UtMzY+3xZllJ+mK7dWkPMMMSbqglEdYWokoV1+Pk4JB3NlPHFTGg8KbogCEKbIz6DmkhRrouSbIXayP4ejHGDixg7sIgde3yYJnQqSKMg007I50dJy4Y9W1OeS3bnUOVP/Qlmt0hIqhWLqmBVZaIHda2eOqSAAQUmfxwPmCZGdm8+W1nLu/N21ddx2OJvg33dsgd3zlplA5sZILprE5f0CHL+sB7MXR/g1S+3JwRJuekOVFmGBjKFCz+fLEtEDSirDLKzIkBhtpPcTCc2GdFjIwiCcAAR2DSRwZ2saDs34cspxqLKTBzRGafDwp+fm58wv/ac4zvxi+HtsRadQWjj4pTnkgdM4ov/7Eg63rUoA1vVBmylJ2G1KZw0tCMz522pLz//hCLGujdT8fzfD1guLHHqkCnkTOjPs7O20KNjJg6rAg1MtbLKGmxfwq6P/pWwbHB03xMpOON4/vHOhv2v5cSuyJhibdMRJkkSIc3kT88tYHfV/nlg2el27r1qOG6rIoIbQRCEvcSEiCZgxCKEls/GjAQw5jzLvZeW0qckm9c+WZu0aOjNOVvZuK0ayZ2DZ9zFIB3wI5EVMseehzUtIyl3U1Gem5tPa49athr30NOJRk3OPL4LA7vnAuByWBhbrBCd9+pBe6CYRBe+y+DcCH27ZHPz+QNpLL+kNVpL1YzHE/dCAGIrZ9PNUkaH/DRkCX45oSftsl3iA7YJ6MA/Xl2cENRAfIfov/x7ITERSQqCINQTPTZNQA/UIasWfEs/I1a+hc4nmzzxaerJuwBvL9hDxxwbSvexSNl9UL278DhVLDY7vhVfEl58L3eefBl11g7sqY2Ql2EjN8tNmtVEP+FS/Fp8jx/FNLnh7P74wxp6NIKy+N8NXlNe9RG/n/ZrooaUNJdmH4tFxr/os4Zf6PIZ/GHabzFtbmyqdMil3sLPE4rqrN9em7JsZ4WfQDiGxy5+lQVBEEAENk3C1GPIdhe6vwbJ6iAmWamsS70yCqDaG0HTNEIxmV89vYqMNBv3TiuB13+PbHPi7D4UuWodSs1XuMu3YIR8WMZdRKjr8UlJ3GTTxGNTcDgt1PmqG7gi6L4qZCOGaTbcXyNjoNdVNFiu+arJcykEDERQ04QiKdJdHCgY0UVgIwiCsJcYimoCssWO5q3ENvICwhP/yOyVdXTvkNlg/V4d0rDZ7diVeHBQ64tQ4dPJGHUOuafdgBkLE9m9AWtWYXw77Jz2WAtKMBqZpKtJVmwd+zRYbmvfHVXSscgNn0M3ZWydBzR6jgbzkQhHjMthQZFTJ+ySpPiGj4IgCEKcCGyagJKWhdR9LAvDnfndC6v5z6w1DOqZhz1FegJVkTljRCGmYiES1Rg/OJ7Ic1t1DCw2yt/6G4E184nsWEvddx9S/vbfyRp3IZI7t6H5vgDENBN3/xORLMk70kqKBXfv0ex67hbUmk0oSuoPTU0zcHQdjOxwJxdKMp5BpxDbsgSrLNYcNyW7RWLyqM4py44fWITdIn6NBUEQ9hF/EZuAJEkELJk888FqID5K85+P1vC7i4bQrUNGfb3OhR7+74pSMj1Onv1kK8/NWMfEkZ05ZVgnhhY7qZ3zatK5zWiImq/+96NWHgVlDwUX/glbYZf6Y9b8zuSdeTO1896CWISqDx/Dbja843JQSqPgl3/C3qF3/TFLdnvypvwa35JPqZr1NDaRXbpJmbrJ6WNKmHZy9/ql+TarwhnHd+HCCT2RxDCgIAhCPTEw30TWbatNWCG0YUctj72xjIkjOjP1xO4UZjtwW0xMZO54YRHbyn1IEkwc2RmbRSEtsJ3aBjI6R3auRdFCIDU+BKEbJkFbPjmnTUerju9bE6veTdWnL6DtnTujB2oh7AOrM/U5dANTUrF37EX6sNPANNF8VdR8/b/6nFbRPVuR8/uJFVFNSDZMJg7tyAmDiojGdKwWBYcqJ82xEgRBaOtEYHOEKIqMzQyg6DpaOL5pWmG2iwkjOpGf5UIzDL5dvpvXPl2Lbpj885YTeH/+TnoXZ7GtPJ5M0jTj/2WlWVAOsaWsaZoggaLEN27TdBNZkrCrUsKHnWGY6JEQ5W/9rbGTHeJaOrVz32q4wgFLwVVVwmYEkQwNU1YJS64W/eG7Lxu6YYJFkVCllrvhna4bWKX9GddbcrsKgiA0FxHYHAF2OQbl66j64j9oNbuRHen0uvCf/HJCT96YvY6tZT5sVoXjBxbx+0uP47VP1yFJsHlXHcP7FtClKJ2NO+roU5JFv/ZWutatxuIpbvB61vzOGBYHpiGxsczPCx+uZnu5D4dNZcLwTkwe1Rn5gA9n2elBstoxo+Gkc8l2N5LDk7zd8AFM1YElu319D00iCWt+MRHDxKlEia6fT8W8t9EDtShpWaSPmoqteDBBvWVNcJUkiRjw5mfr+GLRDmKaQdeiDK6c0oc8j/2QwZ4gCILQMok5NodJVSXMnSuoePtvaDW7AXAU92NPTYi//3cRW8vivTGRqM4nC7by2ifr+PV5A9lW5mXpugrueWY+l0zqjdOmcss5vfDPewP/V/8luGERnsGnJl9QUck++XJMi4tNZV7+9NwCtu/t8QlFNN6Zs5GHX1uKIcUnBCuKjBH0knX8+SnvP3vC1YSl1MNQ+0QkB1kTrwU5OQ5OHz2VqOzEpuiEFn9IzafPx4e3AN1XTfWspwiv+Ayr0rJ6FzTgT88t4JMF24jtTSWxYUctdzwxlyp/BLmBVUiCIAhCyyYCm8Nk0wPUfP5SwjFz1KU8997KlPU37KilsjZE172TiEMRjXkrdnHfdSNxmiFCy2cD4F34EUpaNrmn3YC9Qy/UzELc/U6gYOrvqVs0i3A4wnPvr0p5jZWbqqjxR+P3Zwap+vAxopU7yD/7tzi6DETNLMDZdTD559yGmt2eQ41o6LpJzF1E4eUP4Oo7FjWzEHvHvuSddye2ficRMVQsehDv9zNSPt/77TtYjZYzwViWJbaV+9ixx59UZpjwwoer0USHjSAIwjFJDEUdrliovoei/pCp1M+bSWX5hgoKszsDoMgSKzZU0bEgjZxcL7D/E7Xmy1dQPDm4e4/CYXdj6zqE8hdvw9SiWIZfRllVw8HC2q01jOlXgKxFiVXvIla9i8Cab3H3GYO9Q280byUVHz6GZ/iZKL1PqU+A2eDLNCRichaOsZfi0qOYskrEtGDo8fs1Qt6ktAv7mHoMM+QDe4pl481AVWUWrtnTYPkPW6oxTBH1C4IgHItEYHOYJNUCSBwYkMiSlDLT9j55mU6CkRiP/OYEbFIMDwGkiA9ZSd5zRvdWUjf/PQDyi0sxtShIMqoqo8gSeoqJrh6XlSElDhzRCsywj4Lz7iC8cz1189/Du/CjhLqKOxOVKHYzgBGsQ3WlI2Gi+WvQnDn4cVHli8ZXajmt6LpCZZ1EtlMnQw1iBGqRLDZk1fIj2qlliE/QtjVY7rK3nHttDqYkEYrq1AWiuB0WXHYVBVKm3rDJGhYjhBGoQbLYwO4hhLPFTsAWBKH1E4HNYdIUJ46SgYQ27c/Mba/bwrghHfh4/tak+rIsUZDt4paHv8JmVbh6cjd6eecSWzKD3F9MR3FlJPUAAVgLSjD3znFxdh+K3aIwekA75ixJnNCb7rbyfxd2x/z4H+wu31x/3FE8gLwzb2bPOw/FgyMA1YqjY0+qP3+W4Jr55Ey6lsCKL/CvmIPtpOv4aHuUD+bvqv+Qyk63c93Z/VH1EPoPs9m9/JP6BJu5v7gRNT0PrS65J8SS3R7D6m50gvLRFIvpjOhbyH8/XpuyfPKoYmyKhK63vQ9nXZJ48u0VLF67/+dY0j6d3104GJucmFfMpUQILHyfyoUf1b8PlLRs8s7+HSFHPrrYt1EQhGYgetsPU8RQyTz5ctSM/PpjdTMe4uzx3Shu50moK8sS153Vn5nz4gFHJKrz6DtrqOswFtnhpvbb98iZdA2S1ZHwPMWdQdbkGzCrtuHoMpCMcRcR0+CXp/akQ17i8M5Vp3RE+fJxogcENQChzcvwLf+CtNKT9t6MQsEF91D77XsE18zH3rkfWl0F/uVfYG3XjcW+XN6btzPhm3dVXZgH/7uY/AwH0WWzErKG13zzBjkTrkI+aLhJdnrIOeM3hEl8Tc3NYVW4ceoApIPmCPcuzuLkoR3b5lJqWebFGasTghqATTvruP+lhWgHHFNVicj6Bfi+n5HwPtB9VZS9ei8Oo+FNHwVBEJqS6LE5TKYJATmN3Gn3oFdtJ7JrLdbiQZQFovz+kqGUVQdYsaESl8NCpwIPH327he9Xlyec4635e7ii1ziiiz+g5us3yDv9V+iRIKE9O7AWlGDNaU/Np89iBL3kTf09PsMNmFgkuPuKYeyqDLBqcxW56Q6O62xS9mVyTxFAcN1C2l18H2p2e2yd+yMB/uWfA5DW73iqPtubDbzfJN78cFfKc4QiGmu2VtPjoOXfWk0ZVZ+/TOEv7yFStZPYnm1Y8jtjySshJKdhtLBAQTJNBnbL4bFbx7FkXQW+QJTS7rnkpNuRG8nB1ZqFYzrfrtidsmzLbi/+UIz0vck2bUaQim/fTlnXjASJ7V6H0n5Qm+z1EgSheYnA5ggwDBM/DuTsHlgLexMzTObP3UqfLtm8PHMVN18wmDc/X8+LH65O+fxdlUGMnrkARMs2Uf7mX8k75zYUUyPw/fvU7Fofr6io6Ob+4QDTNFGAjjkuSgrSME0TozL18Er8CQamrGKUjMGvG7i0KtDj38Mlix0jtHejQLuHam9Vg6fZWhGmtzszaV+bWMU2/D98i2XwmSjtBxHTDSKGySGXXR1EVWVkWcYwjENOaj4sholDkRjbrwCQ0HU95TyStiIc1RpN0l7ri5DhUDFNkEw95ZDpPrGKbSgdhqCL8ShBEI4yMRR1BBmGiaYZSBKEIjEKsp3ceO5ANmyvxao2nAW7U74LxZv4TVmSZbzffUh0X1ADWHOKMEg+j2maxGI6mmaguBvOIo6sgMVRP8wiW2xIanzjPCMSRHFnxOsFa8jLbHjoqEuBA81bmbLMWlBCLKYTi+k/eQKpokCaHEDa+DWROc/Cui9Jk3woStMGG5pmoGl6m9+Tz25rOIs4QJbHXt9GhqSgpGU3WNda0KVtDucJgtDsRGDTBHRD4uwTOrNuaw03PzSHR/+3jBH9ClN+aEgSnD08j+gPc+qP2dr3IFaxnQNXWgFkjD2fCMkrpw5kWNOwte+esszd93hiSnwzPkUGtDBpA8YB4Fv2OZ4hk+L3tPxDzj++fcpzeFxWunXIQqspSyqTnR7UvOKftSJGliUcoT3sfv5Wqj9+Bv+KL6n57Hl2P/cb7P5dYsO8o8BhkRk3uEPKsp6dMnHZ93fwRiQnmWPPTVlXdnqw5P+894EgCMLhEoFNE1AkiWBU4p+vL8EwQdMNPvh6EzedP5Asz/7AxOOyctv5fXGunYUZie9JYyseQNak6/Gt/qa+nmR1kH3qlZg5JYf8sAiZNrJ/cRP24v77D0oyrn4nkDbqPCJ6vMfHTpCyV/6ErbAbaQPGE9m1HklRSR92OrHKHfRkExed1AmbZX8PUYf8NH5zwWA27vRiGz61vrcHwJLbgfwL7iUkuX5Wm9kJUfHug5jRUMJxU4tS8c7fcDSSgVw4MkzdYNrJ3Rk3uIgD48jSbrnccsEglAMCbRUD2Z5G+ogzk94HeaffeDRvWxAEIYFktuVJBQfQdYPq6sP/8FRVmfQ0G18s2cXDry1JKCtpn84vRpfgcljI8tixWmQspka6EkLRQig2GxHFTV1UIU0OY9UDSIaOZHcTltzEfuR0BUkCmxxD1YKY0RCSzUlMcdUHNQCu0C7KXr4DJJm0AeNxdhuCqWuomfnIiooeDmLY0/GZTrwhHVWRcTlUMKEuEMVjl/EoYYj4kRQLhsVFGPshAy9VlcnMdFFTE0iYP+PWKtn9/K0NPq/g0r8SsOY3WN5aNNQ+R5UsEY4ZBMMaDpuK3SIjYyYM1bnlAGXP3ISz22DcfcdiGgaSohKrKaNuwQd4jjsNep7YJK+hRbRRCybap3HN0T65uWlH5TpCnJg83CRMAuFYwpFuHTIYU9oeTTdYvHYPE0d04lcPfglAUZ6bOy8dgj+ok+W0YFcghhPN4sImR1GMGCoRDMWetMpEksAqa6hGFEOSiUpOdN0galqRFAPZbkGTLOiSBYccQjZ1dNmCae6NkkwD39LP8K/6BnffMdhCJRixCLbiUoK4USTIdO7t2NsbtGQ64xvYBUwXWPf20Bjx1/2zW0zXGi/XYvAj8mhKUnyDuZhuIktgVeRG53qoqozVCIBpost2IrroxMQwsSsSdtfejQpNM/kna5qYeozAmvkE1sxPPkUkgHLwWnpBEISjQAQ2TUBWLfTqnAWAqkj86tyB7KkJ8tG3W/AHo/QuzgYk+pZks3JTFYN65OFUTXDs3/HWKutYAuXUfv0K0bItqJ5sPKPOwVrQg9DeTNkW2cAWraL269eJbF+D4krHM3wKrk79iJZtpnbeG2i1FVjzOpIxeirhbavwLpyFtaAzmWOm4e57PP6Vc7C170HmmHPxLfmU6i//i2xz4ZEk3F2H4dcbn9NzpEj2NCSLHTOWnIEcRUV2ZRw6bpIlaoMxXvl4LT9sqSYjzcZZJ3Slf9fshGzn+7iVMOF1C6haOBMjEsRRXIpn1NmElIyfupCrzdElK7Z23YnsWpey3F5cSkgTK6IEQTj6xFDUXkdyKCoz00V5lZ9n3ltF7+Islq6vYMnaisR6isztlwzhxQ9X8YdLh1FVWU37gmxMw0RRJCx7VlHx1gNJ5/cMPwNr6WnEsODwb6fsv3cnbJAG4Oo9EktWe2q/eSPhePaEq/Cv/IrIjviS8Nwpv8a79HMyh59O+Vt/278j8V72zv1Jn3ADQaPh9AM/VUPdwBbFxFz/NTWfPZ/0nPQx56H2PZVoI70psiyxqybMnU/NSxoOGz+kiAtO7oF8wFvdKYepnfkIkW2JS/Al1UrBJfcTULOaZZXUsTKMIMsSjuAuyl7+Q9L7z9axNxmTfn1E3zcHOlbaqLmI9mmcGIpq/US/exMI+32gRbl6Sl9K2mckBTUQn1D89hcbuOvy4bjUGAX58aAGwG4Gqf7k2ZTn9i54H4sRxE6Y6k+eSfpQAQisnoetXVdQEjvkar56Hc+gU+sfV89+iZxTLqduwQdJQQ1AeMty8Fck7c7bFGK6hKXrcHLPvg1LbgeQZCxZ7ciZcjO2Pic2GtQAxAz41zvLU87x+XzhDoKR/UNdkiSBtzwpqIH4ZOXar17BKjc+NNbWGYZJ1JlP4SV/wd65P0gyiiudjLHnkzX5V00W1AiCIByKGIpqApIepaImyH9mr6ZPccN7fazeXA2AKdkSd7uNBtB91amfZBpoVTtRs9oR3ZN6h2GA6O6NWLLaEavYVn/MCHqRLfs/cHR/DaYeI7RlRYPnCa5bgOW4TsR+7MzlwxAyrCh5fcg8+04UdAxkInvnDB1KRNPZVtZwRvXVm6sZ0TsfXTdQVZng2uR5IfX3sWExGSdGiYhfj0bFDBnNVkDaxBvJMGOARER24tdEJ7AgCM1H/OVuCpKEqsjIEtisDW/Mpyrx7Nwh08SuyvVpByQ5xXNkFWu/k9C7jKLcnkM7SePgrOIJt2Cx1e8qnFggH/RQQVJUTD2WXBeQrQ68YQ1FkUiXw0gRP0gg2dyE+XFBx0+h6wYhDvy2/+POL0tSfOJwA5+pdpuasKuwbG147tD+jO1CY+Kr76Lx1XdaFMnqRDINRNsJgtCcRGDTFKwustPhurP6E47pvPzRDymrjRrQjm9X7ObNz9dz/indGdm3ENk00S1OrHmd9vfIKCr20+/gf0vCfPn8BnRjPeef0JETSkoJb1qSfGJJxprbkVh1Yr4nS3Z7tLr9w2LWvE4YVjeuvsfjX/ZZynsMFZby8ofLuWy4k+o5z6LVxvNcqRn5ZE++gVhaB2JG83+Q2S0Kpd1zUw77yRL06JhZP0wVi+m4e46k7tt3Up7L3X88UdneYrKRt0SSJOHGR/VHTxHe2+MnWe2kjzwbZ6/jCeo/YgmbIAhCExBzbJqAzW7DYrdS44/y+cLtXDSxZ1Kd3AwHJw7pSLeOGURiOs9/sJqlGypRFJmIaSf7tBuRbPFdgq2Dz+ClBUFmLy5D3/vh/P6C3WiDp6G40pPOnX3yZfhWzEk4JlntZI2/kLrvZ8Qf25xkT55O0LDjGXEmanpe0nmsx53Fl2v8XHJ8HuEP7q8PagC02nLKX/0TNq32Z7fTkSSZBlee3pcMd/LcjuvOHoBNSQy+NFsG6SPOSqqrZhaQdtwviGrNH6y1ZE4pRMUb/1cf1ACY0TC1X/6X2IYFqIpoP0EQmkeLWhX10Ucf8f7777Nq1Sq8Xi+dOnXioosu4uyzz45P+NzrjTfe4Nlnn2XXrl0UFxdz8803M27cuMO69pFcFWW1W6n1hfnXOyvIy3TQtSiDjDQ7360uwxeI0qckm/wsJ0+/u4KhvQuorAvx/epysjx27r9+FComigJOw09o/ff48kuZ/sSSpGGWgmwnvz2jmNzQJiKbFqOk5eAecCKGPQM55ie4cg6xyu3Yinrh6j6EwMZlRLYux9ahD45uxxGU3eh6fIWLkwDRnWsIrf0W2ZmO1mUsX22IsLs2xrlp3xNZ+UXK1+suPRnbyAt+dCDQlCsSZFkiYpgsXVfJ4rV7yM1wcNLQjqTZVaQUb3O7HEUJVeNf+glGyI+z50gs7boTMF3NlgzzWFjRIkng8G+l/L9/TFkuOz3kXXQ/AdPZJNc/FtqoOYn2aZxYFdX6taihqBdffJH27dtz++23k5mZybx587jrrrsoKytj+vTpAMyYMYO77rqLa6+9luHDhzNz5kymT5/Of//7X0pLS5v3BexlmCaabrJll5cB3XJ558uNBMJRjh9YhMvuZPb329i6d6Lrlt1eunXIAKDaGyamG/G5Nzr4cKP2PJFAbTjl3JGyqiC/eW4V/7zleHK6jMRAxh/TQQOkDCyDzsKCjm4q1Gk6lh4n4OwxFt2U8WkG7J0PHM9O7kQpGoKz42CiSPz6r18QjupcfkonzG2bGnyt0d0bcBpRoPlXwRiGiQUY0TuPEX3ykYgPOzU08SZsWJHsBdjGXIpkmmiGREQ3OJyNBtsCWZYbnbhuBL1IehTkpglsBEEQGtOiApsnn3ySrKys+scjRoygtraWF154geuvvx5ZlnnkkUeYPHkyN910EwDDhw9n3bp1PP744zzzzDPNdOf7SZKEpuvIMpR2z6Ffl2xyMxxYVJnNu+pwO61cMrk373+9iaXrKsjLdFDnjwDgsKmoByV71DQDu7XxH5MsSURiJvWRyl77VzLpex/v+3aS+luKrhvoOsSIT3oOR3X2eDVkTy408EGmZuRhyJYWNR/lp3wLM02IRvfVFwHNj2GaJpb0htNbSBYbKBbRnIIgNIsWNcfmwKBmn169euH3+wkGg2zfvp0tW7YwceLEhDqTJk3i22+/JRpN3ovlaJIkCX9E54GXF5HjNDl1eCe8gSiz5m/hL//+ntc+Xcez763k/734PScMKmJQjzxGl7Zn/qp4puzTRhVjtyT/SJw2pb5X52A9O2XiaGTl1c/hsimcPqYLAF8s24PRd1KDddOGTTnkHjNC62IYJmpOEbLDnbLcPfBkosrPS4YqCIJwuFr8J9KiRYvIz8/H7XazaVN8SKS4uDihTpcuXYjFYmzfvr05brGeDjz4ymLCEZ3augAfz9/KojV7WLmxKqGephs8+r+lXDSpF/NX7iYS1RndN5eJQ3LrN+k7kALcesEgOhUkjtN2LvRw87SBCVmXj4RoROP4ge0ZN7iIQCjGZ2tj2E64PHHDP0Ula8I1GM68ZtmhV2heQdzkT7sbxZ2RcNzZfRjuwacRFfsbCoLQTFrUUNTBFi5cyMyZM7ntttsAqKurA8Dj8STU2/d4X/nPpaqHF+fVhTQ27KjlzvN7UVYZYHCvfJ54c3nKurphsm5rDeN6pzNloAfLziXEPvuM9Mm/Iipbkuo7VYm7Lx9GXSBKVV2I7AwH6U4LVkVqssDiogk9OXt8N3aU+4hldabgykHEqncjSRKWrEJ0xUnMVPgpzaYocsL/hUTHUvtEnAXkXfQXTH81esiHJSMf3eomjA21Cf+yHEtt1BxE+zROtE/r12IDm7KyMm6++WaGDRvGxRdf3OTXk2WJzMzD6z6v9NcAkJVmZUeFH0uaE1WRmDC8E2kuKzv2+Plu1f4l25W1QQbueI/QhsVEMbHkdMBukXC5XGghP2YkiGRzIu/dQE+2OsjLdtGNTIJhDV03cDosKPKhVyT5gzHAxO386fuLdCo4IJDMKTxkfSMWxYyFkax2ZDX19Twex0++j7bk2GkfF3p6DsGwhqlKuGwWjtYg1LHTRs1DtE/jRPu0Xi0ysPF6vVx11VVkZGTw6KOPIsvxyDo9Pb5ni8/nIzc3N6H+geU/h2GYeL3Bn/VcSZLwRXRiMQOLKrN0Ux2DumZQq6lMn1rKZ99tY/2OWroWZXD3lcN57ZN49um+xZlEP9nAvlmW9k590XSD2I611C14H1tRLyyZ+XiXfIbur8besQ+u0pPZXGfhv5+tJxjSGNonP77ayiqnzJMUNWDlpkpmzd+KacBJQztQ2j0Pu9LwLr0/l4qGGq7C+90HxCq3Y8nthGfoL9DsWWhmfB6Qosh4PA683tAR37W4NTiW2keWJQIRnc8X7WDRmj2kOS1MGduFjvlu1CbcxuZYaqPmINqncc3RPof7pVn4aVpcYBMOh7nmmmvw+Xy8/vrrpKXtn1dSUlICwKZNm+r/ve+xxWKhQ4cOh3Xtn7OngSxLVPoi3PHkXEb0K2TCiM68M28744/ryHeLy3nl47X1dTfuqOOLhdv57UVD+ODrTRRkWgkH4sNnksWGZ8hEIpsWUznjCdIGnIjur6b6sxfqnx8t24RvySekT7mTnXv8VNWF2bCjlg+/2cz9N4zGflDPqobE319ZzLptNfXH1m6roVNBGndeNhTlCEY2qgKUrWb3Ow+yL1CLlm0msPIrcs+5DTOnJ7q+/3q6bog9NhrR0ttHksAXNrnt8W/wh/an41iyroJJIzpz1gldErKpN4WW3kbNTbRP40T7tF4tapBR0zRuuukmNm3axLPPPkt+fuKS0g4dOtC5c2dmzZqVcHzmzJmMGDECq/Xob+MeM+GxN5cR1QzmLNlJYY6Ls07oQkCTee2TtUn1o5rBKx+v4Zoz+7N20x6seZ2wtutO+rn3gNVO1cfPAhKunsOpm/9+0vPNWATmvsg5I/cPCXkDUV77dC2SvP/HqSgyP2ytTghq9tla5uP7NXuO6Biz3QhQNfMJktb4mgZVMx7Hbh7+5odCy2FKMv+euTohqNln5rdb8IfF7GFBEJpHiwps7r33Xr744guuvfZa/H4/S5curf9v31LuG2+8kQ8//JBHHnmEBQsW8Mc//pHly5dz/fXXN8s9h6M6W3Z76x8//c4KOhem88OWGlKMDAGweZeXcERjxqJKjFN+x6KiafzxrZ3UBTRMLYqakUe0YjsNbQQS2b2RHoWJG+LNXbaL6AEX1E2TTxZsO/ip9T77bhvRI9gNa4a8GJHUQ3lG0AvhhjNvC8eemG6w8IfyBsu//6HssCfjC4Ig/Bwtaihq7ty5ANx///1JZbNnz6aoqIjTTjuNUCjEM888w9NPP01xcTGPPfYYAwcOPNq326AaX+qdgg9kscj8ckJPygIG73xbhgTYLArOM3+DZLWjeSsPcRUz+ZFp1idWPtQgwI9JGaAoMlHdJKobKLKETZWxomExgmAYGIqNiGRPGGL6kbebeB1VJhQz0HUTiypjVyXRRXyYJAmsso7FCIFpoMt2IthSzsP6OUxAUSRO6JfPyf0zUSWDSr/O/+btYfMur9gCQBCEZtOiApvPP//8R9WbOnUqU6dObeK7+XHsVoWO+WlsK9/fI/HaJ+u4+8rhSFLqCbqdCz3o0SiGYfLFwm1cd3Z/ijNlogvewL9iDqauUXD+XcSjlOQTWAtKWFaWuBnhyH6FWFUZc28vjCpJnHxcB1Ztqkp6PsBJx3XEqsgNTp4zJYkte/w8895Kduzxk5lm44HLe6MvfZfKVXPB0LDkFJF10uVoGR2RHB4kqwMzGko6l+xIA0dayt2JDVnm/a83M+vbLYSjOjkZdi6e2IvenbOafI5GayXLEi7DS+3slwmuXwimgbWwC1knX0HEWYh2BLKxWxWJh68pRVn1EdGPvsTUonTMyOf2E6bx6bb2HNcrXwSngiA0C9FXfJgsEkyfOgDLAd3uV53ZD5dN5oJTeiTXV2Wum9iB7OplzF6whZOO68T9/16IPxjBv+obTD2+LDuw5lvSh05Oer6kWpFGXsJb88rqj6U5LZx/Sg8w9n+Q6LpBn5JsurRPXilWlOdmaO/8BoMaWZbYXR3ij8/MZ8cePwC/Pr2Y2My/E1gxB4z4/IlY5Q7KX/sTqm8nUdVN9sRrU5xNInvSdYSl5FUBhiTx5NvLeXfORsLReNqHytow/3h1CUvWV4p9Jn4mJ37KX7mb4LrvwIz/jKO7N1L2n7uwR6uRjsCKJZcURvnqX0SWf4KpxYNsrbac8Kx/Mqk4RGaKLOuCIAhHQ4vqsTkWGYZJrsfGQzcdz4dzN7FxRx1d2qfhD8YY2yeTnnm9ePe7Cqq8UXp1cDNpUDbKty9Su2czk065i+83VtK7OIs3v9nJBb1PILosPjHat+RTMkZPJe+Mm/Et/wLdX4OtQ2/cgyawsc5CXlYtLoeFob3zGT+kA3ZFShpmUDG5/eIhLF1fyScLtmKYJicO6ciQnnmoNDw6pJnw3Acr6x9neezkSdXEqnamrF8z+0Uyzrgdo7AvBZf+Fe+C94hV7sCa2xHPsClEbVkph6v8YY1Fa/akPOfLH/1A/y7ZWJpw2XBrpKoy0c0r0P3Jk8YxdOq+eR3nidcQ0Q8zDUegmsjO5MnxAKGv/0t2hx4EEfuECIJw9InA5ggwDROnKjFtfDeQwK6YVEQ05Kq1ZHzzAlf1PB7DkYFctZTIm/Mx9vZ4eCwaa7ZU07Uog6+X7sTo1TXhvLXfvIHiyqDg4v9HzFTQZRt+DQqzJH4zbSC6YdQPJ6WaO2GaoGAyrGcug7rlYAIWOR6MNTbKoxkmm3ftnxBdlO9G3rW6wfrRss0opkbItBG15eMcdyWSEcOUrfg1KeUQlCxLbCvzJh132FRimoE3ECUc1bHYjmwerNZOkQyCGxY2WB7evhqPESVyGEGHoshEdq9vsFzzViBrEZBFYCMIwtEnApsjyNANLBYZHQmLKiPZ3RhBL5HFH6SuL6t4XDYCYY10tw0p4k+uEwliGCYh01YfIMSDGBMFftQGU5pmxCcnKzpWI4heuwfJYkV2ZxHCxcGnkCUJp10luHfJbjCkYTgzGzy/ZHNiShKY8WAqosmArdGM36Zp4nHFhyskCc4Y2Z7xfdJRglVIFjtVmgOHCGp+MgMZJS27wXLFmV7/s/q5TNNEdTX8fkBW43nFxBQpQRCagQhsjjDThJAGaQ4ripyH7vTElzsfxN6pL3M3BRlT2o4n31rOlad2gh/eS6rn7j+OqOKEw9wWxKlECS/9iKr579XPu5DtLnLP+A3RjOKECaU2VWLiiM689cUGADburMWY3B+kV+ufeyDP4IlEZRcNrm9PwTShMMeFy65y3eRiulR9Q/StTzH2nj/dkYbn7N8RTStC08V41I8Vixm4B5yEb/HHKcs9Q08nKjsT5mP9VIZhYskvRlKt9fNrDuTqM4qY7IxnhRUEQTjKxOzMJpDptmHqMcpCVqwTfoNkcyaUqxn5eE65hjA2tuz2Uto9l97FWcSqdifUsxZ2JW3YGUS1w/tgVxQZfedq6r59JyEwMcIByv/3/3AYiXvM6JrBxBGd6FMS/+ZvmvDa3D3YT50OUuJbxtaxN84BJxPTfvrX8ww1wv9dXkpXYxPR5R8n3lvIx57X/ozDSO7FEhoXs2WQefLl1K/938vZcySWzqVHZLVSWHKTd87tSEpiwlZrXmfSR08joos/LYIgNA/J/DEbmrQBum5QXX34u+OqqkxQM5m7bCcLVpWj6xrXntoBV2AneMshpzMVZiary3WO65mLRTKwOexs21VL9xyQKzdiBmuxte+J5M4haNoPe08Qpxym+o0/Nzj5N+P4C5D7TiQWS/yKrUsSNb4IqzZVke62Udo1gzQzSGT7avSgF3vHPpjOLILGj18Bo6oymZkuamoCWOu2YARqqZ79IrqvOmX9rFOvwigZ02Zy3hzYPocTgFhlDZsRJLx1JUYsjKNTP3R7OiH9yO3OrcomdjNAdPd6dG8VtvbdkdLyjsh7ttHrHqE2aq1E+zSuOdonNzft0JWEI0YMRTUBwzBwOazsqQ5S64/w2+dXk5vpIN1VQEVtNXX+Mgb1yGNixyDe9/+GdM591AYVlPRCpLQcDMNE25uxWzHMhF8+i0VBkuIjCbqu43BYMTHRYkZ9YKKqMrIsYZoQi+nIGGi1qVcfAcQqtmJPsQZYMU1y06ycOKg9phkP/ny4UTrF9+gJ6kZ9J4vVGp8Po2mpJzIfTJYlNG8lisXSYFADxJNpdpXQxbDGTxI1VKJ4UEpGI0ng14wjPjSkGRJ+3MjtBiEXSYQOeD8IgiA0FxHYNAFVkanzR+hUmEbt+ggAFTUhKmr2b17Xs3MmVCzDUjqRoDWTrWU7WbqugpOHdiQvy8ncZbvYsttLr85ZHNc7H6dFJhA1+GLhdjbsqOOi8R3IxIvv29kQC2HtMRpnQQk+ycHXS3exdmsNndt5GN2/HQ5FxZLXkejujSnv11bUC6OBORemmZwc9MDeE7scRQlVE/j+c4yQH2evkaj5XQiYjka/tRuGiS2zkOiutViy2zfYm2Rt3wP9MOaDtHVHo6fLMMwjtqOxIAjC4RKBTRMoyHbRpySL3sVZLFufnBrBZlXo3TmbsG00q7YHeeyf3wDQo2Mm/nCM//vHl2h7932Zt2I3r3yylj9dM4JHXlvC9j1+/nBBbywrP2TPkv3JQIM/zMOSX4Ln9N/w5ufr8QVjzFuxm/99tp77rh1B5+N/Sflrf0q6F9nmxNGpD4GfETzY5RjRZR/F5+7sFVgzD0tOB3Kn3oFPP8RyX2cm4W0/kD7sF1TO/FfyvTk9WNt1x/9jUjYIgiAIAmLycJOQJInMNDtfLt7Bby8cTMYBu7C2y3Fx6y8H8/KsH3jz23JWbds/cXfK8V3419sr6oOafUIRjX++toSJI4tx2lV6Z2sElyRmOAeIlW8iuOorLp7Ys/6Yphv8vxe/R84sJPuUK5Ad7voyS04Hcqf8mspZT2E3UyewbIwSqUkIaurvo3I7/iUfYzlE2Bw07WSMvxjTMMgadyHyAZOsrfmdyb/gHkKSu5EzCIIgCEIi0WPTRJasq2BHuZ9uRRlc9os+2K0KkiRRWRfi+Q9WsbsywMYdddxywSC+WLQdVZGRgEAolvJ8O/b4yc10MHZge0LLP2vwutGVsxl41uiEY95AFD0cILBmPjmnXgWygiQrxGrLqZr1LJq3AoK14HCmPmkKFotCYOnXDZb7l35GXumpxBrZCM40wY8LW+fjsEgxCrsNwYyGQbWiqS6C2DFEb40gCILwE4jApomEwjGcDpUMh4SdANGwyafLali0tqJ+uxdNN8hwW7FZ4hNvY4eYoW8YJjZVTploch8zFk7ZDWfqOuFtqwhvW5X6eXryfiSNkSQwIw2vIjNiYX7MDm2mCWHTQhgL4IR9i3YMftTz2xJFkbGZQWRMdEkhYh65bN2CIAithRiKaiLDeudz7fg8umx5m6zZf6Zw7gNc3mEDD1zRF5cjvvdH35JMcgIbeejKnhRmO3E7LShy6j1r0pwWYprBwjV7sPca0+B1LcWD2OU7KGeUIiE70pDtyYkoAZAVlLScn/T6NM3A2XNkg+XOLoPQJJEI8UhxKlHUHYuofuPPlD37a+reewB73SZscuoePkEQhLZKBDZNpMAWIPL2HwmumYcZC2MEvUQXvYfjq8f43dldURWZy04sIvrFM+jv3svvzuzEtyt2c9rokpTnu2Ryb2bN38LOigB+VxGW3I5JdSSrg7ThZ/H8zMQ8PtNO7kFUcZF50mUpz50+6hwiPzGvj2GYyJlF2Np1S74Pi430secTNURKhCPBpmiEFn9I5Qf/JFa1E1OLEtm1gfJX74Vdq1BVsTOzIAjCPmIoqglEQkFqvn4DMxZOKtMqt9FequDha0uR5j6HFqgFwPLDp8jmYEraZ/O7Cwfz+mfrKK8O0rnAwy8nxnt0Mt02QmGNu19azT+u+R3R1V8SWv4ZRiyKrcsQ0keeTYXuQlVk7FaFgmwX55/Sgy7tPGiaia1oAPnT7qb2q1eJVmzHkplP+uhzkfK7EfoZ2Z6Dpp2sKb8htPorfItnYURCOLoMJH30uYSUDLGnyRFi0YNUfvdhyrLqz54n/+L78fPj50cJgiC0ZmLn4b2O5M7DNjNI+fO3YEZSrzRy9RwOFjuBFV/WH1PcmWT/8v8RlJxYZImIZmKYJoosoUp7e0hkCc0E3Ygfd9pkpIgfCRNNcRDTZSQJdDOeoVuRJSwy6AdMwJVlCRsRZFPDkBSikj2h/Oe9ZgmrHozfh2xvdDt9sStq4w5uH0kCW/U6Kt74fw0+p+CyvxGw5B7Fu2xe4j3UONE+jRM7D7d+osemichWO3oDgY1kdWCEA0n1ZVlG0s14UAKo8t4N8LQgZjSMZHMRU51ETBkJk1DUIBS1EY7puOwSDks8D2VEMwhFNOw2FdmicOAkXMMwCWEFrHsPH35cq2km2r7VT0d4d1tJApscQ61vAycx1bU3g3jrZ5ogWxqfqyTJ4tdYEARhH/EXsQnY0rNw9D8Z/9zXU5a7ug1hzwePJhxT+p5MAAd7lwMhSeCWglR/9CThLSvilWSVtEGn4ho6hZqYlUf+t4TVm+PpCCyqzOljSujbJYc/P78AwzCRJBjWu4ArTu+Dcgx2zEkSuOUg1bP+RXjz8vhBWSVt4Mm4hp5BQG8bk5PltBwkqyPlajhrXicMi3Pf20YQBKHNaxtfe48yRVFI6z8OS0HyROC0wROJlG2K79eyl6V9T2qz+iYs93ZIESrf+dv+oAbA0PAtnIG3ppY/PbegPqiB+FLxt77YwKpNVQzqkQfEv+3PX1XGY28uw0iRC6qli7fB3/cHNRBvg0UfEVw8A2sbCcvDspPcM38DcuI8KNnuIvu0Gwljb6Y7EwRBaHnayEfD0be5ViZ/0i0odTsI//ANWJ24+o0jJLsI1FXh6F+HpEeJdRrO9qiHJT/4mVZYgLE3t48UqiFavjnpvJLNyR6fzq7K1POBPpq3mevOHsDCH8rrjy1bX0koquOyHFtxrBSuJVq2KWWZb9EsCkpPIUrr35lY0yWkrC60u/IhgmvmEavYjq1jb+zFpQRwi71sBEEQDiACmyayeXcdt729gkeuLyWnyyBMLYIvqHPnm6vwB2P06zoIVZZYs6wGiVrunz6afX0qsiw1mI1bdWeyo7LhDfoCYQ1VSQ5g6vwRXJk/bUl3c2qsDQBMLRrv9bK0/sAGIKZLxPBg6TcZuyRhGAY+zUBsYigIgpBIBDZNpDDLxQ2nlZCthohWbke2WMnIyOdvl/bgla/K+XJZORZV5q4LetLJFSayeiaKOxNnh96EcaF4slOeVw/WkZ/Z8NCDzaKQaqFbmtOaonbLZRgmalrqNgDiaSEOMam2NYrFjvDsbEEQhFZGBDZNpEe+Qu7SD6ict+iAoxKZx0/jwh4ezuhfRHZeLtUfPsKe3Rv3V5EVcs+6FTm3GDU9D60usdfCCPkpTFfJTLNR44skXXfc4CLmrtiVcKykfTpOuxqfdHMscWWiZuSj1ZYnF/UeQ0xxHvFVWIIgCMKx7diadHGMME2T2MbviW1adHAJNXNexZqWiX37QgKLZhI9MKgBMHQq3v47khEj79w/YMlql1Ds6DqYrOwM7r16BPlZiZuyje7fjmF9C5m3fHf9seJ2Hm67cPAxGcGGcJJ37h3JbdBlMJ4x04j8jE0FBUEQhNZNbNC315HcoC/NEmXXv/+QsqcBwDNkIukjzmTHk9MxtdTJJ7MmXgclI7HqAaSIFz3kR0nLQlddhHQLsiwRNUx8oRiBkEa2x47DKmOYEIzo1PjCpLttuO0qCqQcnmoOP3VzLFmWsBNC3tcG7ix0S7wNWiOxudqhiTZqnGifxokN+lq/Y/GLfMtnGOhBb4PFuq8Gw6TBoAZA99cgmyYBww4WO1jy9hbsu4SJCmQ6LGTuTaqJYSIDbquMO3tvb45pHtPTSw3DJEjqNhAEQRCEg4mhqCYg2RzYO/VpsNzeqTdEAlhyihqu07E3ui6+bQmCIAjCTyECmyag2JxkjJ2WtKEagJKWjZqWzZ63/07m2PNSPt9aUILkyTvm5voKgiAIQnMTgU0TidmzKbjwPmztusYPSDLOHsPInXw9lZ88T6xqJ8ENi8k/9/dYstvHq6hW3ANPIefM3xI0xG6ygiAIgvBTiTk2TUQ3FWLOduRMuZnY3h2EQ5uXUf7W3zBj8XQK/uVfYMkvJue8u0GLgKQQVVz4tOa8c0EQBEE4donApgkZhokhwZ53/gFm8nwZyWJHTyugOmYjplmwWRRspsTBGQ0lRSYcM9B0A5tFxq7KDc7mVxSZsGYQ1QwsqozDIqMf4ysjrCpYdT+mroFiJSK70HQxTicIgiAkE4FNE4vJdpzdjyO4dkHCccWVjjL59zw7r4ZvV3yOYUJ+lpOrpvSluCANyTSRJImYCS/P+IG5y3dhGCZ5mQ4u/0UfurVPRzpoEo4hSXy5ZCdvfbGBQCiG3aoweVQxE0d0RjaOzeDGrYbxL3iP6mWzMbUostNDxuhzcXQZSkg/tnZTFgRBEJqemGPTxKKGQsbxv0R2JO5joIy/nj+9sZW5y3ezL4dheXWQ+174jp2VwXiuJBP+9PwCvl66sz7R4Z6aEPe/tJDN5T6UA3JCSbLMR/O38tLMHwiEYgCEozpvfbGB/8xagykfg9m9lSi1s57Ct+ij+qXxRtBL9SfPEls3F4vYn08QBEE4SIsKbLZu3crdd9/NlClT6N27N6eddlrKem+88Qannnoq/fr14/TTT+eLL744ynf645kmBJUMCi6+n4yx52Nr1w1X3+PZLRewuyr1hoDPf7gKzYTd1UF27PGnrPPih6uJHjAcE9EM3v86dSbsOUt2EI4dez02StRPaNOSlGV137yBzTj8DRUFQRCE1qVFBTbr169nzpw5dOrUiS5duqSsM2PGDO666y4mTpzIM888Q2lpKdOnT2fp0qVH92Z/Al038JkupL4T8Jz+O9LGXc7yTbUN1t+y24thwqpNVaS7rThsySOGO/b40Q4YXvKHY8QamEtjmqTMK9WSSZJErGpng+VGJIgZbTjLuSAIgtA2tag5NuPHj+ekk04C4Pbbb2flypVJdR555BEmT57MTTfdBMDw4cNZt24djz/+OM8888zRvN2fTNNMNCyoukRuhqPBem6nBcOEYX0KyEq347CqWC0K78zZwNqtNQA4bCqyJMPefYVthxiXcaYIjloy0zRRXOmN1JCQLGKOjSAIgpCoRX3ayXLjHUjbt29ny5Yt/Pa3v004PmnSJB544AGi0ShWa8v/sNM0g0E98lBkCd1IXt1z20XH8cbsdcxeuL3+mMOmcsM5A7Bbt7FsfQUTRnTGrkroe4ejHFaF7h0zWbetJul8+VlO3I4W9aP+UaS0HGSnByNFegpHl4FoIru3IAiCcJAWNRR1KJs2xeeQFBcXJxzv0qULsViM7du3p3pai2RXJH5/yXGoSuKPYNKozmwr8yYENQChiMbDry3hzOO70L9rDpNHdk5IuaAAN08bmJTxOyPNxh8uHYr1GJw8HMJF/rl/QLa7E45bcorIPPkKIsaxF6wJgiAITeuY+mSoq6sDwOPxJBzf93hf+c+lqocf5+1bqaQohz5X9/YeHv/tODbuqGVPbYj2uW7cDgsPvrIoZX1NN9hW7uM35w9CMg04qIfLrcrcd80IymtCbC/zUZjjol2uC6dFxjBM5BYQ3PyU9gGIugspuOwBtMrtaHUVWPM6IaXlEZYdKK1wK5uf2j5tkWijxon2aZxon9bvmApsmpIsS2Rmuo7Y+TyehufQ7BONxUgDcjIKeebdFbwyaw03nz+Iytpwg8/ZWeEnzW1FVWQkKXWgkp/tonfnLBRFbhHBTCo/pn32c0FWfsKRn/LsY9FPa5+2SbRR40T7NE60T+t1TAU26enxyaQ+n4/c3Nz6416vN6H85zAME683eHg3SPxbgMfjwOsNpczOLUkQjuksWFXG0g3V5HosTBjWgbNHt2fu8l2UVQfpXOhhy+7keSUA3TtkUlETYuWmKnp1zsRtU6nPlilJBKI6Xy/dydqtNXQu9DB+SAfS7AfUaWaHap+2TrTPoYk2apxon8Y1R/scyS/NwqEdU4FNSUkJEJ9rs+/f+x5bLBY6dOhwWOdvKE3Bz6HrRtL5JEkiENX4/ZPz8AVj9cc/WrCDG8/ozv1XlnL/6z9wyeTe3Pf8d0nnzHDb6FCQxh1PzuWeq0bw5+e+Y/rUAbTPciBJEmW1Ie5+6lsisfiM2kVr9vDunI3cedlQigvcGC0oDUGq9hH2E+1zaKKNGifap3GifVqvY2qQsUOHDnTu3JlZs2YlHJ85cyYjRoxo8SuiDAmeeHtFQlCzzxPvr0eW4PRRxXgDUa4+ox/p7v2vp3vHTG46fyCRqIYvEOXVT9Zw0aSePPjKYqKGSUQ3+Mcri+uDmn10w+TvrywmorWcoEYQBEEQmkqL6rEJhULMmTMHgJ07d+L3++uDmKFDh5KVlcWNN97IrbfeSseOHRk2bBgzZ85k+fLl/Oc//2nOW/9RIprB6s3VKct0w2TjjloGdyvkuofm0qV9Olec3herqqAoElt2eXn4tSWM6t+OnsVZLFhZxnkn9aDaG8Yf0pCkeEqGVAKhGHWBKDnulh34CYIgCMLhalGBTVVVFb/+9a8Tju17/NJLLzFs2DBOO+00QqEQzzzzDE8//TTFxcU89thjDBw4sDlu+ScxUuxZc6BoTAc9voPw6s3VlFcHOWVYJzrmp5HpsdEhL42YptO3cyZnHpdNnjXEL8d3/FHn1sRYuyAIgtAGtKjApqioiLVr1x6y3tSpU5k6depRuKMjy25RaJ/rZmdF6vxP3TtmooarGdorD4tFZfSAdrz9xQZe+3Qt6S4bJw/ryAmDilB/+Jjolx+wJxJidMc+ZKkXEbTl4nZY8IeSh7lURSbLY2/qlycIgiAIze6YmmNzrLPIEtef3Y9UK7AnHFdIul1CrtvJxePbM6xPPve/9D1rt9VgmlDrj/DG7PW8+OFKDIsTIxwA0yC6dQVl//49rkgFN0wdkPK6F03siVVpmcu+BUEQBOFIEoHNUaTrBu2ynPzj12MY2isPj8tK50IPvzm3N9PGdUb176b2y1eQ63bz6ifrUq7Q/v6HCoJZ3ZHUA+bLGDo1n/+bAR0c/OW6UfTtko3HZaV7x0z+eMUwRvcrhEMMVQmCIAhCa9CihqLaAtMwSXdYuP7s/miaRp0vxPJN1Xy1dDfjeqajB2oxFCc7K3Y3eI4fdgQYlFlArGJb/bHw1lVkmjEKMlzcfG4pmmGiyhKKdOj5N4IgCILQWojAphmYJpiagYHMX19Zzp6aEAADOvYEScbi8iBLDXeyuO0qZiyScEyy2AEJ0zSRAIsUv5CIaQRBEIS2RAxFNSObKjN51P6Ensu3h8k/5zZsNZsZ2jsv5XMUWaJbvgWttjzhuLXPOL5Z58eQxI9UEARBaLvEp2Az0jWDkf0K6V2cBcAen05w81L8s5/m4tE55GUm5jKRJbjlvH5IS99NOK7mdCTYZTyPv72SzxZuE8ndBEEQhDZLDEU1M8U0uXnaQMqrg+Ragvhf/wxTj2F89FfuPWM6m31Wlm0NkJOmMnxAR9ZtrSZjzHl4M9sjhb0YHQZSZmTx9/+sQTdM3vtqEycMKsIqFkEJgiAIbZAIbI4yWZZQFBnDMOsTsCmmSfssJy4tyG49vg+N7q9Bf+/PdMrIp1tOEbKRjhy28sOOKKaSyZqaPtT6ImxeWkdV3Z7684ciGlHNwGoRvTaCIAhC2yMCm6NEliWcBNGqthHZ/gNqRh7OTv0ISW40Iz7pF8UKsgqGVv88Ka8L0UFns3R7hJofoozpnUn7TCvdsjJ48L1NVNWFE65jtypYVRHUCIIgCG2TCGyOAkkCl+ljz+t/Tpz0K6vkn/t7yChBMyRiqgt3/3H4l34KgKX7SDYWncZDTy6rX930JlBcmMbtU4r4w5T23P3G9oQcUZNGFmNXZQyRQkEQBEFog8RX+6PAJmvUfP5i0komDI09b/4VuxkPTCKaTNrIs3H2Ghkv7n86D721JmnJ9ubdPt5dXIe0eT6Xjm8PxIOn8cd1YNLITiKoEQRBENos0WNzFKh6iND6RSnLTC2KVrkNObc3hmHi1+y4TricjBMuZPZqX4P70MxeXMYvLhzAwGw7918/Co/Lit0iix2GBUEQhDZNBDZHgalrgIlkseHuPQpLThFGyId/5Tdo3gqMkA+HHEGXZCKGhbCuoqk2av3VDZ4zqhkYkoKkx8jz2OIHRVAjCIIgtHEisDkaVDvO3qPw9D8B39LP8S6cheLOJGPMOegBL5aMHCpfvQc1PYv0kedAentkzUu/Dg5ea+CUxe08KN7dSAX9QcQzgiAIggCIwOaoiKouPKUnUfbafWDoAGh1e4jsXItnyCSMaAStZhdazS7CW1aSdfJlRHasJa//aXTvkMG67bUJ55MkuPykDrhCq4nKTtCb4UUJgiAIQgskJg8fBRY9RNUnz9cHNQfyLpyJrKhwQCqEms//g7vf8XikEL+ZkMMZoztjtyoAdGmfzn2X9KOjtQZH9+FEdfEjFARBEIR9RI/NUSBpIWKV2xssj1ZsR03PrV81ZeoxzGiYmL+a8OyXmdBtKBMumYzkysQqxbDVbsE7fyaOSTeA7GjwvIIgCILQ1ojA5iiQJAl7xz6klZ6IpFqRZJnQ5mX4ln+JGYsgKQqSasFz3GQcnfpgGjqWzEJMqwPJYgNPAZoJsboa9JiX8NKPQI8hKwourRpJUdEUBxHDGt/or4mpqozNCEAstPfaTiKGylG4tCAIgiA0SgQ2R4Nqw9ltCFWfvYgR9IIk4+w+hPyzbmXP+49gK+pJdnYR3oUzKV/4EZgGiiuDrJMvw3b+33juo018/+l6TBMy02xcNuFyBhRA+Wt/QqurAMDeqS9Zp1xFQMnAaMLVUVZZQy5fR8Wnz6N7K+PX7tyfrFOuJCCnN+m1BUEQBOFQxASNJqaqMtFty6me/e94UANgGgTXfkf1F/8h/+zfgqxQ9clzBDcsAjO+uZ4eqKVOs/LHl1by3ZrK+t6QGl+Ef7yxmh/KdSTr/mGo8NaVlL/yR5z4m+y1yLKE6t1BxVsP1Ac1AOEty/deO9Bk1xYEQRCEH0MENk3MpvupnfNKyrLonq0YsQixqp3EqncllCmuDHbH0iirCqZ87r8/3wGlpycc0wO1RHeuQVGa5sdqI0zNFy+nLNP9NcR2r0dRRFpxQRAEofmIwKaJSXoU3V/bYHm0bBO6L3kjPktWIWt2hhp83u7KAIYrJ+l4ZPPyJgtsFHSiuzc1WB7euqLJri0IgiAIP4b4FGpqioqkWBoudmUkDCntowe95Kc3/DynXUXWIknH1cwCpCbasc8wJZS0zAbL1cwCMcdGEARBaFYisGliUdmJq98JKcskqwPF4caa3Q5JtSaUxap20rPQgkVN/SOadFwh8g+fHnRCGXv7bqje7SjykR8SiipOPMOmpC6UZBxdj0PTRAJOQRAEofmIwKaJRXWZtBFnY+vYO+G4bHOS94vp+Nd+hylB7i9uRLLaE+rYti7gj5cNrt+cb58hPXM5tX86kU2L649JioWciVfjXfwJ5a/9GYd55CcRa5qJrdtwnL1HJxyXFAu5Z/+WiJJ2xK8pCIIgCD+FZB6NjU+OAbpuUF19+Kt6VFUmM9NFTU0goffCKUeRI7XEyjejOD0oaZkYkSCxiu1Uz3kVa04HMkacgR4OYIT9yO16s8NvIXvnV0TbD2SXX8UbjNE534Gjci1Ow4+z2xDCW1ch2+wo7iy8Cz8itHkZALln/Y5oXp8mGRpyyFGUmJ9o+WZkmxM1pwNhyYlmHDpObqh9hDjRPocm2qhxon0a1xztk5srvvQdTWIfm6MkZFqxpbXD3LUePRxAD/up+uR5zGh8gnBk13rK3/obstODbHdDwSB8UYPC7iPRl39K4Y7VFBo62vwqorpGFLDmlxCpq0a32DFXzSWybVX99TRvBVK+RFNkyAwZVlCykIuyMU0Im+Yxm4hTkiRkRcIwQZEQHwSCIAjHOBHYHAWGJBEMRZBDVRjVu4ju2Yo1p4j8M2/Bu/hjgusX1td1dOyNZ8hkajCpqg3w4PxaXPbBTD5+EplVy+GrfwNgHXo2u22dmFEFFd4Y/Tr2ZNSwC+HLfxGr2Io1v4SQ0bQf0sf6RGFDkiivCTFj7hYC4Rgj+xUwsHseVvnYf22CIAhtlQhsmpghSXyzfBcj8nzUfvAAGBoQ31DPu/gTck+7Hj3oJbJzHfZOfXF0GUhV1MLdr62honb/cu95K3YzaVh7Tht5PtTsYql1ME89Nr++fPkGeHeeyv9dfAP2BS8ipeXu2+tPSMGQJN79ahMfzt1cf2zZ+gpyMuzcd81IrGI7HkEQhGOSmDzcxLyhGJ3SDWKzn6wPauqZBlWfvUj6cZMBSB96Gv7tG3hnYU1CULPPzAU7CRaUwnHn8swHa5LKA2GNpz/ZiXPybwia9qRyYb+6YDQhqNmnsjbM219uQBb78QiCIByTxF/vJmSxKHy7fBd5Dh3dX5OyjhHyo3hyyLr4b0hWB3qnIcxZtqfBcy7ZEWXVjhANjZSs3lKDLyaLhJSNsFgU5i7b3WD5l4t2ENVFAwqCIByLxFBUE4tpxiEn1kZMlXV7ogzo4CbD6qK4XZA1W1MHQtGYSZpT5a5p3XHbFcpqo7z57R62l/vq6xiGSVSWkCUJmyKh62JM6mAxXQfAqsr065qDzaKwYUcte2pCaIZ5rM6FFgRBaPNEYNOENE1neL9C6vzVpNldGOH9y8kVTw6qJ5vME36JLOv0SasjVllJYO0Cbh3ahxUDu/LPdzcknE+WJSaUZqFsmY9vwXsYQS/dstpx50nn8uXOIl79cgedCtL4YWsNT7y5jCyPnXNP6saQnnnIYjJsPU3TGdmvHXaijOvhRNk0DznqJ9Z3EBVKJ75aE8AiAyIeFARBOOaIfWz2aqp9bExZZta3Gzkpr5Lwp4/j7D4Uz8CT0EM+LDkdqJz5JNGy+FwP2e4mY+RZ6IFawrrEO9U9+Xjh/iGTW87uSc89HxNaNSfpuvYTr+aJJS4mjizhxRmrEpJnThlbwpTRxTQ4fnUUtZQ9NqyKRnDZp4TmvZ5wXMkuInfqHQRNZ7MM57WU9mnJRBs1TrRP48Q+Nq3fMTnHZuPGjVx22WWUlpYyatQoHnjgAaLRaHPfVkppcohzhqSTll9I4WV/JX3EFCo/exHZ6qTstfvqgxoAI+yn+vOXsOQUYWxZwrkj88jLdNC50MP1Z/djcGd7yqAGIDrvVX5zZgkfz9+SlBH8g282E9GaP6hpSazRuqSgBkCv2kFw8UwsSoonCYIgCC3eMTcUVVdXxyWXXELnzp159NFHKS8v5/777yccDnP33Xc39+0lcChRwqu+pHbuW5haPPBSM/LJmXw9sYptGEFvyufVLfiAtAHjsElB/nJxT7SqHajSLsyqhr9dGCEftVU1LFlXkVxmmFR5w7TLECulID55OLhyXoPlvqWfUTBoMlGSk5MKgiAILdsxF9i89tprBAIBHnvsMTIyMgDQdZ17772Xa665hvz8/Oa9wb0URcbYuoqaOa8mHNdqy/EumoXiaLhrMla5HTU9FyMSwP+/uwCQrHYcE65u9JqG1PCP8+B8U22ZJIERaXjY0YxFOWa3UhYEQWjjjrmhqK+++ooRI0bUBzUAEydOxDAM5s6d23w3dhBV81P71Wspy7TacixZBQ0+V0nLxoxG0A/o0TGjYSTVgmxzpnyOragnm6v1lGVZHjsepzVlWVsUixk4ug1tsNxRPABNEu0lCIJwLDrmAptNmzZRUlKScMzj8ZCbm8umTZua6a6SyaaBVluesixatgl7+x5IauoPT8+QCVgKu1A3752E43Xz3yPntBuQFEvCcSUti8yx59GtUw4eV+I5HTaV319yHNZj7ifddEzTRM5sh61d96QySbGQccKFRIxjrjNTEARB4BgcivJ6vXg8nqTj6enp1NXVHda5VfXwP/2VfTvWygpqeh5aXerN9mI1ZeRNuYmKGU9ghP17j0qkDToZV49hYHWieHKJVe9fFaWH/FhyO1F41UNEtq4gVrkDa0Exlqx26BYXmTY7f//VGNZvr2Xdtlo6FaTRpyQLp0WJf5jLzZ8nYF/7KM28s28EJ9ln3EJw5Zf4Fs3CiASxFw8gc+z5RO3ZKGbztFVLaZ+WTLRR40T7NE60T+t3zAU2TUWWJTIzXUfsfPbMHDKPn0bF+48klUlWB7LDTc3X/yP3tOuRrHaMcAA1PRfZ5kR2elAdaeSfeTN6oBatrhLZlY7qzkRNy4qfP7sw5XU9QGGOm7EDi47Ya2kKHk9LmJjrwjbqLDylJ4JpItkcKDYnLWGKdcton5ZNtFHjRPs0TrRP63XMBTYejwefz5d0vK6ujvT09J99XsMw8XqDh654CIoi4/E48HpDqEV9SR99LnXfvg16PE+U4skh76xbkexpZE++EawOsDpRo2FMi5VATIIwEA4ACqjZSDnZ8T1VNKDm8PfaaU4Htk/L2RHZFv9f0IRg87Zvy2yflkW0UeNE+zSuOdrnSH5pFg7tmAtsSkpKkubS+Hw+Kioqkube/FRHcrMmXTeIaFas/SZQ2GcsZrAOSbVg2tIISU503YzPcNIATQcsoJm0ldU4um6IzcMaIdrn0EQbNU60T+NE+7Rex9wg49ixY5k3bx5e7/4VQ7NmzUKWZUaNGtWMd5ZaVJfxm24Cjvb4LXkEDEc8qBEEQRAE4Yg75gKbadOm4XK5uOGGG/jmm2946623eOCBB5g2bVqL2cNGEARBEITmccwFNunp6fz73/9GURRuuOEGHnzwQc455xxuv/325r41QRAEQRCa2TE3xwagS5cuvPjii819G4IgCIIgtDDHXI+NIAiCIAhCQ0RgIwiCIAhCqyECG0EQBEEQWg0R2AiCIAiC0GqIwEYQBEEQhFZDBDaCIAiCILQaIrARBEEQBKHVEIGNIAiCIAithghsBEEQBEFoNSTTNEVGRsA0TQzjyDSFosjousga2xDRPo0T7XNooo0aJ9qncUe7fRRF9CEcTSKwEQRBEASh1RBhpCAIgiAIrYYIbARBEARBaDVEYCMIgiAIQqshAhtBEARBEFoNEdgIgiAIgtBqiMBGEARBEIRWQwQ2giAIgiC0GiKwEQRBEASh1RCBjSAIgiAIrYYIbARBEARBaDVEYCMIgiAIQqshAhtBEARBEFoNEdgcIRs3buSyyy6jtLSUUaNG8cADDxCNRpv7tprc1q1bufvuu5kyZQq9e/fmtNNOS1nvjTfe4NRTT6Vfv36cfvrpfPHFF0l1fD4fd9xxB0OHDmXgwIH86le/Ys+ePU39EprURx99xHXXXcfYsWMpLS1lypQpvPnmmxyce7atts+cOXO48MILGT58OH379uXEE0/kL3/5Cz6fL6He559/zumnn06/fv049dRTeeutt5LOFY1G+etf/8qoUaMoLS3lsssuY9OmTUfrpRwVgUCAsWPH0qNHD1asWJFQ1lbfQ2+//TY9evRI+u/vf/97Qr222j5tkQhsjoC6ujouueQSYrEYjz76KDfffDP/+9//uP/++5v71prc+vXrmTNnDp06daJLly4p68yYMYO77rqLiRMn8swzz1BaWsr06dNZunRpQr2bbrqJuXPncs899/D3v/+dzZs3c9VVV6Fp2lF4JU3jxRdfxOFwcPvtt/Pkk08yduxY7rrrLh5//PH6Om25fWpra+nfvz/33nsvzz33HJdddhnvvvsuv/71r+vrLFy4kOnTp1NaWsozzzzDxIkT+cMf/sCsWbMSznXffffxxhtvcPPNN/Poo48SjUa59NJLk4KkY9kTTzyBrutJx9vye2ifZ599ltdff73+v1/+8pf1ZaJ92hhTOGz/+te/zNLSUrOmpqb+2GuvvWb26tXLLCsra74bOwp0Xa//92233WZOnjw5qc4pp5xi3nLLLQnHzjvvPPPKK6+sf7x48WKze/fu5tdff11/bOPGjWaPHj3MGTNmNMGdHx1VVVVJx+68805z0KBB9W3Xltsnlddff93s3r17/e/O5Zdfbp533nkJdW655RZz4sSJ9Y93795t9urVy3zttdfqj9XU1JilpaXm008/fXRuvIlt2LDBLC0tNV999VWze/fu5vLly+vL2vJ76K233jK7d++e8ndtn7bcPm2R6LE5Ar766itGjBhBRkZG/bGJEydiGAZz585tvhs7CmS58bfQ9u3b2bJlCxMnTkw4PmnSJL799tv64bqvvvoKj8fDqFGj6uuUlJTQq1cvvvrqqyN/40dJVlZW0rFevXrh9/sJBoNtvn1S2fd7FIvFiEajLFiwgAkTJiTUmTRpEhs3bmTHjh0AfPPNNxiGkVAvIyODUaNGtZr2ue+++5g2bRrFxcUJx8V7qHGifdoeEdgcAZs2baKkpCThmMfjITc3t9WN8f9U+17/wX+Mu3TpQiwWY/v27fX1iouLkSQpoV5JSUmra8NFixaRn5+P2+0W7bOXrutEIhFWrVrF448/zvjx4ykqKmLbtm3EYrGk3699w577XvumTZvIzs4mPT09qV5raJ9Zs2axbt06brjhhqQy8R6KO+200+jVqxcnnngiTz31VP2QnWiftkdt7htoDbxeLx6PJ+l4eno6dXV1zXBHLce+139w++x7vK/c6/WSlpaW9Pz09HRWrlzZxHd59CxcuJCZM2dy2223AaJ99hk3bhzl5eUAjBkzhgcffBA4/PbxeDzH/O9gKBTi/vvv5+abb8btdieVt/X3UG5uLjfeeCMDBgxAkiQ+//xzHn74YcrLy7n77rvbfPu0RSKwEYSjpKysjJtvvplhw4Zx8cUXN/fttChPP/00oVCIDRs28OSTT3LttdfywgsvNPdttQhPPvkk2dnZnH322c19Ky3SmDFjGDNmTP3j0aNHY7PZ+Pe//821117bjHcmNBcxFHUEeDyelCsv6urqkrrG25p9r//g9vF6vQnlHo8Hv9+f9PzW0oZer5errrqKjIwMHn300fq5SaJ94nr27MnAgQOZOnUqTzzxBAsWLODTTz897Pbxer3HdPvs3LmT559/nl/96lf4fD68Xi/BYBCAYDBIIBAQ76EUJk6ciK7r/PDDD6J92iAR2BwBqcZgfT4fFRUVSXMD2pp9r//g9tm0aRMWi4UOHTrU19u8eXPS/i6bN28+5tswHA5zzTXX4PP5ePbZZxO6u0X7JOvRowcWi4Vt27bRsWNHLBZLyvaB/e1XUlJCZWVl0rBTqvlvx5IdO3YQi8W4+uqrOe644zjuuOPqeyEuvvhiLrvsMvEeOgTRPm2PCGyOgLFjxzJv3rz6bwAQn+wny3LCDPu2qEOHDnTu3Dlpz5GZM2cyYsQIrFYrEG/Duro6vv322/o6mzdvZvXq1YwdO/ao3vORpGkaN910E5s2beLZZ58lPz8/obytt08qy5YtIxaLUVRUhNVqZdiwYXz88ccJdWbOnEmXLl0oKioC4sMPsizzySef1Nepq6vjm2++Oabbp1evXrz00ksJ//3+978H4N577+WPf/yjeA+lMHPmTBRFoXfv3qJ92iAxx+YImDZtGi+//DI33HAD11xzDeXl5TzwwANMmzYt6YOstQmFQsyZMweId5v7/f76PyBDhw4lKyuLG2+8kVtvvZWOHTsybNgwZs6cyfLly/nPf/5Tf56BAwcyevRo7rjjDm677TZsNhsPPfQQPXr04JRTTmmW13Yk3HvvvXzxxRfcfvvt+P3+hA3BevfujdVqbdPtM336dPr27UuPHj2w2+2sWbOG5557jh49enDSSScBcN1113HxxRdzzz33MHHiRBYsWMCHH37IQw89VH+egoICzjnnHB544AFkWSY/P5+nnnqKtLQ0pk2b1lwv77B5PB6GDRuWsqxPnz706dMHoE2/h6644gqGDRtGjx49AJg9ezb/+9//uPjii8nNzQXadvu0RZJ5cL+b8LNs3LiRP//5zyxZsgSXy8WUKVO4+eab678NtFY7duzgxBNPTFn20ksv1f9RfuONN3jmmWfYtWsXxcXF3HLLLYwbNy6hvs/n4y9/+QuffvopmqYxevRo7rzzzmM6OBw/fjw7d+5MWTZ79uz6Hoe22j5PP/00M2fOZNu2bZimSfv27Tn55JO54oorElYAzZ49m4cffpjNmzfTrl07rr76as4555yEc0WjUR566CHee+89AoEAgwYN4s4772xwR+xj1YIFC7j44ot588036devX/3xtvoeuu+++/j6668pKyvDMAw6d+7M1KlTueiiixKWbrfV9mmLRGAjCIIgCEKrIebYCIIgCILQaojARhAEQRCEVkMENoIgCIIgtBoisBEEQRAEodUQgY0gCIIgCK2GCGwEQRAEQWg1RGAjCIIgCEKrIQIbQRAEQRBaDRHYCIIgCILQaohcUYLQBr399tv1yRQPdtVVV3Hrrbce5TsSBEE4MkRgIwht2K9+9av6fFX7dO/evZnuRhAE4fCJwEYQ2rCxY8cmJFI8XMFgEKfTecTOJwiC8FOJOTaCICTYuXMn99xzD6eeeir9+/dn2LBh/OpXv2LHjh0J9d5++2169OjBd999xz333MOIESM4/vjj68vnzJnDBRdcQGlpKQMHDuTqq69m/fr1R/vlCILQxogeG0Fow/x+P9XV1QnHVqxYwZIlS5g8eTIFBQXs3LmTV199lYsvvpgZM2bgcDgS6t97771kZWVxww03EAwGAXj33Xe5/fbbGT16NLfeeiuhUIhXX32VCy64gHfeeSdp+EsQBOFIEYGNILRhl156adKxZcuWMWHChIRj48aN47zzzuPjjz/mjDPOSChLT0/nxRdfRFEUAAKBAP/3f//H1KlT+fOf/1xf78wzz2TChAk89dRTCccFQRCOJBHYCEIbdvfdd1NcXJxwzG631/87Fovh9/vp2LEjHo+H1atXJwU25557bn1QAzBv3jy8Xi+TJ09O6A2SZZkBAwawYMGCpnkxgiAIiMBGENq0/v37J00eDofDPPXUU7z99tuUl5djmmZ9mc/nSzrHwcNKW7ZsAeCSSy5JeU23232Ydy0IgtAwEdgIgpDgz3/+M2+//TaXXHIJpaWlpKWlIUkSN998c0KQs4/NZkt4vK/OAw88QG5ublL9A3t3BEEQjjQR2AiCkGDfPJrbb7+9/lgkEknZW5NKhw4dAMjOzmbkyJFNco+CIAgNEcu9BUFIkKpH5eWXX0bX9R/1/DFjxuB2u3nqqaeIxWJJ5QevwhIEQTiSRI+NIAgJTjjhBN577z3cbjddu3Zl6dKlzJs3j4yMjB/1fLfbzT333MPvfvc7zjrrLCZNmkRWVha7du1izpw5DBo0iLvvvrtpX4QgCG2WCGwEQUjwhz/8AVmW+eCDD4hEIgwaNIgXXniBK6+88kef4xe/+AV5eXk8/fTTPPfcc0SjUfLz8xkyZAhnnXVWE969IAhtnWSmmg0oCIIgCIJwDBJzbARBEARBaDVEYCMIgiAIQqshAhtBEARBEFoNEdgIgiAIgtBqiMBGEARBEIRWQwQ2giAIgiC0GiKwEQRBEASh1RCBjSAIgiAIrYYIbARBEARBaDVEYCMIgiAIQqshAhtBEARBEFoNEdgIgiAIgtBqiMBGEARBEIRW4/8DaKzo869pAJEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The higher the fare the higher the survival rate in general. However, the greatest portion of the passangers were paying fares lower than 100."
      ],
      "metadata": {
        "id": "k7UwlohZyaeu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "ZyHK0_pG6-8Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e444328-46c4-4b22-8c30-6b6ba1d5d1f8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 585.222x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x787d6d09c790>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 585.222x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# In case you believe that other plots are interesting to vizualize the data set, you can provide them here.\n",
        "\n",
        "sns.relplot(data=df,x=\"Pclass\", y=\"Age\",hue=\"Survived\")\n",
        "plt.show()\n",
        "sns.relplot(data=df,x=\"Embarked\", y=\"Fare\",hue=\"Survived\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Categorical encoding\n",
        "\n",
        "Certain columns (sex, embarked, etc.) are categorical. We need to transform them into numerical features."
      ],
      "metadata": {
        "id": "pssKmKCC_qKm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new column \"Sex_num\" where the strings \"male\" and \"female\" are replaced by a binary integer.\n",
        "df[\"Sex_num\"] = (df[\"Sex\"]==\"male\").astype(\"int\") #That's ok because there is no missing data in the \"Sex\" column"
      ],
      "metadata": {
        "id": "cMpH7bx-AF85"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the following code doing?\n",
        "pd.get_dummies(df['Embarked'])"
      ],
      "metadata": {
        "id": "elJwhJPNBJru",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "1220f9ba-0553-48ca-b4e8-8e9cf784a451"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     C  Q  S\n",
              "0    0  0  1\n",
              "1    1  0  0\n",
              "2    0  0  1\n",
              "3    0  0  1\n",
              "4    0  0  1\n",
              "..  .. .. ..\n",
              "886  0  0  1\n",
              "887  0  0  1\n",
              "888  0  0  1\n",
              "889  1  0  0\n",
              "890  0  1  0\n",
              "\n",
              "[891 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35946b13-e702-4c83-8d2e-522304edf286\" 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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code is coverting one categorical value to 3 different numerical values to be learnable"
      ],
      "metadata": {
        "id": "H6tRj6odr73O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using pd.get_dummies, create new columns in the dataframe df, one for each embarkation port\n",
        "ports = pd.get_dummies(df['Embarked'])\n",
        "df2 = pd.DataFrame(df)\n",
        "df2[[\"C\",\"Q\",\"S\"]] = ports\n",
        "df = df2\n",
        "df\n"
      ],
      "metadata": {
        "id": "56hT28iOCMG3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "bfee98fe-5439-4f6f-a13b-f7495168b7de"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     PassengerId  Survived  Pclass  \\\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",
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              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      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  Sex_num  C  Q  S  \n",
              "0        0         A/5 21171   7.2500   NaN        S        1  0  0  1  \n",
              "1        0          PC 17599  71.2833   C85        C        0  1  0  0  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S        0  0  0  1  \n",
              "3        0            113803  53.1000  C123        S        0  0  0  1  \n",
              "4        0            373450   8.0500   NaN        S        1  0  0  1  \n",
              "..     ...               ...      ...   ...      ...      ... .. .. ..  \n",
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              "890      0            370376   7.7500   NaN        Q        1  0  1  0  \n",
              "\n",
              "[891 rows x 16 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <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>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 16 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\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\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We may drop the two columns \"Sex\" and \"Embarked\", as we now included their onehot counterparts in the dataframe. Check that the following code drops theses columns\n",
        "df.drop(columns=[\"Sex\",\"Embarked\"])\n"
      ],
      "metadata": {
        "id": "DfNWAo5uCsTx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "cd5dda56-bf15-40df-8e7e-a505b0645455"
      },
      "execution_count": 74,
      "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",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name   Age  SibSp  Parch  \\\n",
              "0                              Braund, Mr. Owen Harris  22.0      1      0   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
              "2                               Heikkinen, Miss. Laina  26.0      0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
              "4                             Allen, Mr. William Henry  35.0      0      0   \n",
              "..                                                 ...   ...    ...    ...   \n",
              "886                              Montvila, Rev. Juozas  27.0      0      0   \n",
              "887                       Graham, Miss. Margaret Edith  19.0      0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN      1      2   \n",
              "889                              Behr, Mr. Karl Howell  26.0      0      0   \n",
              "890                                Dooley, Mr. Patrick  32.0      0      0   \n",
              "\n",
              "               Ticket     Fare Cabin  Sex_num  C  Q  S  \n",
              "0           A/5 21171   7.2500   NaN        1  0  0  1  \n",
              "1            PC 17599  71.2833   C85        0  1  0  0  \n",
              "2    STON/O2. 3101282   7.9250   NaN        0  0  0  1  \n",
              "3              113803  53.1000  C123        0  0  0  1  \n",
              "4              373450   8.0500   NaN        1  0  0  1  \n",
              "..                ...      ...   ...      ... .. .. ..  \n",
              "886            211536  13.0000   NaN        1  0  0  1  \n",
              "887            112053  30.0000   B42        0  0  0  1  \n",
              "888        W./C. 6607  23.4500   NaN        0  0  0  1  \n",
              "889            111369  30.0000  C148        1  1  0  0  \n",
              "890            370376   7.7500   NaN        1  0  1  0  \n",
              "\n",
              "[891 rows x 14 columns]"
            ],
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              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Sex_num</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <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>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        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",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\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\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use df.head(). Did the dropped column disappear? Solve possible issued using the argument inplace=True, in the drop method\n",
        "df.head()"
      ],
      "metadata": {
        "id": "Z7jd_UfyDfad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "eb105379-cca5-43c6-af0a-8f828b2960c2"
      },
      "execution_count": 75,
      "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",
              "                                                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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"df\",\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\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\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\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What does the matrix df.corr() represent? Provide your interpretation of the numbers you see.\n",
        "sns.set(rc = {'figure.figsize':(13,11)})\n",
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "metadata": {
        "id": "X3CEZZygERoZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0d2634bd-2fe4-4318-be24-13fa892f7911"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-76-9c62aa7c9f4a>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df.corr(),annot=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1100 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the covarience matrix of all the features we have in that dataset. We can see that there is a great correlation between sex and survival, also There is a correlation between age and the survival rate, and finally the age and the class of the passenger. There is also less correlation between the city of embarktion and survival rate, but this seems to be coming from the fare correlation. There are some meaninless correlation happening too, as the correlation between the cities."
      ],
      "metadata": {
        "id": "efAQ2qdK3Gzp"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2uY85k_t6-8U"
      },
      "source": [
        "# Data cleansing\n",
        "Check the number of missing values in \"Age\". You can either\n",
        "\n",
        "\n",
        "*   Do nothing (the rows with missing \"Age\" will be ignored in the training stage)\n",
        "*   Or fill the missing values with some value of your choice.\n",
        "\n",
        "\n",
        "Do something smart.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Do something smart to handle the problem of missing values in the \"Age\" column\n",
        "m = (df[\"Age\"].mean())\n",
        "df[\"Age\"] = df[\"Age\"].apply(lambda x: m if pd.isna(x) else x)\n",
        "nan_count = pd.isna(df[\"Age\"]).sum()\n",
        "df\n"
      ],
      "metadata": {
        "id": "JfnlnXzlIq4u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "59f52a56-5115-423b-8165-0187325ec2aa"
      },
      "execution_count": 77,
      "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",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex        Age  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
              "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
              "4                             Allen, Mr. William Henry    male  35.000000   \n",
              "..                                                 ...     ...        ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.000000   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
              "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
              "890                                Dooley, Mr. Patrick    male  32.000000   \n",
              "\n",
              "     SibSp  Parch            Ticket     Fare Cabin Embarked  Sex_num  C  Q  S  \n",
              "0        1      0         A/5 21171   7.2500   NaN        S        1  0  0  1  \n",
              "1        1      0          PC 17599  71.2833   C85        C        0  1  0  0  \n",
              "2        0      0  STON/O2. 3101282   7.9250   NaN        S        0  0  0  1  \n",
              "3        1      0            113803  53.1000  C123        S        0  0  0  1  \n",
              "4        0      0            373450   8.0500   NaN        S        1  0  0  1  \n",
              "..     ...    ...               ...      ...   ...      ...      ... .. .. ..  \n",
              "886      0      0            211536  13.0000   NaN        S        1  0  0  1  \n",
              "887      0      0            112053  30.0000   B42        S        0  0  0  1  \n",
              "888      1      2        W./C. 6607  23.4500   NaN        S        0  0  0  1  \n",
              "889      0      0            111369  30.0000  C148        C        1  1  0  0  \n",
              "890      0      0            370376   7.7500   NaN        Q        1  0  1  0  \n",
              "\n",
              "[891 rows x 16 columns]"
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              "      <th>886</th>\n",
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              "<p>891 rows × 16 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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\": 13.002015226002882,\n        \"min\": 0.42,\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\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "tm2QgXVa6-8U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "c20d78c2-6d85-4f7d-bcd8-e8a49c1fa3c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'missing_columns = [col for col in mycolumns if col not in df.columns]\\nif missing_columns:\\n    print(\"Columns missing in DataFrame:\", missing_columns)\\nelse:\\n    print(\"All columns present in DataFrame.\")'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "# We will train a logistic regression. Write below the columns which you would like to keep as features for logistic regression.\n",
        "# n.b.: We will NOT keep the columns Ticket and Cabin. Why?\n",
        "#mycolumns2 = [\"Age\", \"Pclass\", \"Fare\", \"C\", \"Q\", \"S\", \"Sex_num\"]\n",
        "mycolumns2 = [\"Age\", \"Fare\",  \"Sex_num\"]\n",
        "mycolumns = [\"Pclass\",  \"Sex_num\"]\n",
        "#mycolumns3 = [\"Age\", \"Fare\", \"C\", \"Q\", \"S\", \"Sex_num\"]\n",
        "df[mycolumns]\n",
        "\n",
        "##nan_count = pd.isna(df[\"Cabin\"]).sum()\n",
        "##print (nan_count)\n",
        "'''missing_columns = [col for col in mycolumns if col not in df.columns]\n",
        "if missing_columns:\n",
        "    print(\"Columns missing in DataFrame:\", missing_columns)\n",
        "else:\n",
        "    print(\"All columns present in DataFrame.\")'''"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because the ticket itself is not relevent, such as the id of each passenger, just a catergorical variable with alot of categories. and the cabin is missing in 687 times of the data"
      ],
      "metadata": {
        "id": "fmMNNotW8cLs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic regression\n"
      ],
      "metadata": {
        "id": "hMDarQFuTEYR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a logistic regression classifier using sklearn, based on the above dataframe and the chosen columns.\n",
        "# Explain the meaning of the parameter in the logistic regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression(penalty='l2',C=1.0,max_iter=1000)"
      ],
      "metadata": {
        "id": "MGxoSeWOTCFp"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model\n",
        "lr.fit(df[mycolumns],df[\"Survived\"])"
      ],
      "metadata": {
        "id": "G84-coE2Tzy1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "986f2546-375e-4cc1-c2ae-b32d80201f8a"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the coefficients of the logistic regression. Discuss the possible positive/negative impact on survival of certain parameters\n",
        "lr.coef_"
      ],
      "metadata": {
        "id": "wDcs4mnJUJ_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa25f45c-b313-437c-fb8d-46bf0f8fd71e"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.93682543, -2.55361558]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UimdQsrb6-8W"
      },
      "source": [
        "# Performance evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "V-iZ4QGf6-8W"
      },
      "outputs": [],
      "source": [
        "# Load the test data\n",
        "Xtest = pd.read_csv(\"testtitanic.csv\")\n",
        "ytest = pd.read_csv(\"gender_submission.csv\")\n",
        "dftest = pd.merge(Xtest,ytest)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Process the test features in order to obtain the same format as for the train set\n",
        "# Do something smart to handle the problem of missing values in the \"Age\" column\n",
        "dfback = dftest\n",
        "m2 = (dftest[\"Age\"].mean())\n",
        "dftest[\"Age\"] = dftest[\"Age\"].apply(lambda x: m2 if pd.isna(x) else x)\n",
        "# We may drop the two columns \"Sex\" and \"Embarked\", as we now included their onehot counterparts in the dataframe. Check that the following code drops theses columns\n",
        "# Using pd.get_dummies, create new columns in the dataframe df, one for each embarkation port\n",
        "ports2 = pd.get_dummies(dftest['Embarked'])\n",
        "dftest[[\"C\",\"Q\",\"S\"]] = ports2\n",
        "pd.get_dummies(dftest['Embarked'])\n",
        "dftest[\"Sex_num\"] = (dftest[\"Sex\"]==\"male\").astype(\"int\") #That's ok because there is no missing data in the \"Sex\" column\n",
        "dftest.drop(columns=[\"Sex\",\"Embarked\"], inplace = True)\n",
        "dftest"
      ],
      "metadata": {
        "id": "2r85LzRHVRRY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "1ebc19db-a985-4155-95aa-6cafa8830c6f"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass                                          Name  \\\n",
              "0            892       3                              Kelly, Mr. James   \n",
              "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
              "2            894       2                     Myles, Mr. Thomas Francis   \n",
              "3            895       3                              Wirz, Mr. Albert   \n",
              "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
              "..           ...     ...                                           ...   \n",
              "413         1305       3                            Spector, Mr. Woolf   \n",
              "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
              "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
              "416         1308       3                           Ware, Mr. Frederick   \n",
              "417         1309       3                      Peter, Master. Michael J   \n",
              "\n",
              "          Age  SibSp  Parch              Ticket      Fare Cabin  Survived  C  \\\n",
              "0    34.50000      0      0              330911    7.8292   NaN         0  0   \n",
              "1    47.00000      1      0              363272    7.0000   NaN         1  0   \n",
              "2    62.00000      0      0              240276    9.6875   NaN         0  0   \n",
              "3    27.00000      0      0              315154    8.6625   NaN         0  0   \n",
              "4    22.00000      1      1             3101298   12.2875   NaN         1  0   \n",
              "..        ...    ...    ...                 ...       ...   ...       ... ..   \n",
              "413  30.27259      0      0           A.5. 3236    8.0500   NaN         0  0   \n",
              "414  39.00000      0      0            PC 17758  108.9000  C105         1  1   \n",
              "415  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN         0  0   \n",
              "416  30.27259      0      0              359309    8.0500   NaN         0  0   \n",
              "417  30.27259      1      1                2668   22.3583   NaN         0  1   \n",
              "\n",
              "     Q  S  Sex_num  \n",
              "0    1  0        1  \n",
              "1    0  1        0  \n",
              "2    1  0        1  \n",
              "3    0  1        1  \n",
              "4    0  1        0  \n",
              "..  .. ..      ...  \n",
              "413  0  1        1  \n",
              "414  0  0        0  \n",
              "415  0  1        1  \n",
              "416  0  1        1  \n",
              "417  0  0        1  \n",
              "\n",
              "[418 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e41f8054-5701-4a9b-b224-5d1eb0523081\" 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>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Survived</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>Sex_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>34.50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>47.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>62.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>27.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>22.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>39.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>38.50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 14 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dftest",
              "summary": "{\n  \"name\": \"dftest\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.634534168325064,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          28.0,\n          34.5,\n          41.0\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          5\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          0\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          \"SC/PARIS 2167\"\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          27.4458\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          \"C31\"\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\": \"C\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a dataframe with only the columns you want to keep (and the label)\n",
        "dftest=dftest[mycolumns+[\"Survived\"]]\n",
        "dftest.head()"
      ],
      "metadata": {
        "id": "b7d9vEy3aupb",
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          "height": 206
        },
        "outputId": "397813c5-a2c1-47d0-f18b-bf2bd9a76493"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Sex_num  Survived\n",
              "0       3        1         0\n",
              "1       3        0         1\n",
              "2       2        1         0\n",
              "3       3        1         0\n",
              "4       3        0         1"
            ],
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              "\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dftest",
              "summary": "{\n  \"name\": \"dftest\",\n  \"rows\": 418,\n  \"fields\": [\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\": \"Sex_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\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}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if there are some missing values. If needed, row containing missing values can be dropped using the dropna() method (inplace)\n",
        "#m3 = dftest[\"Fare\"].mean()\n",
        "#dftest[\"Fare\"] = dftest[\"Fare\"].apply(lambda x: m3 if pd.isna(x) else x)\n",
        "\n",
        "#columns_with_nan = dftest.columns[dftest.isna().any()].tolist()\n",
        "\n",
        "# Print the columns with NaN values\n",
        "#print(\"Columns with NaN values:\", columns_with_nan)"
      ],
      "metadata": {
        "id": "qA4j2tfqW64X"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the hard predictions on the test set (using only the columns to keep)\n",
        "yhat = lr.predict(dftest[mycolumns])\n",
        "yhat"
      ],
      "metadata": {
        "id": "i0kPfKM5WaWh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0bf59a5-5ea2-424d-e058-40a80960fef7"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
              "       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
              "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the accuracy score\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(yhat,dftest.Survived)"
      ],
      "metadata": {
        "id": "c8_oRIPFXxrB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3d5974a-8fde-45ca-f235-19efd1cffce3"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the soft predictions unsing predict_proba\n",
        "soft_predictions = lr.predict_proba(dftest[mycolumns])\n",
        "soft_predictions"
      ],
      "metadata": {
        "id": "3xhmTCSocRwU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8149373d-2d11-4867-9159-7cae25ed1467"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.77524159, 0.22475841],\n",
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              "       [0.89797955, 0.10202045],\n",
              "       [0.77524159, 0.22475841],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.57476595, 0.42523405],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.77524159, 0.22475841],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.77524159, 0.22475841],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.57476595, 0.42523405],\n",
              "       [0.57476595, 0.42523405],\n",
              "       [0.77524159, 0.22475841],\n",
              "       [0.77524159, 0.22475841],\n",
              "       [0.57476595, 0.42523405],\n",
              "       [0.40645459, 0.59354541],\n",
              "       [0.40645459, 0.59354541],\n",
              "       [0.40645459, 0.59354541],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.40645459, 0.59354541],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.09515191, 0.90484809],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.89797955, 0.10202045],\n",
              "       [0.89797955, 0.10202045]])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import RocCurveDisplay\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "RocCurveDisplay.from_estimator(lr,dftest[mycolumns],dftest[\"Survived\"],ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wQ2IRIy5dpPR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "2e15074e-bb2f-4711-e0ee-b54fd98eb8c2"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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HjnkuDgAAAHCFHA3HHjhwwGH5oYce0n/+8x/17dtXvXv3VuXKlSVJJ0+e1KpVq5SamqquXbu6vloAAADACTkKvQ8//LBMJpNDm2EYkqRff/3Vvi69TZIGDBiggwcPuqpOAAAAwGk5Cr3Tpk3L7zoAAACAfJOj0NujR4/8rgMAAADIN059kA0AAAAoTJy+r1hycrK+/vpr/f7774qPj5fNZnNYbzKZNHXq1DwXCAAAAOSVU6H3zJkzevzxx3XmzBlZLBbFx8fL399f8fHxslqtKlGihHx9fV1dKwAAAOAUp6Y3vPHGG0pISFBERIQiIyNlGIbeffdd7du3T2PGjFHRokW1aNEiV9cKAAAAOMWp0PvDDz+oX79+ql27tjw8/ncILy8vPfnkk2rUqBFTGwAAAFBgOBV6k5KSVL58eUmSn5+fTCaT4uPj7evvv/9+7dmzxzUVAgAAAHnkVOi98847FRUVJUny9PRUmTJl9PPPP9vXHz16VN7e3i4pEAAAAMgrpz7I1qhRI23evFnPPvuspGv38Z0/f77i4uJks9m0du1adevWzaWFAgAAAM5yKvQOGzZMv/76q1JSUuTl5aWnnnpK58+f19dffy0PDw916dJF48ePd3WtAAAAgFOcCr3lypVTuXLl7Mve3t6aMmWKpkyZ4rLCAAAAAFfhiWwAAABwezka6Z09e3auD2wymRQWFpbr/QAAAABXI/QCAADA7eUo9B46dCi/6wAAAADyDXN6AQAA4PYIvQAAAHB7hF4AAAC4PUIvAAAA3B6hFwAAAG6P0AsAAAC3l+fQe/78eR06dEhXr151RT0AAACAyzkdejdt2qSOHTuqRYsW6tGjh3755RdJUkxMjLp3765Nmza5rEgAAAAgL5wKvd98841GjBihEiVKKCwsTIZh2NcFBgaqTJkyWr16tcuKBAAAAPLCqdA7Z84c1atXTytWrNBjjz2WYX2dOnV08ODBPBcHAAAAuIJToffIkSMKDQ3Ncn2pUqUUHR3tdFEAAACAKzkVen18fJSYmJjl+lOnTikgIMDZmgAAAACXcir0NmzYUJ999pnS0tIyrLtw4YIiIiLUtGnTPBcHAAAAuIJToff555/X33//rV69emnVqlUymUzavn273n33XXXt2lWGYSgsLMzVtQIAAABOcSr0BgUFafny5QoICNCMGTNkGIYWLVqkefPmKTg4WMuXL1eFChVcXSsAAADgFE9nd7z77rsVHh6u2NhYnTx5UoZhqGLFigoMDHRlfQAAAECeORV6jx49qmrVqkmS/P39Vbt2bZcWBQAAALiSU9MbunTpoq5du2ru3Lk6efKkq2sCAAAAXMqp0Pvqq68qMDBQM2fOVMeOHdWzZ08tXLhQZ86ccXV9AAAAQJ45FXr79u2rJUuWaNu2bXr55Zfl4+Ojt99+W23btlWfPn20ZMkSRUVFubpWAAAAwClOhd50pUqVUv/+/fXRRx/p22+/1YsvviiTyaTXX39drVu3dlWNAAAAQJ7kKfRer3Tp0rr77rsVFBSkokWLymazuerQAAAAQJ44fcsySTIMQ7t27dK6deu0adMmXbp0SRaLRZ07d1anTp1cVSMAAACQJ06F3p9++knr16/X119/rejoaPn5+alt27YKDQ3Vgw8+KE/PPGVpAAAAwKWcSqf9+/eXr6+vWrVqpU6dOqlZs2by8vJydW0AAACASzgVemfMmKGWLVvK29vb1fUAAAAALudU6O3QoYOr6wAAAADyTY5C7+zZs2UymfT000/Lw8NDs2fPvuk+JpNJYWFheS4QAAAAyKtchd6hQ4fKy8uL0AsAAIBCJUeh99ChQ9kuAwAAAAWZyx5OAQAAABRUToXeGjVq6Isvvshy/bp161SjRg2niwIAAABcyanQaxhGtuutVqtMJpNTBR07dkxPPPGE6tSpoyZNmuiNN95QSkpKjvaNiorSiy++qEaNGql27doKDQ3V2rVrnaoDAAAA7sPpR6dlFWoTEhK0fft2lShRItfHjI2N1cCBA1WlShXNmjVLUVFRmj59upKSkjRhwoRs9z1//rz69Omju+66S5MmTZKfn5+OHDmS48AMAAAA95Xj0Dt79mzNmTNH0rXAO3bsWI0dOzbTbQ3D0IABA3JdzMqVK3XlyhXNnj1bAQEBkq6NGr/22msaPny4ypQpk+W+b775psqWLauFCxfKbDZLkho3bpzrGgAAAOB+chx6a9WqpUcffVSGYWj58uVq0qSJqlSp4rCNyWSSj4+P7rnnHrVv3z7XxWzbtk2NGze2B15JCg0N1cSJE7Vjxw717Nkz0/0SEhK0fv16TZ061R54AQAAgHQ5Dr0tWrRQixYtJEmJiYnq27ev7rvvPpcWc/z4cT388MMObRaLRaVLl9bx48ez3O/AgQNKTU2Vp6en+vfvr3379ikgIEDdu3fX888/ryJFijhdk6dn/t/gwmr73xxpD7NJnmZuqlEYmf/bb2b6r9CiDws3+q/wow8Lv4Lch07N6Z02bZqr65AkxcXFyWKxZGj39/dXbGxslvtdvHhRkvTKK6+od+/eevbZZ7V//37NnDlTHh4eeuGFF5yqx8PDpBIlijm1b24kJafZ/20p7qOi3k5PtUYBYLH43O4SkEf0YeFG/xV+9GHhVxD7MEfp6rPPPpMkdevWTSaTyb58M927d3eyrNyx2WySpAcffFDjxo2TJDVq1EhXrlzR4sWLFRYWpqJFizpxXENxcVddWmtmklOs9n/HxScq8WrBe3eEmzObPWSx+CguLlFWq+12lwMn0IeFG/1X+NGHhd+t7kOLxSfHo8o5Cr3jxo2TyWRSp06d5OXlZQ+W2TGZTLkOvRaLRfHx8RnaY2Nj5e/vn+1+0rWge73GjRtr7ty5OnnypEJCQnJVS7q0tPzvsOvPYbMaSjP4Ri/MrFbbLXndIP/Qh4Ub/Vf40YeFX0HswxyF3s2bN0uSvLy8HJZdLSgoKMPc3fj4eF24cEFBQUFZ7letWrVsj5ucnOyS+gAAAFA45Sj0li9fPttlV2nevLnmzp3rMLc3MjJSHh4eatKkSbb1BQcHa+fOnerfv7+9fefOnSpatOhNQzEAAADcm8smjxqGoe+//15bt25VQkKCU8fo27evihUrprCwMG3fvl2rV6/WG2+8ob59+zrco3fgwIFq166dw76jRo3SN998oylTpmjHjh2aO3euFi9erEGDBsnX1zdP1wYAAIDCzanbBLz77rvau3evli1bJula4B08eLB++OEHGYahcuXKKTw8XJUqVcrVcf39/bVkyRJNmjRJYWFhKlasmHr16qVRo0Y5bGez2WS1Wh3aWrdurXfeeUfvvfeeVqxYoTvuuEMjRozQsGHDnLlEAAAAuBGnQu/XX3+tNm3a2JcjIyP1/fffa9SoUapevbomTJigWbNm6c0338z1satWrarw8PBst0kP2zfq1KmTOnXqlOtzAgAAwL05FXqjoqJUuXJl+/LGjRtVrVo1DR8+XJLUr18/rVixwjUVAgAAAHnk1JxeT09PpaSkSPrfXN5mzZrZ15csWVKXLl1yTYUAAABAHjkVeu+++26tXbtWsbGxWr16tS5fvmx/RLEknT17ViVKlHBZkQAAAEBeODW9ISwsTE899ZT9YRB169Z1eDDE1q1bVatWLddUCAAAAOSRU6G3SZMmWrNmjXbs2CGLxeLw4bHY2FjVq1fP4YNuAAAAwO3kVOiVrj0FLbOHPvj7++ull17KU1EAAACAKzkdeiXp1KlT2rZtm86ePStJKleunJo3b66KFSu6pDgAAADAFZwOvdOnT9fSpUtls9kc2j08PDRw4EC9+OKLeS4OAAAAcAWnQu/ixYsVHh6uDh06aPDgwapataok6dixYwoPD1d4eLjKlCmjQYMGubJWAAAAwClOhd6IiAi1bt1aM2bMcGi/77779O677yo5OVkrV64k9AIAAKBAcOo+vWfOnFHTpk2zXN+0aVOdOXPG6aIAAAAAV3Iq9JYsWVKHDh3Kcv2hQ4cUGBjodFEAAACAKzkVejt27KhPPvlE8+fP19WrV+3tV69e1fz58/XJJ5843LsXAAAAuJ2cmtP73HPP6eDBg3rnnXc0c+ZM3XHHHZKk8+fPKy0tTQ0bNtTIkSNdWigAAADgLKdCr4+Pj5YsWaJNmzY53Ke3adOmatGihVq3bi2TyeTSQgEAAABn5Tr0/vLLLzp9+rQCAgLUrFkztW3bNj/qAgAAAFwmx6E3ISFBQ4cO1c8//2xvK1WqlObPn68aNWrkR20AAACAS+T4g2wLFy7Uvn371K5dO73yyit6/PHHFRsby5PXAAAAUODleKR348aNat++vWbOnGlvCwoK0quvvqpTp06pYsWK+VIgAAAAkFc5Huk9c+aMmjRp4tDWtGlTGYahqKgolxcGAAAAuEqOQ29SUpJ8fX0d2tKXU1NTXVsVAAAA4EK5untDYmKiLl++bF+OjY2VJF25csWhPV1AQEBeagMAAABcIlehd+LEiZo4cWKG9hEjRmS6/cGDB52rCgAAAHChHIfeZ599Nj/rAAAAAPINoRcAAABuL8cfZAMAAAAKqxyF3i+//FKGYeT64IZh6Msvv8z1fgAAAIAr5Sj0Tp06VR06dNCCBQt06tSpm25/8uRJzZ07V+3atdO0adPyXCQAAACQFzma07tp0yYtWbJEH3zwgd555x2VL19eNWvWVIUKFeTv7y/DMBQbG6szZ87ot99+07lz5xQQEKABAwZo0KBB+XwJAAAAQPZyFHp9fX319NNPa+jQodqyZYs2b96sffv2aePGjfZpDyaTSZUqVVL9+vXVpk0btWrVSkWKFMnX4gEAAICcyNV9ej09PdWuXTu1a9dOkmS1Wu0PqPD395fZbHZ9hQAAAEAe5Sr03shsNiswMNBVtQAAAAD5gluWAQAAwO0RegEAAOD2CL0AAABwe4ReAAAAuD1CLwAAANxenu7ekJKSogMHDig6Olp169blTg4AAAAokJwe6V26dKmaNm2qRx99VCNGjNDhw4clSTExMWrYsKE++eQTlxUJAAAA5IVToXf16tWaOnWqmjVrpilTptifyiZJgYGBatSokdatW+eyIgEAAIC8cCr0fvDBB2rTpo3efvtttWrVKsP6e+65R0eOHMlzcQAAAIArOBV6T548qebNm2e5PiAgQJcvX3a2JgAAAMClnAq9FotFly5dynL90aNHVbp0aaeLAgAAAFzJqdDbvHlzRUREKC4uLsO6I0eO6OOPP1br1q3zXBwAAADgCk7dsuz5559X79691aVLF7Vq1Uomk0mfffaZVq9erQ0bNqh06dJ65plnXF0rAAAA4BSnRnrLlCmjTz/9VM2aNdP69etlGIY+//xzbdmyRZ07d1ZERAT37AUAAECB4fTDKUqWLKkpU6ZoypQpiomJkc1mU2BgoDw8eMgbAAAAChanEur48eP1yy+/2JcDAwNVqlQpe+Ddv3+/xo8f75oKAQAAgDxyKvSuWbNGf/31V5brT58+rc8++8zZmgAAAACXype5COfPn1fRokXz49AAAABAruV4Tu+mTZu0efNm+3JERIR27tyZYbv4+Hjt3LlT9957r2sqBAAAAPIox6H32LFjioyMlCSZTCb98ssv+u233xy2MZlM8vX1Vf369TVu3DjXVgoAAAA4Kcehd/jw4Ro+fLgkqXr16poyZYq6du2ab4UBAAAAruLULcsOHTrk6joAAACAfMNNdQEAAOD2nH44xdatWxUeHq7ff/9d8fHxMgwjwzYHDx7MU3EAAACAKzg10vv111/rqaee0sWLF9WpUyfZbDZ17txZnTp1UtGiRRUSEqKwsDBX1woAAAA4xamR3nnz5ql27dpavny5YmNjtWLFCj388MNq3LixTp8+rT59+qhChQqurhUAAABwilMjvceOHVOnTp1kNpvl6XktN6elpUmSKlSooH79+mnBggWuqxIAAADIA6dCb9GiRVWkSBFJksVikZeXly5cuGBfX6pUKZ0+fdo1FQIAAAB55FToveuuu3Ts2DH7co0aNfT5558rLS1NycnJ+vLLL3XnnXe6rEgAAAAgL5wKve3atdPmzZuVkpIiSXrqqaf0448/qn79+mrUqJF++uknDRs2zKWFAgAAAM5y6oNsQ4YM0ZAhQ+zLrVq10rJly7RhwwaZzWa1aNFCjRo1clmRAAAAQF44fZ/eG9WrV0/16tWzLyckJMjPz89VhwcAAACc5vInskVHR+udd95Rq1atXH1oAAAAwCm5GumNjo7WZ599pr/++kv+/v5q37697r33XklSVFSU3n//fa1Zs0bJyclq0KBBvhQMAAAA5FaOQ++xY8fUv39/Xb582f7I4YULF+rNN9+UyWTSyy+/rJSUFLVv315Dhgyxh2EAAADgdstx6J0xY4auXr2qiRMnql69ejp9+rSmTZumqVOnKj4+Xq1atdKYMWNUsWLF/KwXAAAAyLUch96ffvpJ/fr1U9++fSVJ1apVk9ls1tChQ9WjRw9NmzYt34oEAAAA8iLHH2S7fPmyQkJCHNqqV68uSWrbtq1rqwIAAABcKMeh12azydPTcWA4fdnX19e1VQEAAAAulKu7N/z222/y9va2L1+5ckUmk0l79uxRfHx8hu3bt2+f64KOHTumyZMna9++fSpWrJi6deum559/Xl5eXjk+Rnh4uKZNm6aWLVtq3rx5ua4BAAAA7iVXoXfJkiVasmRJhvbZs2dnaDOZTDp48GCuiomNjdXAgQNVpUoVzZo1S1FRUZo+fbqSkpI0YcKEHB3jwoULmjNnjkqWLJmrcwMAAMB95Tj0Ll26ND/rkCStXLlSV65c0ezZsxUQECBJslqteu211zR8+HCVKVPmpsd488031bp1a509ezafqwUAAEBhkePQeyseNrFt2zY1btzYHnglKTQ0VBMnTtSOHTvUs2fPbPf/6aeftGnTJkVGRuqFF17I52oBAABQWORqekN+O378uB5++GGHNovFotKlS+v48ePZ7mu1WjVp0iQ99dRTuuOOO1xWk6eny5/UnIHVZtj/7WE2ydOc/+eE65n/229m+q/Qog8LN/qv8KMPC7+C3IcFKvTGxcXJYrFkaPf391dsbGy2+y5fvlyJiYkaNGiQy+rx8DCpRIliLjteVpKS0+z/thT3UVHvAtUtyCWLxed2l4A8og8LN/qv8KMPC7+C2Iduka6io6M1c+ZMvf7667m6y8PN2GyG4uKuuux4WUlOsdr/HRefqMSrBe/dEW7ObPaQxeKjuLhEWa22210OnEAfFm70X+FHHxZ+t7oPLRafHI8qF6jQa7FYMr31WWxsrPz9/bPcb8aMGQoJCVG9evUUFxcnSUpLS1NaWpri4uLk6+ub4R7DOZWWlv8ddv05bFZDaQbf6IWZ1Wq7Ja8b5B/6sHCj/wo/+rDwK4h9WKBCb1BQUIa5u/Hx8bpw4YKCgoKy3O/PP//U7t27Vb9+/Qzr6tevrwULFqh58+YurxcAAACFQ4EKvc2bN9fcuXMd5vZGRkbKw8NDTZo0yXK/l156yT7Cm27q1KkqWrSoRo8eneHxyQAAAPhncTr0nj17VnPnztWuXbt06dIlzZkzR/Xr11dMTIzee+899ezZUzVr1szVMfv27atly5YpLCxMw4cPV1RUlN544w317dvX4R69AwcO1NmzZ7Vx40ZJUo0aNTIcy2KxyNfXVw0bNnT2EgEAAOAmnPrE1NGjR9WjRw+tX79eFSpUUHx8vNLSrt2BIDAwUHv27NGHH36Y6+P6+/tryZIlMpvNCgsL09tvv61evXpp3LhxDtvZbDZZrdYsjgIAAAA4cmqk980331Tx4sUVEREhSXrwwQcd1rdo0ULr1693qqCqVasqPDw8222WLVt20+PkZBsAAAD8Mzg10rt7927169dPgYGBMplMGdaXK1dOUVFReS4OAAAAcAWnQq9hGCpatGiW62NiYlx6v1wAAAAgL5wKvTVr1tTWrVszXZeWlqavvvpK9913X54KAwAAAFzFqdA7bNgwfffdd5o4caKOHDki6dpT0Xbu3KnBgwfr+PHjGjZsmEsLBQAAAJzl1AfZWrRooWnTpmnq1Kn2D7ONHTtWhmHIz89Pr7/+eqYPigAAAABuB6fv09u9e3e1b99eO3fu1IkTJ2Sz2VSpUiU1bdpUfn5+rqwRAAAAyBOnQq9hGDKZTPL19VXbtm1dXRMAAADgUk7N6W3WrJkmT56sPXv2uLoeAAAAwOWcGult0KCBVq9erY8++khlypRRaGioQkNDVbt2bVfXBwAAAOSZU6H3nXfeUVJSkrZs2aL169drxYoVCg8PV/ny5dWpUyeFhoaqRo0arq4VAAAAcIrTH2QrWrSofYT36tWr+uabb7Ru3TqFh4drwYIFqly5siIjI11ZKwAAAOAUp+b03sjX11ddunTRm2++qX/961/y9fXVyZMnXXFoAAAAIM+cHulNl5iYqG+++Ubr16/Xd999p5SUFFWqVEmhoaGuqA8AAADIM6dCb3Jysr799lutW7dO27ZtU2JiosqXL68BAwaoU6dOqlmzpqvrBAAAAJzmVOht1KiRkpKSdMcdd6h3797q1KmT7rvvPlfXBgAAALiEU6G3Z8+eCg0NVb169VxdDwAAAOByToXef//7366uAwAAAMg3OQq9u3fvliTVr1/fYflm0rcHAAAAbqcchd4BAwbIZDLpl19+kZeXl305K4ZhyGQy6eDBgy4rFAAAAHBWjkLv0qVLJUleXl4OywAAAEBhkKPQ26BBg2yXAQAAgILMqSeyPf744/r++++zXP/DDz/o8ccfd7ooAAAAwJWcCr0//vijLl68mOX6mJiYHH/YDQAAAMhvToVeSdl+kO3kyZMqVqyYs4cGAAAAXCrH9+lds2aN1qxZY19+//33FRERkWG7+Ph4HT58WM2bN3dNhQAAAEAe5Tj0JiYm6tKlS/blK1euyMMj40Cxr6+v+vbtq7CwMNdUCAAAAORRjkPvo48+qkcffVSS1Lp1a7388stq06ZNvhUGAAAAuIpTjyH+5ptvXF0HAAAAkG9yFHrPnj0rSSpXrpzD8s2kbw8AAADcTjkKva1bt3Z4DHH68s3wGGIAAAAUBDkKvVOnTpXJZFKRIkUclgEAAIDCIEeht2fPntkuAwAAAAWZ0w+nyExKSoquXr3qykMCAAAAeeZU6P3qq680depUh7bZs2erbt26ql+/vsLCwnTlyhWXFAgAAADklVOhd/HixUpMTLQv7927V7Nnz1bTpk01cOBAfffdd5o7d67LigQAAADywqn79J46dUo9evSwL3/55ZcqVaqUZs+eLU9PTxmGoQ0bNuiFF15wWaEAAACAs5wa6U1JSZG3t7d9eceOHWrevLk8Pa9l6KpVq+rvv/92TYUAAABAHjkVeitUqKCdO3dKkn799VedPHlSzZo1s6+Pjo6Wr6+vayoEAAAA8sip6Q19+vTRlClTdPToUUVFRals2bJq1aqVff3evXtVrVo1lxUJAAAA5IVToXfAgAHy9vbW1q1bde+99+rJJ59U0aJFJUmXL1/WhQsX1K9fP5cWCgAAADjLqdArSb1791bv3r0ztAcEBOjTTz/NU1EAAACAKzkdetMdPXpUZ86ckSSVL1+eaQ0AAAAocJwOvZs2bdL06dPtgTddhQoVNG7cOLVp0ybPxQEAAACu4FTo3bp1q0aOHKly5cpp1KhRqlq1qiTp2LFjioiI0IgRIzR37lw1b97cpcUCAAAAznAq9L733nsKCQnRRx995HBrsjZt2qh///569NFHNWfOHEIvAAAACgSn7tN7+PBhde/ePdN78fr6+qpHjx46fPhwnosDAAAAXMGp0Ovt7a3Y2Ngs18fGxjo8sQ0AAAC4nZwKvQ0bNtTSpUu1b9++DOt++eUXLVu2TI0bN85zcQAAAIArODWnd+zYserbt68effRR1a5dW3fddZck6c8//9T+/ftVsmRJjRkzxqWFAgAAAM5yaqS3YsWKWrt2rQYMGKDY2FitW7dO69atU2xsrB5//HF9/vnnqlChgqtrBQAAAJyS65Feq9WqmJgYWSwWvfTSS3rppZfyoy4AAADAZXI80msYht555x3Vr19fzZs31wMPPKCwsDBdvnw5H8sDAAAA8i7HI72ffvqp5s+fr7Jly6pZs2Y6deqUNm/eLJvNpvfffz8/awQAAADyJMehd8WKFapZs6aWL1+uokWLSpImT56s5cuXKyYmRoGBgflWJAAAAJAXOZ7ecOrUKXXr1s0eeCXp0Ucflc1m08mTJ/OlOAAAAMAVchx6Y2NjM4zmlihRQpKUnJzs2qoAAAAAF8rVLctMJlN+1QEAAADkm1zdsuztt9/WvHnz7Ms2m02S9Morr8jHx8dhW5PJpLVr17qgRAAAACBvchx669evn2k7H2ADAABAQZfj0Lts2bL8rAMAAADIN049hhgAAAAoTAi9AAAAcHuEXgAAALg9Qi8AAADcHqEXAAAAbo/QCwAAALeXq4dT3CgqKkq7d+9WdHS0OnTooLJly8pqtSo+Pl7FixeX2Wx2VZ0AAACA05wKvYZhaPr06froo4+UlpYmk8mk4OBglS1bVlevXlXr1q01cuRIDRo0yMXlAgAAALnn1PSGhQsXaunSpRo8eLA++OADGYZhX1e8eHG1b99eGzZscFmRAAAAQF44FXo//vhjde/eXaNHj1b16tUzrA8JCdGJEyfyWhsAAADgEk6F3nPnzun+++/Pcr2Pj48SEhKcLgoAAABwJafm9JYsWVLnzp3Lcv2BAwd05513OlXQsWPHNHnyZO3bt0/FihVTt27d9Pzzz8vLyyvLfc6fP6/w8HDt2LFDf/31l4oXL6769etr9OjRKl++vFN1AAAAwH04NdLbrl07rVy5UqdOnbK3mUwmSdL27du1Zs0adezYMdfHjY2N1cCBA5WamqpZs2Zp1KhRioiI0PTp07Pd78CBA9q4caNCQ0P13nvvady4cfrjjz/0yCOPKCYmJtd1AAAAwL04NdI7cuRI7dq1S926dVO9evVkMpm0YMECzZgxQz///LNq1Kihp556KtfHXblypa5cuaLZs2crICBAkmS1WvXaa69p+PDhKlOmTKb7PfDAA1q/fr08Pf93OXXr1lXLli312WefafDgwc5cJgAAANyEUyO9xYsXV0REhJ588klFRUXJ29tbu3fvVnx8vMLCwrR8+XL5+Pjk+rjbtm1T48aN7YFXkkJDQ2Wz2bRjx44s97NYLA6BV5LKli2rwMBAnT9/Ptd1AAAAwL04/XCKokWL6plnntEzzzzjsmKOHz+uhx9+2KHNYrGodOnSOn78eK6O9eeffyo6OlpVq1bNU02envn/0Dqr7X+3fPMwm+Rp5kF5hZH5v/1mpv8KLfqwcKP/Cj/6sPAryH2YpyeyuVpcXJwsFkuGdn9/f8XGxub4OIZhaPLkybrjjjvUuXNnp+vx8DCpRIliTu+fU0nJafZ/W4r7qKh3geoW5JLFkvu/cqBgoQ8LN/qv8KMPC7+C2IdOpavx48ffdBuTyaSpU6c6c/g8mzVrln744QctXLhQvr6+Th/HZjMUF3fVhZVlLjnFav93XHyiEq8WvHdHuDmz2UMWi4/i4hJltdpudzlwAn1YuNF/hR99WPjd6j60WHxyPKrsVOjdtWtXhjabzaYLFy7IarUqMDDQqTm9FotF8fHxGdpjY2Pl7++fo2NERERozpw5mjJliho3bpzrGm6Ulpb/HXb9OWxWQ2kG3+iFmdVquyWvG+Qf+rBwo/8KP/qw8CuIfehU6P3mm28ybU9NTdWqVau0ZMkSLV68ONfHDQoKyjB3Nz4+XhcuXFBQUNBN99+4caNeffVVjRw5Ur169cr1+QEAAOCeXPp39CJFiqh///5q0qSJJk2alOv9mzdvrp07dyouLs7eFhkZKQ8PDzVp0iTbfXft2qXRo0frkUceUVhYWK7PDQAAAPeVL5NHq1evrt27d+d6v759+6pYsWIKCwvT9u3btXr1ar3xxhvq27evwz16Bw4cqHbt2tmXjx07prCwMFWpUkXdunXTzz//bP/vr7/+csk1AQAAoPDKl9sE7Ny506k5vf7+/lqyZIkmTZqksLAwFStWTL169dKoUaMctrPZbLJa//fhr19++UXx8fGKj49Xv379HLbt0aPHTZ/oBgAAAPfmVOidPXt2pu3x8fHavXu3fv/9dw0bNsypgqpWrarw8PBst1m2bJnDcs+ePdWzZ0+nzgcAAAD359LQ6+/vr4oVK+q1115T796981QYAAAA4CpOhd5Dhw65ug4AAAAg3+T6g2xJSUmaNm1alrctAwAAAAqaXIfeokWLatWqVYqOjs6PegAAAACXc+qWZffcc4/++OMPV9cCAAAA5AunQu9LL72kdevW6eOPP1ZaWpqrawIAAABcKscfZNu9e7eqVq2qwMBAjRs3TiaTSRMmTNDkyZNVpkwZeXt7O2xvMpm0du1alxcMAAAA5FaOQ+/jjz+uN998U126dFFAQIACAgJ011135WdtAAAAgEvkOPQahiHDMCRlfDgEAAAAUJA5NacXAAAAKExyFXpNJlN+1QEAAADkm1w9kW3s2LEaO3ZsjrY1mUz6/fffnSoKAAAAcKVchd4HH3xQVapUyadSAAAAgPyRq9DbvXt3de3aNb9qAQAAAPIFH2QDAACA2yP0AgAAwO0RegEAAOD2cjyn99ChQ/lZBwAAAJBvGOkFAACA2yP0AgAAwO0RegEAAOD2CL0AAABwe4ReAAAAuD1CLwAAANweoRcAAABuj9ALAAAAt0foBQAAgNsj9AIAAMDtEXoBAADg9gi9AAAAcHuEXgAAALg9Qi8AAADcHqEXAAAAbo/QCwAAALdH6AUAAIDbI/QCAADA7RF6AQAA4PYIvQAAAHB7hF4AAAC4PUIvAAAA3B6hFwAAAG6P0AsAAAC3R+gFAACA2yP0AgAAwO0RegEAAOD2CL0AAABwe4ReAAAAuD1CLwAAANweoRcAAABuj9ALAAAAt0foBQAAgNsj9AIAAMDtEXoBAADg9gi9AAAAcHuEXgAAALg9Qi8AAADcHqEXAAAAbo/QCwAAALdH6AUAAIDbI/QCAADA7RF6AQAA4PY8b3cBAIBbz2azyWpNu91luJTNZlJSklkpKcmyWo3bXQ6cQB8Wfq7sQ7PZUx4erhufJfQCwD+IYRiKi4tRYmLC7S4lX1y86CGbzXa7y0Ae0IeFnyv70MfHTxZLoEwmU56PRegFgH+Q9MDr51dCXl7eLvlFUpCYzSZGCAs5+rDwc0UfGoahlJRkJSRckiT5+5fMc12EXgD4h7DZrPbA6+dnud3l5AtPTw+lpTFKWJjRh4Wfq/rQy8tbkpSQcEnFi5fI81QHPsgGAP8QVqtV0v9+kQBAQZf+88oVn0Eg9ALAP4y7TWkA4L5c+fOK0AsAAAC3R+gFAACA2yP0AgAKnUWL5qldu2a35Fx79/6kpk3r6dCh33O8z6JF8/Trr79kaG/atJ6WL1+W4+OcO3dWTZvWs//XuvWDevTRh7Vo0TwlJyfl+DiFyYIFc29Z315vzpwZeuWVFzNdN27caDVtWk+RkV9lWJfeR1u2bMqwLj4+Xk2b1tO6dV84tKempioiYrmGDBmgdu2aqXXrJho4sK8WLZqn+Ph411xQNg4d+l1Tp76mxx7rpWbN6utf/3o+x/umpqZqzpwZeuihDmrbtqmef/4Z/fXXiQzbnTx5Qs8//4zatm2qhx7qoPfem6HU1FT7+qtXryg0tLX27/857xeUQ9y9AQCAbISEVNfcuR+ocuW7crzPBx8skI+Pr2rVus+hfe7cD1S27J25rmH48DDdf389JSUlavv2bfrggwWKiYnW2LEv5fpYBV23bj3UqFGTW3rOixcv6NNPIzRnzsIM6+LiYrVr1/eSpI0bv1bHjp3zdK7k5GS98MII/f77b+rR4xENHfq0vLy89Mcfh7R6dYSuXEnQyJEv5OkcN7N//y/65Zd9qlnzXiUnJ+dq3//7vze1efMGPfvsKJUufYeWLl2s5557RsuWRcjPz0+SFBcXp5Ejn1LFipU0ZcqbunDhvGbPfldJSUkaPfraGwtf32Lq1auP5s9/T7Nnz3f5NWamwIXeY8eOafLkydq3b5+KFSumbt266fnnn5eXl1e2+xmGoQULFmj58uWKiYlRjRo1NH78eNWpU+fWFA4AcEvFivnp3ntrueRYzh6nQoWK9n3r1Wugkyf/VGTkV3rhhXEufWJVVpKTk+TtXTTfzyNJd9xRRoGBpW/JudJ9/vmnqlChkqpXr5Fh3ZYtm5Wamqp69Rrop5926dKlGJUoEej0uRYtmqv9+3/W22/PUv36De3tdevWU48ej2T6FwJX69Wrj3r37idJevbZYTne7/z5KH355ecaPfpFdenSTZJUo0ZNPfxwF33++Wo99thASdKaNZ/o6tUrmjr1TVks/pKu3T3mnXde1+OPD1apUtf6t3Pnh/TBBwt05MgfuvvuYFdeYqYK1PSG2NhYDRw4UKmpqZo1a5ZGjRqliIgITZ8+/ab7LliwQDNnztSgQYM0b948lS5dWoMHD9apU6duQeUAgILm2LGjGj36WbVt21QdOrTQK6/8S3///bfDNgkJCfrPf/6tdu2aq0uXdpo3b45WrPhQTZvWs2+T2fSGL7/8XP3791br1k3UqVMbPf30EB08eECS7Pu+994M+7SEvXt/sq+7cXrDzp3b9fTTg9WmTRN17NhKzz47TH/8cSjba7v77hAlJyfr8uVL9rb4+Hi99dZ0devWQa1aNdbgwf31448/OOxnGIY++GCBHnqog9q1a6ZXXnlRu3fvcqgxvc5ly8L13nsz9dBDHdSlS3v7/suXL1Pfvj3VqlVjPfJIN61a9ZHDOc6fj9K//z1OXbu2V+vWD+qRRx7SzJlv53h9ZtMb/v77nF555V/q0KGF2rZtqtGjn9WxY0cdtunVq6veeed1rV4doYcf7qIOHVpo/PgXdOnSJd1MZORXatWqTabrNm6MVIUKFTVixGhZrVZt3rzhpsfLSnJyktasWa1mzVo4BN503t7eqlevgdPHzyln3yj9+OMPstlsatWqrb3NYvFX/fqN9P33O+xt33+/Q/XqNbAHXklq3bqdbDabw2uybNk7VaPGPVq/3nH6R34pUCO9K1eu1JUrVzR79mwFBARIuvbO4LXXXtPw4cNVpkyZTPdLTk7WvHnzNHjwYA0aNEiS9MADD6hjx45atGiRXn311VtzAQBQCBmGoZTU2/cwAK8iHi6/jVpU1N8KCxuq8uUr6N//nqSUlGTNn/++RowYpiVLVsjXt5gkaerU17R3724988xIlS1bVmvXfqbDhw9me+yff96r6dMnqV+/AWrcuImSkpJ08OABJSRcm4s5d+4HeuqpJ9SrVx+1bdtRknTXXZlPjdi8eYNeffVlNW3aQhMnTlGRIp7av/8XXbhwQcHB1bO5vnPy9S0mf/8ASdfmWY4aFaaYmGgNHfqMSpe+Qxs2rNPYsc9p8eKPVLVqNUnSJ5+s0uLF8/Xoo4/rgQfqa8+e3Xr99cmZnuOTT1aoZs1aGjfu3/Z7pM6Y8Za++OIzPf74YNWsea9++22/3n9/lry9vdW9ey9J0uTJE3Xx4gU9//wYlSgRqKiovx2+pjdbf6OrV69oxIjhMplMGjNmvLy8vLV06WKFhQ3VkiUrVKZMWfu227dv0+nTpzR69IuKjb2smTPf0f/93xt67bVpWR7/9OlTOnfubIapKNK1gP7LL/s0aNCTqlq1mqpWraaNG79Wr159szxedg4dOqTExKtq2PBBp/aXpLS0m9+v1mw258utCf/664RKlAiUxeL4cJvKlavoq6/W2pdPnjyhTp0ectimePHiKlmylE6ePOHQXqtWbe3evcvltWamQIXebdu2qXHjxvbAK0mhoaGaOHGiduzYoZ49e2a63969e5WQkKDQ0FB7m5eXl9q1a6eNGzfmd9kAUGgZhqFpH+7V0TOxt62GahX8Nf6xui79JR0RsVxWa5refXe2fbQpOLi6+vd/ROvWfaFevfrqzz+Pa9u2LXrlldfs8zQbNnxQjz7aK9tj//77AVks/goLe87e9uCDTe3/Tp+GcMcdZbOdzmAYhubMmaH69Rtp2rS37O2NGzfNsK3NZigtLU1JSUnavn2rvv32Gw0b9ozMZrMkacOG9Tpy5LDCw1forruC/nstjXXq1CmFhy/UpEnTZbVa9eGH4erUqauefnqEJKlBg0aKjb2sL7/8PMM5ixf319Spb9r75cyZ01q9OkJjxoxXt27Xfh/Xr99QSUlJ/x097ikPDw8dPHhAw4eHqU2b9vZjhYZ2sf/7Zutv9NVXX+jvv89p2bIIValy7c3D/ffX1cMPd1FExAqNGDHKYfvp09+xT4k8d+6sli37QDabLcvRzfQR+qpV786wbtOmr2UYhtq16yBJatu2o+bNm60zZ06rfPkKWdaclYsXz0uSQ1DPjXPnzuqRRx666XYvvTRRnTp1deoc2YmPj7fP271e8eIWxcX972dIXFy8/PyKZ7JdccXFxTm0VasWrI8/XqmrV6/Y34zmlwIVeo8fP66HH37Yoc1isah06dI6fvx4tvtJUlBQkEN71apVtWTJEiUlJaloUefmInl65v8MEKvtf8+n9jCb5GkuULNOkEPm//abmf4rtNy9D222LEKlmzyrIj0zm0zSL7/8rLp16zn8ebVy5SqqVu1u7d//i3r16mufrtC0aQv7Nh4eHmrSpFmGP9lfLySkuuLiYjVlyqtq376jatWq49TvmL/+Oqnz56MUFvb8TbedOHG8w3KbNu3t8yela392rlq1mipWrOQwEli/fkNt2LBeknThwnlFR19U06bNHY7VtGmLTENvo0YPOrwRSR+Na9mytcM56tVroI8+WqLz56NUtuydCg6urhUrPpTZ7Kn69RuqQoWKDsfNbn1m73t++WWfgoKq2gOvdO1P6vXqNczwyf86deo6fAaoSpUgpaWl6dKlGJUsWSrjwSVFR1+Uh4eH/P39M6zbuDFSwcHVValSFUlSu3YdNH/+HG3cGKlBg57M9Hg54ewbvFKlSmvhwqU33e7OO8s5dXxXuP77MCf8/QNkGIZiYmKyDb1msynPmaxAhd64uLgMQ+aS5O/vr9jYrEch4uLi5OXlJW9vx0drWiwWGYah2NhYp34geXiYVKJE/r7rkK69269R5dqk+NIl/XhaUiFnsfjc7hKQR+7ah0lJZl286JHhl8e/B9YrdNMbPDyubZ/ZL0Gz2UMJCXEKDg7JsL5kyZKKj4+Tp6eHLl2KlqenpwICLBm2uf7Y178Z8vT0UMOGDTVx4iRFRKzQ6NEj5OXlrdat2+j558c4BKesfkmntyckXBvxKlPmjix/maefOyxspOrVa6D4+Hh98skqbd68QfXq1VOPHtdGpePiYvXHH4fVsmWjTI5hlqenhy5fjrZf3/XnK1WqpMP1Xd9+/XJ8fKwMw1Dnzv+bz3m9ixfPq0KF8poyZbrmzp2jBQve09tvT1flylX01FNh9jmzN1ufLv3cCQnxCgwsmeFrVKpUSf355zGHdouluMNy0aLXArDVmpbl1zgtLVWenp4qUsTs0P7nn8d15MgfGjr0KSUmXpEk+ftbVKNGTW3cGKknn7z2ATBv7yL/3cPIcA4Pj2uDWl5eReTp6WGfpnnhQpRTAc7T01vVq2c97SVdbqY3mEwmmUw5G+SzWCy6cuVKhm2vXImXxeJvb7dYiuvq1YzbJSTEKyDA/4Y+upbd0tJSMq3BZjP9902Jr9MDmOkKVOgtaGw2Q3FxV2/JuV5+/AFZLD6Kj0+S1Xr7fvnAeWazhywWH8XFJdKHhZS792FKSrJsNpusVkNpaY7XZ/a4fW+2rVZDknHT7a5n++9fyK6/DpPpWh9arTYVL25RTEx0huuMjo5WxYqVlZZmU4kSJZWWlqbLl+Mc/mQbHR3tcOz014LVarO3tWsXqnbtQnX58mVt3/6tZs58Vx4eZo0fP8Hhum48//Xtfn7XwnZU1PlMt7v+3GXLltPdd18LO/fdV1dDhz6uefPeU9u2ofLx8VHx4hZVrXq3xo//d6bHSUuzKSCgpP36rj/fxYvRGa5Pkmw2x6+vn59FJpNJ7723UEWKFNGNKlWqbD/PuHET9K9/vaLDhw9qyZJFeuWVcVq+fLXKl6+Q7foKFf43ZSD93MWLW/TXXyczfI0uXoxW8eKWG2p2/Jpfe21lvLbr+fkVV0pKiq5cSXQYPFu/fp2kax+sW7Bgbob9Dhz4XSEh1VWsWHF5eHjowoWLGc7x99/XpjP4+wcoLc2mu+8OkY+Pr77/fqc6d+6WaT3ZyY/pDYZhyDCU5dfnehUrVlZMTLRiYi47DFKeOPGnvf9Npmt/VTlx4k+HYyYkJOjixYv27790sbHX3vz5+Vmy/H6x2WyKjb2qxERrhvUWi0+O/zpXoEKvxWLJ9KbMsbGxmf7Z4fr9UlJSlJyc7PCCjYuLk8lkynbfm8nJi8AVPD2vjXRk942JwoE+LPzctQ/TA4C7Moz//b927Tpau3aNw18Q//rrhI4dO6rOna+FhpCQa7en+u67b+1zSm02m3bs+C7H5wwICFCXLt31/fc7HD6g4+npqZSU7O9/WqlSZd1xRxmtW/eF2rRpl+Nzms1mPf30SI0aFaa1az9Vnz6PqV69Bvr++x0qVaq0/XZQNypd+g6VLFlS3323Vc2atbS3f/fdtzk67wMP1Jd07XfyjVMkMuPh4aEaNe7R0KHPaPv2bRnmwd5sfbratevo228366+/TtinGcTFxemnn37UQw/1yFHt2Uk/5rlzZx2mUGza9LXuuaeWhg8Pc9g+LS1NL744Shs2rFdISHV5exdV9eo1tX37VvttwNJ999238vLyVvXq90iSvL2LqkePh7Vy5Ufau/cn1a1bz2H75ORk/fbbfvvX+ka3e3pDgwaN5OHhoa1bv1HXrt0lXeuLH3/cpUGDhki69v3XuHEThYcvVnx8vIoXvza3d8uWTfLw8FCDBo5/jfj777Py8/NTYGDJbM+d1ZvI3ChQoTcoKCjD3N34+HhduHAhw3zdG/eTpD///NNh2P/48eMqV65cnofDAQAFj9Vqy/AULLPZQ8HBNdW796P66qsvNHr0s3r88cFKSUnWggXvq0yZsvYRsKCgqmrevJVmzHhLyclJKlPmTq1du0YpKcnZ/ml40aJ5io29rPvvf0AlSgTq2LGj2rXre/Xp85h9m8qV79L27dt03333y8fHR5UqVc4wX9FkMiks7Dm9+urLevnlserYsbOKFPHSgQO/qnr1mmrSJOunktWv31C1a9fRqlXL9fDDfdSxY2d9/vmnevbZ4erXr78qVqykhIQEHTlyWKmpqXrqqWdlNpvVv/8TmjnzbQUGllTdutduU/bTTz9KuvltrCpVqqyePR/R5MkT1K/fANWsea/S0tJ06tRf2rfvJ02b9rYSEhI0evSz6tCh039H/lL1yScR8vMrruDg6jddn5nOnbsqImK5xo59/r8Pcrh29waz2ZwhZDqjRo17ZDabdfjwQXvo/e23/Tp79owGDhySIZhK1z5suHnzBoWFPScPDw8NGTJcY8c+p5deGqsOHTrJ29tbe/fu1qpVy/XYYwPtwU+Shgx5Sr//fkBjxjynnj0fUf36DVWkSBEdPfqHVq+OUJMmzbIMvUWKFFH16jXzfM2XLl3Szz/vkSRdvnxZiYmJ9u+lxo2b2nNTnz7dVbbsnZox431J1+6h3KVLN82ZM0MeHh72h1P4+fmpW7f/fSarR49eiohYqfHjX9Djjw/WhQvnNWfODHXr1jPDm7JDhw7q3ntr35L7TReo0Nu8eXPNnTvX4Z15ZGTkfz9YkPXTWerWrSs/Pz+tX7/eHnpTU1O1YcMGNW9+83ejAIDCJyUlWf/+97gM7f/+93/UoUMnzZ49X3Pm/J/+859X5OFhVv36DTRixGiH8Dl+/AS9++4bmjNnhry8vNSxYxcFBVXV6tURWZ63evWaiohYoW++2aSrV6+odOk71K/fAA0cOMS+zejRL2rGjLc0ZsxIJScna+bMuZmGpzZt2svbu6iWLl2siRNflpeXl0JCqqt585Y3vf4nnhiqUaPCtGHDenXq1FUzZ76vxYvna+nSxYqOvih//wAFB4eoR49H7Pv06tVH8fFxWrPmE33yyUrVq9dAzzzznCZOHK9ixTJ+Kv9Gzz8/VpUqVdbnn3+q8PCF8vHxVaVKle3zcb28vFS1ajWtXr1KUVF//3cUtIbefffarUhTUlKyXZ8ZX99imjVrnmbNekdvvDFVNptVtWrdpzlzFjh9F4Tr+fj4qFGjB/XDDzvVoUMnSdc+wFa0aNEs790bGtpZ27Zt0b59e/TAA/XVsGFjvf32TC1ZsliTJ09QWlraf+/tO0oPP9zHYV9vb2+9++4crVnzsSIj1+mzzz6RzWaoYsWK6tixsx55JO9B/mb+/PNYhu+d9OWPP15rHym2Wq2yWh2nFDz33Bj5+Phq7tzZunr1imrVuk//93/vOUwRslgsmjHjfb377psaP/4F+foWU9eu3TVs2DMOx0pLS9NPP/2oZ54ZmR+XmYHJMIwC8/eu2NhYde7cWXfddZeGDx+uqKgoTZ8+XV27dtWECf+bJzVw4ECdPXvW4XZk8+fP16xZszRmzBgFBwdrxYoV2r59uz7//HNVrFgxs9PdlNVqU0zMlTxfV054enqoRIliunTpilv+WfWfgD4s/Ny9D1NTUxQdfU4lS96pIkWyf8plYeXp6ZHnvgsLGyoPDw/NmjXPRVUVbAsWvK+VKz/UunWbb9lT17Ljij7Mre3bt+m1117RF19s4K/DLpDTPty5c7tee+1lrVmzXr6+vpluc7OfW4GBxQrnnF5/f38tWbJEkyZNUlhYmIoVK6ZevXpp1CjHe/Bd+yCG4zuPoUOHyjAMLV682P4Y4kWLFjkdeAEA7u/bbzcrKupvBQVVU3JykjZujNQvv+zT1Klv3XznQujEiT/19dfrVKvWfSpSxFN79+7RypUfqnv3XgUi8N4uTZo0U8WKlfTFF5/pkUece/AEcm/lyg/Vt2//LAOvqxWokd6ChpFe5AZ9WPi5ex8y0pvRrl3fa9682Tp16pTS0lJVqVIVPfbY42rfPvTmOxdCf/99TlOnvqYjR/6wT81o3z5UgwcPk6dnwRgHux0jvdK1h1QcPXrE/gEtOC8nfXj16tX/ht7Hsr0/rytHegm92SD0Ijfow8LP3fuQ0IvCgD4s/FzZh64Mve752CEAAADgOoReAAAAuD1CLwD8wzCrDUBh4cqfV4ReAPiHMJvNknTTJ4UBQEGR/vPKbM77By0Lxkc1AQD5zsPDLB8fPyUkXJIkeXl5Z/vkscLIZjO5/eOW3R19WPi5og8Nw1BKSrISEi7Jx8fPJU9sI/QCwD+IxRIoSfbg6248PDxks/HJ/8KMPiz8XNmHPj5+9p9beUXoBYB/EJPJJH//kipevISs1rTbXY5Lmc0m+fv7Kjb2KiOFhRR9WPi5sg/NZk+XjPCmI/QCwD+Qh4eHPDzc6169np4eKlq0qBITrdzntZCiDwu/gtyHfJANAAAAbo/QCwAAALdH6AUAAIDbMxncpTxLhmHIZrt1Xx6z2UNWa8Ga/4LcoQ8LP/qwcKP/Cj/6sPC7lX3o4WHK8a0XCb0AAABwe0xvAAAAgNsj9AIAAMDtEXoBAADg9gi9AAAAcHuEXgAAALg9Qi8AAADcHqEXAAAAbo/QCwAAALdH6AUAAIDbI/QCAADA7RF6AQAA4PYIvQAAAHB7hF4AAAC4PULvLXDs2DE98cQTqlOnjpo0aaI33nhDKSkpN93PMAzNnz9fLVu2VO3atdWnTx/9/PPP+V8wMnCmD8+fP6833nhD3bp10/3336/mzZvrhRde0JkzZ25R1bies9+H1wsPD1dISIiGDx+eT1UiK3npv6ioKL344otq1KiRateurdDQUK1duzafK8aNnO3DS5cuacKECWrZsqXq1KmjLl26aMWKFbegYlzv5MmTmjBhgrp166aaNWuqS5cuOdqvIGUZz9ty1n+Q2NhYDRw4UFWqVNGsWbMUFRWl6dOnKykpSRMmTMh23wULFmjmzJkaM2aMQkJC9NFHH2nw4MH6/PPPVbFixVt0BXC2Dw8cOKCNGzfq4Ycf1n333adLly7p/fff1yOPPKIvv/xSgYGBt/Aq/tny8n2Y7sKFC5ozZ45KliyZz9XiRnnpv/Pnz6tPnz666667NGnSJPn5+enIkSO5fsODvMlLHz733HM6fvy4Ro8erTvvvFPbtm3Tq6++KrPZrN69e9+iK8CRI0e0detW3XfffbLZbDIMI0f7FagsYyBfzZ0716hTp45x6dIle9vKlSuNGjVqGH///XeW+yUlJRl169Y13n77bXtbcnKy0apVK2PixIn5WDFu5GwfxsbGGqmpqQ5t586dM0JCQoxFixblV7nIhLN9eL2xY8ca//rXv4z+/fsbw4YNy6dKkZm89N+YMWOMPn36GGlpaflcJbLjbB+eP3/eCA4ONlavXu3Q/thjjxmPP/54fpWLTFitVvu/X3zxRaNz58433aegZRmmN+Szbdu2qXHjxgoICLC3hYaGymazaceOHVnut3fvXiUkJCg0NNTe5uXlpXbt2mnbtm35WTJu4GwfWiwWeXo6/jGlbNmyCgwM1Pnz5/OrXGTC2T5M99NPP2nTpk164YUX8rFKZMXZ/ktISND69ev16KOPymw234JKkRVn+zAtLU2SVLx4cYd2Pz+/HI80wjU8PHIfGQtaliH05rPjx48rKCjIoc1isah06dI6fvx4tvtJyrBv1apVdfbsWSUlJbm+WGTK2T7MzJ9//qno6GhVrVrVlSXiJvLSh1arVZMmTdJTTz2lO+64Iz/LRBac7b8DBw4oNTVVnp6e6t+/v+655x41adJEb775plJTU/O7bFzH2T6888471bRpU82dO1dHjx5VQkKC1q1bpx07duixxx7L77KRRwUtyzCnN5/FxcXJYrFkaPf391dsbGy2+3l5ecnb29uh3WKxyDAMxcbGqmjRoi6vFxk524c3MgxDkydP1h133KHOnTu7skTcRF76cPny5UpMTNSgQYPyqTrcjLP9d/HiRUnSK6+8ot69e+vZZ5/V/v37NXPmTHl4eDByfwvl5Xtw1qxZGjVqlP3nptls1iuvvKIOHTrkS61wnYKWZQi9wC0ya9Ys/fDDD1q4cKF8fX1vdznIgejoaM2cOVOvv/66vLy8bnc5yCWbzSZJevDBBzVu3DhJUqNGjXTlyhUtXrxYYWFhDB4UcIZhaPz48Tpx4oTefvttlS5dWjt37tTUqVPl7+/PAAJyhdCbzywWi+Lj4zO0x8bGyt/fP9v9UlJSlJyc7PAOKS4uTiaTKdt94VrO9uH1IiIiNGfOHE2ZMkWNGzd2dYm4CWf7cMaMGQoJCVG9evUUFxcn6docw7S0NMXFxcnX1zfDvG24Xl5+jkrXgu71GjdurLlz5+rkyZMKCQlxbbHIlLN9+O233yoyMlJr166191XDhg0VHR2t6dOnE3oLuIKWZZjTm8+CgoIyzFeKj4/XhQsXMsxxuXE/6doc0OsdP35c5cqVY3TiFnK2D9Nt3LhRr776qkaOHKlevXrlV5nIhrN9+Oeff2r37t2qX7++/b+9e/dq+/btql+/vnbu3JnfpUPO91+1atWyPW5ycrJL6sPNOduHR48eldlsVnBwsEN7jRo1dP78eSUmJuZLvXCNgpZlCL35rHnz5tq5c6d9lEiSIiMj5eHhoSZNmmS5X926deXn56f169fb21JTU7VhwwY1b948X2uGI2f7UJJ27dql0aNH65FHHlFYWFh+l4osONuHL730kpYuXerwX/Xq1VWnTh0tXbpUtWvXvhXl/+M523/ly5dXcHBwhjcnO3fuVNGiRW8aiuE6eelDq9Wqw4cPO7QfOHBAJUuWlI+PT77VjLwraFmGv8vls759+2rZsmUKCwvT8OHDFRUVpTfeeEN9+/ZVmTJl7NsNHDhQZ8+e1caNGyVJ3t7eGj58uGbNmqXAwEAFBwdrxYoVunz5soYMGXK7Lucfydk+PHbsmMLCwlSlShV169bN4Qk0gYGBqlSp0q2+lH8sZ/uwRo0aGY5lsVjk6+urhg0b3rL6/+mc7T9JGjVqlJ555hlNmTJFLVu21K+//qrFixdryJAhzK2/hZztw+bNm6tcuXIaOXKkwsLCdMcdd2j79u1as2aNRowYcbsu5x8pMTFRW7dulSSdOXNGCQkJioyMlCQ1aNBAgYGBBT7LEHrzmb+/v5YsWaJJkyYpLCxMxYoVU69evTRq1CiH7Ww2m6xWq0Pb0KFDZRiGFi9erJiYGNWoUUOLFi3iaWy3mLN9+Msvvyg+Pl7x8fHq16+fw7Y9evTQ9OnTb0n9yNv3IW6/vPRf69at9c477+i9997TihUrdMcdd2jEiBEaNmzYrbyEfzxn+9DPz0/h4eF699139dZbbyk+Pl4VKlTQuHHj1L9//1t9Gf9o0dHReu655xza0peXLl2qhg0bFvgsYzK4uzMAAADcHHN6AQAA4PYIvQAAAHB7hF4AAAC4PUIvAAAA3B6hFwAAAG6P0AsAAAC3R+gFAACA2yP0AgAAwO0RegE3tmvXLoWEhGjXrl23u5R8FRISolmzZuVo29atW2vcuHH5XJF7ePXVV/XEE0/c1hoGDBigAQMG5GjbcePGqXXr1vlckWvPGxISov/85z8uq+X06dMKCQnRp59+6rJjZiY1NVUtWrTQRx99lK/nAVyJxxADBdCnn36q8ePHZ7pu6NChGjNmzC2uKOdurN3Ly0vlypVTkyZN9Mwzz6hUqVL5XsPevXu1Y8cODRw4UBaLJd/PlxOtW7fWmTNn7Ms+Pj6qVq2a+vfvr+7duzt1zK1bt2r//v0aMWKEi6r8n1OnTumTTz7RwoUL7W2nT59WmzZt7MseHh4qU6aM7rnnHj377LOqUaOGy+u4UVRUlCIiItS2bdtbcr5/mvPnz2vp0qX65Zdf9Ntvv+nq1av2R8xer0iRInriiSc0d+5c9erVS97e3repYiDnCL1AATZy5EhVqFDBoS04OPg2VZM76bWnpKRoz549WrFihbZu3aovv/xSPj4+Lj3X/v37ZTab7cv79u3T7Nmz1aNHjwyhNzIyUiaTyaXnz6kaNWrYR04vXLigjz/+WC+++KJSUlLUu3fvXB9v69at+uijj/Il9C5dulTly5dXo0aNMqzr0qWLmjdvLpvNpmPHjmnFihXatm2bIiIiXB5EFy1a5LB8/vx5zZ49W+XLl89wrkmTJskwDJee/5/mzz//1IIFC1SlShWFhIRo3759WW7bs2dPvfXWW/riiy/Uq1evW1gl4BxCL1CANW/eXLVq1brdZTjl+tofeeQRBQQE6IMPPtDmzZvVpUsXl54rN6NMXl5eLj13bpQpU0bdunWzL/fs2VNt2rRReHi4U6E3v6SmpuqLL75Q3759M11fs2ZNh+uoW7eunn76aa1YscKlf6qXctdfRYoUcem5/4nuuece7dq1SwEBAYqMjMw29FosFjVt2lRr1qwh9KJQYE4vUAidOXNGr776qjp06KDatWurYcOGGjlypE6fPn3TfU+cOKERI0aoSZMmqlWrlpo3b65Ro0YpPj7eYbvPP/9cPXv2VO3atdWgQQONGjVK586dc7rm9BHD9BrT0tI0Z84ctW3bVvfee69at26td955RykpKQ77/frrrxoyZIgaNmyo2rVrq3Xr1hmmflw/p3fWrFl64403JElt2rRRSEiIQkJC7Oe9fk7vr7/+qpCQEK1ZsyZDvd99951CQkK0ZcsWe1tUVJTGjx+vBx98UPfee686d+6sTz75xOmvSWBgoIKCgvTXX385tP/0008aOXKkWrZsqXvvvVctWrTQ1KlTlZSUZN9m3Lhx9vmU6dcYEhJiX2+z2RQeHq7OnTurVq1aevDBBzVhwgTFxsbetK49e/bo0qVLevDBB3N0HTf2rSStX7/e/vpp2LChxowZo6ioKIf9Lly4oPHjx6t58+a699571bRpUz399NMOx7l+Tu+uXbvs4Wr8+PH2a06fv3r93NrU1FQ1aNAg02lCCQkJqlWrll5//XV7W0pKimbOnKl27drZv+ZvvPFGhtdjTi1atEh9+/a1v2579uypyMjILLdfu3atOnTooFq1aqlnz57avXt3hm2cff2lpqbq2LFjOn/+/E239fPzU0BAwE23S/fggw9qz549unz5co73AW4XRnqBAiwhIUExMTEObYGBgfr111+1b98+de7cWWXLltWZM2e0YsUKPf744/rqq6+ynD6QkpKiIUOGKCUlRf3791epUqUUFRWlb7/9VnFxcSpevLgk6f3339eMGTMUGhqqXr16KSYmRh9++KEee+wxffbZZ07Nk00Pdum/UF955RWtWbNGHTp00BNPPKH9+/dr3rx5OnbsmObMmSNJio6O1pAhQ1SiRAkNGzZMFotFp0+f1saNG7M8T7t27XTixAl9+eWXGj9+vEqUKGH/ut2oVq1aqlixotavX68ePXo4rFu3bp38/f3VtGlTSdLFixfVu3dvmUwmPfbYYwoMDNS2bdv08ssvKyEhQYMGDcr11yQtLU1RUVHy9/d3aI+MjFRSUpL69eungIAA7d+/Xx9++KH+/vtvzZw5U5LUp08fnT9/Xjt27LCH/OtNmDBBa9asUc+ePTVgwACdPn1aH330kX7//XetWLEi21HRffv2yWQyqWbNmjm6jhv7Nn1ed61atTR69GhFR0dr6dKl2rt3r8PrZ8SIETp69Kj69++v8uXLKyYmRjt27NC5c+cyTOuRpKpVq2rkyJGaOXOm+vTpowceeEDStZHmGxUpUkRt27bVxo0b9dprrzmMGG/atEkpKSnq1KmTpGtvEJ5++mnt2bNHvXv3VtWqVfXHH39oyZIlOnHihN57770cfR2ut3TpUrVu3Vpdu3ZVamqqvvrqKz333HOaN2+eWrZs6bDt7t27tW7dOg0YMEBeXl5asWKFnnzySX388cf26Ux5ef1FRUWpU6dO6tGjh6ZPn57ra8nOPffcI8MwtG/fPrVq1cqlxwZczgBQ4KxevdoIDg7O9D/DMIzExMQM++zbt88IDg421qxZY2/74YcfjODgYOOHH34wDMMwfv/9dyM4ONhYv359luc+ffq0UaNGDeP99993aD98+LBRs2bNDO1Z1b5z504jOjraOHfunPHVV18ZDRo0MGrXrm38/fffxsGDB43g4GDj5Zdfdth3+vTpRnBwsPH9998bhmEYGzduNIKDg439+/dne87g4GBj5syZ9uWFCxcawcHBxqlTpzJs26pVK+PFF1+0L7/99tvGPffcY1y+fNnelpycbNSrV88YP368ve2ll14ymjRpYsTExDgcb9SoUcYDDzyQaZ/ceN7Bgwcb0dHRRnR0tHH48GFj7NixRnBwsPHaa685bJvZsebNm2eEhIQYZ86csbe99tpr9tfE9Xbv3m0EBwcba9eudWjftm1bpu03GjNmjNGgQYMM7adOnTKCg4ONWbNmGdHR0caFCxeMXbt2Gd27dzeCg4ONr7/+2khJSTEaN25sdOnSxUhKSrLvu2XLFiM4ONiYMWOGYRiGERsbawQHBxsLFy7Mtpb+/fsb/fv3ty/v37/fCA4ONlavXp1h2xdffNFo1aqVffm7774zgoODjW+++cZhu6FDhxpt2rSxL3/22WdG9erVjd27dztst2LFCiM4ONjYs2dPtjXeeF7DyNiHKSkpRpcuXYzHH3/coT39+/rXX3+1t505c8aoVauWERYWZm/L6esvvY+u//qkt13/us+J9evXO/z8yExUVJQRHBxszJ8/P1fHBm4HpjcABdiECRP0wQcfOPwnSUWLFrVvk5qaqkuXLqlSpUqyWCz6/fffszyen5+fJGn79u1KTEzMdJuNGzfKZrMpNDRUMTEx9v9KlSqlypUr5/j2Z4MGDVLjxo3VokULjRo1SsWKFdPs2bNVpkwZbd26VZIy3A5r8ODBkmRfnz7y/O233yo1NTVH582tTp06KTU1VRs2bLC37dixQ3FxcfaRQMMwtGHDBrVu3VqGYTh8XZo2bar4+HgdOHDgpufavn27GjdurMaNG6tr1672KST/+te/HLa7vn+vXr2qmJgY3X///TIMI9v+TRcZGanixYurSZMmDrXec8898vX1vWkfXr58OcPo8/VmzZqlxo0bq0mTJhowYID++usvjRkzRu3bt9dvv/2m6Oho9evXz2GudcuWLRUUFKRvv/3Wfo1FihTRjz/+mKMpF85o1KiRSpQooXXr1tnbYmNjtXPnTnvfSte+XlWrVlVQUJDD1yt92oYzt/y7vg9jY2MVHx+vBx54INP+u//++3Xvvffal8uVK6c2bdpo+/btslqteX79VahQQYcPH3b5KK8k++vk0qVLLj824GpMbwAKsNq1a2f6QbakpCTNmzdPn376qaKiohw+sX7j3NzrVaxYUU888YQ++OADffHFF6pXr55at26thx56yB4wT5w4IcMw1L59+0yP4emZsx8bEyZM0F133SWz2axSpUrprrvukofHtffZZ86ckYeHhypVquSwT+nSpWWxWOy39mrQoIE6dOig2bNnKzw8XA0aNFDbtm3VtWtXl30grXr16goKCtL69ev1yCOPSLo2taFEiRL20BMTE6O4uDitWrVKq1atyvQ4N05Dycx9992n559/XlarVUeOHNH777+vuLi4DFMNzp49q5kzZ+qbb77JEAgTEhJuep6TJ08qPj5ejRs3znR9dHT0TY9hZHMXhD59+qhjx44ymUyyWCy6++677f1x9uxZSdJdd92VYb+goCDt2bNH0rUPqI0ZM0avv/66mjRpovvuu08tW7ZU9+7dVbp06ZvWlxOenp5q3769vvzyS6WkpMjLy0sbNmxQamqqQ+g9efKkjh07lqev1422bNmi999/XwcPHnSYF5zZnUMqV66coa1KlSpKTExUTEyMPDw8XPL6yw/pr5PbdUcUIDcIvUAhNGnSJH366acaOHCg6tSpo+LFi8tkMmnUqFE3vWXTuHHj1KNHD23evFk7duzQ5MmTNW/ePEVERKhs2bKy2WwymUxasGCBw23A0vn6+uaoxqwC+/Vu9ovSZDJp5syZ+vnnn7VlyxZ99913eumll/TBBx9o1apVKlasWI5quZlOnTpp7ty5iomJkZ+fn7755ht17tzZHvBtNpsk6aGHHsow9zfd9R8iy0qJEiXsHw5r1qyZgoKCNHz4cC1dutQ+6m21WvXEE08oNjZWTz75pIKCguTr66uoqCiNGzfOXkt2bDabSpYsqbfeeivT9ZnNb75eQECA4uLislxfuXLlHH/ILTuDBg1S69attWnTJm3fvl0zZszQ/PnztWTJkhzPJ76Zzp07a9WqVdq2bZvatm2ryMhIBQUFqXr16vZtbDabgoODs7w3dtmyZXN1zp9++klPP/206tevr4kTJ6p06dIqUqSIVq9erS+//DLX1+Cq119+SH9Tlj53HijICL1AIfT111+re/fuDk8WS05OznaU93rpn3p/5plntHfvXvXr108rVqzQqFGjVKlSJRmGoQoVKmQ6WucK5cuXl81m08mTJ1W1alV7+8WLFxUXF6fy5cs7bF+nTh3VqVNHo0aN0hdffKExY8Zo3bp19pHZG+V21KlTp06aPXu2NmzYoFKlSikhIUGdO3e2rw8MDFSxYsVks9lcEvbStWzZUg0aNNDcuXPVp08f+fr66o8//tCJEyf0+uuvOzy0YseOHRn2z+o6K1WqpO+//15169Z1+DN7TgUFBemLL75QfHy8/S8AOVWuXDlJ1+73euPI6Z9//mlff32tgwcP1uDBg3XixAl1795dixcvzjKw57Zv69evr9KlS2vdunWqW7eufvjhBz311FMZajh06JAaN27skhHLr7/+Wt7e3lq0aJHDXyRWr16d6fYnT57M0HbixAn5+PjY36Dkx+vPFdLvtHH99zFQUDGnFyiEMhuBXbZsmaxWa7b7JSQkKC0tzaEtODhYHh4e9j/Btm/fXmazWbNnz84wamwYhkvm7rVo0UKStGTJEof29DnL6etjY2Mz1JD+QILsbiWVfveKnL4JqFq1qoKDg7Vu3TqtW7dOpUuXVv369e3rzWazOnTooK+//lp//PFHhv3z8qflJ598UpcvX1ZERIQk2aeAXH/dhmFo6dKlGfZNv84bR2VDQ0NltVozvetAWlpatqO40rU3GYZh6LfffsvdxUi69957VbJkSa1cudKhj7Zu3apjx47Z71yQmJio5ORkh30rVaqkYsWK5ahvb3YN6Tw8PNSxY0dt2bJFa9euVVpamsPUBuna1yv9SW83SkpK0tWrV3N0rnRms1kmk8nh+/H06dPavHlzptvv27fPYU7uuXPntHnzZjVp0kRmsznPr7/c3LIstw4cOCCTyaQ6deq4/NiAqzHSCxRCLVu21Oeffy4/Pz9Vq1ZNP//8s3bu3HnT+2v+8MMP+s9//qOOHTuqSpUqslqt+vzzz+2/VKVrweP555/X22+/rTNnzqht27YqVqyYTp8+rU2bNql3794aMmRInuqvXr26evTooVWrVikuLk7169fXr7/+qjVr1qht27b2ubRr1qzRihUr1LZtW1WqVElXrlxRRESE/Pz81Lx58yyPf88990iS3n33XXXq1ElFihRRq1atsp2a0alTJ82cOVPe3t7q1auXPXyme+GFF7Rr1y717t1bjzzyiKpVq6bY2FgdOHBA33//vX788UenvhYtWrRQcHCwwsPD9dhjjykoKEiVKlXS66+/rqioKPn5+enrr7/ONOSlX+fkyZPVtGlTmc1mde7cWQ0aNFCfPn00b948HTx4UE2aNFGRIkV04sQJRUZG6uWXX1bHjh2zrOmBBx5QQECAvv/++yznuWalSJEiGjNmjMaPH6/+/furc+fO9luWlS9f3n5rrRMnTmjQoEHq2LGjqlWrJrPZrE2bNunixYsOo+w3Sv/A5sqVK1WsWDH5+vqqdu3aqlixYpb7hIaGatmyZZo5c6aCg4MzjEp269ZN69ev18SJE7Vr1y7VrVtXVqtVx48fV2RkpBYuXJirh8S0aNFCH3zwgZ588kl16dJF0dHRWr58uSpVqqTDhw9n2D44OFhDhgxxuGWZJIcn7eXl9ZfbW5alv1k6evSopGv37E6fi/3MM884bLtz507VrVuX6Q0oFAi9QCH08ssvy8PDQ1988YWSk5NVt25d+y/Z7ISEhKhp06basmWLoqKi5OPjo5CQEC1YsMBhpGbYsGGqUqWKwsPD7ffMLVu2rJo0aWK/+X9eTZ48WRUqVNCaNWu0adMmlSpVSsOHD9ezzz5r36ZBgwb69ddftW7dOl28eFHFixdX7dq19dZbb2UbcmrXrq3nnntOK1eu1HfffSebzabNmzffNPT+3//9nxITExUaGpphfalSpfTxxx9rzpw52rhxo1asWKGAgABVq1ZNY8aMydPXYvDgwRo3bpy++OIL9ezZU3PnzrXPtfb29la7du302GOPOTwFTbo2Kj9gwAB99dVXWrt2rQzDsAfG//znP7r33nu1cuVKvfvuuzKbzSpfvrweeuihTO9rez0vLy917dpVkZGRGj16dK6vp2fPnipatKgWLFigt956S76+vmrbtq3Gjh1rv0dv2bJl1blzZ33//fdau3atzGazgoKC9H//93/2N2CZKVKkiKZPn6533nlHr776qtLS0jRt2rRsXw9169bVnXfeqXPnzmUY5ZWujQbPmTNH4eHh+vzzz7Vx40b5+PioQoUKGjBgQK6n+TRu3FhTpkzRggULNHXqVFWoUEFjxozRmTNnMg299evXV506dTRnzhydPXtW1apV07Rp0xzmHefn6+9GM2bMcFi+flrG9aE3Pj5e27dv18SJE116fiC/mIybfeoFAPCPc+rUKYWGhmrBggW5Hu3FP0N4eLgWLlyoTZs2OTV3HLjVmNMLAMigYsWKevjhhzV//vzbXQoKoNTUVIWHh+vpp58m8KLQYKQXAAAAbo+RXgAAALg9Qi8AAADcHqEXAAAAbo/QCwAAALdH6AUAAIDbI/QCAADA7RF6AQAA4PYIvQAAAHB7hF4AAAC4PUIvAAAA3N7/AydXkjuMmao8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bonus question: do whatever you find useful\n",
        "\n",
        "Here are some examples:\n",
        "\n",
        "*   Try to include useful elements of the Cabin number\n",
        "*   Try different regularization parameters\n",
        "* Try L1 regularization instead of ridge\n",
        "* Try linear regression\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aJOoT9ldfKzk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, at the beginning I included both fare and pclass in the features that I want to consider in addition to the cities, but the acccuracy was 0.94. Then, I tried [sex_num, age , and fare] and the accuracy was 0.98. Then I tried with only [sex_num and Pclass] and it was 1.0 accuracy, which is the perfect accuracy."
      ],
      "metadata": {
        "id": "p514w_Huut-X"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3XoIdcGfPOaW"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}