Titanic_kaggle.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1WSvD-SGwkII_Y8b4SjF8LOnrX-tg2KST",
      "authorship_tag": "ABX9TyNDl5+EwTEB7o/Za0N7wKJW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/salim4n/kaggle_competition/blob/main/Titanic_kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Résultat de  0.76555 sur kaggle"
      ],
      "metadata": {
        "id": "UTECU18XBo8q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o_lXzj0v1-YH"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "# Permet d'afficher les graphiques directement dans la sortie Colab\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Chemin vers les fichiers CSV\n",
        "train_csv_path = \"data/train.csv\"\n",
        "test_csv_path = \"data/test.csv\""
      ],
      "metadata": {
        "id": "tix3QfRA2Zpb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Charger les données\n",
        "train_data = pd.read_csv(train_csv_path)\n",
        "test_data = pd.read_csv(test_csv_path)"
      ],
      "metadata": {
        "id": "aoi0GUxr2xSr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Afficher les premières lignes du jeu de données d'entraînement\n",
        "print(train_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMKC4ELI20Hx",
        "outputId": "5201f527-1099-47f0-db04-7b25d7c0e365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   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",
            "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                           Allen, Mr. William Henry    male  35.0      0   \n",
            "\n",
            "   Parch            Ticket     Fare Cabin Embarked  \n",
            "0      0         A/5 21171   7.2500   NaN        S  \n",
            "1      0          PC 17599  71.2833   C85        C  \n",
            "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
            "3      0            113803  53.1000  C123        S  \n",
            "4      0            373450   8.0500   NaN        S  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Informations sur les colonnes et les valeurs manquantes\n",
        "print(train_data.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4-4eFhq3DEw",
        "outputId": "f9f94509-7c8a-4da2-a0f4-b2f183114382"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistiques descriptives\n",
        "print(train_data.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESSjc9L-3Is0",
        "outputId": "99327089-91c8-4b30-d891-b7ecb3b3760c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
            "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
            "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
            "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
            "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
            "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
            "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
            "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
            "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
            "\n",
            "            Parch        Fare  \n",
            "count  891.000000  891.000000  \n",
            "mean     0.381594   32.204208  \n",
            "std      0.806057   49.693429  \n",
            "min      0.000000    0.000000  \n",
            "25%      0.000000    7.910400  \n",
            "50%      0.000000   14.454200  \n",
            "75%      0.000000   31.000000  \n",
            "max      6.000000  512.329200  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vérifier les valeurs manquantes\n",
        "print(train_data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyu00E0O3NWN",
        "outputId": "8be1a211-4749-4987-fdd8-efa2221a34f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      0\n",
            "Survived         0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age            177\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             0\n",
            "Cabin          687\n",
            "Embarked         2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualiser les valeurs manquantes avec une heatmap\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(train_data.isnull(), cmap='viridis', cbar=False)\n",
        "plt.title(\"Valeurs Manquantes\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "T4TOTpm53UxL",
        "outputId": "ac4d8cbb-3097-4289-e25c-cff34750ac1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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LIyQREREREbnHFct7fs6dO0deXh5Vq1a1Ka9atSqHDh3KV99kMuV763Qn9944GZxvaZwiIiIicudbk7qvuEModlE+dYs7hGKVbF5iV727YrW3hIQE3N3dbT4nyZ8kiYiIiIiI3EixJD9VqlTB2dmZM2fO2JSfOXMGb2/vfPXj4uLIzs62+QQQcrvCFRERERGRe0CxDHsrXbo0DRo0YN26dXTs2BH4Y8GDdevWMXDgwHz1jUYjRqPRpkxD3kRENNQDNNRDRETsVyzJD8CwYcPo1asXDRs25KGHHmLatGnk5uYSGxtbXCGJiNx1dOMvIiJiv2JLfp566inOnj3LG2+8QXp6OvXq1WP16tX5FkEQEREREREpCsX2np+b1dqpS3GHICIiIiIidwB7V3srtp4fEREREZGioPmPGgZtr7tiqWsREREREZGbpZ4fEZG7mJ526mmniIjYr8h7fhISEnjwwQepUKECXl5edOzYkcOHD9vUSU9Pp0ePHnh7e1OuXDkeeOABPv/886IORURERERExKrIe342bdrEgAEDePDBB7l+/TojR46kTZs2HDx4kHLlygHQs2dPsrKyWL58OVWqVCExMZGuXbuya9cu6tevX9QhiYjcs9TrISKic6HY75av9nb27Fm8vLzYtGkTTZs2BaB8+fLMmjWLHj16WOt5eHgwadIknn32Wbva1WpvIiIiIiICd9Bqb9nZ2QBUrlzZWhYZGcmiRYuIjo6mYsWKLF68mCtXrtCsWbNbHY6IiIiI3GM0/1G9X/a6pcmP2WxmyJAhNG7cmPvvv99avnjxYp566ik8PDxwcXGhbNmyJCUlERQUVGA7JpMJk8lk27YlDyeD860MX0RERETuArrxF3vd0uRnwIABHDhwgK1bt9qUjx49mqysLNauXUuVKlVYtmwZXbt2ZcuWLdSuXTtfOwkJCcTHx9uUBRBKIOG3MnwRkTuennbqpkdEdC4EnQvtdcvm/AwcOJAvv/ySzZs3ExAQYC0/fvw4QUFBHDhwgPDw/yQvrVq1IigoiNmzZ+drq6Cen07uvdXzIyIlni74uuCLiEgxzvmxWCwMGjSIpKQkNm7caJP4AFy+fBkAJyfbVbadnZ0xm80Ftmk0GjEajTZlSnxEREREBPQgCPQgyF5FnvwMGDCAxMREvvzySypUqEB6ejoA7u7uuLq6EhISQlBQEP3792fKlCl4eHiwbNkykpOTWblyZVGHIyJyT9PFTkRExH5FnvzMmjULIN/KbXPnzqV3796UKlWKr7/+mhEjRtC+fXsuXbpEUFAQ8+fP57HHHivqcERE7ml62qkEUERE7HdLhr39nVq1avH5558X9a5FREoc3fiLiIjYz+nvq4iIiIiIiNz9bvlLTkVE5NbRsDf1fomIiP2U/IiI3MV04y8iImI/JT8iIncx9fwoARQREfsV+ZyfsWPHYjAYbD4hISE2dbZv306LFi0oV64cbm5uNG3alN9//72oQxEREREREbG6JT0/4eHhrF279j87cfnPbrZv307btm2Ji4tjxowZuLi4sG/fvnwvPRURkb+nXg8RERH73ZLkx8XFBW9v7wK3DR06lMGDBzNixAhrWXBw8K0IQ0RERERKAD0IEnvdku6Wo0eP4uPjQ82aNYmJiSElJQWAjIwMduzYgZeXF5GRkVStWpVHH32UrVu33oowRERERERErIq856dRo0bMmzeP4OBg0tLSiI+P55FHHuHAgQOcOHEC+GNe0JQpU6hXrx4ff/wxLVu25MCBA9SqVavANk0mEyaTyabMbMnDyeBc1OGLiIiIyF1Gi7+o98teRd7z065dO7p06UKdOnWIiori66+/Jisri8WLF2M2mwHo378/sbGx1K9fn3fffZfg4GA++uijG7aZkJCAu7u7zeckh4o6dBERERERuYfd8lUGKlasyH333cexY8eoVq0aAGFhYTZ1QkNDrUPjChIXF0d2drbNJ4CQG9YXERERERH5X7f8PT+XLl3i+PHj9OjRA39/f3x8fDh8+LBNnSNHjtCuXbsbtmE0GjEajTZlGvImIiIiIqAhX6Chf/Yq8uTnlVdeoX379vj5+ZGamsqYMWNwdname/fuGAwGhg8fzpgxY6hbty716tVj/vz5HDp0iKVLlxZ1KCIiIiJSAujGXwlgstm+ekWe/Pz22290796d8+fP4+npSZMmTfjuu+/w9PQEYMiQIVy5coWhQ4eSmZlJ3bp1SU5OJjAwsKhDERG55+mCrwu+iIjYz2CxWCzFHURhtHbqUtwhiIiIiMgdQA+C9CAo2bzErnq3fMEDERERERGRO4GSHxERERERKRGU/IiIiIiISIlQ5Ase5OXlMXbsWBYsWEB6ejo+Pj707t2bUaNGYTAY8tV//vnn+eCDD3j33XcZMmRIUYcjIiIiIve4kj7fRexX5MnPpEmTmDVrFvPnzyc8PJxdu3YRGxuLu7s7gwcPtqmblJTEd999h4+PT1GHISJSImiSr256RETnQtC50F5Fnvxs27aNDh06EB0dDYC/vz+ffvop33//vU2906dPM2jQINasWWOtKyIijtHFTkRExH5FPucnMjKSdevWceTIEQD27dvH1q1badeunbWO2WymR48eDB8+nPDw8KIOQUREREREJJ8i7/kZMWIEOTk5hISE4OzsTF5eHuPHjycmJsZaZ9KkSbi4uOQbBncjJpMJk8lkU2a25OFkcC7S2EVERERE5N5V5D0/ixcvZuHChSQmJvLDDz8wf/58pkyZwvz58wHYvXs306dPZ968eQUugFCQhIQE3N3dbT4nOVTUoYuIiIiIyD3MYLFYLEXZYI0aNRgxYgQDBgywlr311lssWLCAQ4cOMW3aNIYNG4aT03/yrry8PJycnKhRowanTp3K12ZBPT+d3Hur50dEREREtOABmgOabF5iV70iH/Z2+fJlm8QGwNnZGbPZDECPHj1o1aqVzfaoqCh69OhBbGxsgW0ajUaMRqNNmRIfEREREQHd+Iv9ijz5ad++PePHj8fX15fw8HD27NnDO++8Q58+fQDw8PDAw8PD5julSpXC29ub4ODgog5HRERERO5x6vlRAmivIk9+ZsyYwejRo3nxxRfJyMjAx8eH/v3788YbbxT1rkREREREdOMvdivyOT+3S2unLsUdgoiIiIjcAdTzowSw2Ob8iIiIiIjcTiX9xl/sp+RHRERERO5q6vlRAmivIn/Pj4iIiIiIyJ1IPT8iIiIicldTr4fYy+HkZ/PmzUyePJndu3eTlpZGUlISHTt2tG7/4osvmD17Nrt37yYzM5M9e/ZQr1496/bMzEzGjBnDN998Q0pKCp6ennTs2JFx48bh7u5eFMckIiIiIiWIhr0pAbSXw8PecnNzqVu3LjNnzrzh9iZNmjBp0qQCt6emppKamsqUKVM4cOAA8+bNY/Xq1fTt29fRUEREREREROzmcM9Pu3btaNeu3Q239+jRA4BTp04VuP3+++/n888/t/4dGBjI+PHjeeaZZ7h+/TouLhqJJyIiIiIiRe+OyDSys7Nxc3NT4iMiIiIiDtOQL7FXsWcb586dY9y4cTz33HM3rGMymTCZTDZlZkseTgbnWx2eiIiIiNzhNOdHCaC9inWp65ycHKKjowkLC2Ps2LE3rJeQkIC7u7vN5ySHbl+gIiIiIiJy1yu25OfixYu0bduWChUqkJSURKlSpW5YNy4ujuzsbJtPACG3MVoREREREbnbFcuwt5ycHKKiojAajSxfvpwyZcr8ZX2j0YjRaLQp05A3ERERERFxhMPJz6VLlzh27Jj175MnT7J3714qV66Mr68vmZmZpKSkkJqaCsDhw4cB8Pb2xtvbm5ycHNq0acPly5dZsGABOTk55OTkAODp6Ymzs5IaEREREREpeg4nP7t27aJ58+bWv4cNGwZAr169mDdvHsuXLyc2Nta6vVu3bgCMGTOGsWPH8sMPP7Bjxw4AgoKCbNo+efIk/v7+Dh+EiEhJpUm+muQrIiL2M1gsFktxB1EYrZ26FHcIIiIiIiJyB0g2L7GrXrEvdS0iIiIicjPUC65ecHsV61LXIiIiIiIit4uSHxERERERKREcTn42b95M+/bt8fHxwWAwsGzZshvWff755zEYDEybNs2mPDMzk5iYGNzc3KhYsSJ9+/bl0qVLjoYiIiIiIiJiN4eTn9zcXOrWrcvMmTP/sl5SUhLfffcdPj4++bbFxMTw008/kZyczMqVK9m8eTPPPfeco6GIiIiIiIjYzeEFD9q1a0e7du3+ss7p06cZNGgQa9asITo62mbbzz//zOrVq9m5cycNGzYEYMaMGTz22GNMmTKlwGRJRERERETkZhX5am9ms5kePXowfPhwwsPD823fvn07FStWtCY+AK1atcLJyYkdO3bQqVOnog5JRERERO5hWulM7FXkyc+kSZNwcXFh8ODBBW5PT0/Hy8vLNggXFypXrkx6enqB3zGZTJhMJpsysyUPJ4Nz0QQtIiIiIiL3vCJNfnbv3s306dP54YcfMBgMRdZuQkIC8fHxNmUBhBJI/p4lEZGSRO+20BNfEdG5EHQutFeRJj9btmwhIyMDX19fa1leXh4vv/wy06ZN49SpU3h7e5ORkWHzvevXr5OZmYm3t3eB7cbFxTFs2DCbsk7uvYsydBGRu5IudiIiIvYr0uSnR48etGrVyqYsKiqKHj16EBsbC0BERARZWVns3r2bBg0aALB+/XrMZjONGjUqsF2j0YjRaLQp05A3ERERERFxhMPJz6VLlzh27Jj175MnT7J3714qV66Mr68vHh4eNvVLlSqFt7c3wcHBAISGhtK2bVv69evH7NmzuXbtGgMHDqRbt25a6U1ERERERG4Zh5OfXbt20bx5c+vffw5H69WrF/PmzbOrjYULFzJw4EBatmyJk5MTnTt35r333nM0FBERERERDQEWuxksFouluIMojNZOXYo7BBERERG5A2jBAyWAyeYldtVzusVxiIiIiIiI3BGK/D0/IiJy++hpp552ioiI/ZT8iIjcxXTjLyIiYj8lPyIidzH1/CgBFBER+zmc/GzevJnJkyeze/du0tLSSEpKomPHjjZ1fv75Z1577TU2bdrE9evXCQsL4/PPP7e+/DQ9PZ3hw4eTnJzMxYsXCQ4O5vXXX6dz585FclAiIiWFbvxFRETs5/CCB7m5udStW5eZM2cWuP348eM0adKEkJAQNm7cyI8//sjo0aMpU6aMtU7Pnj05fPgwy5cvZ//+/Tz55JN07dqVPXv2FP5IRERERERE/sJNLXVtMBjy9fx069aNUqVK8cknn9zwe+XLl2fWrFn06NHDWubh4cGkSZN49tln7dq3lroWEREREdAQYNBIAHuXui7SOT9ms5mvvvqKV199laioKPbs2UNAQABxcXE2CVJkZCSLFi0iOjqaihUrsnjxYq5cuUKzZs2KMhwRkXueLvi64IuIzgNivyJNfjIyMrh06RITJ07krbfeYtKkSaxevZonn3ySDRs28OijjwKwePFinnrqKTw8PHBxcaFs2bIkJSURFBRUYLsmkwmTyWRTZrbk4WRwLsrwRUTuOrrgi4joQRDoemCvIn3JqdlsBqBDhw4MHTqUevXqMWLECB5//HFmz55trTd69GiysrJYu3Ytu3btYtiwYXTt2pX9+/cX2G5CQgLu7u42n5McKsrQRURERETkHlekyU+VKlVwcXEhLCzMpjw0NJSUlBTgjwUR/u///o+PPvqIli1bUrduXcaMGUPDhg1vuIhCXFwc2dnZNp8AQooydBERERERuccV6bC30qVL8+CDD3L48GGb8iNHjuDn5wfA5cuXAXByss27nJ2drT1H/8toNGI0Gm3KNORNREREREQc4XDyc+nSJY4dO2b9++TJk+zdu5fKlSvj6+vL8OHDeeqpp2jatCnNmzdn9erVrFixgo0bNwIQEhJCUFAQ/fv3Z8qUKXh4eLBs2TKSk5NZuXJlkR2YiIiIiIjIf3N4qeuNGzfSvHnzfOW9evVi3rx5AHz00UckJCTw22+/ERwcTHx8PB06dLDWPXr0KCNGjGDr1q1cunSJoKAgXnnlFZulr/+OlroWERERERGwf6nrm3rPT3FS8iMiIiIioNXeQKu9Fct7fkRE5PbSBV8XfBERsZ+SHxGRu5hu/EVEdC4U+yn5ERG5i6nnRzc9IiJiP4eTn82bNzN58mR2795NWloaSUlJdOzY0br9zJkzvPbaa3zzzTdkZWXRtGlTZsyYQa1atWza2b59O6+//jo7duzA2dmZevXqsWbNGlxdXW/6oERESgrd+IuI6EEQ6HpgL4dfcpqbm0vdunULfCGpxWKhY8eOnDhxgi+//JI9e/bg5+dHq1atyM3Ntdbbvn07bdu2pU2bNnz//ffs3LmTgQMH5nv3j4iIiIiISFG5qdXeDAaDTc/PkSNHCA4O5sCBA4SHhwNgNpvx9vZmwoQJPPvsswA8/PDDtG7dmnHjxhU6cK32JiIiIiKgnh9Qz0+xrPZmMpkAKFOmjLXMyckJo9HI1q1befbZZ8nIyGDHjh3ExMQQGRnJ8ePHCQkJYfz48TRp0qQowxERERGREqCk3/iL/Yp0nFlISAi+vr7ExcVx4cIFrl69yqRJk/jtt99IS0sD4MSJEwCMHTuWfv36sXr1ah544AFatmzJ0aNHC2zXZDKRk5Nj8zFb8ooydBERERERuccVac9PqVKl+OKLL+jbty+VK1fG2dmZVq1a0a5dO/4cXWc2mwHo378/sbGxANSvX59169bx0UcfkZCQkK/dhIQE4uPjbcoCCCWQ8KIMX0TkrqOhHnriKyI6F4LOhfYq8hUGGjRowN69e8nKyiItLY3Vq1dz/vx5atasCUC1atUACAsLs/leaGgoKSkpBbYZFxdHdna2zSeAkKIOXURERERE7mG37D0/7u7uABw9epRdu3ZZFzfw9/fHx8eHw4cP29Q/cuQI7dq1K7Ato9GI0Wi0KXMyON+CqEVE7i560iciImI/h5OfS5cucezYMevfJ0+eZO/evVSuXBlfX1+WLFmCp6cnvr6+7N+/n5deeomOHTvSpk0b4I8V4oYPH86YMWOoW7cu9erVY/78+Rw6dIilS5cW3ZGJiIiIiIj8F4eTn127dtG8eXPr38OGDQOgV69ezJs3j7S0NIYNG8aZM2eoVq0aPXv2ZPTo0TZtDBkyhCtXrjB06FAyMzOpW7cuycnJBAYG3uThiIiIiIiIFOym3vNTnPSeHxEREREBLXgAGgZt73t+inzBAxERERERkTuRkh8RERERESkRlPyIiIiIiEiJcMuWuhYRkVtP49w1zl1EROznUPKTkJDAF198waFDh3B1dSUyMpJJkyYRHBwMQGZmJmPGjOGbb74hJSUFT09POnbsyLhx46zv/flv58+fp27dupw+fZoLFy5QsWLFIjkoEZGSQjf+IiIi9nMo+dm0aRMDBgzgwQcf5Pr164wcOZI2bdpw8OBBypUrR2pqKqmpqUyZMoWwsDB++eUXnn/+eVJTUwt8h0/fvn2pU6cOp0+fLrIDEhEREZGSRQ+CxF43tdT12bNn8fLyYtOmTTRt2rTAOkuWLOGZZ54hNzcXF5f/5FqzZs1i0aJFvPHGG7Rs2dLhnh8tdS0iIiIioCHAoATQ3qWub2rOT3Z2NgCVK1f+yzpubm42ic/Bgwd588032bFjBydOnLiZEEREREREROxS6OTHbDYzZMgQGjduzP33319gnXPnzjFu3Diee+45a5nJZKJ79+5MnjwZX19fu5Ifk8mEyWSy3b8lDyeDc2HDFxG5J+hpp552ioiI/Qqd/AwYMIADBw6wdevWArfn5OQQHR1NWFgYY8eOtZbHxcURGhrKM888Y/e+EhISiI+PtykLIJRAwgsVu4jIvUI3/iIiIvYr1Ht+Bg4cyMqVK9mwYQPVq1fPt/3ixYu0bduWChUqkJSURKlSpazb1q9fz5IlS3BxccHFxYWWLVsCUKVKFcaMGVPg/uLi4sjOzrb5BBBSmNBFRERERKSEcqjnx2KxMGjQIJKSkti4cSMBAQH56uTk5BAVFYXRaGT58uWUKVPGZvvnn3/O77//bv17586d9OnThy1bthAYGFjgfo1GI0aj0aZMQ95ERDTsDdT7JSIi9nMo+RkwYACJiYl8+eWXVKhQgfT0dADc3d1xdXUlJyeHNm3acPnyZRYsWEBOTg45OTkAeHp64uzsnC/BOXfuHAChoaF6z4+IiIN04y8iImI/h5KfWbNmAdCsWTOb8rlz59K7d29++OEHduzYAUBQUJBNnZMnT+Lv71/4SEVERERERG6Cw8Pe/kqzZs3+tk5RfEdERP6gYW/q/RIREfvd1Ht+RESkeOnGX0RExH6FWu1NRERERETkbqOeHxGRu5iGvan3S0RE7OdQ8pOQkMAXX3zBoUOHcHV1JTIykkmTJhEcHGyt06xZMzZt2mTzvf79+zN79mwA9u3bx8SJE9m6dSvnzp3D39+f559/npdeeqkIDkdEpGTRjb+IiIj9HEp+Nm3axIABA3jwwQe5fv06I0eOpE2bNhw8eJBy5cpZ6/Xr148333zT+nfZsmWt/7179268vLxYsGABNWrUYNu2bTz33HM4OzszcODAIjgkERERESlJ9CBI7GWw3MRSa2fPnsXLy4tNmzbRtGlT4I+en3r16jFt2jS72xkwYAA///wz69evt/s7rZ26OBquiIiIiNyDNARYCWCyeYld9W5qzk92djYAlStXtilfuHAhCxYswNvbm/bt2zN69Gib3p+C2vnfNkRERERE7FHSb/zFfoVOfsxmM0OGDKFx48bcf//91vKnn34aPz8/fHx8+PHHH3nttdc4fPgwX3zxRYHtbNu2jUWLFvHVV1/dcF8mkwmTyWS7f0seTgbnwoYvInJP0NNO3fSIiM6FoHOhvQo97O2FF15g1apVbN26lerVq9+w3vr162nZsiXHjh0jMDDQZtuBAwdo3rw5L730EqNGjbphG2PHjiU+Pt6mLIBQAg3hhQldRERERETuIfYOeytU8jNw4EC+/PJLNm/eTEBAwF/Wzc3NpXz58qxevZqoqChr+cGDB2nevDnPPvss48eP/8s2Cur56eTeWz0/IiIiIqKeH9Tzc0vm/FgsFgYNGkRSUhIbN27828QHYO/evQBUq1bNWvbTTz/RokULevXq9beJD4DRaMRoNNqUKfEREdEFH3TBFxER+zmU/AwYMIDExES+/PJLKlSoQHp6OgDu7u64urpy/PhxEhMTeeyxx/Dw8ODHH39k6NChNG3alDp16gB/DHVr0aIFUVFRDBs2zNqGs7Mznp6eRXx4IiL3Nt34i4iI2M+hYW8Gg6HA8rlz59K7d29+/fVXnnnmGQ4cOEBubi41atSgU6dOjBo1Cjc3N6Dg+TsAfn5+nDp1yu7AtdS1iIh6fkAJoIjoXAg6F97SOT93AiU/IiIiIiICt+k9PyIiUrz0tFNPO0VE50LQudBeSn5ERO5iutiJiIjYT8mPiIiIiNzV9CBI7KXkR0TkLqahHrrpERGdC0HnQns5lPwkJCTwxRdfcOjQIVxdXYmMjGTSpEkEBwdb66SnpzN8+HCSk5O5ePEiwcHBvP7663Tu3NlaJzMzk0GDBrFixQqcnJzo3Lkz06dPp3z58kV3ZCIiJYAudiIiIvZzKPnZtGkTAwYM4MEHH+T69euMHDmSNm3acPDgQcqVKwdAz549ycrKYvny5VSpUoXExES6du3Krl27qF+/PgAxMTGkpaWRnJzMtWvXiI2N5bnnniMxMbHoj1BE5B6mp51KAEVExH43tdT12bNn8fLyYtOmTTRt2hSA8uXLM2vWLHr06GGt5+HhwaRJk3j22Wf5+eefCQsLY+fOnTRs2BCA1atX89hjj/Hbb7/h4+Nj17611LWIiIiIiMBtWuo6OzsbgMqVK1vLIiMjWbRoEdHR0VSsWJHFixdz5coVmjVrBsD27dupWLGiNfEBaNWqFU5OTuzYsYNOnTrdTEgiIiWKen7U8yMiOheCzoX2KnTyYzabGTJkCI0bN+b++++3li9evJinnnoKDw8PXFxcKFu2LElJSQQFBQF/zAny8vKyDcLFhcqVK5Oenl7gvkwmEyaTyXb/ljycDM6FDV9E5J6gi52IiIj9nAr7xQEDBnDgwAE+++wzm/LRo0eTlZXF2rVr2bVrF8OGDaNr167s37+/0EEmJCTg7u5u8znJoUK3JyIiIiIiJU+hen4GDhzIypUr2bx5M9WrV7eWHz9+nP/7v//jwIEDhIeHA1C3bl22bNnCzJkzmT17Nt7e3mRkZNi0d/36dTIzM/H29i5wf3FxcQwbNsymrJN778KELiIiIiIiJZRDPT8Wi4WBAweSlJTE+vXrCQgIsNl++fLlPxp1sm3W2dkZs9kMQEREBFlZWezevdu6ff369ZjNZho1alTgfo1GI25ubjYfDXkTERERERFHONTzM2DAABITE/nyyy+pUKGCdY6Ou7s7rq6uhISEEBQURP/+/ZkyZQoeHh4sW7aM5ORkVq5cCUBoaCht27alX79+zJ49m2vXrjFw4EC6detm90pvIiIiIiIijnKo52fWrFlkZ2fTrFkzqlWrZv0sWrQIgFKlSvH111/j6elJ+/btqVOnDh9//DHz58/nscces7azcOFCQkJCaNmyJY899hhNmjThww8/LNojExERERER+S839Z6f4qT3/IiIiIiICNym9/yIiIiIiBQ3vedHrz6wl5IfEZG7mC74uuCLiIj9lPyIiNzFdOMvIqJzodjP4QUP6tSpY11uOiIiglWrVlm3X7lyhQEDBuDh4UH58uXp3LkzZ86csWlj586dtGzZkooVK1KpUiWioqLYt09PLkVERERE5NZyaMGDFStW4OzsTK1atbBYLMyfP5/JkyezZ88ewsPDeeGFF/jqq6+YN28e7u7uDBw4ECcnJ7799lsALl26hJ+fH0888QQjRozg+vXrjBkzhq1bt/Lrr79SqlQpuwPXggciIhr2BnriKyI6F4LOhfYueHDTq71VrlyZyZMn889//hNPT08SExP55z//CcChQ4cIDQ1l+/btPPzww+zatYsHH3yQlJQUatSoAcD+/fupU6cOR48eJSgoyO79KvkREREREVDyA0p+bvlqb3l5eSxZsoTc3FwiIiLYvXs3165do1WrVtY6ISEh+Pr6WpOf4OBgPDw8mDNnDiNHjiQvL485c+YQGhqKv79/YUMRESmxdMHXBV9EROzncPKzf/9+IiIiuHLlCuXLlycpKYmwsDD27t1L6dKlqVixok39qlWrkp6eDkCFChXYuHEjHTt2ZNy4cQDUqlWLNWvW4OJy41BMJhMmk8mmzGzJw8ng7Gj4IiIiIiJSQjmc/AQHB7N3716ys7NZunQpvXr1YtOmTXZ99/fff6dv3740btyYTz/9lLy8PKZMmUJ0dDQ7d+7E1dW1wO8lJCQQHx9vUxZAKIGEOxq+iMg9Rb0eIiIi9rvpOT+tWrUiMDCQp556ipYtW3LhwgWb3h8/Pz+GDBnC0KFDrcPd0tLScHL6Y6G5q1evUqlSJebMmUO3bt0K3EdBPT+d3Hur50dERERERG79nJ8/mc1mTCYTDRo0oFSpUqxbt47OnTsDcPjwYVJSUoiIiADg8uXLODk5YTAYrN//82+z2XzDfRiNRoxGo02ZEh8RERERAc1/BI0EsJdD7/mJi4tj8+bNnDp1iv379xMXF8fGjRuJiYnB3d2dvn37MmzYMDZs2MDu3buJjY0lIiKChx9+GIDWrVtz4cIFBgwYwM8//8xPP/1EbGwsLi4uNG/e/JYcoIiIiIiICDjY85ORkUHPnj1JS0vD3d2dOnXqsGbNGlq3bg3Au+++i5OTE507d8ZkMhEVFcX7779v/X5ISAgrVqwgPj6eiIgInJycqF+/PqtXr6ZatWpFe2QiIiIiIiL/5abn/BQXvedHRERERETgNs75EREREREpTprzozk/9nJozo+IiIiIiMjdSj0/IiJ3MT3t1NNOERGxn5IfEZG7mG78RURE7OfQsLdZs2ZRp04d3NzccHNzIyIiglWrVlm39+/fn8DAQFxdXfH09KRDhw4cOnQoXzvz5s2jTp06lClTBi8vLwYMGHDzRyIiIiIiIvIXHOr5qV69OhMnTqRWrVpYLBbmz59Phw4d2LNnD+Hh4TRo0ICYmBh8fX3JzMxk7NixtGnThpMnT+Ls/MdLSd955x2mTp3K5MmTadSoEbm5uZw6depWHJuIiIiIlADqBRd73fRS15UrV2by5Mn07ds337Yff/yRunXrcuzYMQIDA7lw4QL/+Mc/WLFiBS1btryZ3WqpaxERERERAW7DUtd5eXksWbKE3NxcIiIi8m3Pzc1l7ty5BAQEUKNGjT+CSk7GbDZz+vRpQkNDuXjxIpGRkUydOtVaR0RERETEEVr8Rb1f9nI4+dm/fz8RERFcuXKF8uXLk5SURFhYmHX7+++/z6uvvkpubi7BwcEkJydTunRpAE6cOIHZbGbChAlMnz4dd3d3Ro0aRevWrfnxxx+t9f6XyWTCZDLZlJkteTgZnB0NX0TknqILvi74IiJiP4eHvV29epWUlBSys7NZunQp//73v9m0aZM1AcrOziYjI4O0tDSmTJnC6dOn+fbbbylTpgwTJkzg9ddfZ82aNbRp0waAs2fP4u3tzddff01UVFSB+xw7dizx8fE2ZQGEEmgIL8wxi4iIiIjIPcTeYW8Ov+S0dOnSBAUF0aBBAxISEqhbty7Tp0+3bnd3d6dWrVo0bdqUpUuXcujQIZKSkgCoVq0agE1PkaenJ1WqVCElJeWG+4yLiyM7O9vmE0CIo6GLiIiIiEgJdtPv+TGbzfmGpP3JYrFgsVis2xs3bgzA4cOHqV69OgCZmZmcO3cOPz+/G+7DaDRiNBptyjTkTUREREREHOFQ8hMXF0e7du3w9fXl4sWLJCYmsnHjRtasWcOJEydYtGgRbdq0wdPTk99++42JEyfi6urKY489BsB9991Hhw4deOmll/jwww9xc3MjLi6OkJAQmjdvfksOUERERETubZr/qPmP9nIo+cnIyKBnz56kpaXh7u5OnTp1WLNmDa1btyY1NZUtW7Ywbdo0Lly4QNWqVWnatCnbtm3Dy8vL2sbHH3/M0KFDiY6OxsnJiUcffZTVq1dTqlSpIj84EREREbn36cZf7HXT7/kpLnrPj4iInnaCbnpEROdC0Lnwlr/nR0REil9Jv9iJiIg4QsmPiMhdTE87lQCKiM4DYj8lPyIidzFd8EVE9CAIdD2wl0Pv+Zk1axZ16tTBzc0NNzc3IiIiWLVqlU2d7du306JFC8qVK4ebmxtNmzbl999/z9eWyWSiXr16GAwG9u7de1MHISIiIiIi8ncc6vmpXr06EydOpFatWlgsFubPn0+HDh3Ys2cP4eHhbN++nbZt2xIXF8eMGTNwcXFh3759ODnlz7FeffVVfHx82LdPmbqIiIiIFJ56PcReN73aW+XKlZk8eTJ9+/bl4YcfpnXr1owbN+4vv7Nq1SqGDRvG559/Tnh4OHv27KFevXoO7VervYmIaKgH6KZHRHQuBJ0Lb/lqb3l5eSxZsoTc3FwiIiLIyMhgx44dxMTEEBkZyfHjxwkJCWH8+PE0adLE+r0zZ87Qr18/li1bRtmyZQu7exERQRc7ERERRzg05wdg//79lC9fHqPRyPPPP09SUhJhYWGcOHECgLFjx9KvXz9Wr17NAw88QMuWLTl69CgAFouF3r178/zzz9OwYUO792kymcjJybH5mC15joYuIiIiIiIlmMM9P8HBwezdu5fs7GyWLl1Kr1692LRpE2azGYD+/fsTGxsLQP369Vm3bh0fffQRCQkJzJgxg4sXLxIXF+fQPhMSEoiPj7cpCyCUQMIdDV9E5J6ioR7q/RIREfvd9JyfVq1aERgYyIgRI6hZsyaffPIJzzzzjHX7U089hYuLCwsXLqRjx46sWLECg8Fg3Z6Xl4ezszMxMTHMnz+/wH2YTCZMJpNNWSf33jgZnG8mdBERERERuQfc8jk/fzKbzZhMJvz9/fHx8eHw4cM2248cOUK7du0AeO+993jrrbes21JTU4mKimLRokU0atTohvswGo0YjUabMiU+IiLq+QH1/IiIzoWgc6G9HEp+4uLiaNeuHb6+vly8eJHExEQ2btzImjVrMBgMDB8+nDFjxlC3bl3q1avH/PnzOXToEEuXLgXA19fXpr3y5csDEBgYSPXq1YvokERESg5d7EREdC4U+zmU/GRkZNCzZ0/S0tJwd3enTp06rFmzhtatWwMwZMgQrly5wtChQ8nMzKRu3bokJycTGBh4S4IXEREREVHPjxJAe930nJ/iovf8iIiIiAgo+QElP/bO+XF4qWsREREREZG7kZIfEREREREpEZT8iIiIiIhIiaDkR0RERERESgSHkp9Zs2ZRp04d3NzccHNzIyIiglWrVlm3Hz9+nE6dOuHp6Ymbmxtdu3blzJkz1u2nTp2ib9++BAQE4OrqSmBgIGPGjOHq1atFd0QiIiIiIiIFcCj5qV69OhMnTmT37t3s2rWLFi1a0KFDB3766Sdyc3Np06YNBoOB9evX8+2333L16lXat2+P2WwG4NChQ5jNZj744AN++ukn3n33XWbPns3IkSNvycGJiIiIiIj86aaXuq5cuTKTJ0+mRo0atGvXjgsXLuDm5gZAdnY2lSpV4ptvvqFVq1YFfn/y5MnMmjWLEydOOLRfLXUtIiIiIqClrkFLXd/ypa7z8vL47LPPyM3NJSIiApPJhMFgwGg0WuuUKVMGJycntm7desN2srOzqVy5cmHDEBERERERsYuLo1/Yv38/ERERXLlyhfLly5OUlERYWBienp6UK1eO1157jQkTJmCxWBgxYgR5eXmkpaUV2NaxY8eYMWMGU6ZM+ct9mkwmTCaTTZnZkoeTwdnR8EVEREREpIRyeNjb1atXSUlJITs7m6VLl/Lvf/+bTZs2ERYWxjfffMMLL7zAyZMncXJyonv37hw8eJCHHnqIWbNm2bRz+vRpHn30UZo1a8a///3vv9zn2LFjiY+PtykLIJRAQ7gjoYuIiIiIyD3I3mFvNz3np1WrVgQGBvLBBx9Yy86dO4eLiwsVK1bE29ubl19+meHDh1u3p6am0qxZMx5++GHmzZuHk9Nfj74rqOenk3tv9fyIiIiIiOb8oDk/9iY/Dg97+19mszlfYlKlShUA1q9fT0ZGBk888YR12+nTp2nevDkNGjRg7ty5f5v4ABiNRpu5RIASHxERERERcYhDyU9cXBzt2rXD19eXixcvkpiYyMaNG1mzZg0Ac+fOJTQ0FE9PT7Zv385LL73E0KFDCQ4OBv5IfJo1a4afnx9Tpkzh7Nmz1ra9vb2L8LBERERERERsOZT8ZGRk0LNnT9LS0nB3d6dOnTqsWbOG1q1bA3D48GHi4uLIzMzE39+f119/naFDh1q/n5yczLFjxzh27BjVq1e3afsmR9+JiIiIiIj8pZue81Nc9J4fEREREQHN+QHN+bnl7/kRERERERG5myj5ERERERGREuGmV3sTEZHio6EeGuohIiL2u6men4kTJ2IwGBgyZAgAmZmZDBo0iODgYFxdXfH19WXw4MFkZ2fbfC8lJYXo6GjKli2Ll5cXw4cP5/r16zcTioiIiIiIyF8qdM/Pzp07+eCDD6hTp461LDU1ldTUVKZMmUJYWBi//PILzz//PKmpqSxduhSAvLw8oqOj8fb2Ztu2baSlpdGzZ09KlSrFhAkTbv6IRERKEPV6iIjoXCj2K9Rqb5cuXeKBBx7g/fff56233qJevXpMmzatwLpLlizhmWeeITc3FxcXF1atWsXjjz9OamoqVatWBWD27Nm89tprnD17ltKlS9sVg1Z7ExERERHQEGBQAnhLV3sbMGAA0dHRtGrV6m/rZmdn4+bmhovLH51M27dvp3bt2tbEByAqKoqcnBx++umnwoQjIiIiIiLytxwe9vbZZ5/xww8/sHPnzr+te+7cOcaNG8dzzz1nLUtPT7dJfADr3+np6QW2YzKZMJlMNmVmSx5OBmdHwxcRERERkRLKoeTn119/5aWXXiI5OZkyZcr8Zd2cnByio6MJCwtj7NixNxMjCQkJxMfH25QFEEog4TfVrojI3U5DPTTUQ0RE7OdQ8rN7924yMjJ44IEHrGV5eXls3ryZ//u//8NkMuHs7MzFixdp27YtFSpUICkpiVKlSlnre3t78/3339u0e+bMGeu2gsTFxTFs2DCbsk7uvR0JXUTknqQbfxEREfs5lPy0bNmS/fv325TFxsYSEhLCa6+9hrOzMzk5OURFRWE0Glm+fHm+HqKIiAjGjx9PRkYGXl5eACQnJ+Pm5kZYWFiB+zUajRiNRpsyDXkTERERERFHOJT8VKhQgfvvv9+mrFy5cnh4eHD//feTk5NDmzZtuHz5MgsWLCAnJ4ecnBwAPD09cXZ2pk2bNoSFhdGjRw/efvtt0tPTGTVqFAMGDMiX4IiIiIiIiBSVQr/npyA//PADO3bsACAoKMhm28mTJ/H398fZ2ZmVK1fywgsvEBERQbly5ejVqxdvvvlmUYYiIlIiaM6Phv6JiIj9CvWenzuB3vMjIiIiIiJg/3t+irTnR0RERETkdlMvuHrB7aXkR0RERETuarrxF3sp+RERERGRu5p6fpQA2supuAMQERERERG5HW4q+Zk4cSIGg4EhQ4bk22axWGjXrh0Gg4Fly5bZbNu5cyctW7akYsWKVKpUiaioKPbtU8YuIiIiIiK3TqGTn507d/LBBx9Qp06dArdPmzYNg8GQr/zSpUu0bdsWX19fduzYwdatW6lQoQJRUVFcu3atsOGIiIiIiIj8pUIlP5cuXSImJoZ//etfVKpUKd/2vXv3MnXqVD766KN82w4dOkRmZiZvvvkmwcHBhIeHM2bMGM6cOcMvv/xSmHBERERERET+VqEWPBgwYADR0dG0atWKt956y2bb5cuXefrpp5k5cybe3t75vhscHIyHhwdz5sxh5MiR5OXlMWfOHEJDQ/H39y/UQYiIiIhIyaXJ/mIvh5Ofzz77jB9++IGdO3cWuH3o0KFERkbSoUOHArdXqFCBjRs30rFjR8aNGwdArVq1WLNmDS4uBYdjMpkwmUw2ZWZLHk4GZ0fDFxEREZF7jFZ7UwJoL4eSn19//ZWXXnqJ5ORkypQpk2/78uXLWb9+PXv27LlhG7///jt9+/alcePGfPrpp+Tl5TFlyhSio6PZuXMnrq6u+b6TkJBAfHy8TVkAoQQS7kj4IiIiInIP0o2/2MtgsVgs9lZetmwZnTp1wtn5Pz0ueXl5GAwGnJyceOGFF5g5cyZOTk42252cnHjkkUfYuHGjdbhbWlqatd7Vq1epVKkSc+bMoVu3bvn2W1DPTyf33ur5EZEST087ddMjIjoXgs6FyeYldtVzqOenZcuW7N+/36YsNjaWkJAQXnvtNapUqUL//v1ttteuXZt3332X9u3bA3/MCXJycrJZCe7Pv81mc4H7NRqNGI1GmzIlPiIiutiJiIDOhWI/h5KfChUqcP/999uUlStXDg8PD2t5QYsc+Pr6EhAQAEDr1q0ZPnw4AwYMYNCgQZjNZiZOnIiLiwvNmzcv7HGIiIiISAmlnh8lgPYq1GpvNyMkJIQVK1YQHx9PREQETk5O1K9fn9WrV1OtWrXbHY6IyF1NF3xd8EVE5wGxn0Nzfu4krZ26FHcIIiLFTsmPbnpEROdC0Lnwlsz5ERGRO0tJv9iJiIg4wunvq4iIiIiIiNz91PMjInIX01AP9X6JiIj9bqrnZ+LEiRgMBoYMGQLAqVOnMBgMBX6WLLEdhzdv3jzq1KlDmTJl8PLyYsCAATcTioiIiIiIyF8qdM/Pzp07+eCDD6hTp461rEaNGqSlpdnU+/DDD5k8eTLt2rWzlr3zzjtMnTqVyZMn06hRI3Jzczl16lRhQxEREREREflbhUp+Ll26RExMDP/617946623rOXOzs753vOTlJRE165dKV++PAAXLlxg1KhRrFixgpYtW1rr/XcSJSIiIiIiUtQKlfwMGDCA6OhoWrVqZZP8/K/du3ezd+9eZs6caS1LTk7GbDZz+vRpQkNDuXjxIpGRkUydOpUaNWoUJhwRkRJL811ERETs53Dy89lnn/HDDz+wc+fOv607Z84cQkNDiYyMtJadOHECs9nMhAkTmD59Ou7u7owaNYrWrVvz448/Urp06XztmEwmTCaTTZnZkoeTwdnR8EVEREREpIRyaMGDX3/9lZdeeomFCxdSpkyZv6z7+++/k5iYSN++fW3KzWYz165d47333iMqKoqHH36YTz/9lKNHj7Jhw4YC20pISMDd3d3mc5JDjoQuIiIiIiIlnEPJz+7du8nIyOCBBx7AxcUFFxcXNm3axHvvvYeLiwt5eXnWukuXLuXy5cv07NnTpo1q1aoBEBYWZi3z9PSkSpUqpKSkFLjfuLg4srOzbT4BhDgSuoiIiIiIlHAODXtr2bIl+/fvtymLjY0lJCSE1157DWfn/wxDmzNnDk888QSenp429Rs3bgzA4cOHqV69OgCZmZmcO3cOPz+/AvdrNBoxGo02ZRryJiIiIiIijnAo+alQoQL333+/TVm5cuXw8PCwKT927BibN2/m66+/ztfGfffdR4cOHXjppZf48MMPcXNzIy4ujpCQEJo3b17IwxAREREREflrhX7Pz1/56KOPqF69Om3atClw+8cff8zQoUOJjo7GycmJRx99lNWrV1OqVKlbEY6IiIiI3MO08qXYy2CxWCzFHURhtHbqUtwhiIiIiIjIHSDZvMSuerek50dERERE5HZZk7qvuEModur9so+SHxGRu5gu+Lrgi4iI/ZT8iIjcxXTjLyIiYj+H3vMjIiIiIiJyt3Ko52fs2LHEx8fblAUHB3Po0CEArly5wssvv8xnn32GyWQiKiqK999/n6pVq+Zr6/z589StW5fTp09z4cIFKlasWPijEBEpoTTsTb1fIiJiP4d7fsLDw0lLS7N+tm7dat02dOhQVqxYwZIlS9i0aROpqak8+eSTBbbTt29f6tSpU/jIRUREREREHODwnB8XFxe8vb3zlWdnZzNnzhwSExNp0aIFAHPnziU0NJTvvvuOhx9+2Fp31qxZZGVl8cYbb7Bq1aqbCF9EpGRTr4eIiIj9HO75OXr0KD4+PtSsWZOYmBhSUlIA2L17N9euXaNVq1bWuiEhIfj6+rJ9+3Zr2cGDB3nzzTf5+OOPcXLSlCMREREREbk9HOr5adSoEfPmzSM4OJi0tDTi4+N55JFHOHDgAOnp6ZQuXTrf3J2qVauSnp4OgMlkonv37kyePBlfX19OnDhh135NJhMmk8mmzGzJw8ng7Ej4IiIiIiJSgjmU/LRr187633Xq1KFRo0b4+fmxePFiXF1d//b7cXFxhIaG8swzzzgUZEJCQr6FFgIIJZBwh9oRERERkXuPhgCLvW7qPT8VK1bkvvvu49ixY7Ru3ZqrV6+SlZVl0/tz5swZ6xyh9evXs3//fpYuXQqAxWIBoEqVKrz++uv5Epw/xcXFMWzYMJuyTu69byZ0EZF7glZ7002PiOhcCDoX2uumkp9Lly5x/PhxevToQYMGDShVqhTr1q2jc+fOABw+fJiUlBQiIiIA+Pzzz/n999+t39+5cyd9+vRhy5YtBAYG3nA/RqMRo9FoU6YhbyIiutiJiIg4wqHk55VXXqF9+/b4+fmRmprKmDFjcHZ2pnv37ri7u9O3b1+GDRtG5cqVcXNzY9CgQURERFhXevvfBOfcuXMAhIaG6j0/IiKFoKedSgBFRMR+DiU/v/32G927d+f8+fN4enrSpEkTvvvuOzw9PQF49913cXJyonPnzjYvORURkVtDN/4iIiL2M1j+nHhzl2nt1KW4QxARERERkTtAsnmJXfVuas6PiIgULw17U++XiOhcCDoX2kvJj4jIXUwXOxEREfsp+RERERGRu5oeBIm9HEp+xo4dm+9dPMHBwRw6dAiA/v37s3btWlJTUylfvjyRkZFMmjSJkJAQAPbt28fEiRPZunUr586dw9/fn+eff56XXnqpiA5HRKRk0VAP3fSIiM6FoHOhvRzu+QkPD2ft2rX/acDlP000aNCAmJgYfH19yczMZOzYsbRp04aTJ0/i7OzM7t278fLyYsGCBdSoUYNt27bx3HPP4ezszMCBA4vmiERERERERArgcPLj4uKCt7d3gduee+4563/7+/vz1ltvUbduXU6dOkVgYCB9+vSxqV+zZk22b9/OF198oeRHRKQQ9KRPRETEfk6OfuHo0aP4+PhQs2ZNYmJiSElJKbBebm4uc+fOJSAggBo1atywvezsbCpXruxoGCIiIiIiIg5xKPlp1KgR8+bNY/Xq1cyaNYuTJ0/yyCOPcPHiRWud999/n/Lly1O+fHlWrVpFcnIypUuXLrC9bdu2sWjRIpseIxERERERkVvhpl5ympWVhZ+fH++88w59+/YF/ujJycjIIC0tjSlTpnD69Gm+/fZbypQpY/PdAwcO0Lx5c1566SVGjRr1l/sxmUyYTCabsk7uvXEyOBc2dBERERG5R2jBAw2Dtvclpw4Pe/tvFStW5L777uPYsWPWMnd3d2rVqkXTpk1ZunQphw4dIikpyeZ7Bw8epGXLljz33HN/m/gAJCQk4O7ubvM5yaGbCV1EREREREqYm3rPz6VLlzh+/Dg9evQocLvFYsFisdj02vz000+0aNGCXr16MX78eLv2ExcXx7Bhw2zKOrn3LnTcIiIiInLvKOm9HmI/h5KfV155hfbt2+Pn50dqaipjxozB2dmZ7t27c+LECRYtWkSbNm3w9PTkt99+Y+LEibi6uvLYY48Bfwx1a9GiBVFRUQwbNoz09HQAnJ2d8fT0vOF+jUYjRqPRpkxD3kRENNQDdNMjIjoXgs6F9nIo+fntt9/o3r0758+fx9PTkyZNmvDdd9/h6enJtWvX2LJlC9OmTePChQtUrVqVpk2bsm3bNry8vABYunQpZ8+eZcGCBSxYsMDarp+fH6dOnSrSAxMRKQl0sRMR0blQ7HdTCx4Up9ZOXYo7BBERERG5A6jnRwmgvQse3NScHxERERGR4lbSb/zFfkp+REREROSupp4fJYD2UvIjInIX0wVfF3wREbGfkh8RkbuYbvxFRETs59BLTseOHYvBYLD5hISE2NTZvn07LVq0oFy5cri5udG0aVN+//136/bMzExiYmJwc3OjYsWK9O3bl0uXLhXN0YiIiIiIiNyAwz0/4eHhrF279j8NuPynie3bt9O2bVvi4uKYMWMGLi4u7Nu3Dyen/+RYMTExpKWlkZyczLVr14iNjeW5554jMTHxJg9FRERERETkxhxOflxcXPD29i5w29ChQxk8eDAjRoywlgUHB1v/++eff2b16tXs3LmThg0bAjBjxgwee+wxpkyZgo+Pj6PhiIiIiIiI2MXh5Ofo0aP4+PhQpkwZIiIiSEhIwNfXl4yMDHbs2EFMTAyRkZEcP36ckJAQxo8fT5MmTYA/eoYqVqxoTXwAWrVqhZOTEzt27KBTp05Fd2QiIiWAFjzQvCcREbGfQ8lPo0aNmDdvHsHBwaSlpREfH88jjzzCgQMHOHHiBPDHvKApU6ZQr149Pv74Y1q2bMmBAweoVasW6enpeHl52Qbg4kLlypVJT0+/4X5NJhMmk8mmzGzJw8ng7Ej4IiL3HN34i4iI2M+hBQ/atWtHly5dqFOnDlFRUXz99ddkZWWxePFizGYzAP379yc2Npb69evz7rvvEhwczEcffXRTQSYkJODu7m7zOcmhm2pTRERERERKlpta6rpixYrcd999HDt2jBYtWgAQFhZmUyc0NJSUlBQAvL29ycjIsNl+/fp1MjMzbziPCCAuLo5hw4bZlHVy730zoYuI3BM07E29XyIiYr+bSn4uXbrE8ePH6dGjB/7+/vj4+HD48GGbOkeOHKFdu3YAREREkJWVxe7du2nQoAEA69evx2w206hRoxvux2g0YjQabco05E1ERDf+IiIijnAo+XnllVdo3749fn5+pKamMmbMGJydnenevTsGg4Hhw4czZswY6tatS7169Zg/fz6HDh1i6dKlwB+9QG3btqVfv37Mnj2ba9euMXDgQLp166aV3kRERERE5JZyKPn57bff6N69O+fPn8fT05MmTZrw3Xff4enpCcCQIUO4cuUKQ4cOJTMzk7p165KcnExgYKC1jYULFzJw4EBatmyJk5MTnTt35r333ivaoxIREREREfkfBovFYinuIAqjtVOX4g5BRERERETuAMnmJXbVc2i1NxERERERkbvVTS14ICIiIiJS3LTypRbAsZd6fkREREREpERwqOdn7NixxMfH25QFBwdz6NAfLxxNT09n+PDhJCcnc/HiRYKDg3n99dfp3Lmztf6RI0cYPnw43377LVevXqVOnTqMGzeO5s2bF8HhiIiULHraqaedIiJiP4eHvYWHh7N27dr/NODynyZ69uxJVlYWy5cvp0qVKiQmJtK1a1d27dpF/fr1AXj88cepVasW69evx9XVlWnTpvH4449z/Pjxv3zRqYiI5KcbfxEREfs5POzNxcUFb29v66dKlSrWbdu2bWPQoEE89NBD1KxZk1GjRlGxYkV2794NwLlz5zh69CgjRoygTp061KpVi4kTJ3L58mUOHDhQdEclIiIiIiLyPxzu+Tl69Cg+Pj6UKVOGiIgIEhIS8PX1BSAyMpJFixYRHR1NxYoVWbx4MVeuXKFZs2YAeHh4EBwczMcff8wDDzyA0Wjkgw8+wMvLiwYNGhTpgYmIlAQa9qbeLxERsZ9DyU+jRo2YN28ewcHBpKWlER8fzyOPPMKBAweoUKECixcv5qmnnsLDwwMXFxfKli1LUlISQUFBABgMBtauXUvHjh2pUKECTk5OeHl5sXr1aipVqnRLDlBE5F6mG38RERH7OZT8tGvXzvrfderUoVGjRvj5+bF48WL69u3L6NGjycrKYu3atVSpUoVly5bRtWtXtmzZQu3atbFYLAwYMAAvLy+2bNmCq6sr//73v2nfvj07d+6kWrVqBe7XZDJhMplsysyWPJwMzoU4ZBERERERKYkMFovFcjMNPPjgg7Rq1Ypnn32WoKAgDhw4QHh4uHV7q1atCAoKYvbs2axbt442bdpw4cIF3NzcrHVq1apF3759GTFiRIH7KGiVuQBCCTSEF1hfREREREoODQHWSIBk8xK76t3Ue34uXbrE8ePHqVatGpcvX/6jQSfbJp2dnTGbzQA3rOPk5GStU5C4uDiys7NtPgGE3EzoIiIiIiJSwjg07O2VV16hffv2+Pn5kZqaypgxY3B2dqZ79+5UrFiRoKAg+vfvz5QpU/Dw8GDZsmUkJyezcuVKACIiIqhUqRK9evXijTfewNXVlX/961+cPHmS6OjoG+7XaDRiNBptyjTkTUREREREHOFQ8vPbb7/RvXt3zp8/j6enJ02aNOG7777D09MTgK+//poRI0bQvn17Ll26RFBQEPPnz+exxx4DoEqVKqxevZrXX3+dFi1acO3aNcLDw/nyyy+pW7dkd9WJiBSGhnpoqIeIiNjvpuf8FJfWTl2KOwQRkWKn5EfJj4joXAg6F9o758fh9/yIiMido6Rf7EREQOdCsZ+SHxGRu5ieduqmR0R0LgSdC+2l5EdE5C6mi52IiIj9bmqpaxERERERkbuFw8nP6dOneeaZZ/Dw8MDV1ZXatWuza9cu6/YvvviCNm3a4OHhgcFgYO/evQW2s337dlq0aEG5cuVwc3OjadOm/P7774U+EBERERERkb/i0LC3Cxcu0LhxY5o3b86qVavw9PTk6NGjVKpUyVonNzeXJk2a0LVrV/r161dgO9u3b6dt27bExcUxY8YMXFxc2LdvX76Xn4qIiIiI/B0NARZ7ObTU9YgRI/j222/ZsmXL39Y9deoUAQEB7Nmzh3r16tlse/jhh2ndujXjxo1zOOA/aalrERFN8gXd9IiIzoWgc6G9S1071NWyfPlyGjZsSJcuXfDy8qJ+/fr861//ciiwjIwMduzYgZeXF5GRkVStWpVHH32UrVu3OtSOiIiIiIiIIxwa9nbixAlmzZrFsGHDGDlyJDt37mTw4MGULl2aXr162d0GwNixY5kyZQr16tXj448/pmXLlhw4cIBatWrl+47JZMJkMtmUmS15OBmcHQlfROSeU9Kf9ImIiDjCoZ4fs9nMAw88wIQJE6hfvz7PPfcc/fr1Y/bs2Q61AdC/f39iY2OpX78+7777LsHBwXz00UcFfichIQF3d3ebz0kOORK6iIiIiIiUcA4lP9WqVSMsLMymLDQ0lJSUFIfaABxqJy4ujuzsbJtPACGOhC4iIiIiIiWcQ8PeGjduzOHDh23Kjhw5gp+fn91t+Pv74+PjU2A77dq1K/A7RqMRo9FoU6YhbyIiIiICGgIs9nMo+Rk6dCiRkZFMmDCBrl278v333/Phhx/y4YcfWutkZmaSkpJCamoqgDXJ8fb2xtvbG4PBwPDhwxkzZgx169alXr16zJ8/n0OHDrF06dIiPDQRERERKQm02psSQHs5tNQ1wMqVK4mLi+Po0aMEBAQwbNgwm/f5zJs3j9jY2HzfGzNmDGPHjrX+PXHiRGbOnElmZiZ169bl7bffpkmTJnbHoaWuRUREREQE7F/q2uHk506h5EdEREREQD0/oJ6fW/KeHxERERERkbuVQ3N+RERERETuNCW910Psp54fEREREREpEdTzIyIiIiJ3Nc35Ue+XvRzu+Tl9+jTPPPMMHh4euLq6Urt2bXbt2lVg3eeffx6DwcC0adMK3G4ymahXrx4Gg4G9e/c6GoqIiIiIiIjdHEp+Lly4QOPGjSlVqhSrVq3i4MGDTJ06lUqVKuWrm5SUxHfffYePj88N23v11Vf/cruIiIiIiEhRcWjY26RJk6hRowZz5861lgUEBOSrd/r0aQYNGsSaNWuIjo4usK1Vq1bxzTff8Pnnn7Nq1SoHwxYREREREXGMQz0/y5cvp2HDhnTp0gUvLy/q16/Pv/71L5s6ZrOZHj16MHz4cMLDwwts58yZM/Tr149PPvmEsmXLFj56EREREREROzmU/Jw4cYJZs2ZRq1Yt1qxZwwsvvMDgwYOZP3++tc6kSZNwcXFh8ODBBbZhsVjo3bs3zz//PA0bNrRrvyaTiZycHJuP2ZLnSOgiIiIiIlLCOTTszWw207BhQyZMmABA/fr1OXDgALNnz6ZXr17s3r2b6dOn88MPP2AwGApsY8aMGVy8eJG4uDi795uQkEB8fLxNWQChBFJwz5KIiIiIiMj/cij5qVatGmFhYTZloaGhfP755wBs2bKFjIwMfH19rdvz8vJ4+eWXmTZtGqdOnWL9+vVs374do9Fo007Dhg2JiYmx6UX6U1xcHMOGDbMp6+Te25HQRUTuSVreVcu7ioiI/RxKfho3bszhw4dtyo4cOYKfnx8APXr0oFWrVjbbo6Ki6NGjB7GxsQC89957vPXWW9btqampREVFsWjRIho1alTgfo1GY75kycng7EjoIiIiIiJSwjmU/AwdOpTIyEgmTJhA165d+f777/nwww/58MMPAfDw8MDDw8PmO6VKlcLb25vg4GAAm14hgPLlywMQGBhI9erVC30gIiIlkXo9RERE7OdQ8vPggw+SlJREXFwcb775JgEBAUybNo2YmJhbFZ+IiPwFDXtTAigiIvYzWCwWS3EHURitnboUdwgiIiIicgfQgyA9CEo2L7GrnkNLXYuIiIiIiNytHBr2JiIiIiJypynpvR5iPyU/IiIiInJX07A3JYD2cnjY2+nTp3nmmWfw8PDA1dWV2rVrs2vXLut2g8FQ4Gfy5MkAnDp1ir59+xIQEICrqyuBgYGMGTOGq1evFt1RiYiIiIiI/A+Hen4uXLhA48aNad68OatWrcLT05OjR49SqVIla520tDSb76xatYq+ffvSuXNnAA4dOoTZbOaDDz4gKCiIAwcO0K9fP3Jzc5kyZUoRHJKIiIiIiEh+Dq32NmLECL799lu2bNli9w46duzIxYsXWbdu3Q3rTJ48mVmzZnHixAm729VqbyIiGuoBGuohIjoXgs6F9q725lDPz/Lly4mKiqJLly5s2rSJf/zjH7z44ov069evwPpnzpzhq6++Yv78+X/ZbnZ2NpUrV3YkFBERERERQDf+Yj+Hkp8TJ04wa9Yshg0bxsiRI9m5cyeDBw+mdOnS9OrVK1/9+fPnU6FCBZ588skbtnns2DFmzJjxl0PeTCYTJpPJpsxsycPJ4OxI+CIi9xxd8EVEROzn0LC30qVL07BhQ7Zt22YtGzx4MDt37mT79u356oeEhNC6dWtmzJhRYHunT5/m0UcfpVmzZvz73/++4X7Hjh1LfHy8TVkAoQQawu0NXURERETuURr2podht+Qlp9WqVSMsLMymLDQ0lJSUlHx1t2zZwuHDh3n22WcLbCs1NZXmzZsTGRnJhx9++Jf7jYuLIzs72+YTQIgjoYuIiIiISAnn0LC3xo0bc/jwYZuyI0eO4Ofnl6/unDlzaNCgAXXr5s9CT58+TfPmzWnQoAFz587FyemvczCj0YjRaLQp05A3ERERERFxhEPJz9ChQ4mMjGTChAl07dqV77//ng8//DBfz01OTg5Llixh6tSp+do4ffo0zZo1w8/PjylTpnD27FnrNm9v70IehoiIiIiUVCV9yJfYz6Hk58EHHyQpKYm4uDjefPNNAgICmDZtGjExMTb1PvvsMywWC927d8/XRnJyMseOHePYsWNUr17dZpsD049ERASNcwfd9IiIzoWgc6G9HFrw4E6i9/yIiIiICCj5ASU/t2TBAxERERERkbuVkh8RERERESkRlPyIiIiIiEiJ4NCCByIiIiIid5qSPt9F7Odw8uPv788vv/ySr/zFF19k5syZXLlyhZdffpnPPvsMk8lEVFQU77//PlWrVrXWTUlJ4YUXXmDDhg2UL1+eXr16kZCQgIuLcjEREUdokq9uekRE50LQudBeDmcbO3fuJC8vz/r3gQMHaN26NV26/LH62tChQ/nqq69YsmQJ7u7uDBw4kCeffJJvv/0WgLy8PKKjo/H29mbbtm2kpaXRs2dPSpUqxYQJE4rosERESgZd7EREROx300tdDxkyhJUrV3L06FFycnLw9PQkMTGRf/7znwAcOnSI0NBQtm/fzsMPP8yqVat4/PHHSU1NtfYGzZ49m9dee42zZ89SunRpu/arpa5FREREBNTzA3oYZu9S1zc1zuzq1assWLCAYcOGYTAY2L17N9euXaNVq1bWOiEhIfj6+lqTn+3bt1O7dm2bYXBRUVG88MIL/PTTT9SvX/9mQhIRERGREqak3/iL/W4q+Vm2bBlZWVn07t0bgPT0dEqXLk3FihVt6lWtWpX09HRrnf9OfP7c/ue2gphMJkwmk02Z2ZKHk8H5ZsIXEREREZES5KaSnzlz5tCuXTt8fHyKKp4CJSQkEB8fb1MWQCiBhN/S/YqIiIjInU/D3tT7Za9Cv+fnl19+Ye3atTz77LPWMm9vb65evUpWVpZN3TNnzuDt7W2tc+bMmXzb/9xWkLi4OLKzs20+AYQUNnQRERERESmBCp38zJ07Fy8vL6Kjo61lDRo0oFSpUqxbt85advjwYVJSUoiIiAAgIiKC/fv3k5GRYa2TnJyMm5sbYWFhBe7LaDTi5uZm89GQNxERERERcUShhr2ZzWbmzp1Lr169bN7N4+7uTt++fRk2bBiVK1fGzc2NQYMGERERwcMPPwxAmzZtCAsLo0ePHrz99tukp6czatQoBgwYgNFoLJqjEhEREZESQ0O+xF6FSn7Wrl1LSkoKffr0ybft3XffxcnJic6dO9u85PRPzs7OrFy5khdeeIGIiAjKlStHr169ePPNNwt/FCIiJZTGueumR0R0LgSdC+110+/5KS56z4+IiC74oAu+iIjcpvf8iIhI8dKNv4iIHgSBrgf2UvIjIiIiInc13fiLvQq92puIiIiIiMjdxKGeH39/f3755Zd85S+++CLjxo1jzJgxfPPNN6SkpODp6UnHjh0ZN24c7u7u1ro7d+5kxIgR7N69G4PBwEMPPcTbb79N3brK2EVERETEcRr2pt4veznU87Nz507S0tKsn+TkZAC6dOlCamoqqampTJkyhQMHDjBv3jxWr15N3759rd+/dOkSbdu2xdfXlx07drB161YqVKhAVFQU165dK9ojExERERER+S83tdrbkCFDWLlyJUePHsVgMOTbvmTJEp555hlyc3NxcXFh165dPPjgg6SkpFCjRg0A9u/fT506dTh69ChBQUF271urvYmIiIgIqOcH1PNj72pvhZ7zc/XqVRYsWECfPn0KTHwAsrOzcXNzs74INTg4GA8PD+bMmcPVq1f5/fffmTNnDqGhofj7+xc2FBERERERkb9V6NXeli1bRlZWFr179y5w+7lz5xg3bhzPPfectaxChQps3LjROhcIoFatWqxZs8aaIBXEZDJhMplsysyWPJwMzoUNX0TknqCnnXraKSIi9iv0sLeoqChKly7NihUr8m3LycmhdevWVK5cmeXLl1OqVCkAfv/9d5o1a0ZISAgDBw4kLy+PKVOmcOjQIXbu3Imrq2uB+xo7dizx8fE2ZQGEEmgIL0zoIiIiInIP0YMgPQiyd9hboZKfX375hZo1a/LFF1/QoUMHm20XL14kKiqKsmXLsnLlSsqUKWPdNmfOHEaOHElaWhpOTn+MuLt69SqVKlVizpw5dOvWrcD9FdTz08m9t3p+RERERETJD0p+7E1+CjXsbe7cuXh5eREdHW1TnpOTQ1RUFEajkeXLl9skPgCXL1/GycnJZo7Qn3+bzeYb7s9oNGI0Gm3KlPiIiIiIiIgjHF7wwGw2M3fuXHr16mUzTycnJ4c2bdqQm5vLnDlzyMnJIT09nfT0dPLy8gBo3bo1Fy5cYMCAAfz888/89NNPxMbG4uLiQvPmzYvuqERERERERP6Hwz0/a9euJSUlhT59+tiU//DDD+zYsQMg35LVJ0+exN/fn5CQEFasWEF8fDwRERE4OTlRv359Vq9eTbVq1W7iMERERERERP7aTb3npzjpPT8iIhrnDhrnLiI6F4LOhbd0zo+IiIiIyJ2ipN/4i/2U/IiI3MV0wRcREbGfkh8RERERuatp2JsehtnL4dXeRERERERE7kYO9fz4+/vzyy+/5Ct/8cUXmTlzJs2aNWPTpk022/r378/s2bNtyubNm8c777zDkSNHcHNzo0uXLsycObMQ4YuIiIhISadeD7GXQ8nPzp07re/sAThw4ACtW7emS5f/rLzWr18/3nzzTevfZcuWtWnjnXfeYerUqUyePJlGjRqRm5vLqVOnChm+iIiIiJR0GvamBNBeDiU/np6eNn9PnDiRwMBAHn30UWtZ2bJl8fb2LvD7Fy5cYNSoUaxYsYKWLVtay+vUqeNIGCIiIiIiIg4r9Jyfq1evsmDBAvr06YPBYLCWL1y4kCpVqnD//fcTFxfH5cuXrduSk5Mxm82cPn2a0NBQqlevTteuXfn1119v7ihERERERET+RqFXe1u2bBlZWVn07t3bWvb000/j5+eHj48PP/74I6+99hqHDx/miy++AODEiROYzWYmTJjA9OnTcXd3Z9SoUbRu3Zoff/yR0qVLF7gvk8mEyWSyKTNb8nAyOBc2fBERERERKWEKnfzMmTOHdu3a4ePjYy177rnnrP9du3ZtqlWrRsuWLTl+/DiBgYGYzWauXbvGe++9R5s2bQD49NNP8fb2ZsOGDURFRRW4r4SEBOLj423KAgglkPDChi8iIiIiIiVMoZKfX375hbVr11p7dG6kUaNGABw7dozAwECqVasGQFhYmLWOp6cnVapUISUl5YbtxMXFMWzYMJuyTu69CxO6iMg9RZN8NclXRETsV6jkZ+7cuXh5eREdHf2X9fbu3QtgTXoaN24MwOHDh6levToAmZmZnDt3Dj8/vxu2YzQaMRqNNmUa8iYioht/ERERRxgsFovFkS+YzWYCAgLo3r07EydOtJYfP36cxMREHnvsMTw8PPjxxx8ZOnQo1atXt3n3T8eOHTl27Bgffvghbm5uxMXFceLECfbu3UupUqXsjqO1U5e/ryQiIiIiIve8ZPMSu+o5vNrb2rVrSUlJoU+fPjblpUuXZu3atbRp04aQkBBefvllOnfuzIoVK2zqffzxxzRq1Ijo6GgeffRRSpUqxerVqx1KfERERERERBzlcM/PnUI9PyIiIiICmv8IGgZ9y3p+RERERERE7kZKfkREREREpEQo9Ht+RESk+Gmoh4Z6iIiI/RxKfvz9/fnll1/ylb/44ovMnDmT9PR0hg8fTnJyMhcvXiQ4OJjXX3+dzp075/uOyWSiUaNG7Nu3jz179lCvXr1CH4SISEmlG38RERH7OTTsbefOnaSlpVk/ycnJAHTp8sfiAz179uTw4cMsX76c/fv38+STT9K1a1f27NmTr61XX30VHx+fIjgEERERERGRv+dQz4+np6fN3xMnTiQwMJBHH30UgG3btjFr1iweeughAEaNGsW7777L7t27qV+/vvV7q1at4ptvvuHzzz9n1apVN3sMIiIiIlKCqRdc7FXoBQ+uXr3KggUL6NOnDwaDAYDIyEgWLVpEZmYmZrOZzz77jCtXrtCsWTPr986cOUO/fv345JNPKFu27E0fgIiIiIiIiD0KveDBsmXLyMrKonfv3tayxYsX89RTT+Hh4YGLiwtly5YlKSmJoKAgACwWC7179+b555+nYcOGnDp1yq59mUwmTCaTTZnZkoeTwbmw4YuI3BO04IGe+IqIzoWgc6G9Cp38zJkzh3bt2tnM2xk9ejRZWVmsXbuWKlWqsGzZMrp27cqWLVuoXbs2M2bM4OLFi8TFxTm0r4SEBOLj423KAgglkPDChi8ick/QxU5ERMR+BovFYnH0S7/88gs1a9bkiy++oEOHDgAcP36coKAgDhw4QHj4f5KSVq1aERQUxOzZs+nYsSMrVqywDpMDyMvLw9nZmZiYGObPn1/g/grq+enk3ls9PyIiIiKinh/0MCzZvMSueoXq+Zk7dy5eXl5ER0dbyy5fvgyAk5PtNCJnZ2fMZjMA7733Hm+99ZZ1W2pqKlFRUSxatIhGjRrdcH9GoxGj0WhTpsRHREREREQc4XDyYzabmTt3Lr169cLF5T9fDwkJISgoiP79+zNlyhQ8PDxYtmwZycnJrFy5EgBfX1+btsqXLw9AYGAg1atXv5njEBEpkfS0U087RUTEfg6v9rZ27VpSUlLo06ePTXmpUqX4+uuv8fT0pH379tSpU4ePP/6Y+fPn89hjjxVZwCIiIiIiIoVRqDk/d4LWTl2KOwQRERERuQOoF1y94Ld0zo+IiIiIyJ2ipN/4i/2U/IiIiIjIXU09P0oA7eXwnB8REREREZG7kXp+REREROSupl4PsZdDyU9eXh5jx45lwYIFpKen4+PjQ+/evRk1apT1xaVffPEFs2fPZvfu3WRmZrJnzx7q1atnbSMzM5MxY8bwzTffkJKSgqenJx07dmTcuHG4u7sX6cGJiIiIyL1Pw96UANrLoeRn0qRJzJo1i/nz5xMeHs6uXbuIjY3F3d2dwYMHA5Cbm0uTJk3o2rUr/fr1y9dGamoqqampTJkyhbCwMH755Reef/55UlNTWbp0adEclYiIiIiUGLrxF3s5tNT1448/TtWqVZkzZ461rHPnzri6urJgwQKbuqdOnSIgICBfz09BlixZwjPPPENubq7Ni1P/ipa6FhERERFQzw8oAbR3qWuHFjyIjIxk3bp1HDlyBIB9+/axdetW2rVr53iE/yU7Oxs3Nze7Ex8RERERERFHOZRtjBgxgpycHEJCQnB2diYvL4/x48cTExNT6ADOnTvHuHHjeO65525Yx2QyYTKZbMrMljycDM6F3q+IiIiIiJQsDvX8LF68mIULF5KYmMgPP/zA/PnzmTJlCvPnzy/UznNycoiOjiYsLIyxY8fesF5CQgLu7u42n5McKtQ+RURERESkZHJozk+NGjUYMWIEAwYMsJa99dZbLFiwgEOHbJORv5vzc/HiRaKioihbtiwrV66kTJkyN9xvQT0/ndx7q+dHREo8jXPXOHcREbF/zo9Dw94uX76Mk5NtZ5GzszNms9mRZsjJySEqKgqj0cjy5cv/MvEBMBqNGI1GmzIlPiIiuvEXEQE9CAJdD+zlUPLTvn17xo8fj6+vL+Hh4ezZs4d33nmHPn36WOtkZmaSkpJCamoqAIcPHwbA29sbb29vcnJyaNOmDZcvX2bBggXk5OSQk5MDgKenJ87OSmpERERERKToOTTs7eLFi4wePZqkpCQyMjLw8fGhe/fuvPHGG5QuXRqAefPmERsbm++7Y8aMYezYsWzcuJHmzZsX2P7Jkyfx9/e3KxYtdS0iIiIioJ4fUM+PvcPeHEp+7iRKfkREREREBG7RnB8REbmz6GmnnnaKiM6FoHOhvZT8iIjcxXSxExERsZ+SHxERERG5q+lBkHq/7OVQ8pOXl8fYsWNZsGAB6enp+Pj40Lt3b0aNGoXBYMhX//nnn+eDDz7g3XffZciQIdbyzMxMBg0axIoVK3BycqJz585Mnz6d8uXL3/QBiYiIiEjJoht/JYDJdr55x6HkZ9KkScyaNYv58+cTHh7Orl27iI2Nxd3dncGDB9vUTUpK4rvvvsPHxydfOzExMaSlpZGcnMy1a9eIjY3lueeeIzEx0ZFwRERERERE7OZQ8rNt2zY6dOhAdHQ0AP7+/nz66ad8//33NvVOnz7NoEGDWLNmjbXun37++WdWr17Nzp07adiwIQAzZszgscceY8qUKQUmSyIiIiIiN1LSez3Efg4lP5GRkXz44YccOXKE++67j3379rF161beeecdax2z2UyPHj0YPnw44eHh+drYvn07FStWtCY+AK1atcLJyYkdO3bQqVOnmzgcEZGSRUM9dNMjIjoXgs6F9nIo+RkxYgQ5OTmEhITg7OxMXl4e48ePJyYmxlpn0qRJuLi45BsG96f09HS8vLxsg3BxoXLlyqSnpxf4HZPJhMlksikzW/JwMjg7Er6IiIiI3IN04y/2cij5Wbx4MQsXLiQxMZHw8HD27t3LkCFD8PHxoVevXuzevZvp06fzww8/FLgAQmElJCQQHx9vUxZAKIHk71kSESlJdMEXEVHPD+h6YC8nRyoPHz6cESNG0K1bN2rXrk2PHj0YOnQoCQkJAGzZsoWMjAx8fX1xcXHBxcWFX375hZdffhl/f38AvL29ycjIsGn3+vXrZGZm4u3tXeB+4+LiyM7OtvkEEFKIwxURERERkZLKoZ6fy5cv4+Rkmy85OztjNv+xtlyPHj1o1aqVzfaoqCh69OhBbGwsABEREWRlZbF7924aNGgAwPr16zGbzTRq1KjA/RqNRoxGo02ZhryJiIiIiIgjHEp+2rdvz/jx4/H19SU8PJw9e/bwzjvv0KdPHwA8PDzw8PCw+U6pUqXw9vYmODgYgNDQUNq2bUu/fv2YPXs2165dY+DAgXTr1k0rvYmIOEhDPTTUQ0RE7OdQ8jNjxgxGjx7Niy++SEZGBj4+PvTv35833njDoZ0uXLiQgQMH0rJlS+tLTt977z2H2hAREd34i4iIOMJgsVgsxR1EYbR26lLcIYiIiIjIHUC94HoYlmxeYlc9hxY8EBERERERuVsp+RERERERkRJByY+IiIiIiJQISn5ERERERKREcCj5ycvLY/To0QQEBODq6kpgYCDjxo3jf9dM+Pnnn3niiSdwd3enXLlyPPjgg6SkpFi3p6en06NHD7y9vSlXrhwPPPAAn3/+edEckYiIiIiISAEcWup60qRJzJo1i/nz5xMeHs6uXbuIjY3F3d2dwYMHA3D8+HGaNGlC3759iY+Px83NjZ9++okyZcpY2+nZsydZWVksX76cKlWqkJiYSNeuXdm1axf169cv2iMUERERkXtaSV/pTOzn0FLXjz/+OFWrVmXOnDnWss6dO+Pq6sqCBQsA6NatG6VKleKTTz65YTvly5dn1qxZ9OjRw1rm4eHBpEmTePbZZ+2KRUtdi4iIiAhoqWtQAnhLlrqOjIxk3bp1HDlyBIB9+/axdetW2rVrB4DZbOarr77ivvvuIyoqCi8vLxo1asSyZcvytbNo0SIyMzMxm8189tlnXLlyhWbNmjkSjoiIiIiIiN0cGvY2YsQIcnJyCAkJwdnZmby8PMaPH09MTAwAGRkZXLp0iYkTJ/LWW28xadIkVq9ezZNPPsmGDRt49NFHAVi8eDFPPfUUHh4euLi4ULZsWZKSkggKCipwvyaTCZPJZFNmtuThZHAuzDGLiNwz9LRTTztFRMR+DiU/ixcvZuHChSQmJhIeHs7evXsZMmQIPj4+9OrVC7PZDECHDh0YOnQoAPXq1WPbtm3Mnj3bmvyMHj2arKws1q5dS5UqVVi2bBldu3Zly5Yt1K5dO99+ExISiI+PtykLIJRAwgt10CIi9wrd+IuIiNjPoTk/NWrUYMSIEQwYMMBa9tZbb7FgwQIOHTrE1atXKVeuHGPGjGHUqFHWOq+99hpbt27l22+/5fjx4wQFBXHgwAHCw/+TvLRq1YqgoCBmz56db78F9fx0cu+tnh8REREREbF7zo9DPT+XL1/Gycl2mpCzs7O1x6d06dI8+OCDHD582KbOkSNH8PPzs7YB/GU7/8toNGI0Gm3KlPiIiIiICGgIMGgkgL0cSn7at2/P+PHj8fX1JTw8nD179vDOO+/Qp08fa53hw4fz1FNP0bRpU5o3b87q1atZsWIFGzduBCAkJISgoCD69+/PlClT8PDwYNmyZSQnJ7Ny5coiPTgREREREZE/OTTs7eLFi4wePZqkpCQyMjLw8fGhe/fuvPHGG5QuXdpa76OPPiIhIYHffvuN4OBg4uPj6dChg3X70aNHGTFiBFu3buXSpUsEBQXxyiuv2Cx9/Xe01LWIiIiIgHp+QD0/9g57cyj5uZMo+REREREREbhFc35ERERERO406vlRz4+9HHrJqYiIiIiIyN1KyY+IiIiIiJQIGvYmIiIiInc1DfkSe6nnR0RERERESgaLFMqVK1csY8aMsVy5cqW4QykWJf34LRb9BiX9+C0W/QYWi36Dkn78Fot+g5J+/BaLfoOSfvwWy931G9y1S10Xt5ycHNzd3cnOzsbNza24w7ntSvrxg36Dkn78oN8A9BuU9OMH/QYl/fhBv0FJP364u34DDXsTEREREZESQcmPiIiIiIiUCEp+RERERESkRFDyU0hGo5ExY8ZgNBqLO5RiUdKPH/QblPTjB/0GoN+gpB8/6Dco6ccP+g1K+vHD3fUbaMEDEREREREpEdTzIyIiIiIiJYKSHxERERERKRGU/IiIiIiISImg5EdEREREREoEJT8iIiIiIlIiKPkRERERsVPNmjU5f/58vvKsrCxq1qxZDBGJiCOU/IiIOGDDhg033PbBBx/cxkiK19WrVzl8+DDXr18v7lCKTUZGBlu2bGHLli1kZGQUdzhym5w6dYq8vLx85SaTidOnTxdDRCLiCJfiDkDubE8++aTddb/44otbGMmdKysri4oVKxZ3GLfdsWPHOH78OE2bNsXV1RWLxYLBYCjusG65tm3bMnjwYCZMmECpUqUAOHfuHLGxsWzdupX+/fsXc4S31uXLlxk0aBDz588H4MiRI9SsWZNBgwbxj3/8gxEjRhRzhLfexYsXefHFF/nss8+sN8HOzs489dRTzJw5E3d392KO8Pa5evUqGRkZmM1mm3JfX99iiujWWb58ufW/16xZY/PvnJeXx7p16/D39y+GyIrP8ePHmTt3LsePH2f69Ol4eXmxatUqfH19CQ8PL+7wbqm8vDzmzZvHunXrCvz/wPr164spMvk7Sn7+xrBhw+yu+84779zCSIrHf5/cLRYLSUlJuLu707BhQwB2795NVlaWQ0nS3WzSpEn4+/vz1FNPAdC1a1c+//xzvL29+frrr6lbt24xR3jrnT9/nqeeeor169djMBg4evQoNWvWpG/fvlSqVImpU6cWd4i31IYNG+jZsyfJyckkJiZy8uRJ+vbtS3BwMHv37i3u8G65uLg49u3bx8aNG2nbtq21vFWrVowdO7ZEJD/PPvsse/bsYeXKlURERACwfft2XnrpJfr3789nn31WzBHeekePHqVPnz5s27bNpvzPhyAF9Yzc7Tp27AiAwWCgV69eNttKlSqFv7//PX/++2+bNm2iXbt2NG7cmM2bNzN+/Hi8vLzYt28fc+bMYenSpcUd4i310ksvMW/ePKKjo7n//vtLxMM/gB9//NHuunXq1LmFkRSewWKxWIo7iDtZ8+bNbf7+4YcfuH79OsHBwcAfTz2dnZ1p0KDBPZ/lv/baa2RmZjJ79mycnZ2BP558vPjii7i5uTF58uRijvDWCwgIYOHChURGRpKcnEzXrl1ZtGgRixcvJiUlhW+++aa4Q7zlevbsSUZGBv/+978JDQ1l37591KxZkzVr1jBs2DB++umn4g7xlrt06RLPP/88S5cuxWw2M27cOF599dUScfHz8/Nj0aJFPPzww1SoUMH673/s2DEeeOABcnJyijvEW65cuXKsWbOGJk2a2JRv2bKFtm3bkpubW0yR3T6NGzfGxcWFESNGUK1atXz/27+XHwQFBASwc+dOqlSpUtyhFKuIiAi6dOnCsGHDbM4F33//PU8++SS//fZbcYd4S1WpUoWPP/6Yxx57rLhDua2cnJwwGAx2jfa4Ux+CqOfnb/z3+P533nmHChUqMH/+fCpVqgTAhQsXiI2N5ZFHHimuEG+bjz76iK1bt1oTH/hjqMewYcOIjIwsEclPeno6NWrUAGDlypV07dqVNm3a4O/vT6NGjYo5utvjm2++Yc2aNVSvXt2mvFatWvzyyy/FFNXtdeTIEXbt2kX16tVJTU3l8OHDXL58mXLlyhV3aLfc2bNn8fLyyleem5tbIpI/AA8PjwKHtrm7u1uvDfe6vXv3snv3bkJCQoo7lNvu5MmT1v++cuUKZcqUKcZois/+/ftJTEzMV+7l5cW5c+eKIaLbq3Tp0gQFBRV3GLfdf//vf8+ePbzyyisMHz7cphd86tSpvP3228UV4t/SggcOmDp1KgkJCTYXt0qVKvHWW2+ViK7u69evc+jQoXzlhw4dyjfW9V5VqVIlfv31VwBWr15Nq1atgD+GetypTziKWm5uLmXLls1XnpmZidFoLIaIbq+JEycSERFB69atOXDgAN9//z179uyhTp06bN++vbjDu+UaNmzIV199Zf37z4Tn3//+t/Xid68bNWoUw4YNIz093VqWnp7O8OHDGT16dDFGdvuEhYWViBvcgvzZ2/uPf/yD8uXLc+LECQBGjx7NnDlzijm626dixYqkpaXlK9+zZw//+Mc/iiGi2+vll19m+vTplLQBVH5+ftbPhAkTeO+99+jfvz916tShTp069O/fn2nTpjFu3LjiDvWG1PPjgJycHM6ePZuv/OzZs1y8eLEYIrq9YmNj6du3L8ePH+ehhx4CYMeOHUycOJHY2Nhiju72ePLJJ3n66aepVasW58+fp127dsAfJ/uS8gTokUce4eOPP7ae2AwGA2azmbfffjvfMNF70fTp01m2bJn13/7+++/n+++/Z+TIkTRr1gyTyVTMEd5aEyZMoF27dhw8eJDr168zffp0Dh48yLZt29i0aVNxh3dbzJo1i2PHjuHr62ud2J+SkoLRaOTs2bM2q/798MMPxRVmkfvvIY2TJk3i1VdfZcKECdSuXdu6+Mef3Nzcbnd4t81bb73F/Pnzefvtt+nXr5+1/P7772fatGn07du3GKO7fbp168Zrr73GkiVLrNeBb7/9lldeeYWePXsWd3i33NatW9mwYQOrVq0iPDw83/8HSsIiUPv37ycgICBfeUBAAAcPHiyGiOyjOT8O6NmzJ1u2bGHq1Kk2N//Dhw/nkUcesa5+dK8ym81MmTKF6dOnW5/2VKtWjZdeeomXX37ZZjjcveratWtMnz6dX3/9ld69e1O/fn3+v717j6o53f8A/t67aJddEcq1knIbGd38XKemQhdJZIwyyJhx12iQGY7kLoNGLHEU5SC5Ts2ZI/fcMijaRDdSLiGV6Kbb9/eH1ffYStpzZu8ne39ea7XG9/n2x3vNqt33+T7P8/kAwKZNm6CtrY2pU6cyTih/t2/fhoODAywtLXHmzBmMHDkSKSkpKCgowKVLl9C1a1fWEeXqxYsXH9zrHx8fD1tbWwUnUrx79+5h7dq1SE5ORnFxMSwtLeHv7w9zc3PW0RQiMDCw0d8bEBAgxySKVbvXv1Z9e/6VueBBLVNTU2zfvh0ODg5SZ11SU1MxYMAAFBYWso6oEBUVFZg1axZ2796N6upqqKuro7q6Gl5eXti9e7fSPxN87KXvrl27FJSEHUtLS/Tu3Rs7d+5E8+bNAbz9uZg6dSpu377dZF/+0ORHBqWlpZg/fz7Cw8NRWVkJAFBXV8e3336L9evXq8R+/1q1bwCV+e0e+bCioiJs2bJF6uF31qxZaN++PetoCvHy5UscOnQI9+7dw4IFC6Cnp4ekpCQYGBioxHYPoppkWdlT5pcAmpqaSE1NhZGRkdTk586dO+jXrx+Ki4tZR5Q7juPw8OFDtG3bFi9evMCtW7dQXFwMCwsLmJmZsY5HFOTq1atwc3MDx3F8ZTeJRAKBQIDY2Fh+oaCpocnPX1BSUoJ79+4BALp27apSk56qqiqcO3cO9+7dg5eXF7S1tfHkyRPo6OhALBazjid3ERERaNOmDVxdXQEACxcuxI4dO9CrVy/s378fRkZGjBMSeZNIJHB0dISuri4ePHiAtLQ0mJiYYMmSJcjJyUFkZCTriHL1oWpuAoEAGhoa/Ns/VVFeXo4DBw6gpKQEQ4cOpQc/FWBlZYV58+ZhwoQJUpOf5cuX4+TJk7hw4QLriHJXU1MDkUiElJQU+plXcSUlJdi7dy9/Jrxnz57w8vJq0s/GNPkhjZadnQ0nJyfk5OTgzZs3fHNDX19fvHnzBqGhoawjyl337t2xbds22NvbIyEhAY6Ojti0aRN+//13qKurq8QeX+DtA59EIqm3sdvIkSMZpVIMBwcHWFlZISgoSOrB5/Lly/Dy8sKDBw9YR5Sr97c+va9Tp06YPHkyAgICIBQqV00dPz8/VFZWIiQkBMDb7R39+vXDnTt3oKWlhaqqKpw4cQIDBw5knFT+du3aBbFYjLFjx0qNHzx4EKWlpXX64CiT3377DZMmTcJPP/2E5cuXIzAwEGlpaYiMjMTvv/+OoUOHso6oEJ999hnCwsLQv39/1lEUxtLSEqdPn0arVq1gYWHR4GdhU93yRajgQaM0toGnsj/4+vr6wtraGsnJyWjdujU/7uHhIXXoU5k9fPiQL2xw7NgxjBkzBt9//z0GDRoEOzs7tuEU5Pjx45g4cWK9lZ6Ufa8/AFy/fh07duyoM96xY0ep6l/Kavfu3Vi8eDEmT57Mb2m4evUqIiIisGTJEuTl5eGXX36BhoYGfv75Z8Zp/14nTpzA6tWr+eu9e/ciJycHGRkZMDQ0xJQpU7Bq1SqpanjKas2aNVKFHWrp6+vj+++/V+rJj7u7O2JjY7F8+XK0aNECS5cuhaWlJWJjY1Vm4gO8rXy5YMECbNu2Db1792YdRyHc3d35qqa1TW9V3Z49e7B9+3bcv38fCQkJMDIywqZNm2BiYgJ3d3fW8epFk59GqK+fgyq6cOECLl++XGdbi7GxMR4/fswolWKJxWLk5+fD0NAQJ06cgJ+fHwBAJBKhrKyMcTrFmDNnDsaOHYulS5fCwMCAdRyF09DQqHfrV3p6Otq2bcsgkWJFRERgw4YN+Oqrr/gxNzc3mJubY/v27Th9+jQMDQ2xatUqpZv85OTkoFevXvz1iRMn4OnpyW939fX1VZmGhzk5OfVWeTIyMkJOTg6DRIrz6NEjDBkyBCdPnqxz78qVKyqzEjJx4kSUlpbi888/R/PmzaGpqSl1v6CggFEy+Xm3gIkyFTP5q7Zt24alS5fihx9+wMqVK/mXn61atUJwcDBNfj5lqlCxozFqamrqfav/6NEjaGtrM0ikeEOHDsXUqVNhYWGB9PR0/kEnJSUFxsbGbMMpyLNnz+Dn56eSEx/g7ba+5cuXIzo6GsDb1a6cnBz4+/tjzJgxjNPJ3+XLl+vd4mphYcH3ORo8eLBSPgALhUKpnh5XrlyR6uvTsmVLlan0pa+vD4lEUudz7/2dAcpo2LBhuHjxIvT09KTGL126BFdXV7x8+ZJNMAULDg5mHaFJuH79Ou7evQvgbf8rKysrxokUJyQkBP/85z8xatQorF27lh+3trbG/PnzGSZrGE1+GqmyshKampq4efOmyizvvm/YsGEIDg7mt/wIBAIUFxcjICBAZd52bt26FUuWLMHDhw9x+PBh/o98YmIixo8fzzidYnh6euLcuXNKX9L6QzZs2ABPT0/o6+ujrKwMtra2yM3NxYABA7Bq1SrW8eSuc+fOCAsLk/pDBwBhYWHo3LkzACA/P1+qGbSy6NmzJ2JjY+Hn54eUlBTk5ORI9bbKzs5WmZcC48ePx9y5c6GtrY0vvvgCwNtqcL6+vvj6668Zp5Ov/v37Y9iwYTh79iz/4u/8+fNwc3PDsmXL2IZTIGXe2tgYjx49wvjx43Hp0iW0bNkSwNtKoAMHDkRUVBQ6derENqACZGVl8S0/3qWhoYGSkhIGiRqHCh7IwMTEBEePHsXnn3/OOgoTjx49wvDhw8FxHDIyMmBtbY2MjAy0adMG58+fh76+PuuIRAFKS0sxduxYtG3btt7mhnPnzmWUTLEuXrwIiUSC4uJiWFlZwcHBgXUkhYiJicHYsWPRo0cP2NjYAPjvm8/Dhw9jxIgR2LZtGzIyMrBx40bGaf9eR48exddff43BgwcjJSUFNjY2iI2N5e/7+/sjKyuLXxVUZhUVFfjmm29w8OBBqKu/fY9aU1ODiRMnIjQ0VKmr/tXU1MDT0xMFBQWIi4vD5cuXMXLkSKxcuRK+vr6s4zFRXl6OiooKqTFlb4Xh5OSEly9fIiIiAt27dwcApKWlwcfHBzo6Ojh+/DjjhPLXq1cvrFmzBu7u7lIFgEJCQrBr164mW/SBJj8yCAsLw5EjR7Bnz546y92qoqqqClFRUfxDn6WlJby9vevs9VV2paWlyMnJqfNhX1vnXpmFhYVh+vTpEIlEaN26tVS1G4FAgPv37zNMJz8JCQnIz8/HiBEj+LGIiAgEBASgtLQUo0aNQkhICH8YVpk9ePAAoaGhSE9PB/C2CuK0adNQXFys9Cvjp0+fxu+//4527dphzpw50NLS4u8FBgbC1tZW6YufvNvj5dGjR7h58yY0NTVhbm6uMuX+Kyoq4OrqitLSUkgkEqxZswazZ89mHUuhSkpK4O/vj+joaOTn59e5r+zFbzQ1NXH58uU6Kx+JiYkYMmQISktLGSVTnJ07d2LZsmXYsGEDvv32W+zcuRP37t3DmjVrsHPnzia7CkyTHxlYWFggMzMTlZWVMDIyqlPDvKnOcP8u5eXlEIlErGMwlZeXh8mTJ3/wjY6yf9gDQLt27TB37lwsWrRI6UoZN8TZ2Rl2dnbw9/cHANy6dQtWVlaYNGkSevbsifXr12PatGkqte0FeNv3Z//+/QgPD8f169dV4ndA1alijxeJRFJn7PXr1xg/fjxcXV0xY8YMflwVXoIBwKxZs3D27FmsWLEC33zzDbZu3YrHjx9j+/btWLt2Lby9vVlHlKtu3brhX//6V51GnlevXoWXlxcyMzMZJVOsvXv3YtmyZXz/yw4dOiAwMBDffvst42QfRpMfGQQGBjZ4X9krf+jo6MDDwwMTJkyAg4ODSj341vL29kZ2djaCg4NhZ2eHo0eP4tmzZ1i5ciU2bNjANz9VZnp6erh27ZrKnflp3749YmNjYW1tDQBYvHgx4uPjcfHiRQBv+5sEBATgzp07LGMqzPnz5xEWFobDhw+jQ4cOGD16NMaMGcNvhVN2hYWFCAsL4w869+zZE1OmTFGZXQGq1uOltr/Vu49M717X/lsVyv3XMjQ0RGRkJOzs7KCjo4OkpCSYmppiz5492L9/P/744w/WEeXqt99+w+rVq7F161b+78L169cxZ84c+Pv7q0Qp7FevXvHbG0tLS1FcXMwfgcjMzORbgzQ1NPkhjXb06FHs27cP//73v6Grq4tx48ZhwoQJ/C+9Kmjfvj1+++039OvXDzo6Orh+/Tq6deuGmJgYBAUF8Q/CymzevHlo27at0pUx/hiRSISMjAz+UP/gwYPh7OyMxYsXA3i7Fczc3ByvX79mGVOunj59it27dyMsLAyvXr3CV199hdDQUCQnJ0uVgFZ2tYfbdXV1+c+/xMREvHz5ErGxsXwBAGUWGxuLoKAglenxkp2d3ejvVZWtf2KxGHfu3IGhoSE6deqEI0eOoF+/fsjKyoK5uTmKi4tZR/zbtWrVSmqrd0lJCaqqqvhzb7X/btGihVKW+n7fkCFDcOrUqTrbvdPS0uDg4IBHjx4xStYwqvYmo5cvX+LQoUO4d+8eFixYAD09PSQlJcHAwAAdO3ZkHU+uPDw84OHhgdevX+PQoUPYv38/+vfvDxMTE0yYMAFLly5lHVHuSkpK+LcarVq1Ql5eHrp16wZzc3Ol3/ZYq7q6GkFBQYiLi0OfPn3qFDxQtkPutQwMDJCVlYXOnTujoqICSUlJUqvBr1+/rvP/Qpm4ubnh/PnzcHV1RXBwMJycnKCmplZv2WtlN2vWLIwbNw7btm2DmpoagLe/FzNnzsSsWbNw69YtxgnlT9V6vKjKhEYWJiYmyMrKgqGhIXr06IHo6Gj069cPsbGxfPUzZUPlvaWJxWJ4eHggJiaGnwDevXsX9vb2Ur3gmhpa+ZGBRCKBo6MjdHV18eDBA6SlpcHExARLlixBTk4OIiMjWUdUuDt37sDb2xsSiUQllvptbGywcuVKDB8+HCNHjkTLli2xZs0abN68mZ8UK7t3S/u+TyAQ4MyZMwpMozgzZsxAcnIy1q1bh2PHjiEiIgJPnjzhq1rt3bsXwcHBuHbtGuOk8qGuro65c+dixowZUuc8mjVrpnIrP7VtD2orPNVKS0tD3759VaLhcURERIP3lbkM8po1a2BgYIApU6ZIjYeHhyMvL48/F6is7t+/D2NjY/z6669QU1PD3LlzcerUKbi5uYHjOFRWVmLjxo0qW/lOlZSVlcHR0RGdOnVCVFQUUlJS4ODgAG9v76b9IpQjjebg4MAtWLCA4ziOE4vF3L179ziO47hLly5xRkZGDJMpVllZGXfgwAHO3d2d09DQ4AwNDTl/f3/WsRRiz5493K5duziO47jr169zbdq04YRCIScSibioqCi24Yhc5eXlcUOGDOEEAgGnra3NHTlyROq+vb099/PPPzNKJ38JCQnc1KlTOW1tba5fv35cSEgIl5eXx6mrq3MpKSms4ynUwIEDuaNHj9YZP3r0KPd///d/ig9EFMrIyIi7dOlSnfErV65wxsbGDBIpllAo5J49e8Zff/XVV9zTp0+5Bw8ecIcPH+aSk5MZpmOjrKyMKyoqkvpSFYWFhdznn3/OeXp6cvr6+tz8+fNZR/ooWvmRga6uLpKSktC1a1epeubZ2dno3r07ysvLWUeUq7i4OOzbtw/Hjh2Duro6PD094e3trRL72z+ktLQUqampMDQ0RJs2bVjHIQpQVFQEsVjMb3eqVVBQALFYrNT9TYC3Wz8PHDiA8PBwXL16FdXV1di4cSOmTJnCN3xURu9W+7p79y4WLlyIOXPm8Af+r1y5gq1bt2Lt2rUYN24cq5hMqFqPF5FIhLt376JLly5S4/fv30evXr2U/llAKBTi6dOn/Bbwd5+HVImqlvp+9epVnbHc3FwMHToUI0aMkGqA3VQ/B2jyIwN9fX3ExcXBwsJC6pf95MmTmDJlCh4+fMg6olxpaWlhxIgR8Pb2houLi1KfbyANu379OqKjo+vtdXTkyBFGqYiipaWlISwsDHv27MHLly8xdOhQxMTEsI4lF/VV+6qPqlT7UtUHPwAwMzNDQEAAJkyYIDW+Z88eBAQEKG2vs1o0+XlLVUt9134Wvo/7hCofUsEDGYwcORLLly/nu3cLBALk5OTA398fY8aMYZxO/p49e6bUb3Y/xM/Pr9Hf26T3uP5NoqKiMHHiRAwfPhwnTpzAsGHDkJ6ejmfPnsHDw4N1PKJA3bt3R1BQENasWYPY2FiEh4ezjiQ3WVlZrCM0KQsXLsTZs2exbdu2eh/8lNl3332HH374AZWVlbC3twfwtvntwoUL8eOPPzJOJ38CgaDOw299D8PKLjY2li/17ePjgyFDhsDU1BRGRkbYu3ev0k5+zp49yzrC/4xWfmRQVFQET09PXL9+Ha9fv0aHDh3w9OlTDBgwAH/88UedpqfK4N0a7vUtdb6rqS5v/q8aOuD/LmU+7P+uPn36YNq0aZg1axb/xq9Lly6YNm0a2rdv/9F+WISQT58q93jhOA6LFi3C5s2b+ZVvkUgEf39/lah6KhQK4ezszJc3jo2Nhb29fZ1nIGXfBaCKpb7fVVVVhdWrV2PKlCno1KkT6zgyocnPX3Dx4kVIJBIUFxfD0tISjo6OrCPJjZqaGnJzc6Gvr9/gUmdTXt4kf68WLVogJSUFxsbGaN26Nc6dOwdzc3O+vGVubi7riIT87WJiYuDs7IxmzZp9dGvfyJEjFZSKHVV/8AOA4uJi3L17F5qamjAzM6vT60RZ+fj4NOr7du3aJeckbPXp0wchISGwtbWFo6Mj+vbti19++QWbN29GUFBQk+1x83fS1tbGrVu3YGxszDqKTGjb218wePBgDB48mHUMhThz5gzfsfzMmTMqubT9rqKiIlRXV9fp4l5QUAB1dXWlXf16V6tWrfhGnh07dsTt27dhbm6Oly9forS0lHE6QuRj1KhR/DmHhjq3q8qLIFXs8fI+sVgMGxsb1jEUTtknNY3l4+OD5ORk2NraYtGiRXBzc8OWLVtQUVGBTZs2sY6nEPb29oiPj//kJj+08iODzZs31zsuEAggEolgamqKL774ok4VKKI8nJ2d4ebmhpkzZ0qNh4aGIiYmRqm3etTy8vKCtbU1/Pz8sGLFCoSEhMDd3R0nT56EpaWl0m91IIQAmzZtUqkeL6NHj8bu3buho6OD0aNHN/i99BmomrKzs5GYmAgzMzOYm5uzjqMQoaGhCAwMhLe3N6ysrOpsfWyqq+A0+ZFBly5dkJeXh9LSUrRq1QoAUFhYCC0tLYjFYjx//hwmJiY4e/YsOnfuzDjt38/MzAze3t7w9vaWanKoSvT09HDp0iX07NlTajw1NRWDBg2qt+qRsikoKEB5eTk6dOiAmpoaBAUF4fLlyzAzM8OSJUv43w1ClE1CQgLy8/MxYsQIfiwyMhIBAQEoKSnBqFGjEBISotTbn2pqarB+/XrExMSgoqICDg4OCAgIwPPnz5GYmAhTU1P06dOHdcy/nY+PDzZv3gxtbe2PbvuilRHldubMGcyePRtXrlyps9ujqKgIAwcORGhoKIYMGcIooeIIhcIP3mvSq+CKbiz0Kdu3bx9nZ2fHZWZm8mMZGRmcvb09FxUVxT18+JAbNGgQN2bMGIYp5Wfjxo2ctbU1JxQKOWtray44OJjLzc1lHUuhtLS0OIlEUmdcIpFwmpqaDBIRQhTFycmJW7t2LX8tkUg4dXV1burUqdyGDRu4du3acQEBAewCKsDy5cs5oVDIDRs2jHN3d+dEIhHn4+PDOpZCBAYGciUlJaxjEMbc3Ny4jRs3fvD+r7/+yo0aNUqBiYisaPIjAxMTE+7GjRt1xpOSkrguXbpwHMdxly5d4tq1a6fgZIqVlpbGLV26lDMzM+PU1dW5oUOHchEREaxjKYSdnR03e/bsOuMzZ87kBg8ezCCR4ggEAk4oFDb4paamxjomIXLTrl077tq1a/z1zz//zA0aNIi/jo6O5nr27MkimsKYmppyoaGh/PXJkye55s2bc9XV1QxTKYZQKOSePXvGOgZhzNDQkLtz584H79+9e5fr3LmzAhMRWVHBAxnk5uaiqqqqznhVVRWePn0KAOjQoQN/GFxZdevWDYGBgQgMDMSVK1cwY8YM+Pj4YOLEiayjyd3KlSvh6OiI5ORkODg4AHjb3+HatWs4ceIE43TydfTo0Q/eS0hIwObNm1FTU6PARIQoVmFhIQwMDPjr+Ph4ODs789c2NjZK3+w6JycHLi4u/LWjoyMEAgGePHnyyZW7lRVHpwQI3vY8bKjJu7q6OvLy8hSYiK2SkhLEx8fX2/R87ty5jFI1jCY/Mvjyyy8xbdo07Ny5ExYWFgCAGzduYMaMGXyjs1u3bqFLly4sYyrE1atXsW/fPhw4cACvXr3C2LFjWUdSiEGDBuHKlSsICgpCdHQ0NDU10adPH4SFhSn9OSh3d/c6Y2lpaVi0aBFiY2Ph7e2N5cuXM0hGiGIYGBggKysLnTt3RkVFBZKSkqT6Wr1+/brBhyJlUFVVBZFIJDXWrFkzVFZWMkqkWKpe8ZT8t8qpqalpvfclEgnat2+v4FRs3LhxAy4uLigtLUVJSQn09PTw4sULaGlpQV9fnyY/yiAsLAzffPMNrKys+D9wVVVVcHBwQFhYGIC3pS83bNjAMqbcpKenY+/evdi/fz+ysrJgb2+PdevWYfTo0RCLxazjydX7h3zt7e2xc+dOaGpqso7GxJMnTxAQEICIiAgMHz4cN2/eRO/evVnHIkSuXFxcsGjRIqxbtw7Hjh2DlpaW1KFmiUSCrl27MkwofxzHYfLkyVJFHcrLyzF9+nSpSk/KWvGsW7duH50AFRQUKCgNYcHFxQX/+Mc/4OTkVOdFQFlZGQICAqSKoiizefPmwc3NDaGhodDV1cWVK1fQrFkzTJgwoUlXfKRqb39Bamoq0tPTAQDdu3dH9+7dGSdSDKFQCBsbG3h5eeHrr7+W2v6h7FasWIFly5bB0dERmpqaiIuLw/jx4xEeHs46mkIVFRVh9erVCAkJQd++fbFu3TqVqGhDCAC8ePECo0ePxsWLFyEWixEREQEPDw/+voODA/r3749Vq1YxTClfqtzgUigUIjg4GLq6ug1+36RJkxSUiLDw7NkzWFpaQk1NDbNnz+afAVNTU7F161ZUV1cjKSlJJZ6RWrZsiT///BPdu3dHy5YtkZCQgJ49e+LPP//EpEmTkJqayjpivWjyQxqluroa4eHh8PT0VMlSxmZmZpg/fz6mTZsGADh16hRcXV1RVlbWYKlHZRIUFIR169ahXbt2WL16db3b4AhRBUVFRRCLxXV6uhUUFEAsFqN58+aMkhF5EgqFfKNbotqys7MxY8YMxMXF8WfBBAIBhg8fjq1bt6rE8QcAaNu2Ld/qolu3bggJCcHw4cORmpoKKysrlJSUsI5YL5r8yKC6uhq7d+/G6dOn8fz58zqHu8+cOcMomWKIRCLcvXtXZX6p36WhoYHMzEyp/k0ikQiZmZlKf8i3llAohKamJhwdHRts5Kus210IIapNTU0Nubm5NPkhvMLCQmRmZoLjOJiZmancy+Fhw4Zh8uTJ8PLywnfffQeJRIK5c+diz549KCwsxJ9//sk6Yr3ozI8MfH19sXv3bri6uqJ3794qd/Cxd+/euH//vkpOflT9kC8ATJw4UeV+5gkhpBa9Kybva9WqFWxsbFjHYGb16tV8heNVq1Zh4sSJmDFjBszMzJr0sQBa+ZFBmzZtEBkZKVXmU5UcP34cP/30E1asWAErKyupw60A6nQ6ViZCoRDOzs5Sh3xjY2Nhb2+vEod8CSGEEEKUAU1+ZNChQwecO3cO3bp1Yx2FiXfPtry7AsBxHAQCAaqrq1nEUghVPuRLCCGEEPIhz58/R1paGgCgR48eaNu2LeNEDaPJjww2bNiA+/fvY8uWLSq5/Sc+Pr7B+7a2tgpKQgghhBBCWHr9+jVmzpyJqKgo/gW4mpoaxo0bh61bt360MiIrNPmRgYeHB86ePQs9PT189tlndZrZ0ZYnQgghhBCiCsaNG4cbN24gJCQEAwYMAAAkJCTA19cXffv2RVRUFOOE9aPJjww+tvVJ2bc8nT9/vsH7X3zxhYKSEEIIIYQQllq0aIG4uDgMHjxYavzChQtwcnJqsqWuqdqbDJR9cvMxdnZ2dcbe3f6nzGd+CCGEEELIf7Vu3brerW26urpNuuy3anRn/BtVVVXh1KlT2L59O1/e78mTJyguLmacTP4KCwulvp4/f47jx4/DxsYGJ06cYB2PEEIIIYQoyJIlS+Dn54enT5/yY0+fPsWCBQvwj3/8g2GyhtG2NxlkZ2fDyckJOTk5ePPmDdLT02FiYgJfX1+8efMGoaGhrCMyER8fDz8/PyQmJrKOQgghhBBC5MTCwkJq109GRgbevHkDQ0NDAEBOTg40NDRgZmaGpKQkVjEbRNveZODr6wtra2skJyejdevW/LiHhwe+++47hsnYMjAw4EscEkIIIYQQ5TRq1CjWEf5nNPmRwYULF3D58mU0b95catzY2BiPHz9mlEpxJBKJ1DXHccjNzcXatWvRt29fNqEIIYQQQohCBAQEsI7wP6PJjwxqamrqPdT/6NEjaGtrM0ikWH379oVAIMD7OyX79++P8PBwRqkIIYQQQghLxcXFqKmpkRrT0dFhlKZhdOZHBuPGjYOuri527NgBbW1tSCQStG3bFu7u7jA0NFT6anDZ2dlS10KhEG3btoVIJGKUiBBCCCGEsJCVlYXZs2fj3LlzKC8v58c5joNAIGiyVYBp8iODR48eYfjw4eA4DhkZGbC2tkZGRgbatGmD8+fPQ19fn3VEuUhISEB+fj5GjBjBj0VGRiIgIAAlJSUYNWoUQkJCoKGhwTAlIYQQQghRlEGDBoHjOPj6+sLAwECqEAIA2NraMkrWMJr8yKiqqgoHDhxAcnIyiouLYWlpCW9vb2hqarKOJjfOzs6ws7ODv78/AODWrVuwtLTE5MmT0bNnT6xfvx7Tpk3DsmXL2AYlhBBCCCEKIRaLkZiYiO7du7OOIhOa/JCPat++PWJjY2FtbQ0AWLx4MeLj43Hx4kUAwMGDBxEQEIA7d+6wjEkIIYQQQhTkyy+/xOLFi+Ho6Mg6ikyo4IEMIiIi0KZNG7i6ugIAFi5ciB07dqBXr17Yv38/jIyMGCeUj8LCQhgYGPDX8fHxcHZ25q9tbGzw8OFDFtEIIYQQQggDO3fuxPTp0/H48WP07t0bzZo1k7rfp08fRskaJmQd4FOyevVqfntbQkICtmzZgqCgILRp0wbz5s1jnE5+DAwMkJWVBQCoqKhAUlIS+vfvz99//fp1nR94QgghhBCivPLy8nDv3j34+PjAxsYGffv2hYWFBf/fpopWfmTw8OFDmJqaAgCOHTsGT09PfP/99xg0aBDs7OzYhpMjFxcXLFq0COvWrcOxY8egpaWFIUOG8PclEgm6du3KMCEhhBBCCFGkKVOmwMLCAvv376+34EFTRZMfGYjFYuTn58PQ0BAnTpyAn58fAEAkEqGsrIxxOvlZsWIFRo8eDVtbW4jFYkREREg1eg0PD8ewYcMYJiSEEEIIIYqUnZ2NmJgYfmHgU0GTHxkMHToUU6dOhYWFBdLT0+Hi4gIASElJgbGxMdtwclRbyruoqAhisRhqampS9w8ePAixWMwoHSGEEEIIUTR7e3skJyfT5EeZbd26FUuWLMHDhw9x+PBhtG7dGgCQmJiI8ePHM04nf7q6uvWO6+npKTgJIYQQQghhyc3NDfPmzcOtW7dgbm5e5/z3yJEjGSVrGJW6JoQQQgghhMhEKPxw3TSBQIDq6moFpmk8qvYmg+PHj/O9bYC3K0F9+/aFl5cXCgsLGSYjhBBCCCFEcWpqaj741VQnPgBNfmSyYMECvHr1CgBw69Yt/Pjjj3BxcUFWVhZf/IAQQgghhBBl5eLigqKiIv567dq1ePnyJX+dn5+PXr16MUjWOLTtTQZisRi3b9+GsbExli1bhtu3b+PQoUNISkqCi4sLnj59yjoiIYQQQgghcqOmpobc3Fzo6+sDAHR0dHDz5k2YmJgAAJ49e4YOHTo02dUfWvmRQfPmzVFaWgoAOHXqFF/eWU9Pj18RIoQQQgghRFm9v27yqa2jULU3GQwePBh+fn4YNGgQrl69igMHDgAA0tPT0alTJ8bpCCGEEEIIIQ2hlR8ZbNmyBerq6jh06BC2bduGjh07AgD+85//wMnJiXE6QgghhBBC5EsgEEAgENQZ+1TQmR9CCCGEEEJIowiFQjg7O0NDQwMAEBsbC3t7e7Ro0QIA8ObNGxw/frzJnvmhyc9fVF5ejoqKCqkxHR0dRmkIIYQQQgiRPx8fn0Z9365du+Sc5K+hyY8MSkpK4O/vj+joaOTn59e531RnuIQQQgghhBA68yOThQsX4syZM9i2bRs0NDSwc+dOBAYGokOHDoiMjGQdjxBCCCGEENIAWvmRgaGhISIjI2FnZwcdHR0kJSXB1NQUe/bswf79+/HHH3+wjkgIIYQQQgj5AFr5kUFBQQHfwElHRwcFBQUA3pbAPn/+PMtohBBCCCGEkI+gyY8MTExMkJWVBQDo0aMHoqOjAbytctGyZUuGyQghhBBCCCEfQ9veZLBp0yaoqalh7ty5OHXqFNzc3MBxHCorK7Fx40b4+vqyjkgIIYQQQgj5AHXWAT4FNTU1WL9+PWJiYlBRUYEnT54gICAAqampSExMhKmpKfr06cM6JiGEEEIIIaQBtPLTCCtWrMCyZcvg6OgITU1NxMXFYfz48QgPD2cdjRBCCCGEENJINPlpBDMzM8yfPx/Tpk0DAJw6dQqurq4oKyuDUEjHpgghhBBCCPkU0OSnETQ0NJCZmYnOnTvzYyKRCJmZmejUqRPDZIQQQgghhJDGomWLRqiqqoJIJJIaa9asGSorKxklIoQQQgghhMiKCh40AsdxmDx5MjQ0NPix8vJyTJ8+HS1atODHjhw5wiIeIYQQQgghpBFo8tMIkyZNqjM2YcIEBkkIIYQQQgghfxWd+SGEEEIIIYSoBDrzQwghhBBCCFEJNPkhhBBCCCGEqASa/BBCCCGEEEJUAk1+CCGEEEIIISqBJj+EEEIIIYQQlUCTH0IIIYQQQohKoMkPIYQQQgghRCX8Px/k3Aagz/CXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisation des relations entre les caractéristiques et la variable cible\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(train_data.corr(), annot=True, cmap='coolwarm')\n",
        "plt.title(\"Corrélation entre les Caractéristiques\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "dSA-Uhsj3Y0x",
        "outputId": "fa7bd5dc-d432-4ed9-ddd2-87285e105c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-260d5515dec7>: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(train_data.corr(), annot=True, cmap='coolwarm')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisation des distributions des caractéristiques\n",
        "train_data.hist(figsize=(10, 8))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "rM5fZa7v3mvg",
        "outputId": "d6bd4c98-e73a-4463-9b14-5ef26d0b3682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remplissage des valeurs manquantes dans la colonne \"Age\" avec la médiane\n",
        "median_age = train_data['Age'].median()\n",
        "median_age_test = test_data['Age'].median()\n",
        "train_data['Age'].fillna(median_age, inplace=True)\n",
        "test_data['Age'].fillna(median_age_test, inplace=True)\n",
        "\n",
        "# Suppression de la colonne \"Cabin\"\n",
        "train_data.drop('Cabin', axis=1, inplace=True)\n",
        "test_data.drop('Cabin', axis=1, inplace=True)\n",
        "\n",
        "# Afficher à nouveau les informations sur les valeurs manquantes\n",
        "print(train_data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgUQZIYC3uEI",
        "outputId": "24b8cb53-2a59-4289-e510-b19b024a1678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId    0\n",
            "Survived       0\n",
            "Pclass         0\n",
            "Name           0\n",
            "Sex            0\n",
            "Age            0\n",
            "SibSp          0\n",
            "Parch          0\n",
            "Ticket         0\n",
            "Fare           0\n",
            "Embarked       2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XarV-SMcAdGG",
        "outputId": "d45d9166-bbcd-4a15-c19f-63b3d949b019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pclass    0\n",
            "Sex       0\n",
            "Age       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Suppression de plusieurs colonnes\n",
        "columns_to_drop = ['PassengerId', 'Name', 'Ticket', 'Embarked','Parch','SibSp','Fare']\n",
        "train_data.drop(columns=columns_to_drop, inplace=True)\n",
        "test_data.drop(columns=columns_to_drop, inplace=True)"
      ],
      "metadata": {
        "id": "KkNb7L0k36-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QnwIBBDY4S_j",
        "outputId": "d9fd4afe-6d04-4658-a81a-e95ecc2264b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass     Sex   Age\n",
              "0         0       3    male  22.0\n",
              "1         1       1  female  38.0\n",
              "2         1       3  female  26.0\n",
              "3         1       1  female  35.0\n",
              "4         0       3    male  35.0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-cfcb5051-cc88-4770-9c9d-6730af270a1b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>3</td>\n",
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              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfcb5051-cc88-4770-9c9d-6730af270a1b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2de5e8a1-bb90-4170-aa5c-589ca4d091c2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2de5e8a1-bb90-4170-aa5c-589ca4d091c2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cfcb5051-cc88-4770-9c9d-6730af270a1b 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-cfcb5051-cc88-4770-9c9d-6730af270a1b');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.head()"
      ],
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        "colab": {
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          "height": 206
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        "id": "Qm3b3sw44rBG",
        "outputId": "23156638-caf6-4cdc-e24d-8017084d16f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass     Sex   Age\n",
              "0       3    male  34.5\n",
              "1       3  female  47.0\n",
              "2       2    male  62.0\n",
              "3       3    male  27.0\n",
              "4       3  female  22.0"
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              "      <td>22.0</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4db5e4eb-6c28-451f-a5d2-08b875e013df');\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",
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              "    </div>\n",
              "  </div>\n"
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          "metadata": {},
          "execution_count": 14
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1292
        },
        "id": "Mxde2EMi66Rz",
        "outputId": "b9d4e44a-b904-4a80-a5e1-fc86cfd4ae6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Survived      Pclass         Age\n",
              "count  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.361582\n",
              "std      0.486592    0.836071   13.019697\n",
              "min      0.000000    1.000000    0.420000\n",
              "25%      0.000000    2.000000   22.000000\n",
              "50%      0.000000    3.000000   28.000000\n",
              "75%      1.000000    3.000000   35.000000\n",
              "max      1.000000    3.000000   80.000000"
            ],
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
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              "      <td>891.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.361582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "    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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-50bc73fe-7228-49ef-871c-d2277e66f808 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a1487443-e638-4b3d-a396-604b1ccf9eca 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-a1487443-e638-4b3d-a396-604b1ccf9eca');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc5db7c5360>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_2908878289952775203 = autoviz.get_df('df_2908878289952775203')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_2908878289952775203, *['Survived'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8e691d14-a99b-47e1-ade6-a6044ea6f021\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8e691d14-a99b-47e1-ade6-a6044ea6f021\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_3155923125202782535 = autoviz.get_df('df_3155923125202782535')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_3155923125202782535, *['Pclass'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-227aff46-2569-4066-b119-776edca649bf\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-227aff46-2569-4066-b119-776edca649bf\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1234423358161153183 = autoviz.get_df('df_1234423358161153183')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_1234423358161153183, *['Age'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "1VtuCf0AAAAASUVORK5CYII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ce79e964-ee25-4eef-8578-5760a737dfcc\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ce79e964-ee25-4eef-8578-5760a737dfcc\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc5d511a5f0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_951775417380936621 = autoviz.get_df('df_951775417380936621')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_951775417380936621, *['Survived'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-a84779e5-8802-47d4-94e8-c294f69ea8ef\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-a84779e5-8802-47d4-94e8-c294f69ea8ef\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1488459608111040644 = autoviz.get_df('df_1488459608111040644')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_1488459608111040644, *['Pclass'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-eba7551e-b891-4bee-900e-1b1a5d0abccc\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-eba7551e-b891-4bee-900e-1b1a5d0abccc\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_2583252124578300273 = autoviz.get_df('df_2583252124578300273')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_2583252124578300273, *['Age'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-45de8ea6-7d30-46c7-a4e5-d4026d83f724\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-45de8ea6-7d30-46c7-a4e5-d4026d83f724\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc5d50583a0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4894869781557133012 = autoviz.get_df('df_4894869781557133012')\n",
              "\n",
              "def categorical_histogram(df, colname, figsize=(2, 1.2), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  bars = df[colname].value_counts()\n",
              "  plt.barh(bars.index, bars.values, color=sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(df_4894869781557133012, *['index'], **{})\n",
              "chart"
            ],
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              "AElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-210d8f77-a79a-4548-8972-db0c70e8081f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-210d8f77-a79a-4548-8972-db0c70e8081f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc5d4f22140>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_7764760950539129768 = autoviz.get_df('df_7764760950539129768')\n",
              "\n",
              "def scatter_plots(df, colname_pairs, scatter_plot_size=2.5, size=8, alpha=.6):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * scatter_plot_size, scatter_plot_size))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    ax.scatter(df[x_colname], df[y_colname], s=size, alpha=alpha)\n",
              "    plt.xlabel(x_colname)\n",
              "    plt.ylabel(y_colname)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(df_7764760950539129768, *[[['Survived', 'Pclass'], ['Pclass', 'Age']]], **{})\n",
              "chart"
            ],
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              "AABJRU5ErkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-7303e901-bcca-49f8-aeb4-8550df293281\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-7303e901-bcca-49f8-aeb4-8550df293281\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc5d4df4e20>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_6685633550693291978 = autoviz.get_df('df_6685633550693291978')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_6685633550693291978, *['Survived', 'index'], **{'inner': 'stick'})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-0fec7982-bf37-4e72-af39-cb97f345b0ff\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-0fec7982-bf37-4e72-af39-cb97f345b0ff\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4712698635457489168 = autoviz.get_df('df_4712698635457489168')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_4712698635457489168, *['Pclass', 'index'], **{'inner': 'stick'})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-51b173fa-0452-4bd7-b7e8-30e997d50264\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-51b173fa-0452-4bd7-b7e8-30e997d50264\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_2914465559398532079 = autoviz.get_df('df_2914465559398532079')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_2914465559398532079, *['Age', 'index'], **{'inner': 'stick'})\n",
              "chart"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-fd37eb2e-03ff-4df3-a60d-c585565e284f\">\n",
              "        <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAATEAAADrCAYAAAABt6GVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-fd37eb2e-03ff-4df3-a60d-c585565e284f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-fd37eb2e-03ff-4df3-a60d-c585565e284f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "test_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sVDVSjKh7C7U",
        "outputId": "e5f534ad-9f57-4b50-dbbb-917243916934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Pclass         Age\n",
              "count  418.000000  418.000000\n",
              "mean     2.265550   29.599282\n",
              "std      0.841838   12.703770\n",
              "min      1.000000    0.170000\n",
              "25%      1.000000   23.000000\n",
              "50%      3.000000   27.000000\n",
              "75%      3.000000   35.750000\n",
              "max      3.000000   76.000000"
            ],
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              "\n",
              "\n",
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.265550</td>\n",
              "      <td>29.599282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.841838</td>\n",
              "      <td>12.703770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>76.000000</td>\n",
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              "  </tbody>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed62b957-a16d-40c2-8d6a-796bc8c581a8 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-ed62b957-a16d-40c2-8d6a-796bc8c581a8');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Diviser les données en ensembles d'entraînement et de validation\n",
        "X_train, X_val, y_train, y_val = train_test_split(train_data.drop('Survived', axis=1), train_data['Survived'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Convertir la colonne \"Sex\" en valeurs numériques, petit oubli ...\n",
        "sex_mapping = {'male': 0, 'female': 1}\n",
        "X_train['Sex'] = X_train['Sex'].map(sex_mapping)\n",
        "X_val['Sex'] = X_val['Sex'].map(sex_mapping)\n",
        "# Normalisation des données\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_val_scaled = scaler.transform(X_val)\n",
        "\n",
        "# Construction du modèle\n",
        "model = keras.Sequential([\n",
        "    layers.Input(shape=(X_train_scaled.shape[1],)),\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(1, activation='linear')\n",
        "])\n",
        "\n",
        "# Compilation du modèle\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Entraînement du modèle\n",
        "history = model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_data=(X_val_scaled, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7UWb9gX7E6Z",
        "outputId": "af935f1a-6884-4085-b50e-d64b904d31b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "23/23 [==============================] - 1s 11ms/step - loss: 0.7365 - accuracy: 0.6966 - val_loss: 0.4604 - val_accuracy: 0.7654\n",
            "Epoch 2/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5482 - accuracy: 0.8020 - val_loss: 0.4983 - val_accuracy: 0.7933\n",
            "Epoch 3/10\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5184 - accuracy: 0.7865 - val_loss: 0.5769 - val_accuracy: 0.7709\n",
            "Epoch 4/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5241 - accuracy: 0.8006 - val_loss: 0.5907 - val_accuracy: 0.7709\n",
            "Epoch 5/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.4821 - accuracy: 0.7865 - val_loss: 0.5353 - val_accuracy: 0.7654\n",
            "Epoch 6/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.4755 - accuracy: 0.8118 - val_loss: 0.5138 - val_accuracy: 0.7989\n",
            "Epoch 7/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5199 - accuracy: 0.8160 - val_loss: 0.7130 - val_accuracy: 0.7821\n",
            "Epoch 8/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.4999 - accuracy: 0.7935 - val_loss: 0.5351 - val_accuracy: 0.7989\n",
            "Epoch 9/10\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5297 - accuracy: 0.8118 - val_loss: 0.7311 - val_accuracy: 0.7933\n",
            "Epoch 10/10\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5009 - accuracy: 0.8188 - val_loss: 0.6537 - val_accuracy: 0.7933\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Construction du modèle\n",
        "model = keras.Sequential([\n",
        "    layers.Input(shape=(X_train_scaled.shape[1],)),\n",
        "    layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),\n",
        "    layers.Dense(128, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),\n",
        "    layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),\n",
        "    layers.Dense(1, activation='linear')\n",
        "])\n",
        "\n",
        "# Compilation du modèle\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Entraînement du modèle\n",
        "history = model.fit(X_train_scaled, y_train, epochs=120, batch_size=32, validation_data=(X_val_scaled, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNDBpd308d5a",
        "outputId": "240c7d37-b63a-4513-b8af-fa0e94ba8abf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "23/23 [==============================] - 3s 15ms/step - loss: 0.9102 - accuracy: 0.7346 - val_loss: 0.7016 - val_accuracy: 0.7821\n",
            "Epoch 2/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.7460 - accuracy: 0.7879 - val_loss: 0.9856 - val_accuracy: 0.7654\n",
            "Epoch 3/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.7443 - accuracy: 0.7935 - val_loss: 0.7303 - val_accuracy: 0.7821\n",
            "Epoch 4/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.7173 - accuracy: 0.7837 - val_loss: 0.9174 - val_accuracy: 0.7598\n",
            "Epoch 5/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.7021 - accuracy: 0.8076 - val_loss: 0.8353 - val_accuracy: 0.7821\n",
            "Epoch 6/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6926 - accuracy: 0.8090 - val_loss: 0.7004 - val_accuracy: 0.7765\n",
            "Epoch 7/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.6841 - accuracy: 0.8062 - val_loss: 0.8900 - val_accuracy: 0.8045\n",
            "Epoch 8/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6711 - accuracy: 0.8076 - val_loss: 0.8125 - val_accuracy: 0.8045\n",
            "Epoch 9/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.8076 - val_loss: 0.8664 - val_accuracy: 0.7989\n",
            "Epoch 10/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6589 - accuracy: 0.8118 - val_loss: 0.8688 - val_accuracy: 0.7933\n",
            "Epoch 11/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.6564 - accuracy: 0.8076 - val_loss: 0.5977 - val_accuracy: 0.7765\n",
            "Epoch 12/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6570 - accuracy: 0.8006 - val_loss: 0.8665 - val_accuracy: 0.7877\n",
            "Epoch 13/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.6315 - accuracy: 0.8048 - val_loss: 0.5738 - val_accuracy: 0.7765\n",
            "Epoch 14/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6468 - accuracy: 0.8048 - val_loss: 0.8523 - val_accuracy: 0.7989\n",
            "Epoch 15/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6361 - accuracy: 0.8160 - val_loss: 0.8543 - val_accuracy: 0.7709\n",
            "Epoch 16/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6363 - accuracy: 0.8034 - val_loss: 0.8462 - val_accuracy: 0.7989\n",
            "Epoch 17/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6229 - accuracy: 0.8174 - val_loss: 0.8396 - val_accuracy: 0.7933\n",
            "Epoch 18/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6189 - accuracy: 0.8188 - val_loss: 0.7836 - val_accuracy: 0.7933\n",
            "Epoch 19/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6107 - accuracy: 0.7851 - val_loss: 0.6937 - val_accuracy: 0.8045\n",
            "Epoch 20/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.6171 - accuracy: 0.8216 - val_loss: 0.7789 - val_accuracy: 0.7933\n",
            "Epoch 21/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6128 - accuracy: 0.8118 - val_loss: 0.8205 - val_accuracy: 0.7989\n",
            "Epoch 22/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6064 - accuracy: 0.8104 - val_loss: 0.8167 - val_accuracy: 0.8101\n",
            "Epoch 23/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6282 - accuracy: 0.8006 - val_loss: 0.8504 - val_accuracy: 0.7598\n",
            "Epoch 24/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6197 - accuracy: 0.8062 - val_loss: 0.5686 - val_accuracy: 0.7542\n",
            "Epoch 25/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5888 - accuracy: 0.8006 - val_loss: 0.7471 - val_accuracy: 0.8045\n",
            "Epoch 26/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5695 - accuracy: 0.8048 - val_loss: 0.7483 - val_accuracy: 0.7821\n",
            "Epoch 27/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5814 - accuracy: 0.8006 - val_loss: 0.8019 - val_accuracy: 0.8101\n",
            "Epoch 28/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5950 - accuracy: 0.8062 - val_loss: 0.8058 - val_accuracy: 0.8045\n",
            "Epoch 29/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5916 - accuracy: 0.8104 - val_loss: 0.8051 - val_accuracy: 0.7821\n",
            "Epoch 30/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5840 - accuracy: 0.8174 - val_loss: 0.8022 - val_accuracy: 0.7821\n",
            "Epoch 31/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5857 - accuracy: 0.8090 - val_loss: 0.7945 - val_accuracy: 0.8045\n",
            "Epoch 32/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5796 - accuracy: 0.8062 - val_loss: 0.8010 - val_accuracy: 0.7989\n",
            "Epoch 33/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5799 - accuracy: 0.8188 - val_loss: 0.7968 - val_accuracy: 0.7933\n",
            "Epoch 34/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.5801 - accuracy: 0.8146 - val_loss: 0.7912 - val_accuracy: 0.7821\n",
            "Epoch 35/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5744 - accuracy: 0.8062 - val_loss: 0.7852 - val_accuracy: 0.7989\n",
            "Epoch 36/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5743 - accuracy: 0.7963 - val_loss: 0.7887 - val_accuracy: 0.7933\n",
            "Epoch 37/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5688 - accuracy: 0.8146 - val_loss: 0.7819 - val_accuracy: 0.7989\n",
            "Epoch 38/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5689 - accuracy: 0.8216 - val_loss: 0.7840 - val_accuracy: 0.7933\n",
            "Epoch 39/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5681 - accuracy: 0.8048 - val_loss: 0.7736 - val_accuracy: 0.8045\n",
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            "23/23 [==============================] - 0s 4ms/step - loss: 0.5654 - accuracy: 0.8174 - val_loss: 0.7804 - val_accuracy: 0.7765\n",
            "Epoch 41/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5622 - accuracy: 0.8104 - val_loss: 0.7772 - val_accuracy: 0.7989\n",
            "Epoch 42/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5614 - accuracy: 0.8188 - val_loss: 0.7726 - val_accuracy: 0.7989\n",
            "Epoch 43/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5588 - accuracy: 0.8146 - val_loss: 0.7723 - val_accuracy: 0.7989\n",
            "Epoch 44/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5606 - accuracy: 0.8076 - val_loss: 0.7214 - val_accuracy: 0.7821\n",
            "Epoch 45/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5575 - accuracy: 0.8146 - val_loss: 0.7642 - val_accuracy: 0.7989\n",
            "Epoch 46/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5587 - accuracy: 0.8132 - val_loss: 0.7733 - val_accuracy: 0.7765\n",
            "Epoch 47/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5561 - accuracy: 0.8174 - val_loss: 0.7678 - val_accuracy: 0.7821\n",
            "Epoch 48/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.6319 - accuracy: 0.7107 - val_loss: 0.6967 - val_accuracy: 0.7039\n",
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            "23/23 [==============================] - 0s 5ms/step - loss: 0.5892 - accuracy: 0.7992 - val_loss: 0.6312 - val_accuracy: 0.8101\n",
            "Epoch 50/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5310 - accuracy: 0.8132 - val_loss: 0.7003 - val_accuracy: 0.7933\n",
            "Epoch 51/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5403 - accuracy: 0.8090 - val_loss: 0.7049 - val_accuracy: 0.7933\n",
            "Epoch 52/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5293 - accuracy: 0.8160 - val_loss: 0.7082 - val_accuracy: 0.7654\n",
            "Epoch 53/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5271 - accuracy: 0.8104 - val_loss: 0.6980 - val_accuracy: 0.8045\n",
            "Epoch 54/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5224 - accuracy: 0.8118 - val_loss: 0.6950 - val_accuracy: 0.7877\n",
            "Epoch 55/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5157 - accuracy: 0.8160 - val_loss: 0.6941 - val_accuracy: 0.7877\n",
            "Epoch 56/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5208 - accuracy: 0.8076 - val_loss: 0.6902 - val_accuracy: 0.7877\n",
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            "Epoch 58/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5289 - accuracy: 0.8062 - val_loss: 0.6895 - val_accuracy: 0.7877\n",
            "Epoch 59/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5463 - accuracy: 0.8076 - val_loss: 0.7561 - val_accuracy: 0.7933\n",
            "Epoch 60/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5310 - accuracy: 0.8146 - val_loss: 0.6545 - val_accuracy: 0.7709\n",
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            "23/23 [==============================] - 0s 5ms/step - loss: 0.5118 - accuracy: 0.8104 - val_loss: 0.6818 - val_accuracy: 0.7933\n",
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            "Epoch 66/120\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 0.5241 - accuracy: 0.8076 - val_loss: 0.6777 - val_accuracy: 0.8101\n",
            "Epoch 67/120\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 0.5106 - accuracy: 0.8132 - val_loss: 0.6840 - val_accuracy: 0.7765\n",
            "Epoch 68/120\n",
            "23/23 [==============================] - 0s 10ms/step - loss: 0.5030 - accuracy: 0.8174 - val_loss: 0.7465 - val_accuracy: 0.7933\n",
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            "23/23 [==============================] - 0s 7ms/step - loss: 0.5379 - accuracy: 0.8160 - val_loss: 0.6898 - val_accuracy: 0.7877\n",
            "Epoch 70/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.5347 - accuracy: 0.8132 - val_loss: 0.7487 - val_accuracy: 0.7989\n",
            "Epoch 71/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.5245 - accuracy: 0.8230 - val_loss: 0.6974 - val_accuracy: 0.7765\n",
            "Epoch 72/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.5329 - accuracy: 0.8062 - val_loss: 0.6168 - val_accuracy: 0.7933\n",
            "Epoch 73/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.5155 - accuracy: 0.8090 - val_loss: 0.6691 - val_accuracy: 0.8101\n",
            "Epoch 74/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.5024 - accuracy: 0.8076 - val_loss: 0.6751 - val_accuracy: 0.7877\n",
            "Epoch 75/120\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 0.5029 - accuracy: 0.8174 - val_loss: 0.6752 - val_accuracy: 0.7765\n",
            "Epoch 76/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.4963 - accuracy: 0.8188 - val_loss: 0.6677 - val_accuracy: 0.7989\n",
            "Epoch 77/120\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 0.4963 - accuracy: 0.8146 - val_loss: 0.6747 - val_accuracy: 0.8101\n",
            "Epoch 78/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.4956 - accuracy: 0.8048 - val_loss: 0.6672 - val_accuracy: 0.8045\n",
            "Epoch 79/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.4965 - accuracy: 0.8118 - val_loss: 0.6804 - val_accuracy: 0.7765\n",
            "Epoch 80/120\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.5003 - accuracy: 0.8104 - val_loss: 0.6696 - val_accuracy: 0.7877\n",
            "Epoch 81/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.4974 - accuracy: 0.8132 - val_loss: 0.6749 - val_accuracy: 0.8101\n",
            "Epoch 82/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5110 - accuracy: 0.8146 - val_loss: 0.6672 - val_accuracy: 0.7877\n",
            "Epoch 83/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4906 - accuracy: 0.8160 - val_loss: 0.6652 - val_accuracy: 0.8101\n",
            "Epoch 84/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4908 - accuracy: 0.8146 - val_loss: 0.6703 - val_accuracy: 0.7933\n",
            "Epoch 85/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4910 - accuracy: 0.8076 - val_loss: 0.6724 - val_accuracy: 0.7933\n",
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            "23/23 [==============================] - 0s 5ms/step - loss: 0.5632 - accuracy: 0.7725 - val_loss: 0.5660 - val_accuracy: 0.7207\n",
            "Epoch 87/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5648 - accuracy: 0.7626 - val_loss: 0.5357 - val_accuracy: 0.8101\n",
            "Epoch 88/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.5164 - accuracy: 0.8132 - val_loss: 0.5297 - val_accuracy: 0.7933\n",
            "Epoch 89/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5015 - accuracy: 0.8118 - val_loss: 0.6639 - val_accuracy: 0.8101\n",
            "Epoch 90/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4968 - accuracy: 0.8132 - val_loss: 0.6726 - val_accuracy: 0.7821\n",
            "Epoch 91/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5268 - accuracy: 0.7823 - val_loss: 0.5690 - val_accuracy: 0.7598\n",
            "Epoch 92/120\n",
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            "Epoch 93/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.5020 - accuracy: 0.8104 - val_loss: 0.6682 - val_accuracy: 0.7821\n",
            "Epoch 94/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4902 - accuracy: 0.8188 - val_loss: 0.6645 - val_accuracy: 0.7989\n",
            "Epoch 95/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.8216 - val_loss: 0.6660 - val_accuracy: 0.7933\n",
            "Epoch 96/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4872 - accuracy: 0.8216 - val_loss: 0.6690 - val_accuracy: 0.7821\n",
            "Epoch 97/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4867 - accuracy: 0.8132 - val_loss: 0.6622 - val_accuracy: 0.7933\n",
            "Epoch 98/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4855 - accuracy: 0.8090 - val_loss: 0.6638 - val_accuracy: 0.7765\n",
            "Epoch 99/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4867 - accuracy: 0.8146 - val_loss: 0.6712 - val_accuracy: 0.7933\n",
            "Epoch 100/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4884 - accuracy: 0.8132 - val_loss: 0.6688 - val_accuracy: 0.7765\n",
            "Epoch 101/120\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.4826 - accuracy: 0.8216 - val_loss: 0.6589 - val_accuracy: 0.7989\n",
            "Epoch 102/120\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.4858 - accuracy: 0.8090 - val_loss: 0.6660 - val_accuracy: 0.7765\n",
            "Epoch 103/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4835 - accuracy: 0.8216 - val_loss: 0.6585 - val_accuracy: 0.7821\n",
            "Epoch 104/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4805 - accuracy: 0.8146 - val_loss: 0.6612 - val_accuracy: 0.7821\n",
            "Epoch 105/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4822 - accuracy: 0.8118 - val_loss: 0.6583 - val_accuracy: 0.7933\n",
            "Epoch 106/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4826 - accuracy: 0.8188 - val_loss: 0.6643 - val_accuracy: 0.7765\n",
            "Epoch 107/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4796 - accuracy: 0.8216 - val_loss: 0.6567 - val_accuracy: 0.7933\n",
            "Epoch 108/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4823 - accuracy: 0.8160 - val_loss: 0.6627 - val_accuracy: 0.7821\n",
            "Epoch 109/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5126 - accuracy: 0.8174 - val_loss: 0.7302 - val_accuracy: 0.7989\n",
            "Epoch 110/120\n",
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            "Epoch 111/120\n",
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            "Epoch 112/120\n",
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            "Epoch 113/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5039 - accuracy: 0.8020 - val_loss: 0.5924 - val_accuracy: 0.8101\n",
            "Epoch 114/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4875 - accuracy: 0.8146 - val_loss: 0.6538 - val_accuracy: 0.7877\n",
            "Epoch 115/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5185 - accuracy: 0.8118 - val_loss: 0.6674 - val_accuracy: 0.7765\n",
            "Epoch 116/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4914 - accuracy: 0.8118 - val_loss: 0.6253 - val_accuracy: 0.7709\n",
            "Epoch 117/120\n",
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            "Epoch 118/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.5102 - accuracy: 0.7893 - val_loss: 0.5999 - val_accuracy: 0.8212\n",
            "Epoch 119/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4988 - accuracy: 0.8202 - val_loss: 0.6043 - val_accuracy: 0.7821\n",
            "Epoch 120/120\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4951 - accuracy: 0.8104 - val_loss: 0.6520 - val_accuracy: 0.8045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data['Sex'] = test_data['Sex'].map(sex_mapping)\n",
        "# Faire des prédictions sur le jeu de test\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "y_pred = (y_pred > 0.5).astype(int)\n",
        "\n",
        "# Générer une séquence numérique unique pour les passagers\n",
        "passenger_ids = range(892, 892 + len(test_data))\n",
        "\n",
        "# Créer un DataFrame pour les résultats\n",
        "results = pd.DataFrame({\n",
        "    'PassengerId': passenger_ids,\n",
        "    'Survived': y_pred.flatten()\n",
        "})\n",
        "\n",
        "# Enregistrer les résultats au format CSV\n",
        "results.to_csv('submission.csv', index=False)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "results.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "qiG5cqSa9371",
        "outputId": "710781f7-f72c-4a5a-961e-63446a2ed0df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         0\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e06229dc-a843-4761-a879-c25ed29da782 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-abb1e1c4-5ddd-4bb8-8de3-1ae57b7cf2eb 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-abb1e1c4-5ddd-4bb8-8de3-1ae57b7cf2eb');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}