Titanic_Machine_Learning_from_Disaster.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic - Machine Learning from Disaster.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1f3B2cC8O44jXr1zwWpSn3pBQkYt7v10W",
      "authorship_tag": "ABX9TyOIa+zG7UOUEZeTH79QpqyX"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut5oLxd3meu5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rw9i3J94qUeT"
      },
      "source": [
        "!mkdir titanic_data"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9XQLGRMplS5"
      },
      "source": [
        "!mv titanic.zip titanic_data"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9RYrF1Dmjhr"
      },
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/MyDrive/\") "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6qaAuljLvfb"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9d6lc-A_CtK"
      },
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OX4ovqmiFdQU"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuXi8VYWrR_k"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "from sklearn.preprocessing import OneHotEncoder, Normalizer, MinMaxScaler, StandardScaler\n",
        "from scipy.sparse import hstack"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kk3haVKtXol"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIIEGmgup6WZ"
      },
      "source": [
        "#!unzip titanic.zip"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05h098kTp8WI",
        "outputId": "03f9130e-9a01-4004-87d3-d5639d4f0277"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ML-Projects  Titanic_Machine_Learning_from_Disaster.ipynb  train.csv\n",
            "README.md    titanic_submission.csv\n",
            "test.csv     titanic.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "nvRUey2qrMYl",
        "outputId": "302b14db-8d3d-400e-94fe-f21c5ebf6246"
      },
      "source": [
        "train_data = pd.read_csv('train.csv')\n",
        "train_data.tail()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
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              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...   Fare Cabin  Embarked\n",
              "886          887         0       2  ...  13.00   NaN         S\n",
              "887          888         1       1  ...  30.00   B42         S\n",
              "888          889         0       3  ...  23.45   NaN         S\n",
              "889          890         1       1  ...  30.00  C148         C\n",
              "890          891         0       3  ...   7.75   NaN         Q\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "gz3mM_Kua7cM",
        "outputId": "07a798c5-8f4c-48c9-b3ad-d022e0bef79c"
      },
      "source": [
        "test_data = pd.read_csv('test.csv')\n",
        "test_data.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yg8rKp9K1dLX",
        "outputId": "3824f28b-c538-4c8a-add6-6189dee6346a"
      },
      "source": [
        "print(train_data.shape, test_data.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 12) (418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnZm-wxb1dFr",
        "outputId": "fb530c6b-e1dd-4938-f196-e4abc185dce3"
      },
      "source": [
        "train_data.info()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdDj-yesbfEQ",
        "outputId": "0142a9f2-d778-437d-e739-0bfe2d037d90"
      },
      "source": [
        "test_data.info()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6qvgDu14igP",
        "outputId": "e3cbf6a1-536f-43f1-9b6c-4b7c9c2b3c91"
      },
      "source": [
        "train_data.isna().values.any()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agr4Ll334yXr",
        "outputId": "3ba5ca64-14f5-405a-96bc-f30a4e44b11b"
      },
      "source": [
        "print(\"Total null values in the train dataset are:\\n\",train_data.isna().sum().sum())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total null values in the train dataset are:\n",
            " 866\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gGEFimtcZlH",
        "outputId": "dce780f7-5b40-48ec-fe56-f402c69a8d7f"
      },
      "source": [
        "test_data.isna().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qt9tIWJ2cEAs",
        "outputId": "6c9d72aa-76d2-4db4-bc45-9217f5b01ea6"
      },
      "source": [
        "print(\"total null values in test dataset are: \\n\", test_data.isna().sum().sum())\n",
        "#Age, Fare & cabin have na values"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total null values in test dataset are: \n",
            " 414\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhwPBlXKJbWl"
      },
      "source": [
        "def plot_missing_data(dataset, title):\n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    plt.title(title)  \n",
        "    sns.heatmap(dataset.isna(), cbar=True)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "T0h1YL2OJuKZ",
        "outputId": "2b4d6ec8-8d92-4353-84a2-7373d20ab2b2"
      },
      "source": [
        "plot_missing_data(train_data, \"Training data\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "wquQj0e-ciFH",
        "outputId": "515b7a63-57dc-4ae6-a154-5d398571c631"
      },
      "source": [
        "plot_missing_data(test_data, \"Test data\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "au6NySTq-2cP"
      },
      "source": [
        "Age and Cabin features have max null values in both train & test datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-cTXm-3--mo"
      },
      "source": [
        "Target Variable analysis:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "6oEFilxR_N-1",
        "outputId": "878a8b7b-0537-413f-d807-3c3924e35b58"
      },
      "source": [
        "train_data.Survived.value_counts().to_frame()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived\n",
              "0       549\n",
              "1       342"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DUdEWMhAW5E",
        "outputId": "b360b0d8-926f-4e5c-b53d-fc21d876476f"
      },
      "source": [
        "train_data.Survived.value_counts(normalize=True)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.616162\n",
              "1    0.383838\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kAOOup0AdES"
      },
      "source": [
        "61% of people died and only 38% survived the ship wreck"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "CyOxP6hV7ptZ",
        "outputId": "147733af-b669-4aa8-e47e-01ca2e4e4922"
      },
      "source": [
        "train_data.Survived.value_counts().plot(kind ='bar')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd92bfb50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haDIhPqSAqnp"
      },
      "source": [
        "def categorical_visual(feature, data):\n",
        "  survived = data[data['Survived'] == 1][feature].value_counts()\n",
        "  dead = data[data['Survived']== 0][feature].value_counts()\n",
        "  df = pd.DataFrame([survived, dead])\n",
        "  df.index= ['Passengers survived', 'Passengers Died']\n",
        "  df.plot(kind = 'bar', stacked = True, figsize= (5,5))\n",
        "  plt.legend(loc=\"upper right\")"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2KpWY0J3HG0"
      },
      "source": [
        "#Pclass variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "hgqaeum8Ca19",
        "outputId": "55f38fc3-7652-4889-a225-509dbac76930"
      },
      "source": [
        "categorical_visual(\"Pclass\", train_data)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EwzyObDN33e4",
        "outputId": "e007ad48-be03-43ed-e273-8f05fba4cdf2"
      },
      "source": [
        "train_data.Pclass.isna().values.any()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2QlSLzM1dDM",
        "outputId": "fbaf179a-cc58-48a6-c3a6-ffb2e3887c3e"
      },
      "source": [
        "train_data.Pclass.value_counts(normalize=True)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    0.551066\n",
              "1    0.242424\n",
              "2    0.206510\n",
              "Name: Pclass, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlSX3-wp3WGB"
      },
      "source": [
        "Pclass is a categorical var with 3 class, class 1 people survived the most.\n",
        "Also, there are 55% passengers carrying class 3 ticket & less number of passengers carrying class 1 & 2 tickets i.e only 20 to 24% of total passengers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIiEZiF-9EDT"
      },
      "source": [
        "### Sex variable:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "3vGp1UY38xvX",
        "outputId": "71edd1c0-c32a-4eb9-ceb8-351c4d93873a"
      },
      "source": [
        "categorical_visual(\"Sex\", train_data)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gD5PUm7HIARx",
        "outputId": "96629a51-aed6-430e-870e-0cee2301f443"
      },
      "source": [
        "train_data.Sex.isna().values.any()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6eM8VNAICxs",
        "outputId": "b20e71cb-06f3-4412-ecb8-c0ac5a189c6a"
      },
      "source": [
        "train_data.Sex.value_counts(normalize=True)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      0.647587\n",
              "female    0.352413\n",
              "Name: Sex, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ugkkvdz1rlP"
      },
      "source": [
        "Females have survived in large numbers as compared to males even though there are 64% males and only 35% females among the total passengers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "i5qGvF9dLGLg",
        "outputId": "d09ad713-930d-4384-b8c7-a997b9618748"
      },
      "source": [
        "categorical_visual(\"SibSp\", train_data)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-XeS5gymszFx",
        "outputId": "ec7d7cd2-4e06-4b12-a6de-a051180bb274"
      },
      "source": [
        "sns.barplot(x='SibSp', y = 'Survived', data=train_data)\n",
        "plt.xlabel(\"Number of Siblings/Spouse on board\")\n",
        "plt.ylabel(\"Survival Rate\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Survival Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjTw-_ZeK7z-",
        "outputId": "b12e87e8-4045-4df0-8572-a016af1c08c8"
      },
      "source": [
        "train_data.SibSp.isna().values.any()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a49acO1K_xn",
        "outputId": "a2fd6716-1c27-4fea-9618-2a29b8acfde5"
      },
      "source": [
        "train_data.SibSp.value_counts(normalize=True)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.682379\n",
              "1    0.234568\n",
              "2    0.031425\n",
              "4    0.020202\n",
              "3    0.017957\n",
              "8    0.007856\n",
              "5    0.005612\n",
              "Name: SibSp, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ygmGhZv16Cl"
      },
      "source": [
        "People who had more than 2 siblings have not survived much i.e people with less number(<2) or no siblings or only with spouses have survived in large numbers. Also there are 68% of passengers who don't have any siblings or spouse onboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "p2BnUnQdMY2O",
        "outputId": "d0945977-7580-4b97-b0ef-380262791b62"
      },
      "source": [
        "categorical_visual('Parch', train_data)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWhZ6MwoMYp7",
        "outputId": "bae85c3f-b53d-41b9-ab97-086cafe99efa"
      },
      "source": [
        "train_data.Parch.isna().values.any()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPEOvczVMYb4",
        "outputId": "df26ca1c-29b9-4628-f4d6-9b2f9ad33736"
      },
      "source": [
        "train_data.Parch.value_counts(normalize=True)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.760943\n",
              "1    0.132435\n",
              "2    0.089787\n",
              "5    0.005612\n",
              "3    0.005612\n",
              "4    0.004489\n",
              "6    0.001122\n",
              "Name: Parch, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETKsORig2gPH"
      },
      "source": [
        "People with more number of family members have died. There are 76% of people who don't have any family members on board. This might be the reason that it was easier for them to get rescued easily than who had thier dependents along."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "rmqSTNwjM30j",
        "outputId": "8eb885ef-edd0-43b2-8b3a-f41121dc13d0"
      },
      "source": [
        "categorical_visual('Embarked', train_data)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0jBSoLTMwW2",
        "outputId": "35846465-4cd4-4297-8572-caa625d73c0a"
      },
      "source": [
        "train_data.Embarked.isna().values.any()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YImXKZ4zZZE",
        "outputId": "accb091b-63f2-4f0d-d415-ac6a54397640"
      },
      "source": [
        "train_data.Embarked.isna().sum()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9tisW-oMsC-",
        "outputId": "6974714f-7ff1-43b8-d4bd-1a10c43f4704"
      },
      "source": [
        "train_data.Embarked.value_counts(normalize=True)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    0.724409\n",
              "C    0.188976\n",
              "Q    0.086614\n",
              "Name: Embarked, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJihg0Jc2p8r"
      },
      "source": [
        "There are more number of people fom SouthHampton than Cherbourg or Queens, still the count of survived passengers from SouthHampton is very less."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WGD5dV0dm6m7",
        "outputId": "da9138c3-544c-4068-9260-56382ac6598b"
      },
      "source": [
        "sns.countplot(x='Embarked', data=train_data)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8d3d8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "woaE0cLCmdb9",
        "outputId": "69b5229b-00a1-4ec0-f758-112123994662"
      },
      "source": [
        "train_data.groupby('Embarked').Survived.mean().to_frame()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>0.336957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Survived\n",
              "Embarked          \n",
              "C         0.553571\n",
              "Q         0.389610\n",
              "S         0.336957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdnLGBRBngjW"
      },
      "source": [
        "55% people from Cherbourg have survived, followed by 38% from Queens and least have survived from SouthHampton , only 33%. This might have happened due to more number of females or class 1 tickets from Cherbourg.I will try plotting these 2 together to analyse further."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "-4q3bQu3l4ED",
        "outputId": "b1762990-62bc-432d-8c6f-097d774bb46e"
      },
      "source": [
        "pd.pivot_table(train_data, values='Ticket', index='Survived', columns='Embarked', aggfunc='count')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Embarked</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75</td>\n",
              "      <td>47</td>\n",
              "      <td>427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93</td>\n",
              "      <td>30</td>\n",
              "      <td>217</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Embarked   C   Q    S\n",
              "Survived             \n",
              "0         75  47  427\n",
              "1         93  30  217"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9Vf-wY20PZz"
      },
      "source": [
        "Fare Variable:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHrtAvum3aKd",
        "outputId": "925d76ed-85d6-45bc-a3ea-d8520544f055"
      },
      "source": [
        "train_data.Fare.max()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512.3292"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDo6XZo2wIyL",
        "outputId": "1d647050-62fe-4dd9-eb74-580becebfe84"
      },
      "source": [
        "train_data.Fare.min()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezzhivP-wOZo",
        "outputId": "a63c8440-a7e2-48b3-8b19-5d5f2b6584f1"
      },
      "source": [
        "len(train_data[train_data['Fare'] == 0.0])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEVwfBzLc_gk",
        "outputId": "f24b692e-2b38-4019-919d-e14270d34a8f"
      },
      "source": [
        "len(test_data[test_data['Fare'] == 0.0])"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjZ3zbLpwfZx"
      },
      "source": [
        "15 Passengers in train data & 2 in test data paid 0 Fare which doesn't make sense, so I will replace it with NaN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_e6Yp9HhwtWR"
      },
      "source": [
        "train_data.loc[train_data['Fare'] == 0, 'Fare'] = np.NaN\n",
        "test_data.loc[test_data['Fare'] == 0, 'Fare'] = np.NaN"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec8Sal3-xKBJ",
        "outputId": "4797c4de-372b-4f2a-9672-ce0b34746cc2"
      },
      "source": [
        "train_data.isna().sum()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare            15\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDnOggqI21DR",
        "outputId": "64e52ce9-681a-4ca8-a01d-6b8be50bf21b"
      },
      "source": [
        "train_data.Fare.mean()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32.75564988584471"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVgOmO3Aer7t",
        "outputId": "af0638d9-c9de-4b8f-ed84-6ce056727412"
      },
      "source": [
        "test_data.Fare.mean()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.7988857831325"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "x6Z90R4B2F08",
        "outputId": "0a0cdcb0-ca69-4f52-eef5-3b4682750c2e"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.xlim(0,550)\n",
        "plt.ylabel('Fare')\n",
        "sns.boxplot(train_data.Fare)\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "5YG9TRM23NxC",
        "outputId": "632a7727-9d97-4d0e-a731-09ad2ca8f209"
      },
      "source": [
        "sns.distplot(train_data.Fare, bins= 50)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8c5b750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXt2Jhw02-vP"
      },
      "source": [
        "There are few people who have Fare amount of more than 50, most of them have paid around 30-40 dollars."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "lMuXi450ekCK",
        "outputId": "22201ef8-739c-43c6-93fc-5a2d7513b657"
      },
      "source": [
        "sns.distplot(test_data.Fare, bins= 50)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8ca7690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "OjSxBfEx0big",
        "outputId": "e302eedf-f73b-449f-a5b4-f67b8714862d"
      },
      "source": [
        "sns.FacetGrid(train_data, hue=\"Survived\", height=5) \\\n",
        "  .map(sns.kdeplot, \"Fare\") \\\n",
        "  .add_legend()\n",
        "plt.title(\"Survival status based on Fare\")\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4r6Cn2Z_3bnp"
      },
      "source": [
        "People who paid more amount seem to have survived than people who paid less , so there might be a relation between Fare & Class of people with their survival."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60Cg-Cim0m0B"
      },
      "source": [
        "Age variable:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6F8JpCn3qfj",
        "outputId": "2bea8a11-a98d-473f-9366-1ea4195d2709"
      },
      "source": [
        "train_data.Age.max()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPxm1wFH3uBn",
        "outputId": "f2d9f91c-cb89-436e-c01c-3284c543cb32"
      },
      "source": [
        "train_data.Age.mean()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFXBeZEHdjAK",
        "outputId": "7981fa4f-7a7a-4b07-fa3b-723ebf6b05af"
      },
      "source": [
        "test_data.Age.mean()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.272590361445783"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Fy_-h5i830Gt",
        "outputId": "2dcc16f5-8a7c-4734-f1df-0d94360e129c"
      },
      "source": [
        "sns.boxplot(train_data.Age)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8c89d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDLjFEOQ7rVi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "46e7d7fe-a7f3-4b7a-c296-b753b50335c0"
      },
      "source": [
        "sns.distplot(train_data.Age)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8e21d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xN1bCH644Nwe"
      },
      "source": [
        "Most of the people on board are around 20 to 40 years old, there are less senior citizens and children. Average age is of 29 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "gTfi0Lqgdp0U",
        "outputId": "ffabb988-87a1-452b-bbca-80e3fda49918"
      },
      "source": [
        "sns.distplot(test_data.Age)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8fbc5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8r8sRCbu+Xub6Lv4R1HGBx8uBlWGsS6JMWc1BhqcmkBQf43Upx23u2CzGDU/hiQ93el2KSL/o61xMfws8D/w2sCgX+HO4ipLo0uUc5TWHBmzz0mFmxs1zC1lbUc/q8jqvyxEJu772QdwFzAMaAJxzW4Hhve1kZpeY2WYzKzWze4KsTzCzZwLrl5pZUWD5hWa2wszWBb6f19cPJJGnqqmVQ+2dAz4gAK6emUdKQixPfLDT61JEwq6vAdHqnGs7/MTMYvGPgzgiM4sBHgAuBSYB15vZpB6b3QbUOufGAfcBPwssrwKucM5NBW4BnuxjnRKByqr9/Q8FWQM/IFISYrlmZi6vrN3LvnrNNiPRra8B8a6Z/SOQZGYXAs8BL/eyz2yg1Dm3PRAuT/PZ6TnmA48HHj8PnG9m5pxb5Zw7fKeWDYH3TehjrRJhymqaSYqLYVhKdBzC288cQ6dz/Pa9bV6XIhJWfQ2Ie4BKYB3wFeA14Pu97JMLdB8RVRFYFnQb51wHUA9k9djmGmClc661j7VKhCmraSY/MwmfmdelhER+5hA+PyOXp5aWUdmoH0uJXn29iqkLf6f0nc65Bc65h/pjVLWZTcbf7PSVI6y/w8xKzKyksrIy3OXIcTjU1kllY2tU9D90d+c5Y2nv7OLh97d7XYpI2Bw1IMzvXjOrAjYDmwN3k/tBH157N5Df7XleYFnQbQL9GulAdeB5HvAicLNzLui5vHPuQedcsXOuODs7uw8lSX8rr20ODJBL7nXbgWRMdgqXT8vhyQ93UXuwrfcdRAag3s4gvo3/6qVTnXOZzrlMYA4wz8y+3cu+y4HxZjbazOKB64Ceo68X4u+EBv8MsW8755yZZQCvAvc455Ycw+eRCFNW04wB+UOTvC4l5O4+bxzNbZ08umSH16WIhEVvAXETcL1z7pP/Ac657cCNwM1H2zHQp3A3sAjYBDzrnNtgZj8ysysDmz0CZJlZKfAd/H0dBPYbB/zAzFYHvnq9rFYiT3lNMyPSEkmIG7gD5I7kpBGpXDplJL9bslNnERKVeptqI845V9VzoXOu0szientx59xr+Du0uy/7QbfHLcAXguz3E+Anvb2+RLYu5yiraWZ6fobXpYTNty88iUUb9vGrt7fyz1dM9rockZDq7QziaH8W6U8mOaoDDa20dnRFXQd1dyeNSOXaUwt48sNd7Kg66HU5IiHVW0BMN7OGIF+NwNT+KFAGrp3V/l+YRVnR1UHd07cvHE9CrI+fvLIR3TJFoslRA8I5F+OcSwvyleqc67WJSQa3XdUHSU2MZeiQ6P5RGZ6ayDcvGM9bHx/gfzbqtqQSPY7lntQix2RXdTOFWclYlAyQO5pb541mwohUfrhwA02tHV6XIxISCggJi7rmNuoOtVMUBfMv9UVcjI9/vXoKexta+NfXNnldjkhIKCAkLHYFbhBUGOX9D93NKszkb88cw1NLy3h3i0b2y8CngJCw2FV9kPgYHyPTEr0upV9958KTGD88hb97dg0HGjXbqwxsCggJi13VzRRkDiHGF/39D90lxsVw/w0zaWpt51tPr6azS1c1ycDV13tSi/RZQ0s7++pbOG/i4Bn8/tTSsk89v2zqKP60cje3PLqMz00dddR9b5hTEM7SRI6bAkJCblVZHY7B1f/Q06zCTPbUtfB+aRXZKQmcOjrT65JEjpkCQkKuZGcNPovOCfqOxeemjqKqqZU/r95NQpyPaXnRO+WIRCf1QUjILd9Zw6j0pKicoO9YxPiML80ppDBrCM+WlLNpb4PXJYkcEwWEhFR7Zxery+soHCTjH3oTH+vj5rlF5GQk8dSyMrbsb/S6JJE+UxOThNTaijpa2rsG5PxLPTuaQyUxLoZbTx/Nw+9v58kPd3H1zFxmFAwNy3uJhJLOICSkPiitBmDMsIEXEOGUFB/D7WeMoXDYEJ5bUcHbH+/XxH4S8RQQElIfbKtm0qg0hiTo5LSnpPgYvnx6ETPyM3hz0wGeX1FBe2eX12WJHJH+F0vItLR3sqKsllvmFnpdSsSK9flYMCuPzOR43vr4APsaWjjrpGxG64xLIpDOICRkVuyqpa2ji9PHDvO6lIhmZpx/8ghumVtEXXM7V/73+7y+bq/XZYl8hgJCQuaDbVXE+EyDwvpowshU7j5vHGOGp/C1P6zk+39eR0t7p9dliXxCASEh88G2aqbnpZOi/oc+Gzoknue+Mpe/PXM0v/+ojKseWMJWXQorEUIBISHR2NLO2op65o1T89Kxio/18X8vm8Rjt55KZWMrV9z/Pn9cVqarnMRzCggJieU7a+jscswdm+V1KQPWuROG8/q3zqS4MJPvvbCOu59aRf2hdq/LkkFMbQESEktKq4mP9TFTA8COWc8BepdMGUlyQiyvr9/Lkm1VXFecT8ERBh5qJlgJJ51BSEh8sK2a4sKhJA7y+ZdCwWfG2Sdlc8dZYzHgwcXb+evmA3SpyUn6mQJCTljNwTY27W1Q/0OIFWQO4evnjWdyTjr/s3E/jy3ZQYOanKQfKSDkhC3e6r//sgIi9BLjYrju1HyunpFLWU0zv3p7q65ykn6jgJAT9s7HB8hKjmdabrrXpUQlM6O4KJO7zhlHWmIcv/tgJx9uq/K6LBkEFBByQjq7HO9uqeTsCdn4Btn9p/vb8LREvnL2GCaOTOXltXt5afVuOjSXk4SRAkJOyJqKOmqb2zlnwuC5/7SXEmJj+NJphZw5fhhLd9Rw+xMlHGrT6GsJDwWEnJC/fnwAn8FZ49X/0F98Zlw6ZRRXnZLLe1squfGRpdQ3q/NaQk/jIOSEvLHpALMKh5IxJN7rUgad2aMzSYqP4dnl5Vz8i/f48rwi0hLj+ry/xlBIb3QGIcetvKaZTXsbuGjSSK9LGbSm5qZz8+mF1Bxs46H3ttPQojMJCR0FhBy3RRv2AXDR5BEeVzK4jR+eyq3zimhs6eCRxTtoVEhIiCgg5Lj9z8b9TByZSuEAvP90tCnMSuaW04uoO9TGI+/voKm1w+uSJAooIOS4VDe1UrKzhosm6ewhUowelszNc4uobW7j0fd30KyQkBOkgJDj8vr6fXQ5uHiK+h8iydjsFG46rYiqplYeWbJDl8DKCVFAyHF5ec0exmYnM2lUmtelSA/jhqdw42mFHGhs5Xcf7KBVd6mT46SAkGO2r76FZTtruGJ6DmYaPR2JThqRyvWnFrC77hBPfLSLdo24luOggJBj9sraPTgHV0zP8boUOYpJOWl8YVY+O6sO8oelu+joUkjIsQlrQJjZJWa22cxKzeyeIOsTzOyZwPqlZlYUWJ5lZu+YWZOZ3R/OGuXY/Xn1bibnpDE2O8XrUqQX0/MzuGpGLlv2N/Hs8nI6u3RPCem7sAWEmcUADwCXApOA681sUo/NbgNqnXPjgPuAnwWWtwD/BPx9uOqT47NhTz3rdzfwhVl5XpcifXRqUSaXTR3F+j0NvLCyQjcekj4L5xnEbKDUObfdOdcGPA3M77HNfODxwOPngfPNzJxzB51z7+MPCokgz5VUEB/r46oZuV6XIsdg3rhhXHDycFaV1/Hymj04hYT0QTgDIhco7/a8IrAs6DbOuQ6gHujzXe/N7A4zKzGzksrKyhMsV3rT0t7Ji6t2c/HkkZp7aQA6d8JwzgrMArtowz6FhPRqQHdSO+cedM4VO+eKs7OzvS4n6r2+fi/1h9q5tjjf61LkOJgZF08eyZzRmby3tYr73y71uiSJcOGczXU30P03SV5gWbBtKswsFkgHqsNYkxwn5xyPLdnJ2Oxk5o3r80meRBgz44rpObR1dPFfb2xhSEIst50x2uuyJEKF8wxiOTDezEabWTxwHbCwxzYLgVsCjxcAbzud90aklWW1rK2o58vzRmvswwDnM+PqmXlcOmUkP35lI098uNPrkiRChe0MwjnXYWZ3A4uAGOBR59wGM/sRUOKcWwg8AjxpZqVADf4QAcDMdgJpQLyZXQVc5JzbGK565egefX8naYmxXDNTndPRIMZn/PK6GbT/YSU/eGkDjS0d3HnOWIW/fEpYbxjknHsNeK3Hsh90e9wCfOEI+xaFszbpu9IDTby2fi9fPXssQ+J1j6loER/r4zc3zuS7z63hPxZtpuFQO/dcOlEhIZ/Q/3bp1QPvlJIYG8PtaquOOnExPn7+xVNIS4rjt+9tp7KplX+7eioJsTFelyYRQAEhR7W9somXVu/m9jPHkJWS4HU5EgY+n/HDKyczLCWBn7+xhV3Vzfz2plkM0/Ee9Ab0Za4Sfj99/WOS4mL42zPHeF2KhJGZ8Y3zx/PADTPZsKee+fcvYcOeeq/LEo8pIOSIPthWxf9s3M+d544jO1V/TQ4Gl00bxXNfOZ3OLsfnH/iA3y3ZoQF1g5gCQoJq7+zix69sIjcjSdfJDzJT89J59RtncMb4Ydz78kZuf7yE6qZWr8sSDyggJKjfvruNTXsb+KfLJ5EYpw7LwSYrJYFHbinmn6+YxOKtVVx433u8uKpCZxODjAJCPmPzvkZ+9VYpl08bxSW6peigZWbcOm80C78+j8KsIXz7mTXc/OgydlUf9Lo06ScKCPmUptYOvvaHFaQlxXLvlZO9LkciwMSRaTz/1dP58fzJrCqr44Kfv8uPX9lIXXOb16VJmOky1wHgqaVlx73vDXMK+rxtV5fjH/60lp1VB/n97XN0mWOUO9afqxifj7vPG8ebG/fz2JIdPFdSzjfOH89Ncws1biJK6QxCPvHTv3zMq2v38t2LJ3L62GFelyMRKC0xjqtn5vHaN89kRsFQfvLqJi74+bu8tHo3XbpbXdRRQAjOOX755lYefG87N88t5Ktna8yDHN3EkWk8/jezefK22aQkxPHNp1dz+X+/z183H1BHdhRRQAxynV2OH72ykfve3MLVM3P55ysmay4e6bMzx2fz6tfP4BfXnkJjaztffmw51z/0EavL67wuTUJAATGIVTW1csujy3hsyU5unVfEfy6YToxP4SDHxuczrpqRy1vfOYd7r5jE1v1NXPXAEr72+xVsq2zyujw5ARYtp4PFxcWupKTE6zLC4kQ6qYNxzrG6vI5X1+2lraOLK6fnUFyUGdL3kOh2tIsfmlo7eHjxdh56bzstHV18sTif7148gcxk3aY2EpnZCudccbB1uoppEHHOsfVAE29s3M/uukPkD03i6pl5jEhL9Lo0iSIpCbF864KTuPG0Qu5/u5QnP9rF6+v38g+XTOTa4nx8OksdMBQQg0BtcxvrKupZVV7L/oZW0pPiWDAzj1MKMvCpv0HCZFhKAvdeOZnrZxfwTy+t53svrOPZknL+Y8F0xg1P8bo86QMFRJRqaGln/e561lbUU1bTDEDe0CQWzMxjWn46sT51P8nxO9Zmz/nTcyjMHMKr6/Zy2a8W892LJ3DrvNHq84pwCogo0tzawfo9DaytqGNH1UEcMCo9kYsnjWBqXobagMUzZsaMgqGMG57C8p21/OTVTSzasI9fXDeD3Iwkr8uTI1BADHCdXY4t+xtZWVbLx3sb6XSOrOR4zp04nKm56epfkIiSmhjHQzfP4oWVu/nnhRu47FeL+a8vTOf8k0d4XZoEoYAYoFrbO1m+s4b3S6toaOkgOT6G08ZkckrBUHLSEzWWQSLWH5eVA3DHWWP447Iybnu8hDPHDeOiySN7bXI6lqlj5MQpIAaYto4uFm+tZMm2Klrauxg9LJkrp+cyYWSq2nNlQBmWksBXzx7La+v2sri0ir31LVw3O58h8fq1FCl0JAYI5xzrdtfz+vp91B9qZ9KoNM4+KZv8zCFelyZy3OJifMw/JZe8oUn8efUefvPXbdx0WiHD1TQaERQQA0Btcxt/WlHB9qqD5KQncm1xPkXDkr0uSyRkZhVmMiwlgT8sLeM3727julPzmTAyzeuyBj1d6xjBnHO8sLKCX721ld11h5h/Sg53njtO4SBRqTArmTvPGUtWcjxPfLiL97ZUauI/j+kMIkI1tXZwz5/W8sravRRmDeGLs/IZqstUJcplDInnjrPG8vzKCv6yYR+VTa3MPyVH43Y8ooCIQDurDnLHkyWUHmjiuxdPID0pTiOeZdCIj/Vx3an5vJ2awNsfH6DmYBtfml3AkAT9uupviuUI896WSq68/xujnw8AAAqESURBVH0ONLbyxN/M4a5zxykcZNDxmXHBySP4YnE+5TXN/PrdbRxobPG6rEFHAREhnHM8+N42vvzYMnIyklh41xmcMV53dZPB7ZT8DG4/YzStHV38v3e3sXhrpdclDSoKiAhwqK2Tbz69mn997WMunTKKF+48nYIsXb4qAlAQ6LxOT4rjy48t58mPdnld0qChRj2Pldc089Xfr2Dj3ga+e/EE7jxnrEZBi/QwdEg8Xz1rLItLq/inP69n24Emvn/ZycTG6G/ccNK/roc+KK3iyvvfp6y6mYdvLuauc8cpHESOICEuhoduLub2M0bzuw92csPDS9nfoH6JcFJAeMA5x8OLt3PjI0sZlpLAS3fP02RlIn0Q4zO+f/kkfv7F6ayrqOdzv1zMe1vULxEuCoh+dri/4SevbuKiSSN58a55jMnWzVNEjsXVM/N4+evzGJaSwC2PLeNHL2+kua3D67KijgKiH23YU8+V97/Py2v38N2LJ/CbG2eSomu7RY7LuOGp/Pmuedw4p5BHl+zgkl8s5oPSKq/LiioKiH7Q2eX4zV+3cdUDS6g/1M7jt85Wf4NICCTFx/Djq6bwzB2n4TO44eGlfP2Pq9hVfdDr0qKC/nwNszXlddz78gZWldXxuakj+ZerpmrKDJEQmzMmi7986yweeKeUhxZv5/V1e7l+dgF3njuWUem6Y93xUkCEyYHGFv7jL5t5bkUFw1ISuO/a6Vx1Sq7OGkTCJDEuhr+7aAI3nVbIL9/aylPLynhqWRkXnDycm04r4vSxWfh0z5RjooAIsR1VB3l48XaeX1FBl3N85awx3H3eOFIT47wuTWRQGJ6WyL98fipfPXssf1haxjPLy1i0YT8j0xI57+ThXHjyCOaMydSNifpA/0Ih0NjSzpub9vPymr28s/kAcTE+rpmZyx1njWW0puYW8UR+5hDuuXQi37pgPIs27OMv6/fx0qrdPLW0DJ/BSSNSmZ6XwcRRqRRmDaEwK5ncjCQS42K8Lj1iWDjnWzezS4BfAjHAw865n/ZYnwA8AcwCqoFrnXM7A+u+B9wGdALfcM4tOtp7FRcXu5KSkpB/hmBa2jtZt7ue5TtrWLajhg+2VdPW0cWo9ESumZnHLacXkZ2aELL3e2ppWcheS2Qw6+jsYkfVQXZWN7O7rpnymkMcau/81DbxsT5SEmJJjo8hOSGWpLgYpudnkJYYS2piHKmJsaQl+b9/8jzwfSCGi5mtcM4VB1sXtjMIM4sBHgAuBCqA5Wa20Dm3sdtmtwG1zrlxZnYd8DPgWjObBFwHTAZygDfN7CTn3KePZAg45zjU3smhtk6a2zo/9bi5rYPqg21UNrZS2djKzuqDbK88SEVtM12BXB2bncyX5hRw+bRRzMgfqjZOkQgWG+Nj/IhUxo9IBfz//w+2dVLd1Er1wTYaDrVzsLWDptYODrZ1Un+onX0NLZRWNtHU2kFvf0/Hx/hIS/pscPzv4/8NmJSEWJLiY0iI9QW+YkiI+9/H8TE+zAcxZvjM8Pn8s9zGmGFGv/RnhrOJaTZQ6pzbDmBmTwPzge4BMR+4N/D4eeB+83/q+cDTzrlWYIeZlQZe78NQF7mqvI6rf/1Br9ulJsSSnzmEaXnpXDUjl6m56cwqHEqmrkgSGbDMjJSEWFISYinMOnJz8A1zCujqchxs66ChpYPGlnYaWzpoOOT/3tjSTkNLBw0th58fXtfO/oaWT5Y3t4Xub1yf+QPDZ8Zl00Zx37WnhOy1DwtnQOQC5d2eVwBzjrSNc67DzOqBrMDyj3rsm9vzDczsDuCOwNMmM9scmtKDWx/OFz+6YcBgGQGkzxq9Buzn/dKx79Kvn/UXwC+uO+7dC4+0YkB3UjvnHgQe9LqOcDOzkiO1EUYbfdboNZg+b7R81nCOpN4N5Hd7nhdYFnQbM4sF0vF3VvdlXxERCaNwBsRyYLyZjTazePydzgt7bLMQuCXweAHwtvNfVrUQuM7MEsxsNDAeWBbGWkVEpIewNTEF+hTuBhbhv8z1UefcBjP7EVDinFsIPAI8GeiErsEfIgS2exZ/h3YHcFc4rmAaQKK+Ga0bfdboNZg+b1R81rCOgxARkYFLs7mKiEhQCggREQlKARHBzOwSM9tsZqVmdo/X9YSSmeWb2TtmttHMNpjZNwPLM83sDTPbGvg+1OtaQ8nMYsxslZm9Eng+2syWBo7xM4ELOgY8M8sws+fN7GMz22Rmc6P52JrZtwM/x+vN7I9mlhgNx1YBEaG6TVVyKTAJuD4wBUm06AD+zjk3CTgNuCvw+e4B3nLOjQfeCjyPJt8ENnV7/jPgPufcOKAW//Qz0eCXwF+ccxOB6fg/c1QeWzPLBb4BFDvnpuC/KOfw1EED+tgqICLXJ1OVOOfagMNTlUQF59xe59zKwONG/L9AcvF/xscDmz0OXOVNhaFnZnnAZcDDgecGnId/mhmIks9rZunAWfivUsQ51+acqyOKjy3+K0KTAuO5hgB7iYJjq4CIXMGmKvnMdCPRwMyKgBnAUmCEc25vYNU+YIRHZYXDL4D/A3QFnmcBdc65jsDzaDnGo4FK4LFAc9rDZpZMlB5b59xu4D+BMvzBUA+sIAqOrQJCPGVmKcCfgG855xq6rwsMmoyK67DN7HLggHNuhde19INYYCbwG+fcDOAgPZqTouzYDsV/djQa/+zTycAlnhYVIgqIyBX1042YWRz+cPiDc+6FwOL9ZjYqsH4UcMCr+kJsHnClme3E31x4Hv52+oxAswREzzGuACqcc0sDz5/HHxjRemwvAHY45yqdc+3AC/iP94A/tgqIyNWXqUoGrED7+yPAJufcz7ut6j79yi3AS/1dWzg4577nnMtzzhXhP5ZvO+e+BLyDf5oZiJLP65zbB5Sb2YTAovPxz4oQlccWf9PSaWY2JPBzffjzDvhjq5HUEczMPoe/3frwVCX/4nFJIWNmZwCLgXX8b5v8P+Lvh3gWKAB2AV90ztV4UmSYmNk5wN875y43szH4zygygVXAjYH7oAxoZnYK/s74eGA7cCv+P0ij8tia2Q+Ba/FfnbcKuB1/n8OAPrYKCBERCUpNTCIiEpQCQkREglJAiIhIUAoIEREJSgEhIiJBKSBEQsDMrjIzZ2YTva5FJFQUECKhcT3wfuC7SFRQQIicoMB8Umfgn875usAyn5n9OnA/hDfM7DUzWxBYN8vM3jWzFWa26PD0EyKRRgEhcuLm47/3wRag2sxmAVcDRfjv5XETMBc+mX/qv4EFzrlZwKNA1IyQl+gS2/smItKL6/FPvAf+qRWux/9/6znnXBewz8zeCayfAEwB3vBP20MM/imiRSKOAkLkBJhZJv6ZWaeamcP/C98BLx5pF2CDc25uP5UoctzUxCRyYhYATzrnCp1zRc65fGAHUANcE+iLGAGcE9h+M5BtZp80OZnZZC8KF+mNAkLkxFzPZ88W/gSMxH9fhI3A74GVQH3g9rELgJ+Z2RpgNXB6/5Ur0neazVUkTMwsxTnXZGZZwDJgXuBeCSIDgvogRMLnFTPLwH9PhB8rHGSg0RmEiIgEpT4IEREJSgEhIiJBKSBERCQoBYSIiASlgBARkaD+P75e1WjNPebHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oh60qsWE38Fo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "8b019364-d2f9-457e-834e-53a62fcf5c22"
      },
      "source": [
        "sns.FacetGrid(train_data, hue=\"Survived\", height=5) \\\n",
        "   .map(sns.kdeplot, \"Age\") \\\n",
        "   .add_legend()\n",
        "plt.title(\"Survival status based on age of passenger\")\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPrEs18C6KYx"
      },
      "source": [
        "Lets try to do bivariate analysis for more insights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUR_IK_2Vb7w"
      },
      "source": [
        "def bivariate_barplot(data, feature1, feature2, title):\n",
        "  plt.figure(figsize=(8,5))\n",
        "  plt.title(title)\n",
        "  sns.barplot(x = feature1, y = \"Survived\", hue= feature2, data=data)\n",
        "  plt.ylabel(\"Survival rate\")"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cq-tAe3o6RVN"
      },
      "source": [
        "Pclass & Sex:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "zcDKFOM34x_e",
        "outputId": "7d0e6049-4bef-4c4e-9228-675bd2f1a879"
      },
      "source": [
        "bivariate_barplot(train_data, \"Pclass\", \"Sex\", \"Survival Rate for Sex and class\")"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "KrHUM33UTGc2",
        "outputId": "80907201-90ad-4916-b632-b63cbe14d51c"
      },
      "source": [
        "train_data.groupby(['Pclass', 'Sex']).Survived.mean().to_frame()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>female</th>\n",
              "      <td>0.968085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.368852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>female</th>\n",
              "      <td>0.921053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.157407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>female</th>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.135447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Survived\n",
              "Pclass Sex             \n",
              "1      female  0.968085\n",
              "       male    0.368852\n",
              "2      female  0.921053\n",
              "       male    0.157407\n",
              "3      female  0.500000\n",
              "       male    0.135447"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "9Uw4_DRQTxq-",
        "outputId": "1e740a8a-580f-4c42-c8d3-55c3be51a864"
      },
      "source": [
        "pd.pivot_table(train_data, values='Ticket', columns=['Pclass', 'Sex'], index=\"Survived\",aggfunc='count')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th colspan=\"2\" halign=\"left\">1</th>\n",
              "      <th colspan=\"2\" halign=\"left\">2</th>\n",
              "      <th colspan=\"2\" halign=\"left\">3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>77</td>\n",
              "      <td>6</td>\n",
              "      <td>91</td>\n",
              "      <td>72</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91</td>\n",
              "      <td>45</td>\n",
              "      <td>70</td>\n",
              "      <td>17</td>\n",
              "      <td>72</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Pclass        1           2           3     \n",
              "Sex      female male female male female male\n",
              "Survived                                    \n",
              "0             3   77      6   91     72  300\n",
              "1            91   45     70   17     72   47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIAFWz3pWeg0"
      },
      "source": [
        "From above analysis its clear that Sex and class are important features:\n",
        "- Overall for all classes , females have survived more than men\n",
        "- 96% of females who bought class 1 & 92% who bought class 2 tickets have survived, while only 50% of females with class 3 ticket have survived\n",
        "- Only 36% of males who bought class 1 ticket have survived."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_rovKi8XR-p"
      },
      "source": [
        "Pclass with Parch & SibSp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "SGp2zorWUH6Q",
        "outputId": "14ecf781-0296-4334-df88-ef610269c7c6"
      },
      "source": [
        "bivariate_barplot(train_data, \"Pclass\", \"SibSp\", \"Survival Rate for number of Siblings/Spouse and class\")"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1V-lVkAYw4D"
      },
      "source": [
        "From above analysis its clear that people who bought class 1 tickets have survived along with their family members."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "vsdYtt2DZajB",
        "outputId": "f72f8c35-7bc2-4bd3-95fe-51d6970efdf3"
      },
      "source": [
        "train_data.groupby(['Pclass', 'SibSp']).Survived.mean().to_frame()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>0.562044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.746479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
              "      <th>0</th>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.581818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">3</th>\n",
              "      <th>0</th>\n",
              "      <td>0.236467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.325301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Survived\n",
              "Pclass SibSp          \n",
              "1      0      0.562044\n",
              "       1      0.746479\n",
              "       2      0.800000\n",
              "       3      0.666667\n",
              "2      0      0.416667\n",
              "       1      0.581818\n",
              "       2      0.500000\n",
              "       3      1.000000\n",
              "3      0      0.236467\n",
              "       1      0.325301\n",
              "       2      0.333333\n",
              "       3      0.083333\n",
              "       4      0.166667\n",
              "       5      0.000000\n",
              "       8      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAv3DfYWZ6dT"
      },
      "source": [
        "Its good to know that all people with class 2 ticket and 3 siblings on board have survived (100%)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "NsUEL9a_X55J",
        "outputId": "791d5907-00f2-415b-e3f4-1aa759e0ebd4"
      },
      "source": [
        "bivariate_barplot(train_data, \"Pclass\", \"Parch\", \"Survival Rate for Parch and class\")"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "XZ7N48Nea5YH",
        "outputId": "9dce8060-a989-4660-84fb-692e0943ab1a"
      },
      "source": [
        "pd.pivot_table(train_data, values='Ticket',index='Survived',columns=['Pclass', 'Parch'],aggfunc='count')"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th colspan=\"4\" halign=\"left\">1</th>\n",
              "      <th colspan=\"4\" halign=\"left\">2</th>\n",
              "      <th colspan=\"7\" halign=\"left\">3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>4</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>295.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Pclass       1                      2        ...     3                          \n",
              "Parch        0     1     2    4     0     1  ...     1     2    3    4    5    6\n",
              "Survived                                     ...                                \n",
              "0         64.0  10.0   5.0  1.0  86.0   8.0  ...  35.0  32.0  2.0  3.0  4.0  1.0\n",
              "1         99.0  21.0  16.0  NaN  48.0  24.0  ...  20.0  11.0  1.0  NaN  1.0  NaN\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qbI-nDh7YaZG",
        "outputId": "0b6268c7-430e-41fe-f55c-714b38a3bff5"
      },
      "source": [
        "train_data.groupby(['Pclass', 'Parch']).Survived.mean().to_frame()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>0.607362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.677419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.761905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
              "      <th>0</th>\n",
              "      <td>0.358209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">3</th>\n",
              "      <th>0</th>\n",
              "      <td>0.225722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.255814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Survived\n",
              "Pclass Parch          \n",
              "1      0      0.607362\n",
              "       1      0.677419\n",
              "       2      0.761905\n",
              "       4      0.000000\n",
              "2      0      0.358209\n",
              "       1      0.750000\n",
              "       2      0.812500\n",
              "       3      1.000000\n",
              "3      0      0.225722\n",
              "       1      0.363636\n",
              "       2      0.255814\n",
              "       3      0.333333\n",
              "       4      0.000000\n",
              "       5      0.200000\n",
              "       6      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDWs4_iSaoCR"
      },
      "source": [
        "In case of Parch i.e children & grand parents on board, class 2 people seemed to have survived even more than class 1!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5CbI0Vaohy1"
      },
      "source": [
        "Pclass & Embarked:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "H-7TVphKaS8j",
        "outputId": "e778b3ce-2c19-422e-947e-13f1a2d699ab"
      },
      "source": [
        "sns.countplot(x = \"Embarked\", hue=\"Pclass\", data=train_data)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8855650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U45yXkw6XlC"
      },
      "source": [
        "As suspected earlier, majority of passengers who embarked from Cherbourg carried class 1 ticket and mojority embarked from SouthHampton carried class 3 tickets due to which survival rates are higher for Cherbourg. Queens data is not clear from here as there more people who carried class 3 tickets but still more survived than Southhampton, hence I need to analyse it with Sex variable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e02wGdNv690V"
      },
      "source": [
        "Embarked and Sex:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "6qPAG2ozq4kY",
        "outputId": "9429b8ff-f063-49b7-a58e-9dd6ca44d5b6"
      },
      "source": [
        "bivariate_barplot(train_data, \"Embarked\", \"Sex\", \"Survival rate based on Sex and Embarked\")"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aqth96n1o7Bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2b16b134-f472-457d-abbf-243960de0eee"
      },
      "source": [
        "sns.countplot(x=\"Embarked\", hue=\"Sex\", data=train_data)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd8740e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hl1CGRgV7tX_"
      },
      "source": [
        "As seen from above plots, number of females and males is almost same for passengers embarked from Cherbourg & Queens, hence survival rate of Queens passengers is more than SouthHampton where number of males is much higher than number of females"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yck9IypO8ua7"
      },
      "source": [
        "Now we will try to analyse continuous and categorical feature together to understand Age and Fare variables better\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktKWk-b79AZZ"
      },
      "source": [
        "Age and Sex:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5JibQz27hlr"
      },
      "source": [
        "def swarm_survivors(data, feature1, feature2,title):\n",
        "  fig, ax = plt.subplots(figsize=(20,5))\n",
        "  ax.grid(True)\n",
        "  plt.xticks(list(range(0,100,2)))\n",
        "  sns.swarmplot(x=feature1, y=feature2, hue=\"Survived\", data=data)\n",
        "  plt.title(title)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "uhfU5Z9L_Cl4",
        "outputId": "4e8d48b6-cce7-49cc-cc57-d455e48a960b"
      },
      "source": [
        "swarm_survivors(train_data, \"Age\", \"Sex\", \"Survival according to Age and Sex\")"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IIYQQQrQlV1dXxo4dyy233MKAAQP49NNPcXNzs0+hKysrq/V6Hx8f+7+jo6MJCQnhwIEDLFq0iA8++KDO+SdMmMCf//xnXnjhBeLi4rjuuusoKSkhMDCQffv21dum1tjBT+6EEkIIIYQQQgghhGgjx48f5+TJk/bjffv20aVLF7p27UpcXBwAX3/9teE5Jk+ezGuvvcbZs2cZOHBgned9fX0ZPnw4zz33HLfddhuurq74+/sTGxvLV199BYCmaezfvx+A0aNHs3DhQgAWLFjQIjlBBqGEEEIIIYQQQggh2kxxcTEPPPAAw4cPZ+DAgRw5coRZs2bxt7/9jZkzZzJs2DBcXV0NzzFp0iQWLlzIPffc0+BrqqfkTZ482f7YggULmDNnDoMGDaJfv34sX74cgLfeeov33nuPAQMGkJaW1jJBkel4QgghhBBCCCGEEG1m6NChbNu2jaKiIvz8/OyPjx07lhMnTtR5/axZs+o8Fh4eTlVVVa3Hpk2bxrRp0+zHkyZNorCwsFYdsbGxrFmzps75YmNj2b59u/345ZdfbkqkBsmdUEIIIYQQQgghhBCi1ckglBBCCCGEEEIIIYRodTIIJYQQQgghhBBCCCFanQxCCSGEEEIIIYQQQohWJ4NQQgghhBBCCCGEEKLVye54QghxgZRXWVi+N53kvBJu6BvB4E6BrVLP8TNFfHsgnY5+Hky8PAYfj0v3S71jlruGxuDd4eLIkl1Uztd7UjkRX0GvIeeIDPBq6yYJIYQQQghx0bs4fpoXQohfgMcX7GH90SwA/vdTPLOnDOP6vuEtWkdccj73zf6ZCosVgK/3pPHNY1eilGrRei6EuOQ87pu9w55l6d40lj7a9lnOllYy4d0tZJwtA+DHtzazeuZVRAR4tmm7hBBCCCGEOB9r1qxh5syZWCwWpk+fzvPPP9/idch0PCGEuABO55baB6AArBrM25bU4vV8/nOyfdAGYF9KAXtO57d4PRfC/O21s+w9XcCe0wVt2CLdqkMZ9gEogPzSSpbuTW3DFgkhhBBCiF8Sq1Vj2d40xr+zhWEvr2P8O1tYtjcNq1Vr9jktFguPP/44q1ev5siRI3z55ZccOXKkBVutkzuhhBDiAnBzVSgFmsP3BXfXlr+jp75zurtemn9vqK/dHS6CLBdru4QQQgghRPtntWr87vM4tpzKobTCAkBOcQUvLD3IqoMZfHD/UFxcmv57xs6dO+nRowfdunUD4N5772X58uX07du3RdsvPzULIcQFEBXoxaTLY+zHHdxcmHF19xav56Exsfg6rAF1Ta8wBsa0ztpTrc05y7W9whgQE9CGLdLdMiCCHh197cfRgV5MdOhbIYQQQgghWsuK/em1BqCqnau0sPlkDisPpDfrvGlpaXTq1Ml+HBMTQ1pa2nm1tT5yJ5QQQlwgr00ayPDYIA6lFTJlZBcuC/dr8Tp6R/iz5smxzNmcSJcQb+4f2aVR5QpKK9iXUkBBudX8xRdIn0h/Vs8cy9wtTcvS2rw7uLHiidHM3pTAqYREXpkyhgCvDm3drPOyL6WAk/kWrta0Nl9z63ztPZ3fbrIIIYQQQjibsyWxzgBUtXOVFj7enMjtg6MvcKsaTwahhBDiAnnnh1O8uf4EmgarDmawYPpIekW07EBUesE57p39M6n55wCIzy7h/+7ob1hm44lsZszfTVmlFVcFKjztovjGlWbLklagZ0nIKeH/3W6c5UKotFiZMT+OzSdzAEj7ZBcLpo+4aHbua4pKi5UHP9nFllN6llUZ2/hi+ki8Ori2ccuarqLKyoPzdrL1VC4AqzO2seASzSKEEEII0ZCMs+fO6/mGREdHk5KSYj9OTU0lOrrlfyeQ6XhCCHEB5BSX8/aGk/Y1oXKKK3h7w8kWr2f2pgT7ABTA/J+TOZVVZFjmH6uOUlap3wFl0eDl746iac1f1LClzN4Ybx+AAvhsezKnsorbsEW6dUcy7QNQoC+Y/s3elr9V+UJYezjTPgAFepZl+y7RLEfO2AegAPacLmD5JZpFCCGEEKIhkQFe5/V8Q4YPH87JkydJTEykoqKChQsXMmHChGady4gMQgkhxAVQUFpBldNuFdlF5S1eT33nzDKpx7lMfkndtraF7OK67W6N96yp6mvDxdCu5sguKqvzWFbhpZql6de+EEIIIcSl5rdjYvFyr/9Oby93V6aPjW3Wed3c3Hj33XcZN24cffr04Z577qFfv37n09R6ySCUEEJcAD06+jHQaVHtiZe3/O2tdw6pfc5OwV4M7xpsWMa5HeMHRV0UO+rdOaT2Yt+dg70Z1jWojVpTY1y/iFoLpndwc+G2gZFt2KLmG9c/Ah+H6Wod3Fy49VLN0q/9ZBFCCCGEaMiEQVGMvSy0zkCUl7srYy8LZfzAqGaf+5ZbbuHEiRPEx8fz4osvnm9T63XpLWAhhBAXkbLK+hcFrM+8B69g9qZ4EnNKuGVAZKPXXbJYNSyNvDPp+r7hzHlgGEviUgn392TG1d1MB5Sev7kPUYFebDuVS0BVHi9PHNCoupqjKVlu6BvOx1OH8fWexmep1pR+aaqIAE+++t0oPtmaSGpaBs9OHEmPjo1b26uiyoqbi2rStrmtmSUywIslj17plMXXvOBFKCrQi69+dyVztyaSnp7BcxNH0j3s0swihBBCCNEQFxfFB/cPZeWBdD7enEjG2XNEBngxfWws4wdGNennzLYgg1BCCNEMp3NL+cPCvexLKSDGV/Fhz7P0iwowLLM/tYCV+zNIP3uOSovGNT07EuDtbljm/Z9O8f6P8VRUVjGt7Cgv3NzbcMevskoLK/ans/ZIJkHeHRjSOdB0sCst/xzL96XbsyRkl9A3yt+wTHO89+Mp/veTnuXBsqM834QswT6Ny5KcW8IfFu5jvy3LR70K6RPZ8lk2HM1k1cEzVFRaWH0og8GdAg1fX1Zp4ZklB1h1MINgnw68dFtfJgwy/itVUk4JMxfpWTr5uTC7lbKsP5LJdwcyqKyysObQGdMsF7MNRzNZfTCDSouFNYfPMOgSziKEEEII0RAXF8Xtg6Mvis2Emqrt51sIIcQl6MVlB9mXUgBAarHGU4v2G77+XIWFmV/uJa3gHJoGPxzL4o11xw3LxCXn8dqa4xSXV1Fh1RcdX3PojGGZjzcnsHxfOharRk5xOX/6aj9Z9az74+jP3zhlWbzP8PXNsSspj39/X5Plw00JfH/YOMtHmxJYsV/Pkl2kZzFbe+nP3xxkv0OWPy5q+Sw7E/N4fe2JmiwbzbPM3pTASscsi/eTU8+aV45eWFqTJaXIylOLja+x5tiRkMt/1p2gpMJChRU+2BjPWpMsF6ufHbNY4H8/xbPuSGZbN0sIIYQQQjiQQSghhGiGA6lnax0fzywynDaVlFtCYVlVrcf2O53D2f6Uus8fSDMu49yuSovG0Qzj3fEOpBbUOj52pojyqpadAubcroYeq/V8Wn1ZCo3LOL1nx84UUWmxNrKVjeP8fjX0mNHzFRYrx0z65aBT/qMZhVS1eJa6feBc76Wivj44aNIvQgghhBDiwpJBKCGEaIaR3Wov9j2kcyCeDexSAdA9zJdQX49aj43qHmJYx4huwTjPVhvZzbiM8/Ne7q4MjjGekuRc5vLOgXi4NZylOUbEnn8W7w6uptOrRjiVGdolqMUXWa+v3aO6hTapjE8HVwbEGE/fdL7GhnUJwq2Fs4zoVnfRerN+uVjV1+5LNYsQQgghRHslg1BCCNEMr9w5gHH9wgnwcqdfiAtvTR5i+PoObi58NHUogzsFEuTtzn1XdGbmry4zLNMvKoDX7hpI1xBvgj0Vf7m1D1f3DDMs88CVXXnkqm6E+nagT6Q/H04ZarrulGOW/iGuvHWvcZbm6B8dwL/uGkiXEG9CPBUv3daXq8yyjOrCw2Nja2fxMs7yj4kDuLFvTZb/Th7ckjEAPctrDln+eltfxlxmPAg17cquTB+jZ+kb6c+HU4aZZnnVMUuoK2+2QpaBMYG1svxtfF9G9zDOcrEaGBPIv+4aQOdgPcus8X258hLNIoQQQgjRXsnC5EKIdufnhFw+3BhPZs45KjtmckPf8Bavw9fDjdhQXzILywlRxaYDCgChvh50C/XB1UURG+pNh0bc1XJ3aDJ3R71PXk4mwR1fALoZvl4/tw+xoT509PMkIsDTtA4/z9pZ/BuRpTk6BXkTG+pDNmV0DfE2fb2bqwuxob72LOH+5ll8PdyIDfMhq6ic0MZmyU+CH1/V/99nAox6nDq3bTmJCfayZ+nS2Cxhtn7x9yTc38O0jJ+Huz1LmEvr9UtMUNOyNMe5Cgv/XX+CHYl5hLmUc/nISvw9Wz7P5JBEJke9S15uDsFhLwKxLV6HEEIIIUR79dBDD/Htt9/SsWNHDh061Cp1yCCUEKJdSckrZercnVRU6WvnzJi/m6WPjW7xHb/+vvIwX+5MsR8/tXgfc6YNb/D1VqvGA5/sJCG7BIC45HwUioevMhhUykuE+XeCpYJggIW/hunrIXpog0WW7U3jhaUH7cc7EnPZ8tx1hlMFZ604zMJdNVmeXryPjx9oOEtzJOeW8MDcnVTY1jR6+LPdLHt8NAMNpgp+szeVP3/jmCWPLc9da5jlbysOsXh3qv34T1/t56OpwxpumNUK8ydCXrx+nLIDlAuMeswwy7S5u2plWf74GMPpdV/HpfLiNzXfyHcm5rH5WeMsf11+iK/iarI889V+ZhtlaYbEnBIe+GQnlRYNgOmf7mbFE2PoH208VbCpXlp+iCVOWT6c0rJZyDkFn98F1kr98/LlvfDIjxA5qGXrEUIIIYRoa1YrHFoC29+DwjTwj9b/kNp/Erg0f8LbtGnTeOKJJ5g6dWoLNrY2mY4nhGhXfjyeZR+AArBqtMpuX8671P1wPMtwMe+TWcX2ASj7OczadXIdWCpqjjUrHPuuSe3KKa5gd1K+cRmndmw4Vvs9bAk/HMuyD9pAdb8Y71xWN0s5ccnGWb53Ouf6o5nGC5NnHa4ZgKp2dIVhHRuO1s2y7ohxXzrvnpddVM4ekyzO/bL+aGaLL0z+w7Es+wAU2PqlFXaU+96pL9cdycRi1Rp4dTOd/B6slTXHmgWOrWrZOoQQQggh2prVCovuh5UzIWMflGTr/185ExZP0Z9vpquuuorg4LprhrYkGYQSQrQrnYPrTifqGuLT8vU4nTMqwMtwel2EvycebrWfN536FFzPVKJg4+l4zudUqv73pFYZp+ejA73o4Nay3x7qy2qWv4vTe9yoLE7njAnyMl6Y3D8aXJ2mxjXxPa6vrWZlXBR0amKW6CCvFl+Y3LnvgUZNlWyqzk7n7BTsjauL8ZTHJgtq+udFCCGEEOKSc2gJJPwIlaW1H68shfgf4NDXbdOuRpJBKCFEu3J1zzDuHhpjX9Ln+j4duX1IVIvXM2t8X8L89MELbzd4+c7+KIN1hAK83Xnptr72gahuYT788fqexpX0uB4G/wawnbfXLTDgHsMiM67uzkDbtDB3V8VT1/esMwDg7G8T+tl37vN2g5fv6G/crma4pmdH7rq8pl9u6BvO7YOjDcvMuKobA6Jrsjx9Q0/TgZtZDll83OHlOwYYN8w7GMa9Am629aZCe8E1zxsWubZXRyZeHm3PcmPfcMYPMr7Gfnd1d/pH+9dkubGXaZa/T+hPqG8He5ZXzLI0w3W9OzJxSE0/jOtnnqU5/t/t/WplaY1rjJ43wcB7a477jIf+E1u+HiGEEEKItrT9vboDUNUqS2H7uxe2PU0ka0IJIdoVpRTP3dybjn4enEpK5q+398fDreF1d5prSOcg/nFnf74/nElQZRbXmOz0BnD3sBiqLFaOnSlk8vDOpoMQKEXO9W+yxus3JCUl8NDNk4hy62BYJNinA/+eNIjPtifRKciLKaO6mrbr8s5B/HNiTRazHfgAyqssLN+bTnJeCTf2jWCQyZpbLi6KF27pTbi/B/FJyfxtQj/Tu61CfD14/e6aLFOvbFyWf0zsz9rDmQRXZnGVya51eqGpYLVA1hG4/AEI7Gye5eY+RPh7Ep+UzKzbG59l/vZkOgV7M3VUF9NmDe0SxD8mDmDt4UxCq7IY24gs5VUWlu1N43ReKeP6RRiuuWXPcksfwgM8SUVRA8AAACAASURBVLT1i+GdYzbHzhTy3YEMOvp7ctfl0Xh3MP5xYmiXYF69cwDrjlRnMb/Gyir1LCn5jcuCiwvZN7zNGu8pnE5O5KGbJhHp2jqLubcnPxzLZGdiPu6FVVzT1o0RQgghhLnCtPN7vo3JIJQQol0pLKvk9ne3klZwDoAdb2/muz+MJTrQq0Xr+XhzAi9/d7TmgcBj/PmWPoZlHl+wh/VHswBYvDuV2VOGcb3Bzn1nz1Uy4Z0tpJ8tA4JZ8vZmVv1hLFEGWeKS87lv9s/2NYvWHM7km8euNLxL66NNCbyyqiaLCjzGCyZZHvt8DxuO6Vn+91M8H00dxq/6GGcZ/84WMs6WAbDz7c2smjmWyACjLHncN3uHPcv3RzJZ+qhxltmb4nl11bGaLEHHef7m3oZZWHQ/nFyr/3vvfLhvIfQc13CW0komvOuQ5a3NrJ55leFOhLuT8rjvo5/t6y+tPXyGr02yfLAxnn+urslC0HGeu8k4y4z5cfx0PBvQ+2XOA8O5tnfHBl9fUFrB+He2cKawul+2sOoPYw2z7ErK49cOWZbtTWPJ70YZZvnfT/H8a41jvxzj2UZk2XiiJsvcacO5ppdxltve2UxmYTkQzNdvb2H1zLGN2lXxl+q9H0/x7++P1zwQdJynb+zVdg0SQgghhDn/aH0dKKPnL2IyHa+9O49FyYS4FK05dMY+AAVQUFrJUodduVrK3C2JtY4/255kuGj06dxS+wAU6AtAz9uWZFjH94fO2AagdAWllSzdY5zl85+Tay2avS+lgD2njRfAnru1dpZPtxtnSc4tsQ9AQeOyrD6YYR+0AcgvrWTpHuO/0szfXjvL3tMF7DldYFhm7pba7fh0W5LxAti58TUDUKAv/r7jA8M6Vh2qJ8te436Z/3NyrQXA95wuYG+KWZba/TJvaxJWgywJ2cX2AShoXL+sOnjGPgAFkFdSwTd7jfvls+21s8Ql57PPLIvTNTZvm3GW+Oxi+wAUNC7LdwczbANQusZk+aX7xKlf5m5JRNNaeMF4IYQQQrSsUY+DewMzKty9YdQTzT71fffdx6hRozh+/DgxMTHMmTOn2edqiNwJ1V5VVcB3T8H+hfqaJze+DAON15IRoj2ob3Fw9xZeZLu+c7q7uBjeCeLmqlAKHH+/c3c1XpjZ3a3u82bTvuo7p9n0KufnzbPUPZ/RouwNtaFVyji9Z26uCsN32aWeb4POC5W3RLtaoIzp9VLftW9aR8tcY2ZlnLO6uSgMLjHc69lauDXe41+6OteYm/FnXwghhBAXgf6T4PCyuouTu3tD9+ug/13NPvWXX37ZAg00Jj+dtVc7Z+vTSqyVUJwJyx6FwvS2bpUQrW5cvwh6hfvZj6MCPJk0NKbF6/n9dZfV+iX60Wu7G+72FRXoxaTLa9rRwc2FGVd3N6zjpn6R9Az3tR9HB3px1+XGWR4aE4uvR83AyjW9wkzX0vn9dT1qZXns2h6GWZzb0cHNhUeuMt6F7OYBEVzW0SmLSb84Z7m2VxgDbIuuN+T319bulyeu7YGL0S5sQV1g0H01x26eMPoPhnXcMiCCHk5ZJpr1y+hYfDrUrE12Xe+O9I82zvKHX/WodfzEdcZZOgV711pk3MPNhRlXG/fLLQMi62YZYnwL92/H1M7yq94d6Rdl0i/X9XA6vsxwsKNziDd3DK5ZIN2jEdfYrQMi6R5Ws0thTJAXEy+/uG9Hb2tP1NMvQgghhLjIubjA5M9h/NsQORh8wvT/j38b7pmvP38Rkzuh2qu03bWPrVWQsR/8W37XIyEuJl4dXFn2+Gi+37Kdk6fimXH/vfh7G9/ZAlBRZWV3Uh5hfh5c5jCI1ZBJQ2PoFOTFiv3phFae4bFrepiWeW3SQIbHBnEorZApI7uY1uPVwZXlj49h9qZ4TsQn8urUMQR4GS9M3jvCnzVPjmXO5kS6hHhz/0jzBbDvHtaJzsHe9iyPXmM8OAbw+t0DGdFNzzJ1VBd6dDTO4t3BjRVPjOHDTfGcSkjklSnmWfpE+rN65ljmbml8lnuG61lWHtCzmA30AXD7+2R2vpWijJN0GnEnHmGxjcgymtmbEhqdpW+UPz88eSVrt+0gPCSY664YYtqsycM70yXYh5UH0ulYmckjVzWmXwYxvpsiJSOTa0cOo1PHYMPX+3jUzvLqlDH4m2TpFxXAmievYu6WRLqGenP/yK6m7br3is70D1HsOngYVVnONJMBJYA37hnM7d1dSMnI5LpRw4gJM8+y8vdj+HBjPPGJSY3KciEl5pSQml/K8K7BeLo3brOEvafzOZlv4WpNa5U7lH4zoguDQhS7Dh3BtaqcqWOMr/32KCG7mLSCc03qFyGEEKLNubjAwLv1/y4xMgjVXnUZDYe/qTl29YDoYW3XHiEuFEsVXosnc8ep9frx5/PggZXg4dtgkfSCc0yevZ2UPH0tqftHdublOwYYVrPpRDYz5sdxrtKCq4LYy9K4w+QOknd+OMWb60+gabDqYAYLpo+kV0TDgzdVFiszPo9jk21tnNS5u/jy4RGGO5GlF5zj3tk/k5qvZ4nPLuH/7uhv2K6NJ7KZMX83ZZVWXBV065nG7YONs7y14SRvbThpz/LFwyPpaTCoVmWx8sj83Ww+mWPP8oVJljRbluo1vhJySvh/txtn+el4Fr/7PK5JWd7ccIq3f3BB03oRui+eLx8ONRwgrLRYmTE/zp4l7ZNdLJhunIWC04R/eitTzp7Wj/NnwC2vGbbrx+NZPOqQJbZXOhMGGf8hwWXjP7h242uABsc7wrRvIazhhaYrLVYe+SyOLadqsnwxfSReHRr+ZTw1v7RWvyTmlDJrQj/DdnFiLf0XT6V/1Tmsyg0OzTa9Vdzlp1e4dtO/beXD4YFvIaxng6+vqLLy8Ge72XoqF4D0T3axwCTLhfKftcd554dTAIT5efDlwyNr3YHmrKLKyrRPdrItXs+y5sx2Fkwf0fKDJMfX0P+rB+hfVab3S5850O+Olq3jIvbammO8/1M8AB39PFj4yEi6hTXcL0IIIYQ4fxf3fVqi+YY9pC9I5hUEoT3h7nnga74lthCXvBNroHoACiB9D+w3ntv80eYE+wAUwOc/n+ZEZpFhmVdXHeVcpQUAiwYvf3fUcEHfnOJy3rYN2ujHFby94aRhHeuPZtkHoAD2pxTwtcli3rM3JdgHoEBfEPtUlnGWf6w6SlmltdFZsovKefeHU03Ksu5Ipn3QBvQF080WJp+9Mb7WIvOfbU/mVFaxSZZjtbK8YpIlq6iMd390zFLO27bBgsZm2Xu6wHwB7K1vQ/UAFMDODyHH+D179bujTlmOGNdRlAmbXgdsYUqyYKPxQNfaw5n2ASjQsyzbZ5zlA6d+mbctiYRs435h7V+gSi/jolXB938xfn1hBmx+o+a4OBOqB6QaquLIGfsAFOiLvy83yXIhZBaW8d6PNddUdlF5reP6rDl8xj4ABfri762SZe1foEpfmF7vlxdbvo6LVMbZc3ywMd5+nFVUzrsm/SKEEEK0pPayGUhTc8ggVHvl4so3HR/lNyEL+X3IbE4EjW3rFglxYRRnNu4xB9lF5Y16zFFOce3n80srau0Y5qygtIIqp93AzOrILq6nXQ47mdVbpp5zZpnV4/R8fkndtjqqL4tpHfVkaU6Zpr5neSUVhrvj5ZdU1nk+qxnvsVm7mnVdOmXJLTbOQmkOaJam1VFUN2tWYdOul4YeM2xHSbbx7q2lufVkOdPkdpldYxdCbnEFzt2WVc/77qhZ73FzFGfVPi7Jqr17QjtWX7+0ynsshBBC1MPT05Pc3NxLfiBK0zRyc3Px9PRsdBmZjtdOrTmUwR8X7bcfbzuVw+bnrjWeLiJEe9D7Vlg/C8oL9WPXDtBvomGRiZdH8+2BDPtxTJAXw7sarz8z8fIYZm9KsB+PHxhpuENYj45+DIwJ4EDq2Vr1GrmxbzivrT5GUXkVoO/0Nd5kOtadQ6L57mBNlk7BjckSzUeba7ZqHz8oynBXtcvC/RgQHcDBtJosd5lmieC1Nccprs7i5sL4gZEmWWJYdbBm4KFzsDfDugaZlIlmzpaaLBMGRdW7m1+1XhF+9I/251BaoUMW40XGx/WL4N/f185ym0kWBt0LR1fUHAfFQqeRhkUmDolh7laHLIOjDBeMp2NfiBgIZw441Htfw68HxvXXs5RU6AM+HdxcuNUky8TLY/j+cM2gUtcQb4Z2Me4XBt0HO/5XczzwHuNFM8P7QcQAOHOw9jkMjOsXwetNzHIh9In0o0+kP0czaq6xiUOMr7Gb+kfwn7XHKbVl8XBz4ZYBrZBl0GR9I5NqA+/BcNvCdqRflD+9I/w4dqbmTlGzz74QQgjRUmJiYkhNTSU7O7vW42VlZU0a0GmOlq7D09OTmJjGfw+VEYl2yvEXN4Dckgp2JORxbe+OpmXLKi2mr6lF08BSAW7miz9Xs1g1wzst2ruKKituLsp41y7RPH4R8ND3WH/+H5lpp4kc/xcI72tY5Lre4cydNoyvdqcS7u/JI1d1M91y/rmbehMZ4Mm2U7n4V+Xxyl0DTZs278ErmL0pnsScEm4ZEGm6VlG4vydLHr2SOVsSSUlL55k7R5guZn5933DmPDCMJXF6lhlXdzMcUAJ4/uY+RAV6se1ULgFVebw80Xg9LIBPH9KzJOcUc9OAKNMsEQGeLHl0FHO3JJKaltGoLDf0DefjqcP4ek/js7xwc2+iA73YEZ+NX1VB47I8eAWzNyWQlFvCrQOjTNddigjw5KvfjeKTrXqWZyeONF2Ynd63wn0LsexfhItfBGr0H8DV+Fvwn2/pTUyQF9vjcwmw5PGKWRalYMo3aFvfxpqfiGu/O0zXXYoM8GLJo1c6ZTFeE2dcvwg+mjqMpXtSiQjwZMZV3Q0H+gC48WUI7IwlaStJlcF0v+118yz3f4O27W2s+Um49rsT+hsPJkcFevHV765k7tZE0tMzeG7iSLo3cn2fiior7q6qSYt/N/Z7pVKK+b+9go82JZCSW8wtg6K5baDxNRYd6MWS6iwZGTw/sZXWKhr3DwjqCklbiK8Mo/utJv1yHqosVuM7+erRnH5pLL1fRjB7Uzyn80qZMCi68YOWliqUtYk/K1VVgKt7kwb5mvrzmKZpVFiseLg1fu2w5vRLczT5Z0shhGjn3N3diY2tuyHITz/9xJAh5hvYnI8LUYcRGYRqpzoFezXqMUep+aXMXLiPuOR8onwVH/Y4a7odOqc2wLdPQkEK9PgVTPwIvI3vupi9KZ53NpyirLKKKSVHeOm2Pq3yA+bFqKzSwgtLD7JyfzqB3u78+ZY+plu7i6b731EP3t9zG2WVVUw74MOfY4x3liqrtLBiXzprj2QS5N2BwZ0CTRcZT8s/x/J96exLKSDGVxGfXWy6Tf3+1AJW7s8g/ew5Ki0a1/TsSIC3u2GZDccyWXUwg4pKC2sOnWFIp0DzLPtrsgzpHGg6QOScJSG7hL5R/oZlgtN+4vljT8HZVHAdB70+BK9A4yxHs1h18Iw9y+AmZAn2aVwWt7NJPHR0Og+lxVHs0wXP3C8gwngxc71f0skoLMNi1bi6ZxgBXib9cjTTnmX1oQwGdzLOXlZp4Zk9Eaw6eA/BPh14KQomDDIsQmr+OZbvT2d/SgGd/FxIyC6hT6Rxv2w4beGluKvJKBzBr8rCeaNHJf6exlnWH8nkuwMZVFbV9IuRcxUWlu9Ls/fL5Z2DTO/QS8ovZ2bcQPandqGTnwsf5ZTTO8L4DxfrT1v5a9w1ZBSWNTrLhqOZrD6YQaXFwprDZxjUiCzPLNnP6kNnCPHpwF/H9zUdIErMKWHmwr0cSD1LJz8XPu5dZLjBAEBo2g+8cOxpKEwHt1ug5wfgadyX6x2zHDrDwBjjLM2SnwQHv4L0vYT7xEJePHTs0+LVvLX+JLM3xVNpsfBw5TGeGdfb8PWlFVU889UBVh/KIMzPg7+N79cqd4LtPZ3PtwcyyCwsQ9Pg6l5h+HqY/Gj80z9h2zuMqaoE7ffwq5eMX19RAssehaMrwTccbv4X9L3dsEh8djFPLtzHwbSzdPZzYU6fItNB++8Pn+Fvyw+TVVTGDX3D+c89g02zvLnuBB9tTsBisfBI1XGevrHhTQya61RWMU8u2suhtEK6+Lswp2+R+aC9EEKIdk3WhGqnpo/pxoBo/RdiFwWPXtPd9Jv+S8sOEZecD0B6scbMRXuNK6ksg6+nQ8FpQNMXg97w/wyL7E8p4NVV+vSiSivM3ZrISodpUO3dJ1uT+GZvGlVWjZziCp5dcoAzZ43XBhFNE5ecz7/W1FxjH21OZPUh47Vk5mxJZNm+dCxWjZzicp5Zst90XaAXlx1kX0oBAKnFGk85TH+tz7kKCzO/3EtawTk0DX44lsUb646bZMmzT2GrsOqLjq8xyfLx5gSWO2T501f7Tdef+fM3TlkW7zN8PRUl+mf/bAqg6YvB//iqYZFdSXn2KWwVVvhwUwLfHzbO8tGmBFbs17NkF+lZTNdsWfkkpMUB4FuSDN/MMHx5aUUVM7/cR/pZ/ZfQ9UezeHPdCcMyOxPzeH3tiZosG82zzN6UwErHLIv311lXzNkLSw+y39YvKUVWnlpsfI2VlFfx5ELHLJn8d53x4uc7EnL5z7oTlFRYqLDqi46vNcny4aZ4vj2QYc/ydCOyPL/0APttU1FTiqw8bZKluLyKJxfVzvLWeuMsPztmscD/fopn3RHjNbE+2FiTJauonKcW7yevpMI4y9cH7NNqU4qsPP2VyeelvEj/vBSmoe9a+B1s/JdhkW3xObzhkOX9n+LZcNQ4S7Os/AOk69/rfUsS4ZvftXgV207l8Ob6mizv/RjPD8dM+uWneL47mIFVg8zCcv64aB/5Jv3SVIVllTy5aB8ZZ8uwarD2SCbvmGywQMJP8NM/oKIYV2s5bH4dTqw1LrP5DTiyHDQrFGXA0kegNM+wyHNLDtinO58usvKnJQcMX19YVsmTC/dxplDP8v3hTN75wTjL5pPZvLXhJKUVFsot+u6tPx7PMizTHM99fcA+3Tm50MqfvjLOIoQQov2TQah2Kih7Fyu7fsWaK4+zfeYQnrvJ+K+OQK31XQASskvsa57UqyAZzjn9IJW+x7COA6kFdeut57H26mBa7axVVo0jGWcbeLVojvqup/0m15jzdVlp0TjisH5L/WVq99vxzCLD6QZJuSUUltX+PO1PNe77/Sl1nz+QZlzGuV2VFo2jGca74znnP3amiPIqg6kTufFQ7tSOdONBa+d2NfRYrefT6sti3C912pF5CCyVDb48IbvEvuZWNeevhXXaVc/1VN9jRs9XWKwcM+kX53YczSikytLwYt6JOXWzmLerblaz/AedylRYrBw/Y5LFqczh9ELDKUAJ2cV1vv84n8NZc76/OGetqLJyrImf/UNphViNpjPlnIAKp90DTT4v9WU1+3rRLGlO37Mz9rX4wuT1fc2q72tbreedspZXWTlusmNpU8VnFdvX3Kqp1+TnEef3C0z7ss7zVWWQddSwiPN7ZnYdn8wstu/WWlOmad8rGlOmOZw/l2ZfX4QQQrR/MgjVHiX8BJ/eBnHz6L3n74QvuR0sBoNJNiO6hdQ67h/tb3wrd3A38HOattDVeBe+Ed1C6iyHMNKp3vbMOaunuwtDOpks6Cua5IrYutfYKJNrzLlfvNxdTackjexWe9rpkM6BeLo3vA5H9zBfQn1rTz8a1d24XSO6BTf581JvFpNpPM5lLu8caLymSFgv8Amr/VjXMYZ1jIg9/yzeHVxNp1fVaUenEfo6LA24LNyXUN8OTvUaTymur92juoU2qYxPB1fT6c7O7RjWJchw7aUeHX0J8amdpTHXmFlbzZ5vXJbaZa7oGmy4yHrPcD+Cfc6/X8yz1D6nr4dbk/vlithg4/X9OvYFL6e2m31e6r3GWuF7pXM7uoxp8YXJR8TW7Tez69K53/w83OgfbbI8QBP1jvAn0Gk6tNnnuN6fcUz6ss7zHv4QabyGoHN+s+u4b6R/nSnETb32G1OmOepmMf4cCyGEaP9cZ82a1dZtaDOzZ8+e9cgjj7R1M5okKSmJrl27Gr9ow/9B1uGa49JciL0KgroYFhvVLYSU/FKyisrpHgDvThlJkHeHhgu4uEKXK/W/6FWegwF3wQ3/Z/gLX4ivB11CvDmRWYyLtYonb+jN3cM6Gec5T416zy5QHf2jAyivspCcW0rnYG/+eddA0zVemlPP+bgQdbRmPWF+HsQEeXMiswgXrYqnx5mvuzUwJpCySgvJuSV0CfHhX3cNpFeEcb+M6h5Ccm4JOcUV9LB9XozWd3J1UQzvGsRx211Gdw6J4bmbehsOKnT08yQ60IuT553FeCruyG4hnM7Ts1wWoHh36kjjNZFc3KDzKMg6ov9Vf9BkuP5vhp/9jv6eRAV6cSKzCDetij/d1Jc7TdbdGhgdwLlKC6fzSukS4sNrkwbS02RdFGKvgrxEKM4iz683XvfONVyrys3FhaFdgjmeWUSlxcqdl0fz7DiTfvH3JCrAixNZepZnburL7WZZYgIordCzdA3x4bVJg0yzjOweQnJuqd4vgYp3poww7Bc3VxeGda3JMvHyaP40rpdhlnCnLM/e3JcJJutuDYoJoKTCQooty7/vHsRlJtO9R3UPISm3lNziCnrasvibZtE/L5UWrdFZIgM8OZFZjJtWxXM392W8aZbA2lkmDTSdun5l9xASc0rJLWlcFlzdofNIyDyib+Ix6Nf6OkIuDf+RJyLAkwj/mizP39KP20zW3WqW2KsgLwFKssn174f3vXPAs2UHeyIDvAj39+REZhHumkXPYrLu1uBOgRSXV5GSV0q3MB9eu3tgoxeZh8Z9f3F3dWFol5prbNLQGJ66sSduRrs2BkSDbwRkHaVM88Bt3N+h3x3GjYkZpu/Wmp8EoZfB7e/r/zcwqlsIiTkl5JVU0CtI8fb9I/AzWA/NniVTz3L3sBieuqGX4UBvVKAXYX4enMwqogMWXri1ddbdurJ7aK0s70wxznK+LvWfYdpjHReqHslycdYjWS7Oei5Ulr///e8Zs2bNmu38uCxM3h7V9wuXyYLBAMEdrLwf8R2UbiRNCyPaewTgY1zIJwxCeuj/DukBbuZbPd4ZfJo7I98nPzuDoI7PAd1Ny1wIqfmlvLHuBEk5JdzYL4JHxnYz370ueRts+S+DsjMg4nl9BywDri6KF8J38ULUl+AbBoHPAeY7FjZZwWl9jZ7ceOhzG4z6vfF26BdIWaWFtzacZHt8LiGqnCFXVJouzN0cMUFexIb6kEkZXUJMrmH0fokN9aFbqC9hfh6E+5tfx6EeVj6MWAllW0glnBjvkYC3cRlfD7qF+tjq86aD2Y5iQKdgb2JDfciijK5NyBIb6kNHP08iAsyz+Hm6ERvqS2ZhOSGq2PgXapsUn3686f8fkqtKuSkggulunpjdP9EpSM+STRldQ4zfK9AHIl4M38GLUYv0BX0DngfCjAt18IWQ7lB0hlLCCfY0/9rX0U/vFzcXRbdQHzxMdkYEiAn2smfp4pAlr6SC/6w9zuH0QsZeFsoT1/XAw80VN1cXYsNs/eLvSbi/+W6iHT2szI5cAeVbSSWCGC/za2yoXwHLIz8FzySIuB3cjBdlh5rPi3OWhri5utRcY/6edPRrXJaPIpZD+TZbllGmZexZvJIbnWVySCKTo94lLzeH4LAXgbq7zjhneSl8Oy9FfQV+kbZrzPhuGF9PN7qF+ZBTXE6YS+M+L8k+A3jT/w1SLOe4JTCSh1w7mH9ebJ/9nNwyOgfX9EtOcTn/WXuCY2cKGXtZGE9c28O+m+f8n5NZsS+NcH9Pnry+p+lOh6Ueofw34K/s9MsjzKWYNzwjMFsyOimnhDfXnyA1/xy3DojkwdFdTTcX6RTkTbdQX3JzK+jciGvM3dWFbrZrLDLQi45+NV/HDqae5Z0fTnL2XCX3XdHZvolESXkV/11/gl1J+XR0KWfoyErTwY4wXw9iQ31wt13TjfmaTFBXCOlBaV4unkHG1xcAru58FvgoK/xuJ9Lfiyd9LzP9qSfCs4o5kcug8mdSVCRRXqNNqwnz87Bn0DOZ39HWOdib2FBf8nIr6NyI7y/NoX9/8SG3pIJwl+LWG4A6tQG2v8fAvDyI+Qv0uL7Fqygur+LNdSeIS84n3KWcYaOqTBd/T8gu5r/rT5JecI7xg6J44Mqu5hWdWg/b37dleUnf+MfEvK2JfHsgg6hAL/54Q09iQ437szrLjwfPsaX4CH+8oSc+ZovyN8PGE9l8vDmB/PwyXKOzGXuZ8fdwTdOYty2J7w5kEB3kxZPXm2dpT4rKKnlz3Ul+OnSOrSVHePJ68345lVXMWxtOklFwjtsHRzFlVNcL01ghzoMMQrUnJ77Xv3H5RYJvRyi2LTA54G6IMN+mnLUvwq6PAYgGfSHVKUsbfr2mwYK7Idu2tkHqTv2xsU81XKYgBebfCVVlBAEsuh9+uxY6XdGIgK1H0zQe/GQXJ7P0dTv2nNbXMPjd1QY/Khachs/uAEu5nmXhb+C366DT8IbLHPoaVvy+5jhpCzx5EDq04DdYTYPPJ0GObdHr1J36/0fPbLk6munvK4/w5c7T9uM/Lt7H3GkG71czpOSVMnXuTiqq9LVzZszfzdLHRhtOr1u+L40Xlh60H+9IzGXzs9fh1cFgStrq52DPpwDEAHxjgV8vavDlVqvGA5/sJCG7BNAXUFcoHr6qW4NlTueWMnXOTips6wA9Mn833zw22nBK2rK9dbNsee46w6mCs1YcZuGuFPvx04v38fEDDfeL1arxwNydJOQ4ZFEwfWzDWZJzS3hgbk2Whz/bzbLHRxvv+LV/kb77pv0k2+DJA+BusNPn6mdg7+eArV+WPQb3fWGYZercnSTWyqL47ZiGf7lMzi1h2txdtbIsf3wMA2ICeOKLPWyLzwVgX0oBBROIpgAAIABJREFUJeUW/jq+L1/HpfLiN4fs59iZmMfmZ6817BdWPQP7HLIsB+5d0PDrrVb4fKJ+ZwtAyg5QLjDy0QaLJOaU8MAnO6m06OsATf90NyueGGM49WlJXCp/WVaTZVdiHptMs/wJ9i2oybLMLIsF5k+E/MSaLC5uMMJgofmcU/D5XWCtJBjgy3vhkR8h0mAbwr0L4DuH71nV15hbwwNrf1l2iKV70uzHzy05wP/uH9rg6y1WjSlzdnI6rxTQrzE3F2X4y2hCdjHTHPvls92seGI0/aICeGzBHnYm6usx7j1dwLmKKl68tS+Ld6fwkmO/JOWx+dnr7ANU9Wb55hBL9zpk+foA7//GJMvcHaTknavJ4qqYavBLT3x2MQ/Oq32NrXxijOEOnIt2neal5bY7upPz7ddYaUUVv/7oZ/vaZzsS8/D3cuO63uH8ZdkhvnHI8vzXB3nvN5c3WEeVxcr9c3aQml+Txd1VGf8Cl30cFkwCa5V+jX1xD/xuM4T3a7DIwp2n+atDlt1JeWx85lrDfuHbP8LBxQB0AljhBnfPM87y8Q7SCvQsu5PzcXd14f6RDd8BfzKziAc/2UWVbT2z387bxXd/GGt652xTvbD0ICv2p9uPOyw9yDv3tfC24FnH9L6w98tkmLEZwvu2aDXVuxtX81h6kLcNslRarEyZs7NOv/x6ROeGK8k8ore/1jW2FTo2vLbr5z8nM2vlEf0gOZ+45Hw2PnON4Z2jz399gG9tGwMlbEkku7ict+5t2X45dqaQh+btsq//9+Anu1jz5FjDu00/33Gav9uy7E7OZ3eSeZb25LmvD7DqoL45SMLmRHKLK3hj8uAGX19RpX/2z9g209mdnI+Hmyv3DG/dWSZCnK9fxif6l2DnR/o3qp2z4Yf/g86j4Z75+qDIXR837hxHV9Y+jt8A5cX1vxYg91TNAFRD53B2ap0+fcdOMy9zAcRnl9gHoKqZ7ajGyXVgcdwRSoNjJlmcs5bmQvL2xje0MXJO1AxAVTuyomXraCbnXbd+PJ5luJh3c/x4PMs+AAXoux6Z7PblvLNZTnEFu5KMdy+q05cnvoeqhncIO5lVbB+AqrbGpF0/Hs+yD3SALcsR4zLOu+flFFewOynfuIxTOzYcq/0eOjuRVWQfgGqoXmc/HKsny2GT3b6OOl23JVn6YIRhmW9rHx9fZbgw+bEzRfYBqGprDhnv2LnhaN0s646cobCs0j4A5Xwu52ssu6icPcnG/VLnGju+ynh9v8xDNQNQDZ3DyQ/HsuyDA4B9lzAjzn2dVVTOntPNyGI1+OxnHqoZgKpm9nXs5PdgdehrzQLHVjWtXcVnIHWXYRHn6/b7w2cMFyY/mlFoH4CqtroR15hjv1isGuuPZJFfUmEfgKpW/fn93qlfMgvL2WvSL87X5feHM9EMFiY/nH7WPgBlr9/ks7/haGbdLCY7/X3v9B6fKSxjX0oBW0/l1ll8v7p+53aY7Vh5OL3QPgBlP5dJGU6sAatD/Y24xpzPmXG2zHwBdOfr0uRzfCi90D7QYa/XpF/WH82yD0CBvlHKOpPvL83h3A9mX1+b5fiq2v1irYITq1u8GufPmNl7fCD1bN1+Mb3GVtfNctz4GnN+j9MKzpluYlK3X1q+7zcczaq1AYV+jRnvwOj8HqcVnONQusmGJO2I89c+s99F9qUU2AegqpleY0JcBGQQqr3YNaf28dHlEDu2aXcYOd9W7hcF7ga3zfuG130+uOE7IeqtozFlLoCO/h54Of0V33S6UHAzstTJr+o/z/nwiwA3pztFLoL3GKgzDSPS37NRU5+aVEdw3X4zm8bWObj280rVf55anPstoBO4NryGWkQ9Wc2mPtU3bcVseqHzORuTpYvT89GBXoZ/pa8/S9Pa1dBjtdT5bCh9KkxTygR2NlyrKjLAs07W5mXxwdvdtc7i89Xnci7jovTpVoaCu9Y+DugErgY3MAfE1L0GTb6+OPc9mH/tqy+L6efFud8Cu+jrCjbEv+lZmvX9xfmcykVvmwHnrJ2DvQ2nbkcGeNaZ5mX6NamePuga+v/ZO+/wKqr0j39uSe+9EEhIJwm9hV6UKtLtgq6rIPa2tnV/urviNnftuysqFuwdpdhApPcOoaVBeieN9Pv7Y5JbJjfn3MQAys73eXh0bubcOZ95z8yd+95zvq87nq7Gdubzbe+lbuPIGFMvwerl7y5cWhfu69ZumZfselHfX5U2kn6p+m3Q6+jp79bhtWfvPWXL/rrCYn+Mde4aM+h1RPgJZnPae0/JOO7h64ZRr2bp3HWsvNb9y57Uxzkfx+jS81gXpB5TsnMc4dc+LtJnyy7cx9T9MOp1RPiKx5j6GnNkuX9nZf95rHPPPU4GHT0kLJeS1PcLR8aY+uPHkWX1mjRdbP2qk1A6nW68TqdbJd/zf0Dq5VwGZ+EXYrua+hfFcBNoMnjAjH+JfYRcvWHa3ywJD/8YmPh78TGix8OghdDmhhE3Gfpf17l+ngd5uzrxf1cm4eqk8PYO9ODBSQniRtETVCxT5Cwj74YerUsd9EYY94jiX9OdcvWBaX+1xCUgVh6XC6Qnr0wmqNU/xt0IS+f0lXqJdFbj4oO4anCEucDT5X2CmTVQbIK7eGw0/VsrYhn1Ou6/PJ4omQfBtL8riVig0dh6vQhYfNyd+MOMJHPyJjrIg/svjxceYnx8EPNtWEKYLTFaXjwuhn6tLE4GHQ9cHi/9MvbkzGRz8sTdCE/PFnvv+Lo784QVS0yQB/dPEhvtjo8PZt4gC8ukpBBmSVgYdR+EtU5D1zsp41iWhJr2D/BQfNYajZ5KXATy83DmD1f0MbPEBnty3+VilgkJwcwd1MPMMjkphCv7h2M06Fk6JwWvVv+GUG9XnpjRB1CW9qb0UJYfORl0PDg5QZ6Emv6suQqhwvKceH93f5jyjMWbLzABxj0qbDIxMZi5VqbqU5IVFpGWjG/PEuHXWRZxXPAIgMlLwdCa1AtKhPFiFuKnQr9rLdt9roSUueI2ox+A0NZKZXonmPgH8BUvY/jTrGRzIsjDCZ6eLV7uHuDpwuPTE83JzvgQT+65TDzGJvUJsTHun5YSyvS+YTgZ9Dw9O8XsQxPu48rj05Uxdsf4WJLCLHH53ZREwiVf3tqziK/9QE8XHp/eR8USK2ZJCmHWAMuYmt43VGqAfceEGDOLs0HP76YkEObjRkoPHxaNjTYbbg/r7c/CEZGtLCm2LLPELEFeLjw2zcKSEOLFPRPFcSHxCsXioE1Js5V/At05MdZcgMTZoOeRqQqLUNOfBXelqlyj0Quu+KecZboty90SlinJoTbX+hV9w5iWEiruVxf0p1kp5kqXnk7wZ0lcuqQ+MyFlnmU7eQ4kXtnth3l6toXFy0lhEynE25VHpyWak9CJoV7cNUF8vdBnptL/NiXPVe5lAt09MY7E1mWUzgY9j05LJFjibfn07L74tXpy+ns488dZHS8p7aqmpYRyRT/LtX5l/3AmJ4vH2D0T40hoLdrhbNTz6LQ+5ufG/wU9PTvFXLUzwMNZOsbCfd14eGqiOaGeFObNkvG/DK9dTZpE+lVXx/vjH/8YBYx86qmnOjb8EOhXXx3PZIIjX8CBD5V179nbwNS6RGTs7yBmYufe3DuMzQHz+F43gk3eM0kdP12eIAjqo3zx8QyFCY/Lvad0OohQSqafaQ7EZ/bflC8b50On1sGet6kpzSM4aYy07HRCiBe+7s6EeLlw/6R4edU6nY7SHpfxBRP5uimV6NmP4+0hebB0clNKxptMkDgDUu8AozxZ+NOJYj7ceYb80gpGJveWxqUhuB9fuUxnm8dlMOExwkIdqKpUUwK7XsOUsRG/qH7dXiEJlIpPMUGK8fPwgDoWjJcnoRqaWvhsTw6rD+Xj4WKUGm3rdDoGRfrhpNfhTxVPzR8qrvIIuDkbGBzph8lkYkpKKL8Z1Vvs1wHgHc5PvnNYpx/JJu9ZpI6bImVJCPXC182JUB8XHpiUIK3Ap9PpmBxp4GrnLUzQ7eWueZMwuCtxaWkxsfpQvtmbpi3R5OZsYFAvhWVqcig3O8AS5uNGdKA7LkYDwwPquNGBuCRasTw4KYF4K5Y92eW8uz2b/LN1xIV4YtDr0Ol0DI70w9gWl3lD8W2NS0uLiVUH8/liX67tzC1nd+V6waR8oUi9XZ5c9+nBBr+5rNOPZLP3TIaPlcelf5gbCz13Mdcvg/smJeITIp4Jo9PpGNTLwvLH+RaW6EAPQrxd8HFz4q6JsQyNUsqRuzsbuT6qmhn6rdzdr4VxI1I7xbLFZybDx042s5wpq+WtLVkcyjlLdKCH2b+sPmQAXztNY5vXJAwTHyMkJETKMiVKz1VOW7hMv5875k7C4GYZY18fzOfL/bnoscyqcXc2MijSD5MJpqaEcfPIKJxkfh0+EWzwncM6g8KSOnaK+U+nS2t5a2sWh3PPEh3kYfaWqgsZwFdO0xWWCY8REixmQaejOGIyn5vGs6p5JHGzH8XLzaU9i/UMIWcP0nrMZ1XzcE71uZPo1JlSlnBfN6IC3XFzMjAioI7rx8k9FxPDvPFxMxLm68aDkxKIlVRGVO5jvhj1egJ1yvXSVhkxOsiTYC9n/DycuXNiLIMjlTHm4aLEBWBaShg3ORCXHr5u3Bx8kitcDjIjsJjhYydLWfqEeePtZiTc142HJicQI6kmqNfpmBap5yrnzVyuP8DtcydhcBPf+zxcjNwQVckVum3c07+FMcNTzTMa+/f0xdVgIC7Ek4cmJxDamtDp4afMlHJzMjAyoI5rHYiLLUs8MRIjd3R6iiIm85lpPKuaR5Ew5xE83cTXsYeLkRutWEZbsXQo316s957DeuMotvjMInWM3GQ7KcwbL1cjPfzceNABFr1Ox5BIP/R6HcG6Kp6cP9Qhk/3OKsLPnV7+7rg7GxgVWMc1Y+VJqLrGZj7encO3RwrwdnOySajsyCjlvR2nKamqJy7EC71O1xqXKUpcWkaTMPthaVxAWV76ztZsskpriQvxlPoORfi507OVZaSDLEnh3ni5GInwc+fByfH0llV51Osp7DmVL1rGsco0hsTZv8PDVczi6WKkf4QPOnTM6BfGjSMipSw9/ZW4nC0r5r6pfZmQ2P1FcvT61jGm0xGsr+LJuUOkxvSerkYG9FQ+g67sF86NqXIWUGwXPtuXh4u7J9GdqKTZFZ3PqmI9/d2J8HWjsryE+6f1ZVyCJS4ZxdW8tTWLtPxKYoI9cTEqn5XJ4T54uhrp6efOQ1MS5D+iWul8V0j79kgBn+/Lw83Tk96Bv964XLBjnM2Fnctozt6Gf8yQ7vUMVuliV8fTidb+XwjpdLoo4BtgOzAS2AW8CfwRpWzYDa27vgC4AueA35hMpuM6nW488JDJZJqh0+k8gJeAFMAJeMpkMq0UHXvIkCGm3bt3dzfSedWGDRsYP368srH2EdjxX8sfxz+uzMwoOKj8C0xQZg54O1bW+a0tmRZjQ+A3o6J48krJLyMf3WjlVaCDq9+BpJkd719fBf8ZqZh6g5LoWLwJ/MRf+jqt7f+Fbx6xbKfeocz0Emjxit3mtdg6Hfz3xsFMEfxiU1XXyNTnN5nX+3u7Gllz7xjxjICcPfDmNIuXVPhAuHW9cMbZ8s2Z/GmVJS63ju7NEzPEZpuL3tlt9nXR6eDVGweLf32qq1TicrbVnNrVVzFb9RWYZ3ZBb2zO5M9WLLeN6c3vrxCz3Pr2brN/iF4Hry4YwqSkjr+MVtY1Ms0qLr7uTqy+Z4xwOvfe0+Vc++p2s89P/wgfvrxzlDB58fqmDJ5ebfFEWzQ22jwjoWOWXfyQVmRmWbZgCJcLWDhXAf8ZBZU5yrabH9y+GXwieOqrI7y1Ncu8659nJbNgRBR7ssu5bpmFZUBPX764Y6SQ5bWNGSxdY2FZPDaaxyQsv31rF+uOWVheWziEy/qEsOZQPne+v5e2j5Yr+oXxyvWDOHuukanPbyT/rOJd4OfuxJp7xxDm48b/rTzMO9uyLSyzU1iQGgmnd8DbM5Sy9gARQxWfOwHLso3pPLPmmHn79nExPDqtY0NXQCmwcPI75f91erjuQ4if0uHuZ2sbmfqCLcvae8cS6uPKH748zIrtFpanZ6co5sCnt8NbMyyeRRHDlKIMApb//pTOX9daWJaMj+GRqYlkldRw5Uubzd44kQHufHPvWNycDdz85k42HC8GlLi8cdNQ8ZeL2jJljFW1mu26ByhjzDucJ748xLvbLYUEls5J4YbhkezKKuP617abfX4GR/rx6e0jhGPsPxvS+ds3FpY7xsfw8NREMktqmNkBy03Ld/LTCQvL8puHMj6hY5aK2gamPL+Rwkrl/urv4czae8cQ4u3K418c4v0dFpa/zO3LdcN6sTOzjBtet7AMifTj0yUjOz5fwL83nOLv31h89+6aEMtDU8QzZxcu38nGVhaDXscbNw0RspTXNDD1BQtLQCtLsLcrj31+yKbAw1/n9uXaYb3YkVHKjW/sMLMMjfLjk9vFLGz6J6z7k2V77O9g4hPCJgve2MGmkyVmljdvHsrYeEHFq9oy+PcIxW8LwD0QlmxRlo53pKzN8M4sizdOr5Fwy1oamlqY8dImThQqHo5uTga+uHMkiaHevLz+JM9+d8L8FvdMjOWByeK43Pj6DjafsrC89ZuhwupdZTUNTH1+I0VVSlwCPZ1Zc+8Ym+p97ZS5CVbMtrBEjoLfiD1+Xlp3kn9+b8VyWRwPTBLPnL3h9e1sOVVqZnn7N8MYHddxpcfS6nqmvrCJYiuWtfeO7fZZJy+uO8m/rFjuuzyO+ySzgK9/bbvZX8+g1/HOLcMYFRvIZ3tyePCTA+b95g+O4Nmr+lNSXc/U5zdRUt3G4sI3941ptzzaWltPlbBw+U6zL9aI6AA+WJQq7NcLP5zkuR8sLPdfHs+9kpmz1y3bzrYMhcXYyjIytuO4FFfVM+0FW5Zv7xtDgIBlSytLm//SyJgA3r9NzPLc9yd4Yd1J8/YDk+KlMzQ7K4VlIyXVymd4kJcL39wrZtl8soSb3rSwjIoN4L1bxSz/+v4EL1qxPDQ5nrtksxp/hmy+i3Wz/vXdcV5cf8q8/bspCdw5IZZjBZXMeWUr51p9VOOCPVlz7xicDHqu/u82drb6mBr1Ot69dTip0Y79wH8+WZ799jgv/9ie5XzpfLJckGNU5sN/Ryl+waAUGluyVZnscR50Ic4XgE6n22MymYaoX/+lLMeLBf4JJLb+ux4YDTwEPA4cA8aYTKaBwP8Bz9h5j98D600m0zBgAvCP1sTUpanGOti93Pa1Ax8oD3m731CMVfe/q1TYcFDLt2TZbL+347TYNPpsjsos0wQ7XhUf5NhqSwIKoO4s7O/SRDaxtv/bdnvXG0LT6JzyWhszQJMJ3tyS2eH+oJjTWhtOVtY18emeHHG/dr9ha2aetw9Oi43Jl6v6sWJ7ttA0+kxZrY2xsMKSJe5X2teWBBRAXYUyw66btXyzLcs727JpbBazWBvYtpjgra3iuHxzuMAmLhW1jXwuicu727NtjKYP5Jxlt8Q0uj1LFk0CltOlteYEFLSxZAmPQdrXlgQUwLlyOPABdY3NNl+oQUnw2WPZf6ZCahqtHmNvS1iyS2vMCSg1y5tbMrH+bWP1wXwKztax9lC+OWkDUF7byOd7cznX0GzzhRrgzbZzu+t1SwIKlPuaxDR6+eYsW5atWTbGqO1Umm5JQIEym9Q6uW9Haw7bYdmXw7mGZj7cZctiPre7Xrc1zc7ZCTniH0HUY+ytLVm0tJj4ePcZG3Pm7FLlOskorjYnoMDBMXZ0pSUBBcrDz8GPqG1o4iOrionW/VGuW8s53ZNdzv4zYqNl9Rh7a2vHLOuOFZJeXG1OQDnKsvpQvjlpA0rC4It9uVTXN/GxiuUNM0uWDcvu7HIOyFhUY0wZ8x2PsVNF1eYEFCjG3G9LWFapWEprGvhyv8LyyW5VXFrP7TvbbeOyK6ucgzID7O3/ab8tYDlZWGVOQDnKwuHPLAkogNoSOPSJuM3O12zNmU9vhbx9bD5VbE5AAZxrbOaD1nuh+hlG9rl3orDKnIACB+NyMM+cgAKl8MNX+/MELVAKxlizZG+BvP3CJurr5c3N4s+94wVV5gQUKCyy6+XrA3nmBBQoLCv35wpadE1qljckLGn5lTYFHqxZ1O/1+d4cKmob+PpAnjlpA1BSXS+Ny9vbsmyM2bdllHJUYoD9xuYM4bZaR/MqzQkoUIy5396WJWxjl+WAhEX1Obc1vZS0fDGL+lyqt7tDXx3IMyegQElKfS1heUvFsuVUKccLqoRt1NeHbIz9kqW+j7WxfLDjtDkBBUqxm00nizmUc9acgAJljK2w+lHvYslkMrX7HqV+ptGk0sGPLAkogKp8ZcXTJSqBw+kFVabJZDoEoNPpjgDrTCaTSafTHQKiAB/gbZ1OFweYUGY6qTUZmKnT6R5q3XYFegE25dt0Ot0iYBFAr17dO8vjgkqnV/wrrL+gGV3aVw8qOAjl2Q7NNFIbdBr1OmWac0fSG5V+mKy+rMqmmNv7uwPL0Tot9TIXg5PS1w7kZNCj09k+ezsbBaa5gJOdJU7SJVz2+CVLctSGtk4GfTsTQvXf27PI+mWnD7JYdkHqfjgZ9AhQMBp07Vhky0vU5wvsx0rWxt5rovd00uuFM0Hss0iWu9qNiws6nfLrMFY54rZza+89ZedM/Xc5S8fnS/1eep3Cbq8Pzq1j1aDX2Xx5Nu/bhXHpZFTdxww64RhDb+dj0CCeCdAZFmcRi+Te1y4urbG1d3wng77D14XqYIzpdW33fyuW1nuivTEmu8eoryejXrkeOmSxMztUek12EBeDTodBr7P5wtn2XnbvF9J7jHqMia+Xrp0v+9exXqcscaGl/fXSFRb7n5Uilu4aY5LP/Q7Gpd0Yd3DvM0rur11hER2/Q9m99jt3j5F9htkbY7KiH/bes7sLhUB7Fmm/hDG2/ZtBr0Ovt//5Ij9nnY+l+u+y50RnYxc+j7vwbGmvTWeflaTXcRdk7z7WtXMmuZaNerD6ffd8sFwoqVk7erZS/mZo98xj7z0uhnQ6nRKXBsuDqvRe+b+uLnxW/Jr1SxkN1lNUWqy2W1ASZX8GfjSZTCnAlSgJJrV0wDyTyTSg9V8vk8mUpt7JZDItM5lMQ0wm05CgIMH08V+6jM4w6l6rF3Qw5iEIUFXQcPE2m8HKdPfEOJtnz9vHxYhvGF6hrcbcrTI4w+j7xAdJuAJCrNbQe4XDgBsd6l+nNPZ3YP3Vc9R9wi+vId6uXDPEYkbrbNBz+9joDvcHxYy4zQgSlApIVw0WG9oyfAm4WHktRU+AnkOFTe6aGGsTlyXjY4Tr40N9XLl6sC3L4nFiFvrMgGCrZXHePc5LXO6a0DmWMB83rhocYd52NupZPFZsuDglOdRsagmKce98q/ewp9+M6m02kwYYGx9E/56+wjbq62XJhBizWa49hfu6MX+QimWcxDyyz5WK71qbfHrCgOtxMRpsYqrXYZ7ifMvo3mbTYoDxCUH0i5Cx2MbljgmxQpYevm7MU7Esar1eloyPsXkAun54LwI9XZjWN5Q4K4+SHr5uzBscgauTwSamep1i5AtA6hJwtvKbiZusLGEVsUywjctdE2KFlcvwi7QtKGB0hVH3CI8xvW8osSqWuYMUlkUqlrtsWKz8EOKmQFh/4XHUhs93TVRYrhvWy2bJTEoPbyYmBtPT393GZNzF6MC1nzRLWbrdJp9e0P/aVhbbMdZmqPvb0b3xcLZ8kbgsMZjkcLGH3N0TY1Xbceh0Oq4b1tOGpW8PHyYkBNMrwJ3ZVmbWLlZjrCNd0TeMmCDLBOgIPzfmDuqBm7OB28aoWFr789sxtiyX9wkmKVzsV6Re5qFmUysywMPGmNshln7hRFux9PR3Y87AHrg7G7ltjKV6ljou7lYsk5JC5N6GYx9SbT8s3D0q0IOZVmbWrk56bpOwkDJXKZDRJt9IW3NvexpxJzhZTWZPnAEhSYyKCWRIq+8VKEthF6RGAbQz4pYZc/cO9LAx5naEZUa/MKKt/FZ6+bszq7+kwMLIu9qzBIuXO999WefGWHSQp40BtKuTnlutxok9zewfTm8rlsgAd2bKikV0QffYufZFig325Aor43o3J8v1e6fqs+nmkVF4uzoxc0C4TdW1qAB3m3FqT7eNibapiqy+r9tlUcVFZsofG+zF9L6WZafWLB1pZv9wm+pmUQHu0mIRapYr+oZ1nkUyxrqimQN62LAo15y4KMFtY6LNRYJAWdIv83hq9/nSzcsKL6Ta3cdax9jCEVFmI3lQlo6PjAkgMdSbyVa2Du7OBm6VjLELpc7ek//n1f9aWxuUwHhp4Ytfs34pnlCrWhNM6HS6t1q3P237G3ASeNdkMn2m0+meAm42mUxRKk+oZwBv4O7WWVQDTSbTPtGxf/WeUKCYkecfgN5jICQZSk7Be/OgPAuc3JVqSv2v7ejt2mlXVilf7svDv6GQB6+RG2FiMinL6XL3wtBblD7I1HiOg5vXcOLUSebesBi9u5+8TVdUeAQyN7G30MSgWUuku5tMJjaeLCGrpMb8JUimusZmvj1SwIHDR7l33nizcaxIjRX57NryA4F+vsQPnyYuud6qtPxKtmeU0lSUwW1zLnOI5dM9ORzMOcuC1EjiQ8XGsUrHzkHa1xw7cpDE2Q+Bmzhx0VV1heWnE8Vkl9aav2jLdK6hmdc2ZnAsPYNnFkwwm0aLVFReyYGt3+DmH07qsJEOGWHuyCjlqwN5BDYUcr8D14vJZOKT3Wc4nFvJghGRxEnMiQFoqOXAljWcSk9n7g2L0VnFZWdmGUfyzjIyJpAEqxjnltfy2uZMIv3dWeCgqWcbS1BDIfd1kmXhiEgbo+Wskhp+OlFMdJAHo2MDzbNEzjU08+rGdE6lZ7J04QR8rIxjO2KhPBu2vwJ+0TD0Vocp+DUyAAAgAElEQVSul23pJaw6mO8wCy0tZO37gTN5BQwdPgbXYPGXN4DahiaWbcywy7Ijo5Sj+ZWMig0k3uq8NJTnsmvrOkICAogdOqVzLI2F3He1heVsbSNrD+fj5mxgSnKo2cy7pcXEx3vOcDSvkoWpkVIDbKVjNbDlJbIzTxJ53b/AzZJQ6oglp7yW1zdn0jvAnRtTo4RJyzZtPVXC6kPtWSpqG/jmcAHuLkYmJ4XYsHy0+wxp+ZXcNCJSaoANSlxe/Smd9MwsnlkwAW87cRkdG2hz7Z0urWX51kx6B3hwY2qkQyxbTpWw5lA+wY2F3Hu1fIx1lWXHpu/Iykhn/o2L8bJi2Z6hLLdRsxScreP7owWE+rgxMTHYIZay9D0cPHyIlsYmJs6/1UGW06TlV3HzqCiiHTGbra9m76Y1ZGVlMufGJehcJckxgLIs5doPjIcht4BeGRcNTS18f7SQs+camZIcYuMv0xaX0KZC7r7Ksbh8uOs0xwocZ6mpb2Lnpm/JzMzk6gWL8ZQYLQPUl55h17b1hAUHETNkiplFpM0nS1h7OJ/Q5kLunu8Yy47dO6guyiI5dQrhgfJnqzaWrKxMrrrRMZauqCssPx4vIq/iHJf1CbGp8pheXM2mE8XEh3oxMsbirVRd18iyjRlkZWexdMFEqQE2QF7FOdalFRLu68b4BMeul4M5FezJLoeSDH4zS/4MI2LpSFV1jby6MYPT2Vk8s2CiQ3HJLKnm7S1ZxIV4cd2wXuIfX1p1+EQ6u3/4jGGT5pMUd34SF9X1TXxzuIDjx9K4b/4EPFzkn3uZJdW8tSWLhFAvrh3qGMuBMxV8vG4X114+jL4R3V9Yx1rn20tn/5kKPlm3i+smDSOlh4WltLqeb48U4uPmxKSkEPNEgeYWE+uPFVFw9hyTkkKlBXysdbFYzod+9Z5QoHj0pn1N2vET9Jn78Hk1Jr/YnlC/lOV4Mv0dZTneE8DqDvb5M/A8cFCn0+mBTGDGBerfxVPkCOVfmwJjYda/FX+o8EG2JWsl2nKqhNve2U1tQzMGHUTG5khnkLD5X7Duz4AJjn4BC1cKK+Q1t5i47b3DrD/mCvTl7TeO8MGiVJuZG92mkGQISaZywwaHdtfpdIyLD2KcyGBVJVcnA7MG9MCn4qRDCaiCs3VcvewYp8sUk7nrCtL4y1zHKvj0CfNmwwbH1nm/8uMp/vn9CUwmWHMon/duG06ipBIbTm7Q72oKyoJJPE8JKOg8i06nExr4qtXU3MLt7+4x+8nkLt/J+7elih98zuYQ/PZ0JlW09qn0t9IS8htPFLN4xR7ONSrXS+/4XGYPFP+K/NL6Uzz3Q2tcDufz3q2ptgkXOyy3vHuYjSfcgBTeWX6ED24bjruzwjKstz/DetsaFuZVnOOaZdvJKVd8sTKKa/izpOz6TyeKWbxiN3WNLQpLQi6zJL+Iv7DuJC+sO2lmef+2VHOSIirQo111lqbmFhat2G32k8lZvov3JSxUnFHMvM+2+iyVnoIrnhX2a8PxIm5/d0+nWJ5bd4oX1zdiMgUQeCCdD24LFCYIG5tbWLxij5kl981dvHerhWV4dADDVaagOeW1XPPqcXIrlNdvLjrOUzPFSfsfjxexxIolOiHP/Ou+j7sT1w5rv6T8+XUneWl9a1wOFfDhouHEihIezY3w4fWQsYFIgPeyYeFXSmVCIct2s/daZkmtnOVYEUveU1iMOohJyDP/uu/r7myX5bkfTvBSq0Hr2sMFfHBbqvDX/YamFm57Z7fZGyfvzV28d2uquXKgPZYzZbVc95qFJau0RlqQY/2xQpa8u5f6JoUlNjHfZhaKPXWWhaYG3D+Yx4TMjcr2+18rn69OyhfY1OgAu8azoT6uLBgRJeyLtdalFXLHe0XUNwVh1MGLCflM7ytm+ef3x3nlx3RA8eD7YFEqMYKZCg1NLdz09mG2ZXgAKbz/5mHevXW4OdloV2WZyrXf5olXngVTlgLKzEt75/uHo4Xc+b5tXKZJWJ797jj/3mBh+XBRqnjWRVM9Hh/MY0LWJiYAvL8KFnwJTh1/6TtdWss1y06QfzYQMHFb8XFpQY7vW1kaWlniEvOZmiJm0a//IyM2P6dsHA+Dm1dDgGC2bVM9Hu/PZUL2ZmX7/dWw4AshS1f03ZEC7vpgn8Kih7jEAqamCEzpUZacXtbHftGOmCDPduOtvqmZ3769mx2ZijdOwdu7eeeWYeIxhjJDuTPXC0C/CF/6Rfg6/AwjYrGnukaFZWcrS+Hbu3nnt8PMldDsKaukhmuXbTf7yJ0pPycvyJH2NSmf/paU5nr48Fm46k1IvMLhfjoqTxcj8wdHsKHqlIMJqPYsj0yVsKBUzSyPcjrvCagLoQE9famIcmqXtAnwdOH64e0/Kw16nbBgz8VURyyaOpCrNwy8gcKzG+hzHhNQvwRd9OV4JpMpq20WVOv2zSaT6VPrv5lMpm0mkyneZDINNJlMT5hMpqjWv28wmUwzWv//nMlkWmwymfqaTKbkttf/53ToU3hrOuxbAavvhy9ud7jpX9ceo7Z17W6zCZ5ZkyY29K0tgw1/xewZUlvaut2xNhwvYr2VofGh3LPtTFYvZb22KYPTZbXm7Q92nuZYgdg8srMqq2kwJwdAMbR94YeT4kaXkNYdK7IxND6Qc5bP9koM47e+DBVWD5S734Cidqt5bfTMmjSzSWSzCZ5enSY0Jy6prudFq7iUVDfYVHOxpx/SimwMjQ+cqeCzvWLj2GUbM8wJKFCM7E8ViU09/7ImjbrGFodZiqvqeXn9qU6xfH+00MbQeP+ZCj6XsLD1RUsCCmDXa1B8ouP9gb+sOWbDslTCUlRVx8s/WrPU21SmcYRl3+kKvtgnj4u1Yf5bW7NIL64WtIBnVqepWI4K9y+qquMVNcs6MQvHVkHGBst2zi449LGwyX9/Sm/HkiFhWWo1xppa4yJSYWWdOTkAbWNOPMa+O1pgY86893SF1GhZzfLmliwyS2qEbZauTqO+ycLytCQuhZVKXNpUXFVvs21XaV9BWwIK4MwOuZl3F7R0jYpllZgl/+w5/mMVl6Kqel6RXC9rD+fbmDPvzi6Xm3lved62KMO2V5TElEDtWCRjLP/sOf77ky3Ly7K4HP0KsjZZtk9vU4zXBfr3hlM2hQxe35zJ6dJaQQvlWm+wYvnzKjELZ3NgywuW7ap8pfKhSEe+hLYEFCjm70c+F7fpgpauSbOwtMDSNeIx1hWtPphvTkCBMrN29cH8bj/OhdDqg/nmBBTAjswy1hwSs7zy4ymbQgbLNtre1+zq28cthXKa6+Hb33e5z92pl9fbsrz6Uzp5MhZNmjT96nTRk1Caulnqqk6HP4PqIvv7qmRdJQWUJRKiKmycK7c1RgeoLrS/bwfHAGwqzVzqssdv77Wfo4raBhtjZNDOsfQc2xu3krFsXb0GoNzOebdWRW2DjTGyI/0qrrbDUllnZ0/xe8rir25TXtO+r9ayxyI9hh0W6bjsQlzUxymraRAm08trGtv9vagL51gay660UbGUVotZ7LHKx76dz4eqzt/HO8tfWlNPSydZOjuOu9pGfW3L2pRU1wsTnaXVDahRi6rEY8xuXCRjvytqz9LQaRZ717boGI60ac9vkj7LqI8jO4Zdli7dkwravyZ4T5Op8+dMer5qim2LxABUiftll0XWpgtSsxRVSli64RjgwDn7hcruZ6XknKnbtJigpLP3fge/K5xv2WMprW7oYG9NmjT9WqUloS41qZ31dXr71Z/saO4g2yUr01LCzMsY7CogBiKG2b4m8Z+6PCkEL1dLf5wMOql55KWkOapz3MPXjeG92y+p+DmKDvJkgMpUWx3bS1mTk0JsTMadDXpm9JOMMfW49e0FvUYKm8wdZLtU9cp+YUIj/9hgL/qpponL4mKPRWZQOke1JLCnvxtDo/w72Nt+P67sHy6sLhMX4kVf1dTqeVKWUJtlt85GPVdKljDRTxUXvyjolSpsouaf2T9c6ImVEOpFSg/bparzBomXIU9Jbs8yQ8Ki7ldkgLuNwbI9zR1o24+ZA8KFniUJIV4kq0y1pdd+4gxbw3SDCyTPEfdLdX6iAtwZLGNR9WPWgB5Cn4/EUC+SwtQs8rhYm4x3tGxL9J69Az0YKClKoG4ze0APYXW8PmFe7QzC1bFt3+hKWzNroyskiePSFc1VjcvZA8OFLMnh3jYFOUA+xqamhNoYprsY9dIlf/S7xnY7IA4i2llKCPuhZlPLHovs2lfiYuVLaHSTGseqx0tMkEe7z2hZG9n9ldD+tkUswLbggj0lzWzPktz9Jrjqe5/sOu6KpvcNszHmdnXSM12yfPGXqukpYTbG3G5OBun1oj7HccGe7T6j20n93NMJ/9jzKfVYTwhp/xmtSZOmX78MTz311MXuw0XTsmXLnlq0aNHF7kanlJWVRVRUVMc7eIYo06nbfhEbtkh50HBAI6IDCPB0xtmop79vA3+7boS8zGniDNAZlAp8Yx60rZZnR+7ORib1CaGhuRlPUzXP3ZAqrdz1cyU9ZxfwGL0DPegf4Utzi4mRMYH8dV4//Bwwze7scaYkh6LX6wjydOGuCbFcPURSta8Lx/i5Ol/H8XAxclmfEBqamvE21fDcjanyh7GAWAgfRENTM7ro8ehmvggSw/yRMYH4uTvhZNAx0K+Bv1w3QmoAPiVZ8cEI8HDmnsvimC+ppuhph0W2rj46SHn4bGxuYXRsAH+b18/GNNueRsVaWAb5NfDMtalSljZPjzaWeTIWVyMT+wRT39iCLwpLsiwugXEQNoCG5hZLXCR+ZaNiAvB1d8bT0ER/v2aWOsAyJTkUHSZ6eOq58/JE6ZckT1cjExKDaWhSWJ5fMIIkSXW4mGBPUsKVuIyJVa59b4mP3KhYhcXJoGOQf2tc9B2z6HQ6piaHAiYCPV247/J45si+8Ll6K5X6muopaPHH89rXIEzsUxcb7ElyuA9NnWAZHRuIj7szzgYdg/0blLhIWKakhILJRKCXC/dPipd6rnm5OjEhIZj6pmZ8qeWFBSOk1eFigz1JCvOmqcXUKRZvNyecjXoG+zfwtCNx6SQLrj4QP5mmpkbyWoLwvnYZhDpQ+KOTGhNnYRni38DT14wQJjrNLECItzLGZBXVvN2cGJ8QRH1jM37U8uLCEXKPwuBECOlLQzPoYyaim/mCMlYFGh0XiI+bE27OBgb7NfDna1KlLFNar5cgLxcemJTAzAGSHyzcfCFuMk1NTeSZgvC+bhmEiP2d4kO8SAz1xmSCcfFB/GVuX6lp9pi4QLxdnXAx6hkS0MCfrxazoNNB0kxMJmjw7IFxwmPQV+IJ6uYLcZOguZF8AvG69jXbKrndpLHxQZ1j6YJ8WsdYY3ML/vpaXlwwwrHCHz9D5+sZxsfdifEJwTQ0txCgV+5jMpbEUG9zVeDxCUEsndtXbmYecxm4eFNceQ6P0bfD+MccMszvqhw9X4mh3sSHeGHCxITEYJbO6euQl1RnjvFz9Uv6XvFrOI7G8ss8zoVi+eMf/5j/1FNPLVO//msxJtfkqOIuhzt3wql1UJ4BObvh/Wth/KMQPkDYVK/XsXBEFAtHRLFhwwbxLKg2ufvD5U92qos/Hi9izcEC6hpbWH2wgEG9/IS/vF5qmpAYzIREx422uyI/D2eHjBwvVa0/VsTaQwXUNTaz9lA+A3v6CsdYXWMzj+4N5uuDV+Hn7swTYTpmDxQfI7f8HCv357H/TAURnjrSi6ulZeoP5FSw6mA+eWfP0dRiYnx8MD7u4gfFdccKWXOogIbGZr45XOAQy1cH8vjuaCF+7s4M7OUnNeZWs2QU10jL1B84Y2FpbjExPiFYas6/Lq2ItYctLAMcYHl4byirD12Fv4czT4TDLPFtDOPZLG5Ju5VbcvdQ7RGJa+n7ECo2Zg/I28Bjxx6AylwwTIX4/0qTXevSCs0saw/nS2c21DU2s7I1Lv4ezgyM9JPOAs0pP8fKA3kcOFNBTy89GcU10qTK/jMVrDqQT35lHc0tMC5B+QIo1PG1cHQlQU0NihdRxGDh7ucamlm5P9fMMijSTzpDL6f8HF/tz+VAzll6eunJLKmRJiL2n1bGWBtL25dZkdalFbL2UAGNzc18c6SA/pK4nGuwXC8BHs4MjvKTzpw8U36Orw7kcbCVJaukVlhgoI3l64P5FFTW0dKajJAlIl484sqre2fQ2NzMrYfcefg8TGg9XVZry1JaY1MF0Z4CPV14fHof4T5q/XC0iG8OK3H59kiB9Men2oYmfrc3nLWH5xPk5cKTESamS2p4OBn03DommlvHRLNhwwbhzNQ27TtdzqqD+RRW1ilJooQgaaGU54+48to+JS6LDrnzkAOTuaemhErNuK3VFpdDuWfp5aUnu7RGmoj4NruFJ/dMoKiqjkmNIfwztkle9CWsP8x+heMbNhAWLvnQ66KyS2tZeSCXw7mVRHrrOV1WIy6W0EWl9PDhH1f1Z8OG8l+9CXJKDx+e7STLtL5hUiN+GxmdYdQ9HGnsx/iR47vW0fOk6X3D5LMlNWnS9KuWthzvUlRAjJIc2v4fxWT2xFp4ZxbUi82JL4QOnKngmTXHqKpvorEFlm/J5OtfqXmkpl+m9mSX87dvLGPstU2ZrD0s9rl4Y3MmX+7Po7nFREl1Pb/79IDUF+j3Xx5i/5kKAHKqTTzw0QHh/ucamrn3g33kVpzDZFISZf/6/riEpYy/f3Oc6vomGloUc+tvJCyvb8pgpRXLQ58ckPrPPP6FiuXj/cL9axuauOdDC8u6Y0U8973YMHxXVhn/+NbC8urGDL49ImZ5bWMGXx1QWIqrFBapZ8vX90HuHgA8a7Lhi8Xi/Rtq4LNbW02QTcr9csNfhE12Zpbx7HcnLCw/yVmWbczga2uWjw9IvYce+/wQB1rjcqaqhQc+Fo+xmvom7vtwP3lnlS/UP6QV8vz3kqIEWVvgx6ehoRpDS4NiCH2soyK0il7dmM6qg/lmlgcdYHn084McyDlrZnlQwlJd38R9H9myyAosbM8o5Z/fn6CmoZmGZvjPhnS+Pyr2UfrvTxaWoqp6Hvj4AGU1Yv+RRz87yEFrlk/E10t1fRP3friP/FaW74/KWbaml/AvK5Z/b0hnXVr3e0I9+vkhWxZJXLqiradKeO4HC8srP6az/pgkLhvSWX0onxYTFFbWc/9H+ymXxKWzqqxr5L6P9pN/VkkMfne0kJckBRa2nCrh+R9Omlle/vEUPx7rfi+dRz49yKFcJS6nq1p46NODwv0r6xq578P95iTnt0cKeUli5H+h9MhnBzmcqxRgya5s4aFPxCyaNGnSpOnSl5aEulR1fI3tdl0FZG+9OH2x0sGcinavHbLzmiZNXZW98XRAMsbU47Kx2cTRfHHVwrYvbm06XlhFXWu1PHvKKq2hsq5J1a+zHezd+vcz7f9+MFfcRt2vxmYTafniBLSa/1hBFfVNHbNkltRQpWKxd22L+tXRazZ/z7XHIqkmmbfPdrvwMDQ3drx/6SmoV72n+j3U/bLDKue3/XtDcwvHJHE5pOJPy6+kqbnjYhGZJTVU1XcuLuTttfOamP+QKm4NzS0cL5CwqNocyasUmqxnFFdTrWJRv4daXfl8UZ/jhqYWjnXy2j+cWyk0WU8vqqamwfZ6kl3H9lhl94uuSH3ODuWeFRqTd+kYdljt3dts/q5irW9q4Xhh9/6Qll5Uba4IbDmueLzY+7vsPtYVqc+ZbByfLKw2V2u1tOn+fnVF9saYJk2aNGn635aWhLpUFRCrekEH/jEXpSvWGh4dgHr1TWp09xpza/rf1rDe7cfYCMkYU49BNyeDdHlVarSt2ffAXr64OnW8hDUmyJNATxfbfsWI+zU82r/T14tdFsnSF3WbQb18cTF2zBIb7Emgp63PlKxfw3v/fBZ3Z4N0eRVRo223ew4Hg2DZU1Ci4mkneg9JvwBGRAd2qo2Hs4G+EeJlFuoxNiTST+hvFRvsSYCHbVxkY4zIUe1f6yS/Yyy2bYZF+Qt9YeJDvPD3UI8xscG+vbjIx5jte3q6GDsdl2G9/YUm6wmhXviplt1Krxe7Y6z7PyvV/VCu0+5dHj+8d/u4ycalul9eLsZuX2KVGOqNryounb2OldfE47IrUh9HNl6SwrzbLYf+pTxbtWfp/vOlSZMmTZp+XdKMyS81Y/I2hfaFMzvh7BmlYt74RyHpyu4/TicV4OlCZIA7Jwqr0bc0cd+kRK7qhGl2V3QpmbtpLHIFebkQ4efOicIq9KYmHpzSR2o03S/Cl7rGZrJLa4gM8OBv8/qRIPGrGRETQHZpDSXVDcT6wMsLUoX+Tga9jqFRfhxvnWU0Z2AEj0xNFCYVgr1c6eHrxsmfzSL230iNDuB0mcIS56Pj5YWpQn8no17PkCj/zrF4uxLu68aJwiqMpiYemprUrqJPO5YePpxrbOZ0WS2RAR78fX4/qV8NvcdCWSZUF1HmlYjbtcvF/k56o1Jxr+goNDcoFfkuexIMHXupBHu7Eu7jxokiheV3U5OYJWOJ8KG2QWGJCvDg7/P7S1lSYwLILq1V4uKr46UFw8VxMbTGpbCKxuYW5g7qwUNTEsTG7N5h4B0BRWnUmZwwTnoS+s4X9qt/hA81Dc2caWX5x1X9iZN4vIyICSCrtJbS6gbiW1lEBuAKi3K9NDabHGIJ8XYlzMeVE4XVGE1NPDItiSslfmj9I3xtWeb3k/rVjIwJILOkltKaTrBEWljmDW5lEZiZh/q4EuptYXl0ejIzzkMlWWuWBF8dLy1MlXuIdVJhPm6EeLtyorAKJ1OzwiLx3RrQ05fq+ibOlNUSHeTB36/qR0yQp7CNtRz5fHEy6BlsFZf5gyN4YHK8MC5hPm4Ee7lyokhheeyKZK6QVV/tgkZEB5BZUkNZTQMJfjpevHG40EPMzFKosFw1JIIHJiU4bAB+Pj/3R8YE2rC8tEDM8nP1a3+GuRSPcaGOo7H8Mo+jsfwyj6MZk2s6P3LzhSuehZ2vK18yhkt8US6g5gyMYM7ACDZs2MD4cRd/dpamS08Rfm70DvSgkDoiAzyk+xv0OnoHehAd6EmQlwsh3q7SNp4uRnoHelJYWU+Arlpqyg0w0KuSL8PeAddMCJkBBnm1q57+7vQO9KCIOqIcZHk8ZAePh3+oVMv0fRQIErbxcrVlkVUHAwjydCE60AODXkd0oAcuDpgA9/RTWIqpIyrAXbq/0aCnd6AnvQM9CPZydSgu55wDeN7nCXZ4lRGoq+afrj2Qzp/wDFZmj+qNyn+NLrIWRPi7mVkiHWUJ8lBYvF0J8ZYfw8vFid5BHhRV1ROkdywug70qWBn2NrhmQegsMIpN2QHw7QUBsdRSjKt/b8dYAi0swV6OsUQHelDcCZZATxd6B3qYj9eZMVZS6ti1bzTo+cOMJP4ww/GqYMGuzbwe9iU0bCOHUCLcRkjbBHlZs3jiLKs8i+XaLymto5e/fIx1RZ6uRqKDPCipbo2L6/l5LOzp5050oCelpQ30cuB6cTLoiW4dY2G+SuLnfCiodYw5tY4xR+Jy/fBeXD+8l/IMMyLqvPQr1MeVN24eCsCGDRsI93WTtmkbY86tLE6GX0bBFzVLmI+cRZMmTZo0XdrSklCXqnJ2w5vTlF/2AY6uhMUbz2v5VU2afgk6U1bLwuU7aWhSvHMWr9jN53eMEi6vW7k/l8c+P2Te3pFZyqaHJworRP7x6yN8sPOMefuBj/ebH7TtqqUF3p0Hpa1msWd2KGW1R97dYZPTpbUsfGMnDa0+QItW7OaLO0aJl6Qd/Bi+vteynb0F7jsETh0/+D/11RE+3GVhefDj/bx+U8csLS0mblq+k4ySGkAxg9fp4NYx0R22yS6t4ablFpbb3tnNl3eOElbJ+mJfDo9/YR2XMjY/MkG47PHJrw7z8e4c8/ZDnxzgtYVDOtyflhZYMRfK0pXtMztAp4cRdwhZbl6+y4Zl5Z2jhcu4PtuTw++/OGze3plZxqaHxSz/t/Iwn+yxsPzukwMsk7G8OxfKMmxZUpd03KY0XRmXLY34A3xwLSzaoFTN6kCf7snhiS8tLLsyy9goYfnDysN8qmJ5dUHHLM0tJhYu30l2aS2gjDGjXsfNozpOkmUUV3PTmztpbFY8jW59Zzcr7xzV/ZWyVj8IBz4AIAJgpR6uWdHh7s0tJha8sZPTZbYsN42M6rBNRnE1N6tYvrprlLQCZ2f1xBeH+Xxfrnn7kc8O8u8bxNURO6v04mp+85YVy9u7+fqu0cIKnB/tOs0fVh5RNrLLzWPMkYp3jqqpuYUb39hBTvk5QImLk0HHgvOUWDqfampu4cbXd5BbobDszi7HyaDnxtTIi9wzTZo0adKkqb00T6hLVbvftCSgQDHnzdp88fqjSdMF0o/Hi8wJKECpeiSpXKaubFZS3cCurDJhG3WVuvXHi4Rm3hQfsySg2pT2tfAYPx4vMic6oJXlqJiFtK9st2uK4fR2YZNvVPzrjtmeQ7VOFFWZE1Dm95BU7Vt/zA7LEXGFLPV7llTXsye7XNjmW9V7/pBWSKPAzJuiI5YEVJvU51CldWntWb6XxEU9xoqr6tkrYVHH5Ye0QqExOYWHLQmoNknGGCe+hRYr43ZTi7Q6njouRVX17D0tiYuqzfdHC4XG5Gn5leYEVJtkVS7XHysyJzpASf7IquN1SWmrbLePrVISgB3tnl9pTkC1ae1hcVXYdWntWX442v1V2NTj8tsjhd1uTL4urbA9i6TSn/o6LqisM1fw7C4dyas0J6DapL7mfi06nFdpTkC1SXZP1qRJkyZNmi6WtCTUpSonO1PXBTMhNGm6VGRv2YpsGVsvf9u/63T238emjeo9w33cxEs5vMPAqLou/TueOaQco30fpEuM/NQzRXTgFyVsEqli7eHrJpxxEOrt2m5plKxf9pasyZaxqd/Tkbio3zPCzw0nYVx6gEG1nEwSF9xuxhUAACAASURBVPssnePX65TlVp1p08PPTezv5BOheABaS7a8zt7fO8mvd+h6sf17T393oV9NuG/760l2HduLQVTgeVjG5h9lu+0XBUIfIddOs9i79s8Hi/o+1svfvduNydX3V5Bf++rxZNDr6Onfvc8w4b5u7ZasObKE85eoHr5uGPVqlvOzhFOTJk2aNGn6udKSUJeqht8Obn6W7ZjLoOewi9cfTZoukMbFB3HV4AhzJbbL+wQza6DYOHbx2Gj6ty6lMup13H95PFGB4i8jT12ZRFCrF467EZ6ekyL+8ubmB1OesSSiAuJg/GPCY4yPD2K+DUsIsyVGy4y6D8IHKv+vd4IJv5cmIp6cmWyu3OduhKdni32EfN2deWJGkjkRFRPkwf2T4iQswcwbZGGZlBTCLAnL4rHR9G1dSuVk0PHgpHhp4uYpKxYPJ3h6dl/h/rj7w5SllrgEJiiFHASakBDM3EE9zCyTk0K4UmIaffu4GFJ6eFtYJidIWf44M8VchdDDCZY6xPKMLcs4MQtxUxQz9jYlzoCUecImS8a3Z4nwE7P8aVayDYtsjPl7OPP7K/qYk6FxwZ7ce7l4jF2WGMxcK4P4qcmhUgPsLmn6s+CuVFFrNHrBFf8S7h7g6cLj0xPNLPEhntxzmZhlUp8QG+P+aSmhTO8b9jM73l5/mpVsrqjoSFy6IuVat8Rhel85yx0TYkgKU8aYs0HP76YkdLuXUJCXC49Ns4yxhBAv7pkojssvVUFeLjw23Zbl7l8piyZNmjRpuvSleUJdqgqMo/72XWzfso4AH29SRky52D3SpOmCSKfT8ci0RIK9XTiVkc3/zUrBxSj2QvPzcGblXaM5VVSFr7uzOYkh0sBefvxlTl++PVKAf2MR4+PF5t8A9QNvZmXjKLILSpg0JJkBfn7C/XU6HY9OSyTE24VTmdk8OStZ7oniEcDxmV+zavshgv18mJuagOy3/UG9/Nh2VQtn0zaTU+/JgPjpUparh0TQ3GziWEEl1w7tKU1C6PU6HpuusGRkZfN/M+UsAZ4uPHtVf97ZlkWEn5vQQ8ea5S9z+/Jda1zGxolLrgPUD7qFLxvHcLqwlClDk+jnK46LXq/jsWl9CPV2JSPLsbi0sazYlk1Pf3cWjpB7tQyO9OMvc/vx3ZECApuKGOMwy2hOF5Y5xIJeD5P/DN7hnM7KoNe0Z8AgNg0P7BKLP8/M6cv3RwtbWeTXyzVDe9Lc0sLJomquHdpTas6sjLE+hPi4kpmVzZMzk8Sz4LqqXqnwwFEoOcm2I7mMjZkgbXLtsF60mEycLKrmumG9HGJ57poBPDQlga1bt3HV9O71aWrT0Ch/npmbwg9HiwhsKmJUrHyMdVYGvY4Xrh3Iw1MTHWYJ9nJlzb1jOFlYRYCnC/4eztI2XdEto3szd1APiqrqiQv27PZZYBdSvx3dm7kDe1Bc/etn0aRJkyZNl7a0JNQlqvyz55j/n0PkVngBJublHuafV3dsNKtJ06WiyrpGZr28xeyPsePFTay+Zww9HKguJCvNbq3XN2Xw9Oo087bJ9xiPT+8jbHPne3v5IU3xdfnPrq0sWzCEy5NCOtz/7LlGZr60mbyzdYDCsuaeMcIvsHuyy7lu2fZWz6JyPjtcwRd3jBR/Idn6Ek7fPUEgEAjwfZ2SmBDojnf3su6YwvLx7jO8tnAIl/URs1z50mbyrVnuHSOc3bAnu4zrlu0w+y99d7SQz5eIWZZtTOeZNcfM2zq/4zw6LVHIcvuKPfx4vBhQ4vL6TUOYmChgqW1k5stWLC9sYu29Ywn16biC1+6sMq57bbvZG+e7IwV8JmH570/p/HWthQW/4zwyVcyyeMUeNlixvHHTUCYkBnfcoLYMXh0HVXn0Anh1E9y+Gbw7nkG0K6uM661Yvj9ayKe3jxCy/GdDOn/7xjoux3jYAZafTigsH+06w/KbhzI+oWOWitoGZry0icLKegB2vriZtfeOcaiqYqdldIHQFFqOlTi0+6IVe9jYyvLx7hzeuGmIkKVNPXzdCHI/f5PWX/nxFP/49rh52xhwnAcnJ5yXY3WFJS7E8XtyV+Xr7oyv+/lJcl1o+Xk443eeEnaaNGnSpElTd0lbjneJ6o1NmTYmlZ/tzeFw7tmL2CNNmi6MvjlcYDP2K2ob+dyqKld3afnmTJvtd7ZlCU2jT5fWmhNQoJhZv7U1S3iMbw8XmBNQ0MqyV8zy7vZsG9Ps/WcqpKbRbP+P7fbO16C5qcPds0trzAkocIxl7aF8c9IGoLy2kc/35gpawIpttiz7Tlew97TYnHj5Ztt+vL01S2iAnVlSY05AgcLy5pasDvcHWHPYDss+cVxWbM+2MWfee7qCfRKjZfUYe2tLFi0ClozianMCChyLC0dXQlWeZbu2FA5+JGzyzjZblj3Z5VLT6OVbVCxbxSzpxdXmBBQ4xrL6UL45AQVQVtPAF/vEY+xC6FRRtTkBBYox99uyuFwgvamKy/LNmd1uTK5JkyZNmjRp0mQtLQl1iaqspqHda6V2XtOk6VKTPXNwp24s693Rezrp9cKZIEaDDvWf1aa47Y/R/u+yZV/23lO6JEm9/MrgRLvOWsmeObbQlL2DPpyXNqpzZjToEJ1ltZkv0M50vVv61Q1tpOPF3tiXxt7OrAm1UXu79+z8uFSzGvXtrwebY9gx+j4f5/hCqCvn60Kp3Rgziu9jmjRp0qRJkyZNP1e/jKcgTd2uuVYGwKBUiEqN9r94HdKk6QJpSnIoCVZLOMJ9XJk/OKLbj3P3xDiba2zJhBhpta/5gyz9cDbqWTwuRniMqclhxId4mrd7+Loxb5CY5ZbRvfF0say0Hp8QRL8IX2Ebxj4M1qmaMQ+AvmMfLXU/nI16Fo0VV1Sb1jeUuGAViyQuapYJCUH0bTWQ70h3T7CNy10TYtEL4tLT3525gywG0C5GPYvGiuMyvW8osSqWubK4jOqNh7PlnE5MDCalh5jlnstibbbvmugAy0BblsXjxHEhaZZiYN4mn17Q/9qO90fxnrFmuSwxmORwSVwmxqq244TJjl4B7sy2MrN2cWCMXdE3jJggiwNahJ+bTWwvliIDPGyMuR1huVC6y05cNGnSpEmTJk2azqc0T6hLVKPjAllxy3A+35uDv4czvx3TW2rOrEnTpSA3ZwNf3jmK1zZmcCw9g2cWjDkvfh/zB0fQ08+Nrw7kEdhQyB3jY6Vt/j6/H0Oj/DicW8mCEZFSvxM3ZwMr7xzNjs3fk5l+knk3LsbbTcySGOrNt/eN4bXNmUT6u7MgVW4azcAbqPBLZv+B/TQ3NHDZ6EXSJs9e1Y/hvRWWhSMiiZWwuDsb+equ0by6MZ1T6ZksXTgaHwlLnzBvvrlvDK93guXqoT3p6e/GqoP5BDUUShN9AM/O78/V0Y1U5Z+iz/DJRASLE/YKyyiWbcxwmCUp3Jtv7x/L65sz6R3gzg3D5SzXDO1FL393haWxUJocA3j2qv4Mi/bnaF4lC1PlccHFExb9yP7Na8jISGfODUvQuYkTSsnhPqx/aDzfHS0kxMtF6AXWpmuHKSyrDykstzmQhPnX1QO4NqaRqoJ0+qROJiJIHBcPFyNf3z2aHZu+Iysjnfk3LsZLEpcLpeeuHsDsAT04U17LxMRgqZH/hdINwyOJCvBgzaF8QpsK+e1ocSVNTZo0adKkSZOmnystCXUJa3RcIKMdqKakSdOlpKbmFm5/12JonLt8J+/floqHS/fe7jaeKGbxij2ca2zGoIPe8bnMHiiedfHS+lM898MJTCbFV+i9W1NJCBUkCZqbcPvoasanr2M8wLtfwE1fg3PH9e7yKs5xzbLt5JQrvlgZxTX8WVJ2/acTxSxekU9dYxAGHfwrLpdZA8QsL6w7yQvrTppZ3r8tlXhBwqOpuYVFK3az6aRi5JyzfBfv3zYcd+eO45JbcY5rXt1u9vjKLKnhT7PELBuOF3H7u3uoa2xR4pIgZ9H/9FdSf/obYIJjwco5Du7YNLuxuYXFK/aYWXLf3MV7t4pZcsprVSy1PDUzWdivH48XscSKJTohj5n9OzYMB3h+3UleWt8al0MFfLhouNBwv7G5hd+8c5jNp9yBvqx48zDv35qKm7P4R4sQb1fHEpxtLMeKWPKewmLUQUxCHldKWPQblpK68R/KxvEQuGkVBMV33KCpAfcP5jMh8ydl+72vYOFKcL74CR+9Xic2iL9IWpdWyB3v7aW+qTUuiflM7xt2sbulSZMmTZo0abqEpS3H06RJ0yWldceKbAyND+Sc5TOJmXdX9MyaNM41NgPQbIKnV6cJDX1Lqut5sTVpo2w38OK6k+KDnFgL6ess27l74MAHwibLNmaYE1CgGGKfKqoStvnLmjTqGlscZimuqufl9ac6xfL90UJz0gYUw3SZMfmyn9JtTObf2ZbNqaJqCcsxG5alEhaqCmHjP4DWfWqKYOPfO8Wy73SF1AB72cYMG5a3tmaRXixmeWZ1morlqHD/oqo6XvnROi71vLjulLDNd0cK2XzKluXL/d1v5r3Uaow1tcZFqMp82PQvy3Z1W5wEOrYK2hJQADk74dAnXezx/4aWrkmjvskSl6dXiceYJk2aNGnSpEnTz5WWhNKkSdMlpeKqeode+7kqqbZ9z/LaBpuKYWpV1DbQpKoGJu1XdWH716rsvCZ5zyLJcdRtymva99Va9likx6juQr/stJGdM3WbspoGYXU8akvB1Gz7WhfOsbRfXWmjYimtFrPYY5X3q67da0WV3X+9qPtRWlMvrI5nNy7VBeKDVBfZeU0cy/91qeNSUt2gVcfTpEmTJk2aNJ1XaUkoTZo0XVKanBSCl9XSO2eDnhn9xMt+uiK1EfWV/cKEFa9ig73opzLVlpomJ1wBLt6WbYMzpMwTNpmjWhLY09+NoVFiLx11P67sHy6sqhYX4kVflan2PAnL5KRQG5NxZ6OeK/uJl/3MGWh7jnv5uzMkyk/SxrYfM/uH263mZ1ZIEoT1t31NYsw9Jbk9ywwpi22/IgPcGRIpZpmr4p85IFxofp8Q4kVyuLfNa7IxNiUl1MZk3Nmo5woJS1ek7sesAT2EJuuEJENoX9vX+l8nPkifGeBsMYzH4ALJczrZ0/8tzVWNy9kDw7XqeJo0adKkSZOm8yrNE0qTJk2XlIK9Xfl0yUje3JLJ6dx8Hpo9TOy71EU9MjWRMB9XtpwqwaepnKXz+knbvPWbYbz6UzqZJTVc0S9M6lWEdxjc8i3N2/9Dfu4ZIq78vdCrCODypBDeuGkIn+7JIcTbhcXjYoQJJYBHp/Uh3NeNLadK8G0q5+m5fYX7A7x9yzCWbcwgu7SGqSmhUpZQH1c+XTKCtzalk5uXzwNzUqXG7JOSQnh94RC+2pdFgJc7i8bFSlkem5ZID183tqY7zsKNn2Pa8iINZadxSZ4BfedLWT65fQTLN2eSm5fPw3NThb5LAJOTQ80sgV4eLBoXK06OAY9PTyTCr5WluZylEhadTsc7twxj2aYMzpTVMi0lTOq7FObjxqdLRvL2ZiUuD84dYVP5r7v0++l9iPBzZ2dmKd6NZTw9R+zthU4HN34BW1+EimxImg0pc8VtfCLglm9o2v4qBXm5RMx6AgK1am8i/WFGEj393dmVVYZPYxlPz3bgetGkSZMmTZo0afoZ0pJQmjRpuuSUEOrFX+f1Y8OGMgb1Es826apyy8+xcn8e+89UEOGpI724Wlqm/kBOBasO5pN39hxNLSbGxwfj4+4kbPPvNGf+vXcGDY1N3HzIk8ciTMKZCnWNzXx1II/vjhbi5+7MwF5+0gSRmiWjuIYk1Ywatfw9nHl0mjghplbiiWX89eTzNDfVYzh2O/T8k5Js6EiN57j86ONcfuoL8Ph/9u47PKoqfeD496aHNEoIvUPoTUCxoyDYQbF3xV7Xsqtusa26v921iwWsawNURMCONOnSCZ0UQkIJ6T2ZJHN+f5wkk5lM7p0MMwnE9/M8POTO3DPnvHPunXLmnvfEQq8XLQeIDuaVMn/rIbZWx5KSVczATuaxLEmr4u8bz+ZwQRnjy+N4uW8FMeHm/bJ4VwY/bj+CraKKH7cfZkS31qb761ieZELitx7Hkp7riKVbVADJmdaxtIsM5ckLBpq3xcXAvTOq+8VG4O57oduzjSrviaDAAKad0YtpZ/Ri2bJlnq3WGtkeJv6zUfW8uSOMdzddTEVVFdMSInjcYpz3jy4oMIDbz+zN7Wf2ZtmyZaZXcwohhBBC+IJ82hBCCC/87dsEtqTlAZBepHhkzlbT/UttVTw0azMH80pRCpbsPsori/aYltmYmsN/ftpDUXklNrtObv3TdvO8OO+vSGb+lkNU2RVZReU89tVWjrrJ+1PXX+e5xPLlFtP9vZK6Bpb8E2yFBNpt+gqXXQvNy6yeDtu/1rmBijJg3t3u8/7U8dd5CWytE8vDc8xjKbFV8tCsLRzKL0Mp+HXXUV5dtNe0zO8pObz0y97afpmxPJmfd1jkK1r9Jmyf64jl23ugKNO0yJPfOGJJK7TzyJfmx5hX9q+Cpc+DrUj3y6rXYPf3vq+nCaxNzublRXsptlVhq4J3liWxaKfkhBJCCCGEOJ7IIJQQQnhhW3q+0/aejELKKqoa2Bv2ZxdTUFbpdNtWl8dwtTWt/v3bDpqXcW1XRZVi12Hz1fG2pec5be8+Ukh5ZcOxeOXQZs9uM7vfXgFHEkyLbHN5znYfKaSiyt7g/smZxRSWO/dLguVznOfRbU4ObnLerrJBxnbTIq7t2HW4gEqTWLxyaJOb2yz65Tjlrg8SrPpFCCGEEEI0KRmEEkIIL4zt7Zzse2T31oQFNzzFqE/7SGIjQ51uO7VPO9M6Tundtt5stbG9zcu43h8eHMiIruZTxVzLnNS9tWfTpRqj5+mASzA9z7Ao43J/cAR0GWVa5BSXWEb1aGORZD2S2MgQp9tc+9aVuz44tXesaZl6sYREQueRFvU4t2N0jzaWeaQarcfp9W+z6pfjlLt+sTpfhBBCCCFE05JBKCGE8MILlw1l0uAOxIQHM7hdAK9fbT6gEBIUwHs3jWJEt9a0aRXMtSd356Hx5kmTB3eO4T9Th9GzXSvahhn8/aKBnB3f3rTMzaf15M6zehMbGcLATtHMuHGUZd6purEMaRfI69eYx+KVTsNh8nRo04uy0PYw6V/Qd7x5mZPvhFPvh4j20GEoXPMZhJsPqP3r8qFMHOSI5bWrR5juHxoUyIwbRzO8W2vaRoRw3SndeeBc834Z0kX3S492rWgXZvDUxYM4o5/FINQpdzti6TgUrraO5cW6scQG8qpFLF7pchJcWqdfzv839B7n+3qawLCurfn31KF0b6v75ZlLBnFaX4t+EUIIIYQQTUoSkwshhBdiI0OZceNoAJYtW0b3dq0sy4yMKuDbTp9AWAp0uBgCB1uWuXJ0N64c3Y1ly5Yx7szelvsHBhj0io2gV2wEcVFhdIwJsywTFRZEr9hIMgrKaWcUEW2RlNtba6IvYEZ0PJm2bB5pfRIWQ1AQGMQXre9iXtRFxEWF8VBkP+ItirSPCmXmTY5+6dbWul9G9WjD/PvcXBFk4qrYFK7q9BY52Udp2/6vQC/zAoFBMOkF/c9DUaHB9GofwdHCctoH+K9faN0d2vWlhEzC2lrEcZy7ul0KV3eeTk52Fm3b/w3LfhFCCCGEEE1KBqGEEKIp2O3w2VTI3qe309bpleFOe8Cn1Xy7+SBPfuPIm7QuJZuVj59rOlXwmQU7mL0+rXb70S+38P7NY3zartTsYm7+8Hds1TmN7vhkA9/edzrDTKYKztuczl/n1Y0lh5WPn2MaS5PISdZ9WWWjLcDsa+GOJZbT6xrrqfnb+Wpjeu32n7/aWjvA5jPZSToWe4WOZdY1cOcyfeXaiSYr0U0sS0/MWIQQQgghWiiZjieEEE0hc7djAKqG1epwXnBdPS+ryMaG/bnmZVxWdlu8+yi2St8mwF6y+2jtABSAXcEvO8xXLqsfSzkbU81jaRJ7f9aJxWsoO+z+wefVuPbLr7syfJ+YfO/POuF7DWU/YVfHY59rLFV+6RchhBBCCOE9GYQSQoimEN0JglymxrW1nl7XWD1cpgUaBnS3mJLWw+X+Lq3DCQny7duDa7saus35/ginbU9iaRJt3EzxaoK+7NIm3PeJyd1Nv/NDLE2iifpFCCGEEEJ4TwahhBCiKYS3gUkvOgai2vWDcU/6vJq7zu7DsK4xAAQHGjwyId4yX9XTlw6uXbmvVRA8P2WIz9s1Lj6OqSd1rV3t77xBHZg8ootpmbvO6s3QLo5YHj0v3qMcT37XbyIMv5ba1f4GXAxDpvq8mmcvHVK7cl9EMLwwZajP66DfJBh2jWPbT7E0ifjznWMZeAkMubz52iOEEEIIIeqRnFBCCNFEykfewvyK00k9ksV5owczok0bn9fRNiKE/14xnE/W7Kdrm3BuOrWnZZmTurdh9RPnkpRZxIGdGxnXP87n7QoIMHj5quE8OjGeVavXcOWF1rmN2kWG8tKVjlhuPq2nz9vllYAAMie8ztxWd5KSsp8/XXABnYJCfF7NqB5tWP3EeJIyi0jbuZGzLFZG9EpAAFw+A879O2vXrGbsBVf7vo6m0pJiEUIIIYRooWQQSgghmsh9n2/i111HAXhn/Wpm3jiaCYM6+LSOjam5XDtzbW3+pZ93ZDDv3tMwai5BakBIUAADO0WTscd8v2PVuXU47Vt5dhHuxtQcrp25rjaWX3Zm8M091rH4W35JBZdOX8nh/DIglF9eX8GPD53l0UqEjdVU/ULrbpSF+/ZYbDYtKRYhhBBCiBZGpuMJIUQTOJBdUjsABTox98er9/u8ns/WpjolAN+SlsemA8dBMm8vfLrGOZbNB/LYdCCvGVuk/bD9cPUAlJZbUsE3m9NNSgghhBBCCCFABqGEEKJJBAUauF7AExzo+6tb3D1msK+TWTcRd+0OOQ5iOV7bJYQQQgghxPFOPjULIUQT6Nw6nCtO6lq7HRIUwF1n9/F5Pbed0YvIUMdM63H92zOsa2uf19MUXGM5p397hlYnXW9OFw7tSN+4yNrtLq3DubxO3wohhBBCCCHck5xQQgjRRP5zxTDG9GzD9oMF3HhqD/p1iPJ5HQM6RrPk0bP5eWcGcVGhjB/g+yTjTWVgp2h++tOZvL8yhR5tW3Hj2B7N3SQAWoUEseD+0/l95S+kJCUx9Ya7iA73fWJyIYQQQgghWhoZhBJCiCby5pJEXv11L0rpvEKf3z6W/h19PxAVFx123AzYHIuDeaVcPWMtB/NKAUjJKua5yUOauVVAVQWtZl/JuOSljAP4fD7cNB9CIpq5YUIIIYQQQhzfZDqeEEI0gayict5YvA+larZtvLF4X/M26jg3c3lS7QAUwCdrUkk8WtSMLaq2+3tIXurYTl8P2+Y0X3uEEEIIIYQ4QcgglBBCNIG8EhuVduV0W2ZheTO15sSQWVT/+TkunrOio57dJoQQQgghhHAig1BCCNEE+sZFMcwlqfblJ3VpptacGC4b6Zzsu3vbVozu2aaZWlPHwIshpM40ysBQGHxZ87VHCCGEEEKIE4TkhBJCiCby8a0nM/O3ZFKzizl/SEcmj5BBKDPnDerA+zeNZt6Wg8RFhXLnWb0JDjwOfjuJ7gy3/QTr3uXIoXQ6XvIPaN+/uVslhBBCCCHEcU8GoYQQoom0jQjhiQsGNHczTigTBnVgwqAOzd2M+joOgcnT2b1sGR27jmru1gghhBBCCHFCOA5+UhZCCCGEEEIIIYQQLZ0MQgkhhBBCCCGEEEIIv5NBKCGEEEIIIYQQQgjhdzIIJYQQQgghhBBCCCH8TgahhBBCCCGEEEIIIYTfySCUEEIIIYQQQgghhPA7GYQSQgghhBBCCCGEEH4ng1BCCCGEEEIIIYQQwu9kEEoIIYQQQgghhBBC+J0MQgkhhBBCCCGEEEIIv/NoEMowjGku24GGYTztnyYJIYQQQgghhBBCiJbG0yuhxhuG8YNhGJ0MwxgMrAWi/NguIYQQQgghhBBCCNGCBHmyk1LqOsMwrgYSgGLgOqXUKr+2TAghhBBCCCGEEEK0GJ5Ox+sHPATMBVKBGw3DaOXPhgkhhBBCCCGEEEKIlsPT6XgLgaeUUncBZwP7gPV+a5UQQgghhBBCCCGEaFE8mo4HnKyUKgBQSingZcMwFvqvWUIIIYQQQgghhBCiJfH0SqhwwzA+MAzjJwDDMAYBZ/qvWUIIIYQQQgghhBCiJfF0EOpj4GegU/X2XuBP/miQEEIIIYQQQgghhGh5PB2EilVKfQnYAZRSlUCV31olhBBCCCGEEEIIIVoUTwehig3DaAcoAMMwxgL5fmuVEEIIIYQQQgghhGhRPE1M/giwAOhjGMYqoD1whd9aJYQQQgghhBBCCCFaFNMroQzDGGMYRkel1CbgbOCvQDnwC5DeBO0TQgghhBBCCCGEEC2A1XS8GYCt+u/TgL8BbwG5wEw/tksIIYQQQgghhBBCtCBW0/EClVI51X9fDcxUSs0F5hqGscW/TRNCCCGEEEIIIYQQLYXVlVCBhmHUDFSNB5bUuc/TfFJCCCGEEEIIIYQQ4g/OaiBpFrDcMIwsoBRYAWAYRl9kdTwhhBBCCCGEEEII4SHTQSil1AuGYSwGOgG/KKVU9V0BwAP+bpwQQgghhBBCCCGEaBksp9Qppda6uW2vf5ojhBBCCCGEEEIIIVoiq5xQQgghhBBCCCGEEEIcMxmEEkIIIYQQQgghhBB+J4NQQgghhBBCCCGEEMLvZBBKCCGEEEIIIYQQQvidDEIJIYQQQgghhBBCCL+TQSghhBBCCCGEEEII4XcyCCWEEEIIIYQQQggh/E4GoYQQQgghhBBCCCGE38kglBBCCCGEEEIIIYTwOxmEEkIIIYQQQgghhBB+J4NQQgghhBBCCCGEEMLvZBBKCCGEEEIIIYQQQvhdUHM3QAghr+2UpgAAIABJREFUxB9DSlYxn65JJTWtnI4DChjQMbq5mySEEEIIIYRoQjIIJYQQwu8yC8uZ8tYq8ksrAFj91mp+eOhMesVGNHPLhBBCCCGEEE1FpuMJIYTwu592HKkdgAIorahi/paDzdgiIYQQQgghRFOTQSghhBB+1zo8uN5tbVqFNENLhBBCCCGEEM1FBqGEEEL43aTBHRndo03t9oCOUVx+UpdmbJEQQgghhBCiqUlOKCGEEH4XEhTAl9NGsXbVryQmJXHdDdMICqt/dZQQQgghhBCi5ZJBKCGEEP5XUUbARxM57fBWTgOY+THcsRjC21gUFEIIIYQQQrQUMh1PCCGE/+1aAIe3OrZzkmDLrOZrjxBCCCGEEKLJySCUEEII/7MVe3abEEIIIYQQosWSQSghhBD+N2gyRLR3bIdGw7Crmq89QgghhBBCiCYng1BCCOElu12RllNCpV01d1OOmd9jadUW7lwGpz5AepeL9N9tevinLiGEEEIIIcRxSRKTCyGEF3YcyufuzzaSllNKTKjBuz2yObVPu+Zulld2HMrnrk83kp6rY5nRM5uxvX0ci1Kw6nVY/wFdlB2WR8LktyFQ3oaEEEIIIYT4o5AroYQQwgtPz99BWk4pAPnliie+2dbMLfLeU/N3kJ5bJ5a5foglaQn8PhNUFQYKts2BHd/4vh4hhBBCCCHEcUsGoYQQwguJmUVO26nZJZRXVjVTa45N4lHnWPZnl2CrtPu2kqy99W/L3O3bOoQQQgghhBDHNRmEEkIIL5w7IM5p+8x+sYQGBTZTa47NeDexhAT5+O2h7wQIqDv1zoB+k3xbhxBCCCGEEOK4Jsk4hBDCC89NHkJkaBBrkrKJCyrj1atHNHeTvPbclCFEhAaxNlnH8po/YontB9d8AStfoyA3i+iJj0P3U3xfjxBCCCGEEOK4JYNQQgjhhUh7Ic/Zp4OxksyArsRWDQG6NHezvBJZVcA/7W+CsYrMgK60sw8FOvu+oooSsBUSWFWm/xbmSnLgpycgdTWDQrrBSf0hulNzt0oIIYQQQgivyXQ8IYTwxnePwNZZkJ9G+6w18M0dzd0i733/CGyb7Yhlrh9iydgJX98GRxKIKDkACx6AlBW+r6clWfiQTuCen0Zc5mqYd2dzt0gIIYQQQohjIoNQQgjhjaQlztupq6CyvHnacqzqxbISKm2+rSN5GSiXZOeu9QpnSUudt1N+g6rK5mmLEEIIIYQQPiCDUEII4Y2OQ5232/WDoNDmacux6uASS2w8BIX4to6OQzy7TTi4Pj/tB0CgzKIXQgghhBAnLhmEEkIIb1z0MrQfCEBpWAeY8k4zN+gYXPyKHuDAj7H0OgvOeAQCQ7EbQTB6Ggy6zPf1tCQXv1qnXzrC5LebuUFCCCGEEEIcG/lJVQghvBEbD6fdD4m/kl7Wjn6dT9zV8YiNh1Pvh6TFpJfF0q/TcP/UM/IGKC/kSFoqncdMgwD5HcRUbH849T5IWkJaeSzx/uqX41VpHvw+E3JTYdBkiJ/Y3C0SQgghhBDHSAahhBDCG0tfgN/+C0A/gO/KYPJbzdokry15Hla8BNSNZbpv6yg6Cu+dC2V5et2991fC3SuhXR/f1tOSLHkOVr4KQDzADxVwyevN2qQm9dlUOLhB/73lM7jiQxgytXnbJIQQQgghjon8DC2EEN7Y9Inz9tY5vk/m3VTqxTIbqip8W8euBVCW59iuKIGEr3xbR0vj2i9bvgB7VfO0pall7HQMQNVwfT6EEEIIIcQJRwahhBDCG+FtnLfDoiHgBL24tF4sMWAE+rcOgPC2vq2jpXHbL3+Qt213scrxIoQQQghxwvuDfJoVQggfG/8UBAQDoAiA8U+fuDmOJjztHMsEP8Qy4BLoNtaxHTcYhl/j2zpamvGu/fIMGEazNqnJxHSBU+5xbIe1hjMfbb72CCGEEEIInzhBf7YX/nIgu4S1KdmUFfxBpnyIlqmqAvb9QvujW6DiFAgOty5Tmgt7f4HIOOg9zvrL/oCLSLtpHWnblpJha8Vlo67zrG2payA3BfqMh6gOnpVprJIc2LeoUbFw83eQ8CUJ5Z0ZdtJNvm9TUAjcNB9Wv8H+pL30vP5VCI3yfT3equmXvhP082bBVmln6Z6jbDtSydiKKsKCPbhyrKZfojpAr7Ot+2XQpXDzQkj4im3lXRk+8gaPQvk9JYcDOSWcHd+e9lGhHpVptJIc2PcLRHXSKx/6Y3Ds/BchbiAcSYBT7oZ2va3LVNqqz/2tUDEWgsN83y4vrUvOJj23lLP7tyc20oN+qbTBvp+JzUw47mJpEsXZkLgIojtDzzP/OAOwQgghRAsng1Ci1o8Jh7l/1maq7AqA8tbJ3HGWBx/6hTieVJbDh5Pg0GYGAxz5Gm5fDK1MpvJk7YMPJkJpjt4ecDFc87lpNQu3HuKh2buxq04A5HVK4dbTe5m37fvHYP17+u/gVnDTAug2xrO4PJW5Fz6cqAfVAAZeCld/al4m4Wv45g5QdoYBdI2CU+7ybbsqynS7Dm+lJ8CMTXDHYvfT9Jradw/Dhg/138ERcPMC6Dq6wd3LKqqY+s5qdhwqAODH9BXMu/d0YloFN1zH0d36uKzJizVoClz1P/N2bfsK5t0Jys5wgG5RcPIdpkX+Oi+BL9YdACAiJJDP7xjLiG6tzetpLNdYBl8GV37s2zoAFjwIm6qfoy2f6wG5Lic1vL+tRB9jRxL0uZ8xTx9jYTG+b1sjPTF3G7PXpwG6X2bdOZZhXU36xVaiX5MyEhgCOpbbfz0uYmkSGTvgowugLF9vD70Spr7fvG0SQgghhE+coHNHhD+8+uve2gEogDcW76Oiyt6MLRLCC7sWwqHNju2cZP0F1syatxwDUAC7v4NDW0yLvLpoL3VOF15d5Hz+1FNwCDZ84NiuKIEVL5u3yxtrpjsGoEAnBD+81bzM0hdB2Z237T4+913bkZMEW2b5tg5v5KfDho8c2xXFtSvSNeTH7YdrB6AAkrOKmbsp3byeNdOdE7Pv/BaObDcvs/R5l355AVTDx9jBvFJm/X6gdrvYVsU7yxLN6/DG6jedY9kxTw8a+FLeAedE5LYiy35h53x91VSN7H16wYBmlpZTUjsABbpf3l2eZF5oxzzIqBNL1l7Y9qWfWngcWv2mYwAK9CIGmXuarz1CCCGE8BkZhBK1SmzOU/DKKqvMv1QLcTyqKKl/m63YvIy7+y3K1D9f7NhNBgioKHUeUAD9xdrXvInftYy7th4rL57jJlFRCrj0m0W/uPa9vq3SvB5v4rc1rl9KbVX1xqjctfWYVTRBX9pKqN8vVs+Xm37zxznWSKUV9fuguNyiX9yex80fS5M5TvtSCCGEEMdOBqFErRvG9nDavmJUN8/ynAhxPBlwMUTWybUUEgXDrjYvM+oW59XgOgyB7qeaFrlhbHen7WvGdCM40OQltV0fnZ+prjHTzNvljXqxDHVOCO7OaJd2jLoZAn08W3vQZIho79gOjYZhV/m2Dm/E9tP5ZupyfT5cXDCkE7GRIbXbUaFBTB7Rxbwe137pOAy6WkzFdD0+Rt0KAQ2/JveNi2Rsb8e0U8OA60/p0eD+Xht1q3MsnYZbx9JYcQOgxxl1bjBg9G3mZQZNgVaxju3QGD2Nq5nFd4ji5F6u/dLdpATVsbRzbIcdH7E0mVG3Oq+O2Pkk/U8IIYQQJzzJCSVq3X12H7q3acWiXRnE2DL5x5Qh/qusJIdgW571fnUVHIKQSAiL9k+bmtCR/DIiQgOJCjPJIdNS2e2QfwDDbnHlSF22EijJgtYWX9xA5366YymsfYf0A8l0vew5aGuRq6nn6XD7ItjwcfWqXHdZrg53/7n96NkugsW7j9LalsnfLxls3bZrZsHv70HWbhh+rU7o7Gs9z4Bpi2Djx9Wx3G290t3Zf8beri/Fe5ZxoDyKwec/51ldje2XO5fB2nd1v1z+T2jj2QDJ4fxSosKCiQz17C2ryq44lFdKpadXcl43B35/v7pfroNeZ5ru3jYihPn3n8HsVXvYf+AAj145jm5tW5nX0ftsmPaL7pfW3eBk62OMcU9Au76w7xd2l8cxYJJ1v3x4yxhmrU4iLTOX80f1Y2zvdpZlGs0plu76fPEkabS9CvIOYNg9vDrrujmw/n3I2gMjrtfHtpnI9voYW/cu6QdS6Dr1ef1c+0MjY/noljHMWpNIemYeF4zqxylW/VITy9p3SU9LpevUf0JMV4/qOpRXSkx4MBEeni/Hpb7j4bafdU6wNr3g5DuPr8TkxdkE2/Kt9zsWjT1fhBBCiBPECfwJRfja7iMF/Pvn3aRmlxAdAhOSsjmjX6x1wcb66a/w+wxOs1dB/mVw+UwINBmMKSuAOTdAynIICoNz/gqnP+T7djWBgrIK7vlsI6sSswkLDuCR8+K586w+zd2spnMkAWZfD3mpjA1pA70+sR6I2ToHfngMygv0FRfXztarJZlZ/Qb8/h5dlB2Wt4Ip75gfY6V5sOhp2L8CgsL1YOdp95tWseNQPv/+eTdpOaXEhBpMTMnh1D4WXyx3LYAVL+lYMnZ4FktjlebBr3ViCY2CU+8zLbLjUD53fR9Deu5EYkINZuzPtR682DILfvxLdb+MqI6lU8P7KwWrXof1H1T3SyRMftv0iqv8kgru/HQD61JyCAsO4M+TBjDtDPMBxe0H87n7s42k55bSOtTg3Z7Z1rHsnA+//RdshXB0lx4stIily6p/8OiGD1HKjvHbVZaxUJoLi56C1FU6KX1oNIy9x7xdh7bAr89C/gF6hbSBAxfoAVMTrXbMYdrqx3UsuSfBtbMgqqN5PY1VkuMmlrvNyxzaDHNugvwD+tzv8wX0OM28zM5v4beXqvtltz7GzFaUVErnjdr4MV2UguXRMPkt06vHvHJwE3x5E+SnMTakbXUs5ldORuyYxe2rn9Sx5I2C9rM8jOV/dWKZbhpLbrGNOz/dwPr9uYQHB/LkhQO46dSeXgbZzGqOsQNr9GIBYTGWSfmbhN0O3z8Mmz7hNAWUXAeXvmk9oNxY6Rv1MVaQztiQdtB3FnQ/xbd1CCGEEM1EpuOJWs8s2EFqts5DUWCDx+duw+7rnFD7V8Lat8BeiYGCHd/ANovEsWve0gNQAJVlerAgJ9m37Woi769IYVViNgBlFXb+9eNuDmS7yf3RUv3wF8hLBSDUlgsLHjBNtExZgV65rLw6CfThrbDkBfM6kpfBundBVeljLOErvfqbmTXT9aANQGUpLPoH5KaaFnl6/g7SckoByC9XPPHNNvM6yvLrx7LUIhZvrH7DOZZf/q6TPJt4av4O0nPrxDK3sbFsgWUvmpdJWgK/z3T0y7Y5+vw3MeO3JNal6ITxZRV2Xvh+JwfzSi1i2V4bS1654slvEkz3pzQPvntEDw6AHixZ9i+LWBbrVQ6dYplnXmbV63rQBnS+n5//ppOim/n+UcjXfRdqy4WFD1rEkqvL1MayyToWb6x6zSWWv0L+QfMyrrEssIilJKd+LMv/z7xM4q86+b+qwsAOW2fpgSxf+/5RyNeJxkNtObDQ4kcR11gOboTl/zYvs+8XvWJjbSxf6MFSE+8sT2L9fr0oQWlFFc8t3ElGQZlHIR13VrysB6BA5yD76QkoPNK8bQLY97O+AlDZdb9s+Qx2L/R9Pd/9CQr060OoLVtvCyGEEC2EDEKJWolHnZO+HswrdZtQ9Zhk7vbstrqyXFfEUZC1z2dNakpJR50TqyoFSVl/oGSrrn2dux8qyxvePz+9fhLkeseDax17699mWcalXcquV9YykZjp3G+p2SWUV5qcL/np9ZMNu2vrsXJdQUrZIdt8hbREl+Nyf3YJtkqTxOR5B/QAl1m9rrLcxGpx7ru2y64gJdM8OfW+erEUm6/y6U0sXh1jrv1SZdkv9Z6z7CSoMpnG6jYWfxxjLo/pSSyuZbIT9XSjhuSl6h8dnB6jkeexJ2W84fqYWXvNV5PM3Q9VLq9zlrG4ud/dOVSH6/lSaVekZB0Hyf+94Rq/vVIf/82tqY4x176WlQGFEEK0IDIIJWqNHxDntH1Kr7a+zynRZzwEuEyLij/fvIzr/aExlkmjj1fnujzH0WFBjOnZtoG9WyDXvux1NgSHNbx/+/7QpqfLY0wyr6PveAhwOW77WZRxbVdYa8tk3q59eWa/WEKDTKb9tB8ArV1yIFnF4g3XWMLbQDfzaRyu5/6Z/WIJCTJ5e4gbVD8PlGW/THDpF8OyX8YPdG5X61bBnNSjtXmZerG0N08YHzcIYvwfS73HDG8LXU9uXJm+482n/MUNhhiXHEh+OcZcHrNVO+vE5K5l+p1nPk2uw1CIdsmBZNkv5zknTPekX7xRL5aJ5tOxOg6FaJfE9Vax9HMXy3mmRVxfk9pFhDCim/n5ctxyfX4i2kOXUc3Tlrr6nuecMN0IsOwXr7jGb/U5SQghhDiBSE4oUevpSwcRHhLIqsQs2geV8dp1I31fSdtecN1sWPEqBTkZRE/4s3Wy2eHX6GkmW77QH0TP+dsJm5x86qiu5JVWMHdjOrFRoTxyXrzHyZZbhAv/C6GRkPIbRwI60XHqe+b7BwTC9V/Dr89ATgoMvBhOf9i8TLs+OnfMilcoyM0i+ry/WOZrYeQNeorZ1ll6Zb1z/qbbaeK5yUOIDA1iTVI2cUFlvHr1COtYbphbJ5ZL4HQ/TLE46UYdy7bZOpZz/w4hEaZFnpsyhIjQINYm61he8ySW6+fC4md1LIMutY4lth9c8wWsfE33y8THLXOcXD2mOwWllczbfJC46FAePa8/rULMz5d/ThlCZFgQ65Jz6BBcxqtXDTdvV2AQ3PA1LH6uTiwW06vax1fH8ir5eTnETHwculkMwpx0s55euu1LnaPp3L9DiEUy84te0fmWUldxOLALnS6b4UEsc3Ueqdz9ekXC0x4wL+ONUbdAeaGOJbqTPl+sYrn4Ff26nbpaxzLlXfP9a2JZ/JyOZfAUONU8TxtxA+Caz2HV67pfJj0JXf0wcHHJaxDe2hHLZVaxBMMN3+jzJTcVBl9mmaeNuIFw9Wc6lvxcHYvFIMz1p3SnqLyS+VsO0SkmjEcnxp+4K9yOuR1sRZAwV+fNO/fv5j9YNJWOQ6r75Q3dL+f/DTr74bPSJW/oHxAOrOVwYFc6TZ7u+zqEEEKIZvIH+vb7B2Ar0flf9v2ir7o4//8gtq/HxSuqFMXllZTYqihDUVHlQT6ojJ06H0hOEvS/CM57FoJCLSoqBVshgVWl+kOmFaWgvEjvGxxef7qJO6V5ul0pvzE4uAuM7OvxykKN8dP2w7y5JJHc/BLuC0+1XA5dKUVJeSUltkqKywMp8/V0x6aklE7mvOULPdgx/inLpMnYK6r7spjA4DKoslnXU1mu+95WqMsqk2kvNWzFjmPMdTpfQ7GUF+l/wRH1pwG5EWkv5Dn7dDBWkhnQldiqIUAX80KVZfrLu61Qx+RBLD8kHGb6kkTyCkt4oNUBrj3ZfCU6u13xRslE5hUOpr0K5XFbDyyGR4isKuCf9jfBWEVmQFfa2YcCFgnTq8odsZQX6qlVFgmgvy8bxluFfyW/tJAHy4ZwtUW7sNu5o2oOdzAHVEeoehowH1CMshfyvP1NMFaTaXgWy46KjrxY9ACphSVcWNKJx+wGIVbXCduKobyIoMpS/bdVKApeLzmfb4uGEmeE8ritO6MtC1VWH/vFBIaUQlWFZT1UllUfX9X/lB2wGIjYPhdWvMqYwnyI/oseyDSjVJ1zMsp8Sm1tLFW151hgaJl+LfA4liJ9jHkSSyP7xStVlc6xePA6tr2iEy8UPkR6UfUxpgws10atKAFbEYFVZfr93YKh7NxdNYu7+QrsnaHqWcDiarvibPjpcUhdw6DQHjBqoHnCdCAhPZ8Xf9hFel4JFw3tzGMT4wkyu9oQYNtXsOo1RhcVQszjMPJ6092rFLxWciHzC0fQMSCMJyq6cZJ5DV7Zlp7Hiz/s4mBeqeex2BrXL9irYOmLsP1rfUXcec9BV4uz3+5yjDVmNdnG2PYlrHyN0cVF0PpJGHGt7+soytQ5vdLWMSikB4weBJFx1uWOR0WZ+nxJ+52BoT1h9GC9kuUfRfoGvWBAfjoMvUL/AOHrhR+EEH8IMh2vJfn1GZ2UNT8NEhfBHPMPea6eWbCDrzamczCvlE1Hq3jgi03mBexVMOtqSF6qc5GseweWWSSOzdoHX94Mh7cSUZIO3z+iExab2fQJLH1eJyNPWwefX6WvjDLzw59hy+eQn0b7rLXw9W3m+3sh8WgR932xmR2HCjhUrPjbvO2s3JdlWmb2+jReXrSX/dklbEzNZdrH68kv9eDL2PFo48c6sXZuCqSthS+u1lfgmPn+MZ1gNz+N9llrYO7t5vtXVerHTV6mj7G1b+mBLzNHd8PXt8KRBCJK0nTS4OTlFrF8pBNr18Ty+VXWsXz3iL5yqiaWbyxWbqqJJWW5jmXNdL1Snom9GYXc/8Umdh4u4FCRTrK9Osn8GPv89wO89us+UrNL2JCay20fraewzOIY+/4RfeVUbb9YxVKhn6O6sax8xbTIniOFPDBLx3KwSPH43ATWJGWb17PhA52MOjdFJyn+4ip9NZGZ7x7WicJr++VO090rquzc9vF6ViVmk55byszfknlnmUXumYwdMHcaZCQQUXJAJ9jfv8q0yGfrUnl9se6X9ftzufXj9RSVW3yxXPhQbSxxmathnnksVNqq++U33S+r39QrrJk5sh2+rhvL/ZC62rzM+vd0Yu3c/XBgNXx+pf7CbBrLg5DwJRSkE5e5yrJfqLTp/k75TeeHWv2GTohuGkuCfk2piWX+fZC6xryMN1xjmWe+MqCt0s4tH61nTXI2aTmlzFiezMzfLBbXOLytOpbtRBanwvx74cA68zK/z9Svj3X7xWogbuGDevGGmli+NY+lvLKKWz92xPLu8iRmWMayVb8+NiKWj1fv580liRzIKeH3lBxu+3g9pTbf/mhTXlnFbR+vZ21yTm0sM1dYxHJos3Ms394NaevNy6x9R7/W5+7XCf0/v8J68Gr+/XrQqiCduMyV8K3FSpreOLhJn4dHdzhiSd/g+3oWVMeSn6ZjmW9xFeDxbP69etA+P40OR1ec2LE0lq1Ev6akrtKvySte1se2EEJ4QQahWhLXwZzM3darL9WxYl+m0/amA3nmX5KyE+uvumU1oJSyXCexrStxsXkZ18esKLb+MO5aJm2dz38VX5OURZXL6oG/uTyHrlyf42JbFZtSLQbUjleuz7GtENJ+b1yZA6v1lXENydpTu0JQg4/hKmV5/SuMrMq4jcXii4VrmdRV5leEZO6GApcVxCzatSoxC9cFKldYDHSu2Ot8jBWWV7L5QJ5pmfqxrNSDAA3J3A2Fh5xvsziPV7qNxfx8IWmp83Z5ARy0+JLkWmb/CtMriPYcKSSjwLnfrM5jkpc1+hj7ba9zvxWWVbLFsl9cYkn5zTwx+dGdUOSygpjVsZ+8DHDpmMaeL+X5je+XlOXmicmP7oCiDOfbEv0QizdcY0leapqYfPeRArKKnI+x5XutjrGlHHO/lOXpgYbGlElaarpi6a7DhfVi8ew8dnnM5KVud23oMfNKKtiabnG+NNLOQwVkFTm/zq3Ya/766pPzpTRXD2Y1pow/jmNvjjFvuD6m1We+41m9fjmBY2msw1uhNMf5tj9S/EIIn5JBqJak4xDn7Yg4PU3KQwM7OedZ6tGuFREhJpfZxnSDsBjzNrjqMLT+bR3d3Gb2mEYAdBjUuDJte0OwRc6SRnJ9vvRtUeZlOjqXCTAgvqN5meOWa78ZgTqPiWkZl35p11dPsWxI6x4Q4vL8WB5jbu63OsZcj0uPYnEp066f+VTUNj3rx+KurXW4P8bM86G53h8YYBDfweIYc40/Nh6CQhrev01PCHHJmWXRL+7ODatY6j1mQBC0b+Qx1n6AzsnTAHevc1bnsftjzDz+QS6PGRRgEN/BPO+Y+1hMZtG37a2nk9ZlcYy5bbdVGdf7A4J02xpTJm6Q+TQOd6/ZXp37FmW84fqYcYNNE5P3aBdBuEtupkFWx743sdTrl2C9uENjynQYDIbR4O692kUQFuwca6PPY3f1unB9zJDAAPrGWZwvjdQ7NrLxsbj9DNPIcywwxLpfXMtYnZPecBeLX+pxeUx/nJNNpd75cgLH0lix8RDo8hnnjxS/EMKn/DYIZRjGg4Zh7DIM43M/Pf4zhmE85o/HPmFNfB46V2dNiOoEl71j+uXL1bOXDmZA9YBIuzCDl68cjmHyYZSQVjDlHT3YBXrFunOfMq+k+ylw9uMQFI7dCISTboKhV5qXGXsvDLgYMHSS3gv/W39lLlcXvqy/GAClYXEw5V3TD9beGN2zLQ+e25fQoAACDbj25G5cOtw8J9C0M3tx3qAOGAZEhQXx3OQhdGltMghzPDv1Puh/IbpfYuCil6zzbl34sv7yCZSGddDHj5nQSJjytk5ID9DjdDjn7+Zlep4OZz4GQWH6GBt1Cwy+vJGxvAwxFvmdLnq5dkDE81jeglax1bGcoZPtmhjbux33ndOn9hi77pTuXDy0k2mZO8/qzYSBcbXH2PNThtAxxiKh78Wv1A4ieBZLFEyuE0vPM3VuCBOn9Ynl3nF9CKmO5Yax3bnIIhZOe8CxKlRYjE7UHW1R5qK6sXSEyW+b7h4VFsx/rhhOuwg96Da2d1seOc/iC2Lvs+GMhyEwFLsRBKOnwcDJpkXuPLtP7epl0WFBvHDZEOKirfrl1UbFQlg0TJ6uV6sD3S/jnrSIZZxOKl8Ty5jbYeCl5mXO+JNj1bmwGLj4NZ1s3cwlr0Fs/zqxWCRaDotxjqXXWdax9DlHJ5WvjeUO61i8cfFr+gsZnsUSEx7Mv68YRtvqY+z0vu14aHw/8zr6jofTHnTEcvKd1e+DJs54WK/eBnqFz0tet869c4kjlpLwTtaxtArm31P8WSXBAAAgAElEQVSH0aaV/mxxRt9YD2KZoM/lwJDqWO6CAReZFrlnXB/Ojtev+zHhwbx4+VBiIy3yTTaSu1geHG+RR7PfBJ0gvyaWU+6ufu8wceaj+jkAnWz8kjcgIta8zCVv6B82gJLwztbnizf6necSyz3Q/wLf13PpG/oHJ6pjufRN39fRVC59s+XE0lgR7fRrSngbvd1nvD62hRDCC/5MTH4vMEEp5fl8MHFsYrrqD3q7FuqrNLqf1qjivWMj+GncQbL3riGrPJj+XSdYlinqNYlPRi3kSMZRTh8WzySLhKYADL8WSvM4ciCZzmPusE5qGBKhB6JCInXC1AGXWFah2vVh7ilfsnVPIqq8nKc6j8Hkug6trEDn1MhJ1h+QLT4kAzwyOph7KlaRlraf+FP/oi9tMtEqJIhpZ/QiKjSIuOhQJg22+OKGTmb+9cZ01iRnE15SwWmVdkKCzMePC8sq+N/q/ezPLmHS4I6cN8jzK+I8FhKhB29CovTAgNUXJEDF9uPrk79k294klK2cpzuPrk3Ou/1gPrPXHyAsKJCbTu1J93bVV0H0Hqe/TGbthSFTPUsCOuI6KMvnSNp+Oo+53Xz5dNADRGPvbWQs8Xx1ylck1MYyyjrRcE0s2ft0Uk+rLyLAlaO6UVhayf70dG4c24MAi2MsIjSI988qIz9yEeHRsYQM8iCdb2y8/jKStJj0slj6dbJYUQ4o6HMRn47sz5GsLMaNiGe8B7H8ZUwwD1StJC3tAPGnPmF5vhAapY+x0Gj9+ubBOUn7/rpM0pLqWIZZFrkovhUTz9hCcXY6rYddCBGWrxYw8kYoK+RI+gE6j5lmeYxFhgbx4Vkl5EctIjymPSGDPFi1LdYRS1p5LPF1+iUhXZ8vrUL0+dKtbfX50ufc6mMsEYZdpb84eBJLeZGOZfRtjlgqy2H9+zrXUu9zYHh1KvmafgmLhpjunn1xrRuLLY74jtbHGH3GV8eSpGNp1daDWG6C8mJHLB78+LAqMYv5Ww7SMTqMW07vVTtY1KD2A6rPl5pYrI+xS+NbccHpmyjOOezxMZY08nFmF0/m4MGD/GnUucRbxRIWzcpT3mVR8G7axLTmpvh4LJ+x9gOr+2Up6bYOxLu7OsbF5P6tuPCMzTqW4RdBKw/Ol5NuBlsJhw+m02X0rZb9Eh0WzB1n9qZNq2C6tGnF+AH+SWStY9lEcc4RWg+/2KNYEkc+yZySyzh48CAPn3Qu/Sz7JUY/x+Ft9NW98ZMs66iKG8ycMXPZlZSCUWnj6Q7DrFLyQ0mO/gyTlwZDLnMMfDXEMBz9cuggXUbd4tmPdYmLYfs3+oeaU+62Pi/jBsHY+2D/b6SXxxHvydUzjY0FIHMPrP+AvgfTYXAn66vNgN/2ZvLdtkN0ignn1tN70tqq/zsM1p8V9q8gzdaB/h0GW7fLG17E0mj2Ktj4MQN2LYTIFP2jsNXn8fhJ+r0lN1UnsQ9v7ft2CSH+EAxlMvff6wc1jHeB24A9wGygDzAECAaeUUrNNwzjFmAKEAH0A14CQoAbgXLgQqVUjmEYdwB3Vt+XCNyolCoxDOMZoEgp9ZJhGH2At4D2QAlwh1Jqt1U7R48erTZs8EMSRj9atmwZ48aNc3/n2nf1qh014i+A62Y34sH/rZMz1xh2DVxuviT4NTPXsDbZMUf8P1OHcdWYbg0XKMmB6aOhpDohcVAY3LlcL63dkOTl8OkURw6Wtr3hvt9Nr/J67de9vPbrvtrty0/qwitXWSw7/9FFOhdOjclvm6/gU5ytY6mZIx8UBnf9ZvphYVViFjd+sK42N06v2Ah+efgsgk1W43ll0V7eWOyI5YpRXXnpSvMvcFfNWMPvKY5+eenK4VwxyrPVAU2PsbqSlsJnlzv6pV1fuHed6XShV37ZwxtLEmu3rxzVlf9eOZy9GYVc8uZKyiv1Y7WNCGHxI2fTJiIEPrxA546qMeVd8xV8irOq+6U611ZQONy9AmJNfq1PWgKfXk5tfgwPYnn5lz28WSeWq0Z35T9XWHyx/vB8nWC7xmUzYPg1De6eVVTOhFeWk1eicxqFBQfww4Nn0ru9ybSUxMXw2dQ6sfSDe9eaT+Na/E/nJOkjb7T85X3qO6vZWCef2WtXj2DKSJOrx4qOwvQxOk8N6KlWd6+Edn1MYvkVPrvCEUtsvI7F7IPyr886J0k/6Wb9S7yZ98+D9Dr5zC5/H4aZXKFZmAFvndy4WPb9qhMS18bSH+5dYxHLM86JxUfdApe8zq7DBUyevgpblT5fYiNDWPzoOGLCg+H9CZBeJ5/Z1A/0gGeDsRypjiXfEcs9q/Tr7Fe3wo5vHPuOfxrOfAT2/qKThtfE0n4A3LPaPJZFTzsnFh91q74Kx8x7451zTV3xoR6I9jiWiOpYejVYZMnuDKb9b0NtGqT4DpH89NBZ5oO9v/xDJ0qvMXqavprQNJZz4eDGOrF8BEMavkIzo6CMCa8sp7BM5wBrFRLIz386yzHY6MbiXRnc/okjlv4dovjxoTMtYvm7TmBfY8zt+ipPMzPHOec0uvJjGHxZw/sXHIK3xurcYaB/ULpnlZ7W24Bfd+pYagzoGMUPD1rEUodH72NKwXvnuMTyPxg8pcEih/NLmfjKbxRW58uMCAnkJ4t+YfcPMLvOe1aHIXDXCtOB6+e/28n7K1Nqt28+tQfPTjYZvFEKZp6t8/bUuOoTGGRyhWZ+Orx9qs61B7pf7l1jfqX57u9h9nV1Yhmq31/NBq9+ehLW1rmK85S74YJ/m8cy4yw4sq1OLJ/CIJOrGvPT9TFmK6yOJao6loY/j/60/Qh3f+Y4Jwd1iub7B88wnwHw4xN6EZ7aWO6BCywW5GmsvDTdL3VjuW+t71d4/vFxWPeuY3vsvXD+vxreXyl490zISHDcds0Xnv04RCM+Wx6DpqijqeqRWI7PeiSWxjMMY6NSqt6SsH6ZjqeUuhs4BJyDHmRaopQ6uXr7v4Zh1CStGAJcDowBXgBKlFIjgTXATdX7fKOUGqOUGg7sAqa5qXIm8IBSahTwGGAxZ6GF2vyp8/beH/Vysp7a9InzdsJXpkmjD2SXOA1AAczZkGZex+7vHQNQoJfgTvjSvMyWz52TAOck62TDJuasd27Hgi2HKKswSYKbk+w8AAX1nw9Xe753TtJYWaaXOzbx1YY0p+TMKVnFToNF7nzpEsu3mw9SXtlwLPvdPOac9Qca2PsYuPZLdqJOzm3C9fiYt/kgtkp7dUyOx8optrFoZwZkJToPQEH949zVroXOqydWlupj2czmz3FK0Jrtpl4Xs9e7j6VBWfucB6AANpnH8vOOI7UDUABlFXbmbzlkUgLY/BnOsezTK/6ZcT3Wt842TeadeLTIaQAKYLbVMbZrgWPQBvTy85b94hJL1l44YBGL6/GxdZZFMu/dzgNQAJv+Z16Hu1i2z/WgXXVj2aMXTDDj2i9bvgB7lT7WqhzHWlaRjSW7M+DoLucBKHeP4WrXQueVICtKIGGuvjJ057fuH8s1lszd1osSuI3F5HzJ2Fk/2blVLDsXuMRSrFflMvHl+nSnPNx7M4rYnGaxWITrMbb5M9Nk3mTscB6AAstYfkg4XDsABVBiq2LBVvNz/8sNaU7N2JNRyOY0i2Terq9BVrEcSaifVNvidYyd8x0DUAC2In0ljYkvXd4rdh8pZIuPE5O7jcXi/eWHhCO1A1CgFxf5btth83pcHzNjOxwyTxjv+hnG8rPVkW3OA1Bg3S87vnUMQIFH/VLvMTMSLGOpV8bqPD68xXkACqzf93fMcwzagP57xzzTIl+5PKc7DxeQcNBiVVzXtlvF4g0vYvFKvVgsnuNDm5wHoDwpI4QQDWiKxOQTgScMw9gCLAPCgJqfWZYqpQqVUplAPrCw+vYEoGf130MMw1hhGEYCcD3gdO2rYRiRwGnAV9V1zAAaTBpiGMadhmFsMAxjQ2ZmIwZoTgQ187RrBIWbJ322Kh8aqRObNiAyLIggl18lW4dbTEhyrQMg3OJSbi/KxLi0w11bnYRG6+S6dVldYu6uXRZl3F3q7drW+mXcxdLwqeu2XzyZLtFY3sQf7tyOqLAgAgOMejFCddxh0TpJuFW9Vvd7VcYqFuc2R4UFWx9j9WIxv5Td9fkCavOXNMgX8YfF1G9rHdFhQfVm0rWxOsZ8du43MpbQaPMrdMJi9GIHdXlz7jfFcVndVnevGa3DQ9zH4k27WrXRSfZdk5zXPC9enPtuYzGbwug2Ft+/V7h77Ylxc96Z1hPe2vxKkLAYwOV+y/eKBl4Tzcq4abdVmfr9YhVLa+rF4odj3238Vp8vGiu88bG4a4M/XpNjXB7TXd86cdcvfvgM45PXMavzOMzN+6If3itcn2Pw4Hl2fUxPpgg3ljfvlb6ox6of3fWLP+IXQvwhNMUglAFMVUqNqP7XXSm1q/q+uuv82uts23Hkq/oYuF8pNRR4Fj2IVVcAkFfn8UcopRpcPkkpNVMpNVopNbp9ew9yy5xIxj3pvJrQyBtg6Qvw20t6GpyV8f/Qq7YACgPGP2U6hadtRAh3n+2YfhIVGsSDVglK4yfpZLk1YvvrdpoZe69OtF5jyFTobD617i/n9yekeoqbATw2sT9BJlPeiIjVCW1rhEbDWX82b1f8+Tq5dI32A2CEyfQ9YNoZvegQ7UiuOnlEZ4Z0iTEpAX+e5BzLnyf1J9BksCM2MpQ7z+pdux0VFsQD51okW/XGqfdBZJ2cVkOvslyFzjWWv5w/gMAAg6tHd3da+Whs77Y6iXNknM5zViM0xrpfBlzknA8tbpDOEWXmtPudYxl2teUKPn+e1J/gQMMRy6T+5lNFojroemp4EMt5gzowpqfjg2H/DlFMtZpWedoDzqtiDr9W57EwM+Hp2gFnRUD1dsPnS1x0GHec6TjGosOCuO8ci2NswCXQbWydBxlsOhURcBPLddYrY45/yiWWZ8y/VEd30q8xNcJidGJ7MwMvgW6nOLY7DPEslog6eW1GXG+9AuN41355BgyD607uTu9YxwDRGX1jOSu+PUR3domlNZzlQSxdT64Ty1B9/AeFVifOr37ugsIcyedPf8gllhusc5ZMeLp2oL82FjMxXfRUl7qxWCXBbSgWE3ed3YfYSMcXz6tHd7NehW28ayzPmu8f0xXG1oklvA2c8YhpkQuGdGJkd8eXvkGdopkywnyxhLvO7u0UyzVjutHHbOou1O+X8yxiad0NTrnLsR3eRk/RNDNoCnSpcyV+x2GWC5LcdXaf2sUCQC/8YToN2Rutu+uE7zU86JeLhnVieDdHvwzpEs2lIzqb13P6nxyLOICeimo2dRd4vPq9EXTqvCcusFh9sk0POPkOx3Z4W52o3syQyx2L2QB0GmE+3RX0ogSt6uSZG32bnrprZsLTtT9qeHTut+2lp4XW8CSWwZdD55GO7c4jLWO5d1wfp/xvN4zt7shF2ZDznm1cLN4YMrV+LGbTXb01wRELRqDuJzPt+uj+rtGqnT62hRDCG0opv/wD9gOxwIvAdBz5p0ZW/38LMN11f9f7gCwgDp1PahHwcfXtzwCPVf+9Griy+m8DGO5JG0eNGqVONEuXLjXfoShLqe3zlNo6R6ln2yr1dLT+9+YYpSorLB8/NTVVzfn2WzV7zmyP27TrcL76YdshlVds86xARblSy/6r9r9/q1KleZ6VyT+k1I9PKrXmHaWqqjwqkpFfqr7bekjN+X6xZ3UopdSR7Urt+FapkhzP9rfblUperrZ+/ZJSlZ7FfzivVD23YLv6YGWystvtnjUrv1R9v61xsew81Mh+qWZ5jNVVXqzUru+USlvvcZHM/TvVlvlvqp+++sDpdltllVqyO0OtTsyq/7xs+lyp7x9T6ugezyqxlSm19P/U/g9uU6o037MyeelK/fiEUmvf1f3qAUcsH3pWh1JKbfq0UbFUVdnVqn2Z6vUvF6mKSs+OfZWXVh3LDI9jUftXK7XwYbXl65c8218plbxxkdq68C2VfzTNswK2UqWW/p9K+WCaUqUFHhXJzc1V33z3nVq5epXH54vav6o6lpc9218pR79k7fNo9/LyCvXT4kXqrfdmqNLSMs/qyEtT6ofHlVo70+N+SU5JUnO+/VZ9+dWXzvVXVKkluzLUmqT658u69b+rrxYsUEczjx57LJn79HtKYYbz7TmpSv3wF6XWeR6LSlmp1MI/qS1fv+LZ/koptfETpb7/s1JZiZ7tbytVaum/9DFWVuhRkaKyCvXT9sNqy4Fcz9vlTSwbPq6OJcmj3SttZWrn4s/UD+8/pyrKij0qU5STobZ+/45KXPed5/2SvKLxsRzaotTOBR6/vpaV29SPi35Rb7830+PzJS27WD2zYLv6dE2K5+d+tUa9jx3c3KhYKstLa/ulsrzEszrKCpTauVCp9I0eNysjaat+f5n7scdlGhuLqqpUKnGJ2jL3Ff23J8oKdB0HN3neruTlSi18WG3+5jXPy9TEUubZe4WOZbHaMvdVj2MpLKtQPyYcVtvSPPwsqpRSSUsbH0tjeRGLV3JT1fY5z+v3Jk8d3NS4fqnWqHPSS01RR1PVI7Ecn/VILI0HbFBuxmH8uTpejX8CrwHbDMMIAFIA66WnHP4BrAMyq/+PcrPP9cA7hmH8HT1YNRvY6ma/li+inU6q+e29YK+TByVrj86j1OecBov+mHCY+2dtp8oeBERS0DGZO86y+IULGNAxmgEdoz1rX6UNPjofDm6kB8CMjXD7EvMVnLKT4IPzHLmkkpd5lHA9LjqMi4Z1YlnOHs/aBvqKkcasdmIY0OssclLtponSa6RkFTP1ndXkFNsAWJOUzXs31cvVVr9Z0WFcOLQTy7I9j2Vgp2gGdvKwX7wV0srjpJQAbP+G2Lm3E6uqGA7Qtbz2CoHgwADO6e9mBaQf/qxXyQGdr+Sm+dDt5Pr71agsh48mwaHN+hibuQluX2x+2XjWPvhgoiPHV8pvcM3nFrHMrY7FXieWu83LfP8YrH+vTiwLoNsY0yIBAQan9Y3Flh5kfjVfjcy98OFER16s/Svgaou8DQlfwzd3QG0skc5XO7jz3SP02vCB/nvr83DzAuhqcixXlOl2Hd6q51rP3AR3LDadApB4tIgr3v2dvBKAXC5M3sTb11usKuc2ljvNy3z3MGz4UP+9+XPLWMoqqpj67hp2HCoHuvD19DXMu/d0t9M7ah3dDR9OcuSSSl0JV5nnnpq/5SAPz9mFXQUBQZR22c9Np/YEICQogHPcrBj213kJfLHuKBBAxPqNfH7HWEZ0a3jaZ6mtiqnvrGHn4Tqx3He6Y8pfbF/9zzSWVTo5tZltX8K8uxz90j3K+WoHdxY86MjPteVzuHkhdDFZ7dFWoo+xIwnVx9gWfYyFmV9tGhEa5NFKpbW2zoZv76kTSzSMcZeusm4sDzhysHgYS+AHExmYkcBAgPcWwO2/mseSsZOIj85nWE1erANXwBUfmLdryywdC4phGNAjxvlqh4Z0Gq7/eaDEVsnlb69h9xEb0Jm5b6/lm3tPIzqs4fNl95ECrnp3DQXVebHW78/l9WtGNrj/Mek8wvLq6lq2YgLfP4+BR3fofpm5sLpfLN5rQ6NgYCM++m7+nLj59xFX2y9KL0xgpTGxgJ6q3OccctMM65XRaoRG6asOPbX5M5h/P6AYjgE92uiV2Kx4Fcu55KYFeBxLZGgQ5w9pxLm/6VN9LtfG0hZOutHz8p7yIhavtO5OZtzpjUt63nmk85VaQgjhBb9Nx1NK9VRKZSmlSpVSdymlhiqlBiulLq6+/2Ol1P2u+7vep5R6RynVSyl1slLqAaXULdW3P6OUeqn67xSl1PlKqeFKqUFKqef8FdcJIySi/m2h7sbvHF79dS9VdbJmv7F4HxVVJoljvbHnB+cErbn7rRNOrn3bOZn53h8hfWPD+x/HPliZXDsABbBoZwZbrRLHtiTL/gWqynnbbpIwvuCwXiK+RkUJrLBYuWnXQudksznJ+kufmTVvOSeZ3/0dHNpiXmbpi86J2Ze9aBHLIdhQ5wuhJ7F4Y81058TsuxbUT1jryjWWpS+aJ43OP+gYtAGdAHqFxepgru3ISdJfgE28vyLZKTH7DwlH2HHIInHs0hfq94tZouX8dNjwkWO7oth5RTo3ftx+mB2HHAl9k7OKmbsp3bxda6Y7JzPf+S0c2W5a5OVf9jotZPDKor01Vw67dTCvlFm/OxLEF9uqeGdZYoP7g45l52HnWOZZxbL6TedYdszTybfNLHm+/jFm1i95B5wT59qKLPuFnfN1suka2ftg6xzzMt5wPcaWvtjwvqCXM3eNpe5Kge7smOecBDhrr+XCF7pf6pwf27/WA4Zmlr5ITZJ5AwVLXjDf3wvfbTvM7iOORMuJR4uYv/mgaZmZvyXXDkABzN9yiMSjhSYlmsj2b+BonWM9a4/1AgveWPoCTv1idYwdz5a4xOKHY6zJLHke5345gWMRQohm1BQ5oURzGHuPcw6C/heaX6WAXn2nrrLKKqdBKZ+wFXt2m9P9JW5uK/JNe5qY63MMUGwzWbmrpXHt64pS84GbylLnL3vuHsNVhbvjxeoY88FxWVFWv61O97uLxQ/HsTfxu5Zx11bX+3F5bbCKxYvn2N354u4208f0Qyzu22VxHvsg/lJbFWYvyaW2qnrjOlbPV7Hb1ySL57jCi/PF9RizlZgPQtlKqN8vVuekm37zxznm2g5bsXksvjgnwToWt/1idV663F9h0S9eKPXiGCspd1PGzW1Nzpv3Cl/U4486mkpLiqXe69gJHIsQQjQjGYRqqdr2hntWwdj7YNL/wTVfWBa5YWwPp+0rRnUj7P/bu+84qcp7j+Ofh630IksRpEsTsABWwAIW0AjW2GLXqzdNY4wxuTFqNDe5mhgTYxI1ptrFFjXGuqJYUFFAiiiwSxEUZGlSFnaf+8dzgJnZmXN2Z5+zs7t+36/XvnZndmZ+5ze/M+eceWbO7ynw/DXgwccnN4AubBPd0Hfkeckz15UMgT5jMt++ETvzwF5JM6gN6tqWg/qGnIrY3KSesnLAuZAfMhtNp37QL+UU0qhTRQafkNzMurBtZHNiRp6fPBtc12HQ65Dw+4xOWY4Dzg0/JXOP/tDviJTHiDiFJxs1chme3BA8nVEpyzHyvNBJCeg8APoennxdVC5DJ0PrhMkgitrBiNND73Lmgb2SmvAP6d6Okb0iZvBJPcVr5PnhpzN03jt5sgSo+XykmDise1ID6LZF+UyOaBpdoy7dRkDP8FMxzzm4V9Llsw7qFTopwYAubTi43+7TTo2Bsw/qnfH2AJOGdUtqAN22OJ/JUY2WR16QnEv3fSNzqfGcjrogfHa8LoOTJ37ARL/2h05J/vClqH1kA+yspK5joy8Kb37fZQj0PizhitrmkrBvKK5FLiMvSJ5RcM/9oUfE6aupuYy6MDyXLEwa3j2pAXS74nxO3Dd8HTvroF5JM3Du27M9I3qGn1bZIPY5KXmmsuIOMPxU/3FSt6e1OUWysUrNJY79XkNpTrmIiORQQ/SEklxYt9T1uNm40l1e/nZkz47LDu9Pn07FzJ03ly2VO7h2SvjsYFlp2QEufQXe+iPLyz6m58k3Rs4SQ6+DXc+F2Y+4Weyi3lQ2YqP7dOKx/z6UJ97/lM5tCzn7wN6hbyqbnbFXQaf+UPYaCza0ZPDEiJmYwA2gzvy7O61uyAnQd1z47Vt1gkteoeLNf1BeXsZ+p3zfzbYTps9hcPELrHznKdp26Eibg84Lf4MMbma7Tv1h4XMs2NqZwRN/VotcHnA9oT5fAPudGZ1LNvqMgYteYOW7O3M5PzqXw6+GPfrBwv+4XI67KTrOmQ+4UyU/X+BmH+w7Nvz2rTrBpaXutV++iJ6n3ORmdApxSP89mHr5oTzw9lL27FjM+Yf2DZ+BEODwH1DVcQCffvwe6yrzGX5sxIw/AGc9VKdcOrUu5MlvjeGJ6bNZWr6Uy0+fxF6dImZV6nc4XPQ8vPvX3TOMRdTligkDGVDSko8WzKdyexXXHB8xMyBw7/mjefjNhVR8/imHjNyfg/uFD3Lv0aaIp749hgdnLGXxknKuPuUwenasQy4de7lcogYujrwWSgZC+RvM39iaIcdcH5kLZz0EM+5xpzztf3b0hw9tStw69vYfWLa0jL1Ouck9174d+SOq9hjEioUz2bA9j2FHXx99n7Mehhl3u9Pq9j/HbXPC7MzlrT+wbNlS9jrlZ9F9W/ofCRf+x50y1m5PN5gcWZcfuZkNl77J/I1tGHJ0LV4vdVTStoinvnUYD85YRlm5W8f27NAy9D7jBpbwyGWHMu3d92nfsYTTDh2M8Tw4lpW2XV1dZv6d8vIyek/5H/dc+3bUT9yMux+/wPzK7gyJmrWwMRt/nZuldtlbzNvUlqFRs7A1ZuN/6mZ2XfYW8za1Y+j463K9RCIiTZIGoZqrN+/cPQAFrr/Eod8Jb4T62VyOe/lsjqtYQmVBe/KW/C20kXnW3rgDZvyJHtVV8EoxnHxXdFPvZtQIcUTPDozomblRcLO3zxTYZwqrSksZXJvBxMJW0Q2/U1z/6jr+8dYIqquHc+KLG7n1tGoKQpp6r9+yncue2c6biw+kuKAF38+r4OKxEd+4WTkbXrwe1pXTt7AjlE+MHoiZ/xRMuxW2bXB9Rc580PsbmPVbtvNfz1by1q5c1nLx2Ij1bVcuS10uSydFv+Gfl5jLvCCX7plvby1Mvx3e+TM9bDW82hYm3xn6jav1m7fzv8/O5+0laykuaEGbogIuGhM+oPjhivVc9u8OLK84lA5Fhj+WVUQOxDDvSXj1FqjcCKvnu8HCiFx6TP8J33z3Xqytxkw7PTIXtlTAC9e5Jt4FrVwj46Apf0affsAJL3+DE9YvZVthR1osvS9y8KLV3Ic4f/o1LpeKA9xgYdvwxrs9OrTkqmMGUVq6kj6d0/QTTLV5bXIuRe1q9xoddgoMO8HfpkIAAB7wSURBVIXPSksZEjUwCq5v1mu3BnVZ4Naxtl0z395a1zfqvb/S01p4tR1M/r33Dy1mL1/H5c92ZMU6t47dvXQdo/uETHwAbh/82q9cLms+cutYrXL5W0Iud0TnsteB4RM3pDLGfZNn+Km1r0sWenZsxfePdetY7z1qt46NfOksRi59EwpaQ5sb4MBLYlm2OuvYG8b/hCWlpfSO+oAjWytmwos3wIbl9CvcA5YdD70OiidW3IyBEafBiNP4vLSUoY1hMDFbzSkXEZEc0ul4zdXWNM2u012X6N/XQMUSAAq3r3czE4U1J85G2evw1u+heodr6jj3MZgdQ+NY+cqa/ska/vpGGVXVFotraPvUB5+G3uee1xbz5mLX/H7r9mp+/ux8lq1N05Ml0bNXw7pyAIoqK4IZc0JsXe9mYdsWNIFeOSuWpqZ3TVvEW4tdk/WduSyviMrl++7bk2SbyweuAXiYRS+7WQ5tlXvtz37Ivf5D/GnaIt5esjuXm5+Zx4p1W0Lvc92TH7K8wt1m3TbLtY/NCb09W9bB099zgwPgmtqX/m9ELi+5b7Ql5fJ4+H2m3+4GbcD1FfnPj11T9DDPXAXrE+ryr+9E5FLh7rMrl5nRuWRj+m9ScvmRa1bv0+a1NXN59Rfh9/nkRdf831ZhqIZZD7iBLM9+8sSHu9bDWq1jqbmseA9e/WX4fT5+3jX/35XL/W6w9KvitV/B0jfd39u/hOd+CBtX5XaZGtLTV8AGt30oqvzCXRYREWkmNAjVXO13dnJviE79avY9SbX6o+TL65emb45aH6vTzNST7jqRLH3yec1GvJ+sDm/O+/Fnyf+vtm6WsFBrUl4vFUtgx7bMt1+/vObrafXC8BhZSM2/2sKSqFxSX/trF8OOyvS3BTdgtSNlMCj1MVKtSZNrxGs/bS6rw3P5OOU+ZV98GT7LZza5pKtb6vpQ4z4p/7dV8EX4zHU1nrMvFkFVSAP0tLn4X8dqPGZtcqmrdeWwY2tK3KjnON3+JeI+WUhdxxat3kR1WMf4ijKoStk2ROaS5v/pXkPNVWr+1Tvc+v9VkVrrGNZjERGRXNEgVHPVdyyc/ywccB6M/T5c8Fz0KW8Dj02+3HsMFLXxu1z9x0OLlOUYeJzfGPKVNm5gSVLzd4CjBncJvc9RQ5L/375lAaN6R5yOl7re9jsS8osy375kMHRI6YGU+przYPzg5FN8OrQqYGRdc+l/VHjD+C5DoUNy0+zIXAZMSJ5gAAN7h99nfEpdOrQq4IDe4acWjk+p9di9S0JPxaTLUGgffy41HrNlJ+gZcdpU6n0GjA8/5a/LPtA+pQdSDOtYjcdstUd0Y/K66joc2qX0QIqsy9HJDdNrU5cspG5PjhzUJbxXWbfh0C6lcX1ULnuny+Xoui1oU5b6/LQuiW6y3pyk5q/jJBERaUbUE6o5632I+6mtib+EwtawuJRVLbrR7dS7/C9Tp75w1oPw2m1sWPsZ7SZc3WRnupPGqW/n1tx93ij+8Moi1lSs47sTR0T2azl91F5s2LKdx2auoKRtEVcdM5DWRRGbx0m3uNkdy15jVYs96XZyxOulRR6cM9X1Xlq7BIZ8DQ7zf4rF6aP3YsPW3bl8/5hBtCqMyOX4W92Ac9nrQS53h9++RR6cPRVeusHlMvTE6Fw67+2azL/+GzZUrKHdMddE9jj5+uhebNiyg8ffX0GXdkVcdXR0Lj+bMow2xfm8vXgtXQu2ctvp+4YvV14+nPMovHRjQi7fDb9PycAgl9tYv24t7Y+5BvaKGIQ54DzYugFmP+x6NB31P67fWZjjf+36LZVPZ2VeD7qf9Kda5DLV9ZKpKHMzEh4acWplNkaeD9s2ulzadYcjfxydS13tzOWlG10u+0yBQ74Vfp8ug+GM+2D67a4ux14LPf0PXPz85OG0b1nAjCVr6VawlV+dFrWOFcA5j7nXS0W5m2HtkG+G36fLEPj6P10u6ytcLl+lQZjRF0PlJpgz1fXNO+p/oKA410vVcL72W2jZEZa+xcq8nnSffEeul0hERMQbDULJboWt3UAUsKC0lG5hTVPrY8AEGDCBmaWlHLHvEfHEkK+0Iwd14chBXSgtLeWI/XpE3wG4eGw/Lh7br9Yx1lcVc8OX5/D2xkl0K6rkjqo2hLSydnZsdW/eKze6N1jWc881oLrasmnbDr6s3EGrbXls3VEVfaeq7bBtE1R+SV7BFqgKORVvp5KB7g1/XWzfDJUbyavaWutTfS8Z149LxtW+Lm2LC7hpynAASktL2aNNyLfTdioZVPdcKr+EbZvI37HF/R2h2sLtm4/jiU3D6WKKuKayF6Mi77TDrSeVX5JXuMXVKcqOrcH6FfzYasDzbKLGwGHfcT9x2pXLJve6qU0udaxLNtoVF3DzSbvXsY6tQ741uFOXwa5JfF0MngSDJ/F+aSlH7HNE3Re0KTMGxlzpfr6KWnaAr90OwEelpXRvFdH4XkREpAnR6XgiIln48RNzeGzmClas28J7n1Xx3Qc/CL9D1Q64/+uw5FXXu+fNO9zMX57dN2Mpv3nxY8q/2My75RVc+Jd32Lg1YvDime/B7Adh/TJK1rwJU2OYheqzefDohbBqDq03L3XNz5e85j9OQ/hsLky9CD5LyKVseuhd/vl2Obe/5OryTlkFF/z1HTZtC+nvBPCv77qm5+uX0WX1G/D4peG331EJ950OS6a5deyN37kZ1pqiHZVw/85cyuGN37qG6GFWzYGpF++uy5PfhPI3G2Z5RURERKRWNAglIpKF1z5ek3R5xpK1bAv71tHqBbAhZQaxRS/7X66Fq5Mub9y2g/eXRsyMmboc5a+HNybPxuLSmt/8iiH/BpFFLtMWJq8vG7fu4IPIurySfHnJtPDG5J/Pg00pM4g11ef487mw6bPk6z6JyGVxKZDSILyp5i8iIiLSTGkQSkQkC0O6t0263K+kNUX5IacKdewDhcn3oeuwGJarXdLlvBaGgV3bZrj1zuUYnny588DwxuTZ6JYm13TXNQXp6haRy9CU9SW/hWFg14iJH1Ifs2RweGPyTv2goHXydTGsYw2iUz8oSOkzFbW+ZFEXEREREWlYGoQSEcnCTVOG7RpEKGlpuDWqOXFRG5jye2jV2V3uPcY12/Xs0nH9mDCkC8ZA2+J8bpoyjG7tIxr6nvBrN8ABbCnuClP+4H256DsOxnwP8oqoNvkw6iIYepL/OA2h3+GuV01iLkMmh97l0sP775pVrV1xPjefNIwu7aLqcltCXbrB5DvDb1/cDibf4WarA+gzFo64tlYpNTrF7ZNz6TsuOpf+R7qm8jvrMvoSGHJi/MsqIiIiIrWmxuQiIlkYUNKG549YxtaPX2VpZTsG9qjF9OlDJ8OgSa7JckyNZlsX5XPPeaNZv2U7LQvyKMyvxWcNnQe6mccWvcTybZ3Zu3vEgFq29j8Htm1k1fJy9hx9EbRowp+DTLgexv2A6dNeZeyEiZE3b1OUz73nj2b95u20LKxtXQa5WdQWvcyybSUMrE1dhp3sZl6McR1rMMNOcYNIdcnl6Bvh8GuYPm1areoiIiIiIg1Lg1AiItl45WaYdgvFwECAp7fC5N9H3y+voEEGB9q3LKj9jV++aVeT9L0hyMXzlOCbPoe7j4Kt69gT4J7X4bLXYY/+fuM0pMJWVOW3rNNd2reqS11u3NVYfCDAs5W7ZswK1UDrWIPIJpfC1nWui4iIiIg0jCb8MbSISA7N/Hvy5VkP+W/m3VBq5PIgVEXMqFdX85+CrQmNuLdvhjmP+I3R3KTW5YP7oTqk+b2IiIiISCOnQSgRkWy07Jh8ubgdtGiiXy6tkUt7MCFN1n3EAGjZTL6tE5e0ddFuW0RERESaLh3NiohkY/x10MKdWmVpAeN/2nR7HE34aXIuE2LIZfDXYK+Dd1/usg/se4bfGM3N+NS6XA/G5HSRRERERETqo4l+bC8ikmODj4fznoY5DzN7Ww/2HXlerpcoe4OPh/P+BXMecbkccK7/GPmFcO6TMP12yhZ/TJ+zboPitv7jNCdDT2TJuTN4Z9YczPZKTtv/tFwvkYiIiIhIvWgQSkQkGx9OhakXg61mX4AereHgy3K9VNmZ8yg8dsnuXHq2gYP+y2+M7Vvh3mNg5Sz6ANw1Ey55Kf1pegLAkx+s4MqH5lNt84F8tvQo49xD+uR6sUREREREstZEzx0REcmxV34Otnr35dKfN92m0am5vPJzqK7OfPtszH8KVs7afXntIvjgAb8xmplfPb+Qarv78q9fWIi1NvMdREREREQaOQ1CiYhko3Jz8uXtW5MHcpqS7am5bPGfS+WXtbtOdtlcmTyouaWyKmlQSkRERESkqdEglIhINkZfmHz5gHMhryA3y1Jfoy5KvjzyPMjzfLb20MnQumT35aJ2MOJ0vzGamXMO7pV0+ayDepHXQo3JRURERKTpUk8oEZFsjLsaOvWHhc+xYGtnBk/8Wa6XKHuHXw2dB0DZ6yzY0IrBx93gP0arTnBpKbz1R5aXL6LnKTdBx97+4zQjV0wYyIAubXh78VqKv1zJtccPzfUiiYiIiIjUiwahRESysXI2vHg9rCunb2FHKJ8Ifcfmeqmyt89JsM9JrCotZXCLGL4kay1Mvx3e+TM9bDW82hYm3+n/G1fNzAkj9uSEEXtSWrqGFvoWlIiIiIg0cTodT0QkG89eDevKASiqrICnvp3jBWrkFr0MM+4CW4XBwuyHYO5juV4qERERERFpQBqEEhHJxpqPki9XLIEd23KzLE3BmoU1r1u9oOGXQ0REREREckaDUCIi2Rh4XPLlfkdCflFulqUpGDABWiSeemdg72NztjgiIiIiItLw1IxDRCQbk26BwjZQ9hqrWuxJt5PvyvUSNW6d94Yz7ofXf8OGijW0O+Ya6HVQrpdKREREREQakAahRESyUdQWjr8VgAWlpXRrU5LjBWoCtm+Gyo3kVW11f4uIiIiIyFeKBqFERCR+n82DRy8EW01rcI3cO/Zt2jMKioiIiIhInagnlIiIxG9xKdjq5OsWvZyTRRERERERkdzQIJSIiMSv27DaXSciIiIiIs2WBqFERCR+fcfBmO9BXhHVJh9GXQRDT8r1UomIiIiISAPSIJSIiDSMCT+Fa8qYftg/4IRfQwvtgkREREREvkr0DkBERBpOYSuq8lvleilERERERCQHNAglIiIiIiIiIiKx0yCUiIiIiIiIiIjEToNQIiIiIiIiIiISOw1CiYiIiIiIiIhI7DQIJSIiIiIiIiIisdMglIiIiIiIiIiIxE6DUCIiIiIiIiIiEjsNQomIiIiIiIiISOw0CCUiIiIiIiIiIrHTIJSIiIiIiIiIiMROg1AiIiIiIiIiIhI7DUKJiIiIiIiIiEjsNAglIiIiIiIiIiKx0yCUiIiIiIiIiIjEToNQIiIiIiIiIiISOw1CiYiIiIiIiIhI7DQIJSIiIiIiIiIisdMglIiIiIiIiIiIxE6DUCIiIiIiIiIiEjsNQomIiIiIiIiISOw0CCUiIiIiIiIiIrHTIJSIiIiIiIiIiMROg1AiIiIiIiIiIhI7DUKJiIiIiIiIiEjsNAglIiIiIiIiIiKx0yCUiIiIiIiIiIjEToNQIiIiIiIiIiISO2OtzfUy5IwxZjVQnuvlqKPOwJpmEke5NM44yqVxxlEujTOOcmmccZRL44yjXBpnHOXSOOM0lxgNFUe5NM44yqVxxmmoXHpba0tSr/xKD0I1RcaYd621o5pDHOXSOOMol8YZR7k0zjjKpXHGUS6NM45yaZxxlEvjjNNcYjRUHOXSOOMol8YZp6FyyUSn44mIiIiIiIiISOw0CCUiIiIiIiIiIrHTIFTTc1cziqNcGmcc5dI44yiXxhlHuTTOOMqlccZRLo0zjnJpnHGaS4yGiqNcGmcc5dI44zRULmmpJ5SIiIiIiIiIiMRO34QSEREREREREZHYaRCqCTHGHGeM+cgY84kx5ocxPP5exphXjDHzjDFzjTHf9R0jIVaeMeZ9Y8zTMcboYIx51BizwBgz3xhzSExxrgyerw+NMQ8YY4o9POa9xpjPjTEfJlzXyRjzgjHm4+B3x5ji3BI8Z7ONMY8bYzr4jpHwv6uMMdYY07k+McLiGGO+HeQz1xjzf75jGGP2M8a8ZYz5wBjzrjHmwHrGSPs69F3/kDje6h+1TfFV/7A4vuof8nz5rn+xMWaGMWZWEOeG4Pq+xpi3g+3/Q8aYwhhi3BfsYz4M1vWCOHJJ+P9vjTGb4ohhnJuNMQuN2/5/J6Y4440xM4P6v26MGVCfOMFjJu0ffdY+Io7X+qeLkXB9vWsfFsd3/TPEiKP2ZcaYOTu3J8F1vrf96WJ43e9nipPwP1/b/rQxjMf9fqY4xv+2v8Yxq+/ah8TxfdyX8fjbV+3D4visf4bny3ftBwWPtfNngzHmCp/1D4nhu/Zp4yT8v971D4vhufaZnjPf9a/xPtLEsN/PEMf7fr/WrLX6aQI/QB6wCOgHFAKzgKGeY3QHDgj+bgss9B0jIdb3gPuBp2N8zv4GXBz8XQh0iCFGD2AJ0DK4/DBwvofHHQccAHyYcN3/AT8M/v4h8MuY4hwD5Ad//7K+cdLFCK7fC/gPUA50jimXI4EXgaLgcpcYYjwPTAz+ngSU1jNG2teh7/qHxPFW/7Btis/6h+Tirf4hMXzX3wBtgr8LgLeBg4NtyxnB9X8ELo8hxqTgfwZ4oD4xwuIEl0cB/wA2xfR8XQD8HWhR39pHxFkIDAmu/2/gr/WJEzxO0v7RZ+0j4nitf7oYPmsfkYvX+meIEUfty0jZHuJ/258uhtf9fqY4wfU+t/3pcvG63w+J43vbX+OY1XftQ+L4Pu5Le/zts/Yhufg+7ksXw2vtU+LlAauA3nHUP00M76/9dHHiqH+aXLy/9jPE8VZ/MryPxPN+PySO9/1+bX/0Taim40DgE2vtYmttJfAgMNlnAGvtSmvtzODvjcB83ErrlTGmJ3A8cI/vx06I0R43YPBnAGttpbV2XUzh8oGWxph8oBXwaX0f0Fo7DVibcvVk3M6Q4PeUOOJYa5+31u4ILr4F9PQdI3Ab8APAS2O6DHEuB35hrd0W3ObzGGJYoF3wd3vqWf+Q16HX+meK47P+EdsUb/UPieOt/iExfNffWmt3fkOkIPixwFHAo8H19ap/phjW2meD/1lgBvV/7aeNY4zJA27B1b9eQp6vy4EbrbXVwe3q+9rPFMdr/VP3j8YYg8faZ4oD4Lv+6WL4rH1YHDzXP0MMr7UP4X3fn8r3fj+C131/Gl73+yG81T/kmNVr7TPF8Vn/iONvb7UPieOt/iEx4nztjwcWWWvLie+1vytGzK/9xFwgntd+Yow4X/uJcXzXP/V95Epi2O+nifOp7/1+XWgQqunoASxLuLycGAaIdjLG9AH2x33a69tvcBuh6hgee6e+wGrgL8Z9ff4eY0xr30GstSuAW4GluI3Gemvt877jBLpaa1cGf68CusYUJ9GFwL99P6gxZjKwwlo7y/djpxgIjA2+0vqqMWZ0DDGuAG4xxizDrQvX+nrglNdhbPUPeb17q39ijDjrn5JLLPVPieG9/sad9vMB8DnwAu5bsOsSDhTrvf1PjWGtfTvhfwXAN4Dn6hMjJM63gKcS1uc4YvQHvh58Vf7fxpi9Y4pzMfCsMWY57jn7RT3DpO4f98Bz7TPE2cVj/dPF8Fr7kDi+658uhu/ag3tz87wx5j1jzKXBdb63/eliJPK13a8RJ4Ztf7pc4tjup4vjc9uf6ZjVd+1rc2xc3/qnjRFD7TPl4rP+mWLEdtwHnIH7RgrEd9yXGCOR72P+XXFiPO5LzCXOY/7EON7qn+59JPAenvf7Ue9XfR731ZYGoaQGY0wbYCpwhbV2g+fHPgH43Fr7ns/HTSMfd9rUH6y1+wNf4r7K6pVx52dPxu2o9gRaG2PO8R0nVTBiHdeniAAYY34M7ADu8/y4rYAfAdf5fNwM8oFOuNNmrgYeDr5Z4NPlwJXW2r2AKwk+MauvsNehz/pniuOz/okxgseMpf5pcvFe/zQxvNffWltlrd0P94nUgcDg+j5mVAxjzLCEf98JTLPWvhZDnHHAacDv6vvYITGGAUXAVmvtKOBu4N6Y4lwJTLLW9gT+Avw628dvqP1jLeLUu/7pYhhj9sRz7UNy8Vb/kBjeap9gjLX2AGAi8M3g9bKLp21/xhie9/vp4vje9qeLEcd+P10cn9v+yGNWT7UPjeOp/uliXI//2mfKxWf9M8WI67ivEDgReCT1f76O+zLF8H3MnxgnruP+NLnEcsyfJo63+qd7HwkcV68FrmWclPer3o77akuDUE3HCty5tDv1DK7zKhgJnQrcZ619zPfjA4cBJxpjynCnFB5ljPlnDHGWA8sTPt1/FLcj8W0CsMRau9paux14DDg0hjgAnxljugMEv+P6ijnGmPOBE4Czgx2fT/1xG8FZwXrQE5hpjOnmOQ649eCx4JumM3CfYte7GWaK83B1B7eDqleDQsj4OvRe/0yvd5/1TxMjlvpnyMVr/TPE8F7/nYKv/b8CHAJ0MO4r1OBx+58Q4zgAY8xPgRJc/xtvEuIcCQwAPgnq38oY84nnGMcR1D741+PACB8xUuJMBPZN2M88RP22/zX2j8Dt+K99xv2wx/qny2Uu/mufKRef9U8X4xn81h7Y9Wn1ztNIHsdtT7xu+zPE8L7fTxPncDxv+zPk4n2/nyGOz21/pmNW3/v9jMfGHuufKYbv/X6mOD7rnylGXPv9icBMa+1nweU4jvtTY8R1zJ8YJ67j/tRc4jrmT43js/7p3kcehv/9fsb3q3Ed90XRIFTT8Q6wt3Hd8gtxXwt8ymeAYLT4z8B8a62PT/RqsNZea63taa3tg8vhZWut928OWWtXAcuMMYOCq8YD83zHwX2t8WBjTKvg+RuP6xUTh6dwGz6C30/GEcQYcxzutIMTrbWbfT++tXaOtbaLtbZPsB4sxzV8XuU7FvAE7k0vxpiBuKaSazzH+BR3cA3ujdbH9XmwkNeh1/pniuOz/ulixFH/kOfMW/1DYviuf4kJZqcxxrQEjsZtU14BTg1uVq/6Z4ixwBhzMXAscKYNeunUR4Y471lruyXUf7O1NutZxTLlQkLtcfVZmH0moXVpH6xbJFyXlQz7x7PxWPuQOOf4rH+GGB191j4sFzzWP10M3CfK3moPYNwpS213/o1rGPwhHrf9mWL43u9niPOOz21/yPPldb8fEsfbtj/kmNXrfj9THJ/1zxBjpu/9fshz5q3+ITG87vcTnEnyaXJxHPcnxYjxmH9XnBiP+1Ofr7iO+VPj+Kx/uveR8/C8388QZ77v4746sQ3UAV0/9f/BdbBfiOsP8uMYHn8M7ques4EPgp9JMeZzBPHOjrcf8G6QzxNAx5ji3IB70/MhbsafIg+P+QDunN3tuI31RbjeIC/hNnYvAp1iivMJrv/YznXgj75jpPy/DD+zpKTLpRD4Z1CbmcBRMcQYgzt/exauR9DIesZI+zr0Xf+QON7qX5ttio/6h+Tirf4hMXzXfwTwfhDnQ+C64Pp+uKaRn+A+ect6OxMSYwdu/7Izv+viyCXlNvWdHS9TLh2AZ4A5wJu4b63EEeekIMYsoBToV584CfGOYPcsbN5qHxHHa/3TxfBZ+4hcvNY/QwyvtQ/qPCv4mUtwnIfHbX9IDN/7/bRxUm5TRj22/SG5+N7vZ4rje9tf45jVZ+0j4viuf+jxd31rH5GL7/qni+G19kGc1sAXQPuE63wf96WL4bX2meL4rn+GXLzWPiSO79d+jfeRxLDfzxAnlv1+bX5MsFAiIiIiIiIiIiKx0el4IiIiIiIiIiISOw1CiYiIiIiIiIhI7DQIJSIiIiIiIiIisdMglIiIiIiIiIiIxE6DUCIiIiIiIiIiEjsNQomIiIg0AsaYKcYYa4wZnOtlEREREYmDBqFEREREGoczgdeD3yIiIiLNjgahRERERHLMGNMGGANcBJwRXNfCGHOnMWaBMeYFY8yzxphTg/+NNMa8aox5zxjzH2NM9xwuvoiIiEitaBBKREREJPcmA89ZaxcCXxhjRgInA32AocA3gEMAjDEFwO+AU621I4F7gZtzsdAiIiIidZGf6wUQEREREc4Ebg/+fjC4nA88Yq2tBlYZY14J/j8IGAa8YIwByANWNuziioiIiNSdBqFEREREcsgY0wk4ChhujLG4QSULPJ7pLsBca+0hDbSIIiIiIl7odDwRERGR3DoV+Ie1tre1to+1di9gCbAWOCXoDdUVOCK4/UdAiTFm1+l5xph9crHgIiIiInWhQSgRERGR3DqTmt96mgp0A5YD84B/AjOB9dbaStzA1S+NMbOAD4BDG25xRURERLJjrLW5XgYRERERScMY08Zau8kYswcwAzjMWrsq18slIiIikg31hBIRERFpvJ42xnQACoGfaQBKREREmjJ9E0pERERERERERGKnnlAiIiIiIiIiIhI7DUKJiIiIiIiIiEjsNAglIiIiIiIiIiKx0yCUiIiIiIiIiIjEToNQIiIiIiIiIiISOw1CiYiIiIiIiIhI7P4fhdGv4rJVoY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v90hFuSn_bdA"
      },
      "source": [
        "Sex seems to be an important feature in differentiating survival. Males in the age from 1 to 12 i.e kids have survived more as compared to adult males & teens. 80 year old man has survived.\n",
        "Females of almost all ages seem to have survived."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIdj5EFq_4_T"
      },
      "source": [
        "Age & Pclass:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "eV8zBR5f_PQ2",
        "outputId": "7bad9988-5f48-498d-85ec-a20df1681aa9"
      },
      "source": [
        "swarm_survivors(train_data, \"Pclass\", \"Age\", 'Survival according to Age and Sex')"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwT9ego0AoIl"
      },
      "source": [
        "Pclass is also an important variable. It shows that class 1 people have very few children on board as compared to class 2 and 3. Class 2 has maximum chidren on board. As class 1 people are travelling alone , they have mostly survived."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o853LGEBSjZ"
      },
      "source": [
        "Fare and Sex:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "GnWXVvO8AI_M",
        "outputId": "4397e191-5471-4bfc-a263-c846a1204494"
      },
      "source": [
        "swarm_survivors(train_data, \"Sex\", \"Fare\", 'Survival according to Fare and Sex')"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 42.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 12.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yXxEmywB5p6"
      },
      "source": [
        "As seen from the plot, \n",
        "1. Irrespective of gender, all have survived who paid more than 500$.\n",
        "\n",
        "2. All males who paid between 200 -300$ have died.\n",
        "\n",
        "3. All females who paid between 200-300 dollars have survived.\n",
        "4. Most of the males who paid between 0 to 100 dollars have died but most of the females with same fare have survived.\n",
        "5. 0$ fare needs to be imputed correctly"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DoeNorT6C40B"
      },
      "source": [
        "Fare & Pclass:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "X1Uk5pjnBgjV",
        "outputId": "cfd802d0-5593-4cdf-b861-446d26c2ccb2"
      },
      "source": [
        "swarm_survivors(train_data, \"Pclass\", \"Fare\", 'Survival according to Fare and Pclass')"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 21.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 64.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNtVtSj8DL1L"
      },
      "source": [
        "1. Here, most of the people from class1 have survived as the Fare seems to be higher than class 2 & 3\n",
        "2. No one has paid more than ~90dollars in class 2 and 3.\n",
        "3. Class 2 & 3 fare seems to be almost similar , but still more people from class3 have died, this might be confusing for the model. Fare and Sex seems to be better differentiating the survivals"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNDbna5OERWY"
      },
      "source": [
        "Feature Engineering:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4hUSU77DCjI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e52393dd-6f01-4895-f1a2-c3af10f9b1c5"
      },
      "source": [
        "train_data.columns"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DHZ6fkBpiqe"
      },
      "source": [
        "Combine SibSp & parch as both represent number of family members"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "5Vh_qfLYncd3",
        "outputId": "3d63f012-9aa9-4b0b-e3d9-09a5109034b0"
      },
      "source": [
        "train_data['Family_Size'] = train_data['SibSp'] + train_data['Parch'] + 1\n",
        "train_data.head()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family_Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Family_Size\n",
              "0            1         0       3  ...   NaN        S            2\n",
              "1            2         1       1  ...   C85        C            2\n",
              "2            3         1       3  ...   NaN        S            1\n",
              "3            4         1       1  ...  C123        S            2\n",
              "4            5         0       3  ...   NaN        S            1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Ji3N09oWpiEc",
        "outputId": "dd1b960d-1951-41ca-8056-44333524deeb"
      },
      "source": [
        "sns.countplot(x='Family_Size', hue= \"Survived\",data=train_data)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd89d49d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "W20k-sIopiC4",
        "outputId": "b7c19b38-3bf3-4e95-ecc6-58bc0b49c127"
      },
      "source": [
        "sns.barplot(x='Family_Size', y= \"Survived\", data=train_data)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd89defd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8Oi0sZ7ph-S",
        "outputId": "73fdb780-75d3-4ebb-fc2a-77898625bfe7"
      },
      "source": [
        "train_data.Family_Size.unique()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  1,  5,  3,  7,  6,  4,  8, 11])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "gEXgQ2nigKfv",
        "outputId": "d4df5cee-a55e-4a72-bee9-5f1cd9b93e2a"
      },
      "source": [
        "test_data['Family_Size'] = test_data['SibSp'] + test_data['Parch'] + 1\n",
        "test_data.head()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family_Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Embarked Family_Size\n",
              "0          892       3  ...        Q           1\n",
              "1          893       3  ...        S           2\n",
              "2          894       2  ...        Q           1\n",
              "3          895       3  ...        S           1\n",
              "4          896       3  ...        S           3\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpLv_uTaPYJL"
      },
      "source": [
        "Name :\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsLNdndRPgzX"
      },
      "source": [
        "I will be extracting titles from names which can be used as a useful feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmMWBtqwQz-i",
        "outputId": "2c058381-77df-4549-931c-c79b4f81c622"
      },
      "source": [
        "train_data.Name[0:5]"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                              Braund, Mr. Owen Harris\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
              "2                               Heikkinen, Miss. Laina\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
              "4                             Allen, Mr. William Henry\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AC3eUt9LPfj_"
      },
      "source": [
        "train_data['Title'] = train_data['Name'].apply(lambda x : x.split(sep=',')[1].split(sep='.')[0].strip())"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "eS3_4qXNph7r",
        "outputId": "ab83b054-c1f2-4375-942a-7be620629a1a"
      },
      "source": [
        "train_data.head()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Embarked Family_Size  Title\n",
              "0            1         0       3  ...        S           2     Mr\n",
              "1            2         1       1  ...        C           2    Mrs\n",
              "2            3         1       3  ...        S           1   Miss\n",
              "3            4         1       1  ...        S           2    Mrs\n",
              "4            5         0       3  ...        S           1     Mr\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "SR0vCuvCPc8c",
        "outputId": "900a25c2-1ddb-46d3-8081-8006cdf04eca"
      },
      "source": [
        "#analyze new title feature\n",
        "sns.barplot(\"Survived\", \"Title\", data=train_data)\n",
        "plt.figure(figsize=(40,20))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2880x1440 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x1440 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "-434Z-W7RDeX",
        "outputId": "99993ced-a886-4845-f85e-07de10281242"
      },
      "source": [
        "train_data.Title.value_counts().to_frame()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mr</th>\n",
              "      <td>517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs</th>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dr</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rev</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>the Countess</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ms</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Capt</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Title\n",
              "Mr              517\n",
              "Miss            182\n",
              "Mrs             125\n",
              "Master           40\n",
              "Dr                7\n",
              "Rev               6\n",
              "Major             2\n",
              "Col               2\n",
              "Mlle              2\n",
              "the Countess      1\n",
              "Ms                1\n",
              "Capt              1\n",
              "Sir               1\n",
              "Mme               1\n",
              "Jonkheer          1\n",
              "Lady              1\n",
              "Don               1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sci2tNcRv45"
      },
      "source": [
        "train_data.Title.replace(['Ms', 'Lady', 'the Countess', 'Mme', 'Mlle'], 'Miss', inplace=True)\n",
        "train_data.Title.replace(['Jonkheer', 'Sir', 'Don', 'Capt', 'Major', 'Col'], 'Mr', inplace=True)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "IlFufvQbTmFv",
        "outputId": "b8ee24ee-3e10-4b1f-c3e6-bc2dead5d985"
      },
      "source": [
        "train_data.Title.value_counts().plot(kind='bar')"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd6c8a990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "JFWIqu4gTocn",
        "outputId": "db00af44-2eec-4a00-832d-f4bebf7afc98"
      },
      "source": [
        "sns.barplot('Title', 'Survived', data=train_data)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd6bfc250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzCHqETfUNlJ"
      },
      "source": [
        "As seen,\n",
        "1. Females have survived the most\n",
        "2. Doctors too have survived in large numbers ~42%\n",
        "3. 18% of males have survived\n",
        "4. Master i.e male kids have surprisingly ~58% survival rate\n",
        "5. 6 Revernands have died."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpVLaOi8gV7w"
      },
      "source": [
        "test_data['Title'] = test_data['Name'].apply(lambda x : x.split(sep=',')[1].split(sep='.')[0].strip())"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyW1jgdngdpM"
      },
      "source": [
        "test_data.Title.replace(['Ms', 'Dona'], 'Miss', inplace=True)\n",
        "test_data.Title.replace(['Col'], 'Mr', inplace=True)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZX81aL6hg-L",
        "outputId": "2eab31f8-8e02-45fb-f424-ed0dec9140cf"
      },
      "source": [
        "test_data.Title.value_counts()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr        242\n",
              "Miss       80\n",
              "Mrs        72\n",
              "Master     21\n",
              "Rev         2\n",
              "Dr          1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Z9Ulmdl1oMQ"
      },
      "source": [
        "Impute NA values of Age & Fare features:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1x2vKWM-1miV",
        "outputId": "cf4170fb-01fb-4d05-cb62-7cfc326f44bb"
      },
      "source": [
        "mean_age = train_data.Age.mean()\n",
        "print(round(mean_age))"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmmrRDsO2ySS"
      },
      "source": [
        "As seen before under Age plot, its distribution is not exactly a normal one , but most of the people are between 20 to 40 years old, hence I am taking mean value as a substitute for NaNs in this feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oKgx5w_3IkA"
      },
      "source": [
        "train_data.Age.replace(np.NaN, round(mean_age),inplace=True)\n",
        "test_data.Age.replace(np.NaN, round(test_data.Age.mean()), inplace=True)"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCLmtcR23Ihp",
        "outputId": "d4a71333-bced-4026-a4b0-7275ba9d9edb"
      },
      "source": [
        "train_data.Age.isna().values.any()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v65oA4bJeVC-",
        "outputId": "4c5a060f-0c10-47c9-b0d5-84bdb5adf5f5"
      },
      "source": [
        "test_data.Age.isna().values.any()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzfbriQf5XIt"
      },
      "source": [
        "Fare feature's distribution was extremely left skewed so, replacing NaNs with median will be more useful as median is not much affected by outliers in the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojmhkvE138jw",
        "outputId": "20f88001-393c-43d1-f332-48d855403076"
      },
      "source": [
        "med_Fare = train_data.Fare.median()\n",
        "print(med_Fare)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOL800bn5vB3",
        "outputId": "ddcf4555-2d78-43b0-d5c0-ef8cb9dc5123"
      },
      "source": [
        "train_data.Fare.isna().sum()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJg2j0Tg58Ry"
      },
      "source": [
        "train_data.Fare.replace(np.NaN,med_Fare,inplace=True)\n",
        "test_data.Fare.replace(np.NaN, test_data.Fare.median(), inplace=True)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIzsQbxR6Imz",
        "outputId": "8e666d8f-0b31-413d-ee18-079b9f6d81ab"
      },
      "source": [
        "train_data.Fare.isna().values.any()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xFD6yKyfLVJ",
        "outputId": "dab12890-e458-4920-83f3-95b4a13194cb"
      },
      "source": [
        "test_data.isna().sum()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      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",
              "Cabin          327\n",
              "Embarked         0\n",
              "Family_Size      0\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dNf2Q5U7Nib",
        "outputId": "1cdafa37-2eff-432f-8c17-28de1082b428"
      },
      "source": [
        "train_data.isna().sum()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "Cabin          687\n",
              "Embarked         2\n",
              "Family_Size      0\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsaaRUfGB4Ey",
        "outputId": "9d41e877-0787-48e9-d84b-b6925eeb17a0"
      },
      "source": [
        "train_data.Embarked.unique()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAIlw4NP_gEl"
      },
      "source": [
        "train_data.Embarked.replace(np.NaN, 'S', inplace=True)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzg-YOGbVGxO"
      },
      "source": [
        "Modelling:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xzp5DQ16-4ql"
      },
      "source": [
        "cat_cols = ['Pclass', 'Sex', 'Family_Size', 'Title', 'Embarked']\n",
        "numeric_cols = ['Age', 'Fare']"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbP70MlmKMgF"
      },
      "source": [
        "def num_encode(input_num):\n",
        "  arr_num = np.asarray(input_num)\n",
        "  arr_num = arr_num.reshape(-1,1)\n",
        "  enc = MinMaxScaler()\n",
        "  num_enc = enc.fit_transform(arr_num)\n",
        "  return num_enc"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-9Wt-caJvrF",
        "outputId": "d3727340-f3d0-4b93-9ae2-16307c37d06d"
      },
      "source": [
        "num_enc = []\n",
        "for i in numeric_cols:\n",
        "  num_cat_enc = num_encode(train_data[i])\n",
        "  print(\"Feature {0}, shape{1}\\n\".format(i, num_cat_enc.shape))\n",
        "  num_enc.append(num_cat_enc)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Age, shape(891, 1)\n",
            "\n",
            "Feature Fare, shape(891, 1)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhiZh6giiYYN",
        "outputId": "e1790fe4-fd34-429d-aea3-6f9014a84ca9"
      },
      "source": [
        "num_enc_test = []\n",
        "for i in numeric_cols:\n",
        "  num_cat_test = num_encode(test_data[i])\n",
        "  print(\"Feature {0}, shape{1}\\n\".format(i, num_cat_test.shape))\n",
        "  num_enc_test.append(num_cat_test)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Age, shape(418, 1)\n",
            "\n",
            "Feature Fare, shape(418, 1)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QhgHMmyT93G"
      },
      "source": [
        "#One hot encode categorical columns\n",
        "def onehot(input_cat):\n",
        "  arr_cat = np.asarray(input_cat)\n",
        "  arr_cat = arr_cat.reshape(-1,1)\n",
        "  encoded = OneHotEncoder(handle_unknown='ignore')\n",
        "  input_cat_enc = encoded.fit_transform(arr_cat)\n",
        "  \n",
        "  return input_cat_enc"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mf9PmliJ--yL",
        "outputId": "6fceb09a-1678-42fa-8c1c-4abfbc3b8712"
      },
      "source": [
        "cat_cols_enc = []\n",
        "for i in cat_cols:\n",
        "  onehot_col = onehot(train_data[i])\n",
        "  print(\"Feature {0}, shape{1}\\n\".format(i, onehot_col.shape))\n",
        "  cat_cols_enc.append(onehot_col)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Pclass, shape(891, 3)\n",
            "\n",
            "Feature Sex, shape(891, 2)\n",
            "\n",
            "Feature Family_Size, shape(891, 9)\n",
            "\n",
            "Feature Title, shape(891, 6)\n",
            "\n",
            "Feature Embarked, shape(891, 3)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-NP2omBiqep",
        "outputId": "f1c21d46-3a4d-49a5-d561-da74e1660d16"
      },
      "source": [
        "cat_test_enc = []\n",
        "for i in cat_cols:\n",
        "  onehot_col_test = onehot(test_data[i])\n",
        "  print(\"Feature {0}, shape{1}\\n\".format(i, onehot_col_test.shape))\n",
        "  cat_test_enc.append(onehot_col_test)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Pclass, shape(418, 3)\n",
            "\n",
            "Feature Sex, shape(418, 2)\n",
            "\n",
            "Feature Family_Size, shape(418, 9)\n",
            "\n",
            "Feature Title, shape(418, 6)\n",
            "\n",
            "Feature Embarked, shape(418, 3)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA9hES9vP89q"
      },
      "source": [
        "#normalize numerical variables\n"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6iyAH0HOogm"
      },
      "source": [
        "#del train_enc"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcWb3aSX26kp"
      },
      "source": [
        "#del train_enc\n",
        "#del test_enc"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAX3Ja-zQZvW"
      },
      "source": [
        "train_enc = hstack((cat_cols_enc[0], cat_cols_enc[1], cat_cols_enc[2], \\\n",
        "                    cat_cols_enc[3], cat_cols_enc[4], num_enc[0], num_enc[1]))"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjPDqyaAi7Gy"
      },
      "source": [
        "test_enc = hstack((cat_test_enc[0], cat_test_enc[1], cat_test_enc[2], \\\n",
        "                    cat_test_enc[3], cat_test_enc[4], num_enc_test[0], num_enc_test[1]))"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFNIxjl_PTXy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5852d7b1-d7cc-4c1f-edc8-3f897c8a5fb5"
      },
      "source": [
        "train_enc.shape"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plI8LaQqk96O",
        "outputId": "fde167a9-4bd9-43dd-ad5a-043a7aaf6660"
      },
      "source": [
        "test_enc.shape"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYVwvbBMPgpN",
        "outputId": "6f1a64e7-bb3e-471c-9c75-43e624704623"
      },
      "source": [
        "y_train = np.asarray(train_data.Survived)\n",
        "print(y_train.shape)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0wphvnSBc9X"
      },
      "source": [
        "X_train, X_cv, y_train, y_cv = train_test_split(train_enc, y_train, test_size=0.30, stratify=y_train)"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQRkqKuaBc6Y",
        "outputId": "8f8a7442-fd5a-4d9b-ebe8-85be11b3acc5"
      },
      "source": [
        "print(X_train.shape, y_train.shape)\n",
        "print(X_cv.shape, y_cv.shape)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(623, 25) (623,)\n",
            "(268, 25) (268,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqIJgIvPLjo4"
      },
      "source": [
        "rf_new = RandomForestClassifier(random_state=0, n_estimators=750, max_depth=5)"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKmuSHoWMfTR",
        "outputId": "13f45726-5fb8-4c9b-a082-7e1d72dc462f"
      },
      "source": [
        "rf_new.fit(X_train,y_train)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=5, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=750,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyCiqUgHMfP5",
        "outputId": "46fa0045-fae8-4ed8-fa72-5a9d59718bac"
      },
      "source": [
        "rf_new.score(X_train,y_train)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8571428571428571"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QooBqnGtsKK",
        "outputId": "468927ca-36ee-4c16-9f54-84b95072399d"
      },
      "source": [
        "cross_val_score(rf_new, X_train, y_train, cv=8).mean()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8201590076590076"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIrHU__fMfM-"
      },
      "source": [
        "y_rf = rf_new.predict(X_cv)"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4amYmYOGZ_4",
        "outputId": "a98ec294-b921-4577-ac89-dc73cd711f44"
      },
      "source": [
        "confusion_matrix(y_cv, y_rf)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[149,  16],\n",
              "       [ 28,  75]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPssRdIeDGJ1"
      },
      "source": [
        "log_clf = LogisticRegression(C = 1)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2We2N6NcEQ0m",
        "outputId": "982dc30e-eb44-4896-e838-fd2d81bccd54"
      },
      "source": [
        "log_clf.fit(X_train,y_train)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHJfYnoTEVtN",
        "outputId": "5aed8860-9263-4e32-be50-684849fb597f"
      },
      "source": [
        "\n",
        "log_clf.score(X_train, y_train)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.826645264847512"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcgPqPvoEVpw"
      },
      "source": [
        "pred_log = log_clf.predict(test_enc)"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aezpol0V9OZB"
      },
      "source": [
        "predictions = rf_new.predict(test_enc)"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erf7FcYy96wn"
      },
      "source": [
        "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': pred_log})\n",
        "output.to_csv('titanic_submission.csv', index=False)"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "CIEeXbYk-kfd",
        "outputId": "6afac415-a531-4cbb-82fa-bd7c36088717"
      },
      "source": [
        "out = pd.read_csv('titanic_submission.csv')\n",
        "out.head()"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         1\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oyob4S2b-vPT",
        "outputId": "7482fe45-7223-49db-b777-2f023d6966b4"
      },
      "source": [
        "out.shape"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WoD-CEX-yA9",
        "outputId": "66b00089-b39a-40d7-c364-240f4e9279cb"
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgHF1m6-GRJb"
      },
      "source": [
        "Test data score at Kaggle achieved is 77.751% using logistic regression & 77% using Random Forest"
      ]
    }
  ]
}