Titanic Survival Prediction/Titanic_submission.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AY2D7zVSGUPW"
      },
      "source": [
        "# 1- Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install scikit-optimize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJ_UGMy9HpNQ",
        "outputId": "1245ab0e-38e3-47c1-bb5f-f8d22fcbe91f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-optimize in /usr/local/lib/python3.10/dist-packages (0.9.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.3.2)\n",
            "Requirement already satisfied: pyaml>=16.9 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (23.12.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.23.5)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.2.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyaml>=16.9->scikit-optimize) (6.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->scikit-optimize) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46LVVibyGUPa"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "import sklearn.exceptions\n",
        "from nltk import re\n",
        "from skopt import BayesSearchCV\n",
        "from scipy.stats import f_oneway\n",
        "from scipy.stats import chi2_contingency\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import learning_curve\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVmWFbZlGUPb"
      },
      "source": [
        "# 2- Load data and drop unused column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jromvYJ6GUPc"
      },
      "outputs": [],
      "source": [
        "df_train = pd.read_csv('https://github.com/SamBelkacem/Machine-Learning-Basics/blob/main/Titanic%20Survival%20Prediction/train.csv?raw=true')\n",
        "df_test = pd.read_csv('https://github.com/SamBelkacem/Machine-Learning-Basics/blob/main/Titanic%20Survival%20Prediction/test.csv?raw=true')\n",
        "df_test.insert(loc=1, column=\"Survived\", value=np.nan)\n",
        "df = pd.concat([df_train, df_test], ignore_index=True, sort=False)\n",
        "df = df.drop(['PassengerId'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbKhwJK8GUPc"
      },
      "source": [
        "# 3- Remove duplicates if any and preview the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "nKwO7ei4GUPc",
        "outputId": "0204f498-e717-4f83-8e12-7c5243b80976"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1309\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0       0.0       3                            Braund, Mr. Owen Harris   \n",
              "1       1.0       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2       1.0       3                             Heikkinen, Miss. Laina   \n",
              "3       1.0       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4       0.0       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
              "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
              "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
              "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
              "4    male  35.0      0      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-306d97af-6baa-4adb-8566-505ca2867a3f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-306d97af-6baa-4adb-8566-505ca2867a3f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-306d97af-6baa-4adb-8566-505ca2867a3f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-306d97af-6baa-4adb-8566-505ca2867a3f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5109f9da-2c7c-4269-8615-3d03a8ad7522\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5109f9da-2c7c-4269-8615-3d03a8ad7522')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5109f9da-2c7c-4269-8615-3d03a8ad7522 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "print(len(df))\n",
        "df = df.drop_duplicates()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "DEMZMkdnGUPd",
        "outputId": "a135fb72-bd2e-4f57-baf1-47b4411e5c4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Fare'}, xlabel='Pclass'>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.boxplot(column='Fare', by='Pclass', figsize=(10, 5), vert=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMSzbqwbGUPe"
      },
      "source": [
        "# 4- Summarized information of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXDxn7Q2GUPf",
        "outputId": "190d5a64-a0bd-476e-ca5b-328859b504b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1309 entries, 0 to 1308\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    float64\n",
            " 1   Pclass    1309 non-null   int64  \n",
            " 2   Name      1309 non-null   object \n",
            " 3   Sex       1309 non-null   object \n",
            " 4   Age       1046 non-null   float64\n",
            " 5   SibSp     1309 non-null   int64  \n",
            " 6   Parch     1309 non-null   int64  \n",
            " 7   Ticket    1309 non-null   object \n",
            " 8   Fare      1308 non-null   float64\n",
            " 9   Cabin     295 non-null    object \n",
            " 10  Embarked  1307 non-null   object \n",
            "dtypes: float64(3), int64(3), object(5)\n",
            "memory usage: 122.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9H625zNKGUPf"
      },
      "source": [
        "# 5- Check for unique values and normalize if necessary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPEd0mk1GUPg",
        "outputId": "fe21752a-ae8d-4f42-a5b9-02ded2cfcc92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived       2\n",
              "Pclass         3\n",
              "Name        1307\n",
              "Sex            2\n",
              "Age           98\n",
              "SibSp          7\n",
              "Parch          8\n",
              "Ticket       929\n",
              "Fare         281\n",
              "Cabin        186\n",
              "Embarked       3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_E4auvzGUPh",
        "outputId": "af007eee-3eac-4ce7-b3af-e3963b80969e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "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",
            "5                                      Moran, Mr. James\n",
            "6                               McCarthy, Mr. Timothy J\n",
            "7                        Palsson, Master. Gosta Leonard\n",
            "8     Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
            "9                   Nasser, Mrs. Nicholas (Adele Achem)\n",
            "10                      Sandstrom, Miss. Marguerite Rut\n",
            "11                             Bonnell, Miss. Elizabeth\n",
            "12                       Saundercock, Mr. William Henry\n",
            "13                          Andersson, Mr. Anders Johan\n",
            "14                 Vestrom, Miss. Hulda Amanda Adolfina\n",
            "15                     Hewlett, Mrs. (Mary D Kingcome) \n",
            "16                                 Rice, Master. Eugene\n",
            "17                         Williams, Mr. Charles Eugene\n",
            "18    Vander Planke, Mrs. Julius (Emelia Maria Vande...\n",
            "19                              Masselmani, Mrs. Fatima\n",
            "Name: Name, dtype: object\n",
            "\n",
            "0            A/5 21171\n",
            "1             PC 17599\n",
            "2     STON/O2. 3101282\n",
            "3               113803\n",
            "4               373450\n",
            "5               330877\n",
            "6                17463\n",
            "7               349909\n",
            "8               347742\n",
            "9               237736\n",
            "10             PP 9549\n",
            "11              113783\n",
            "12           A/5. 2151\n",
            "13              347082\n",
            "14              350406\n",
            "15              248706\n",
            "16              382652\n",
            "17              244373\n",
            "18              345763\n",
            "19                2649\n",
            "Name: Ticket, dtype: object\n",
            "\n",
            "0      NaN\n",
            "1      C85\n",
            "2      NaN\n",
            "3     C123\n",
            "4      NaN\n",
            "5      NaN\n",
            "6      E46\n",
            "7      NaN\n",
            "8      NaN\n",
            "9      NaN\n",
            "10      G6\n",
            "11    C103\n",
            "12     NaN\n",
            "13     NaN\n",
            "14     NaN\n",
            "15     NaN\n",
            "16     NaN\n",
            "17     NaN\n",
            "18     NaN\n",
            "19     NaN\n",
            "Name: Cabin, dtype: object\n"
          ]
        }
      ],
      "source": [
        "for column in ['Name', 'Ticket', 'Cabin']:\n",
        "    print()\n",
        "    print(df[column].head(20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmowKwCuGUPh",
        "outputId": "a857eaf8-1c6a-4f28-8679-68642d1bc1a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr         757\n",
              "Miss       262\n",
              "Mrs        200\n",
              "Master      61\n",
              "Officer     23\n",
              "Royalty      6\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "# Keep only the 'Title' in the column 'Name'\n",
        "df[\"Name\"] = df[\"Name\"].apply(lambda x: re.findall(r\"\\,\\s([^.]+)\", x)[0])\n",
        "\n",
        "# Normalize the 'Title' with a dictionary\n",
        "title_dictionary = {\n",
        "    \"Capt\":         \"Officer\",\n",
        "    \"Col\":          \"Officer\",\n",
        "    \"Major\":        \"Officer\",\n",
        "    \"Jonkheer\":     \"Royalty\",\n",
        "    \"Don\":          \"Royalty\",\n",
        "    \"Sir\":          \"Royalty\",\n",
        "    \"Dr\":           \"Officer\",\n",
        "    \"Rev\":          \"Officer\",\n",
        "    \"the Countess\": \"Royalty\",\n",
        "    \"Dona\":         \"Royalty\",\n",
        "    \"Mme\":          \"Mrs\",\n",
        "    \"Mlle\":         \"Miss\",\n",
        "    \"Ms\":           \"Mrs\",\n",
        "    \"Lady\":         \"Royalty\"\n",
        "}\n",
        "\n",
        "df = df.replace({\"Name\": title_dictionary})\n",
        "df[\"Name\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxQXpkB5GUPi",
        "outputId": "51739eea-4832-4d43-b9e6-bad17f375bd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2343       11\n",
            "1601        8\n",
            "2144        8\n",
            "17608       7\n",
            "14879       7\n",
            "           ..\n",
            "2816        1\n",
            "3101306     1\n",
            "113792      1\n",
            "36209       1\n",
            "359309      1\n",
            "Name: Ticket, Length: 924, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Fill missing values in the column 'Ticket'\n",
        "df = df.replace({\"Ticket\": {'LINE': '0'}})\n",
        "\n",
        "# Normalize the column 'Ticket'\n",
        "df[\"Ticket\"] = df[\"Ticket\"].apply(lambda x: re.findall(r'[0-9]+$', x)[0])\n",
        "print(df.Ticket.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilwKNQUXGUPj",
        "outputId": "e9442145-9b13-438e-bd16-aa2a05a4abf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "U    1014\n",
            "C      94\n",
            "B      65\n",
            "D      46\n",
            "E      41\n",
            "A      22\n",
            "F      21\n",
            "G       5\n",
            "T       1\n",
            "Name: Cabin, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Fill missing values with the letter 'U' in the column 'Cabin' and get the first character\n",
        "df.Cabin = df.Cabin.fillna('U')\n",
        "df.Cabin = df.Cabin.map(lambda x: x[0])\n",
        "print(df.Cabin.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7V1IECYRGUPj"
      },
      "source": [
        "# 6- Statistical description of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "72JqJ6wqGUPk",
        "outputId": "d483cc71-64ef-4c55-8b29-327cc18f5c80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Survived       Pclass          Age        SibSp        Parch  \\\n",
              "count  891.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
              "mean     0.383838     2.294882    29.881138     0.498854     0.385027   \n",
              "std      0.486592     0.837836    14.413493     1.041658     0.865560   \n",
              "min      0.000000     1.000000     0.170000     0.000000     0.000000   \n",
              "25%      0.000000     2.000000    21.000000     0.000000     0.000000   \n",
              "50%      0.000000     3.000000    28.000000     0.000000     0.000000   \n",
              "75%      1.000000     3.000000    39.000000     1.000000     0.000000   \n",
              "max      1.000000     3.000000    80.000000     8.000000     9.000000   \n",
              "\n",
              "              Fare  \n",
              "count  1308.000000  \n",
              "mean     33.295479  \n",
              "std      51.758668  \n",
              "min       0.000000  \n",
              "25%       7.895800  \n",
              "50%      14.454200  \n",
              "75%      31.275000  \n",
              "max     512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bbfc0e03-446d-4ad8-9549-e285ddd62575\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1046.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1308.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.294882</td>\n",
              "      <td>29.881138</td>\n",
              "      <td>0.498854</td>\n",
              "      <td>0.385027</td>\n",
              "      <td>33.295479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.837836</td>\n",
              "      <td>14.413493</td>\n",
              "      <td>1.041658</td>\n",
              "      <td>0.865560</td>\n",
              "      <td>51.758668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbfc0e03-446d-4ad8-9549-e285ddd62575')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bbfc0e03-446d-4ad8-9549-e285ddd62575 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bbfc0e03-446d-4ad8-9549-e285ddd62575');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cb8e8f9f-7eda-4b5a-87ca-e620b00be3c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb8e8f9f-7eda-4b5a-87ca-e620b00be3c4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cb8e8f9f-7eda-4b5a-87ca-e620b00be3c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_APJplD4GUPk"
      },
      "source": [
        "# 7- Visualize the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOAJeVQCGUPl"
      },
      "source": [
        "## Distribution of the class label with a bar-chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "qf8mUQKXGUPl",
        "outputId": "45ed88eb-d08f-4b25-f4cc-94e3e3d12997"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of instances')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHPCAYAAAC1PRvJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4z0lEQVR4nO3deVxVdf7H8fdlVZYLoggyuS8pbilNSppWkmQ4g+OSlpPoONkorpSpTW60aExpWZpNqdhMVj+tnNTELbVUNDW33HIdLAVNBdQUBM7vDx/e6QYaFy5cPL6ej8d9PLjf7/ec8zmU8OZ7vudci2EYhgAAAEzKzdUFAAAAlCXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDnCbqlOnjvr37+/qMopl0qRJslgsdm3lVf/x48dlsViUnJxsa+vfv7/8/PzK/NjXWSwWTZo0qdyOB5gNYQcwmSNHjuipp55SvXr1VKlSJVmtVrVr105vvPGGLl++7OryXOqLL76osKGhItcG3Oo8XF0AAOdZtmyZevXqJW9vb/Xr10/NmjVTbm6uNmzYoNGjR2vv3r365z//6eoyneLgwYNyc3Ps77UvvvhCM2fOdChU1K5dW5cvX5anp6eDFTrmZrVdvnxZHh78uAZKin89gEkcO3ZMffr0Ue3atfXll1+qRo0atr74+HgdPnxYy5Ytc2GFzuXt7V2m+8/Ly1NBQYG8vLxUqVKlMj3Wb3H18YFbHZexAJNISkrSxYsXNWfOHLugc12DBg00YsSIG25/7tw5PfPMM2revLn8/PxktVrVpUsX7dq1q9DYN998U02bNpWPj4+qVKmiu+++WwsWLLD1X7hwQSNHjlSdOnXk7e2t6tWr66GHHtK33377m+exYcMG/f73v1elSpVUv359vfPOO0WO+/WanatXr2ry5Mlq2LChKlWqpKpVq6p9+/ZatWqVpGvrbGbOnCnp2hqY6y/pf+tyXn31Vb3++uuqX7++vL29tW/fviLX7Fx39OhRRUdHy9fXV2FhYUpMTJRhGLb+devWyWKxaN26dXbb/XqfN6vtetuvZ3x27NihLl26yGq1ys/PT506ddLmzZvtxiQnJ8tisWjjxo1KSEhQcHCwfH199ac//Ulnzpwp+j8AYELM7AAmsWTJEtWrV0/33ntvibY/evSoFi9erF69eqlu3brKyMjQO++8o44dO2rfvn0KCwuTJL377rsaPny4evbsqREjRujKlSvavXu3tmzZoscff1yS9Le//U2LFi3S0KFDFR4errNnz2rDhg3av3+/WrdufcMa9uzZo86dOys4OFiTJk1SXl6eJk6cqJCQkN+sf9KkSZoyZYr++te/6p577lF2dra2bdumb7/9Vg899JCeeuopnTx5UqtWrdK//vWvIvcxb948XblyRYMGDZK3t7eCgoJUUFBQ5Nj8/Hw9/PDDatu2rZKSkpSSkqKJEycqLy9PiYmJv1nvLxWntl/au3ev7rvvPlmtVj377LPy9PTUO++8o/vvv1/r169XmzZt7MYPGzZMVapU0cSJE3X8+HG9/vrrGjp0qD7++GOH6gRuWQaAW15WVpYhyYiNjS32NrVr1zbi4uJs769cuWLk5+fbjTl27Jjh7e1tJCYm2tpiY2ONpk2b3nTfAQEBRnx8fLFrua5bt25GpUqVjP/+97+2tn379hnu7u7Gr39c/br+li1bGjExMTfdf3x8fKH9GMa185RkWK1W4/Tp00X2zZs3z9YWFxdnSDKGDRtmaysoKDBiYmIMLy8v48yZM4ZhGMbatWsNScbatWt/c583qs0wDEOSMXHiRNv7bt26GV5eXsaRI0dsbSdPnjT8/f2NDh062NrmzZtnSDKioqKMgoICW/uoUaMMd3d3IzMzs8jjAWbDZSzABLKzsyVJ/v7+Jd6Ht7e3bcFvfn6+zp49Kz8/P9155512l58CAwP1ww8/aOvWrTfcV2BgoLZs2aKTJ08W+/j5+flasWKFunXrplq1atnamzRpoujo6N/cPjAwUHv37tWhQ4eKfcxf69Gjh4KDg4s9fujQobavLRaLhg4dqtzcXK1evbrENfyW/Px8rVy5Ut26dVO9evVs7TVq1NDjjz+uDRs22P5/uG7QoEF2l8Xuu+8+5efn67///W+Z1QlUJIQdwASsVquka2tlSqqgoEDTp09Xw4YN5e3trWrVqik4OFi7d+9WVlaWbdyYMWPk5+ene+65Rw0bNlR8fLw2btxot6+kpCR99913qlmzpu655x5NmjRJR48evenxz5w5o8uXL6thw4aF+u68887frD8xMVGZmZlq1KiRmjdvrtGjR2v37t3FPPtr6tatW+yxbm5udmFDkho1aiTp2pqcsnLmzBn9/PPPRX5PmjRpooKCAp04ccKu/ZfhUZKqVKkiSTp//nyZ1QlUJIQdwASsVqvCwsL03XfflXgfL7/8shISEtShQwf9+9//1ooVK7Rq1So1bdrUbt1KkyZNdPDgQX300Udq3769PvnkE7Vv314TJ060jXn00Ud19OhRvfnmmwoLC9M//vEPNW3aVMuXLy/Ved5Mhw4ddOTIEc2dO1fNmjXTe++9p9atW+u9994r9j4qV67s1Jp+/SDE6/Lz8516nN/i7u5eZLvxi8XUgJkRdgCT6Nq1q44cOaLU1NQSbb9o0SI98MADmjNnjvr06aPOnTsrKipKmZmZhcb6+vqqd+/emjdvntLS0hQTE6OXXnpJV65csY2pUaOGhgwZosWLF+vYsWOqWrWqXnrppRsePzg4WJUrVy7yMtTBgweLdQ5BQUEaMGCAPvzwQ504cUItWrSwu4vpRuGjJAoKCgrNVn3//feSrt0pJv1vBuXX38OiLh8Vt7bg4GD5+PgU+T05cOCA3NzcVLNmzWLtC7hdEHYAk3j22Wfl6+urv/71r8rIyCjUf+TIEb3xxhs33N7d3b3QX/oLFy7Ujz/+aNd29uxZu/deXl4KDw+XYRi6evWq8vPz7S57SVL16tUVFhamnJycmx4/OjpaixcvVlpamq19//79WrFixQ23u1Fdfn5+atCggd0xfX19JRUOHyX11ltv2b42DENvvfWWPD091alTJ0nXHkjo7u6ur776ym67WbNmFdpXcWtzd3dX586d9Z///MfucllGRoYWLFig9u3b2y5rAriGW88Bk6hfv74WLFig3r17q0mTJnZPUN60aZMWLlx408+S6tq1qxITEzVgwADde++92rNnjz744INC61I6d+6s0NBQtWvXTiEhIdq/f7/eeustxcTEyN/fX5mZmbrjjjvUs2dPtWzZUn5+flq9erW2bt2q11577abnMHnyZKWkpOi+++7TkCFDlJeXZ3umz2+tvwkPD9f999+viIgIBQUFadu2bbbb36+LiIiQJA0fPlzR0dFyd3dXnz59fuM7W7RKlSopJSVFcXFxatOmjZYvX65ly5bpueeesy1yDggIUK9evfTmm2/KYrGofv36Wrp0qU6fPl1of47U9uKLL2rVqlVq3769hgwZIg8PD73zzjvKyclRUlJSic4HMDXX3gwGwNm+//5748knnzTq1KljeHl5Gf7+/ka7du2MN99807hy5YptXFG3nj/99NNGjRo1jMqVKxvt2rUzUlNTjY4dOxodO3a0jXvnnXeMDh06GFWrVjW8vb2N+vXrG6NHjzaysrIMwzCMnJwcY/To0UbLli0Nf39/w9fX12jZsqUxa9asYtW/fv16IyIiwvDy8jLq1atnzJ4925g4ceJv3nr+4osvGvfcc48RGBhoVK5c2WjcuLHx0ksvGbm5ubYxeXl5xrBhw4zg4GDDYrHY9nn9VvB//OMfheq50a3nvr6+xpEjR4zOnTsbPj4+RkhIiDFx4sRCt++fOXPG6NGjh+Hj42NUqVLFeOqpp4zvvvuu0D5vVJthFL713DAM49tvvzWio6MNPz8/w8fHx3jggQeMTZs22Y25fuv51q1b7dpvdEs8YFYWw2CFGgAAMC/W7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFPjoYK69tj3kydPyt/f36mPkwcAAGXHMAxduHBBYWFhcnO78fwNYUfSyZMn+SwZAABuUSdOnNAdd9xxw37CjiR/f39J175ZfKYMAAC3huzsbNWsWdP2e/xGCDv636cNW61Wwg4AALeY31qCwgJlAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgah6uLgCuVWfsMleXgHJ0fGqMq0sAgHLHzA4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1l4adSZMmyWKx2L0aN25s679y5Yri4+NVtWpV+fn5qUePHsrIyLDbR1pammJiYuTj46Pq1atr9OjRysvLK+9TAQAAFZSHqwto2rSpVq9ebXvv4fG/kkaNGqVly5Zp4cKFCggI0NChQ9W9e3dt3LhRkpSfn6+YmBiFhoZq06ZNOnXqlPr16ydPT0+9/PLL5X4uAACg4nF52PHw8FBoaGih9qysLM2ZM0cLFizQgw8+KEmaN2+emjRpos2bN6tt27ZauXKl9u3bp9WrVyskJER33XWXXnjhBY0ZM0aTJk2Sl5dXeZ8OAACoYFy+ZufQoUMKCwtTvXr11LdvX6WlpUmStm/frqtXryoqKso2tnHjxqpVq5ZSU1MlSampqWrevLlCQkJsY6Kjo5Wdna29e/fe8Jg5OTnKzs62ewEAAHNyadhp06aNkpOTlZKSorffflvHjh3TfffdpwsXLig9PV1eXl4KDAy02yYkJETp6emSpPT0dLugc73/et+NTJkyRQEBAbZXzZo1nXtiAACgwnDpZawuXbrYvm7RooXatGmj2rVr6//+7/9UuXLlMjvuuHHjlJCQYHufnZ1N4AEAwKRcfhnrlwIDA9WoUSMdPnxYoaGhys3NVWZmpt2YjIwM2xqf0NDQQndnXX9f1Dqg67y9vWW1Wu1eAADAnCpU2Ll48aKOHDmiGjVqKCIiQp6enlqzZo2t/+DBg0pLS1NkZKQkKTIyUnv27NHp06dtY1atWiWr1arw8PByrx8AAFQ8Lr2M9cwzz+gPf/iDateurZMnT2rixIlyd3fXY489poCAAA0cOFAJCQkKCgqS1WrVsGHDFBkZqbZt20qSOnfurPDwcD3xxBNKSkpSenq6nn/+ecXHx8vb29uVpwYAACoIl4adH374QY899pjOnj2r4OBgtW/fXps3b1ZwcLAkafr06XJzc1OPHj2Uk5Oj6OhozZo1y7a9u7u7li5dqsGDBysyMlK+vr6Ki4tTYmKiq04JAABUMBbDMAxXF+Fq2dnZCggIUFZW1m23fqfO2GWuLgHl6PjUGFeXAABOU9zf3xVqzQ4AAICzEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpVZiwM3XqVFksFo0cOdLWduXKFcXHx6tq1ary8/NTjx49lJGRYbddWlqaYmJi5OPjo+rVq2v06NHKy8sr5+oBAEBFVSHCztatW/XOO++oRYsWdu2jRo3SkiVLtHDhQq1fv14nT55U9+7dbf35+fmKiYlRbm6uNm3apPnz5ys5OVkTJkwo71MAAAAVlMvDzsWLF9W3b1+9++67qlKliq09KytLc+bM0bRp0/Tggw8qIiJC8+bN06ZNm7R582ZJ0sqVK7Vv3z79+9//1l133aUuXbrohRde0MyZM5Wbm+uqUwIAABWIy8NOfHy8YmJiFBUVZde+fft2Xb161a69cePGqlWrllJTUyVJqampat68uUJCQmxjoqOjlZ2drb17997wmDk5OcrOzrZ7AQAAc/Jw5cE/+ugjffvtt9q6dWuhvvT0dHl5eSkwMNCuPSQkROnp6bYxvww61/uv993IlClTNHny5FJWDwAAbgWlntnJz8/Xzp07df78eYe2O3HihEaMGKEPPvhAlSpVKm0ZDhk3bpyysrJsrxMnTpTr8QEAQPlxOOyMHDlSc+bMkXQt6HTs2FGtW7dWzZo1tW7dumLvZ/v27Tp9+rRat24tDw8PeXh4aP369ZoxY4Y8PDwUEhKi3NxcZWZm2m2XkZGh0NBQSVJoaGihu7Ouv78+pije3t6yWq12LwAAYE4Oh51FixapZcuWkqQlS5bo2LFjOnDggEaNGqW///3vxd5Pp06dtGfPHu3cudP2uvvuu9W3b1/b156enlqzZo1tm4MHDyotLU2RkZGSpMjISO3Zs0enT5+2jVm1apWsVqvCw8MdPTUAAGBCDq/Z+emnn2yzJl988YV69eqlRo0a6S9/+YveeOONYu/H399fzZo1s2vz9fVV1apVbe0DBw5UQkKCgoKCZLVaNWzYMEVGRqpt27aSpM6dOys8PFxPPPGEkpKSlJ6erueff17x8fHy9vZ29NQAAIAJOTyzExISon379ik/P18pKSl66KGHJEk///yz3N3dnVrc9OnT1bVrV/Xo0UMdOnRQaGioPv30U1u/u7u7li5dKnd3d0VGRurPf/6z+vXrp8TERKfWAQAAbl0Oz+wMGDBAjz76qGrUqCGLxWK7NXzLli1q3LhxqYr59ZqfSpUqaebMmZo5c+YNt6ldu7a++OKLUh0XAACYl8NhZ9KkSWrWrJlOnDihXr162S4Xubu7a+zYsU4vEAAAoDRK9Jydnj17Srr22VXXxcXFOaciAAAAJ3J4zU5+fr5eeOEF/e53v5Ofn5+OHj0qSRo/frztlnQAAICKwuGw89JLLyk5OVlJSUny8vKytTdr1kzvvfeeU4sDAAAoLYfDzvvvv69//vOf6tu3r93dVy1bttSBAwecWhwAAEBpORx2fvzxRzVo0KBQe0FBga5eveqUogAAAJzF4bATHh6ur7/+ulD7okWL1KpVK6cUBQAA4CwO3401YcIExcXF6ccff1RBQYE+/fRTHTx4UO+//76WLl1aFjUCAACUmMMzO7GxsVqyZIlWr14tX19fTZgwQfv379eSJUtsT1MGAACoKEr0nJ377rtPq1atcnYtAAAATufwzM7WrVu1ZcuWQu1btmzRtm3bnFIUAACAszgcduLj43XixIlC7T/++KPi4+OdUhQAAICzOBx29u3bp9atWxdqb9Wqlfbt2+eUogAAAJzF4bDj7e2tjIyMQu2nTp2Sh0eJlgABAACUGYfDTufOnTVu3DhlZWXZ2jIzM/Xcc89xNxYAAKhwHJ6KefXVV9WhQwfVrl3b9hDBnTt3KiQkRP/617+cXiAAAEBpOBx2fve732n37t364IMPtGvXLlWuXFkDBgzQY489Jk9Pz7KoEQAAoMRKtMjG19dXgwYNcnYtAAAATleisHPo0CGtXbtWp0+fVkFBgV3fhAkTnFIYAACAMzgcdt59910NHjxY1apVU2hoqCwWi63PYrEQdgAAQIXicNh58cUX9dJLL2nMmDFlUQ8AAIBTOXzr+fnz59WrV6+yqAUAAMDpHA47vXr10sqVK8uiFgAAAKdz+DJWgwYNNH78eG3evFnNmzcvdLv58OHDnVYcAABAaVkMwzAc2aBu3bo33pnFoqNHj5a6qPKWnZ2tgIAAZWVlyWq1urqcclVn7DJXl4BydHxqjKtLAACnKe7vb4dndo4dO1aqwgAAAMqTw2t2AAAAbiUleqjgDz/8oM8//1xpaWnKzc2165s2bZpTCgMAAHAGh8POmjVr9Mc//lH16tXTgQMH1KxZMx0/flyGYah169ZlUSMAAECJOXwZa9y4cXrmmWe0Z88eVapUSZ988olOnDihjh078vwdAABQ4Tg8s7N//359+OGH1zb28NDly5fl5+enxMRExcbGavDgwU4vEgBQApMCXF0BytOkLFdXUGE5PLPj6+trW6dTo0YNHTlyxNb3008/Oa8yAAAAJ3B4Zqdt27basGGDmjRpokceeURPP/209uzZo08//VRt27YtixoBAABKzOGwM23aNF28eFGSNHnyZF28eFEff/yxGjZsyJ1YAACgwnE47NSrV8/2ta+vr2bPnu3UggAAAJzJ4TU79erV09mzZwu1Z2Zm2gUhAACAisDhsHP8+HHl5+cXas/JydGPP/7olKIAAACcpdiXsT7//HPb1ytWrFBAwP9uaczPz9eaNWtUp04dpxYHAABQWsUOO926dZN07ZPN4+Li7Po8PT1Vp04dvfbaa04tDgAAoLSKHXYKCgokSXXr1tXWrVtVrVq1MisKAADAWRy+G+vYsWOF2jIzMxUYGOiMegAAAJzK4QXKr7zyij7++GPb+169eikoKEi/+93vtGvXLqcWBwAAUFoOh53Zs2erZs2akqRVq1Zp9erVSklJUZcuXTR69GinFwgAAFAaDl/GSk9Pt4WdpUuX6tFHH1Xnzp1Vp04dtWnTxukFAgAAlIbDMztVqlTRiRMnJEkpKSmKioqSJBmGUeTzdwAAAFzJ4Zmd7t276/HHH1fDhg119uxZdenSRZK0Y8cONWjQwOkFAgAAlIbDYWf69OmqU6eOTpw4oaSkJPn5+UmSTp06pSFDhji9QAAAgNJwOOx4enrqmWeeKdQ+atQopxQEAADgTA6HHUk6dOiQ1q5dq9OnT9seNnjdhAkTnFIYAACAMzgcdt59910NHjxY1apVU2hoqCwWi63PYrEQdgAAQIXicNh58cUX9dJLL2nMmDFlUQ8AAIBTOXzr+fnz59WrV6+yqAUAAMDpHA47vXr10sqVK8uiFgAAAKdzOOw0aNBA48ePV//+/fXaa69pxowZdi9HvP3222rRooWsVqusVqsiIyO1fPlyW/+VK1cUHx+vqlWrys/PTz169FBGRobdPtLS0hQTEyMfHx9Vr15do0ePVl5enqOnBQAATMrhNTv//Oc/5efnp/Xr12v9+vV2fRaLRcOHDy/2vu644w5NnTpVDRs2lGEYmj9/vmJjY7Vjxw41bdpUo0aN0rJly7Rw4UIFBARo6NCh6t69uzZu3ChJys/PV0xMjEJDQ7Vp0yadOnVK/fr1k6enp15++WVHTw0AAJiQxTAMw9VF/FJQUJD+8Y9/qGfPngoODtaCBQvUs2dPSdKBAwfUpEkTpaamqm3btlq+fLm6du2qkydPKiQkRNK1DyodM2aMzpw5Iy8vr2IdMzs7WwEBAcrKypLVai2zc6uI6oxd5uoSUI6OT41xdQkoT5MCXF0BytOkLFdXUO6K+/vb4ctYZSU/P18fffSRLl26pMjISG3fvl1Xr161ffaWJDVu3Fi1atVSamqqJCk1NVXNmze3BR1Jio6OVnZ2tvbu3XvDY+Xk5Cg7O9vuBQAAzKlYl7ESEhL0wgsvyNfXVwkJCTcdO23aNIcK2LNnjyIjI3XlyhX5+fnps88+U3h4uHbu3CkvLy8FBgbajQ8JCVF6erqka5/A/sugc73/et+NTJkyRZMnT3aoTgAAcGsqVtjZsWOHrl69avv6Rn75gMHiuvPOO7Vz505lZWVp0aJFiouLK7QWyNnGjRtnF9qys7NVs2bNMj0mAABwjWKFnbVr1xb5tTN4eXnZPi09IiJCW7du1RtvvKHevXsrNzdXmZmZdrM7GRkZCg0NlSSFhobqm2++sdvf9bu1ro8pire3t7y9vZ16HgAAoGKqMGt2risoKFBOTo4iIiLk6empNWvW2PoOHjyotLQ0RUZGSpIiIyO1Z88enT592jZm1apVslqtCg8PL/faAQBAxVOiDwJ1lnHjxqlLly6qVauWLly4oAULFmjdunVasWKFAgICNHDgQCUkJCgoKEhWq1XDhg1TZGSk2rZtK0nq3LmzwsPD9cQTTygpKUnp6el6/vnnFR8fz8wNAACQ5OKwc/r0afXr10+nTp1SQECAWrRooRUrVuihhx6SJE2fPl1ubm7q0aOHcnJyFB0drVmzZtm2d3d319KlSzV48GBFRkbK19dXcXFxSkxMdNUpAQCACqbCPWfHFXjODm4XPGfnNsNzdm4vPGfnhuOKtWandevWOn/+vCQpMTFRP//8s3OqBAAAKGPFCjv79+/XpUuXJEmTJ0/WxYsXy7QoAAAAZynWmp277rpLAwYMUPv27WUYhl599VX5+fkVOXbChAlOLRAAAKA0ihV2kpOTNXHiRC1dulQWi0XLly+Xh0fhTS0WC2EHAABUKMUKO3feeac++ugjSZKbm5vWrFmj6tWrl2lhAAAAzuDwrecFBQVlUQcAAECZKNFzdo4cOaLXX39d+/fvlySFh4drxIgRql+/vlOLAwAAKC2HPy5ixYoVCg8P1zfffKMWLVqoRYsW2rJli5o2bapVq1aVRY0AAAAl5vDMztixYzVq1ChNnTq1UPuYMWNsTz8GAACoCBye2dm/f78GDhxYqP0vf/mL9u3b55SiAAAAnMXhsBMcHKydO3cWat+5cyd3aAEAgArH4ctYTz75pAYNGqSjR4/q3nvvlSRt3LhRr7zyihISEpxeIAAAQGk4HHbGjx8vf39/vfbaaxo3bpwkKSwsTJMmTdLw4cOdXiAAAEBpOBx2LBaLRo0apVGjRunChQuSJH9/f6cXBgAA4Awles7OdYQcAABQ0Tm8QBkAAOBWQtgBAACmRtgBAACm5lDYuXr1qjp16qRDhw6VVT0AAABO5VDY8fT01O7du8uqFgAAAKdz+DLWn//8Z82ZM6csagEAAHA6h289z8vL09y5c7V69WpFRETI19fXrn/atGlOKw4AAKC0HA473333nVq3bi1J+v777+36LBaLc6oCAABwEofDztq1a8uiDgAAgDJR4lvPDx8+rBUrVujy5cuSJMMwnFYUAACAszgcds6ePatOnTqpUaNGeuSRR3Tq1ClJ0sCBA/X00087vUAAAIDScDjsjBo1Sp6enkpLS5OPj4+tvXfv3kpJSXFqcQAAAKXl8JqdlStXasWKFbrjjjvs2hs2bKj//ve/TisMAADAGRye2bl06ZLdjM51586dk7e3t1OKAgAAcBaHw859992n999/3/beYrGooKBASUlJeuCBB5xaHAAAQGk5fBkrKSlJnTp10rZt25Sbm6tnn31We/fu1blz57Rx48ayqBEAAKDEHJ7Zadasmb7//nu1b99esbGxunTpkrp3764dO3aofv36ZVEjAABAiTk8syNJAQEB+vvf/+7sWgAAAJyuRGHn/PnzmjNnjvbv3y9JCg8P14ABAxQUFOTU4gAAAErL4ctYX331lerUqaMZM2bo/PnzOn/+vGbMmKG6devqq6++KosaAQAASszhmZ34+Hj17t1bb7/9ttzd3SVJ+fn5GjJkiOLj47Vnzx6nFwkAAFBSDs/sHD58WE8//bQt6EiSu7u7EhISdPjwYacWBwAAUFoOh53WrVvb1ur80v79+9WyZUunFAUAAOAsxbqMtXv3btvXw4cP14gRI3T48GG1bdtWkrR582bNnDlTU6dOLZsqAQAASqhYYeeuu+6SxWKRYRi2tmeffbbQuMcff1y9e/d2XnUAAAClVKywc+zYsbKuAwAAoEwUK+zUrl27rOsAAAAoEyV6qODJkye1YcMGnT59WgUFBXZ9w4cPd0phAAAAzuBw2ElOTtZTTz0lLy8vVa1aVRaLxdZnsVgIOwAAoEJxOOyMHz9eEyZM0Lhx4+Tm5vCd6wAAAOXK4bTy888/q0+fPgQdAABwS3A4sQwcOFALFy4si1oAAACczuHLWFOmTFHXrl2VkpKi5s2by9PT065/2rRpTisOAACgtEoUdlasWKE777xTkgotUAYAAKhIHA47r732mubOnav+/fuXQTkAAADO5fCaHW9vb7Vr164sagEAAHA6h8POiBEj9Oabb5ZFLQAAAE7n8GWsb775Rl9++aWWLl2qpk2bFlqg/OmnnzqtOAAAgNJyeGYnMDBQ3bt3V8eOHVWtWjUFBATYvRwxZcoU/f73v5e/v7+qV6+ubt266eDBg3Zjrly5ovj4eFWtWlV+fn7q0aOHMjIy7MakpaUpJiZGPj4+ql69ukaPHq28vDxHTw0AAJiQwzM78+bNc9rB169fr/j4eP3+979XXl6ennvuOXXu3Fn79u2Tr6+vJGnUqFFatmyZFi5cqICAAA0dOlTdu3fXxo0bJUn5+fmKiYlRaGioNm3apFOnTqlfv37y9PTUyy+/7LRaAQDArcliGIbh6iKuO3PmjKpXr67169erQ4cOysrKUnBwsBYsWKCePXtKkg4cOKAmTZooNTVVbdu21fLly9W1a1edPHlSISEhkqTZs2drzJgxOnPmjLy8vH7zuNnZ2QoICFBWVpasVmuZnmNFU2fsMleXgHJ0fGqMq0tAeZrk2Gw7bnGTslxdQbkr7u9vh2d26tate9Pn6Rw9etTRXdpkZV37DxUUFCRJ2r59u65evaqoqCjbmMaNG6tWrVq2sJOamqrmzZvbgo4kRUdHa/Dgwdq7d69atWpV6Dg5OTnKycmxvc/Ozi5xzQAAoGJzOOyMHDnS7v3Vq1e1Y8cOpaSkaPTo0SUupKCgQCNHjlS7du3UrFkzSVJ6erq8vLwUGBhoNzYkJETp6em2Mb8MOtf7r/cVZcqUKZo8eXKJawUAALcOh8POiBEjimyfOXOmtm3bVuJC4uPj9d1332nDhg0l3kdxjRs3TgkJCbb32dnZqlmzZpkfFwAAlD+nfXR5ly5d9Mknn5Ro26FDh2rp0qVau3at7rjjDlt7aGiocnNzlZmZaTc+IyNDoaGhtjG/vjvr+vvrY37N29tbVqvV7gUAAMzJaWFn0aJFtrU2xWUYhoYOHarPPvtMX375perWrWvXHxERIU9PT61Zs8bWdvDgQaWlpSkyMlKSFBkZqT179uj06dO2MatWrZLValV4eHgpzggAAJiBw5exWrVqZbdA2TAMpaen68yZM5o1a5ZD+4qPj9eCBQv0n//8R/7+/rY1NgEBAapcubICAgI0cOBAJSQkKCgoSFarVcOGDVNkZKTatm0rSercubPCw8P1xBNPKCkpSenp6Xr++ecVHx8vb29vR08PAACYjMNhp1u3bnbv3dzcFBwcrPvvv1+NGzd2aF9vv/22JOn++++3a583b57tg0anT58uNzc39ejRQzk5OYqOjrYLVe7u7lq6dKkGDx6syMhI+fr6Ki4uTomJiY6eGgAAMKEK9ZwdV+E5O7hd8Jyd2wzP2bm98JydG45z2podAACAiqjYl7Hc3Nxu+jBBSbJYLHwmFQAAqFCKHXY+++yzG/alpqZqxowZKigocEpRAAAAzlLssBMbG1uo7eDBgxo7dqyWLFmivn37sigYAABUOCVas3Py5Ek9+eSTat68ufLy8rRz507Nnz9ftWvXdnZ9AAAApeJQ2MnKytKYMWPUoEED7d27V2vWrNGSJUtsn2UFAABQ0RT7MlZSUpJeeeUVhYaG6sMPPyzyshYAAEBFU+ywM3bsWFWuXFkNGjTQ/PnzNX/+/CLHffrpp04rDgAAoLSKHXb69ev3m7eeAwAAVDTFDjvJycllWAYAAEDZ4AnKAADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Fwadr766iv94Q9/UFhYmCwWixYvXmzXbxiGJkyYoBo1aqhy5cqKiorSoUOH7MacO3dOffv2ldVqVWBgoAYOHKiLFy+W41kAAICKzKVh59KlS2rZsqVmzpxZZH9SUpJmzJih2bNna8uWLfL19VV0dLSuXLliG9O3b1/t3btXq1at0tKlS/XVV19p0KBB5XUKAACggvNw5cG7dOmiLl26FNlnGIZef/11Pf/884qNjZUkvf/++woJCdHixYvVp08f7d+/XykpKdq6davuvvtuSdKbb76pRx55RK+++qrCwsLK7VwAAEDFVGHX7Bw7dkzp6emKioqytQUEBKhNmzZKTU2VJKWmpiowMNAWdCQpKipKbm5u2rJlS7nXDAAAKh6XzuzcTHp6uiQpJCTErj0kJMTWl56erurVq9v1e3h4KCgoyDamKDk5OcrJybG9z87OdlbZAACggqmwMztlacqUKQoICLC9atas6eqSAABAGamwYSc0NFSSlJGRYdeekZFh6wsNDdXp06ft+vPy8nTu3DnbmKKMGzdOWVlZtteJEyecXD0AAKgoKmzYqVu3rkJDQ7VmzRpbW3Z2trZs2aLIyEhJUmRkpDIzM7V9+3bbmC+//FIFBQVq06bNDfft7e0tq9Vq9wIAAObk0jU7Fy9e1OHDh23vjx07pp07dyooKEi1atXSyJEj9eKLL6phw4aqW7euxo8fr7CwMHXr1k2S1KRJEz388MN68sknNXv2bF29elVDhw5Vnz59uBMLAABIcnHY2bZtmx544AHb+4SEBElSXFyckpOT9eyzz+rSpUsaNGiQMjMz1b59e6WkpKhSpUq2bT744AMNHTpUnTp1kpubm3r06KEZM2aU+7kAAICKyWIYhuHqIlwtOztbAQEBysrKuu0uadUZu8zVJaAcHZ8a4+oSUJ4mBbi6ApSnSVmurqDcFff3d4VdswMAAOAMhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqpgk7M2fOVJ06dVSpUiW1adNG33zzjatLAgAAFYApws7HH3+shIQETZw4Ud9++61atmyp6OhonT592tWlAQAAFzNF2Jk2bZqefPJJDRgwQOHh4Zo9e7Z8fHw0d+5cV5cGAABc7JYPO7m5udq+fbuioqJsbW5uboqKilJqaqoLKwMAABWBh6sLKK2ffvpJ+fn5CgkJsWsPCQnRgQMHitwmJydHOTk5tvdZWVmSpOzs7LIrtIIqyPnZ1SWgHN2O/4/f1nIMV1eA8nQb/vu+/jPNMG7+//otH3ZKYsqUKZo8eXKh9po1a7qgGqD8BLzu6goAlJmpAa6uwGUuXLiggIAbn/8tH3aqVasmd3d3ZWRk2LVnZGQoNDS0yG3GjRunhIQE2/uCggKdO3dOVatWlcViKdN64XrZ2dmqWbOmTpw4IavV6upyADgR/75vL4Zh6MKFCwoLC7vpuFs+7Hh5eSkiIkJr1qxRt27dJF0LL2vWrNHQoUOL3Mbb21ve3t52bYGBgWVcKSoaq9XKD0PApPj3ffu42YzOdbd82JGkhIQExcXF6e6779Y999yj119/XZcuXdKAAQNcXRoAAHAxU4Sd3r1768yZM5owYYLS09N11113KSUlpdCiZQAAcPsxRdiRpKFDh97wshXwS97e3po4cWKhS5kAbn38+0ZRLMZv3a8FAABwC7vlHyoIAABwM4QdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdmN6+ffs0ZMgQtWrVSjVq1FCNGjXUqlUrDRkyRPv27XN1eQCcJCcnRzk5Oa4uAxUQYQemtnz5crVq1Uo7duxQbGysJkyYoAkTJig2Nla7du1S69attWLFCleXCaCEVq1apUceeURVqlSRj4+PfHx8VKVKFT3yyCNavXq1q8tDBcFDBWFqLVu2VGxsrBITE4vsnzRpkj799FPt3r27nCsDUFrz58/XX//6V/Xs2VPR0dG2jwjKyMjQypUrtWjRIs2ZM0dPPPGEiyuFqxF2YGqVK1fWzp07deeddxbZf/DgQd111126fPlyOVcGoLQaNWqkESNGKD4+vsj+WbNmafr06Tp06FA5V4aKhstYMLU6depo2bJlN+xftmyZateuXY4VAXCWtLQ0RUVF3bC/U6dO+uGHH8qxIlRUpvkgUKAoiYmJevzxx7Vu3TpFRUXZTXOvWbNGKSkpWrBggYurBFASTZs21Zw5c5SUlFRk/9y5cxUeHl7OVaEi4jIWTG/Tpk2aMWOGUlNTlZ6eLkkKDQ1VZGSkRowYocjISBdXCKAk1q1bp65du6pevXpF/jFz9OhRLVu2TB06dHBxpXA1wg4A4JZ1/Phxvf3229q8eXOhP2b+9re/qU6dOq4tEBUCYQcAAJgaC5RxW3vuuef0l7/8xdVlAADKEGEHt7UffvhBx48fd3UZAMpAXFycHnzwQVeXgQqAu7FwW3v//fddXQKAMhIWFiY3N/6mB2t2cBv46aefNHfu3EJ3Y917773q37+/goODXVwhAKAsEXlhalu3blWjRo00Y8YMBQQEqEOHDurQoYMCAgI0Y8YMNW7cWNu2bXN1mQDKwIkTJ1iTB0nM7MDk2rZtq5YtW2r27NmyWCx2fYZh6G9/+5t2796t1NRUF1UIoKxc/7Df/Px8V5cCF2PNDkxt165dSk5OLhR0JMlisWjUqFFq1aqVCyoDUFqff/75TfuPHj1aTpWgoiPswNRCQ0P1zTffqHHjxkX2f/PNN7anrgK4tXTr1k0Wi0U3u0BR1B86uP0QdmBqzzzzjAYNGqTt27erU6dOhR4n/+677+rVV191cZUASqJGjRqaNWuWYmNji+zfuXOnIiIiyrkqVESEHZhafHy8qlWrpunTp2vWrFm2a/fu7u6KiIhQcnKyHn30URdXCaAkIiIitH379huGnd+a9cHtgwXKuG1cvXpVP/30kySpWrVq8vT0dHFFAErj66+/1qVLl/Twww8X2X/p0iVt27ZNHTt2LOfKUNEQdgAAgKnxnB0AAGBqhB0AAGBqhB0AAGBqhB0At4V169bJYrEoMzOzTI/Tv39/devWrUyPAcAxhB0A5erMmTMaPHiwatWqJW9vb4WGhio6OlobN24s0+Pee++9OnXqlAICAsr0OAAqHp6zA6Bc9ejRQ7m5uZo/f77q1atne8Dj2bNnS7Q/wzCUn58vD4+b/zjz8vJSaGhoiY4B4NbGzA6AcpOZmamvv/5ar7zyih544AHVrl1b99xzj8aNG6c//vGPOn78uCwWi3bu3Gm3jcVi0bp16yT973LU8uXLFRERIW9vb82dO1cWi0UHDhywO9706dNVv359u+0yMzOVnZ2typUra/ny5XbjP/vsM/n7++vnn3+WdO1Tsx999FEFBgYqKChIsbGxOn78uG18fn6+EhISFBgYqKpVq+rZZ5/lIXZABUTYAVBu/Pz85Ofnp8WLFysnJ6dU+xo7dqymTp2q/fv3q2fPnrr77rv1wQcf2I354IMP9Pjjjxfa1mq1qmvXrlqwYEGh8d26dZOPj4+uXr2q6Oho+fv76+uvv9bGjRvl5+enhx9+WLm5uZKk1157TcnJyZo7d642bNigc+fO6bPPPivVeQFwPsIOgHLj4eGh5ORkzZ8/X4GBgWrXrp2ee+457d692+F9JSYm6qGHHlL9+vUVFBSkvn376sMPP7T1f//999q+fbv69u1b5PZ9+/bV4sWLbbM42dnZWrZsmW38xx9/rIKCAr333ntq3ry5mjRponnz5iktLc02y/T6669r3Lhx6t69u5o0aaLZs2ezJgiogAg7AMpVjx49dPLkSX3++ed6+OGHtW7dOrVu3VrJyckO7efuu++2e9+nTx8dP35cmzdvlnRtlqZ169Y3/MT7Rx55RJ6envr8888lSZ988omsVquioqIkSbt27dLhw4fl7+9vm5EKCgrSlStXdOTIEWVlZenUqVNq06aNbZ8eHh6F6gLgeoQdAOWuUqVKeuihhzR+/Hht2rRJ/fv318SJE+Xmdu1H0i/XvVy9erXIffj6+tq9Dw0N1YMPPmi7NLVgwYIbzupI1xYs9+zZ02587969bQudL168qIiICO3cudPu9f333xd5aQxAxUXYAeBy4eHhunTpkoKDgyVJp06dsvX9crHyb+nbt68+/vhjpaam6ujRo+rTp89vjk9JSdHevXv15Zdf2oWj1q1b69ChQ6pevboaNGhg9woICFBAQIBq1KihLVu22LbJy8vT9u3bi10vgPJB2AFQbs6ePasHH3xQ//73v7V7924dO3ZMCxcuVFJSkmJjY1W5cmW1bdvWtvB4/fr1ev7554u9/+7du+vChQsaPHiwHnjgAYWFhd10fIcOHRQaGqq+ffuqbt26dpek+vbtq2rVqik2NlZff/21jh07pnXr1mn48OH64YcfJEkjRozQ1KlTtXjxYh04cEBDhgwp84cWAnAcYQdAufHz81ObNm00ffp0dejQQc2aNdP48eP15JNP6q233pIkzZ07V3l5eYqIiNDIkSP14osvFnv//v7++sMf/qBdu3bd9BLWdRaLRY899liR4318fPTVV1+pVq1atgXIAwcO1JUrV2S1WiVJTz/9tJ544gnFxcUpMjJS/v7++tOf/uTAdwRAebAYPBQCAACYGDM7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1P4fo3Q3tx7HQpEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = df['Survived'].value_counts().plot.bar(title=\"Class distribution\", color=['C0', 'C1'])\n",
        "ax.set_xlabel(\"Survived\")\n",
        "ax.set_ylabel(\"Number of instances\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMLB7mZuGUPl"
      },
      "source": [
        "## Numerical feature distributions with boxplots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "i3EHp3aKGUPm",
        "outputId": "d7fa6979-1e50-4963-f049-f2465577e55c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAArgklEQVR4nO3df3TU9Z3v8dckzAyZJJM0SBKCgUMBgSAqRX6MtbvqQqJEDqxxjz8IooftXtlgxQi6eC2KpUXRirUrprt2hVOX0xZP1YWiksUrXpcoMZZdjCUXWEqUmAREMgkDM5PJ3D+4+V7HoGVIyPeTmefjnBwy3+97Ju/vH9+ZF5/5fD9fRzQajQoAAMAgKXY3AAAA8FUEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQbZ3cD56OrqUlNTkzIzM+VwOOxuBwAAnINoNKr29nYVFBQoJeWbx0gGZEBpampSYWGh3W0AAIDz8Mknn+jiiy/+xpoBGVAyMzMlnTlAr9drczcA+lI4HNb27dtVXFwsp9NpdzsA+pDf71dhYaH1Of5NBmRA6f5ax+v1ElCABBMOh+XxeOT1egkoQII6l+kZTJIFAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAFgjI6ODpWVlenee+9VWVmZOjo67G4JgE0G5EqyABLPtGnTVFtbaz0+fPiwMjMzNXXqVO3evdvGzgDYgREUALbrDicOh0Pl5eVat26dysvL5XA4VFtbq2nTptndIoB+5ohGo1G7m4iX3+9XVlaW2trauBcPMMB1dHQoMzNTDodDgUBAqamp2rZtm2bPnq1IJCKPx2Pdoj0jI8PudgH0Qjyf34ygALDVggULJEnl5eUaPHhwzL7Bgwfr9ttvj6kDkBwIKABsdfDgQUnSsmXLzrq/srIypg5AciCgALDV6NGjJUlPPfXUWfc//fTTMXUAkgNzUADYijkoQPJgDgqAASMjI0NTp05VNBqVx+PRnXfeqYMHD+rOO++0wsnUqVMJJ0CSiSugPProo3I4HDE/48ePt/afPn1aFRUVGjJkiDIyMlRWVqaWlpaY12hsbFRpaak8Ho9yc3O1fPlydXZ29s3RABiQdu/ebYWUTZs26f7779emTZuscMI6KEDyiXsEZeLEifrss8+sn3fffdfad99992nLli3avHmzdu7cqaamJt10003W/kgkotLSUoVCIe3atUsbN27Uhg0btHLlyr45GgAD1u7du9Xe3q45c+Zo5MiRmjNnjtrb2wknQJKKaw7Ko48+qldffVV79uzpsa+trU1Dhw7Vpk2bdPPNN0uS9u3bpwkTJqimpkYzZszQ66+/rhtvvFFNTU3Ky8uTJFVVVenBBx/U0aNH5XK5zqkP5qAAiSscDltzUJxOp93tAOhDF3QOyv79+1VQUKBvf/vbmj9/vhobGyVJdXV1CofDmjlzplU7fvx4jRgxQjU1NZKkmpoaTZo0yQonklRSUiK/36/6+vp4WwEAAAkqrnvxTJ8+XRs2bNC4ceP02WefadWqVfre976njz76SM3NzXK5XMrOzo55Tl5enpqbmyVJzc3NMeGke3/3vq8TDAYVDAatx36/X9KZ/2mFw+F4DgGA4brPac5tIPHEc17HFVBuuOEG6/fLLrtM06dP18iRI/Xb3/5WaWlp8bxUXNasWaNVq1b12L59+3Z5PJ4L9ncB2Ke6utruFgD0sUAgcM61vbqbcXZ2ti655BIdOHBAs2bNUigU0okTJ2JGUVpaWpSfny9Jys/P7zHhrfsqn+6as1mxYoW1mqR0ZgSlsLBQxcXFzEEBEkw4HFZ1dbVmzZrFHBQgwXR/A3IuehVQOjo6dPDgQS1YsEBTpkyR0+nUjh07VFZWJklqaGhQY2OjfD6fJMnn8+nHP/6xWltblZubK+nM/5K8Xq+Kioq+9u+43W653e4e251OJ29gQILi/AYSTzzndFwBZdmyZdYlgE1NTXrkkUeUmpqq2267TVlZWVq0aJEqKyuVk5Mjr9ere+65Rz6fTzNmzJAkFRcXq6ioSAsWLNDatWvV3Nyshx9+WBUVFWcNIAAAIDnFFVA+/fRT3Xbbbfr88881dOhQXX311Xrvvfc0dOhQSdK6deuUkpKisrIyBYNBlZSUaP369dbzU1NTtXXrVi1evFg+n0/p6elauHChHnvssb49KgAAMKBxLx4ARmEdFCBxcS8eAAAwoBFQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM06uA8vjjj8vhcGjp0qXWttOnT6uiokJDhgxRRkaGysrK1NLSEvO8xsZGlZaWyuPxKDc3V8uXL1dnZ2dvWgEAAAnkvANKbW2tfvGLX+iyyy6L2X7fffdpy5Yt2rx5s3bu3KmmpibddNNN1v5IJKLS0lKFQiHt2rVLGzdu1IYNG7Ry5crzPwoAAJBQziugdHR0aP78+frnf/5nfetb37K2t7W16Ze//KWefvppXXfddZoyZYpefPFF7dq1S++9954kafv27fr444/10ksv6YorrtANN9ygH/3oR3ruuecUCoX65qgAAMCANuh8nlRRUaHS0lLNnDlTq1evtrbX1dUpHA5r5syZ1rbx48drxIgRqqmp0YwZM1RTU6NJkyYpLy/PqikpKdHixYtVX1+vyZMn9/h7wWBQwWDQeuz3+yVJ4XBY4XD4fA4BgKG6z2nObSDxxHNexx1Qfv3rX+vDDz9UbW1tj33Nzc1yuVzKzs6O2Z6Xl6fm5mar5svhpHt/976zWbNmjVatWtVj+/bt2+XxeOI9BAADQHV1td0tAOhjgUDgnGvjCiiffPKJ7r33XlVXV2vw4MFxN3a+VqxYocrKSuux3+9XYWGhiouL5fV6+60PABdeOBxWdXW1Zs2aJafTaXc7APpQ9zcg5yKugFJXV6fW1lZ95zvfsbZFIhG98847+sd//Ee9+eabCoVCOnHiRMwoSktLi/Lz8yVJ+fn52r17d8zrdl/l013zVW63W263u8d2p9PJGxiQoDi/gcQTzzkd1yTZv/qrv9LevXu1Z88e6+fKK6/U/Pnzrd+dTqd27NhhPaehoUGNjY3y+XySJJ/Pp71796q1tdWqqa6ultfrVVFRUTztAACABBXXCEpmZqYuvfTSmG3p6ekaMmSItX3RokWqrKxUTk6OvF6v7rnnHvl8Ps2YMUOSVFxcrKKiIi1YsEBr165Vc3OzHn74YVVUVJx1lAQAACSf87qK55usW7dOKSkpKisrUzAYVElJidavX2/tT01N1datW7V48WL5fD6lp6dr4cKFeuyxx/q6FQAAMEA5otFo1O4m4uX3+5WVlaW2tjYmyQIJJhwOa9u2bZo9ezZzUIAEE8/nN/fiAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYJ66A8vzzz+uyyy6T1+uV1+uVz+fT66+/bu0/ffq0KioqNGTIEGVkZKisrEwtLS0xr9HY2KjS0lJ5PB7l5uZq+fLl6uzs7JujAQAACSGugHLxxRfr8ccfV11dnT744ANdd911mjt3rurr6yVJ9913n7Zs2aLNmzdr586dampq0k033WQ9PxKJqLS0VKFQSLt27dLGjRu1YcMGrVy5sm+PCgAADGiOaDQa7c0L5OTk6Mknn9TNN9+soUOHatOmTbr55pslSfv27dOECRNUU1OjGTNm6PXXX9eNN96opqYm5eXlSZKqqqr04IMP6ujRo3K5XOf0N/1+v7KystTW1iav19ub9gEYJhwOa9u2bZo9e7acTqfd7QDoQ/F8fg863z8SiUS0efNmnTx5Uj6fT3V1dQqHw5o5c6ZVM378eI0YMcIKKDU1NZo0aZIVTiSppKREixcvVn19vSZPnnzWvxUMBhUMBmMOUDrzRhYOh8/3EAAYqPuc5twGEk8853XcAWXv3r3y+Xw6ffq0MjIy9Morr6ioqEh79uyRy+VSdnZ2TH1eXp6am5slSc3NzTHhpHt/976vs2bNGq1atarH9u3bt8vj8cR7CAAGgOrqartbANDHAoHAOdfGHVDGjRunPXv2qK2tTS+//LIWLlyonTt3xvsycVmxYoUqKyutx36/X4WFhSouLuYrHiDBhMNhVVdXa9asWXzFAySY7m9AzkXcAcXlcmnMmDGSpClTpqi2tlY/+9nPdMsttygUCunEiRMxoygtLS3Kz8+XJOXn52v37t0xr9d9lU93zdm43W653e4e251OJ29gQILi/AYSTzzndK/XQenq6lIwGNSUKVPkdDq1Y8cOa19DQ4MaGxvl8/kkST6fT3v37lVra6tVU11dLa/Xq6Kiot62AgAAEkRcIygrVqzQDTfcoBEjRqi9vV2bNm3S22+/rTfffFNZWVlatGiRKisrlZOTI6/Xq3vuuUc+n08zZsyQJBUXF6uoqEgLFizQ2rVr1dzcrIcfflgVFRVnHSEBAADJKa6A0traqjvuuEOfffaZsrKydNlll+nNN9/UrFmzJEnr1q1TSkqKysrKFAwGVVJSovXr11vPT01N1datW7V48WL5fD6lp6dr4cKFeuyxx/r2qAAAwIDW63VQ7MA6KEDiYh0UIHHF8/nNvXgAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAME5cdzMGgAupoaFBEydOVCQSUWpqqurr6zVu3Di72wJgAwIKACM4HI6Yx5FIROPHj5ckDcCbrgPoJb7iAWC7L4cTt9ut2267TW63+6z7ASQHAgoAWzU0NFi/HzlyRO3t7brlllvU3t6uI0eOnLUOQOIjoACw1cSJEyWdGTkpKCiI2VdQUGCNpHTXAUgOBBQAtopEIpKkH/7wh2fd/8ADD8TUAUgOBBQAtkpNTZUk/ehHPzrr/rVr18bUAUgOBBQAtqqvr5ckBYNBNTU1xexrampSMBiMqQOQHAgoAGz15XVOhg8frszMTL300kvKzMzU8OHDz1oHIPGxDgoA20WjUetS4mAwqJdffrnHfgDJhREUAEaIRqP6wx/+ELPtD3/4A+EESFIEFABGmDdvniZPnhyzbfLkyZo3b549DQGwFQEFgO3mzZun1157TS6XSw888ICef/55PfDAA3K5XHrttdcIKUASckQH4Pip3+9XVlaW2tra5PV67W4HQC+cOnVKHo9HLpdL7e3tcjgc2rZtm2bPnq1oNKrMzEyFQiEFAgGlpaXZ3S6AXojn85sRFAC2Wr58uSSpsrJSLpcrZp/L5dLSpUtj6gAkBwIKAFvt379fkvS3f/u3Z92/aNGimDoAyYGAAsBWY8eOlSS98MILZ93/y1/+MqYOQHJgDgoAWzEHBUgezEEBMGCkpaVp7ty5CoVCyszM1EMPPaQjR47ooYcessLJ3LlzCSdAkmEEBYARui81/qq5c+fq1Vdf7f+GAPQ5RlAADDivvvqqAoGA7r77bl1xxRW6++67FQgECCdAkuJePACMkZaWpmeffdaag+J0Ou1uCYBNGEEBAADGYQQFgDHa2tp0ww03aP/+/Ro7dqxef/11ZWVl2d0WABsQUAAYYcyYMTp48KD1+NixY8rOztbo0aN14MABGzsDYAe+4gFguy+Hk5KSEj3++OMqKSmRJB08eFBjxoyxsz0ANiCgALBVW1ubFU5OnjypLVu2aPz48dqyZYtOnjwp6UxIaWtrs7NNAP2MgALAVqWlpZKk66+/Xl1dXSorK9O9996rsrIydXV1qbi4OKYOQHJgDgoAWzU2NkqS/vSnPykzM9PafvjwYWVmZmrcuHExdQCSAyMoAGw1YsQISdK+ffvkcDhUXl6udevWqby8XA6HQw0NDTF1AJIDS90DsNWRI0d08cUXS5I+//xzZWZmWgu1tbe3a8iQIZKkTz/9VMOHD7ezVQC9xFL3AAaMJUuWWL8PGTJEpaWlqq+vV2lpqRVOvloHIPExBwWArbqv4Bk+fLiOHDmi6upqVVdXW/u7t395jRQAiY8RFAC2Gj16tCTpuuuu04kTJ+Tz+XTRRRfJ5/PpxIkTuuaaa2LqACQH5qAAsFVHR4cyMzPlcDi0f/9+TZ06VX6/X16vV7W1tRo7dqyi0aja29uVkZFhd7sAeoE5KAAGjIyMDE2dOlXRaFRjxozRF198oUgkoi+++EJjxoxRNBrV1KlTCSdAkiGgALDdf/7nf/ZqP4DEQ0ABYKsjR44oFApJOrM425w5czRy5EjNmTNHhw8fliSFQiEdOXLEzjYB9DPmoACwVU5Ojr744gsNGzZMTU1NCofD1jooTqdTw4YNU3Nzs771rW/p+PHjdrcLoBeYgwJgwGhvb5ckPfHEE2fdv3r16pg6AMmBgALAVt3333nwwQfPuv/hhx+OqQOQHAgoAGy1d+9eSdJnn33W4yuc48ePq7m5OaYOQHKIK6CsWbNGU6dOVWZmpnJzczVv3jzrRl7dTp8+rYqKCg0ZMkQZGRkqKytTS0tLTE1jY6NKS0vl8XiUm5ur5cuXq7Ozs/dHA2DAGT58uFwul6QzS92PGDFC27dv14gRI6yl7l0uF/fhAZJMXAFl586dqqio0Hvvvafq6mqFw2EVFxfr5MmTVs19992nLVu2aPPmzdq5c6eampp00003WfsjkYhKS0sVCoW0a9cubdy4URs2bNDKlSv77qgADCjBYNAKKc3NzVq/fr01cuJyuRQMBu1sD4ANenUVz9GjR5Wbm6udO3fqL/7iL9TW1qahQ4dq06ZNuvnmmyWduYX6hAkTVFNToxkzZuj111/XjTfeqKamJuXl5UmSqqqq9OCDD+ro0aPWm9Q34SoeIDEdOXJEkyZNslaS3bt3LyMnQAKJ5/O7VzcLbGtrk3TmMkFJqqurUzgc1syZM62a8ePHa8SIEVZAqamp0aRJk6xwIkklJSVavHix6uvrNXny5B5/JxgMxvwPyu/3S5LC4bDC4XBvDgGAQXJzc/Xpp5+qurpas2bNktPp5BwHEkg85/N5B5Suri4tXbpU3/3ud3XppZdKOjM063K5lJ2dHVObl5dnDdc2NzfHhJPu/d37zmbNmjVatWpVj+3bt2+Xx+M530MAYLAv39EYQGIIBALnXHveAaWiokIfffSR3n333fN9iXO2YsUKVVZWWo/9fr8KCwtVXFzMVzxAggmHwzEjKAASR/c3IOfivALKkiVLtHXrVr3zzju6+OKLre35+fkKhUI6ceJEzChKS0uL8vPzrZrdu3fHvF73VT7dNV/ldrvldrt7bHc6nbyBAQmK8xtIPPGc03FdxRONRrVkyRK98soreuuttzRq1KiY/VOmTJHT6dSOHTusbQ0NDWpsbJTP55Mk+Xw+7d27V62trVZNdXW1vF6vioqK4mkHAAAkqLhGUCoqKrRp0ya99tpryszMtOaMZGVlKS0tTVlZWVq0aJEqKyuVk5Mjr9ere+65Rz6fTzNmzJAkFRcXq6ioSAsWLNDatWvV3Nyshx9+WBUVFWcdJQEAAMknroDy/PPPS5KuueaamO0vvvii7rzzTknSunXrlJKSorKyMgWDQZWUlGj9+vVWbWpqqrZu3arFixfL5/MpPT1dCxcu1GOPPda7IwEAAAmDuxkDMMpX72YMIHFwN2MAADCgEVAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFADG6OjoUFlZme69916VlZWpo6PD7pYA2CSuuxkDwIUybdo01dbWWo8PHz6szMxMTZ06Vbt377axMwB2YAQFgO26w4nD4VB5ebnWrVun8vJyORwO1dbWatq0aXa3CKCfOaLRaNTuJuIVz+2aAZito6NDmZmZcjgcCgQCSk1N1bZt2zR79mxFIhF5PB5Fo1G1t7crIyPD7nYB9EI8n9+MoACw1YIFCyRJ5eXlGjx4cMy+wYMH6/bbb4+pA5AcCCgAbHXw4EFJ0rJly866v7KyMqYOQHIgoACw1ejRoyVJTz311Fn3P/300zF1AJIDc1AA2Io5KEDyYA4KgAEjIyNDU6dOVTQalcfj0cSJE7V8+XJNnDjRCidTp04lnABJhhEUAEbIzs5WW1tbj+1ZWVk6ceJE/zcEoM8xggJgQJk3b95Zw4kktbW1ad68ef3bEADbEVAA2OrUqVN67bXXrMfTp0/XqlWrNH36dGvba6+9plOnTtnRHgCbEFAA2Grx4sXW762trZo8ebJeeeUVTZ48Wa2trWetA5D4mIMCwFbp6ekKBALyer3y+/099mdmZqq9vV0ej0cnT560oUMAfSWez29uFgjAVpFIRNKZNy6Xy6WlS5dq1KhROnTokJ555hm1t7fH1AFIDnzFA8BWo0aNsn7//PPPtXr1ag0bNkyrV6/W559/ftY6AImPgALAVldddZX1e3Z2th566CEdOXJEDz30kLKzs89aByDx8RUPAFt9+umn1u+RSORrl7z/ch2AxMcICgBbjR07VpLk8XjOur97e3cdgORAQAFgqyeffFKS1NnZqcOHDysvL09Op1N5eXk6fPiwOjs7Y+oAJAcCCgBbpaWlae7cuQqFQho5cqRaWloUDofV0tKikSNHKhQKae7cuUpLS7O7VQD9iIACwHaXXHJJr/YDSDws1AbAVqFQSG63+8/WBYNBuVyufugIwIXCzQIBDBg/+clPrN9zcnJUVVWlF198UVVVVcrJyTlrHYDExwgKAFu53W6FQiFJUjgcVjQa1bZt2zR79mw5HA45nU5JksvlUjAYtLNVAL3ECAqAAaM7nFx11VUaNCh2aaZBgwZZdzXurgOQHAgoAGzVPUJSU1NjXVLcrbOzU7t3746pA5AcCCgAbPUP//APkqRoNKphw4bphRde0PHjx/XCCy9o2LBh6v4WursOQHJgDgoAW3EVD5A8mIMCYMBwuVxavnz5N9YsX76ccAIkGQIKANutXbv2a0PK8uXLtXbt2n7uCIDdCCgAjPD222/HtR1AYiOgALDdtGnTVFtbK4fDofLycq1bt07l5eVyOByqra3VtGnT7G4RQD9jkiwAW3V0dCgzM1MOh0OBQECpqanWQm2RSEQej0fRaFTt7e3KyMiwu10AvcAkWQADxoIFCyRJ5eXlikaj+sEPfqBHH31UP/jBDxSNRnX77bfH1AFIDoP+fAkAXDgHDx6UJDU2Nsrj8Vjb9+zZo6qqKv3lX/5lTB2A5MAICgBbjR49WpK0c+dOOZ1OjR49WgUFBRo9erScTqd27twZUwcgOTAHBYCtjh49qtzc3D9b19raqqFDh/ZDRwAuFOagABgwVq1a1ad1ABIDAQWArT7++OM+rQOQGAgoAGx1rpNfmSQLJBeu4gFgq/b2dut3l8ulpUuXatSoUTp06JCeeeYZhUKhHnUAEh8jKABslZqaav0ejUbV1dVl/Xx5Dv+X6wAkPkZQANhq6NChOnbsmCQpHA7rqaee+to6AMkj7hGUd955R3PmzFFBQYEcDodeffXVmP3RaFQrV67UsGHDlJaWppkzZ2r//v0xNcePH9f8+fPl9XqVnZ2tRYsWqaOjo1cHAmBgKigoiHk8ZswYjR07VmPGjPnGOgCJLe6AcvLkSV1++eV67rnnzrp/7dq1evbZZ1VVVaX3339f6enpKikp0enTp62a+fPnq76+XtXV1dq6daveeecd/d3f/d35HwWAAWv8+PExjw8cOKD9+/frwIED31gHILH1aqE2h8OhV155RfPmzZN0ZvSkoKBA999/v5YtWyZJamtrU15enjZs2KBbb71Vf/zjH1VUVKTa2lpdeeWVkqQ33nhDs2fP1qeffnpO/0tioTYgcZw6dUoej0cpKSnq6urqsb97eyAQUFpamg0dAugr8Xx+9+kclEOHDqm5uVkzZ860tmVlZWn69OmqqanRrbfeqpqaGmVnZ1vhRJJmzpyplJQUvf/++/rrv/7rHq8bDAYVDAatx36/X9KZ76vD4XBfHgKAfjZo0CDNmTNHW7ZsUWpqqjwej4LBoNxutwKBgCKRiObMmaNBgwZxvgMDXDzncJ8GlObmZklSXl5ezPa8vDxrX3Nzc49lrQcNGqScnByr5qvWrFlz1lUkt2/fHnNzMQAD06JFi7Rjxw4FAgHrcuLuy4s9Ho8WLVqkbdu22dkigD4QCATOuXZAXMWzYsUKVVZWWo/9fr8KCwtVXFzMVzxAArjqqqusN67U1FRFIhHr30AgoNWrV2vXrl02dwmgt7q/ATkXfRpQ8vPzJUktLS0aNmyYtb2lpUVXXHGFVdPa2hrzvM7OTh0/ftx6/le53W653e4e251Op5xOZx91D8AOHR0d+uCDD6zHkUgk5l9J+uCDDxQMBpWRkdHv/QHoO/F8ZvfpQm2jRo1Sfn6+duzYYW3z+/16//335fP5JEk+n08nTpxQXV2dVfPWW2+pq6tL06dP78t2AAwACxYs6NM6AIkh7hGUjo6OmMv/Dh06pD179ignJ0cjRozQ0qVLtXr1ao0dO1ajRo3SD3/4QxUUFFhX+kyYMEHXX3+9vv/976uqqkrhcFhLlizRrbfeyjoHQBJqaGiwfr/oootUVFSkY8eO6aKLLtLHH39sLeL25ToAiS/uy4zffvttXXvttT22L1y4UBs2bFA0GtUjjzyif/qnf9KJEyd09dVXa/369brkkkus2uPHj2vJkiXasmWLUlJSVFZWpmefffach2+5zBhIHGlpaTHrJH2dwYMH69SpU/3QEYALJZ7P716tg2IXAgqQOAYNGhQz3+Srlxl3S01NVWdnpx0tAugj8Xx+c7NAALYaPHhwzOPutU++ejniV+sAJDYCCgBbTZgwoU/rACQGAgoAW53ryAgjKEByIaAAsNX777/fp3UAEgMBBYCtzvXeHNyHB0guBBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoACwlcPh6NM6AImBgAIAAIxDQAFgq3O9X+kAvK8pgF4goAAAAOMQUAAAgHEG2d0AgIEvEAho37595/Xc66+/Xm+88Yb12O12q7MzokGDUhUMBmPqPvzww/P6G+PHj5fH4zmv5wKwhyM6AL/Y9fv9ysrKUltbm7xer93tAEnvww8/1JQpU+xu42vV1dXpO9/5jt1tAEkvns9vRlAA9Nr48eNVV1d3Xs8NhUK6+uqrFYlEvrYmNTVV7777rlwu13n3B2BgIaAA6DWPx9OrEYrKyko9+eSTyszMVEdHh6LRqBwOhzIyMtTe3q7KykrNmDGjDzsGYDoCCgDbrV27VpK0bt0663LiaDSqU6dOafny5dZ+AMmDOSgAjBEKhfQ/V6/VL37/vv5H6XT9+OEHzvtrHQDmYQ4KgAHJ5XJp/qLF2hyarPmLZhBOgCTGOigAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4wyyuwEA9jp07KROBjvtbsNy8OhJ699Bg8x5i0p3D9Koi9LtbgNIGuac/QD63aFjJ3XtU2/b3cZZ3f/yXrtb6OF/LbuGkAL0EwIKkMS6R06eueUKjcnNsLmbM06eCmrr2zW68Rqf0tPcdrcjSTrQ2qGlv9lj1EgTkOgIKAA0JjdDlw7PsrsNSVI4HFbzUOk7I78lp9NpdzsAbMIkWQAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcVgHBUhiwchppQw+okP+BqUMNmOhts7OTjV1NumPx/9ozFL3h/wdShl8RMHIaUlmrBcDJDozzn4Atmg6eVjpo36uh3bb3UlP699Yb3cLMdJHSU0nr9AU5dndCpAUCChAEitIH6mTh+7Rz265QqMNWeq+s7NT//Huf+i7V3/XmBGUg60duvc3e1Rw7Ui7WwGShhlnPwBbuFMHq+v0cI3yjlPREDO+ugiHwzo06JAm5EwwZqn7rtNt6jp9VO7UwXa3AiQNAgqQxE6FI5Kkj4602dzJ/3fyVFAfHJXyD39h1M0CAfQvAgqQxA7+vw/ef/jdXps7+apB+tWBWrub6CHdzVsm0F9sPduee+45Pfnkk2pubtbll1+un//855o2bZqdLQFJpXhiviRpdG6G0pypNndzRsNnbbr/5b366c2TNG6YGV87SWfCyaiL0u1uA0gatgWU3/zmN6qsrFRVVZWmT5+uZ555RiUlJWpoaFBubq5dbQFJJSfdpVunjbC7jRidnZ2SpNFD03XpcHMCCoD+ZdtCbU8//bS+//3v66677lJRUZGqqqrk8Xj0L//yL3a1BAAADGHLCEooFFJdXZ1WrFhhbUtJSdHMmTNVU1PToz4YDCoYDFqP/X6/pDOz/cPh8IVvGMA3CgQCamho6JPX+j+ftSnYfEAf7XEp1NI3Iyjjxo2Tx+Ppk9cCcP7i+cy2JaAcO3ZMkUhEeXmxCx7l5eVp3759PerXrFmjVatW9di+fft23nQAAxw8eFD3339/n77mgo1991o//elPNXr06L57QQDnJRAInHPtgJiSvmLFClVWVlqP/X6/CgsLVVxcLK/Xa2NnAKQzbzpXX311n7xWx6mg3vzftSr53lRl9NFlxoygAGbo/gbkXNgSUC666CKlpqaqpaUlZntLS4vy8/N71LvdbrndPd+onE6nMQs5AcksKyurz67AC4fDaj9xXN+7agbnN5Bg4jmnbZkk63K5NGXKFO3YscPa1tXVpR07dsjn89nREgAAMIhtX/FUVlZq4cKFuvLKKzVt2jQ988wzOnnypO666y67WgIAAIawLaDccsstOnr0qFauXKnm5mZdccUVeuONN3pMnAUAAMnH1kmyS5Ys0ZIlS+xsAQAAGMi2hdoAAAC+DgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADCOrSvJnq9oNCopvts2AxgYwuGwAoGA/H4/dzMGEkz353b35/g3GZABpb29XZJUWFhocycAACBe7e3tysrK+sYaR/RcYoxhurq61NTUpMzMTDkcDrvbAdCH/H6/CgsL9cknn8jr9drdDoA+FI1G1d7eroKCAqWkfPMskwEZUAAkLr/fr6ysLLW1tRFQgCTGJFkAAGAcAgoAADAOAQWAUdxutx555BG53W67WwFgI+agAAAA4zCCAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAC6oO++8Uw6Ho8fPgQMH7G4NgMEG5L14AAws119/vV588cWYbUOHDo3rNSKRiBwOx59dHhtAYuBMB3DBud1u5efnx/z87Gc/06RJk5Senq7CwkL9/d//vTo6OqznbNiwQdnZ2fq3f/s3FRUVye12q7GxUcFgUMuWLdPw4cOVnp6u6dOn6+2337bv4ABcEAQUALZISUnRs88+q/r6em3cuFFvvfWWHnjggZiaQCCgJ554Qi+88ILq6+uVm5urJUuWqKamRr/+9a/1X//1X/qbv/kbXX/99dq/f79NRwLgQmChNgAX1J133qmXXnpJgwcPtrbdcMMN2rx5c0zdyy+/rLvvvlvHjh2TdGYE5a677tKePXt0+eWXS5IaGxv17W9/W42NjSooKLCeO3PmTE2bNk0/+clP+uGIAPQH5qAAuOCuvfZaPf/889bj9PR0/fu//7vWrFmjffv2ye/3q7OzU6dPn1YgEJDH45EkuVwuXXbZZdbz9u7dq0gkoksuuSTm9YPBoIYMGdI/BwOgXxBQAFxw6enpGjNmjPX4T3/6k2688UYtXrxYP/7xj5WTk6N3331XixYtUigUsgJKWlqaHA6H9byOjg6lpqaqrq5OqampMX8jIyOjfw4GQL8goADod3V1derq6tJPf/pT66qc3/72t3/2eZMnT1YkElFra6u+973vXeg2AdiISbIA+t2YMWMUDof185//XP/93/+tX/3qV6qqqvqzz7vkkks0f/583XHHHfrd736nQ4cOaffu3VqzZo1+//vf90PnAPoLAQVAv7v88sv19NNP64knntCll16qf/3Xf9WaNWvO6bkvvvii7rjjDt1///0aN26c5s2bp9raWo0YMeICdw2gP3EVDwAAMA4jKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAY5/8CSGRma78rMdIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "categorical_columns = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Embarked\", \"Name\", \"Cabin\"]\n",
        "for column in df.drop(categorical_columns + ['Survived', 'Ticket'], axis=1):\n",
        "    plt.figure()\n",
        "    df.boxplot([column])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vcKXRx5GUPm"
      },
      "source": [
        "## Numerical feature distributions with histograms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "l-BNP9CKGUPm",
        "outputId": "2d4b53bd-e8a9-4cc9-b74f-1077d6018c41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.drop(categorical_columns + ['Survived'], axis=1).hist(bins=15, figsize=(15, 5), rwidth=0.8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuWifmjCGUPn"
      },
      "source": [
        "## Normalize highly skewed columns with log transformation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 875
        },
        "id": "C7xIj0oGGUPn",
        "outputId": "3df9c59b-3d8a-4137-f976-5c8ff8f2d732"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['Fare'] = df['Fare'].apply(lambda x: np.log(1+x))\n",
        "df['Fare'].hist(bins=15, figsize=(7, 5), rwidth=0.8)\n",
        "plt.show()\n",
        "df.boxplot(['Fare'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYntp-0EGUPn"
      },
      "source": [
        "## Categorical feature value counts with bar charts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QHbA7j8YGUPn",
        "outputId": "a60a46df-72f0-4c55-8a94-a8ca35389d0b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for column in categorical_columns:\n",
        "    ax = df[column].value_counts().plot.bar(title=column)\n",
        "    ax.set_xlabel(\"Value\")\n",
        "    ax.set_ylabel(\"Number of instances\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBDkZukTGUPo"
      },
      "source": [
        "## Numerical feature correlations with a heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "m9HZyGhWGUPo",
        "outputId": "e9941a12-5607-4fed-f817-8e4ab79f0964"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-97-b7eec00aec4e>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df.drop(categorical_columns, axis=1).corr(), annot=True, cmap=plt.cm.Reds)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAH/CAYAAAA434sNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABE3klEQVR4nO3deVxU9f7H8fcMyiAgoCK44Zbmkoo7yk3TwiXLsrLMFs3rtTIrb1QqldIqWlczy5strqWplXLNrYzUMjFzw3It0VwhEZVFAR3O7w9/d24jkAwywXFez/s4j0dz5vs953O689BPn+9yLIZhGAIAAICpWMs6AAAAALiOJA4AAMCESOIAAABMiCQOAADAhEjiAAAATIgkDgAAwIRI4gAAAEyIJA4AAMCESOIAAABMiCQOAADAhEjiAAAArsC3336rvn37qlatWrJYLIqPj79sn7Vr16pt27ay2Wxq1KiRZs+e7fJ9SeIAAACuQHZ2tsLDwzVt2rRitT9w4IBuueUWde/eXdu3b9c///lP/eMf/9CXX37p0n0thmEYJQkYAAAAziwWi5YsWaJ+/foV2Wb06NFavny5fv75Z8e5e++9V6dPn9aqVauKfS8qcQAAAJfIzc1VRkaG05Gbm1sq105MTFRUVJTTuV69eikxMdGl61QolWhKwaOWgLIOAXAyPftwWYcAOLHPeLmsQwAK8HpiUpnd2525Q43YaL300ktO52JjY/Xiiy9e8bVTUlIUGhrqdC40NFQZGRk6d+6cKlWqVKzrlJskDgAAoLyIiYlRdHS00zmbzVZG0RSOJA4AAJiSO+eE2Ww2tyVtNWrUUGpqqtO51NRUBQQEFLsKJzEnDgAA4C/VuXNnJSQkOJ1bvXq1Onfu7NJ1SOIAAIApWS0Wtx2uyMrK0vbt27V9+3ZJF7cQ2b59uw4dOiTp4tDsoEGDHO0fffRRJScna9SoUdqzZ4/+/e9/a9GiRXrqqadce36XWgMAAMDJ5s2b1aZNG7Vp00aSFB0drTZt2mjcuHGSpOPHjzsSOklq0KCBli9frtWrVys8PFyTJk3Shx9+qF69erl0X+bEAQAAUyovlahu3brpz7bdLextDN26ddO2bduu6L4kcQAAwJSsro16XnXKSxILAAAAF1CJAwAApuTplShPf34AAABTohIHAABMydWtQK42VOIAAABMiEocAAAwJU+vRHn68wMAAJgSlTgAAGBK7BMHAAAA06ESBwAATMnTK1EkcQAAwJQsbDECAAAAs6ESBwAATMnTK1Ge/vwAAACmRCUOAACYEluMAAAAwHSoxAEAAFPy9EqUpz8/AACAKVGJAwAApmT18H3iSOIAAIApefpwoqc/PwAAgClRiQMAAKbEFiMAAAAwHSpxAADAlDy9EuXpzw8AAGBKVOIAAIApWeXZk+KoxAEAAJgQlTgAAGBKnr46lSQOAACYkqcPJ3r68wMAAJgSlTgAAGBKnj6cSiUOAADAhKjEAQAAU2KLEQAAAJgOlTgAAGBKzIkDAACA6VCJAwAApuTplSiSOAAAYEoMpwIAAMB0qMQBAABTYosRAAAAmA6VOAAAYEqePieu2EncnXfeWeyLLl68uETBAAAAoHiKncQFBgY6/tkwDC1ZskSBgYFq3769JGnLli06ffq0S8keAABASXl4Ia74SdysWbMc/zx69Gjdc889mj59ury8vCRJdrtdjz32mAICAko/SgAAADgp0Zy4mTNnav369Y4ETpK8vLwUHR2tyMhIvfHGG6UWIAAAQGE8fU5ciVanXrhwQXv27Clwfs+ePcrPz7/ioAAAAC7HKovbDjMoUSVuyJAhGjp0qPbv36+OHTtKkn744QdNmDBBQ4YMKdUAAQAAUFCJkrh//etfqlGjhiZNmqTjx49LkmrWrKlnn31WTz/9dKkGCAAAUBhPH04tURJntVo1atQojRo1ShkZGZLEggYAAIC/UInf2HDhwgV9/fXX+uSTT2SxXEyFjx07pqysrFILDgAAoChWNx5mUKJK3G+//abevXvr0KFDys3NVY8ePVS5cmVNnDhRubm5mj59emnHCQAAgD8oUbI5cuRItW/fXqdOnVKlSpUc5++44w4lJCSUWnAAAABFsbjxMIMSVeK+++47bdiwQd7e3k7n69evr6NHj5ZKYAAAAChaiZK4/Px82e32AuePHDmiypUrX3FQAAAAl2O1mKVm5h4lGk7t2bOnpkyZ4vhssViUlZWl2NhY9enTp7RiAwAAKBLDqSUwadIk9erVS82bN1dOTo7uu+8+/fLLLwoODtYnn3xS2jECAADgEiVK4urUqaOkpCQtWLBAO3bsUFZWloYOHar777/faaEDAACAu5ilYuYuJUricnJy5OPjowceeKC04wEAAEAxlGhOXEhIiAYPHqzVq1fzwnsAAFAmPH1OXImSuDlz5ujs2bO6/fbbVbt2bf3zn//U5s2bSzs2AAAAFKFESdwdd9yhTz/9VKmpqRo/frx27dqlTp066dprr9XLL79c2jECAAAUYLFY3HaYwRW9Hqxy5coaMmSIvvrqK+3YsUN+fn566aWXSis2AAAAFOGKkricnBwtWrRI/fr1U9u2bZWenq5nn322tGIDAAAokqfPiSvR6tQvv/xS8+fPV3x8vCpUqKD+/fvrq6++UteuXUs7PhRToy6R6vnsSNVt11pBtWrq3X4DlfSf5WUdFq4ChmFo6rvv69Ml8crIzFLb8FZ68bnRql+v7p/2m7fwU82Y87FOnDypptc21tjRz6hVi+skSUeOHdNNt/QrtN+U18fr5h5RWrx0mWJiC5+esSFhlapVrXpFz4Wry/wdBzVz236lnc1Vk+AAPd/1OrUKrVJo29X7j+v9zb/q0JlsXcg3VDfIT0NaN9RtTes42rzzw16t/OWYUrJyVNHLqubVAzWyUxOF1yj8migbV1SJugqUKIm74447dOutt2ru3Lnq06ePKlasWNpxwUU2Pz8dSfpZG2Z+pEeXzC/rcHAV+WD2XH30yUJNeDlWdWrX0lv/fk9DRzypFZ8vlM1mK7TPii9XK27SFL30/BiFt7hOc+Yv0NDHntSq+E9VrWpV1QwN1frVK5z6LPw8XjPmfqyuf4uUJPXpGaUukZ2c2oyJfVl5uXkkcHCy8pdjmrh+l2K7tVSrGkH6aPsBPbx0k5bf303VfAv+RgNtFfVI+0ZqUMVfFb2sWncwVc8nJKlqJW9dXy9EklQ/yF/P39BCYQG+yrmQr7lJyRq29AeterC7qlYq/HcP/NVKlMSlpqbyjtRyZueq1dq5anVZh4GrjGEYmjt/gYYP+7uiut8gSXr9lRcVGdVbX69Zp1t69yy036yP5+ueO/vprtv7SpJeen6M1n73vT6P/0IP/32wvLy8VD042KnP12vW6uYeN8nP11eS5OPjIx8fH8f36emn9MOmzXo19gV3PCpMbPb2ZN19XZjubB4mSYrt3lLrfkvV4t2HNaxdowLtO9Zx/u09GN5Q8XuOaOvxU44k7tYmtZ3ajL6+uT7fdVh70zLVOYwkrrwwyfoDtyl2JTIjI8Pxz4ZhKCMjo8gDwNXhyNFjOpF2UpERHR3nKlf2V3iL67Rtx0+F9sk7f147d+9RZEQHxzmr1arIiA5F9vl5127t3rtP/fvdXmQs8ctWyMfHR72jbizh0+BqlGfP167fz6hTWHXHOavFos51qmt7yqnL9jcMQ4mH03TwVLba1yq8wptnz9einw+psncFNQ0OKLXYgStV7EpclSpVdPz4cYWEhCgoKKjQ5beGYchischut//ptXJzc5Wbm+t0zi5DXqaZSgh4hhNpJyWpwPBltWpVlXbyZKF9Tp06LbvdXmif5IO/Fdrns/iluqZBA7Vt3arIWD6LX6pbb+7lVJ0DTp/Lk90wFHzJEGc1X28ln84qsl9m7nl1m/21ztvzZbVYNPaGFoqsW92pzdoDqXr6q63KOW9XdT+bPry9k6pU8nbLc6BkLB6eNxQ7ifvmm29U9f//UP7mm2+uaA+VuLi4AluRtJO32osSNVCWlq5YpdhX4xyf35v6ptvvmZOTo2Urv9Rjw4YW2WZb0g7tP3BAr7/6otvjgWfw866gxQO66uz5C9p4JE2vr9+lsABfp6HWjnWqafGArjqdk6dPdx5S9KotWnD39YXOswPKQrGTuBtuuMHxz926dbuim8bExCg6Otrp3NOBtYtoDeCvcuMNXRT+/ytIJSnvfJ4k6WR6ukKq/+8vt5Mn09W0ybWFXqNKlSB5eXnpZHq60/mTJ9MVXK1agfarvv5GOTk56ndrnyLj+nTJf9SsybVq0byZS8+Dq19QJW95WSxKO+c8unPybJ6C/yTZslosqhfkJ0lqVj1Qyaey9MGW/U5JnG/FCqoXVEH15KfwGlXU+6Nv9Pmuw3q4fcF5digbnl2HK+Hq3MaNG+vFF1/UL7/8UqKb2mw2BQQEOB0MpQJlz9/PT/XqhjmORg0bqnpwNSX+8KOjTVZWlpJ+3qk2rVoWeg3vihV1XbOmTn3y8/OVuGlzoX0+j1+qG2/oqqpVC9+6IfvsWa1cnaD+/W67wqfD1cjby6rmIYHaeDjNcS7fMLTxSJpau7AdSL4h5V1mKpBRjDbAX6lESdxjjz2m5cuXq2nTpurQoYPeeustpaSklHZscIHNz091wluqTvjFvySDG9RXnfCWqhJW5zI9gaJZLBYNuu9evfvhTCWs/VZ7f/lVo8a+qJDqwY7VqpI0+JHH9PGCRY7PQx64T4uW/EdLli7T/uQDenH8RJ07d0533n6r0/V/O3RYP27dpv53FL2gYcWXq2W323XbLTeX/gPiqvBQ64b6bNchxe8+rP3pmXpp7U86d8GuO5pdXK06ZvU2Td6w29H+/c2/asOhEzp8Jlv70zM1a9t+fbH3iPo2ufjn5dnzF/Rm4h4lpZzS0Yyz2vn7aT2fkKTU7Bz1alSrTJ4RhWOz3xJ46qmn9NRTT2nfvn2aN2+epk2bpmeeeUbdu3fXAw88oEGDBpV2nLiMeu3bKHrt//bduvvNi/OaEmfP05whw8sqLFwFhj00SOfO5Wjcq+OVkZmldq3D9eG0t5z2iDt8+KhOnT7t+NynVw+lnzqlqe++rxMnT6pZk2v14bS3Cgynfv6fL1QjNETXd44o8v6fxy9Vjxu7KYBtjVCEmxvXUvq5XL29aZ/SsnPVtHqA3uvb0TGcejzznKx/mMd97sIFvbzuJ6Vm5chWwUsNq/hrYo82urnxxQTNy2LRgVNZGrnnsE6dO68gn4pqERqkj+6MVONq/A7LE6tZsi03sRiGYZTGhTZu3Kjhw4drx44dl12dWphHLSzbRvkyPftwWYcAOLHPKPwNFkBZ8npiUpnde0VwTbddu0/acZfaT5s2TW+88YZSUlIUHh6ut99+Wx07diyy/ZQpU/Tuu+/q0KFDCg4OVv/+/RUXF+fSCvwSVeL+aNOmTZo/f74WLlyojIwM3X333Vd6SQAAgMsqL1uMLFy4UNHR0Zo+fboiIiI0ZcoU9erVS3v37lVISEiB9vPnz9eYMWM0c+ZMRUZGat++fXrooYdksVg0efLkYt+3RHPi9u3bp9jYWF177bX629/+pt27d2vixIlKTU3VggULSnJJAAAAU5o8ebKGDRumIUOGqHnz5po+fbp8fX01c+bMQttv2LBBf/vb33Tfffepfv366tmzpwYOHKhNmza5dN8SJXFNmzbVqlWrNGLECB05ckRffvmlBg0aJH9//5JcDgAAwGXuXNiQm5tb4I1Ul76oQJLy8vK0ZcsWRUVFOc5ZrVZFRUUpMTGx0LgjIyO1ZcsWR9KWnJysFStWqE+fordaKozLSZzdbtd7772nVatWaeTIkQoNDXX1EgAAAOVaXFycAgMDnY64uLgC7dLS0mS32wvkQ6GhoUXu3HHffffp5Zdf1vXXX6+KFSvqmmuuUbdu3fTcc8+5FKPLSZyXl5eeeOIJnf7DSjQAAIC/msXiviMmJkZnzpxxOmJiYkol7rVr12r8+PH697//ra1bt2rx4sVavny5XnnlFZeuU6KFDS1atFBycrIaNGhQku4AAADlms1mc9pKqSjBwcHy8vJSamqq0/nU1FTVqFGj0D5jx47Vgw8+qH/84x+SpJYtWyo7O1sPP/ywnn/+eVmtxauxlWhO3KuvvqpnnnlGy5Yt0/HjxwuMGQMAALhbedjs19vbW+3atVNCQoLjXH5+vhISEtS5c+dC+5w9e7ZAoubl5SVJcmXntxJV4v478e62226T5Q8bKBqGIYvFUqJ94gAAAMwoOjpagwcPVvv27dWxY0dNmTJF2dnZGjJkiCRp0KBBql27tmNOXd++fTV58mS1adNGERER+vXXXzV27Fj17dvXkcwVR4mSuDVr1pSkGwAAQKmxlpN94gYMGKATJ05o3LhxSklJUevWrbVq1SrHYodDhw45Vd5eeOEFWSwWvfDCCzp69KiqV6+uvn376rXXXnPpvqX2xoYrxRsbUN7wxgaUN7yxAeVRWb6xIaF6bbdd+6YTR9127dJSokrct99++6ffd+3atUTBAAAAoHhKlMR169atwLk/zo1jThwAAHA3S/kYTS0zJVqdeurUKafj999/16pVq9ShQwd99dVXpR0jAAAALlGiSlxgYGCBcz169JC3t7eio6O1ZcuWKw4MAADgz3h4Ia5klbiihIaGau/evaV5SQAAABSiRJW4HTt2OH02DEPHjx/XhAkT1Lp169KICwAA4E9ZPLwWV6IkrnXr1rJYLAV2Fe7UqZNmzpxZKoEBAACgaCVK4g4cOOD02Wq1qnr16vLx8SmVoAAAAC7H6tmFONfmxCUmJmrZsmWqV6+e41i3bp26du2qunXr6uGHH1Zubq67YgUAAHAoD+9OLUsuJXEvv/yydu7c6fj8008/aejQoYqKitKYMWP0xRdfON4LBgAAAPdxKYnbvn27brrpJsfnBQsWKCIiQh988IGio6M1depULVq0qNSDBAAAuBSVOBecOnXK8TJXSVq3bp1uvvlmx+cOHTro8GHeNwkAAOBuLiVxoaGhjkUNeXl52rp1qzp16uT4PjMzUxUrVizdCAEAAAphceP/zMClJK5Pnz4aM2aMvvvuO8XExMjX11ddunRxfL9jxw5dc801pR4kAAAAnLm0xcgrr7yiO++8UzfccIP8/f01Z84ceXt7O76fOXOmevbsWepBAgAAXMpijoKZ27iUxAUHB+vbb7/VmTNn5O/vLy8vL6fvP/30U/n7+5dqgAAAACioRJv9BgYGFnq+atWqVxQMAABAcZXqC+BNqERJHAAAQFnz8NFUj09iAQAATIlKHAAAMCWLh69soBIHAABgQlTiAACAKXl2HY5KHAAAgClRiQMAAKZEJQ4AAACmQyUOAACYkqevTiWJAwAApmT17ByO4VQAAAAzohIHAABMyeLhpTgqcQAAACZEJQ4AAJiSh69roBIHAABgRlTiAACAKVGJAwAAgOlQiQMAAKbEZr8AAAAm5OE5HMOpAAAAZkQlDgAAmJKnD6dSiQMAADAhKnEAAMCUPLwQRyUOAADAjKjEAQAAU7J6eCmOShwAAIAJUYkDAACm5OGFOJI4AABgTmwxAgAAANOhEgcAAEzJ4uGlKA9/fAAAAHOiEgcAAEyJOXEAAAAwHSpxAADAlDy8EEclDgAAwIyoxAEAAFPy9DlxJHEAAMCUPDyHYzgVAADAjKjEAQAAU7J6eCmOShwAAIAJUYkDAACm5OGFOCpxAAAAZkQlDgAAmJKnbzFCJQ4AAMCEyk0lbnr24bIOAXDyqF9YWYcAOJm2+p2yDgEoVzy8EFd+kjgAAABXeHoSx3AqAACACVGJAwAApmSxenYpjkocAACACVGJAwAApsScOAAAAJgOlTgAAGBKVg8vxVGJAwAAMCEqcQAAwJQ8vBBHJQ4AAMCMqMQBAABTsnh4KY4kDgAAmJKH53AMpwIAAJgRSRwAADAli8XitsNV06ZNU/369eXj46OIiAht2rTpT9ufPn1aI0aMUM2aNWWz2XTttddqxYoVLt2T4VQAAIArsHDhQkVHR2v69OmKiIjQlClT1KtXL+3du1chISEF2ufl5alHjx4KCQnRZ599ptq1a+u3335TUFCQS/cliQMAAKZUXubETZ48WcOGDdOQIUMkSdOnT9fy5cs1c+ZMjRkzpkD7mTNnKj09XRs2bFDFihUlSfXr13f5vgynAgAAXCI3N1cZGRlOR25uboF2eXl52rJli6KiohznrFaroqKilJiYWOi1ly5dqs6dO2vEiBEKDQ1VixYtNH78eNntdpdiJIkDAACm5M45cXFxcQoMDHQ64uLiCsSQlpYmu92u0NBQp/OhoaFKSUkpNO7k5GR99tlnstvtWrFihcaOHatJkybp1Vdfden5GU4FAAC4RExMjKKjo53O2Wy2Url2fn6+QkJC9P7778vLy0vt2rXT0aNH9cYbbyg2NrbY1yGJAwAApmRx43iizWYrVtIWHBwsLy8vpaamOp1PTU1VjRo1Cu1Ts2ZNVaxYUV5eXo5zzZo1U0pKivLy8uTt7V2sGBlOBQAAplQethjx9vZWu3btlJCQ4DiXn5+vhIQEde7cudA+f/vb3/Trr78qPz/fcW7fvn2qWbNmsRM4iSQOAADgikRHR+uDDz7QnDlztHv3bg0fPlzZ2dmO1aqDBg1STEyMo/3w4cOVnp6ukSNHat++fVq+fLnGjx+vESNGuHRfhlMBAIA5WcvHHiMDBgzQiRMnNG7cOKWkpKh169ZatWqVY7HDoUOHZLX+r24WFhamL7/8Uk899ZRatWql2rVra+TIkRo9erRL97UYhmGU6pOU1NkzZR0B4ORRv7CyDgFwMm31O2UdAlCAV9SgMrv3mW7hbrt24Nokt127tFCJAwAA5lRedvstI8yJAwAAMCEqcQAAwJRK8qL6qwmVOAAAABOiEgcAAMypnKxOLSskcQAAwJwYTgUAAIDZUIkDAACmZPHw4VQqcQAAACZEJQ4AAJgTc+IAAABgNlTiAACAKTEnDgAAAKZDJQ4AAJiTh8+JI4kDAADmxHAqAAAAzIZKHAAAMCWLhw+nUokDAAAwISpxAADAnJgTBwAAALOhEgcAAMyJOXEAAAAwGypxAADAlCweXooiiQMAAObEcCoAAADMhkocAAAwJQtbjAAAAMBsqMQBAABzYk4cAAAAzIZKHAAAMCfmxAEAAMBsqMQBAABTsnj4nDiSOAAAYE4MpwIAAMBsqMQBAABz8vDhVCpxAAAAJkQlDgAAmJKnL2ygEgcAAGBCVOIAAIA5sToVAAAAZkMlDgAAmBJz4kooLy9Pe/fu1YULF0ozHgAAgOKxWtx3mIDLSdzZs2c1dOhQ+fr66rrrrtOhQ4ckSU888YQmTJhQ6gECAACgIJeTuJiYGCUlJWnt2rXy8fFxnI+KitLChQtLNTgAAIAiWSzuO0zA5Tlx8fHxWrhwoTp16uQ0Fn3ddddp//79pRocAAAACudyEnfixAmFhIQUOJ+dne3xEwwBAMBfx2KSuWvu4vJwavv27bV8+XLH5/8mbh9++KE6d+5cepEBAACgSC5X4saPH6+bb75Zu3bt0oULF/TWW29p165d2rBhg9atW+eOGD2KYRia+u77+nRJvDIys9Q2vJVefG606ter+6f95i38VDPmfKwTJ0+q6bWNNXb0M2rV4jpJ0pFjx3TTLf0K7Tfl9fG6uUeUFi9dppjYlwttsyFhlapVrXpFzwXP06hLpHo+O1J127VWUK2aerffQCX9Z/nlOwIumr9us2Z+vVFpGVlqUjtUz9/TU63q1y607S/HTuid5eu081CKjqWf0Zi7emjQjR2d2mTn5GrqsnX6evtepWedVbM6oYq5u6da1qv1VzwOXOHhI4AuV+Kuv/56bd++XRcuXFDLli311VdfKSQkRImJiWrXrp07YvQoH8yeq48+WagXnxujRXNnqlKlSho64knl5uYW2WfFl6sVN2mKRjzyDy2ZP1dNr22soY89qZPp6ZKkmqGhWr96hdPxxKMPy9fXV13/FilJ6tMzqkCb6yM7qWO7tiRwKBGbn5+OJP2sBSOeLutQcBVbuWWXJi7+Wo/16aLPxgxV0zohevidBTqZmV1o+5zz51WnWhVF395dwQF+hbYZO2+5Nuw+oImDb1f8c8MU2ayhhk6dr9TTGe58FMBlJdrs95prrtEHH3xQ2rF4PMMwNHf+Ag0f9ndFdb9BkvT6Ky8qMqq3vl6zTrf07llov1kfz9c9d/bTXbf3lSS99PwYrf3ue30e/4Ue/vtgeXl5qXpwsFOfr9es1c09bpKfr68kycfHx2m1cXr6Kf2wabNejX3BHY8KD7Bz1WrtXLW6rMPAVW52wg+6O7K17uwcLkmKvbeP1v38qxYnJmlYz8gC7VvWq+WoqE3+z5oC3+fkndfq7Xv0ziN3q33jiyMgj9/SVWt/+kULvtuqkX27ue9h4DrmxLkmIyOj0CMzM1N5eXnuiNFjHDl6TCfSTioy4n+l/cqV/RXe4jpt2/FToX3yzp/Xzt17FBnRwXHOarUqMqJDkX1+3rVbu/fuU/9+txcZS/yyFfLx8VHvqBtL+DQA4F55F+zadfi4OjVt4DhntVrUuWkDbU8+UqJr2vPzZc835F3BucbhU7GCtu4/fEXxovRZLBa3HWbgciUuKCjoTx+uTp06euihhxQbGyurtfAcMTc3t8DwoM2eK5vN5mo4V5UTaSclqcDwZbVqVZV28mShfU6dOi273V5on+SDvxXa57P4pbqmQQO1bd2qyFg+i1+qW2/u5VSdA4Dy5HTWWdnzDQVXdh4WrVbZT8kphf+ZeTl+Pja1blBb01et1zU1glUtwE/LN+/U9gNHVbd6ldIIGyg1LlfiZs+erVq1aum5555TfHy84uPj9dxzz6l27dp699139fDDD2vq1Kl/+vaGuLg4BQYGOh1x/5p8RQ9iRktXrFKbyBscx1/xCrOcnBwtW/ml+ve7rcg225J2aP+BA3/aBgCuVhMG3y7DkLo9P1WtR07QvLU/qk/762Q1SXXGo3j4a7dcrsTNmTNHkyZN0j333OM417dvX7Vs2VLvvfeeEhISVLduXb322mt67rnnCr1GTEyMoqOjnc7Z7DmuhmJ6N97QReH/v4JUkvLOXxyOPpmerpDq/5vDdvJkupo2ubbQa1SpEiQvLy/HIoY/9gmuVq1A+1Vff6OcnBz1u7VPkXF9uuQ/atbkWrVo3syl5wGAv1KQv6+8rBalXbKI4WRmdpGLFoqjbvUqmvvUgzqbm6fsnFxVD6ys6BmLVSc46AojBkqXy5W4DRs2qE2bNgXOt2nTRomJiZIurmD97ztVC2Oz2RQQEOB0eOJQqr+fn+rVDXMcjRo2VPXgakr84UdHm6ysLCX9vFNtWrUs9BreFSvqumZNnfrk5+crcdPmQvt8Hr9UN97QVVWrFj4skH32rFauTqAKB6Dc867gpeZhNbVx70HHufx8Qxv3HlTrhnWu+Pq+Nm9VD6ysM2fP6fvdybqxVeH/MY0y5OGv3XI5iQsLC9OMGTMKnJ8xY4bCwsIkSSdPnlSVKswdcJXFYtGg++7Vux/OVMLab7X3l181auyLCqke7FitKkmDH3lMHy9Y5Pg85IH7tGjJf7Rk6TLtTz6gF8dP1Llz53Tn7bc6Xf+3Q4f149Zt6n9H0QsaVny5Wna7XbfdcnPpPyA8is3PT3XCW6pO+MX/mAhuUF91wluqStiV/+UK/NdDN0Xos++3KX7jDu1PSdNLC1bqXO553dHp4pzfMXOWOq1Czbtg1+7DKdp9OEXn7Xalns7U7sMp+u33/41mrN+1X9/t3K8jaae1YXeyHprysRqEVtMd/78CFigvXB5O/de//qW7775bK1euVIcOF1dEbt68Wbt379bnn38uSfrxxx81YMCA0o3UQwx7aJDOncvRuFfHKyMzS+1ah+vDaW85VSoPHz6qU6dPOz736dVD6adOaeq77+vEyZNq1uRafTjtrQLDqZ//5wvVCA3R9Z0jirz/5/FL1ePGbgqoXLnUnw2epV77Nopeu8Lx+e434yRJibPnac6Q4WUVFq4yN7drrvTMbL29bJ3SMrPVtHao3htxr4ID/CVJx0+dcZrLduJMpu6a8L9CxKyEjZqVsFEdGtfVnH8+KEnKPJerKUvXKOV0pgJ9fdSzdVONvK2bKnp5/bUPh8szScXMXSyGYRiudjp48KCmT5+uffv2SZKaNGmiRx55RFlZWWrRokXJIjl7pmT9ADd51C+srEMAnExb/U5ZhwAU4BU1qMzufeFJ9039qTB1qduuXVpKtNlv/fr1HatPMzIy9Mknn2jAgAHavHmz7HZ7qQYIAABQKA+vxLk8J+6/vv32Ww0ePFi1atXSpEmT1L17d23cuLE0YwMAACia1eq+wwRcqsSlpKRo9uzZmjFjhjIyMnTPPfcoNzdX8fHxat68ubtiBAAAwCWKnWr27dtXTZo00Y4dOzRlyhQdO3ZMb7/9tjtjAwAAKJqHbzFS7ErcypUr9eSTT2r48OFq3LixO2MCAADAZRS7Erd+/XplZmaqXbt2ioiI0DvvvKO0tDR3xgYAAFA0D6/EFTuJ69Spkz744AMdP35cjzzyiBYsWKBatWopPz9fq1evVmZmpjvjBAAAwB+4vPzCz89Pf//737V+/Xr99NNPevrppzVhwgSFhITottt4VRMAAPiLUIkruSZNmuj111/XkSNH9Mknn5RWTAAAALiMEm32eykvLy/169dP/fr1K43LAQAAXJ5J9nNzF89+egAAAJMqlUocAADAX84kc9fchSQOAACYk4cncQynAgAAmBCVOAAAYE5U4gAAAGA2JHEAAMCcrFb3HS6aNm2a6tevLx8fH0VERGjTpk3F6rdgwQJZLJYSbdNGEgcAAHAFFi5cqOjoaMXGxmrr1q0KDw9Xr1699Pvvv/9pv4MHD+qZZ55Rly5dSnRfkjgAAGBO5eS1W5MnT9awYcM0ZMgQNW/eXNOnT5evr69mzpxZZB+73a77779fL730kho2bFiixyeJAwAAKKG8vDxt2bJFUVFRjnNWq1VRUVFKTEwsst/LL7+skJAQDR06tMT3ZnUqAAAwJzeuTs3NzVVubq7TOZvNJpvN5nQuLS1NdrtdoaGhTudDQ0O1Z8+eQq+9fv16zZgxQ9u3b7+iGKnEAQAAc3LjcGpcXJwCAwOdjri4uCsOOTMzUw8++KA++OADBQcHX9G1qMQBAABcIiYmRtHR0U7nLq3CSVJwcLC8vLyUmprqdD41NVU1atQo0H7//v06ePCg+vbt6ziXn58vSapQoYL27t2ra665plgxksQBAABTspRgK5DiKmzotDDe3t5q166dEhISHNuE5OfnKyEhQY8//niB9k2bNtVPP/3kdO6FF15QZmam3nrrLYWFhRU7RpI4AACAKxAdHa3Bgwerffv26tixo6ZMmaLs7GwNGTJEkjRo0CDVrl1bcXFx8vHxUYsWLZz6BwUFSVKB85dDEgcAAMypnLx2a8CAATpx4oTGjRunlJQUtW7dWqtWrXIsdjh06JCsbqgaWgzDMEr9qiVx9kxZRwA4edSv+CVt4K8wbfU7ZR0CUIBX1KAyu7f9pSFuu7ZX7Cy3Xbu0UIkDAADmVE4qcWWFLUYAAABMiEocAAAwJw+vxJHEAQAAc3LjFiNm4NlPDwAAYFJU4gAAgDl5+HAqlTgAAAATohIHAADMiUocAAAAzIZKHAAAMCcqcQAAADAbKnEAAMCcPHyfOJI4AABgTgynAgAAwGyoxAEAAHOiEgcAAACzoRIHAADMycMXNnj20wMAAJgUlTgAAGBOzIkDAACA2VCJAwAA5uThlTiSOAAAYE4ensQxnAoAAGBCVOIAAIA5scUIAAAAzIZKHAAAMCfmxAEAAMBsqMQBAABzohIHAAAAs6ESBwAAzMni2bUokjgAAGBOVoZTAQAAYDJU4gAAgDl5+HCqZz89AACASVGJAwAA5sQWIwAAADAbKnEAAMCcrJ5di/LspwcAADApKnEAAMCcPHxOHEkcAAAwJ7YYAQAAgNlQiQMAAObk4cOpVOIAAABMiEocAAAwJw/fYqTcJHH2GS+XdQiAk2mr3ynrEAAnI3o8XtYhAAVMNwaVdQgeq9wkcQAAAC5hThwAAADMhkocAAAwJw/fJ44kDgAAmJOV4VQAAACYDJU4AABgTh4+nOrZTw8AAGBSVOIAAIA5scUIAAAAzIZKHAAAMCfmxAEAAMBsqMQBAABzYp84AAAAmA2VOAAAYE4evjqVJA4AAJgTCxsAAABgNlTiAACAObGwAQAAAGZDJQ4AAJgTc+IAAABgNlTiAACAOXn4FiNU4gAAAEyIShwAADAnD58TRxIHAADMiS1GAAAAYDZU4gAAgDl5+HCqZz89AACASVGJAwAA5sQWIwAAADAbKnEAAMCcrJ5di/LspwcAACgF06ZNU/369eXj46OIiAht2rSpyLYffPCBunTpoipVqqhKlSqKior60/ZFIYkDAADmZLG473DBwoULFR0drdjYWG3dulXh4eHq1auXfv/990Lbr127VgMHDtSaNWuUmJiosLAw9ezZU0ePHnXpviRxAADAnCxW9x0umDx5soYNG6YhQ4aoefPmmj59unx9fTVz5sxC28+bN0+PPfaYWrduraZNm+rDDz9Ufn6+EhISXLovSRwAAMAlcnNzlZGR4XTk5uYWaJeXl6ctW7YoKirKcc5qtSoqKkqJiYnFutfZs2d1/vx5Va1a1aUYSeIAAIA5uXE4NS4uToGBgU5HXFxcgRDS0tJkt9sVGhrqdD40NFQpKSnFeozRo0erVq1aTolgcbA6FQAA4BIxMTGKjo52Omez2Ur9PhMmTNCCBQu0du1a+fj4uNSXJA4AAJiTG7cYsdlsxUragoOD5eXlpdTUVKfzqampqlGjxp/2/de//qUJEybo66+/VqtWrVyOkeFUAACAEvL29la7du2cFiX8d5FC586di+z3+uuv65VXXtGqVavUvn37Et2bShwAADCncvLarejoaA0ePFjt27dXx44dNWXKFGVnZ2vIkCGSpEGDBql27dqOOXUTJ07UuHHjNH/+fNWvX98xd87f31/+/v7Fvi9JHAAAwBUYMGCATpw4oXHjxiklJUWtW7fWqlWrHIsdDh06JOsfhn7fffdd5eXlqX///k7XiY2N1Ysvvljs+5LEAQAAc3JxPzd3evzxx/X4448X+t3atWudPh88eLBU7kkSBwAAzKmcDKeWlfKTwgIAAKDYqMQBAABzKkfDqWXBs58eAADApKjEAQAAc7IyJw4AAAAmQyUOAACYE3PiAAAAYDZU4gAAgDl5+D5xJHEAAMCcGE4FAACA2VCJAwAApmTx8OFUKnEAAAAmRCUOAACYE3PiAAAAYDZU4gAAgDlRiQMAAIDZUIkDAADmZPXs1akkcQAAwJwYTgUAAIDZUIkDAADmxGa/AAAAMBsqcQAAwJyYEwcAAACzKXESt3//fr3wwgsaOHCgfv/9d0nSypUrtXPnzlILDgAAoEgWi/sOEyhRErdu3Tq1bNlSP/zwgxYvXqysrCxJUlJSkmJjY0s1QAAAABRUoiRuzJgxevXVV7V69Wp5e3s7zt94443auHFjqQUHAABQJIvVfYcJlGhhw08//aT58+cXOB8SEqK0tLQrDgoAAOCyPPyNDSVKNYOCgnT8+PEC57dt26batWtfcVAAAAD4cyVK4u69916NHj1aKSkpslgsys/P1/fff69nnnlGgwYNKu0YAQAACmI41XXjx4/XiBEjFBYWJrvdrubNm8tut+u+++7TCy+8UNoxerT5Ow5q5rb9SjubqybBAXq+63VqFVql0Lar9x/X+5t/1aEz2bqQb6hukJ+GtG6o25rWcbR554e9WvnLMaVk5aiil1XNqwdqZKcmCq9R+DWBS81ft1kzv96otIwsNakdqufv6alW9QuvwP9y7ITeWb5OOw+l6Fj6GY25q4cG3djRqU12Tq6mLlunr7fvVXrWWTWrE6qYu3uqZb1af8XjwIM06hKpns+OVN12rRVUq6be7TdQSf9ZXtZhASXmcqppGIZSUlI0depUJScna9myZfr444+1Z88effTRR/Ly8nJHnB5p5S/HNHH9Lj3W4Vp9NqCLmlYL0MNLN+nk2dxC2wfaKuqR9o00v//ftGRgV93ZrI6eT0jS+t9+d7SpH+Sv529oofiBXfXRnZGqHVBJw5b+oPRzhV8T+KOVW3Zp4uKv9VifLvpszFA1rROih99ZoJOZ2YW2zzl/XnWqVVH07d0VHOBXaJux85Zrw+4Dmjj4dsU/N0yRzRpq6NT5Sj2d4c5HgQey+fnpSNLPWjDi6bIOBaXFw7cYcbkSZxiGGjVqpJ07d6px48YKCwtzR1yQNHt7su6+Lkx3Nr/47zi2e0ut+y1Vi3cf1rB2jQq071gn2Onzg+ENFb/niLYeP6Xr64VIkm5t4lwxGX19c32+67D2pmWqc5jNTU+Cq8XshB90d2Rr3dk5XJIUe28frfv5Vy1OTNKwnpEF2resV8tRUZv8nzUFvs/JO6/V2/fonUfuVvvGdSVJj9/SVWt/+kULvtuqkX27ue9h4HF2rlqtnatWl3UYQKlxuRJntVrVuHFjnTx50h3x4P/l2fO16/cz6hRW3XHOarGoc53q2p5y6rL9DcNQ4uE0HTyVrfa1qhZ5j0U/H1Jl7wpqGhxQarHj6pR3wa5dh4+rU9MGjnNWq0WdmzbQ9uQjJbqmPT9f9nxD3hWc/3vSp2IFbd1/+IriBeABmBPnugkTJujZZ5/Vu+++qxYtWrjcPzc3V7m5zsN3Fc5fkK0ir3L9r9Pn8mQ3DAVXcq6OVfP1VvLprCL7ZeaeV7fZX+u8PV9Wi0Vjb2ihyLrVndqsPZCqp7/aqpzzdlX3s+nD2zupSiXvIq4IXHQ666zs+YaCKzsPi1ar7KfklJL9R52fj02tG9TW9FXrdU2NYFUL8NPyzTu1/cBR1a3OPE0A+DMlypoGDRqks2fPKjw8XN7e3qpUqZLT9+np6X/aPy4uTi+99JLTubG9Oym2T8HhGLjGz7uCFg/oqrPnL2jjkTS9vn6XwgJ8nYZaO9appsUDuup0Tp4+3XlI0au2aMHd16uaL8Op+OtNGHy7Xvh4mbo9P1VeVouah9VQn/bXadehgtsYAYATk8xdc5cSJXFTpky5opvGxMQoOjraOZAPx17RNa82QZW85WWxKO2SBQcnz+Yp+E+SLavFonpBFyslzaoHKvlUlj7Yst8pifOtWEH1giqonvwUXqOKen/0jT7fdVgPty84zw74ryB/X3lZLUq7ZBHDyczsIhctFEfd6lU096kHdTY3T9k5uaoeWFnRMxarTnDQFUYM4KpnkmFPdylREjd48OAruqnNZpPN5pyI2BlKdeLtZVXzkEBtPJymqIY1JEn5hqGNR9J0X6v6xb5OviHl2e1/2sYoRhvAu4KXmofV1Ma9BxUV3kSSlJ9vaOPeg7rvhvZXfH1fm7d8bd46c/acvt+drKf73XjF1wSAq9kVZ045OTnKy8tzOhcQwCT50vBQ64aK+Xq7WoQEqmVokOYmHdC5C3bd0eziatUxq7cpxM9H0ZHNJEnvb/5VLUICFRboqzx7vr797Xd9sfeIxt3QUpJ09vwFvbf5V93YIFTBvjadzsnT/J9+U2p2jno1Yk8uXN5DN0UoZu5StahbUy3r19LcbzbpXO553dGplSRpzJylCgmqrOjbu0u6uBhi//ETkqTzdrtST2dq9+EU+dq8VS/k4oKb9bv2yzCkBqHVdOhEut5YkqAGodV0x/+vgAVKi83PT9UbNXR8Dm5QX3XCWyo7/ZROHS7Z4hyUMSuVOJdlZ2dr9OjRWrRoUaGrVO1UdUrFzY1rKf1crt7etE9p2blqWj1A7/Xt6BhOPZ55TtY/zAc4d+GCXl73k1KzcmSr4KWGVfw1sUcb3dz4YoLmZbHowKksjdxzWKfOnVeQT0W1CA3SR3dGqnG1ymXyjDCXm9s1V3pmtt5etk5pmdlqWjtU7424V8EB/pKk46fOOP0mT5zJ1F0TZjg+z0rYqFkJG9WhcV3N+eeDkqTMc7masnSNUk5nKtDXRz1bN9XI27qpIntOopTVa99G0WtXOD7f/WacJClx9jzNGTK8rMICSsxiGIbhaqcRI0ZozZo1euWVV/Tggw9q2rRpOnr0qN577z1NmDBB999/v8uB2N9m80WUM82oBKF8GdHj8bIOAShgulF2G3Mbeze67dqWJp3cdu3SUqJK3BdffKG5c+eqW7duGjJkiLp06aJGjRqpXr16mjdvXomSOAAAABRfiQaT09PT1bDhxXkFAQEBji1Frr/+en377belFx0AAEBRPHyz3xJF2bBhQx04cECS1LRpUy1atEjSxQpdUFBQqQUHAACAwrmUxCUnJys/P19DhgxRUlKSJGnMmDGaNm2afHx89NRTT+nZZ591S6AAAABO3PHi+/8eJuDSnLjGjRvr+PHjeuqppyRJAwYM0NSpU7Vnzx5t2bJFjRo1UqtWrdwSKAAAAP7HpSTu0oWsK1asUFxcnBo2bKh69eqVamAAAAB/yiRz19yF1yQAAABzMsmwp7u4lMJaLBZZLvkXdulnAAAAuJ/Lw6kPPfSQ472nOTk5evTRR+Xn5/zy68WLF5dehAAAAIXhtVvFd+mL7x944IFSDQYAAADF41ISN2vWLHfFAQAA4BoPn9Ll2XVIAAAAk2J1KgAAMCcP32LEs58eAADApKjEAQAAc/LwOXEkcQAAwKQ8O4ljOBUAAMCEqMQBAABz8vDhVCpxAAAAJkQlDgAAmBOVOAAAAJgNlTgAAGBSVOIAAABgMlTiAACAOXn4nDiSOAAAYE6encMxnAoAAGBGVOIAAIBJeXYpjkocAACACVGJAwAA5uThCxuoxAEAAJgQlTgAAGBOVOIAAABgNlTiAACASXl2JY4kDgAAmBPDqQAAALgS06ZNU/369eXj46OIiAht2rTpT9t/+umnatq0qXx8fNSyZUutWLHC5XuSxAEAAJOyuPEovoULFyo6OlqxsbHaunWrwsPD1atXL/3++++Ftt+wYYMGDhyooUOHatu2berXr5/69eunn3/+2bWnNwzDcKmHm9jffrqsQwCcNQsv6wgAJyN6PF7WIQAFTDcyyuzeRuoBt13bEtqg2G0jIiLUoUMHvfPOO5Kk/Px8hYWF6YknntCYMWMKtB8wYICys7O1bNkyx7lOnTqpdevWmj59erHvSyUOAACYk8XitiM3N1cZGRlOR25uboEQ8vLytGXLFkVFRTnOWa1WRUVFKTExsdCwExMTndpLUq9evYpsXxSSOAAAgEvExcUpMDDQ6YiLiyvQLi0tTXa7XaGhoU7nQ0NDlZKSUui1U1JSXGpfFFanAgAAc3Lj6tSYmBhFR0c7nbPZbG67X0mQxAEAAFzCZrMVK2kLDg6Wl5eXUlNTnc6npqaqRo0ahfapUaOGS+2LwnAqAAAwqbJfnert7a127dopISHBcS4/P18JCQnq3LlzoX06d+7s1F6SVq9eXWT7olCJAwAApmQpJ5v9RkdHa/DgwWrfvr06duyoKVOmKDs7W0OGDJEkDRo0SLVr13bMqRs5cqRuuOEGTZo0SbfccosWLFigzZs36/3333fpviRxAAAAV2DAgAE6ceKExo0bp5SUFLVu3VqrVq1yLF44dOiQrNb/DX5GRkZq/vz5euGFF/Tcc8+pcePGio+PV4sWLVy6L/vEAUVhnziUM+wTh/KoLPeJ08kj7rt2tTruu3YpYU4cAACACTGcCgAATKp8zIkrK1TiAAAATIhKHAAAMKdysjq1rFCJAwAAMCEqcQAAwJw8vBJHEgcAAEzKs5M4hlMBAABMiEocAAAwJw8fTqUSBwAAYEJU4gAAgDl5diGOShwAAIAZUYkDAAAm5dmlOCpxAAAAJkQlDgAAmJOHr04liQMAAObk4Ukcw6kAAAAmRCUOAACYFJU4AAAAmAyVOAAAYE7MiQMAAIDZUIkDAADmRCUOAAAAZkMlDgAAmJRnV+JI4gAAgDkxnAoAAACzsRiGYZR1ECgdubm5iouLU0xMjGw2W1mHA0jid4nyh98krhYkcVeRjIwMBQYG6syZMwoICCjrcABJ/C5R/vCbxNWC4VQAAAATIokDAAAwIZI4AAAAEyKJu4rYbDbFxsYyURflCr9LlDf8JnG1YGEDAACACVGJAwAAMCGSOAAAABMiiQMAADAhkrir1Nq1a2WxWHT69Gm33uehhx5Sv3793HoPAABQEEmcm504cULDhw9X3bp1ZbPZVKNGDfXq1Uvff/+9W+8bGRmp48ePKzAw0K33gedKTEyUl5eXbrnllrIOBR7uoYceksViKXD8+uuvZR0a4FYVyjqAq91dd92lvLw8zZkzRw0bNlRqaqoSEhJ08uTJEl3PMAzZ7XZVqPDn/9d5e3urRo0aJboHUBwzZszQE088oRkzZujYsWOqVatWWYcED9a7d2/NmjXL6Vz16tVduobdbpfFYpHVSn0D5sAv1Y1Onz6t7777ThMnTlT37t1Vr149dezYUTExMbrtttt08OBBWSwWbd++3amPxWLR2rVrJf1vWHTlypVq166dbDabZs6cKYvFoj179jjd780339Q111zj1O/06dPKyMhQpUqVtHLlSqf2S5YsUeXKlXX27FlJ0uHDh3XPPfcoKChIVatW1e23366DBw862tvtdkVHRysoKEjVqlXTqFGjxA41nikrK0sLFy7U8OHDdcstt2j27NlO3y9dulSNGzeWj4+Punfvrjlz5hQY3l+/fr26dOmiSpUqKSwsTE8++aSys7P/2gfBVeO/Ix1/PN566y21bNlSfn5+CgsL02OPPaasrCxHn9mzZysoKEhLly5V8+bNZbPZdOjQIeXm5uqZZ55R7dq15efnp4iICMefyUB5QhLnRv7+/vL391d8fLxyc3Ov6FpjxozRhAkTtHv3bvXv31/t27fXvHnznNrMmzdP9913X4G+AQEBuvXWWzV//vwC7fv16ydfX1+dP39evXr1UuXKlfXdd9/p+++/l7+/v3r37q28vDxJ0qRJkzR79mzNnDlT69evV3p6upYsWXJFzwVzWrRokZo2baomTZrogQce0MyZMx0J/YEDB9S/f3/169dPSUlJeuSRR/T888879d+/f7969+6tu+66Szt27NDChQu1fv16Pf7442XxOLhKWa1WTZ06VTt37tScOXP0zTffaNSoUU5tzp49q4kTJ+rDDz/Uzp07FRISoscff1yJiYlasGCBduzYobvvvlu9e/fWL7/8UkZPAhTBgFt99tlnRpUqVQwfHx8jMjLSiImJMZKSkgzDMIwDBw4Ykoxt27Y52p86dcqQZKxZs8YwDMNYs2aNIcmIj493uu6bb75pXHPNNY7Pe/fuNSQZu3fvdup36tQpwzAMY8mSJYa/v7+RnZ1tGIZhnDlzxvDx8TFWrlxpGIZhfPTRR0aTJk2M/Px8xzVzc3ONSpUqGV9++aVhGIZRs2ZN4/XXX3d8f/78eaNOnTrG7bfffuX/omAqkZGRxpQpUwzDuPg7CA4OdvxmR48ebbRo0cKp/fPPP+/0exw6dKjx8MMPO7X57rvvDKvVapw7d87t8ePqMnjwYMPLy8vw8/NzHP379y/Q7tNPPzWqVavm+Dxr1ixDkrF9+3bHud9++83w8vIyjh496tT3pptuMmJiYtz3EEAJUIlzs7vuukvHjh3T0qVL1bt3b61du1Zt27YtMPx0Oe3bt3f6fO+99+rgwYPauHGjpItVtbZt26pp06aF9u/Tp48qVqyopUuXSpI+//xzBQQEKCoqSpKUlJSkX3/9VZUrV3ZUEKtWraqcnBzt379fZ86c0fHjxxUREeG4ZoUKFQrEhavf3r17tWnTJg0cOFDSxd/BgAEDNGPGDMf3HTp0cOrTsWNHp89JSUmaPXu247fm7++vXr16KT8/XwcOHPhrHgRXle7du2v79u2OY+rUqfr666910003qXbt2qpcubIefPBBnTx50jGFRLo4f7hVq1aOzz/99JPsdruuvfZap9/nunXrtH///rJ4NKBILGz4C/j4+KhHjx7q0aOHxo4dq3/84x+KjY3Vd999J0lO88rOnz9f6DX8/PycPteoUUM33nij5s+fr06dOmn+/PkaPnx4kTF4e3urf//+mj9/vu69917Nnz9fAwYMcCyQyMrKUrt27QoM0UquTw7G1W3GjBm6cOGC00IGwzBks9n0zjvvFOsaWVlZeuSRR/Tkk08W+K5u3bqlFis8h5+fnxo1auT4fPDgQd16660aPny4XnvtNVWtWlXr16/X0KFDlZeXJ19fX0lSpUqVZLFYHP2ysrLk5eWlLVu2yMvLy+ke/v7+f83DAMVEElcGmjdvrvj4eEdydPz4cbVp00aSnBY5XM7999+vUaNGaeDAgUpOTta999572fY9evTQzp079c033+jVV191fNe2bVstXLhQISEhCggIKLR/zZo19cMPP6hr166SpAsXLmjLli1q27ZtsWOGuV24cEFz587VpEmT1LNnT6fv+vXrp08++URNmjTRihUrnL778ccfnT63bdtWu3btcvpLFyhNW7ZsUX5+viZNmuRYbbpo0aLL9mvTpo3sdrt+//13denSxd1hAlemrMdzr2ZpaWlG9+7djY8++shISkoykpOTjUWLFhmhoaHG3//+d8MwDKNTp05Gly5djF27dhlr1641OnbsWOicuP/OJfqjjIwMo1KlSkZ4eLhx0003OX1XWL/8/HwjLCzMCA8Pd5pPZxiGkZ2dbTRu3Njo1q2b8e233xrJycnGmjVrjCeeeMI4fPiwYRiGMWHCBKNq1arGkiVLjN27dxvDhg0zKleuzJw4D7JkyRLD29vbOH36dIHvRo0aZbRv395ITk42KlasaIwaNcrYu3evsXDhQqNOnTqGJEe/pKQko1KlSsaIESOMbdu2Gfv27TPi4+ONESNG/NWPhKvA4MGDC/w5tH37dkOSMWXKFGP//v3G3Llzjdq1azv9uThr1iwjMDCwwPXuv/9+o379+sbnn39uJCcnGz/88IMxfvx4Y9myZe5/GMAFzIlzI39/f0VEROjNN99U165d1aJFC40dO1bDhg1zDDvNnDlTFy5cULt27fTPf/7TqTp2OZUrV1bfvn2VlJSk+++//7LtLRaLBg4cWGh7X19fffvtt6pbt67uvPNONWvWTEOHDlVOTo6jMvf000/rwQcf1ODBg9W5c2dVrlxZd9xxhwv/RmB2M2bMUFRUVKGbSN91113avHmzMjMz9dlnn2nx4sVq1aqV3n33XcfqVJvNJklq1aqV1q1bp3379qlLly5q06aNxo0bx15zKDXh4eGaPHmyJk6cqBYtWmjevHmKi4srVt9Zs2Zp0KBBevrpp9WkSRP169dPP/74I0P9KHcshsFGXwDc67XXXtP06dN1+PDhsg4FAK4azIkDUOr+/e9/q0OHDqpWrZq+//57vfHGG+wBBwCljCQOQKn75Zdf9Oqrryo9PV1169bV008/rZiYmLIOCwCuKgynAgAAmBALGwAAAEyIJA4AAMCESOIAAABMiCQOAADAhEjiAAAATIgkDgAAwIRI4gAAAEyIJA4AAMCESOIAAABM6P8A4lrTCLv8YAUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(df.drop(categorical_columns, axis=1).corr(), annot=True, cmap=plt.cm.Reds)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXkqwnftGUPo"
      },
      "source": [
        "## Numerical Feature-Class distributions with grouped histograms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "ddVbXtitGUPp",
        "outputId": "bb02d984-6e00-4775-f07a-6a0664c88238"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for column in df.drop(categorical_columns + ['Survived', 'Ticket'], axis=1):\n",
        "    df.groupby('Survived')[column].hist(bins=25, figsize=(8, 4), alpha=0.4, log=True)\n",
        "    plt.title(column)\n",
        "    plt.xlabel(\"Value\")\n",
        "    plt.ylabel(\"Number of instances\")\n",
        "    plt.legend(['Survived 0', 'Survived 1'])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaMx_hTqGUPp"
      },
      "source": [
        "## Numerical Feature-Class distributions with side-by-side boxplots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "dPdfnS5NGUPp",
        "outputId": "2f2dbe2c-cd9b-4c39-a0b7-301cc3bf0894"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for column in df.drop(categorical_columns + ['Survived', 'Ticket'], axis=1):\n",
        "    df.boxplot(column=column, by='Survived', figsize=(10, 5), vert=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVCU-wpCGUPq"
      },
      "source": [
        "## Categorical Feature-Class correlations with grouped bar charts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MGQ5BYv9GUPq",
        "outputId": "50eadf80-1696-44c7-8be4-4679ad429501"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for column in categorical_columns:\n",
        "    # Cross tabulation between the categorical feature and the class label\n",
        "    crosstab = pd.crosstab(index=df[column], columns=df['Survived'])\n",
        "    # Grouped bar chart between the categorical feature and the class label\n",
        "    crosstab.plot.bar(figsize=(7, 4), rot=0, log=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBOQDPVrGUPq"
      },
      "source": [
        "## Numerical Feature-Output-variable relationships with ANOVA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAov2ihtGUPr",
        "outputId": "35d7e232-3405-4134-c98f-6893ae8ccb66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age: F_onewayResult(statistic=nan, pvalue=nan)\n",
            "Fare: F_onewayResult(statistic=108.54151903409304, pvalue=4.646670286164067e-24)\n"
          ]
        }
      ],
      "source": [
        "for column in df.drop(categorical_columns + ['Survived', 'Ticket'], axis=1):\n",
        "    print(column + \": \" + str(f_oneway(*df.groupby(\"Survived\")[column].apply(list))))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LK79WKpGUPu"
      },
      "source": [
        "## Categorical Feature-Output-variable relationships with Chi-Square Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "31AlArvXGUPy",
        "outputId": "e7340fa2-1367-4ffd-b080-54b482f84bf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pclass, pvalue= 4.549251711298793e-23\n",
            "Sex, pvalue= 1.1973570627755645e-58\n",
            "SibSp, pvalue= 1.5585810465902116e-06\n",
            "Parch, pvalue= 9.703526421039997e-05\n",
            "Embarked, pvalue= 1.769922284120912e-06\n",
            "Name, pvalue= 1.3901480936748393e-60\n",
            "Cabin, pvalue= 6.326020042314704e-18\n"
          ]
        }
      ],
      "source": [
        "for column in categorical_columns:\n",
        "    print(column + \", pvalue= \" + str(chi2_contingency(pd.crosstab(index=df[\"Survived\"], columns=df[column]))[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Grj8pVNhGUPy"
      },
      "source": [
        "# 8- Feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vT4ZbMOsGUPz"
      },
      "source": [
        "## Drop irrelevant features according to the previous analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tb5-hDRyGUPz"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['Ticket'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nezk8tGCGUPz"
      },
      "source": [
        "## Check missing values in columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9KaJ22-GUPz",
        "outputId": "60132ded-5801-40f9-90c3-da7f470e032c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    418\n",
              "Pclass        0\n",
              "Name          0\n",
              "Sex           0\n",
              "Age         263\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          1\n",
              "Cabin         0\n",
              "Embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmD6o51XGUPz"
      },
      "source": [
        "## Fill missing values in columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lclrSdIhGUPz",
        "outputId": "ddbbc932-99dc-4a71-8589-8c02c76c42db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex     Pclass  Name   \n",
            "female  1       Miss       30.0\n",
            "                Mrs        45.0\n",
            "                Officer    49.0\n",
            "                Royalty    39.0\n",
            "        2       Miss       20.0\n",
            "                Mrs        30.0\n",
            "        3       Miss       18.0\n",
            "                Mrs        31.0\n",
            "male    1       Master      6.0\n",
            "                Mr         41.5\n",
            "                Officer    52.0\n",
            "                Royalty    40.0\n",
            "        2       Master      2.0\n",
            "                Mr         30.0\n",
            "                Officer    41.5\n",
            "        3       Master      6.0\n",
            "                Mr         26.0\n",
            "Name: Age, dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-105-35d59e2daf1b>:4: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
            "To preserve the previous behavior, use\n",
            "\n",
            "\t>>> .groupby(..., group_keys=False)\n",
            "\n",
            "To adopt the future behavior and silence this warning, use \n",
            "\n",
            "\t>>> .groupby(..., group_keys=True)\n",
            "  df.Age = grouped.Age.apply(lambda x: x.fillna(x.median()))\n"
          ]
        }
      ],
      "source": [
        "# Group by Sex, Pclass, and Title and then use the median for the column 'Age'\n",
        "grouped = df.groupby(['Sex', 'Pclass', 'Name'])\n",
        "print(grouped.Age.median())\n",
        "df.Age = grouped.Age.apply(lambda x: x.fillna(x.median()))\n",
        "\n",
        "# Use the most frequent value for the column 'Embarked'\n",
        "df.Embarked = df.Embarked.fillna(df.Embarked.value_counts().index[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wd974B3ZGUPz",
        "outputId": "77a71a47-4d40-478a-e398-11a0dc667629"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age: F_onewayResult(statistic=3.1685041795129862, pvalue=0.07541266650120756)\n"
          ]
        }
      ],
      "source": [
        "print('Age' + \": \" + str(f_oneway(*df.groupby('Survived')['Age'].apply(list))))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DK6m2MpFGUPz"
      },
      "source": [
        "## Create a new column 'Family' from the columns 'SibSp' and 'Parch'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqQEg8vRGUPz"
      },
      "outputs": [],
      "source": [
        "df[\"Family\"] = df[\"SibSp\"] + df[\"Parch\"]\n",
        "df = df.drop(['SibSp', 'Parch'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PTZALImGUPz"
      },
      "source": [
        "## Convert categorical columns into numerical ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "INiVqy6vGUPz"
      },
      "outputs": [],
      "source": [
        "# Label encoder for the column 'Sex', female -> 0, male -> 1\n",
        "df['Sex'] = LabelEncoder().fit_transform(df.Sex.values)\n",
        "\n",
        "# One Hot Encoder for the columns 'Name', 'Cabin', and 'Embarked'\n",
        "for column in ['Name', 'Cabin', 'Embarked']:\n",
        "        df[column] = pd.Categorical(df[column])\n",
        "        dfDummies = pd.get_dummies(df[column], prefix=column)\n",
        "        df = df.drop(column, axis=1)\n",
        "        df = pd.concat([df, dfDummies], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYUYD2BwGUPz"
      },
      "source": [
        "## Handle outliers in numerical columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "41lXkMoBGUPz",
        "outputId": "fd9766b9-fd50-48be-f71d-703d8ec3f908"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAtAElEQVR4nO3deXRUZZ7/8U+WSmWBBAlYSSTBjFGCGkTRhsiiMFkGl9OZBEW7PU0r2n0cQCGoTZwGOo4aRQFbDaCeNLT2oCwTmRahDY0aAiYscUDSthicsCgkLJoUJKRSqdTvDyf165KoVFJ1K8v7dQ6ncp/71K1vOOdSH+597vMEOJ1OpwAAAAwS6O8CAABA30L4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYKtjfBXxXW1ubjh07pv79+ysgIMDf5QAAgAvgdDp15swZxcXFKTDwh69tdLvwcezYMcXHx/u7DAAA0AlHjx7VkCFDfrBPtwsf/fv3l/Rt8ZGRkX6uBoA32e12lZSUKCMjQyaTyd/lAPAiq9Wq+Ph41/f4D+l24aP9VktkZCThA+hl7Ha7wsPDFRkZSfgAeqkLGTLBgFMAAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwCGcDgcKi0t1bZt21RaWiqHw+HvkgD4iUfhw+FwaP78+UpMTFRYWJguu+wy/cd//IecTqerj9Pp1IIFCxQbG6uwsDClpaWpurra64UD6DmKi4uVlJSk9PR0LVmyROnp6UpKSlJxcbG/SwPgBx6Fj2effVbLly/Xyy+/rL///e969tlntWjRIr300kuuPosWLdKLL76oFStWaOfOnYqIiFBmZqaam5u9XjyA7q+4uFhTpkxRSkqKysrK9Oabb6qsrEwpKSmaMmUKAQTogwKc/3jZ4kfcdtttslgsKioqcrXl5OQoLCxMf/rTn+R0OhUXF6e5c+fqkUcekSQ1NDTIYrFo1apVuuuuu370M6xWq6KiotTQ0MDaLkAP53A4lJSUpJSUFG3YsEEOh0ObNm3SLbfcoqCgIGVlZamqqkrV1dUKCgryd7kAusCT72+PFpa78cYb9eqrr+rzzz/XFVdcoX379mn79u1asmSJJKmmpka1tbVKS0tzvScqKkqjR49WeXl5h+HDZrPJZrO5FS99uwCV3W73pDwA3UxpaakOHTqkN954Qw6Hw3VOt78++uijmjBhgj744APddNNN/iwVQBd58p3tUfiYN2+erFarkpOTFRQUJIfDoaeeeko///nPJUm1tbWSJIvF4vY+i8Xi2vddBQUFys/PP6+9pKRE4eHhnpQHoJvZtm2bJOnLL7/U6dOnXe1btmyRJJ07d06StHnzZjU2NhpfIACvaWpquuC+HoWPtWvX6j//8z+1evVqXXXVVdq7d69mz56tuLg4TZs2zeNCJSkvL0+5ubmubavVqvj4eGVkZHDbBejhIiIitGTJEg0ZMkSjR4+W3W7Xli1blJ6eLpPJpIqKCknS5MmTufIB9HDtdy4uhEfh49FHH9W8efNct09SUlJ0+PBhFRQUaNq0aYqJiZEk1dXVKTY21vW+uro6jRw5ssNjms1mmc3m89pNJpNMJpMn5QHoZiZOnKhLL71UixYt0oYNG1ztJpNJQUFBeu6555SYmKiJEycy5gPo4Tz5zvboaZempiYFBrq/JSgoSG1tbZKkxMRExcTEaOvWra79VqtVO3fuVGpqqicfBaAXCAoK0uLFi7Vx40ZlZWWpoqJC586dU0VFhbKysrRx40Y9//zzBA+gj/Hoysftt9+up556SgkJCbrqqqv0P//zP1qyZInuu+8+SVJAQIBmz56tJ598UpdffrkSExM1f/58xcXFKSsryxf1A+jmsrOztX79es2dO1cTJkxwtScmJmr9+vXKzs72Y3UA/MGjR23PnDmj+fPn6+2339aJEycUFxenu+++WwsWLFBISIikbycZW7hwoV599VXV19dr3LhxWrZsma644ooL+gwetQV6J4fDoQ8++ECbN2/W5MmTudUC9DKefH97FD6MQPgAei+73e6a54MxXUDv4sn3N2u7AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDgCEcDodKS0u1bds2lZaWyuFw+LskAH5C+ADgc8XFxUpKSlJ6erqWLFmi9PR0JSUlqbi42N+lAfADwgcAnyouLtaUKVOUkpKisrIyvfnmmyorK1NKSoqmTJlCAAH6ICYZA+AzDodDSUlJSklJ0YYNG+RwOFyTjAUFBSkrK0tVVVWqrq5mtlOgh2OSMQDdQllZmQ4dOqTHH3/8vEUpAwMDlZeXp5qaGpWVlfmpQgD+QPgA4DPHjx+XJF199dUd7m9vb+8HoG8gfADwmdjYWElSVVVVh/vb29v7AegbCB8AfGb8+PG69NJL9fTTT6utrc1tX1tbmwoKCpSYmKjx48f7qUIA/kD4AOAzQUFBWrx4sTZu3KisrCxVVFTo3LlzqqioUFZWljZu3Kjnn3+ewaZAHxPs7wIA9G7Z2dlav3695s6dqwkTJrjaExMTtX79emVnZ/uxOgD+wJUPAIb47lP9370NA6DvIHwA8Kn2ScZGjBjhNsnYiBEjmGQM6KOYZAyAzzDJGNB3MMkYgG6BScYAdITwAcBnmGQMQEcIHwB8hknGAHSE8AHAZ5hkDEBHCB8AfIZJxgB0hEnGAPgUk4wB+C4etQVgCIfDoQ8++ECbN2/W5MmTNXHiRK54AL2IJ9/fXPkAYIigoCDddNNNamxs1E033UTwAPowwgcAQ7S0tOill17S+++/r4MHD2rWrFkKCQnxd1kA/IABpwB87rHHHlNERIQeeeQRbdq0SY888ogiIiL02GOP+bs0AH7AlQ8APvXYY4/pueeek8Vi0c9+9jM1NjYqIiJCq1ev1nPPPSdJWrRokZ+rBGAkBpwC8JmWlhZFREQoIiJCAwYM0OHDh137hg4dqvr6ejU2NqqxsZFbMEAPx9ouALqFZcuWqbW1VQ0NDR2uatvQ0KDW1lYtW7bM36UCMJBH4ePSSy9VQEDAeX9mzJghSWpubtaMGTMUHR2tfv36KScnR3V1dT4pHED3V11dLUlKT0/X2rVrtXPnTr3xxhvauXOn1q5dq/T0dLd+APoGj8Z87N69Ww6Hw7VdVVWl9PR03XHHHZKkOXPm6N1339W6desUFRWlmTNnKjs7Wzt27PBu1QB6hICAAElSQ0OD+vfvr9bWVknSpk2bNG/ePF133XVu/QD0DR6Fj8GDB7ttP/PMM7rssst00003qaGhQUVFRVq9erUmTZokSVq5cqWGDx+uiooKjRkzxntVA+gRRo8ercLCQu3atUsWi0X5+fkym82y2WxauHChdu3a5eoHoO/o9JiPlpYW/elPf9J9992ngIAAVVZWym63Ky0tzdUnOTlZCQkJKi8v90qxAHoWi8Xi+rmtrU1tbW1yOp2unzvqB6D36/Sjths2bFB9fb1++ctfSpJqa2sVEhKiAQMGuPWzWCyqra393uPYbDbZbDbXttVqlSTZ7XbZ7fbOlgegG9i3b58kadCgQfr666/1b//2b659wcHBGjRokE6dOqV9+/Zp4sSJ/ioTgBd48p3d6fBRVFSkyZMnKy4urrOHkCQVFBQoPz//vPaSkhKFh4d36dgA/OvDDz+UJJ06dUqjRo1STEyM7Ha7TCaTamtrVVlZ6ep3xRVX+LFSAF3V1NR0wX07FT4OHz6sv/71ryouLna1xcTEqKWlRfX19W5XP+rq6hQTE/O9x8rLy1Nubq5r22q1Kj4+XhkZGczzAfRwBw8e1KZNm/SrX/1KJSUlrrAhfbuq7QMPPKDXXntNkyZN0i233OLHSgF0VfudiwvRqUnGfve73+mVV17R0aNHFRz8bX5paGjQ4MGD9eabbyonJ0eSdODAASUnJ6u8vPyCB5wyyRjQe7RPMhYdHa3Dhw+rrKzMtart+PHjNXToUJ0+fZpJxoBewKeTjLW1tWnlypWaNm2aK3hIUlRUlKZPn67c3Fx98MEHqqys1L333qvU1FSedAH6qJCQEM2ZM0d1dXUaOnSoqqurdfXVV6u6ulpDhw5VXV2d5syZQ/AA+hiPb7v89a9/1ZEjR3Tfffedt2/p0qUKDAxUTk6ObDabMjMzmbkQ6OPa121ZunTpeQNOH330UdZ1Afog1nYBYIiWlha99NJLev/99zVp0iTNmjWLKx5AL+LJ9zer2gIwREhIiB566CElJSXplltukclk8ndJAPyEheUAGMLhcKi0tFTbtm1TaWmp21INAPoWwgcAnysuLlZSUpLS09O1ZMkSpaenKykpye1xfQB9B+EDgE8VFxdrypQpSklJUVlZmd58802VlZUpJSVFU6ZMIYAAfRADTgH4jMPhUFJSklJSUrRhwwY5HA5t2rRJt9xyi4KCgpSVlaWqqipVV1crKCjI3+UC6AKfzvMBABeqrKxMhw4d0uOPPy6n0+k25sPpdCovL081NTUqKyvzd6kADET4AOAzx48flyR98cUXHY75+N///V+3fgD6BsIHAJ+JjY2VJN1zzz0djvm455573PoB6BsY8wHAZ/5xbZcvv/xSTqfTNeYjICBAQ4YMYW0XoJdgzAeAbuGjjz5Sa2urTpw4oezsbFVUVOjcuXOqqKhQdna2Tpw4odbWVn300Uf+LhWAgQgfAHymfSzHG2+8oU8++UQTJkzQ3XffrQkTJmj//v1644033PoB6BsIHwB8pn0sxyuvvKLDhw+77Tt06JBeeeUVt34A+gbGfADwGYfDof79++vcuXMKCQnR7NmzlZiYqJqaGr3wwgtqaWlRWFiYzpw5wzwfQA/HmA8A3UJLS4vOnTsnSUpLS9Ntt92mAQMG6LbbblNaWpok6dy5c2ppafFnmQAMRvgA4DOPPvqoJOlf//VftW/fPrcxH5988omysrLc+gHoG4L9XQCA3qu6ulqSVFlZqa+++spt35dffqnAwEC3fgD6Bq58APCZyy+/XJJ05MgRSVJmZqaeeeYZZWZmurW39wPQNzDgFIDP1NbWup5k+eabbxQREeGaZKyxsVEXXXSRpG8ftY2JifFnqQC6yJPvb267APCZKVOmuH4eNGiQLr30Up07d05hYWE6dOiQW7/t27f7oUIA/kD4AOAz7bdVIiIi1NjYqC+++MJtf3t7ez8AfQPhA4DPJCQk6OjRo2psbFRAQICSkpIUEBAgp9OpgwcPqrGx0dUPQN9B+ADgM2vWrNGQIUMkSadOnVL//v1dYz7OnDmj6OhoVz8AfQfhA4DPzJw50/VzdHS0oqKi5HQ6FRAQoIaGBrd+b7/9tj9KBOAHhA8APtM+xiMoKEgOh8MtcPxj+3fHggDo3ZjnA4DPXHbZZZK+XeNFkqKiohQZGamoqCi39vZ+APoGwgcAn3n55ZddP58+fVonT57U66+/rpMnT+r06dMd9gPQ+3HbBYDPTJ061fVzdHS0Bg0aJLvdLpPJpFOnTrn1Y54PoO8gfADwmfb5O4KDg9Xa2uoWOP6xnXk+gL6F2y4AfKZ9/o7W1lZJ0uDBg3XxxRdr8ODBbu3M8wH0LVz5AOAzq1ev1tChQyV9u87L/v37tXnzZk2ePFkpKSmu9VxWr17tzzIBGIyF5QD4zLhx47Rjx44f7Td27FjGfAA9nCff3x7fdvnqq690zz33KDo6WmFhYUpJSdGePXtc+51OpxYsWKDY2FiFhYUpLS1N1dXVnv8WAHq8Cx3LwZgPoG/xKHx88803Gjt2rEwmkzZv3qxPP/1Uixcvdi2LLUmLFi3Siy++qBUrVmjnzp2KiIhQZmammpubvV48gO7tQsdyMOYD6Fs8uu0yb9487dixQ2VlZR3udzqdiouL09y5c/XII49IkhoaGmSxWLRq1SrdddddP/oZ3HYBeo+NGzfq9ttvd21feeWVysrK0oYNG/Tpp5+62t955x3ddttt/igRgJf47LbLn//8Z11//fW64447dPHFF+vaa6/Va6+95tpfU1Oj2tpapaWludqioqI0evRolZeXe/hrAOjpvvrqK7ftSy65RBEREbrkkkt+sB+A3s2jKx+hoaGSpNzcXN1xxx3avXu3Hn74Ya1YsULTpk3TRx99pLFjx+rYsWOKjY11ve/OO+9UQEBAhytX2mw22Ww217bValV8fLxOnTrFlQ+gh4uPj1ddXZ1rDZfvam+3WCw6evSoHyoE4C1Wq1WDBg26oCsfHj1q29bWpuuvv15PP/20JOnaa69VVVWVK3x0RkFBgfLz889rLykpUXh4eKeOCaB7qK+vlyT90z/9k+bPn6+nn35ap06d0qBBg/T444/riSee0MGDB1VfX69Nmzb5t1gAXdLU1HTBfT0KH7Gxsbryyivd2oYPH67/+q//kiTXM/t1dXVuVz7q6uo0cuTIDo+Zl5en3Nxc13b7lY+MjAyufAA9XExMjA4fPqzq6mqtWbNGy5YtU11dnSwWixYvXqyDBw+6+t1yyy1+rhZAV1it1gvu61H4GDt2rA4cOODW9vnnn7smEUpMTFRMTIy2bt3qChtWq1U7d+7Ugw8+2OExzWazzGbzee0mk0kmk8mT8gB0M7t379bFF18sSdq/f78mTZrk2tf+70Z7P853oGfz5Bz2KHzMmTNHN954o55++mndeeed2rVrl1599VW9+uqrkqSAgADNnj1bTz75pC6//HIlJiZq/vz5iouLU1ZWlke/BIDuo6mpSZ999lmn3tuvXz+dPXtWR44ckcVi0ZDEK/Rlzec6fPiwa//Ro0c7PeYjOTmZW7RAD+PxDKcbN25UXl6eqqurlZiYqNzcXD3wwAOu/U6nUwsXLtSrr76q+vp6jRs3TsuWLdMVV1xxQcfnUVug+/n44481atQof5fRocrKSl133XX+LgPo8zz5/mZ6dQA/qitXPtqdPHlS9/ximk6d/kaDoi/Sn17/o2uBua7gygfQPXjy/c3CcgB+VHh4uFeuLmzZ9TdlLa/QhgfHaOTQaC9UBqAn8nhtFwAAgK4gfAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGMqj8PG73/1OAQEBbn+Sk5Nd+5ubmzVjxgxFR0erX79+ysnJUV1dndeLBgAAPZfHVz6uuuoqHT9+3PVn+/btrn1z5szRO++8o3Xr1qm0tFTHjh1Tdna2VwsGAAA9W7DHbwgOVkxMzHntDQ0NKioq0urVqzVp0iRJ0sqVKzV8+HBVVFRozJgxXa8WAAD0eB6Hj+rqasXFxSk0NFSpqakqKChQQkKCKisrZbfblZaW5uqbnJyshIQElZeXf2/4sNlsstlsrm2r1SpJstvtstvtnpYHoBtrbW11vXJ+A72LJ+e0R+Fj9OjRWrVqlYYNG6bjx48rPz9f48ePV1VVlWpraxUSEqIBAwa4vcdisai2tvZ7j1lQUKD8/Pzz2ktKShQeHu5JeQC6uaNnJSlYFRUV+qrK39UA8KampqYL7utR+Jg8ebLr5xEjRmj06NEaOnSo1q5dq7CwME8O5ZKXl6fc3FzXttVqVXx8vDIyMhQZGdmpYwLonvYd+Vrav0djxozRNQkD/V0OAC9qv3NxITy+7fKPBgwYoCuuuEIHDx5Uenq6WlpaVF9f73b1o66ursMxIu3MZrPMZvN57SaTSSaTqSvlAehmgoODXa+c30Dv4sk53aV5Ps6ePasvvvhCsbGxGjVqlEwmk7Zu3eraf+DAAR05ckSpqald+RgAANCLeHTl45FHHtHtt9+uoUOH6tixY1q4cKGCgoJ09913KyoqStOnT1dubq4GDhyoyMhIzZo1S6mpqTzpAgAAXDwKH19++aXuvvtunT59WoMHD9a4ceNUUVGhwYMHS5KWLl2qwMBA5eTkyGazKTMzU8uWLfNJ4QAAoGfyKHy89dZbP7g/NDRUhYWFKiws7FJRAACg92JtFwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoboUPp555hkFBARo9uzZrrbm5mbNmDFD0dHR6tevn3JyclRXV9fVOgEAQC/R6fCxe/duvfLKKxoxYoRb+5w5c/TOO+9o3bp1Ki0t1bFjx5Sdnd3lQgEAQO/QqfBx9uxZ/fznP9drr72miy66yNXe0NCgoqIiLVmyRJMmTdKoUaO0cuVKffTRR6qoqPBa0QAAoOcK7sybZsyYoVtvvVVpaWl68sknXe2VlZWy2+1KS0tztSUnJyshIUHl5eUaM2bMecey2Wyy2WyubavVKkmy2+2y2+2dKQ9AN9Xa2up65fwGehdPzmmPw8dbb72ljz/+WLt37z5vX21trUJCQjRgwAC3dovFotra2g6PV1BQoPz8/PPaS0pKFB4e7ml5ALqxo2clKVgVFRX6qsrf1QDwpqampgvu61H4OHr0qB5++GFt2bJFoaGhHhfWkby8POXm5rq2rVar4uPjlZGRocjISK98BoDuYd+Rr6X9ezRmzBhdkzDQ3+UA8KL2OxcXwqPwUVlZqRMnTui6665ztTkcDm3btk0vv/yy3nvvPbW0tKi+vt7t6kddXZ1iYmI6PKbZbJbZbD6v3WQyyWQyeVIegG4uODjY9cr5DfQunpzTHoWPf/7nf9b+/fvd2u69914lJyfrN7/5jeLj42UymbR161bl5ORIkg4cOKAjR44oNTXVk48CAAC9lEfho3///rr66qvd2iIiIhQdHe1qnz59unJzczVw4EBFRkZq1qxZSk1N7XCwKQAA6Hs69bTLD1m6dKkCAwOVk5Mjm82mzMxMLVu2zNsfAwAAeqguh48PP/zQbTs0NFSFhYUqLCzs6qEBAEAvxNouAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABDeX2SMQDdR82pRjXaWv1dhssXJxtdr+3rvHQXEeZgJQ6K8HcZQJ/Qvc5+AF5Tc6pRE5//0N9ldGju+v0/3skPPnjkZgIIYADCB9BLtV/xeGHqSCVd3M/P1Xyr8ZxNGz8s1203pyoi7PzVrP3l4Imzmr1mb7e6SgT0ZoQPoJdLurifrr4kyt9lSJLsdrtqB0vXDb3Io+W3AfQuDDgFAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUB6Fj+XLl2vEiBGKjIxUZGSkUlNTtXnzZtf+5uZmzZgxQ9HR0erXr59ycnJUV1fn9aIBAEDP5VH4GDJkiJ555hlVVlZqz549mjRpkn7605/qb3/7myRpzpw5euedd7Ru3TqVlpbq2LFjys7O9knhAACgZwr2pPPtt9/utv3UU09p+fLlqqio0JAhQ1RUVKTVq1dr0qRJkqSVK1dq+PDhqqio0JgxY7xXNQAA6LE8Ch//yOFwaN26dWpsbFRqaqoqKytlt9uVlpbm6pOcnKyEhASVl5d/b/iw2Wyy2WyubavVKkmy2+2y2+2dLQ/o81pbW12v3eVcaq+ju9TTrjv+XQE9jSfnjsfhY//+/UpNTVVzc7P69eunt99+W1deeaX27t2rkJAQDRgwwK2/xWJRbW3t9x6voKBA+fn557WXlJQoPDzc0/IA/J+jZyUpWNu3b9fhfv6uxt2WLVv8XYKb7vx3BfQUTU1NF9zX4/AxbNgw7d27Vw0NDVq/fr2mTZum0tJSTw/jkpeXp9zcXNe21WpVfHy8MjIyFBkZ2enjAn3d345Z9fz+Co0bN05XxXWPc8lut2vLli1KT0+XyWTydzku3fHvCuhp2u9cXAiPw0dISIiSkpIkSaNGjdLu3bv1+9//XlOnTlVLS4vq6+vdrn7U1dUpJibme49nNptlNpvPazeZTN3qHyegpwkODna9drdzqbud39357wroKTw5d7o8z0dbW5tsNptGjRolk8mkrVu3uvYdOHBAR44cUWpqalc/BgAA9BIeXfnIy8vT5MmTlZCQoDNnzmj16tX68MMP9d577ykqKkrTp09Xbm6uBg4cqMjISM2aNUupqak86QIAAFw8Ch8nTpzQL37xCx0/flxRUVEaMWKE3nvvPaWnp0uSli5dqsDAQOXk5MhmsykzM1PLli3zSeEAAKBn8ih8FBUV/eD+0NBQFRYWqrCwsEtFAQCA3ou1XQAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoTxe1RZAz2BzNCsw9CvVWA8oMLSfv8uRJLW2tupY6zH9/eu/u1aS7Q5qrGcVGPqVbI5mSVH+Lgfo9brP2Q/Aq441HlZE4kt6fJe/Kznfsr90vzWfIhKlY40jNUoWf5cC9HqED6CXiosYqsaaWfr91JG67OLuc+Vjx/YdGjtubLe68vHFibN6eM1exU0c6u9SgD6h+5z9ALzKHBSqtuZLlBg5TFdGd49bCXa7XTXBNRo+cLhMJpO/y3Fpa25QW/NJmYNC/V0K0Ccw4BQAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYKtjfBQDwjXN2hySp6qsGP1fy/zWes2nPSSnm8DeKCDP7uxyXgyfO+rsEoE/xKHwUFBSouLhYn332mcLCwnTjjTfq2Wef1bBhw1x9mpubNXfuXL311luy2WzKzMzUsmXLZLFYvF48gO/3xf99oc4r3u/nSr4rWG8c3O3vIjoUYeb/Y4ARPDrTSktLNWPGDN1www1qbW3V448/royMDH366aeKiIiQJM2ZM0fvvvuu1q1bp6ioKM2cOVPZ2dnasWOHT34BAB3LuCpGknTZxf0UZgryczXfOnC8QXPX79fiKSkaFhvl73LcRJiDlTgowt9lAH1CgNPpdHb2zSdPntTFF1+s0tJSTZgwQQ0NDRo8eLBWr16tKVOmSJI+++wzDR8+XOXl5RozZsyPHtNqtSoqKkoNDQ2KjIzsbGkAuqG9h08ra3mFNjw4RiOHRvu7HABe5Mn3d5euMTY0fHsveeDAgZKkyspK2e12paWlufokJycrISHhe8OHzWaTzWZzK16S7Ha77HZ7V8oD0M20tra6Xjm/gd7Fk3O60+Gjra1Ns2fP1tixY3X11VdLkmpraxUSEqIBAwa49bVYLKqtre3wOAUFBcrPzz+vvaSkROHh4Z0tD0A3dPSsJAWroqJCX1X5uxoA3tTU1HTBfTsdPmbMmKGqqipt3769s4eQJOXl5Sk3N9e1bbVaFR8fr4yMDG67AL3MviNfS/v3aMyYMbomYaC/ywHgRe13Li5Ep8LHzJkztXHjRm3btk1DhgxxtcfExKilpUX19fVuVz/q6uoUExPT4bHMZrPM5vMfuTOZTDKZTJ0pD0A3FRwc7Hrl/AZ6F0/OaY8mGXM6nZo5c6befvttvf/++0pMTHTbP2rUKJlMJm3dutXVduDAAR05ckSpqamefBQAAOilPLryMWPGDK1evVr//d//rf79+7vGcURFRSksLExRUVGaPn26cnNzNXDgQEVGRmrWrFlKTU29oCddAABA7+dR+Fi+fLkk6eabb3ZrX7lypX75y19KkpYuXarAwEDl5OS4TTIGAAAgeRg+LmRKkNDQUBUWFqqwsLDTRQEAgN6LheUAAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIbyOHxs27ZNt99+u+Li4hQQEKANGza47Xc6nVqwYIFiY2MVFhamtLQ0VVdXe6teAADQw3kcPhobG3XNNdeosLCww/2LFi3Siy++qBUrVmjnzp2KiIhQZmammpubu1wsAADo+YI9fcPkyZM1efLkDvc5nU698MIL+u1vf6uf/vSnkqTXX39dFotFGzZs0F133dW1agEAQI/ncfj4ITU1NaqtrVVaWpqrLSoqSqNHj1Z5eXmH4cNms8lms7m2rVarJMlut8tut3uzPAB+1tra6nrl/AZ6F0/Oaa+Gj9raWkmSxWJxa7dYLK5931VQUKD8/Pzz2ktKShQeHu7N8gD42dGzkhSsiooKfVXl72oAeFNTU9MF9/Vq+OiMvLw85ebmuratVqvi4+OVkZGhyMhIP1YGwNv2Hfla2r9HY8aM0TUJA/1dDgAvar9zcSG8Gj5iYmIkSXV1dYqNjXW119XVaeTIkR2+x2w2y2w2n9duMplkMpm8WR4APwsODna9cn4DvYsn57RX5/lITExUTEyMtm7d6mqzWq3auXOnUlNTvflRAACgh/L4ysfZs2d18OBB13ZNTY327t2rgQMHKiEhQbNnz9aTTz6pyy+/XImJiZo/f77i4uKUlZXlzboBAEAP5XH42LNnjyZOnOjabh+vMW3aNK1atUqPPfaYGhsb9atf/Ur19fUaN26c/vKXvyg0NNR7VQMAgB7L4/Bx8803y+l0fu/+gIAAPfHEE3riiSe6VBgAAOidWNsFAAAYivABAAAMRfgAAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAowgcAADAU4QMAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGIrwAQAADEX4AAAAhiJ8AAAAQxE+AACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABDET4AAIChCB8AAMBQhA8AAGAon4WPwsJCXXrppQoNDdXo0aO1a9cuX30UAADoQXwSPtasWaPc3FwtXLhQH3/8sa655hplZmbqxIkTvvg4AADQgwT74qBLlizRAw88oHvvvVeStGLFCr377rv6wx/+oHnz5vniIwH4UFNTkz777LMuH+fA8XrZag/q71Vhajs9oOuFSUpOTlZ4eLhXjgXAGF4PHy0tLaqsrFReXp6rLTAwUGlpaSovLz+vv81mk81mc21brVZJkt1ul91u93Z5ADqhqqpKo0eP9trxfvZHrx1KO3fu1LXXXuu9AwLoFE++s70ePk6dOiWHwyGLxeLWbrFYOvyfU0FBgfLz889rLykp4X8zQDdhs9m0ePHiLh/H3iZ93SwNDJVMXrrpe+jQIR0/ftw7BwPQaU1NTRfc1ye3XTyRl5en3Nxc17bValV8fLwyMjIUGRnpx8oAeJvdbteWLVuUnp4uk8nk73IAeFH7nYsL4fXwMWjQIAUFBamurs6tva6uTjExMef1N5vNMpvN57WbTCb+cQJ6Kc5voPfx5Jz2+tMuISEhGjVqlLZu3epqa2tr09atW5WamurtjwMAAD2MT2675Obmatq0abr++uv1k5/8RC+88IIaGxtdT78AAIC+yyfhY+rUqTp58qQWLFig2tpajRw5Un/5y1/OG4QKAAD6Hp8NOJ05c6Zmzpzpq8MDAIAeirVdAACAoQgfAADAUIQPAABgKMIHAAAwFOEDAAAYivABAAAMRfgAAACGInwAAABD+X1V2+9yOp2SPFsdD0DPYLfb1dTUJKvVysJyQC/T/r3d/j3+Q7pd+Dhz5owkKT4+3s+VAAAAT505c0ZRUVE/2CfAeSERxUBtbW06duyY+vfvr4CAAH+XA8CLrFar4uPjdfToUUVGRvq7HABe5HQ6debMGcXFxSkw8IdHdXS78AGg97JarYqKilJDQwPhA+jDGHAKAAAMRfgAAACGInwAMIzZbNbChQtlNpv9XQoAP2LMBwAAMBRXPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThA4BXlJeXKygoSLfeequ/SwHQzfG0CwCvuP/++9WvXz8VFRXpwIEDiouL83dJALoprnwA6LKzZ89qzZo1evDBB3Xrrbdq1apVbvv//Oc/6/LLL1doaKgmTpyoP/7xjwoICFB9fb2rz/bt2zV+/HiFhYUpPj5eDz30kBobG439RQAYgvABoMvWrl2r5ORkDRs2TPfcc4/+8Ic/uJbVrqmp0ZQpU5SVlaV9+/bp17/+tf793//d7f1ffPGF/uVf/kU5OTn65JNPtGbNGm3fvl0zZ870x68DwMe47QKgy8aOHas777xTDz/8sFpbWxUbG6t169bp5ptv1rx58/Tuu+9q//79rv6//e1v9dRTT+mbb77RgAEDdP/99ysoKEivvPKKq8/27dt10003qbGxUaGhof74tQD4CFc+AHTJgQMHtGvXLt19992SpODgYE2dOlVFRUWu/TfccIPbe37yk5+4be/bt0+rVq1Sv379XH8yMzPV1tammpoaY34RAIYJ9ncBAHq2oqIitba2ug0wdTqdMpvNevnlly/oGGfPntWvf/1rPfTQQ+ftS0hI8FqtALoHwgeATmttbdXrr7+uxYsXKyMjw21fVlaW3nzzTQ0bNkybNm1y27d792637euuu06ffvqpkpKSfF4zAP9jzAeATtuwYYOmTp2qEydOKCoqym3fb37zG73//vtau3athg0bpjlz5mj69Onau3ev5s6dqy+//FL19fWKiorSJ598ojFjxui+++7T/fffr4iICH366afasmXLBV89AdBzMOYDQKcVFRUpLS3tvOAhSTk5OdqzZ4/OnDmj9evXq7i4WCNGjNDy5ctdT7uYzWZJ0ogRI1RaWqrPP/9c48eP17XXXqsFCxYwVwjQS3HlA4DhnnrqKa1YsUJHjx71dykA/IAxHwB8btmyZbrhhhsUHR2tHTt26LnnnmMOD6API3wA8Lnq6mo9+eST+vrrr5WQkKC5c+cqLy/P32UB8BNuuwAAAEMx4BQAABiK8AEAAAxF+AAAAIYifAAAAEMRPgAAgKEIHwAAwFCEDwAAYCjCBwAAMBThAwAAGOr/AaHfl5hUkzHsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age: -2.25, 59.75\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdc0lEQVR4nO3df2zc9X348dfFPl9ixzY/En4EHJYlJSkJhJEfNKJUhBF+tKAxtYhRGD9UIRVogaWo+4KmddFoXSSaQX8s7cpKqnYMBhRWtUDjliWMQUZiRBfQkpE0bbIEmroFnx03l4tz3z+63PDigM9522c7j4cUOffx53OfVyJ9fE997uPPZUqlUikAABIYV+0BAICxQ1gAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAytcO9w/3798fOnTujsbExMpnMcO8eABiEUqkUXV1dMWXKlBg37tDnJYY9LHbu3BktLS3DvVsAIIHt27fHySeffMjvD3tYNDY2RsTvBmtqahru3QNDqFgsxqpVq+LCCy+MbDZb7XGAhPL5fLS0tJRfxw9l2MPiwNsfTU1NwgLGmGKxGPX19dHU1CQsYIx6r8sYXLwJACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhASTR3d0dH/3oR+O2226Lj370o9Hd3V3tkYAqGPY7bwJjz8KFC2PdunXlx7/4xS+isbExFixYEC+99FIVJwOGmzMWwGE5EBWZTCauueaa+Ju/+Zu45pprIpPJxLp162LhwoXVHhEYRplSqVQazh3m8/lobm6Ozs5OnxUCo1x3d3c0NjZGJpOJnp6eqKmpiaeeeio+/OEPR29vb9TX15c/annixInVHhc4DAN9/XbGAhi0P/3TP42IiGuuuSbGjx/f53vjx4+Pj3/8433WA8Y+YQEM2pYtWyIi4o477uj3+0uXLu2zHjD2CQtg0KZPnx4REffee2+/31++fHmf9YCxzzUWwKC5xgKOHK6xAIbcxIkTY8GCBVEqlaK+vj6uv/762LJlS1x//fXlqFiwYIGogCOIMxbAYfu/97E4wH0sYOxwxgIYNi+99FJ0dXXFZZddFqecckpcdtll0dXVJSrgCOTOm0ASEydOjMcff7x8jUU2m632SEAVOGMBACQjLACAZCoOix07dsQ111wTxx57bEyYMCFOP/30WL9+/VDMBgCMMhVdY/HWW2/FOeecE4sXL46nn346Jk+eHK+//nocffTRQzUfADCKVBQW99xzT7S0tMSDDz5YXjZt2rTkQwEAo1NFYfH9738/LrroorjiiitizZo1cdJJJ8XNN98cN9544yG3KRQKUSgUyo/z+XxERBSLxSgWi4McGxiJDhzTjm0YewZ6XFcUFj/72c9ixYoVsXTp0rjrrrti3bp1ceutt0ZdXV1cd911/W7T2toay5YtO2j5qlWror6+vpLdA6NEW1tbtUcAEuvp6RnQehXdebOuri7mz58fL7zwQnnZrbfeGuvWrYsXX3yx3236O2PR0tISHR0d7rwJY0yxWIy2trZYsmSJ+1jAGJPP52PSpEnveefNis5YnHjiiXHaaaf1Wfb+978/Hn/88UNuk8vlIpfLHbQ8m836wQNjlOMbxp6BHtMV/brpOeecE5s2beqz7L/+67/ilFNOqeRpAIAxqqKw+LM/+7NYu3ZtfOELX4jNmzfHQw89FH/3d38Xt9xyy1DNBwCMIhWFxYIFC+KJJ56If/zHf4w5c+bEX//1X8d9990XV1999VDNBwCMIhV/CNmll14al1566VDMAgCMcj4rBABIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIJmKPysEoD+bNm2K2bNnR29vb9TU1MRrr70WM2fOrPZYwDATFsBhy2QyfR739vbGrFmzIiKiVCpVYySgSrwVAhyWd0ZFLpeLq666KnK5XL/fB8Y+YQEM2qZNm8p/37FjR3R1dcWVV14ZXV1dsWPHjn7XA8Y2b4UAgzZ79uyI+N2ZiqOPPjpuvfXWWLt2bTzzzDOxfPnyyOVyUSgUYvbs2bFv374qTwsMh0xpmN8Azefz0dzcHJ2dndHU1DScuwYSO/A2x8yZM/s9K/G+970vXn/99YhwrQWMdgN9/XbGAhi0mpqa6O3tjU2bNkVdXV3cfvvtMW3atNi6dWvcd9995aioqamp8qTAcHGNBTBo69evL/9906ZNcffdd8eJJ54Yd999d58zGO9cDxjbnLEABu2BBx4o/33atGkRETF+/PjYs2fPQet99atfHdbZgOpwxgIYtANvdbzT/42KQ60HjE3CAhi0973vfUnXA0Y/YQEM2l/8xV+U//7WW2/F3r1748knn4y9e/fGW2+91e96wNgmLIBB+9jHPlb++/HHHx933XVX7NixI+666644/vjj+10PGNtcvAlHuJ6enti4ceOgtt28eXNERJx55pnxyiuvxL333tvn+3Pnzo2f/vSnsXnz5nj55Zcrfv5Zs2ZFfX39oGYDqsMNsuAI9/LLL8e8efOqPUa/2tvb46yzzqr2GEC4QRYwQLNmzYr29vZBbdvZ2Rnnn39+RET867/+a2zv3BtLH90Qy684PVqa6+Lcc8+NiIhnn302mpubBzUbMLo4YwEclhkzZsSWLVsiImLRh86PzSddGDN2rIoXn3s2IiKmT59efssEGL0G+votLIDD9s64eCdRAWPHQF+//VYIcNg2b94cb7/9dsydtzBqGifF3HkL4+233xYVcAQSFkASzc3NsfLxp+Lkm1fGysefGtQ1FcDoJywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDIVhcVf/dVfRSaT6fNn1qxZQzUbADDK1Fa6wezZs+PHP/7x/z5BbcVPAQCMURVXQW1tbZxwwglDMQsAMMpVHBavv/56TJkyJcaPHx+LFi2K1tbWmDp16iHXLxQKUSgUyo/z+XxERBSLxSgWi4MYGRip9u3bV/7q+IaxZaDHdEVhcfbZZ8fKlStj5syZ8cYbb8SyZcvi3HPPjVdffTUaGxv73aa1tTWWLVt20PJVq1ZFfX19JbsHRrjt3RERtbF27drY8Wq1pwFS6unpGdB6mVKpVBrsTt5+++045ZRTYvny5fGJT3yi33X6O2PR0tISHR0d0dTUNNhdAyPQT7f9Jj72zfXx2I3zY+7UY6o9DpBQPp+PSZMmRWdn57u+fh/WlZdHHXVUnHrqqbF58+ZDrpPL5SKXyx20PJvNRjabPZzdAyPMgYu5a2trHd8wxgz0mD6s+1h0d3fHli1b4sQTTzycpwEAxoiKwuKOO+6INWvWxM9//vN44YUX4o//+I+jpqYmrrrqqqGaDwAYRSp6K+S///u/46qrropf//rXMXny5PjgBz8Ya9eujcmTJw/VfADAKFJRWDz88MNDNQcAMAb4rBAAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZA4rLL74xS9GJpOJ22+/PdE4AMBoNuiwWLduXXzjG9+IM844I+U8AMAoNqiw6O7ujquvvjq++c1vxtFHH516JgBglKodzEa33HJLfOQjH4kLLrgg7r777nddt1AoRKFQKD/O5/MREVEsFqNYLA5m98AItW/fvvJXxzeMLQM9pisOi4cffjhefvnlWLdu3YDWb21tjWXLlh20fNWqVVFfX1/p7oERbHt3RERtrF27Nna8Wu1pgJR6enoGtF5FYbF9+/a47bbboq2tLcaPHz+gbe68885YunRp+XE+n4+Wlpa48MILo6mpqZLdAyPcT7f9JmLD+vjABz4Qc6ceU+1xgIQOvOPwXioKi/b29ti1a1ecddZZ5WW9vb3x3HPPxVe/+tUoFApRU1PTZ5tcLhe5XO6g58pms5HNZivZPTDC1dbWlr86vmFsGegxXVFY/OEf/mFs2LChz7IbbrghZs2aFX/+539+UFQAAEeWisKisbEx5syZ02dZQ0NDHHvssQctBwCOPO68CQAkM6hfN32n1atXJxgDABgLnLEAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJ1FZ7AGBwtnbsjt2FfdUeo48tv9pd/lpbO3J+vDTkamPapIZqjwFHhJFz5AMDtrVjdyy+d3W1xzikzzy2odojHORf7jhPXMAwEBYwCh04U3HflWfGjOMmVnma/7X7t4X4weoX49LzFkXDhFy1x4mIiM27uuP2R14ZcWd3YKwSFjCKzThuYsw5qbnaY5QVi8V4c3LEWaccHdlsttrjAFXg4k0AIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZCoKixUrVsQZZ5wRTU1N0dTUFIsWLYqnn356qGYDAEaZisLi5JNPji9+8YvR3t4e69evj/PPPz/+6I/+KF577bWhmg8AGEVqK1n5sssu6/P485//fKxYsSLWrl0bs2fPTjoYADD6VBQW79Tb2xuPPvpo7N69OxYtWnTI9QqFQhQKhfLjfD4fERHFYjGKxeJgdw9HtH379pW/jqTj6MAsI2mmkfp/BaPNQI+fisNiw4YNsWjRotizZ09MnDgxnnjiiTjttNMOuX5ra2ssW7bsoOWrVq2K+vr6SncPRMT27oiI2nj++efjFxOrPc3B2traqj1C2Uj/v4LRoqenZ0DrZUqlUqmSJ967d29s27YtOjs747HHHosHHngg1qxZc8i46O+MRUtLS3R0dERTU1Mluwb+x2s783H5irXx5E0fiNlTRs5xVCwWo62tLZYsWRLZbLba40TEyP2/gtEmn8/HpEmTorOz811fvys+Y1FXVxczZsyIiIh58+bFunXr4v77749vfOMb/a6fy+Uil8sdtDybzY6YHzww2tTW1pa/jsTjaCQd3yP9/wpGi4EeP4d9H4v9+/f3OSMBABy5Kjpjceedd8Yll1wSU6dOja6urnjooYdi9erV8aMf/Wio5gMARpGKwmLXrl1x7bXXxhtvvBHNzc1xxhlnxI9+9KNYsmTJUM0HAIwiFYXF3//93w/VHADAGOCzQgCAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJDPrTTYHqKfTuiXHjd8TW/KYYN37kfLLWvn37Yue+nfGfv/nP8q20q21rvjvGjd8Rhd49EdFc7XFgzBsZRz5QkZ27fxEN074Sd71U7Un697fP/G21R+ijYVrEzt1nxrw4vtqjwJgnLGAUmtJwSuze+um4/8ozY/pxI+uMxb89/29xzgfPGTFnLLbs6o7bHnklpiw+pdqjwBFhZBz5QEVyNeNj/56TYlrTzDjt2JFzer9YLMbW2q3x/mPeP2I+SXT/ns7Yv+dXkasZX+1R4Ijg4k0AIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkU1vtAYDK/bbYGxERr+7orPIkfe3+bSHW/yrihF+8FQ0TctUeJyIiNu/qrvYIcEQRFjAKbfmfF8v/970NVZ6kP7Xxnc3rqj3EQRpyftzBcHCkwSh04ewTIiJi+nETY0K2psrT/K9Nb3TGZx7bEF/62Okx88Tmao9T1pCrjWmTGqo9BhwRhAWMQsc01MWfLJxa7TEOsm/fvoiImD65IeacNHLCAhg+Lt4EAJIRFgBAMsICAEimorBobW2NBQsWRGNjYxx33HFx+eWXx6ZNm4ZqNgBglKkoLNasWRO33HJLrF27Ntra2qJYLMaFF14Yu3fvHqr5AIBRpKLfCnnmmWf6PF65cmUcd9xx0d7eHh/60IeSDgYAjD6H9eumnZ2/u+vfMcccc8h1CoVCFAqF8uN8Ph8REcViMYrF4uHsHhhhDvy66b59+xzfMMYM9JgedFjs378/br/99jjnnHNizpw5h1yvtbU1li1bdtDyVatWRX19/WB3D4xA27sjImpj7dq1sePVak8DpNTT0zOg9TKlUqk0mB3cdNNN8fTTT8fzzz8fJ5988iHX6++MRUtLS3R0dERTU9Ngdg2MUD/d9pv42DfXx2M3zo+5Uw99JhMYffL5fEyaNCk6Ozvf9fV7UGcsPvWpT8UPfvCDeO655941KiIicrlc5HIHfxhRNpuNbDY7mN0DI1RtbW35q+MbxpaBHtMVhUWpVIpPf/rT8cQTT8Tq1atj2rRpgxoOABibKgqLW265JR566KH453/+52hsbIw333wzIiKam5tjwoQJQzIgADB6VHQfixUrVkRnZ2ecd955ceKJJ5b/PPLII0M1HwAwilT8VggAwKH4rBAAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASKa22gMA1dXT0xMbN25M8lyb3ng7Cm9ujv98dULs//VRh/18s2bNivr6+sMfDBg2wgKOcBs3box58+Ylfc6PfzvN87S3t8dZZ52V5smAYSEs4Ag3a9asaG9vT/Jc3b8txA//5cX4yOJFMXFC7rCfb9asWQmmAoaTsIAjXH19fbKzAsViMd7q2BWLFs6PbDab5DmB0cXFmwBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJBMxWHx3HPPxWWXXRZTpkyJTCYTTz755BCMBQCMRhWHxe7du2Pu3Lnxta99bSjmAQBGsdpKN7jkkkvikksuGYpZAIBRzjUWAEAyFZ+xqFShUIhCoVB+nM/nIyKiWCxGsVgc6t0Dw+jAMe3YhrFnoMf1kIdFa2trLFu27KDlq1ativr6+qHePVAFbW1t1R4BSKynp2dA62VKpVJpsDvJZDLxxBNPxOWXX37Idfo7Y9HS0hIdHR3R1NQ02F0DI1CxWIy2trZYsmRJZLPZao8DJJTP52PSpEnR2dn5rq/fQ37GIpfLRS6XO2h5Npv1gwfGKMc3jD0DPaYrDovu7u7YvHlz+fHWrVvjlVdeiWOOOSamTp1a6dMBAGNIxWGxfv36WLx4cfnx0qVLIyLiuuuui5UrVyYbDAAYfSoOi/POOy8O47IMAGAMcx8LACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIyyAJNrb26Ouri4uv/zyqKuri/b29mqPBFTBoMLia1/7Wvze7/1ejB8/Ps4+++x46aWXUs8FjCKZTCbmz5/fZ9n8+fMjk8lUaSKgWioOi0ceeSSWLl0an/vc5+Lll1+OuXPnxkUXXRS7du0aivmAEe6d8ZDJZOLiiy8+aBlw5Kg4LJYvXx433nhj3HDDDXHaaafF17/+9aivr49vfetbQzEfMIK98+2OLVu2RKFQiE9+8pNRKBRiy5Yt/a4HjG21lay8d+/eaG9vjzvvvLO8bNy4cXHBBRfEiy++2O82hUIhCoVC+XE+n4+IiGKxGMVicTAzAyPEgbc/MplMtLS0lI/pYrEYLS0tkclkolQqxfz582Pv3r3VHBU4TAN9za4oLDo6OqK3tzeOP/74PsuPP/742LhxY7/btLa2xrJlyw5avmrVqqivr69k98AIddFFF8VTTz1VftzW1hYREYsXL45nn302IqLP94HRp6enZ0DrZUqlUmmgT7pz58446aST4oUXXohFixaVl3/2s5+NNWvWxL//+78ftE1/ZyxaWlqio6MjmpqaBrprYASqq6uLiN+dsSgUClEsFqOtrS2WLFkS2Ww2crlcHPgR44wFjG75fD4mTZoUnZ2d7/r6XdEZi0mTJkVNTU388pe/7LP8l7/8ZZxwwgn9bpPL5SKXyx20PJvNRjabrWT3wAizfv36mD9/fpRKpdi+fXu0tLRExO+O7+3bt5ejYv369Y53GOUGegxXdPFmXV1dzJs3L37yk5+Ul+3fvz9+8pOf9DmDARwZ5s2bV/779OnTI5fLxZe//OXI5XIxffr0ftcDxraKzlhERCxdujSuu+66mD9/fixcuDDuu+++2L17d9xwww1DMR8wwpVKpfKvlJZKpfI1Fe/8PnDkqDgsrrzyyvjVr34Vf/mXfxlvvvlmnHnmmfHMM88cdEEncOQolUrR3t7e5yZZ69evd6YCjkAVXbyZQj6fj+bm5ve8+AMYfYrFYjz11FPx4Q9/2DUVMMYM9PXbZ4UAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJVHxL78N14Eaf+Xx+uHcNDLFisRg9PT2Rz+fdeRPGmAOv2+91w+5hD4uurq6IiPLHKwMAo0dXV1c0Nzcf8vvD/lkh+/fvj507d0ZjY2P5ExGBsSGfz0dLS0ts377dZwHBGFMqlaKrqyumTJkS48Yd+kqKYQ8LYOzyIYOAizcBgGSEBQCQjLAAksnlcvG5z30ucrlctUcBqsQ1FgBAMs5YAADJCAsAIBlhAQAkIywAgGSEBdCv66+/PjKZzEF/Nm/eXO3RgBFs2D8rBBg9Lr744njwwQf7LJs8eXJFz9Hb2xuZTOZdbwEMjB2OdOCQcrlcnHDCCX3+3H///XH66adHQ0NDtLS0xM033xzd3d3lbVauXBlHHXVUfP/734/TTjstcrlcbNu2LQqFQtxxxx1x0kknRUNDQ5x99tmxevXq6v3jgCEhLICKjBs3Lr785S/Ha6+9Ft/+9rfj2Wefjc9+9rN91unp6Yl77rknHnjggXjttdfiuOOOi0996lPx4osvxsMPPxz/8R//EVdccUVcfPHF8frrr1fpXwIMBTfIAvp1/fXXx3e/+90YP358edkll1wSjz76aJ/1HnvssfjkJz8ZHR0dEfG7MxY33HBDvPLKKzF37tyIiNi2bVv8/u//fmzbti2mTJlS3vaCCy6IhQsXxhe+8IVh+BcBw8E1FsAhLV68OFasWFF+3NDQED/+8Y+jtbU1Nm7cGPl8Pvbt2xd79uyJnp6eqK+vj4iIurq6OOOMM8rbbdiwIXp7e+PUU0/t8/yFQiGOPfbY4fnHAMNCWACH1NDQEDNmzCg//vnPfx6XXnpp3HTTTfH5z38+jjnmmHj++efjE5/4ROzdu7ccFhMmTIhMJlPerru7O2pqaqK9vT1qamr67GPixInD848BhoWwAAasvb099u/fH1/60pfKv+XxT//0T++53R/8wR9Eb29v7Nq1K84999yhHhOoIhdvAgM2Y8aMKBaL8ZWvfCV+9rOfxXe+8534+te//p7bnXrqqXH11VfHtddeG9/73vdi69at8dJLL0Vra2v88Ic/HIbJgeEiLIABmzt3bixfvjzuueeemDNnTvzDP/xDtLa2DmjbBx98MK699tr4zGc+EzNnzozLL7881q1bF1OnTh3iqYHh5LdCAIBknLEAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMn8f/U94K2RUGRyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fare: 0.252508733003725, 5.407363458970095\n"
          ]
        }
      ],
      "source": [
        "for column in ['Age', 'Fare']:\n",
        "    df.boxplot(column)\n",
        "    plt.show()\n",
        "    IQR = df[column].quantile(0.75) - df[column].quantile(0.25)\n",
        "    lower_boundary = df[column].quantile(0.25) - (IQR * 1.5)\n",
        "    upper_boundary = df[column].quantile(0.75) + (IQR * 1.5)\n",
        "    print(column + \": \" + str(lower_boundary) + \", \" + str(upper_boundary))\n",
        "    df.loc[df[column] >= upper_boundary, column] = upper_boundary\n",
        "    df.loc[df[column] <= lower_boundary, column] = lower_boundary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9vrVwXgGUPz"
      },
      "source": [
        "# 9- Model construction: Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "Vospuu4zGUP0",
        "outputId": "80888f71-0837-43c7-a9d5-aca46e8b5dd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass  Sex   Age      Fare  Family  Name_Master  Name_Miss  \\\n",
              "0       0.0       3    1  22.0  2.110213       1            0          0   \n",
              "1       1.0       1    0  38.0  4.280593       1            0          0   \n",
              "2       1.0       3    0  26.0  2.188856       0            0          1   \n",
              "3       1.0       1    0  35.0  3.990834       1            0          0   \n",
              "4       0.0       3    1  35.0  2.202765       0            0          0   \n",
              "\n",
              "   Name_Mr  Name_Mrs  ...  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
              "0        1         0  ...        0        0        0        0        0   \n",
              "1        0         1  ...        1        0        0        0        0   \n",
              "2        0         0  ...        0        0        0        0        0   \n",
              "3        0         1  ...        1        0        0        0        0   \n",
              "4        1         0  ...        0        0        0        0        0   \n",
              "\n",
              "   Cabin_T  Cabin_U  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0        0        1           0           0           1  \n",
              "1        0        0           1           0           0  \n",
              "2        0        1           0           0           1  \n",
              "3        0        0           0           0           1  \n",
              "4        0        1           0           0           1  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-741d6be5-75b2-44bf-a9da-880768f2b2fa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Family</th>\n",
              "      <th>Name_Master</th>\n",
              "      <th>Name_Miss</th>\n",
              "      <th>Name_Mr</th>\n",
              "      <th>Name_Mrs</th>\n",
              "      <th>...</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_T</th>\n",
              "      <th>Cabin_U</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2.110213</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>4.280593</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2.188856</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>3.990834</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>2.202765</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-741d6be5-75b2-44bf-a9da-880768f2b2fa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-741d6be5-75b2-44bf-a9da-880768f2b2fa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-741d6be5-75b2-44bf-a9da-880768f2b2fa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d2610952-e138-4153-80b0-956d2007136a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d2610952-e138-4153-80b0-956d2007136a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2610952-e138-4153-80b0-956d2007136a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfemF7ghGUP0"
      },
      "source": [
        "## Train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UZYxLQ7OGUP0"
      },
      "outputs": [],
      "source": [
        "# Check that there are no duplicates before splitting\n",
        "train, test = df[:len(df_train)], df[len(df_train):]\n",
        "y_train = train['Survived']\n",
        "X_train = train.drop(['Survived'], axis=1)\n",
        "X_test = test.drop('Survived', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13r6fqlQGUP0"
      },
      "source": [
        "## Tune model hyperparameters with Bayesian search"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RS-kCsKGUP0",
        "outputId": "d3801dfd-ad74-4344-c410-c0788f18f5e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
            "0.826762833825551\n",
            "OrderedDict([('colsample_bytree', 0.8), ('eval_metric', 'logloss'), ('gamma', 1.5), ('learning_rate', 0.1), ('max_depth', 9), ('min_child_weight', 1), ('n_estimators', 100), ('nthread', 4), ('objective', 'binary:logistic'), ('subsample', 0.7), ('use_label_encoder', False)])\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric='logloss',\n",
            "              feature_types=None, gamma=1.5, grow_policy=None,\n",
            "              importance_type=None, interaction_constraints=None,\n",
            "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
            "              max_cat_to_onehot=None, max_delta_step=None, max_depth=9,\n",
            "              max_leaves=None, min_child_weight=1, missing=nan,\n",
            "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
            "              n_jobs=None, nthread=4, num_parallel_tree=None, ...)\n"
          ]
        }
      ],
      "source": [
        "# define a search space\n",
        "params = {\n",
        "    'nthread': [4],\n",
        "    'objective': ['binary:logistic'],\n",
        "    'n_estimators': [15, 30, 50, 70, 100],\n",
        "    'max_depth': [3, 6, 9],\n",
        "    'learning_rate': [0.001, 0.01, 0.1],\n",
        "    'min_child_weight': [1, 5, 10],\n",
        "    'gamma': [0.5, 1, 1.5, 2, 5],\n",
        "    'subsample': [0.5, 0.7, 1.0],\n",
        "    'colsample_bytree': [0.6, 0.8, 1.0],\n",
        "    'use_label_encoder': [False],\n",
        "    'eval_metric': ['logloss']}\n",
        "\n",
        "# instantiate the search, CV: the number of folds for the cross-validation, n_iter controls the number of searches\n",
        "Bsearch = BayesSearchCV(XGBClassifier(), params, cv=5, scoring='f1_weighted', n_iter=5, random_state=5, verbose=True)\n",
        "\n",
        "# fit the search with data\n",
        "Bsearch.fit(X_train, y_train)\n",
        "\n",
        "# examine the best model\n",
        "print(Bsearch.best_score_)\n",
        "print(Bsearch.best_params_)\n",
        "print(Bsearch.best_estimator_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2EhyFtBGUP0"
      },
      "source": [
        "## Learning curve for various training set sizes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "5Ud0tmprGUP0",
        "outputId": "0459c2a3-b488-4d56-b462-b27c8c5c58be"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create CV training and test scores for various training set sizes (40 different sizes)\n",
        "train_sizes, train_scores, test_scores = learning_curve(Bsearch.best_estimator_, X_train, y_train, train_sizes=np.linspace(0.01, 1.0, 40), cv=5, scoring='f1_weighted')\n",
        "\n",
        "# Create means and standard deviations of training set scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Create means and standard deviations of test set scores\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Draw lines\n",
        "plt.plot(train_sizes, train_mean, color='r', label='Training score')\n",
        "plt.plot(train_sizes, test_mean, color='g', label='Cross-validation score')\n",
        "\n",
        "# Draw bands\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color='#DDDDDD')\n",
        "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color='#DDDDDD')\n",
        "\n",
        "# Create plot\n",
        "plt.title('Learning Curve')\n",
        "plt.xlabel('Training Set Size'), plt.ylabel('F1-Score'), plt.legend(loc='best')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eB13Q5tvGUP0"
      },
      "source": [
        "## Train the model on the train set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yjTsj9IyGUP0"
      },
      "outputs": [],
      "source": [
        "model = Bsearch.best_estimator_.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fkYuoDtGUP0"
      },
      "source": [
        "## Evaluate the model on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3N4BSdgMGUP0"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(X_test).astype(np.int64)\n",
        "df_submission = pd.DataFrame({'PassengerId': df_test['PassengerId'], 'Survived': predictions})\n",
        "df_submission.to_csv('Titanic_predictions.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2W8YfrFAGUP0"
      },
      "source": [
        "## Feature importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "A6BIDKDfGUP0",
        "outputId": "e58fe0c7-4799-4353-a3eb-6e3635f0a3b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feat_importances = pd.Series(np.round(model.feature_importances_, 2), index=X_train.columns)\n",
        "colors = [\"C\" + str(count) for count in range(0, len(feat_importances))]\n",
        "feat_importances.nlargest(30).plot(kind='barh', title='Feature importance', color=colors, figsize=(13, 8))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}