u6_titanic_eda.ipynb

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        "#  **EDA y Machine Learning**\n",
        "\n",
        "Tenemos un Dataset con los pasajeros del Titanic, indicando para cada uno una serie de atributos.. y nos solicitan: Construir un modelo que nos permita estimar la posibilidad de sobrevivir, dados una serie de atributos del pasajero.\n",
        "\n",
        "https://www.kaggle.com/c/titanic/data"
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        "\n",
        "# 1. Comprensión del Negocio\n",
        "\n",
        "\n",
        "**Objetivo del Negocio:**\n",
        "\n",
        "Predecir si los pasajeros sobrevivieron o no al desastre del Titanic basándonos en características.\n",
        "\n",
        "\n",
        "**Objetivos del Proyecto**\n",
        "\n",
        "Construir un modelo que nos permita estimar la posibilidad de sobrevivir, dados una serie de atributos del pasajero\n",
        "\n"
      ]
    },
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        "# 2. Comprensión de los Datos\n",
        "\n",
        "**Pasos**\n",
        "\n",
        "*   Obtener los Datos: Obtener los datos desde Kaggle.\n",
        "*   Análisis Exploratorio de Datos (EDA): Comprender las características principales del dataset y su estructura.\n"
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        "# importamos librerias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "sns.set_style(\"whitegrid\")\n"
      ]
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      "source": [
        "# levantamos el csv\n",
        "titanic = pd.read_csv(\"/content/train.csv\")"
      ]
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
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              "3            4         1       1   \n",
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              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
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              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.354126134736337,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "titanic.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hpm_GwtM7Bng",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00e0ef0c-7bf8-422f-cfec-921fe196585d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "titanic.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "C0jgPdcP7Fcm",
        "outputId": "e94b8a84-f8aa-4110-f02f-488d99cfda26"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "titanic.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLQmUms-7b3R",
        "outputId": "28a5656c-346c-4e6a-c073-552b65a235cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ],
      "source": [
        "titanic.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wh0dMDyorTUx"
      },
      "source": [
        "**Comprensión de los datos**\n",
        "\n",
        "\n",
        "*   Survived: Indicador de supervivencia (0 = No, 1 = Sí).\n",
        "*   Pclass: Clase del billete (1 = 1ª clase, 2 = 2ª clase, 3 = 3ª clase).\n",
        "*   Name: Nombre del pasajero (se elimina porque no es relevante para el modelo).\n",
        "*   Sex: Sexo del pasajero (codificado como 0 o 1).\n",
        "*   Age: Edad del pasajero.\n",
        "*   SibSp: Número de hermanos/cónyuges a bordo del Titanic.\n",
        "*   Parch: Número de padres/hijos a bordo del Titanic.\n",
        "*   Ticket: Número del billete (se elimina porque no es relevante para el modelo).\n",
        "*   Fare: Precio del billete.\n",
        "*   Cabin: Número de cabina (se elimina por tener demasiados valores nulos).\n",
        "*   Embarked: Puerto de embarque (C = Cherbourg; Q = Queenstown; S = Southampton, codificado como 0 o 1).\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOWT2Hub7IE4"
      },
      "source": [
        "Resumiendo:\n",
        "* Tienen 891 registros\n",
        "* Tienen 12 columnas\n",
        "* Hay columnas que tienen nulls\n",
        "* Hay 2 columnas flotantes\n",
        "* Hay 5 columnas enteras\n",
        "* Hay 5 columnas object. Que parece tienen datos de cadenas de texto"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW2PKHYm8AoH"
      },
      "source": [
        "### Puedo averiguar algo de las columnas con datos categoricos?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "I89BFBJz8CRK",
        "outputId": "2e90dfe7-780f-4586-e481-4e9e2c9aaa0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    891\n",
              "Survived         2\n",
              "Pclass           3\n",
              "Name           891\n",
              "Sex              2\n",
              "Age             88\n",
              "SibSp            7\n",
              "Parch            7\n",
              "Ticket         681\n",
              "Fare           248\n",
              "Cabin          147\n",
              "Embarked         3\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "titanic.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "78bdGxQB8YV3",
        "outputId": "a3da879c-1ccc-4014-fd0c-611eef7c3e65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      2\n",
              "Sex           2\n",
              "Cabin       147\n",
              "Pclass        3\n",
              "Ticket      681\n",
              "Embarked      3\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "categoricos = titanic[[\"Survived\", \"Sex\", \"Cabin\", \"Pclass\", \"Ticket\", \"Embarked\"]]\n",
        "categoricos.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSTPQrUYzwbH"
      },
      "source": [
        "# 3. Preparación de los Datos\n",
        "\n",
        "\n",
        "**Pasos:**\n",
        "\n",
        "*    Limpieza de Datos: Manejo de valores nulos y eliminación de columnas irrelevantes.\n",
        "*    Transformación de Datos: Codificación de variables categóricas y normalización de datos si es necesario.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcONkV2N8f_Z"
      },
      "source": [
        "Tengo datos nulos o NA?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "vaTTO7pZ8ks0",
        "outputId": "ec5df0e7-7503-4e0b-dfcf-c36ba8dbd970"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "pd.isnull(titanic).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6PwtrCT87c3"
      },
      "source": [
        "## Age\n",
        "\n",
        "Algunas opciones:\n",
        "\n",
        "* Podemos remplazarla por un valor fijo\n",
        "* Podemos reemplazarla por una estadistica ej: media\n",
        "* Podemos reemplazarla por una estadistica ej: mediana u otras\n",
        "* Podemos reemplazarla por el resultado de una funcion a construida"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8lhCxGV9VgW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "f16480ce-6917-425e-b047-66b82c50f009"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7972b246a080>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "ages = titanic[[\"Age\"]]\n",
        "sns.histplot(ages[\"Age\"], kde=True)\n",
        "\n",
        "p = ages[\"Age\"].mean()\n",
        "m = ages[\"Age\"].median()\n",
        "q25 = ages[\"Age\"].quantile([.25]).loc[0.25]\n",
        "\n",
        "sns.histplot(ages[\"Age\"], kde=True)\n",
        "plt.axvline(p, color=\"red\")\n",
        "plt.axvline(m, color=\"green\")\n",
        "plt.axvline(q25, color=\"orange\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "_1nNHHYE9wkg",
        "outputId": "a4dbc1cf-5351-472b-fb38-26422068197e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-77-d6517595b502>:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  titanic[\"Age\"].fillna(mage, inplace = True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "\n",
        "#completamos los valores faltantes con la mediana\n",
        "mage = titanic[\"Age\"].median()\n",
        "titanic[\"Age\"].fillna(mage, inplace = True)\n",
        "\n",
        "pd.isnull(titanic).sum()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3H0mvGq-A7_"
      },
      "source": [
        "## Embarked"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "aov3TBNw9-WB",
        "outputId": "8d0aaf22-895c-446b-e0fd-1679e163bcb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Embarked', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "emb = titanic[[\"Embarked\"]]\n",
        "sns.histplot(emb[\"Embarked\"], kde=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SZcHA2Rf-Zrb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb80dae5-2c98-48cb-cbb6-9d19a05f1416"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-79-84275a798562>:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  titanic[\"Embarked\"].fillna(\"S\", inplace=True)\n"
          ]
        }
      ],
      "source": [
        "# reemplazamos nulos por S\n",
        "titanic[\"Embarked\"].fillna(\"S\", inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "HZhgrSjx-f3k",
        "outputId": "08d69100-c65a-4dcc-e4dc-dc1f4e700193"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "pd.isnull(titanic).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4-OBEpd-8ZG"
      },
      "source": [
        "## Hay duplicados? Como verificamos?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "S9Z05Cco-_NH",
        "outputId": "aac5ec61-d37d-4233-87f1-774575eaa99c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "886    False\n",
              "887    False\n",
              "888    False\n",
              "889    False\n",
              "890    False\n",
              "Length: 891, dtype: bool"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> bool</label>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "titanic.duplicated()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rtjlx5ZY_BZU",
        "outputId": "4d8c2909-1518-4a2b-cd4c-8f0b69098e6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "titanic.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IpB1FnSf_Hxm"
      },
      "outputs": [],
      "source": [
        "titanic.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGPVWjF-_QYI"
      },
      "source": [
        "**Análisis de cada variable**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjWheTgC_Znq"
      },
      "source": [
        "### Survived"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftNe-1ZQ_cDu",
        "outputId": "abcf8fb5-85a0-4ac2-9553-3db170bd3913"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "# Que datos hay?\n",
        "titanic[\"Survived\"].unique()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3wdYy7B_i1V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "05fc4fd4-e5f1-4056-8efc-93d86edaa695"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    0.616162\n",
              "1    0.383838\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.616162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.383838</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "# En que proporción?\n",
        "titanic[\"Survived\"].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OkKiTq8Q_pfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "e53335c6-edce-46a9-d782-2a0f14fdd519"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Para graficar\n",
        "psurvived = titanic[\"Survived\"].value_counts(normalize=True).sort_index()\n",
        "\n",
        "plt.pie(psurvived, labels=[\"No Sobrevivio\", \"Sobrevivio\"], autopct='%.0f%%');\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoSyHESHAOS_"
      },
      "source": [
        "## Pclass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KZSA2NpjAQWI"
      },
      "outputs": [],
      "source": [
        "# que valores hay?\n",
        "titanic[\"Pclass\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6e3vhzwxATzx"
      },
      "outputs": [],
      "source": [
        "# cuantos de cada clase\n",
        "titanic[\"Pclass\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XZVbwkBCAcKB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "ce6f9826-109b-4863-9cd0-1f52bd0beb00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# en que proporción?\n",
        "titanic[\"Pclass\"].value_counts(normalize=True).sort_index()\n",
        "\n",
        "ppc = titanic[\"Pclass\"].value_counts(normalize=True).sort_index()\n",
        "# 1 = 1st, 2 = 2nd, 3 = 3rd\n",
        "plt.pie(ppc, labels=[\"1st\", \"2nd\", \"3rd\"], autopct='%.0f%%');\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xKmAX1XRAv0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "518f04fc-5e05-4bdc-ae85-16f0cc61494e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# que pasa con los sobrevivientes y la clase?\n",
        "\n",
        "sns.barplot(x=\"Pclass\", y=\"Survived\", data=titanic)\n",
        "plt.title(\"Distribución de sobrevivientes por Clase\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xNq3rsJEA4Q8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d2f4ea4a-80b4-4c1c-c0cb-ac35a64bd8ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(data=titanic, x=\"Pclass\", hue=\"Survived\" )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DxOXysssdSl2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "33135cae-5c8b-4ee4-92f8-08931e942319"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Survived\n",
              "0       1  0.629630\n",
              "1       2  0.472826\n",
              "2       3  0.242363"
            ],
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              "\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-76417369-bdb8-4cd5-942e-c8b662c35a92 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic[['Pclass', 'Survived']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19479759330588634,\n        \"min\": 0.24236252545824846,\n        \"max\": 0.6296296296296297,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6296296296296297,\n          0.47282608695652173,\n          0.24236252545824846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "# nuericamente\n",
        "titanic[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNniFqyPBUvH"
      },
      "source": [
        "## Sex (Género)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U2wCWvwYBmRW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "bce1f974-85e0-4e01-b20a-036ab29b5ab2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x=\"Sex\", y=\"Survived\", data=titanic)\n",
        "plt.title(\"Distribución de sobrevivientes por género\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKeLHcdSBxUU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "7104d830-ddc8-4ee9-e896-16ba33518cc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(data=titanic, x=\"Sex\", hue=\"Survived\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I5ymfwm-CGAH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f62aca78-a526-4c6f-e741-66ee9b29f0c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total sobrevivientes: 342\n",
            "Proporción de mujeres que sobrevivieron:\n",
            "0.6812865497076024\n",
            "Proporción de hombres que sobrevivieron:\n",
            "0.31871345029239767\n"
          ]
        }
      ],
      "source": [
        "tot_sobrevivientes_mujeres = titanic[titanic.Sex == \"female\"][\"Survived\"].sum()\n",
        "tot_sobrevivientes_hombres = titanic[titanic.Sex == \"male\"][\"Survived\"].sum()\n",
        "\n",
        "print(\"Total sobrevivientes: \" + str((tot_sobrevivientes_mujeres + tot_sobrevivientes_hombres)))\n",
        "print(\"Proporción de mujeres que sobrevivieron:\")\n",
        "print(tot_sobrevivientes_mujeres/(tot_sobrevivientes_mujeres + tot_sobrevivientes_hombres))\n",
        "print(\"Proporción de hombres que sobrevivieron:\")\n",
        "print(tot_sobrevivientes_hombres/(tot_sobrevivientes_mujeres + tot_sobrevivientes_hombres))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2NHj2ZLjglbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "8b61376a-d118-4a3d-85a1-421ba54df191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(data=titanic, x=\"Survived\", hue=\"Sex\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXRfJtJuCrbI"
      },
      "source": [
        "## Fare\n",
        "La tarifa o valor del Ticket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w0nmm0aciVbC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d247385d-de97-416e-d2f9-a70319913024"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Fare\n",
              "count  891.000000\n",
              "mean    32.204208\n",
              "std     49.693429\n",
              "min      0.000000\n",
              "25%      7.910400\n",
              "50%     14.454200\n",
              "75%     31.000000\n",
              "max    512.329200"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f52700f8-8f44-413c-b722-9a2cd9831bf8\" 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>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\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-f52700f8-8f44-413c-b722-9a2cd9831bf8')\"\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-f52700f8-8f44-413c-b722-9a2cd9831bf8 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-f52700f8-8f44-413c-b722-9a2cd9831bf8');\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-dd2cd01b-1669-45dc-ac57-a0cc94f438e5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd2cd01b-1669-45dc-ac57-a0cc94f438e5')\"\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-dd2cd01b-1669-45dc-ac57-a0cc94f438e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic[[\\\"Fare\\\"]]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228577,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "# como son los valores?\n",
        "titanic[[\"Fare\"]].describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgSq9m4mCvBk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "0ea422b3-f549-4917-f115-93770410386a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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VHx+vuXPnaunSpQP7rgAAwKDmsizLcrqJcAWDQfvSZb6jBiIL4xuIXOGMb+7FAwAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAjHH+/HlNnjxZX/va1zR58mSdP3/e6ZYAOCTW6QYAQJJGjBihhoYG+/nx48eVlpam4cOH69y5cw52BsAJzKAAcNzl4WTatGnatGmTpk2bJklqaGjQiBEjnGwPgAMIKAAcdf78eTuc+Hw+/eY3v9HUqVP1m9/8Rj6fT9KlkMLXPUB0IaAAcNSsWbMkSdOnT1dSUlLIuaSkJE2dOjWkDkB0IKAAcFR9fb0k6amnnrri+bVr14bUAYgOBBQAjho5cqQk6Z//+Z+veP4HP/hBSB2A6EBAAeCovXv3SpIOHDigtra2kHNtbW06dOhQSB2A6EBAAeCoYcOGafjw4ZKk5ORkzZgxQ/v379eMGTOUnJwsSRo+fLiGDRvmZJsArjH2QQHguHPnztmXGh8+fFiHDx+2z7EPChCdmEEBYIRz587p7NmzGj58uOLi4jR8+HCdPXuWcAJEKQIKACOUlJToxhtvVENDg7q7u9XQ0KAbb7xRJSUlTrcGwAEEFACOKykpUXl5udLS0lRZWandu3ersrJSaWlpKi8vJ6QAUchlWZbldBPhCgaDqq6ultfrldvtdrodAP3g9/uVmJiotLQ0nTlzRi6Xyx7flmVp1KhRamlpUXt7uzwej9PtAuiHcD6/mUEB4KhNmzYpEAho3bp1io0NXbcfGxurtWvXKhAIaNOmTQ51CMAJBBQAjjp9+rQk6a677rri+d7jvXUAogMBBYCjMjMzJUm7du264vne4711AKIDa1AAOIo1KED0YA0KgEHD4/Fo2bJlamho0KhRo/TSSy+pqalJL730kkaNGqWGhgYtW7aMcAJEGXaSBeC4Z599VpL0ox/9SP/0T/9kH4+NjVVxcbF9HkD04CseAMbw+/16/vnndfDgQU2bNk1Llixh5gSIIOF8fjODAsAYHo9Hjz76KP8AAcAaFAAAYB4CCgBjtLW1ae7cuZo/f77mzp2rtrY2p1sC4BC+4gFghKlTp+rw4cP289OnTys5OVm33XabDh065GBnAJzADAoAx/WGE5fLpcLCQm3btk2FhYVyuVw6fPiwpk6d6nSLAK4xAgoAR7W1tdnhpKOjQy+//LKys7P18ssvq6Ojww4pfN0DRBcCCgBHffvb35YkFRYWKi4uTnv27NHu3bu1Z88excXF6b777gupAxAdWIMCwFG9NwH0er3KysrSH//4R/vc6NGj9fDDD2vr1q3cLBCIMsygAHBU700AH3vsMeXk5Gjfvn167733tG/fPuXk5Ki4uDikDkB0YCdZAI66cOGCUlNTJUnt7e267rrr7PHd1dWlxMRESVJra6tSUlIc7BRAf3GzQACDRlVVlf04KSlJCxcu1MmTJ7Vw4UIlJSVdsQ5A5COgAHDU2bNnJUl5eXmyLMu+xHjbtm2yLEt5eXkhdQCiAwEFgKNuuOEGSdKLL74on8+n2bNnKzMzU7Nnz5bP59MLL7wQUgcgOhBQADhq5syZGj16tNavX6+enp6Qcz09PXr66ac1ZswYzZw506EOATiBy4wBOMrtduu5557TvHnzQhbBnj592n6+Y8cOFsQDUYYZFACOKysr69d5AJGHGRQAjrp8q3ufz6cDBw7owIEDmj59uqZPn67k5GR7q/vLr+oBENmYQQHgqMu3uk9MTNRXvvIV/cM//IO+8pWvKDExka3ugShFQAHgqN4t7FesWHHF88uXLw+pAxAdCCgAHNW7hf2GDRuueP6HP/xhSB2A6MBW9wAc1dbWpuTkZLlcLnV0dCguLs4e393d3UpISJBlWfL5fKxBAQa5L2yr+5/+9KeaN2+ecnNzNWPGDD300EOqq6sLqenq6lJpaammTZum3NxcLVmyRM3NzSE19fX1evDBBzVlyhTNmDFDzzzzjAKBQDitAIgQSUlJuu2222RZlhISEkK2uu8NJ7fddhvhBIgyYQWUQ4cOacGCBXr99de1ZcsWBQIBLVq0SB0dHXbN+vXr9e6776qiokKvvvqqGhsbVVRUZJ8PBoNavHixuru7tX37dpWVlWnnzp3auHHjwL0rAIPKoUOH7JDyp1vd33bbbTp06JDTLQK41qx+aGlpsbKzs61Dhw5ZlmVZFy9etG655Rbrl7/8pV1TW1trZWdnW1VVVZZlWdaePXus8ePHW01NTXbNtm3brLy8PKurq6tPvzcQCFi//e1vrUAg0J/2ARjG5/NZs2fPtjIzM63Zs2dbPp/P6ZYADKBwPr/7tQ+Kz+eTJHu3x6NHj6q7u1v5+fl2TWZmpkaOHGl/51RdXa3s7Gylp6fbNQUFBVqzZo1qa2s1ceLEPv/+YDDYn/YBGCY+Pl5vvPGGampqlJOTI7fbzTgHIkg44/mqA0pPT4/Wr1+vvLw8ZWdnS5Kam5sVFxenoUOHhtSmpaWpqanJrrk8nEiyn/fW9FVNTc3Vtg/AcIxvILpddUApLS3VqVOntG3btoHsJyy9/8ICEDmCwWDIDAqAyNE7vvviqgLK2rVrtWfPHr322msaMWKEfTw9PV3d3d26ePFiyCxKS0uLMjIy7JojR46EvF7vVT69NX3ldrv5AwZEKMY3EN3CuorHsiytXbtW77zzjl555RXdeOONIecnTZqkuLg47d+/3z5WV1en+vp6eb1eSZLX69UHH3yglpYWu+b9999XUlKSsrKy+vFWAABApAhrBqW0tFS7du3Spk2blJiYaK8ZSU5O1pAhQ5ScnKx58+aprKxMKSkpSkpK0rp165Sbm2sHlIKCAmVlZamkpETFxcVqampSRUWFFixYII/HM+BvEAAADD5hBZR///d/l/TZm3Y9/fTTuueeeyRJq1atUkxMjJYuXSq/36+CggKtXr3arnW73aqsrNSaNWs0f/58xcfHa+7cuVq6dGl/3wsAAIgQbHUPwCiMbyByfWFb3QMAAFwLBBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAME5YNwsEgC+S3+/X888/r4MHD2ratGlasmQJdzkHohQzKACMUFJSosTERK1YsUL/8R//oRUrVigxMVElJSVOtwbAAQQUAI4rKSlReXm50tLSVFlZqd27d6uyslJpaWkqLy8npABRyGVZluV0E+HiduxA5PD7/UpMTFRaWprOnDkjl8tlj2/LsjRq1Ci1tLSovb2dr3uAQS6cz29mUAA4atOmTQoEAlq3bp1iY0OXxcXGxmrt2rUKBALatGmTQx0CcAIBBYCjTp8+LUm66667rni+93hvHYDoQEAB4KjMzExJ0q5du654vvd4bx2A6MAaFACOYg0KED3C+fxmHxQAjvJ4PFq2bJnKy8v1V3/1Vxo/frx8Pp+Sk5N18uRJNTY2qri4mHACRBlmUAAYISsr64rrTDIzM1VbW+tARwAGGlfxABhU5syZ82cXwZ4+fVpz5sy5tg0BcBwBBYCjOjs79dZbb0mSXC6XCgsLtW3bNhUWFsrlckmS3nrrLXV2djrZJoBrjIACwFFLliyxH7e2tsrr9Wrnzp3yer1qbW29Yh2AyMcaFACOysjIUHNzszIzM/Xhhx8qEAjY52JjY/XlL39ZdXV1Sk9PV1NTk4OdAugv1qAAGDR6enokXVprcqV78dTV1YXUAYgOBBQAjpo+fbr9uK6uTvfff7/S09N1//332+HkT+sARD4CCgBHzZw5036cmpqqlStX6sMPP9TKlSuVmpp6xToAkY+N2gA46uOPP7Yfd3d3q7y8/C/WAYh8zKAAcFTvPXZGjx59xfM33XRTSB2A6EBAAeCohx56SLGxsers7NQnn3yi2bNnKzMzU7Nnz9Ynn3yiTz/9VLGxsXrooYecbhXANURAAeCo3nvxNDQ0KDMzU8eOHdOFCxd07NgxZWZmqqGhQcuWLeNePECUYR8UAEZITU3VhQsXPnM8JSUlZMM2AIMX+6AAGFSysrKuGE4k6cKFC8rKyrrGHQFwGgEFgKMuXLhg3yjw+uuvD9mo7frrr5d0aRO3PxdgAEQmAgoAR91+++2SLq1F+fjjj0M2avv444/ttSe9dQCiAwEFgKOOHz8uSVq+fLliY0O3ZoqNjdXSpUtD6gBEBwIKAEfFx8dLkg4cOHDF84cOHQqpAxAdCCgAHLVkyRJJ0p49e9TR0RFyrqOjQ++9915IHYDoQEAB4KgVK1bYjxMTE3X77berqqpKt99+uxITE69YByDyEVAAOMrj8ai4uNh+/s477+iBBx7QO++8Yx8rLi5mozYgynCzQACOe/bZZyVJGzZs0OV7R7pcLq1YscI+DyB6MIMCwAh79uzRn25sbVmW9uzZ40xDABxFQAHguKlTp+rw4cNyuVwqLCzUtm3bVFhYKJfLpcOHD2vq1KlOtwjgGiOgAHBUW1ubHU46Ojr08ssvKzs7Wy+//LI6OjrskNLW1uZ0qwCuIQIKAEd9+9vfliQVFhbKsiwVFRXp4YcfVlFRkSzL0n333RdSByA6sEgWgKN678Pz0UcfKSEhwT5+8OBBVVZWatasWSF1AKIDMygAHJWZmSlJ2rt3r+Li4pSZmakvf/nLyszMVFxcnPbu3RtSByA6uKw/XTY/CASDQVVXV8vr9crtdjvdDoB+aGpqsu9a/HkaGxuVkZFxDToC8EUJ5/ObGRQAjiotLR3QOgCRgYACwFF9vUsxdzMGogsBBYCj6urq7Mculyvk3OXPL68DEPkIKAAc9emnn9qPMzIyVFlZqd27d6uysjJkzcnldQAiHwEFgKPi4+Ptx62trTp9+rTa29t1+vRptba2XrEOQORjHxQAjpo4caL++Mc/SpL8fr/Ky8v/bB2A6BH2DMrhw4f1ve99TwUFBRo3bpx+/etfh5y3LEs//vGPVVBQoMmTJ+s73/mO/cenV2trqx577DHl5eXpr//6r7Vq1Sq1t7f3640AGJxuuukm+7HL5dLYsWN1yy23aOzYsSFrUC6vAxD5wg4oHR0dGjdunFavXn3F8y+99JJeffVVrVmzRq+//rri4+O1aNEidXV12TUrVqxQbW2ttmzZosrKSv32t7/VD37wg6t/FwAGrezsbPuxZVk6deqUjh07plOnToXc3fjyOgCRr18btY0bN04vvviivv71r0u69Mdl5syZ+u53v6tFixZJknw+n/Lz81VWVqY777xTp0+f1h133KE33nhDOTk5kqT33ntPDz74oPbu3avhw4f/xd/LRm1A5PD7/UpMTJTH41FHR8dnzickJMjv96u9vV0ej8eBDgEMlHA+vwd0DcqZM2fU1NSk/Px8+1hycrKmTJmiqqoq3XnnnaqqqtLQoUPtcCJJ+fn5iomJ0ZEjR/T3f//3ff59wWBwINsH4AC3261HH31UGzZsUEZGhm644QZduHBBKSkpOnv2rJqamrRixQq53W7GPDDIhTOGBzSgNDU1SZLS0tJCjqelpam5uVmS1NzcrGHDhoU2ERurlJQU++f7qqamph/dAjDFvffeq7ffflvHjx//zN+BiRMn6t5771V1dbUzzQFwxKC+iicnJ4eveIAI8MQTT+j48eNKT09XV1eXOjs7FR8fr+uuu07Hjx/X9u3bVVZW5nSbAPopGAz2eXJhQANK76ZKLS0tITf/amlp0fjx4yVJ6enpOn/+fMjPBQIBXbhwIewbgbndbgIKMMj5/X5VVFTI5XLZM63SpfVrPp9PLpdLFRUVeuqpp1iDAkSRAd2obdSoUcrIyND+/fvtY21tbfqf//kf5ebmSpJyc3N18eJFHT161K45cOCAenp6NHny5IFsB8AgsGnTJgUCAf259fqWZSkQCGjTpk3XuDMATgp7BqW9vV0fffSR/fzMmTM6ceKEUlJSNHLkSC1cuFA/+clPdNNNN2nUqFH68Y9/rOuvv96+0iczM1MzZ87U97//fZWWlqq7u1tPPvmk7rzzzj5dwQMgshw7dsx+nJGRoYkTJ+rixYsaOnRoyJqUy+sARL6wLzM+ePCgFi5c+Jnjc+fOVVlZmSzL0saNG/X666/r4sWLuvXWW7V69WqNGTPGrm1tbdWTTz6p//7v/1ZMTIy+8Y1v6F/+5V+UmJjYpx64zBiIHMOHD1djY+NfrLv++uvV0NBwDToC8EUJ5/O7X/ugOIWAAkSO+Ph4+0aAbrdbw4cP16effqohQ4aooaHBvixxyJAh6uzsdLJVAP3k2D4oABCuIUOG2AElGAyqvr7+z9YBiB7czRiAo8aOHTugdQAiAwEFgKMSEhIGtA5AZCCgAHDUiRMnBrQOQGQgoABwlM/nG9A6AJGBgALAUX29MocreIDoQkABAADGIaAAMIrL5ZLH45HL5XK6FQAOYh8UAEaxLEt+v9/pNgA4jBkUAABgHAIKAEelpqYOaB2AyEBAAeCovq41YU0KEF0IKAAcdeHChQGtAxAZCCgAHNXT0zOgdQAiAwEFgKP6epdi7mYMRBcuMwYwIOrq6tTa2hr2z33961/Xrl277Ofp6eny+/3yeDxqbm4Oqfv9738f9uunpqbq5ptvDvvnADiLgAKg35qbmzV27NgB+Rrm8lByuV27doUEmb5yu906d+6c0tPT+9sagGuIgAKg39LT03Xq1KmrmkHx+/0qKChQMBj8szVut1v79u2Tx+MJ+/VTU1MJJ8AgREABMCD68zXK8uXLVV5eri996Uvq6OhQV1eXrrvuOiUkJOiTTz7R8uXLNX369AHsFoDpCCgAHPfss89Kkn70ox8pEAhIkrq6uhQMBlVcXGyfBxA9uIoHgBGeffZZtbe3a9myZZKkZcuWqb29nXACRCkCCgBjeDwefetb35Ikfetb37qqNScAIgMBBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGCcWKcbAOCsU6dOyefzOd2G7eTJk/Z/3W63w938f8nJyRo7dqzTbQBRg4ACRLFTp04pOzvb6TauaOHChU638BkffPABIQW4RggoQBTrnTl57bXXNGHCBIe7uSQYDOp3v/udbr31VmNmUE6cOKHCwkKjZpqASEdAAaAJEyYoLy/P6TYkXQooMTEx8nq9xgQUANcei2QBAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMZhHxQgyo1Icim+9QOp3pB/r/T0XOrnrEuKMaOn+NYPNCLJ5XQbQFQhoABRbvGtHk14b7H0ntOdXOKWNFGS/q/DjVxmgi79fwJw7RBQgCj309/5Nf8HL2vC+PFOtyJJCvb06A9/+IPGjRsntyEzKCdOntRPn7tP/8fpRoAoQkABoty5NkudqdnSSK/TrVwSDKqzwZJumCIZstV957kenWuznG4DiCoEFAD6/e9/73QLtt6bBfb09BhzL54TJ0443QIQdQgoQBQLBAKSpAceeMDhTgaH5ORkp1sAooajAWXr1q3avHmzmpqaNH78eH3/+9/X5MmTnWwJiCpTp07VwYMHFRtrzr9Vjh07poULF+rnP/+5brnlFqfbsSUnJ2vs2LFOtwFEDcf+Kr399tt6+umnVVpaqilTpuiVV17RokWLtHv3bqWlpTnVFhB1pk6d6nQLIYLBoCRp/PjxysvLc7gbAE5xbIn8li1b9I//+I+aN2+esrKyVFpaqiFDhmjHjh1OtQQAAAzhyAyK3+/XsWPHtHjxYvtYTEyM8vPzVVVV1efX6f2XFgDn1dXVqbW1td+vc/z48ZD/9ldqaqpuvvnmAXktAP0Tzue2IwHlk08+UTAY/MxXOWlpaaqrq+vz69TU1Ax0awCuQmtrq77xjW+op6dnwF7zO9/5zoC8jtvt1q9+9SulpqYOyOsBuDbMWRl3FXJycoy5DBGIdidPnhyQGZSenh5VVVUpNzdXMQOwURszKIA5gsFgnycXHAkoX/rSl+R2u9XS0hJyvKWlRenp6X1+HbfbTUABDDFQV7gEg0HFxsbK6/UyvoEo5sgiWY/Ho1tuuUX79++3j/X09Gj//v3Kzc11oiUAAGAQx77i+e53v6vHH39ckyZN0uTJk/XKK6+os7NT99xzj1MtAQAAQzgWUO644w6dP39eGzduVFNTkyZMmKCf/exnYX3FAwAAIpOji2QLCwtVWFjoZAsAAMBAZtzLHAAA4DIEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOI7uJHu1LMuSdOmupwAiS++4ZnwDkad3XPd+jn+eQRlQenp6JEk1NTUOdwLgi8L4BiJX7+f453FZfYkxhunp6VEgEFBMTIxcLpfT7QAAgD6wLEs9PT2KjY1VTMznrzIZlAEFAABENhbJAgAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAMMLhw4f1ve99TwUFBRo3bpx+/etfO90SAAcRUAAYoaOjQ+PGjdPq1audbgWAAQblzQIBRJ5Zs2Zp1qxZTrcBwBDMoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA5X8QAwQnt7uz766CP7+ZkzZ3TixAmlpKRo5MiRDnYGwAkuy7Isp5sAgIMHD2rhwoWfOT537lyVlZU50BEAJxFQAACAcViDAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBx/h9odkX+dU8ubAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#sns.catplot(titanic[[\"Fare\"]], kind=\"box\")\n",
        "ax = plt.boxplot(titanic[[\"Fare\"]])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2FpjhOiYk_gl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "db66813f-208b-46e5-fae5-eb8d0407039c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-96-293a72c7a7f5>:6: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  titanic[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          FareBand  Survived\n",
              "0   (-0.001, 7.91]  0.197309\n",
              "1   (7.91, 14.454]  0.303571\n",
              "2   (14.454, 31.0]  0.454955\n",
              "3  (31.0, 512.329]  0.581081"
            ],
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              "    }\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-7fee606e-b811-4b16-a723-f05926f3bfa3 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-7fee606e-b811-4b16-a723-f05926f3bfa3');\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-5be398c5-afb4-426c-95b8-133565795928\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5be398c5-afb4-426c-95b8-133565795928')\"\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-5be398c5-afb4-426c-95b8-133565795928 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic[['FareBand', 'Survived']]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"FareBand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"(7.91, 14.454]\",\n          \"(31.0, 512.329]\",\n          \"(-0.001, 7.91]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1685205000908493,\n        \"min\": 0.19730941704035873,\n        \"max\": 0.581081081081081,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.30357142857142855,\n          0.581081081081081,\n          0.19730941704035873\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "#cambiamos el enfoque...\n",
        "# vamos a armar 3 o 4 grupos\n",
        "titanic['FareBand'] = pd.qcut(titanic['Fare'], 4)\n",
        "\n",
        "#podemos ver el promedio y la cantidad (como funciona pd.qcut())\n",
        "titanic[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuMJ_f9dD1mY"
      },
      "source": [
        "## Age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZchOn3v4D5-b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "outputId": "6c838f3c-a9b5-421c-be70-eb645335e625"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-97-ed12dfa8de09>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(edad_sobrevivientes, kde=False)\n",
            "<ipython-input-97-ed12dfa8de09>:9: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(edad_no_sobrevivientes, kde=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "edad_sobrevivientes  = titanic[titanic.Survived == 1][\"Age\"]\n",
        "edad_no_sobrevivientes = titanic[titanic.Survived == 0][\"Age\"]\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot(edad_sobrevivientes, kde=False)\n",
        "plt.axis([0, 100, 0, 100])\n",
        "plt.title(\"sobrevivientes\")\n",
        "plt.ylabel(\"Cantidad\")\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.distplot(edad_no_sobrevivientes, kde=False)\n",
        "plt.axis([0, 100, 0, 100])\n",
        "plt.title(\"No sobrevivieron\")\n",
        "plt.subplots_adjust(right=1.7)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 946
        },
        "id": "j-4HrQbwEL4T",
        "outputId": "2374ac90-12e2-401b-959b-e6ddda7591ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-98-0bc838a802e6>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(edad_sobrevivientes, kde=False, bins=20, color=\"green\", label='Sobrevivientes')\n",
            "<ipython-input-98-0bc838a802e6>:6: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(edad_no_sobrevivientes, kde=False, bins=20, color=\"red\", label='No Sobrevivientes')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# edades / sobrevivientes\n",
        "plt.figure(figsize = (14,6))\n",
        "plt.title('Edades Sobrevivientes / No sobrevivientes')\n",
        "sns.set_color_codes(\"pastel\")\n",
        "sns.distplot(edad_sobrevivientes, kde=False, bins=20, color=\"green\", label='Sobrevivientes')\n",
        "sns.distplot(edad_no_sobrevivientes, kde=False, bins=20, color=\"red\", label='No Sobrevivientes')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rCiEZX0_EbO5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "21dca7d3-67c0-4fe8-8db8-e000fdbf2aa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.stripplot(x=\"Survived\", y=\"Age\", data=titanic, jitter=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gha6fRJPElco"
      },
      "source": [
        "## Embarked"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKgPopx1Epe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "7eecc2ba-2f3d-4a3b-e84b-5414a9ad1c86"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WPteuXdPChQu1YcMGnT9/3np4SVK2N92/yvogu/mwy19dvXrVZoxuvhLv7NmzcnR0tB52yeLj4yNPT0+dPXtWktS4cWO1a9dO8+fP17/+9S81btxYbdu2VadOnW57gntWjTkFU+l/r40srq6u2Q45lC5dWhUrVsz2nJQuXTrb+SFS9nlWqlQp+fj4WLdFunE4aPbs2dq9e7d1nma5ecwfeOCBbNt46tQp1ahRQyVK3P6t/dtvv9WHH36ow4cP25wD9ddtudVzkHW4OcvFixeVkpKS4+uwVq1ayszM1B9//KHatWsrPDxcL7/8stq1ayeLxaLQ0FA988wzqlOnzm3rzXKnMcx6Td5co4uLix588EGbsZZyHsOCFh8fr2vXruX4PpNTW17eY/m6jtwj7NzHPDw8VKFChVueUHuzU6dO6YUXXlDNmjU1duxYVapUSc7Oztq+fbv+9a9/Zftr3cnJqTDKvqOMjAz169dPV65c0YABA1SzZk25u7vr/PnzGjt2bK5OCC5omZmZat68uQYMGJDj8psDU35FRERozpw56tq1q4YPHy4vLy85Ojpq+vTpNmGlVq1a2rJli7777jvt2LFDX331lVatWqVXXnlF4eHhkqSpU6daz7MJDg5W6dKl5eDgYHNeTU6ylo0ePVp169bNsc/NV1vd6mqum4NETsvnzp2r/fv369tvv9WOHTs0btw4LV26VGvXrr3lXsusGt9++235+PhkW37z3L3VXL5V++3G51YSEhLUq1cveXh4KDw8XNWqVZOrq6sOHTqkd999N9u8ze9VN3v37tVLL72kRo0aadKkSfLx8ZGzs7PWr1+vzz//PF/rzK1GjRrpv//9r7Zt26YffvhBn3zyiT7++GNNmTLFZs/ZvXI3Vy7dam7efDJ5XuT1PZYrr3KPsHOfa9WqldauXat9+/YpJCTktn2/+eYbpaWl6cMPP1TlypWt7Xfzzbl3+jD7qypVquj333+XYRg29zt+/LhNv99//10nTpzQrFmz1LlzZ2v7zVd8ZG3DyZMnsz3WzevMSdb9T5w4YbP7/uLFi9n2JlWrVk3Jyclq1qzZHdd7KydPnsy27Vl7KKpUqSJJ2rp1q5o0aaLp06fb3DchIUFly5a1aXN3d1f79u3Vvn17paWladiwYYqIiNDgwYPl6uqqrVu3qnPnzjbfBJ2amnrbvTqSrGPh4eGR7+2tUqWKMjMzdfLkSZsT1uPi4pSQkGDd3izBwcEKDg7WiBEjtGnTJo0aNUpffPHFLT9As2r09va+q+ckL06ePKlHHnnEejspKUmxsbF69NFHJUl79uzR5cuXNX/+fDVq1Mja7+ar326nWrVq+uWXX5Senn7L7z3aunWrXF1dtWTJEpu9GuvXr7fpl/UcnDp1ymZPyc2H08qVKyc3N7ccXzPHjh2To6OjzV7eMmXKqGvXruratauSkpLUq1cvzZs3L1dh505jmPWaPHbsmM1rMi0tTWfOnCnQ5zprz+TNe/Gy9i5l8fb2lqura47vMze3FcZ7LG7gnJ373IABA+Tu7q4JEyYoLi4u2/JTp07p448/lvS/v2JvPpxx85tkXmRd0XOnD1BJevTRR3XhwgWbS2RTUlK0bt06m35Zx+r/WqdhGFq2bJlNvwoVKqhu3brauHGjzeP/8MMPiomJuWM9zZo1k7Ozs1asWGHzWFnj9VdPPfWU9u3bpx07dmRblpCQoOvXr9/x8S5cuGBzlVFiYqI+/fRT1a1b17p3wsnJKdtehS+//FLnz5+3abt06ZLNbRcXF9WqVUuGYVjPfclpr8Xy5cvv+Jerv7+/qlWrpn/+8585HiLNzSG7li1bSso+lkuXLrVZfuXKlWzbm7U3KaevJMjSokULeXh4aOHChTme65ObGvNq7dq1No+1evVqXb9+3fpBndO8TUtLy/Gy41t54okndOnSJetl8X+VtV4nJyc5ODjYPI9nzpzRtm3bbPpn1bV8+XKb9pufEycnJzVv3lzbtm2zCWZxcXH6/PPP1aBBA+thwZvnXalSpVStWrXbPld/dacxzHpNLl++3GYcP/nkE129etU6bwqCh4eHypYtq71799q03/x8OTk5qVmzZtq2bZvN6/DkyZPZ3g8K4z0WN7Bn5z5XrVo1vfvuuxoxYoTat29v/QbltLQ07du3T1u2bLGewNe8eXM5OztryJAh6t69u5KSkvTvf/9b3t7e1hNk86pu3bpycnLSokWLdPXqVbm4uOiRRx7J8RLQ5557TitXrtSYMWN06NAh+fj46LPPPsu2K7dmzZqqVq2aZs2apfPnz8vDw0Nbt27N8TyKkSNHavDgwXr++efVtWtXXb58WStWrFDt2rWVnJx829rLlSunF198UQsXLtTgwYPVsmVL/frrr/r++++z7UXp37+/vvnmGw0ZMkRhYWF6+OGHlZKSot9//11bt27Vtm3bcrwM9a8eeughjR8/XlFRUfL29tb69esVHx+vGTNmWPs89thjWrBggV5//XWFhITo999/16ZNm7JdBt2/f3+VL19e9evXl7e3t44dO6YVK1aoZcuW1g+mxx57TJ999pk8PDzk6+ur/fv3a9euXTbn/uTE0dFR06ZN08CBA9WxY0d16dJFDzzwgM6fP68ff/xRHh4eioiIuO066tSpo7CwMK1du1YJCQlq1KiRoqKitHHjRrVt29b61/3GjRu1evVqtW3bVtWqVVNSUpLWrVsnDw8P6wdgTjw8PDR58mSNHj1aXbp0Ufv27VWuXDmdO3dO27dvV/369TVx4sTb1phX6enpeuGFF/TUU0/p+PHjWrVqlRo0aKA2bdpIkkJCQuTl5aWxY8eqd+/ecnBw0GeffZanQ2KdO3fWp59+qhkzZujAgQNq0KCBUlJSFBkZqR49eqht27Zq2bKlli5dqgEDBqhjx46Kj4/XqlWrVK1aNUVHR1vXVbduXXXs2FGrVq3S1atXFRISot27d+e4h+Lvf/+7du3apeeff17PP/+8nJyctHbtWqWlpem1116z9uvQoYMaN26shx9+WGXKlFFUVJS2bt2a4wnV+RnDcuXKafDgwZo/f74GDBig1q1bW/sFBATo6aefzvVY5sazzz6rjz76SOPHj5e/v7/27t2b4x6uoUOHaufOnerRo4d69OihzMxM6/vM4cOHrf0K4z0WNxB2oDZt2ug///mPlixZom3btmn16tVycXGRn5+fxo4da73aqWbNmpo7d65mz56tWbNmqXz58urRo4fKlSuncePG5euxfXx8NGXKFC1cuFDjx49XRkaGli1blmPYcXNz07/+9S9NnTpVK1asUMmSJdWpUyc9+uijNufCODs7KyIiQtOmTdPChQvl6uqqxx9/XD179tQzzzxjs85HH31Uc+bM0ezZs/Xee++pWrVqmjFjhrZt26Y9e/bcsf6///3vcnFx0Zo1a/Tjjz8qMDBQ//znPzV48OBstS9fvlwLFy7Uli1b9Omnn8rDw0MPPfSQhg0bZnPS4a089NBDeuONN/T222/r+PHjqlq1qj744AObqzGGDBmilJQUbdq0SV988YXq1aunhQsX6r333rNZV7du3bRp0yYtXbpUycnJqlixonr37q2XX37Z2mf8+PFydHTUpk2blJqaqvr161s/JO+kSZMmWrt2rf7xj39oxYoVSk5Olo+PjwIDA9WtW7c73l+Spk2bpqpVq2rjxo36+uuvVb58eQ0ePFhDhw619mncuLGioqL0xRdfKC4uTqVLl1ZgYKDefffdbAHvZp06dVKFChX00UcfacmSJUpLS9MDDzyghg0bFsoVOhMnTtSmTZs0d+5cpaenq0OHDpowYYL1sGTZsmUVERGhWbNmafbs2fL09NTTTz+tpk2bqn///rl6jKw/HD788EN9/vnn+uqrr1SmTBnVr1/f+uV1TZs21VtvvaVFixZp+vTpqlq1qkaNGqWzZ8/ahB1Jmj59usqWLatNmzZp27ZtatKkiT766KNse0hq166tlStX6r333tPChQtlGIYCAwP1zjvvWL9jR7pxZdc333yjH374QWlpaapcubL+/ve/53r77jSGkjRs2DCVK1dOK1as0IwZM+Tl5aXnnntOI0eOLPCfNHnllVd08eJFbd26VV9++aUeffRRLV68ONuFCP7+/lq0aJHefvttzZkzR5UqVVJ4eLiOHTtmc1iwMN5jcYODkZ8z6QDcU61bt1bt2rW1cOFCe5eSTWRkpF544QWtXLnS5qsGgIKyYcMGvf766/rkk08UEBBg73IKzMsvv6yYmJhs34SOgsc5OwDuyoULFyQp26E7AP9z80/VnDhxQt9//70aN25sp4ruLxzGApAvycnJ2rRpk5YtW6aKFSvyVfXAbbRt21ZhYWHW7/tZs2aNnJ2dc3VYGHePsAMgXy5evKipU6fKYrFoxowZNt9YC8BWixYttHnzZsXGxsrFxUXBwcEaOXJkgX3HFm6Pc3YAAICp8acYAAAwNcIOAAAwNc7Z0Y3fLbp+/bocHR3z9PMFAADAfgzDUGZmpkqUKHHb8wYJO5KuX7+uqKgoe5cBAADyISAg4La/YE/Y0f9+kyYgIMBuv9R9v8vIyFBUVBTPAQoccwuFgXlVNGQ9D3e6GpSwo//98raTkxOT1s54DlBYmFsoDMyrouFOp6BwgjIAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg6KDDc3N3uXAAAwIcJOIcvMNOxdQrHg5OSkevXqycnJyd6lFBvMLQDInRL2LsDsHB0dNO+bIzp7KcXepRR5ycnJcnd3t3cZxUKVsm4a1rq2vcsAgGKBsHMPnL2UohPxSfYuo0gzDCkxMVEeHoYcHOxdDQDATDiMBQAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATM3uYef8+fMaNWqUmjRposDAQHXq1ElRUVHW5YZhaM6cOQoNDVVgYKBeeOEFnThxwmYdly9f1quvvqr69eurYcOGGjdunJKSku7xlgAAgKLIrmHnypUr6tGjh5ydnbVo0SJt3rxZY8aMkZeXl7XPokWLtHz5ck2ePFnr1q2Tm5ub+vfvr9TUVGufUaNGKSYmRkuXLlVERIT27t2riRMn2mOTAABAEVPCng++aNEiVaxYUTNmzLC2Pfjgg9Z/G4ahZcuW6aWXXlLbtm0lSW+//baaNWumr7/+Wh06dNDRo0e1Y8cOffLJJwoICJAkTZgwQYMGDdLo0aP1wAMP3NuNAgAARYpd9+x888038vf3V3h4uJo2barOnTtr3bp11uVnzpxRbGysmjVrZm0rXbq0goKCtG/fPknSvn375OnpaQ06ktSsWTM5OjrqwIED925jAABAkWTXPTunT5/W6tWr1a9fPw0ZMkRRUVGaNm2anJ2dFRYWptjYWEmSt7e3zf28vb0VFxcnSYqLi1O5cuVslpcoUUJeXl7W++dWRkbGXWxNzpycnCRJhlHgqzaXrAEyDBlysG8txUhhzFmzyRojxgoFiXlVNOR2/O0adgzDkL+/v0aOHClJqlevno4cOaI1a9YoLCzsntfz1xOjC4Kbm5vq1aun5ORkJSYmFui6zSqRE8tzJdn9RiCMjo5WSkqKnaspHgr69Q1IzKviwq5hx8fHR7Vq1bJpq1mzprZu3WpdLknx8fGqUKGCtU98fLzq1KkjSSpfvrwuXrxos47r16/rypUr1vvnVkBAgHVPTEFyd3eXhwe7dm7LMJSYlCSPUqUkB/bs3Im7u7skyc/Pz86VFH0ZGRmKiooqtNc37k/Mq6Ih63m4E7uGnfr16+v48eM2bSdOnFCVKlUkSVWrVpWPj48iIyNVt25dSVJiYqJ++eUX9ejRQ5IUEhKihIQEHTx4UP7+/pKk3bt3KzMzU4GBgXmqx8nJqdAmLZ/ft2c9dOXgwFjlAW+yuVeYr2/cv5hXxYNdT1Du27evfvnlF0VEROjkyZPatGmT1q1bp+eff16S5ODgoD59+ujDDz/Utm3bFB0drdGjR6tChQrWq7Nq1aqlFi1a6I033tCBAwf0888/a+rUqerQoQNXYgEAAPvu2QkMDNT8+fP1/vvva8GCBapatarGjRunp59+2tpn4MCBSklJ0cSJE5WQkKAGDRpo8eLFcnV1tfZ59913NXXqVPXt21eOjo564oknNGHCBHtsEgAAKGLsGnYkqVWrVmrVqtUtlzs4OGj48OEaPnz4LfuUKVNG7733XmGUBwAAijm7/1wEAABAYSLsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU7Nr2Jk3b578/Pxs/nvyySety1NTUzVlyhQ1adJEISEhGjZsmOLi4mzWce7cOQ0aNEhBQUFq2rSpZs2apevXr9/rTQEAAEVUCXsXULt2bS1dutR628nJyfrv6dOna/v27Zo9e7ZKly6tqVOnaujQoVqzZo0kKSMjQ4MHD1b58uW1Zs0aXbhwQWPGjJGzs7NGjhx5z7cFAAAUPXY/jOXk5CQfHx/rf+XKlZMkXb16VevXr9fYsWPVtGlT+fv7a/r06dq3b5/2798vSdq5c6diYmL0zjvvqG7dumrZsqWGDx+ulStXKi0tzY5bBQAAigq779k5efKkQkND5erqquDgYL366quqXLmyDh48qPT0dDVr1szat1atWqpcubL279+v4OBg7d+/XxaLReXLl7f2CQ0N1eTJkxUTE6N69erlqZaMjIwC264sWXuqDKPAV20uWQNkGDLkYN9aipHCmLNmkzVGjBUKEvOqaMjt+Ns17AQGBmrGjBmqUaOGYmNjtWDBAvXs2VObNm1SXFycnJ2d5enpaXMfb29vxcbGSpLi4uJsgo4k6+2sPnkRFRWVzy3JmZubm+rVq6fk5GQlJiYW6LrNKjEpyd4lFAvJ7jcCYXR0tFJSUuxcTfFQ0K9vQGJeFRd2DTstW7a0/rtOnToKCgpSq1at9OWXX6pkyZL3vJ6AgACbc4YKiru7uzw82LVzW4ahxKQkeZQqJTmwZ+dO3N3dJUl+fn52rqToy8jIUFRUVKG9vnF/Yl4VDVnPw53Y/TDWX3l6euqhhx7SqVOn1KxZM6WnpyshIcFm7058fLx8fHwk3diLc+DAAZt1ZF2tldUnL5ycnApt0vL5fXvWQ1cODoxVHvAmm3uF+frG/Yt5VTzY/QTlv0pKStLp06fl4+Mjf39/OTs7KzIy0rr82LFjOnfunIKDgyVJwcHB+v333xUfH2/ts2vXLnl4eMjX1/delw8AAIogu+7ZmTVrllq1aqXKlSvrwoULmjdvnhwdHdWxY0eVLl1aXbt21cyZM+Xl5SUPDw9NmzZNISEh1rATGhoqX19fjR49Wq+99ppiY2M1e/Zs9ezZUy4uLvbcNAAAUETYNez8+eefGjlypC5fvqxy5cqpQYMGWrdunfXy83HjxsnR0VHh4eFKS0tTaGioJk2aZL2/k5OTIiIiNHnyZHXr1k1ubm4KCwtTeHi4vTYJAAAUMXYNOx988MFtl7u6umrSpEk2AedmVapU0aJFiwq6NAAAYBJF6pwdAACAgkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAAplZkws5HH30kPz8/vfXWW9a21NRUTZkyRU2aNFFISIiGDRumuLg4m/udO3dOgwYNUlBQkJo2bapZs2bp+vXr97p8AABQRBWJsHPgwAGtWbNGfn5+Nu3Tp0/Xt99+q9mzZ2v58uW6cOGChg4dal2ekZGhwYMHKz09XWvWrNHMmTO1ceNGzZ07915vAgAAKKLsHnaSkpL02muvadq0afLy8rK2X716VevXr9fYsWPVtGlT+fv7a/r06dq3b5/2798vSdq5c6diYmL0zjvvqG7dumrZsqWGDx+ulStXKi0tzU5bBAAAihK7h50333xTLVu2VLNmzWzaDx48qPT0dJv2WrVqqXLlytaws3//flksFpUvX97aJzQ0VImJiYqJibkn9QMAgKKthD0ffPPmzfr111/1ySefZFsWFxcnZ2dneXp62rR7e3srNjbW2uevQUeS9XZWn7zIyMjI833uxMnJSZJkGAW+anPJGiDDkCEH+9ZSjBTGnDWbrDFirFCQmFdFQ27H325h548//tBbb72lf/7zn3J1dbVXGTaioqIKdH1ubm6qV6+ekpOTlZiYWKDrNqvEpCR7l1AsJLvfCITR0dFKSUmxczXFQ0G/vgGJeVVc2C3sHDp0SPHx8erSpYu1LSMjQz/99JNWrlypJUuWKD09XQkJCTZ7d+Lj4+Xj4yPpxl6cAwcO2Kw362qtrD55ERAQYN0TU5Dc3d3l4cGundsyDCUmJcmjVCnJgT07d+Lu7i5J2U7qR3YZGRmKiooqtNc37k/Mq6Ih63m4E7uFnUceeUSbNm2yaXv99ddVs2ZNDRw4UJUqVZKzs7MiIyPVrl07SdKxY8d07tw5BQcHS5KCg4MVERGh+Ph4eXt7S5J27dolDw8P+fr65rkmJyenQpu0fH7fnvXQlYMDY5UHvMnmXmG+vnH/Yl4VD3YLOx4eHrJYLDZt7u7uKlOmjLW9a9eumjlzpry8vOTh4aFp06YpJCTEGnZCQ0Pl6+ur0aNH67XXXlNsbKxmz56tnj17ysXF5V5vEgAAKILseoLynYwbN06Ojo4KDw9XWlqaQkNDNWnSJOtyJycnRUREaPLkyerWrZvc3NwUFham8PBwO1YNAACKkiIVdpYvX25z29XVVZMmTbIJODerUqWKFi1aVNilAQCAYsru37MDAABQmAg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1PIVdtq0aaNLly5la09ISFCbNm3uuigAAICCkq+wc/bsWWVmZmZrT0tL0/nz5++6KAAAgIKSp9/G2rZtm/XfO3bsUOnSpa23MzMzFRkZqSpVqhRcdQAAAHcpT2HnlVdekSQ5ODho7NixtisqUUJVqlTJ1g4AAGBPeQo7v/32mySpdevW+uSTT1SuXLlCKQoAAKCg5CnsZPnmm28Kug4AAIBCka+wI0mRkZGKjIxUfHx8tpOVZ8yYcdeFAQAAFIR8hZ358+drwYIF8vf3l4+PjxwcHAq6LgAAgAKRr7CzZs0azZgxQ507dy7gcgAAAApWvr5nJz09XfXr1y/oWgAAAApcvsLO3/72N23atKmgawEAAChw+TqMlZqaqnXr1ikyMlJ+fn4qUcJ2Na+//nqBFAcAAHC38hV2oqOjVadOHUnS77//brOMk5UBAEBRkq+ws3z58oKuAwAAoFDk65wdAACA4iJfe3Z69+5928NVy5Yty3dBAAAABSlfYadu3bo2t69fv67Dhw/ryJEjfPcOAAAoUvIVdsaNG5dj+7x585ScnHxXBQEAABSkAj1n5+mnn9b69esLcpUAAAB3pUDDzr59++Ti4lKQqwQAALgr+TqMNXToUJvbhmEoNjZWBw8e1Msvv1wghQEAABSEfIWd0qVL29x2cHBQjRo1FB4ertDQ0AIpDAAAoCDkK+zMmDGjoOsAAAAoFPkKO1kOHjyoo0ePSpJq166tevXqFUhRAAAABSVfYSc+Pl4jRozQnj175OnpKUlKSEhQkyZN9MEHH6hcuXIFWiQAAEB+5etqrKlTpyopKUmbN2/Wnj17tGfPHn3++edKTEzUtGnTCrpGAACAfMtX2NmxY4cmTZqkWrVqWdt8fX01adIkff/99wVWHAAAwN3KV9jJzMyUs7NztvYSJUooMzPzrosCAAAoKPkKO4888ojeeustnT9/3tp2/vx5zZgxQ02bNi2w4gAAAO5Wvk5Qnjhxol566SW1adNGFStWlCT9+eefql27tt55550CLRAAAOBu5CvsVKpUSRs3btSuXbt07NgxSVKtWrXUrFmzAi0OAADgbuXpMFZkZKTat2+vxMREOTg4qHnz5urdu7d69+6tgIAAdejQQXv37i2sWgEAAPIsT2Hn448/1nPPPScPD49sy0qXLq1u3bpp6dKlBVYcAADA3cpT2ImOjlaLFi1uubx58+Y6dOjQXRcFAABQUPIUduLi4lSixK1P8ylRooQuXrx410UBAAAUlDyFnQceeEBHjhy55fLo6Gj5+Pjken2rVq1Sp06dVL9+fdWvX1/dunXT9u3brctTU1M1ZcoUNWnSRCEhIRo2bJji4uJs1nHu3DkNGjRIQUFBatq0qWbNmqXr16/nZbMAAICJ5SnstGzZUnPmzFFqamq2ZdeuXdO8efPUqlWrXK+vYsWKGjVqlDZs2KD169frkUce0SuvvGINVNOnT9e3336r2bNna/ny5bpw4YKGDh1qvX9GRoYGDx6s9PR0rVmzRjNnztTGjRs1d+7cvGwWAAAwsTxdev7SSy/pq6++Urt27dSzZ0/VqFFDknTs2DGtWrVKGRkZGjJkSK7X17p1a5vbI0aM0OrVq7V//35VrFhR69ev17vvvmv9osLp06erffv22r9/v4KDg7Vz507FxMRo6dKlKl++vOrWravhw4fr3Xff1dChQ+Xi4pKXzQMAACaUp7BTvnx5rVmzRpMnT9b7778vwzAkSQ4ODgoNDdXEiRNVvnz5fBWSkZGhLVu2KDk5WSEhITp48KDS09NtvrunVq1aqly5sjXs7N+/XxaLxeYxQ0NDNXnyZMXExKhevXp5rqGgOTk5SZL+b6hwK1kDZBgy5GDfWoqRwpizZpM1RowVChLzqmjI7fjn+UsFq1SpokWLFunKlSs6efKkJKl69ery8vLK66ok3TjPp3v37kpNTZW7u7sWLFggX19fHT58WM7OzvL09LTp7+3trdjYWEk3Tpi+OVxl3c7qkxdRUVH52oZbcXNzU7169ZScnKzExMQCXbdZJSYl2buEYiHZ/UYgjI6OVkpKip2rKR4K+vUNSMyr4iJf36AsSV5eXgoMDLzrAmrUqKFPP/1UV69e1datWzVmzBitWLHirtebHwEBAdY9MQXJ3d1dHh7s2rktw1BiUpI8SpWSHNizcyfu7u6SJD8/PztXUvRlZGQoKiqq0F7fuD8xr4qGrOfhTvIddgqKi4uLqlevLkny9/dXVFSUli1bpqeeekrp6elKSEiw2bsTHx9vveKrfPnyOnDggM36sq7WystVYVmcnJwKbdLy+X171kNXDg6MVR7wJpt7hfn6xv2LeVU85OtXzwtTZmam0tLS5O/vL2dnZ0VGRlqXHTt2TOfOnVNwcLAkKTg4WL///rvi4+OtfXbt2iUPDw/5+vre69IBAEARZNc9O++9954effRRVapUSUlJSfr888+1Z88eLVmyRKVLl1bXrl01c+ZMeXl5ycPDQ9OmTVNISIg17ISGhsrX11ejR4/Wa6+9ptjYWM2ePVs9e/bkSiwAACDJzmEnPj5eY8aM0YULF1S6dGn5+flpyZIlat68uSRp3LhxcnR0VHh4uNLS0hQaGqpJkyZZ7+/k5KSIiAhNnjxZ3bp1k5ubm8LCwhQeHm6vTQIAAEWMXcPO9OnTb7vc1dVVkyZNsgk4N8u6OgwAACAnRe6cHQAAgIJE2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZm17CzcOFCde3aVSEhIWratKlefvllHTt2zKZPamqqpkyZoiZNmigkJETDhg1TXFycTZ9z585p0KBBCgoKUtOmTTVr1ixdv379Xm4KAAAoouwadvbs2aOePXtq3bp1Wrp0qa5fv67+/fsrOTnZ2mf69On69ttvNXv2bC1fvlwXLlzQ0KFDrcszMjI0ePBgpaena82aNZo5c6Y2btyouXPn2mOTAABAEWPXsLNkyRJ16dJFtWvXVp06dTRz5kydO3dOhw4dkiRdvXpV69ev19ixY9W0aVP5+/tr+vTp2rdvn/bv3y9J2rlzp2JiYvTOO++obt26atmypYYPH66VK1cqLS3NjlsHAACKgiJ1zs7Vq1clSV5eXpKkgwcPKj09Xc2aNbP2qVWrlipXrmwNO/v375fFYlH58uWtfUJDQ5WYmKiYmJh7VzwAACiSSti7gCyZmZmaPn266tevL4vFIkmKi4uTs7OzPD09bfp6e3srNjbW2uevQUeS9XZWn9zKyMjIb/m35OTkJEkyjAJftblkDZBhyJCDfWspRgpjzppN1hgxVihIzKuiIbfjX2TCzpQpU3TkyBGtWrXKbjVERUUV6Prc3NxUr149JScnKzExsUDXbVaJSUn2LqFYSHa/EQijo6OVkpJi52qKh4J+fQMS86q4KBJh580339R3332nFStWqGLFitb28uXLKz09XQkJCTZ7d+Lj4+Xj42Ptc+DAAZv1ZV2tldUntwICAqx7YgqSu7u7PDzYtXNbhqHEpCR5lColObBn507c3d0lSX5+fnaupOjLyMhQVFRUob2+cX9iXhUNWc/Dndg17BiGoalTp+q///2vli9frgcffNBmub+/v5ydnRUZGal27dpJko4dO6Zz584pODhYkhQcHKyIiAjFx8fL29tbkrRr1y55eHjI19c3T/U4OTkV2qTl8/v2rIeuHBwYqzzgTTb3CvP1jfsX86p4sGvYmTJlij7//HP94x//UKlSpazn2JQuXVolS5ZU6dKl1bVrV82cOVNeXl7y8PDQtGnTFBISYg07oaGh8vX11ejRo/Xaa68pNjZWs2fPVs+ePeXi4mLHrQMAAEWBXcPO6tWrJUm9e/e2aZ8xY4a6dOkiSRo3bpwcHR0VHh6utLQ0hYaGatKkSda+Tk5OioiI0OTJk9WtWze5ubkpLCxM4eHh925DAABAkWXXsBMdHX3HPq6urpo0aZJNwLlZlSpVtGjRooIsDQAAmESR+p4dAACAgkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAWB6bm5u9i4BgB0RdoBiyMvNWZmZhr3LKBacnJxUr149OTk52buUYoF5BTOy6w+BAsifUq4l5OjooHnfHNHZSyn2LqfIS05Olru7u73LKPKqlHXTsNa17V0GUOAIO0AxdvZSik7EJ9m7jCLNMKTExER5eBhycLB3NQDsgcNYAADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Owadn766ScNGTJEoaGh8vPz09dff22z3DAMzZkzR6GhoQoMDNQLL7ygEydO2PS5fPmyXn31VdWvX18NGzbUuHHjlJSUdA+3AgAAFGV2DTvJycny8/PTpEmTcly+aNEiLV++XJMnT9a6devk5uam/v37KzU11dpn1KhRiomJ0dKlSxUREaG9e/dq4sSJ92oTAABAEWfXsNOyZUuNGDFCjz/+eLZlhmFo2bJleumll9S2bVvVqVNHb7/9ti5cuGDdA3T06FHt2LFD06ZNU1BQkBo2bKgJEyZo8+bNOn/+/L3eHAAAUASVsHcBt3LmzBnFxsaqWbNm1rbSpUsrKChI+/btU4cOHbRv3z55enoqICDA2qdZs2ZydHTUgQMHcgxRt5ORkVFg9WdxcnKSJBlGga/aXLIGyDBkyMG+tRQzzK07YG7lWWG8F5pN1hgxVvaV2/EvsmEnNjZWkuTt7W3T7u3trbi4OElSXFycypUrZ7O8RIkS8vLyst4/L6KiovJZbc7c3NxUr149JScnKzExsUDXbVaJnG+VK9eulfq//19jbuUSc+vOkt1vhMHo6GilpKTYuZrioaA/N1A4imzYsYeAgADrnpiC5O7uLg8P/vy+LcNQYlKSPEqVkhz46/tOSpZ0+7//l5SHB39Z3hZzK9fc3d0lSX5+fnaupOjLyMhQVFRUoX1uIHeynoc7KbJhx8fHR5IUHx+vChUqWNvj4+NVp04dSVL58uV18eJFm/tdv35dV65csd4/L5ycnApt0vIee3vWwwsODoxVHjFet8fcyjs+vHOvMD83UHCK7PfsVK1aVT4+PoqMjLS2JSYm6pdfflFISIgkKSQkRAkJCTp48KC1z+7du5WZmanAwMB7XjMAACh67LpnJykpSadOnbLePnPmjA4fPiwvLy9VrlxZffr00Ycffqjq1auratWqmjNnjipUqKC2bdtKkmrVqqUWLVrojTfe0JQpU5Senq6pU6eqQ4cOeuCBB+y1WQAAoAixa9g5ePCg+vTpY709Y8YMSVJYWJhmzpypgQMHKiUlRRMnTlRCQoIaNGigxYsXy9XV1Xqfd999V1OnTlXfvn3l6OioJ554QhMmTLjn2wIAAIomu4adJk2aKDo6+pbLHRwcNHz4cA0fPvyWfcqUKaP33nuvMMoDAAAmUGTP2QEAACgIhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAPLBzc3N3iUglwg7AABJkpebszIz+R2/3HByclK9evX4qYhcsve8KrK/jQUAuLdKuZaQo6OD5n1zRGcv8avnd5KcnGz98VTcWpWybhrWurZdayDsAABsnL2UohPxSfYuo0gzjBu/1+jhYfADs8UAh7EAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpmSbsrFy5Uq1bt1ZAQICeffZZHThwwN4lAQCAIsAUYeeLL77QjBkz9Morr2jjxo2qU6eO+vfvr/j4eHuXBgAA7MwUYWfp0qV67rnn1LVrV/n6+mrKlCkqWbKk1q9fb+/SAACAnRX7sJOWlqZDhw6pWbNm1jZHR0c1a9ZM+/bts2NlAACgKChh7wLu1qVLl5SRkSFvb2+bdm9vbx07dixX6zAMQ9KN4OTk5FSg9Tk5OalamZIq4WAU6HpNxzCU4uEgt5IlJQcHe1dT5FUs7ayMjAzmVm4wt3KNeZUHzKtcq+xVUhkZGcrIyCjwdWetM+tz/FaKfdgpCJmZmZKkX3/9tVDW36SMpDKFsmqTcZZU8C8Gc4rX/v3xzK1cY27lDvMqb5hXuXNV+/fvL9RHyPocv5ViH3bKli0rJyenbCcjx8fHq3z58rlaR4kSJRQQECBHR0c5kNABACgWDMNQZmamSpS4fZwp9mHHxcVFDz/8sCIjI9W2bVtJNxJeZGSkevXqlat1ODo6ysXFpTDLBAAAdlLsw44k9evXT2PGjJG/v78CAwP18ccfKyUlRV26dLF3aQAAwM5MEXbat2+vixcvau7cuYqNjVXdunW1ePHiXB/GAgAA5uVg3OkUZgAAgGKs2H/PDgAAwO0QdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKmZ4tJzFG8XL17UnDlztH37dsXFxcnLy0t16tTRyy+/rAYNGti7PBRjsbGxioiI0Hfffafz58/L29tbdevWVd++fdW0aVN7l4di6I8//tDcuXO1Y8cOXb58WT4+PmrTpo1eeeUVlS1b1t7l4Ra49Bx217NnT6Wnp2vkyJF68MEHFR8fr8jISPn6+qpNmzb2Lg/F1JkzZ9SjRw95enoqPDxcFotF169f186dO7V27Vpt2bLF3iWimDl9+rS6deumhx56SH//+99VtWpVHTlyRO+8847S09O1du1alSlTxt5lIgeEHdhVQkKCGjVqpOXLl6tx48b2LgcmMnDgQEVHR2vLli1yd3e3WZaQkCBPT087VYbiasCAATpy5Ii2bt2qkiVLWttjY2P1+OOP65lnntGUKVPsWCFuhXN2YFfu7u5yd3fX119/rbS0NHuXA5O4fPmyduzYoZ49e2YLOpIIOsizy5cva+fOnXr++edtgo4k+fj4qFOnTvryyy/F/oOiibADuypRooRmzpypTz/9VA0bNlT37t31/vvv67fffrN3aSjGTp06JcMwVLNmTXuXApM4efKkDMNQrVq1clxeq1YtXblyRRcvXrzHlSE3CDuwu3bt2mnHjh368MMP1aJFC+3Zs0ddunTRhg0b7F0aiin+ukZhudPccnZ2vkeVIC8IOygSXF1d1bx5c73yyitas2aNwsLCNG/ePHuXhWKqevXqcnBw0LFjx+xdCkyiWrVqcnBw0NGjR3NcfvToUZUrV45DpEUUYQdFkq+vr5KTk+1dBoqpMmXKKDQ0VCtXrsxxHiUkJNihKhRnZcuWVfPmzbVq1Spdu3bNZllsbKw2bdqksLAwO1WHOyHswK4uXbqkPn366LPPPtNvv/2m06dP68svv9TixYu57Bx3ZdKkScrMzNSzzz6rrVu36sSJEzp69KiWLVumbt262bs8FENvvPGG0tLS1L9/f/3000/6448/9P333+vFF1/UQw89pFdeecXeJeIWuPQcdpWWlqZ58+bphx9+0KlTp3T9+nVVrFhRTz75pIYMGZLtqgcgLy5cuGD9UsELFy6oXLlyevjhh/XCCy+oSZMm9i4PxdCZM2c0f/587dixQ/Hx8TIMQ0888YTefvttubm52bs83AJhBwCAfJo7d66WLl2qpUuXKjg42N7l4BYIOwAA3IX169fr6tWr6tOnjxwdOTukKCLsAAAAUyOCAgAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAChW5s2bp2eeeaZQ1v3jjz/Kz8+vQH9O4syZM/Lz89Phw4cLbJ0A8qaEvQsAYF5jx47Vxo0bs7WHhoZqyZIldqgIwP2IsAOgULVo0UIzZsywaXNxcbFTNbeWnp5u7xIAFBIOYwEoVC4uLvLx8bH5z8vLS5Lk5+enNWvWaPDgwQoKCtJTTz2lffv26eTJk+rdu7eCg4PVvXt3nTp1Ktt616xZo5YtWyooKEjDhw/X1atXrcsOHDigfv36qUmTJmrQoIF69eqlQ4cO2dzfz89Pq1at0pAhQxQcHKyIiIhsj5GSkqIBAwaoe/fu1kNb//73v/XUU08pICBATz75pFauXGlznwMHDqhz584KCAhQly5dOHwFFAGEHQB29Y9//EPPPPOMPv30U9WsWVOvvvqqJk6cqEGDBmn9+vUyDENvvvmmzX1OnTqlL7/8UhEREVq8eLEOHz6syZMnW5cnJSWpc+fOWrVqldatW6fq1atr0KBBSkxMtFnP/Pnz9fjjj2vTpk3q2rWrzbKEhAT169dPhmFo6dKl8vT01H/+8x/NmTNHI0aM0BdffKGRI0dq7ty51kN1SUlJGjx4sGrVqqUNGzZo2LBhmjVrVuEMHIDcMwCgkIwZM8aoW7euERwcbPPfhx9+aBiGYVgsFuODDz6w9t+3b59hsViMf//739a2zz//3AgICLDenjt3rlG3bl3jzz//tLZt377dqFOnjnHhwoUc68jIyDBCQkKMb775xtpmsViMt956y6bf7t27DYvFYsTExBidOnUyhg0bZqSmplqXt23b1ti0aZPNfRYsWGB069bNMAzDWLNmjdG4cWPj2rVr1uWrVq0yLBaL8euvv95xvAAUDs7ZAVComjRpYrPXRZL1MJZ043BSFm9vb0mSxWKxaUtNTVViYqI8PDwkSZUqVdIDDzxg7RMSEqLMzEwdP35cPj4+iouL0+zZs7Vnzx7Fx8crMzNTKSkpOnfunE0d/v7+Odb84osvKjAwUB988IGcnJwkScnJyTp16pTGjx+vN954w9r3+vXrKl26tCTp6NGj8vPzk6urq01tAOyLsAOgULm5ual69eq3XO7s7Gz9t4ODwy3bMjMzc/2YY8aM0eXLlzV+/HhVrlxZLi4u6tatW7aTkN3d3XO8f8uWLfXVV18pJibGGsaSk5MlSVOnTlVQUJBNf37pGijaeIUCKHb++OMPnT9/3np7//79cnR0VI0aNSRJ/+///T/17t1bLVu2VO3ateXi4qJLly7lev2jRo1SWFiYXnjhBcXExEiSypcvrwoVKuj06dOqXr26zX8PPvigJKlWrVqKjo5WamqqTW0A7IuwA6BQpaWlKTY21ua/ixcv3tU6XV1dNXbsWP3222/au3evpk2bpqeeeko+Pj6SpIceekj/+c9/dPToUf3yyy8aNWqUSpYsmafHGDNmjDp16qS+ffvq6NGjkqTw8HB99NFHWrZsmY4fP67o6GitX79eS5culSR17NhRDg4OmjBhgmJiYrR9+3b985//vKttBXD3OIwFoFDt2LFDoaGhNm01atTQli1b8r3OatWq6fHHH9fAgQN15coVPfbYY5o0aZJ1+VtvvaU33nhDYWFhqlSpkkaMGKG33347z48zbtw4ZWZmqm/fvlq+fLmeffZZlSxZUkuWLNHbb78td3d3WSwW9e3bV5JUqlQpRUREaNKkSercubN8fX01atQoDRs2LN/bCuDuORiGYdi7CAAAgMLCYSwAAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBq/x/CGa1wzx0iCwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "emb = titanic[[\"Embarked\"]]\n",
        "sns.histplot(emb[\"Embarked\"], kde=False)\n",
        "plt.title(\"Cantidad de pasajeros embarcados por lugar\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gwcOUqj4Eu4t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ee82745a-3a10-4d86-b491-c7afd7ad3f02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Embarked', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BACAIWCwWRUREGF2GKXFjEAAAmJqhYWft2rUaNGiQunfvru7du2vYsGHavHmzd/4DDzwgu93u83nmmWd81nH8+HGNHj1a3bp1U2pqqp5//nlVV1df6l0BAABBytDbWC1bttSUKVPUtm1beTwevf/++xo3bpzee+89XXvttZKke++9VxMnTvQuExkZ6f3b5XJpzJgxstlsys3NVVFRkTIyMhQWFqYnn3zyku8PAAAIPoaGnVtuucVn+oknntAbb7yhvLw8b9iJiIhQfHz8eZffunWrCgoKlJOTI5vNpk6dOmnSpElasGCBxo8fr/Dw8FrV43K5/NsRKSADqMyiLscRAICaqun3TdAMUHa5XProo49UXl6ulJQUb/v69ev1wQcfKD4+XjfffLPGjh3rvbqTl5enxMRE2Ww2b/+0tDRlZmaqoKBAnTt3rlUNe/bs8av2yMjIWm/LzPLz81VRUWF0GQAASAqCsJOfn6/77rtPlZWVioqK0iuvvKKEhARJ0l133aXWrVurRYsWys/P14IFC3T48GEtWbJEklRcXOwTdCR5px0OR61rSUpK4gpNANjtdqNLAAA0AC6Xq0YXKgwPO+3atdP777+v06dP6+OPP1ZGRoZWr16thIQEDRs2zNvPbrcrPj5eDz74oAoLC3X11VcHvBar1UrYCQCOIQAgmBj+6Hl4eLjatm2rLl26aPLkyerYsaNWrlx53r7dunWTJO8bJm02m4qLi336nJ2+0DgfAADQsBgedn7K7XZf8A2S+/fvl/R/QSY5OVlfffWVSkpKvH0+++wzRUdHe2+FAQCAhs3Q21gLFy7UTTfdpFatWqmsrEwbNmzQ9u3btXz5chUWFmr9+vVKT09X06ZNlZ+fr3nz5qlHjx7q2LGjpB8HIyckJGjatGmaOnWqHA6HFi1apOHDh9f6SSwAAGBOhoadkpISZWRkqKioSDExMbLb7Vq+fLn69Omjb7/9Vtu2bdPKlStVXl6uVq1a6fbbb9fYsWO9y1utVi1dulSZmZkaNmyYIiMjNXjwYJ/38gAAgIbN0LAzd+7cC85r1aqVVq9efdF1XHnllXrttdcCWRYAADCRoBuzAwAAEEiEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqGhp21a9dq0KBB6t69u7p3765hw4Zp8+bN3vmVlZXKyspSz549lZKSogkTJqi4uNhnHcePH9fo0aPVrVs3paam6vnnn1d1dfWl3hUAABCkDA07LVu21JQpU/Tuu+9q3bp16tWrl8aNG6evv/5akjR37lz9/e9/16JFi7Rq1SoVFRVp/Pjx3uVdLpfGjBkjp9Op3NxcZWdn67333tPixYuN2iUAABBkQo3c+C233OIz/cQTT+iNN95QXl6eWrZsqXXr1mnBggVKTU2V9GP4GTBggPLy8pScnKytW7eqoKBAOTk5stls6tSpkyZNmqQFCxZo/PjxCg8Pr1U9LpfL732xWq1+L2s2dTmOAADUVE2/bwwNO//J5XLpo48+Unl5uVJSUrR37145nU717t3b26dDhw5q3bq1N+zk5eUpMTFRNpvN2yctLU2ZmZkqKChQ586da1XDnj17/Ko9MjKy1tsys/z8fFVUVBhdBgAAkoIg7OTn5+u+++5TZWWloqKi9MorryghIUH79+9XWFiYmjRp4tM/Li5ODodDklRcXOwTdCR5p8/2qY2kpCSu0ASA3W43ugQAQAPgcrlqdKHC8LDTrl07vf/++zp9+rQ+/vhjZWRkaPXq1YbUYrVaCTsBwDEEAAQTw8NOeHi42rZtK0nq0qWL9uzZo5UrV+rOO++U0+lUaWmpz9WdkpISxcfHS/rxKs7u3bt91nf2aa2zfQAAQMMWdO/ZcbvdqqqqUpcuXRQWFqZt27Z55x06dEjHjx9XcnKyJCk5OVlfffWVSkpKvH0+++wzRUdHKyEh4VKXDgAAgpChV3YWLlyom266Sa1atVJZWZk2bNig7du3a/ny5YqJidHQoUOVnZ2t2NhYRUdHa86cOUpJSfGGnbS0NCUkJGjatGmaOnWqHA6HFi1apOHDh9f6SSwAAGBOhoadkpISZWRkqKioSDExMbLb7Vq+fLn69OkjSZoxY4YsFosmTpyoqqoqpaWladasWd7lrVarli5dqszMTA0bNkyRkZEaPHiwJk6caNQuAQCAIBPi8Xg8RhdhNJfL5X2cvS6Da4cv2qAD/3sigJVdXjpe2VxrHr/L6DIAAA1ETb+/g27MDgAAQCARdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkZGnb+8Ic/aOjQoUpJSVFqaqrGjh2rQ4cO+fR54IEHZLfbfT7PPPOMT5/jx49r9OjR6tatm1JTU/X888+rurr6Uu4KAAAIUqFGbnz79u0aPny4kpKS5HK59Lvf/U6jRo3Sn//8Z0VFRXn73XvvvZo4caJ3OjIy0vu3y+XSmDFjZLPZlJubq6KiImVkZCgsLExPPvnkJd0fAAAQfAwNO8uXL/eZzs7OVmpqqvbt26cePXp42yMiIhQfH3/edWzdulUFBQXKycmRzWZTp06dNGnSJC1YsEDjx49XeHh4ve4DAAAIboaGnZ86ffq0JCk2Ntanff369frggw8UHx+vm2++WWPHjvVe3cnLy1NiYqJsNpu3f1pamjIzM1VQUKDOnTvXePsul8vv2q1Wq9/Lmk1djiMAADVV0++boAk7brdbc+fOVffu3ZWYmOhtv+uuu9S6dWu1aNFC+fn5WrBggQ4fPqwlS5ZIkoqLi32CjiTvtMPhqFUNe/bs8av2yMjIWoUqs8vPz1dFRYXRZQAAICmIwk5WVpa+/vprrV271qd92LBh3r/tdrvi4+P14IMPqrCwUFdffXVAa0hKSuIKTQDY7XajSwAANAAul6tGFyqCIuw8++yz+vTTT7V69Wq1bNnyZ/t269ZNknTkyBFdffXVstls2r17t0+f4uJiSbrgOJ8LsVqthJ0A4BgCAIKJoY+eezwePfvss/rkk0+0YsUKtWnT5qLL7N+/X9L/BZnk5GR99dVXKikp8fb57LPPFB0drYSEhPopHAAAXDb8CjsjRoxQaWnpOe0//PCDRowYUeP1ZGVl6YMPPtDChQvVuHFjORwOORwOnTlzRpJUWFioV155RXv37tWxY8f017/+VRkZGerRo4c6duwo6cfByAkJCZo2bZoOHDigLVu2aNGiRRo+fDhPYgEAAP9uY23fvl1Op/Oc9srKSv3jH/+o8XreeOMNST++OPA/zZs3T0OGDFFYWJi2bdumlStXqry8XK1atdLtt9+usWPHevtarVYtXbpUmZmZGjZsmCIjIzV48GCf9/IAAICGq1Zh58CBA96/CwoKfJ52crvd2rJli6644ooary8/P/9n57dq1UqrV6++6HquvPJKvfbaazXeLgAAaDhqFXbuvvtuhYSEKCQkRCNHjjxnfkREhGbOnBmw4gAAAOqqVmHnr3/9qzwej2677Ta9/fbbat68uXdeWFiY4uLieBIHAAAElVqFnSuvvFKS7+0sAACAYOb3e3a++eYbffHFFyopKZHb7faZN378+DoXBgAAEAh+hZ233npLmZmZatasmWw2m0JCQrzzQkJCCDsAACBo+BV2Xn31VT3++OMaPXp0oOsBAAAIKL9eKnjq1Cndeeedga4FAAAg4PwKO3fccYe2bt0a6FoAAAACzq/bWG3bttVLL72kL7/8UomJiQoN9V1NbX4yAgAAoD75FXbefPNNRUVFafv27dq+fbvPvJCQEMIOAAAIGn6Fnb/97W+BrgMAAKBe+DVmBwAA4HLh15Wd3/72tz87f968eX4VAwAAEGh+hZ3S0lKf6erqan399dcqLS1Vr169AlIYAABAIPgVdl555ZVz2txutzIzM9WmTZs6FwUAABAoARuzY7FY9OCDD2rFihWBWiUAAECdBXSA8tGjR1VdXR3IVQIAANSJX7exfjoA2ePxyOFw6NNPP9XgwYMDUhgAAEAg+BV2/vWvf/lMWywWNW/eXNOnT9fQoUMDUhguP3ExEfK4XQqxWI0uxXAcBwAIHn6FnVWrVgW6DphATES4QixWFb87Xc7iQ0aXY5gwW3vZhmQbXQYA4P/zK+ycdeLECR069OOXWvv27dW8efOAFIXLm7P4kJzf7Te6DAAAJPkZdsrLyzV79mz96U9/ktvtliRZrVb98pe/1NNPP63IyMiAFgkAAOAvv57Gys7O1o4dO/Tqq69q586d2rlzp37/+99rx44dys7m8j0AAAgefoWdjz/+WM8995zS09MVHR2t6Ohopaena/bs2fr4448DXSMAAIDf/Ao7Z86ckc1mO6c9Li5OZ86cqXNRAAAAgeJX2ElOTtbixYtVWVnpbTtz5oyWLFmi5OTkQNUGAABQZ34NUJ4xY4YeeeQR3XTTTerYsaMk6cCBAwoPD9frr78e0AIBAADqwq+wY7fb9Ze//EXr16/3Pnp+1113adCgQYqIiAhogQAAAHXhV9j5wx/+oLi4ON17770+7e+8845OnDih0aNHB6Q4AACAuvJrzM6bb76p9u3bn9N+7bXXKjc3t85FAQAABIpfYcfhcCg+Pv6c9ubNm8vhcNR4PX/4wx80dOhQpaSkKDU1VWPHjvXeFjursrJSWVlZ6tmzp1JSUjRhwgQVFxf79Dl+/LhGjx6tbt26KTU1Vc8//zy/vg4AACT5GXZatWqlf/7zn+e0/+Mf/1CLFi1qvJ7t27dr+PDheuutt5STk6Pq6mqNGjVK5eXl3j5z587V3//+dy1atEirVq1SUVGRxo8f753vcrk0ZswYOZ1O5ebmKjs7W++9954WL17sz64BAACT8WvMzj333KO5c+equrpavXr1kiRt27ZNL7zwgh5++OEar2f58uU+09nZ2UpNTdW+ffvUo0cPnT59WuvWrdOCBQuUmpoq6cfwM2DAAOXl5Sk5OVlbt25VQUGBcnJyZLPZ1KlTJ02aNEkLFizQ+PHjFR4e7s8uAgAAk/Ar7DzyyCM6efKksrKy5HQ6JUmNGjXSI488ojFjxvhdzOnTpyVJsbGxkqS9e/fK6XSqd+/e3j4dOnRQ69atvWEnLy9PiYmJPi85TEtLU2ZmpgoKCtS5c+cab9/lcvldu9Vq9XtZmFNdzicAwMXV9N9Zv8JOSEiIpk6dqrFjx+rgwYOKiIjQNddcU6erKG63W3PnzlX37t2VmJgoSSouLlZYWJiaNGni0zcuLs47Nqi4uPictzmfna7N+CFJ2rNnj1+1R0ZG1ipUoWHIz89XRUWF0WUAQIPnV9g5q3HjxuratWtACsnKytLXX3+ttWvXBmR9/khKSuIKDQLGbrcbXQIAmJrL5arRhYo6hZ1AefbZZ/Xpp59q9erVatmypbfdZrPJ6XSqtLTU5+pOSUmJ92kwm82m3bt3+6zv7NNa53ti7OdYrVbCDgKGcwkAgoNfT2MFisfj0bPPPqtPPvlEK1asUJs2bXzmd+nSRWFhYdq2bZu37dChQzp+/Lj3N7iSk5P11VdfqaSkxNvns88+U3R0tBISEi7JfgAAgOBl6JWdrKwsbdiwQb///e/VuHFj7xibmJgYRUREKCYmRkOHDlV2drZiY2MVHR2tOXPmKCUlxRt20tLSlJCQoGnTpmnq1KlyOBxatGiRhg8fzpNYAADA2LDzxhtvSJIeeOABn/Z58+ZpyJAhkn780VGLxaKJEyeqqqpKaWlpmjVrlrev1WrV0qVLlZmZqWHDhikyMlKDBw/WxIkTL92OAACAoGVo2MnPz79on0aNGmnWrFk+AeenrrzySr322muBLA0AAJiEoWN2AAAA6hthBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmJqhYWfHjh167LHHlJaWJrvdrk2bNvnMnz59uux2u89n1KhRPn1OnjypyZMnq3v37rrhhhs0Y8YMlZWVXcrdAAAAQSzUyI2Xl5fLbrdr6NChGj9+/Hn79O3bV/PmzfNOh4eH+8yfMmWKHA6HcnJy5HQ6NWPGDD3zzDNauHBhvdYOAAAuD4aGnfT0dKWnp/9sn/DwcMXHx5933sGDB7Vlyxa98847SkpKkiTNnDlTo0eP1rRp03TFFVcEvGYAAHB5MTTs1MT27duVmpqqJk2aqFevXnr88cfVrFkzSdKuXbvUpEkTb9CRpN69e8tisWj37t3q169frbblcrn8rtNqtfq9LMypLucTAODiavrvbFCHnb59+6pfv3666qqrdPToUf3ud7/To48+qjfffFNWq1XFxcVq3ry5zzKhoaGKjY2Vw+Go9fb27NnjV52RkZHq3LmzX8vCvPLz81VRUWF0GQDQ4AV12Bk4cKD377MDlG+77Tbv1Z5AS0pK4goNAsZutxtdAgCYmsvlqtGFiqAOOz/Vpk0bNWvWTEeOHFFqaqpsNptOnDjh06e6ulqnTp264Difn2O1Wgk7CBjOJQAIDpfVe3a+++47nTx50htkUlJSVFpaqr1793r7fP7553K73eratatRZQIAgCBi6JWdsrIyFRYWeqePHTum/fv3KzY2VrGxsVqyZIn69+8vm82mo0eP6oUXXlDbtm3Vt29fSVKHDh3Ut29fPf3008rKypLT6dTs2bM1cOBAnsQCAACSDA47e/fu1YgRI7zTZ9+nM3jwYGVmZuqrr77S+++/r9OnT6tFixbq06ePJk2a5POunQULFmj27NkaOXKkLBaLbr/9ds2cOfOS7wsAAAhOhoadnj17Kj8//4Lzly9fftF1NG3alBcIAgCAC7qsxuwAAADUFmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmqFhZ8eOHXrssceUlpYmu92uTZs2+cz3eDx66aWXlJaWpq5du+rBBx/UN99849Pn5MmTmjx5srp3764bbrhBM2bMUFlZ2SXcCwAAEMwMDTvl5eWy2+2aNWvWeee/9tprWrVqlTIzM/XWW28pMjJSo0aNUmVlpbfPlClTVFBQoJycHC1dulQ7d+7UM888c6l2AQAABLlQIzeenp6u9PT0887zeDxauXKlfvOb3+i2226TJM2fP1+9e/fWpk2bNHDgQB08eFBbtmzRO++8o6SkJEnSzJkzNXr0aE2bNk1XXHFFrepxuVx+74vVavV7WZhTXc4nAMDF1fTfWUPDzs85duyYHA6Hevfu7W2LiYlRt27dtGvXLg0cOFC7du1SkyZNvEFHknr37i2LxaLdu3erX79+tdrmnj17/Ko1MjJSnTt39mtZmFd+fr4qKiqMLgMAGrygDTsOh0OSFBcX59MeFxen4uJiSVJxcbGaN2/uMz80NFSxsbHe5WsjKSmJKzQIGLvdbnQJAGBqLperRhcqgjbsGMFqtRJ2EDCcSwAQHIL20fP4+HhJUklJiU97SUmJbDabJMlms+nEiRM+86urq3Xq1Cnv8gAAoGEL2rBz1VVXKT4+Xtu2bfO2/fDDD/ryyy+VkpIiSUpJSVFpaan27t3r7fP555/L7Xara9eul7xmAAAQfAy9jVVWVqbCwkLv9LFjx7R//37FxsaqdevWGjFihF599VW1bdtWV111lV566SW1aNHC+3RWhw4d1LdvXz399NPKysqS0+nU7NmzNXDgwFo/iQUAAMzJ0LCzd+9ejRgxwjs9b948SdLgwYOVnZ2tRx99VBUVFXrmmWdUWlqq66+/XsuWLVOjRo28yyxYsECzZ8/WyJEjZbFYdPvtt2vmzJmXfF8ABC+P26UQC2OoOA5oqAwNOz179lR+fv4F54eEhGjSpEmaNGnSBfs0bdpUCxcurI/yAJhEiMWq4neny1l8yOhSDBNmay/bkGyjywAMwdNYABoEZ/EhOb/bb3QZAAwQtAOUAQAAAoGwAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wA5iYy+02ugQAMBy/eg6YmNVi0cy1W3S46JTRpRimt721xt3Z3egyABiIsAOY3OGiUzrwvyeMLsMw18Q3MboEAAbjNhYAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1oA47L7/8sux2u8/njjvu8M6vrKxUVlaWevbsqZSUFE2YMEHFxcUGVgwAAIJNqNEFXMy1116rnJwc77TVavX+PXfuXG3evFmLFi1STEyMZs+erfHjxys3N9eIUgEAQBAK+rBjtVoVHx9/Tvvp06e1bt06LViwQKmpqZJ+DD8DBgxQXl6ekpOTL3GlAAAgGAV92Dly5IjS0tLUqFEjJScna/LkyWrdurX27t0rp9Op3r17e/t26NBBrVu39jvsuFwuv+v8zytOgFS38ylQOC/xU8FwXgKBUtPzOajDTteuXTVv3jy1a9dODodDr7zyioYPH67169eruLhYYWFhatKkic8ycXFxcjgcfm1vz549fi0XGRmpzp07+7UszCs/P18VFRWGbZ/zEudj9HkJGCGow056err3744dO6pbt266+eab9eGHHyoiIiLg20tKSuK/hBEwdrvd6BKAc3BewkxcLleNLlQEddj5qSZNmuiaa65RYWGhevfuLafTqdLSUp+rOyUlJecd41MTVquVsIOA4VxCMDL6vHS53bJagvpB4EuC43BpXVZhp6ysTEePHlV8fLy6dOmisLAwbdu2Tf3795ckHTp0SMePH2dwMgAEKavFoplrt+hw0SmjSzFMuxaxmvPrvkaX0aAEddh5/vnndfPNN6t169YqKirSyy+/LIvForvuuksxMTEaOnSosrOzFRsbq+joaM2ZM0cpKSmEHQAIYoeLTunA/54wugw0IEEddr777js9+eSTOnnypJo3b67rr79eb731lpo3by5JmjFjhiwWiyZOnKiqqiqlpaVp1qxZBlcNAACCSVCHnRdffPFn5zdq1EizZs0i4AAAgAtidBQAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AAJdQXEyEPG6X0WUEhUt1HIL65yIAADCbmIhwhVisKn53upzFh4wuxzBhtvayDcm+JNsi7AAAYABn8SE5v9tvdBkNArexAACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqZkm7KxZs0a33HKLkpKSdM8992j37t1GlwQAAIKAKcLOxo0bNW/ePI0bN07vvfeeOnbsqFGjRqmkpMTo0gAAgMFMEXZycnJ07733aujQoUpISFBWVpYiIiK0bt06o0sDAAAGCzW6gLqqqqrSvn37NGbMGG+bxWJR7969tWvXrhqtw+PxeNdltVr9qsNqteralrEKt4b4tbwZtIlrLJfLJWt8otyWcKPLMYw17hq5XC65XC6jS+G8FOflWcFyXnJOck6eFYhz8uyyZ7/HLyTEc7EeQe7777/XTTfdpNzcXKWkpHjb58+frx07dujtt9++6Dqqqqq0Z8+e+iwTAADUk6SkJIWHXzg4XvZXdgIhNDRUSUlJslgsCglpuP+1AQDA5cTj8cjtdis09OfjzGUfdpo1ayar1XrOYOSSkhLZbLYarcNisfxsIgQAAJevy36Acnh4uK677jpt27bN2+Z2u7Vt2zaf21oAAKBhuuyv7EjSQw89pIyMDHXp0kVdu3bVihUrVFFRoSFDhhhdGgAAMJgpws6AAQN04sQJLV68WA6HQ506ddKyZctqfBsLAACY12X/NBYAAMDPuezH7AAAAPwcwg4AADA1wg4AADA1wg4AADA1UzyNBeOdOHFCL730kjZv3qzi4mLFxsaqY8eOGjt2rK6//nqjy0MD5XA4tHTpUn366af6/vvvFRcXp06dOmnkyJFKTU01ujwAlwhhBwExYcIEOZ1OZWdnq02bNiopKdG2bdt08uRJo0tDA3Xs2DH96le/UpMmTTRt2jQlJiaqurpaW7duVVZWlj766COjS0QD8+2332rx4sXasmWLTp48qfj4eN16660aN26cmjVrZnR5psaj56iz0tJS9ejRQ6tWrdKNN95odDmAJOnRRx9Vfn6+PvroI0VFRfnMKy0tVZMmTQyqDA3R0aNHNWzYMF1zzTV6/PHHddVVV+nrr7/WCy+8IKfTqTfffFNNmzY1ukzTYswO6iwqKkpRUVHatGmTqqqqjC4H0MmTJ7VlyxYNHz78nKAjiaCDSy4rK0thYWF6/fXXdeONN6p169ZKT09XTk6Ovv/+e7344otGl2hqhB3UWWhoqLKzs/X+++/rhhtu0H333aff/e53OnDggNGloYEqLCyUx+NR+/btjS4F0MmTJ7V161b9+te/VkREhM+8+Ph4DRo0SB9++KG40VJ/CDsIiP79+2vLli169dVX1bdvX23fvl1DhgzRu+++a3RpaID40kAwOXLkiDwejzp06HDe+R06dNCpU6d04sSJS1xZw0HYQcA0atRIffr00bhx45Sbm6vBgwfr5ZdfNrosNEBt27ZVSEiIDh06ZHQpgNfFQnhYWNglqqThIeyg3iQkJKi8vNzoMtAANW3aVGlpaVqzZs15z8HS0lIDqkJDdfXVVyskJEQHDx487/yDBw+qefPmjCWrR4Qd1Nm///1vjRgxQn/605904MABHT16VB9++KGWLVumW2+91ejy0EDNmjVLbrdb99xzjz7++GN98803OnjwoFauXKlhw4YZXR4akGbNmqlPnz5au3atzpw54zPP4XBo/fr1Gjx4sEHVNQw8eo46q6qq0ssvv6z/+Z//UWFhoaqrq9WyZUvdcccdeuyxx84ZkAdcKkVFRd6XChYVFal58+a67rrr9OCDD6pnz55Gl4cG5JtvvtF9992nDh06nPPoudVq1Zo1a9S4cWOjyzQtwg4AAJfAsWPHtGTJEm3ZskUlJSXyeDy6/fbbNX/+fEVGRhpdnqkRdgAAMMDixYuVk5OjnJwcJScnG12OqRF2AAAwyLp163T69GmNGDFCFgvDaOsLYQcAAJgaMRIAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQfAZeXll1/WL3/5y3pZ9xdffCG73R7Q3846duyY7Ha79u/fH7B1AqidUKMLAGBe06dP13vvvXdOe1pampYvX25ARQAaIsIOgHrVt29fzZs3z6ctPDzcoGouzOl0Gl0CgHrCbSwA9So8PFzx8fE+n9jYWEmS3W5Xbm6uxowZo27duunOO+/Url27dOTIET3wwANKTk7Wfffdp8LCwnPWm5ubq/T0dHXr1k2TJk3S6dOnvfN2796thx56SD179tT111+v+++/X/v27fNZ3m63a+3atXrssceUnJyspUuXnrONiooKPfLII7rvvvu8t7befvtt3XnnnUpKStIdd9yhNWvW+Cyze/du3X333UpKStKQIUO4fQUEAcIOAEP9/ve/1y9/+Uu9//77at++vSZPnqxnnnlGo0eP1rp16+TxePTss8/6LFNYWKgPP/xQS5cu1bJly7R//35lZmZ655eVlenuu+/W2rVr9dZbb6lt27YaPXq0fvjhB5/1LFmyRP369dP69es1dOhQn3mlpaV66KGH5PF4lJOToyZNmuiDDz7QSy+9pCeeeEIbN27Uk08+qcWLF3tv1ZWVlWnMmDHq0KGD3n33XU2YMEHPP/98/Rw4ADXnAYB6kpGR4enUqZMnOTnZ5/Pqq696PB6PJzEx0fPiiy96++/atcuTmJjoefvtt71tGzZs8CQlJXmnFy9e7OnUqZPnu+++87Zt3rzZ07FjR09RUdF563C5XJ6UlBTP3/72N29bYmKi57nnnvPp9/nnn3sSExM9BQUFnkGDBnkmTJjgqays9M6/7bbbPOvXr/dZ5pVXXvEMGzbM4/F4PLm5uZ4bb7zRc+bMGe/8tWvXehITEz3/+te/Lnq8ANQPxuwAqFc9e/b0ueoiyXsbS/rxdtJZcXFxkqTExESftsrKSv3www+Kjo6WJLVq1UpXXHGFt09KSorcbrcOHz6s+Ph4FRcXa9GiRdq+fbtKSkrkdrtVUVGh48eP+9TRpUuX89b88MMPq2vXrnrxxRdltVolSeXl5SosLNRTTz2lp59+2tu3urpaMTExkqSDBw/KbrerUaNGPrUBMBZhB0C9ioyMVNu2bS84PywszPt3SEjIBdvcbneNt5mRkaGTJ0/qqaeeUuvWrRUeHq5hw4adMwg5KirqvMunp6frL3/5iwoKCrxhrLy8XJI0e/ZsdevWzac/v1YNBDf+HwrgsvPtt9/q+++/907n5eXJYrGoXbt2kqR//vOfeuCBB5Senq5rr71W4eHh+ve//13j9U+ZMkWDBw/Wgw8+qIKCAkmSzWZTixYtdPToUbVt29bn06ZNG0lShw4dlJ+fr8rKSp/aABiLsAOgXlVVVcnhcPh8Tpw4Uad1NmrUSNOnT9eBAwe0c+dOzZkzR3feeafi4+MlSddcc40++OADHTx4UF9++aWmTJmiiIiIWm0jIyNDgwYN0siRI3Xw4EFJ0sSJE/XHP/5RK1eu1OHDh5Wfn69169YpJydHknTXXXcpJCREM2fOVEFBgTZv3qzXX3+9TvsKoO64jQWgXm3ZskVpaWk+be3atdNHH33k9zqvvvpq9evXT48++qhOnTqlX/ziF5o1a5Z3/nPPPaenn35agwcPVqtWrfTEE09o/vz5td7OjBkz5Ha7NXLkSK1atUr33HOPIiIitHz5cs2fP19RUVFKTEzUyJEjJUmNGzfW0qVLNWvWLN19991KSEjQlClTNGHCBL/3FUDdhXg8Ho/RRQAAANQXbmMBAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABT+38n/j4gAklz3wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(data=titanic, x=\"Embarked\", hue=\"Survived\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUs_QbWyqBYr"
      },
      "outputs": [],
      "source": [
        "sns.countplot(data=titanic, x=\"Survived\", hue=\"Embarked\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xNxLEwxHt54"
      },
      "source": [
        "# Análisis multivariado: Buscamos correlaciones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "77IJemmYHxje",
        "outputId": "7d639c0f-44e8-4fc7-c030-bfe221092f81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
              "PassengerId     1.000000 -0.005007 -0.035144  0.034212 -0.057527 -0.001652   \n",
              "Survived       -0.005007  1.000000 -0.338481 -0.064910 -0.035322  0.081629   \n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.339898  0.083081  0.018443   \n",
              "Age             0.034212 -0.064910 -0.339898  1.000000 -0.233296 -0.172482   \n",
              "SibSp          -0.057527 -0.035322  0.083081 -0.233296  1.000000  0.414838   \n",
              "Parch          -0.001652  0.081629  0.018443 -0.172482  0.414838  1.000000   \n",
              "Fare            0.012658  0.257307 -0.549500  0.096688  0.159651  0.216225   \n",
              "\n",
              "                 Fare  \n",
              "PassengerId  0.012658  \n",
              "Survived     0.257307  \n",
              "Pclass      -0.549500  \n",
              "Age          0.096688  \n",
              "SibSp        0.159651  \n",
              "Parch        0.216225  \n",
              "Fare         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdb57811-364a-47f3-b1c1-b7d40eca3068\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>0.034212</td>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.064910</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.339898</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.034212</td>\n",
              "      <td>-0.064910</td>\n",
              "      <td>-0.339898</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.233296</td>\n",
              "      <td>-0.172482</td>\n",
              "      <td>0.096688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.233296</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.172482</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.096688</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
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              "    animation:\n",
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              "  }\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3824571839654439,\n        \"min\": -0.0575268337844415,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          -0.0050066607670665175,\n          -0.0016520124027188366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42395721445618667,\n        \"min\": -0.33848103596101514,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0050066607670665175,\n          1.0,\n          0.08162940708348335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.506096478938897,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.03514399403038102,\n          -0.33848103596101514,\n          0.018442671310748508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4471732159013244,\n        \"min\": -0.33989832839258594,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.03421211199737627,\n          -0.06491041993052588,\n          -0.17248195403694871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41032992843273136,\n        \"min\": -0.23329633125248891,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0575268337844415,\n          -0.035322498885735576,\n          0.41483769862015624\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38928955954868316,\n        \"min\": -0.17248195403694871,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0016520124027188366,\n          0.08162940708348335,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45580984533458957,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.012658219287491099,\n          0.2573065223849626,\n          0.21622494477076448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "#calculamos coeficiente de corr de Pearson\n",
        "titanic.corr(method=\"pearson\", numeric_only=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bddQolf5H-w1"
      },
      "source": [
        "Observamos que faltan algunos atributos importantes como el Genero (sex) y Embarked porque son categórico... lo transformamos\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "pRFbvvAITBCC",
        "outputId": "b2df2de2-650b-4e63-f3fc-c57912ec26c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      0\n",
              "      ..\n",
              "886    0\n",
              "887    1\n",
              "888    1\n",
              "889    0\n",
              "890    0\n",
              "Name: Sex, Length: 891, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "# sex\n",
        "titanic[\"Sex\"].map( {\"female\": 1, \"male\": 0} ).astype(int)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZZxNSxwKIJnS"
      },
      "outputs": [],
      "source": [
        "titanic[\"Sex\"] = titanic[\"Sex\"].map( {\"female\": 1, \"male\": 0} ).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hAM-rIMKIMDc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "901c50ee-a8e8-4619-a796-74939822509e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name  Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    0  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina    1  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1   \n",
              "4                             Allen, Mr. William Henry    0  35.0      0   \n",
              "..                                                 ...  ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    0  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith    1  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"    1  28.0      1   \n",
              "889                              Behr, Mr. Karl Howell    0  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    0  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked         FareBand  \n",
              "0        0         A/5 21171   7.2500   NaN        S   (-0.001, 7.91]  \n",
              "1        0          PC 17599  71.2833   C85        C  (31.0, 512.329]  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S   (7.91, 14.454]  \n",
              "3        0            113803  53.1000  C123        S  (31.0, 512.329]  \n",
              "4        0            373450   8.0500   NaN        S   (7.91, 14.454]  \n",
              "..     ...               ...      ...   ...      ...              ...  \n",
              "886      0            211536  13.0000   NaN        S   (7.91, 14.454]  \n",
              "887      0            112053  30.0000   B42        S   (14.454, 31.0]  \n",
              "888      2        W./C. 6607  23.4500   NaN        S   (14.454, 31.0]  \n",
              "889      0            111369  30.0000  C148        C   (14.454, 31.0]  \n",
              "890      0            370376   7.7500   NaN        Q   (-0.001, 7.91]  \n",
              "\n",
              "[891 rows x 13 columns]"
            ],
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              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973194,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FareBand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"(31.0, 512.329]\",\n          \"(14.454, 31.0]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "titanic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oPDxsmKlIU9A"
      },
      "outputs": [],
      "source": [
        "#Embarked\n",
        "titanic[\"Embarked\"]\n",
        "\n",
        "titanic['Embarked'] = titanic['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zq7IiO4YIwX5"
      },
      "outputs": [],
      "source": [
        "titanic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJ89zK9cI3uw"
      },
      "source": [
        "Vemos la correlación nuevamente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "nzcjpHJyI50Q",
        "outputId": "5d3799ee-e518-40e6-f954-15b3b38ea3da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
              "PassengerId     1.000000 -0.005007 -0.035144 -0.042939  0.034212 -0.057527   \n",
              "Survived       -0.005007  1.000000 -0.338481  0.543351 -0.064910 -0.035322   \n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.131900 -0.339898  0.083081   \n",
              "Sex            -0.042939  0.543351 -0.131900  1.000000 -0.081163  0.114631   \n",
              "Age             0.034212 -0.064910 -0.339898 -0.081163  1.000000 -0.233296   \n",
              "SibSp          -0.057527 -0.035322  0.083081  0.114631 -0.233296  1.000000   \n",
              "Parch          -0.001652  0.081629  0.018443  0.245489 -0.172482  0.414838   \n",
              "Fare            0.012658  0.257307 -0.549500  0.182333  0.096688  0.159651   \n",
              "Embarked       -0.030467  0.106811  0.045702  0.116569 -0.009165 -0.059961   \n",
              "\n",
              "                Parch      Fare  Embarked  \n",
              "PassengerId -0.001652  0.012658 -0.030467  \n",
              "Survived     0.081629  0.257307  0.106811  \n",
              "Pclass       0.018443 -0.549500  0.045702  \n",
              "Sex          0.245489  0.182333  0.116569  \n",
              "Age         -0.172482  0.096688 -0.009165  \n",
              "SibSp        0.414838  0.159651 -0.059961  \n",
              "Parch        1.000000  0.216225 -0.078665  \n",
              "Fare         0.216225  1.000000  0.062142  \n",
              "Embarked    -0.078665  0.062142  1.000000  "
            ],
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              "      <th>PassengerId</th>\n",
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              "      <th>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.042939</td>\n",
              "      <td>0.034212</td>\n",
              "      <td>-0.057527</td>\n",
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              "      <td>-0.030467</td>\n",
              "    </tr>\n",
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              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
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              "    </tr>\n",
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              "      <th>Sex</th>\n",
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              "      <td>0.543351</td>\n",
              "      <td>-0.131900</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.081163</td>\n",
              "      <td>0.114631</td>\n",
              "      <td>0.245489</td>\n",
              "      <td>0.182333</td>\n",
              "      <td>0.116569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.034212</td>\n",
              "      <td>-0.064910</td>\n",
              "      <td>-0.339898</td>\n",
              "      <td>-0.081163</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.233296</td>\n",
              "      <td>-0.172482</td>\n",
              "      <td>0.096688</td>\n",
              "      <td>-0.009165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.114631</td>\n",
              "      <td>-0.233296</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>-0.059961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>0.245489</td>\n",
              "      <td>-0.172482</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>-0.078665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.182333</td>\n",
              "      <td>0.096688</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>-0.030467</td>\n",
              "      <td>0.106811</td>\n",
              "      <td>0.045702</td>\n",
              "      <td>0.116569</td>\n",
              "      <td>-0.009165</td>\n",
              "      <td>-0.059961</td>\n",
              "      <td>-0.078665</td>\n",
              "      <td>0.062142</td>\n",
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              "\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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33981129339343985,\n        \"min\": -0.0575268337844415,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.012658219287491099,\n          -0.0050066607670665175,\n          -0.0575268337844415\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3927801703869347,\n        \"min\": -0.33848103596101514,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.2573065223849626,\n          1.0,\n          -0.035322498885735576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.440624150994496,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.5494996199439076,\n          -0.33848103596101514,\n          0.08308136284568686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35713603780428826,\n        \"min\": -0.13190049060473927,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.18233283380287013,\n          0.5433513806577549,\n          0.11463081038942237\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3897490961993397,\n        \"min\": -0.33989832839258594,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.0966884221803649,\n          -0.06491041993052588,\n          -0.23329633125248891\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36515890057518297,\n        \"min\": -0.23329633125248891,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.159651043242161,\n          -0.035322498885735576,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35211468449192235,\n        \"min\": -0.17248195403694871,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.21622494477076448,\n          0.08162940708348335,\n          0.41483769862015624\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.396454904028999,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          0.2573065223849626,\n          0.159651043242161\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3342977454417153,\n        \"min\": -0.07866469555645676,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.06214157262848517,\n          0.10681138570891952,\n          -0.05996144194119927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "#calculamos coeficiente de corr de Pearson\n",
        "titanic.corr(method=\"pearson\", numeric_only=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrOVXVLqJQbL"
      },
      "outputs": [],
      "source": [
        "# aramos una columna que represente la familia\n",
        "titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch'] + 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "3h297_HJJTwa",
        "outputId": "0d364da5-0735-4d99-9191-f18aa51ca26f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name  Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    0  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina    1  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1   \n",
              "4                             Allen, Mr. William Henry    0  35.0      0   \n",
              "..                                                 ...  ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    0  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith    1  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"    1  28.0      1   \n",
              "889                              Behr, Mr. Karl Howell    0  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    0  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin  Embarked         FareBand  \\\n",
              "0        0         A/5 21171   7.2500   NaN         0   (-0.001, 7.91]   \n",
              "1        0          PC 17599  71.2833   C85         1  (31.0, 512.329]   \n",
              "2        0  STON/O2. 3101282   7.9250   NaN         0   (7.91, 14.454]   \n",
              "3        0            113803  53.1000  C123         0  (31.0, 512.329]   \n",
              "4        0            373450   8.0500   NaN         0   (7.91, 14.454]   \n",
              "..     ...               ...      ...   ...       ...              ...   \n",
              "886      0            211536  13.0000   NaN         0   (7.91, 14.454]   \n",
              "887      0            112053  30.0000   B42         0   (14.454, 31.0]   \n",
              "888      2        W./C. 6607  23.4500   NaN         0   (14.454, 31.0]   \n",
              "889      0            111369  30.0000  C148         1   (14.454, 31.0]   \n",
              "890      0            370376   7.7500   NaN         2   (-0.001, 7.91]   \n",
              "\n",
              "     FamilySize  \n",
              "0             2  \n",
              "1             2  \n",
              "2             1  \n",
              "3             2  \n",
              "4             1  \n",
              "..          ...  \n",
              "886           1  \n",
              "887           1  \n",
              "888           4  \n",
              "889           1  \n",
              "890           1  \n",
              "\n",
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              "      <th>PassengerId</th>\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    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-2a3b9166-8655-4bdd-a6e0-05f98d5e5a74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ac81aa9e-7824-4062-b6c7-6290bc167541\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('titanic')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ac81aa9e-7824-4062-b6c7-6290bc167541 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('titanic');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973194,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FareBand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"(31.0, 512.329]\",\n          \"(14.454, 31.0]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FamilySize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "titanic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDsuJVnuJi7s"
      },
      "source": [
        "Limpiamos columnas que creemos que no van a servir para el clasificador"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hhaf9DPUJoQJ"
      },
      "outputs": [],
      "source": [
        "titanic.drop(labels = [\"PassengerId\", \"Cabin\", \"Ticket\", \"Name\"], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_D0NY2Z9Ju6U"
      },
      "outputs": [],
      "source": [
        "titanic.drop(labels = [\"Fare\", \"FareBand\"], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9F1p-gJYJ0MW"
      },
      "outputs": [],
      "source": [
        "titanic.drop(labels = [\"SibSp\", \"Parch\"], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hnDC-zggJ11S",
        "outputId": "d6eef39b-9550-4597-b7aa-6cb9f7022b8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass  Sex   Age  Embarked  FamilySize\n",
              "0           0       3    0  22.0         0           2\n",
              "1           1       1    1  38.0         1           2\n",
              "2           1       3    1  26.0         0           1\n",
              "3           1       1    1  35.0         0           2\n",
              "4           0       3    0  35.0         0           1\n",
              "..        ...     ...  ...   ...       ...         ...\n",
              "886         0       2    0  27.0         0           1\n",
              "887         1       1    1  19.0         0           1\n",
              "888         0       3    1  28.0         0           4\n",
              "889         1       1    0  26.0         1           1\n",
              "890         0       3    0  32.0         2           1\n",
              "\n",
              "[891 rows x 6 columns]"
            ],
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              "  <div id=\"df-af638899-a469-4a92-811f-1e0cff0427dc\" class=\"colab-df-container\">\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>35.0</td>\n",
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              "      <th>...</th>\n",
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              "      <th>886</th>\n",
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              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
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              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        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-7c1e9aee-b29e-456a-9f74-b6ad40429f6e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7c1e9aee-b29e-456a-9f74-b6ad40429f6e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "      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-7c1e9aee-b29e-456a-9f74-b6ad40429f6e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f76d58bf-ffed-4230-8980-d6caba6cec37\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('titanic')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f76d58bf-ffed-4230-8980-d6caba6cec37 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('titanic');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973194,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FamilySize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "titanic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08XzEXxSKWAC"
      },
      "source": [
        "# 4. Modelado\n",
        "\n",
        "**Pasos:**\n",
        "*    Selección del Modelo: Elegir el modelo de machine learning apropiado.\n",
        "*    Entrenamiento del Modelo: Dividir el dataset en conjuntos de entrenamiento y prueba, y entrenar el modelo.\n",
        "*    Predicción: Usar el modelo entrenado para hacer predicciones.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Byp8uKbsKaeB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ctqi0JrtKeti"
      },
      "outputs": [],
      "source": [
        "X = titanic.drop('Survived', axis=1)\n",
        "y = titanic['Survived']\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0iXS0ERDuyxA",
        "outputId": "629840ed-191b-4d32-a8e0-294a718f2c75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass  Sex   Age  Embarked  FamilySize\n",
              "0         3    0  22.0         0           2\n",
              "1         1    1  38.0         1           2\n",
              "2         3    1  26.0         0           1\n",
              "3         1    1  35.0         0           2\n",
              "4         3    0  35.0         0           1\n",
              "..      ...  ...   ...       ...         ...\n",
              "886       2    0  27.0         0           1\n",
              "887       1    1  19.0         0           1\n",
              "888       3    1  28.0         0           4\n",
              "889       1    0  26.0         1           1\n",
              "890       3    0  32.0         2           1\n",
              "\n",
              "[891 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Pclass</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>35.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>2</td>\n",
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              "      <th>887</th>\n",
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              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea83610e-5c27-4d9a-8e2f-45017f794350')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ea83610e-5c27-4d9a-8e2f-45017f794350');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
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              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      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",
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              "    (() => {\n",
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              "        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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
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            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
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      "cell_type": "code",
      "source": [
        "y"
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      "metadata": {
        "colab": {
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          "height": 458
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        "id": "Vt_igfMYu4os",
        "outputId": "2abd57c0-5475-4001-89f8-3fe7d8229af7"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "221jKnydu7PL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kob6YsqtLACJ"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "rPp5-PpCLEaM",
        "outputId": "7b38a26e-26f8-4793-af31-8fcb8af34a16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
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              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
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              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Evaluación\n",
        "\n",
        "**Pasos:**\n",
        "*   Métricas de Evaluación: Utilizar métricas como accuracy, matriz de confusión, y reporte de clasificación.\n",
        "*   Explicación de la Evaluación\n",
        "Accuracy: La proporción de predicciones correctas entre el total de predicciones.\n",
        "*   Matriz de Confusión: Muestra los verdaderos positivos, falsos positivos, verdaderos negativos y falsos negativos.\n"
      ],
      "metadata": {
        "id": "q34c9Qztgr44"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzRx9yRwLPwC",
        "outputId": "525b4ae7-c0b5-4b26-955e-921d228d0d63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7877094972067039\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jq1SQzjaLir3"
      },
      "source": [
        "vamos a predecir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2XzEb6svLxvb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gRR-vFIRL2nN"
      },
      "outputs": [],
      "source": [
        "# clase\n",
        "# genero\n",
        "# edad\n",
        "# embarque\n",
        "# family size\n",
        "new_data = np.array([1, 0, 3.0, 3, 4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "an61xaFwMGRs",
        "outputId": "5385a4b7-b6a8-46bd-94ff-e4af760dfddb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: [1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "new_data=new_data.reshape(1, -1)\n",
        "prediction = model.predict(new_data)\n",
        "print('Prediction:', prediction)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "gO19oBDbMaGn",
        "outputId": "49e8aea7-6a41-44d9-f9bb-a6e5364613dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(model)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Despliegue\n",
        "\n",
        "**Pasos:**\n",
        "*   Guardado del Modelo: Guardar el modelo entrenado para uso futuro.\n",
        "*   Carga del Modelo: Demostrar cómo cargar el modelo guardado.\n"
      ],
      "metadata": {
        "id": "V-lRolj5g-aT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "Dxe4cDrNhYN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Guardar el modelo\n",
        "joblib.dump(model, 'titanic_model.pkl')\n",
        "\n",
        "# Cargar el modelo (ejemplo)\n",
        "loaded_model = joblib.load('titanic_model.pkl')\n"
      ],
      "metadata": {
        "id": "lK44EcvHhVEp"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}