18_RandomForest.ipynb

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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/GustavoBD-Dev/AnalyticalModelsWithPythonCourse/blob/Session-6/18_RandomForest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Bosques Aleatorios**"
      ],
      "metadata": {
        "id": "N7tzOjKd3rqr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Ejemplo: Dataset *Titanic***"
      ],
      "metadata": {
        "id": "HqTA0lUt3roJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "file_id = '1z0ENAU-18OtvLFSI4Gs52eMoachtGdBq'\n",
        "raw_url = f'https://drive.google.com/uc?id={file_id}&export=download'"
      ],
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        "id": "XsTpZg_I8JXH"
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      "execution_count": null,
      "outputs": []
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        "id": "PhUTWdoswuCV",
        "outputId": "c4256d45-ba0f-4e9a-bdfd-c404c5c9cf84"
      },
      "outputs": [
        {
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              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   711.000000  711.000000  711.000000  711.000000  711.000000   \n",
              "mean    448.483826    0.403657    2.241913   29.621899    0.514768   \n",
              "std     258.849806    0.490976    0.836147   14.493108    0.931147   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     222.500000    0.000000    1.000000   20.000000    0.000000   \n",
              "50%     444.000000    0.000000    2.000000   28.000000    0.000000   \n",
              "75%     677.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    5.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  711.000000  711.000000  \n",
              "mean     0.431786   34.534323  \n",
              "std      0.854517   52.968619  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    8.050000  \n",
              "50%      0.000000   15.550000  \n",
              "75%      1.000000   32.750000  \n",
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              "      <th>mean</th>\n",
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              "      <td>0.403657</td>\n",
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              "      <td>29.621899</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>222.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>444.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.550000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>677.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>32.750000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.72561968654225,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          448.48382559774967,\n          444.0,\n          711.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 251.23060286812972,\n        \"min\": 0.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.40365682137834036,\n          1.0,\n          0.4909755914078956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 250.71742650747447,\n        \"min\": 0.8361472666583222,\n        \"max\": 711.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          711.0,\n          2.241912798874824,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 241.86281157598285,\n        \"min\": 0.42,\n        \"max\": 711.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.62189873417721,\n          28.0,\n          711.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 251.0058523502335,\n        \"min\": 0.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          711.0,\n          0.5147679324894515,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 250.9659391718171,\n        \"min\": 0.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          711.0,\n          0.4317862165963432,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277.67691775305497,\n        \"min\": 0.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.534323206751054,\n          15.55,\n          711.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Cargamos el dataset\n",
        "df= pd.read_csv(raw_url)\n",
        "# Descripción genereal del conjunto de datos\n",
        "df.describe()"
      ]
    },
    {
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      "source": [
        "df.head()"
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            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>35.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 258,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 711,\n        \"samples\": [\n          643,\n          497,\n          263\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\": 711,\n        \"samples\": [\n          \"Skoog, Miss. Margit Elizabeth\",\n          \"Eustis, Miss. Elizabeth Mussey\"\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\": 14.493108101648481,\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\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\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\": 541,\n        \"samples\": [\n          \"345364\",\n          \"244367\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.968618723425166,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          7.875,\n          146.5208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"C7\",\n          \"C104\"\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}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cambiamos la variable categórica Sex por la variable numérica Sex_male (Donde male = 1, fremale = 0)\n",
        "df = pd.get_dummies(df, columns=['Sex'], drop_first=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3RBHZCIW32vP",
        "outputId": "68df9ca3-9e95-4523-c310-7c3718ca290d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "                                                Name   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
              "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare Cabin Embarked  Sex_male  \n",
              "0         A/5 21171   7.2500   NaN        S      True  \n",
              "1          PC 17599  71.2833   C85        C     False  \n",
              "2  STON/O2. 3101282   7.9250   NaN        S     False  \n",
              "3            113803  53.1000  C123        S     False  \n",
              "4            373450   8.0500   NaN        S      True  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 258,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 711,\n        \"samples\": [\n          643,\n          497,\n          263\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\": 711,\n        \"samples\": [\n          \"Skoog, Miss. Margit Elizabeth\",\n          \"Eustis, Miss. Elizabeth Mussey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.493108101648481,\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\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\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\": 541,\n        \"samples\": [\n          \"345364\",\n          \"244367\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.968618723425166,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          7.875,\n          146.5208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"C7\",\n          \"C104\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seleccionamos las características para el modelo\n",
        "data = df[['Sex_male', 'Fare', 'Age','Pclass', 'SibSp','Survived']]\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t775kszF33zx",
        "outputId": "c8fe559d-a8c2-4272-f5a1-ac0f953c395b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sex_male     Fare   Age  Pclass  SibSp  Survived\n",
              "0      True   7.2500  22.0       3      1         0\n",
              "1     False  71.2833  38.0       1      1         1\n",
              "2     False   7.9250  26.0       3      0         1\n",
              "3     False  53.1000  35.0       1      1         1\n",
              "4      True   8.0500  35.0       3      0         0"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "     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-68a08b57-3f28-47f0-b169-1fb7e3554938 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",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"Sex_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.968618723425166,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          7.875,\n          146.5208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.493108101648481,\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\": \"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\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Información del dataset\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwFqcpxv345H",
        "outputId": "170fbc43-0f2f-4e26-9d56-92bc34e83948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 711 entries, 0 to 710\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Sex_male  711 non-null    bool   \n",
            " 1   Fare      711 non-null    float64\n",
            " 2   Age       711 non-null    float64\n",
            " 3   Pclass    711 non-null    int64  \n",
            " 4   SibSp     711 non-null    int64  \n",
            " 5   Survived  711 non-null    int64  \n",
            "dtypes: bool(1), float64(2), int64(3)\n",
            "memory usage: 28.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividimos los datos en entrenamiento y prueba\n",
        "from sklearn.model_selection import train_test_split\n",
        "# X son nuestras variables independientes\n",
        "X = data.drop([\"Survived\"],axis = 1)\n",
        "\n",
        "# y es nuestra variable dependiente\n",
        "y = data.Survived\n",
        "\n",
        "# División 75% de datos para entrenamiento, 25% de daatos para test\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0)"
      ],
      "metadata": {
        "id": "N8bWSM9c363A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creaamos el modelo de Bosques Aleatorios (y configuramos el número de estimadores (árboles de decisión))\n",
        "BA_model = RandomForestClassifier(n_estimators = 19,\n",
        "                                  random_state = 2016,\n",
        "                                  min_samples_leaf = 8,)"
      ],
      "metadata": {
        "id": "tR4VbcnX38YZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Entrenamiento**"
      ],
      "metadata": {
        "id": "HXw_dEiA3-X_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BA_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "e4_ZR3tK4ARO",
        "outputId": "169976a8-4482-4d24-947c-a91ca776bbd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_leaf=8, n_estimators=19, random_state=2016)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_leaf=8, n_estimators=19, random_state=2016)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=8, n_estimators=19, random_state=2016)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Accuracy promedio (Usando datos de Test)**"
      ],
      "metadata": {
        "id": "7cx7IHWa4DYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy promedio\n",
        "BA_model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umiyPVpz4HqB",
        "outputId": "da875655-dc8f-4f06-e149-1f35e4d1e9a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.848314606741573"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Matriz de Confusión**"
      ],
      "metadata": {
        "id": "-P5_XRP34ITo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Predicción del modelo usando los datos de prueba\n",
        "y_pred = BA_model.predict(X_test)\n",
        "matriz = confusion_matrix(y_test,y_pred)\n",
        "\n",
        "plot_confusion_matrix(conf_mat=matriz, figsize=(6,6), show_normed=False)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "06bVS3k14LTA",
        "outputId": "493cbeef-69d9-457c-d81f-3c30427e720a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}