XGBoost.ipynb

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        "<a href=\"https://colab.research.google.com/github/juliasalto/proyectoml/blob/main/XGBoost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "En este notebook desarrollo la construcción de un modelo XGBoost para el proyecto"
      ],
      "metadata": {
        "id": "UoDKdLQNBozU"
      }
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      "execution_count": null,
      "metadata": {
        "id": "9CFSvxTvjLLa"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.metrics as metrics\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats\n",
        "import plotly.express as px\n",
        "import plotly.figure_factory as ff\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "from google.colab import files\n",
        "from google.colab import auth\n",
        "from google.cloud import bigquery"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/rh_data - rh_data (2).csv\")"
      ],
      "metadata": {
        "id": "UaXjyu3ojP81"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
      ],
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        "id": "VjaUh4xjepTt",
        "outputId": "76ad2f4e-52fd-4687-994a-0d9448b29e3f"
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              "   Age AgeCategory Attrition     BusinessTravel                Department  \\\n",
              "0   51           D        No      Travel_Rarely                     Sales   \n",
              "1   31           B       Yes  Travel_Frequently  Research and Development   \n",
              "2   32           B        No  Travel_Frequently  Research and Development   \n",
              "3   38           C        No         Non-Travel  Research and Development   \n",
              "4   32           B        No      Travel_Rarely  Research and Development   \n",
              "\n",
              "   DistanceFromHome    DistanceCategory  Education EducationCategory  \\\n",
              "0                 6  Mid-Short Distance          2         Graduated   \n",
              "1                10   Mid-Long Distance          1    Undergraduated   \n",
              "2                17       Long Distance          4           Mastery   \n",
              "3                 2      Short Distance          5         Doctorate   \n",
              "4                10   Mid-Long Distance          1    Undergraduated   \n",
              "\n",
              "  EducationField  ...  Over18  PercentSalaryHike StandardHours  \\\n",
              "0  Life Sciences  ...       Y                 11             8   \n",
              "1  Life Sciences  ...       Y                 23             8   \n",
              "2          Other  ...       Y                 15             8   \n",
              "3  Life Sciences  ...       Y                 11             8   \n",
              "4        Medical  ...       Y                 12             8   \n",
              "\n",
              "   StockOptionLevel TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
              "0                 0               1.0                     6              1   \n",
              "1                 1               6.0                     3              5   \n",
              "2                 3               5.0                     2              5   \n",
              "3                 3              13.0                     5              8   \n",
              "4                 2               9.0                     2              6   \n",
              "\n",
              "   CategoryYearsAtCompany YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                       A                       0                     0  \n",
              "1                       B                       1                     4  \n",
              "2                       B                       0                     3  \n",
              "3                       C                       7                     5  \n",
              "4                       C                       0                     4  \n",
              "\n",
              "[5 rows x 30 columns]"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4410 entries, 0 to 4409\n",
            "Data columns (total 30 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Age                      4410 non-null   int64  \n",
            " 1   AgeCategory              4410 non-null   object \n",
            " 2   Attrition                4410 non-null   object \n",
            " 3   BusinessTravel           4410 non-null   object \n",
            " 4   Department               4410 non-null   object \n",
            " 5   DistanceFromHome         4410 non-null   int64  \n",
            " 6   DistanceCategory         4410 non-null   object \n",
            " 7   Education                4410 non-null   int64  \n",
            " 8   EducationCategory        4410 non-null   object \n",
            " 9   EducationField           4410 non-null   object \n",
            " 10  EmployeeCount            4410 non-null   int64  \n",
            " 11  EmployeeID               4410 non-null   int64  \n",
            " 12  Gender                   4410 non-null   object \n",
            " 13  JobLevel                 4410 non-null   int64  \n",
            " 14  LevelCategory            4410 non-null   object \n",
            " 15  JobRole                  4410 non-null   object \n",
            " 16  MaritalStatus            4410 non-null   object \n",
            " 17  MonthlyIncome            4410 non-null   int64  \n",
            " 18  IncomeCategory           4410 non-null   object \n",
            " 19  NumCompaniesWorked       4391 non-null   float64\n",
            " 20  Over18                   4410 non-null   object \n",
            " 21  PercentSalaryHike        4410 non-null   int64  \n",
            " 22  StandardHours            4410 non-null   int64  \n",
            " 23  StockOptionLevel         4410 non-null   int64  \n",
            " 24  TotalWorkingYears        4401 non-null   float64\n",
            " 25  TrainingTimesLastYear    4410 non-null   int64  \n",
            " 26  YearsAtCompany           4410 non-null   int64  \n",
            " 27  CategoryYearsAtCompany   4410 non-null   object \n",
            " 28  YearsSinceLastPromotion  4410 non-null   int64  \n",
            " 29  YearsWithCurrManager     4410 non-null   int64  \n",
            "dtypes: float64(2), int64(14), object(14)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "F0gnx2dde1Ak",
        "outputId": "09f23095-426c-489e-afc5-8f1d95056ccc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Age  DistanceFromHome    Education  EmployeeCount   EmployeeID  \\\n",
              "count  4410.000000       4410.000000  4410.000000         4410.0  4410.000000   \n",
              "mean     36.923810          9.192517     2.912925            1.0  2205.500000   \n",
              "std       9.133301          8.105026     1.023933            0.0  1273.201673   \n",
              "min      18.000000          1.000000     1.000000            1.0     1.000000   \n",
              "25%      30.000000          2.000000     2.000000            1.0  1103.250000   \n",
              "50%      36.000000          7.000000     3.000000            1.0  2205.500000   \n",
              "75%      43.000000         14.000000     4.000000            1.0  3307.750000   \n",
              "max      60.000000         29.000000     5.000000            1.0  4410.000000   \n",
              "\n",
              "          JobLevel  MonthlyIncome  NumCompaniesWorked  PercentSalaryHike  \\\n",
              "count  4410.000000    4410.000000         4391.000000        4410.000000   \n",
              "mean      2.063946   65029.312925            2.694830          15.209524   \n",
              "std       1.106689   47068.888559            2.498887           3.659108   \n",
              "min       1.000000   10090.000000            0.000000          11.000000   \n",
              "25%       1.000000   29110.000000            1.000000          12.000000   \n",
              "50%       2.000000   49190.000000            2.000000          14.000000   \n",
              "75%       3.000000   83800.000000            4.000000          18.000000   \n",
              "max       5.000000  199990.000000            9.000000          25.000000   \n",
              "\n",
              "       StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
              "count         4410.0       4410.000000        4401.000000   \n",
              "mean             8.0          0.793878          11.279936   \n",
              "std              0.0          0.851883           7.782222   \n",
              "min              8.0          0.000000           0.000000   \n",
              "25%              8.0          0.000000           6.000000   \n",
              "50%              8.0          1.000000          10.000000   \n",
              "75%              8.0          1.000000          15.000000   \n",
              "max              8.0          3.000000          40.000000   \n",
              "\n",
              "       TrainingTimesLastYear  YearsAtCompany  YearsSinceLastPromotion  \\\n",
              "count            4410.000000     4410.000000              4410.000000   \n",
              "mean                2.799320        7.008163                 2.187755   \n",
              "std                 1.288978        6.125135                 3.221699   \n",
              "min                 0.000000        0.000000                 0.000000   \n",
              "25%                 2.000000        3.000000                 0.000000   \n",
              "50%                 3.000000        5.000000                 1.000000   \n",
              "75%                 3.000000        9.000000                 3.000000   \n",
              "max                 6.000000       40.000000                15.000000   \n",
              "\n",
              "       YearsWithCurrManager  \n",
              "count           4410.000000  \n",
              "mean               4.123129  \n",
              "std                3.567327  \n",
              "min                0.000000  \n",
              "25%                2.000000  \n",
              "50%                3.000000  \n",
              "75%                7.000000  \n",
              "max               17.000000  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>Education</th>\n",
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              "      <th>EmployeeID</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.0</td>\n",
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              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36.923810</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2205.500000</td>\n",
              "      <td>2.063946</td>\n",
              "      <td>65029.312925</td>\n",
              "      <td>2.694830</td>\n",
              "      <td>15.209524</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.793878</td>\n",
              "      <td>11.279936</td>\n",
              "      <td>2.799320</td>\n",
              "      <td>7.008163</td>\n",
              "      <td>2.187755</td>\n",
              "      <td>4.123129</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.133301</td>\n",
              "      <td>8.105026</td>\n",
              "      <td>1.023933</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1273.201673</td>\n",
              "      <td>1.106689</td>\n",
              "      <td>47068.888559</td>\n",
              "      <td>2.498887</td>\n",
              "      <td>3.659108</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.851883</td>\n",
              "      <td>7.782222</td>\n",
              "      <td>1.288978</td>\n",
              "      <td>6.125135</td>\n",
              "      <td>3.221699</td>\n",
              "      <td>3.567327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10090.000000</td>\n",
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              "      <td>11.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1103.250000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>49190.000000</td>\n",
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              "      <td>8.0</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>14.000000</td>\n",
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              "      <td>1.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1547.4757495510028,\n        \"min\": 9.133301271011144,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          36.923809523809524,\n          36.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DistanceFromHome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1555.6444661348714,\n        \"min\": 1.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.19251700680272,\n          7.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1558.214606633186,\n        \"min\": 1.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.912925170068027,\n          3.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EmployeeCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1558.8674460279533,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4410.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EmployeeID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1589.0805476546325,\n        \"min\": 1.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4410.0,\n          2205.5,\n          3307.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobLevel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1558.404797573631,\n        \"min\": 1.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4410.0,\n          2.0639455782312925,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62104.76922790216,\n        \"min\": 4410.0,\n        \"max\": 199990.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          65029.31292517007,\n          49190.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumCompaniesWorked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1551.3848574629706,\n        \"min\": 0.0,\n        \"max\": 4391.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.6948303347756775,\n          2.0,\n          4391.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PercentSalaryHike\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1554.1887032359211,\n        \"min\": 3.659107516298374,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.209523809523809,\n          14.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StandardHours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1556.748603431432,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4410.0,\n          8.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockOptionLevel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1558.835067961458,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4410.0,\n          0.7938775510204081,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalWorkingYears\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1551.4852278651285,\n        \"min\": 0.0,\n        \"max\": 4401.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.279936378095888,\n          10.0,\n          4401.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TrainingTimesLastYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1558.2578069712736,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4410.0,\n          2.7993197278911564,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearsAtCompany\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1555.6786454268577,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.0081632653061225,\n          5.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearsSinceLastPromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1557.9451560324437,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4410.0,\n          2.1877551020408164,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearsWithCurrManager\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1557.3259464582432,\n        \"min\": 0.0,\n        \"max\": 4410.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.12312925170068,\n          3.0,\n          4410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imputación de nulos"
      ],
      "metadata": {
        "id": "1T47_V2zBufl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_total_working_years = df['TotalWorkingYears'].mean()\n",
        "\n",
        "df['TotalWorkingYears'].fillna(mean_total_working_years, inplace=True)\n",
        "print(df['TotalWorkingYears'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yV8PF1_qe4Ar",
        "outputId": "22b0e13c-1e0e-45f6-fb22-0735dd4ba2ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_num_companies = df['NumCompaniesWorked'].mean()\n",
        "\n",
        "df['NumCompaniesWorked'].fillna(mean_num_companies, inplace=True)\n",
        "print(df['NumCompaniesWorked'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bs3p_5UHe7O3",
        "outputId": "eebe8792-a68c-4e07-ef23-2d91085cce10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conteo de variables categóricas"
      ],
      "metadata": {
        "id": "rOL6I7AaBxOP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.groupby('Attrition').size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0TeBu1XVe-q9",
        "outputId": "5edb1074-c226-43c9-fd8a-4af6636760e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attrition\n",
            "No     3699\n",
            "Yes     711\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variables = ['AgeCategory', 'BusinessTravel', 'JobRole', 'EducationCategory', 'EducationField', 'LevelCategory', 'Department', 'MaritalStatus', 'IncomeCategory', 'EducationCategory', 'DistanceCategory']\n",
        "\n",
        "df_attrition_yes = df[df['Attrition'] == 'Yes']\n",
        "\n",
        "for variable in variables:\n",
        "    print(f\"Conteo para {variable} con Attrition = Yes:\")\n",
        "    print(df_attrition_yes[variable].value_counts())\n",
        "    print(\"\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfnPMIxcfHpN",
        "outputId": "112cdda6-c826-4e3f-ceca-cfb5c909d8b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conteo para AgeCategory con Attrition = Yes:\n",
            "AgeCategory\n",
            "A    300\n",
            "B    198\n",
            "D    126\n",
            "C     87\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para BusinessTravel con Attrition = Yes:\n",
            "BusinessTravel\n",
            "Travel_Rarely        468\n",
            "Travel_Frequently    207\n",
            "Non-Travel            36\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para JobRole con Attrition = Yes:\n",
            "JobRole\n",
            "Sales Executive              165\n",
            "Research Scientist           159\n",
            "Laboratory Technician        126\n",
            "Healthcare Representative     57\n",
            "Research Director             57\n",
            "Manufacturing Director        48\n",
            "Manager                       42\n",
            "Sales Representative          36\n",
            "Human Resources               21\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para EducationCategory con Attrition = Yes:\n",
            "EducationCategory\n",
            "Degree            267\n",
            "Mastery           186\n",
            "Graduated         159\n",
            "Undergraduated     78\n",
            "Doctorate          21\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para EducationField con Attrition = Yes:\n",
            "EducationField\n",
            "Life Sciences       303\n",
            "Medical             225\n",
            "Marketing            75\n",
            "Technical Degree     45\n",
            "Human Resources      33\n",
            "Other                30\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para LevelCategory con Attrition = Yes:\n",
            "LevelCategory\n",
            "Intermediate    285\n",
            "Entry           252\n",
            "Experienced      96\n",
            "Advanced         51\n",
            "Expert           27\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para Department con Attrition = Yes:\n",
            "Department\n",
            "Research and Development    453\n",
            "Sales                       201\n",
            "Human Resources              57\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para MaritalStatus con Attrition = Yes:\n",
            "MaritalStatus\n",
            "Single      360\n",
            "Married     252\n",
            "Divorced     99\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para IncomeCategory con Attrition = Yes:\n",
            "IncomeCategory\n",
            "Mid Income         207\n",
            "Low Income         201\n",
            "Mid-Low Income     156\n",
            "Mid-High Income     99\n",
            "High Income         48\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para EducationCategory con Attrition = Yes:\n",
            "EducationCategory\n",
            "Degree            267\n",
            "Mastery           186\n",
            "Graduated         159\n",
            "Undergraduated     78\n",
            "Doctorate          21\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "Conteo para DistanceCategory con Attrition = Yes:\n",
            "DistanceCategory\n",
            "Short Distance        210\n",
            "Long Distance         180\n",
            "Mid-Short Distance    162\n",
            "Mid-Long Distance     159\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*One-hot encoding* a variables categóricas"
      ],
      "metadata": {
        "id": "Xvc51YpijWYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['AgeCategory', 'DistanceCategory', 'BusinessTravel', 'Department', 'EducationCategory', 'EducationField', 'LevelCategory', 'JobRole', 'MaritalStatus', 'IncomeCategory', 'CategoryYearsAtCompany'], drop_first=True)\n",
        "\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "gna7kxIqiKvi",
        "outputId": "954f3fbd-eb7e-4065-ab59-94ef9d90dd1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Attrition  DistanceFromHome  Education  EmployeeCount  EmployeeID  \\\n",
              "0   51        No                 6          2              1           1   \n",
              "1   31       Yes                10          1              1           2   \n",
              "2   32        No                17          4              1           3   \n",
              "3   38        No                 2          5              1           4   \n",
              "4   32        No                10          1              1           5   \n",
              "\n",
              "   Gender  JobLevel  MonthlyIncome  NumCompaniesWorked  ...  \\\n",
              "0  Female         1         131160                 1.0  ...   \n",
              "1  Female         1          41890                 0.0  ...   \n",
              "2    Male         4         193280                 1.0  ...   \n",
              "3    Male         3          83210                 3.0  ...   \n",
              "4    Male         1          23420                 4.0  ...   \n",
              "\n",
              "  JobRole_Sales Representative  MaritalStatus_Married  MaritalStatus_Single  \\\n",
              "0                        False                   True                 False   \n",
              "1                        False                  False                  True   \n",
              "2                        False                   True                 False   \n",
              "3                        False                   True                 False   \n",
              "4                        False                  False                  True   \n",
              "\n",
              "   IncomeCategory_Low Income  IncomeCategory_Mid Income  \\\n",
              "0                      False                      False   \n",
              "1                      False                      False   \n",
              "2                      False                      False   \n",
              "3                      False                       True   \n",
              "4                       True                      False   \n",
              "\n",
              "   IncomeCategory_Mid-High Income  IncomeCategory_Mid-Low Income  \\\n",
              "0                            True                          False   \n",
              "1                           False                           True   \n",
              "2                           False                          False   \n",
              "3                           False                          False   \n",
              "4                           False                          False   \n",
              "\n",
              "   CategoryYearsAtCompany_B  CategoryYearsAtCompany_C  \\\n",
              "0                     False                     False   \n",
              "1                      True                     False   \n",
              "2                      True                     False   \n",
              "3                     False                      True   \n",
              "4                     False                      True   \n",
              "\n",
              "   CategoryYearsAtCompany_D  \n",
              "0                     False  \n",
              "1                     False  \n",
              "2                     False  \n",
              "3                     False  \n",
              "4                     False  \n",
              "\n",
              "[5 rows x 59 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8246be42-a961-4122-aaba-e492439d2421\" 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>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>...</th>\n",
              "      <th>JobRole_Sales Representative</th>\n",
              "      <th>MaritalStatus_Married</th>\n",
              "      <th>MaritalStatus_Single</th>\n",
              "      <th>IncomeCategory_Low Income</th>\n",
              "      <th>IncomeCategory_Mid Income</th>\n",
              "      <th>IncomeCategory_Mid-High Income</th>\n",
              "      <th>IncomeCategory_Mid-Low Income</th>\n",
              "      <th>CategoryYearsAtCompany_B</th>\n",
              "      <th>CategoryYearsAtCompany_C</th>\n",
              "      <th>CategoryYearsAtCompany_D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>51</td>\n",
              "      <td>No</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>131160</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31</td>\n",
              "      <td>Yes</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>41890</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>193280</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>3</td>\n",
              "      <td>83210</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>23420</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8246be42-a961-4122-aaba-e492439d2421')\"\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",
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              "  </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-8246be42-a961-4122-aaba-e492439d2421 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-8246be42-a961-4122-aaba-e492439d2421');\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-41140c61-4669-47e3-8e82-397cd0db5f49\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41140c61-4669-47e3-8e82-397cd0db5f49')\"\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",
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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",
              "    }\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-41140c61-4669-47e3-8e82-397cd0db5f49 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": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definición de variables X e Y"
      ],
      "metadata": {
        "id": "Ju0m8NvgjZ6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Attrition', 'EmployeeID', 'EmployeeCount','Gender', 'Over18', 'StandardHours', 'Age', 'DistanceFromHome', 'Education', 'MonthlyIncome', 'JobLevel', 'TotalWorkingYears', 'YearsAtCompany'])\n",
        "\n",
        "# Definir la variable objetivo 'y' como la columna 'Attrition' (convertida en binaria)\n",
        "y = df['Attrition'].apply(lambda x: 1 if x == 'Yes' else 0)"
      ],
      "metadata": {
        "id": "zkRY3uREjXE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "División del conjunto de datos en entrenamiento y prueba (70/30)"
      ],
      "metadata": {
        "id": "bRdgBP2hkBxK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "QC12CzQ9j_g_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Balanceo de conjunto de entrenamiento"
      ],
      "metadata": {
        "id": "PiDHJfGikGL_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Crear un objeto SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "# Aplicar SMOTE solo al conjunto de entrenamiento\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "# Verificar la distribución de clases después del balanceo\n",
        "print(\"Distribución de clases después del balanceo:\")\n",
        "print(y_train_balanced.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVuGv1nwkD-d",
        "outputId": "f7eebfe1-18b7-4c6b-8d55-c5313e89626e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribución de clases después del balanceo:\n",
            "Attrition\n",
            "0    2584\n",
            "1    2584\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest para ver importancia de variables"
      ],
      "metadata": {
        "id": "2R_QCdZAkcpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Entrenar un modelo de Random Forest\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Obtener la importancia de las características\n",
        "importances = rf_model.feature_importances_\n",
        "\n",
        "# Mostrar las características más importantes\n",
        "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': importances})\n",
        "print(feature_importance_df.sort_values(by='Importance', ascending=False))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqbBmU5LkMdG",
        "outputId": "72f15a93-a867-452e-87aa-f5d691af4ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                Feature  Importance\n",
            "5                  YearsWithCurrManager    0.088994\n",
            "0                    NumCompaniesWorked    0.064975\n",
            "1                     PercentSalaryHike    0.064946\n",
            "38                 MaritalStatus_Single    0.063897\n",
            "3                 TrainingTimesLastYear    0.049426\n",
            "4               YearsSinceLastPromotion    0.044065\n",
            "2                      StockOptionLevel    0.030246\n",
            "12     BusinessTravel_Travel_Frequently    0.029350\n",
            "40            IncomeCategory_Mid Income    0.023044\n",
            "28           LevelCategory_Intermediate    0.022627\n",
            "25                  LevelCategory_Entry    0.022089\n",
            "39            IncomeCategory_Low Income    0.020861\n",
            "37                MaritalStatus_Married    0.020034\n",
            "20         EducationField_Life Sciences    0.019938\n",
            "6                         AgeCategory_B    0.019589\n",
            "14  Department_Research and Development    0.019107\n",
            "34           JobRole_Research Scientist    0.018634\n",
            "18            EducationCategory_Mastery    0.018633\n",
            "11      DistanceCategory_Short Distance    0.018393\n",
            "13         BusinessTravel_Travel_Rarely    0.018189\n",
            "30        JobRole_Laboratory Technician    0.017855\n",
            "10  DistanceCategory_Mid-Short Distance    0.017768\n",
            "35              JobRole_Sales Executive    0.016926\n",
            "26            LevelCategory_Experienced    0.016845\n",
            "22               EducationField_Medical    0.016778\n",
            "15                     Department_Sales    0.016315\n",
            "17          EducationCategory_Graduated    0.016291\n",
            "9    DistanceCategory_Mid-Long Distance    0.015916\n",
            "42        IncomeCategory_Mid-Low Income    0.015315\n",
            "7                         AgeCategory_C    0.015118\n",
            "8                         AgeCategory_D    0.014927\n",
            "44             CategoryYearsAtCompany_C    0.014801\n",
            "33            JobRole_Research Director    0.014410\n",
            "41       IncomeCategory_Mid-High Income    0.013625\n",
            "45             CategoryYearsAtCompany_D    0.012980\n",
            "19     EducationCategory_Undergraduated    0.012154\n",
            "43             CategoryYearsAtCompany_B    0.011975\n",
            "21             EducationField_Marketing    0.010477\n",
            "32       JobRole_Manufacturing Director    0.008838\n",
            "24      EducationField_Technical Degree    0.008344\n",
            "36         JobRole_Sales Representative    0.007845\n",
            "31                      JobRole_Manager    0.007313\n",
            "23                 EducationField_Other    0.006626\n",
            "29              JobRole_Human Resources    0.005523\n",
            "27                 LevelCategory_Expert    0.004365\n",
            "16          EducationCategory_Doctorate    0.003635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGBoost (30% de prueba)"
      ],
      "metadata": {
        "id": "uTgHcmWruOp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "xgboost_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')\n",
        "\n",
        "xgboost_model.fit(X_train_balanced, y_train_balanced)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "OBhA6sQruccS",
        "outputId": "9bee2314-a734-435d-dbea-4757df243616"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:16:53] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric='logloss',\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)"
            ],
            "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>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</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\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "# Predecir en el conjunto de prueba\n",
        "y_pred = xgboost_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYTIKBiOuS3M",
        "outputId": "b5e61f4d-404f-495a-ae03-408c35ccd978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9863945578231292\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      1115\n",
            "           1       0.98      0.93      0.96       208\n",
            "\n",
            "    accuracy                           0.99      1323\n",
            "   macro avg       0.99      0.96      0.97      1323\n",
            "weighted avg       0.99      0.99      0.99      1323\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizar el árbol número 0\n",
        "xgb.plot_tree(xgboost_model, num_trees=0)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "J7FujMqUuM9-",
        "outputId": "2ffb1d59-c347-4fae-b551-b9f500412b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Realizar validación cruzada con 5 pliegues\n",
        "cross_val_scores = cross_val_score(xgboost_model, X, y, cv=5, scoring='accuracy')\n",
        "\n",
        "print(\"Cross-validation scores:\", cross_val_scores)\n",
        "print(\"Mean cross-validation score:\", cross_val_scores.mean())\n"
      ],
      "metadata": {
        "id": "l4GSVAqFxGwT",
        "outputId": "0a4fcf2a-fed8-4d91-8095-d6b231727647",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:27:52] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:27:55] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:27:55] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:27:56] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:27:56] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [1.         1.         1.         0.99886621 1.        ]\n",
            "Mean cross-validation score: 0.9997732426303856\n"
          ]
        }
      ]
    }
  ]
}