Preparatorio_data_science.ipynb

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      "authorship_tag": "ABX9TyOpUI+uCnlUsSKLAVAIu50L",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wlopezm-unal/Machine-learning/blob/main/Preparatorio_data_science.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": [
        "# LImpieza de datos y modelo básico de Machine learning"
      ],
      "metadata": {
        "id": "Uw_Ug9peJFrx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este colab se desea proceder con la limpieza de datos y posterior a un primer análisis de machine learning para determinar qué tipo de empleados son más probables en abandonar la compañia observando que variables pueden influir para determinar que puede influir en el abandono de la empresa"
      ],
      "metadata": {
        "id": "wbEEnKf9JLuw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0YiEQ08Jrm6b"
      },
      "outputs": [],
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/AbandonoEmpleados.csv', sep=';', index_col='id', na_values='#N/D')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "mMJ7IKOnsKPE",
        "outputId": "d7e00e02-3ea2-4bbf-c2ff-ac0f6490d42f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      edad abandono             viajes            departamento  \\\n",
              "id                                                               \n",
              "1       41      Yes      Travel_Rarely                   Sales   \n",
              "2       49       No  Travel_Frequently  Research & Development   \n",
              "4       37      Yes      Travel_Rarely  Research & Development   \n",
              "5       33       No  Travel_Frequently  Research & Development   \n",
              "7       27       No      Travel_Rarely  Research & Development   \n",
              "...    ...      ...                ...                     ...   \n",
              "2061    36       No  Travel_Frequently  Research & Development   \n",
              "2062    39       No      Travel_Rarely  Research & Development   \n",
              "2064    27       No      Travel_Rarely  Research & Development   \n",
              "2065    49       No  Travel_Frequently                   Sales   \n",
              "2068    34       No      Travel_Rarely  Research & Development   \n",
              "\n",
              "      distancia_casa      educacion        carrera  empleados  \\\n",
              "id                                                              \n",
              "1                  1  Universitaria  Life Sciences          1   \n",
              "2                  8     Secundaria  Life Sciences          1   \n",
              "4                  2     Secundaria          Other          1   \n",
              "5                  3  Universitaria  Life Sciences          1   \n",
              "7                  2  Universitaria        Medical          1   \n",
              "...              ...            ...            ...        ...   \n",
              "2061              23         Master        Medical          1   \n",
              "2062               6     Secundaria        Medical          1   \n",
              "2064               4         Master  Life Sciences          1   \n",
              "2065               2     Secundaria        Medical          1   \n",
              "2068               8            NaN        Medical          1   \n",
              "\n",
              "     satisfaccion_entorno  sexo  ... satisfaccion_companeros  horas_quincena  \\\n",
              "id                               ...                                           \n",
              "1                   Media   3.0  ...                    Baja              80   \n",
              "2                    Alta   2.0  ...                Muy_Alta              80   \n",
              "4                Muy_Alta   2.0  ...                   Media              80   \n",
              "5                Muy_Alta   3.0  ...                    Alta              80   \n",
              "7                    Baja   3.0  ...                Muy_Alta              80   \n",
              "...                   ...   ...  ...                     ...             ...   \n",
              "2061                 Alta   4.0  ...                    Alta              80   \n",
              "2062             Muy_Alta   2.0  ...                    Baja              80   \n",
              "2064                Media   4.0  ...                   Media              80   \n",
              "2065             Muy_Alta   NaN  ...                Muy_Alta              80   \n",
              "2068                Media   4.0  ...                    Baja              80   \n",
              "\n",
              "     nivel_acciones anos_experiencia num_formaciones_ult_ano  conciliacion  \\\n",
              "id                                                                           \n",
              "1                 0                8                       0           NaN   \n",
              "2                 1               10                       3           NaN   \n",
              "4                 0                7                       3           NaN   \n",
              "5                 0                8                       3           NaN   \n",
              "7                 1                6                       3           NaN   \n",
              "...             ...              ...                     ...           ...   \n",
              "2061              1               17                       3           NaN   \n",
              "2062              1                9                       5           NaN   \n",
              "2064              1                6                       0           NaN   \n",
              "2065              0               17                       3           NaN   \n",
              "2068              0                6                       3      Muy_Alta   \n",
              "\n",
              "      anos_compania anos_en_puesto anos_desde_ult_promocion  \\\n",
              "id                                                            \n",
              "1                 6            NaN                        0   \n",
              "2                10            NaN                        1   \n",
              "4                 0            2.0                        0   \n",
              "5                 8            3.0                        3   \n",
              "7                 2            NaN                        2   \n",
              "...             ...            ...                      ...   \n",
              "2061              5            4.0                        0   \n",
              "2062              7            NaN                        1   \n",
              "2064              6            NaN                        0   \n",
              "2065              9            NaN                        0   \n",
              "2068              4            NaN                        1   \n",
              "\n",
              "      anos_con_manager_actual  \n",
              "id                             \n",
              "1                           5  \n",
              "2                           7  \n",
              "4                           0  \n",
              "5                           0  \n",
              "7                           2  \n",
              "...                       ...  \n",
              "2061                        3  \n",
              "2062                        7  \n",
              "2064                        3  \n",
              "2065                        8  \n",
              "2068                        2  \n",
              "\n",
              "[1470 rows x 31 columns]"
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              "      <td>3.0</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f76e8a6a-177a-4c2e-90d8-2dec5b30676b');\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-8e835446-e5e8-401c-90f8-ee75a456784e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e835446-e5e8-401c-90f8-ee75a456784e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8e835446-e5e8-401c-90f8-ee75a456784e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We be able to know if there one or more null data\n",
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uk74s4qCsWdx",
        "outputId": "571d73d9-687d-4e61-9999-f94c0019f04e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anos_en_puesto              1238\n",
              "conciliacion                1011\n",
              "sexo                         199\n",
              "educacion                    101\n",
              "satisfaccion_trabajo          76\n",
              "implicacion                   18\n",
              "edad                           0\n",
              "nivel_acciones                 0\n",
              "evaluacion                     0\n",
              "satisfaccion_companeros        0\n",
              "horas_quincena                 0\n",
              "anos_experiencia               0\n",
              "horas_extra                    0\n",
              "num_formaciones_ult_ano        0\n",
              "anos_compania                  0\n",
              "anos_desde_ult_promocion       0\n",
              "incremento_salario_porc        0\n",
              "salario_mes                    0\n",
              "mayor_edad                     0\n",
              "num_empresas_anteriores        0\n",
              "abandono                       0\n",
              "estado_civil                   0\n",
              "puesto                         0\n",
              "nivel_laboral                  0\n",
              "satisfaccion_entorno           0\n",
              "empleados                      0\n",
              "carrera                        0\n",
              "distancia_casa                 0\n",
              "departamento                   0\n",
              "viajes                         0\n",
              "anos_con_manager_actual        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLNg-N4dsluA",
        "outputId": "2735bcba-5501-4753-d3a4-c45037ff2c59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1470 entries, 1 to 2068\n",
            "Data columns (total 31 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   edad                      1470 non-null   int64  \n",
            " 1   abandono                  1470 non-null   object \n",
            " 2   viajes                    1470 non-null   object \n",
            " 3   departamento              1470 non-null   object \n",
            " 4   distancia_casa            1470 non-null   int64  \n",
            " 5   educacion                 1369 non-null   object \n",
            " 6   carrera                   1470 non-null   object \n",
            " 7   empleados                 1470 non-null   int64  \n",
            " 8   satisfaccion_entorno      1470 non-null   object \n",
            " 9   sexo                      1271 non-null   float64\n",
            " 10  implicacion               1452 non-null   object \n",
            " 11  nivel_laboral             1470 non-null   int64  \n",
            " 12  puesto                    1470 non-null   object \n",
            " 13  satisfaccion_trabajo      1394 non-null   object \n",
            " 14  estado_civil              1470 non-null   object \n",
            " 15  salario_mes               1470 non-null   int64  \n",
            " 16  num_empresas_anteriores   1470 non-null   int64  \n",
            " 17  mayor_edad                1470 non-null   object \n",
            " 18  horas_extra               1470 non-null   object \n",
            " 19  incremento_salario_porc   1470 non-null   int64  \n",
            " 20  evaluacion                1470 non-null   object \n",
            " 21  satisfaccion_companeros   1470 non-null   object \n",
            " 22  horas_quincena            1470 non-null   int64  \n",
            " 23  nivel_acciones            1470 non-null   int64  \n",
            " 24  anos_experiencia          1470 non-null   int64  \n",
            " 25  num_formaciones_ult_ano   1470 non-null   int64  \n",
            " 26  conciliacion              459 non-null    object \n",
            " 27  anos_compania             1470 non-null   int64  \n",
            " 28  anos_en_puesto            232 non-null    float64\n",
            " 29  anos_desde_ult_promocion  1470 non-null   int64  \n",
            " 30  anos_con_manager_actual   1470 non-null   int64  \n",
            "dtypes: float64(2), int64(14), object(15)\n",
            "memory usage: 367.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#eliminar estas columnas ya que superan el 60% de nan\n",
        "df.drop(columns=['anos_en_puesto','conciliacion'], inplace=True)"
      ],
      "metadata": {
        "id": "cZtncLgU10sq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RV0YRu9k2Mnb",
        "outputId": "af0fd626-bb89-41e3-849a-d6fe1a494965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sexo                        199\n",
              "educacion                   101\n",
              "satisfaccion_trabajo         76\n",
              "implicacion                  18\n",
              "edad                          0\n",
              "mayor_edad                    0\n",
              "anos_desde_ult_promocion      0\n",
              "anos_compania                 0\n",
              "num_formaciones_ult_ano       0\n",
              "anos_experiencia              0\n",
              "nivel_acciones                0\n",
              "horas_quincena                0\n",
              "satisfaccion_companeros       0\n",
              "evaluacion                    0\n",
              "incremento_salario_porc       0\n",
              "horas_extra                   0\n",
              "estado_civil                  0\n",
              "num_empresas_anteriores       0\n",
              "salario_mes                   0\n",
              "abandono                      0\n",
              "puesto                        0\n",
              "nivel_laboral                 0\n",
              "satisfaccion_entorno          0\n",
              "empleados                     0\n",
              "carrera                       0\n",
              "distancia_casa                0\n",
              "departamento                  0\n",
              "viajes                        0\n",
              "anos_con_manager_actual       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cambiar los valores categoricas por numericas\n",
        "def graficos_eda_categoria(cat):\n",
        "  #calcular el número de filas\n",
        "  from math import ceil\n",
        "\n",
        "  #las filas análiza las variables categoricas\n",
        "  filas=ceil(cat.shape[1]/2)\n",
        "\n",
        "  #Definir el grafico\n",
        "  f, ax=plt.subplot(nrows=filas, ncols=2, figsize=(16, filas*16))\n",
        "\n",
        "  #aplanar el grafico\n",
        "  ax=ax.flat\n",
        "\n",
        "  #crear bucle\n",
        "\n",
        "  for cada, variable in enumerate(cat):\n",
        "    cat[variable].value_counts().plotbarh(ax=ax[cada])\n",
        "    ax[cada].set_title(variable, fontsize=12, fontweight='bold')\n",
        "    ax[cada].tick_params(labelsize=12)"
      ],
      "metadata": {
        "id": "TLXjs2YW2jfE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def graficos_eda_categoria(cat):\n",
        "    # Calcular el número de filas\n",
        "    filas = math.ceil(cat.shape[1] / 2)\n",
        "\n",
        "    # Definir el gráfico\n",
        "    fig, ax = plt.subplots(nrows=filas, ncols=2, figsize=(16, filas * 8))\n",
        "\n",
        "    # Aplanar el gráfico\n",
        "    ax = ax.flatten()\n",
        "\n",
        "    # Crear bucle\n",
        "    for i, variable in enumerate(cat.columns):\n",
        "        cat[variable].value_counts().plot.barh(ax=ax[i])\n",
        "        ax[i].set_title(variable, fontsize=12, fontweight='bold')\n",
        "        ax[i].tick_params(labelsize=12)\n",
        "\n",
        "    # Ajustar el espaciado entre subplots\n",
        "    plt.tight_layout()\n",
        "\n",
        "# Ejemplo de uso\n",
        "# Suponiendo que 'cat' es un DataFrame con variables categóricas\n",
        "\n"
      ],
      "metadata": {
        "id": "JBkzQxFMDgZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graficos_eda_categoria(df.select_dtypes('O'))"
      ],
      "metadata": {
        "id": "IyBs-mjCDjU7",
        "outputId": "2dd4ba63-249c-4bbe-c72e-1124d3ea205f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x5600 with 14 Axes>"
            ],
            "image/png": 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MnTo1TZo0+VnPuagpLy+v7RIAABYb/lwEAADg/3vzzTfTuXPnGqFGkrRs2bLa+6uvvjpbbLFFWrZsmfLy8qyxxhoZPnz4PJ1n+vTpOeWUU7LyyiunvLw87dq1y//8z/9k+vTp1dqNHTs23bt3z9JLL52mTZumU6dOOeGEExbo2iorK7PaaqvlzTffrLZ91qxZOf/889O5c+c0bNgwrVq1yuDBg/PZZ59Va9e+ffvssMMOuffee7PuuuumYcOGWWONNXLrrbdWazdixIiUlZXlgQceyKGHHpqWLVtm+eWXL+4fPXp0evTokSZNmmSppZZKnz598tJLL1Xr48MPP8ygQYOy/PLLp7y8PG3atMnOO++cCRMmVGs3L30NHDgwTZs2zfvvv59ddtklTZs2TWVlZY4++ujMnDkzSTJhwoRUVlYmSYYOHVq8hdeQIUOSJM8//3wxGGrYsGFat26d/fffP5MmTSqeZ8iQITnmmGOSJB06dCj2Mbvm7z5j46mnnkpZWVmuueaaGj+nMWPGpKysLHfddVdx2/vvv5/9998/rVq1Snl5eTp37pyrrrqqxrEAAEsKKzYAAAD+vxVXXDH/+te/8uKLL2bNNdf83rbDhw9P586ds9NOO6VevXq58847c+ihh2bWrFn51a9+NdfjZs2alZ122ikPP/xwDjrooKy++up54YUXct555+X111/P7bffniR56aWXssMOO2TttdfOqaeemvLy8rzxxht55JFHFujavvnmm/z73/9O8+bNq20fPHhwRowYkUGDBuWII47I22+/nYsuuijPPPNMHnnkkdSvX7/Ydvz48dlrr71y8MEHZ8CAAbn66quzxx575J577snWW29drd9DDz00lZWVOfnkkzN16tQkybXXXpsBAwakd+/eOfvss/Pll19m+PDh6d69e5555pm0b98+SbL77rvnpZdeyuGHH5727dvn448/ztixY/Puu+8W28xrX0kyc+bM9O7dOxtuuGH++Mc/5r777ss555yTjh075pBDDkllZWWGDx+eQw45JLvuumt22223JMnaa6+d5NuA6a233sqgQYPSunXrvPTSS7n88svz0ksv5bHHHktZWVl22223vP766xk1alTOO++8tGjRIkmKgcl3rb/++llppZVy4403ZsCAAdX2/fWvf03z5s3Tu3fvJMlHH32UjTbaKGVlZTnssMNSWVmZ0aNH55e//GWmTJmSo446aj7+VwAAsHgoKxQKhdouAqhuypQpadasWaqqqlJRUVHb5QAAS5Al/XvI2LFjs9122yVJunXrlh49emTLLbdMr169qv2CP0m++uqrNGrUqNq2bbfdNuPHj6+2KmL28xpmP5T6uuuuy4ABA/LAAw+ke/fuxXaXXXZZDj744DzyyCPZZJNNcv755+fXv/51Pvnkk+IvyedV+/bt06lTp4wcOTLJtysgfv/73+faa6/Nr371q1x00UVJkocffjg9evTIyJEj079//+LxY8aMybbbbltte/v27fPOO+/klltuKf7if8qUKVlttdXSunXrPP3000lSDEm6d++e+++/P3Xr/j/27jsqqmt///gzgA4IgohgCwpibLFFjMaC2LEHY+daMLHFa4vRJHYsETXGmtiiEaNgFFssscZyY0z9aowliRWN3kRFBSyICvz+8Mdcx0EFLOTo+7XWrOXss88+n3MG1wIe9t72kqSrV6/K29tbbdq00bx58yzXOnfunEqWLKm2bdtq3rx5iouLk7u7uz788EMNGjQo3fvL6FjSnRkbixYt0pgxYzRixAhL30qVKsnOzk4///yzJCk2Nlaenp4aNWqUZaZGmvQ+6y+++EIdOnTQf/7zHwUEBEiSJk+erMGDB+vkyZNWwUra86tdu7YiIiIkSUOHDtXkyZN17tw5S9h08+ZN5c+fX6+//roWLFggSerWrZu++uorHThwQB4eHpbxOnTooI0bN+qvv/6yqU26Myvo7hlACQkJ8vb2lveA5bIz50r3ueL5EjOhaXaXAAB4TjyJnzFYigoAAAAA/r8GDRrou+++U4sWLbR//35NmjRJQUFBKly4sNauXWvV9+5fJsfHxys2NlaBgYE6ceKE4uPj73uN6OholS5dWqVKlVJsbKzlVbduXUnSjh07JMmyHNaXX36plJSUTN/Lli1b5OnpKU9PT5UrV06LFy9W165d9eGHH1rV4ubmpgYNGljV4u/vLxcXF0staQoVKqSWLVta3ru6uqpz587at2+f/v77b6u+3bt3t4Qa0p3QKC4uTh06dLC6lr29vapWrWq5lpOTk3LmzKmdO3faLIeV2bHu1qtXL6v3AQEBOnHiRIae5d2f9Y0bNxQbG6tXX31VkiyBTma1a9dOt27dslrKa8uWLYqLi1O7du0kSampqVq5cqWaN2+u1NRUq3sNCgpSfHz8fa8fHh4uNzc3y8vb2ztLdQIAAPwTEWwAAAAAwF1eeeUVrVq1SpcvX9aPP/6oIUOG6MqVK2rdurUOHz5s6fftt9+qfv36cnZ2Vp48eeTp6WnZ/+JBwcbRo0d16NAhS+iQ9ipRooSk/21S3q5dO9WoUUPdunVT/vz51b59ey1fvjzDIUfVqlW1detWbdq0SZMnT1aePHl0+fJl5cyZ06qW+Ph4eXl52dRz9epVmw3TixcvLpPJZNWWVve9+1/4+vra3Lck1a1b1+ZaW7ZssVzLbDZr4sSJ2rhxo/Lnz69atWpp0qRJVsFJRsdK4+joaLMklLu7+32Dk3tdunRJ/fv3V/78+eXk5CRPT0/L/T3os36QChUqqFSpUlq2bJmlbdmyZcqXL58l5Lpw4YLi4uI0b948m/vs2rWrpPtvaj9kyBDFx8dbXn/++WeW6gQAAPgnYo8NAAAAAEhHzpw59corr+iVV15RiRIl1LVrV0VHR2vUqFE6fvy46tWrp1KlSmnKlCny9vZWzpw59dVXX2nq1KkPDB9SUlJUrlw5TZkyJd3jaX9Z7+TkpP/85z/asWOHNmzYoE2bNmnZsmWqW7eutmzZYjUbIj358uVT/fr1JUlBQUEqVaqUmjVrpunTp2vgwIGWWry8vCxLVt0rvf0hMure5ZHSnsnixYtVoEABm/4ODv/78XTAgAFq3ry51qxZo82bN2vEiBEKDw/X9u3b9fLLL2dqLEkPfVYP07ZtW+3Zs0eDBw9WxYoV5eLiopSUFDVq1ChLs2nStGvXTh988IFiY2OVO3durV27Vh06dLDUnzZ2x44dbfbiSJO2D8i9zGazzGZzlmsDAAD4JyPYAAAAAICHqFy5siTpr7/+kiStW7dOSUlJWrt2rYoUKWLpl94SSPfy8/PT/v37Va9ePZvZD/eys7NTvXr1VK9ePU2ZMkXjx4/XsGHDtGPHDktokVFNmzZVYGCgxo8fr549e8rZ2Vl+fn7atm2batSoke4+Dfc6duyYUlNTreo+cuSIJNnsKXEvPz8/SZKXl1eGavfz89M777yjd955R0ePHlXFihX10UcfacmSJZkeKyPu91lcvnxZX3/9tUaPHq2RI0da2tNmjWRkjPtp166dRo8erZUrVyp//vxKSEhQ+/btLcc9PT2VO3duJScnP7b7BAAAeBawFBUAAAAA/H87duxQamqqTftXX30lSSpZsqSk/80AuLtvfHy8Fi5c+NBrtG3bVmfPntWnn35qcywxMVHXrl2TdGf5o3tVrFhRkqw2hc6M9957TxcvXrRcu23btkpOTtbYsWNt+t6+fVtxcXFWbf/973+1evVqy/uEhAR9/vnnqlixYrozJ+4WFBQkV1dXjR8/Xrdu3bI5fuHCBUnS9evXdePGDatjfn5+yp07t+W+MzpWZuTKdWdD7XvvOb3PWpKmTZtmM4azs3O6Y9xP6dKlVa5cOS1btkzLli1TwYIFVatWLatrt2rVSitXrtTBgwdtzs/KfQIAADwLmLEBAAAAAP9f3759df36dbVs2VKlSpXSzZs3tWfPHi1btkw+Pj6WfQ0aNmyonDlzqnnz5urZs6euXr2qTz/9VF5eXpZZHffTqVMnLV++XL169dKOHTtUo0YNJScn6/fff9fy5cu1efNmVa5cWWPGjNF//vMfNW3aVEWLFtX58+c1a9YsvfDCC6pZs2aW7q9x48YqW7aspkyZon//+98KDAxUz549FR4erl9++UUNGzZUjhw5dPToUUVHR2v69Olq3bq15fwSJUrozTff1E8//aT8+fPrs88+07lz5zIU6Li6umr27Nnq1KmTKlWqpPbt28vT01OnT5/Whg0bVKNGDX388cc6cuSI6tWrp7Zt26pMmTJycHDQ6tWrde7cOctshoyOlRlOTk4qU6aMli1bphIlSihv3rwqW7asypYta9nn49atWypcuLC2bNmikydP2ozh7+8vSRo2bJjat2+vHDlyqHnz5pbAIz3t2rXTyJEj5ejoqDfffFN2dtZ/fzhhwgTt2LFDVatWVffu3VWmTBldunRJe/fu1bZt29INwAAAAJ51BBsAAAAA8P9NnjxZ0dHR+uqrrzRv3jzdvHlTRYoUUe/evTV8+HDlyZNH0p2ZGytWrNDw4cM1aNAgFShQQG+99ZY8PT31xhtvPPAadnZ2WrNmjaZOnarPP/9cq1evVq5cuVSsWDH179/fshl3ixYtFBMTo88++0yxsbHKly+fAgMDNXr0aLm5uWX5HgcNGqTQ0FBFRkYqNDRUc+bMkb+/v+bOnauhQ4fKwcFBPj4+6tixo2rUqGF17osvvqiZM2dq8ODB+uOPP+Tr66tly5YpKCgoQ9cOCQlRoUKFNGHCBH344YdKSkpS4cKFFRAQYAmNvL291aFDB3399ddavHixHBwcVKpUKS1fvlytWrXK1FiZNX/+fPXt21dvv/22bt68qVGjRqls2bKKiopS37599cknnyg1NVUNGzbUxo0bVahQIavzX3nlFY0dO1Zz5szRpk2blJKSopMnTz402Bg+fLiuX7+udu3a2RzPnz+/fvzxR40ZM0a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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "graficos_eda_categoria(df.select_dtypes('O'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bMX_-AQS5H5L",
        "outputId": "6b69cc16-0a6a-4950-93f5-66174317b2b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x5600 with 14 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mayor de edad solo tiene un valor\n",
        "#educación: imputar por universitarios,\n",
        "#satisfacción_trabajo: imputar por Alta\n",
        "#Implicación: impuación por Alta"
      ],
      "metadata": {
        "id": "8CxipeZO1vEh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns='mayor_edad', inplace=True)"
      ],
      "metadata": {
        "id": "57Mbhmwt3UO8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['educacion'].value_counts().idxmax())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWSXgkxc5nUf",
        "outputId": "832bdda1-5638-41aa-ed6d-fabb8abac315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Universitaria\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#reemplazar los valores nulos por valores que más se repiten en el dataframe\n",
        "df['educacion']=df['educacion'].fillna(df['educacion'].value_counts().idxmax())\n",
        "df['satisfaccion_trabajo']=df['educacion'].fillna(df['educacion'].value_counts().idxmax())\n",
        "df['implicacion']=df['educacion'].fillna(df['educacion'].value_counts().idxmax())"
      ],
      "metadata": {
        "id": "Z1hQHEmX5dfA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(df['implicacion'].value_counts().idxmax())\n"
      ],
      "metadata": {
        "id": "K7a1h-hA6aiv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Observar los valores unicos para cada columna del dataframe\n",
        "for i in df.columns:\n",
        "    print(f'la cantidad de valores para la columna {i} es: {df[i].unique()}')\n",
        "    print('-'*90)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9Ikb-Th2SWZ",
        "outputId": "823a8c37-ee0d-4a29-9b13-626991824132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "la cantidad de valores para la columna edad es: [41 49 37 33 27 32 59 30 38 36 35 29 31 34 28 22 53 24 21 42 44 46 39 43\n",
            " 50 26 48 55 45 56 23 51 40 54 58 20 25 19 57 52 47 18 60]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna abandono es: ['Yes' 'No']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna viajes es: ['Travel_Rarely' 'Travel_Frequently' 'Non-Travel']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna departamento es: ['Sales' 'Research & Development' 'Human Resources']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna distancia_casa es: [ 1  8  2  3 24 23 27 16 15 26 19 21  5 11  9  7  6 10  4 25 12 18 29 22\n",
            " 14 20 28 17 13]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna educacion es: ['Universitaria' 'Secundaria' 'Master' 'Primaria']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna carrera es: ['Life Sciences' 'Other' 'Medical' 'Marketing' 'Technical Degree'\n",
            " 'Human Resources']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna empleados es: [1]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna satisfaccion_entorno es: ['Media' 'Alta' 'Muy_Alta' 'Baja']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna sexo es: [ 3.  2.  4. nan  1.]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna implicacion es: ['Universitaria' 'Secundaria' 'Master' 'Primaria']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna nivel_laboral es: [2 1 3 4 5]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna puesto es: ['Sales Executive' 'Research Scientist' 'Laboratory Technician'\n",
            " 'Manufacturing Director' 'Healthcare Representative' 'Manager'\n",
            " 'Sales Representative' 'Research Director' 'Human Resources']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna satisfaccion_trabajo es: ['Universitaria' 'Secundaria' 'Master' 'Primaria']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna estado_civil es: ['Single' 'Married' 'Divorced']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna salario_mes es: [5993 5130 2090 ... 9991 5390 4404]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna num_empresas_anteriores es: [8 1 6 9 0 4 5 2 7 3]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna horas_extra es: ['Yes' 'No']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna incremento_salario_porc es: [11 23 15 12 13 20 22 21 17 14 16 18 19 24 25]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna evaluacion es: ['Alta' 'Muy_Alta']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna satisfaccion_companeros es: ['Baja' 'Muy_Alta' 'Media' 'Alta']\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna horas_quincena es: [80]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna nivel_acciones es: [0 1 3 2]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna anos_experiencia es: [ 8 10  7  6 12  1 17  5  3 31 13  0 26 24 22  9 19  2 23 14 15  4 29 28\n",
            " 21 25 20 11 16 37 38 30 40 18 36 34 32 33 35 27]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna num_formaciones_ult_ano es: [0 3 2 5 1 4 6]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna anos_compania es: [ 6 10  0  8  2  7  1  9  5  4 25  3 12 14 22 15 27 21 17 11 13 37 16 20\n",
            " 40 24 33 19 36 18 29 31 32 34 26 30 23]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna anos_desde_ult_promocion es: [ 0  1  3  2  7  4  8  6  5 15  9 13 12 10 11 14]\n",
            "------------------------------------------------------------------------------------------\n",
            "la cantidad de valores para la columna anos_con_manager_actual es: [ 5  7  0  2  6  8  3 11 17  1  4 12  9 10 15 13 16 14]\n",
            "------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Observar cuantos valores null hay en las columnas del dataframe\n",
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsSNX7lGtORj",
        "outputId": "c0111293-ca66-4a8a-fbe9-d6e7de14c3fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sexo                        199\n",
              "edad                          0\n",
              "salario_mes                   0\n",
              "anos_desde_ult_promocion      0\n",
              "anos_compania                 0\n",
              "num_formaciones_ult_ano       0\n",
              "anos_experiencia              0\n",
              "nivel_acciones                0\n",
              "horas_quincena                0\n",
              "satisfaccion_companeros       0\n",
              "evaluacion                    0\n",
              "incremento_salario_porc       0\n",
              "horas_extra                   0\n",
              "num_empresas_anteriores       0\n",
              "estado_civil                  0\n",
              "abandono                      0\n",
              "satisfaccion_trabajo          0\n",
              "puesto                        0\n",
              "nivel_laboral                 0\n",
              "implicacion                   0\n",
              "satisfaccion_entorno          0\n",
              "empleados                     0\n",
              "carrera                       0\n",
              "educacion                     0\n",
              "distancia_casa                0\n",
              "departamento                  0\n",
              "viajes                        0\n",
              "anos_con_manager_actual       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#completar los valores null de sexo con los valores que más se repita\n",
        "df['sexo'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SO1lDOCZlnbE",
        "outputId": "c242bea9-43c6-4349-c448-b44be4dfcfad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0    739\n",
              "2.0    328\n",
              "4.0    130\n",
              "1.0     74\n",
              "Name: sexo, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['educacion'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaLW6FDjv2xy",
        "outputId": "70b13177-909f-4c93-994f-c6098975b757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Universitaria', 'Secundaria', 'Master', 'Primaria'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#observar cuandos valores N/D estan presentes en nuestro dataframe\n",
        "for columna in df.columns:\n",
        "    conteo = df[columna].value_counts().get('#N/D', 0)\n",
        "    print(f\"La columna {columna} tiene {conteo} repeticiones del valor '#N/D'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Kr_FZrrwJj5",
        "outputId": "07b335f7-ee18-4e59-cbcb-4c75fa296f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La columna edad tiene 0 repeticiones del valor '#N/D'\n",
            "La columna abandono tiene 0 repeticiones del valor '#N/D'\n",
            "La columna viajes tiene 0 repeticiones del valor '#N/D'\n",
            "La columna departamento tiene 0 repeticiones del valor '#N/D'\n",
            "La columna distancia_casa tiene 0 repeticiones del valor '#N/D'\n",
            "La columna educacion tiene 0 repeticiones del valor '#N/D'\n",
            "La columna carrera tiene 0 repeticiones del valor '#N/D'\n",
            "La columna empleados tiene 0 repeticiones del valor '#N/D'\n",
            "La columna satisfaccion_entorno tiene 0 repeticiones del valor '#N/D'\n",
            "La columna sexo tiene 0 repeticiones del valor '#N/D'\n",
            "La columna implicacion tiene 0 repeticiones del valor '#N/D'\n",
            "La columna nivel_laboral tiene 0 repeticiones del valor '#N/D'\n",
            "La columna puesto tiene 0 repeticiones del valor '#N/D'\n",
            "La columna satisfaccion_trabajo tiene 0 repeticiones del valor '#N/D'\n",
            "La columna estado_civil tiene 0 repeticiones del valor '#N/D'\n",
            "La columna salario_mes tiene 0 repeticiones del valor '#N/D'\n",
            "La columna num_empresas_anteriores tiene 0 repeticiones del valor '#N/D'\n",
            "La columna horas_extra tiene 0 repeticiones del valor '#N/D'\n",
            "La columna incremento_salario_porc tiene 0 repeticiones del valor '#N/D'\n",
            "La columna evaluacion tiene 0 repeticiones del valor '#N/D'\n",
            "La columna satisfaccion_companeros tiene 0 repeticiones del valor '#N/D'\n",
            "La columna horas_quincena tiene 0 repeticiones del valor '#N/D'\n",
            "La columna nivel_acciones tiene 0 repeticiones del valor '#N/D'\n",
            "La columna anos_experiencia tiene 0 repeticiones del valor '#N/D'\n",
            "La columna num_formaciones_ult_ano tiene 0 repeticiones del valor '#N/D'\n",
            "La columna anos_compania tiene 0 repeticiones del valor '#N/D'\n",
            "La columna anos_desde_ult_promocion tiene 0 repeticiones del valor '#N/D'\n",
            "La columna anos_con_manager_actual tiene 0 repeticiones del valor '#N/D'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cuál es la tasa de abandono"
      ],
      "metadata": {
        "id": "GSy8wPfDTBx6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.abandono.value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZziYH2pTDYq",
        "outputId": "dc6a920f-b8a5-4e5b-d973-74a4dfb31e57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     83.877551\n",
              "Yes    16.122449\n",
              "Name: abandono, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿Hay un tipo de empleado que deja la empresa?\n",
        "Se desea evaluar cual tipo de empleado es más probable que abandone la empresa"
      ],
      "metadata": {
        "id": "O8bFoVm-Tl7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#cambiar los valores para que se adapten mejor a los datos tipo categoricos\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "O_codificador=OrdinalEncoder()\n",
        "\n",
        "df['abandono_encoded']=O_codificador.fit_transform(df[['abandono']]).astype('int')"
      ],
      "metadata": {
        "id": "5ak8DKyRdYE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#eliminar las siguientes columnas ya que no son datos informativos, además,   la columna 'abandono' se dropea porque se cambio sus datos a uno tipo categorico en otra columna\n",
        "df.drop(columns=['abandono', 'sexo'], inplace=True)"
      ],
      "metadata": {
        "id": "o7ujIr8udgVg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'abandono_encoded':'abandono'}, inplace=True)"
      ],
      "metadata": {
        "id": "8Y15jRsBdwim"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#verificar valores null\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dpMzAQGd32-",
        "outputId": "1dc1c27b-7340-45fc-a08b-bc672aa6d9d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "edad                        0\n",
              "viajes                      0\n",
              "departamento                0\n",
              "distancia_casa              0\n",
              "educacion                   0\n",
              "carrera                     0\n",
              "empleados                   0\n",
              "satisfaccion_entorno        0\n",
              "implicacion                 0\n",
              "nivel_laboral               0\n",
              "puesto                      0\n",
              "satisfaccion_trabajo        0\n",
              "estado_civil                0\n",
              "salario_mes                 0\n",
              "num_empresas_anteriores     0\n",
              "horas_extra                 0\n",
              "incremento_salario_porc     0\n",
              "evaluacion                  0\n",
              "satisfaccion_companeros     0\n",
              "horas_quincena              0\n",
              "nivel_acciones              0\n",
              "anos_experiencia            0\n",
              "num_formaciones_ult_ano     0\n",
              "anos_compania               0\n",
              "anos_desde_ult_promocion    0\n",
              "anos_con_manager_actual     0\n",
              "abandono                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#transformar abandono por numerico\n",
        "df['abandono'].df.abandono.map({'No':0, 'Yes':1})"
      ],
      "metadata": {
        "id": "Mv_BXJ-CToIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#analisis por educación\n",
        "temp=df.groupby('educacion').abandono.mean().sort_values(ascending=False)*100\n",
        "temp.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "aDRkaMLwUEpb",
        "outputId": "f925bf45-6827-4309-ba6d-ea4d774add5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='educacion'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "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": [
        "#análisis por estado civil\n",
        "temp_civil=df.groupby('estado_civil').abandono.mean().sort_values(ascending=False)*100\n",
        "temp_civil.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "n78MsbGYUmL9",
        "outputId": "6c2246b8-6023-41ee-990a-318591cf8377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='estado_civil'>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "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": [
        "#análisis por horas extras\n",
        "temp_horas=df.groupby('horas_extra').abandono.mean().sort_values(ascending=False)*100\n",
        "temp_horas.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "LWK0bSD5VFI7",
        "outputId": "b5014ff4-7d50-48b6-f9bf-6cdb83e9d696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='horas_extra'>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "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": [
        "temp_puesto=df.groupby('puesto').abandono.mean().sort_values(ascending=False)*100\n",
        "temp_puesto.plot.bar()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "ET8hFBV7Vkx7",
        "outputId": "e239d75e-1ede-4ba2-d84e-36f25423131f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='puesto'>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "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": [
        "#abandono-salario\n",
        "temp_diferencia=df.groupby('abandono').salario_mes.mean()\n",
        "temp_diferencia.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "LtJTYXsoWt0L",
        "outputId": "fb61d3bf-4eb8-436e-c3f3-846ba6f4b53d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='abandono'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "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": [
        "#salario año\n",
        "df['salario_año']=df.salario_mes.transform(lambda x: x*12)\n",
        "df[['salario_año', 'salario_mes']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "9m9zk8W1Y5Az",
        "outputId": "f01c54e7-7ffe-4ac6-e3d3-5b42d3cc6f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      salario_año  salario_mes\n",
              "id                            \n",
              "1           71916         5993\n",
              "2           61560         5130\n",
              "4           25080         2090\n",
              "5           34908         2909\n",
              "7           41616         3468\n",
              "...           ...          ...\n",
              "2061        30852         2571\n",
              "2062       119892         9991\n",
              "2064        73704         6142\n",
              "2065        64680         5390\n",
              "2068        52848         4404\n",
              "\n",
              "[1470 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d83b9c6e-0150-400c-9d2a-fb0cf59eb0fd\" 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>salario_año</th>\n",
              "      <th>salario_mes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>71916</td>\n",
              "      <td>5993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61560</td>\n",
              "      <td>5130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25080</td>\n",
              "      <td>2090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34908</td>\n",
              "      <td>2909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>41616</td>\n",
              "      <td>3468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2061</th>\n",
              "      <td>30852</td>\n",
              "      <td>2571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2062</th>\n",
              "      <td>119892</td>\n",
              "      <td>9991</td>\n",
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              "    <tr>\n",
              "      <th>2064</th>\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calcular el impacto economico de cada empleado si deja el empledo\n",
        "condiciones=[(df['salario_año']<30000),\n",
        "             (df['salario_año']>=30000) & (df['salario_año']<50000),\n",
        "             (df['salario_año']>=50000) & (df['salario_año']<75000),\n",
        "             (df['salario_año']>=75000)\n",
        "             ]\n",
        "resultado=[df.salario_año*0.161,df.salario_año*0.197, df.salario_año*0.204, df.salario_año*0.21 ]\n",
        "#aplicar condiciones\n",
        "df['impacto__abandono']=np.select(condiciones, resultado, default=000)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "iTTZNspVaHoU",
        "outputId": "49389598-5cdd-4fc4-d292-de742a5b8444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      edad             viajes            departamento  distancia_casa  \\\n",
              "id                                                                      \n",
              "1       41      Travel_Rarely                   Sales               1   \n",
              "2       49  Travel_Frequently  Research & Development               8   \n",
              "4       37      Travel_Rarely  Research & Development               2   \n",
              "5       33  Travel_Frequently  Research & Development               3   \n",
              "7       27      Travel_Rarely  Research & Development               2   \n",
              "...    ...                ...                     ...             ...   \n",
              "2061    36  Travel_Frequently  Research & Development              23   \n",
              "2062    39      Travel_Rarely  Research & Development               6   \n",
              "2064    27      Travel_Rarely  Research & Development               4   \n",
              "2065    49  Travel_Frequently                   Sales               2   \n",
              "2068    34      Travel_Rarely  Research & Development               8   \n",
              "\n",
              "          educacion        carrera  empleados satisfaccion_entorno  \\\n",
              "id                                                                   \n",
              "1     Universitaria  Life Sciences          1                Media   \n",
              "2        Secundaria  Life Sciences          1                 Alta   \n",
              "4        Secundaria          Other          1             Muy_Alta   \n",
              "5     Universitaria  Life Sciences          1             Muy_Alta   \n",
              "7     Universitaria        Medical          1                 Baja   \n",
              "...             ...            ...        ...                  ...   \n",
              "2061         Master        Medical          1                 Alta   \n",
              "2062     Secundaria        Medical          1             Muy_Alta   \n",
              "2064         Master  Life Sciences          1                Media   \n",
              "2065     Secundaria        Medical          1             Muy_Alta   \n",
              "2068  Universitaria        Medical          1                Media   \n",
              "\n",
              "        implicacion  nivel_laboral  ... horas_quincena nivel_acciones  \\\n",
              "id                                  ...                                 \n",
              "1     Universitaria              2  ...             80              0   \n",
              "2        Secundaria              2  ...             80              1   \n",
              "4        Secundaria              1  ...             80              0   \n",
              "5     Universitaria              1  ...             80              0   \n",
              "7     Universitaria              1  ...             80              1   \n",
              "...             ...            ...  ...            ...            ...   \n",
              "2061         Master              2  ...             80              1   \n",
              "2062     Secundaria              3  ...             80              1   \n",
              "2064         Master              2  ...             80              1   \n",
              "2065     Secundaria              2  ...             80              0   \n",
              "2068  Universitaria              2  ...             80              0   \n",
              "\n",
              "     anos_experiencia  num_formaciones_ult_ano  anos_compania  \\\n",
              "id                                                              \n",
              "1                   8                        0              6   \n",
              "2                  10                        3             10   \n",
              "4                   7                        3              0   \n",
              "5                   8                        3              8   \n",
              "7                   6                        3              2   \n",
              "...               ...                      ...            ...   \n",
              "2061               17                        3              5   \n",
              "2062                9                        5              7   \n",
              "2064                6                        0              6   \n",
              "2065               17                        3              9   \n",
              "2068                6                        3              4   \n",
              "\n",
              "     anos_desde_ult_promocion  anos_con_manager_actual abandono salario_año  \\\n",
              "id                                                                            \n",
              "1                           0                        5        1       71916   \n",
              "2                           1                        7        0       61560   \n",
              "4                           0                        0        1       25080   \n",
              "5                           3                        0        0       34908   \n",
              "7                           2                        2        0       41616   \n",
              "...                       ...                      ...      ...         ...   \n",
              "2061                        0                        3        0       30852   \n",
              "2062                        1                        7        0      119892   \n",
              "2064                        0                        3        0       73704   \n",
              "2065                        0                        8        0       64680   \n",
              "2068                        1                        2        0       52848   \n",
              "\n",
              "      impacto__abandono  \n",
              "id                       \n",
              "1             14670.864  \n",
              "2             12558.240  \n",
              "4              4037.880  \n",
              "5              6876.876  \n",
              "7              8198.352  \n",
              "...                 ...  \n",
              "2061           6077.844  \n",
              "2062          25177.320  \n",
              "2064          15035.616  \n",
              "2065          13194.720  \n",
              "2068          10780.992  \n",
              "\n",
              "[1470 rows x 29 columns]"
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              "      <td>Baja</td>\n",
              "      <td>Universitaria</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41616</td>\n",
              "      <td>8198.352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2061</th>\n",
              "      <td>36</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>23</td>\n",
              "      <td>Master</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>Alta</td>\n",
              "      <td>Master</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>30852</td>\n",
              "      <td>6077.844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2062</th>\n",
              "      <td>39</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>6</td>\n",
              "      <td>Secundaria</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>Muy_Alta</td>\n",
              "      <td>Secundaria</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>119892</td>\n",
              "      <td>25177.320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2064</th>\n",
              "      <td>27</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>4</td>\n",
              "      <td>Master</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>Media</td>\n",
              "      <td>Master</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>73704</td>\n",
              "      <td>15035.616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2065</th>\n",
              "      <td>49</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Sales</td>\n",
              "      <td>2</td>\n",
              "      <td>Secundaria</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>Muy_Alta</td>\n",
              "      <td>Secundaria</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>64680</td>\n",
              "      <td>13194.720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2068</th>\n",
              "      <td>34</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>8</td>\n",
              "      <td>Universitaria</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>Media</td>\n",
              "      <td>Universitaria</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>52848</td>\n",
              "      <td>10780.992</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1470 rows × 29 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-48bbb9b2-b6b7-4b41-9cbf-3bc5409f16f6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      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-48bbb9b2-b6b7-4b41-9cbf-3bc5409f16f6 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-48bbb9b2-b6b7-4b41-9cbf-3bc5409f16f6');\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-f3aeab27-6b58-4ac4-884a-74b6115c01c0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3aeab27-6b58-4ac4-884a-74b6115c01c0')\"\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",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\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-f3aeab27-6b58-4ac4-884a-74b6115c01c0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cuanto ha costado el problema último año\n",
        "costo_total=df.loc[df.abandono==1].impacto__abandono.sum()\n",
        "print(f'El impacto que ha tenido la empresa el último año es: {costo_total} dolares')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjHC6-NndFwU",
        "outputId": "272db881-045d-41a2-d5ad-2815fa06dee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El impacto que ha tenido la empresa el último año es: 2719005.912 dolares\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cuanto cuesta los empleados que no estan motivados? (implicacion== baja)\n",
        "\n"
      ],
      "metadata": {
        "id": "xDiikQBSeHvL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df.abandono==1) & (df.implicacion=='Baja')].impacto__abandono.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIfMEqfAeF8D",
        "outputId": "9d0eeeb6-e2a3-49c3-900a-799ecaa2c86b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot correlation between the variables and Diabetes_binary\n",
        "df.corr()['abandono'][:-1].sort_values().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "SQav5LukXYP0",
        "outputId": "02bf7cd2-2253-41df-d0c0-4d99ff480326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-61-f04be9ec25cf>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()['abandono'][:-1].sort_values().plot(kind='bar')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "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": [
        "import seaborn as sns\n",
        "# Scatter plot:\n",
        "\n",
        "sns.set()\n",
        "cols = df.columns\n",
        "sns.pairplot(df[cols], size = 2.5)\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "tMG0KgE_CcX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA VARIABLES NUMERICAS"
      ],
      "metadata": {
        "id": "hKzfSN_K63kH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def estadisticos_count(num):\n",
        "\n",
        "  #calculamos describe en las columnas\n",
        "  estadisticos=num.describe().T\n",
        "\n",
        "  #añadir la mediana\n",
        "  estadisticos['median']=num.median()\n",
        "\n",
        "  estadisticos=estadisticos.iloc[:, [0,1,8,2,3,4,5,6,7]]\n",
        "\n",
        "  return estadisticos\n"
      ],
      "metadata": {
        "id": "8QWZD_Ze66Pn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "estadisticos_count(df.select_dtypes('number'))"
      ],
      "metadata": {
        "id": "lIuiwlR28Z1X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "66a05240-1d9d-49ec-c473-f5fd4afc077a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count          mean     median           std  \\\n",
              "edad                      1470.0     36.923810     36.000      9.135373   \n",
              "distancia_casa            1470.0      9.192517      7.000      8.106864   \n",
              "empleados                 1470.0      1.000000      1.000      0.000000   \n",
              "nivel_laboral             1470.0      2.063946      2.000      1.106940   \n",
              "salario_mes               1470.0   6502.931293   4919.000   4707.956783   \n",
              "num_empresas_anteriores   1470.0      2.693197      2.000      2.498009   \n",
              "incremento_salario_porc   1470.0     15.209524     14.000      3.659938   \n",
              "horas_quincena            1470.0     80.000000     80.000      0.000000   \n",
              "nivel_acciones            1470.0      0.793878      1.000      0.852077   \n",
              "anos_experiencia          1470.0     11.279592     10.000      7.780782   \n",
              "num_formaciones_ult_ano   1470.0      2.799320      3.000      1.289271   \n",
              "anos_compania             1470.0      7.008163      5.000      6.126525   \n",
              "anos_desde_ult_promocion  1470.0      2.187755      1.000      3.222430   \n",
              "anos_con_manager_actual   1470.0      4.123129      3.000      3.568136   \n",
              "abandono                  1470.0      0.161224      0.000      0.367863   \n",
              "salario_año               1470.0  78035.175510  59028.000  56495.481397   \n",
              "impacto__abandono         1470.0  15986.410473  12041.712  12151.287856   \n",
              "\n",
              "                                min        25%        50%        75%  \\\n",
              "edad                         18.000     30.000     36.000      43.00   \n",
              "distancia_casa                1.000      2.000      7.000      14.00   \n",
              "empleados                     1.000      1.000      1.000       1.00   \n",
              "nivel_laboral                 1.000      1.000      2.000       3.00   \n",
              "salario_mes                1009.000   2911.000   4919.000    8379.00   \n",
              "num_empresas_anteriores       0.000      1.000      2.000       4.00   \n",
              "incremento_salario_porc      11.000     12.000     14.000      18.00   \n",
              "horas_quincena               80.000     80.000     80.000      80.00   \n",
              "nivel_acciones                0.000      0.000      1.000       1.00   \n",
              "anos_experiencia              0.000      6.000     10.000      15.00   \n",
              "num_formaciones_ult_ano       0.000      2.000      3.000       3.00   \n",
              "anos_compania                 0.000      3.000      5.000       9.00   \n",
              "anos_desde_ult_promocion      0.000      0.000      1.000       3.00   \n",
              "anos_con_manager_actual       0.000      2.000      3.000       7.00   \n",
              "abandono                      0.000      0.000      0.000       0.00   \n",
              "salario_año               12108.000  34932.000  59028.000  100548.00   \n",
              "impacto__abandono          1949.388   6881.604  12041.712   21115.08   \n",
              "\n",
              "                                max  \n",
              "edad                          60.00  \n",
              "distancia_casa                29.00  \n",
              "empleados                      1.00  \n",
              "nivel_laboral                  5.00  \n",
              "salario_mes                19999.00  \n",
              "num_empresas_anteriores        9.00  \n",
              "incremento_salario_porc       25.00  \n",
              "horas_quincena                80.00  \n",
              "nivel_acciones                 3.00  \n",
              "anos_experiencia              40.00  \n",
              "num_formaciones_ult_ano        6.00  \n",
              "anos_compania                 40.00  \n",
              "anos_desde_ult_promocion      15.00  \n",
              "anos_con_manager_actual       17.00  \n",
              "abandono                       1.00  \n",
              "salario_año               239988.00  \n",
              "impacto__abandono          50397.48  "
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "      <td>1470.0</td>\n",
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              "      <td>1.000</td>\n",
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              "      <td>1009.000</td>\n",
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              "      <td>4919.000</td>\n",
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              "      <td>2.498009</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
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              "      <td>4.00</td>\n",
              "      <td>9.00</td>\n",
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              "      <td>1470.0</td>\n",
              "      <td>15.209524</td>\n",
              "      <td>14.000</td>\n",
              "      <td>3.659938</td>\n",
              "      <td>11.000</td>\n",
              "      <td>12.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>18.00</td>\n",
              "      <td>25.00</td>\n",
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              "      <th>horas_quincena</th>\n",
              "      <td>1470.0</td>\n",
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              "      <td>80.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>80.000</td>\n",
              "      <td>80.000</td>\n",
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              "      <td>80.00</td>\n",
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              "      <td>0.852077</td>\n",
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              "      <td>7.780782</td>\n",
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              "      <td>56495.481397</td>\n",
              "      <td>12108.000</td>\n",
              "      <td>34932.000</td>\n",
              "      <td>59028.000</td>\n",
              "      <td>100548.00</td>\n",
              "      <td>239988.00</td>\n",
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              "    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-c5cc423a-52bd-42f5-aae5-1919052b6e3e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRLob4uDlBP7",
        "outputId": "47b020a6-24a1-4d3c-9287-f5f46646008c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "edad                        0\n",
              "viajes                      0\n",
              "departamento                0\n",
              "distancia_casa              0\n",
              "educacion                   0\n",
              "carrera                     0\n",
              "empleados                   0\n",
              "satisfaccion_entorno        0\n",
              "implicacion                 0\n",
              "nivel_laboral               0\n",
              "puesto                      0\n",
              "satisfaccion_trabajo        0\n",
              "estado_civil                0\n",
              "salario_mes                 0\n",
              "num_empresas_anteriores     0\n",
              "horas_extra                 0\n",
              "incremento_salario_porc     0\n",
              "evaluacion                  0\n",
              "satisfaccion_companeros     0\n",
              "horas_quincena              0\n",
              "nivel_acciones              0\n",
              "anos_experiencia            0\n",
              "num_formaciones_ult_ano     0\n",
              "anos_compania               0\n",
              "anos_desde_ult_promocion    0\n",
              "anos_con_manager_actual     0\n",
              "abandono                    0\n",
              "salario_año                 0\n",
              "impacto__abandono           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación de los datos del modelo\n",
        "\n",
        "Transformar todas las variables categoricas a numericas"
      ],
      "metadata": {
        "id": "0XagFMCjemMO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml=df.copy()\n",
        "df_ml.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slgyHTBVflGl",
        "outputId": "fd8ff186-5557-4c9a-91bc-52b22fcc1163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1470 entries, 1 to 2068\n",
            "Data columns (total 29 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   edad                      1470 non-null   int64  \n",
            " 1   viajes                    1470 non-null   object \n",
            " 2   departamento              1470 non-null   object \n",
            " 3   distancia_casa            1470 non-null   int64  \n",
            " 4   educacion                 1470 non-null   object \n",
            " 5   carrera                   1470 non-null   object \n",
            " 6   empleados                 1470 non-null   int64  \n",
            " 7   satisfaccion_entorno      1470 non-null   object \n",
            " 8   implicacion               1470 non-null   object \n",
            " 9   nivel_laboral             1470 non-null   int64  \n",
            " 10  puesto                    1470 non-null   object \n",
            " 11  satisfaccion_trabajo      1470 non-null   object \n",
            " 12  estado_civil              1470 non-null   object \n",
            " 13  salario_mes               1470 non-null   int64  \n",
            " 14  num_empresas_anteriores   1470 non-null   int64  \n",
            " 15  horas_extra               1470 non-null   object \n",
            " 16  incremento_salario_porc   1470 non-null   int64  \n",
            " 17  evaluacion                1470 non-null   object \n",
            " 18  satisfaccion_companeros   1470 non-null   object \n",
            " 19  horas_quincena            1470 non-null   int64  \n",
            " 20  nivel_acciones            1470 non-null   int64  \n",
            " 21  anos_experiencia          1470 non-null   int64  \n",
            " 22  num_formaciones_ult_ano   1470 non-null   int64  \n",
            " 23  anos_compania             1470 non-null   int64  \n",
            " 24  anos_desde_ult_promocion  1470 non-null   int64  \n",
            " 25  anos_con_manager_actual   1470 non-null   int64  \n",
            " 26  abandono                  1470 non-null   int64  \n",
            " 27  salario_año               1470 non-null   int64  \n",
            " 28  impacto__abandono         1470 non-null   float64\n",
            "dtypes: float64(1), int64(16), object(12)\n",
            "memory usage: 344.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "#categoricas\n",
        "cat=df_ml.select_dtypes('O')\n",
        "\n",
        "#instancia\n",
        "ohe=OneHotEncoder(sparse=False)\n",
        "\n",
        "#entrenamos\n",
        "ohe.fit(cat)\n",
        "\n",
        "#aplicamos la transfomacion\n",
        "cat_ohe=ohe.transform(cat)\n",
        "\n",
        "#se evaluan las variables categoricas\n",
        "cat_one=pd.DataFrame(cat_ohe, columns=ohe.get_feature_names_out(input_features=cat.columns)).reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfbCI_xzesy1",
        "outputId": "3be0956a-80cf-423b-cf2c-30c5a8a99b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_one"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "H6IDlcObfuPN",
        "outputId": "0907de8a-45d0-4c1a-ced8-31121126fe38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      viajes_Non-Travel  viajes_Travel_Frequently  viajes_Travel_Rarely  \\\n",
              "0                   0.0                       0.0                   1.0   \n",
              "1                   0.0                       1.0                   0.0   \n",
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              "1468                0.0                       1.0                   0.0   \n",
              "1469                0.0                       0.0                   1.0   \n",
              "\n",
              "      departamento_Human Resources  departamento_Research & Development  \\\n",
              "0                              0.0                                  0.0   \n",
              "1                              0.0                                  1.0   \n",
              "2                              0.0                                  1.0   \n",
              "3                              0.0                                  1.0   \n",
              "4                              0.0                                  1.0   \n",
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              "1467                           0.0                                  1.0   \n",
              "1468                           0.0                                  0.0   \n",
              "1469                           0.0                                  1.0   \n",
              "\n",
              "      departamento_Sales  educacion_Master  educacion_Primaria  \\\n",
              "0                    1.0               0.0                 0.0   \n",
              "1                    0.0               0.0                 0.0   \n",
              "2                    0.0               0.0                 0.0   \n",
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              "4                    0.0               0.0                 0.0   \n",
              "...                  ...               ...                 ...   \n",
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              "1468                 1.0               0.0                 0.0   \n",
              "1469                 0.0               0.0                 0.0   \n",
              "\n",
              "      educacion_Secundaria  educacion_Universitaria  ...  \\\n",
              "0                      0.0                      1.0  ...   \n",
              "1                      1.0                      0.0  ...   \n",
              "2                      1.0                      0.0  ...   \n",
              "3                      0.0                      1.0  ...   \n",
              "4                      0.0                      1.0  ...   \n",
              "...                    ...                      ...  ...   \n",
              "1465                   0.0                      0.0  ...   \n",
              "1466                   1.0                      0.0  ...   \n",
              "1467                   0.0                      0.0  ...   \n",
              "1468                   1.0                      0.0  ...   \n",
              "1469                   0.0                      1.0  ...   \n",
              "\n",
              "      estado_civil_Married  estado_civil_Single  horas_extra_No  \\\n",
              "0                      0.0                  1.0             0.0   \n",
              "1                      1.0                  0.0             1.0   \n",
              "2                      0.0                  1.0             0.0   \n",
              "3                      1.0                  0.0             0.0   \n",
              "4                      1.0                  0.0             1.0   \n",
              "...                    ...                  ...             ...   \n",
              "1465                   1.0                  0.0             1.0   \n",
              "1466                   1.0                  0.0             1.0   \n",
              "1467                   1.0                  0.0             0.0   \n",
              "1468                   1.0                  0.0             1.0   \n",
              "1469                   1.0                  0.0             1.0   \n",
              "\n",
              "      horas_extra_Yes  evaluacion_Alta  evaluacion_Muy_Alta  \\\n",
              "0                 1.0              1.0                  0.0   \n",
              "1                 0.0              0.0                  1.0   \n",
              "2                 1.0              1.0                  0.0   \n",
              "3                 1.0              1.0                  0.0   \n",
              "4                 0.0              1.0                  0.0   \n",
              "...               ...              ...                  ...   \n",
              "1465              0.0              1.0                  0.0   \n",
              "1466              0.0              1.0                  0.0   \n",
              "1467              1.0              0.0                  1.0   \n",
              "1468              0.0              1.0                  0.0   \n",
              "1469              0.0              1.0                  0.0   \n",
              "\n",
              "      satisfaccion_companeros_Alta  satisfaccion_companeros_Baja  \\\n",
              "0                              0.0                           1.0   \n",
              "1                              0.0                           0.0   \n",
              "2                              0.0                           0.0   \n",
              "3                              1.0                           0.0   \n",
              "4                              0.0                           0.0   \n",
              "...                            ...                           ...   \n",
              "1465                           1.0                           0.0   \n",
              "1466                           0.0                           1.0   \n",
              "1467                           0.0                           0.0   \n",
              "1468                           0.0                           0.0   \n",
              "1469                           0.0                           1.0   \n",
              "\n",
              "      satisfaccion_companeros_Media  satisfaccion_companeros_Muy_Alta  \n",
              "0                               0.0                               0.0  \n",
              "1                               0.0                               1.0  \n",
              "2                               1.0                               0.0  \n",
              "3                               0.0                               0.0  \n",
              "4                               0.0                               1.0  \n",
              "...                             ...                               ...  \n",
              "1465                            0.0                               0.0  \n",
              "1466                            0.0                               0.0  \n",
              "1467                            1.0                               0.0  \n",
              "1468                            0.0                               1.0  \n",
              "1469                            0.0                               0.0  \n",
              "\n",
              "[1470 rows x 48 columns]"
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              "      <th></th>\n",
              "      <th>viajes_Non-Travel</th>\n",
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          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num=df.select_dtypes('number').reset_index(drop=True)\n",
        "num"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "So6LN5U7gRD9",
        "outputId": "91ba6c56-9b00-40ff-a56e-9eb8d8ae5776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      edad  distancia_casa  empleados  nivel_laboral  salario_mes  \\\n",
              "0       41               1          1              2         5993   \n",
              "1       49               8          1              2         5130   \n",
              "2       37               2          1              1         2090   \n",
              "3       33               3          1              1         2909   \n",
              "4       27               2          1              1         3468   \n",
              "...    ...             ...        ...            ...          ...   \n",
              "1465    36              23          1              2         2571   \n",
              "1466    39               6          1              3         9991   \n",
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              "1468    49               2          1              2         5390   \n",
              "1469    34               8          1              2         4404   \n",
              "\n",
              "      num_empresas_anteriores  incremento_salario_porc  horas_quincena  \\\n",
              "0                           8                       11              80   \n",
              "1                           1                       23              80   \n",
              "2                           6                       15              80   \n",
              "3                           1                       11              80   \n",
              "4                           9                       12              80   \n",
              "...                       ...                      ...             ...   \n",
              "1465                        4                       17              80   \n",
              "1466                        4                       15              80   \n",
              "1467                        1                       20              80   \n",
              "1468                        2                       14              80   \n",
              "1469                        2                       12              80   \n",
              "\n",
              "      nivel_acciones  anos_experiencia  num_formaciones_ult_ano  \\\n",
              "0                  0                 8                        0   \n",
              "1                  1                10                        3   \n",
              "2                  0                 7                        3   \n",
              "3                  0                 8                        3   \n",
              "4                  1                 6                        3   \n",
              "...              ...               ...                      ...   \n",
              "1465               1                17                        3   \n",
              "1466               1                 9                        5   \n",
              "1467               1                 6                        0   \n",
              "1468               0                17                        3   \n",
              "1469               0                 6                        3   \n",
              "\n",
              "      anos_compania  anos_desde_ult_promocion  anos_con_manager_actual  \\\n",
              "0                 6                         0                        5   \n",
              "1                10                         1                        7   \n",
              "2                 0                         0                        0   \n",
              "3                 8                         3                        0   \n",
              "4                 2                         2                        2   \n",
              "...             ...                       ...                      ...   \n",
              "1465              5                         0                        3   \n",
              "1466              7                         1                        7   \n",
              "1467              6                         0                        3   \n",
              "1468              9                         0                        8   \n",
              "1469              4                         1                        2   \n",
              "\n",
              "      abandono  salario_año  impacto__abandono  \n",
              "0            1        71916          14670.864  \n",
              "1            0        61560          12558.240  \n",
              "2            1        25080           4037.880  \n",
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              "4            0        41616           8198.352  \n",
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              "1466         0       119892          25177.320  \n",
              "1467         0        73704          15035.616  \n",
              "1468         0        64680          13194.720  \n",
              "1469         0        52848          10780.992  \n",
              "\n",
              "[1470 rows x 17 columns]"
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          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml=pd.concat([cat_one, num], axis=1)\n",
        "df_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "bFut78J9gaId",
        "outputId": "0405f141-fa81-4520-becb-e3be17bddecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      viajes_Non-Travel  viajes_Travel_Frequently  viajes_Travel_Rarely  \\\n",
              "0                   0.0                       0.0                   1.0   \n",
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              "1469                0.0                       0.0                   1.0   \n",
              "\n",
              "      departamento_Human Resources  departamento_Research & Development  \\\n",
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              "\n",
              "      nivel_acciones  anos_experiencia  num_formaciones_ult_ano  \\\n",
              "0                  0                 8                        0   \n",
              "1                  1                10                        3   \n",
              "2                  0                 7                        3   \n",
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              "4                  1                 6                        3   \n",
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              "1467               1                 6                        0   \n",
              "1468               0                17                        3   \n",
              "1469               0                 6                        3   \n",
              "\n",
              "      anos_compania  anos_desde_ult_promocion  anos_con_manager_actual  \\\n",
              "0                 6                         0                        5   \n",
              "1                10                         1                        7   \n",
              "2                 0                         0                        0   \n",
              "3                 8                         3                        0   \n",
              "4                 2                         2                        2   \n",
              "...             ...                       ...                      ...   \n",
              "1465              5                         0                        3   \n",
              "1466              7                         1                        7   \n",
              "1467              6                         0                        3   \n",
              "1468              9                         0                        8   \n",
              "1469              4                         1                        2   \n",
              "\n",
              "      abandono  salario_año  impacto__abandono  \n",
              "0            1        71916          14670.864  \n",
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              "1469         0        52848          10780.992  \n",
              "\n",
              "[1470 rows x 65 columns]"
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>71916</td>\n",
              "      <td>14670.864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>61560</td>\n",
              "      <td>12558.240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25080</td>\n",
              "      <td>4037.880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34908</td>\n",
              "      <td>6876.876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41616</td>\n",
              "      <td>8198.352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>30852</td>\n",
              "      <td>6077.844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>119892</td>\n",
              "      <td>25177.320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>73704</td>\n",
              "      <td>15035.616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1468</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>64680</td>\n",
              "      <td>13194.720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1469</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>52848</td>\n",
              "      <td>10780.992</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1470 rows × 65 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-f21ca909-7f70-46c1-9faa-3c7378b7c988')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f21ca909-7f70-46c1-9faa-3c7378b7c988 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-f21ca909-7f70-46c1-9faa-3c7378b7c988');\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-986e9582-e665-4b06-b2e1-f624e8be8c4b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-986e9582-e665-4b06-b2e1-f624e8be8c4b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-986e9582-e665-4b06-b2e1-f624e8be8c4b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df_ml.drop(columns='abandono')\n",
        "y=df_ml['abandono']"
      ],
      "metadata": {
        "id": "LEiS-o9bh2Uk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#separación de entrenamiento y test\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_X, test_X, train_y, test_y=train_test_split(x , y, test_size=0.3)"
      ],
      "metadata": {
        "id": "FgUdOIjaiGEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsN9cwc-kbZ3",
        "outputId": "62bb98d1-1b33-4989-f539-c2bf0db442d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "viajes_Non-Travel                      0\n",
              "viajes_Travel_Frequently               0\n",
              "viajes_Travel_Rarely                   0\n",
              "departamento_Human Resources           0\n",
              "departamento_Research & Development    0\n",
              "                                      ..\n",
              "anos_compania                          0\n",
              "anos_desde_ult_promocion               0\n",
              "anos_con_manager_actual                0\n",
              "salario_año                            0\n",
              "impacto__abandono                      0\n",
              "Length: 64, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTIB_VqcjT-0",
        "outputId": "64e194f1-d768-44c2-f469-d69356424ee1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "234     1\n",
              "444     0\n",
              "95      0\n",
              "811     0\n",
              "174     0\n",
              "       ..\n",
              "1138    0\n",
              "367     0\n",
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              "959     0\n",
              "Name: abandono, Length: 1029, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Instantiate the DecisionTreeClassifier\n",
        "ac = DecisionTreeClassifier(max_depth=4)\n",
        "\n",
        "# Train the model\n",
        "ac.fit(train_X, train_y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "1RiwD7apijd1",
        "outputId": "643b9bff-3d8d-4c9b-ff9e-bd046a8ae11d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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>DecisionTreeClassifier(max_depth=4)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ac.predict(test_X)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9E3wlF7osSDb",
        "outputId": "6ada33f7-8454-4286-a95f-0a9122752436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
          "execution_count": 76
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}