02 - preprocesado.ipynb

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      "source": [
        "###**Preprocesado de datos: Proyecto Visas**\n",
        "Por: Claudia Yaneth Giraldo Vergara\n",
        "\n",
        "C.C. 1001745178"
      ]
    },
    {
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      "source": [
        "**Aclaración**\n",
        "\n",
        "Debido al gran tamaño de la base de datos original descargada de Kaggle ('H-1B_Disclosure_Data_FY17.csv') se ha creado una nueva base de datos de menor tamaño ('Visas_reducida') a partir de la base de datos original con 10000 filas seleccionadas de manera aleatoria, esto con el fin de acelerar la carga del archivo a Google Colab."
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import resample\n",
        "import matplotlib.pyplot as plt\n"
      ],
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              "   Unnamed: 0         CASE_NUMBER CASE_STATUS CASE_SUBMITTED DECISION_DATE  \\\n",
              "0      354115  I-200-17078-371645   CERTIFIED     2017-03-19    2017-03-23   \n",
              "1      520870  I-200-17174-799976   CERTIFIED     2017-06-27    2017-07-03   \n",
              "2      435723  I-200-17095-547953   CERTIFIED     2017-04-14    2017-04-20   \n",
              "3       27054  I-200-16295-236668   CERTIFIED     2016-10-21    2016-10-27   \n",
              "4       87671  I-200-16356-398915   CERTIFIED     2016-12-21    2016-12-28   \n",
              "\n",
              "  VISA_CLASS EMPLOYMENT_START_DATE EMPLOYMENT_END_DATE  \\\n",
              "0       H-1B            2017-09-15          2020-09-14   \n",
              "1       H-1B            2017-06-30          2020-06-29   \n",
              "2       H-1B            2017-08-05          2020-08-04   \n",
              "3       H-1B            2016-11-07          2019-11-06   \n",
              "4       H-1B            2017-02-03          2020-02-02   \n",
              "\n",
              "                       EMPLOYER_NAME EMPLOYER_BUSINESS_DBA  ... H1B_DEPENDENT  \\\n",
              "0               HSK TECHNOLOGIES INC                   NaN  ...             N   \n",
              "1                      ACCENTURE LLP                   NaN  ...             N   \n",
              "2  TATA CONSULTANCY SERVICES LIMITED                   NaN  ...             Y   \n",
              "3   URBAN SCIENCE APPLICATIONS, INC.                   NaN  ...             N   \n",
              "4      TECH MAHINDRA (AMERICAS),INC.                   NaN  ...             Y   \n",
              "\n",
              "  WILLFUL_VIOLATOR SUPPORT_H1B LABOR_CON_AGREE PUBLIC_DISCLOSURE_LOCATION  \\\n",
              "0                N         NaN             NaN                        NaN   \n",
              "1                N         NaN             NaN                        NaN   \n",
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              "4                N           Y               Y                        NaN   \n",
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              "  WORKSITE_CITY WORKSITE_COUNTY  WORKSITE_STATE WORKSITE_POSTAL_CODE  \\\n",
              "0         MIAMI      MIAMI-DADE              FL                33131   \n",
              "1       ATLANTA          FULTON              GA                30308   \n",
              "2   SAN ANTONIO           BEXAR              TX                78288   \n",
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              "4    RICHARDSON          COLLIN              TX                75082   \n",
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      ],
      "source": [
        "# Carga de datos \n",
        "url = 'https://raw.githubusercontent.com/claudiagiraldo/Proyecto/main/Visas_reducida.csv'\n",
        "data = pd.read_csv(url)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Limpieza de datos**"
      ],
      "metadata": {
        "id": "6YOSa5zyujkt"
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      "source": [
        "data = data.drop('VISA_CLASS', axis = 1)\n",
        "data = data.drop('Unnamed: 0', axis = 1)\n",
        "\n",
        "nan_porcentajes = (data.isna().sum())*100/len(data)\n",
        "columnas = nan_porcentajes[nan_porcentajes > 40].index\n",
        "data = data.drop(columnas, axis=1)\n",
        "\n",
        "for col in data.columns:\n",
        "    if pd.api.types.is_numeric_dtype(data[col]):\n",
        "        data[col] = data[col].fillna(data[col].median())\n",
        "    elif pd.api.types.is_string_dtype(data[col]):\n",
        "        data[col] = data[col].fillna(data[col].mode()[0])\n"
      ],
      "metadata": {
        "id": "zWI4iT7bumTa"
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      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se escogen las variables que se considera que pueden influir en la variable 'CASE_STATUS', esto también con el fin de hacer el preprocesamiento de datos más fácil."
      ],
      "metadata": {
        "id": "JPBw2fzdv4rQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.loc[:, ['CASE_STATUS', 'EMPLOYER_NAME', 'AGENT_REPRESENTING_EMPLOYER', 'SOC_NAME', 'FULL_TIME_POSITION', 'PW_WAGE_LEVEL', 'H1B_DEPENDENT', 'WILLFUL_VIOLATOR']]\n",
        "data.head()"
      ],
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        "id": "qD94kxs6xxSt",
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      "execution_count": 80,
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              "  CASE_STATUS                      EMPLOYER_NAME AGENT_REPRESENTING_EMPLOYER  \\\n",
              "0   CERTIFIED               HSK TECHNOLOGIES INC                           N   \n",
              "1   CERTIFIED                      ACCENTURE LLP                           Y   \n",
              "2   CERTIFIED  TATA CONSULTANCY SERVICES LIMITED                           Y   \n",
              "3   CERTIFIED   URBAN SCIENCE APPLICATIONS, INC.                           Y   \n",
              "4   CERTIFIED      TECH MAHINDRA (AMERICAS),INC.                           N   \n",
              "\n",
              "                            SOC_NAME FULL_TIME_POSITION PW_WAGE_LEVEL  \\\n",
              "0  SOFTWARE DEVELOPERS, APPLICATIONS                  Y       Level I   \n",
              "1                MANAGEMENT ANALYSTS                  Y       Level I   \n",
              "2                     WEB DEVELOPERS                  Y     Level III   \n",
              "3  SOFTWARE DEVELOPERS, APPLICATIONS                  Y     Level III   \n",
              "4  SOFTWARE DEVELOPERS, APPLICATIONS                  Y      Level II   \n",
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              "  H1B_DEPENDENT WILLFUL_VIOLATOR  \n",
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              "1             N                N  \n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CERTIFIED</td>\n",
              "      <td>TATA CONSULTANCY SERVICES LIMITED</td>\n",
              "      <td>Y</td>\n",
              "      <td>WEB DEVELOPERS</td>\n",
              "      <td>Y</td>\n",
              "      <td>Level III</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CERTIFIED</td>\n",
              "      <td>URBAN SCIENCE APPLICATIONS, INC.</td>\n",
              "      <td>Y</td>\n",
              "      <td>SOFTWARE DEVELOPERS, APPLICATIONS</td>\n",
              "      <td>Y</td>\n",
              "      <td>Level III</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CERTIFIED</td>\n",
              "      <td>TECH MAHINDRA (AMERICAS),INC.</td>\n",
              "      <td>N</td>\n",
              "      <td>SOFTWARE DEVELOPERS, APPLICATIONS</td>\n",
              "      <td>Y</td>\n",
              "      <td>Level II</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65f05302-bc76-4a33-a98b-1b1db800fb56')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-65f05302-bc76-4a33-a98b-1b1db800fb56 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-65f05302-bc76-4a33-a98b-1b1db800fb56');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codificación de valores**\n",
        "\n",
        "Se convierten los valores o etiquetas de forma numérica para que sean legibles para el algoritmo. Las variables 'CASE_STATUS', 'FULL_TIME_POSITION', 'PW_WAGE_LEVEL', 'H1B_DEPENDENT', 'WILLFUL_VIOLATOR', 'AGENT_REPRESENTING_EMPLOYER' son fáciles de convertir, mientras que 'SOC_NAME' y 'EMPLOYER_NAME' son más complejas."
      ],
      "metadata": {
        "id": "pBNP7ez7p04v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*La variable 'CASE_STATUS' se convierte en una variable binaria, se le asignan valores de 1 para indicar que la visa fue aceptada, y 0 para indicar que la visa fue denegada.*"
      ],
      "metadata": {
        "id": "XNZrEMUFocMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['CASE_STATUS'] = data['CASE_STATUS'].map({'CERTIFIED' :1, 'CERTIFIED-WITHDRAWN' : 1, 'WITHDRAWN' : 1, 'DENIED' : 0})"
      ],
      "metadata": {
        "id": "j1ABmTZvoW7f"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['FULL_TIME_POSITION'] = data['FULL_TIME_POSITION'].map({'N' : 0, 'Y' : 1})\n",
        "data['PW_WAGE_LEVEL'] = data['PW_WAGE_LEVEL'].map({'Level I' : 1, 'Level II' : 2, 'Level III' : 3, 'Level IV' : 4})\n",
        "data['H1B_DEPENDENT'] = data['H1B_DEPENDENT'].map({'N' : 0, 'Y' : 1})\n",
        "data['WILLFUL_VIOLATOR'] = data['WILLFUL_VIOLATOR'].map({'N' : 0, 'Y' : 1})\n",
        "data['AGENT_REPRESENTING_EMPLOYER'] = data['AGENT_REPRESENTING_EMPLOYER'].map({'N' : 0, 'Y' : 1})\n",
        "\n",
        "\n",
        "data['SOC_NAME1'] = 'otros'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('COMPUTER','SOFTWARE')] = 'it'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('CHIEF','MANAGMENT')] = 'gerente'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('MECHANICAL')] = 'mecanico'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('DATABASE')] = 'base de datos'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('SALES','MARKET')] = 'scm'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('FINANCIAL')] = 'finanzas'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('PUBLIC','FUNDRAISING')] = 'pr'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('EDUCATION','LAW')] = 'administrativo'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('AUDITORS','COMPLIANCE')] = 'auditoria'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('DISTRIBUTION','LOGISTIC')] = 'logistica'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('RECRUITERS','HUMAN')] = 'rrhh'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('AGRICULTURAL','FARM')] = 'agri'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('CONSTRUCTION','ARCHITECTURAL')] = 'estate'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('FORENCSIC','HEALTH')] = 'propiedades'\n",
        "data['SOC_NAME1'][data['SOC_NAME'].str.contains('TEACHERS')] = 'educacion'\n",
        "\n",
        "data['EMPLOYER_NAME1'] = 'otros'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('INFOSYS')] = 'infosys'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('TATA')] = 'tata'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('CAPGEMINI')] = 'capgemini'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('IBM')] = 'ibm'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('MAHINDRA')] = 'mahindra'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('DELOITTE')] = 'deloitte'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('ERNST')] = 'E&Y'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('GOOGLE')] = 'google'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('WIPRO')] = 'wipro'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('HCL')] = 'hcl'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('MICROSOFT')] = 'microsoft'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('AMAZON')] = 'amazon'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('SYNTEL')] = 'syntel'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('COGNIZANT')] = 'cognizant'\n",
        "data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('APPLE')] = 'apple'\n",
        "data = data.drop(['EMPLOYER_NAME', 'SOC_NAME'], axis = 1)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mv4_KWuADDb4",
        "outputId": "a3a4ac19-2530-4afa-fc1d-deed39cc186a"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-cabfcd5a19cf>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('COMPUTER','SOFTWARE')] = 'it'\n",
            "<ipython-input-82-cabfcd5a19cf>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('CHIEF','MANAGMENT')] = 'gerente'\n",
            "<ipython-input-82-cabfcd5a19cf>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('MECHANICAL')] = 'mecanico'\n",
            "<ipython-input-82-cabfcd5a19cf>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('DATABASE')] = 'base de datos'\n",
            "<ipython-input-82-cabfcd5a19cf>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('SALES','MARKET')] = 'scm'\n",
            "<ipython-input-82-cabfcd5a19cf>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('FINANCIAL')] = 'finanzas'\n",
            "<ipython-input-82-cabfcd5a19cf>:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('PUBLIC','FUNDRAISING')] = 'pr'\n",
            "<ipython-input-82-cabfcd5a19cf>:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('EDUCATION','LAW')] = 'administrativo'\n",
            "<ipython-input-82-cabfcd5a19cf>:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('AUDITORS','COMPLIANCE')] = 'auditoria'\n",
            "<ipython-input-82-cabfcd5a19cf>:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('DISTRIBUTION','LOGISTIC')] = 'logistica'\n",
            "<ipython-input-82-cabfcd5a19cf>:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('RECRUITERS','HUMAN')] = 'rrhh'\n",
            "<ipython-input-82-cabfcd5a19cf>:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('AGRICULTURAL','FARM')] = 'agri'\n",
            "<ipython-input-82-cabfcd5a19cf>:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('CONSTRUCTION','ARCHITECTURAL')] = 'estate'\n",
            "<ipython-input-82-cabfcd5a19cf>:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('FORENCSIC','HEALTH')] = 'propiedades'\n",
            "<ipython-input-82-cabfcd5a19cf>:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['SOC_NAME1'][data['SOC_NAME'].str.contains('TEACHERS')] = 'educacion'\n",
            "<ipython-input-82-cabfcd5a19cf>:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('INFOSYS')] = 'infosys'\n",
            "<ipython-input-82-cabfcd5a19cf>:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('TATA')] = 'tata'\n",
            "<ipython-input-82-cabfcd5a19cf>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('CAPGEMINI')] = 'capgemini'\n",
            "<ipython-input-82-cabfcd5a19cf>:29: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('IBM')] = 'ibm'\n",
            "<ipython-input-82-cabfcd5a19cf>:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('MAHINDRA')] = 'mahindra'\n",
            "<ipython-input-82-cabfcd5a19cf>:31: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('DELOITTE')] = 'deloitte'\n",
            "<ipython-input-82-cabfcd5a19cf>:32: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('ERNST')] = 'E&Y'\n",
            "<ipython-input-82-cabfcd5a19cf>:33: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('GOOGLE')] = 'google'\n",
            "<ipython-input-82-cabfcd5a19cf>:34: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('WIPRO')] = 'wipro'\n",
            "<ipython-input-82-cabfcd5a19cf>:35: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('HCL')] = 'hcl'\n",
            "<ipython-input-82-cabfcd5a19cf>:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('MICROSOFT')] = 'microsoft'\n",
            "<ipython-input-82-cabfcd5a19cf>:37: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('AMAZON')] = 'amazon'\n",
            "<ipython-input-82-cabfcd5a19cf>:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('SYNTEL')] = 'syntel'\n",
            "<ipython-input-82-cabfcd5a19cf>:39: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('COGNIZANT')] = 'cognizant'\n",
            "<ipython-input-82-cabfcd5a19cf>:40: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['EMPLOYER_NAME1'][data['EMPLOYER_NAME'].str.contains('APPLE')] = 'apple'\n"
          ]
        },
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          "output_type": "execute_result",
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            "text/plain": [
              "   CASE_STATUS  AGENT_REPRESENTING_EMPLOYER  FULL_TIME_POSITION  \\\n",
              "0            1                            0                   1   \n",
              "1            1                            1                   1   \n",
              "2            1                            1                   1   \n",
              "3            1                            1                   1   \n",
              "4            1                            0                   1   \n",
              "\n",
              "   PW_WAGE_LEVEL  H1B_DEPENDENT  WILLFUL_VIOLATOR SOC_NAME1 EMPLOYER_NAME1  \n",
              "0              1              0                 0     otros          otros  \n",
              "1              1              0                 0     otros          otros  \n",
              "2              3              1                 0     otros           tata  \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se utiliza la librería sklearn para convertir los datos de 'SOC_NAME' y 'EMPLOYER_NAME' en valores categóricos."
      ],
      "metadata": {
        "id": "AOI44FB0D57y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cod_variables = preprocessing.LabelEncoder()\n",
        "cod_variables.fit(data.SOC_NAME1)\n",
        "data['SOC_N']=cod_variables.transform(data['SOC_NAME1'])\n",
        "\n",
        "cod_variables = preprocessing.LabelEncoder()\n",
        "cod_variables.fit(data.EMPLOYER_NAME1)\n",
        "data['EMP_N']=cod_variables.transform(data['EMPLOYER_NAME1'])\n",
        "\n",
        "data = data.drop(['SOC_NAME1', 'EMPLOYER_NAME1'], axis=1)"
      ],
      "metadata": {
        "id": "9JF7WbkMDPpq"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uHqlK-ImdP6Q",
        "outputId": "96380153-752a-4147-b09a-c5549824ed01"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CASE_STATUS  AGENT_REPRESENTING_EMPLOYER  FULL_TIME_POSITION  \\\n",
              "0            1                            0                   1   \n",
              "1            1                            1                   1   \n",
              "2            1                            1                   1   \n",
              "3            1                            1                   1   \n",
              "4            1                            0                   1   \n",
              "\n",
              "   PW_WAGE_LEVEL  H1B_DEPENDENT  WILLFUL_VIOLATOR  SOC_N  EMP_N  \n",
              "0              1              0                 0     11     12  \n",
              "1              1              0                 0     11     12  \n",
              "2              3              1                 0     11     14  \n",
              "3              3              0                 0     11     12  \n",
              "4              2              1                 0     11     10  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Datos desbalanceados**\n",
        "\n",
        "Se encontró un desbalance de los datos de la variable a predecir ('CASE_STATUS'), como se muestra en el siguiente gráfico"
      ],
      "metadata": {
        "id": "VT1tPqbSiri1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de la variable 'CASE_STATUS'\n",
        "status_contador = data['CASE_STATUS'].value_counts()\n",
        "plt.bar(status_contador.index, status_contador.values)\n",
        "plt.title('Número de aplicaciones por resultado final del caso')\n",
        "plt.xlabel('Resultado del caso')\n",
        "plt.ylabel('Número de aplicaciones')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "9aNDxRIDzTz8",
        "outputId": "1cf8dc70-665e-44b7-ae08-aa720faa2288"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se opta por realizar una reducción de muestreo de la clase mayoritaria"
      ],
      "metadata": {
        "id": "2uq3P8nHjDWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separa muestras\n",
        "aceptadas = data[data['CASE_STATUS'] == 1]\n",
        "rechazadas = data[data['CASE_STATUS'] == 0]\n",
        "\n",
        "# Realiza la reducción de muestreo de la clase mayoritaria (aceptadas)\n",
        "downsampled_certified = resample(aceptadas, \n",
        "                                 replace=False,   \n",
        "                                 n_samples=len(rechazadas),   \n",
        "                                 random_state=42)  \n",
        "\n",
        "# Combinar muestras\n",
        "data = pd.concat([downsampled_certified, rechazadas])\n",
        "\n",
        "# Dataframe final\n",
        "data = data.sample(frac=1, random_state=42)"
      ],
      "metadata": {
        "id": "FjdTIvpL5xWv"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aunque se pierde una gran cantidad de datos, se obtienen datos balanceados, como se observa en el siguiente gráfico:"
      ],
      "metadata": {
        "id": "5_AQB6mSjULR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de la variable 'CASE_STATUS'\n",
        "status_contador = data['CASE_STATUS'].value_counts()\n",
        "plt.bar(status_contador.index, status_contador.values)\n",
        "plt.title('Número de aplicaciones por resultado final del caso')\n",
        "plt.xlabel('Resultado del caso')\n",
        "plt.ylabel('Número de aplicaciones')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Dd6FYy8-1BWB",
        "outputId": "2782cc9e-46d7-4e22-e769-86939f10032e"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finalmente, con la base de datos lista se procede a seleccionar la variable dependiente y la independiente, y se divide el dataset en 80% para train y 20% para test."
      ],
      "metadata": {
        "id": "N0mMLtlujhfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Variable dependiente e independiente\n",
        "x = data.drop(['CASE_STATUS'], axis=1) # Variables independientes\n",
        "y = data['CASE_STATUS'] # Variable dependiente\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "8IAm42qYEUh5"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "De esta manera, los datos ya estan listos para utilizarlos en los algoritmos"
      ],
      "metadata": {
        "id": "Js8DUopZuX-q"
      }
    }
  ]
}