decision_tree_model.ipynb

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      "cell_type": "markdown",
      "source": [
        "En este notebook desarrollo la construcción de un modelo de árbol de decisión para el proyecto"
      ],
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        "id": "m7CfYhoSAS8q"
      }
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        "id": "aJWJC_-Z1Ybw"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.metrics as metrics\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats\n",
        "import plotly.express as px\n",
        "import plotly.figure_factory as ff\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "from google.colab import files\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/rh_data - rh_data (2).csv\")"
      ],
      "metadata": {
        "id": "1l_YOLVq8fXU"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "   Age AgeCategory Attrition     BusinessTravel                Department  \\\n",
              "0   51           D        No      Travel_Rarely                     Sales   \n",
              "1   31           B       Yes  Travel_Frequently  Research and Development   \n",
              "2   32           B        No  Travel_Frequently  Research and Development   \n",
              "3   38           C        No         Non-Travel  Research and Development   \n",
              "4   32           B        No      Travel_Rarely  Research and Development   \n",
              "\n",
              "   DistanceFromHome    DistanceCategory  Education EducationCategory  \\\n",
              "0                 6  Mid-Short Distance          2         Graduated   \n",
              "1                10   Mid-Long Distance          1    Undergraduated   \n",
              "2                17       Long Distance          4           Mastery   \n",
              "3                 2      Short Distance          5         Doctorate   \n",
              "4                10   Mid-Long Distance          1    Undergraduated   \n",
              "\n",
              "  EducationField  ...  Over18  PercentSalaryHike StandardHours  \\\n",
              "0  Life Sciences  ...       Y                 11             8   \n",
              "1  Life Sciences  ...       Y                 23             8   \n",
              "2          Other  ...       Y                 15             8   \n",
              "3  Life Sciences  ...       Y                 11             8   \n",
              "4        Medical  ...       Y                 12             8   \n",
              "\n",
              "   StockOptionLevel TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
              "0                 0               1.0                     6              1   \n",
              "1                 1               6.0                     3              5   \n",
              "2                 3               5.0                     2              5   \n",
              "3                 3              13.0                     5              8   \n",
              "4                 2               9.0                     2              6   \n",
              "\n",
              "   CategoryYearsAtCompany YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                       A                       0                     0  \n",
              "1                       B                       1                     4  \n",
              "2                       B                       0                     3  \n",
              "3                       C                       7                     5  \n",
              "4                       C                       0                     4  \n",
              "\n",
              "[5 rows x 30 columns]"
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    {
      "cell_type": "markdown",
      "source": [
        "Imputación de nulos"
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        "mean_total_working_years = df['TotalWorkingYears'].mean()\n",
        "\n",
        "df['TotalWorkingYears'].fillna(mean_total_working_years, inplace=True)\n",
        "print(df['TotalWorkingYears'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bDcNDUW8omT",
        "outputId": "f678129b-efcf-48f7-8fbf-196cb0e2bbc1"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_num_companies = df['NumCompaniesWorked'].mean()\n",
        "\n",
        "df['NumCompaniesWorked'].fillna(mean_num_companies, inplace=True)\n",
        "print(df['NumCompaniesWorked'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wxurGbC8qzn",
        "outputId": "53d8b2e8-85a2-4a87-c95f-3bbba871cbf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.groupby('Attrition').size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbGPKvm08rto",
        "outputId": "d74008d2-639c-4874-fa5f-85cf91da877c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attrition\n",
            "No     3699\n",
            "Yes     711\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*One-hot encoding* de variables categóricas"
      ],
      "metadata": {
        "id": "iXiRlTcE7Wsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['AgeCategory', 'DistanceCategory', 'BusinessTravel', 'Department', 'EducationCategory', 'EducationField', 'LevelCategory', 'JobRole', 'MaritalStatus', 'IncomeCategory', 'CategoryYearsAtCompany'], drop_first=True)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "gKL5TTT-8xJ0",
        "outputId": "8efec2e4-7486-4262-e485-cb0b6d5182ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Attrition  DistanceFromHome  Education  EmployeeCount  EmployeeID  \\\n",
              "0   51        No                 6          2              1           1   \n",
              "1   31       Yes                10          1              1           2   \n",
              "2   32        No                17          4              1           3   \n",
              "3   38        No                 2          5              1           4   \n",
              "4   32        No                10          1              1           5   \n",
              "\n",
              "   Gender  JobLevel  MonthlyIncome  NumCompaniesWorked  ...  \\\n",
              "0  Female         1         131160                 1.0  ...   \n",
              "1  Female         1          41890                 0.0  ...   \n",
              "2    Male         4         193280                 1.0  ...   \n",
              "3    Male         3          83210                 3.0  ...   \n",
              "4    Male         1          23420                 4.0  ...   \n",
              "\n",
              "  JobRole_Sales Representative  MaritalStatus_Married  MaritalStatus_Single  \\\n",
              "0                        False                   True                 False   \n",
              "1                        False                  False                  True   \n",
              "2                        False                   True                 False   \n",
              "3                        False                   True                 False   \n",
              "4                        False                  False                  True   \n",
              "\n",
              "   IncomeCategory_Low Income  IncomeCategory_Mid Income  \\\n",
              "0                      False                      False   \n",
              "1                      False                      False   \n",
              "2                      False                      False   \n",
              "3                      False                       True   \n",
              "4                       True                      False   \n",
              "\n",
              "   IncomeCategory_Mid-High Income  IncomeCategory_Mid-Low Income  \\\n",
              "0                            True                          False   \n",
              "1                           False                           True   \n",
              "2                           False                          False   \n",
              "3                           False                          False   \n",
              "4                           False                          False   \n",
              "\n",
              "   CategoryYearsAtCompany_B  CategoryYearsAtCompany_C  \\\n",
              "0                     False                     False   \n",
              "1                      True                     False   \n",
              "2                      True                     False   \n",
              "3                     False                      True   \n",
              "4                     False                      True   \n",
              "\n",
              "   CategoryYearsAtCompany_D  \n",
              "0                     False  \n",
              "1                     False  \n",
              "2                     False  \n",
              "3                     False  \n",
              "4                     False  \n",
              "\n",
              "[5 rows x 59 columns]"
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definición de variables X e Y"
      ],
      "metadata": {
        "id": "COs2lmzQFA8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Attrition', 'EmployeeID', 'EmployeeCount','Gender', 'Over18', 'StandardHours', 'Age', 'DistanceFromHome', 'Education', 'MonthlyIncome', 'JobLevel', 'TotalWorkingYears', 'YearsAtCompany'])\n",
        "\n",
        "# Definir la variable objetivo 'y' como la columna 'Attrition' (convertida en binaria)\n",
        "y = df['Attrition'].apply(lambda x: 1 if x == 'Yes' else 0)"
      ],
      "metadata": {
        "id": "IIh_T0i580IE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "División de datos en entrenamiento (70%) y prueba (30%)"
      ],
      "metadata": {
        "id": "-k5XDcDlFECc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "A8XjsTKH82oL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Balanceo de conjunto de entrenamiento"
      ],
      "metadata": {
        "id": "kzljWKbrFJq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Crear un objeto SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "# Aplicar SMOTE solo al conjunto de entrenamiento\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "# Verificar la distribución de clases después del balanceo\n",
        "print(\"Distribución de clases después del balanceo:\")\n",
        "print(y_train_balanced.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBw7F4N68496",
        "outputId": "bfb93b90-74da-4158-e0fe-c652793fbae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribución de clases después del balanceo:\n",
            "Attrition\n",
            "0    2584\n",
            "1    2584\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenamiento del árbol de decisión (30% de prueba)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "y5wnyn3589fO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Crear el modelo de árbol de decisión\n",
        "decision_tree_model = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Entrenar el modelo\n",
        "decision_tree_model.fit(X_train_balanced, y_train_balanced)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "358e-Ehb893g",
        "outputId": "50841dc2-cf76-47e0-870b-35780338c99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicción, rendimiento y validación cruzada del modelo"
      ],
      "metadata": {
        "id": "i_o1YpqKAlql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "# Validación cruzada (K=5)\n",
        "cv_scores = cross_val_score(decision_tree_model, X_train_balanced, y_train_balanced, cv=5)\n",
        "\n",
        "# Realizar predicciones con el conjunto de prueba\n",
        "y_pred_tree = decision_tree_model.predict(X_test)\n",
        "\n",
        "# Evaluar el rendimiento del modelo\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred_tree))\n",
        "\n",
        "# Mostrar los resultados de la validación cruzada\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Mean CV Score: {cv_scores.mean()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8I2hplDx9GJf",
        "outputId": "30156519-8615-4ad4-d23f-92f12f612f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9758125472411187\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      1115\n",
            "           1       0.94      0.90      0.92       208\n",
            "\n",
            "    accuracy                           0.98      1323\n",
            "   macro avg       0.96      0.94      0.95      1323\n",
            "weighted avg       0.98      0.98      0.98      1323\n",
            "\n",
            "Cross-Validation Scores: [0.95631068 0.94983819 0.95623987 0.93841167 0.95461912]\n",
            "Mean CV Score: 0.9510839063639176\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gráfico del primer modelo obtenido (decision_tree_model)"
      ],
      "metadata": {
        "id": "8jRLU4vMmnyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "import graphviz\n",
        "\n",
        "# Exportar el árbol a formato DOT con los nombres de características corregidos\n",
        "dot_data = export_graphviz(decision_tree_model,\n",
        "                           out_file=None,\n",
        "                           feature_names=X_train_balanced.columns,\n",
        "                           class_names=['Clase 0', 'Clase 1'],\n",
        "                           filled=True, rounded=True,\n",
        "                           special_characters=True)\n",
        "\n",
        "# Crear una visualización con graphviz\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph.render(\"decision_tree\")  # Guardar el archivo en formato .pdf o .png si lo prefieres\n",
        "graph.view()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ixZ6Vx2QcE0M",
        "outputId": "defd614d-813d-4e21-eb9c-844c14df2f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'decision_tree.pdf'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Curva de aprendizaje del árbol de decisión (sin parámetros).\n",
        "En este gráfico se puede observar el *overfitting* del\n",
        "modelo en el conjunto de entrenamiento y la mejora del rendimiento del conjunto de prueba a medida que aumentan los datos"
      ],
      "metadata": {
        "id": "T5UbPETcFQXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "train_sizes, train_scores, test_scores = learning_curve(decision_tree_model, X_train_balanced, y_train_balanced, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Calcular la media y desviación estándar\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Graficar la curva de aprendizaje\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Entrenamiento\")\n",
        "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Validación cruzada\")\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "plt.xlabel(\"Tamaño de Entrenamiento\")\n",
        "plt.ylabel(\"Exactitud\")\n",
        "plt.title(\"Curva de Aprendizaje (Árbol de Decisión)\")\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "4d7tY4aW-BgO",
        "outputId": "86d24679-b0ef-4ae9-f753-2fb3706c7c3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importancia de las variables en el modelo"
      ],
      "metadata": {
        "id": "-aiHov59FW7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtener la importancia de las características\n",
        "feature_importances = decision_tree_model.feature_importances_\n",
        "\n",
        "# Ordenar las características más importantes\n",
        "sorted_indices = np.argsort(feature_importances)[::-1]\n",
        "\n",
        "# Crear un DataFrame para visualizar la importancia de las características\n",
        "feature_importances_df = pd.DataFrame({\n",
        "    'Feature': X_train.columns[sorted_indices],\n",
        "    'Importance': feature_importances[sorted_indices]\n",
        "})\n",
        "\n",
        "# Visualizar las características más importantes\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.barplot(x='Importance', y='Feature', data=feature_importances_df)\n",
        "plt.title('Importancia de las Características (Árbol de Decisión)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "IXM6hhdA-DBK",
        "outputId": "70520ddd-edc1-435d-a347-60846d377ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba del modelo con datos nuevos"
      ],
      "metadata": {
        "id": "vCbI0mPSdzxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_1 = pd.read_csv(\"/content/rh_data - Hoja 2.csv\")"
      ],
      "metadata": {
        "id": "1cZu52-0d2kd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Eliminar las columnas innecesarias en X_new\n",
        "X_new = df_1.drop(columns=['Attrition', 'EmployeeID', 'EmployeeCount', 'Gender', 'Over18', 'StandardHours',\n",
        "                           'Age', 'DistanceFromHome', 'Education', 'MonthlyIncome', 'JobLevel',\n",
        "                           'TotalWorkingYears', 'YearsAtCompany'])\n",
        "\n",
        "# Aplicar get_dummies para codificar las variables categóricas en X_new\n",
        "X_new_encoded = pd.get_dummies(X_new)\n",
        "X_new_encoded = X_new_encoded.reindex(columns=X_train_balanced.columns, fill_value=0)\n",
        "\n",
        "# Predicciones con el modelo\n",
        "y_new_pred = decision_tree_model.predict(X_new_encoded)\n",
        "\n",
        "# Ver las predicciones\n",
        "print(y_new_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73hnZ8jmg2zA",
        "outputId": "731eb2f2-99b9-496e-a41d-6804871dca46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicción y rendimiento del modelo con datos nuevos"
      ],
      "metadata": {
        "id": "FzpFUoHuAtP6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Evaluar las predicciones\n",
        "accuracy = accuracy_score(y_new, y_new_pred)\n",
        "report = classification_report(y_new, y_new_pred)\n",
        "\n",
        "# Mostrar los resultados\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvofcgY1hwkA",
        "outputId": "3b30f598-0434-4386-be54-545e772e5648"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8947368421052632\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94        16\n",
            "           1       0.67      0.67      0.67         3\n",
            "\n",
            "    accuracy                           0.89        19\n",
            "   macro avg       0.80      0.80      0.80        19\n",
            "weighted avg       0.89      0.89      0.89        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicción del modelo en el df de datos nuevos"
      ],
      "metadata": {
        "id": "HymlyUeKAx-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_1['Attrition_Pred'] = y_new_pred\n",
        "\n",
        "df_1.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "lM1HqJMCiDLy",
        "outputId": "b62524aa-13ff-42e6-a38a-da5fe5d6eada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age AgeCategory Attrition     BusinessTravel                Department  \\\n",
              "0   34           B        No      Travel_Rarely           Human Resources   \n",
              "1   45           D       Yes  Travel_Frequently  Research and Development   \n",
              "2   32           B        No  Travel_Frequently  Research and Development   \n",
              "3   33           B        No         Non-Travel  Research and Development   \n",
              "4   60           D        No      Travel_Rarely  Research and Development   \n",
              "\n",
              "   DistanceFromHome    DistanceCategory  Education EducationCategory  \\\n",
              "0                 7  Mid-Short Distance          5         Doctorate   \n",
              "1                10   Mid-Long Distance          2         Graduated   \n",
              "2                 6  Mid-Short Distance          3            Degree   \n",
              "3                 2      Short Distance          2         Graduated   \n",
              "4                18       Long Distance          1    Undergraduated   \n",
              "\n",
              "  EducationField  ...  PercentSalaryHike  StandardHours StockOptionLevel  \\\n",
              "0  Life Sciences  ...                 11              8                0   \n",
              "1  Life Sciences  ...                 23              8                1   \n",
              "2          Other  ...                 15              8                3   \n",
              "3  Life Sciences  ...                 11              8                3   \n",
              "4        Medical  ...                 12              8                2   \n",
              "\n",
              "   TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
              "0                  1                     6              1   \n",
              "1                  6                     3              5   \n",
              "2                  5                     2              5   \n",
              "3                 13                     5              8   \n",
              "4                  9                     2              6   \n",
              "\n",
              "  CategoryYearsAtCompany  YearsSinceLastPromotion YearsWithCurrManager  \\\n",
              "0                      A                        0                    0   \n",
              "1                      B                        1                    4   \n",
              "2                      B                        0                    3   \n",
              "3                      C                        7                    5   \n",
              "4                      C                        0                    4   \n",
              "\n",
              "   Attrition_Pred  \n",
              "0               1  \n",
              "1               1  \n",
              "2               0  \n",
              "3               0  \n",
              "4               0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <td>6</td>\n",
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              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-565d5635-1d36-45f9-ad8c-057758eaa2b7 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-565d5635-1d36-45f9-ad8c-057758eaa2b7');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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-9f61d53e-92ff-4bc2-b393-728fffe955ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_1"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Revisión de hiperparámetros para revisar el sobreajuste del modelo"
      ],
      "metadata": {
        "id": "7DoHfSxJnXVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Revisar los hiperparámetros actuales del modelo\n",
        "print(decision_tree_model.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnfz73VTiwwI",
        "outputId": "7b993c23-e8ec-49c6-c752-a3f882ceefe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Búsqueda de los hiperparámetros que mejor se ajusten al modelo, y ejecución de los mismos"
      ],
      "metadata": {
        "id": "JWEXNf1cngL3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Definir los hiperparámetros que queremos probar\n",
        "param_grid = {\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'criterion': ['gini', 'entropy']  # Prueba con distintos criterios de pureza\n",
        "}\n",
        "\n",
        "# Crear el modelo base\n",
        "decision_tree_model_1 = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Definir el GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=decision_tree_model_1, param_grid=param_grid, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Ajustar el GridSearch con los datos de entrenamiento\n",
        "grid_search.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Mostrar los mejores parámetros encontrados\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bMLkTQTjKMQ",
        "outputId": "4b35d7fe-c501-43c8-c35a-48374c0e87e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
            "Best hyperparameters: {'criterion': 'gini', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtener los mejores hiperparámetros\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Entrenar el modelo con los mejores parámetros\n",
        "best_model_tuned = DecisionTreeClassifier(**best_params, random_state=42)\n",
        "best_model_tuned.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Hacer predicciones con los nuevos datos\n",
        "y_new_pred_tuned = best_model_tuned.predict(X_new_encoded)\n",
        "\n",
        "# Evaluar el modelo ajustado\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "accuracy_tuned = accuracy_score(y_new, y_new_pred_tuned)\n",
        "report_tuned = classification_report(y_new, y_new_pred_tuned)\n",
        "\n",
        "print(f\"Accuracy: {accuracy_tuned}\")\n",
        "print(\"Classification Report:\\n\", report_tuned)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dWNIA2cjzuq",
        "outputId": "f48a9d5d-3156-4f19-ceea-68b95685dfa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8947368421052632\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94        16\n",
            "           1       0.67      0.67      0.67         3\n",
            "\n",
            "    accuracy                           0.89        19\n",
            "   macro avg       0.80      0.80      0.80        19\n",
            "weighted avg       0.89      0.89      0.89        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba con `max_depth = 15`"
      ],
      "metadata": {
        "id": "drz1B43xHyve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenar el modelo con el mejor max_depth\n",
        "best_depth = grid_search.best_params_['max_depth']\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=10, random_state=42)\n",
        "decision_tree_model.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Evaluar el rendimiento en el conjunto de prueba\n",
        "y_pred_tree = decision_tree_model.predict(X_test)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyFuzLY6Hnq5",
        "outputId": "a0968abb-b3a6-40e3-e848-04ff0aafe6ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8639455782312925\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.90      0.92      1115\n",
            "           1       0.56      0.66      0.60       208\n",
            "\n",
            "    accuracy                           0.86      1323\n",
            "   macro avg       0.75      0.78      0.76      1323\n",
            "weighted avg       0.87      0.86      0.87      1323\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicciones con el modelo\n",
        "y_new_pred = decision_tree_model.predict(X_new_encoded)\n",
        "\n",
        "# Ver las predicciones\n",
        "print(y_new_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vK9tx207kuSw",
        "outputId": "6854dab9-154d-4302-fc27-678d9d891f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Evaluar las predicciones\n",
        "accuracy = accuracy_score(y_new, y_new_pred)\n",
        "report = classification_report(y_new, y_new_pred)\n",
        "\n",
        "# Mostrar los resultados\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dX740Zymkz0E",
        "outputId": "ee696676-4ba1-4138-9c37-acc61ec3d876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7368421052631579\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.81      0.84        16\n",
            "           1       0.25      0.33      0.29         3\n",
            "\n",
            "    accuracy                           0.74        19\n",
            "   macro avg       0.56      0.57      0.56        19\n",
            "weighted avg       0.77      0.74      0.75        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sizes, train_scores, test_scores = learning_curve(decision_tree_model, X_train_balanced, y_train_balanced, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Calcular la media y desviación estándar\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Graficar la curva de aprendizaje\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Entrenamiento\")\n",
        "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Validación cruzada\")\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "plt.xlabel(\"Tamaño de Entrenamiento\")\n",
        "plt.ylabel(\"Exactitud\")\n",
        "plt.title(\"Curva de Aprendizaje (Árbol de Decisión)\")\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "_5UWIdJMHtSX",
        "outputId": "74cd5cf7-7d31-4240-9400-73f7018d266f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba con `max_depth = 5`"
      ],
      "metadata": {
        "id": "YOubWy_-HcSB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenar el modelo con el mejor max_depth\n",
        "best_depth = grid_search.best_params_['max_depth']\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=5, random_state=42)\n",
        "decision_tree_model.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Evaluar el rendimiento en el conjunto de prueba\n",
        "y_pred_tree = decision_tree_model.predict(X_test)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhgxudXf-Mt6",
        "outputId": "214168a8-a662-4d58-f21c-c164a53ed135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7430083144368859\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.79      0.84      1115\n",
            "           1       0.30      0.48      0.37       208\n",
            "\n",
            "    accuracy                           0.74      1323\n",
            "   macro avg       0.60      0.64      0.60      1323\n",
            "weighted avg       0.80      0.74      0.76      1323\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicciones con el modelo\n",
        "y_new_pred = decision_tree_model.predict(X_new_encoded)\n",
        "\n",
        "# Ver las predicciones\n",
        "print(y_new_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrfcbeJClG1S",
        "outputId": "1be624e9-e46e-44c8-f78f-7026abaef968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Evaluar las predicciones\n",
        "accuracy = accuracy_score(y_new, y_new_pred)\n",
        "report = classification_report(y_new, y_new_pred)\n",
        "\n",
        "# Mostrar los resultados\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6MBATg4lIMA",
        "outputId": "0ddfa6b9-5834-407b-900c-01d4b0b0dcf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7894736842105263\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.81      0.87        16\n",
            "           1       0.40      0.67      0.50         3\n",
            "\n",
            "    accuracy                           0.79        19\n",
            "   macro avg       0.66      0.74      0.68        19\n",
            "weighted avg       0.85      0.79      0.81        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sizes, train_scores, test_scores = learning_curve(decision_tree_model, X_train_balanced, y_train_balanced, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Calcular la media y desviación estándar\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Graficar la curva de aprendizaje\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Entrenamiento\")\n",
        "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Validación cruzada\")\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "plt.xlabel(\"Tamaño de Entrenamiento\")\n",
        "plt.ylabel(\"Exactitud\")\n",
        "plt.title(\"Curva de Aprendizaje (Árbol de Decisión)\")\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "yD1Macw8-ShP",
        "outputId": "fd405ac8-e6d0-45e7-9205-5e2597bb83f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba con `max_depth = 5 ` y ` min_samples_split = 5`"
      ],
      "metadata": {
        "id": "ybupg56wn5xh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenar el modelo con el mejor max_depth\n",
        "best_depth = grid_search.best_params_['max_depth']\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=5, random_state=42, min_samples_split=5)\n",
        "decision_tree_model.fit(X_train_balanced, y_train_balanced)\n",
        "\n",
        "# Evaluar el rendimiento en el conjunto de prueba\n",
        "y_pred_tree = decision_tree_model.predict(X_test)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXaVeZG6l0Eu",
        "outputId": "b771cdd5-0971-44fe-d7e2-6711d8ef2009"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7430083144368859\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.79      0.84      1115\n",
            "           1       0.30      0.48      0.37       208\n",
            "\n",
            "    accuracy                           0.74      1323\n",
            "   macro avg       0.60      0.64      0.60      1323\n",
            "weighted avg       0.80      0.74      0.76      1323\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicciones con el modelo\n",
        "y_new_pred = decision_tree_model.predict(X_new_encoded)\n",
        "\n",
        "# Ver las predicciones\n",
        "print(y_new_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XILIOSNQmAKP",
        "outputId": "1437702a-b0dd-4d01-e0d3-ac7b6e648e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Evaluar las predicciones\n",
        "accuracy = accuracy_score(y_new, y_new_pred)\n",
        "report = classification_report(y_new, y_new_pred)\n",
        "\n",
        "# Mostrar los resultados\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swzAdzALmJD8",
        "outputId": "9db1ebcd-ac11-4291-fbea-a58127b51ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7894736842105263\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.81      0.87        16\n",
            "           1       0.40      0.67      0.50         3\n",
            "\n",
            "    accuracy                           0.79        19\n",
            "   macro avg       0.66      0.74      0.68        19\n",
            "weighted avg       0.85      0.79      0.81        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sizes, train_scores, test_scores = learning_curve(decision_tree_model, X_train_balanced, y_train_balanced, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Calcular la media y desviación estándar\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Graficar la curva de aprendizaje\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Entrenamiento\")\n",
        "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Validación cruzada\")\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "plt.xlabel(\"Tamaño de Entrenamiento\")\n",
        "plt.ylabel(\"Exactitud\")\n",
        "plt.title(\"Curva de Aprendizaje (Árbol de Decisión)\")\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "A-4fp5C0mM2s",
        "outputId": "4ab05dbe-2728-4b27-d537-f517b6ebe91b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}