ClasificacionBank_LeivaCarlosEzequiel.ipynb

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      "source": [
        "#**MACHINE LEARNING** 🤖📊\n",
        "##**CLASIFICACIÓN - DESAFIO**\n"
      ]
    },
    {
      "cell_type": "markdown",
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        "## **CONSIGNAS:** 📌\n",
        "\n",
        "1) Deberá elegir un dataset no trabajado en clases para clasificación\n",
        "\n",
        "2) Elija un dataset para tarea de clasificación del siguiente enlace. [aquí](https://archive.ics.uci.edu/datasets?Task=Classification&skip=0&take=10&sort=desc&orderBy=NumHits&search=)\n",
        "\n",
        "3) No podrá repetir o utilizar el mismo dataset que algún otro estudiante. Para ello debe postear en este grupo cual es su dataset elegido. Una vez elegido no podrá cambiarlo.\n",
        "\n",
        "4) Debe postear aquí como entrega el enlace a su notebook colab público, también almacenado en su github.\n",
        "\n",
        "5) En su notebook debe tener como mínimo los siguientes apartados:\n",
        "\n",
        "5.1 Presentación del dataset\n",
        "\n",
        "5.2 Pre-procesamiento del dataset\n",
        "\n",
        "5.3 Explicación del objetivo (qué es lo que se quiere clasificar, si es binaria o no, cuáles son las clases, etc)\n",
        "\n",
        "5.4 Elección de modelos de clasificación a entrenar (minimo dos modelos, y uno de ellos debe entrenarlo con diferentes hiperparámetros para mostrar su desempeño)\n",
        "\n",
        "5.4 División del dataset para entrenar (set de entrenamiento, testeo, si usará o no cross validation)\n",
        "\n",
        "5.5 Entrenamiento de los modelos\n",
        "\n",
        "5.6 Resultados obtenidos y su análisis (contrastar métricas, matriz de confusión)\n",
        "\n",
        "5.7 Análisis de la experiencia, elección de modelo candidato\n",
        "\n",
        "--------------\n",
        "\n",
        "\n"
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        "#**Elección y presentación del dataset:**\n",
        "\n",
        "El dataset \"Bank Marketing\" se organiza en dos carpetas: bank y bank-additional, cada una con un archivo que contiene el 10% de los datos (bank.csv y bank-additional.csv) y otro con el dataset completo (bank-full.csv y bank-additional-full.csv). bank-additional es la versión más actualizada y contiene 20 variables en lugar de 17, lo que aporta mayor riqueza de información. Para el modelo de clasificación, se utilizará bank-additional-full.csv por su detalle y confiabilidad."
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              "   age        job  marital    education  default housing loan    contact  \\\n",
              "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
              "1   57   services  married  high.school  unknown      no   no  telephone   \n",
              "2   37   services  married  high.school       no     yes   no  telephone   \n",
              "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
              "4   56   services  married  high.school       no      no  yes  telephone   \n",
              "\n",
              "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
              "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
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              "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
              "0          93.994          -36.4      4.857       5191.0  no  \n",
              "1          93.994          -36.4      4.857       5191.0  no  \n",
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              "3          93.994          -36.4      4.857       5191.0  no  \n",
              "4          93.994          -36.4      4.857       5191.0  no  \n",
              "\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "# Cargar el dataset\n",
        "df = pd.read_csv('bank-additional-full.csv', sep=';')\n",
        "\n",
        "# Mostrar las primeras 10 filas\n",
        "df.head(5)"
      ]
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      "source": [
        "##**Definición del Objetivo del Modelo**\n",
        "El propósito de este modelo es clasificar a los clientes en función de su decisión de suscribir o no a un depósito a plazo fijo. Este es un problema de clasificación binaria, donde las clases se definen como:\n",
        "\n",
        "**YES:** El cliente suscribió un depósito.\n",
        "\n",
        "**NO:** El cliente no suscribió un depósito."
      ],
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    {
      "cell_type": "code",
      "source": [
        "#importacion librerias\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score"
      ],
      "metadata": {
        "id": "ewplsQN0pYNn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Pre-procesamiento del Dataset:**\n",
        "\n",
        "En esta sección, se identifican las variables categóricas y numéricas del dataset. Luego, se lleva a cabo la codificación de las variables categóricas y se separan las características (X) de la variable objetivo (y). Finalmente, se realiza la división del dataset en conjuntos de entrenamiento y prueba, así como el escalado de las características para mejorar el rendimiento de los modelos."
      ],
      "metadata": {
        "id": "fjSwSu90p5mV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Variables categóricas y numéricas\n",
        "cat_cols = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']\n",
        "num_cols = ['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed']\n",
        "\n",
        "# Codificación de variables categóricas\n",
        "df_encoded = pd.get_dummies(df, drop_first=True)\n",
        "\n",
        "# Separar características (X) y variable objetivo (y)\n",
        "X = df_encoded.drop('y_yes', axis=1)\n",
        "y = df_encoded['y_yes']\n",
        "\n",
        "# Dividir el dataset en conjunto de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Escalado de características\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "XMEMRSo8rKy4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Selección de Modelos de Clasificación**\n",
        "Se seleccionan y entrenan varios modelos de clasificación, que incluyen:\n",
        "\n",
        "- Árbol de Decisión\n",
        "- Máquinas de Vectores de Soporte (SVM)\n",
        "- Regresión Logística, con la intención de ajustar sus hiperparámetros."
      ],
      "metadata": {
        "id": "od8THU8eAs3f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Árbol de Decisión\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "dt_model.fit(X_train, y_train)\n",
        "\n",
        "# 2. Máquinas de Vectores de Soporte (SVM)\n",
        "svm_model = SVC(kernel='linear', random_state=42)\n",
        "svm_model.fit(X_train, y_train)\n",
        "\n",
        "# 3. Regresión Logística\n",
        "logistic_model = LogisticRegression(max_iter=1000, random_state=42)\n",
        "logistic_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "vRYzG34KrYnI",
        "outputId": "a6d1ce98-dece-43f6-860b-0c8fa45397fd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Evaluación de los Modelos**\n",
        "Se evalúa el rendimiento de cada modelo utilizando métricas como la matriz de confusión y el informe de clasificación. Esto permite analizar la efectividad de cada enfoque en la predicción de la suscripción a depósitos."
      ],
      "metadata": {
        "id": "k3RzJKUuBD5-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SJoUCRLtLqn",
        "outputId": "81277e57-2fcb-415b-802a-93c9a0ee0848"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Árbol de Decisión\n",
            "[[6814  489]\n",
            " [ 448  487]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.93      0.94      7303\n",
            "        True       0.50      0.52      0.51       935\n",
            "\n",
            "    accuracy                           0.89      8238\n",
            "   macro avg       0.72      0.73      0.72      8238\n",
            "weighted avg       0.89      0.89      0.89      8238\n",
            "\n",
            "Máquinas de Vectores de Soporte\n",
            "[[7146  157]\n",
            " [ 639  296]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.92      0.98      0.95      7303\n",
            "        True       0.65      0.32      0.43       935\n",
            "\n",
            "    accuracy                           0.90      8238\n",
            "   macro avg       0.79      0.65      0.69      8238\n",
            "weighted avg       0.89      0.90      0.89      8238\n",
            "\n",
            "Regresión Logística\n",
            "[[7103  200]\n",
            " [ 529  406]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.93      0.97      0.95      7303\n",
            "        True       0.67      0.43      0.53       935\n",
            "\n",
            "    accuracy                           0.91      8238\n",
            "   macro avg       0.80      0.70      0.74      8238\n",
            "weighted avg       0.90      0.91      0.90      8238\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluar el modelo de Árbol de Decisión\n",
        "dt_predictions = dt_model.predict(X_test)\n",
        "print(\"Árbol de Decisión\")\n",
        "print(confusion_matrix(y_test, dt_predictions))\n",
        "print(classification_report(y_test, dt_predictions))\n",
        "\n",
        "# Evaluar el modelo SVM\n",
        "svm_predictions = svm_model.predict(X_test)\n",
        "print(\"Máquinas de Vectores de Soporte\")\n",
        "print(confusion_matrix(y_test, svm_predictions))\n",
        "print(classification_report(y_test, svm_predictions))\n",
        "\n",
        "# Evaluar el modelo de Regresión Logística\n",
        "logistic_predictions = logistic_model.predict(X_test)\n",
        "print(\"Regresión Logística\")\n",
        "print(confusion_matrix(y_test, logistic_predictions))\n",
        "print(classification_report(y_test, logistic_predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los resultados muestran que, aunque todos los modelos presentan un rendimiento sólido en la clasificación de la clase negativa, tienen dificultades para detectar la clase positiva, evidenciando un desbalance en el conjunto de datos. La Regresión Logística destaca como el modelo más efectivo, aunque su capacidad para identificar instancias de la clase positiva es limitada. Para abordar este problema, planeo optimizar el modelo de Regresión Logística ajustando sus hiperparámetros, con el objetivo de mejorar la precisión y el recall en la detección de la clase positiva."
      ],
      "metadata": {
        "id": "zhj50WZWGmmK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Visualización**\n",
        "\n",
        "Se usa seaborn para graficar la matriz de confusión, que muestra la cantidad de predicciones correctas e incorrectas para cada clase ('no' y 'yes')."
      ],
      "metadata": {
        "id": "Md1dtUGeFiXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "# Evaluar el modelo de Árbol de Decisión\n",
        "dt_predictions = dt_model.predict(X_test)\n",
        "print(\"Árbol de Decisión\")\n",
        "print(confusion_matrix(y_test, dt_predictions))\n",
        "print(classification_report(y_test, dt_predictions))\n",
        "\n",
        "# Graficar la matriz de confusión para Árbol de Decisión\n",
        "dt_cm = confusion_matrix(y_test, dt_predictions)\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(dt_cm, annot=True, fmt='d', cmap='Blues', xticklabels=['no', 'yes'], yticklabels=['no', 'yes'])\n",
        "plt.ylabel('Etiqueta real')\n",
        "plt.xlabel('Etiqueta predicha')\n",
        "plt.title('Matriz de Confusión - Árbol de Decisión')\n",
        "plt.show()\n",
        "\n",
        "# Evaluar el modelo SVM\n",
        "svm_predictions = svm_model.predict(X_test)\n",
        "print(\"Máquinas de Vectores de Soporte\")\n",
        "print(confusion_matrix(y_test, svm_predictions))\n",
        "print(classification_report(y_test, svm_predictions))\n",
        "\n",
        "# Graficar la matriz de confusión para SVM\n",
        "svm_cm = confusion_matrix(y_test, svm_predictions)\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(svm_cm, annot=True, fmt='d', cmap='Blues', xticklabels=['no', 'yes'], yticklabels=['no', 'yes'])\n",
        "plt.ylabel('Etiqueta real')\n",
        "plt.xlabel('Etiqueta predicha')\n",
        "plt.title('Matriz de Confusión - SVM')\n",
        "plt.show()\n",
        "\n",
        "# Evaluar el modelo de Regresión Logística\n",
        "logistic_predictions = logistic_model.predict(X_test)\n",
        "print(\"Regresión Logística\")\n",
        "print(confusion_matrix(y_test, logistic_predictions))\n",
        "print(classification_report(y_test, logistic_predictions))\n",
        "\n",
        "# Graficar la matriz de confusión para Regresión Logística\n",
        "logistic_cm = confusion_matrix(y_test, logistic_predictions)\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(logistic_cm, annot=True, fmt='d', cmap='Blues', xticklabels=['no', 'yes'], yticklabels=['no', 'yes'])\n",
        "plt.ylabel('Etiqueta real')\n",
        "plt.xlabel('Etiqueta predicha')\n",
        "plt.title('Matriz de Confusión - Regresión Logística')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U6L77AAaEPpX",
        "outputId": "38dc7392-aaee-4378-e658-1dbf8c8c32fb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Árbol de Decisión\n",
            "[[6814  489]\n",
            " [ 448  487]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.93      0.94      7303\n",
            "        True       0.50      0.52      0.51       935\n",
            "\n",
            "    accuracy                           0.89      8238\n",
            "   macro avg       0.72      0.73      0.72      8238\n",
            "weighted avg       0.89      0.89      0.89      8238\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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cN23axLNnz/Dz8wMM+5689957nD59muTk5FcOzcxMQT0OC5ocvb2Dra0tS5YsYcKECbRp0ybLeqampqhUKp0W5K1btzK9ktfGxiZD8jHU3bt3+fTTT6lXrx6zZs3Se7n0g+Xl0Urz5s3TeW1qakqHDh3YvHkzV65cybCe113p6OrqSp8+fdi3bx//+9//MsxPS0tj9uzZ3L59G1NTU5o3b84vv/yic5l+dHS09kK7nPi5Dpqf/e+88w5Hjx7VKV+8eLHO69TUVOLj43XKSpYsiYuLi06X3statmxJampqhqGAc+fORaVS5VhLzdfXV2d61Zjz9C6fkSNH0rFjR52pT58+lCtXjnXr1um97X///ZetW7dqXyckJLB69Wq8vb21re2WLVty5swZQkNDtfWePHnCsmXLKFOmTKa/QF6lZcuWnDp1ijNnzmjL7t27lyFuPz8/1Go106ZNIzk5OcN6snOF7sWLFxk6dCgODg4MGDAAMOx70qFDB+7fv5/h2ICsW9gF/TgsaHL8Sqisfs69qFWrVsyZM4ePPvqIrl27EhMTw6JFi/Dw8ODSpUs6dWvWrMn+/fuZM2cOLi4uuLu7Z9qf+SqDBw/m3r17jBo1ih9//FFnXrVq1bL8qent7U2XLl1YvHgx8fHxfPjhhxw4cEDbD/yiGTNmcOjQIWrXrk2fPn3w9PQkNjaWc+fOsX//fmJjY18Z4+zZs7l58yaDBw9my5YttG7dGgcHByIjI9m4cSPh4eF07twZgClTphASEkK9evXo378/ZmZmfP/99yQlJTFz5kyD3pvX6d27NzNmzKB3797UqlWLo0ePaluu6R49ekSpUqXo2LEjXl5e2Nrasn//fs6ePcvs2bOzXHebNm1o3LgxX331Fbdu3cLLy4t9+/bxyy+/MHTo0CyH8+WWpKQkNm/eTLNmzbK8eOjjjz9m/vz5xMTEvHaYMGj693v16sXZs2dxdHRk5cqVREdH6/zKHTNmjHaY9ODBgylWrBirVq0iIiKCzZs3Y2JiWBtt1KhRrFmzho8++oghQ4Zoh3q6ubnpfL/UajVLliyhW7du1KhRg86dO1OiRAkiIyPZuXMndevWzTT5vuzYsWMkJiaSmprKgwcPOHHiBL/++it2dnZs3bpVp0tJ3+9J9+7dWb16NcOGDePMmTPUr1+fJ0+esH//fvr370/btm0zxFFYjsM8k52hQi8O9XyVzIZ6rlixQilXrpxiaWmpVKxYUQkKCsp0iGZ4eLjSoEEDxdraWgG0wz7T6967dy/D9l5eT/pwvMymF4eJZebZs2fK4MGDleLFiys2NjZKmzZtlH/++SfTZaOjo5UBAwYorq6uirm5ueLk5KQ0bdpUWbZs2Su3kS4lJUVZvny5Ur9+fcXOzk4xNzdX3NzclJ49e2YYBnru3DnFz89PsbW1VYoUKaI0btxYOXnypE6drD6f9CF6hw4d0pZlNtRTUTRDcnv16qXY2dkpRYsWVT799FMlJiZGZ/+TkpKUkSNHKl5eXkrRokUVGxsbxcvLS1m8eLHOul4eYqcomuGGgYGBiouLi2Jubq6UK1dOmTVrls5wPkXRDLHLbAifm5tbpkOB38TmzZsVQFmxYkWWdQ4fPqwAyvz58xVF0RxblStXzrRu+nG/d+9epVq1atpjPbOhkTdv3lQ6duyo2NvbK1ZWVsoHH3yg7NixQ6eOvkM9FUVRLl26pDRs2FCxsrJS3n33XWXy5MnKihUrdIZ6pjt06JDi5+en2NnZKVZWVsp7772n9OjRQ/ntt99euY304yh9Mjc3V0qUKKE0aNBAmTp1qhITE5Ppcvp+T54+fap89dVXiru7u7Zex44ddYY4F8bjMK+oFOUtO0shhBAi2wrlLZ2FEEK8miR/IYQwQpL8hRDCCEnyF0IIIyTJXwghjJAkfyGEMEKS/IUQwgi9Xc861JN19YH5HYLIQ7FnXn8Vqig8rA271U/G5Q3ID8/OF95jq1AmfyGEyJJKOjxAkr8QwtiYZP0AJ2MiyV8IYVxy6DbxbztJ/kII4yLdPoAkfyGEsZGWPyDJXwhhbKTlD0jyF0IYG2n5A5L8hRDGRlr+gCR/IYSxkZY/IMlfCGFspOUPSPIXQhgbucgLkOQvhDA20vIHJPkLIYyNifT5gyR/IYSxkZY/IMlfCGFsZLQPIMlfCGFspOUPSPIXQhgbafkDkvyFEMZGWv6AJH8hhLGRlj8gyV8IYWzkIi9Akr8QwthItw8gyV8IYWyk2weQ5C+EMDbS8gck+QshjI0kfwDkXRBCGBeVSv/JQHfu3OHzzz+nePHiWFtbU7VqVX777TftfEVRGDduHM7OzlhbW+Pr68sff/yhs47Y2Fj8/f1Rq9XY29vTq1cvHj9+rFPn0qVL1K9fHysrK1xdXZk5c6bBsUryF0IYF5WJ/pMBHj58SN26dTE3N2f37t1cu3aN2bNn4+DgoK0zc+ZMFixYwNKlSzl9+jQ2Njb4+fmRmJiorePv78/Vq1cJCQlhx44dHD16lC+//FI7PyEhgebNm+Pm5kZYWBizZs1iwoQJLFu2zLC3QVEUxaAl3gLW1QfmdwgiD8WeWZjfIYg8ZG2ezeXb6Z8kn2378vWV/s+YMWM4ceIEx44dy3S+oii4uLgwfPhwRowYAUB8fDyOjo4EBwfTuXNnrl+/jqenJ2fPnqVWrVoA7Nmzh5YtW3L79m1cXFxYsmQJX331FVFRUVhYWGi3vW3bNsLDw/WOV1r+QgjjYkDLPykpiYSEBJ0pKSkp09X++uuv1KpVi06dOlGyZEmqV6/ODz/8oJ0fERFBVFQUvr6+2jI7Oztq165NaGgoAKGhodjb22sTP4Cvry8mJiacPn1aW6dBgwbaxA/g5+fHjRs3ePjwod5vgyR/IYRRUZmY6D1Nnz4dOzs7nWn69OmZrvevv/5iyZIllCtXjr1799KvXz8GDx7MqlWrAIiKigLA0dFRZzlHR0ftvKioKEqWLKkz38zMjGLFiunUyWwdL25DHzLaRwhhVFQGnMgdO3Ysw4YN0ymztLTMtG5aWhq1atVi2rRpAFSvXp0rV66wdOlSAgIC3jzgXCItfyGEcVHpP1laWqJWq3WmrJK/s7Mznp6eOmWVKlUiMjISACcnJwCio6N16kRHR2vnOTk5ERMTozM/JSWF2NhYnTqZrePFbehDkr8QwqioVCq9J0PUrVuXGzdu6JT9/vvvuLm5AeDu7o6TkxMHDhzQzk9ISOD06dP4+PgA4OPjQ1xcHGFhYdo6Bw8eJC0tjdq1a2vrHD16lOTkZG2dkJAQKlSooDOy6HUk+QshjEpuJf/AwEBOnTrFtGnT+PPPP1m/fj3Lli1jwIAB2u0OHTqUKVOm8Ouvv3L58mW6d++Oi4sL7dq1AzS/FD766CP69OnDmTNnOHHiBAMHDqRz5864uLgA0LVrVywsLOjVqxdXr17lp59+Yv78+Rm6p15H+vyFEEbF0KSur/fff5+tW7cyduxYJk2ahLu7O/PmzcPf319bZ9SoUTx58oQvv/ySuLg46tWrx549e7CystLWWbduHQMHDqRp06aYmJjQoUMHFixYoJ1vZ2fHvn37GDBgADVr1uSdd95h3LhxOtcC6EPG+Yu3nozzNy7ZHedv12WN3nXjN3TL3sYKMGn5CyGMi9zUE5DkL4QwMrnV7fO2keQvhDAqkvw1JPkLIYyKiYkMcgRJ/kIIYyMNf0CSvxDCyEi3j4YkfyGEUZHkryHJXwhhVCT5a0jyF0IYF8n9gCR/IYSRkZa/hiR/IYRRkeSvIclfCGFUJPlrSPIXQhgVlYkkf5DkL4QwMtLy15DkL4QwKpL8NST5CyGMiiR/DbnDUQHjUsKOlVO6c/vQt8SGzuHsz/+lhmdp7Xwbawvmju7En3smExs6h3Obv6J3x3o66/iifV32/jCE6GOzeHZ+IXa21lluz8LcjFM/juHZ+YVUK/9uru2XMNzK5cvwrlKBmTOmasvu37/HV2NG0rRhXeq8703nTp+wP2SvznLXr13lP717Us+nFg3r1mbShG94+vRJXodfcBnwAPfCrEAl/7CwMNauXcvatWs5d+5cfoeT5+yLWnMweBjJKWm0G7iY6h2mMmbOFh4mPNXW+XZ4B5p96EnPr1bj3X4KC9cdZu7oTrRqWFVbp4iVOSEnrzFr5b7XbnPa0LbcvRefK/sj3tyVy5fYtPFHypevoFP+9djR3LoVwbyFS9i0ZTtNfZsxavhQwq9fAyAmJpr/9O5J6dKlWbv+ZxYt/YGbf/7BuK/G5sduFEi59Qzft02B6PaJiYmhc+fOHD58GHt7ewDi4uJo3LgxP/74IyVKlMjfAPPI8J7NuB31kP9MWKst+/vfBzp16ni5s3bHaY6F/QHAyi0n6NWhLrUqu7HzyGUAFq4/DED9muVeub3mdT1pWqcSXUYu56N6lXNwT0R2PH36hP+OGcm4CVP44fslOvMuXjjPV9+Mp2rVagD0+U9/1q5exbWrV6lYyZOjRw5jZmbG2K/Ha29d/PW4iXRq/zGRkX9TurRbnu9PQVPYk7q+CkTLf9CgQTx69IirV68SGxtLbGwsV65cISEhgcGDB+d3eHmmVcOqnLsWybqZX/D3gemEbhhNz08+1Klz6mIErRtWxaWEHQANapWjnFtJ9p+6btC2ShYryuJvutDrm9U8ffY8x/ZBZN+0KZOo36AhdXw+zDDPy7s6e/fsJj4+jrS0NPbs2knS8yRqffABAMnPn2Nubq5zz3rL/3s4+PlzYXmzAwWctPw1CkTLf8+ePezfv59KlSppyzw9PVm0aBHNmzd/5bJJSUkkJSXplClpqahMTHMl1tzk/u479OlUnwVrDzJzxT5qVnZj9qiOPE9JZd320wAM+3Yji77pws19U0lOTiVNSaP/5A2cOHfToG0tm/Q5P2w6zrlrkZR2LpYbuyPewJ5dOwm/fo11P27KdP7M2fMYPSKQhnVrY2ZmhpWVFXPmLdS26N+vXYfZs2YQvHI5/t268+zpMxbMnQ3A/Xv38mw/CrLCntT1VSCSf1paGubm5hnKzc3NSUtLe+Wy06dPZ+LEiTplpo7vY+78QY7GmBdMTFScuxbJ+IXbAbh44zaVPZzp07GeNvn379yQD6qWocOQpUTejaVeDQ/mjfmUu/fiOXT6hl7b6d+lIUWLWOl1TkDknai7d5k5YypLf1iJpaVlpnUWL5zPo0cJfL88GHt7Bw4d3M+oEUMJWrWOcuUr4OFRjklTZzB75gz+N38OJiYmdPHvRvHi72AiFzcBcpFXugKR/Js0acKQIUPYsGEDLi4uANy5c4fAwECaNm36ymXHjh3LsGHDdMpK1h+da7Hmpqj7CVz/K0qnLDwiinZNvQGwsjRn4qA2fDbsB/YcvwrAlT/+pVqFUgzt1lTv5N/o/fLUruZO/Ol5OuUn1o3ix92/0WfcmmzvizDctWtXiY19QJdP22vLUlNTORd2lp82rGPb9j38uH4tm7btwMNDcz6nQsWKnD/3Gz9tWMfX4ycB0LJVG1q2asOD+/exLmKNChVrVwfzbinXfNmvgkZa/hoFIvkvXLiQjz/+mDJlyuDqqjlAIyMjqVq1KmvXrn3lspaWlhlaSW9jlw9A6IW/KO9WUqesXOmSRN6NBcDczBQLczPSFEWnTmpqmkGtuuEzNzFh0Q7ta+cSduxYMpBuY4I4e/nWm++AyJbadeqwaet2nbJxX4/F3b0sPXv1ITHxGQAmKt1TdSYmphmOCYDi77wDwLYtm7CwtKSOT91civztIrlfo0Akf1dXV86dO8eBAwe4fl1z4rJSpUr4+vrmc2R5639rD3IoeDgjv2jO5pBzvF+5DF90qMvAyRsAePQkkaO//cG0oe14lphM5N1Y6tf0wL/1B4yes0W7HsfiRXEsrua90povf5VyLjx6ksg/UQ95mPCUf6Ie6mz38VPNOZO//rnHnZi4vNlZkYGNjS0e5crrlFlbF8HO3h6PcuVJTk7GtbQbUyaNI3DEaOzt7Dl0cD+nQk+wYNH32mV+XL8WL+/qFClShNDQk8ybPZPBQ4ejVqvzepcKJGn5axSI5A9w8OBBDh48SExMDGlpaZw/f57169cDsHLlynyOLm+EXYvks+E/MGnQx/z3yxbcuvOAkbM28+Pu37R1uo9ZyaRBbQmeFoCDugiRd2OZsGgHP2w8rq3Tu2N9vu7bUvt6/8pAAPqMW8Pa/zt3IN4+5ubmLFyyjAVzZzNkQF+ePntKadfSTJ46g/oNGmrrXbl8iSWL/sfTp09wdy/L1+Mm0vrjdvkXeAEjuV9DpSiZ/F7MYxMnTmTSpEnUqlULZ2fnDH+Zt27datD6rKsPzMnwRAEXe2Zhfocg8pB1xrEhBqkweu/rK/2fG9/66V13woQJGQafVKhQgfDwcAASExMZPnw4P/74I0lJSfj5+bF48WIcHR219SMjI+nXrx+HDh3C1taWgIAApk+fjpnZ/2+nHz58mGHDhnH16lVcXV35+uuv6dGjh95xpisQLf+lS5cSHBxMt27d8jsUIUQhl5st/8qVK7N//37t6xeTdmBgIDt37mTjxo3Y2dkxcOBA2rdvz4kTJwDNyf1WrVrh5OTEyZMnuXv3Lt27d8fc3Jxp06YBEBERQatWrejbty/r1q3jwIED9O7dG2dnZ/z89P9DBQUk+T9//pwPP8x4QYsQQuS03BzyamZmhpOTU4by+Ph4VqxYwfr162nSpAkAQUFBVKpUiVOnTlGnTh327dvHtWvX2L9/P46Ojnh7ezN58mRGjx7NhAkTsLCwYOnSpbi7uzN7tubajUqVKnH8+HHmzp1rcPIvEFf49u7dW9u/L4QQuUml0n9KSkoiISFBZ3r5otIX/fHHH7i4uFC2bFn8/f2JjIwENPctS05O1hnEUrFiRUqXLk1oaCgAoaGhVK1aVacbyM/Pj4SEBK5evaqt8/JAGD8/P+06DFEgWv6JiYksW7aM/fv3U61atQwXfM2ZMyefIhNCFDaGtPwzu4h0/PjxTJgwIUPd2rVrExwcTIUKFbh79y4TJ06kfv36XLlyhaioKCwsLLT3Lkvn6OhIVJTm2p6oqCidxJ8+P33eq+okJCTw7NkzrK2zvoPvywpE8r906RLe3t4AXLlyRWeeDMsSQuQkQ3JKZheRZnX1dYsWLbT/rlatGrVr18bNzY2ff/7ZoKScVwpE8j906FB+hyCEMBKGJP/MLiLVl729PeXLl+fPP/+kWbNmPH/+nLi4OJ3Wf3R0tPYcgZOTE2fOnNFZR3R0tHZe+v/Ty16so1arDf4DUyD6/IUQIq8Y0uefHY8fP+bmzZs4OztTs2ZNzM3NOXDggHb+jRs3iIyMxMfHBwAfHx8uX75MTEyMtk5ISAhqtRpPT09tnRfXkV4nfR2GkOQvhDAquXVL5xEjRnDkyBFu3brFyZMn+eSTTzA1NaVLly7Y2dnRq1cvhg0bxqFDhwgLC6Nnz574+PhQp04dAJo3b46npyfdunXj4sWL7N27l6+//poBAwZof3307duXv/76i1GjRhEeHs7ixYv5+eefCQwMNPh9KBDdPkIIkVdy6zTi7du36dKlCw8ePKBEiRLUq1ePU6dOaR9GNXfuXExMTOjQoYPORV7pTE1N2bFjB/369cPHxwcbGxsCAgKYNGmSto67uzs7d+4kMDCQ+fPnU6pUKZYvX27wME8oIFf45jS5wte4yBW+xiW7V/jWnKz/Ocawbxpnb2MFmF4tfwcHB71/AsXGxmYrICGEyE0ygFBDr+Q/b968XA5DCCHyhgwf19Ar+QcEBOR2HEIIkSck92tk64RvYmIiz5/rPvxb7hkuhCjI5HGWGgYP9Xzy5AkDBw6kZMmS2NjY4ODgoDMJIURBlltDPd82Bif/UaNGcfDgQZYsWYKlpSXLly9n4sSJuLi4sHr16tyIUQghckxeXeRV0Bnc7bN9+3ZWr15No0aN6NmzJ/Xr18fDwwM3NzfWrVuHv79/bsQphBA5orC36PVlcMs/NjaWsmXLApr+/fShnfXq1ePo0aM5G50QQuQwaflrGJz8y5YtS0REBKC5H/XPP/8MaH4RvHy7UiGEKGikz1/D4OTfs2dPLl68CMCYMWNYtGgRVlZWBAYGMnLkyBwPUAghcpIkfw2D+/xfvIGQr68v4eHhhIWF4eHhQbVq1XI0OCGEyGmFPKfrLdvj/N3c3HBzc8upeIQQIlcV9ha9vgzu9klNTWXy5Mm8++672Nra8tdffwHwzTffsGLFihwPUAghcpKJiUrvqTAzOPlPnTqV4OBgZs6ciYWFhba8SpUqLF++PEeDE0KInCajfTQMTv6rV69m2bJl+Pv7Y2pqqi338vIiPDw8R4MTQoicZqJS6T0VZgb3+d+5cwcPD48M5WlpaSQnJ+dIUEIIkVsKeU7Xm8Etf09PT44dO5ahfNOmTVSvXj1HghJCiNwiQz01DG75jxs3joCAAO7cuUNaWhpbtmzhxo0brF69mh07duRGjEIIkWMK+XlcvRnc8m/bti3bt29n//792NjYMG7cOK5fv8727dtp1qxZbsQohBA5Rlr+Gga1/FNSUpg2bRpffPEFISEhuRWTEELkmkKe0/VmUMvfzMyMmTNnkpKSklvxCCFErlIZ8F9hZnC3T9OmTTly5EhuxCKEELnO1ESl91SYGXzCt0WLFowZM4bLly9Ts2ZNbGxsdOZ//PHHORacEELkNOn20TA4+ffv3x+AOXPmZJinUqlITU3NflRCCJFLCvvFW/oyOPmnpaXlRhxCCJEnJPdrGNznL4QQb7O8Guo5Y8YMVCoVQ4cO1ZYlJiYyYMAAihcvjq2tLR06dCA6OlpnucjISFq1akWRIkUoWbIkI0eOzDDI5vDhw9SoUQNLS0s8PDwIDg42OD5J/kIIo5IXN3Y7e/Ys33//fYZnnAQGBrJ9+3Y2btzIkSNH+Pfff2nfvr12fmpqKq1ateL58+ecPHmSVatWERwczLhx47R1IiIiaNWqFY0bN+bChQsMHTqU3r17s3fvXoNilOQvhDAquX1jt8ePH+Pv788PP/yAg4ODtjw+Pp4VK1YwZ84cmjRpQs2aNQkKCuLkyZOcOnUKgH379nHt2jXWrl2Lt7c3LVq0YPLkySxatIjnz58DsHTpUtzd3Zk9ezaVKlVi4MCBdOzYkblz5xr2PrzR3gkhxFtKZcCUlJREQkKCzpSUlPTK9Q8YMIBWrVrh6+urUx4WFkZycrJOecWKFSldujShoaEAhIaGUrVqVRwdHbV1/Pz8SEhI4OrVq9o6L6/bz89Puw59SfIXQhgVQ/r8p0+fjp2dnc40ffr0LNf9448/cu7cuUzrREVFYWFhgb29vU65o6MjUVFR2jovJv70+enzXlUnISGBZ8+e6f0+ZPsxjuk/RdKp1ersrFIIIXKVIRdvjR07lmHDhumUWVpaZlr3n3/+YciQIYSEhGBlZZWtGPOCwS3/p0+fMnDgQEqWLImNjQ0ODg46kxBCFGSGnPC1tLRErVbrTFkl/7CwMGJiYqhRowZmZmaYmZlx5MgRFixYgJmZGY6Ojjx//py4uDid5aKjo3FycgLAyckpw+if9Nevq6NWq7G2ttb7fTA4+Y8cOZKDBw+yZMkSLC0tWb58ORMnTsTFxYXVq1cbujohhMhTuTXUs2nTply+fJkLFy5op1q1auHv76/9t7m5OQcOHNAuc+PGDSIjI/Hx8QHAx8eHy5cvExMTo60TEhKCWq3G09NTW+fFdaTXSV+Hvgzu9tm+fTurV6+mUaNG9OzZk/r16+Ph4YGbmxvr1q3D39/f0FUKIUSeya1b9hQtWpQqVarolNnY2FC8eHFtea9evRg2bBjFihVDrVYzaNAgfHx8qFOnDgDNmzfH09OTbt26MXPmTKKiovj6668ZMGCA9hdH3759WbhwIaNGjeKLL77g4MGD/Pzzz+zcudOgeA1u+cfGxlK2bFlA078fGxsLQL169Th69KihqxNCiDyVn/fznzt3Lq1bt6ZDhw40aNAAJycntmzZop1vamrKjh07MDU1xcfHh88//5zu3bszadIkbR13d3d27txJSEgIXl5ezJ49m+XLl+Pn52dQLAa3/MuWLUtERASlS5emYsWK/Pzzz3zwwQds3749w1lsIYQoaPLy7g6HDx/WeW1lZcWiRYtYtGhRlsu4ubmxa9euV663UaNGnD9/PluxGdzy79mzJxcvXgRgzJgxLFq0CCsrKwIDAxk5cmS2ghFCiNyW2xd5vS0MbvkHBgZq/+3r60t4eDhhYWF4eHhkuJRZCCEKmkKe0/VmcMt/9erVOle4ubm50b59eypWrCijfYQQBZ48w1fjjbp94uPjM5Q/evSInj175khQQgiRW/Lixm5vA4O7fRRFyfQv4u3bt7Gzs8uRoIQQIrcU9scz6kvv5F+9enXtT6GmTZtiZvb/F01NTSUiIoKPPvooV4IUQoicUti7c/Sld/Jv164dABcuXMDPzw9bW1vtPAsLC8qUKUOHDh1yPMA38fDswvwOQeQhRcnvCMTbRO5mqaF38h8/fjwAZcqU4bPPPnsrblwkhBAvk5a/hsF/BAMCAkhMTGT58uWMHTtWe4XvuXPnuHPnTo4HKIQQOclEpf9UmBl8wvfSpUv4+vpiZ2fHrVu36NOnD8WKFWPLli1ERkbKcE8hRIFW2JO6vgxu+QcGBtKjRw/++OMPna6fli1byr19hBAFnozz1zC45f/bb7+xbNmyDOXvvvuu9kkzQghRUEnLX8Pg5G9paUlCQkKG8t9//50SJUrkSFBCCJFbCnmDXm8Gd/t8/PHHTJo0ieTkZEDzEyoyMpLRo0cXmKGeQgiRFTOVSu+pMDM4+c+ePZvHjx9TsmRJnj17RsOGDfHw8KBo0aJMnTo1N2IUQogcI7d30DC428fOzo6QkBCOHz/OpUuXePz4MTVq1MDX1zc34hNCiBxV2G/VrC+Dk3+6evXqUa9evZyMRQghcp3kfg2Dk/+LjxPLzLhx4944GCGEyG0y2kfD4OS/detWndfJyclERERgZmbGe++9J8lfCFGgSbePhsHJP7PnRiYkJNCjRw8++eSTHAlKCCFyi+R+jRy5wZ1arWbixIl88803ObE6IYTINXJvH403PuH7svj4+Eyf8CWEEAWJikKe1fVkcPJfsGCBzmtFUbh79y5r1qyhRYsWORaYEELkBjO5oT/wBsl/7ty5Oq9NTEwoUaIEAQEBjB07NscCE0KI3FDYb9imL4OTf0RERG7EIYQQeaKw9+XrS34ACSGMSm7d3mHJkiVUq1YNtVqNWq3Gx8eH3bt3a+cnJiYyYMAAihcvjq2tLR06dCA6OlpnHZGRkbRq1YoiRYpQsmRJRo4cSUpKik6dw4cPU6NGDSwtLfHw8CA4OPiN3geDW/6ffPKJ3j+btmzZYnBAQgiRm3JrnH+pUqWYMWMG5cqVQ1EUVq1aRdu2bTl//jyVK1cmMDCQnTt3snHjRuzs7Bg4cCDt27fnxIkTAKSmptKqVSucnJw4efIkd+/epXv37pibmzNt2jRA0/PSqlUr+vbty7p16zhw4AC9e/fG2dkZPz8/g+JVKYphj7/u0aMHW7duxc7Ojlq1agEQFhZGfHw87dq10/nDEBQUZFAwOSUx5fV1ROEhD3A3Ltbm2Vt+wXH9u64H13PP1raKFSvGrFmz6NixIyVKlGD9+vV07NgRgPDwcCpVqkRoaCh16tRh9+7dtG7dmn///RdHR0cAli5dyujRo7l37x4WFhaMHj2anTt3cuXKFe02OnfuTFxcHHv27DEoNoNb/o6Ojnz66acsXboUU1NTQPMXq3///qjVambNmmXoKoUQIs8Y0vBPSkoiKSlJp8zS0hJLS8tXLpeamsrGjRt58uQJPj4+hIWFkZycrHMDzIoVK1K6dGlt8g8NDaVq1araxA/g5+dHv379uHr1KtWrVyc0NDTDTTT9/PwYOnSo/jv1fwzu81+5ciUjRozQJn4AU1NThg0bxsqVKw0OQAgh8pIJKr2n6dOnY2dnpzNNnz49y3VfvnwZW1tbLC0t6du3L1u3bsXT05OoqCgsLCywt7fXqe/o6Kh9AmJUVJRO4k+fnz7vVXUSEhJ49uyZQe+DwS3/lJQUwsPDqVChgk55eHg4aWlphq5OCCHylCEt/7FjxzJs2DCdsle1+itUqMCFCxeIj49n06ZNBAQEcOTIkTcNNVcZnPx79uxJr169uHnzJh988AEAp0+fZsaMGfTs2TPHAxRCiJxkZsBYT326eF5kYWGBh4cHADVr1uTs2bPMnz+fzz77jOfPnxMXF6fT+o+OjsbJyQkAJycnzpw5o7O+9NFAL9Z5eYRQdHQ0arUaa2trveOEN0j+3333HU5OTsyePZu7d+8C4OzszMiRIxk+fLihqxNCiDyVl9d4paWlkZSURM2aNTE3N+fAgQPax93euHGDyMhIfHx8APDx8WHq1KnExMRQsmRJAEJCQlCr1Xh6emrr7Nq1S2cbISEh2nUYwuDRPi9Kf5C7Wq1+01XkChntY1xktI9xye5onxVnIvWu2+uD0nrXHTt2LC1atKB06dI8evSI9evX8+2337J3716aNWtGv3792LVrF8HBwajVagYNGgTAyZMnAc1JYm9vb1xcXJg5cyZRUVF069aN3r176wz1rFKlCgMGDOCLL77g4MGDDB48mJ07dxo81DNbN3YraElfCCFeJ7da/jExMXTv3p27d+9iZ2dHtWrVtIkfNLfGMTExoUOHDiQlJeHn58fixYu1y5uamrJjxw769euHj48PNjY2BAQE6DxAy93dnZ07dxIYGMj8+fMpVaoUy5cvNzjxg54t/xo1anDgwAEcHByoXr36Ky/yOnfunMFB5DRp+RsXafkbl+y2/IPP6t/y7/G+/i3/t41eLf+2bdtqT3q0bdtWbowkhHhrSf7SyFaff0ElLX/jUviOYPEq2W35r/7tH73rdq/lmr2NFWAGX+RVtmxZHjx4kKE8Li6OsmXL5khQQgiRW0xUKr2nwszgE763bt0iNTU1Q3lSUhK3b9/OkaCEECK3FO6Urj+9k/+vv/6q/ffevXuxs7PTvk5NTeXAgQO4u2fvJkhCCJHbCnmDXm96J/927doBmpMlAQEBOvPMzc0pU6YMs2fPztHghBAip5lK9gcMSP7p9+1xd3fn7NmzvPPOO7kWlBBC5BYZ7aMhj3EUQhgVSf0aeo/2admyJfHx8drXM2bMIC4uTvv6wYMH2vtPCCFEQaVSqfSeCjO9k//evXt1Hmowbdo0YmNjta9TUlK4ceNGzkYnhBA5zMSAqTDTu9vn5WvBCuG1YUIII1DYW/T6ytaN3YQQ4m0jqV9D7+SfWR+Y/AUVQrxtJG1pGNTt06NHD+0N3hITE+nbty82NjYAGR5yLIQQBZGJtP0BA5L/yxd2ff755xnqdO/ePfsRCSFELirs9+zRl97JPygoKDfjEEKIPCG5X0NO+AohjIp0+2hI8hdCGBVp+WtI8hdCGBVJ/hoF7iK21NRULly4wMOHD/M7FCFEIaQy4L/CLN+T/9ChQ1mxYgWgSfwNGzakRo0auLq6cvjw4fwNTghR6Jio9J8Ks3xP/ps2bcLLywuA7du3ExERQXh4OIGBgXz11Vf5HJ0QorCRlr9Gvif/+/fv4+TkBMCuXbvo1KkT5cuX54svvuDy5cv5HJ0QorBRqfSfCrN8T/6Ojo5cu3aN1NRU9uzZQ7NmzQB4+vQppqam+RxdwbLih2V4Va7AzOlTM8xTFIX+/+mNV+UKHDywX2felcuX6PNFAPXq1KKez/v07dOLG+HheRW2eEMrly/Du0oFZs74/5/3/fv3+GrMSJo2rEud973p3OkT9ofs1c4/e+Y03lUqZDpduXwpP3ajwDFVqfSeCrN8H+3Ts2dPPv30U5ydnVGpVPj6+gJw+vRpKlasmM/RFRxXLl9i08YfKV++Qqbz165elem9lp4+eUL///ShYeMmfPXNeFJSU1my8H/0+7IXew8cxtzcPLdDF28gq8/767GjefQogXkLl+Bg78DuXdsZNXwo63/aTMVKnnhXr87+w8d1lln0v/mcOR1K5SpV83IXCqzC3p2jr3xv+U+YMIHly5fz5ZdfcuLECe29g0xNTRkzZkw+R1cwPH3yhLGjRzJ+4hTUdnYZ5odfv87qVSuZOHlahnkREX8RHx/HgIGDKeNeFg+PcvTtP4AHD+5z999/8yJ8YaCnT5/w3zEjGTdhCkXVup/3xQvn6dL1c6pWrUYpV1f6/Kc/RYuquXb1KgDm5ha8804J7WRnZ8/hQwdo26693Ijx/0i3j0a+J3+Ajh07EhgYqPNc4ICAANq2bZuPURUc06ZMokGDhtTx+TDDvGfPnjF21HD++/U43ilRIsP8Mu7u2Nvbs3XLJpKfPycxMZGtmzdRtux7uLz7bl6ELww0bcok6mfxeXt5V2fvnt3Ex8eRlpbGnl07SXqeRK0PPsh0XUcOHyQ+Lo627TrkdthvDZUBkyGmT5/O+++/T9GiRSlZsiTt2rXL8ICrxMREBgwYQPHixbG1taVDhw5ER0fr1ImMjKRVq1YUKVKEkiVLMnLkSFJSUnTqHD58mBo1amBpaYmHhwfBwcEGRlsAkn9qaiqTJ0/m3XffxdbWlr/++guAb775RjsE9FWSkpJISEjQmQrTHUZ379rJ9evXGBw4PNP5s76djlf16jRu4pvpfBsbW5YHr2Hn9l/5oKYXPu9X58SJYyz6/gfMzPK910+8ZM+unYRfv8bgoZl/3jNnzyMlJYWGdWvzQY2qTJk0jjnzFlK6tFum9bdu2YRP3Xo4/t+gCqG5sZu+kyGOHDnCgAEDOHXqFCEhISQnJ9O8eXOePHmirRMYGMj27dvZuHEjR44c4d9//6V9+/ba+ampqbRq1Yrnz59z8uRJVq1aRXBwMOPGjdPWiYiIoFWrVjRu3JgLFy4wdOhQevfuzd69ezFEvif/qVOnEhwczMyZM7GwsNCWV6lSheXLl792+enTp2NnZ6czzfp2em6GnGei7t5l5oypTP92lrY77EWHDx7g7OlTjBr93yzXkZiYyIRvvsK7eg3WrP+JVWs34OFRnoH9/kNiYmJuhi8MlP55T5uR+ecNsHjhfB49SuD75cGs+3Ezn3fvyagRQ/nj94yPUI2OiiL0xHE+ad8xt0N/qxjS8jekcblnzx569OhB5cqV8fLyIjg4mMjISMLCwgCIj49nxYoVzJkzhyZNmlCzZk2CgoI4efIkp06dAmDfvn1cu3aNtWvX4u3tTYsWLZg8eTKLFi3i+fPnACxduhR3d3dmz55NpUqVGDhwIB07dmTu3LkGvQ/5nvxXr17NsmXL8Pf31xnd4+XlRbgeI1LGjh1LfHy8zjRy9NjcDDnPXLt2ldgHD+jcqT01qnlSo5onv509w/p1a6hRzZPQ0JP8808k9Xze184HGD50EL16dANg187t/PvvHSZNnU6VqtWo5uXNjJnfcefObQ4dPJCfuydecu3aVWJjH9Dl0/bU9PKkppcnYb+dYcO6NdT08uSfyEh+XL+WCZOnUbuODxUqVqRv/4FUrlyFnzasy7C+X7Ztxs7enoaNmuTD3hRgBmT/zBqX06fr17iMj48HoFixYgCEhYWRnJysHdQCULFiRUqXLk1oaCgAoaGhVK1aFUdHR20dPz8/EhISuPp/53VCQ0N11pFeJ30d+sr33/137tzBw8MjQ3laWhrJycmvXd7S0jJDKykxJYvKb5nadeqwadt2nbLxX42lTNmy9OzVBwd7Bzp++pnO/I7t2jBi9FgaNmoMaFr+JioTnZN9KhMTVKhQ0tJyfyeE3mrXqcOmrbqf97ivx+Lurvm8ExOfAWCi0m2zmZiYkpbJM7Z/2baFNm3ayYiulxgy2mfs2LEMGzZMpyyrX2UvSktLY+jQodStW5cqVaoAEBUVhYWFBfb29jp1HR0diYqK0tZ5MfGnz0+f96o6CQkJPHv2DGtra732Ld+Tv6enJ8eOHcPNTbfPctOmTVSvXj2foioYbGxsKVeuvE6ZdZEi2NvZa8szO8nr7OxCqVKuAPj4fMjc72YybfJEuvh3I01JY+XyZZiZmfJ+7dq5vxNCbzY2tni8/HlbF8HO3h6PcuVJTk7GtbQbUyaNI3DEaOzt7Dl0cD+nQk+wYNH3OsudOX2KO7dv80kH6fJ5mSFd+Zk1LvUxYMAArly5wvHjx19fOZ/ke/IfN24cAQEB3Llzh7S0NLZs2cKNGzdYvXo1O3bsyO/w3nruZd9jwaKlLF28kO7+n6FSmVCxUiUWf7+cEiVK5nd4wgDm5uYsXLKMBXNnM2RAX54+e0pp19JMnjqD+g0a6tTdumUTXt7VcS/7Xj5FW3Dl9hDOgQMHsmPHDo4ePUqpUqW05U5OTjx//py4uDid1n90dLT2LgdOTk6cOXNGZ33po4FerPPyCKHo6GjUarXerX4AlaK89HsxHxw7doxJkyZx8eJFHj9+TI0aNRg3bhzNmzd/o/UVlm4foZ/8P4JFXrLOZi/WbxEJetet5a7Wu66iKAwaNIitW7dy+PBhypUrpzM/Pj6eEiVKsGHDBjp00Ay9vXHjBhUrViQ0NJQ6deqwe/duWrduzd27dylZUtM4W7ZsGSNHjiQmJgZLS0tGjx7Nrl27dG5/07VrV2JjY9mzZ4/e8eZ78g8ICKBXr140aNAgx9Ypyd+4SPI3LtlN/mG39E/+Ncvon/z79+/P+vXr+eWXX6hQ4f9fmW1nZ6dtkffr149du3YRHByMWq1m0KBBAJw8eRLQDPX09vbGxcWFmTNnEhUVRbdu3ejduzfTpmku4oyIiKBKlSoMGDCAL774goMHDzJ48GB27tyJn5+f3vHme/Jv164du3btws3NjZ49e9KjRw9cXFyytU5J/sZFkr9xyW7yP2dA8q9hQPLP6grqoKAgevToAWgGYAwfPpwNGzaQlJSEn58fixcv1nbpAPz999/069ePw4cPY2NjQ0BAADNmzNC5Lufw4cMEBgZy7do1SpUqxTfffKPdht7x5nfyB7h37x5r1qxh1apVXLt2DV9fX7744gvatXuzkQqS/I1L/h/BIi9lO/n/bUDyd9M/+b9t8n2cP0CJEiUYNmwYFy9e5PTp03h4eNC9e3dcXFwIDAzkjz/+yO8QhRCFhNzPX6NAJP90d+/eJSQkhJCQEExNTWnZsiWXL1/G09PT4KvXhBAiM3JjN4187/ZJTk7m119/JSgoiH379lGtWjV69+5N165dUas1P7m2bt3KF198ofdzfaXbx7hIt49xyW63z8XIR3rX9SpdNHsbK8DyfZy/s7MzaWlpdOnShTNnzuDt7Z2hTuPGjTNcFSeEEG+kkLfo9ZXvLf81a9bQqVMnrKyscmyd0vI3LtLyNy7Zbflf+uex3nWrudpmb2MFWL4n/9wgyd+4FL4jWLxKdpP/ldv6J/8qpQpv8s/3bh8hhMhT0u0DSPIXQhiZwj6EU1+S/IUQRqWwD+HUlyR/IYRRkdyvIclfCGFcJPsDkvyFEEZG+vw1JPkLIYyK9PlrSPIXQhgVyf0akvyFEEYlq/vuGxtJ/kIIoyK5X0OSvxDCqEju15DkL4QwLpL9AUn+QggjI0M9NST5CyGMivT5a0jyF0IYFcn9GpL8hRDGRbI/IMlfCGFkpM9fQ5K/EMKomEjuByT5CyGMjJzw1TDJ7wCEECJvqQyYDHP06FHatGmDi4sLKpWKbdu26cxXFIVx48bh7OyMtbU1vr6+/PHHHzp1YmNj8ff3R61WY29vT69evXj8WPe5w5cuXaJ+/fpYWVnh6urKzJkzDY5Vkr8QwqioVPpPhnry5AleXl4sWrQo0/kzZ85kwYIFLF26lNOnT2NjY4Ofnx+JiYnaOv7+/ly9epWQkBB27NjB0aNH+fLLL7XzExISaN68OW5uboSFhTFr1iwmTJjAsmXLDHsfFEVRDN/Fgi0xJb8jEHmp8B3B4lWszbO3/L9xz/Wu62Jv8cbbUalUbN26lXbt2gGaVr+LiwvDhw9nxIgRAMTHx+Po6EhwcDCdO3fm+vXreHp6cvbsWWrVqgXAnj17aNmyJbdv38bFxYUlS5bw1VdfERUVhYWFJr4xY8awbds2wsPD9Y5PWv5CCKNiSMs/KSmJhIQEnSkpKemNthsREUFUVBS+vr7aMjs7O2rXrk1oaCgAoaGh2NvbaxM/gK+vLyYmJpw+fVpbp0GDBtrED+Dn58eNGzd4+PCh3vFI8hdCGBWVAf9Nnz4dOzs7nWn69OlvtN2oqCgAHB0ddcodHR2186KioihZsqTOfDMzM4oVK6ZTJ7N1vLgNfchoHyGEcTGgL3/s2LEMGzZMp8zS0jKHA8ofkvyFEEbFkPO4lpaWOZbsnZycAIiOjsbZ2VlbHh0djbe3t7ZOTEyMznIpKSnExsZql3dyciI6OlqnTvrr9Dr6kG4fIYRRMVGp9J5ykru7O05OThw4cEBblpCQwOnTp/Hx8QHAx8eHuLg4wsLCtHUOHjxIWloatWvX1tY5evQoycnJ2johISFUqFABBwcHveOR5C+EMC65N8yfx48fc+HCBS5cuABoTvJeuHCByMhIVCoVQ4cOZcqUKfz6669cvnyZ7t274+Lioh0RVKlSJT766CP69OnDmTNnOHHiBAMHDqRz5864uLgA0LVrVywsLOjVqxdXr17lp59+Yv78+Rm6p177NshQT/G2K3xHsHiV7A71vP9Y/wTxjq1hPeOHDx+mcePGGcoDAgIIDg5GURTGjx/PsmXLiIuLo169eixevJjy5ctr68bGxjJw4EC2b9+OiYkJHTp0YMGCBdja2mrrXLp0iQEDBnD27FneeecdBg0axOjRow2KVZK/eOsVviNYvEp2k/+DJ/oniOI2hfe0aOHdMyGEyITc1VNDkr8QwqjIjd005ISvEEIYIWn5CyGMirT8NST5CyGMivT5a0jyF0IYFWn5a0jyF0IYFUn+GpL8hRBGRbp9NCT5CyGMirT8NST5CyGMiuR+DUn+QgjjItkfkOQvhDAy0uevIclfCGFUpM9fo1De1dMYJSUlMX36dMaOHVtoHjMnsiaft8guSf6FREJCAnZ2dsTHx6NWq/M7HJHL5PMW2SU3dhNCCCMkyV8IIYyQJH8hhDBCkvwLCUtLS8aPHy8n/4yEfN4iu+SErxBCGCFp+QshhBGS5C+EEEZIkr8QQhghSf5CCGGEJPkLIYQRkuQvhBBGSJL/W6ZRo0YMHjyYUaNGUaxYMZycnJgwYYJ2fmRkJG3btsXW1ha1Ws2nn35KdHR0/gUsDLJ69WqKFy9OUlKSTnm7du3o1q0bAL/88gs1atTAysqKsmXLMnHiRFJSUgBQFIUJEyZQunRpLC0tcXFxYfDgwXm+H+ItoIi3SsOGDRW1Wq1MmDBB+f3335VVq1YpKpVK2bdvn5Kamqp4e3sr9erVU3777Tfl1KlTSs2aNZWGDRvmd9hCT0+fPlXs7OyUn3/+WVsWHR2tmJmZKQcPHlSOHj2qqNVqJTg4WLl586ayb98+pUyZMsqECRMURVGUjRs3Kmq1Wtm1a5fy999/K6dPn1aWLVuWX7sjCjBJ/m+Zhg0bKvXq1dMpe//995XRo0cr+/btU0xNTZXIyEjtvKtXryqAcubMmbwOVbyhfv36KS1atNC+nj17tlK2bFklLS1Nadq0qTJt2jSd+mvWrFGcnZ21dcuXL688f/48T2MWbx/p9nkLVatWTee1s7MzMTExXL9+HVdXV1xdXbXzPD09sbe35/r163kdpnhDffr0Yd++fdy5cweA4OBgevTogUql4uLFi0yaNAlbW1vt1KdPH+7evcvTp0/p1KkTz549o2zZsvTp04etW7dqu4SEeJE8yestZG5urvNapVKRlpaWT9GInFa9enW8vLxYvXo1zZs35+rVq+zcuROAx48fM3HiRNq3b59hOSsrK1xdXblx4wb79+8nJCSE/v37M2vWLI4cOZLhuBHGTZJ/IVKpUiX++ecf/vnnH23r/9q1a8TFxeHp6ZnP0QlD9O7dm3nz5nHnzh18fX21n2eNGjW4ceMGHh4eWS5rbW1NmzZtaNOmDQMGDKBixYpcvnyZGjVq5FX44i0gyb8Q8fX1pWrVqvj7+zNv3jxSUlLo378/DRs2pFatWvkdnjBA165dGTFiBD/88AOrV6/Wlo8bN47WrVtTunRpOnbsiImJCRcvXuTKlStMmTKF4OBgUlNTqV27NkWKFGHt2rVYW1vj5uaWj3sjCiLp8y9EVCoVv/zyCw4ODjRo0ABfX1/Kli3LTz/9lN+hCQPZ2dnRoUMHbG1tadeunbbcz8+PHTt2sG/fPt5//33q1KnD3Llztcnd3t6eH374gbp161KtWjX279/P9u3bKV68eD7tiSio5JbOQhRQTZs2pXLlyixYsCC/QxGFkCR/IQqYhw8fcvjwYTp27Mi1a9eoUKFCfockCiHp8xeigKlevToPHz7k22+/lcQvco20/IUQwgjJCV8hhDBCkvyFEMIISfIXQggjJMlfCCGMkCR/IYQwQpL8hdaECRPw9vbO7zDeGrdu3UKlUnHhwgUADh8+jEqlIi4uTq/lGzVqxNChQ3MtPiFeRZJ/IZR++9+Xp48++khbR6VSsW3bNp3lRowYwYEDB/I42sLzR+fDDz/k7t272NnZ5XcoQryWXORVSH300UcEBQXplFlaWr5ymfT7wxub5OTkHLndsYWFBU5OTjkQkRC5T1r+hZSlpSVOTk46k4ODAwBlypQB4JNPPkGlUmlfv9wCT01NZdiwYdjb21O8eHFGjRpFQECAzo3GypQpw7x583S27e3trfNc4bi4OHr37k2JEiVQq9U0adKEixcvApoHlUycOJGLFy9qf6EEBwcDMGfOHKpWrYqNjQ2urq7079+fx48fv3K/VSoVS5YsoUWLFlhbW1O2bFk2bdqknZ/eVfPTTz/RsGFDrKysWLduHQDLly+nUqVKWFlZUbFiRRYvXqyz7jNnzlC9enWsrKyoVasW58+f15mfWbfPiRMnaNSoEUWKFMHBwQE/Pz8ePnyonZ+Wlpbl85jf9D0QQh+S/I3Q2bNnAQgKCuLu3bva1y+bPXs2wcHBrFy5kuPHjxMbG8vWrVsN3l6nTp2IiYlh9+7dhIWFUaNGDZo2bUpsbCyfffYZw4cPp3Llyty9e5e7d+/y2WefAWBiYsKCBQu4evUqq1at4uDBg4waNeq12/vmm2/o0KEDFy9exN/fn86dO2d4ktmYMWMYMmQI169fx8/Pj3Xr1jFu3DimTp3K9evXmTZtGt988w2rVq0CNA9Rad26NZ6enoSFhTFhwgRGjBjxyjguXLhA06ZN8fT0JDQ0lOPHj9OmTRtSU1O1dVatWoWNjQ2nT59m5syZTJo0iZCQEO38N30PhHit/HyGpMgdAQEBiqmpqWJjY6MzTZ06VVsHULZu3aqz3Pjx4xUvLy/ta2dnZ2XmzJna18nJyUqpUqWUtm3basvc3NyUuXPn6qzHy8tLGT9+vKIoinLs2DFFrVYriYmJOnXee+895fvvv890u1nZuHGjUrx48VfWAZS+ffvqlNWuXVvp16+foiiKEhERoQDKvHnzMsSzfv16nbLJkycrPj4+iqIoyvfff68UL15cefbsmXb+kiVLFEA5f/68oiiKcujQIQVQHj58qCiKonTp0kWpW7dulrG+6nnMWdHnPRBCH9LnX0g1btyYJUuW6JQVK1ZM7+Xj4+O5e/cutWvX1paZmZlRq1YtFANuB3Xx4kUeP36c4X7yz5494+bNm69cdv/+/UyfPp3w8HASEhJISUkhMTGRp0+fUqRIkSyX8/HxyfA6fUROuhcfbvPkyRNu3rxJr1696NOnj7Y8JSVFe/L2+vXrVKtWDSsrqyy387ILFy7QqVOnV9bJ6nnM6d70PRDidST5F1I2NjavfNRfTjExMcnwxyA5OVn778ePH+Ps7Mzhw4czLGtvb5/lem/dukXr1q3p168fU6dOpVixYhw/fpxevXrx/PnzbCc+GxsbnRgBfvjhB50/dgCmpqZvvA1ra+vX1nnV85hz+z0Qxk36/I2Uubm5Tt/zy+zs7HB2dub06dPaspSUFMLCwnTqlShRgrt372pfJyQkEBERoX1do0YNoqKiMDMzw8PDQ2d65513AM0omZdjCQsLIy0tjdmzZ1OnTh3Kly/Pv//+q9e+nTp1KsPrSpUqZVnf0dERFxcX/vrrrwwxuru7A5rnI1+6dInExMQst/OyatWqZWvobHbeAyFeR5J/IZWUlERUVJTOdP/+fe38MmXKcODAAaKionRGn7xoyJAhzJgxg23bthEeHk7//v0zXMDUpEkT1qxZw7Fjx7h8+TIBAQE6rWVfX198fHxo164d+/bt49atW5w8eZKvvvqK3377TRtLREQEFy5c4P79+yQlJeHh4UFycjL/+9//+Ouvv1izZg1Lly7Va983btzIypUr+f333xk/fjxnzpxh4MCBr1xm4sSJTJ8+nQULFvD7779z+fJlgoKCmDNnDqB5pq5KpaJPnz5cu3aNXbt28d13371ynWPHjuXs2bP079+fS5cuER4ezpIlS3Q+h1fJznsgxGvl90kHkfMCAgIUIMNUoUIFbZ1ff/1V8fDwUMzMzBQ3NzdFUTKeeE1OTlaGDBmiqNVqxd7eXhk2bJjSvXt3nRO+8fHxymeffaao1WrF1dVVCQ4O1jnhqyiKkpCQoAwaNEhxcXFRzM3NFVdXV8Xf31+JjIxUFEVREhMTlQ4dOij29vYKoAQFBSmKoihz5sxRnJ2dFWtra8XPz09ZvXq1zgnVzADKokWLlGbNmimWlpZKmTJllJ9++kk7P/2Eb/pJ2hetW7dO8fb2ViwsLBQHBwelQYMGypYtW7TzQ0NDFS8vL8XCwkLx9vZWNm/e/MoTvoqiKIcPH1Y+/PBDxdLSUrG3t1f8/Py08xs2bKgMGTJEJ4a2bdsqAQEB2tdv8h4IoQ95mIswSI8ePYiLi8twdXBBoVKp2Lp1q861CEKIjKTbRwghjJAkfyGEMELS7SOEEEZIWv5CCGGEJPkLIYQRkuQvhBBGSJK/EEIYIUn+QghhhCT5CyGEEZLkL4QQRkiSvxBCGKH/B1wWOlic+ocHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Máquinas de Vectores de Soporte\n",
            "[[7146  157]\n",
            " [ 639  296]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.92      0.98      0.95      7303\n",
            "        True       0.65      0.32      0.43       935\n",
            "\n",
            "    accuracy                           0.90      8238\n",
            "   macro avg       0.79      0.65      0.69      8238\n",
            "weighted avg       0.89      0.90      0.89      8238\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Regresión Logística\n",
            "[[7103  200]\n",
            " [ 529  406]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.93      0.97      0.95      7303\n",
            "        True       0.67      0.43      0.53       935\n",
            "\n",
            "    accuracy                           0.91      8238\n",
            "   macro avg       0.80      0.70      0.74      8238\n",
            "weighted avg       0.90      0.91      0.90      8238\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Optimización de Hiperparámetros con GridSearchCV**\n",
        "Se ajustaron los hiperparámetros del modelo de Regresión Logística para optimizar su rendimiento. Se utiliza **GridSearchCV** para evaluar diversas combinaciones de parámetros, buscando mejorar el balanceo de clases mediante el uso de `class_weight='balanced'`."
      ],
      "metadata": {
        "id": "QGUG7EO6BN9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Definir el modelo con pipeline\n",
        "logistic_model = LogisticRegression(max_iter=1000, random_state=42, class_weight='balanced')\n",
        "\n",
        "# Definir los hiperparámetros que se desean ajustar\n",
        "param_grid = {\n",
        "    'C': [0.01, 0.1, 0.5, 1, 1.3, 1.5],\n",
        "}\n",
        "\n",
        "# Configurar GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=logistic_model, param_grid=param_grid,\n",
        "                           scoring='accuracy', cv=5, verbose=1, n_jobs=-1)\n",
        "\n",
        "# Ajustar el modelo\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Imprimir los mejores hiperparámetros\n",
        "print(f\"Mejores hiperparámetros: {grid_search.best_params_}\")\n",
        "\n",
        "# Imprimir la mejor puntuación\n",
        "print(f\"Mejor puntuación (accuracy): {grid_search.best_score_}\")\n",
        "\n",
        "# Realizar predicciones con el mejor modelo\n",
        "best_model = grid_search.best_estimator_\n",
        "y_pred = best_model.predict(X_test)\n",
        "\n",
        "# Matriz de confusión\n",
        "confusion = confusion_matrix(y_test, y_pred)\n",
        "print(\"Matriz de Confusión:\")\n",
        "print(confusion)\n",
        "\n",
        "# Informe de clasificación\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Informe de Clasificación:\")\n",
        "print(report)\n",
        "\n",
        "# Precisión\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Precisión: {accuracy:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxQCw2eV9jwY",
        "outputId": "408b946c-85cc-4d10-9fd0-6a451269eb75"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
            "Mejores hiperparámetros: {'C': 1.5}\n",
            "Mejor puntuación (accuracy): 0.8603338391502275\n",
            "Matriz de Confusión:\n",
            "[[6263 1040]\n",
            " [  99  836]]\n",
            "Informe de Clasificación:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.86      0.92      7303\n",
            "        True       0.45      0.89      0.59       935\n",
            "\n",
            "    accuracy                           0.86      8238\n",
            "   macro avg       0.72      0.88      0.76      8238\n",
            "weighted avg       0.92      0.86      0.88      8238\n",
            "\n",
            "Precisión: 0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La optimización de hiperparámetros del modelo de Regresión Logística con GridSearchCV resultó en una mejora del rendimiento, alcanzando una precisión de 0.86 tras ajustar C a 1.5. Aunque la precisión para la clase negativa es alta, la detección de la clase positiva sigue siendo un desafío, evidenciado por su menor precisión y recall. Esto indica que, aunque se logró un mejor equilibrio en el rendimiento general, aún hay oportunidades de mejora. Para futuros ajustes, se podrían explorar técnicas como la selección de características y la normalización de datos, así como experimentar con otros valores de C y ajustes adicionales de hiperparámetros."
      ],
      "metadata": {
        "id": "_LSUhX43HU8E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Visualización de Resultados del Modelo de Regresión Logística**\n",
        "\n",
        "Este gráfico presenta la matriz de confusión y el informe de clasificación del modelo de regresión logística. La matriz de confusión ilustra las predicciones correctas e incorrectas, mientras que el informe de clasificación detalla métricas como precisión, recall y f1-score, proporcionando una evaluación concisa del rendimiento del modelo en la clasificación de las clases.\n",
        "\n"
      ],
      "metadata": {
        "id": "UtMVVNCkKu-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import pandas as pd\n",
        "\n",
        "# Crear la matriz de confusión\n",
        "confusion = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Configurar la figura\n",
        "plt.figure(figsize=(4, 4))\n",
        "sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['False', 'True'],\n",
        "            yticklabels=['False', 'True'])\n",
        "plt.xlabel('Predicción')\n",
        "plt.ylabel('Real')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.show()\n",
        "\n",
        "# Obtener el informe de clasificación\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "# Convertir el informe en un DataFrame para graficar\n",
        "report_df = pd.DataFrame(report).transpose()\n",
        "\n",
        "# Eliminar la fila de 'accuracy' si no se desea mostrar\n",
        "report_df = report_df.drop('accuracy', axis=0)\n",
        "\n",
        "# Graficar precisión, recall y F1-score\n",
        "plt.figure(figsize=(6, 4))\n",
        "report_df[['precision', 'recall', 'f1-score']].plot(kind='bar',\n",
        "                                                     color=['#1f77b4', '#ff7f0e', '#2ca02c'])\n",
        "plt.title('Informe de Clasificación')\n",
        "plt.ylabel('Score')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(title='Métricas', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "plt.grid(axis='y')\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "id": "6F_s6SFqKuoi",
        "outputId": "14c83f21-c2e6-4020-e760-31bd6fdd79cf"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "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": [
        "# **Análisis del Desbalanceo de Clases**\n"
      ],
      "metadata": {
        "id": "dem4w7yHEDBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# **Análisis del Desbalanceo de Clases**\n",
        "\n",
        "# Imprimir las primeras filas del DataFrame para una vista general\n",
        "print(df.head())\n",
        "\n",
        "# Analizar la distribución de la variable objetivo\n",
        "class_distribution = df['y'].value_counts(normalize=True) * 100\n",
        "print(\"\\nDistribución de Clases:\")\n",
        "print(class_distribution)\n",
        "\n",
        "# Graficar la distribución de clases\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Crear un gráfico de barras para visualizar el desbalanceo\n",
        "plt.figure(figsize=(8, 5))\n",
        "class_distribution.plot(kind='bar', color=['blue', 'orange'])\n",
        "plt.title('Distribución de Clases en la Variable Objetivo')\n",
        "plt.xlabel('Clase')\n",
        "plt.ylabel('Porcentaje')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "QkUwLBx6D682",
        "outputId": "305cf268-43e0-48b8-f234-20ac0d104a25"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   age        job  marital    education  default housing loan    contact  \\\n",
            "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
            "1   57   services  married  high.school  unknown      no   no  telephone   \n",
            "2   37   services  married  high.school       no     yes   no  telephone   \n",
            "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
            "4   56   services  married  high.school       no      no  yes  telephone   \n",
            "\n",
            "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
            "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
            "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
            "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
            "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
            "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
            "\n",
            "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
            "0          93.994          -36.4      4.857       5191.0  no  \n",
            "1          93.994          -36.4      4.857       5191.0  no  \n",
            "2          93.994          -36.4      4.857       5191.0  no  \n",
            "3          93.994          -36.4      4.857       5191.0  no  \n",
            "4          93.994          -36.4      4.857       5191.0  no  \n",
            "\n",
            "[5 rows x 21 columns]\n",
            "\n",
            "Distribución de Clases:\n",
            "y\n",
            "no     88.734583\n",
            "yes    11.265417\n",
            "Name: proportion, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La distribución de clases revela un fuerte desbalance, con un 88.73% de datos en la clase negativa (\"no\") y solo un 11.27% en la clase positiva (\"yes\"). Este desbalance puede perjudicar el rendimiento de los modelos, ya que tienden a predecir más la clase mayoritaria, lo que se refleja en resultados previos con baja precisión y recall para la clase positiva. Para mejorar esto, se podrían aplicar técnicas de muestreo, como el sobremuestreo de la clase minoritaria o ajustar hiperparámetros para optimizar la detección de la clase positiva."
      ],
      "metadata": {
        "id": "9MDt7xKqD6eD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANÁLISIS GENERAL\n",
        "\n",
        "El análisis realizado abarca varios modelos, incluyendo Árbol de Decisión, Máquinas de Vectores de Soporte y Regresión Logística, cada uno con sus respectivas evaluaciones de rendimiento. Aunque se observaron métricas aceptables, los resultados también evidencian un notable desbalance de clases, donde la clase negativa predomina significativamente sobre la positiva. Este desbalance afectó la capacidad de los modelos para identificar correctamente la clase minoritaria, lo que resalta la necesidad de aplicar técnicas de optimización y ajuste de hiperparámetros, especialmente en el modelo de Regresión Logística, donde se empleó GridSearchCV para mejorar su rendimiento.\n",
        "\n",
        "El desafío de manejar este desbalanceo y optimizar los modelos ha sido significativo, ya que requiere una mayor práctica y entrenamiento para comprender cómo ajustar correctamente los parámetros y seleccionar las estrategias adecuadas. Sin embargo, cada nueva experiencia en estas prácticas me permitirá ir mejorando mis habilidades y acercarme a resultados más óptimos en futuras implementaciones."
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        "### Recursos Utilizados\n",
        "\n",
        "- **NumPy**: Operaciones matemáticas y manipulación de arreglos.\n",
        "- **Pandas**: Carga y limpieza de datos.\n",
        "- **Scikit-learn**: Modelos de aprendizaje automático y optimización de hiperparámetros con GridSearchCV.\n",
        "- **Matplotlib y Seaborn**: Visualización de datos y resultados.\n",
        "- **ChatGPT**: Consultas y análisis sobre desbalanceo de clases.\n",
        "\n",
        "### Prompts sobre Desbalanceo de Clases\n",
        "\n",
        "1. **Conceptos Básicos**:\n",
        "   - \"¿Qué es el desbalanceo de clases en el contexto del aprendizaje automático y por qué es un problema importante?\"\n",
        "\n",
        "2. **Causas del Desbalanceo**:\n",
        "   - \"¿Cuáles son las principales causas del desbalanceo de clases en un conjunto de datos?\"\n",
        "\n",
        "3. **Efectos del Desbalanceo**:\n",
        "   - \"¿Cómo afecta el desbalanceo de clases a la precisión y rendimiento de un modelo de clasificación?\"\n",
        "\n",
        "4. **Métodos para Tratar el Desbalanceo**:\n",
        "   - \"¿Cuáles son algunas técnicas comunes para tratar el desbalanceo de clases en un conjunto de datos?\"\n",
        "   - \"¿Cómo se puede aplicar la técnica de sobremuestreo (oversampling) y submuestreo (undersampling)?\"\n",
        "\n",
        "5. **Uso de `class_weight='balanced'`**:\n",
        "   - \"¿Cómo puedo utilizar el parámetro `class_weight='balanced'` en Scikit-learn para abordar el desbalanceo de clases?\"\n",
        "\n",
        "6. **Implementación de Código**:\n",
        "   - \"Proporciona un ejemplo de código en Python que muestre cómo ajustar un modelo de clasificación utilizando `class_weight='balanced'` para manejar el desbalanceo de clases.\"\n",
        "\n",
        "7. **Evaluación de Modelos Desbalanceados**:\n",
        "   - \"¿Qué métricas son más adecuadas para evaluar modelos de clasificación en un contexto de desbalanceo de clases?\"\n",
        "\n",
        "8. **Recomendaciones de Mejora**:\n",
        "   - \"¿Qué recomendaciones puedes dar para mejorar un modelo de clasificación que enfrenta desbalanceo de clases?\"\n",
        "\n",
        "### Recursos Adicionales\n",
        "\n",
        "- **Artículos**:\n",
        "  - \"Understanding Class Imbalance\" - Un artículo que describe los conceptos y técnicas para manejar el desbalanceo de clases.\n",
        "  \n",
        "- **Documentación de Scikit-learn**:\n",
        "  - [Scikit-learn: Class Weight](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)\n",
        "\n",
        "- **Clases de Aprendizaje Automático**: Conocimientos prácticos en manejo de datos y evaluación de modelos.\n"
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