Modelo_Clasificación_Multiple.ipynb

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        "# **Modelos de Clasificación Múltiple**\n",
        "\n",
        "Se presentan varios modelos para abordar un problema de clasificación múltiple.\n",
        "\n",
        "**Contexto**: Utilizando datos disponibles de Colombia, específicamente la base de datos de estudiantes inscritos en programas de educación superior, el objetivo es clasificar a los estudiantes utilizando como variables independientes su género y estrato socioeconómico. Aunque existen otras columnas en nuestro conjunto de datos, hemos optado por un modelo parsimonioso, priorizando la simplicidad.\n",
        "\n",
        "Para llevar a cabo nuestros modelos, emplearemos diversas bibliotecas, incluyendo **Scikit-Learn, TensorFlow y Keras**. Además, acompañaremos el código con comentarios explicativos para facilitar su comprensión y legibilidad. Agradecemos cualquier comentario, sugerencia o aporte, ya que serán recibidos de la mejor manera.\n",
        "\n",
        "# **Multiple Classification Models**\n",
        "\n",
        "We present several models to address a multiple classification problem.\n",
        "\n",
        "**Context**: Using available data from Colombia, specifically the database of students enrolled in higher education programs, the aim is to classify students using their gender and socio-economic stratum as independent variables. Although there are other columns in our dataset, we have opted for a parsimonious model, prioritizing simplicity.\n",
        "\n",
        "To implement our models, we will use various libraries, including **Scikit-Learn, TensorFlow, and Keras**. Furthermore, we will accompany the code with explanatory comments to facilitate understanding and readability. We welcome any comments, suggestions, or contributions, as they will be greatly appreciated.\n",
        "\n",
        "\n",
        "***Create By: Cristian Diaz***"
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      "source": [
        "#Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy as sc\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "from io import StringIO\n",
        "\n",
        "#Leer los archivos de la API\n",
        "URL = 'https://www.datos.gov.co/resource/89us-fxi5.csv?$query=SELECT%0A%20%20%60vigencia%60%2C%0A%20%20%60estrato%60%2C%0A%20%20%60tipo%60%2C%0A%20%20%60genero%60%2C%0A%20%20%60estado_civil%60%2C%0A%20%20%60nacionalidad%60%2C%0A%20%20%60depa_proce_colegio%60%2C%0A%20%20%60ciudad_proce_colegio%60%2C%0A%20%20%60depa_resi%60%2C%0A%20%20%60ciudad_resi%60%2C%0A%20%20%60facultad%60%2C%0A%20%20%60programa%60%2C%0A%20%20%60modalidad%60%2C%0A%20%20%60metodologia%60%2C%0A%20%20%60jornada%60%2C%0A%20%20%60sede%60'\n",
        "\n",
        "df = pd.read_csv(StringIO(requests.get(URL).text))"
      ],
      "metadata": {
        "id": "aGnIRjV0BM-7"
      },
      "execution_count": 143,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df=df.drop(columns=['ciudad_resi','vigencia', 'tipo','jornada','sede','modalidad','metodologia','nacionalidad','depa_proce_colegio','ciudad_proce_colegio', 'depa_resi','programa','estado_civil'])\n"
      ],
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      "execution_count": 144,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df.columns #Nombre de la columna\n",
        "df['genero'].unique() # Valores unicos de cada columna"
      ],
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            "text/plain": [
              "array(['MASCULINO', 'FEMENINO'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      ]
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    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow import keras\n",
        "\n",
        "# Supongamos que tienes un DataFrame llamado 'clientes' con las características mencionadas\n",
        "\n",
        "# Preprocesamiento de datos\n",
        "# Codifica las características categóricas (genero, facultad)\n",
        "le_genero = LabelEncoder()\n",
        "df['genero'] = le_genero.fit_transform(df['genero'])\n",
        "\n",
        "le_facultad = LabelEncoder()\n",
        "df['facultad'] = le_facultad.fit_transform(df['facultad'])"
      ],
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        "id": "PuBENA1DZRMl"
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      "execution_count": 145,
      "outputs": []
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              "     estrato  genero  facultad\n",
              "0          2       1         0\n",
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              "..       ...     ...       ...\n",
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              "998        1       0         2\n",
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              "\n",
              "[1000 rows x 3 columns]"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dividir el conjunto de entrenamiento y testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "Eb3tmuMHaXnl"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Escalando la variables\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "ktlqM_UG3r2c"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creando el modelo\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "kvOE091348U8",
        "outputId": "86cd2354-e06d-439d-e720-82fe575c5857"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Matriz de confusion y porcentaje de precision\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred) #Precision del 54%, dado que su precision no es mayor que el 60% entonces no se deberia tomar este modelo de Maquina de Soporte Vectorial"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WENJgY6y5erL",
        "outputId": "9d1123f8-6a81-44ac-8f1c-3d4fe2c81fb3"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0 14 12]\n",
            " [ 0 61 49]\n",
            " [ 0 40 74]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.54"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, BatchNormalization,Dropout\n",
        "from tensorflow.keras import optimizers\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "VYt7koJUXCYZ"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(13)\n",
        "tf.debugging.set_log_device_placement(False)"
      ],
      "metadata": {
        "id": "swFhRhfsYKPL"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mezclar la da data\n",
        "df = df.sample(frac=1)\n",
        "df"
      ],
      "metadata": {
        "id": "ck7JJnpAYvg7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "18a4459a-da92-4d30-ca20-ba325b98ae0c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     estrato     genero                                    facultad\n",
              "128        3   FEMENINO       FACULTAD DE CIENCIAS AGROINDUSTRIALES\n",
              "454        1  MASCULINO  FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS\n",
              "147        3  MASCULINO  FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS\n",
              "357        2   FEMENINO  FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS\n",
              "986        1   FEMENINO            FACULTAD DE CIENCIAS DE LA SALUD\n",
              "..       ...        ...                                         ...\n",
              "715        1   FEMENINO            FACULTAD DE CIENCIAS DE LA SALUD\n",
              "820        2  MASCULINO            FACULTAD DE CIENCIAS DE LA SALUD\n",
              "629        3   FEMENINO            FACULTAD DE CIENCIAS DE LA SALUD\n",
              "674        2   FEMENINO            FACULTAD DE CIENCIAS DE LA SALUD\n",
              "136        4  MASCULINO  FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS\n",
              "\n",
              "[1000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d58c4d6b-448f-41f3-b551-b1d590d86897\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>estrato</th>\n",
              "      <th>genero</th>\n",
              "      <th>facultad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>3</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS AGROINDUSTRIALES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454</th>\n",
              "      <td>1</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>3</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>2</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>986</th>\n",
              "      <td>1</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS DE LA SALUD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>1</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS DE LA SALUD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>2</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS DE LA SALUD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>3</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS DE LA SALUD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>674</th>\n",
              "      <td>2</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS DE LA SALUD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>4</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>FACULTAD DE CIENCIAS BASICAS Y TECNOLOGIAS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d58c4d6b-448f-41f3-b551-b1d590d86897')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d58c4d6b-448f-41f3-b551-b1d590d86897 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-d58c4d6b-448f-41f3-b551-b1d590d86897');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-97731450-40f0-4d54-8b10-85beb008e683\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97731450-40f0-4d54-8b10-85beb008e683')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-97731450-40f0-4d54-8b10-85beb008e683 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dividir el conjunto de prueba y de testeo\n",
        "train_dataset, temp_test_Dataset = train_test_split(df,test_size=0.2)"
      ],
      "metadata": {
        "id": "2atYckBcNrDl"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "bVAyHN_mYU0Z",
        "outputId": "d6732517-12e0-4b15-ae96-11919f4f6cf3"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     estrato  genero  facultad\n",
              "960        3       1         2\n",
              "276        1       1         1\n",
              "531        3       0         1\n",
              "544        2       0         1\n",
              "974        2       1         2\n",
              "..       ...     ...       ...\n",
              "350        1       1         1\n",
              "77         1       1         0\n",
              "896        4       1         2\n",
              "523        1       0         1\n",
              "806        1       0         2\n",
              "\n",
              "[800 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>estrato</th>\n",
              "      <th>genero</th>\n",
              "      <th>facultad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>960</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>974</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>523</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>806</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 3 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4d48216-88f7-4324-8bf1-95767f101588 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-c4d48216-88f7-4324-8bf1-95767f101588');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b64f893d-57b5-408e-b6ec-4b9eda06ca26\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b64f893d-57b5-408e-b6ec-4b9eda06ca26')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b64f893d-57b5-408e-b6ec-4b9eda06ca26 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_stats = train_dataset.describe()\n",
        "train_stats.pop(\"estrato\")\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nX9KhycxX8h5",
        "outputId": "07551e95-da2d-4f42-9766-73ea54ab16a0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          count     mean       std  min  25%  50%  75%  max\n",
              "genero    800.0  0.45125  0.497929  0.0  0.0  0.0  1.0  1.0\n",
              "facultad  800.0  1.30000  0.695057  0.0  1.0  1.0  2.0  2.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3601860a-cb5a-4210-8329-e24b24577b46\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>genero</th>\n",
              "      <td>800.0</td>\n",
              "      <td>0.45125</td>\n",
              "      <td>0.497929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facultad</th>\n",
              "      <td>800.0</td>\n",
              "      <td>1.30000</td>\n",
              "      <td>0.695057</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3601860a-cb5a-4210-8329-e24b24577b46 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-3601860a-cb5a-4210-8329-e24b24577b46');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-df9a2873-49d5-4f85-af66-dedc085679d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df9a2873-49d5-4f85-af66-dedc085679d4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df9a2873-49d5-4f85-af66-dedc085679d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Forma de los conjuntos de datos\n",
        "print(train_dataset.shape)\n",
        "print(temp_test_Dataset.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzHj-q7KN_qH",
        "outputId": "4fec3065-34cc-4a4b-c1ba-b5718540be91"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(800, 3)\n",
            "(200, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dividir el conjunto de test en 50% y 50% validacion\n",
        "test_dataset, valid_dataset = train_test_split(temp_test_Dataset, test_size=0.5)\n",
        "print(test_dataset.shape)\n",
        "print(valid_dataset.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aefyDsUnOLPk",
        "outputId": "5da18442-56dc-4d9f-af72-9383ea9d0d02"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 3)\n",
            "(100, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mostrar el tipo de dato de conjunto de prueba:  {type(test_dataset)}\")\n",
        "print(f\"Conjunto de entrenamiento             : {train_dataset.shape}\")\n",
        "print(f\"Conjunto de prueba                    : {test_dataset.shape}\")\n",
        "print(f\"Conjunto de validacion                : {valid_dataset.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phnTqYacPMGN",
        "outputId": "5e537c27-0635-40c3-b86a-e6de2037b8e3"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mostrar el tipo de dato de conjunto de prueba:  <class 'pandas.core.frame.DataFrame'>\n",
            "Conjunto de entrenamiento             : (800, 3)\n",
            "Conjunto de prueba                    : (100, 3)\n",
            "Conjunto de validacion                : (100, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels1 = train_dataset.pop('estrato')\n",
        "test_labels1 = test_dataset.pop('estrato')\n",
        "valid_labels1 = valid_dataset.pop('estrato')"
      ],
      "metadata": {
        "id": "GjV1vdlJQerM"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Codificando las etiquetas\n",
        "train_labels = pd.get_dummies(train_labels1,prefix ='Label')\n",
        "valid_labels = pd.get_dummies(valid_labels1 , prefix = 'Label')\n",
        "test_labels = pd.get_dummies(test_labels1 , prefix = 'Label')"
      ],
      "metadata": {
        "id": "koKaf8UjR2B_"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalizando el conjunto de datos\n",
        "def norm(x):\n",
        "  return (x - train_stats['mean']) / train_stats['std']\n",
        "\n",
        "normed_train_data = norm(train_dataset)\n",
        "normed_test_data = norm(test_dataset)\n",
        "normed_valid_dataset = norm(valid_dataset)"
      ],
      "metadata": {
        "id": "U0roevblWb-u"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostrando un ejemplo de los datos despues de estar normalizados\n",
        "normed_train_data.head(10)"
      ],
      "metadata": {
        "id": "WzMMYYNoZBmI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normed_valid_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUek8nVhb7px",
        "outputId": "be18c917-49c8-4222-b441-2c28429491f9"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Construyendo el modelo\n",
        "def build_model1_two_hidden_layers():\n",
        "  model = Sequential() #OJO con las dimensiones de las neuronas dado que no utilizamos una capa plana\n",
        "  model.add(Dense(40,activation='relu',input_shape = (normed_train_data.shape[1],)))\n",
        "  model.add(Dense(30,activation='relu'))\n",
        "  model.add(Dense(20,activation='relu'))\n",
        "  model.add(Dense(10,activation='relu'))\n",
        "  model.add(Dense(5,activation='softmax'))\n",
        "  learning_rate=0.0001\n",
        "  optimizer = optimizers.Adam(learning_rate)\n",
        "  model.compile(loss ='categorical_crossentropy',\n",
        "                optimizer = optimizer,\n",
        "                metrics = ['accuracy'])\n",
        "  return model\n",
        "\n",
        "EPOCHS = 50\n",
        "batch_size = 100\n",
        "model = build_model1_two_hidden_layers()\n",
        "print('Aqui el resumen del modelo: ')\n",
        "model.summary()\n",
        "\n",
        "with tf.device('/CPU:0'):\n",
        "  history = model.fit(\n",
        "      normed_train_data,\n",
        "      train_labels,\n",
        "      batch_size=batch_size,\n",
        "      epochs=EPOCHS,\n",
        "      verbose=1,\n",
        "      shuffle=True,\n",
        "      steps_per_epoch= int(normed_train_data.shape[0]/batch_size),\n",
        "      validation_data = (normed_valid_dataset, valid_labels),\n",
        "  )"
      ],
      "metadata": {
        "id": "qAR9HgxqhoVq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e58a99e-00d7-4dc7-849a-e1370e0691d1"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aqui el resumen del modelo: \n",
            "Model: \"sequential_42\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_121 (Dense)           (None, 40)                120       \n",
            "                                                                 \n",
            " dense_122 (Dense)           (None, 30)                1230      \n",
            "                                                                 \n",
            " dense_123 (Dense)           (None, 20)                620       \n",
            "                                                                 \n",
            " dense_124 (Dense)           (None, 10)                210       \n",
            "                                                                 \n",
            " dense_125 (Dense)           (None, 5)                 55        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2235 (8.73 KB)\n",
            "Trainable params: 2235 (8.73 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 2s 35ms/step - loss: 1.6859 - accuracy: 0.0250 - val_loss: 1.6874 - val_accuracy: 0.0300\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.6784 - accuracy: 0.0250 - val_loss: 1.6798 - val_accuracy: 0.0300\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.6714 - accuracy: 0.0250 - val_loss: 1.6731 - val_accuracy: 0.0300\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.6650 - accuracy: 0.0250 - val_loss: 1.6662 - val_accuracy: 0.0300\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6582 - accuracy: 0.0250 - val_loss: 1.6596 - val_accuracy: 0.0300\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.6518 - accuracy: 0.0250 - val_loss: 1.6535 - val_accuracy: 0.0600\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.6456 - accuracy: 0.0637 - val_loss: 1.6477 - val_accuracy: 0.0700\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6396 - accuracy: 0.0675 - val_loss: 1.6425 - val_accuracy: 0.0700\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.6341 - accuracy: 0.1488 - val_loss: 1.6375 - val_accuracy: 0.1400\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6290 - accuracy: 0.1813 - val_loss: 1.6329 - val_accuracy: 0.1400\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6245 - accuracy: 0.1813 - val_loss: 1.6289 - val_accuracy: 0.1400\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.6205 - accuracy: 0.1813 - val_loss: 1.6251 - val_accuracy: 0.1400\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.6171 - accuracy: 0.1937 - val_loss: 1.6221 - val_accuracy: 0.2000\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6142 - accuracy: 0.2562 - val_loss: 1.6190 - val_accuracy: 0.2000\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.6108 - accuracy: 0.2562 - val_loss: 1.6157 - val_accuracy: 0.2000\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 1.6072 - accuracy: 0.2562 - val_loss: 1.6127 - val_accuracy: 0.2000\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.6042 - accuracy: 0.2775 - val_loss: 1.6098 - val_accuracy: 0.2100\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.6015 - accuracy: 0.2788 - val_loss: 1.6069 - val_accuracy: 0.2100\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5987 - accuracy: 0.2788 - val_loss: 1.6040 - val_accuracy: 0.2100\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5963 - accuracy: 0.2788 - val_loss: 1.6016 - val_accuracy: 0.2100\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5941 - accuracy: 0.2788 - val_loss: 1.5992 - val_accuracy: 0.2100\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5919 - accuracy: 0.2788 - val_loss: 1.5968 - val_accuracy: 0.2100\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5897 - accuracy: 0.3038 - val_loss: 1.5943 - val_accuracy: 0.2400\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5877 - accuracy: 0.3113 - val_loss: 1.5920 - val_accuracy: 0.2400\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5856 - accuracy: 0.3113 - val_loss: 1.5898 - val_accuracy: 0.2400\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5836 - accuracy: 0.3113 - val_loss: 1.5876 - val_accuracy: 0.2400\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5815 - accuracy: 0.3113 - val_loss: 1.5853 - val_accuracy: 0.2400\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5793 - accuracy: 0.3113 - val_loss: 1.5828 - val_accuracy: 0.2400\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.5768 - accuracy: 0.3113 - val_loss: 1.5802 - val_accuracy: 0.2400\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.5744 - accuracy: 0.3225 - val_loss: 1.5776 - val_accuracy: 0.3000\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5719 - accuracy: 0.3413 - val_loss: 1.5749 - val_accuracy: 0.3600\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5693 - accuracy: 0.3500 - val_loss: 1.5721 - val_accuracy: 0.3600\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5665 - accuracy: 0.3500 - val_loss: 1.5693 - val_accuracy: 0.3600\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5639 - accuracy: 0.3500 - val_loss: 1.5665 - val_accuracy: 0.3600\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5610 - accuracy: 0.3500 - val_loss: 1.5632 - val_accuracy: 0.3600\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5580 - accuracy: 0.3575 - val_loss: 1.5600 - val_accuracy: 0.3600\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.5551 - accuracy: 0.3650 - val_loss: 1.5566 - val_accuracy: 0.3600\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5520 - accuracy: 0.3650 - val_loss: 1.5534 - val_accuracy: 0.3600\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5492 - accuracy: 0.3650 - val_loss: 1.5503 - val_accuracy: 0.3600\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.5461 - accuracy: 0.3650 - val_loss: 1.5472 - val_accuracy: 0.3600\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.5433 - accuracy: 0.3650 - val_loss: 1.5439 - val_accuracy: 0.3600\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5401 - accuracy: 0.3650 - val_loss: 1.5405 - val_accuracy: 0.3600\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5370 - accuracy: 0.3650 - val_loss: 1.5370 - val_accuracy: 0.3600\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5337 - accuracy: 0.3650 - val_loss: 1.5335 - val_accuracy: 0.3600\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.5302 - accuracy: 0.3650 - val_loss: 1.5300 - val_accuracy: 0.3600\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.5268 - accuracy: 0.3650 - val_loss: 1.5263 - val_accuracy: 0.3600\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5233 - accuracy: 0.3650 - val_loss: 1.5226 - val_accuracy: 0.3900\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.5197 - accuracy: 0.3787 - val_loss: 1.5188 - val_accuracy: 0.3900\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5160 - accuracy: 0.3787 - val_loss: 1.5149 - val_accuracy: 0.3900\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5121 - accuracy: 0.3787 - val_loss: 1.5106 - val_accuracy: 0.3900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resumen del modelo despues de cada epoca: ')\n",
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "i5POvbDBdOcP",
        "outputId": "7b66eaad-b315-417d-c71d-ca0c2b31dfbd"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resumen del modelo despues de cada epoca: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy  epoch\n",
              "95  1.502276   0.36875  1.499306          0.32     95\n",
              "96  1.500727   0.36875  1.497645          0.32     96\n",
              "97  1.499032   0.36875  1.495964          0.32     97\n",
              "98  1.497492   0.36875  1.494227          0.32     98\n",
              "99  1.495764   0.36875  1.492644          0.32     99"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-375e35f9-92e7-4649-a68a-3b5c4f9dba40\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.502276</td>\n",
              "      <td>0.36875</td>\n",
              "      <td>1.499306</td>\n",
              "      <td>0.32</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1.500727</td>\n",
              "      <td>0.36875</td>\n",
              "      <td>1.497645</td>\n",
              "      <td>0.32</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1.499032</td>\n",
              "      <td>0.36875</td>\n",
              "      <td>1.495964</td>\n",
              "      <td>0.32</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1.497492</td>\n",
              "      <td>0.36875</td>\n",
              "      <td>1.494227</td>\n",
              "      <td>0.32</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1.495764</td>\n",
              "      <td>0.36875</td>\n",
              "      <td>1.492644</td>\n",
              "      <td>0.32</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-375e35f9-92e7-4649-a68a-3b5c4f9dba40')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-375e35f9-92e7-4649-a68a-3b5c4f9dba40 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-375e35f9-92e7-4649-a68a-3b5c4f9dba40');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9b568059-ff71-4665-a1a1-81e64426e8f2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b568059-ff71-4665-a1a1-81e64426e8f2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9b568059-ff71-4665-a1a1-81e64426e8f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example_batch = normed_test_data[:10]\n",
        "example_result = model.predict(example_batch)\n",
        "print('predicted values: ')\n",
        "example_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fwIoyzDdl0P",
        "outputId": "5122c6da-fa59-4494-9f72-14e21c7d8a43"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 96ms/step\n",
            "predicted values: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.22886261, 0.22214216, 0.19421993, 0.17205201, 0.18272318],\n",
              "       [0.24540722, 0.2921845 , 0.16835083, 0.17048883, 0.1235688 ],\n",
              "       [0.2421894 , 0.2569368 , 0.16741328, 0.18758628, 0.14587428],\n",
              "       [0.24540722, 0.2921845 , 0.16835083, 0.17048883, 0.1235688 ],\n",
              "       [0.27923924, 0.23402646, 0.14357081, 0.16146265, 0.18170087],\n",
              "       [0.22886261, 0.22214216, 0.19421993, 0.17205201, 0.18272318],\n",
              "       [0.22886261, 0.22214216, 0.19421993, 0.17205201, 0.18272318],\n",
              "       [0.251936  , 0.27382463, 0.16729996, 0.14696784, 0.15997161],\n",
              "       [0.26446265, 0.35326084, 0.13824701, 0.13330667, 0.11072288],\n",
              "       [0.22886263, 0.22214217, 0.19421995, 0.17205203, 0.18272321]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ver como fue el entrenamiento\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Precision del modelo')\n",
        "plt.ylabel('precision')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Train','Cross-Validation'],loc='upper left')\n",
        "plt.show()\n",
        "# Mala precision menos del 40%"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dLAYCHjOd3XZ",
        "outputId": "92b7561e-70e9-488e-c5fb-6daa5453811e"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ver como fue el entrenamiento\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Perdida del modelo')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Train','Cross-Validation'],loc='upper left')\n",
        "plt.show()\n",
        "#No disminuye lo suficiente hacia cero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2tnV-_xNeptJ",
        "outputId": "8ba21186-7585-4889-9868-c3792fc3c33a"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train Split')\n",
        "loss,accuracy=model.evaluate(normed_train_data , train_labels, verbose=1)\n",
        "print(\"Precision : {:5.2f} \".format(accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtJknO-vjhw3",
        "outputId": "72c94376-1ac7-4609-8805-ecdc76b8e93b"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Split\n",
            "25/25 [==============================] - 1s 4ms/step - loss: 1.5099 - accuracy: 0.3787\n",
            "Precision :  0.38 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Evaluation Split')\n",
        "loss,accuracy=model.evaluate(normed_valid_dataset , valid_labels, verbose=2)\n",
        "print(\"Precision : {:5.2f} \".format(accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kmb79brrj9Q8",
        "outputId": "11b49e8a-4d4d-4ac0-8e93-145a113d16f4"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation Split\n",
            "4/4 - 0s - loss: 1.5106 - accuracy: 0.3900 - 54ms/epoch - 14ms/step\n",
            "Precision :  0.39 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Test Split')\n",
        "loss,accuracy=model.evaluate(normed_test_data , test_labels, verbose=2)\n",
        "print(\"Precision : {:5.2f} \".format(accuracy)) #50% de precision a la hora de predecir ;-;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_uY5DckkOSG",
        "outputId": "abca6e3d-514e-41bc-9dbc-e8dd24f5fd12"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Split\n",
            "4/4 - 0s - loss: 1.4959 - accuracy: 0.5000 - 32ms/epoch - 8ms/step\n",
            "Precision :  0.50 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se propone un tercer modelo simplificado\n",
        "model1=Sequential()\n",
        "model1.add(Dense(64,activation=\"relu\",input_dim=2))\n",
        "model1.add(Dense(32,activation=\"relu\"))\n",
        "model1.add(Dense(16,activation=\"relu\"))\n",
        "model1.add(Dense(1,activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "gVO2y3WqWi9N"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(\n",
        "    loss=\"binary_crossentropy\",\n",
        "    optimizer=\"adam\",\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "3nFWqLLTWt69"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ajustando el modelo\n",
        "historial = model1.fit(X,y,batch_size=5,epochs=150)"
      ],
      "metadata": {
        "id": "W4pkyBLXZEFE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "286cbf2c-2ba2-40aa-abcd-a66aa5fca0a4"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -986914.3750 - accuracy: 0.4300\n",
            "Epoch 2/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -1228550.7500 - accuracy: 0.4300\n",
            "Epoch 3/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -1504309.0000 - accuracy: 0.4300\n",
            "Epoch 4/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1818992.0000 - accuracy: 0.4300\n",
            "Epoch 5/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -2175038.2500 - accuracy: 0.4300\n",
            "Epoch 6/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -2582743.0000 - accuracy: 0.4300\n",
            "Epoch 7/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -3043477.7500 - accuracy: 0.4300\n",
            "Epoch 8/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -3544370.7500 - accuracy: 0.4300\n",
            "Epoch 9/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -4092627.0000 - accuracy: 0.4300\n",
            "Epoch 10/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -4692386.0000 - accuracy: 0.4300\n",
            "Epoch 11/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -5356119.5000 - accuracy: 0.4300\n",
            "Epoch 12/150\n",
            "200/200 [==============================] - 2s 11ms/step - loss: -6085754.5000 - accuracy: 0.4300\n",
            "Epoch 13/150\n",
            "200/200 [==============================] - 1s 7ms/step - loss: -6886832.0000 - accuracy: 0.4300\n",
            "Epoch 14/150\n",
            "200/200 [==============================] - 1s 7ms/step - loss: -7757050.5000 - accuracy: 0.4300\n",
            "Epoch 15/150\n",
            "200/200 [==============================] - 1s 6ms/step - loss: -8682051.0000 - accuracy: 0.4300\n",
            "Epoch 16/150\n",
            "200/200 [==============================] - 1s 6ms/step - loss: -9675259.0000 - accuracy: 0.4300\n",
            "Epoch 17/150\n",
            "200/200 [==============================] - 1s 7ms/step - loss: -10745081.0000 - accuracy: 0.4300\n",
            "Epoch 18/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -11886330.0000 - accuracy: 0.4300\n",
            "Epoch 19/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -13100440.0000 - accuracy: 0.4300\n",
            "Epoch 20/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -14396177.0000 - accuracy: 0.4300\n",
            "Epoch 21/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -15789351.0000 - accuracy: 0.4300\n",
            "Epoch 22/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -17273686.0000 - accuracy: 0.4300\n",
            "Epoch 23/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -18848834.0000 - accuracy: 0.4300\n",
            "Epoch 24/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -20521468.0000 - accuracy: 0.4300\n",
            "Epoch 25/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -22292552.0000 - accuracy: 0.4300\n",
            "Epoch 26/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -24168128.0000 - accuracy: 0.4300\n",
            "Epoch 27/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -26146856.0000 - accuracy: 0.4300\n",
            "Epoch 28/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -28238896.0000 - accuracy: 0.4300\n",
            "Epoch 29/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -30459846.0000 - accuracy: 0.4300\n",
            "Epoch 30/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -32812448.0000 - accuracy: 0.4300\n",
            "Epoch 31/150\n",
            "200/200 [==============================] - 0s 2ms/step - loss: -35268548.0000 - accuracy: 0.4300\n",
            "Epoch 32/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -37884400.0000 - accuracy: 0.4300\n",
            "Epoch 33/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -40623864.0000 - accuracy: 0.4300\n",
            "Epoch 34/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -43491992.0000 - accuracy: 0.4300\n",
            "Epoch 35/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -46498984.0000 - accuracy: 0.4300\n",
            "Epoch 36/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -49649952.0000 - accuracy: 0.4300\n",
            "Epoch 37/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -52938808.0000 - accuracy: 0.4300\n",
            "Epoch 38/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -56384064.0000 - accuracy: 0.4300\n",
            "Epoch 39/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -59959344.0000 - accuracy: 0.4300\n",
            "Epoch 40/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -63678480.0000 - accuracy: 0.4300\n",
            "Epoch 41/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -67517368.0000 - accuracy: 0.4300\n",
            "Epoch 42/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -71510176.0000 - accuracy: 0.4300\n",
            "Epoch 43/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -75654240.0000 - accuracy: 0.4300\n",
            "Epoch 44/150\n",
            "200/200 [==============================] - 3s 16ms/step - loss: -79945728.0000 - accuracy: 0.4300\n",
            "Epoch 45/150\n",
            "200/200 [==============================] - 1s 6ms/step - loss: -84412272.0000 - accuracy: 0.4300\n",
            "Epoch 46/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -89068832.0000 - accuracy: 0.4300\n",
            "Epoch 47/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -93934128.0000 - accuracy: 0.4300\n",
            "Epoch 48/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -99014136.0000 - accuracy: 0.4300\n",
            "Epoch 49/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -104274112.0000 - accuracy: 0.4300\n",
            "Epoch 50/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -109740760.0000 - accuracy: 0.4300\n",
            "Epoch 51/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -115378192.0000 - accuracy: 0.4300\n",
            "Epoch 52/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -121206528.0000 - accuracy: 0.4300\n",
            "Epoch 53/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -127230728.0000 - accuracy: 0.4300\n",
            "Epoch 54/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -133470552.0000 - accuracy: 0.4300\n",
            "Epoch 55/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -139942160.0000 - accuracy: 0.4300\n",
            "Epoch 56/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -146618336.0000 - accuracy: 0.4300\n",
            "Epoch 57/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -153522528.0000 - accuracy: 0.4300\n",
            "Epoch 58/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -160622048.0000 - accuracy: 0.4300\n",
            "Epoch 59/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -167973168.0000 - accuracy: 0.4300\n",
            "Epoch 60/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -175512896.0000 - accuracy: 0.4300\n",
            "Epoch 61/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -183314464.0000 - accuracy: 0.4300\n",
            "Epoch 62/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -191345136.0000 - accuracy: 0.4300\n",
            "Epoch 63/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -199668336.0000 - accuracy: 0.4300\n",
            "Epoch 64/150\n",
            "200/200 [==============================] - 1s 6ms/step - loss: -208233648.0000 - accuracy: 0.4300\n",
            "Epoch 65/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -217090128.0000 - accuracy: 0.4300\n",
            "Epoch 66/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -226263440.0000 - accuracy: 0.4300\n",
            "Epoch 67/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -235714304.0000 - accuracy: 0.4300\n",
            "Epoch 68/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -245513536.0000 - accuracy: 0.4300\n",
            "Epoch 69/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -255570464.0000 - accuracy: 0.4300\n",
            "Epoch 70/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -265892064.0000 - accuracy: 0.4300\n",
            "Epoch 71/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -276473728.0000 - accuracy: 0.4300\n",
            "Epoch 72/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -287353024.0000 - accuracy: 0.4300\n",
            "Epoch 73/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -298555456.0000 - accuracy: 0.4300\n",
            "Epoch 74/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -310075104.0000 - accuracy: 0.4300\n",
            "Epoch 75/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -321943040.0000 - accuracy: 0.4300\n",
            "Epoch 76/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -334151872.0000 - accuracy: 0.4300\n",
            "Epoch 77/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -346578496.0000 - accuracy: 0.4300\n",
            "Epoch 78/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -359327072.0000 - accuracy: 0.4300\n",
            "Epoch 79/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -372401504.0000 - accuracy: 0.4300\n",
            "Epoch 80/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -385870720.0000 - accuracy: 0.4300\n",
            "Epoch 81/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -399837856.0000 - accuracy: 0.4300\n",
            "Epoch 82/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -414129600.0000 - accuracy: 0.4300\n",
            "Epoch 83/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -428846368.0000 - accuracy: 0.4300\n",
            "Epoch 84/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -443688800.0000 - accuracy: 0.4300\n",
            "Epoch 85/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -458781536.0000 - accuracy: 0.4300\n",
            "Epoch 86/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -474270816.0000 - accuracy: 0.4300\n",
            "Epoch 87/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -490188256.0000 - accuracy: 0.4300\n",
            "Epoch 88/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -506423072.0000 - accuracy: 0.4300\n",
            "Epoch 89/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -523134496.0000 - accuracy: 0.4300\n",
            "Epoch 90/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -540209344.0000 - accuracy: 0.4300\n",
            "Epoch 91/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -557738688.0000 - accuracy: 0.4300\n",
            "Epoch 92/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -575436352.0000 - accuracy: 0.4300\n",
            "Epoch 93/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -593512896.0000 - accuracy: 0.4300\n",
            "Epoch 94/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -612160640.0000 - accuracy: 0.4300\n",
            "Epoch 95/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -631342208.0000 - accuracy: 0.4300\n",
            "Epoch 96/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -650950720.0000 - accuracy: 0.4300\n",
            "Epoch 97/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -670835584.0000 - accuracy: 0.4300\n",
            "Epoch 98/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -691174784.0000 - accuracy: 0.4300\n",
            "Epoch 99/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -712012928.0000 - accuracy: 0.4300\n",
            "Epoch 100/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -733235520.0000 - accuracy: 0.4300\n",
            "Epoch 101/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -754912448.0000 - accuracy: 0.4300\n",
            "Epoch 102/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -777063296.0000 - accuracy: 0.4300\n",
            "Epoch 103/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -799828928.0000 - accuracy: 0.4300\n",
            "Epoch 104/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -823192704.0000 - accuracy: 0.4300\n",
            "Epoch 105/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -847083456.0000 - accuracy: 0.4300\n",
            "Epoch 106/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -871303744.0000 - accuracy: 0.4300\n",
            "Epoch 107/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -896130432.0000 - accuracy: 0.4300\n",
            "Epoch 108/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -921397952.0000 - accuracy: 0.4300\n",
            "Epoch 109/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -947237824.0000 - accuracy: 0.4300\n",
            "Epoch 110/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -973626304.0000 - accuracy: 0.4300\n",
            "Epoch 111/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1000537600.0000 - accuracy: 0.4300\n",
            "Epoch 112/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1027965056.0000 - accuracy: 0.4300\n",
            "Epoch 113/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1055946624.0000 - accuracy: 0.4300\n",
            "Epoch 114/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1084507264.0000 - accuracy: 0.4300\n",
            "Epoch 115/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1113667584.0000 - accuracy: 0.4300\n",
            "Epoch 116/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1143356288.0000 - accuracy: 0.4300\n",
            "Epoch 117/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1173642880.0000 - accuracy: 0.4300\n",
            "Epoch 118/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1204373248.0000 - accuracy: 0.4300\n",
            "Epoch 119/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -1235556608.0000 - accuracy: 0.4300\n",
            "Epoch 120/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -1267243008.0000 - accuracy: 0.4300\n",
            "Epoch 121/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -1299653120.0000 - accuracy: 0.4300\n",
            "Epoch 122/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1332843648.0000 - accuracy: 0.4300\n",
            "Epoch 123/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1366523648.0000 - accuracy: 0.4300\n",
            "Epoch 124/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1400715008.0000 - accuracy: 0.4300\n",
            "Epoch 125/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1435399040.0000 - accuracy: 0.4300\n",
            "Epoch 126/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1470648448.0000 - accuracy: 0.4300\n",
            "Epoch 127/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1506288128.0000 - accuracy: 0.4300\n",
            "Epoch 128/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1542474240.0000 - accuracy: 0.4300\n",
            "Epoch 129/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1579273856.0000 - accuracy: 0.4300\n",
            "Epoch 130/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1616770560.0000 - accuracy: 0.4300\n",
            "Epoch 131/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1655043840.0000 - accuracy: 0.4300\n",
            "Epoch 132/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1694094720.0000 - accuracy: 0.4300\n",
            "Epoch 133/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1733666304.0000 - accuracy: 0.4300\n",
            "Epoch 134/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1774135424.0000 - accuracy: 0.4300\n",
            "Epoch 135/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1815442176.0000 - accuracy: 0.4300\n",
            "Epoch 136/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1857293440.0000 - accuracy: 0.4300\n",
            "Epoch 137/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1900109952.0000 - accuracy: 0.4300\n",
            "Epoch 138/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -1943790720.0000 - accuracy: 0.4300\n",
            "Epoch 139/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -1987912576.0000 - accuracy: 0.4300\n",
            "Epoch 140/150\n",
            "200/200 [==============================] - 1s 5ms/step - loss: -2032592128.0000 - accuracy: 0.4300\n",
            "Epoch 141/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -2078453248.0000 - accuracy: 0.4300\n",
            "Epoch 142/150\n",
            "200/200 [==============================] - 1s 4ms/step - loss: -2124889856.0000 - accuracy: 0.4300\n",
            "Epoch 143/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2172195584.0000 - accuracy: 0.4300\n",
            "Epoch 144/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2219978752.0000 - accuracy: 0.4300\n",
            "Epoch 145/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2268622592.0000 - accuracy: 0.4300\n",
            "Epoch 146/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2317939968.0000 - accuracy: 0.4300\n",
            "Epoch 147/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2368045824.0000 - accuracy: 0.4300\n",
            "Epoch 148/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2418994688.0000 - accuracy: 0.4300\n",
            "Epoch 149/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2470483968.0000 - accuracy: 0.4300\n",
            "Epoch 150/150\n",
            "200/200 [==============================] - 1s 3ms/step - loss: -2523015936.0000 - accuracy: 0.4300\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Precision del modelo')\n",
        "plt.ylabel('precision')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Train','Cross-Validation'],loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MpjCiobft1do",
        "outputId": "6ab680f5-4e9a-450f-af38-1bc3b08f58c2"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qFZKTkxEZGVmjfXBwMI4ePYrDhw+Lr8cffxz9+vXD4cOH4evrC39/f3h6emocs6ioCPv27av1mACgUCjEZ/7c69k/1XN+bGxsmnrZRM1S/bfH+WdERE0n+RBYXFwcRo0ahfDwcPTo0QOJiYkoKSlBbGwsAGDkyJHw8fFBQkICrKyscN9992l83snJCQA0tk+ZMgXz589HUFAQ/P39MXPmTHh7e9d4XlBzcNiLpMK/PSKi5pM8AYqJiUF+fj7i4+ORk5OD0NBQJCUliZOYs7Oz77mg2d3eeustlJSUYNy4cSgoKEDv3r2RlJQEKysrXVwCERERGRmZIAiC1EEYmqKiIjg6OqKwsLDGcFhZWRmysrLg7+9v8gmVn58fpkyZgilTpkgdiknh3yARUe3q+/6+m+QPQiTdk8lk9b5mz57dpOMeOHAA48aN026wREREeiD5EBjp3uXLl8XfN27ciPj4eGRkZIjb7OzsxN8FQYBSqYS5+b3/NNzc3LQbKBERSaOiFCi9ot9zKuwBa2f9nvMOTIBMwJ2PFHB0dIRMJhO3paSkoF+/fti6dSv++9//4ujRo9i+fTt8fX0RFxeHvXv3oqSkBB07dkRCQoLGE7bvHgKTyWRYtWoVfv31V/z222/w8fHB+++/j8cff1yv10tERI1QnA8s7w7cvK7f8/aOA6Jm6fecd2ACpAWCIOBmpVLv57W2MNNaRdC0adOwePFiBAQEwNnZGefPn8fgwYPxzjvvQKFQYP369RgyZAgyMjLQpk2bOo8zZ84cLFq0CO+99x4+/PBDPPfcczh37hxcXFy0EicREWnZiS3q5EcmB8ws9XdeubQpCBMgLbhZqUSn+N/0ft4Tc6NhY6md/wnnzp2LRx55RHzv4uKCkJAQ8f28efOwefNm/PTTT5gwYUKdxxk9ejRGjBgBAHj33XexbNky7N+/HwMHDtRKnEREpGXHt6h/PjIP6Fn3f99bGk6CJgBAeHi4xvvi4mJMnToVHTt2hJOTE+zs7HDy5ElkZ2fXe5yuXbuKv9va2sLBwUFcuoGIiAzMjRzg3B71752ekDYWPWMPkBZYW5jhxNxoSc6rLba2thrvp06dih07dmDx4sUIDAyEtbU1nn76aVRUVNR7HAsLC433MplMXK6EiIgMzMmfAQiATzjgVPsSUC0VEyAtkMlkWhuKMhR79uzB6NGj8eSTTwJQ9widPXtW2qCIiEi7qoe/Oj8paRhS4BAY1SooKAg//PADDh8+jL///hvPPvsse3KIiFoSEx7+ApgAUR2WLFkCZ2dn9OzZE0OGDEF0dDS6desmdVhERKQtJjz8BXApjFpxKQwyZPwbJCKtWPMocG43MOCdFlP9xaUwiIiIqG4mPvwFcBI0ERGR6Wnk8JcgCEjceRp/ZmpvuYyh9/vguYi2WjteYzEBIiIiMjWNrP76Lv0Cliaf1moI3f2kXSGACRAREZEpaeTwV2Z+MWb9dBwAMLqnHx4I0E7iEuBmd+9GOsQEiIiIyJQ0YvirvEqJSd8cQmmFEpEBrTDzsU4wk2tnDUqpcRI0ERGRKWnE8NeipAwcv1QEZxsLfBAT2mKSH4AJEBERkem4kdvg4a9dp/Lw+e4sAMB7T4fA07FlPXaDCRAREZGpOPkTGjL8lVdUhqmb/gagnvcT1clDTwHqDxMgIiIiUyEOfw2ts4lKJeD1bw/jakkFOno5YNqgYL2Epm9MgKjFGT16NIYOHSq+f+ihhzBlypR6P+Pn54fExMRmn1tbxyEi0roGDn998r9/sefMVVhbmOHDEffDysJMTwHqFxMgE5OTk4OJEyciICAACoUCvr6+GDJkCJKTk6UODRMnTkTHjh1r3ZednQ0zMzP89NNPjT7uDz/8gHnz5jU3PA1r166Fk5NTje0HDhzAuHHjtHouIiKt0Bj+alNrk0PZ1/H+9gwAwOzHOyHQXdpSdV1iAmRCzp49i7CwMPz+++947733cPToUSQlJaFfv3547bXXav1MZWWl3uJ76aWXcOrUKfz555819q1duxbu7u4YPHhwo4/r4uICe3t7bYR4T25ubrCxsdHLuYiIGuUew19FZZWYtOEQqlQCHu3qhWHhLXuBVCZAJmT8+PGQyWTYv38/nnrqKbRv3x6dO3dGXFwc9u7dCwCQyWRYsWIFHn/8cdja2uKdd94BAKxYsQLt2rWDpaUlOnTogC+++EI8riAImD17Ntq0aQOFQgFvb29MmjRJ3P/xxx8jKCgIVlZW8PDwwNNPP11rfKGhoejWrRtWr16tsV0QBKxduxajRo2CTCbDSy+9BH9/f1hbW6NDhw5YunRpvdd99xBYXl4ehgwZAmtra/j7++Orr76q8ZklS5agS5cusLW1ha+vL8aPH4/i4mIAQEpKCmJjY1FYWAiZTAaZTIbZs2cDqDkElp2djSeeeAJ2dnZwcHDAsGHDkJubK+6fPXs2QkND8cUXX8DPzw+Ojo4YPnw4bty4Ue81ERE1yj2GvwRBwH83H8P5azfh42SNd5/sApms5ZS814YPQtQGQQAqS/V/XgsboIF/oNeuXUNSUhLeeecd2Nra1th/53DO7NmzsWDBAiQmJsLc3BybN2/G5MmTkZiYiKioKPzyyy+IjY1F69at0a9fP3z//ff44IMPsGHDBnTu3Bk5OTn4+2919cBff/2FSZMm4YsvvkDPnj1x7do1/PHHH3XG+dJLL2HatGlYunSpGGdKSgqysrLw4osvQqVSoXXr1ti0aRNatWqFP//8E+PGjYOXlxeGDRvWoHsxevRoXLp0Cbt27YKFhQUmTZqEvLw8jTZyuRzLli2Dv78//v33X4wfPx5vvfUWPv74Y/Ts2ROJiYmIj49HRoa6q9jOrmY3sUqlEpOf1NRUVFVV4bXXXkNMTAxSUlLEdpmZmdiyZQt++eUXXL9+HcOGDcOCBQvE5JPIFJVVKlFQqr8e6JbO5vD3cICACs9uuCZzBwrLNPbvPJmLn/6+BDO5DMtG3A9HawuJItUfJkDaUFkKvOut//POuARY1kxmanPmzBkIgoDg4HvP5n/22WcRGxsrvh8xYgRGjx6N8ePHA4DYY7R48WL069cP2dnZ8PT0RFRUFCwsLNCmTRv06NEDgLoHxNbWFo899hjs7e3Rtm1b3H///fWe+4033sCmTZswevRoAMCaNWvQu3dvtG/fHgAwZ84csb2/vz/S0tLw7bffNigB+ueff7Bt2zbs378f3bt3BwB8/vnnNeYe3dlj5Ofnh/nz5+OVV17Bxx9/DEtLSzg6OkImk8HT07POcyUnJ+Po0aPIysqCr6+6K3n9+vXo3LkzDhw4IJ5fpVJh7dq14jDdCy+8gOTkZCZAZLK2HLqI/245huLyKqlDaTE2WK7HA3Jg0fmO+Cyh7jmfcY+0R1hbZz1GJh0OgZkIQRAa3DY8PFzj/cmTJ9GrVy+Nbb169cLJkycBAM888wxu3ryJgIAAjB07Fps3b0ZVlfo/XI888gjatm2LgIAAvPDCC/jqq69QWqruLfvqq69gZ2cnvv744w84OTnhP//5jzgMVlRUhO+//x4vvfSSeO7ly5cjLCwMbm5usLOzw6effors7OwGXdvJkydhbm6OsLAwcVtwcHCNCc07d+5E//794ePjA3t7e7zwwgu4evWqGHtDz+Xr6ysmPwDQqVMnODk5ifcOUCdYd85R8vLyqtEjRWQKyiqVmPb9EUzZeBjF5VUwk8tgYcZXc19eZoXoITsFANiBiDrbDezsiVf6tpP4r0B/2AOkDRY26t4YKc7bQEFBQZDJZDh16tQ929Y2RFYfX19fZGRkYOfOndixYwfGjx+P9957D6mpqbC3t8fBgweRkpKC7du3Iz4+HrNnz8aBAwfw+OOPIyIiQjyOj48PAPUwWP/+/XHmzBns2rULZmZmeOaZZwAAGzZswNSpU/H+++8jMjIS9vb2eO+997Bv375GxVyfs2fP4rHHHsOrr76Kd955By4uLti9ezdeeuklVFRUaH2Ss4WFZlezTCaDSqXS6jmIDN2ZvGJM+PogTuXcgEwGTHo4CJP6B7WopRcks38VsFVd/ZU6dpTU0RgMJkDaIJM1eChKKi4uLoiOjsby5csxadKkGklOQUFBrWXdANCxY0fs2bMHo0bd/j/Onj170KlTJ/G9tbU1hgwZgiFDhuC1115DcHAwjh49im7dusHc3BxRUVGIiorCrFmz4OTkhN9//x3/+c9/aq3O6tevH/z9/bFmzRrs2rULw4cPF+Pds2cPevbsKQ7HAeo5NA0VHByMqqoqpKeni0NQGRkZKCgoENukp6dDpVLh/fffh1yu7iT99ttvNY5jaWkJpVJZ77k6duyI8+fP4/z582Iv0IkTJ1BQUKBx74hM3ZZDFzFj81GUVijhaqfA0uGh6BXoKnVYLUcDHn5oipgAmZDly5ejV69e6NGjB+bOnYuuXbuiqqoKO3bswIoVKzSGZe705ptvYtiwYbj//vsRFRWFn3/+GT/88AN27twJQF2irlQqERERARsbG3z55ZewtrZG27Zt8csvv+Dff//Fgw8+CGdnZ2zduhUqlQodOnSoM06ZTIYXX3wRS5YswfXr1/HBBx+I+4KCgrB+/Xr89ttv8Pf3xxdffIEDBw7A39+/QfegQ4cOGDhwIF5++WWsWLEC5ubmmDJlCqytrcU2gYGBqKysxIcffoghQ4Zgz549WLlypcZx/Pz8UFxcjOTkZISEhMDGxqZGz1BUVBS6dOmC5557DomJiaiqqsL48ePRt2/fGsOMRI1WWQakrwFuXpc6kiarUqqQ+k8+zl0qwssAWrtaY+B9nrA9vx84L3V0LYRK2eC1v0wNEyATEhAQgIMHD+Kdd97BG2+8gcuXL8PNzQ1hYWFYsWJFnZ8bOnQoli5disWLF2Py5Mli78xDDz0EQF1BtmDBAsTFxUGpVKJLly74+eef0apVKzg5OeGHH37A7NmzUVZWhqCgIHzzzTfo3LlzvbGOHj0as2bNQufOnTWGyV5++WUcOnQIMTExkMlkGDFiBMaPH49t27Y1+D6sWbMGY8aMQd++feHh4YH58+dj5syZ4v6QkBAsWbIECxcuxPTp0/Hggw8iISEBI0eOFNv07NkTr7zyCmJiYnD16lXMmjVLLIWvJpPJ8OOPP2LixIl48MEHIZfLMXDgQHz44YcNjpWoTkc3AUnTpI6iWcwB9AfQv/qbqBjAXuniadFad6/z4YemSiY0ZnasiSgqKoKjoyMKCwvh4OCgsa+srAxZWVnw9/eHlVXLWhmXjAP/BgkA8OtU4MAqwLsb4NNN6mgaJetKCdL+vYoqpQArCzn6BLnBq4WtNG5Q5OZAt5GAR/3/8GwJ6vv+vht7gIiIjFH+rYKGHmOB0GeljaWByiqVmPPzCXxzUl21GRnQCkuHh8LdgckP6R8TICIiY1SdALkZx0rdmfnFeO2r21VeE/sFYnJUe1Z5kWSYABERGZuSq0BJvvp3t7oLCgzFj4cvYsYPR1FSoYSrnSUSY+5H7yBWeZG0DOJBiMuXL4efnx+srKwQERGB/fv319n2hx9+QHh4OJycnGBrayuuo3Sn0aNHi2s0Vb8GDhyo68sgItKP/FsVm05tDPoRHGWVSkz/4QgmbziMkgolIgNaYeukPkx+yCBI3gO0ceNGxMXFYeXKlYiIiEBiYiKio6ORkZEBd3f3Gu1dXFzwf//3fwgODoalpaW4LpW7uzuio6PFdgMHDsSaNWvE9wqFQqtxc+44SYV/e3R7+Ktj/e0kVGPI6+EgTOaDDcmASJ4ALVmyBGPHjhXXnlq5ciV+/fVXrF69GtOm1SzxrC69rjZ58mSsW7cOu3fv1kiAFApFves0NVX1U3tLS0s1nh1DpC/Vy3Hc/QRpMiF5txIgd8Oc/8MhLzIGkiZAFRUVSE9Px/Tp08VtcrkcUVFRSEtLu+fnBUHA77//joyMDCxcuFBjX0pKCtzd3eHs7IyHH34Y8+fPR6tWrZods5mZGZycnMS1mmxsbCBr4IrsRM0hCAJKS0uRl5cHJycnmJmZSR0SSaURE6BP595A9rWGr2HXXDtO5GLDAfVTDB8IcMGy4fezyosMkqQJ0JUrV6BUKuHh4aGx3cPDo941qwoLC+Hj44Py8nKYmZnh448/xiOPPCLuHzhwIP7zn//A398fmZmZmDFjBgYNGoS0tLRavzTKy8tRXl4uvi8qKqo37uqeJS5YSVJwcnLSSe8mGZEGJECCIGBFaiYWJWXoKajbOORFxkDyIbCmsLe3x+HDh8WlCOLi4hAQECAOjw0fPlxs26VLF3Tt2hXt2rVDSkoK+vfvX+N4CQkJmDNnToPPL5PJ4OXlBXd3d1RWVjb7eogaysLCgj0/pq4BFWBVShXifzqOr/epn7fT0csBlub6qXmxtTTDqw+1Q58gN72cj6ipJE2AXF1dYWZmhtzcXI3tubm59f4LVy6XIzAwEAAQGhqKkydPIiEhocb8oGoBAQFwdXXFmTNnak2Apk+fjri4OPF9UVGRuHhlfczMzPhlRET6dY8KsJLyKkz4+iB2ZeRDJgPiH+uE2F4NWyuPyJRIWgZvaWmJsLAwJCcni9tUKhWSk5MRGRnZ4OOoVCqNIay7XbhwAVevXoWXl1et+xUKBRwcHDReREQGqZ4KsLwbZYj5NA27MvKhMJdj5fNhTH6I6iD5EFhcXBxGjRqF8PBw9OjRA4mJiSgpKRGrwkaOHAkfHx8kJCQAUA9XhYeHo127digvL8fWrVvxxRdfiIt5FhcXY86cOXjqqafg6emJzMxMvPXWWwgMDNSoEiMiMkp1VICdybuBUasP4GLBTbjYWuKzUeHo1sZZggCJjIPkCVBMTAzy8/MRHx+PnJwchIaGIikpSZwYnZ2dDbn8dkdVSUkJxo8fjwsXLsDa2hrBwcH48ssvERMTA0A9LHXkyBGsW7cOBQUF8Pb2xoABAzBv3jytPwuIiEjvapkAvfffqxi3/i8UlVXB39UWa2O7o20rw31AIpEh4GrwtWjMarJERHr1XqB6EvTYXYBPN/x4+CLe3HQEFUoVwto6Y9XIcLjYWkodJZEkuBo8EVFLdEcFmODaHitTMrEwSd0jNOg+T3wQEworCxZmEDUEEyAiImNxRwXY98cKxORnTG9/zBjcEXI+c4eowZgAEREZi1vzf5SuwXh/u/oBhxMfDsQbAwx/RXgiQ2MQq8ETEVED3KoAO1bhjcuFZfB2tMJr/QIlDorIODEBIiIyFrd6gL47r67wmvJIe875IWoiJkBERMbiVgJ0uMwLQe52eKpba4kDIjJenANERGQM7qgAOyN4IzG6AxcaJWoG9gARERmDWxVg51VuCG7jiQGdPCQOiMi4MQEiIjICV88eAQD8I7TG2wODIZOx94eoOZgAEREZgeOH9wEAypyD8EBAK4mjITJ+TICIiAzc8UuFsLj6DwAgpFuExNEQtQxMgIiIDNyipAwEyi8AAFoHdZM4GqKWgQkQEZEBS8u8iiP/ZMJNVqTe4ManPhNpAxMgIiIDJQgCFiadQpDsonqDUxvA0lbaoIhaCCZAREQGavuJXBw+X4DOFpfUG9w6ShsQUQvCBIiIyABVKVV47zf1gqePe3H4i0jbmAARERmgHw5dxJm8YjjZWKCL4rJ6ozt7gIi0hQkQEZGBKatUInGHuuz9tYcCYX5V3RMEt2AJoyJqWZgAEREZmJWpmbhUWAYvRyu8EGInrgEG1/bSBkbUgjABIiIyIFsOXUTiztMAgLcGdoDVdfXvcGoDKOwkjIyoZWECRERkIPacuYI3v/sbADC2jz+evL+1uAgqK8CItIsJEBGRAThxqQgvf5GOSqWAISHemD7oVsKTd0r9kxVgRFrFBIiISGIXrpdi9Jr9KC6vwgMBLlj8TFfI5bdWe8+/lQCxAoxIq5gAERFJqKC0AqPXHEDejXJ08LDHJy+EQ2FudrtBdQLECjAirWICREQkkbJKJcau/wtn8orh5WiFtS92h6O1xe0GJVdZAUakI0yAiIgkoFQJeH3jYRw4ex32VuZYG9sDXo7Wmo2qe39YAUakdUyAiIj0TBAEzPvlBLYdy4GlmRyrRoajg6d9zYasACPSGSZARER69un//sXaP88CAJbEhOCBgFa1N2QFGJHOmEsdABFRU/x2PAc7T+RCJUgdSeOUVynxyxH12l7/fbQjHuvqXXdjVoAR6QwTICIyKmWVSsz5+QS+2Z8tdSjNMqa3P8b0Cai/ESvAiHSGCRARGY1/84sx/quDOJVzAzIZ8HxEW7R2tr73Bw1MGxcbRHf2rL8RK8CIdIoJEBEZhR8PX8SMH46ipEKJVraWSBweij5BblKHpTusACPSKSZARGTQ7h7yivB3wbIR98PDwUriyHSMFWBEOsUEiIgM1t1DXhP7BWJS/yCYm5lAASsrwIh0igkQERkkkxvyuhsrwIh0yiD+GbV8+XL4+fnBysoKERER2L9/f51tf/jhB4SHh8PJyQm2trYIDQ3FF198odFGEATEx8fDy8sL1tbWiIqKwunTp3V9GUSkJfN/OYHJGw6jpEKJCH8XbJ3cx7SSH4AVYEQ6JnkCtHHjRsTFxWHWrFk4ePAgQkJCEB0djby8vFrbu7i44P/+7/+QlpaGI0eOIDY2FrGxsfjtt9/ENosWLcKyZcuwcuVK7Nu3D7a2toiOjkZZWZm+LouImuhUThE+252lHvJ6OBBfjYlo+fN97sYKMCKdkwmCIOljxCIiItC9e3d89NFHAACVSgVfX19MnDgR06ZNa9AxunXrhkcffRTz5s2DIAjw9vbGG2+8galTpwIACgsL4eHhgbVr12L48OH3PF5RUREcHR1RWFgIBweHpl8cETXarB+PYV3aOQy6zxMrng+TOhxpnN0DrB2srgCbclTqaIiMRmO+vyXtAaqoqEB6ejqioqLEbXK5HFFRUUhLS7vn5wVBQHJyMjIyMvDggw8CALKyspCTk6NxTEdHR0RERDTomEQknZsVSvxw6CIAYESPNhJHIyFWgBHpnKSToK9cuQKlUgkPDw+N7R4eHjh16lSdnyssLISPjw/Ky8thZmaGjz/+GI888ggAICcnRzzG3ces3ne38vJylJeXi++LioqadD1E1Dy/HLmEG2VV8HWxRu9AV6nDkU5+hvonK8CIdMYoq8Ds7e1x+PBhFBcXIzk5GXFxcQgICMBDDz3UpOMlJCRgzpw52g2SiBqt+lk/w7u3gVwukzgaCeXd6gFiBRiRzkg6BObq6gozMzPk5uZqbM/NzYWnZ92PiZfL5QgMDERoaCjeeOMNPP3000hISAAA8XONOeb06dNRWFgovs6fP9+cyyKiJjiVU4SD2QUwl8vwTHhrqcORFivAiHRO0gTI0tISYWFhSE5OFrepVCokJycjMjKywcdRqVTiEJa/vz88PT01jllUVIR9+/bVeUyFQgEHBweNFxHp1zf71L0/j3TygLu9iVV93YkVYER6IfkQWFxcHEaNGoXw8HD06NEDiYmJKCkpQWxsLABg5MiR8PHxEXt4EhISEB4ejnbt2qG8vBxbt27FF198gRUrVgAAZDIZpkyZgvnz5yMoKAj+/v6YOXMmvL29MXToUKkuk4jqwcnPd+AaYER6IXkCFBMTg/z8fMTHxyMnJwehoaFISkoSJzFnZ2dDLr/dUVVSUoLx48fjwoULsLa2RnBwML788kvExMSIbd566y2UlJRg3LhxKCgoQO/evZGUlAQrKxP+VyWRAePk5zuwAoxILyR/DpAh4nOAiPTrPx/vwcHsArwZ3QGv9QuUOhxpbX0T2P8p0HMSMGCe1NEQGRWjeQ4QEREnP9+FFWBEesEEiIgkxcnPd2EFGJFeMAEiIslw8vNdWAFGpDdMgIhIMpz8fBdWgBHpDRMgIpIMn/x8F1aAEekNEyAikgQnP9eCa4AR6Q0TICKSRPXk56iOnPwsYgUYkd4wASIivbtz8vOzEZz8LGIFGJHeMAEiIr379ehl3CirQmtnTn4WsQKMSK+YABGR3n297xwAdek7Jz/fwgowIr1iAkREesXJz3UQK8A4/EWkD0yAiEivOPm5DmIFGBMgIn2QfDV4ItKOskolLly/iQvXS3Gx4Oat32+iuKxS6tA07Mu6BoCTn2tgBRiRXjEBIjJCKpWAz3b/iyMXCsVE50pxudRhNZhfKxtOfr6bWAHGZwAR6QMTICIj9PORS3h366ka220tzeDrYoPWztZo7az+6WBtAUOaZiyTyfBAgAsnP99JowKMCRCRPjABIjIygiBgRUomAGBoqDcG3ucpJjuO1haQyZhYGB1WgBHpHRMgIiOzKyMPp3JuwNbSDHMevw+ONhZSh0TNxQowIr1jFRiRkfl4l7r35/kH2jL5aSlYAUakd0yAiIzIgbPX8Ne567A0k+PF3v5Sh0PawgowIr1jAkRkRKrn/jwV1hoeDnyGTovBCjAivWMCRGQkTl4uwu+n8iCXAS8/GCB1OKQtrAAjkgQTICIjsTJV3fszqIsX/FxtJY6GtIYVYESSYAJEZASyr5bi578vAQBe7dtO4mhIq1gBRiQJJkBERuDTPzKhEoAH27vhPh9HqcMhbWIFGJEkmAARGbi8G2X49q8LAIDxD7H3p8VhBRiRJJgAERm4NXvOoqJKhfvbOCHC30XqcEjbWAFGJAkmQEQGrKisEl+mnQOgnvvDZS5aGFaAEUmGCRCRAfty7zncKK9CkLsdojp6SB0OaRsrwIgkwwSIyECVVSqxevdZAMArfdtx9fSWiBVgRJJhAkRkoDalX8CV4nL4OFnj8VBvqcMhXWAFGJFkmAARGaAqpQqf/k/94MOxffxhYcb/q7ZIrAAjkgz/q0pkgH49ehnnr92Ei60lYrq3kToc0hVWgBFJhgkQkYERBEFc9DS2px+sLc0kjoh0ghVgRJIylzoAopZi/i8n8PmeLAiCdo5na2mGkZF+2jkYGR5WgBFJij1ARFogCAI2pV/QWvIDqCu/HG0stHdAMiysACOSFHuAiLTgSnEFCm9WQiYD9rz9MCzNm/dvCwu5nMlPS8cKMCJJGUQP0PLly+Hn5wcrKytERERg//79dbZdtWoV+vTpA2dnZzg7OyMqKqpG+9GjR0Mmk2m8Bg4cqOvLIBN2Jq8YAODrbANvJ2u42ima9WLyYwLy2ANEJCXJE6CNGzciLi4Os2bNwsGDBxESEoLo6Gjk5eXV2j4lJQUjRozArl27kJaWBl9fXwwYMAAXL17UaDdw4EBcvnxZfH3zzTf6uBwyUWfy1QlQoDvnclADVc8BcmcCRCQFyROgJUuWYOzYsYiNjUWnTp2wcuVK2NjYYPXq1bW2/+qrrzB+/HiEhoYiODgYn332GVQqFZKTkzXaKRQKeHp6ii9nZ2d9XA6ZqMw8JkDUCKwAI5KcpAlQRUUF0tPTERUVJW6Ty+WIiopCWlpag45RWlqKyspKuLhorpKdkpICd3d3dOjQAa+++iquXr1a5zHKy8tRVFSk8SJqjOohsEA3JkDUANW9P46sACOSiqQJ0JUrV6BUKuHhobnIo4eHB3Jychp0jLfffhve3t4aSdTAgQOxfv16JCcnY+HChUhNTcWgQYOgVCprPUZCQgIcHR3Fl6+vb9MvikxSdQLUjj1A1BDVFWAc/iKSjFFXgS1YsAAbNmxASkoKrKysxO3Dhw8Xf+/SpQu6du2Kdu3aISUlBf37969xnOnTpyMuLk58X1RUxCSIGuxGWSVyisoAcAiMGogVYESSk7QHyNXVFWZmZsjNzdXYnpubC09Pz3o/u3jxYixYsADbt29H165d620bEBAAV1dXnDlzptb9CoUCDg4OGi+ihsrMLwEAuNkr4GjN6i1qAFaAEUlO0gTI0tISYWFhGhOYqyc0R0ZG1vm5RYsWYd68eUhKSkJ4ePg9z3PhwgVcvXoVXl5eWomb6E6c/0ONxgowIslJXgUWFxeHVatWYd26dTh58iReffVVlJSUIDY2FgAwcuRITJ8+XWy/cOFCzJw5E6tXr4afnx9ycnKQk5OD4mL1l1BxcTHefPNN7N27F2fPnkVycjKeeOIJBAYGIjo6WpJrpJbt9vwfW4kjIaPACjAigyD5HKCYmBjk5+cjPj4eOTk5CA0NRVJSkjgxOjs7G3L57TxtxYoVqKiowNNPP61xnFmzZmH27NkwMzPDkSNHsG7dOhQUFMDb2xsDBgzAvHnzoFAo9HptZBrYA0SNwgowIoMgeQIEABMmTMCECRNq3ZeSkqLx/uzZs/Uey9raGr/99puWIiO6t3/FhyDaSxwJGQVWgBEZBINIgIiMVUWVCueulQJgBZhWCQJQeVPqKHQj97j6JydAE0mKCRBRM5y9WgKlSoCdwhweDhxi1QqVElgdDVw4IHUkusUEiEhSkk+CJjJmdz4AUSaTSRxNC5G9t+UnP9YuQMBDUkdBZNLYA0TUDJwArQMntqh/dh0OPPq+pKHojLkVYMb//BJJqcn/Dzx9+jR27dqFvLw8qFQqjX3x8fHNDozIGJzhIqjapVICJ35U/37fU6ySIiKdaVICtGrVKrz66qtwdXWFp6enRte/TCZjAkQmgwmQlmXvBYpzAStHDhERkU41KQGaP38+3nnnHbz99tvajofIaKhUAv69wgRIq6qHv4IfA8wtJQ2FiFq2Jk2Cvn79Op555hltx0JkVC4W3ERZpQqWZnL4OltLHY7xu3P4q9NQSUMhopavSQnQM888g+3bt2s7FiKjUj385e9qC3MzFlQ2G4e/iEiPmjQEFhgYiJkzZ2Lv3r3o0qULLCw0V8CeNGmSVoIjMmSc/6NlHP4iIj1qUgL06aefws7ODqmpqUhNTdXYJ5PJmACRSbjzGUDUTBz+IiI9a1IClJWVpe04iIzOmXz2AGkNh7+ISM+aPXFBEAQIgqCNWIiMhiAIfAiiNnH4i4j0rMkJ0Pr169GlSxdYW1vD2toaXbt2xRdffKHN2IgM1pXiChTerIRMBgS42UodjnHj8BcRSaBJQ2BLlizBzJkzMWHCBPTq1QsAsHv3brzyyiu4cuUKXn/9da0GSWRoMm8Nf/k628DKwkziaIwch7+ISAJNSoA+/PBDrFixAiNHjhS3Pf744+jcuTNmz57NBIhaPFaAaRGHv4hIAk0aArt8+TJ69uxZY3vPnj1x+fLlZgdFZOjECjAOfzWPSgmc+En9O4e/iEiPmpQABQYG4ttvv62xfePGjQgKCmp2UESGLpMVYNqRvRcozuHwFxHpXZOGwObMmYOYmBj873//E+cA7dmzB8nJybUmRkQtDYfAtKR6+KvDoxz+IiK9alIP0FNPPYV9+/bB1dUVW7ZswZYtW+Dq6or9+/fjySef1HaMRAaluLwKlwvLAACBbvYSR2PE7hz+6sz/bhCRfjWpBwgAwsLC8OWXX2ozFiKjkHmr98fVTgFHG4t7tKY6cfiLiCTU4ASoqKgIDg4O4u/1qW5H1BLdHv7iBOhm4fAXEUmowQmQs7MzLl++DHd3dzg5OUEmk9VoIwgCZDIZlEqlVoMkMiRcAkMLOPxFRBJrcAL0+++/w8XFBQCwa9cunQVEZOi4BIYWcPiLiCTW4ASob9++tf5OZGoyxSEwToBuMg5/EZHEmlQFlpSUhN27d4vvly9fjtDQUDz77LO4fv261oIjMjQVVSqcu1YKgENgTcbhLyIyAE1KgN58801xIvTRo0cRFxeHwYMHIysrC3FxcVoNkMiQnL1aAqVKgJ3CHB4OCqnDMU4c/iIiA9CkMvisrCx06tQJAPD9999jyJAhePfdd3Hw4EEMHjxYqwESGRJxCQx3u1oLAagBOPxFRAagSQmQpaUlSkvVwwA7d+4UF0V1cXG5Z4k8kTEzmAnQggCc3gFcPyttHE1xfIv6J4e/iEhCTUqAevfujbi4OPTq1Qv79+/Hxo0bAQD//PMPWrdurdUAiQyJQawBJgjA9v8CaR9JF0NzcfiLiCTWpAToo48+wvjx4/Hdd99hxYoV8PHxAQBs27YNAwcO1GqARIZE8jXA7k5+2g8CzI1sLpJMBnR5hsNfRCSpJiVAbdq0wS+//FJj+wcffNDsgIgMlUolSNsDdHfyM3gx0GOs/uMgImoBuBQGUQNdLLiJskoVLM3k8HW21u/JmfwQEWkVl8IgaqDqJTD8XW1hbtakJ0g0DZMfIiKt41IYRA2UKZbA63ERVEEAdsxk8kNEpGUN/mds3759YW5uLv5e36uxli9fDj8/P1hZWSEiIgL79++vs+2qVavQp08fODs7w9nZGVFRUTXaC4KA+Ph4eHl5wdraGlFRUTh9+nSj4yK6k95L4KuTnz8/VL9n8kNEpDVN6sdfs2YNNm3aVGP7pk2bsG7dukYda+PGjYiLi8OsWbNw8OBBhISEIDo6Gnl5ebW2T0lJwYgRI7Br1y6kpaXB19cXAwYMwMWLF8U2ixYtwrJly7By5Urs27cPtra2iI6ORllZWeMulOgOdz4EUeeY/BAR6ZRMEAShsR9q3749PvnkE/Tr109je2pqKsaNG4eMjIwGHysiIgLdu3fHRx+pu/hVKhV8fX0xceJETJs27Z6fVyqVcHZ2xkcffYSRI0dCEAR4e3vjjTfewNSpUwEAhYWF8PDwwNq1azF8+PB7HrOoqAiOjo4oLCzkhG4CAAil1/HCe1+juKwKS2JCEOCq4yTo2PfA3o/VvzP5ISJqkMZ8fzepDD47Oxv+/v41trdt2xbZ2dkNPk5FRQXS09Mxffp0cZtcLkdUVBTS0tIadIzS0lJUVlaK85OysrKQk5ODqKgosY2joyMiIiKQlpZWawJUXl6O8vJy8T2fZk0aqiqg+vgBfCnkAAoAW/R4biY/REQ60aQEyN3dHUeOHIGfn5/G9r///hutWrVq8HGuXLkCpVIJDw8Pje0eHh44depUg47x9ttvw9vbW0x4cnJyxGPcfczqfXdLSEjAnDlzGhw3mZirZ2BWnIMqQY48uRu8Ha10f05zK6DXJOD+53V/LiIiE9SkBGjEiBGYNGkS7O3t8eCDDwJQD39Nnjy5QUNM2rJgwQJs2LABKSkpsLJq+pfS9OnTNVaxLyoqgq+vrzZCpJYgX52MHxEC8GGbFVgT20PigIiIqLmalADNmzcPZ8+eRf/+/cXKMJVKhZEjR+Ldd99t8HFcXV1hZmaG3Nxcje25ubnw9PSs97OLFy/GggULsHPnTnTt2lXcXv253NxceHl5aRwzNDS01mMpFAooFEa2nADpz60E6B9Va2nXACMiIq1pUhWYpaUlNm7ciFOnTuGrr77CDz/8gMzMTKxevRqWlg1f38fS0hJhYWFITk4Wt6lUKiQnJyMyMrLOzy1atAjz5s1DUlISwsPDNfb5+/vD09NT45hFRUXYt29fvcckqlPeSQDAacGHCRARUQvRpB6gan5+fhAEAe3atRN7ghorLi4Oo0aNQnh4OHr06IHExESUlJQgNjYWADBy5Ej4+PggISEBALBw4ULEx8fj66+/hp+fnzivx87ODnZ2dpDJZJgyZQrmz5+PoKAg+Pv7Y+bMmfD29sbQoUObc7lkqvLVVY2nhdYYzASIiKhFaFLWUlpaiokTJ4rP/Pnnn38QEBCAiRMnwsfHp0Hl69ViYmKQn5+P+Ph45OTkIDQ0FElJSeIk5uzsbMjltzuqVqxYgYqKCjz99NMax5k1axZmz54NAHjrrbdQUlKCcePGoaCgAL1790ZSUlKz5gmRiaqqgOpqJuRQJ0CBbvZSR0RERFrQpOcATZ48GXv27EFiYiIGDhyII0eOICAgAD/++CNmz56NQ4cO6SJWveFzgKjaxYx0+HzzMIoEaywM2Y53/tP13h8iIiJJNOb7u0lzgLZs2YKPPvoIvXv31lgUtXPnzsjMzGzKIYkMTkl5FdZsSQIAXLZoi/jHO0scERERaUuTEqD8/Hy4u7vX2F5SUlLrKvFExkYQBLz53d+wv3EGANAmuBsU5mYSR0VERNrSpAQoPDwcv/76q/i+Oun57LPPWGlFLcLHKZnYejQHHeQXAADW3uz9ISJqSZo0Cfrdd9/FoEGDcOLECVRVVWHp0qU4ceIE/vzzT6Smpmo7RiK9SsnIw+Lt6sqvSPsrQAkA92BpgyIiIq1qUg9Q79698ffff6OqqgpdunTB9u3b4e7ujrS0NISFhWk7RiK9OXulBJO+OQRBAJ4P94LjzVtr27l1lDYwIiLSqkb3AFVWVuLll1/GzJkzsWrVKl3ERCSJkvIqjPviLxSVVaFbGyfE97QAjlUBCgfAwVvq8IiISIsa3QNkYWGB77//XhexEEmmetLzP7nFcLNXYMXzYbC8dlq9060DwMn9REQtSpOGwIYOHYotW7ZoORQi6axIVU96tjCTYeXz3eDhYCWuAQY3zv8hImppmjQJOigoCHPnzsWePXsQFhYGW1tbjf2TJk3SSnBE+pCSkYf3flNPep7z+H0Ia+ui3nFrDTAmQERELU+TEqDPP/8cTk5OSE9PR3p6usY+mUzGBIh07rv0C5j3ywlUKlXNPlZZpRKCAIzo0QbPRrS5vePWGmCsACMianmalABlZWWJv1evpMEHIJI+rfrfvyi8Wam14/UOdMXsxzvd3lBVAVy79VRzVoAREbU4TV4N/vPPP8cHH3yA06fVE0WDgoIwZcoUjBkzRmvBEdUm+2opMnJvwEwuw6+TesPWssl/xgDU85t9nKw1k/irZwAVK8CIiFqqJn1zxMfHY8mSJZg4caL45Oe0tDS8/vrryM7Oxty5c7UaJNGdtp/IAQD08HNBsKeOFqsVJ0CzAoyIqCVqUgK0YsUKrFq1CiNGjBC3Pf744+jatSsmTpzIBIh0aseJXADAI508dHcSVoAREbVoTSqDr6ysRHh4eI3tYWFhqKqqanZQRHW5XlKBv85dB6DjBIgVYERELVqTEqAXXngBK1asqLH9008/xXPPPdfsoIjqsisjD0qVgGBPe/i62OjuRKwAIyJq0Zo1CXr79u144IEHAAD79u1DdnY2Ro4cibi4OLHdkiVLmh8l0S16Gf5iBRgRUYvXpATo2LFj6NatGwAgM1P9ReHq6gpXV1ccO3ZMbMfSeNKmskolUv/JB6DjBIgVYERELV6TEqBdu3ZpOw6ie0r79ypKK5TwcFCgi4+j7k7ECjAiohavSXOAiKRQPfwV1dFDt72LrAAjImrxmACRUVCpBOzUx/wfgBVgREQmgAkQGYUjFwuRd6McdgpzRLZrpduTsQKMiKjFYwJERqG696dvezcozM10dyJWgBERmQQmQGQUxPk/ndx1eyJWgBERmQQmQGTw7lz8tF8HHSdArAAjIjIJTIDI4N25+KmTjaVuT8YKMCIik8AEiAzezpN6qv4CWAFGRGQimACRQSsorcCBs3pY/LQaK8CIiEwCEyAyaL+f0tPipwArwIiITAgTIDJoeln8tBorwIiITAYTIDJYelv8tBorwIiITAYTIDJYdy5+ep+3Dhc/rXZnAkRERC0aEyAyWHcufiqX66FHRqwA4/wfIqKWjgkQGSS9Ln5ajRVgREQmgwkQGaSjtxY/tbU00/3ip8BdFWBMgIiIWjrJE6Dly5fDz88PVlZWiIiIwP79++tse/z4cTz11FPw8/ODTCZDYmJijTazZ8+GTCbTeAUH8wvN2FQPf/XtoOPFT6tpVID56P58REQkKUkToI0bNyIuLg6zZs3CwYMHERISgujoaOTl5dXavrS0FAEBAViwYAE8PT3rPG7nzp1x+fJl8bV7925dXQLpiF7L3wFWgBERmRhJE6AlS5Zg7NixiI2NRadOnbBy5UrY2Nhg9erVtbbv3r073nvvPQwfPhwKhaLO45qbm8PT01N8ubq66uoSSAf0uvhpNVaAERGZFMkSoIqKCqSnpyMqKup2MHI5oqKikJaW1qxjnz59Gt7e3ggICMBzzz2H7OzsetuXl5ejqKhI40XS2XFr7a/ufs66X/y0GivAiIhMimQJ0JUrV6BUKuHhoTnE4eHhgZycnCYfNyIiAmvXrkVSUhJWrFiBrKws9OnTBzdu3KjzMwkJCXB0dBRfvr6+TT4/Nd/t6q+6hzm1jhVgREQmRfJJ0No2aNAgPPPMM+jatSuio6OxdetWFBQU4Ntvv63zM9OnT0dhYaH4On/+vB4jprudylH3wD0Q4KKfE7ICjIjI5JhLdWJXV1eYmZkhNzdXY3tubm69E5wby8nJCe3bt8eZM2fqbKNQKOqdU0T6U1JeheullQCg+8VPq7ECjIjI5EjWA2RpaYmwsDAkJyeL21QqFZKTkxEZGam18xQXFyMzMxNeXl5aOybpzsWCmwAABytzOFhZ6OekrAAjIjI5kvUAAUBcXBxGjRqF8PBw9OjRA4mJiSgpKUFsbCwAYOTIkfDx8UFCQgIA9cTpEydOiL9fvHgRhw8fhp2dHQIDAwEAU6dOxZAhQ9C2bVtcunQJs2bNgpmZGUaMGCHNRVKjXLheCgDwcdZT7w/ACjAiIhMkaQIUExOD/Px8xMfHIycnB6GhoUhKShInRmdnZ0Muv91JdenSJdx///3i+8WLF2Px4sXo27cvUlJSAAAXLlzAiBEjcPXqVbi5uaF3797Yu3cv3Nzc9Hpt1DQXr6t7gFo7W+vvpKwAIyIyOZImQAAwYcIETJgwodZ91UlNNT8/PwiCUO/xNmzYoK3QSAIXbiVAPk56TIBYAUZEZHJaXBUYGbcLBXruAWIFGBGRSZK8B4joTheu38QM86/w4u+/Ab/X39unHQIgqFgBRkRkYpgAkUG5eP0mnjFLhVyo0u+J2w9kBRgRkQlhAkQGo6xSicriq3C2KlZvmHQYMLfS/YllcsBOT2uOERGRQWACRAbjYsFN+MnUy6AI9l6QufhLHBEREbVUnARNBuPC9dsJkMylncTREBFRS8YEiAzGxes34S+/tRAue3+IiEiHmACRwbhwvRRtZbfWhmvFHiAiItIdJkBkMC4W3IS/rLoHiAkQERHpDhMgMhgXrt+83QPkEiBtMERE1KIxASKDceNaHpxlt0rgOQeIiIh0iAkQGYSKKhVsSs4BAJR2noClrcQRERFRS8YEiAzC5cKbaAv1/B85J0ATEZGOMQEig6B+BpB6/o+M83+IiEjHmACRQbh4/Sb8qp8BxB4gIiLSMSZAZBAuXC+9owSePUBERKRbTIDIIFwouLMEnj1ARESkW0yAyCBcv8oSeCIi0h8mQGQQzK7/CwCosPFgCTwREekcEyCSXJVSBfvSbPUbDn8REZEeMAEiyeUUlaGNoJ4AbeHGBIiIiHSPCRBJ7sIdJfAylsATEZEeMAEiyV284yGILIEnIiJ9YAJEklM/Bbr6GUDsASIiIt1jAkSSu3YlhyXwRESkV0yASHKqq5kAgJtW7iyBJyIivWACRJKzLDoLAKh0ZO8PERHpBxMgkpRKJcDh1jOAzFw5/4eIiPSDCRBJKu9GOdpAPQHayiNI4miIiMhUMAEiSV24XiqWwLMHiIiI9IUJEEnqYgFL4ImISP+YAJGk8vNYAk9ERPrHBIgkVZ53BgBQbOnGEngiItIbJkAkKfk19TOASu3aShwJERGZEiZAJCnrYnUJvODMNcCIiEh/JE+Ali9fDj8/P1hZWSEiIgL79++vs+3x48fx1FNPwc/PDzKZDImJic0+JklHEAS4lJ0HAFh6BEocDRERmRJJE6CNGzciLi4Os2bNwsGDBxESEoLo6Gjk5eXV2r60tBQBAQFYsGABPD09tXJMks6V4gr43noGkJ1XB4mjISIiUyJpArRkyRKMHTsWsbGx6NSpE1auXAkbGxusXr261vbdu3fHe++9h+HDh0OhUGjlmCQd9TOA1AmQhRt7gIiISH8kS4AqKiqQnp6OqKio28HI5YiKikJaWppej1leXo6ioiKNF+leXt5llsATEZEkJEuArly5AqVSCQ8PD43tHh4eyMnJ0esxExIS4OjoKL58fX2bdH5qnJLLpwEABeauLIEnIiK9knwStCGYPn06CgsLxdf58+elDskkKK+oS+BvWDPhJCIi/TKX6sSurq4wMzNDbm6uxvbc3Nw6Jzjr6pgKhaLOOUWkOxYFWQCAckcOfxERkX5J1gNkaWmJsLAwJCcni9tUKhWSk5MRGRlpMMck3bEvPQcAkHMRVCIi0jPJeoAAIC4uDqNGjUJ4eDh69OiBxMRElJSUIDY2FgAwcuRI+Pj4ICEhAYB6kvOJEyfE3y9evIjDhw/Dzs4OgYGBDTomGQZBEOBacRGQATaeQVKHQ0REJkbSBCgmJgb5+fmIj49HTk4OQkNDkZSUJE5izs7Ohlx+u5Pq0qVLuP/++8X3ixcvxuLFi9G3b1+kpKQ06JhkGApKK9Hm1jOAnFt3lDgaIiIyNTJBEASpgzA0RUVFcHR0RGFhIRwcHKQOp0U6ceYcOn3ZVf1mxiVWgRERUbM15vubVWAkicJLpwAAV+WtmPwQEZHeMQEiSZTlngEAXFP4SBwJERGZIiZAJI1r6mcAldi1lTgQIiIyRUyASBJWReoSeJUTnwFERET6xwSIJOFcpn7atqU7S+CJiEj/mACRJDyVFwEAdj4dJI6EiIhMERMg0ruia3lwgnoVeDffYImjISIiU8QEiPTuSvZJAEAenGFr7yhxNEREZIqYAJHelVw+DQDIM2cJPBERSYMJEOldVb46ASqwbiNxJEREZKqYAJHemRecBQBUOPAZQEREJA0mQKR3tiXqZwChVTtpAyEiIpPFBIj0zrVCXQJv7clnABERkTSYAJF+3bwOB6EIAODkwxJ4IiKSBhMg0qubOf8AAHIEZ3i7t5I4GiIiMlVMgEivCi5kAAAuyDzhaG0hcTRERGSqmACRXpXlqkvgr1j6ShwJERGZMiZApFfC1UwAQIktnwFERETSYQJEeqUoOgsAqHLylzYQIiIyaeZSB0AthLISSF8LFJyrt1mr0n8BABZufAYQERFJhwkQNZ+yEvjuReDkT/dsagWgXDCHvXd73cdFRERUByZA1Dx3Jj9mlkD4i+qfdVifdg4pFUF43dVVj0ESERFpYgJETXd38hPzFdB+QI1mxeVV2HrkMr5Lv4D9N68BABY7W+s7WiIiIhETIGqaeyQ/KpWAvVlX8d1fF7DtWA5uVioBAHIZ8FS31nCxrbuXiIiISNeYAFHj1ZP8nLtagu8PXsT36RdwseCm+JEAN1s8E+aLJ+/3gaejlVSRExERAWACRI1VR/JTXF6FV79Mxx+nr4hN7a3MMSTEG8+EtUaorxNkMpmEgRMREd3GBIgarp6en50ncvHH6SuQy4DeQW54Oqw1BnTygJWFmcRBExER1cQEiBrmHnN+/sxU9/yM6ROAGYM7ShUlERFRg/BJ0HRvDaj2+jPzKgCgZzuu8E5ERIaPCRDd21+r601+zl8rxYXrN2Eul6G7n4tEQRIRETUcEyC6tyPfqn9Gza71OT/Vw1+hvk6wVXBUlYiIDB8TIKpfwXng4l8AZMB9T9fahMNfRERkbJgAUf1O/Kj+2bYXYO9RY7cgCGICFNmOy1sQEZFxYAJE9Tu+Wf2z89Bad2fmFyP/RjkU5nLc38ZJb2ERERE1BxMgqtudw18dH6+1SXXvT7ifM5/5Q0RERsMgEqDly5fDz88PVlZWiIiIwP79++ttv2nTJgQHB8PKygpdunTB1q1bNfaPHj0aMplM4zVw4EBdXkLLdI/hLwD480z1/B8OfxERkfGQPAHauHEj4uLiMGvWLBw8eBAhISGIjo5GXl5ere3//PNPjBgxAi+99BIOHTqEoUOHYujQoTh27JhGu4EDB+Ly5cvi65tvvtHH5bQs9xj+UqkEpP1bPf+HE6CJiMh4SJ4ALVmyBGPHjkVsbCw6deqElStXwsbGBqtXr661/dKlSzFw4EC8+eab6NixI+bNm4du3brho48+0minUCjg6ekpvpydnfVxOS1HA4a/TlwuQuHNStgpzNHVx1G/8RERETWDpAlQRUUF0tPTERUVJW6Ty+WIiopCWlparZ9JS0vTaA8A0dHRNdqnpKTA3d0dHTp0wKuvvoqrV6/WGUd5eTmKioo0XiavIcNft57/E+HvAnMzyXNpIiKiBpP0W+vKlStQKpXw8ND8gvXw8EBOTk6tn8nJybln+4EDB2L9+vVITk7GwoULkZqaikGDBkGpVNZ6zISEBDg6OoovX1/fZl5ZC3CP4S8Ad5S/c/iLiIiMS4t8bO/w4cPF37t06YKuXbuiXbt2SElJQf/+/Wu0nz59OuLi4sT3RUVFpp0ENWD4q1Kpwv6sawA4AZqIiIyPpD1Arq6uMDMzQ25ursb23NxceHp61voZT0/PRrUHgICAALi6uuLMmTO17lcoFHBwcNB4mbQGDH8duVCA0golnG0sEOxpr8fgiIiImk/SBMjS0hJhYWFITk4Wt6lUKiQnJyMyMrLWz0RGRmq0B4AdO3bU2R4ALly4gKtXr8LLy0s7gbd0DRn+OnN7+Esul+khKCIiIu2RfOZqXFwcVq1ahXXr1uHkyZN49dVXUVJSgtjYWADAyJEjMX36dLH95MmTkZSUhPfffx+nTp3C7Nmz8ddff2HChAkAgOLiYrz55pvYu3cvzp49i+TkZDzxxBMIDAxEdHS0JNdoVBow/AWAy18QEZFRk3wOUExMDPLz8xEfH4+cnByEhoYiKSlJnOicnZ0Nufx2ntazZ098/fXX+O9//4sZM2YgKCgIW7ZswX333QcAMDMzw5EjR7Bu3ToUFBTA29sbAwYMwLx586BQKCS5RqMiDn/1rHP4q6xSifTs6wC4ACoRERknmSAIgtRBGJqioiI4OjqisLDQ9OYDfRYFXDgADF4M9Bhba5M/z1zBs5/tg4eDAnun94dMxiEwIiKSXmO+vyUfAiMDUnBenfw0cPirZztXJj9ERGSUmADRbQ0Y/gJuPwCRz/8hIiJjxQSIbjuxRf2z85N1Nikur8LfFwoBcP4PEREZLyZApNbA4a8DWdegVAlo42KD1s42+ouPiIhIi5gAkVojh7/Y+0NERMaMCRCpNWD4C+D6X0RE1DIwAaIGD39dL6nAictFAJgAERGRcWMCRA0e/tqXdRWCAAS528Hd3kpPwREREWkfEyBq9PAX5/8QEZGxYwJk6ho4/AVw/S8iImo5JF8LzKQc+x449KXUUWgqzlf/vMfwV15RGc7kFUMmAx4IcNFTcERERLrBBEifrp8DMn+XOoradXmm3t1p/6p7fzp7O8DJxlIfEREREekMEyB9aj8QcPCROoqarByAoAH1NvnzzO31v4iIiIwdEyB98uikfhmhP//l+l9ERNRyMAHSo6KyShTdrJQ6jEbLu1GO89duwlwuQ3c/zv8hIiLjxwRIj77cew6LkjKkDqPJQnydYKfgnwwRERk/fpvpkblcBoW5cT55wNJcjpGRbaUOg4iISCtkgiAIUgdhaIqKiuDo6IjCwkI4ODhIHQ4RERE1QGO+v42zO4KIiIioGZgAERERkclhAkREREQmhwkQERERmRwmQERERGRymAARERGRyWECRERERCaHCRARERGZHCZAREREZHKYABEREZHJYQJEREREJocJEBEREZkcJkBERERkcpgAERERkckxlzoAQyQIAgCgqKhI4kiIiIiooaq/t6u/x+vDBKgWN27cAAD4+vpKHAkRERE11o0bN+Do6FhvG5nQkDTJxKhUKly6dAn29vaQyWRaPXZRURF8fX1x/vx5ODg4aPXYVBPvt37xfusX77d+8X7rV1PutyAIuHHjBry9vSGX1z/Lhz1AtZDL5WjdurVOz+Hg4MD/A+kR77d+8X7rF++3fvF+61dj7/e9en6qcRI0ERERmRwmQERERGRymADpmUKhwKxZs6BQKKQOxSTwfusX77d+8X7rF++3fun6fnMSNBEREZkc9gARERGRyWECRERERCaHCRARERGZHCZAREREZHKYAOnR8uXL4efnBysrK0RERGD//v1Sh9Qi/O9//8OQIUPg7e0NmUyGLVu2aOwXBAHx8fHw8vKCtbU1oqKicPr0aWmCbQESEhLQvXt32Nvbw93dHUOHDkVGRoZGm7KyMrz22mto1aoV7Ozs8NRTTyE3N1eiiI3bihUr0LVrV/FhcJGRkdi2bZu4n/datxYsWACZTIYpU6aI23jPtWf27NmQyWQar+DgYHG/Lu81EyA92bhxI+Li4jBr1iwcPHgQISEhiI6ORl5entShGb2SkhKEhIRg+fLlte5ftGgRli1bhpUrV2Lfvn2wtbVFdHQ0ysrK9Bxpy5CamorXXnsNe/fuxY4dO1BZWYkBAwagpKREbPP666/j559/xqZNm5CamopLly7hP//5j4RRG6/WrVtjwYIFSE9Px19//YWHH34YTzzxBI4fPw6A91qXDhw4gE8++QRdu3bV2M57rl2dO3fG5cuXxdfu3bvFfTq91wLpRY8ePYTXXntNfK9UKgVvb28hISFBwqhaHgDC5s2bxfcqlUrw9PQU3nvvPXFbQUGBoFAohG+++UaCCFuevLw8AYCQmpoqCIL6/lpYWAibNm0S25w8eVIAIKSlpUkVZovi7OwsfPbZZ7zXOnTjxg0hKChI2LFjh9C3b19h8uTJgiDw71vbZs2aJYSEhNS6T9f3mj1AelBRUYH09HRERUWJ2+RyOaKiopCWliZhZC1fVlYWcnJyNO69o6MjIiIieO+1pLCwEADg4uICAEhPT0dlZaXGPQ8ODkabNm14z5tJqVRiw4YNKCkpQWRkJO+1Dr322mt49NFHNe4twL9vXTh9+jS8vb0REBCA5557DtnZ2QB0f6+5GKoeXLlyBUqlEh4eHhrbPTw8cOrUKYmiMg05OTkAUOu9r95HTadSqTBlyhT06tUL9913HwD1Pbe0tISTk5NGW97zpjt69CgiIyNRVlYGOzs7bN68GZ06dcLhw4d5r3Vgw4YNOHjwIA4cOFBjH/++tSsiIgJr165Fhw4dcPnyZcyZMwd9+vTBsWPHdH6vmQARUZO99tprOHbsmMaYPWlfhw4dcPjwYRQWFuK7777DqFGjkJqaKnVYLdL58+cxefJk7NixA1ZWVlKH0+INGjRI/L1r166IiIhA27Zt8e2338La2lqn5+YQmB64urrCzMysxsz13NxceHp6ShSVaai+v7z32jdhwgT88ssv2LVrF1q3bi1u9/T0REVFBQoKCjTa8543naWlJQIDAxEWFoaEhASEhIRg6dKlvNc6kJ6ejry8PHTr1g3m5uYwNzdHamoqli1bBnNzc3h4ePCe65CTkxPat2+PM2fO6PzvmwmQHlhaWiIsLAzJycniNpVKheTkZERGRkoYWcvn7+8PT09PjXtfVFSEffv28d43kSAImDBhAjZv3ozff/8d/v7+GvvDwsJgYWGhcc8zMjKQnZ3Ne64lKpUK5eXlvNc60L9/fxw9ehSHDx8WX+Hh4XjuuefE33nPdae4uBiZmZnw8vLS/d93s6dRU4Ns2LBBUCgUwtq1a4UTJ04I48aNE5ycnIScnBypQzN6N27cEA4dOiQcOnRIACAsWbJEOHTokHDu3DlBEARhwYIFgpOTk/Djjz8KR44cEZ544gnB399fuHnzpsSRG6dXX31VcHR0FFJSUoTLly+Lr9LSUrHNK6+8IrRp00b4/fffhb/++kuIjIwUIiMjJYzaeE2bNk1ITU0VsrKyhCNHjgjTpk0TZDKZsH37dkEQeK/14c4qMEHgPdemN954Q0hJSRGysrKEPXv2CFFRUYKrq6uQl5cnCIJu7zUTID368MMPhTZt2giWlpZCjx49hL1790odUouwa9cuAUCN16hRowRBUJfCz5w5U/Dw8BAUCoXQv39/ISMjQ9qgjVht9xqAsGbNGrHNzZs3hfHjxwvOzs6CjY2N8OSTTwqXL1+WLmgj9uKLLwpt27YVLC0tBTc3N6F///5i8iMIvNf6cHcCxHuuPTExMYKXl5dgaWkp+Pj4CDExMcKZM2fE/bq81zJBEITm9yMRERERGQ/OASIiIiKTwwSIiIiITA4TICIiIjI5TICIiIjI5DABIiIiIpPDBIiIiIhMDhMgIiIiMjlMgIiIGiAlJQUymazGukREZJyYABEREZHJYQJEREREJocJEBEZBZVKhYSEBPj7+8Pa2hohISH47rvvANwenvr111/RtWtXWFlZ4YEHHsCxY8c0jvH999+jc+fOUCgU8PPzw/vvv6+xv7y8HG+//TZ8fX2hUCgQGBiIzz//XKNNeno6wsPDYWNjg549eyIjI0O3F05EOsEEiIiMQkJCAtavX4+VK1fi+PHjeP311/H8888jNTVVbPPmm2/i/fffx4EDB+Dm5oYhQ4agsrISgDpxGTZsGIYPH46jR49i9uzZmDlzJtauXSt+fuTIkfjmm2+wbNkynDx5Ep988gns7Ow04vi///s/vP/++/jrr79gbm6OF198US/XT0TaxcVQicjglZeXw8XFBTt37kRkZKS4fcyYMSgtLcW4cePQr18/bNiwATExMQCAa9euoXXr1li7di2GDRuG5557Dvn5+di+fbv4+bfeegu//vorjh8/jn/++QcdOnTAjh07EBUVVSOGlJQU9OvXDzt37kT//v0BAFu3bsWjjz6KmzdvwsrKSsd3gYi0iT1ARGTwzpw5g9LSUjzyyCOws7MTX+vXr0dmZqbY7s7kyMXFBR06dMDJkycBACdPnkSvXr00jturVy+cPn0aSqUShw8fhpmZGfr27VtvLF27dhV/9/LyAgDk5eU1+xqJSL/MpQ6AiOheiouLAQC//vorfHx8NPYpFAqNJKiprK2tG9TOwsJC/F0mkwFQz08iIuPCHiAiMnidOnWCQqFAdnY2AgMDNV6+vr5iu71794q/X79+Hf/88w86duwIAOjYsSP27Nmjcdw9e/agffv2MDMzQ5cuXaBSqTTmFBFRy8UeICIyePb29pg6dSpef/11qFQq9O7dG4WFhdizZw8cHBzQtm1bAMDcuXPRqlUreHh44P/+7//g6uqKoUOHAgDeeOMNdO/eHfPmzUNMTAzS0tLw0Ucf4eOPPwYA+Pn5YdSoUXjxxRexbNkyhISE4Ny5c8jLy8OwYcOkunQi0hEmQERkFObNmwc3NzckJCTg33//hZOTE7p164YZM2aIQ1ALFizA5MmTcfr0aYSGhuLnn3+GpaUlAKBbt2749ttvER8fj3nz5sHLywtz587F6NGjxXOsWLECM2bMwPjx43H16lW0adMGM2bMkOJyiUjHWAVGREavukLr+vXrcHJykjocIjICnANEREREJocJEBEREZkcDoERERGRyWEPEBEREZkcJkBERERkcpgAERERkclhAkREREQmhwkQERERmRwmQERERGRymAARERGRyWECRERERCaHCRARERGZnP8HA2K/4K6RYh4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}