Random_Forest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importación de librerías y lectura del Data Frame"
      ],
      "metadata": {
        "id": "2IKijoxVmtHq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b9kJSf8FMznj"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, mean_squared_error\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario = pd.read_csv(\"/content/drive/MyDrive/secondary_data.csv\", delimiter=';')"
      ],
      "metadata": {
        "id": "uglN4IA9NV_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploración de los datos\n",
        "\n",
        "Teniendo en cuenta que se utilizaran los mismos criterios que la parte 2, esta sección tiene menos contenido. Cabe aclarar que en este modelo al no ser obligatorio usar todas las columnas, se fueron probando distintas combinaciones hasta conseguir el score pedido.\n"
      ],
      "metadata": {
        "id": "D8FVlwy7JwR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elimino las columnas que no voy a usar."
      ],
      "metadata": {
        "id": "bWNA1_rAFqBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario = df_secundario.drop(\"cap-surface\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"stem-root\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"stem-surface\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"veil-type\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"veil-color\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"spore-print-color\", axis = 1)"
      ],
      "metadata": {
        "id": "s5JQzMwONeUu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imputación de los Nans."
      ],
      "metadata": {
        "id": "z4ZwyJ7772r_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputa los Nans dedos un df, la nombre de la features y un caracter.\n",
        "def imputar_nans(df, nombre_feature, caracter):\n",
        "\n",
        "  df[nombre_feature] = df[nombre_feature].fillna(caracter)\n"
      ],
      "metadata": {
        "id": "KRjkqdkSNzM_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imputar_nans(df_secundario, 'gill-attachment', 'o')\n",
        "imputar_nans(df_secundario, 'gill-spacing', 'o')\n",
        "imputar_nans(df_secundario, 'ring-type', 'o')"
      ],
      "metadata": {
        "id": "4oiWiKSFOBrv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación de los datos\n",
        "\n",
        "En esta sección se preparan los datos para realizar un Random Forest de clasificación.\n"
      ],
      "metadata": {
        "id": "ivXIC9lKNNVv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separo el target del resto del Data Frame."
      ],
      "metadata": {
        "id": "pdDd8CPsE3dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_secundario.drop('class', axis=1)"
      ],
      "metadata": {
        "id": "34_sAwjGM0Eh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_secundario['class']"
      ],
      "metadata": {
        "id": "rIYmFoEjOGIa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de entrenamiento y uno de medición."
      ],
      "metadata": {
        "id": "u1KOgzISE_0M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_med, y_train, y_med = train_test_split(X, y, test_size=0.2, random_state=13)"
      ],
      "metadata": {
        "id": "IRjdLgLwOHlm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de testeo y uno de validación a partir del de medición."
      ],
      "metadata": {
        "id": "qYDaSNgUFICC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, X_validation, y_test, y_validation = train_test_split(X_med, y_med, test_size=0.5, random_state=13)"
      ],
      "metadata": {
        "id": "lueeoYuoTlBM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiY8HtVtqnCp"
      },
      "source": [
        "Voy a utilizar una variable binaria {0, 1} para representar al target (\"class\") en donde 1 es para indicar que el hongo es venenoso ('p') y 0 si el hongo es comestible ('e')."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train =  y_train.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "v27Jqx3ITpGX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_validation =  y_validation.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "zjMRg2MCTtOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test =  y_test.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "tZ3aG0hcUI7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HBc5FyxVTxm"
      },
      "source": [
        "Aplico un encoding a todas las columnas. Al igual que en la parte 2, al no tener una gran cantidad de columnas por cada clase, también voy a utilizar OHE para todas las columnas categóricas."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Aplica OHE a cada columna dada una feature\n",
        "def aplicar_ohe(nombre_feature, columnas):\n",
        "\n",
        "  global X_train, X_test, X_validation\n",
        "  ohe = OneHotEncoder()\n",
        "  encoded_train = ohe.fit_transform(X_train[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_test = ohe.fit_transform(X_test[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_validation = ohe.transform(X_validation[[nombre_feature]]).todense().astype(int)\n",
        "  X_train = X_train.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_train))\n",
        "  X_test = X_test.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_test))\n",
        "  X_validation = X_validation.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_validation))\n",
        "  X_train = X_train.rename(columns=columnas)\n",
        "  X_test = X_test.rename(columns=columnas)\n",
        "  X_validation = X_validation.rename(columns=columnas)"
      ],
      "metadata": {
        "id": "xvLmDwWNUKQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-shape\":"
      ],
      "metadata": {
        "id": "ZEf8p-yKFp1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-shape', {0:'b0',1:'c0',2:'f0',3:'o0',4:'p0',5:'s0',6:'x0'})"
      ],
      "metadata": {
        "id": "J3KrDH5WFr5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-color\":"
      ],
      "metadata": {
        "id": "C3D7ZEZfGRWE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-color', {0:'b2',1:'e2',2:'g2',3:'k2',4:'l2',5:'n2',6:'o2', 7:'p2', 8:'r2', 9:'u2', 10:'w2', 11:'y2'})"
      ],
      "metadata": {
        "id": "oW1-2Z8QGWS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"does-bruise-or-bleed\":"
      ],
      "metadata": {
        "id": "X0MyYuWoGb6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('does-bruise-or-bleed', {0:'f3',1:'t3'})"
      ],
      "metadata": {
        "id": "EiRcQbbAGf6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kFw7zvUm9Mc"
      },
      "source": [
        "Con \"gill-attachment\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-attachment', {0:'a4',1:'d4',2:'e4',3:'f4',4:'o4',5:'p4',6:'s4', 7:'x4'})"
      ],
      "metadata": {
        "id": "GXKLZfybGqFO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wg7UsPOo85D"
      },
      "source": [
        "Con \"gill-spacing\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-spacing', {0:'c5',1:'d5',2:'f5',3:'o5'})"
      ],
      "metadata": {
        "id": "F1Y9kCZuGw93"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEhrQKlCpoua"
      },
      "source": [
        "Con \"gill-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-color', {0:'b6',1:'e6',2:'f6',3:'g6',4:'k6',5:'n6',6:'o6', 7:'p6', 8:'r6', 9:'u6', 10:'w6', 11:'y6'})"
      ],
      "metadata": {
        "id": "sXOV_2M_G7Iq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLwKgt4ir9KL"
      },
      "source": [
        "Con \"stem-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('stem-color', {0:'b9',1:'e9',2:'f9',3:'g9',4:'k9',5:'l9',6:'n9', 7:'o9', 8:'p9', 9:'r9', 10:'u9', 11:'w9', 12:'y9'})"
      ],
      "metadata": {
        "id": "0RFuDi2WHSt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by1Uz3RIuxmf"
      },
      "source": [
        "Con has-ring"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('has-ring', {0:'f12',1:'t12'})"
      ],
      "metadata": {
        "id": "4Uh0Kt0zHo_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqMqt4rpvfgp"
      },
      "source": [
        "Con \"ring-type\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('ring-type', {0:'e13',1:'f13',2:'g13',3:'l13',4:'m13',5:'o13',6:'p13', 7:'r13', 8:'z13'})"
      ],
      "metadata": {
        "id": "fz9XyQVMHv7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDOleFR7yGgZ"
      },
      "source": [
        "Con \"habitat\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('habitat', {0:'d15',1:'g15',2:'h15',3:'l15',4:'m15',5:'p15',6:'u15', 7:'w15'})"
      ],
      "metadata": {
        "id": "XWDj_7MQH_pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kId_v6Uy6kD"
      },
      "source": [
        "Con \"season\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('season', {0:'a16',1:'s16',2:'u16',3:'w16'})"
      ],
      "metadata": {
        "id": "ttOb0llwIGr1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "NO1F6KyBUmnq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "60021649-1234-44a6-fa01-5791b1a02535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
              "0             15.74         5.02       28.73   0   0   1   0   0   0   0  ...   \n",
              "1              1.27         3.79        5.63   0   0   1   0   0   0   0  ...   \n",
              "2             12.05         6.03       23.98   0   0   1   0   0   0   0  ...   \n",
              "3              8.08         6.04       14.95   0   0   0   0   0   0   1  ...   \n",
              "4              6.44        12.23       12.77   0   0   0   0   0   0   1  ...   \n",
              "...             ...          ...         ...  ..  ..  ..  ..  ..  ..  ..  ...   \n",
              "48850          8.53         3.63       25.28   0   0   0   1   0   0   0  ...   \n",
              "48851         10.66         5.10       19.52   0   0   1   0   0   0   0  ...   \n",
              "48852          1.62         3.73        2.63   0   0   0   0   0   0   1  ...   \n",
              "48853          1.29         5.00        2.45   0   0   0   0   0   0   1  ...   \n",
              "48854          2.46         3.41        2.38   0   0   1   0   0   0   0  ...   \n",
              "\n",
              "       h15  l15  m15  p15  u15  w15  a16  s16  u16  w16  \n",
              "0        0    0    0    0    0    0    1    0    0    0  \n",
              "1        0    0    0    0    0    0    1    0    0    0  \n",
              "2        0    0    0    0    0    0    1    0    0    0  \n",
              "3        0    0    0    0    0    0    1    0    0    0  \n",
              "4        0    0    0    0    0    0    0    0    1    0  \n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
              "48850    0    0    0    0    0    0    0    1    0    0  \n",
              "48851    0    0    0    0    0    0    1    0    0    0  \n",
              "48852    0    0    0    0    0    0    1    0    0    0  \n",
              "48853    0    0    0    0    0    0    1    0    0    0  \n",
              "48854    0    0    0    0    0    0    0    0    1    0  \n",
              "\n",
              "[48855 rows x 84 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e16008e5-f9fd-4b75-9fbc-b91580fa2391\">\n",
              "    <div 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>cap-diameter</th>\n",
              "      <th>stem-height</th>\n",
              "      <th>stem-width</th>\n",
              "      <th>b0</th>\n",
              "      <th>c0</th>\n",
              "      <th>f0</th>\n",
              "      <th>o0</th>\n",
              "      <th>p0</th>\n",
              "      <th>s0</th>\n",
              "      <th>x0</th>\n",
              "      <th>...</th>\n",
              "      <th>h15</th>\n",
              "      <th>l15</th>\n",
              "      <th>m15</th>\n",
              "      <th>p15</th>\n",
              "      <th>u15</th>\n",
              "      <th>w15</th>\n",
              "      <th>a16</th>\n",
              "      <th>s16</th>\n",
              "      <th>u16</th>\n",
              "      <th>w16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.74</td>\n",
              "      <td>5.02</td>\n",
              "      <td>28.73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.27</td>\n",
              "      <td>3.79</td>\n",
              "      <td>5.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.05</td>\n",
              "      <td>6.03</td>\n",
              "      <td>23.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.08</td>\n",
              "      <td>6.04</td>\n",
              "      <td>14.95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.44</td>\n",
              "      <td>12.23</td>\n",
              "      <td>12.77</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48850</th>\n",
              "      <td>8.53</td>\n",
              "      <td>3.63</td>\n",
              "      <td>25.28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48851</th>\n",
              "      <td>10.66</td>\n",
              "      <td>5.10</td>\n",
              "      <td>19.52</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48852</th>\n",
              "      <td>1.62</td>\n",
              "      <td>3.73</td>\n",
              "      <td>2.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48853</th>\n",
              "      <td>1.29</td>\n",
              "      <td>5.00</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48854</th>\n",
              "      <td>2.46</td>\n",
              "      <td>3.41</td>\n",
              "      <td>2.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48855 rows × 84 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e16008e5-f9fd-4b75-9fbc-b91580fa2391')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e16008e5-f9fd-4b75-9fbc-b91580fa2391 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-e16008e5-f9fd-4b75-9fbc-b91580fa2391');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "bcfBD9HpmEx7",
        "outputId": "1a9783c8-956c-4510-8bc0-b05a072c8a61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
              "0             2.38         8.13        2.59   0   0   0   0   1   0   0  ...   \n",
              "1             2.35         5.48        2.31   1   0   0   0   0   0   0  ...   \n",
              "2             7.70        14.78       18.18   1   0   0   0   0   0   0  ...   \n",
              "3             5.64         6.76       12.15   0   1   0   0   0   0   0  ...   \n",
              "4             5.69         7.84       14.56   0   0   0   0   0   0   1  ...   \n",
              "...            ...          ...         ...  ..  ..  ..  ..  ..  ..  ..  ...   \n",
              "6102         10.65        10.00       22.49   0   0   0   0   0   0   1  ...   \n",
              "6103          5.20        12.43       13.25   0   1   0   0   0   0   0  ...   \n",
              "6104          8.51         4.52       13.00   0   0   1   0   0   0   0  ...   \n",
              "6105          9.27        11.46       32.54   0   0   0   0   0   0   1  ...   \n",
              "6106          6.26         6.09       14.68   0   0   0   0   0   0   1  ...   \n",
              "\n",
              "      h15  l15  m15  p15  u15  w15  a16  s16  u16  w16  \n",
              "0       0    0    1    0    0    0    1    0    0    0  \n",
              "1       0    0    0    0    0    0    0    0    1    0  \n",
              "2       0    0    0    0    0    0    1    0    0    0  \n",
              "3       0    0    0    0    0    0    1    0    0    0  \n",
              "4       0    0    0    0    0    0    1    0    0    0  \n",
              "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
              "6102    0    0    0    0    0    0    1    0    0    0  \n",
              "6103    0    0    0    0    0    0    0    0    1    0  \n",
              "6104    0    0    0    0    0    1    1    0    0    0  \n",
              "6105    0    0    0    0    0    0    1    0    0    0  \n",
              "6106    0    0    0    0    0    0    1    0    0    0  \n",
              "\n",
              "[6107 rows x 84 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-250740c4-9a68-4ecd-92f6-315c43db3d12\">\n",
              "    <div 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>cap-diameter</th>\n",
              "      <th>stem-height</th>\n",
              "      <th>stem-width</th>\n",
              "      <th>b0</th>\n",
              "      <th>c0</th>\n",
              "      <th>f0</th>\n",
              "      <th>o0</th>\n",
              "      <th>p0</th>\n",
              "      <th>s0</th>\n",
              "      <th>x0</th>\n",
              "      <th>...</th>\n",
              "      <th>h15</th>\n",
              "      <th>l15</th>\n",
              "      <th>m15</th>\n",
              "      <th>p15</th>\n",
              "      <th>u15</th>\n",
              "      <th>w15</th>\n",
              "      <th>a16</th>\n",
              "      <th>s16</th>\n",
              "      <th>u16</th>\n",
              "      <th>w16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.38</td>\n",
              "      <td>8.13</td>\n",
              "      <td>2.59</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.35</td>\n",
              "      <td>5.48</td>\n",
              "      <td>2.31</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.70</td>\n",
              "      <td>14.78</td>\n",
              "      <td>18.18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.64</td>\n",
              "      <td>6.76</td>\n",
              "      <td>12.15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.69</td>\n",
              "      <td>7.84</td>\n",
              "      <td>14.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6102</th>\n",
              "      <td>10.65</td>\n",
              "      <td>10.00</td>\n",
              "      <td>22.49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6103</th>\n",
              "      <td>5.20</td>\n",
              "      <td>12.43</td>\n",
              "      <td>13.25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6104</th>\n",
              "      <td>8.51</td>\n",
              "      <td>4.52</td>\n",
              "      <td>13.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6105</th>\n",
              "      <td>9.27</td>\n",
              "      <td>11.46</td>\n",
              "      <td>32.54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6106</th>\n",
              "      <td>6.26</td>\n",
              "      <td>6.09</td>\n",
              "      <td>14.68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 84 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-250740c4-9a68-4ecd-92f6-315c43db3d12')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-250740c4-9a68-4ecd-92f6-315c43db3d12 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-250740c4-9a68-4ecd-92f6-315c43db3d12');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "1hMoSHfEmFiD",
        "outputId": "b0f88a31-1d5d-4b6f-d76e-6b986e0a9809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
              "0             7.81         6.27       18.18   0   0   0   0   0   1   0  ...   \n",
              "1            10.96         3.89       26.20   0   0   0   1   0   0   0  ...   \n",
              "2             9.27        11.77       18.76   0   0   0   0   0   0   1  ...   \n",
              "3             7.93         7.56       13.68   0   0   0   0   0   0   1  ...   \n",
              "4             9.96         8.33       15.72   0   0   0   0   0   0   1  ...   \n",
              "...            ...          ...         ...  ..  ..  ..  ..  ..  ..  ..  ...   \n",
              "6102         13.53         6.19       16.40   0   0   1   0   0   0   0  ...   \n",
              "6103          3.55         4.84        6.47   0   0   0   0   0   0   1  ...   \n",
              "6104          4.16         5.16       10.51   0   0   0   0   0   0   1  ...   \n",
              "6105          4.51         6.44        9.02   0   0   0   0   0   0   1  ...   \n",
              "6106          4.10         5.69        7.02   0   0   0   0   0   0   1  ...   \n",
              "\n",
              "      h15  l15  m15  p15  u15  w15  a16  s16  u16  w16  \n",
              "0       0    0    0    0    0    0    1    0    0    0  \n",
              "1       0    0    0    0    0    0    0    1    0    0  \n",
              "2       0    0    0    0    0    0    1    0    0    0  \n",
              "3       0    0    0    0    0    0    1    0    0    0  \n",
              "4       0    0    0    0    0    0    0    0    1    0  \n",
              "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
              "6102    0    0    0    0    0    0    1    0    0    0  \n",
              "6103    0    0    0    0    0    0    0    0    0    1  \n",
              "6104    0    0    0    0    0    0    0    0    1    0  \n",
              "6105    0    0    0    0    0    0    1    0    0    0  \n",
              "6106    0    0    0    0    0    0    0    0    1    0  \n",
              "\n",
              "[6107 rows x 84 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7057a8f6-196e-4675-bf23-f9c18901aef2\">\n",
              "    <div 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>cap-diameter</th>\n",
              "      <th>stem-height</th>\n",
              "      <th>stem-width</th>\n",
              "      <th>b0</th>\n",
              "      <th>c0</th>\n",
              "      <th>f0</th>\n",
              "      <th>o0</th>\n",
              "      <th>p0</th>\n",
              "      <th>s0</th>\n",
              "      <th>x0</th>\n",
              "      <th>...</th>\n",
              "      <th>h15</th>\n",
              "      <th>l15</th>\n",
              "      <th>m15</th>\n",
              "      <th>p15</th>\n",
              "      <th>u15</th>\n",
              "      <th>w15</th>\n",
              "      <th>a16</th>\n",
              "      <th>s16</th>\n",
              "      <th>u16</th>\n",
              "      <th>w16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.81</td>\n",
              "      <td>6.27</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.96</td>\n",
              "      <td>3.89</td>\n",
              "      <td>26.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.27</td>\n",
              "      <td>11.77</td>\n",
              "      <td>18.76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.93</td>\n",
              "      <td>7.56</td>\n",
              "      <td>13.68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.96</td>\n",
              "      <td>8.33</td>\n",
              "      <td>15.72</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6102</th>\n",
              "      <td>13.53</td>\n",
              "      <td>6.19</td>\n",
              "      <td>16.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6103</th>\n",
              "      <td>3.55</td>\n",
              "      <td>4.84</td>\n",
              "      <td>6.47</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6104</th>\n",
              "      <td>4.16</td>\n",
              "      <td>5.16</td>\n",
              "      <td>10.51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6105</th>\n",
              "      <td>4.51</td>\n",
              "      <td>6.44</td>\n",
              "      <td>9.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6106</th>\n",
              "      <td>4.10</td>\n",
              "      <td>5.69</td>\n",
              "      <td>7.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 84 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7057a8f6-196e-4675-bf23-f9c18901aef2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7057a8f6-196e-4675-bf23-f9c18901aef2 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-7057a8f6-196e-4675-bf23-f9c18901aef2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aclaración: La elección del nombre de las columnas es el mismo usado en la parte 2."
      ],
      "metadata": {
        "id": "cyRdm80Y94i8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entrenamiento y validación del modelo\n",
        "\n",
        "En esta sección se encuentra el entrenamiento y búsqueda de híper parámetros del modelo mediante los set de entrenamiento y validación.\n"
      ],
      "metadata": {
        "id": "-1sNwpHWGBpN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo un Random Forest de clasificación."
      ],
      "metadata": {
        "id": "PSQMFuaHhL-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(random_state = 16, n_estimators=12, min_samples_split=2, min_samples_leaf=2,max_features=1, max_depth=None)\n",
        "\n",
        "params = {'n_estimators': [1, 2, 3, 4,11, 12],\n",
        " 'min_samples_split': [1, 2],\n",
        " 'min_samples_leaf': [0, 2],\n",
        " 'max_depth': [1, None],\n",
        " 'max_features': [1, 2, 3]}\n",
        "\n",
        "rs = RandomizedSearchCV(rf_model, param_distributions=params, n_iter=10, random_state=13, scoring='roc_auc')"
      ],
      "metadata": {
        "id": "sow8ZmXRnGoy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rs.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "pKtEKq9gnSNk",
        "outputId": "d5cb855c-38e0-4d2b-83b6-b2438b414dde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "20 fits failed out of a total of 50.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "20 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\", line 340, in fit\n",
            "    self._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 600, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 97, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_leaf' parameter of RandomForestClassifier must be an int in the range [1, inf) or a float in the range (0.0, 1.0). Got 0 instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.99998289 0.99999118        nan        nan 0.61077411        nan\n",
            "        nan 0.53564435 0.53541567 0.53564435]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=RandomForestClassifier(max_features=1,\n",
              "                                                    min_samples_leaf=2,\n",
              "                                                    n_estimators=12,\n",
              "                                                    random_state=16),\n",
              "                   param_distributions={'max_depth': [1, None],\n",
              "                                        'max_features': [1, 2, 3],\n",
              "                                        'min_samples_leaf': [0, 2],\n",
              "                                        'min_samples_split': [1, 2],\n",
              "                                        'n_estimators': [1, 2, 3, 4, 11, 12]},\n",
              "                   random_state=13, scoring='roc_auc')"
            ],
            "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>RandomizedSearchCV(estimator=RandomForestClassifier(max_features=1,\n",
              "                                                    min_samples_leaf=2,\n",
              "                                                    n_estimators=12,\n",
              "                                                    random_state=16),\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [1, None],\n",
              "                                        &#x27;max_features&#x27;: [1, 2, 3],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [0, 2],\n",
              "                                        &#x27;min_samples_split&#x27;: [1, 2],\n",
              "                                        &#x27;n_estimators&#x27;: [1, 2, 3, 4, 11, 12]},\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(max_features=1,\n",
              "                                                    min_samples_leaf=2,\n",
              "                                                    n_estimators=12,\n",
              "                                                    random_state=16),\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [1, None],\n",
              "                                        &#x27;max_features&#x27;: [1, 2, 3],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [0, 2],\n",
              "                                        &#x27;min_samples_split&#x27;: [1, 2],\n",
              "                                        &#x27;n_estimators&#x27;: [1, 2, 3, 4, 11, 12]},\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=1, min_samples_leaf=2, n_estimators=12,\n",
              "                       random_state=16)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=1, min_samples_leaf=2, n_estimators=12,\n",
              "                       random_state=16)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxtUvmrInWbk",
        "outputId": "a3cf3f8b-04ed-4a8a-ece5-44255224c2e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 12,\n",
              " 'min_samples_split': 2,\n",
              " 'min_samples_leaf': 2,\n",
              " 'max_features': 1,\n",
              " 'max_depth': None}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = rs.best_estimator_"
      ],
      "metadata": {
        "id": "LBJqUGQEnc7s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entreno el Random Forest con los mejores híper parámetros."
      ],
      "metadata": {
        "id": "xyxOkD8M-fq0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "aZSrvUKHnif9",
        "outputId": "87ede009-4a07-482c-c7b9-dbb9ab51d13f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features=1, min_samples_leaf=2, n_estimators=12,\n",
              "                       random_state=16)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=1, min_samples_leaf=2, n_estimators=12,\n",
              "                       random_state=16)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=1, min_samples_leaf=2, n_estimators=12,\n",
              "                       random_state=16)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation = model.predict(X_validation)"
      ],
      "metadata": {
        "id": "r6ey51Gknl2S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTsCGLpKnuI2",
        "outputId": "c03f1b52-469a-4b54-f381-9489a14e677b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo una matriz de confusión con el set de validación."
      ],
      "metadata": {
        "id": "04bTU5s5hfgt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_validation = confusion_matrix(y_validation, y_pred_validation)"
      ],
      "metadata": {
        "id": "83-ELWbOoDX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matriz_confusion_validation, annot=True, cmap='Reds', fmt='g')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.xlabel('Prediccin')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RasaIhT7-5su",
        "outputId": "592bcf37-2598-4e71-807f-534383690fa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Métricas del set de validación."
      ],
      "metadata": {
        "id": "zAiR6QdGXpx7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_validation, y_pred_validation))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q47Z160Dnvmt",
        "outputId": "cbf0ab7f-acfb-4be9-86d9-bc3c37d4ff9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc = roc_auc_score(y_validation, model.predict_proba(X_validation)[:, 1])\n",
        "print(\"ROC AUC SCORE:\", roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUjrFK4Fn0Pq",
        "outputId": "6e6e70b5-715a-4fa3-95a7-1754b28fbfc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC SCORE: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pruebas realizadas\n",
        "\n",
        "En esta sección están las pruebas realizados en el set de validación en busca de los híper parámetros.\n",
        "\n"
      ],
      "metadata": {
        "id": "Q_sNE_eOHz7d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pruebas:\n",
        "* Prueba 1: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con solamente features numéricas:\n",
        "RMSE: 0.461201, ROC AUC SCORE: 0.87774856337182, Matriz de confusión: ([[2093,630],[669,2715]]).\n",
        "\n",
        "* Prueba 2: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\",con features numéricas, \"habitat\" y \"season\":\n",
        "RMSE: 0.374170, ROC AUC SCORE: 0.9368132118569683, Matriz de confusión: ([[2287,436],[419,2965]]).\n",
        "\n",
        "* Prueba 3: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con features numéricas, \"does-bruise-or-bleed\", \"habitat\" y \"season\":\n",
        "RMSE: 0.337348, ROC AUC SCORE: 0.956939354713243,Matriz de confusión: ([[2369,354],[341,3043]]).\n",
        "\n",
        "* Prueba 4: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con features numéricas, \"does-bruise-or-bleed\", \"has-ring\", \"habitat\" y \"season\":\n",
        "RMSE: 0.307112, ROC AUC SCORE: 0.9668071931684304,Matriz de confusion: ([[2405,318],[258,3126]]).\n",
        "\n",
        "* Prueba 5: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con features numéricas, \"cap-color\", \"gill-color\", \"stem-color\":\n",
        "RMSE: 0.172632, ROC AUC SCORE: 0.9942523477877359,Matriz de confusión: ([[2639,84],[98,3286]]).\n",
        "\n",
        "* Prueba 6: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con todas las\n",
        "features sin Nans imputados:\n",
        "RMSE: 0.073509, ROC AUC SCORE: 0.9989480317824955,Matriz de confusión: ([[2701,22],[11,3373]]).\n",
        "\n",
        "* Prueba 7: Con \"n_estimators\" = 10, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con todas las features sin Nans imputados, \"gill-attachment\" y \"gill-spacing\":\n",
        "RMSE: 0.012796, ROC AUC SCORE: 0.9999998914769466,Matriz de confusión: ([[2722,1],[0,3384]]).\n",
        "\n",
        "* Prueba 8: Con \"n_estimators\" = 11, \"min_samples_split\" = 3, \"min_samples_leaf\" = 1,\"max_features\" = 3, \"max_depth\" = \"None\", con todas las features sin Nans imputados , \"gill-attachment\" y \"gill-spacing\":\n",
        "RMSE: 0.018097, ROC AUC SCORE: 0.99999983721542,Matriz de confusión: ([[2722,1],[1,3383]]).\n",
        "\n",
        "* Prueba 9: Con \"n_estimators\" = 12, \"min_samples_split\" = 2, \"min_samples_leaf\" = 1,\"max_features\" = 1, \"max_depth\" = \"None\", con todas las features sin Nans imputados, \"gill-attachment\" y \"gill-spacing\":\n",
        "RMSE: 0.018097, ROC AUC SCORE: 0.9999991860771,Matriz de confusión: ([[2722,1],[1,3383]]).\n",
        "\n",
        "* Prueba 10: Con \"n_estimators\" = 12, \"min_samples_split\" = 2, \"min_samples_leaf\" = 2,\"max_features\" = 1, \"max_depth\" = \"None\", con todas las features sin Nans imputados, \"gill-attachment\", \"ring-type\" y \"gill-spacing\":\n",
        "RMSE: 0.000000, ROC AUC SCORE: 1.0, Matriz de confusión([[2723, 0],[0,3384]]).\n",
        "\n",
        "Para este ejercicio, decidí ir agregando columnas de poco y ver las métricas e importancias de las features por cada prueba. Una vez que obtuve buenos resultados, para conseguir un Roc Auc Score de 1, empecé a probar distintos valores de híper parámetros modificando el diccionario de \"params\" hasta conseguir el resultado buscado.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-ztSuHGaoU6l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importancias de las Features por prueba\n",
        "\n",
        "En esta sección se encuentra la evolución de la importancia de las features dependiendo de la prueba.\n",
        "Si bien esto no está pedido en el enunciado, decidí incluirlo para ver cómo van evolucionando las features a medida que busco los híper parámetros y en especial, al agregar o eliminar columnas.\n"
      ],
      "metadata": {
        "id": "UTkgEP-hIYY1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sumar_importancias(df_importancias, df_importancias_sumadas, features_individuales, feature):\n",
        "\n",
        "  suma_importancias = 0\n",
        "\n",
        "  for i in features_individuales:\n",
        "    suma_importancias += df_importancias[i].sum()\n",
        "  df_importancias_sumadas[feature] = suma_importancias"
      ],
      "metadata": {
        "id": "O4y_1XDWoG1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importancias_individuales = pd.DataFrame(model.feature_importances_).T\n",
        "importancias_individuales.columns = X_train.columns\n",
        "importancias_sumadas = pd.DataFrame()\n",
        "importancias_sumadas['cap-diameter'] = importancias_individuales['cap-diameter']\n",
        "importancias_sumadas['stem-height'] = importancias_individuales['stem-height']\n",
        "importancias_sumadas['stem-width'] = importancias_individuales['stem-width']\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b0','c0','f0','o0','p0','s0','x0'], 'cap-shape')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b2','e2','g2','k2','l2','n2','o2','p2','r2','u2','w2','y2'], 'cap-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f3', 't3'], 'does-bruise-or-bleed')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['a4','d4','e4','f4','o4','p4','s4','x4'], 'gill-attachment')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['c5','d5','f5','o5'], 'gill-spacing')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b6','e6','f6','g6','k6','n6','o6', 'p6','r6','u6','w6','y6'], 'gill-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b9','e9','f9','g9','k9','l9','n9','o9','p9','r9','u9','w9','y9'], 'stem-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f12','t12'], 'has-ring')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['e13','f13','g13','l13','m13','o13','p13','r13','z13'], 'ring-type')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['d15','g15','h15','l15','m15','p15','u15','w15'], 'habitat')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['a16','s16','u16','w16'], 'season')\n",
        "importancias_sumadas = importancias_sumadas.T.rename(columns={0:\"Importancia\"})"
      ],
      "metadata": {
        "id": "6bkIfMjzvE1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 1"
      ],
      "metadata": {
        "id": "EnqDbyO4vahm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ntKVPNI5TZyP",
        "outputId": "6272b7b1-c53b-4e36-ad4f-22f0ff2326b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 2"
      ],
      "metadata": {
        "id": "Jbz2Dc7Xw3mM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "G2r8hdeFvW-g",
        "outputId": "c3bfa2a5-41f6-4722-8a96-05388946002e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 3"
      ],
      "metadata": {
        "id": "SK3CSp0-yPR2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "84xFLfzkw64n",
        "outputId": "df52c586-a3e7-4116-8699-818a380dd073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 4"
      ],
      "metadata": {
        "id": "uAnChCRG0Hif"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "a6MwvluQySV-",
        "outputId": "55715685-99a9-42a2-b5dd-0d9876f70649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 5"
      ],
      "metadata": {
        "id": "x-YdhWDj1iWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "sl3WCf_a0KBu",
        "outputId": "97e6c74f-b702-4bb2-c896-52459b952447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAHHCAYAAAAh/VVAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABVYklEQVR4nO3deVhU1f8H8PewzLDvi4AKKIqIoAlaigqFCm5pWmSRgruZmWW45AJorrmmuaQlahaalWaZZiqppOauKRIqiAu5yyLKNuf3hz/ut5FFwKER7vv1PPPI3HvuuZ97GIa35947KIQQAkRERERU6+npugAiIiIi+m8w+BERERHJBIMfERERkUww+BERERHJBIMfERERkUww+BERERHJBIMfERERkUww+BERERHJBIMfERERkUww+BFRhcXExEChUFT7fiIjI+Hm5qa1/v6ruomqU0JCAhQKBRISEnRdCtVgDH5Ua8XFxUGhUODIkSO6LqXKli5diri4OF2XQVUQFBQEhUJR6uPcuXPVsk++XrTn8e+ZhYUFAgMD8fPPP+u6tGdKWa/xOnXq6Lq0Um3btg0xMTG6LkOnDHRdABGVbenSpbCzs0NkZKSuSwEATJo0CePHj9d1GTVG3bp1MXPmzBLLnZ2dq2V/z9rrpabr1KkT+vfvDyEELl26hGXLlqFHjx745ZdfEBISouvynhnF4/RvxsbGOqqmfNu2bcNnn30m6/DH4Ef0DMrNzYWJiYmuyyjBwMAABgZ826goS0tLvPXWW7ou46kIIfDw4cNn9hd5dWrcuLHG969Pnz5o2rQpFi1axOD3L4+Pk7YUFhZCrVZDqVRqvW8546lekpXIyEiYmZkhPT0d3bt3h5mZGVxcXPDZZ58BAE6fPo2XXnoJpqamcHV1xddff62xffHp471792LYsGGwtbWFhYUF+vfvj7t375bY39KlS+Ht7Q2VSgVnZ2e88847uHfvnkaboKAgNGvWDEePHkWHDh1gYmKCjz76CG5ubjhz5gx+//136fRJUFAQAODOnTv48MMP4ePjAzMzM1hYWKBLly44efKkRt/F1wRt3LgR06dPR926dWFkZITg4GCcP3++RL2HDh1C165dYW1tDVNTU/j6+mLRokXS+tKulVu9ejVeeuklODg4QKVSoWnTpli2bFmFvyebN29Gs2bNYGRkhGbNmuGHH34otZ1arcbChQvh7e0NIyMjODo6YtiwYaWOe0VUtO4jR44gJCQEdnZ2MDY2hru7OwYOHFilfT4uLy8P0dHR8PDwgEqlQr169TB27Fjk5eVVutbyXi9lXeNY/HpOS0vT6Kd79+7YsWMH/P39YWxsjBUrVgAA7t27h9GjR6NevXpQqVTw8PDA7NmzoVarNfqNj4+Hn58fzM3NYWFhAR8fH43XUVnUajUWLVoEHx8fGBkZwd7eHqGhoRqXaxQWFmLatGlo2LAhVCoV3Nzc8NFHH5UYs+Lj2L9/P1q3bg0jIyM0aNAAa9eufWIdZfHy8oKdnR0uXLigsXzLli3o1q0bnJ2doVKp0LBhQ0ybNg1FRUUa7Yp/1s+ePYsXX3wRJiYmcHFxwZw5c0rs68qVK+jVqxdMTU3h4OCA999/v8QxFvv222/h5+cHY2Nj2NnZ4a233sLVq1c12jzte9/TuHHjBgYNGgRHR0cYGRmhefPmWLNmjUabtLQ0KBQKzJ07FwsXLpS+v2fPngUAnDt3Dq+++ipsbGxgZGQEf39//Pjjjxp9FBQUIDY2Fo0aNYKRkRFsbW3Rrl077Ny5UxqD4uP992lpueF/3Ul2ioqK0KVLF3To0AFz5szB+vXrMXLkSJiammLixIkIDw9H7969sXz5cvTv3x9t2rSBu7u7Rh8jR46ElZUVYmJikJycjGXLluHSpUtS0AIe/bKNjY1Fx44d8fbbb0vtDh8+jMTERBgaGkr93b59G126dEHfvn3x1ltvwdHREUFBQXj33XdhZmaGiRMnAgAcHR0BABcvXsTmzZvx2muvwd3dHdevX8eKFSsQGBiIs2fPljiVOGvWLOjp6eHDDz9EZmYm5syZg/DwcBw6dEhqs3PnTnTv3h1OTk547733UKdOHSQlJeGnn37Ce++9V+Z4Llu2DN7e3nj55ZdhYGCArVu3YsSIEVCr1XjnnXfK/V78+uuv0izKzJkzcfv2bQwYMAB169Yt0XbYsGGIi4vDgAEDMGrUKKSmpmLJkiU4fvx4ifGsiIrUfePGDXTu3Bn29vYYP348rKyskJaWhu+//75C+ygqKsKtW7c0lhkZGcHMzAxqtRovv/wy9u/fj6FDh8LLywunT5/GggUL8Pfff2Pz5s2VqnXhwoVlvl4qKzk5GW+88QaGDRuGIUOGwNPTE7m5uQgMDMTVq1cxbNgw1K9fH3/88QcmTJiAjIwMLFy4EMCj19Ebb7yB4OBgzJ49GwCQlJSExMTEcl9HADBo0CDExcWhS5cuGDx4MAoLC7Fv3z4cPHgQ/v7+AIDBgwdjzZo1ePXVVzFmzBgcOnQIM2fORFJSUon/NJw/fx6vvvoqBg0ahIiICHz55ZeIjIyEn58fvL29Kz0umZmZuHv3Lho2bKixPC4uDmZmZvjggw9gZmaG3bt3Y8qUKcjKysInn3yi0fbu3bsIDQ1F7969ERYWhk2bNmHcuHHw8fFBly5dAAAPHjxAcHAw0tPTMWrUKDg7O2PdunXYvXt3iZqKfyZatWqFmTNn4vr161i0aBESExNx/PhxWFlZSW218d5XmocPH5Z4nZubm0OlUuHBgwcICgrC+fPnMXLkSLi7u+Pbb79FZGQk7t27V+I1sXr1ajx8+BBDhw6FSqWCjY0Nzpw5g4CAALi4uGD8+PEwNTXFxo0b0atXL3z33Xd45ZVXADx6z505cyYGDx6M1q1bIysrC0eOHMGxY8fQqVMnDBs2DNeuXcPOnTuxbt26Jx5XrSWIaqnVq1cLAOLw4cPSsoiICAFAzJgxQ1p29+5dYWxsLBQKhYiPj5eWnzt3TgAQ0dHRJfr08/MT+fn50vI5c+YIAGLLli1CCCFu3LghlEql6Ny5sygqKpLaLVmyRAAQX375pbQsMDBQABDLly8vcQze3t4iMDCwxPKHDx9q9CuEEKmpqUKlUompU6dKy/bs2SMACC8vL5GXlyctX7RokQAgTp8+LYQQorCwULi7uwtXV1dx9+5djX7VarX0dXR0tHj8bSM3N7dEfSEhIaJBgwYllj+uRYsWwsnJSdy7d09a9uuvvwoAwtXVVVq2b98+AUCsX79eY/vt27eXuvxxVa37hx9+KPEaqqji7+vjj4iICCGEEOvWrRN6enpi3759GtstX75cABCJiYmVqlWIsl8vpR2/EP97PaempkrLXF1dBQCxfft2jbbTpk0Tpqam4u+//9ZYPn78eKGvry/S09OFEEK89957wsLCQhQWFpYclHLs3r1bABCjRo0qsa74NXjixAkBQAwePFhj/YcffigAiN27d5c4jr1790rLbty4IVQqlRgzZswT6wEgBg0aJG7evClu3Lghjhw5IkJDQwUA8cknn2i0Le37M2zYMGFiYiIePnwoLSt+Taxdu1ZalpeXJ+rUqSP69OkjLVu4cKEAIDZu3Cgtu3//vvDw8BAAxJ49e4QQQuTn5wsHBwfRrFkz8eDBA6ntTz/9JACIKVOmSMue9r2vvHEq7bF69WqNY/nqq6+kbfLz80WbNm2EmZmZyMrKEkI8ev8CICwsLMSNGzc09hEcHCx8fHw0xlKtVou2bduKRo0aScuaN28uunXrVm6977zzTqk/C3LCU70kS4MHD5a+trKygqenJ0xNTREWFiYt9/T0hJWVFS5evFhi+6FDh2rMML399tswMDDAtm3bAAC//fYb8vPzMXr0aOjp/e/HbMiQIbCwsChxZ6BKpcKAAQMqXL9KpZL6LSoqwu3bt2FmZgZPT08cO3asRPsBAwZoXCfTvn17AJCO7fjx40hNTcXo0aM1ZggAPPFUyL+v/crMzMStW7cQGBiIixcvIjMzs8ztMjIycOLECURERMDS0lJa3qlTJzRt2lSj7bfffgtLS0t06tQJt27dkh5+fn4wMzPDnj17yq2xqnUXj8VPP/2EgoKCSu/Dzc0NO3fu1HiMHTtWOiYvLy80adJE45heeuklANA4pqqOcVW5u7uXuIbt22+/Rfv27WFtba1Rb8eOHVFUVIS9e/cCeDRm9+/fl06vVdR3330HhUKB6OjoEuuKX4PFP18ffPCBxvoxY8YAQImfq6ZNm0qvdQCwt7eHp6dnqT/Tpfniiy9gb28PBwcH+Pv7Y9euXRg7dmyJ/f/7+5OdnY1bt26hffv2yM3NLXEHt5mZmcb1cEqlEq1bt9aoadu2bXBycsKrr74qLTMxMcHQoUM1+jpy5Ahu3LiBESNGwMjISFrerVs3NGnSpNQ7kJ/2va80PXv2LPE6L379bNu2DXXq1MEbb7whtTc0NMSoUaOQk5OD33//XaOvPn36wN7eXnp+584d7N69G2FhYdLY3rp1C7dv30ZISAhSUlKk09pWVlY4c+YMUlJSKlS3XPFUL8lO8bVD/2ZpaYm6deuWCDmWlpalXkPWqFEjjedmZmZwcnKSrpW6dOkSgEdvoP+mVCrRoEEDaX0xFxeXSl3AXHwt1NKlS5GamqpxLZGtrW2J9vXr19d4bm1tDQDSsRVfs9SsWbMK11AsMTER0dHROHDgAHJzczXWZWZmaoS6fyseg8fHEkCJAJuSkoLMzEw4ODiU2teNGzeqpe7AwED06dMHsbGxWLBgAYKCgtCrVy+8+eabUKlUT9yHqakpOnbsWOq6lJQUJCUllXgtlnZMVR3jqirt9F5KSgpOnTr1xHpHjBiBjRs3okuXLnBxcUHnzp0RFhaG0NDQcvd54cIFODs7w8bGpsw2ly5dgp6eHjw8PDSW16lTB1ZWViV+rh5/3QOPXvsVvS60Z8+eGDlyJPLz83H48GHMmDEDubm5Gv+ZA4AzZ85g0qRJ2L17N7KysjTWPR7MS3ufsba2xqlTpzSO08PDo0S7x99PynqfAYAmTZpg//79Gsu08d5Xmrp165b5Or906RIaNWpUYsy8vLw0jqHY46+98+fPQwiByZMnY/LkyaXu48aNG3BxccHUqVPRs2dPNG7cGM2aNUNoaCj69esHX1/fCh2HXDD4kezo6+tXarkQojrLAVD5jz6YMWMGJk+ejIEDB2LatGmwsbGBnp4eRo8eXeJCe6D6ju3ChQsIDg5GkyZNMH/+fNSrVw9KpRLbtm3DggULSq2lKtRqNRwcHLB+/fpS15cVRspS0boVCgU2bdqEgwcPYuvWrdixYwcGDhyIefPm4eDBgzAzM3uqY/Lx8cH8+fNLXV+vXr1K1VqesmZtH7/5oFhpr0e1Wo1OnTpJM5aPa9y4MQDAwcEBJ06cwI4dO/DLL7/gl19+werVq9G/f/8SF/RXVUUvyH/a1/2/A03Xrl1hZ2eHkSNH4sUXX0Tv3r0BPLrhJTAwEBYWFpg6dSoaNmwIIyMjHDt2DOPGjSvx/dHl+8yz+N73uMdfe8Xj9+GHH5Z5J3XxfwQ6dOiACxcuYMuWLfj111+xatUqLFiwAMuXL9eY6ZQ7Bj+iKkhJScGLL74oPc/JyUFGRga6du0KAHB1dQXw6CL5Bg0aSO3y8/ORmppa5v+OH1fWL7hNmzbhxRdfxBdffKGx/N69e7Czs6vUsQCQLlb/66+/KlwbAGzduhV5eXn48ccfNWZXKnLqtXiMSjstk5ycXKK+3377DQEBAVr5WJHK1v3CCy/ghRdewPTp0/H1118jPDwc8fHxT/XLpGHDhjh58iSCg4PLDTKVqbWsfopneO/du6dxKv/x2ZYn1ZuTk1Oh14dSqUSPHj3Qo0cPqNVqjBgxAitWrMDkyZNLzNb9u/8dO3bgzp07Zc76ubq6Qq1WIyUlRZoxAoDr16/j3r170muqugwbNgwLFizApEmT8Morr0h/ReP27dv4/vvv0aFDB6ltampqlffj6uqKv/76C0IIje/p4z8X/36fKb5E4N9tq3s8KsLV1RWnTp2CWq3WmPUrPgX+pBqL3z8NDQ0r9NqzsbHBgAEDMGDAAOTk5KBDhw6IiYmRflbleBfv43iNH1EVfP755xrXfC1btgyFhYXSXXkdO3aEUqnEp59+qvG/5i+++AKZmZno1q1bhfZjampa4uNfgEf/Q3/8f+PffvttiY9wqKiWLVvC3d0dCxcuLLG/8v7XXzxT8O82mZmZWL169RP36eTkhBYtWmDNmjUap8N27twpfYRDsbCwMBQVFWHatGkl+iksLCx1jMpT0brv3r1b4vhbtGgBAGV+tEZFhYWF4erVq1i5cmWJdQ8ePMD9+/crVStQ9uulONgXX4cHAPfv36/UDFxYWBgOHDiAHTt2lFh37949FBYWAnh0h/q/6enpSafayhuzPn36QAiB2NjYEuuKj734P1bFdxAXK541rejPVVUZGBhgzJgxSEpKwpYtWwCU/v3Jz8/H0qVLq7yfrl274tq1a9i0aZO0LDc3F59//rlGO39/fzg4OGD58uUaY/vLL78gKSmp2sejIrp27Yp//vkHGzZskJYVFhZi8eLFMDMzQ2BgYLnbOzg4ICgoCCtWrEBGRkaJ9Tdv3pS+fvy1Z2ZmBg8PD42xMTU1BYBKv2fUJpzxI6qC/Px8BAcHIywsDMnJyVi6dCnatWuHl19+GcCjU48TJkxAbGwsQkND8fLLL0vtWrVqVeEPO/Xz88OyZcvw8ccfw8PDAw4ODnjppZfQvXt3TJ06FQMGDEDbtm1x+vRprF+/XmN2sTL09PSkv0rQokULDBgwAE5OTjh37hzOnDlT6i97AOjcubM0uzNs2DDk5ORg5cqVcHBwKPVN+nEzZ85Et27d0K5dOwwcOBB37tzB4sWL4e3tjZycHKldYGAghg0bhpkzZ+LEiRPo3LkzDA0NkZKSgm+//RaLFi3SuBD+SSpa95o1a7B06VK88soraNiwIbKzs7Fy5UpYWFhIIaSq+vXrh40bN2L48OHYs2cPAgICUFRUhHPnzmHjxo3S5+hVZozLer107twZ9evXx6BBgxAVFQV9fX18+eWXsLe3R3p6eoXqjYqKwo8//oju3btLH4ly//59nD59Gps2bUJaWhrs7OwwePBg3LlzBy+99BLq1q2LS5cuYfHixWjRooXGLN3jXnzxRfTr1w+ffvopUlJSEBoaCrVajX379uHFF1/EyJEj0bx5c0RERODzzz+XTrH++eefWLNmDXr16qUxC19dIiMjMWXKFMyePRu9evVC27ZtYW1tjYiICIwaNQoKhQLr1q17qtOkQ4YMwZIlS9C/f38cPXoUTk5OWLduXYkPdTc0NMTs2bMxYMAABAYG4o033pA+zsXNzQ3vv//+0x7uUxs6dChWrFiByMhIHD16FG5ubti0aRMSExOxcOFCmJubP7GPzz77DO3atYOPjw+GDBmCBg0a4Pr16zhw4ACuXLkifX5p06ZNERQUBD8/P9jY2ODIkSPYtGkTRo4cKfXl5+cHABg1ahRCQkKgr6+Pvn37Vs/BP6v+69uIif4rZX2ci6mpaYm2gYGBwtvbu8RyV1dXjY8HKO7z999/F0OHDhXW1tbCzMxMhIeHi9u3b5fYfsmSJaJJkybC0NBQODo6irfffrvEx6WUtW8hhPjnn39Et27dhLm5uQAgfVTHw4cPxZgxY4STk5MwNjYWAQEB4sCBAyIwMFDj4zyKP87l22+/1ei3+KMTij9yodj+/ftFp06dhLm5uTA1NRW+vr5i8eLF0vrSPhbkxx9/FL6+vsLIyEi4ubmJ2bNniy+//LLEx4SU5bvvvhNeXl5CpVKJpk2biu+//15ERERofJxLsc8//1z4+fkJY2NjYW5uLnx8fMTYsWPFtWvXyt1HVes+duyYeOONN0T9+vWFSqUSDg4Oonv37uLIkSNPPK7yvq/F8vPzxezZs4W3t7dQqVTC2tpa+Pn5idjYWJGZmVmpWoUo+/UihBBHjx4Vzz//vFAqlaJ+/fpi/vz5ZX6cS1kfiZGdnS0mTJggPDw8hFKpFHZ2dqJt27Zi7ty50scbbdq0SXTu3Fk4ODhI+xo2bJjIyMh44pgVFhaKTz75RDRp0kQolUphb28vunTpIo4ePSq1KSgoELGxscLd3V0YGhqKevXqiQkTJmh81Ed5x/H4z0hZAIh33nmn1HUxMTEaH6uSmJgoXnjhBWFsbCycnZ3F2LFjxY4dOzTaFO+7tNdEaa/3S5cuiZdfflmYmJgIOzs78d5770kfX/TvPoUQYsOGDeK5554TKpVK2NjYiPDwcHHlypUS+3ia976ylDdOxa5fvy4GDBgg7OzshFKpFD4+PiXee4rfkx7/qJxiFy5cEP379xd16tQRhoaGwsXFRXTv3l1s2rRJavPxxx+L1q1bCysrK2FsbCyaNGkipk+frvHRW4WFheLdd98V9vb2QqFQyPKjXRRC6ODqTaIaqvjDUg8fPix9oCwREVFNwWv8iIiIiGSCwY+IiIhIJhj8iIiIiGSC1/gRERERyQRn/IiIiIhkgsGPiIiISCb4Ac4kUavVuHbtGszNzflnbYiIiGoIIQSys7Ph7Oys8afxSsPgR5Jr165JfxieiIiIapbLly+jbt265bZh8CNJ8Z/OuXz5MiwsLHRcDREREVVEVlYW6tWrV6E/gcfgR5Li07sWFhYMfkRERDVMRS7T4s0dRERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwa6LoCePc2id0BPZaLrMojoGZY2q5uuSyCiKuCMHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPjVAGlpaVAoFDhx4oSuSyEiIqIaTBbBLzIyEr169dJ1GUREREQ6JYvgR0B+fr6uSyAiIiIdq1XBb9OmTfDx8YGxsTFsbW3RsWNHREVFYc2aNdiyZQsUCgUUCgUSEhIAAJcvX0ZYWBisrKxgY2ODnj17Ii0tTeqveKZwxowZcHR0hJWVFaZOnYrCwkJERUXBxsYGdevWxerVq59Y25kzZ9C9e3dYWFjA3Nwc7du3x4ULFwAAarUaU6dORd26daFSqdCiRQts37693P5+//13tG7dGiqVCk5OThg/fjwKCwul9UFBQRg5ciRGjx4NOzs7hISEVH5AiYiIqFapNcEvIyMDb7zxBgYOHIikpCQkJCSgd+/eiI6ORlhYGEJDQ5GRkYGMjAy0bdsWBQUFCAkJgbm5Ofbt24fExESYmZkhNDRUY3Zs9+7duHbtGvbu3Yv58+cjOjoa3bt3h7W1NQ4dOoThw4dj2LBhuHLlSpm1Xb16FR06dIBKpcLu3btx9OhRDBw4UApqixYtwrx58zB37lycOnUKISEhePnll5GSklJmf127dkWrVq1w8uRJLFu2DF988QU+/vhjjXZr1qyBUqlEYmIili9fXqKfvLw8ZGVlaTyIiIio9lIIIYSui9CGY8eOwc/PD2lpaXB1ddVYFxkZiXv37mHz5s3Ssq+++goff/wxkpKSoFAoADw6HWplZYXNmzejc+fOiIyMREJCAi5evAg9vUcZuUmTJnBwcMDevXsBAEVFRbC0tMSqVavQt2/fUmv76KOPEB8fj+TkZBgaGpZY7+LignfeeQcfffSRtKx169Zo1aoVPvvsM6SlpcHd3R3Hjx9HixYtMHHiRHz33XcatS9duhTjxo1DZmYm9PT0EBQUhKysLBw7dqzMMYuJiUFsbGyJ5fVGb4SeyqTM7YiI0mZ103UJRPT/srKyYGlpiczMTFhYWJTbttbM+DVv3hzBwcHw8fHBa6+9hpUrV+Lu3btltj958iTOnz8Pc3NzmJmZwczMDDY2Nnj48KF0ChYAvL29pdAHAI6OjvDx8ZGe6+vrw9bWFjdu3AAAdOnSRerP29sbAHDixAm0b9++1NCXlZWFa9euISAgQGN5QEAAkpKSSq09KSkJbdq0kUJfcfucnByNmUc/P78yjx8AJkyYgMzMTOlx+fLlctsTERFRzWag6wK0RV9fHzt37sQff/yBX3/9FYsXL8bEiRNx6NChUtvn5OTAz88P69evL7HO3t5e+vrxsKZQKEpdplarAQCrVq3CgwcPNLY1Njau+oE9BVNT03LXq1QqqFSq/6gaIiIi0rVaE/yARwEsICAAAQEBmDJlClxdXfHDDz9AqVSiqKhIo23Lli2xYcMGODg4PHFatDJcXFxKLPP19cWaNWtQUFBQIjRaWFjA2dkZiYmJCAwMlJYnJiaidevWpe7Dy8sL3333HYQQ0qxfYmIizM3NUbduXa0dCxEREdUuteZU76FDhzBjxgwcOXIE6enp+P7773Hz5k14eXnBzc0Np06dQnJyMm7duoWCggKEh4fDzs4OPXv2xL59+5CamoqEhASMGjWq3Bs1qmLkyJHIyspC3759ceTIEaSkpGDdunVITk4GAERFRWH27NnYsGEDkpOTMX78eJw4cQLvvfdeqf2NGDECly9fxrvvvotz585hy5YtiI6OxgcffKBxWpqIiIjo32rNjJ+FhQX27t2LhQsXIisrC66urpg3bx66dOkCf39/JCQkwN/fHzk5OdizZw+CgoKwd+9ejBs3Dr1790Z2djZcXFwQHBys1RlAALC1tcXu3bsRFRWFwMBA6Ovro0WLFtJ1faNGjUJmZibGjBmDGzduoGnTpvjxxx/RqFGjUvtzcXHBtm3bEBUVhebNm8PGxgaDBg3CpEmTtFo3ERER1S615q5eenrFdwXxrl4iehLe1Uv07JDlXb1EREREVD4GPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZMNB1AfTs+Ss2BBYWFroug4iIiLSMM35EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsG/1UslNIveAT2Via7LIKr10mZ103UJRCQznPEjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGPyIiIiKZYPAjIiIikgkGvwpwc3PDwoULpecKhQKbN28GAKSlpUGhUODEiRPVtv+EhAQoFArcu3ev2vZBREREtZ+BrguoCQ4fPgxTU1Ndl0FERET0VBj8KsDe3l7XJTy1/Px8KJVKXZdBREREOsRTvQCys7MRHh4OU1NTODk5YcGCBQgKCsLo0aMBlDzVWxWJiYkICgqCiYkJrK2tERISgrt37wIA8vLyMGrUKDg4OMDIyAjt2rXD4cOHy+3vu+++g7e3N1QqFdzc3DBv3jyN9W5ubpg2bRr69+8PCwsLDB069KnqJyIiopqPwQ/ABx98gMTERPz444/YuXMn9u3bh2PHjmmt/xMnTiA4OBhNmzbFgQMHsH//fvTo0QNFRUUAgLFjx+K7777DmjVrcOzYMXh4eCAkJAR37twptb+jR48iLCwMffv2xenTpxETE4PJkycjLi5Oo93cuXPRvHlzHD9+HJMnTy7RT15eHrKysjQeREREVHvJ/lRvdnY21qxZg6+//hrBwcEAgNWrV8PZ2Vlr+5gzZw78/f2xdOlSaZm3tzcA4P79+1i2bBni4uLQpUsXAMDKlSuxc+dOfPHFF4iKiirR3/z58xEcHCyFucaNG+Ps2bP45JNPEBkZKbV76aWXMGbMmDLrmjlzJmJjY7VxiERERFQDyH7G7+LFiygoKEDr1q2lZZaWlvD09KxSf97e3jAzM4OZmZkU5Ipn/Epz4cIFFBQUICAgQFpmaGiI1q1bIykpqdRtkpKSNNoDQEBAAFJSUqRZRADw9/cvt9YJEyYgMzNTely+fLlCx0hEREQ1k+xn/LRt27ZtKCgoAAAYGxtr/Ptfe9KdyCqVCiqV6j+qhoiIiHRN9jN+DRo0gKGhocbNFJmZmfj777+r1J+rqys8PDzg4eEBFxcXAICvry927dpVavuGDRtCqVQiMTFRWlZQUIDDhw+jadOmpW7j5eWl0R54dPNI48aNoa+vX6W6iYiIqPaT/Yyfubk5IiIiEBUVBRsbGzg4OCA6Ohp6enpQKBRa2ceECRPg4+ODESNGYPjw4VAqldizZw9ee+012NnZ4e2335b2X79+fcyZMwe5ubkYNGhQqf2NGTMGrVq1wrRp0/D666/jwIEDWLJkicY1hERERESPk/2MH/DoZok2bdqge/fu6NixIwICAuDl5QUjIyOt9N+4cWP8+uuvOHnyJFq3bo02bdpgy5YtMDB4lLtnzZqFPn36oF+/fmjZsiXOnz+PHTt2wNrautT+WrZsiY0bNyI+Ph7NmjXDlClTMHXqVI0bO4iIiIgepxBCCF0X8ay5f/8+XFxcMG/evDJn3WqjrKwsWFpaot7ojdBTmei6HKJaL21WN12XQES1QPHv78zMTFhYWJTbVvanegHg+PHjOHfuHFq3bo3MzExMnToVANCzZ08dV0ZERESkPQx+/2/u3LlITk6GUqmEn58f9u3bBzs7O12XRURERKQ1DH4AnnvuORw9elTXZRARERFVK97cQURERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTDH5EREREMsHgR0RERCQTBrougJ49f8WGwMLCQtdlEBERkZZxxo+IiIhIJhj8iIiIiGSCwY+IiIhIJhj8iIiIiGSCwY+IiIhIJhj8iIiIiGSCwY+IiIhIJhj8iIiIiGSCwY+IiIhIJhj8iIiIiGSCwY+IiIhIJvi3eqmEZtE7oKcy0XUZRFQFabO66boEInqGccaPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/IiIiIhkgsGPiIiISCYY/GqAyMhI9OrVS9dlEBERUQ3H4EdEREQkEwx+MiCEQGFhoa7LICIiIh2TXfBTq9WYM2cOPDw8oFKpUL9+fUyfPh0AMG7cODRu3BgmJiZo0KABJk+ejIKCAmnbmJgYtGjRAitWrEC9evVgYmKCsLAwZGZmVnmfAHD69Gm89NJLMDY2hq2tLYYOHYqcnJwy+8vLy8OoUaPg4OAAIyMjtGvXDocPH5bWJyQkQKFQ4JdffoGfnx9UKhX2799f1SEjIiKiWkJ2wW/ChAmYNWsWJk+ejLNnz+Lrr7+Go6MjAMDc3BxxcXE4e/YsFi1ahJUrV2LBggUa258/fx4bN27E1q1bsX37dhw/fhwjRoyo8j7v37+PkJAQWFtb4/Dhw/j222/x22+/YeTIkWX2N3bsWHz33XdYs2YNjh07Bg8PD4SEhODOnTsa7caPH49Zs2YhKSkJvr6+JfrJy8tDVlaWxoOIiIhqL4UQQui6iP9KdnY27O3tsWTJEgwePPiJ7efOnYv4+HgcOXIEwKMZv48//hiXLl2Ci4sLAGD79u3o1q0brl69ijp16lR6nytXrsS4ceNw+fJlmJqaAgC2bduGHj164Nq1a3B0dERkZCTu3buHzZs34/79+7C2tkZcXBzefPNNAEBBQQHc3NwwevRoREVFISEhAS+++CI2b96Mnj17lnl8MTExiI2NLbG83uiN0FOZPHF8iOjZkzarm65LIKL/WFZWFiwtLZGZmQkLC4ty28pqxi8pKQl5eXkIDg4udf2GDRsQEBCAOnXqwMzMDJMmTUJ6erpGm/r160uhDwDatGkDtVqN5ORk7Nu3D2ZmZtJj/fr1T9xnUlISmjdvLoU+AAgICJD6fNyFCxdQUFCAgIAAaZmhoSFat26NpKQkjbb+/v7ljseECROQmZkpPS5fvlxueyIiIqrZDHRdwH/J2Ni4zHUHDhxAeHg4YmNjERISAktLS8THx2PevHkV7t/f3x8nTpyQnjs6OiItLe0pKn46/w6TpVGpVFCpVP9RNURERKRrsprxa9SoEYyNjbFr164S6/744w+4urpi4sSJ8Pf3R6NGjXDp0qUS7dLT03Ht2jXp+cGDB6GnpwdPT08YGxvDw8NDepibm5e7TwDw8vLCyZMncf/+fWlZYmKi1OfjGjZsCKVSicTERGlZQUEBDh8+jKZNm1ZqPIiIiEheZDXjZ2RkhHHjxmHs2LFQKpUICAjAzZs3cebMGTRq1Ajp6emIj49Hq1at8PPPP+OHH34otY+IiAjMnTsXWVlZGDVqFMLCwkq9vu9J+xw0aBDCw8MRHR2NiIgIxMTE4ObNm3j33XfRr18/6QaQfzM1NcXbb7+NqKgo2NjYoH79+pgzZw5yc3MxaNAgrY8ZERER1R6yCn4AMHnyZBgYGGDKlCm4du0anJycMHz4cAwaNAjvv/8+Ro4ciby8PHTr1g2TJ09GTEyMxvYeHh7o3bs3unbtijt37qB79+5YunRplfYJACYmJtixYwfee+89tGrVCiYmJujTpw/mz59fZn+zZs2CWq1Gv379kJ2dDX9/f+zYsQPW1tZPPT5ERERUe1X5rt5169Zh+fLlSE1NxYEDB+Dq6oqFCxfC3d293DtJa7KYmBhs3rxZ4zq+2qT4riDe1UtUc/GuXiL5qfa7epctW4YPPvgAXbt2xb1791BUVAQAsLKywsKFC6vSJRERERFVsyoFv8WLF2PlypWYOHEi9PX1peX+/v44ffq01oojIiIiIu2pUvBLTU3Fc889V2K5SqXSuDu1tomJiam1p3mJiIio9qtS8HN3dy81AG3fvh1eXl5PWxMRERERVYMq3dX7wQcf4J133sHDhw8hhMCff/6Jb775BjNnzsSqVau0XSMRERERaUGVgt/gwYNhbGyMSZMmITc3F2+++SacnZ2xaNEi9O3bV9s1EhEREZEWVDr4FRYW4uuvv0ZISAjCw8ORm5uLnJwcODg4VEd9RERERKQllb7Gz8DAAMOHD8fDhw8BPPoAYoY+IiIiomdflW7uaN26NY4fP67tWoiIiIioGlXpGr8RI0ZgzJgxuHLlCvz8/GBqaqqx3tfXVyvFEREREZH2VCn4Fd/AMWrUKGmZQqGAEAIKhUL6Sx5ERERE9OyoUvBLTU3Vdh1EREREVM2qFPxcXV21XQcRERERVbMqBb+1a9eWu75///5VKoaIiIiIqk+Vgt97772n8bygoAC5ublQKpUwMTFh8CMiIiJ6BlXp41zu3r2r8cjJyUFycjLatWuHb775Rts1EhEREZEWKIQQQludHTlyBG+99RbOnTunrS7pP5SVlQVLS0tkZmbCwsJC1+UQERFRBVTm93eVZvzKYmBggGvXrmmzSyIiIiLSkipd4/fjjz9qPBdCICMjA0uWLEFAQIBWCiMiIiIi7apS8OvVq5fGc4VCAXt7e7z00kuYN2+eNuoiIiIiIi2rUvBTq9XaroOIiIiIqlmVrvGbOnUqcnNzSyx/8OABpk6d+tRFEREREZH2VemuXn19fWRkZMDBwUFj+e3bt+Hg4MC/1VtD8a5eIiKimqfa7+oVQkChUJRYfvLkSdjY2FSlSyIiIiKqZpW6xs/a2hoKhQIKhQKNGzfWCH9FRUXIycnB8OHDtV4kERERET29SgW/hQsXQgiBgQMHIjY2FpaWltI6pVIJNzc3tGnTRutFEhEREdHTq1Twi4iIAAC4u7ujbdu2MDQ0rJaiiIiIiEj7qvRxLoGBgdLXDx8+RH5+vsZ63hhARERE9OypUvDLzc3F2LFjsXHjRty+fbvEet7VW7M1i94BPZWJrssgIiKqVdJmddN1CVW7qzcqKgq7d+/GsmXLoFKpsGrVKsTGxsLZ2Rlr167Vdo1EREREpAVVmvHbunUr1q5di6CgIAwYMADt27eHh4cHXF1dsX79eoSHh2u7TiIiIiJ6SlWa8btz5w4aNGgA4NH1fHfu3AEAtGvXDnv37tVedURERESkNVUKfg0aNEBqaioAoEmTJti4cSOARzOBVlZWWiuOiIiIiLSnSsFvwIABOHnyJABg/Pjx+Oyzz2BkZIT3338fUVFRWi2QiIiIiLSjStf4vf/++9LXHTt2xLlz53D06FF4eHjA19dXa8URERERkfZUKfj928OHD+Hq6gpXV1dt1ENERERE1aRKp3qLioowbdo0uLi4wMzMDBcvXgQATJ48GV988YVWCyQiIiIi7ahS8Js+fTri4uIwZ84cKJVKaXmzZs2watUqrRVHRERERNpTpeC3du1afP755wgPD4e+vr60vHnz5jh37pzWiiMiIiIi7alS8Lt69So8PDxKLFer1SgoKHjqooiIiIhI+6oU/Jo2bYp9+/aVWL5p0yY899xzT10UEREREWlfle7qnTJlCiIiInD16lWo1Wp8//33SE5Oxtq1a/HTTz9pu0YiIiIi0oJKzfhdvHgRQgj07NkTW7duxW+//QZTU1NMmTIFSUlJ2Lp1Kzp16lRdtRIRERHRU6jUjF+jRo2QkZEBBwcHtG/fHjY2Njh9+jQcHR2rqz4iIiIi0pJKzfgJITSe//LLL7h//75WCyIiIiKi6lGlmzuKPR4EiYiIiOjZVangp1AooFAoSiyjp+Pm5oaFCxeW20ahUGDz5s3ltomMjESvXr20VhcRERHVLpW6xk8IgcjISKhUKgCP/k7v8OHDYWpqqtHu+++/116FVRAZGYl79+49MSg9Kw4fPlxiDMuTlpYGd3d3HD9+HC1atKi+woiIiKhWqVTwi4iI0Hj+1ltvabUYubK3t9d1CURERCQDlTrVu3r16go9/iubNm2Cj48PjI2NYWtri44dOyIqKgpr1qzBli1bpFPTCQkJAIDLly8jLCwMVlZWsLGxQc+ePZGWlib1V3yqdMaMGXB0dISVlRWmTp2KwsJCREVFwcbGBnXr1n3iMfr7+2Pu3LnS8169esHQ0BA5OTkAgCtXrkChUOD8+fMASp7qTUlJQYcOHWBkZISmTZti586dGv27u7sDAJ577jkoFAoEBQVprJ87dy6cnJxga2uLd955h39NhYiIiAA85c0dupSRkYE33ngDAwcORFJSEhISEtC7d29ER0cjLCwMoaGhyMjIQEZGBtq2bYuCggKEhITA3Nwc+/btQ2JiIszMzBAaGor8/Hyp3927d+PatWvYu3cv5s+fj+joaHTv3h3W1tY4dOgQhg8fjmHDhuHKlStl1hYYGCiFTSEE9u3bBysrK+zfvx8A8Pvvv8PFxaXMP3vXu3dvKJVKHDp0CMuXL8e4ceM02vz5558AgN9++w0ZGRkap9b37NmDCxcuYM+ePVizZg3i4uIQFxdXap15eXnIysrSeBAREVHtVaODX2FhIXr37g03Nzf4+PhgxIgRMDMzg7GxMVQqFerUqYM6depAqVRiw4YNUKvVWLVqFXx8fODl5YXVq1cjPT1dCmkAYGNjg08//RSenp4YOHAgPD09kZubi48++giNGjXChAkToFQqpRBXmqCgIOzfvx9FRUU4deoUlEolwsPDpf0kJCQgMDCw1G1/++03nDt3DmvXrkXz5s3RoUMHzJgxQ6NN8alhW1tb1KlTBzY2NtI6a2trLFmyBE2aNEH37t3RrVs37Nq1q9R9zZw5E5aWltKjXr16FRl6IiIiqqFqbPBr3rw5goOD4ePjg9deew0rV67E3bt3y2x/8uRJnD9/Hubm5jAzM4OZmRlsbGzw8OFDXLhwQWrn7e0NPb3/DYujoyN8fHyk5/r6+rC1tcWNGzcAAF26dJH68/b2BgC0b98e2dnZOH78OH7//XcEBgYiKChICn6///57idOzxZKSklCvXj04OztLy9q0aVPhcfH29oa+vr703MnJSar1cRMmTEBmZqb0uHz5coX3Q0RERDVPlf5W77NAX18fO3fuxB9//IFff/0VixcvxsSJE3Ho0KFS2+fk5MDPzw/r168vse7fN1cYGhpqrFMoFKUuU6vVAIBVq1bhwYMHGttaWVmhefPmSEhIwIEDB9CpUyd06NABr7/+Ov7++2+kpKSUOeP3tMqr9XEqlUq6Q5uIiIhqvxob/IBHoSYgIAABAQGYMmUKXF1d8cMPP0CpVKKoqEijbcuWLbFhwwY4ODjAwsJCazW4uLiUujwwMBB79uzBn3/+ienTp8PGxgZeXl6YPn06nJyc0Lhx41K38/LywuXLl5GRkQEnJycAwMGDBzXaKJVKAChxjERERETlqbGneg8dOoQZM2bgyJEjSE9Px/fff4+bN2/Cy8sLbm5uOHXqFJKTk3Hr1i0UFBQgPDwcdnZ26NmzJ/bt24fU1FQkJCRg1KhR5d6oUVVBQUHYsWMHDAwM0KRJE2nZ+vXry53t69ixIxo3boyIiAicPHkS+/btw8SJEzXaODg4wNjYGNu3b8f169eRmZmp9fqJiIio9qmxwc/CwgJ79+5F165d0bhxY0yaNAnz5s1Dly5dMGTIEHh6esLf3x/29vZITEyEiYkJ9u7di/r166N3797w8vLCoEGD8PDhQ63OABZr37491Gq1RsgLCgpCUVFRmdf3AYCenh5++OEHPHjwAK1bt8bgwYMxffp0jTYGBgb49NNPsWLFCjg7O6Nnz55ar5+IiIhqH4XgH9yl/5eVlfXo7t7RG6GnMtF1OURERLVK2qxu1dJv8e/vzMzMJ05m1dgZPyIiIiKqHAY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIpkw0HUB9Oz5KzYEFhYWui6DiIiItIwzfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQywb/VSyU0i94BPZWJrssgIpKdtFnddF0C1XKc8SMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQa/p5CQkACFQoF79+49VT8KhQKbN2/+z/dLRERE8lKjg19kZCR69eql6zKeWkZGBrp06aLVPmNiYtCiRQut9klEREQ1m4GuCyCgTp06ui6BiIiIZKBGzPht2rQJPj4+MDY2hq2tLTp27IioqCisWbMGW7ZsgUKhgEKhQEJCAgDg8uXLCAsLg5WVFWxsbNCzZ0+kpaVJ/RXPFM6YMQOOjo6wsrLC1KlTUVhYiKioKNjY2KBu3bpYvXp1heo7evQo/P39YWJigrZt2yI5OVlj/ZYtW9CyZUsYGRmhQYMGiI2NRWFhobT+8VO9f/zxB1q0aAEjIyP4+/tj8+bNUCgUOHHiRIX2GxcXh9jYWJw8eVIam7i4uAqPNxEREdVOz3zwy8jIwBtvvIGBAwciKSkJCQkJ6N27N6KjoxEWFobQ0FBkZGQgIyMDbdu2RUFBAUJCQmBubo59+/YhMTERZmZmCA0NRX5+vtTv7t27ce3aNezduxfz589HdHQ0unfvDmtraxw6dAjDhw/HsGHDcOXKlSfWOHHiRMybNw9HjhyBgYEBBg4cKK3bt28f+vfvj/feew9nz57FihUrEBcXh+nTp5faV1ZWFnr06AEfHx8cO3YM06ZNw7hx4yq139dffx1jxoyBt7e3NDavv/56ie3z8vKQlZWl8SAiIqLa65k/1ZuRkYHCwkL07t0brq6uAAAfHx8AgLGxMfLy8jROlX711VdQq9VYtWoVFAoFAGD16tWwsrJCQkICOnfuDACwsbHBp59+Cj09PXh6emLOnDnIzc3FRx99BACYMGECZs2ahf3796Nv377l1jh9+nQEBgYCAMaPH49u3brh4cOHMDIyQmxsLMaPH4+IiAgAQIMGDTBt2jSMHTsW0dHRJfr6+uuvoVAosHLlShgZGaFp06a4evUqhgwZUuH9Ghsbw8zMDAYGBuWeRp45cyZiY2PLPTYiIiKqPZ75Gb/mzZsjODgYPj4+eO2117By5UrcvXu3zPYnT57E+fPnYW5uDjMzM5iZmcHGxgYPHz7EhQsXpHbe3t7Q0/vf4Ts6OkqBEgD09fVha2uLGzduAAC6dOki9eft7a2xT19fX+lrJycnAJC2O3nyJKZOnSpta2ZmhiFDhiAjIwO5ubkl6k9OToavry+MjIykZa1bty71WMvbb0VMmDABmZmZ0uPy5csV3paIiIhqnmd+xk9fXx87d+7EH3/8gV9//RWLFy/GxIkTcejQoVLb5+TkwM/PD+vXry+xzt7eXvra0NBQY51CoSh1mVqtBgCsWrUKDx48KHXbfz8vnmUs3i4nJwexsbHo3bt3iXr+He6qorz9VoRKpYJKpXqqGoiIiKjmeOaDH/Ao1AQEBCAgIABTpkyBq6srfvjhByiVShQVFWm0bdmyJTZs2AAHBwdYWFhorQYXF5cqbdeyZUskJyfDw8OjQu09PT3x1VdfIS8vTwplhw8frvR+SxsbIiIikrdn/lTvoUOHMGPGDBw5cgTp6en4/vvvcfPmTXh5ecHNzQ2nTp1CcnIybt26hYKCAoSHh8POzg49e/bEvn37kJqaioSEBIwaNapCN2po25QpU7B27VrExsbizJkzSEpKQnx8PCZNmlRq+zfffBNqtRpDhw5FUlISduzYgblz5wL436xeRbi5uSE1NRUnTpzArVu3kJeXp5XjISIioprrmQ9+FhYW2Lt3L7p27YrGjRtj0qRJmDdvHrp06YIhQ4bA09MT/v7+sLe3R2JiIkxMTLB3717Ur18fvXv3hpeXFwYNGoSHDx9qdQawokJCQvDTTz/h119/RatWrfDCCy9gwYIF0o0qj7OwsMDWrVtx4sQJtGjRAhMnTsSUKVMAVO7UcJ8+fRAaGooXX3wR9vb2+Oabb7RyPERERFRzKYQQQtdFUPnWr1+PAQMGIDMzE8bGxtW2n6ysLFhaWqLe6I3QU5lU236IiKh0abO66boEqoGKf39nZmY+cZKrRlzjJzdr165FgwYN4OLigpMnT2LcuHEICwur1tBHREREtR+D3zPon3/+wZQpU/DPP//AyckJr732Wpkf+ExERERUUQx+z6CxY8di7Nixui6DiIiIapln/uYOIiIiItIOBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJA10XQM+ev2JDYGFhoesyiIiISMs440dEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkEwx+RERERDLB4EdEREQkE/xbvVRCs+gd0FOZ6LoMIiKqpLRZ3XRdAj3jOONHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweBHREREJBMMfkREREQyweAHICgoCKNHj5aeu7m5YeHChTqrh4iIiKg6MPiV4vDhwxg6dKiuy0BcXBysrKx0XQYRERHVEga6LuBZZG9vr+sStKqoqAgKhQJ6esz5REREcqbTJKBWqzFnzhx4eHhApVKhfv36mD59OgBg3LhxaNy4MUxMTNCgQQNMnjwZBQUF0rYxMTFo0aIFVqxYgXr16sHExARhYWHIzMwsd5/3799H//79YWZmBicnJ8ybN69Em8dP9c6fPx8+Pj4wNTVFvXr1MGLECOTk5Ejri2fmfvrpJ3h6esLExASvvvoqcnNzsWbNGri5ucHa2hqjRo1CUVGRtF1eXh4+/PBDuLi4wNTUFM8//zwSEhIAAAkJCRgwYAAyMzOhUCigUCgQExPzxO3+Xc+PP/6Ipk2bQqVSIT09vaLfFiIiIqqldDrjN2HCBKxcuRILFixAu3btkJGRgXPnzgEAzM3NERcXB2dnZ5w+fRpDhgyBubk5xo4dK21//vx5bNy4EVu3bkVWVhYGDRqEESNGYP369WXuMyoqCr///ju2bNkCBwcHfPTRRzh27BhatGhR5jZ6enr49NNP4e7ujosXL2LEiBEYO3Ysli5dKrXJzc3Fp59+ivj4eGRnZ6N379545ZVXYGVlhW3btuHixYvo06cPAgIC8PrrrwMARo4cibNnzyI+Ph7Ozs744YcfEBoaitOnT6Nt27ZYuHAhpkyZguTkZACAmZnZE7dr1KiRVM/s2bOxatUq2NrawsHBoWrfJCIiIqo1FEIIoYsdZ2dnw97eHkuWLMHgwYOf2H7u3LmIj4/HkSNHADya8fv4449x6dIluLi4AAC2b9+Obt264erVq6hTp06JPnJycmBra4uvvvoKr732GgDgzp07qFu3LoYOHSrN8rm5uWH06NEaN3z826ZNmzB8+HDcunULwKMZtgEDBuD8+fNo2LAhAGD48OFYt24drl+/LgW20NBQuLm5Yfny5UhPT0eDBg2Qnp4OZ2dnqe+OHTuidevWmDFjBuLi4jB69Gjcu3dPWl/R7QYMGIATJ06gefPmZY5pXl4e8vLypOdZWVmoV68e6o3eCD2VSZnbERHRsyltVjddl0A6kJWVBUtLS2RmZsLCwqLctjqb8UtKSkJeXh6Cg4NLXb9hwwZ8+umnuHDhAnJyclBYWFjiYOrXry+FPgBo06YN1Go1kpOTkZKSgi5dukjrVqxYgWbNmiE/Px/PP/+8tNzGxgaenp7l1vrbb79h5syZOHfuHLKyslBYWIiHDx8iNzcXJiaPApKJiYkU+gDA0dERbm5uUugrXnbjxg0AwOnTp1FUVITGjRtr7CsvLw+2trZl1lLR7ZRKJXx9fcs9rpkzZyI2NrbcNkRERFR76Cz4GRsbl7nuwIEDCA8PR2xsLEJCQmBpaYn4+PhSr8cri7+/P06cOCE9d3R0xMWLFytdZ1paGrp37463334b06dPh42NDfbv349BgwYhPz9fCn6GhoYa2ykUilKXqdVqAI9mH/X19XH06FHo6+trtPt3WHxcRbczNjaGQqEo99gmTJiADz74QHpePONHREREtZPOgl+jRo1gbGyMXbt2lTjV+8cff8DV1RUTJ06Ull26dKlEH+np6bh27Zp0yvPgwYPQ09ODp6cnjI2N4eHhodG+YcOGMDQ0xKFDh1C/fn0AwN27d/H3338jMDCw1DqPHj0KtVqNefPmSXfFbty4seoH/v+ee+45FBUV4caNG2jfvn2pbZRKpcbNIBXdrqJUKhVUKtVT9UFEREQ1h86Cn5GREcaNG4exY8dCqVQiICAAN2/exJkzZ9CoUSOkp6cjPj4erVq1ws8//4wffvih1D4iIiIwd+5cZGVlYdSoUQgLCyv1+j7g0YzYoEGDEBUVJd3wMHHixHI/5sTDwwMFBQVYvHgxevTogcTERCxfvvypj79x48YIDw9H//79MW/ePDz33HO4efMmdu3aBV9fX3Tr1g1ubm7IycnBrl270Lx5c5iYmFRoOyIiIqLS6PTjXCZPnowxY8ZgypQp8PLywuuvv44bN27g5Zdfxvvvv4+RI0eiRYsW+OOPPzB58uQS23t4eKB3797o2rUrOnfuDF9fX407bUvzySefoH379ujRowc6duyIdu3awc/Pr8z2zZs3x/z58zF79mw0a9YM69evx8yZM5/62AFg9erV6N+/P8aMGQNPT0/06tULhw8flmYj27Zti+HDh+P111+Hvb095syZU6HtiIiIiEqjs7t6n1ZMTAw2b96scR0fPZ3iu4J4Vy8RUc3Eu3rlqTJ39fJPORARERHJBIMfERERkUzU2OAXExPD07xERERElVBjgx8RERERVQ6DHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMMPgRERERyQSDHxEREZFMGOi6AHr2/BUbAgsLC12XQURERFrGGT8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimWDwIyIiIpIJBj8iIiIimTDQdQH07BBCAACysrJ0XAkRERFVVPHv7eLf4+Vh8CPJ7du3AQD16tXTcSVERERUWdnZ2bC0tCy3DYMfSWxsbAAA6enpT3zhkPZlZWWhXr16uHz5MiwsLHRdjuxw/HWL469bHH/detrxF0IgOzsbzs7OT2zL4EcSPb1Hl3xaWlryB1+HLCwsOP46xPHXLY6/bnH8detpxr+iEza8uYOIiIhIJhj8iIiIiGSCwY8kKpUK0dHRUKlUui5Fljj+usXx1y2Ov25x/HXrvxx/hajIvb9EREREVONxxo+IiIhIJhj8iIiIiGSCwY+IiIhIJhj8iIiIiGSCwa+W++yzz+Dm5gYjIyM8//zz+PPPP8tt/+2336JJkyYwMjKCj48Ptm3bprFeCIEpU6bAyckJxsbG6NixI1JSUqrzEGo0bY9/ZGQkFAqFxiM0NLQ6D6FGq8z4nzlzBn369IGbmxsUCgUWLlz41H3KnbbHPyYmpsTrv0mTJtV4BDVbZcZ/5cqVaN++PaytrWFtbY2OHTuWaM/3/8rR9vhr7f1fUK0VHx8vlEql+PLLL8WZM2fEkCFDhJWVlbh+/Xqp7RMTE4W+vr6YM2eOOHv2rJg0aZIwNDQUp0+fltrMmjVLWFpais2bN4uTJ0+Kl19+Wbi7u4sHDx78V4dVY1TH+EdERIjQ0FCRkZEhPe7cufNfHVKNUtnx//PPP8WHH34ovvnmG1GnTh2xYMGCp+5Tzqpj/KOjo4W3t7fG6//mzZvVfCQ1U2XH/8033xSfffaZOH78uEhKShKRkZHC0tJSXLlyRWrD9/+Kq47x19b7P4NfLda6dWvxzjvvSM+LioqEs7OzmDlzZqntw8LCRLdu3TSWPf/882LYsGFCCCHUarWoU6eO+OSTT6T19+7dEyqVSnzzzTfVcAQ1m7bHX4hHP/g9e/aslnprm8qO/7+5urqWGjyepk+5qY7xj46OFs2bN9dilbXX075WCwsLhbm5uVizZo0Qgu//laXt8RdCe+//PNVbS+Xn5+Po0aPo2LGjtExPTw8dO3bEgQMHSt3mwIEDGu0BICQkRGqfmpqKf/75R6ONpaUlnn/++TL7lKvqGP9iCQkJcHBwgKenJ95++23cvn1b+wdQw1Vl/HXRZ21VnWOVkpICZ2dnNGjQAOHh4UhPT3/acmsdbYx/bm4uCgoKYGNjA4Dv/5VRHeNfTBvv/wx+tdStW7dQVFQER0dHjeWOjo74559/St3mn3/+Kbd98b+V6VOuqmP8ASA0NBRr167Frl27MHv2bPz+++/o0qULioqKtH8QNVhVxl8XfdZW1TVWzz//POLi4rB9+3YsW7YMqampaN++PbKzs5+25FpFG+M/btw4ODs7S+GF7/8VVx3jD2jv/d+gUq2JSKf69u0rfe3j4wNfX180bNgQCQkJCA4O1mFlRNWvS5cu0te+vr54/vnn4erqio0bN2LQoEE6rKx2mTVrFuLj45GQkAAjIyNdlyM7ZY2/tt7/OeNXS9nZ2UFfXx/Xr1/XWH79+nXUqVOn1G3q1KlTbvvifyvTp1xVx/iXpkGDBrCzs8P58+efvuhapCrjr4s+a6v/aqysrKzQuHFjvv4f8zTjP3fuXMyaNQu//vorfH19peV8/6+46hj/0lT1/Z/Br5ZSKpXw8/PDrl27pGVqtRq7du1CmzZtSt2mTZs2Gu0BYOfOnVJ7d3d31KlTR6NNVlYWDh06VGafclUd41+aK1eu4Pbt23ByctJO4bVEVcZfF33WVv/VWOXk5ODChQt8/T+mquM/Z84cTJs2Ddu3b4e/v7/GOr7/V1x1jH9pqvz+/9S3h9AzKz4+XqhUKhEXFyfOnj0rhg4dKqysrMQ///wjhBCiX79+Yvz48VL7xMREYWBgIObOnSuSkpJEdHR0qR/nYmVlJbZs2SJOnTolevbsydv5y6Dt8c/OzhYffvihOHDggEhNTRW//fabaNmypWjUqJF4+PChTo7xWVbZ8c/LyxPHjx8Xx48fF05OTuLDDz8Ux48fFykpKRXuk/6nOsZ/zJgxIiEhQaSmporExETRsWNHYWdnJ27cuPGfH9+zrrLjP2vWLKFUKsWmTZs0Pi4kOztbow3f/ytG2+Ovzfd/Br9abvHixaJ+/fpCqVSK1q1bi4MHD0rrAgMDRUREhEb7jRs3isaNGwulUim8vb3Fzz//rLFerVaLyZMnC0dHR6FSqURwcLBITk7+Lw6lRtLm+Ofm5orOnTsLe3t7YWhoKFxdXcWQIUMYOspRmfFPTU0VAEo8AgMDK9wnadL2+L/++uvCyclJKJVK4eLiIl5//XVx/vz5//CIapbKjL+rq2up4x8dHS214ft/5Whz/LX5/q8QQojKzRESERERUU3Ea/yIiIiIZILBj4iIiEgmGPyIiIiIZILBj4iIiEgmGPyIiIiIZILBj4iIiEgmGPyIiIiIZILBj4iIqlVkZCR69eql6zKICAA/wJmISEsiIyNx7949bN68WdellJCWlgZ3d3ccP34cLVq0+E/3nZmZCSEErKys/tP9ElFJBrougIiIqld+fr5O929paanT/RPR//BULxFRNQgKCsK7776L0aNHw9raGo6Ojli5ciXu37+PAQMGwNzcHB4eHvjll1+kbRISEqBQKPDzzz/D19cXRkZGeOGFF/DXX39p9P3dd9/B29sbKpUKbm5umDdvnsZ6Nzc3TJs2Df3794eFhQWGDh0Kd3d3AMBzzz0HhUKBoKAgAMDhw4fRqVMn2NnZwdLSEoGBgTh27JhGfwqFAqtWrcIrr7wCExMTNGrUCD/++KNGmzNnzqB79+6wsLCAubk52rdvjwsXLgAoeap3+/btaNeuHaysrGBra4vu3btLbYmoejH4ERFVkzVr1sDOzg5//vkn3n33Xbz99tt47bXX0LZtWxw7dgydO3dGv379kJubq7FdVFQU5s2bh8OHD8Pe3h49evRAQUEBAODo0aMICwtD3759cfr0acTExGDy5MmIi4vT6GPu3Llo3rw5jh8/jsmTJ+PPP/8EAPz222/IyMjA999/DwDIzs5GREQE9u/fj4MHD6JRo0bo2rUrsrOzNfqLjY1FWFgYTp06ha5duyI8PBx37twBAFy9ehUdOnSASqXC7t27cfToUQwcOBCFhYWljsv9+/fxwQcf4MiRI9i1axf09PTwyiuvQK1WP/WYE9ETCCIi0oqIiAjRs2dPIYQQgYGBol27dtK6wsJCYWpqKvr16ycty8jIEADEgQMHhBBC7NmzRwAQ8fHxUpvbt28LY2NjsWHDBiGEEG+++abo1KmTxn6joqJE06ZNpeeurq6iV69eGm1SU1MFAHH8+PFyj6GoqEiYm5uLrVu3SssAiEmTJknPc3JyBADxyy+/CCGEmDBhgnB3dxf5+flPHJfS3Lx5UwAQp0+fLrc2Inp6nPEjIqomvr6+0tf6+vqwtbWFj4+PtMzR0REAcOPGDY3t2rRpI31tY2MDT09PJCUlAQCSkpIQEBCg0T4gIAApKSkoKiqSlvn7+1eoxuvXr2PIkCFo1KgRLC0tYWFhgZycHKSnp5d5LKamprCwsJDqPnHiBNq3bw9DQ8MK7TMlJQVvvPEGGjRoAAsLC7i5uQFAiX0Skfbx5g4iomryeBBSKBQayxQKBQBUyylOU1PTCrWLiIjA7du3sWjRIri6ukKlUqFNmzYlbggp7ViK6zY2Nq5UbT169ICrqytWrlwJZ2dnqNVqNGvWTOc3oRDJAWf8iIieMQcPHpS+vnv3Lv7++294eXkBALy8vJCYmKjRPjExEY0bN4a+vn6ZfSqVSgDQmBUs3nbUqFHo2rWrdMPIrVu3KlWvr68v9u3bJ12HWJ7bt28jOTkZkyZNQnBwMLy8vHD37t1K7Y+Iqo7Bj4joGTN16lTs2rULf/31FyIjI2FnZyfdFTtmzBjs2rUL06ZNw99//401a9ZgyZIl+PDDD8vt08HBAcbGxti+fTuuX7+OzMxMAECjRo2wbt06JCUl4dChQwgPD6/0DN7IkSORlZWFvn374siRI0hJScG6deuQnJxcoq21tTVsbW3x+eef4/z589i9ezc++OCDSu2PiKqOwY+I6Bkza9YsvPfee/Dz88M///yDrVu3SjN2LVu2xMaNGxEfH49mzZphypQpmDp1KiIjI8vt08DAAJ9++ilWrFgBZ2dn9OzZEwDwxRdf4O7du2jZsiX69euHUaNGwcHBoVL12traYvfu3cjJyUFgYCD8/PywcuXKUq/509PTQ3x8PI4ePYpmzZrh/fffxyeffFKp/RFR1fEvdxARPSMSEhLw4osv4u7du/wrF0RULTjjR0RERCQTDH5EREREMsFTvUREREQywRk/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIplg8CMiIiKSCQY/IiIiIpn4PwLdeK2gaLIJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 6"
      ],
      "metadata": {
        "id": "JOZTSpyY2gfe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "5RPb21ih1kJX",
        "outputId": "3c2ec181-d05e-4a7a-ca2b-4224343cb0cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 7"
      ],
      "metadata": {
        "id": "KKwRx0r32trG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "49zhFNa22iKN",
        "outputId": "0a1d931e-a77c-41ab-ea39-1cff3e60704e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 262
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pruebo 8"
      ],
      "metadata": {
        "id": "s8V5Ewg76Q0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "67xj0QVj5C5S",
        "outputId": "a7c366bf-c3d2-4c84-a7f5-5e7cc3fd6a28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 304
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArsAAAHHCAYAAAC/a5ClAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACAbElEQVR4nO3deVxO6f8/8NfddrfcLdoXLWhRKUsxiCkTsjOMjDHIbjBpEBq02BlZhrEM85ExTIx9G7ssIUTWJGtmNLK2aLTd5/eHX+c7txaVErfX8/G4H7qvc53rvM91n+rt6jrXkQiCIICIiIiISAmpVHcARERERERVhckuERERESktJrtEREREpLSY7BIRERGR0mKyS0RERERKi8kuERERESktJrtEREREpLSY7BIRERGR0mKyS0RERERKi8kuEX10wsPDIZFIqvw4AQEBsLOzq7T23lXcRFUpJiYGEokEMTEx1R0KfSSY7BK956KioiCRSHDu3LnqDqXCli5diqioqOoOgyrAx8cHEomk2Nf169er5Ji8XirP65+Znp4evL29sXv37uoO7b1S0jVubm5e3aEVa8+ePQgPD6/uMD4YatUdABEpv6VLl8LY2BgBAQHVHQoAYPLkyZg4cWJ1h/HBqFmzJmbNmlWk3NLSskqO975dLx+6Nm3aoF+/fhAEAffu3cOyZcvQuXNn/Pnnn/Dz86vu8N4bhf30X1paWtUUTen27NmDn376iQlvGTHZJaIqk52dDW1t7eoOowg1NTWoqfHHX1np6+vj66+/ru4w3oogCHj58uV7m7xUJUdHR4XPr0ePHnBxccGiRYuY7P7H6/1UWfLz8yGXy6GhoVHpbVPZcBoD0QcoICAAMpkMKSkp6NSpE2QyGaysrPDTTz8BAC5fvozPPvsMOjo6sLW1xfr16xX2L5wacezYMQwbNgxGRkbQ09NDv3798OzZsyLHW7p0KVxdXSGVSmFpaYmRI0fi+fPnCnV8fHxQr149xMfH49NPP4W2tja+//572NnZ4erVqzh69Kj4p0EfHx8AwNOnTzFu3Di4ublBJpNBT08P7du3x8WLFxXaLpzjt3HjRsyYMQM1a9aEpqYmfH19cfPmzSLxxsXFoUOHDqhRowZ0dHTg7u6ORYsWiduLm/u6evVqfPbZZzA1NYVUKoWLiwuWLVtW5s9k27ZtqFevHjQ1NVGvXj1s3bq12HpyuRwLFy6Eq6srNDU1YWZmhmHDhhXb72VR1rjPnTsHPz8/GBsbQ0tLC7Vq1cLAgQMrdMzX5eTkICwsDPb29pBKpbC2tsb48eORk5NT7lhLu15KmrNceD3fvXtXoZ1OnTph37598PT0hJaWFlasWAEAeP78OYKCgmBtbQ2pVAp7e3vMmTMHcrlcod3o6Gh4eHhAV1cXenp6cHNzU7iOSiKXy7Fo0SK4ublBU1MTJiYmaNeuncJUpPz8fEybNg116tSBVCqFnZ0dvv/++yJ9VngeJ06cQJMmTaCpqYnatWvj119/fWMcJXF2doaxsTFu3bqlUL59+3Z07NgRlpaWkEqlqFOnDqZNm4aCggKFeoXf69euXUOrVq2gra0NKysrzJ07t8ix/vrrL3Tr1g06OjowNTXFd999V+QcC/3xxx/w8PCAlpYWjI2N8fXXX+Pvv/9WqPO2P/veRlpaGgYNGgQzMzNoamqifv36WLNmjUKdu3fvQiKRYN68eVi4cKH4+V67dg0AcP36dXzxxRcwNDSEpqYmPD09sWPHDoU28vLyEBERAQcHB2hqasLIyAgtWrTAgQMHxD4oPN//TrmgknFog+gDVVBQgPbt2+PTTz/F3LlzsW7dOowaNQo6OjqYNGkS+vTpg+7du2P58uXo168fmjVrhlq1aim0MWrUKBgYGCA8PBxJSUlYtmwZ7t27JyaXwKsEIyIiAq1bt8Y333wj1jt79ixiY2Ohrq4utvfkyRO0b98eX375Jb7++muYmZnBx8cH3377LWQyGSZNmgQAMDMzAwDcvn0b27ZtQ8+ePVGrVi08fPgQK1asgLe3N65du1bkz+SzZ8+GiooKxo0bh/T0dMydOxd9+vRBXFycWOfAgQPo1KkTLCwsMHr0aJibmyMxMRG7du3C6NGjS+zPZcuWwdXVFV26dIGamhp27tyJESNGQC6XY+TIkaV+Fvv37xdHy2bNmoUnT55gwIABqFmzZpG6w4YNQ1RUFAYMGIDAwEDcuXMHS5YswYULF4r0Z1mUJe60tDS0bdsWJiYmmDhxIgwMDHD37l1s2bKlTMcoKCjA48ePFco0NTUhk8kgl8vRpUsXnDhxAkOHDoWzszMuX76MBQsW4MaNG9i2bVu5Yl24cGGJ10t5JSUloXfv3hg2bBiGDBkCJycnZGdnw9vbG3///TeGDRsGGxsbnDx5EiEhIUhNTcXChQsBvLqOevfuDV9fX8yZMwcAkJiYiNjY2FKvIwAYNGgQoqKi0L59ewwePBj5+fk4fvw4Tp8+DU9PTwDA4MGDsWbNGnzxxRcYO3Ys4uLiMGvWLCQmJhb5j9LNmzfxxRdfYNCgQejfvz/+97//ISAgAB4eHnB1dS13v6Snp+PZs2eoU6eOQnlUVBRkMhnGjBkDmUyGw4cPIzQ0FBkZGfjhhx8U6j579gzt2rVD9+7d4e/vj02bNmHChAlwc3ND+/btAQD//vsvfH19kZKSgsDAQFhaWmLt2rU4fPhwkZgKvycaN26MWbNm4eHDh1i0aBFiY2Nx4cIFGBgYiHUr42dfcV6+fFnkOtfV1YVUKsW///4LHx8f3Lx5E6NGjUKtWrXwxx9/ICAgAM+fPy9yTaxevRovX77E0KFDIZVKYWhoiKtXr8LLywtWVlaYOHEidHR0sHHjRnTr1g2bN2/G559/DuDVz9xZs2Zh8ODBaNKkCTIyMnDu3DmcP38ebdq0wbBhw/DgwQMcOHAAa9eufeN5EQCBiN5rq1evFgAIZ8+eFcv69+8vABBmzpwplj179kzQ0tISJBKJEB0dLZZfv35dACCEhYUVadPDw0PIzc0Vy+fOnSsAELZv3y4IgiCkpaUJGhoaQtu2bYWCggKx3pIlSwQAwv/+9z+xzNvbWwAgLF++vMg5uLq6Ct7e3kXKX758qdCuIAjCnTt3BKlUKkydOlUsO3LkiABAcHZ2FnJycsTyRYsWCQCEy5cvC4IgCPn5+UKtWrUEW1tb4dmzZwrtyuVy8euwsDDh9R9/2dnZReLz8/MTateuXaT8dQ0aNBAsLCyE58+fi2X79+8XAAi2trZi2fHjxwUAwrp16xT237t3b7Hlr6to3Fu3bi1yDZVV4ef6+qt///6CIAjC2rVrBRUVFeH48eMK+y1fvlwAIMTGxpYrVkEo+Xop7vwF4f+u5zt37ohltra2AgBh7969CnWnTZsm6OjoCDdu3FAonzhxoqCqqiqkpKQIgiAIo0ePFvT09IT8/PyinVKKw4cPCwCEwMDAItsKr8GEhAQBgDB48GCF7ePGjRMACIcPHy5yHseOHRPL0tLSBKlUKowdO/aN8QAQBg0aJDx69EhIS0sTzp07J7Rr104AIPzwww8KdYv7fIYNGyZoa2sLL1++FMsKr4lff/1VLMvJyRHMzc2FHj16iGULFy4UAAgbN24Uy168eCHY29sLAIQjR44IgiAIubm5gqmpqVCvXj3h33//Fevu2rVLACCEhoaKZW/7s6+0firutXr1aoVz+e2338R9cnNzhWbNmgkymUzIyMgQBOHVzy8Agp6enpCWlqZwDF9fX8HNzU2hL+VyudC8eXPBwcFBLKtfv77QsWPHUuMdOXJksd8LVDxOYyD6gA0ePFj82sDAAE5OTtDR0YG/v79Y7uTkBAMDA9y+fbvI/kOHDlUYSfzmm2+gpqaGPXv2AAAOHjyI3NxcBAUFQUXl/35cDBkyBHp6ekXu6JZKpRgwYECZ45dKpWK7BQUFePLkCWQyGZycnHD+/Pki9QcMGKAw761ly5YAIJ7bhQsXcOfOHQQFBSmMBAF445/5/juXMz09HY8fP4a3tzdu376N9PT0EvdLTU1FQkIC+vfvD319fbG8TZs2cHFxUaj7xx9/QF9fH23atMHjx4/Fl4eHB2QyGY4cOVJqjBWNu7Avdu3ahby8vHIfw87ODgcOHFB4jR8/XjwnZ2dn1K1bV+GcPvvsMwBQOKeK9nFF1apVq8ic1D/++AMtW7ZEjRo1FOJt3bo1CgoKcOzYMQCv+uzFixfin47LavPmzZBIJAgLCyuyrfAaLPz+GjNmjML2sWPHAkCR7ysXFxfxWgcAExMTODk5Ffs9XZxffvkFJiYmMDU1haenJw4dOoTx48cXOf5/P5/MzEw8fvwYLVu2RHZ2dpGVN2QymcL8Vg0NDTRp0kQhpj179sDCwgJffPGFWKatrY2hQ4cqtHXu3DmkpaVhxIgR0NTUFMs7duyIunXrFrtyxNv+7CtO165di1znhdfPnj17YG5ujt69e4v11dXVERgYiKysLBw9elShrR49esDExER8//TpUxw+fBj+/v5i3z5+/BhPnjyBn58fkpOTxSkbBgYGuHr1KpKTk8sUN70ZpzEQfaAK5wL+l76+PmrWrFkksdPX1y92TqiDg4PCe5lMBgsLC3Hu47179wC8+qXxXxoaGqhdu7a4vZCVlVW5bsIonNu4dOlS3LlzR2FuoJGRUZH6NjY2Cu9r1KgBAOK5Fc5BrFevXpljKBQbG4uwsDCcOnUK2dnZCtvS09MVEtn/KuyD1/sSQJGkPTk5Genp6TA1NS22rbS0tCqJ29vbGz169EBERAQWLFgAHx8fdOvWDV999RWkUukbj6Gjo4PWrVsXuy05ORmJiYlFrsXizqmifVxRxf3pOjk5GZcuXXpjvCNGjMDGjRvRvn17WFlZoW3btvD390e7du1KPeatW7dgaWkJQ0PDEuvcu3cPKioqsLe3Vyg3NzeHgYFBke+r16974NW1X9Z53l27dsWoUaOQm5uLs2fPYubMmcjOzlb4DywAXL16FZMnT8bhw4eRkZGhsO31/4wU93OmRo0auHTpksJ52tvbF6n3+s+Tkn7OAEDdunVx4sQJhbLK+NlXnJo1a5Z4nd+7dw8ODg5F+szZ2VnhHAq9fu3dvHkTgiBgypQpmDJlSrHHSEtLg5WVFaZOnYquXbvC0dER9erVQ7t27dC3b1+4u7uX6TyoKCa7RB8oVVXVcpULglCV4QAo/zI9M2fOxJQpUzBw4EBMmzYNhoaGUFFRQVBQUJGbhYCqO7dbt27B19cXdevWxfz582FtbQ0NDQ3s2bMHCxYsKDaWipDL5TA1NcW6deuK3V5SAlaSssYtkUiwadMmnD59Gjt37sS+ffswcOBAREZG4vTp05DJZG91Tm5ubpg/f36x262trcsVa2lKGp1//QaqQsVdj3K5HG3atBFHpl/n6OgIADA1NUVCQgL27duHP//8E3/++SdWr16Nfv36FbkpqaLKelPR2173/03iOnToAGNjY4waNQqtWrVC9+7dAby6ac/b2xt6enqYOnUq6tSpA01NTZw/fx4TJkwo8vlU58+Z9/Fn3+tev/YK+2/cuHElroBR+J+fTz/9FLdu3cL27duxf/9+rFq1CgsWLMDy5csVRrSp7JjsEn3EkpOT0apVK/F9VlYWUlNT0aFDBwCAra0tgFc3+tSuXVusl5ubizt37pQ4CvK6kn6pb9q0Ca1atcIvv/yiUP78+XMYGxuX61wAiDfcXLlypcyxAcDOnTuRk5ODHTt2KIyilWVaQWEfFfcnx6SkpCLxHTx4EF5eXpWyBFZ5427atCmaNm2KGTNmYP369ejTpw+io6Pf6hdonTp1cPHiRfj6+paavJUn1pLaKRzJf/78ucI0lddH1d4Ub1ZWVpmuDw0NDXTu3BmdO3eGXC7HiBEjsGLFCkyZMqXIqOx/29+3bx+ePn1a4uiura0t5HI5kpOTxZFBAHj48CGeP38uXlNVZdiwYViwYAEmT56Mzz//XHya2ZMnT7BlyxZ8+umnYt07d+5U+Di2tra4cuUKBEFQ+Exf/77478+Zwukv/61b1f1RFra2trh06RLkcrnC6G7h9I43xVj481NdXb1M156hoSEGDBiAAQMGICsrC59++inCw8PF71WuvlA+nLNL9BH7+eefFeZwLlu2DPn5+eLd1K1bt4aGhgZ+/PFHhdGRX375Benp6ejYsWOZjqOjo1NkqTLg1UjM66Muf/zxR5HlhsqqUaNGqFWrFhYuXFjkeKWN7hSOCP23Tnp6OlavXv3GY1pYWKBBgwZYs2aNwp96Dxw4IC43VMjf3x8FBQWYNm1akXby8/OL7aPSlDXuZ8+eFTn/Bg0aAECJy0CVlb+/P/7++2+sXLmyyLZ///0XL168KFesQMnXS+F/Zgrn1QLAixcvyjXS6u/vj1OnTmHfvn1Ftj1//hz5+fkAXq0s8l8qKirin5FL67MePXpAEAREREQU2VZ47oX/mSxc+aFQ4eh4Wb+vKkpNTQ1jx45FYmIitm/fDqD4zyc3NxdLly6t8HE6dOiABw8eYNOmTWJZdnY2fv75Z4V6np6eMDU1xfLlyxX69s8//0RiYmKV90dZdOjQAf/88w82bNggluXn52Px4sWQyWTw9vYudX9TU1P4+PhgxYoVSE1NLbL90aNH4tevX3symQz29vYKfaOjowMA5f6Z8bHiyC7RRyw3Nxe+vr7w9/dHUlISli5dihYtWqBLly4AXv1ZPSQkBBEREWjXrh26dOki1mvcuHGZF2D38PDAsmXLMH36dNjb28PU1BSfffYZOnXqhKlTp2LAgAFo3rw5Ll++jHXr1imMIpeHioqK+HSoBg0aYMCAAbCwsMD169dx9erVYhMcAGjbtq04ijds2DBkZWVh5cqVMDU1LfYX0+tmzZqFjh07okWLFhg4cCCePn2KxYsXw9XVFVlZWWI9b29vDBs2DLNmzUJCQgLatm0LdXV1JCcn448//sCiRYsUbuZ5k7LGvWbNGixduhSff/456tSpg8zMTKxcuRJ6enpi4lVRffv2xcaNGzF8+HAcOXIEXl5eKCgowPXr17Fx40Zxndvy9HFJ10vbtm1hY2ODQYMGITg4GKqqqvjf//4HExMTpKSklCne4OBg7NixA506dRKX73rx4gUuX76MTZs24e7duzA2NsbgwYPx9OlTfPbZZ6hZsybu3buHxYsXo0GDBgqjsa9r1aoV+vbtix9//BHJyclo164d5HI5jh8/jlatWmHUqFGoX78++vfvj59//lmcPnDmzBmsWbMG3bp1U/hrS1UJCAhAaGgo5syZg27duqF58+aoUaMG+vfvj8DAQEgkEqxdu/atpgAMGTIES5YsQb9+/RAfHw8LCwusXbu2yINm1NXVMWfOHAwYMADe3t7o3bu3uPSYnZ0dvvvuu7c93bc2dOhQrFixAgEBAYiPj4ednR02bdqE2NhYLFy4ELq6um9s46effkKLFi3g5uaGIUOGoHbt2nj48CFOnTqFv/76S1xf3MXFBT4+PvDw8IChoSHOnTuHTZs2YdSoUWJbHh4eAIDAwED4+flBVVUVX375ZdWcvDJ418s/EFH5lLT0mI6OTpG63t7egqura5FyW1tbhaVsCts8evSoMHToUKFGjRqCTCYT+vTpIzx58qTI/kuWLBHq1q0rqKurC2ZmZsI333xTZGmvko4tCILwzz//CB07dhR0dXUFAOKyUi9fvhTGjh0rWFhYCFpaWoKXl5dw6tQpwdvbW2HpqcKlx/744w+FdguX+SlcHqjQiRMnhDZt2gi6urqCjo6O4O7uLixevFjcXtwSVjt27BDc3d0FTU1Nwc7OTpgzZ47wv//9r8iSViXZvHmz4OzsLEilUsHFxUXYsmWL0L9/f4Wlxwr9/PPPgoeHh6ClpSXo6uoKbm5uwvjx44UHDx6UeoyKxn3+/Hmhd+/ego2NjSCVSgVTU1OhU6dOwrlz5954XqV9roVyc3OFOXPmCK6uroJUKhVq1KgheHh4CBEREUJ6enq5YhWEkq8XQRCE+Ph44ZNPPhE0NDQEGxsbYf78+SUuPVbS8k2ZmZlCSEiIYG9vL2hoaAjGxsZC8+bNhXnz5olL8W3atElo27atYGpqKh5r2LBhQmpq6hv7LD8/X/jhhx+EunXrChoaGoKJiYnQvn17IT4+XqyTl5cnRERECLVq1RLU1dUFa2trISQkRGFZqtLO4/XvkZIAEEaOHFnstvDwcIUlwGJjY4WmTZsKWlpagqWlpTB+/Hhh3759CnUKj13cNVHc9X7v3j2hS5cugra2tmBsbCyMHj1aXGrvv20KgiBs2LBBaNiwoSCVSgVDQ0OhT58+wl9//VXkGG/zs68kpfVToYcPHwoDBgwQjI2NBQ0NDcHNza3Iz57Cn0mvL+tW6NatW0K/fv0Ec3NzQV1dXbCyshI6deokbNq0Sawzffp0oUmTJoKBgYGgpaUl1K1bV5gxY4bCMpH5+fnCt99+K5iYmAgSiYTLkL2BRBCqYeY2EVWrwgXcz549Ky5yT0REpIw4Z5eIiIiIlBaTXSIiIiJSWkx2iYiIiEhpcc4uERERESktjuwSERERkdJisktERERESosPlSClI5fL8eDBA+jq6vKRikRERB8IQRCQmZkJS0tLhccyvy0mu6R0Hjx4AGtr6+oOg4iIiCrg/v37qFmzZqW1x2SXlE7hYxvv378PPT29ao6GiIiIyiIjIwPW1tZlevxyeTDZJaVTOHVBT0+PyS4REdEHprKnIPIGNSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpqVV3AERVpV7YPqhItas7jA/O3dkdqzsEIiKiSsORXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZPcjtGnTJri5uUFLSwtGRkZo3bo1Xrx4AQBYtWoVnJ2doampibp162Lp0qUK+06YMAGOjo7Q1tZG7dq1MWXKFOTl5YnbL168iFatWkFXVxd6enrw8PDAuXPnxO2bN2+Gq6srpFIp7OzsEBkZqdC+nZ0dZs6ciYEDB0JXVxc2Njb4+eefq7A3iIiISJnxoRIfmdTUVPTu3Rtz587F559/jszMTBw/fhyCIGDdunUIDQ3FkiVL0LBhQ1y4cAFDhgyBjo4O+vfvDwDQ1dVFVFQULC0tcfnyZQwZMgS6uroYP348AKBPnz5o2LAhli1bBlVVVSQkJEBdXR0AEB8fD39/f4SHh6NXr144efIkRowYASMjIwQEBIgxRkZGYtq0afj++++xadMmfPPNN/D29oaTk1Ox55STk4OcnBzxfUZGRhX1HhEREX1oJIIgCNUdBL0758+fh4eHB+7evQtbW1uFbfb29pg2bRp69+4tlk2fPh179uzByZMni21v3rx5iI6OFkdv9fT0sHjxYjE5/q8+ffrg0aNH2L9/v1g2fvx47N69G1evXgXwamS3ZcuWWLt2LQBAEASYm5sjIiICw4cPLzaG8PBwREREFCm3DtrIJ6hVAJ+gRkRE1SEjIwP6+vpIT0+Hnp5epbXLaQwfmfr168PX1xdubm7o2bMnVq5ciWfPnuHFixe4desWBg0aBJlMJr6mT5+OW7duiftv2LABXl5eMDc3h0wmw+TJk5GSkiJuHzNmDAYPHozWrVtj9uzZCvsmJibCy8tLIR4vLy8kJyejoKBALHN3dxe/lkgkMDc3R1paWonnFBISgvT0dPF1//79t+ojIiIiUh5Mdj8yqqqqOHDgAP7880+4uLhg8eLFcHJywpUrVwAAK1euREJCgvi6cuUKTp8+DQA4deoU+vTpgw4dOmDXrl24cOECJk2ahNzcXLH98PBwXL16FR07dsThw4fh4uKCrVu3livGwmkPhSQSCeRyeYn1pVIp9PT0FF5EREREAOfsfpQkEgm8vLzg5eWF0NBQ2NraIjY2FpaWlrh9+zb69OlT7H4nT56Era0tJk2aJJbdu3evSD1HR0c4Ojriu+++Q+/evbF69Wp8/vnncHZ2RmxsrELd2NhYODo6QlVVtXJPkoiIiAhMdj86cXFxOHToENq2bQtTU1PExcXh0aNHcHZ2RkREBAIDA6Gvr4927dohJycH586dw7NnzzBmzBg4ODggJSUF0dHRaNy4MXbv3q0wavvvv/8iODgYX3zxBWrVqoW//voLZ8+eRY8ePQAAY8eORePGjTFt2jT06tULp06dwpIlS4qs+EBERERUWZjsfmT09PRw7NgxLFy4EBkZGbC1tUVkZCTat28PANDW1sYPP/yA4OBg6OjowM3NDUFBQQCALl264LvvvsOoUaOQk5ODjh07YsqUKQgPDwfwaorEkydP0K9fPzx8+BDGxsbo3r27ePNYo0aNsHHjRoSGhmLatGmwsLDA1KlTFVZiICIiIqpMXI2BlE7h3ZxcjaFiuBoDERFVB67GQERERERUTkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBZXYyCldSXCjw+YICIi+shxZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFG9RIadUL28cnqFG58QlyRETKhSO7RERERKS0mOwSERERkdJisktERERESovJLhEREREpLSa7HxkfHx8EBQVVeP+oqCgYGBiUWic8PBwNGjSo8DGIiIiIKguTXap048aNw6FDh8T3AQEB6NatW7nbYdJMREREb4tLj1Glk8lkkMlk1R0GEREREUd2P0ZyuRzjx4+HoaEhzM3NER4eLm6bP38+3NzcoKOjA2tra4wYMQJZWVlF2ti2bRscHBygqakJPz8/3L9/X9z23xHZ8PBwrFmzBtu3b4dEIoFEIkFMTAwAYMKECXB0dIS2tjZq166NKVOmIC8vD8Cr6RIRERG4ePGiuF9UVFRVdQkREREpKY7sfoTWrFmDMWPGIC4uDqdOnUJAQAC8vLzQpk0bqKio4Mcff0StWrVw+/ZtjBgxAuPHj8fSpUvF/bOzszFjxgz8+uuv0NDQwIgRI/Dll18iNja2yLHGjRuHxMREZGRkYPXq1QAAQ0NDAICuri6ioqJgaWmJy5cvY8iQIdDV1cX48ePRq1cvXLlyBXv37sXBgwcBAPr6+u+gd4iIiEiZMNn9CLm7uyMsLAwA4ODggCVLluDQoUNo06aNws1rdnZ2mD59OoYPH66Q7Obl5WHJkiX45JNPALxKnp2dnXHmzBk0adJE4VgymQxaWlrIycmBubm5wrbJkycrHGvcuHGIjo7G+PHjoaWlBZlMBjU1tSL7vS4nJwc5OTni+4yMjPJ1CBERESktTmP4CLm7uyu8t7CwQFpaGgDg4MGD8PX1hZWVFXR1ddG3b188efIE2dnZYn01NTU0btxYfF+3bl0YGBggMTGxXHFs2LABXl5eMDc3h0wmw+TJk5GSklLu85k1axb09fXFl7W1dbnbICIiIuXEZPcjpK6urvBeIpFALpfj7t276NSpE9zd3bF582bEx8fjp59+AgDk5uZWagynTp1Cnz590KFDB+zatQsXLlzApEmTKnSckJAQpKeni6//zh8mIiKijxunMZAoPj4ecrkckZGRUFF59f+gjRs3FqmXn5+Pc+fOiVMWkpKS8Pz5czg7OxfbroaGBgoKChTKTp48CVtbW0yaNEksu3fv3hv3K45UKoVUKn1jPSIiIvr4cGSXRPb29sjLy8PixYtx+/ZtrF27FsuXLy9ST11dHd9++y3i4uIQHx+PgIAANG3atMh83UJ2dna4dOkSkpKS8PjxY+Tl5cHBwQEpKSmIjo7GrVu38OOPP2Lr1q1F9rtz5w4SEhLw+PFjhXm5RERERGXBZJdE9evXx/z58zFnzhzUq1cP69atw6xZs4rU09bWxoQJE/DVV1/By8sLMpkMGzZsKLHdIUOGwMnJCZ6enjAxMUFsbCy6dOmC7777DqNGjUKDBg1w8uRJTJkyRWG/Hj16oF27dmjVqhVMTEzw+++/V/o5ExERkXKTCIIgVHcQRJUpIyPj1Y1qQRuhItWu7nDoA3N3dsfqDoGI6KNU+Ps7PT0denp6ldYuR3aJiIiISGkx2SUiIiIipcVkl4iIiIiUFpNdIiIiIlJaXGeXlNaVCL9KneBOREREHx6O7BIRERGR0mKyS0RERERKi8kuERERESktJrtEREREpLR4gxoprXph+/gEtQ8En1pGRERVhSO7RERERKS0mOwSERERkdJisktERERESovJLhEREREpLSa7RERERKS0mOwqIR8fHwQFBVV3GAgICEC3bt2qOwwiIiL6iHHpMaoyixYtgiAI1R0GERERfcSY7FKlKygogEQigb6+fnWHQkRERB85TmNQUnK5HOPHj4ehoSHMzc0RHh4ubps/fz7c3Nygo6MDa2trjBgxAllZWeL2e/fuoXPnzqhRowZ0dHTg6uqKPXv2lHisqKgoGBgYYMeOHXBxcYFUKkVKSkqRaQw+Pj4IDAwsMS4AuH79Olq0aAFNTU24uLjg4MGDkEgk2LZtWyX1DBEREX1MOLKrpNasWYMxY8YgLi4Op06dQkBAALy8vNCmTRuoqKjgxx9/RK1atXD79m2MGDEC48ePx9KlSwEAI0eORG5uLo4dOwYdHR1cu3YNMpms1ONlZ2djzpw5WLVqFYyMjGBqalruuAoKCtCtWzfY2NggLi4OmZmZGDt27BvPNScnBzk5OeL7jIyMcvQUERERKTMmu0rK3d0dYWFhAAAHBwcsWbIEhw4dQps2bRRuXrOzs8P06dMxfPhwMdlNSUlBjx494ObmBgCoXbv2G4+Xl5eHpUuXon79+hWO68CBA7h16xZiYmJgbm4OAJgxYwbatGlTapuzZs1CRETEG2MkIiKijw+nMSgpd3d3hfcWFhZIS0sDABw8eBC+vr6wsrKCrq4u+vbtiydPniA7OxsAEBgYiOnTp8PLywthYWG4dOmS2I6rqytkMhlkMhnat28vlmtoaBQ5ZnnjSkpKgrW1tZjoAkCTJk3e2GZISAjS09PF1/3799+4DxEREX0cmOwqKXV1dYX3EokEcrkcd+/eRadOneDu7o7NmzcjPj4eP/30EwAgNzcXADB48GDcvn0bffv2xeXLl+Hp6YnFixcDAPbs2YOEhAQkJCRg1apVYvtaWlqQSCQVjuttSKVS6OnpKbyIiIiIACa7H534+HjI5XJERkaiadOmcHR0xIMHD4rUs7a2xvDhw7FlyxaMHTsWK1euBADY2trC3t4e9vb2sLKyqtTYnJyccP/+fTx8+FAsO3v2bKUeg4iIiD4uTHY/Mvb29sjLy8PixYtx+/ZtrF27FsuXL1eoExQUhH379uHOnTs4f/48jhw5Amdn5yqPrU2bNqhTpw769++PS5cuITY2FpMnTwaAMo0aExEREb2Oye5Hpn79+pg/fz7mzJmDevXqYd26dZg1a5ZCnYKCAowcORLOzs5o164dHB0dxZvXqpKqqiq2bduGrKwsNG7cGIMHD8akSZMAAJqamlV+fCIiIlI+EoGPuKL3WGxsLFq0aIGbN2+iTp06ZdonIyMD+vr6sA7aCBWpdhVHSJXh7uyO1R0CERFVs8Lf3+np6ZV6/w2XHqP3ytatWyGTyeDg4ICbN29i9OjR8PLyKnOiS0RERPRfTHbpvZKZmYkJEyYgJSUFxsbGaN26NSIjI6s7LCIiIvpAMdml90q/fv3Qr1+/6g6DiIiIlASTXVJaVyL8uOYuERHRR46rMRARERGR0mKyS0RERERKi8kuERERESktJrtEREREpLSY7BIRERGR0uJqDKS06oXt4xPUiIioVHyCo/LjyC4RERERKS0mu0RERESktJjsEhEREZHSYrJLREREREqLyS69E3fv3oVEIkFCQkJ1h0JEREQfESa7H4iAgAB069atusMgIiIi+qAw2aUPRm5ubnWHQERERB8YJrvvmU2bNsHNzQ1aWlowMjJC69atERwcjDVr1mD79u2QSCSQSCSIiYkBANy/fx/+/v4wMDCAoaEhunbtirt374rtFY4Iz5w5E2ZmZjAwMMDUqVORn5+P4OBgGBoaombNmli9evUbY7t69So6deoEPT096OrqomXLlrh16xYAQC6XY+rUqahZsyakUikaNGiAvXv3ltre0aNH0aRJE0ilUlhYWGDixInIz88Xt/v4+GDUqFEICgqCsbEx/Pz8yt+hRERE9FFjsvseSU1NRe/evTFw4EAkJiYiJiYG3bt3R1hYGPz9/dGuXTukpqYiNTUVzZs3R15eHvz8/KCrq4vjx48jNjYWMpkM7dq1UxgFPXz4MB48eIBjx45h/vz5CAsLQ6dOnVCjRg3ExcVh+PDhGDZsGP76668SY/v777/x6aefQiqV4vDhw4iPj8fAgQPF5HTRokWIjIzEvHnzcOnSJfj5+aFLly5ITk4usb0OHTqgcePGuHjxIpYtW4ZffvkF06dPV6i3Zs0aaGhoIDY2FsuXLy+2rZycHGRkZCi8iIiIiABAIgiCUN1B0Cvnz5+Hh4cH7t69C1tbW4VtAQEBeP78ObZt2yaW/fbbb5g+fToSExMhkUgAvPpTv4GBAbZt24a2bdsiICAAMTExuH37NlRUXv3fpm7dujA1NcWxY8cAAAUFBdDX18eqVavw5ZdfFhvb999/j+joaCQlJUFdXb3IdisrK4wcORLff/+9WNakSRM0btwYP/30E+7evYtatWrhwoULaNCgASZNmoTNmzcrxL506VJMmDAB6enpUFFRgY+PDzIyMnD+/PlS+y08PBwRERFFyq2DNvIJakREVCo+Qe39kZGRAX19faSnp0NPT6/S2uXI7nukfv368PX1hZubG3r27ImVK1fi2bNnJda/ePEibt68CV1dXchkMshkMhgaGuLly5fi9AIAcHV1FRNdADAzM4Obm5v4XlVVFUZGRkhLSwMAtG/fXmzP1dUVAJCQkICWLVsWm+hmZGTgwYMH8PLyUij38vJCYmJisbEnJiaiWbNmYqJbWD8rK0thhNnDw6PE8y8UEhKC9PR08XX//v037kNEREQfB7XqDoD+j6qqKg4cOICTJ09i//79WLx4MSZNmoS4uLhi62dlZcHDwwPr1q0rss3ExET8+vUEVSKRFFsml8sBAKtWrcK///6rsK+WllbFT+wt6OjovLGOVCqFVCp9B9EQERHRh4bJ7ntGIpHAy8sLXl5eCA0Nha2tLbZu3QoNDQ0UFBQo1G3UqBE2bNgAU1PTSh3ut7KyKlLm7u6ONWvWIC8vr0iirKenB0tLS8TGxsLb21ssj42NRZMmTYo9hrOzMzZv3gxBEMTR3djYWOjq6qJmzZqVdi5ERET0ceM0hvdIXFwcZs6ciXPnziElJQVbtmzBo0eP4OzsDDs7O1y6dAlJSUl4/Pgx8vLy0KdPHxgbG6Nr1644fvw47ty5g5iYGAQGBpZ6s1lFjBo1ChkZGfjyyy9x7tw5JCcnY+3atUhKSgIABAcHY86cOdiwYQOSkpIwceJEJCQkYPTo0cW2N2LECNy/fx/ffvstrl+/ju3btyMsLAxjxoxRmHJBRERE9DY4svse0dPTw7Fjx7Bw4UJkZGTA1tYWkZGRaN++PTw9PRETEwNPT09kZWXhyJEj8PHxwbFjxzBhwgR0794dmZmZsLKygq+vb6WO9AKAkZERDh8+jODgYHh7e0NVVRUNGjQQ5+kGBgYiPT0dY8eORVpaGlxcXLBjxw44ODgU256VlRX27NmD4OBg1K9fH4aGhhg0aBAmT55cqXETERHRx42rMZDSKbybk6sxEBHRm3A1hvcHV2MgIiIiIionJrtEREREpLSY7BIRERGR0mKyS0RERERKi6sxkNK6EuFX6atSEBER0YeFI7tEREREpLSY7BIRERGR0mKyS0RERERKi8kuERERESkt3qBGSqte2D4+QY2IiADwSWkfM47sEhEREZHSYrJLREREREqLyS4RERERKS0mu0RERESktJjsfiTs7OywcOFC8b1EIsG2bdsAAHfv3oVEIkFCQkKVHT8mJgYSiQTPnz+vsmMQERERvY6rMXwkzp49Cx0dneoOg4iIiOidYrL7kTAxManuEN5abm4uNDQ0qjsMIiIi+oBwGoOSyMzMRJ8+faCjowMLCwssWLAAPj4+CAoKAlB0GkNFxMbGwsfHB9ra2qhRowb8/Pzw7NkzAEBOTg4CAwNhamoKTU1NtGjRAmfPni21vc2bN8PV1RVSqRR2dnaIjIxU2G5nZ4dp06ahX79+0NPTw9ChQ98qfiIiIvr4MNlVEmPGjEFsbCx27NiBAwcO4Pjx4zh//nyltZ+QkABfX1+4uLjg1KlTOHHiBDp37oyCggIAwPjx47F582asWbMG58+fh729Pfz8/PD06dNi24uPj4e/vz++/PJLXL58GeHh4ZgyZQqioqIU6s2bNw/169fHhQsXMGXKlGLbysnJQUZGhsKLiIiICOA0BqWQmZmJNWvWYP369fD19QUArF69GpaWlpV2jLlz58LT0xNLly4Vy1xdXQEAL168wLJlyxAVFYX27dsDAFauXIkDBw7gl19+QXBwcJH25s+fD19fXzGBdXR0xLVr1/DDDz8gICBArPfZZ59h7NixpcY2a9YsREREvO0pEhERkRLiyK4SuH37NvLy8tCkSROxTF9fH05OThVqz9XVFTKZDDKZTExeC0d2i3Pr1i3k5eXBy8tLLFNXV0eTJk2QmJhY7D6JiYkK9QHAy8sLycnJ4mgxAHh6er4x3pCQEKSnp4uv+/fvv3EfIiIi+jhwZJeK2LNnD/Ly8gAAWlpaCv++a2VZQUIqlUIqlb6DaIiIiOhDw5FdJVC7dm2oq6sr3BCWnp6OGzduVKg9W1tb2Nvbw97eHlZWVgAAd3d3HDp0qNj6derUgYaGBmJjY8WyvLw8nD17Fi4uLsXu4+zsrFAfeHUDnKOjI1RVVSsUNxEREdHrOLKrBHR1ddG/f38EBwfD0NAQpqamCAsLg4qKCiQSSaUcIyQkBG5ubhgxYgSGDx8ODQ0NHDlyBD179oSxsTG++eYb8fg2NjaYO3cusrOzMWjQoGLbGzt2LBo3boxp06ahV69eOHXqFJYsWaIwJ5iIiIjobXFkV0nMnz8fzZo1Q6dOndC6dWt4eXnB2dkZmpqaldK+o6Mj9u/fj4sXL6JJkyZo1qwZtm/fDjW1V/9fmj17Nnr06IG+ffuiUaNGuHnzJvbt24caNWoU216jRo2wceNGREdHo169eggNDcXUqVMVbk4jIiIielsSQRCE6g6CKt+LFy9gZWWFyMjIEkdXlVVGRgb09fVhHbQRKlLt6g6HiIjeA3dnd6zuEOgNCn9/p6enQ09Pr9La5TQGJXHhwgVcv34dTZo0QXp6OqZOnQoA6Nq1azVHRkRERFR9mOwqkXnz5iEpKQkaGhrw8PDA8ePHYWxsXN1hEREREVUbJrtKomHDhoiPj6/uMIiIiIjeK7xBjYiIiIiUFkd2SWldifCr1AnuRERE9OHhyC4RERERKS0mu0RERESktJjsEhEREZHSYrJLREREREqLN6iR0qoXto9PUCOid45P6iJ6v3Bkl4iIiIiUFpNdIiIiIlJaTHaJiIiISGkx2SUiIiIipcVk9z1mZ2eHhQsXiu8lEgm2bdsGALh79y4kEgkSEhKqJbay+BBiJCIiIuXG1RjeY2fPnoWOjk51h1Fh1tbWSE1NhbGxcXWHQkRERB8pJrvvMRMTk+oO4a2oqqrC3Ny8usMgIiKijxinMVSjzMxM9OnTBzo6OrCwsMCCBQvg4+ODoKAgAEWnMZTXvXv30LlzZ9SoUQM6OjpwdXXFnj17AAAxMTGQSCTYvXs33N3doampiaZNm+LKlSvi/k+ePEHv3r1hZWUFbW1tuLm54ffff1c4hlwux9y5c2Fvbw+pVAobGxvMmDEDQNFpDIXHPHToEDw9PaGtrY3mzZsjKSlJoc3p06fD1NQUurq6GDx4MCZOnIgGDRpUuB+IiIjo48VktxqNGTMGsbGx2LFjBw4cOIDjx4/j/Pnzldb+yJEjkZOTg2PHjuHy5cuYM2cOZDKZQp3g4GBERkbi7NmzMDExQefOnZGXlwcAePnyJTw8PLB7925cuXIFQ4cORd++fXHmzBlx/5CQEMyePRtTpkzBtWvXsH79epiZmZUa16RJkxAZGYlz585BTU0NAwcOFLetW7cOM2bMwJw5cxAfHw8bGxssW7as0vqEiIiIPi6cxlBNMjMzsWbNGqxfvx6+vr4AgNWrV8PS0rLSjpGSkoIePXrAzc0NAFC7du0idcLCwtCmTRsAwJo1a1CzZk1s3boV/v7+sLKywrhx48S63377Lfbt24eNGzeiSZMmyMzMxKJFi7BkyRL0798fAFCnTh20aNGi1LhmzJgBb29vAMDEiRPRsWNHvHz5Epqamli8eDEGDRqEAQMGAABCQ0Oxf/9+ZGVlldheTk4OcnJyxPcZGRll6R4iIiL6CHBkt5rcvn0beXl5aNKkiVimr68PJyenCrXn6uoKmUwGmUyG9u3bAwACAwMxffp0eHl5ISwsDJcuXSqyX7NmzcSvDQ0N4eTkhMTERABAQUEBpk2bBjc3NxgaGkImk2Hfvn1ISUkBACQmJiInJ0dM1svK3d1d/NrCwgIAkJaWBgBISkpS6BMARd6/btasWdDX1xdf1tbW5YqHiIiIlBeTXSWxZ88eJCQkICEhAatWrQIADB48GLdv30bfvn1x+fJleHp6YvHixWVu84cffsCiRYswYcIEHDlyBAkJCfDz80Nubi4AQEtLq0Kxqquri19LJBIAr+b+VlRISAjS09PF1/379yvcFhERESkXJrvVpHbt2lBXV8fZs2fFsvT0dNy4caNC7dna2sLe3h729vawsrISy62trTF8+HBs2bIFY8eOxcqVKxX2O336tPj1s2fPcOPGDTg7OwMAYmNj0bVrV3z99deoX78+ateurRCfg4MDtLS0cOjQoQrFXBwnJyeFPgFQ5P3rpFIp9PT0FF5EREREwFvM2V27di2WL1+OO3fu4NSpU7C1tcXChQtRq1YtdO3atTJjVEq6urro378/goODYWhoCFNTU4SFhUFFRUUc7XxbQUFBaN++PRwdHfHs2TMcOXJETGQLTZ06FUZGRjAzM8OkSZNgbGyMbt26AXiVzG7atAknT55EjRo1MH/+fDx8+BAuLi4AAE1NTUyYMAHjx4+HhoYGvLy88OjRI1y9ehWDBg2qUMzffvsthgwZAk9PTzRv3hwbNmzApUuXip1vTERERPQmFRrZXbZsGcaMGYMOHTrg+fPnKCgoAAAYGBi81VJZH5v58+ejWbNm6NSpE1q3bg0vLy84OztDU1OzUtovKCjAyJEj4ezsjHbt2sHR0RFLly5VqDN79myMHj0aHh4e+Oeff7Bz505oaGgAACZPnoxGjRrBz88PPj4+MDc3FxPhQlOmTMHYsWMRGhoKZ2dn9OrVS5x/WxF9+vRBSEgIxo0bh0aNGuHOnTsICAiotD4hIiKij4tEEAShvDu5uLhg5syZ6NatG3R1dXHx4kXUrl0bV65cgY+PDx4/flwVsSq9Fy9ewMrKCpGRkRUeGS2rmJgYtGrVCs+ePYOBgUGVHutttWnTBubm5li7dm2Z6mdkZLy6US1oI1Sk2lUcHRGRoruzO1Z3CEQfpMLf3+np6ZU6JbFC0xju3LmDhg0bFimXSqV48eLFWwf1sbhw4QKuX7+OJk2aID09HVOnTgWAj3oaSHZ2NpYvXw4/Pz+oqqri999/x8GDB3HgwIHqDo2IiIg+QBVKdmvVqoWEhATY2toqlO/du7fInFAq3bx585CUlAQNDQ14eHjg+PHjMDY2ru6wqo1EIsGePXswY8YMvHz5Ek5OTti8eTNat25d3aERERHRB6hCye6YMWMwcuRIvHz5EoIg4MyZM/j9998xa9YscdkrerOGDRsiPj6+Wo7t4+ODCsxgqXJaWlo4ePBgdYdBRERESqJCye7gwYOhpaWFyZMnIzs7G1999RUsLS2xaNEifPnll5UdIxERERFRhZT7BrX8/HysX78efn5+MDMzQ3Z2NrKysmBqalpVMRKVS1VNcCciIqKqU1W/v8u99JiamhqGDx+Oly9fAgC0tbWZ6BIRERHRe6lC6+w2adIEFy5cqOxYiIiIiIgqVYXm7I4YMQJjx47FX3/9BQ8PD+jo6Chsd3d3r5TgiIiIiIjeRoUeKqGiUnRAWCKRQBAESCQS8YlqRNWBc3aJiIg+PO/dQyWI3nf1wvbxCWpERESV6EN8QmCFkt3XHyZBRERERPQ+qlCy++uvv5a6vV+/fhUKhoiIiIioMlUo2R09erTC+7y8PGRnZ0NDQwPa2tpMdomIiIjovVChpceePXum8MrKykJSUhJatGiB33//vbJjJCIiIiKqkAolu8VxcHDA7Nmzi4z6EhERERFVl0pLdoFXT1d78OBBZTb53rGzs8PChQvF9xKJBNu2bQMA3L17FxKJBAkJCdUSW0WEh4ejQYMG1R0GERERUZWo0JzdHTt2KLwXBAGpqalYsmQJvLy8KiWw99XZs2eLPESjsvn4+KBBgwYKSXVMTAxatWqFZ8+ewcDAoEqP/z6RSCTYunUrunXrVt2hEBER0QeoQsnu64mHRCKBiYkJPvvsM0RGRlZGXO8tExOT6g6BiIiIiMqoQtMY5HK5wqugoAD//PMP1q9fDwsLi8qO8Z3KzMxEnz59oKOjAwsLCyxYsAA+Pj4ICgoCUHQaQ3k9efIEvXv3hpWVFbS1teHm5qZwU19AQACOHj2KRYsWQSKRQCKR4O7du2jVqhUAoEaNGpBIJAgICAAA7N27Fy1atICBgQGMjIzQqVMn3Lp1S+GYf/31F3r37g1DQ0Po6OjA09MTcXFxCnXWrl0LOzs76Ovr48svv0RmZqa4zcfHB99++y2CgoJQo0YNmJmZYeXKlXjx4gUGDBgAXV1d2Nvb488//1Ro88qVK2jfvj1kMhnMzMzQt29fPH78WKHdwMBAjB8/HoaGhjA3N0d4eLi43c7ODgDw+eefQyKRiO+JiIiIyqpCye7UqVORnZ1dpPzff//F1KlT3zqo6jRmzBjExsZix44dOHDgAI4fP47z589XWvsvX76Eh4cHdu/ejStXrmDo0KHo27cvzpw5AwBYtGgRmjVrhiFDhiA1NRWpqamwtrbG5s2bAQBJSUlITU3FokWLAAAvXrzAmDFjcO7cORw6dAgqKir4/PPPIZfLAQBZWVnw9vbG33//jR07duDixYsYP368uB0Abt26hW3btmHXrl3YtWsXjh49itmzZyvEvWbNGhgbG+PMmTP49ttv8c0336Bnz55o3rw5zp8/j7Zt26Jv377idfH8+XN89tlnaNiwIc6dO4e9e/fi4cOH8Pf3L9Kujo4O4uLiMHfuXEydOhUHDhwA8GrKCACsXr0aqamp4vvX5eTkICMjQ+FFREREBFRwGkNERASGDx8ObW3FR7FmZ2cjIiICoaGhlRLcu5aZmYk1a9Zg/fr18PX1BfAq0bK0tKy0Y1hZWWHcuHHi+2+//Rb79u3Dxo0b0aRJE+jr64vrFZubm4v1DA0NAQCmpqYKc3Z79Oih0P7//vc/mJiY4Nq1a6hXrx7Wr1+PR48e4ezZs2Ib9vb2CvvI5XJERUVBV1cXANC3b18cOnQIM2bMEOvUr18fkydPBgCEhIRg9uzZMDY2xpAhQwAAoaGhWLZsGS5duoSmTZtiyZIlaNiwIWbOnKkQm7W1NW7cuAFHR0cAgLu7O8LCwgC8WtFjyZIlOHToENq0aSNOGTEwMFDoi9fNmjULERERJW4nIiKij1eFRnYFQYBEIilSfvHiRTGh+hDdvn0beXl5aNKkiVimr68PJyenCrXn6uoKmUwGmUyG9u3bAwAKCgowbdo0uLm5wdDQEDKZDPv27UNKSkqFjpGcnIzevXujdu3a0NPTE//UX9heQkICGjZsWOrnYmdnJya6AGBhYYG0tDSFOu7u7uLXqqqqMDIygpubm1hmZmYGAOJ+Fy9exJEjR8Tzl8lkqFu3LgAoTLP4b7slHftNQkJCkJ6eLr7u379frv2JiIhIeZVrZLdwvqhEIoGjo6NCwltQUICsrCwMHz680oP8UO3Zswd5eXkAAC0tLQDADz/8gEWLFmHhwoVwc3ODjo4OgoKCkJubW6FjdO7cGba2tli5ciUsLS0hl8tRr149sb3C45ZGXV1d4b1EIlGY5lBSnf+WFV4L/50+0blzZ8yZM6fI8f47r7ssx34TqVQKqVRarn2IiIjo41CuZHfhwoUQBAEDBw5EREQE9PX1xW0aGhqws7NDs2bNKj3Id6V27dpQV1fH2bNnYWNjAwBIT0/HjRs38Omnn5a7PVtb2yJlsbGx6Nq1K77++msAr5LDGzduwMXFRayjoaGBgoIChf00NDQAQKH8yZMnSEpKwsqVK9GyZUsAwIkTJxT2c3d3x6pVq/D06dN3OureqFEjbN68GXZ2dlBTq9BsGQCvkuHX+4KIiIiorMqVhfTv3x8AUKtWLTRv3rzIqNyHTldXF/3790dwcDAMDQ1hamqKsLAwqKioFDttoyIcHBywadMmnDx5EjVq1MD8+fPx8OFDhWTXzs4OcXFxuHv3LmQyGQwNDWFrawuJRIJdu3ahQ4cO0NLSQo0aNWBkZISff/4ZFhYWSElJwcSJExWO17t3b8ycORPdunXDrFmzYGFhgQsXLsDS0rJK/2MycuRIrFy5Er179xZXW7h58yaio6OxatUqqKqqlqkdOzs7HDp0CF5eXpBKpahRo0aVxUxERETKp0Jzdr29vcVE9+XLl0p1J/z8+fPRrFkzdOrUCa1bt4aXlxecnZ2hqalZKe1PnjwZjRo1gp+fH3x8fGBubl5k3eJx48ZBVVUVLi4uMDExQUpKCqysrBAREYGJEyfCzMwMo0aNgoqKCqKjoxEfH4969erhu+++ww8//KDQloaGBvbv3w9TU1N06NABbm5umD17dpmTzYqytLREbGwsCgoK0LZtW7i5uSEoKAgGBgZQUSn7ZRcZGYkDBw7A2toaDRs2rMKIiYiISBlJBEEQyrtTdnY2xo8fj40bN+LJkydFtivTn51fvHgBKysrREZGYtCgQdUdDpVBRkYG9PX1YR20ESpS7TfvQERERGVyd3bHKmu78Pd3eno69PT0Kq3dCo3sBgcH4/Dhw1i2bBmkUilWrVqFiIgIWFpa4tdff6204KrDhQsX8Pvvv+PWrVs4f/48+vTpAwDo2rVrNUdGREREROVVoTuHdu7ciV9//RU+Pj4YMGAAWrZsCXt7e9ja2mLdunVigvihmjdvHpKSkqChoQEPDw8cP34cxsbG1R0WEREREZVThZLdp0+fonbt2gAAPT09PH36FADQokULfPPNN5UXXTVo2LAh4uPjqzsMIiIiIqoEFUp2a9eujTt37sDGxgZ169YVn/61c+dOhad7EVWnKxF+lTrnh4iIiD48FZqzO2DAAFy8eBEAMHHiRPz000/Q1NTEd999h+Dg4EoNkIiIiIiooiq0GsPr7t27h/j4eNjb2xd5/CvRu1ZVd3MSERFR1amq398Vf7TV//fy5UvY2toW+7QwIiIiIqLqVKFpDAUFBZg2bRqsrKwgk8lw+/ZtAMCUKVPwyy+/VGqAREREREQVVaFkd8aMGYiKisLcuXOhoaEhlterVw+rVq2qtOCIiIiIiN5GhaYx/Prrr/j555/h6+uL4cOHi+X169fH9evXKy04ordRL2wfn6D2kanKJ/sQEdGHqUIju3///Tfs7e2LlMvlcuTl5b11UERERERElaFCya6LiwuOHz9epHzTpk1o2LDhWwdFRERERFQZKjSNITQ0FP3798fff/8NuVyOLVu2ICkpCb/++it27dpV2TESEREREVVIuUZ2b9++DUEQ0LVrV+zcuRMHDx6Ejo4OQkNDkZiYiJ07d6JNmzZVFSsRERERUbmUK9l1cHDAo0ePAAAtW7aEoaEhLl++jOzsbJw4cQJt27Z964B8fHwQFBT01u1UlJ2dHRYuXFglbYeHh6NBgwZV0nZlCwgIQLdu3Uqt864+q6r8TIiIiEi5lSvZff1ha3/++SdevHhRqQEps3HjxuHQoUPVHQYRERHRR+OtnqBWCU8aVgp5eXlQV1d/Yz2ZTAaZTPYOIiq7goICSCQSqKhU6F5FIiIiovdauTIciUQCiURSpKyiXrx4gX79+kEmk8HCwgKRkZEK2589e4Z+/fqhRo0a0NbWRvv27ZGcnKxQ58SJE2jZsiW0tLRgbW2NwMBAhdHmpUuXwsHBAZqamjAzM8MXX3zxxrgyMzPRu3dv6OjowMrKCj/99FORc162bBm6dOkCHR0d8SEbBgYGCvW2bdum0D+vT2OIiYlBkyZNoKOjAwMDA3h5eeHevXvi9u3bt6NRo0bQ1NRE7dq1ERERgfz8/FJjf1OfFca5Y8cOuLi4QCqVIiUlpcT2IiIiYGJiAj09PQwfPhy5ubkl1s3JycG4ceNgZWUFHR0dfPLJJ4iJiVGo86bPKy0tDZ07d4aWlhZq1aqFdevWlXq+RERERKUp9zSGgIAAdO/eHd27d8fLly8xfPhw8X3hq6yCg4Nx9OhRbN++Hfv370dMTAzOnz8vbg8ICMC5c+ewY8cOnDp1CoIgoEOHDuJavrdu3UK7du3Qo0cPXLp0CRs2bMCJEycwatQoAMC5c+cQGBiIqVOnIikpCXv37sWnn376xrh++OEH1K9fHxcuXMDEiRMxevRoHDhwQKFOeHg4Pv/8c1y+fBkDBw4s8zkXys/PR7du3eDt7Y1Lly7h1KlTGDp0qJgcHz9+HP369cPo0aNx7do1rFixAlFRUZgxY0ap7b6pzwAgOzsbc+bMwapVq3D16lWYmpoW29ahQ4eQmJiImJgY/P7779iyZQsiIiJKPPaoUaNw6tQpREdH49KlS+jZsyfatWsnJttv+rwK479//z6OHDmCTZs2YenSpUhLSyv1nHNycpCRkaHwIiIiIgIAiVCOuQgDBgwoU73Vq1e/sU5WVhaMjIzw22+/oWfPngCAp0+fombNmhg6dChGjhwJR0dHxMbGonnz5gCAJ0+ewNraGmvWrEHPnj0xePBgqKqqYsWKFWK7J06cgLe3N168eIE9e/ZgwIAB+Ouvv6Crq1um2O3s7ODs7Iw///xTLPvyyy+RkZGBPXv2AHg1shsUFIQFCxaIdaKiohAUFITnz5+LZdu2bcPnn38uTvcIDw/Htm3bkJCQgKdPn8LIyAgxMTHw9vYuEkfr1q3h6+uLkJAQsey3337D+PHj8eDBg2JjT05OfmOfRUVFYcCAAUhISED9+vVL7IeAgADs3LkT9+/fh7b2q6eQLV++HMHBwUhPT4eKigp8fHzQoEEDLFy4ECkpKahduzZSUlJgaWmpcB5NmjTBzJkz3/h5paSkwMnJCWfOnEHjxo0BANevX4ezszMWLFhQ4s1w4eHhxSbh1kEb+QS1jwyfoEZE9OHKyMiAvr4+0tPToaenV2ntlmvOblmS2LK6desWcnNz8cknn4hlhoaGcHJyAgAkJiZCTU1NYbuRkRGcnJyQmJgIALh48SIuXbqk8KduQRAgl8tx584dtGnTBra2tqhduzbatWuHdu3a4fPPP4e2tjbWrVuHYcOGifv9+eefaNmyJQCgWbNmCrE2a9asyGoAnp6eb3X+hoaGCAgIgJ+fH9q0aYPWrVvD398fFhYW4rnFxsYqjOQWFBTg5cuXyM7OxpgxY/Dbb7+J27KyssrUZwCgoaEBd3d3AEBKSgpcXFzEbd9//z2+//57AK8e/1yY6Bb2Q1ZWFu7fvw9bW1uF87l8+TIKCgrg6OioUJ6TkwMjIyPxnEr7vG7cuAE1NTV4eHiI2+vWrVtkesjrQkJCMGbMGPF9RkYGrK2tS92HiIiIPg5vdYNadcvKysKwYcMQGBhYZJuNjQ00NDRw/vx5xMTEYP/+/QgNDUV4eDjOnj2LLl26KCSFVlZW5Tq2jo6OwnsVFZUiN+y96dHJq1evRmBgIPbu3YsNGzZg8uTJOHDgAJo2bYqsrCxEREQUOy1EU1MTU6dOxbhx48oVcyEtLS1xuoSlpSUSEhLEbYaGhhVqMysrC6qqqoiPj4eqqqrCtsKb8t70ed24caNCx5ZKpZBKpRXal4iIiJRbtSW7derUgbq6OuLi4mBjYwPg1c1VN27cgLe3N5ydnZGfn4+4uDiFP8knJSWJI5GNGjXCtWvXYG9vX+Jx1NTU0Lp1a7Ru3RphYWEwMDDA4cOH0b179xKnNpw+fbrIe2dn51LPx8TEBJmZmXjx4oWYCP83iSxJw4YN0bBhQ4SEhKBZs2ZYv349mjZtikaNGiEpKanEczM1NS0y17YsffY6NTW1Eo9x8eJF/Pvvv9DS0gLwqh9kMlmxo6YNGzZEQUEB0tLSxBHy173p86pbty7y8/MRHx8vTmNISkpSmBpCREREVB7Vtt6UTCbDoEGDEBwcjMOHD+PKlSsICAgQl8BycHBA165dMWTIEJw4cQIXL17E119/DSsrK3Tt2hUAMGHCBJw8eRKjRo1CQkICkpOTsX37dvGGp127duHHH39EQkIC7t27h19//RVyuVycKlGS2NhYzJ07Fzdu3MBPP/2EP/74A6NHjy51n08++QTa2tr4/vvvcevWLaxfvx5RUVEl1r9z5w5CQkJw6tQp3Lt3D/v370dycrKYVIeGhuLXX39FREQErl69isTERERHR2Py5MkltlmWPiuP3NxcDBo0CNeuXcOePXsQFhaGUaNGFbtMmaOjI/r06YN+/fphy5YtuHPnDs6cOYNZs2Zh9+7dAN78eTk5OaFdu3YYNmwY4uLiEB8fj8GDB4vJNhEREVF5Veviqj/88ANatmyJzp07o3Xr1mjRooXCfM3Vq1fDw8MDnTp1QrNmzSAIAvbs2SOuaevu7o6jR4/ixo0baNmyJRo2bIjQ0FDxBikDAwNs2bIFn332GZydnbF8+XL8/vvvcHV1LTWusWPH4ty5c2jYsCGmT5+O+fPnw8/Pr9R9DA0N8dtvv2HPnj1wc3PD77//jvDw8BLra2tr4/r16+jRowccHR3Fm/IK5xH7+flh165d2L9/Pxo3boymTZtiwYIFRebKvu5NfVYevr6+cHBwwKeffopevXqhS5cupZ7T6tWr0a9fP4wdOxZOTk7o1q0bzp49K47cv+nzKmzD0tIS3t7e6N69O4YOHVriahFEREREb1Ku1RiIPgSFd3NyNYaPD1djICL6cFXVagx8bBYRERERKS0mu0RERESktJjsEhEREZHSYrJLRERERErrg36oBFFprkT4VeoEdyIiIvrwcGSXiIiIiJQWk10iIiIiUlpMdomIiIhIaTHZJSIiIiKlxRvUSGnVC9vHJ6gREb0BnzxIyo4ju0RERESktJjsEhEREZHSYrJLREREREqLyS4RERERKS0mu/TOBAQEoFu3btUdBhEREX1EmOwSERERkdJisksfDEEQkJ+fX91hEBER0QeEye4HSi6XY+7cubC3t4dUKoWNjQ1mzJgBAJgwYQIcHR2hra2N2rVrY8qUKcjLyxP3DQ8PR4MGDbBixQpYW1tDW1sb/v7+SE9Pr/AxAeDy5cv47LPPoKWlBSMjIwwdOhRZWVkltpeTk4PAwECYmppCU1MTLVq0wNmzZ8XtMTExkEgk+PPPP+Hh4QGpVIoTJ05UtMuIiIjoI8Rk9wMVEhKC2bNnY8qUKbh27RrWr18PMzMzAICuri6ioqJw7do1LFq0CCtXrsSCBQsU9r958yY2btyInTt3Yu/evbhw4QJGjBhR4WO+ePECfn5+qFGjBs6ePYs//vgDBw8exKhRo0psb/z48di8eTPWrFmD8+fPw97eHn5+fnj69KlCvYkTJ2L27NlITEyEu7t7kXZycnKQkZGh8CIiIiICAIkgCEJ1B0Hlk5mZCRMTEyxZsgSDBw9+Y/158+YhOjoa586dA/BqZHf69Om4d+8erKysAAB79+5Fx44d8ffff8Pc3Lzcx1y5ciUmTJiA+/fvQ0dHBwCwZ88edO7cGQ8ePICZmRkCAgLw/PlzbNu2DS9evECNGjUQFRWFr776CgCQl5cHOzs7BAUFITg4GDExMWjVqhW2bduGrl27lnh+4eHhiIiIKFJuHbSRT1AjInoDPkGN3hcZGRnQ19dHeno69PT0Kq1djux+gBITE5GTkwNfX99it2/YsAFeXl4wNzeHTCbD5MmTkZKSolDHxsZGTHQBoFmzZpDL5UhKSsLx48chk8nE17p16954zMTERNSvX19MdAHAy8tLbPN1t27dQl5eHry8vMQydXV1NGnSBImJiQp1PT09S+2PkJAQpKeni6/79++XWp+IiIg+HmrVHQCVn5aWVonbTp06hT59+iAiIgJ+fn7Q19dHdHQ0IiMjy9y+p6cnEhISxPdmZma4e/fuW0T8dv6bQBdHKpVCKpW+o2iIiIjoQ8KR3Q+Qg4MDtLS0cOjQoSLbTp48CVtbW0yaNAmenp5wcHDAvXv3itRLSUnBgwcPxPenT5+GiooKnJycoKWlBXt7e/Glq6tb6jEBwNnZGRcvXsSLFy/EstjYWLHN19WpUwcaGhqIjY0Vy/Ly8nD27Fm4uLiUqz+IiIiISsKR3Q+QpqYmJkyYgPHjx0NDQwNeXl549OgRrl69CgcHB6SkpCA6OhqNGzfG7t27sXXr1mLb6N+/P+bNm4eMjAwEBgbC39+/2Pm6bzrmoEGD0KdPH4SFhaF///4IDw/Ho0eP8O2336Jv377iTWz/paOjg2+++QbBwcEwNDSEjY0N5s6di+zsbAwaNKjS+4yIiIg+Tkx2P1BTpkyBmpoaQkND8eDBA1hYWGD48OEYNGgQvvvuO4waNQo5OTno2LEjpkyZgvDwcIX97e3t0b17d3To0AFPnz5Fp06dsHTp0godEwC0tbWxb98+jB49Go0bN4a2tjZ69OiB+fPnl9je7NmzIZfL0bdvX2RmZsLT0xP79u1DjRo13rp/iIiIiACuxvBRCg8Px7Zt2xTm5SqTwrs5uRoDEdGbcTUGel9wNQYiIiIionJisktERERESovJ7kcoPDxcaacwEBEREf0Xk10iIiIiUlpcjYGU1pUIv0qd4E5EREQfHo7sEhEREZHSYrJLREREREqLyS4RERERKS0mu0RERESktHiDGimtemH7+AQ1IqoQPlWMSHlwZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJfKJCAgAN26davuMIiIiIjKhckuERERESktJrtEREREpLSY7L6n5HI55s6dC3t7e0ilUtjY2GDGjBkAgAkTJsDR0RHa2tqoXbs2pkyZgry8PHHf8PBwNGjQACtWrIC1tTW0tbXh7++P9PT0Uo+5adMmuLm5QUtLC0ZGRmjdujVevHihUGfevHmwsLCAkZERRo4cqXDctWvXwtPTE7q6ujA3N8dXX32FtLQ0cXtMTAwkEgl2794Nd3d3aGpqomnTprhy5YrCMU6cOIGWLVtCS0sL1tbWCAwMLBIHERERUVkw2X1PhYSEYPbs2ZgyZQquXbuG9evXw8zMDACgq6uLqKgoXLt2DYsWLcLKlSuxYMEChf1v3ryJjRs3YufOndi7dy8uXLiAESNGlHi81NRU9O7dGwMHDkRiYiJiYmLQvXt3CIIg1jly5Ahu3bqFI0eOYM2aNYiKikJUVJS4PS8vD9OmTcPFixexbds23L17FwEBAUWOFRwcjMjISJw9exYmJibo3LmzmDTfunUL7dq1Q48ePXDp0iVs2LABJ06cwKhRo96iN4mIiOhjJRH+m83QeyEzMxMmJiZYsmQJBg8e/Mb68+bNQ3R0NM6dOwfg1cju9OnTce/ePVhZWQEA9u7di44dO+Lvv/+Gubl5kTbOnz8PDw8P3L17F7a2tkW2BwQEICYmBrdu3YKqqioAwN/fHyoqKoiOji42rnPnzqFx48bIzMyETCZDTEwMWrVqhejoaPTq1QsA8PTpU9SsWRNRUVHw9/fH4MGDoaqqihUrVojtnDhxAt7e3njx4gU0NTWLHCcnJwc5OTni+4yMDFhbW8M6aCOfoEZEFcInqBG9exkZGdDX10d6ejr09PQqrV2O7L6HEhMTkZOTA19f32K3b9iwAV5eXjA3N4dMJsPkyZORkpKiUMfGxkZMdAGgWbNmkMvlSEpKwvHjxyGTycTXunXrUL9+ffj6+sLNzQ09e/bEypUr8ezZM4U2XV1dxUQXACwsLBSmKcTHx6Nz586wsbGBrq4uvL29AaBIbM2aNRO/NjQ0hJOTExITEwEAFy9eRFRUlEJ8fn5+kMvluHPnTrH9MWvWLOjr64sva2vrEvuWiIiIPi5Mdt9DWlpaJW47deoU+vTpgw4dOmDXrl24cOECJk2ahNzc3DK37+npiYSEBPHVpUsXqKqq4sCBA/jzzz/h4uKCxYsXw8nJSSHBVFdXV2hHIpFALpcDAF68eAE/Pz/o6elh3bp1OHv2LLZu3QoA5YotKysLw4YNU4jv4sWLSE5ORp06dYrdJyQkBOnp6eLr/v37ZT4eERERKTe16g6AinJwcICWlhYOHTpUZBrDyZMnYWtri0mTJoll9+7dK9JGSkoKHjx4AEtLSwDA6dOnoaKiAicnJ2hpacHe3r7IPhKJBF5eXvDy8kJoaChsbW2xdetWjBkz5o0xX79+HU+ePMHs2bPFkdXCaRWvO336NGxsbAAAz549w40bN+Ds7AwAaNSoEa5du1ZsfCWRSqWQSqVlrk9EREQfDya77yFNTU1MmDAB48ePh4aGBry8vPDo0SNcvXoVDg4OSElJQXR0NBo3bozdu3eLI6ivt9G/f3/MmzcPGRkZCAwMhL+/f7HzdQEgLi4Ohw4dQtu2bWFqaoq4uDg8evRITELfxMbGBhoaGli8eDGGDx+OK1euYNq0acXWnTp1KoyMjGBmZoZJkybB2NhYfGDFhAkT0LRpU4waNQqDBw+Gjo4Orl27hgMHDmDJkiVl60AiIiKi/4/TGN5TU6ZMwdixYxEaGgpnZ2f06tULaWlp6NKlC7777juMGjUKDRo0wMmTJzFlypQi+9vb26N79+7o0KED2rZtC3d3dyxdurTE4+np6eHYsWPo0KEDHB0dMXnyZERGRqJ9+/ZlitfExARRUVH4448/4OLigtmzZ2PevHnF1p09ezZGjx4NDw8P/PPPP9i5cyc0NDQAAO7u7jh69Chu3LiBli1bomHDhggNDRVHqImIiIjKg6sxKKHw8HBs27YNCQkJ1R2KgsLVGJ49ewYDA4MqO07h3ZxcjYGIKoqrMRC9e1yNgYiIiIionJjsEhEREZHS4jQGUjqcxkBEb4vTGIjePU5jICIiIiIqJy49RkrrSoRfpf7PkIiIiD48HNklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2iYiIiEhp8QY1Ulr1wvZx6TEiovccl3mjqsaRXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWkx2qVLY2dlh4cKFpdaRSCTYtm1bqXUCAgLQrVu3SouLiIiIPm5Mdt9TH1rSd/bsWQwdOrTM9e/evQuJRIKEhISqC4qIiIg+elxnlyqFiYlJdYdAREREVARHdqvZpk2b4ObmBi0tLRgZGaF169YIDg7GmjVrsH37dkgkEkgkEsTExAAA7t+/D39/fxgYGMDQ0BBdu3bF3bt3xfYKR4RnzpwJMzMzGBgYYOrUqcjPz0dwcDAMDQ1Rs2ZNrF69utS4PD09MW/ePPF9t27doK6ujqysLADAX3/9BYlEgps3bwIoOo0hOTkZn376KTQ1NeHi4oIDBw4otF+rVi0AQMOGDSGRSODj46Owfd68ebCwsICRkRFGjhyJvLy88nQrEREREQAmu9UqNTUVvXv3xsCBA5GYmIiYmBh0794dYWFh8Pf3R7t27ZCamorU1FQ0b94ceXl58PPzg66uLo4fP47Y2FjIZDK0a9cOubm5YruHDx/GgwcPcOzYMcyfPx9hYWHo1KkTatSogbi4OAwfPhzDhg3DX3/9VWJs3t7eYoItCAKOHz8OAwMDnDhxAgBw9OhRWFlZwd7evsi+crkc3bt3h4aGBuLi4rB8+XJMmDBBoc6ZM2cAAAcPHkRqaiq2bNkibjty5Ahu3bqFI0eOYM2aNYiKikJUVFSJsebk5CAjI0PhRURERAQw2a1WqampyM/PR/fu3WFnZwc3NzeMGDECMpkMWlpakEqlMDc3h7m5OTQ0NLBhwwbI5XKsWrUKbm5ucHZ2xurVq5GSkiImpgBgaGiIH3/8EU5OThg4cCCcnJyQnZ2N77//Hg4ODggJCYGGhoaYuBbHx8cHJ06cQEFBAS5dugQNDQ306dNHPE5MTAy8vb2L3ffgwYO4fv06fv31V9SvXx+ffvopZs6cqVCncNqDkZERzM3NYWhoKG6rUaMGlixZgrp166JTp07o2LEjDh06VGKss2bNgr6+vviytrZ+U9cTERHRR4LJbjWqX78+fH194ebmhp49e2LlypV49uxZifUvXryImzdvQldXFzKZDDKZDIaGhnj58iVu3bol1nN1dYWKyv99tGZmZnBzcxPfq6qqwsjICGlpaQCA9u3bi+25uroCAFq2bInMzExcuHABR48ehbe3N3x8fMRk9+jRo0WmHhRKTEyEtbU1LC0txbJmzZqVuV9cXV2hqqoqvrewsBBjLU5ISAjS09PF1/3798t8LCIiIlJuvEGtGqmqquLAgQM4efIk9u/fj8WLF2PSpEmIi4srtn5WVhY8PDywbt26Itv+e4OYurq6wjaJRFJsmVwuBwCsWrUK//77r8K+BgYGqF+/PmJiYnDq1Cm0adMGn376KXr16oUbN24gOTm5xJHdt1VarMWRSqWQSqVVEgsRERF92JjsVjOJRAIvLy94eXkhNDQUtra22Lp1KzQ0NFBQUKBQt1GjRtiwYQNMTU2hp6dXaTFYWVkVW+7t7Y0jR47gzJkzmDFjBgwNDeHs7IwZM2bAwsICjo6Oxe7n7OyM+/fvIzU1FRYWFgCA06dPK9TR0NAAgCLnSERERFSZOI2hGsXFxWHmzJk4d+4cUlJSsGXLFjx69AjOzs6ws7PDpUuXkJSUhMePHyMvLw99+vSBsbExunbtiuPHj+POnTuIiYlBYGBgqTebVZSPjw/27dsHNTU11K1bVyxbt25dqaO6rVu3hqOjI/r374+LFy/i+PHjmDRpkkIdU1NTaGlpYe/evXj48CHS09MrPX4iIiIiJrvVSE9PD8eOHUOHDh3g6OiIyZMnIzIyEu3bt8eQIUPg5OQET09PmJiYIDY2Ftra2jh27BhsbGzQvXt3ODs7Y9CgQXj58mWljvQWatmyJeRyuUJi6+Pjg4KCghLn6wKAiooKtm7din///RdNmjTB4MGDMWPGDIU6ampq+PHHH7FixQpYWlqia9eulR4/ERERkUQQBKG6gyCqTBkZGa9WZQjaCBWpdnWHQ0REpbg7u2N1h0DvicLf3+np6ZU6iMeRXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFpcdIaV2J8KuSG/eIiIjow8GRXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSIiIiJSWlyNgZRWvbB9fIIaEZWKT+8iUn4c2SUiIiIipcVkl4iIiIiUFpNdIiIiIlJaTHaJiIiISGkx2f3IxcTEQCKR4Pnz52/VjkQiwbZt2975cYmIiIhKw2S3mgUEBKBbt27VHcZbS01NRfv27Su1zfDwcDRo0KBS2yQiIqKPC5ceo0phbm5e3SEQERERFcGR3Xdk06ZNcHNzg5aWFoyMjNC6dWsEBwdjzZo12L59OyQSCSQSCWJiYgAA9+/fh7+/PwwMDGBoaIiuXbvi7t27YnuFI8IzZ86EmZkZDAwMMHXqVOTn5yM4OBiGhoaoWbMmVq9eXab44uPj4enpCW1tbTRv3hxJSUkK27dv345GjRpBU1MTtWvXRkREBPLz88Xtr09jOHnyJBo0aABNTU14enpi27ZtkEgkSEhIKNNxo6KiEBERgYsXL4p9ExUVVeb+JiIiIgKY7L4Tqamp6N27NwYOHIjExETExMSge/fuCAsLg7+/P9q1a4fU1FSkpqaiefPmyMvLg5+fH3R1dXH8+HHExsZCJpOhXbt2yM3NFds9fPgwHjx4gGPHjmH+/PkICwtDp06dUKNGDcTFxWH48OEYNmwY/vrrrzfGOGnSJERGRuLcuXNQU1PDwIEDxW3Hjx9Hv379MHr0aFy7dg0rVqxAVFQUZsyYUWxbGRkZ6Ny5M9zc3HD+/HlMmzYNEyZMKNdxe/XqhbFjx8LV1VXsm169ehXbRk5ODjIyMhReRERERACnMbwTqampyM/PR/fu3WFrawsAcHNzAwBoaWkhJydHYRrAb7/9BrlcjlWrVkEikQAAVq9eDQMDA8TExKBt27YAAENDQ/z4449QUVGBk5MT5s6di+zsbHz//fcAgJCQEMyePRsnTpzAl19+WWqMM2bMgLe3NwBg4sSJ6NixI16+fAlNTU1ERERg4sSJ6N+/PwCgdu3amDZtGsaPH4+wsLAiba1fvx4SiQQrV66EpqYmXFxc8Pfff2PIkCFlPq6WlhZkMhnU1NTeOEVi1qxZiIiIKLUOERERfZw4svsO1K9fH76+vnBzc0PPnj2xcuVKPHv2rMT6Fy9exM2bN6GrqwuZTAaZTAZDQ0O8fPkSt27dEuu5urpCReX/PkIzMzMxiQYAVVVVGBkZIS0tDQDQvn17sT1XV1eFY7q7u4tfW1hYAIC438WLFzF16lRxX5lMhiFDhiA1NRXZ2dlF4k9KSoK7uzs0NTXFsiZNmhR7rqUdt6xCQkKQnp4uvu7fv1+u/YmIiEh5cWT3HVBVVcWBAwdw8uRJ7N+/H4sXL8akSZMQFxdXbP2srCx4eHhg3bp1RbaZmJiIX6urqytsk0gkxZbJ5XIAwKpVq/Dvv/8Wu+9/3xeOJhful5WVhYiICHTv3r1IPP9NaCuitOOWlVQqhVQqfas4iIiISDkx2X1HJBIJvLy84OXlhdDQUNja2mLr1q3Q0NBAQUGBQt1GjRphw4YNMDU1hZ6eXqXFYGVlVaH9GjVqhKSkJNjb25epvpOTE3777Tfk5OSISejZs2fLfdzi+oaIiIioPDiN4R2Ii4vDzJkzce7cOaSkpGDLli149OgRnJ2dYWdnh0uXLiEpKQmPHz9GXl4e+vTpA2NjY3Tt2hXHjx/HnTt3EBMTg8DAwDLdbFbZQkND8euvvyIiIgJXr15FYmIioqOjMXny5GLrf/XVV5DL5Rg6dCgSExOxb98+zJs3D8D/jd6WhZ2dHe7cuYOEhAQ8fvwYOTk5lXI+RERE9PFgsvsO6Onp4dixY+jQoQMcHR0xefJkREZGon379hgyZAicnJzg6ekJExMTxMbGQltbG8eOHYONjQ26d+8OZ2dnDBo0CC9fvqzUkd6y8vPzw65du7B//340btwYTZs2xYIFC8Sb7V6np6eHnTt3IiEhAQ0aNMCkSZMQGhoKoHzTHnr06IF27dqhVatWMDExwe+//14p50NEREQfD4kgCEJ1B0HKb926dRgwYADS09OhpaVVpcfKyMiAvr4+rIM2QkWqXaXHIqIP293ZHas7BCL6/wp/f6enp1fq4B7n7FKV+PXXX1G7dm1YWVnh4sWLmDBhAvz9/as80SUiIiL6Lya7VCX++ecfhIaG4p9//oGFhQV69uxZ4kMoiIiIiKoKk12qEuPHj8f48eOrOwwiIiL6yPEGNSIiIiJSWhzZJaV1JcKvWlavICIiovcHR3aJiIiISGkx2SUiIiIipcVkl4iIiIiUFpNdIiIiIlJavEGNlFa9sH18ghoRVSo+cY3ow8ORXSIiIiJSWkx2iYiIiEhpMdklIiIiIqXFZJeIiIiIlBaTXSXi4+ODoKAg8b2dnR0WLlxYbfEQERERVTcmu0rs7NmzGDp0aHWHgaioKBgYGFR3GERERPQR4tJjSszExKS6Q6hUBQUFkEgkUFHh/9GIiIiobJg1VAK5XI65c+fC3t4eUqkUNjY2mDFjBgBgwoQJcHR0hLa2NmrXro0pU6YgLy9P3Dc8PBwNGjTAihUrYG1tDW1tbfj7+yM9Pb3UY7548QL9+vWDTCaDhYUFIiMji9R5fRrD/Pnz4ebmBh0dHVhbW2PEiBHIysoStxeOwO7atQtOTk7Q1tbGF198gezsbKxZswZ2dnaoUaMGAgMDUVBQIO6Xk5ODcePGwcrKCjo6Ovjkk08QExMDAIiJicGAAQOQnp4OiUQCiUSC8PDwN+7333h27NgBFxcXSKVSpKSklPVjISIiIuLIbmUICQnBypUrsWDBArRo0QKpqam4fv06AEBXVxdRUVGwtLTE5cuXMWTIEOjq6mL8+PHi/jdv3sTGjRuxc+dOZGRkYNCgQRgxYgTWrVtX4jGDg4Nx9OhRbN++Haampvj+++9x/vx5NGjQoMR9VFRU8OOPP6JWrVq4ffs2RowYgfHjx2Pp0qVinezsbPz444+Ijo5GZmYmunfvjs8//xwGBgbYs2cPbt++jR49esDLywu9evUCAIwaNQrXrl1DdHQ0LC0tsXXrVrRr1w6XL19G8+bNsXDhQoSGhiIpKQkAIJPJ3rifg4ODGM+cOXOwatUqGBkZwdTUtMh55eTkICcnR3yfkZHxpo+MiIiIPhISQRCE6g7iQ5aZmQkTExMsWbIEgwcPfmP9efPmITo6GufOnQPwamR3+vTpuHfvHqysrAAAe/fuRceOHfH333/D3Ny8SBtZWVkwMjLCb7/9hp49ewIAnj59ipo1a2Lo0KHiaK6dnR2CgoIUblr7r02bNmH48OF4/PgxgFcjqQMGDMDNmzdRp04dAMDw4cOxdu1aPHz4UExS27VrBzs7OyxfvhwpKSmoXbs2UlJSYGlpKbbdunVrNGnSBDNnzkRUVBSCgoLw/PlzcXtZ9xswYAASEhJQv379Evs0PDwcERERRcqtgzbyCWpEVKn4BDWiqpORkQF9fX2kp6dDT0+v0trlyO5bSkxMRE5ODnx9fYvdvmHDBvz444+4desWsrKykJ+fX+QDtLGxERNdAGjWrBnkcjmSkpKQnJyM9u3bi9tWrFiBevXqITc3F5988olYbmhoCCcnp1JjPXjwIGbNmoXr168jIyMD+fn5ePnyJbKzs6Gt/Sop1NbWFhNdADAzM4OdnZ2Y6BaWpaWlAQAuX76MgoICODo6KhwrJycHRkZGJcZS1v00NDTg7u5e6nmFhIRgzJgx4vuMjAxYW1uXug8RERF9HJjsviUtLa0St506dQp9+vRBREQE/Pz8oK+vj+jo6GLn15bE09MTCQkJ4nszMzPcvn273HHevXsXnTp1wjfffIMZM2bA0NAQJ06cwKBBg5Cbmysmu+rq6gr7SSSSYsvkcjmAV6PMqqqqiI+Ph6qqqkK9/ybIryvrflpaWpBIJKWem1QqhVQqLbUOERERfZyY7L4lBwcHaGlp4dChQ0WmMZw8eRK2traYNGmSWHbv3r0ibaSkpODBgwfin/NPnz4NFRUVODk5QUtLC/b29gr169SpA3V1dcTFxcHGxgYA8OzZM9y4cQPe3t7FxhkfHw+5XI7IyEhxNYONGzdW/MT/v4YNG6KgoABpaWlo2bJlsXU0NDQUbmgr635EREREb4vJ7lvS1NTEhAkTMH78eGhoaMDLywuPHj3C1atX4eDggJSUFERHR6Nx48bYvXs3tm7dWmwb/fv3x7x585CRkYHAwED4+/sXO18XeDXyOWjQIAQHB4s3bU2aNKnUJbns7e2Rl5eHxYsXo3PnzoiNjcXy5cvf+vwdHR3Rp08f9OvXD5GRkWjYsCEePXqEQ4cOwd3dHR07doSdnR2ysrJw6NAh1K9fH9ra2mXaj4iIiOhtcemxSjBlyhSMHTsWoaGhcHZ2Rq9evZCWloYuXbrgu+++w6hRo9CgQQOcPHkSU6ZMKbK/vb09unfvjg4dOqBt27Zwd3dXWCGhOD/88ANatmyJzp07o3Xr1mjRogU8PDxKrF+/fn3Mnz8fc+bMQb169bBu3TrMmjXrrc8dAFavXo1+/fph7NixcHJyQrdu3XD27Flx1Ll58+YYPnw4evXqBRMTE8ydO7dM+xERERG9La7GUM3Cw8Oxbds2hXm59HYK7+bkagxEVNm4GgNR1amq1Rg4sktERERESovJLhEREREpLSa71Sw8PJxTGIiIiIiqCJNdIiIiIlJaXHqMlNaVCL9KneBOREREHx6O7BIRERGR0mKyS0RERERKi8kuERERESktJrtEREREpLSY7BIRERGR0mKyS0RERERKi8kuERERESktJrtEREREpLSY7BIRERGR0mKyS0RERERKi8kuERERESktJrtEREREpLSY7BIRERGR0mKyS0RERERKi8kuERERESktteoOgKiyCYIAAMjIyKjmSIiIiKisCn9vF/4eryxMdknpPHnyBABgbW1dzZEQERFReWVmZkJfX7/S2mOyS0rH0NAQAJCSklKp3ywfuoyMDFhbW+P+/fvQ09Or7nDeK+yb4rFfisd+KRn7pnjsl+K93i+CICAzMxOWlpaVehwmu6R0VFReTUXX19fnD5Vi6OnpsV9KwL4pHvuleOyXkrFvisd+Kd5/+6UqBql4gxoRERERKS0mu0RERESktJjsktKRSqUICwuDVCqt7lDeK+yXkrFvisd+KR77pWTsm+KxX4r3rvpFIlT2+g5ERERERO8JjuwSERERkdJisktERERESovJLhEREREpLSa7RERERKS0mOzSB+Gnn36CnZ0dNDU18cknn+DMmTOl1v/jjz9Qt25daGpqws3NDXv27FHYLggCQkNDYWFhAS0tLbRu3RrJyclVeQpVojL7JS8vDxMmTICbmxt0dHRgaWmJfv364cGDB1V9GpWusq+X/xo+fDgkEgkWLlxYyVG/G1XRN4mJiejSpQv09fWho6ODxo0bIyUlpapOoUpUdr9kZWVh1KhRqFmzJrS0tODi4oLly5dX5SlUifL0y9WrV9GjRw/Y2dmV+j1S3r5+H1V2v8yaNQuNGzeGrq4uTE1N0a1bNyQlJVXhGVSdqrhmCs2ePRsSiQRBQUHlC0oges9FR0cLGhoawv/+9z/h6tWrwpAhQwQDAwPh4cOHxdaPjY0VVFVVhblz5wrXrl0TJk+eLKirqwuXL18W68yePVvQ19cXtm3bJly8eFHo0qWLUKtWLeHff/99V6f11iq7X54/fy60bt1a2LBhg3D9+nXh1KlTQpMmTQQPD493eVpvrSqul0JbtmwR6tevL1haWgoLFiyo4jOpfFXRNzdv3hQMDQ2F4OBg4fz588LNmzeF7du3l9jm+6gq+mXIkCFCnTp1hCNHjgh37twRVqxYIaiqqgrbt29/V6f11srbL2fOnBHGjRsn/P7774K5uXmx3yPlbfN9VBX94ufnJ6xevVq4cuWKkJCQIHTo0EGwsbERsrKyqvhsKldV9M1/69rZ2Qnu7u7C6NGjyxUXk1167zVp0kQYOXKk+L6goECwtLQUZs2aVWx9f39/oWPHjgpln3zyiTBs2DBBEARBLpcL5ubmwg8//CBuf/78uSCVSoXff/+9Cs6galR2vxTnzJkzAgDh3r17lRP0O1BV/fLXX38JVlZWwpUrVwRbW9sPMtmtir7p1auX8PXXX1dNwO9IVfSLq6urMHXqVIU6jRo1EiZNmlSJkVet8vbLf5X0PfI2bb4vqqJfXpeWliYAEI4ePfo2ob5zVdU3mZmZgoODg3DgwAHB29u73MkupzHQey03Nxfx8fFo3bq1WKaiooLWrVvj1KlTxe5z6tQphfoA4OfnJ9a/c+cO/vnnH4U6+vr6+OSTT0ps831TFf1SnPT0dEgkEhgYGFRK3FWtqvpFLpejb9++CA4Ohqura9UEX8Wqom/kcjl2794NR0dH+Pn5wdTUFJ988gm2bdtWZedR2arqmmnevDl27NiBv//+G4Ig4MiRI7hx4wbatm1bNSdSySrSL9XR5rv2rs4hPT0dAGBoaFhpbVa1quybkSNHomPHjkW+78qKyS691x4/foyCggKYmZkplJuZmeGff/4pdp9//vmn1PqF/5anzfdNVfTL616+fIkJEyagd+/e0NPTq5zAq1hV9cucOXOgpqaGwMDAyg/6HamKvklLS0NWVhZmz56Ndu3aYf/+/fj888/RvXt3HD16tGpOpJJV1TWzePFiuLi4oGbNmtDQ0EC7du3w008/4dNPP638k6gCFemX6mjzXXsX5yCXyxEUFAQvLy/Uq1evUtp8F6qqb6Kjo3H+/HnMmjWrwm2oVXhPIlJaeXl58Pf3hyAIWLZsWXWHU63i4+OxaNEinD9/HhKJpLrDea/I5XIAQNeuXfHdd98BABo0aICTJ09i+fLl8Pb2rs7wqtXixYtx+vRp7NixA7a2tjh27BhGjhwJS0vLCo9O0cdh5MiRuHLlCk6cOFHdoVS7+/fvY/To0Thw4AA0NTUr3A5Hdum9ZmxsDFVVVTx8+FCh/OHDhzA3Ny92H3Nz81LrF/5bnjbfN1XRL4UKE9179+7hwIEDH8yoLlA1/XL8+HGkpaXBxsYGampqUFNTw7179zB27FjY2dlVyXlUharoG2NjY6ipqcHFxUWhjrOz8wezGkNV9Mu///6L77//HvPnz0fnzp3h7u6OUaNGoVevXpg3b17VnEglq0i/VEeb71pVn8OoUaOwa9cuHDlyBDVr1nzr9t6lquib+Ph4pKWloVGjRuLP36NHj+LHH3+EmpoaCgoKytQOk116r2loaMDDwwOHDh0Sy+RyOQ4dOoRmzZoVu0+zZs0U6gPAgQMHxPq1atWCubm5Qp2MjAzExcWV2Ob7pir6Bfi/RDc5ORkHDx6EkZFR1ZxAFamKfunbty8uXbqEhIQE8WVpaYng4GDs27ev6k6mklVF32hoaKBx48ZFlki6ceMGbG1tK/kMqkZV9EteXh7y8vKgoqL4K1ZVVVUcDX/fVaRfqqPNd62qzkEQBIwaNQpbt27F4cOHUatWrcoI952qir7x9fXF5cuXFX7+enp6ok+fPkhISICqqmrZGirX7WxE1SA6OlqQSqVCVFSUcO3aNWHo0KGCgYGB8M8//wiCIAh9+/YVJk6cKNaPjY0V1NTUhHnz5gmJiYlCWFhYsUuPGRgYCNu3bxcuXbokdO3a9YNceqwy+yU3N1fo0qWLULNmTSEhIUFITU0VXzk5OdVyjhVRFdfL6z7U1Riqom+2bNkiqKurCz///LOQnJwsLF68WFBVVRWOHz/+zs+voqqiX7y9vQVXV1fhyJEjwu3bt4XVq1cLmpqawtKlS9/5+VVUefslJydHuHDhgnDhwgXBwsJCGDdunHDhwgUhOTm5zG1+CKqiX7755htBX19fiImJUfjZm52d/c7P721URd+8riKrMTDZpQ/C4sWLBRsbG0FDQ0No0qSJcPr0aXGbt7e30L9/f4X6GzduFBwdHQUNDQ3B1dVV2L17t8J2uVwuTJkyRTAzMxOkUqng6+srJCUlvYtTqVSV2S937twRABT7OnLkyDs6o8pR2dfL6z7UZFcQqqZvfvnlF8He3l7Q1NQU6tevL2zbtq2qT6PSVXa/pKamCgEBAYKlpaWgqakpODk5CZGRkYJcLn8Xp1NpytMvJf0M8fb2LnObH4rK7peSfvauXr363Z1UJamKa+a/KpLsSgRBECo0tkxERERE9J7jnF0iIiIiUlpMdomIiIhIaTHZJSIiIiKlxWSXiIiIiJQWk10iIiIiUlpMdomIiIhIaTHZJSIiIiKlxWSXiIioDAICAtCtW7fqDoOIyokPlSAiIgUBAQF4/vw5tm3bVt2hFHH37l3UqlULFy5cQIMGDd7psdPT0yEIAgwMDN7pcYno7ahVdwBERERlkZubW63H19fXr9bjE1HFcBoDERGVyMfHB99++y2CgoJQo0YNmJmZYeXKlXjx4gUGDBgAXV1d2Nvb488//xT3iYmJgUQiwe7du+Hu7g5NTU00bdoUV65cUWh78+bNcHV1hVQqhZ2dHSIjIxW229nZYdq0aejXrx/09PQwdOhQ1KpVCwDQsGFDSCQS+Pj4AADOnj2LNm3awNjYGPr6+vD29sb58+cV2pNIJFi1ahU+//xzaGtrw8HBATt27FCoc/XqVXTq1Al6enrQ1dVFy5YtcevWLQBFpzHs3bsXLVq0gIGBAYyMjNCpUyexLhG9P5jsEhFRqdasWQNjY2OcOXMG3377Lb755hv07NkTzZs3x/nz59G2bVv07dsX2dnZCvsFBwcjMjISZ8+ehYmJCTp37oy8vDwAQHx8PPz9/fHll1/i8uXLCA8Px5QpUxAVFaXQxrx581C/fn1cuHABU6ZMwZkzZwAABw8eRGpqKrZs2QIAyMzMRP/+/XHixAmcPn0aDg4O6NChAzIzMxXai4iIgL+/Py5duoQOHTqgT58+ePr0KQDg77//xqeffgqpVIrDhw8jPj4eAwcORH5+frH98uLFC4wZMwbnzp3DoUOHoKKigs8//xxyufyt+5yIKpFARET0H/379xe6du0qCIIgeHt7Cy1atBC35efnCzo6OkLfvn3FstTUVAGAcOrUKUEQBOHIkSMCACE6Olqs8+TJE0FLS0vYsGGDIAiC8NVXXwlt2rRROG5wcLDg4uIivre1tRW6deumUOfOnTsCAOHChQulnkNBQYGgq6sr7Ny5UywDIEyePFl8n5WVJQAQ/vzzT0EQBCEkJESoVauWkJub+8Z+Kc6jR48EAMLly5dLjY2I3i2O7BIRUanc3d3Fr1VVVWFkZAQ3NzexzMzMDACQlpamsF+zZs3Erw0NDeHk5ITExEQAQGJiIry8vBTqe3l5ITk5GQUFBWKZp6dnmWJ8+PAhhgwZAgcHB+jr60NPTw9ZWVlISUkp8Vx0dHSgp6cnxp2QkICWLVtCXV29TMdMTk5G7969Ubt2bejp6cHOzg4AihyTiKoXb1AjIqJSvZ78SSQShTKJRAIAVfLnex0dnTLV69+/P548eYJFixbB1tYWUqkUzZo1K3JTW3HnUhi3lpZWuWLr3LkzbG1tsXLlSlhaWkIul6NevXrVfiMdESniyC4REVWJ06dPi18/e/YMN27cgLOzMwDA2dkZsbGxCvVjY2Ph6OgIVVXVEtvU0NAAAIXR38J9AwMD0aFDB/Gmt8ePH5crXnd3dxw/flycV1yaJ0+eICkpCZMnT4avry+cnZ3x7Nmzch2PiN4NJrtERFQlpk6dikOHDuHKlSsICAiAsbGxuJrB2LFjcejQIUybNg03btzAmjVrsGTJEowbN67UNk1NTaGlpYW9e/fi4cOHSE9PBwA4ODhg7dq1SExMRFxcHPr06VPukdpRo0YhIyMDX375Jc6dO4fk5GSsXbsWSUlJRerWqFEDRkZG+Pnnn3Hz5k0cPnwYY8aMKdfxiOjdYLJLRERVYvbs2Rg9ejQ8PDzwzz//YOfOneLIbKNGjbBx40ZER0ejXr16CA0NxdSpUxEQEFBqm2pqavjxxx+xYsUKWFpaomvXrgCAX375Bc+ePUOjRo3Qt29fBAYGwtTUtFzxGhkZ4fDhw8jKyoK3tzc8PDywcuXKYufwqqioIDo6GvHx8ahXrx6+++47/PDDD+U6HhG9G3yCGhERVaqYmBi0atUKz54949PGiKjacWSXiIiIiJQWk10iIiIiUlqcxkBERERESosju0RERESktJjsEhEREZHSYrJLREREREqLyS4RERERKS0mu0RERESktJjsEhEREZHSYrJLREREREqLyS4RERERKS0mu0RERESktP4fxfyMphEGV6YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 9"
      ],
      "metadata": {
        "id": "wCPMa6dy60Rw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "gmsocsyL6SfE",
        "outputId": "089aab02-96e9-41ad-c8b1-b22743590ed6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 315
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importancia de las Features para la última prueba (Ejercicio extra)\n",
        "\n",
        "En esta sección esta la importancia final al obtener un Auc Roc Score de 1 para la última prueba realizada, obtenida al finalizar la etapa de entrenamiento.\n"
      ],
      "metadata": {
        "id": "p_h3ETomBkXp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 10"
      ],
      "metadata": {
        "id": "Ix_lgqdM9D7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Random Forest\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ML1pfJ2QvZPX",
        "outputId": "d0b57168-3c16-4516-99fc-ecbf5ee97b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 378
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este caso y en relación a las importancias de las features de la parte 2, podemos ver a simple vista que al no utilizar todas las columnas, es muy probable que las importancias sean bastante distintas, no obstante puedo realizar las comparaciones.\n",
        "Al igual que en la Regresión Logística, las feautres de \"sason\", \"does-bruise-or-bleed\" y \"has-ring\" mantienen un rango de importancia menor y similar. Las features numéricas; \"stem-height\", \"cap-diameter\" y en especial \"stem-widht\" son mucho mas importantes para el Random Forest en comparación a la parte 2, que prácticamente poseen importancia nula. A su vez, las features mas importantes para ambos modelos no coinciden en nada, en donde \"gill-attachment\", \"gill-color\", \"stem-color\" y \"cap-color\" y \"stem-width\" son de gran valor para este modelo, pero aportan muy poco para la Regresión Logística, en donde tal como se dijo en la parte 2, las más features mas significativas para este segundo modelo son \"veil-color\", \"habitat\", \"spore-print-color\", \"stem-root\" y \"stem-surface\".\n"
      ],
      "metadata": {
        "id": "isSZBy32CKAg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aclaracion: Para esta parte no se realizo el testo en test ya que no se pide en el enunciado."
      ],
      "metadata": {
        "id": "31jLA_CEOh6u"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X4ixETRYlswT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}