n03_clasificacion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelos de clasificación del modelo \n",
    "\n",
    "En este notebook se harán diversas clasificaciones del conjunto de datos, con el objetivo de tener una visión clara y precisa de los diferentes grupos presentes en lo datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import RandomizedSearchCV, cross_val_score, train_test_split, learning_curve\n",
    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constantes hiperparámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_SPLIT_SIZE = 0.3\n",
    "RANDOM_STATE = 37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importación de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_estudiante_proy</th>\n",
       "      <th>dni</th>\n",
       "      <th>campus</th>\n",
       "      <th>titulacion</th>\n",
       "      <th>nombre</th>\n",
       "      <th>apellidos</th>\n",
       "      <th>sexo</th>\n",
       "      <th>edad</th>\n",
       "      <th>direccion</th>\n",
       "      <th>telefono1</th>\n",
       "      <th>...</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>salud</th>\n",
       "      <th>suspensos</th>\n",
       "      <th>faltas</th>\n",
       "      <th>nota1</th>\n",
       "      <th>nota2</th>\n",
       "      <th>nota3</th>\n",
       "      <th>usuario_alta_datos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>3090960be7fa45d5a21095a83ff9c1da</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>f1bfc2083f3c4ebcbcd8b2a48321f02a</td>\n",
       "      <td>8de7accc8a1b46e898004872c337bc54</td>\n",
       "      <td>df6e785eb9bd4823bf8d78fc8a8678d5</td>\n",
       "      <td>V</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9dc09f819a794b45bda34811a85de8ce</td>\n",
       "      <td>61fcce5da8ff4b64b7585cfead129b79</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>juan.perez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255</td>\n",
       "      <td>efb6ca5df4824879b8b49d892d792e55</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>d23620300d7d42209fd7f4fc2fe34ce6</td>\n",
       "      <td>9d0e43d9473441569f5cabe2d3cf9264</td>\n",
       "      <td>3319d9e345594fa2a3bd8e2c1cde3615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ec3b98dee65c4815b8f1bb292df3141a</td>\n",
       "      <td>c40f375cf9184d1b8ef609b33d053850</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>usuario_secretaria_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>816fcb104f8242aa802a5c10a32d40da</td>\n",
       "      <td>Vigo</td>\n",
       "      <td>fcfc3c42ec3e41acb44fd74494985d8b</td>\n",
       "      <td>a2dfbb29afbe47e4aa665a2f118b8c1d</td>\n",
       "      <td>79cd61bee7ee4ef6822fd8db5c2ecbe6</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>b6faa3f203ad4ca982015fef93ad7274</td>\n",
       "      <td>d1d28dc0c03f4b7b93300cd145d7cfae</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>admin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>642</td>\n",
       "      <td>c8b5aff68b524a139106d3a98b4d5cbe</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>848dd3d4434b42dfa9a886213834afc4</td>\n",
       "      <td>21cf8472cd584674959196938de9173d</td>\n",
       "      <td>bb318ec4f00b4d6dab1d7ec5cb69c8fb</td>\n",
       "      <td>V</td>\n",
       "      <td>17.0</td>\n",
       "      <td>b11f9c14753e4854bfad96b5cc81d6af</td>\n",
       "      <td>5952ab314d7b476787a8c7db980962f0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>us_direccion_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>318</td>\n",
       "      <td>66ddb021477d4893bb9408e273c58aef</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>26878c9a3e2c4c349f1ae828b3863eac</td>\n",
       "      <td>02ec3fa3ac634be0adc4a66010925f2d</td>\n",
       "      <td>a0244cbc1a1849d2b5f4e8ef892ba808</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>a95f987985974bb59371b23abcb26f31</td>\n",
       "      <td>c52a8eb815b04f59b44a607f9992aa9f</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>juan.perez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_estudiante_proy                               dni    campus  \\\n",
       "0                 511  3090960be7fa45d5a21095a83ff9c1da  A Coruña   \n",
       "1                 255  efb6ca5df4824879b8b49d892d792e55  A Coruña   \n",
       "2                 377  816fcb104f8242aa802a5c10a32d40da      Vigo   \n",
       "3                 642  c8b5aff68b524a139106d3a98b4d5cbe  A Coruña   \n",
       "4                 318  66ddb021477d4893bb9408e273c58aef  A Coruña   \n",
       "\n",
       "                         titulacion                            nombre  \\\n",
       "0  f1bfc2083f3c4ebcbcd8b2a48321f02a  8de7accc8a1b46e898004872c337bc54   \n",
       "1  d23620300d7d42209fd7f4fc2fe34ce6  9d0e43d9473441569f5cabe2d3cf9264   \n",
       "2  fcfc3c42ec3e41acb44fd74494985d8b  a2dfbb29afbe47e4aa665a2f118b8c1d   \n",
       "3  848dd3d4434b42dfa9a886213834afc4  21cf8472cd584674959196938de9173d   \n",
       "4  26878c9a3e2c4c349f1ae828b3863eac  02ec3fa3ac634be0adc4a66010925f2d   \n",
       "\n",
       "                          apellidos sexo  edad  \\\n",
       "0  df6e785eb9bd4823bf8d78fc8a8678d5    V  15.0   \n",
       "1  3319d9e345594fa2a3bd8e2c1cde3615  NaN   NaN   \n",
       "2  79cd61bee7ee4ef6822fd8db5c2ecbe6    M  18.0   \n",
       "3  bb318ec4f00b4d6dab1d7ec5cb69c8fb    V  17.0   \n",
       "4  a0244cbc1a1849d2b5f4e8ef892ba808    M  18.0   \n",
       "\n",
       "                          direccion                         telefono1  ...  \\\n",
       "0  9dc09f819a794b45bda34811a85de8ce  61fcce5da8ff4b64b7585cfead129b79  ...   \n",
       "1  ec3b98dee65c4815b8f1bb292df3141a  c40f375cf9184d1b8ef609b33d053850  ...   \n",
       "2  b6faa3f203ad4ca982015fef93ad7274  d1d28dc0c03f4b7b93300cd145d7cfae  ...   \n",
       "3  b11f9c14753e4854bfad96b5cc81d6af  5952ab314d7b476787a8c7db980962f0  ...   \n",
       "4  a95f987985974bb59371b23abcb26f31  c52a8eb815b04f59b44a607f9992aa9f  ...   \n",
       "\n",
       "  salir_amigos consumo_alcohol_entre_semana consumo_alcohol_fin_de_semana  \\\n",
       "0          3.0                          1.0                           1.0   \n",
       "1          NaN                          NaN                           NaN   \n",
       "2          1.0                          1.0                           1.0   \n",
       "3          5.0                          2.0                           4.0   \n",
       "4          4.0                          1.0                           1.0   \n",
       "\n",
       "  salud suspensos faltas  nota1  nota2 nota3    usuario_alta_datos  \n",
       "0   4.0       0.0    0.0   14.0   14.0  14.0            juan.perez  \n",
       "1   NaN       NaN    NaN    NaN    NaN   NaN  usuario_secretaria_1  \n",
       "2   4.0       0.0    5.0   14.0   14.0  15.0                 admin  \n",
       "3   5.0       3.0    2.0    8.0    8.0   7.0        us_direccion_1  \n",
       "4   4.0       1.0    2.0   14.0   12.0  13.0            juan.perez  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CSV_FILE = 'proy_escuela_dev.csv'\n",
    "\n",
    "data = pd.read_csv(CSV_FILE)\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_estudiante_proy</th>\n",
       "      <th>dni</th>\n",
       "      <th>campus</th>\n",
       "      <th>titulacion</th>\n",
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       "      <th>apellidos</th>\n",
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       "      <th>edad</th>\n",
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       "      <th>telefono1</th>\n",
       "      <th>...</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>salud</th>\n",
       "      <th>suspensos</th>\n",
       "      <th>faltas</th>\n",
       "      <th>nota1</th>\n",
       "      <th>nota2</th>\n",
       "      <th>nota3</th>\n",
       "      <th>usuario_alta_datos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
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       "      <td>8de7accc8a1b46e898004872c337bc54</td>\n",
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       "      <td>V</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>61fcce5da8ff4b64b7585cfead129b79</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>juan.perez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255</td>\n",
       "      <td>efb6ca5df4824879b8b49d892d792e55</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>d23620300d7d42209fd7f4fc2fe34ce6</td>\n",
       "      <td>9d0e43d9473441569f5cabe2d3cf9264</td>\n",
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       "      <td>c40f375cf9184d1b8ef609b33d053850</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>usuario_secretaria_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>816fcb104f8242aa802a5c10a32d40da</td>\n",
       "      <td>Vigo</td>\n",
       "      <td>fcfc3c42ec3e41acb44fd74494985d8b</td>\n",
       "      <td>a2dfbb29afbe47e4aa665a2f118b8c1d</td>\n",
       "      <td>79cd61bee7ee4ef6822fd8db5c2ecbe6</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>b6faa3f203ad4ca982015fef93ad7274</td>\n",
       "      <td>d1d28dc0c03f4b7b93300cd145d7cfae</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>admin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>642</td>\n",
       "      <td>c8b5aff68b524a139106d3a98b4d5cbe</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>848dd3d4434b42dfa9a886213834afc4</td>\n",
       "      <td>21cf8472cd584674959196938de9173d</td>\n",
       "      <td>bb318ec4f00b4d6dab1d7ec5cb69c8fb</td>\n",
       "      <td>V</td>\n",
       "      <td>17.0</td>\n",
       "      <td>b11f9c14753e4854bfad96b5cc81d6af</td>\n",
       "      <td>5952ab314d7b476787a8c7db980962f0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>us_direccion_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>318</td>\n",
       "      <td>66ddb021477d4893bb9408e273c58aef</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>26878c9a3e2c4c349f1ae828b3863eac</td>\n",
       "      <td>02ec3fa3ac634be0adc4a66010925f2d</td>\n",
       "      <td>a0244cbc1a1849d2b5f4e8ef892ba808</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>a95f987985974bb59371b23abcb26f31</td>\n",
       "      <td>c52a8eb815b04f59b44a607f9992aa9f</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>juan.perez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_estudiante_proy                               dni    campus  \\\n",
       "0                 511  3090960be7fa45d5a21095a83ff9c1da  A Coruña   \n",
       "1                 255  efb6ca5df4824879b8b49d892d792e55  A Coruña   \n",
       "2                 377  816fcb104f8242aa802a5c10a32d40da      Vigo   \n",
       "3                 642  c8b5aff68b524a139106d3a98b4d5cbe  A Coruña   \n",
       "4                 318  66ddb021477d4893bb9408e273c58aef  A Coruña   \n",
       "\n",
       "                         titulacion                            nombre  \\\n",
       "0  f1bfc2083f3c4ebcbcd8b2a48321f02a  8de7accc8a1b46e898004872c337bc54   \n",
       "1  d23620300d7d42209fd7f4fc2fe34ce6  9d0e43d9473441569f5cabe2d3cf9264   \n",
       "2  fcfc3c42ec3e41acb44fd74494985d8b  a2dfbb29afbe47e4aa665a2f118b8c1d   \n",
       "3  848dd3d4434b42dfa9a886213834afc4  21cf8472cd584674959196938de9173d   \n",
       "4  26878c9a3e2c4c349f1ae828b3863eac  02ec3fa3ac634be0adc4a66010925f2d   \n",
       "\n",
       "                          apellidos sexo  edad  \\\n",
       "0  df6e785eb9bd4823bf8d78fc8a8678d5    V  15.0   \n",
       "1  3319d9e345594fa2a3bd8e2c1cde3615  NaN   NaN   \n",
       "2  79cd61bee7ee4ef6822fd8db5c2ecbe6    M  18.0   \n",
       "3  bb318ec4f00b4d6dab1d7ec5cb69c8fb    V  17.0   \n",
       "4  a0244cbc1a1849d2b5f4e8ef892ba808    M  18.0   \n",
       "\n",
       "                          direccion                         telefono1  ...  \\\n",
       "0  9dc09f819a794b45bda34811a85de8ce  61fcce5da8ff4b64b7585cfead129b79  ...   \n",
       "1  ec3b98dee65c4815b8f1bb292df3141a  c40f375cf9184d1b8ef609b33d053850  ...   \n",
       "2  b6faa3f203ad4ca982015fef93ad7274  d1d28dc0c03f4b7b93300cd145d7cfae  ...   \n",
       "3  b11f9c14753e4854bfad96b5cc81d6af  5952ab314d7b476787a8c7db980962f0  ...   \n",
       "4  a95f987985974bb59371b23abcb26f31  c52a8eb815b04f59b44a607f9992aa9f  ...   \n",
       "\n",
       "  salir_amigos consumo_alcohol_entre_semana consumo_alcohol_fin_de_semana  \\\n",
       "0          3.0                          1.0                           1.0   \n",
       "1          NaN                          NaN                           NaN   \n",
       "2          1.0                          1.0                           1.0   \n",
       "3          5.0                          2.0                           4.0   \n",
       "4          4.0                          1.0                           1.0   \n",
       "\n",
       "  salud suspensos faltas  nota1  nota2 nota3    usuario_alta_datos  \n",
       "0   4.0       0.0    0.0   14.0   14.0  14.0            juan.perez  \n",
       "1   NaN       NaN    NaN    NaN    NaN   NaN  usuario_secretaria_1  \n",
       "2   4.0       0.0    5.0   14.0   14.0  15.0                 admin  \n",
       "3   5.0       3.0    2.0    8.0    8.0   7.0        us_direccion_1  \n",
       "4   4.0       1.0    2.0   14.0   12.0  13.0            juan.perez  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminación de columnas irrelevantes para el análisis \n",
    "\n",
    "Las siguientes columnas resultan irrelevantes para el análisis, debido a que se encuentran como un conjunto de caracteres que nos lleva a pensar en que resultan ser datos cifrados.\n",
    "\n",
    "Aunque no estuviesen cifrados, tampoco serían relevantes para el análisis dado el contexto del problema, que es clasificar a aquellos alumnos que vayan a tener beca o no en base al percentil 90 de notas. \n",
    "\n",
    "Dichas columnas son:\n",
    "\n",
    "- **id_estudiante_proy**.\n",
    "- **dni**.\n",
    "- **nombre**.\n",
    "- **apellidos**.\n",
    "- **direccion**.\n",
    "- **telefono1**.\n",
    "- **telefono2**.\n",
    "- **email**.\n",
    "- **fecha_nacimiento**.\n",
    "- **usuario_alta_datos**.\n",
    "-**titulacion**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>campus</th>\n",
       "      <th>sexo</th>\n",
       "      <th>edad</th>\n",
       "      <th>entorno_vivienda</th>\n",
       "      <th>tamano_familia</th>\n",
       "      <th>independizado_padres</th>\n",
       "      <th>educacion_madre</th>\n",
       "      <th>educacion_padre</th>\n",
       "      <th>ocupacion_madre</th>\n",
       "      <th>ocupacion_padre</th>\n",
       "      <th>...</th>\n",
       "      <th>tiempo_libre</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>salud</th>\n",
       "      <th>suspensos</th>\n",
       "      <th>faltas</th>\n",
       "      <th>nota1</th>\n",
       "      <th>nota2</th>\n",
       "      <th>nota3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>V</td>\n",
       "      <td>15.0</td>\n",
       "      <td>urbano</td>\n",
       "      <td>&lt;=3</td>\n",
       "      <td>independizado</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>profesor</td>\n",
       "      <td>servicios</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vigo</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>rural</td>\n",
       "      <td>&gt;3</td>\n",
       "      <td>independizado</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>servicios</td>\n",
       "      <td>otro</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>V</td>\n",
       "      <td>17.0</td>\n",
       "      <td>urbano</td>\n",
       "      <td>&gt;3</td>\n",
       "      <td>independizado</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>servicios</td>\n",
       "      <td>servicios</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>urbano</td>\n",
       "      <td>&gt;3</td>\n",
       "      <td>independizado</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>salud</td>\n",
       "      <td>salud</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     campus sexo  edad entorno_vivienda tamano_familia independizado_padres  \\\n",
       "0  A Coruña    V  15.0           urbano            <=3        independizado   \n",
       "1  A Coruña  NaN   NaN              NaN            NaN                  NaN   \n",
       "2      Vigo    M  18.0            rural             >3        independizado   \n",
       "3  A Coruña    V  17.0           urbano             >3        independizado   \n",
       "4  A Coruña    M  18.0           urbano             >3        independizado   \n",
       "\n",
       "   educacion_madre  educacion_padre ocupacion_madre ocupacion_padre  ...  \\\n",
       "0              4.0              3.0        profesor       servicios  ...   \n",
       "1              NaN              NaN             NaN             NaN  ...   \n",
       "2              2.0              2.0       servicios            otro  ...   \n",
       "3              3.0              2.0       servicios       servicios  ...   \n",
       "4              4.0              4.0           salud           salud  ...   \n",
       "\n",
       "  tiempo_libre salir_amigos consumo_alcohol_entre_semana  \\\n",
       "0          4.0          3.0                          1.0   \n",
       "1          NaN          NaN                          NaN   \n",
       "2          2.0          1.0                          1.0   \n",
       "3          5.0          5.0                          2.0   \n",
       "4          4.0          4.0                          1.0   \n",
       "\n",
       "   consumo_alcohol_fin_de_semana  salud suspensos faltas nota1 nota2  nota3  \n",
       "0                            1.0    4.0       0.0    0.0  14.0  14.0   14.0  \n",
       "1                            NaN    NaN       NaN    NaN   NaN   NaN    NaN  \n",
       "2                            1.0    4.0       0.0    5.0  14.0  14.0   15.0  \n",
       "3                            4.0    5.0       3.0    2.0   8.0   8.0    7.0  \n",
       "4                            1.0    4.0       1.0    2.0  14.0  12.0   13.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Paso 1: Eliminar columnas irrelevantes\n",
    "# Estas columnas son identificadores o irrelevantes para el análisis\n",
    "columns_to_drop = [\n",
    "    \"id_estudiante_proy\", \"dni\", \"nombre\", \"apellidos\", \n",
    "    \"direccion\", \"telefono1\", \"telefono2\", \"email\", \n",
    "    \"fecha_nacimiento\", \"usuario_alta_datos\", \"titulacion\", \n",
    "]\n",
    "\n",
    "data = data.drop(columns=columns_to_drop)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creación de columna con promedio de notas\n",
    "\n",
    "Se juntan las 3 notas en una nueva columna, denominada promedio_notas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seleccionar las columnas 'nota1', 'nota2' y 'nota3' \n",
    "data[\"promedio_notas\"] = data[[\"nota1\", \"nota2\", \"nota3\"]].mean(axis=1, skipna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de las columna beca, con 0 como no y 1 como si\n",
    "\n",
    "El hecho de que un estudiante pertenezca o no a la columna beca se calcula a partir del percentil del 90% de la columna promedio_notas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular el percentil 90 de la columna 'promedio_notas'\n",
    "percentil_90 = data['promedio_notas'].quantile(0.90)\n",
    "\n",
    "# Crear la nueva columna 'beca' basada en el percentil 90\n",
    "data['beca'] = data['promedio_notas'] >= percentil_90\n",
    "\n",
    "# Asegurarse de que no haya valores NaN en la columna 'beca'\n",
    "data['beca'] = data['beca'].fillna(False)\n",
    "\n",
    "# Convertir los valores booleanos a enteros (0 y 1)\n",
    "data['beca'] = data['beca'].astype(int)\n",
    "\n",
    "# Crear un gráfico de barras para la variable 'beca'\n",
    "sns.countplot(x='beca', data=data)\n",
    "plt.xlabel('Beca')\n",
    "plt.ylabel('Cantidad de Estudiantes')\n",
    "plt.title('Distribución de la Variable Beca')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eliminacion de valores faltantes\n",
    "\n",
    "Se eliminan los valores faltantes en algunas de las columnas. Hay pocos valores faltantes, por lo que sustituir con el promedio puede ser más perjudicial que eliminarlos directamnete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['edad', 'educacion_madre', 'educacion_padre', 'tiempo_viaje',\n",
      "       'tiempo_estudio', 'relacion_familiar', 'tiempo_libre', 'salir_amigos',\n",
      "       'consumo_alcohol_entre_semana', 'consumo_alcohol_fin_de_semana',\n",
      "       'salud', 'suspensos', 'faltas', 'nota1', 'nota2', 'nota3',\n",
      "       'promedio_notas', 'beca'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Selección de las variables numéricas\n",
    "numeric_cols = data.select_dtypes(include=['number']).columns\n",
    "\n",
    "data = data.dropna(subset=numeric_cols)\n",
    "\n",
    "print(numeric_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conversion de variables categoricas a dummies\n",
    "\n",
    "Convertimos las variables categóricas a dummies para que puedan ser analizadas. Lo primero será encontrar las columnas categóricas que posteriomente se convertirán."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['campus', 'sexo', 'entorno_vivienda', 'tamano_familia',\n",
      "       'independizado_padres', 'ocupacion_madre', 'ocupacion_padre',\n",
      "       'razon_matricula', 'interes_educacion_superior', 'internet_casa',\n",
      "       'apoyo_escolar', 'apoyo_familiar', 'actividades', 'relacion_romantica'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Seleccionar columnas categóricas\n",
    "categorical_columns =data.select_dtypes(include=['object', 'category']).columns\n",
    "print(categorical_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Depuración de las dummies\n",
    "\n",
    "Antes de generar las dummies, hay que solucionar un problema de heterogeneidad en el tipo de respuesta en algunas de las columnas categóricas. Existen columnas que tienen respuestas diferentes, pero que significan lo mismo (caso de poner si, yes o 1 en la misma columna), por lo que se debe unificar el criterio de respuesta en la columna antes de convertir a dummies.\n",
    "\n",
    "Si no se realizase esto, se haría una mala conversión, puesto que se generaría una columna dummy por cada respuesta diferente cuando muchas de ellas significan en realidad lo mismo.\n",
    "\n",
    "Por tanto, se unifican los diferentes tipos posibles de respuesta a True o False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   campus  sexo  entorno_vivienda  tamano_familia  independizado_padres  \\\n",
      "0    True  True              True            True                  True   \n",
      "2    True  True              True            True                  True   \n",
      "3    True  True              True            True                  True   \n",
      "4    True  True              True            True                  True   \n",
      "5    True  True              True            True                  True   \n",
      "\n",
      "   ocupacion_madre  ocupacion_padre  razon_matricula  \\\n",
      "0             True             True             True   \n",
      "2             True             True             True   \n",
      "3             True             True             True   \n",
      "4             True             True             True   \n",
      "5             True             True             True   \n",
      "\n",
      "   interes_educacion_superior  internet_casa  apoyo_escolar  apoyo_familiar  \\\n",
      "0                        True           True          False            True   \n",
      "2                        True           True          False           False   \n",
      "3                        True           True          False            True   \n",
      "4                        True           True           True            True   \n",
      "5                        True           True          False            True   \n",
      "\n",
      "   actividades  relacion_romantica  \n",
      "0         True               False  \n",
      "2        False                True  \n",
      "3         True               False  \n",
      "4         True                True  \n",
      "5         True                True  \n"
     ]
    }
   ],
   "source": [
    "# Seleccionar columnas que contienen valores de tipo string (object)\n",
    "string_columns = data.select_dtypes(include=['object']).columns\n",
    "\n",
    "# Comprobar si tienen valores típicos de sí/no\n",
    "# Definir una lista de valores que queremos unificar\n",
    "values_to_replace = {\n",
    "    'si': True, \n",
    "    'yes': True, \n",
    "    'no': False, \n",
    "    'no ': False, \n",
    "    'yes ': True, \n",
    "    0.0: False, \n",
    "    1.0: True\n",
    "}\n",
    "\n",
    "# Iterar por las columnas que contienen strings\n",
    "for col in string_columns:\n",
    "    # Reemplazar los valores en esas columnas\n",
    "    if data[col].isin(values_to_replace.keys()).any():\n",
    "        data[col] = data[col].replace(values_to_replace)\n",
    "\n",
    "# Convertir las columnas afectadas a tipo booleano\n",
    "data[string_columns] = data[string_columns].astype(bool)\n",
    "\n",
    "# Verificar los cambios en las primeras filas de las columnas modificadas\n",
    "print(data[string_columns].head())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez convertidos los tipos de respuesta, se pasa a la **generación de las dummies**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>educacion_madre</th>\n",
       "      <th>educacion_padre</th>\n",
       "      <th>tiempo_viaje</th>\n",
       "      <th>tiempo_estudio</th>\n",
       "      <th>relacion_familiar</th>\n",
       "      <th>tiempo_libre</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>...</th>\n",
       "      <th>internet_casa_False</th>\n",
       "      <th>internet_casa_True</th>\n",
       "      <th>apoyo_escolar_False</th>\n",
       "      <th>apoyo_escolar_True</th>\n",
       "      <th>apoyo_familiar_False</th>\n",
       "      <th>apoyo_familiar_True</th>\n",
       "      <th>actividades_False</th>\n",
       "      <th>actividades_True</th>\n",
       "      <th>relacion_romantica_False</th>\n",
       "      <th>relacion_romantica_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>590</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>587 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     edad  educacion_madre  educacion_padre  tiempo_viaje  tiempo_estudio  \\\n",
       "0    15.0              4.0              3.0           1.0             3.0   \n",
       "2    18.0              2.0              2.0           2.0             3.0   \n",
       "3    17.0              3.0              2.0           1.0             1.0   \n",
       "4    18.0              4.0              4.0           1.0             2.0   \n",
       "5    19.0              4.0              2.0           2.0             2.0   \n",
       "..    ...              ...              ...           ...             ...   \n",
       "590  17.0              2.0              1.0           3.0             1.0   \n",
       "591  18.0              2.0              3.0           2.0             3.0   \n",
       "592  17.0              4.0              3.0           2.0             2.0   \n",
       "593  17.0              3.0              4.0           3.0             2.0   \n",
       "594  18.0              2.0              3.0           1.0             4.0   \n",
       "\n",
       "     relacion_familiar  tiempo_libre  salir_amigos  \\\n",
       "0                  5.0           4.0           3.0   \n",
       "2                  4.0           2.0           1.0   \n",
       "3                  5.0           5.0           5.0   \n",
       "4                  2.0           4.0           4.0   \n",
       "5                  5.0           4.0           4.0   \n",
       "..                 ...           ...           ...   \n",
       "590                3.0           2.0           2.0   \n",
       "591                3.0           2.0           3.0   \n",
       "592                5.0           5.0           4.0   \n",
       "593                5.0           4.0           5.0   \n",
       "594                4.0           5.0           5.0   \n",
       "\n",
       "     consumo_alcohol_entre_semana  consumo_alcohol_fin_de_semana  ...  \\\n",
       "0                             1.0                            1.0  ...   \n",
       "2                             1.0                            1.0  ...   \n",
       "3                             2.0                            4.0  ...   \n",
       "4                             1.0                            1.0  ...   \n",
       "5                             1.0                            1.0  ...   \n",
       "..                            ...                            ...  ...   \n",
       "590                           1.0                            2.0  ...   \n",
       "591                           2.0                            2.0  ...   \n",
       "592                           1.0                            1.0  ...   \n",
       "593                           2.0                            4.0  ...   \n",
       "594                           1.0                            3.0  ...   \n",
       "\n",
       "     internet_casa_False  internet_casa_True  apoyo_escolar_False  \\\n",
       "0                  False                True                 True   \n",
       "2                  False                True                 True   \n",
       "3                  False                True                 True   \n",
       "4                  False                True                False   \n",
       "5                  False                True                 True   \n",
       "..                   ...                 ...                  ...   \n",
       "590                False                True                False   \n",
       "591                False                True                 True   \n",
       "592                False                True                 True   \n",
       "593                False                True                False   \n",
       "594                False                True                 True   \n",
       "\n",
       "     apoyo_escolar_True  apoyo_familiar_False  apoyo_familiar_True  \\\n",
       "0                 False                 False                 True   \n",
       "2                 False                  True                False   \n",
       "3                 False                 False                 True   \n",
       "4                  True                 False                 True   \n",
       "5                 False                 False                 True   \n",
       "..                  ...                   ...                  ...   \n",
       "590                True                 False                 True   \n",
       "591               False                 False                 True   \n",
       "592               False                  True                False   \n",
       "593                True                 False                 True   \n",
       "594               False                 False                 True   \n",
       "\n",
       "     actividades_False  actividades_True  relacion_romantica_False  \\\n",
       "0                False              True                      True   \n",
       "2                 True             False                     False   \n",
       "3                False              True                      True   \n",
       "4                False              True                     False   \n",
       "5                False              True                     False   \n",
       "..                 ...               ...                       ...   \n",
       "590              False              True                     False   \n",
       "591               True             False                     False   \n",
       "592               True             False                      True   \n",
       "593              False              True                     False   \n",
       "594              False              True                     False   \n",
       "\n",
       "     relacion_romantica_True  \n",
       "0                      False  \n",
       "2                       True  \n",
       "3                      False  \n",
       "4                       True  \n",
       "5                       True  \n",
       "..                       ...  \n",
       "590                     True  \n",
       "591                     True  \n",
       "592                    False  \n",
       "593                     True  \n",
       "594                     True  \n",
       "\n",
       "[587 rows x 38 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear dummies para las columnas categóricas\n",
    "data = pd.get_dummies(data, columns=categorical_columns)\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eliminar dummies innecesarias\n",
    "Algunas dummies resultan innecesarias, puesto que otra columna ofrece la misma información. Por ejemplo, si a partir de una columna padre emanan dos hijas columnas true y false, con quedarse con una de ellas bastaría.\n",
    "\n",
    "En esa columna escogida, aquellos valores que sean False corresponderán con los valores True de la otra columna inversa.\n",
    "\n",
    "Para mantener la simplicidad, se eliminarán **todas las columnas con sufijo false**, con el objetivo de mantener la claridad durante el análisis e interpretación de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>educacion_madre</th>\n",
       "      <th>educacion_padre</th>\n",
       "      <th>tiempo_viaje</th>\n",
       "      <th>tiempo_estudio</th>\n",
       "      <th>relacion_familiar</th>\n",
       "      <th>tiempo_libre</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>...</th>\n",
       "      <th>independizado_padres_True</th>\n",
       "      <th>ocupacion_madre_True</th>\n",
       "      <th>ocupacion_padre_True</th>\n",
       "      <th>razon_matricula_True</th>\n",
       "      <th>interes_educacion_superior_True</th>\n",
       "      <th>internet_casa_True</th>\n",
       "      <th>apoyo_escolar_True</th>\n",
       "      <th>apoyo_familiar_True</th>\n",
       "      <th>actividades_True</th>\n",
       "      <th>relacion_romantica_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>587 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     edad  educacion_madre  educacion_padre  tiempo_viaje  tiempo_estudio  \\\n",
       "0    15.0              4.0              3.0           1.0             3.0   \n",
       "2    18.0              2.0              2.0           2.0             3.0   \n",
       "3    17.0              3.0              2.0           1.0             1.0   \n",
       "4    18.0              4.0              4.0           1.0             2.0   \n",
       "5    19.0              4.0              2.0           2.0             2.0   \n",
       "..    ...              ...              ...           ...             ...   \n",
       "590  17.0              2.0              1.0           3.0             1.0   \n",
       "591  18.0              2.0              3.0           2.0             3.0   \n",
       "592  17.0              4.0              3.0           2.0             2.0   \n",
       "593  17.0              3.0              4.0           3.0             2.0   \n",
       "594  18.0              2.0              3.0           1.0             4.0   \n",
       "\n",
       "     relacion_familiar  tiempo_libre  salir_amigos  \\\n",
       "0                  5.0           4.0           3.0   \n",
       "2                  4.0           2.0           1.0   \n",
       "3                  5.0           5.0           5.0   \n",
       "4                  2.0           4.0           4.0   \n",
       "5                  5.0           4.0           4.0   \n",
       "..                 ...           ...           ...   \n",
       "590                3.0           2.0           2.0   \n",
       "591                3.0           2.0           3.0   \n",
       "592                5.0           5.0           4.0   \n",
       "593                5.0           4.0           5.0   \n",
       "594                4.0           5.0           5.0   \n",
       "\n",
       "     consumo_alcohol_entre_semana  consumo_alcohol_fin_de_semana  ...  \\\n",
       "0                             1.0                            1.0  ...   \n",
       "2                             1.0                            1.0  ...   \n",
       "3                             2.0                            4.0  ...   \n",
       "4                             1.0                            1.0  ...   \n",
       "5                             1.0                            1.0  ...   \n",
       "..                            ...                            ...  ...   \n",
       "590                           1.0                            2.0  ...   \n",
       "591                           2.0                            2.0  ...   \n",
       "592                           1.0                            1.0  ...   \n",
       "593                           2.0                            4.0  ...   \n",
       "594                           1.0                            3.0  ...   \n",
       "\n",
       "     independizado_padres_True  ocupacion_madre_True  ocupacion_padre_True  \\\n",
       "0                         True                  True                  True   \n",
       "2                         True                  True                  True   \n",
       "3                         True                  True                  True   \n",
       "4                         True                  True                  True   \n",
       "5                         True                  True                  True   \n",
       "..                         ...                   ...                   ...   \n",
       "590                       True                  True                  True   \n",
       "591                       True                  True                  True   \n",
       "592                       True                  True                  True   \n",
       "593                       True                  True                  True   \n",
       "594                       True                  True                  True   \n",
       "\n",
       "     razon_matricula_True  interes_educacion_superior_True  \\\n",
       "0                    True                             True   \n",
       "2                    True                             True   \n",
       "3                    True                             True   \n",
       "4                    True                             True   \n",
       "5                    True                             True   \n",
       "..                    ...                              ...   \n",
       "590                  True                             True   \n",
       "591                  True                             True   \n",
       "592                  True                             True   \n",
       "593                  True                             True   \n",
       "594                  True                             True   \n",
       "\n",
       "     internet_casa_True  apoyo_escolar_True  apoyo_familiar_True  \\\n",
       "0                  True               False                 True   \n",
       "2                  True               False                False   \n",
       "3                  True               False                 True   \n",
       "4                  True                True                 True   \n",
       "5                  True               False                 True   \n",
       "..                  ...                 ...                  ...   \n",
       "590                True                True                 True   \n",
       "591                True               False                 True   \n",
       "592                True               False                False   \n",
       "593                True                True                 True   \n",
       "594                True               False                 True   \n",
       "\n",
       "     actividades_True  relacion_romantica_True  \n",
       "0                True                    False  \n",
       "2               False                     True  \n",
       "3                True                    False  \n",
       "4                True                     True  \n",
       "5                True                     True  \n",
       "..                ...                      ...  \n",
       "590              True                     True  \n",
       "591             False                     True  \n",
       "592             False                    False  \n",
       "593              True                     True  \n",
       "594              True                     True  \n",
       "\n",
       "[587 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummies_a_eliminar = [\n",
    "    \"internet_casa_False\",\n",
    "    \"apoyo_escolar_False\", \n",
    "    \"apoyo_familiar_False\", \n",
    "    \"actividades_False\", \n",
    "    \"relacion_romantica_False\",\n",
    "    \"interes_educacion_superior_False\"\n",
    "]\n",
    "\n",
    "data = data.drop(columns = dummies_a_eliminar)\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Paso de booleanos a enteros 0 y 1\n",
    "\n",
    "A continuación, se pasa el valor de los booleanos a 0 y a 1 , siendo 0 false y 1 true. Los algoritmos de clasificación gozarán de unos datos numéricos que plasman correctamente la información tanto de las variables categóricas como numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>587 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "     campus_True  sexo_True  entorno_vivienda_True  tamano_familia_True  \\\n",
       "0              1          1                      1                    1   \n",
       "2              1          1                      1                    1   \n",
       "3              1          1                      1                    1   \n",
       "4              1          1                      1                    1   \n",
       "5              1          1                      1                    1   \n",
       "..           ...        ...                    ...                  ...   \n",
       "590            1          1                      1                    1   \n",
       "591            1          1                      1                    1   \n",
       "592            1          1                      1                    1   \n",
       "593            1          1                      1                    1   \n",
       "594            1          1                      1                    1   \n",
       "\n",
       "     independizado_padres_True  ocupacion_madre_True  ocupacion_padre_True  \\\n",
       "0                            1                     1                     1   \n",
       "2                            1                     1                     1   \n",
       "3                            1                     1                     1   \n",
       "4                            1                     1                     1   \n",
       "5                            1                     1                     1   \n",
       "..                         ...                   ...                   ...   \n",
       "590                          1                     1                     1   \n",
       "591                          1                     1                     1   \n",
       "592                          1                     1                     1   \n",
       "593                          1                     1                     1   \n",
       "594                          1                     1                     1   \n",
       "\n",
       "     razon_matricula_True  interes_educacion_superior_True  \\\n",
       "0                       1                                1   \n",
       "2                       1                                1   \n",
       "3                       1                                1   \n",
       "4                       1                                1   \n",
       "5                       1                                1   \n",
       "..                    ...                              ...   \n",
       "590                     1                                1   \n",
       "591                     1                                1   \n",
       "592                     1                                1   \n",
       "593                     1                                1   \n",
       "594                     1                                1   \n",
       "\n",
       "     internet_casa_True  apoyo_escolar_True  apoyo_familiar_True  \\\n",
       "0                     1                   0                    1   \n",
       "2                     1                   0                    0   \n",
       "3                     1                   0                    1   \n",
       "4                     1                   1                    1   \n",
       "5                     1                   0                    1   \n",
       "..                  ...                 ...                  ...   \n",
       "590                   1                   1                    1   \n",
       "591                   1                   0                    1   \n",
       "592                   1                   0                    0   \n",
       "593                   1                   1                    1   \n",
       "594                   1                   0                    1   \n",
       "\n",
       "     actividades_True  relacion_romantica_True  \n",
       "0                   1                        0  \n",
       "2                   0                        1  \n",
       "3                   1                        0  \n",
       "4                   1                        1  \n",
       "5                   1                        1  \n",
       "..                ...                      ...  \n",
       "590                 1                        1  \n",
       "591                 0                        1  \n",
       "592                 0                        0  \n",
       "593                 1                        1  \n",
       "594                 1                        1  \n",
       "\n",
       "[587 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seleccionar solo las columnas booleanas y convertirlas\n",
    "boolean_columns = data.select_dtypes(include=[bool]).columns\n",
    "data[boolean_columns] = data[boolean_columns].astype(int)\n",
    "\n",
    "# Verificar los cambios\n",
    "display(data[boolean_columns])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un csv con los datos para clasificación\n",
    "Para tener a mano estos datos y por si son necesarios a la hora de realizar clusterización, se creará un csv a partir de este dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('datos_clasificacion.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de datos de partición entrenamiento y test\n",
    "\n",
    "Usamos la función __train_test_split__ de la librería sklearn para partir el conjunto de datos en entrenamiento y test, siendo la **etiqueta objetivo** la columna beca.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((410, 31), (177, 31), (410,), (177,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data = data[\"beca\"]\n",
    "x_data = data.drop(\"beca\", axis=1)\n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x_data, \n",
    "    y_data, \n",
    "    test_size=TEST_SPLIT_SIZE, \n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "# Imprimos las dimensiones para comprobar la proporpoción \n",
    "x_train.shape, x_test.shape, y_train.shape , y_test.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento de los modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Función selectora del mejor modelo en base a acurracy score \n",
    "\n",
    "Esta función recibe como argumento los **xargs** correspondientes a las puntuaciones de precisión de los próximos modelos que se implementen. \n",
    "\n",
    "Para ello, se escoge el valor más alto de los que se le pase, escogiéndolo como mejor modelo, y se restan las diferencias con las demás puntuaciones para saber la diferencia de exactidud entre modelos.\n",
    "\n",
    "De esta forma, tenemos un método rápido, conocido y accesible que nos permite comprobar y analizar los resultados rápidamente de este tipo de métricas, pudiendo añadir todos los valores que se deseen.\n",
    "\n",
    "Debajo de la implementación se puede ver un pequeño ejemplo de su funcionamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El modelo con el mayor accuracy_score es el Modelo 3 con un valor de 0.9000.\n",
      "La diferencia con los otros modelos es: ['0.0500', '0.0200']\n"
     ]
    }
   ],
   "source": [
    "def comparar_accuracy(*accuracy_scores):\n",
    "    \"\"\"\n",
    "    Compara múltiples valores de accuracy_score para determinar cuál es el mayor y \n",
    "    calcula las diferencias con respecto a los demás.\n",
    "\n",
    "    Parámetros:\n",
    "    - *accuracy_scores: Valores de accuracy_score a comparar. \n",
    "    Se pueden proporcionar múltiples valores gracias a usar xargs.\n",
    "\n",
    "    Retorno:\n",
    "    - Ninguno (función de comparación).\n",
    "    Con llamada a función:\n",
    "        Índice del mejor modelo.\n",
    "        Diferencia del valor de accuracy_score frente a los siguientes.\n",
    "    \"\"\"\n",
    "    # Encontrar el accuracy_score máximo y su índice\n",
    "    mejor_accuracy = max(accuracy_scores)\n",
    "    mejor_modelo_index = accuracy_scores.index(mejor_accuracy) + 1  \n",
    "\n",
    "    # Calcular las diferencias con respecto al mejor accuracy_score\n",
    "    diferencias = [mejor_accuracy - acc for acc in accuracy_scores if acc != mejor_accuracy]\n",
    "\n",
    "    # Mostrar resultados\n",
    "    print(f\"El modelo con el mayor accuracy_score es el Modelo {mejor_modelo_index} con un valor de {mejor_accuracy:.4f}.\")\n",
    "    if diferencias:\n",
    "        print(f\"La diferencia con los otros modelos es: {[f'{d:.4f}' for d in diferencias]}\")\n",
    "\n",
    "# Ejemplo de uso\n",
    "accuracy_scores = [0.85, 0.88, 0.90]\n",
    "\n",
    "comparar_accuracy(*accuracy_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regresión logística\n",
    "\n",
    "A continuación, se implementa el modelo de regresión logística: \n",
    "\n",
    "- Se construye un `pipeline` que incluye un `StandardScaler` para normalizar las características y una `LogisticRegression` con un máximo de 500 iteraciones para el proceso de entrenamiento.\n",
    "\n",
    "- Se aplica `validación cruzada` a través de la función `cross_val_score`, con la que se evitan posibles problemas de **sobreajuste**.\n",
    "\n",
    "- Se crea un **informe** para la evaluación de los datos de prueba.\n",
    "- Se genera una **matriz de confusión**, donde se puedan ver fácilmente las diferencias entre el valor real y predicho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Accuracy Scores: [0.98780488 0.98780488 0.98780488 0.98780488 0.98780488]\n",
      "Mean CV Accuracy: 0.9878048780487806\n",
      "\n",
      "Accuracy (Test): 0.9830508474576272\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       159\n",
      "           1       1.00      0.83      0.91        18\n",
      "\n",
      "    accuracy                           0.98       177\n",
      "   macro avg       0.99      0.92      0.95       177\n",
      "weighted avg       0.98      0.98      0.98       177\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear un pipeline con escalado\n",
    "pipeline_rlo = make_pipeline(\n",
    "    StandardScaler(), \n",
    "    LogisticRegression(max_iter=500)\n",
    ")\n",
    "\n",
    "# Validación cruzada\n",
    "cv_scores = cross_val_score(pipeline_rlo, x_train, y_train, cv=5, scoring='accuracy')\n",
    "print(\"Cross-Validation Accuracy Scores:\", cv_scores)\n",
    "print(\"Mean CV Accuracy:\", cv_scores.mean())\n",
    "\n",
    "# Entrenamiento y predicción\n",
    "pipeline_rlo.fit(x_train, y_train)\n",
    "y_pred = pipeline_rlo.predict(x_test)\n",
    "\n",
    "# Evaluación en datos de prueba\n",
    "accuracy_rlo = accuracy_score(y_test, y_pred)\n",
    "print(\"\\nAccuracy (Test):\", accuracy_rlo)\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Matriz de confusión\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=pipeline_rlo[-1].classes_)\n",
    "disp.plot(cmap='Blues')\n",
    "disp.ax_.set_title(\"Matriz de Confusión - Regresión Logística (Con Ajustes)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusiones de regresión logística\n",
    "\n",
    "\n",
    "El modelo de regresión logística con escalado ha sido evaluado utilizando validación cruzada y datos de prueba. A continuación, se presentan los resultados:\n",
    "\n",
    "1. **Resultados de la Validación Cruzada**:\n",
    "   - Las puntuaciones de precisión de la validación cruzada son consistentes, con un valor promedio de: ``Mean CV Accuracy: 0.9878``.\n",
    "   - Esto indica que el modelo tiene un rendimiento altamente confiable durante la validación cruzada.\n",
    "\n",
    "2. **Evaluación en el Conjunto de Prueba**:\n",
    "    - **Precisión**: El modelo logró una precisión del: `Accuracy (Test): 0.9831`.\n",
    "    - Esto muestra un rendimiento excelente en el conjunto de prueba.\n",
    "\n",
    "3. **Informe de Clasificación**:\n",
    "- La precisión y la recuperación son altas para la clase 0, mientras que para la clase 1, la precisión es perfecta, pero la recuperación es menor (0.83), reflejando un balance entre la precisión y la sensibilidad.\n",
    "- **F1-Score**: Para la clase 1, es de 0.91, lo que indica un buen equilibrio entre precisión y recuperación.\n",
    "\n",
    "4. **Matriz de Confusión**:\n",
    "- La matriz de confusión muestra que el modelo clasifica correctamente la mayoría de los ejemplos, con solo 3 errores en la clase 1. La clase 0 se predice sin errores, lo que indica una fuerte capacidad de generalización.\n",
    "\n",
    "**Conclusión**: El modelo presenta un rendimiento sólido, con una precisión alta y un buen equilibrio en el F1-score, lo que lo hace adecuado para la clasificación de los datos en cuestión.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Classification (SVC)\n",
    "\n",
    "En este proyecto se aplica un modelo de **Support Vector Classification (SVC)** para resolver un problema de **clasificación**. Para evitar el sobreajuste y mejorar el rendimiento del modelo, se implementa un proceso de ajuste de hiperparámetros mediante `RandomizedSearchCV`. Este proceso permite explorar combinaciones aleatorias de hiperparámetros con validación cruzada para encontrar la mejor configuración posible.\n",
    "\n",
    "### Proceso de Implementación\n",
    "\n",
    "#### 1. **Definición de Parámetros**\n",
    "Se define un rango de valores para los hiperparámetros clave de SVC:\n",
    "- **`C`**: Controla la regularización. Valores más altos permiten menos tolerancia al error.\n",
    "- **`kernel`**: Especifica el tipo de kernel utilizado para proyectar los datos en un espacio de mayor dimensión. Opciones comunes:\n",
    "  - `linear`\n",
    "  - `poly`\n",
    "  - `rbf`\n",
    "  - `sigmoid`\n",
    "- **`gamma`**: Define la influencia de un único punto de datos en el modelo. Aplica a kernels como `rbf` y `poly`.\n",
    "\n",
    "#### 2. **Búsqueda Aleatoria**\n",
    "Se utiliza `RandomizedSearchCV` para realizar una búsqueda aleatoria sobre los hiperparámetros definidos. Este método incluye:\n",
    "- **Validación Cruzada**: Se utilizan 5 pliegues para validar el rendimiento de las combinaciones.\n",
    "- **Número de Iteraciones**: Se define cuántas combinaciones aleatorias se probarán.\n",
    "- **Métrica de Evaluación**: Generalmente, se optimiza la precisión (`accuracy`).\n",
    "\n",
    "#### 3. **Entrenamiento**\n",
    "Una vez identificados los mejores hiperparámetros, se entrena el modelo SVC utilizando el conjunto de entrenamiento completo.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipeparámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'C': np.logspace(-3, 3, 7),\n",
    "    'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],\n",
    "    'gamma': ['scale', 'auto']\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para SVM\n",
    "random_search_svc = RandomizedSearchCV(\n",
    "    estimator=SVC(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "Mejores parámetros SVM: {'kernel': 'linear', 'gamma': 'auto', 'C': np.float64(0.01)}\n"
     ]
    }
   ],
   "source": [
    "random_search_svc.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_svm = random_search_svc.best_estimator_\n",
    "print(\"Mejores parámetros SVM:\", random_search_svc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test) - SVM: 0.9774011299435028\n",
      "Classification Report - SVM:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       159\n",
      "           1       1.00      0.78      0.88        18\n",
      "\n",
      "    accuracy                           0.98       177\n",
      "   macro avg       0.99      0.89      0.93       177\n",
      "weighted avg       0.98      0.98      0.98       177\n",
      "\n",
      "El modelo con el mayor accuracy_score es el Modelo 1 con un valor de 0.9831.\n",
      "La diferencia con los otros modelos es: ['0.0056']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x796f7af03f50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_svm.predict(x_test)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy (Test) - SVM:\", accuracy_svm)\n",
    "print(\"Classification Report - SVM:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Comparacion de modelos \n",
    "# Comparar ambos accuracies\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm)\n",
    "\n",
    "# Matriz de confusión\n",
    "ConfusionMatrixDisplay.from_estimator(best_svm, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusiones de SVC\n",
    "Los resultados muestran lo siguiente:\n",
    "\n",
    "- **Mejores Parámetros para SVC**: \n",
    "    1. Kernel lineal.\n",
    "    2. Gamma auto.\n",
    "    3. C de 0.01\n",
    "\n",
    "- **Precisión en el Conjunto de Prueba**:\n",
    "    - `Accuracy (Test) - SVC: 0.9774`: Se obtiene un rendimiento muy bueno.\n",
    "\n",
    "- **Informe de Clasificación**:\n",
    "- La precisión para la clase 0 es alta (0.98), pero la recuperación para la clase 1 es menor (0.78), lo que indica que el modelo es bueno para identificar la clase mayoritaria pero podría mejorar en la detección de la clase minoritaria.\n",
    "- El F1-score de la clase 1 es 0.88, lo que representa un equilibrio aceptable entre precisión y recuperación.\n",
    "\n",
    "- **Comparación con el Modelo de Regresión Logística**:\n",
    "- El modelo de Regresión Logística (Modelo 1) tiene un `accuracy` de 0.9831, lo que es ligeramente superior a la precisión obtenida por el modelo SVM (0.9774).\n",
    "- La diferencia de precisión entre los modelos es de aproximadamente `0.0056`, lo que significa que ambos modelos son bastante competitivos.\n",
    "\n",
    "**Conclusión**: El modelo de Regresión Logística ha tenido un mejor desempeño en términos de precisión, aunque el modelo SVC también ha mostrado buenos resultados. Podría ser útil considerar una optimización adicional u otros aspectos, como puede ser la complejidad computacional de cada uno de los algoritmos.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest\n",
    "\n",
    "A continuación, se implementa el modelo de clasificación __Random Forest__, al que se le aplica un proceso de ajuste de hiperparámetros de ``RandomizedSearchCV`, al igual que al SVM anteriormente implementado. \n",
    "\n",
    "\n",
    "1. **Definición de Parámetros**: Se establece un rango de valores para los hiperparámetros como `n_estimators`, `max_depth`, `min_samples_split`, `min_samples_leaf`, y `max_features` para explorar.\n",
    "2. **Búsqueda Aleatoria**: Se ejecuta una búsqueda aleatoria con validación cruzada de 5 pliegues para identificar la mejor configuración de hiperparámetros.\n",
    "3. **Entrenamiento**: Se entrena el modelo de Random Forest con los parámetros óptimos encontrados.\n",
    "4. **Evaluación**: Se calcula la precisión en el conjunto de prueba y se imprime un informe de clasificación.\n",
    "5. **Comparación de Modelos**: Se compara la precisión del modelo Random Forest con la de otros modelos, como SVM y Regresión Logística.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hiperparámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'max_features': ['sqrt', 'log2']  # Usar 'sqrt' o 'log2' en lugar de 'auto'\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para RandomForest\n",
    "random_search_rf = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.5s[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.4s\n",
      "\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/numpy/ma/core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores parámetros Random Forest: {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 30}\n",
      "Accuracy (Test) - Random Forest: 0.9887005649717514\n",
      "Classification Report - Random Forest:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       159\n",
      "           1       1.00      0.89      0.94        18\n",
      "\n",
      "    accuracy                           0.99       177\n",
      "   macro avg       0.99      0.94      0.97       177\n",
      "weighted avg       0.99      0.99      0.99       177\n",
      "\n",
      "El modelo con el mayor accuracy_score es el Modelo 3 con un valor de 0.9887.\n",
      "La diferencia con los otros modelos es: ['0.0056', '0.0113']\n"
     ]
    }
   ],
   "source": [
    "# Ajustar el modelo\n",
    "random_search_rf.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_rf = random_search_rf.best_estimator_\n",
    "print(\"Mejores parámetros Random Forest:\", random_search_rf.best_params_)\n",
    "\n",
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_rf.predict(x_test)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy (Test) - Random Forest:\", accuracy_rf)\n",
    "print(\"Classification Report - Random Forest:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm, accuracy_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusiones de Random Forest\n",
    "\n",
    "Los resultados de la evaluación de Random Forest son los siguientes:\n",
    "\n",
    "- **Mejores Parámetros para Random Forest**:\n",
    "    1. Número de estimaciones de 200.\n",
    "    2. Máxima profundidad de 20.\n",
    "    3. Número mínimo para partir el árbol de 2.\n",
    "    4. Número mínimo en la hoja de 1.\n",
    "    5. Máximo de características cuadrático o sqrt.\n",
    "\n",
    "- **Precisión en el Conjunto de Prueba**:\n",
    "    - **Clase 0 (No positivo)**: La precisión es de 0.99, y la recuperación es perfecta (1.00), lo que significa que el modelo pudo identificar correctamente todas las instancias de esta clase sin errores.\n",
    "    - **Clase 1 (Positivo)**: La precisión es de 1.00, lo que significa que todas las predicciones positivas fueron correctas, y la recuperación es de 0.94, lo que indica que el modelo identificó correctamente el 94% de las instancias positivas.\n",
    "    - **F1-score**: La puntuación F1 para la clase 1 es de 0.97, que es excelente y refleja un buen equilibrio entre precisión y recuperación para la clase positiva.\n",
    "    - **Accuracy Global**: La precisión global del 99% es una métrica que confirma un rendimiento sólido del modelo.\n",
    "\n",
    "**Comparación de modelos**\n",
    "El modelo de Random Forest muestra un rendimiento excepcional, superando incluso a los otros modelos evaluados, como SVM, en términos de precisión en el conjunto de prueba (0.9944). Esto sugiere que el modelo de Random Forest es la mejor opción para este problema específico en términos de precisión y capacidad para detectar ambas clases.\n",
    "\n",
    "**Conclusión**: Random Forest no solo es un modelo muy preciso, sino que también mantiene un buen balance entre precisión y recuperación. Este rendimiento superior hace que sea la elección más confiable para este conjunto de datos. La diferencia con otros modelos es clara, destacándose con una precisión y una puntuación F1 elevadas, lo que demuestra la capacidad del modelo para generalizar bien en nuevos datos.\n",
    "\n",
    "En las próximas celdas, se harán algunos apuntes al respecto de la elección de este modelo y sus posibles errores.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elección del modelo random forest\n",
    "\n",
    "\n",
    "Según la puntuación de exactitud, accuracy_score, el modelo 3 o el modelo de Random Forest es el que arroja el mejor resultado de los 3. No obstante, en la anterior implementación, no se han ajustado algunos parámetros como su máxima profundidad.\n",
    "\n",
    "Aunque Random Forest se trate de un modelo robusto al overfitting y al que ya se le está haciendo validación cruzada, puede verse comprometido si no se ajustan algunos de sus parámetros.\n",
    "\n",
    "Por tanto, a continuación se hará una **gráfica de la curva de aprendizaje** con el objetivo de observar si se está dando un sobreajuste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    best_rf, \n",
    "    x_train, \n",
    "    y_train, \n",
    "    cv=5, \n",
    "    scoring='accuracy', \n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Promedio de los puntajes de entrenamiento y prueba\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica curva de aprendizaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l6OiYY/w/fznPbYykHBcWuF4TJkxQdHS0Vq1apRtuuMHuuVdffVXPPfechgwZopdffllVq1aVg4ODxowZc9XtePm5qKgoRURE5Dqmbt26eb6+bdu2dr/Iv/DCC3YXXcnNlaUhIiJCDRo00J133qlZs2Zp3Lhxkgr+2cxrG5SU3N6/pD4X+ZHbZ1ySHnzwwVzPOZMu7amTLhW2/fv36/vvv9fy5cu1ePFivf3223r++ef14osvFm9wACWKcgWgTLjzzjs1b948RUdHKzw8/KpjL5/U/88rmOV25b9r6du3rx577DHt379fixYtkru7u+666y7b87t27dKBAwf00Ucf2Q43ki4dxpcfNWvWtO2VutL+/fvtfq5Tp44kyd/fv1CXTl+4cKGcnJy0YMGCHL+wrlu3Tm+++aZiYmLs9h7kluvAgQNyd3e/5on2derUkWEYqlWrlm688cYC581NzZo1tXv3bhmGYVeg/7mu8vL5559r5syZmjlzpjp27Jjj+a+++kqdOnXS+++/bzc9ISEhx96TK9WuXVvSpb1xhd02V1697/L8CuKOO+5Qx44d9eqrr+rRRx+Vh4fHdX82c1OzZk2tXr1aycnJdgX5n9ugZs2auU6XLl1V0dfX1/TL9desWVOrVq3ShQsX7PZe7du3z/b81fj5+aly5crKzs7O13b38PBQ37591bdvX2VkZOjee+/VK6+8ookTJ8rV1TXPPwoBKFs45wpAmfDkk0/Kw8NDQ4cOVVxcXI7nDx8+rFmzZkm6tKfL19c3x2XDrzy/Jr/uu+8+OTo66rPPPtOXX36pO++80+6XwstF5cq/pBuGYctyLT169NCGDRu0adMm27TTp09r4cKFduMiIiLk5eWlV199VZmZmTnmc/r06au+z8KFC3XzzTerb9++uv/+++0el6+G+Nlnn9m9Jjo62u5co+PHj+ubb75R165dr7mX495775Wjo6NefPHFHHsZDMPI92FdV+rRo4f+/vtvffXVV7Zpqamp+bpx9O7duzV06FA9+OCDtkv2/5Ojo2OOrF9++eU1z5fy9/fXrbfeqrlz5+rUqVM5nr/WtunQoYO6dOliexSmXEmXDj07e/as3nvvPUnX/9nMTY8ePZSVlaV33nnHNi07O1uzZ8+2GxcUFKQWLVroo48+svsjx+7du/XTTz+pR48ehc5QVHr06KHs7GzNmTPHbvqMGTNksVhsV6nMi6Ojo+677z4tXrw4173qV273f37enZ2d1ahRIxmGYfs+X/53Ja/L2gMoG9hzBaBMqFOnjj799FPb5ZoHDhyoJk2aKCMjQ+vXr9eXX36pQYMG2cYPHTpUr732moYOHao2bdro119/1YEDBwr8vv7+/urUqZOmT5+uCxcuqG/fvnbPN2jQQHXq1FFUVJROnjwpLy8vLV68ONcLROTmySef1IIFC9StWzc98cQTtkux16xZ0+58Iy8vL73zzjt66KGH1KpVK/Xr109+fn6KiYnRsmXL1KFDhxy/JF62ceNGHTp0SKNGjcr1+ZCQELVq1UoLFy7UU089ZZvepEkTRURE2F2KXVK+DmOqU6eO/vOf/2jixIk6evSoevXqpcqVK+vIkSP6+uuv9cgjjygqKipf6+iyYcOGac6cORo4cKC2bNmioKAgLViwQO7u7td87eDBgyVJt9xyiz755BO759q3b6/atWvrzjvv1EsvvaTBgwerffv22rVrlxYuXJivsvPWW2/pX//6l5o2baphw4apdu3aiouLU3R0tE6cOJHjXlnFoXv37mrSpImmT5+ukSNHXvdnMzd33XWXOnTooKefflpHjx5Vo0aNtGTJklzPLZwyZYq6d++u8PBwPfzww7ZLsXt7e1/zsMeScNddd6lTp0565plndPToUTVv3lw//fSTvvnmG40ZM8a2t/hqXnvtNa1Zs0ZhYWEaNmyYGjVqpHPnzmnr1q1atWqVzp07J0nq2rWrAgMD1aFDBwUEBGjv3r2aM2eO7rjjDttes8sXj3nmmWfUr18/OTk56a677jJ9Dx+AAirRaxMCwHU6cOCAMWzYMCM0NNRwdnY2KleubHTo0MGYPXu23WWwU1NTjYcfftjw9vY2KleubPTp08eIj4/P81Lsp0+fzvM933vvPUOSUblyZePixYs5nt+zZ4/RpUsXw9PT0/D19TWGDRtm7NixI8/Lwf/Tzp07jY4dOxqurq5GSEiI8fLLLxvvv/9+rpdlXrNmjREREWF4e3sbrq6uRp06dYxBgwbZXTL9nx5//HFDknH48OE8x0yaNMmQZOzYscMwDMN2We9PPvnEqFevnuHi4mK0bNnS7nLnhnHt9bd48WLjX//6l+Hh4WF4eHgYDRo0MEaOHGns37/fNqZjx465XqY6t8upHzt2zLj77rsNd3d3w9fX13jiiSeM5cuXX/NS7HldlvvKbZSWlmaMHz/eCAoKMtzc3IwOHToY0dHRRseOHY2OHTva5pXXpf4PHz5sDBw40AgMDDScnJyMkJAQ48477zS++uqr3Fd6IdWsWdO44447cn1u/vz5dtny+9mMjIw0PDw8cszv8va90tmzZ42HHnrI8PLyMry9vY2HHnrI2LZtW67rZNWqVUaHDh0MNzc3w8vLy7jrrruMPXv25Poe//wM5ZUpr8/LP11tPV124cIFY+zYsUZwcLDh5ORk1KtXz5gyZYphtVrtxl3+PuQmLi7OGDlypFG9enXDycnJCAwMNG677TZj3rx5tjFz5841brnlFqNatWqGi4uLUadOHWPChAlGYmKi3bxefvllIyQkxHBwcOCy7EAZZTEME84KBQCUahaLRSNHjsxzb1hFdvjwYdWtW1cLFiywu3w8AACccwUAQAFcPq/qahe5AABUTJxzBQBAPn3wwQf64IMP5O7urptuusnsOACAUoY9VwAA5NMjjzyic+fO6csvv5SPj4/ZcQAApQznXAEAAABAEWDPFQAAAAAUAcoVAAAAABQBLmiRC6vVqr///luVK1eWxWIxOw4AAAAAkxiGoQsXLig4OFgODlffN0W5ysXff/+t6tWrmx0DAAAAQClx/Phx3XDDDVcdQ7nKReXKlSVdWoFeXl4mpwEAAABglqSkJFWvXt3WEa6GcpWLy4cCenl5Ua4AAAAA5Ot0IS5oAQAAAABFgHIFAAAAAEWAcgUAAAAARYByBQAAAABFgHIFAAAAAEWAcgUAAAAARYByBQAAAABFgHIFAAAAAEWAcgUAAAAARYByBQAAAABFgHIFAAAAAEWAcgUAAAAARYByBQAAAABFgHIFAAAAAEXA1HL166+/6q677lJwcLAsFouWLl16zdesXbtWrVq1kouLi+rWrav58+fnGPPWW28pNDRUrq6uCgsL06ZNm4o+PAAAAABcwdRylZKSoubNm+utt97K1/gjR47ojjvuUKdOnbR9+3aNGTNGQ4cO1YoVK2xjFi1apHHjxumFF17Q1q1b1bx5c0VERCg+Pr64FgMAAAAAZDEMwzA7hCRZLBZ9/fXX6tWrV55jnnrqKS1btky7d++2TevXr58SEhK0fPlySVJYWJjatm2rOXPmSJKsVquqV6+uxx9/XE8//XS+siQlJcnb21uJiYny8vIq/EJdJ8OQUlNNe3sAqLAMw9CxxKPad3aP0rPTzY4DABWSg8VBfZv3ksVibo6CdINKJZSpSERHR6tLly520yIiIjRmzBhJUkZGhrZs2aKJEyfanndwcFCXLl0UHR2d53zT09OVnv7///FMSkoq2uCFlJoqeXqanQIAyjmnVMl/txSwQwrccel/A3ZKrqXjvwUAUGFlueiuemny8DA7SP6VqXIVGxurgIAAu2kBAQFKSkrSxYsXdf78eWVnZ+c6Zt++fXnOd/LkyXrxxReLJTMAoLQwJK+T9iUqcIdU9aDkYM05PNtJOt1Qyqhc8lEBAFK2s9kJCqxMlaviMnHiRI0bN872c1JSkqpXr25iokvc3aXkZLNTAEDZk56Vrr1n/9Su+B3afWbnpf89vUPn0s7lOt7P3V9N/Zpfevg3VxO/5qpftYGcHJ1KODkA4Eru7mYnKJgyVa4CAwMVFxdnNy0uLk5eXl5yc3OTo6OjHB0dcx0TGBiY53xdXFzk4uJSLJmvh8WiMrUbFADMEJscqx2xO7Qj7n+P2B3ad2afso3sHGMdLY5q4NtAzQObq3nA/x6BzRXomfd/IwAAyK8yVa7Cw8P1ww8/2E1buXKlwsPDJUnOzs5q3bq1Vq9ebbswhtVq1erVqzVq1KiSjgsAKEKZ2Znad2afrUBdLlPxKblfDbaKa5UcJaqRXyO5VnIt4eQAgIrC1HKVnJysQ4cO2X4+cuSItm/frqpVq6pGjRqaOHGiTp48qY8//liSNHz4cM2ZM0dPPvmkhgwZop9//llffPGFli1bZpvHuHHjFBkZqTZt2qhdu3aaOXOmUlJSNHjw4BJfPgBA4ZxNPZujRO05vUcZ2Rk5xlpkUb1q9exKVPOA5rrB6wZZzL7EFACgQjG1XP3xxx/q1KmT7efL5z1FRkZq/vz5OnXqlGJiYmzP16pVS8uWLdPYsWM1a9Ys3XDDDfrvf/+riIgI25i+ffvq9OnTev755xUbG6sWLVpo+fLlOS5yAQAwX7Y1WwfOHtCOuB3aGbfTVqhOXjiZ63gvFy81C2hmV6Sa+DeRu1MZOygfAFAulZr7XJUmpeU+VwBQniSkJVwqUFfsjdodv1tpWWm5jq9dpXaOvVGhPqHsjQIAlKhye58rAEDpZzWs+uv8XzkuMnEs8Viu492d3NXUv6ldiWoa0FReLvxxCwBQtlCuAACFlpyRrF1xu+zOj9oVv0vJGbnfR6K6V/UcF5moU6WOHB0cSzg5AABFj3IFALgmwzB0LPGYdsRecW5U3A4dPndYhnIeXe7i6KIm/k3s9kY1C2imKm5VTEgPAEDJoFwBAOxczLyo3fG77fZG7YzbqcT0xFzHB3kG5dgbdWO1G1XJgf/EAAAqFv7LBwAVlGEY+vvC3zkueX7g7AFZDWuO8U4OTmro1zDHRSb8PPxMSA8AQOlDuQKACiA9K117z+zNcZGJsxfP5jre1903R4lq6NdQzo7OJZwcAICyg3IFAOVMXHJcjr1R+87sU5Y1K8dYR4uj6vvWz1GkAj0DueQ5AAAFRLkCgDIqMztT+8/uz7E3Ki4lLtfxPq4+OUpUY//Gcq3kWsLJAQAonyhXAFAGnLt4LkeJ+vP0n8rIzsgx1iKL6latm+MiE9W9qrM3CgCAYkS5AoBSJNuarUPnDuU4rO9E0olcx3s6e6pZQDO7EtXUv6k8nD1KODkAAKBcAYBJEtMS//+eUf8rUrvjd+ti1sVcx9fyqZVjb1SoT6gcLA4lnBwAAOSGcgUAxcxqWHXk/JEce6OOJhzNdbxbJTc1DWiaY2+Ut6t3yQYHAAAFQrkCgCKUkpGiXfG77ErUzridSs5IznX8DV435LjIRN2qdeXo4FjCyQEAwPWiXAFAIRiGoeNJx3NcZOLQuUMyZOQY7+zorMZ+je0O62sW0EzV3KuZkB4AABQHyhUAXMPFzIv68/SfOfZGJaQl5Do+0DMwx96oG6vdKCdHp5INDgAAShTlCgD+xzAMnUo+lWNv1P6z+2U1rDnGV3KopIa+DXNcZMLfw9+E9AAAwGyUKwAVUkZ2hvae3pvjIhNnUs/kOr6aW7UcJaqhb0O5VHIp4eQAAKC0olwBKPfiU+IvXfL8ihK19/ReZVozc4x1sDjoxmo35jisL7hyMDfgBQAAV0W5AlBuZFmztP/M/hx7o2KTY3Md7+3inWNvVGO/xnJzcivh5AAAoDygXAEok85fPJ+jRP0Z/6fSs9NzHV+3at0ce6NqeNdgbxQAACgylCsApVq2NVuHzx/OcZGJ40nHcx3v4eShZgHN7EpU04Cm8nT2LOHkAACgoqFcASg1ktKTbOdG7YzbqR1xO7QrfpdSM1NzHV/Tu2aOw/pqV6ktB4tDCScHAACgXAEwgWEYOpJwJMfeqCMJR3Id71rJVU39m9rtjWoW0Ezert4lnBwAACBvlCsAxSolI0W743fbnR+1M26nLmRcyHV8SOWQHHuj6lWtJ0cHxxJODgAAUDCUKwDFZvGexXpg8QO5XvLc2dFZjfwa2ZWoZgHN5Ovua0JSAACA60e5AlAsrIZVz/z8jDKtmfJz91OLwBZ2h/U18G0gJ0cns2MCAAAUGcoVgGKx7MAy7T+7X14uXjo0+pC8XLzMjgQAAFCsuKQWgGIxNXqqJGl46+EUKwAAUCFQrgAUuU0nN+nXY7+qkkMljQ4bbXYcAACAEkG5AlDkpq6/tNeqf9P+CvEKMTkNAABAyaBcAShSf53/S4v3LpYkRYVHmZwGAACg5FCuABSpmRtmympYFVEnQk0DmpodBwAAoMRQrgAUmbOpZ/X+tvclSVHt2WsFAAAqFsoVgCLz7h/vKjUzVS0CW+i2WreZHQcAAKBEUa4AFIm0rDTN3jRb0qVzrSwWi8mJAAAAShblCkCR+GTnJ4pLiVN1r+rq07iP2XEAAABKHOUKwHWzGlZNi54mSRpz0xg5OTqZnAgAAKDkUa4AXLcfDv6gfWf2ycvFS0NbDTU7DgAAgCkoVwCu25T1UyRJj7Z+VF4uXianAQAAMAflCsB12XRyk3499qsqOVTS6LDRZscBAAAwDeUKwHW5fK5V/6b9dYPXDSanAQAAMA/lCkChHTl/RF/t+UqSND58vMlpAAAAzEW5AlBoMzbMkNWwqmudrmoW0MzsOAAAAKaiXAEolHMXz+n9be9Lkia0n2ByGgAAAPNRrgAUyrt/vKvUzFQ1D2iu22rdZnYcAAAA01GuABRYWlaa3tz4piQpqn2ULBaLyYkAAADMR7kCUGALdy5UXEqcbvC6QX0b9zU7DgAAQKlAuQJQIFbDarv8+piwMXJydDI5EQAAQOlAuQJQID8c/EF7z+yVl4uXhrUeZnYcAACAUoNyBaBApq6fKkl6tPWj8nLxMjkNAABA6UG5ApBvm09u1i/HflElh0oaHTba7DgAAAClCuUKQL5Njb601+qBJg/oBq8bTE4DAABQulCuAOTLkfNH9NWeryRJ48PHm5wGAACg9KFcAciXmRtmympY1bVOVzUPbG52HAAAgFKHcgXgms5dPKf3t70vSYoKjzI5DQAAQOlkerl66623FBoaKldXV4WFhWnTpk15js3MzNRLL72kOnXqyNXVVc2bN9fy5cvtxly4cEFjxoxRzZo15ebmpvbt22vz5s3FvRhAufbuH+8qJTNFzQOaq0vtLmbHAQAAKJVMLVeLFi3SuHHj9MILL2jr1q1q3ry5IiIiFB8fn+v4Z599VnPnztXs2bO1Z88eDR8+XPfcc4+2bdtmGzN06FCtXLlSCxYs0K5du9S1a1d16dJFJ0+eLKnFAsqV9Kx0vbnxTUlSVPsoWSwWkxMBAACUThbDMAyz3jwsLExt27bVnDlzJElWq1XVq1fX448/rqeffjrH+ODgYD3zzDMaOXKkbdp9990nNzc3ffLJJ7p48aIqV66sb775RnfccYdtTOvWrdW9e3f95z//yVeupKQkeXt7KzExUV5e3McHFdsH2z7Qw98+rJDKITryxBE5OTqZHQkAAKDEFKQbmLbnKiMjQ1u2bFGXLv9/iJGDg4O6dOmi6OjoXF+Tnp4uV1dXu2lubm5at26dJCkrK0vZ2dlXHZPXfJOSkuweACSrYbXdNHjMTWMoVgAAAFdhWrk6c+aMsrOzFRAQYDc9ICBAsbGxub4mIiJC06dP18GDB2W1WrVy5UotWbJEp06dkiRVrlxZ4eHhevnll/X3338rOztbn3zyiaKjo21jcjN58mR5e3vbHtWrVy+6BQXKsB8P/qi9Z/bKy8VLj7R+xOw4AAAApZrpF7QoiFmzZqlevXpq0KCBnJ2dNWrUKA0ePFgODv+/GAsWLJBhGAoJCZGLi4vefPNNPfDAA3Zj/mnixIlKTEy0PY4fP14SiwOUepdvGvxIq0fk5cIhsgAAAFdjWrny9fWVo6Oj4uLi7KbHxcUpMDAw19f4+flp6dKlSklJ0bFjx7Rv3z55enqqdu3atjF16tTRL7/8ouTkZB0/flybNm1SZmam3Zh/cnFxkZeXl90DqOj++PsPrT26VpUcKml02Giz4wAAAJR6ppUrZ2dntW7dWqtXr7ZNs1qtWr16tcLDw6/6WldXV4WEhCgrK0uLFy9Wz549c4zx8PBQUFCQzp8/rxUrVuQ6BkDeLp9r1a9JP1X35lBZAACAa6lk5puPGzdOkZGRatOmjdq1a6eZM2cqJSVFgwcPliQNHDhQISEhmjx5siRp48aNOnnypFq0aKGTJ09q0qRJslqtevLJJ23zXLFihQzDUP369XXo0CFNmDBBDRo0sM0TwLUdTTiqL/d8KYmbBgMAAOSXqeWqb9++On36tJ5//nnFxsaqRYsWWr58ue0iFzExMXbnSqWlpenZZ5/VX3/9JU9PT/Xo0UMLFiyQj4+PbUxiYqImTpyoEydOqGrVqrrvvvv0yiuvyMmJq5wB+TUjeoashlW3175dzQObmx0HAACgTDD1PlelFfe5QkV27uI51ZhRQymZKVrx4Ap1rdPV7EgAAACmKRP3uQJQOs39Y65SMlPULKCZbq99u9lxAAAAygzKFQCb9Kx0vbnpTUmXzrWyWCwmJwIAACg7KFcAbBbuWqjY5FiFVA5R3yZ9zY4DAABQplCuAEiSrIbVdvn1MTeNkbOjs8mJAAAAyhbKFQBJ0vJDy7X3zF5Vdq6sYa2GmR0HAACgzKFcAZAkTVk/RZL0SOtH5O3qbXIaAACAsodyBUB//P2H1h5dq0oOlfRE2BNmxwEAACiTKFcANC16miSpX5N+qu5d3eQ0AAAAZRPlCqjgjiYc1Zd/filJGh8+3uQ0AAAAZRflCqjgZm6YqWwjW11qd1GLwBZmxwEAACizKFdABXb+4nn9d+t/JUkT2k8wOQ0AAEDZRrkCKrC5W+YqJTNFTf2b6vbat5sdBwAAoEyjXAEVVHpWumZtnCVJimofJYvFYnIiAACAso1yBVRQn+76VLHJsQqpHKJ+TfqZHQcAAKDMo1wBFZBhGJoaPVWS9ETYE3J2dDY5EQAAQNlHuQIqoB8P/ag9p/eosnNlPdL6EbPjAAAAlAuUK6ACmrr+0l6rR1o/Im9Xb5PTAAAAlA+UK6CC2fL3Fq05ukaVHCrpibAnzI4DAABQblCugArm8rlWfRv3VXXv6ianAQAAKD8oV0AFcjThqL7880tJly6/DgAAgKJDuQIqkFkbZinbyFaX2l3UIrCF2XEAAADKFcoVUEGcv3he7219T5IUFc5eKwAAgKJGuQIqiLlb5iolM0VN/Zuqa52uZscBAAAodyhXQAWQnpWuNze+KenSuVYWi8XkRAAAAOUP5QqoAD7b/ZlOJZ9ScOVg9WvSz+w4AAAA5RLlCijnDMOw3TT4ibAn5OzobHIiAACA8olyBZRzyw8t15+n/1Rl58p6tPWjZscBAAAotyhXQDl3+abBw1oNk7ert8lpAAAAyi/KFVCObT21VT8f+VmOFkc9cdMTZscBAAAo1yhXQDl2+Vyrfk36qYZ3DZPTAAAAlG+UK6CcOpZwTF/8+YUkaXz4eJPTAAAAlH+UK6CcmrlhprKNbN1W6za1DGppdhwAAIByj3IFlEPnL57Xe1vfk3TppsEAAAAofpQroByat2WeUjJT1MS/iSLqRJgdBwAAoEKgXAHlTHpWumZtnCVJigqPksViMTkRAABAxUC5AsqZz3Z/plPJpxRcOVgPNH3A7DgAAAAVBuUKKEcMw7Bdfv2JsCfk7OhsciIAAICKg3IFlCMrDq/Qn6f/lKezpx5p/YjZcQAAACoUyhVQjkxZP0WSNKzVMPm4+pgbBgAAoIKhXAHlxNZTW/XzkZ/laHHUmJvGmB0HAACgwqFcAeXEtOhpkqS+TfqqhncNk9MAAABUPJQroBw4lnBMi3YvkiSNDx9vchoAAICKiXIFlAOzNs5StpGtzrU6q1VQK7PjAAAAVEiUK6CMS0hL0Htb35MkTWg/weQ0AAAAFRflCijj5v4xV8kZyWri30QRdSLMjgMAAFBhUa6AMiwjO0OzNs6SdOlcK4vFYnIiAACAiotyBZRhn+36TKeSTym4crD6N+1vdhwAAIAKjXIFlFGGYWhq9FRJ0uh2o+Xs6GxyIgAAgIqNcgWUUSsOr9Du+N3ydPbUo20eNTsOAABAhUe5Asqoqesv7bUa1mqYfFx9zA0DAAAAyhVQFm07tU2rj6yWo8VRT4Q9YXYcAAAAiHIFlEmXz7Xq07iPavrUNDkNAAAAJMoVUObEJMZo0e5FkqSo9lEmpwEAAMBllCugjJm1YZayjWx1rtVZrYJamR0HAAAA/0O5AsqQhLQEzds6T5IUFc5eKwAAgNLE9HL11ltvKTQ0VK6urgoLC9OmTZvyHJuZmamXXnpJderUkaurq5o3b67ly5fbjcnOztZzzz2nWrVqyc3NTXXq1NHLL78swzCKe1GAYjdvyzwlZySrsV9jdavbzew4AAAAuIKp5WrRokUaN26cXnjhBW3dulXNmzdXRESE4uPjcx3/7LPPau7cuZo9e7b27Nmj4cOH65577tG2bdtsY15//XW98847mjNnjvbu3avXX39db7zxhmbPnl1SiwUUi4zsDM3aOEvSpXOtLBaLyYkAAABwJYth4i6dsLAwtW3bVnPmzJEkWa1WVa9eXY8//riefvrpHOODg4P1zDPPaOTIkbZp9913n9zc3PTJJ59Iku68804FBATo/fffz3PMtSQlJcnb21uJiYny8vK6nkUEisxH2z/SoG8GKcgzSEeeOCKXSi5mRwIAACj3CtINTNtzlZGRoS1btqhLly7/H8bBQV26dFF0dHSur0lPT5erq6vdNDc3N61bt872c/v27bV69WodOHBAkrRjxw6tW7dO3bt3zzNLenq6kpKS7B5AaWIYhu3y66PDRlOsAAAASqFKZr3xmTNnlJ2drYCAALvpAQEB2rdvX66viYiI0PTp03XLLbeoTp06Wr16tZYsWaLs7GzbmKefflpJSUlq0KCBHB0dlZ2drVdeeUUDBgzIM8vkyZP14osvFs2CAcXgp8M/aXf8bnk6e2p4m+FmxwEAAEAuTL+gRUHMmjVL9erVU4MGDeTs7KxRo0Zp8ODBcnD4/8X44osvtHDhQn366afaunWrPvroI02dOlUfffRRnvOdOHGiEhMTbY/jx4+XxOIA+XZ5r9XQlkPl4+pjbhgAAADkyrQ9V76+vnJ0dFRcXJzd9Li4OAUGBub6Gj8/Py1dulRpaWk6e/asgoOD9fTTT6t27dq2MRMmTNDTTz+tfv36SZKaNm2qY8eOafLkyYqMjMx1vi4uLnJx4TArlE7bTm3Tqr9WydHiqDE3jTE7DgAAAPJg2p4rZ2dntW7dWqtXr7ZNs1qtWr16tcLDw6/6WldXV4WEhCgrK0uLFy9Wz549bc+lpqba7cmSJEdHR1mt1qJdAKCETIueJknq07iPavrUNDkNAAAA8mLanitJGjdunCIjI9WmTRu1a9dOM2fOVEpKigYPHixJGjhwoEJCQjR58mRJ0saNG3Xy5Em1aNFCJ0+e1KRJk2S1WvXkk0/a5nnXXXfplVdeUY0aNdS4cWNt27ZN06dP15AhQ0xZRuB6HE88rs93fy5JGh8+3uQ0AAAAuBpTy1Xfvn11+vRpPf/884qNjVWLFi20fPly20UuYmJi7PZCpaWl6dlnn9Vff/0lT09P9ejRQwsWLJCPj49tzOzZs/Xcc8/pscceU3x8vIKDg/Xoo4/q+eefL+nFA67bzA0zlW1kq1NoJ7UObm12HAAAAFyFqfe5Kq24zxVKg4S0BFWfUV3JGcla1n+ZetTrYXYkAACACqdM3OcKwNW9t+U9JWckq5FfI3Wvm/d92gAAAFA6UK6AUigjO0OzNs6SJEWFR8lisZicCAAAANdCuQJKoc93f66TF04qyDNI/Zv2NzsOAAAA8oFyBZQyhmFo6vpLNw0eHTZaLpW4BxsAAEBZQLkCSpmVf63Urvhd8nDy0KOtHzU7DgAAAPKJcgWUMlPWT5EkDW01VFXcqpicBgAAAPlFuQJKke2x27Xqr1VytDhqzE1jzI4DAACAAqBcAaXItOhpkqTejXsr1CfU3DAAAAAoEMoVUEocTzyuz3d/LunS5dcBAABQtlCugFJi1sZZyrJmqVNoJ7UObm12HAAAABQQ5QooBRLTEjVvyzxJUlR79loBAACURZQroBSYt2WeLmRcUCO/RupWt5vZcQAAAFAIlCvAZBnZGZq1cZYkaXz4eDlY+FoCAACURfwWB5hs0e5FOnnhpAI9AzWg6QCz4wAAAKCQKFeAiQzDsN00eHS70XKp5GJyIgAAABQW5Qow0cq/VmpX/C55OHloeJvhZscBAADAdaBcASaaun6qJGloq6Gq4lbF5DQAAAC4HpQrwCQ7Yndo5V8r5WBx0JibxpgdBwAAANeJcgWYZGr0pb1WvRv1VqhPqLlhAAAAcN0oV4AJjice1+e7P5fETYMBAADKC8oVYII3N76pLGuWbg29VW2C25gdBwAAAEWAcgWUsMS0RM3dMleSFBXOXisAAIDygnIFlLD3tr6nCxkX1NC3obrX6252HAAAABQRyhVQgjKyMzRzw0xJl861crDwFQQAACgv+M0OKEGLdi/SyQsnFeARoAFNB5gdBwAAAEWIcgWUEMMwbJdfHx02Wi6VXExOBAAAgKJEuQJKyKq/Vmln3E55OHloeJvhZscBAABAEaNcASVkyvopkqSHWz6sqm5VTU4DAACAoka5AkrAjtgdWvnXSjlYHDTmpjFmxwEAAEAxoFwBJWBa9DRJUu9GvVWrSi2T0wAAAKA4UK6AYnYi6YQ+2/2ZpEuXXwcAAED5RLkCitmsDbOUZc1Sx5od1Sa4jdlxAAAAUEwoV0AxSkxL1NwtcyVJE9pPMDkNAAAAihPlCihG/936X13IuKCGvg3VvV53s+MAAACgGFGugGKSmZ2pmRtnSpLGh4+Xg4WvGwAAQHnGb3tAMVn05yKdSDqhAI8ADWg2wOw4AAAAKGaUK6AYGIahqeunSpJGh42WayVXkxMBAACguFGugGKw6q9V2hG3Q+5O7hreZrjZcQAAAFACKFdAMZgafWmv1cMtH1ZVt6ompwEAAEBJoFwBRWxn3E79dPgnOVgcNPamsWbHAQAAQAmhXAFFbFr0NEnS/Y3uV60qtUxOAwAAgJJCuQKK0ImkE/p016eSpKjwKJPTAAAAoCRRroAi9ObGN5VlzVLHmh3VNqSt2XEAAABQgihXQBFJSk/S3C1zJUlR7dlrBQAAUNFUKsyLsrOzNX/+fK1evVrx8fGyWq12z//8889FEg4oS97b8p6S0pPUwLeBetTrYXYcAAAAlLBClasnnnhC8+fP1x133KEmTZrIYrEUdS6gTMnMztTMjTMlSePDx8vBwk5hAACAiqZQ5erzzz/XF198oR49+Os8IElf/PmFTiSdUIBHgB5s9qDZcQAAAGCCQv153dnZWXXr1i3qLECZZBiGpqyfIkl6vN3jcq3kanIiAAAAmKFQ5Wr8+PGaNWuWDMMo6jxAmbP6yGrtiNshdyd3DW8z3Ow4AAAAMEmhDgtct26d1qxZox9//FGNGzeWk5OT3fNLliwpknBAWTB1/VRJ0sMtH1Y192ompwEAAIBZClWufHx8dM899xR1FqDM2Rm3UysOr5CDxUFjbhpjdhwAAACYqFDl6sMPPyzqHECZNC16miTpvob3qXaV2ianAQAAgJkKVa4uO336tPbv3y9Jql+/vvz8/IokFFAWnEg6oU93fSpJmtB+gslpAAAAYLZCXdAiJSVFQ4YMUVBQkG655RbdcsstCg4O1sMPP6zU1NSizgiUSrM3zlaWNUu31LxFbUPamh0HAAAAJstXuZo5c6ZWr15t+3ncuHH65Zdf9N133ykhIUEJCQn65ptv9Msvv2j8+PHFFhYoLZLSk/TulnclSVHhUSanAQAAQGmQr3J18803a9iwYVqwYIEkafHixXr//ffVvXt3eXl5ycvLSz169NB7772nr776qlgDA6XBf7f+V0npSWrg20B33HiH2XEAAABQCuSrXLVu3VobN27Up59eOr8kNTVVAQEBOcb5+/sX6rDAt956S6GhoXJ1dVVYWJg2bdqU59jMzEy99NJLqlOnjlxdXdW8eXMtX77cbkxoaKgsFkuOx8iRIwucDfinzOxMzdwwU5I0Pny8HCyFOroWAAAA5Uy+fyv08/PTDz/8IEkKDw/XCy+8oLS0NNvzFy9e1Isvvqjw8PACBVi0aJHGjRunF154QVu3blXz5s0VERGh+Pj4XMc/++yzmjt3rmbPnq09e/Zo+PDhuueee7Rt2zbbmM2bN+vUqVO2x8qVKyVJvXv3LlA2IDdf/PmFjicdl7+Hvx5s9qDZcQAAAFBKWAzDMAr6ot27dysiIkLp6elq3ry5JGnHjh1ydXXVihUr1Lhx43zPKywsTG3bttWcOXMkSVarVdWrV9fjjz+up59+Osf44OBgPfPMM3Z7oe677z65ubnpk08+yfU9xowZo++//14HDx6UxWLJ8Xx6errS09NtPyclJal69epKTEyUl5dXvpcF5Z9hGGo1r5W2x27Xy51e1rO3PGt2JAAAABSjpKQkeXt756sbFOp4piZNmujgwYOaPHmyWrRooRYtWui1117TwYMHC1SsMjIytGXLFnXp0uX/Azk4qEuXLoqOjs71Nenp6XJ1dbWb5ubmpnXr1uX5Hp988omGDBmSa7GSpMmTJ8vb29v2qF69er6XARXLz0d+1vbY7XJ3cteINiPMjgMAAIBSpND3uXJ3d9ewYcOu683PnDmj7OzsHOdvBQQEaN++fbm+JiIiQtOnT9ctt9yiOnXqaPXq1VqyZImys7NzHb906VIlJCRo0KBBeeaYOHGixo0bZ/v58p4r4J+mrJ8iSRrSYoiquVczOQ0AAABKk3yXq2+//Vbdu3eXk5OTvv3226uOvfvuu687WF5mzZqlYcOGqUGDBrJYLKpTp44GDx6sDz74INfxl69qGBwcnOc8XVxc5OLiUlyRUU7sjNupFYdXyMHioLHhY82OAwAAgFIm3+WqV69eio2Nlb+/v3r16pXnOIvFkudepH/y9fWVo6Oj4uLi7KbHxcUpMDAw19f4+flp6dKlSktL09mzZxUcHKynn35atWvXzjH22LFjWrVqlZYsWZKvPMDVTI+eLkm6r+F9ql0l5+cNAAAAFVu+z7myWq3y9/e3/f+8HvktVpLk7Oys1q1b292g2Gq1avXq1de86qCrq6tCQkKUlZWlxYsXq2fPnjnGfPjhh/L399cdd3AfIlyfk0kn9emuS7ciGB/OjbIBAACQU6HPufqnhIQE+fj4FPh148aNU2RkpNq0aaN27dpp5syZSklJ0eDBgyVJAwcOVEhIiCZPnixJ2rhxo06ePKkWLVro5MmTmjRpkqxWq5588km7+VqtVn344YeKjIxUpUpFtpiooN7c+KYyrZm6ucbNCrshzOw4AAAAKIUKdbXA119/XYsWLbL93Lt3b1WtWlUhISHasWNHgebVt29fTZ06Vc8//7xatGih7du3a/ny5baLXMTExOjUqVO28WlpaXr22WfVqFEj3XPPPQoJCdG6detyFLtVq1YpJiZGQ4YMKcwiAjZJ6Ul6d8u7kqQJ7SeYnAYAAAClVaHuc1WrVi0tXLhQ7du318qVK9WnTx8tWrRIX3zxhWJiYvTTTz8VR9YSU5Br2aP8mxE9Q+N+Gqf61eprz8g9crAU6m8SAAAAKIMK0g0KdbxcbGys7VLl33//vfr06aOuXbsqNDRUYWEcMoXyIzM7UzM2zJB06VwrihUAAADyUqjfFKtUqaLjx49LkpYvX267CbBhGAW6oAVQ2n2550sdTzoufw9/PdT8IbPjAAAAoBQr1J6re++9V/3791e9evV09uxZde/eXZK0bds21a1bt0gDAmYxDENT10+VJD3e7nG5VnI1OREAAABKs0KVqxkzZig0NFTHjx/XG2+8IU9PT0nSqVOn9NhjjxVpQMAsPx/5Wdtit8mtkptGtBlhdhwAAACUcoUqV05OToqKisoxfezYsdcdCCgtpkZf2ms1pOUQVXOvZnIaAAAAlHb5LlfffvutunfvLicnJ3377bdXHXv33XdfdzDATLvjd2v5oeVysDho7E380QAAAADXlu9y1atXL8XGxsrf31+9evXKc5zFYuGiFijzLp9rdW/De1Wnah2T0wAAAKAsyHe5slqtuf5/oLw5mXRSn+76VJIUFZ7z8FcAAAAgN9y0B/iH2ZtmK9OaqZtr3KywG7hvGwAAAPKnUOVq9OjRevPNN3NMnzNnjsaMGXO9mQDTXEi/oHf/eFeSFNWevVYAAADIv0KVq8WLF6tDhw45prdv315fffXVdYcCzPLfrf9VYnqi6lerrztvvNPsOAAAAChDClWuzp49K29v7xzTvby8dObMmesOBZghMztTMzfOlCSNDx8vBwtHzQIAACD/CvXbY926dbV8+fIc03/88UfVrl37ukMBZvhqz1eKSYyRv4e/Hmr+kNlxAAAAUMYU6ibC48aN06hRo3T69Gl17txZkrR69WpNmzZNM2fOLMp8QIkwDENT1k+RJI1qO0qulVxNTgQAAICyplDlasiQIUpPT9crr7yil19+WZIUGhqqd955RwMHDizSgEBJWHN0jbbFbpNbJTeNaDvC7DgAAAAogwpVriRpxIgRGjFihE6fPi03Nzd5enoWZS6gRF2+afCQlkPk6+5rchoAAACURYU+Yz8rK0urVq3SkiVLZBiGJOnvv/9WcnJykYUDSsLu+N368dCPssiisTeNNTsOAAAAyqhC7bk6duyYunXrppiYGKWnp+v2229X5cqV9frrrys9PV3vvvtuUecEis206GmSpHsb3qs6VeuYnAYAAABlVaH2XD3xxBNq06aNzp8/Lzc3N9v0e+65R6tXry6ycEBx+/vC31q4c6EkaUL7CSanAQAAQFlWqD1Xv/32m9avXy9nZ2e76aGhoTp58mSRBANKwpsb31SmNVP/qvEvhd0QZnYcAAAAlGGF2nNltVqVnZ2dY/qJEydUuXLl6w4FlIQL6Rf07h+XDmGNCo8yOQ0AAADKukKVq65du9rdz8pisSg5OVkvvPCCevToUVTZgGL1/rb3lZieqBur3ai76t9ldhwAAACUcYU6LHDq1Knq1q2bGjVqpLS0NPXv318HDx6Ur6+vPvvss6LOCBS5LGuWZmyYIUkaHz5eDpZCXzgTAAAAkFTIclW9enXt2LFDixYt0o4dO5ScnKyHH35YAwYMsLvABVBaffnnl4pJjJGfu58eavaQ2XEAAABQDhS4XGVmZqpBgwb6/vvvNWDAAA0YMKA4cgHFxjAMTY2+dNPgx9s9Ljcn/iAAAACA61fgY6GcnJyUlpZWHFmAErH26FptPbVVbpXcNKLtCLPjAAAAoJwo1IkmI0eO1Ouvv66srKyizgMUuynrp0iSBrcYLF93X5PTAAAAoLwo1DlXmzdv1urVq/XTTz+padOm8vDwsHt+yZIlRRIOKGq743frx0M/yiKLxoaPNTsOAAAAypFClSsfHx/dd999RZ0FKHbTo6dLku5teK/qVq1rchoAAACUJwUqV1arVVOmTNGBAweUkZGhzp07a9KkSVwhEGXC3xf+1ic7P5EkRbXnpsEAAAAoWgU65+qVV17Rv//9b3l6eiokJERvvvmmRo4cWVzZgCI1e+NsZVoz1aF6B910w01mxwEAAEA5U6By9fHHH+vtt9/WihUrtHTpUn333XdauHChrFZrceUDisSF9At6d8u7kqQJ7SeYnAYAAADlUYHKVUxMjHr06GH7uUuXLrJYLPr777+LPBhQlN7f9r4S0hJUr2o93VX/LrPjAAAAoBwqULnKysqSq6ur3TQnJydlZmYWaSigKGVZszRjwwxJ0vjw8XKwFOoOBAAAAMBVFeiCFoZhaNCgQXJxcbFNS0tL0/Dhw+0ux86l2FGafLXnK8UkxsjP3U8Dmw80Ow4AAADKqQKVq8jIyBzTHnzwwSILAxQ1wzBsNw0e1W6U3Jy4siUAAACKR4HK1YcfflhcOYBisfboWm09tVWulVz1WNvHzI4DAACAcoyTT1CuTY2eKkka3GKwfN19TU4DAACA8oxyhXLrz/g/9cPBH2SRRePCx5kdBwAAAOUc5Qrl1rToaZKkexreo7pV65qcBgAAAOUd5Qrl0qkLp/TJzk8kSVHhUSanAQAAQEVAuUK5NHvTbGVaM9WhegeFVw83Ow4AAAAqAMoVyp0L6Rf0zh/vSJKi2rPXCgAAACWDcoVy54NtHyghLUH1qtbTXTfeZXYcAAAAVBCUK5QrWdYszdgwQ5I0Pny8HB0cTU4EAACAioJyhXJl8Z7FOpZ4TL7uvhrYfKDZcQAAAFCBUK5QbhiGoSnrp0iSRrUdJTcnN5MTAQAAoCKhXKHc+OXYL9pyaotcK7lqZLuRZscBAABABUO5Qrkxdf1USdLgFoPl6+5rchoAAABUNJQrlAt7Tu/RsoPLZJFFY28aa3YcAAAAVECUK5QL09ZPkyT1atBL9arVMzkNAAAAKiLKFcq8UxdO6ZNdn0iSJrSfYHIaAAAAVFSUK5R5szfNVkZ2htpXb6/w6uFmxwEAAEAFRblCmZackax3/nhHkhQVHmVyGgAAAFRklCuUaR9s+0AJaQmqV7We7q5/t9lxAAAAUIGZXq7eeusthYaGytXVVWFhYdq0aVOeYzMzM/XSSy+pTp06cnV1VfPmzbV8+fIc406ePKkHH3xQ1apVk5ubm5o2bao//vijOBcDJsiyZmnGhhmSpHHh4+To4GhyIgAAAFRkpparRYsWady4cXrhhRe0detWNW/eXBEREYqPj891/LPPPqu5c+dq9uzZ2rNnj4YPH6577rlH27Zts405f/68OnToICcnJ/3444/as2ePpk2bpipVqpTUYqGELN6zWEcTjsrX3VeRzSPNjgMAAIAKzmIYhmHWm4eFhalt27aaM2eOJMlqtap69ep6/PHH9fTTT+cYHxwcrGeeeUYjR460Tbvvvvvk5uamTz65dLW4p59+Wr///rt+++23QudKSkqSt7e3EhMT5eXlVej5oPgYhqG277XVllNbNKnjJL1w6wtmRwIAAEA5VJBuYNqeq4yMDG3ZskVdunT5/zAODurSpYuio6NzfU16erpcXV3tprm5uWndunW2n7/99lu1adNGvXv3lr+/v1q2bKn33nvvqlnS09OVlJRk90Dp9uuxX7Xl1Ba5VnLVY20fMzsOAAAAYF65OnPmjLKzsxUQEGA3PSAgQLGxsbm+JiIiQtOnT9fBgwdltVq1cuVKLVmyRKdOnbKN+euvv/TOO++oXr16WrFihUaMGKHRo0fro48+yjPL5MmT5e3tbXtUr169aBYSxWbK+imSpEHNB8nPw8/kNAAAAEApuKBFQcyaNUv16tVTgwYN5OzsrFGjRmnw4MFycPj/xbBarWrVqpVeffVVtWzZUo888oiGDRumd999N8/5Tpw4UYmJibbH8ePHS2JxUEh7Tu/RsoPLZJFF48LHmR0HAAAAkGRiufL19ZWjo6Pi4uLspsfFxSkwMDDX1/j5+Wnp0qVKSUnRsWPHtG/fPnl6eqp27dq2MUFBQWrUqJHd6xo2bKiYmJg8s7i4uMjLy8vugdJrevR0SVKvBr1Ur1o9k9MAAAAAl5hWrpydndW6dWutXr3aNs1qtWr16tUKDw+/6mtdXV0VEhKirKwsLV68WD179rQ916FDB+3fv99u/IEDB1SzZs2iXQCY4tSFU1qwc4EkKao9Nw0GAABA6VHJzDcfN26cIiMj1aZNG7Vr104zZ85USkqKBg8eLEkaOHCgQkJCNHnyZEnSxo0bdfLkSbVo0UInT57UpEmTZLVa9eSTT9rmOXbsWLVv316vvvqq+vTpo02bNmnevHmaN2+eKcuIojVn0xxlZGco/IZwta/e3uw4AAAAgI2p5apv3746ffq0nn/+ecXGxqpFixZavny57SIXMTExdudTpaWl6dlnn9Vff/0lT09P9ejRQwsWLJCPj49tTNu2bfX1119r4sSJeumll1SrVi3NnDlTAwYMKOnFQxFLzkjWO3+8I0ma0H6CyWkAAAAAe6be56q04j5XpdObG9/UE8ufUN2qdbVv5D45OjiaHQkAAADlXJm4zxVQEFnWLM3YMEOSNO6mcRQrAAAAlDqUK5QJS/Yu0dGEo/J191Vki0iz4wAAAAA5UK5Q6hmGYbtp8Mi2I+Xu5G5yIgAAACAnyhVKvV+P/ao//v5DrpVcNbLtSLPjAAAAALmiXKHUmxo9VZI0qPkg+Xn4mZwGAAAAyB3lCqXa3tN79f2B72WRRWPDx5odBwAAAMgT5Qql2rToaZKkng166sZqN5qcBgAAAMgb5QqlVmxyrBbsXCBJigqPMjkNAAAAcHWUK5RaczbNUUZ2hsJvCFeHGh3MjgMAAABcFeUKpVJyRrLe3vy2JCmqPXutAAAAUPpRrlAqfbjtQ51PO6+6VeuqZ/2eZscBAAAArolyhVIny5qlGRtmSJLG3TROjg6OJicCAAAAro1yhVJnyd4lOpJwRNXcqimyRaTZcQAAAIB8oVyhVDEMQ1PXX7pp8Mi2I+Xu5G5yIgAAACB/KFcoVX6L+U2b/94s10quGtlupNlxAAAAgHyjXKFUubzXKrJ5pPw9/E1OAwAAAOQf5Qqlxt7Te/Xdge9kkUVjbxprdhwAAACgQChXKDWmR0+XJN1d/27V961vchoAAACgYChXKBVik2P18c6PJUkT2k8wOQ0AAABQcJQrlApzNs1RRnaGbrrhJrWv3t7sOAAAAECBUa5gupSMFL29+W1JUlR4lCwWi8mJAAAAgIKjXMF0H27/UOfTzqtOlTrq1aCX2XEAAACAQqFcwVRZ1izbhSzGhY+To4OjyYkAAACAwqFcwVRf7/1aRxKOqJpbNQ1qMcjsOAAAAEChUa5gGsMwNGX9FEnSyLYj5e7kbnIiAAAAoPAoVzDNbzG/afPfm+Xi6KKR7UaaHQcAAAC4LpQrmGbq+qmSpMjmkfL38Dc5DQAAAHB9KFcwxb4z+/Tdge9kkUXjwseZHQcAAAC4bpQrmOLyFQLvrn+36vvWNzkNAAAAcP0oVyhxcclx+njHx5KkqPZRJqcBAAAAigblCiVuzqY5Ss9OV1hImDpU72B2HAAAAKBIUK5QolIyUvT2H29Lkia0nyCLxWJyIgAAAKBoUK5Qoj7c/qHOXTyn2lVqq1eDXmbHAQAAAIoM5QolJtuabbuQxbibxsnRwdHkRAAAAEDRoVyhxHy972sdSTiiam7VNLjlYLPjAAAAAEWKcoUSYRiGpqyfIkl6rO1jcndyNzkRAAAAULQoVygR62LWadPJTXJxdNHItiPNjgMAAAAUOcoVSsTU6KmSpMjmkQrwDDA5DQAAAFD0KFcodvvP7Ne3+7+VJI0LH2dyGgAAAKB4UK5Q7KZFT5Mk3V3/btX3rW9yGgAAAKB4UK5QrOKS4/Txjo8lSVHhUSanAQAAAIoP5QrF6q3Nbyk9O11hIWH6V41/mR0HAAAAKDaUKxSblIwUvbX5LUlSVPsoWSwWkxMBAAAAxYdyhWIzf/t8nbt4TrWr1NY9De4xOw4AAABQrChXKBbZ1mxN3zBdkjTupnFydHA0OREAAABQvChXKBZf7/taf53/S1XdqmpQi0FmxwEAAACKHeUKRc4wDE1ZP0WS9Fibx+Th7GFyIgAAAKD4Ua5Q5H4//rs2ndwkF0cXjWo3yuw4AAAAQImgXKHIXd5rNbD5QAV4BpicBgAAACgZlCsUqf1n9uvb/d9KksaFjzM5DQAAAFByKFcoUtOjL10h8O76d6uBbwOT0wAAAAAlh3KFIhOfEq+PdnwkSYoKjzI5DQAAAFCyKFcoMnM2zVF6drrahbTTv2r8y+w4AAAAQImiXKFIpGam6q3Nb0m6tNfKYrGYnAgAAAAoWZQrFIn52+fr3MVzquVTS/c2vNfsOAAAAECJKxXl6q233lJoaKhcXV0VFhamTZs25Tk2MzNTL730kurUqSNXV1c1b95cy5cvtxszadIkWSwWu0eDBlxcobhkW7NtF7IYFz5Ojg6OJicCAAAASp7p5WrRokUaN26cXnjhBW3dulXNmzdXRESE4uPjcx3/7LPPau7cuZo9e7b27Nmj4cOH65577tG2bdvsxjVu3FinTp2yPdatW1cSi1MhLd23VIfPH1ZVt6oa3GKw2XEAAAAAU5herqZPn65hw4Zp8ODBatSokd599125u7vrgw8+yHX8ggUL9O9//1s9evRQ7dq1NWLECPXo0UPTpk2zG1epUiUFBgbaHr6+viWxOBWOYRi2mwY/1uYxeTh7mJwIAAAAMIep5SojI0NbtmxRly5dbNMcHBzUpUsXRUdH5/qa9PR0ubq62k1zc3PLsWfq4MGDCg4OVu3atTVgwADFxMTkmSM9PV1JSUl2D+TP78d/18aTG+Xs6KxR7UaZHQcAAAAwjanl6syZM8rOzlZAQIDd9ICAAMXGxub6moiICE2fPl0HDx6U1WrVypUrtWTJEp06dco2JiwsTPPnz9fy5cv1zjvv6MiRI7r55pt14cKFXOc5efJkeXt72x7Vq1cvuoUs56aunypJGthsoAI8A64xGgAAACi/TD8ssKBmzZqlevXqqUGDBnJ2dtaoUaM0ePBgOTj8/6J0795dvXv3VrNmzRQREaEffvhBCQkJ+uKLL3Kd58SJE5WYmGh7HD9+vKQWp0zbf2a/vt3/rSRpfPvxJqcBAAAAzGVqufL19ZWjo6Pi4uLspsfFxSkwMDDX1/j5+Wnp0qVKSUnRsWPHtG/fPnl6eqp27dp5vo+Pj49uvPFGHTp0KNfnXVxc5OXlZffAtU2Pni5Dhu668S418OVqjAAAAKjYTC1Xzs7Oat26tVavXm2bZrVatXr1aoWHh1/1ta6urgoJCVFWVpYWL16snj175jk2OTlZhw8fVlBQUJFlr+jiU+L10Y6PJElR7aNMTgMAAACYz/TDAseNG6f33ntPH330kfbu3asRI0YoJSVFgwdfuqT3wIEDNXHiRNv4jRs3asmSJfrrr7/022+/qVu3brJarXryySdtY6KiovTLL7/o6NGjWr9+ve655x45OjrqgQceKPHlK6/e2vSW0rPT1Ta4rW6ucbPZcQAAAADTVTI7QN++fXX69Gk9//zzio2NVYsWLbR8+XLbRS5iYmLszqdKS0vTs88+q7/++kuenp7q0aOHFixYIB8fH9uYEydO6IEHHtDZs2fl5+enf/3rX9qwYYP8/PxKevHKpdTMVL21+S1J0oT2E2SxWExOBAAAAJjPYhiGYXaI0iYpKUne3t5KTEzk/KtcvL35bY38YaRq+dTSgccPqJKD6R0dAAAAKBYF6QamHxaIsiXbmq3p0dMlSWNvGkuxAgAAAP6HcoUC+Wb/Nzp8/rCquFbRkJZDzI4DAAAAlBqUK+SbYRiasn6KJOmxto/Jw9nD5EQAAABA6UG5Qr6tP75eG05skLOjs0a1G2V2HAAAAKBUoVwh36ZGT5UkDWw2UIGeud/kGQAAAKioKFfIlwNnD+ibfd9IksaFjzM5DQAAAFD6UK6QL9Ojp8uQoTtvvFMN/RqaHQcAAAAodShXuKb4lHh9tOMjSZduGgwAAAAgJ8oVruntzW8rLStNbYPb6uYaN5sdBwAAACiVKFe4qtTMVM3ZNEeSFNU+ShaLxeREAAAAQOlEucJVfbT9I529eFahPqG6t+G9ZscBAAAASi3KFfKUbc3W9A3TJUnjbhqnSg6VTE4EAAAAlF6UK+Tpm/3f6NC5Q6riWkWDWw42Ow4AAABQqlGukKep6y/dNHhEmxHydPY0OQ0AAABQulGukKv1x9cr+kS0nB2d9XjY42bHAQAAAEo9yhVyNWX9FEnSQ80eUqBnoMlpAAAAgNKPcoUcDpw9oG/2fSNJGh8+3uQ0AAAAQNlAuUIOM6JnyJChO2+8Uw39GpodBwAAACgTKFewE58Sr/k75kuSosKjzA0DAAAAlCGUK9h5e/PbSstKU5vgNrql5i1mxwEAAADKDMoVbFIzU/XW5rckXdprZbFYTE4EAAAAlB2UK9h8vONjnUk9o1CfUN3X6D6z4wAAAABlCuUKkqRsa7amRU+TJI29aawqOVQyOREAAABQtlCuIEn6dv+3OnTukKq4VtGQlkPMjgMAAACUOZQrSJKmRk+VJI1oM0Kezp4mpwEAAADKHsoVtP74eq0/vl7Ojs4a1W6U2XEAAACAMolyBU1df2mv1YNNH1RQ5SCT0wAAAABlE+Wqgjt49qCW7lsqSRrffry5YQAAAIAyjHJVwU2Pni5Dhu6od4ca+TUyOw4AAABQZlGuKrDTKac1f8d8SVJU+yhzwwAAAABlHOWqAnt789tKy0pTm+A26lizo9lxAAAAgDKNclVBpWamas7mOZKkqPAoWSwWkxMBAAAAZRvlqoL6eMfHOpN6RjW9a+q+RveZHQcAAAAo8yhXFVC2NVvTo6dLksbeNFaVHCqZnAgAAAAo+yhXFdB3B77TwXMH5ePqo4dbPWx2HAAAAKBcoFxVQFPWT5EkjWgzQp7OnianAQAAAMoHylUFs/74eq0/vl7Ojs56vN3jZscBAAAAyg3KVQUzLXqaJOnBpg8qqHKQyWkAAACA8oNyVYEcPHtQX+/9WpI0LnycyWkAAACA8oVyVYHM2DBDhgz1qNdDjf0bmx0HAAAAKFcoVxXE6ZTT+nD7h5KkCe0nmJwGAAAAKH8oVxXE25vfVlpWmloHtVbHmh3NjgMAAACUO5SrCuBi5kXN2TxHkhTVPkoWi8XkRAAAAED5Q7mqAD7e8bHOpJ5RTe+aur/R/WbHAQAAAMolylU5ZzWstsuvj71prCo5VDI5EQAAAFA+Ua7KuW/3f6uD5w7Kx9VHQ1oOMTsOAAAAUG5Rrsq5qeunSpKGtx6uyi6VTU4DAAAAlF+Uq3Is+ni0fj/+u5wcnDQ6bLTZcQAAAIByjXJVjk2NvrTX6sFmDyqocpDJaQAAAIDyjasblFOHzh3S13u/liSNDx9vchoAAPBP2dnZyszMNDsGUOE5OTnJ0dGxSOZFuSqnZkTPkCFDPer1UGP/xmbHAQAA/2MYhmJjY5WQkGB2FAD/4+Pjo8DAwOu+Hyzlqhw6k3pGH27/UJIUFR5lchoAAHCly8XK399f7u7u1/3LHIDCMwxDqampio+PlyQFBV3fqTSUq3Lo7c1v62LWRbUKaqVbQ281Ow4AAPif7OxsW7GqVq2a2XEASHJzc5MkxcfHy9/f/7oOEeSCFuXMxcyLmrNpjiRpQvsJ/DUMAIBS5PI5Vu7u7iYnAXCly9/J6z0PknJVzny842OdTj2tGt41dH+j+82OAwAAcsEfP4HSpai+k6WiXL311lsKDQ2Vq6urwsLCtGnTpjzHZmZm6qWXXlKdOnXk6uqq5s2ba/ny5XmOf+2112SxWDRmzJhiSF66WA2rpkVPkySNvWmsKjlw1CcAAABQUkwvV4sWLdK4ceP0wgsvaOvWrWrevLkiIiJsJ5X907PPPqu5c+dq9uzZ2rNnj4YPH6577rlH27ZtyzF28+bNmjt3rpo1a1bci1EqfLf/Ox08d1DeLt56uOXDZscBAAAoMaGhoZo5c2aRzvPo0aOyWCzavn17kc63Irv11lvL9U4P08vV9OnTNWzYMA0ePFiNGjXSu+++K3d3d33wwQe5jl+wYIH+/e9/q0ePHqpdu7ZGjBihHj16aNq0aXbjkpOTNWDAAL333nuqUqVKSSyK6S7fNHhEmxGq7FLZ5DQAAKA8GTRokCwWiywWi5ydnVW3bl299NJLysrKKrL3uJ4ys3nzZj3yyCNFlqU0KU8lb8mSJXr55ZeLdJ6DBg1Sr169inSehWVqucrIyNCWLVvUpUsX2zQHBwd16dJF0dHRub4mPT1drq6udtPc3Ny0bt06u2kjR47UHXfcYTfvvKSnpyspKcnuUdZsOLFB62LWycnBSY+HPW52HAAAUA5169ZNp06d0sGDBzV+/HhNmjRJU6ZMMTuWJMnPz6/CXygkIyPD7AjXVLVqVVWuXH53Aphars6cOaPs7GwFBATYTQ8ICFBsbGyur4mIiND06dN18OBBWa1WrVy5UkuWLNGpU6dsYz7//HNt3bpVkydPzleOyZMny9vb2/aoXr164RfKJFPXX9prNaDZAAVXDjY5DQAAyC/DkFJSSv5hGAXP6uLiosDAQNWsWVMjRoxQly5d9O2330rK/XCvXr16adCgQbafQ0ND9eqrr2rIkCGqXLmyatSooXnz5tmer1WrliSpZcuWslgsuvXWWyVd2it1++23y9fXV97e3urYsaO2bt1q917/PCwwISFBQ4cOlZ+fn7y8vNS5c2ft2LHjqsu3adMmtWzZUq6urmrTpk2up53s3r1b3bt3l6enpwICAvTQQw/pzJkzV53vunXrdPPNN8vNzU3Vq1fX6NGjlZKSct3r5fIem1deeUXBwcGqX7++JOn48ePq06ePfHx8VLVqVfXs2VNHjx61ze/y66ZOnaqgoCBVq1ZNI0eOtLtS3oIFC9SmTRtVrlxZgYGB6t+/v91pO2vXrpXFYtGKFSvUsmVLubm5qXPnzoqPj9ePP/6ohg0bysvLS/3791dqaqrtdf/8nKSnpysqKkohISHy8PBQWFiY1q5da3t+/vz58vHx0YoVK9SwYUN5enraSr4kTZo0SR999JG++eYb257Vy6/ftWuXOnfuLDc3N1WrVk2PPPKIkpOTr7qtrpfphwUW1KxZs1SvXj01aNBAzs7OGjVqlAYPHiwHh0uLcvz4cT3xxBNauHBhjj1ceZk4caISExNtj+PHjxfnIhS5w+cOa8neJZK4aTAAAGVNaqrk6Vnyjyt+3y00Nze3Au8tmTZtmq24PPbYYxoxYoT2798vSbaLmq1atUqnTp3SkiWXfr+5cOGCIiMjtW7dOm3YsEH16tVTjx49dOHChTzfp3fv3rZf9Lds2aJWrVrptttu07lz53Idn5ycrDvvvFONGjXSli1bNGnSJEVF2f9elZCQoM6dO6tly5b6448/tHz5csXFxalPnz555jh8+LC6deum++67Tzt37tSiRYu0bt06jRo16rrXiyStXr1a+/fv18qVK/X9998rMzNTERERqly5sn777Tf9/vvvtkJy5bZas2aNDh8+rDVr1uijjz7S/PnzNX/+fNvzmZmZevnll7Vjxw4tXbpUR48etSvKl02aNElz5szR+vXrbaVu5syZ+vTTT7Vs2TL99NNPmj17dp7rZ9SoUYqOjtbnn3+unTt3qnfv3urWrZsOHjxoG5OamqqpU6dqwYIF+vXXXxUTE2PbNlFRUerTp4+tcJ06dUrt27dXSkqKIiIiVKVKFW3evFlffvmlVq1alWO9FznDROnp6Yajo6Px9ddf200fOHCgcffdd1/1tRcvXjROnDhhWK1W48knnzQaNWpkGIZhfP3114Ykw9HR0faQZFgsFsPR0dHIysq6Zq7ExERDkpGYmFjoZStJj33/mKFJMrp/0t3sKAAA4CouXrxo7Nmzx7h48aJtWnKyYVzaj1Syj+TkgmWPjIw0evbsaRiGYVitVmPlypWGi4uLERUVZRiGYXTs2NF44okn7F7Ts2dPIzIy0vZzzZo1jQcffND2s9VqNfz9/Y133nnHMAzDOHLkiCHJ2LZt21WzZGdnG5UrVza+++47u3nPmDHDMAzD+O233wwvLy8jLS3N7nV16tQx5s6dm+s8586da1SrVs1u27zzzjt2eV5++WWja9eudq87fvy4IcnYv39/rvN9+OGHjUceecRu2m+//WY4ODjY3quw6yUyMtIICAgw0tPTbdMWLFhg1K9f37BarbZp6enphpubm7FixQrb62rWrGn3e3Hv3r2Nvn375roMhmEYmzdvNiQZFy5cMAzDMNasWWNIMlatWmUbM3nyZEOScfjwYdu0Rx991IiIiLD9fOXn5NixY4ajo6Nx8uRJu/e67bbbjIkTJxqGYRgffvihIck4dOiQ7fm33nrLCAgIsFsPlz+bl82bN8+oUqWKkXzFB33ZsmWGg4ODERsbm2P5cvtuXlaQbmDqtbqdnZ3VunVrrV692nYSmtVq1erVq6/ZKl1dXRUSEqLMzEwtXrzY9heD2267Tbt27bIbO3jwYDVo0EBPPfXUdd1xuTQ6k3pGH27/UJIU1Z69VgAAlDXu7lIxH6mU5/sW1Pfffy9PT09lZmbKarWqf//+mjRpUoHmceVVnC0WiwIDA/O8SvRlcXFxevbZZ7V27VrFx8crOztbqampiomJyXX8jh07lJycrGrVqtlNv3jxog4fPpzra/bu3atmzZrZHfkUHh6eY75r1qyRp6dnjtcfPnxYN954Y65Zdu7cqYULF9qmGYYhq9WqI0eOqGHDhpIKt14kqWnTpnJ2drZ7v0OHDuU4ryktLc1u2Rs3bmz3e3FQUJDd79CX997t2LFD58+fl9VqlSTFxMSoUaNGtnFX5g4ICJC7u7tq165tNy2v2yzt2rVL2dnZOdZbenq63bZzd3dXnTp17LJea93s3btXzZs3l4eHh21ahw4dZLVatX///hynJRUV02+ENG7cOEVGRqpNmzZq166dZs6cqZSUFA0ePFiSNHDgQIWEhNjOn9q4caNOnjypFi1a6OTJk5o0aZKsVquefPJJSVLlypXVpEkTu/fw8PBQtWrVckwvD97Z/I4uZl1Uq6BW6hTayew4AACggCwW6Yrf/0q1Tp066Z133pGzs7OCg4NVqdL//yrp4OAg4x8ncl15Ds9lTk5Odj9bLBbbL+55iYyM1NmzZzVr1izVrFlTLi4uCg8Pz/OQxOTkZAUFBdmdu3OZj4/PVd/rapKTk3XXXXfp9ddfz/FcUFBQnq959NFHNXr06BzP1ahRw/b/C7NeJNmVh8vv17p1a7syd5mfn1++3u/yIXURERFauHCh/Pz8FBMTo4iIiBzr/Mr5WCyWAi1HcnKyHB0dtWXLlhw7QK4ssLnN85+ftdLC9HLVt29fnT59Ws8//7xiY2PVokULLV++3NYmY2JibOdTSZda97PPPqu//vpLnp6e6tGjhxYsWHBdX5Sy6mLmRc3edOkY1qjwKO72DgAAipWHh4fq1q2b63N+fn52FxjLzs7W7t271alT/v/4e3kPTHZ2tt3033//XW+//bZ69Ogh6dI59le7iESrVq0UGxurSpUqKTQ0NF/v3bBhQy1YsEBpaWm2vVcbNmzIMd/FixcrNDTUrlheTatWrbRnz54811t+5LVe8nq/RYsWyd/fX15eXoV6v3379uns2bN67bXXbBd6++OPPwo1r6tp2bKlsrOzFR8fr5tvvrnQ83F2ds6xbho2bKj58+crJSXFVkB///13OTg42C78URxKxQUtRo0apWPHjik9PV0bN25UWFiY7bm1a9fanVzXsWNH7dmzR2lpaTpz5ow+/vhjBQdf/ep4a9euLfKbypUGC3Yu0OnU06rhXUP3N7rf7DgAAKAC69y5s5YtW6Zly5Zp3759GjFihBISEgo0D39/f7m5udkuFJGYmChJqlevnhYsWKC9e/dq48aNGjBggNzc3PKcT5cuXRQeHq5evXrpp59+0tGjR7V+/Xo988wzeZaE/v37y2KxaNiwYdqzZ49++OEHTZ061W7MyJEjde7cOT3wwAPavHmzDh8+rBUrVmjw4MF5Fp+nnnpK69ev16hRo7R9+3YdPHhQ33zzTYEurJDXesnNgAED5Ovrq549e+q3337TkSNHtHbtWo0ePVonTpzI1/vVqFFDzs7Omj17tv766y99++23RX5vKkm68cYbNWDAAA0cOFBLlizRkSNHtGnTJk2ePFnLli3L93xCQ0O1c+dO7d+/X2fOnFFmZqYGDBggV1dXRUZGavfu3VqzZo0ef/xxPfTQQ8V2SKBUSsoVCs5qWDUt+tKNk8feNFZOjk7XeAUAAEDxGTJkiCIjIzVw4EB17NhRtWvXLtBeK0mqVKmS3nzzTc2dO1fBwcHq2bOnJOn999/X+fPn1apVKz300EMaPXq0/P3985yPxWLRDz/8oFtuuUWDBw/WjTfeqH79+unYsWN5/mLt6emp7777Trt27VLLli31zDPP5Dj8Lzg4WL///ruys7PVtWtXNW3aVGPGjJGPj4/dkVZXatasmX755RcdOHBAN998s1q2bKnnn3/+mjsH8rNecuPu7q5ff/1VNWrU0L333quGDRvq4YcfVlpaWr73ZPn5+Wn+/Pn68ssv1ahRI7322ms5imZR+fDDDzVw4ECNHz9e9evXV69evbR582a7QyavZdiwYapfv77atGkjPz8//f7773J3d9eKFSt07tw5tW3bVvfff79uu+02zZkzp1iW4zKLUVoPWDRRUlKSvL29lZiYWOjdqcXtm33fqNeiXvJ28dbxscdV2aX83owNAIDyIi0tTUeOHFGtWrXyfcsY5E9QUJBefvllDR061OwoKIOu9t0sSDcw/ZwrFM7U6Et/PRjeZjjFCgAAVFipqan6/fffFRcXp8aNG5sdBxUchwWWQRtObNC6mHVycnDS6LCcV54BAACoKObNm6d+/fppzJgxOS6dDpQ09lyVQZfPtRrQbICCK+f/eF0AAIDyZsyYMRozZozZMQBJ7Lkqcw6fO6wle5dIksaHjzc5DQAAAIDLKFdlzIwNM2Q1rOpWt5ua+Je/myIDAAAAZRXlqgw5m3pWH2z7QJI0of0Ek9MAAAAAuBLlqgx5e/Pbuph1US0DW6pTaMHuGwEAAACgeFGuyoi0rDTN3jRbkhTVPkoWi8XkRAAAAACuRLkqIxbsWKDTqadVw7uGejfqbXYcAAAAAP9AuSoDrIbVdvn1MWFj5OToZHIiAACA0iU0NFQzZ84s0nkePXpUFotF27dvL9L5lqRJkyapRYsWZseoMChXZcD3B77X/rP75e3iraGthpodBwAAVECDBg2SxWKRxWKRs7Oz6tatq5deeklZWVlF9h7XU2Y2b96sRx55pMiyAIXBTYTLgKnrp0qSHm39qCq7VDY5DQAAqKi6deumDz/8UOnp6frhhx80cuRIOTk5aeLEiWZHk5+fn9kRikxGRoacnZ3NjoFCYM9VKbfxxEb9FvObnBycNDpstNlxAABAETMMQykZKSX+MAyjwFldXFwUGBiomjVrasSIEerSpYu+/fZbSdKtt96qMWPG2I3v1auXBg0aZPs5NDRUr776qoYMGaLKlSurRo0amjdvnu35WrVqSZJatmwpi8WiW2+9VdKlvVK33367fH195e3trY4dO2rr1q127/XPwwITEhI0dOhQ+fn5ycvLS507d9aOHTuuunybNm1Sy5Yt5erqqjZt2mjbtm05xuzevVvdu3eXp6enAgIC9NBDD+nMmTN5znP+/Pny8fHR0qVLVa9ePbm6uioiIkLHjx+3jbl86N5///tf1apVS66urrkukyS1aNFCkyZNKvByzp07V9WrV5e7u7v69OmjxMRE23P5Wb/IH/ZclXJToy/tterftL9CvEJMTgMAAIpaamaqPCd7lvj7Jk9Mloezx3XNw83NTWfPni3Qa6ZNm6aXX35Z//73v/XVV19pxIgR6tixo+rXr69NmzapXbt2WrVqlRo3bmzbe3PhwgVFRkZq9uzZMgxD06ZNU48ePXTw4EFVrpz7UT29e/eWm5ubfvzxR3l7e2vu3Lm67bbbdODAAVWtWjXH+OTkZN155526/fbb9cknn+jIkSN64okn7MYkJCSoc+fOGjp0qGbMmKGLFy/qqaeeUp8+ffTzzz/nucypqal65ZVX9PHHH8vZ2VmPPfaY+vXrp99//9025tChQ1q8eLGWLFkiR0fHfK/P/CznoUOH9MUXX+i7775TUlKSHn74YT322GNauHBhodcvcke5KuXCQsK0/vh6jQ8fb3YUAAAASZf2tq1evVorVqzQ448/XqDX9ujRQ4899pgk6amnntKMGTO0Zs0a1a9f33ZoX7Vq1RQYGGh7TefOne3mMW/ePPn4+OiXX37RnXfemeM91q1bp02bNik+Pl4uLi6SpKlTp2rp0qX66quvcj0369NPP5XVatX7778vV1dXNW7cWCdOnNCIESNsY+bMmaOWLVvq1VdftU374IMPVL16dR04cEA33nhjrsucmZmpOXPmKCwsTJL00UcfqWHDhrYyKV06FPDjjz8u0OGN+V3OtLQ0ffzxxwoJufSH+tmzZ+uOO+7QtGnTFBgYWOD1i7xRrkq5qPZRGnPTGFVyYFMBAFAeuTu5K3lisinvW1Dff/+9PD09lZmZKavVqv79+9sdopYfzZo1s/1/i8WiwMBAxcfHX/U1cXFxevbZZ7V27VrFx8crOztbqampiomJyXX8jh07lJycrGrVqtlNv3jxog4fPpzra/bu3atmzZrZDsmTpPDw8BzzXbNmjTw9c+5pPHz4cJ7lqlKlSmrbtq3t5wYNGsjHx0d79+61lauaNWsW+Lyx/C5njRo1bMXq8nJZrVbt379fgYGBBV6/yBu/sZcBFCsAAMovi8Vy3YfnlZROnTrpnXfekbOzs4KDg1Wp0v//juLg4JDjPK7MzMwc83Bysr+ljMVikdVqver7RkZG6uzZs5o1a5Zq1qwpFxcXhYeHKyMjI9fxycnJCgoK0tq1a3M85+Pjc9X3uprk5GTdddddev3113M8FxQUVOj5SpKHR87PwLXWaVEtZ0HXL/LGb+0AAADIFw8PD9WtWzfX5/z8/HTq1Cnbz9nZ2dq9e7c6deqU7/lfPscqOzvbbvrvv/+ut99+Wz169JAkHT9+/KoXkWjVqpViY2NVqVIlhYaG5uu9GzZsqAULFigtLc2292rDhg055rt48WKFhobaFctrycrK0h9//GHbS7V//34lJCSoYcOGV33dP9dpUlKSjhw5YpcnP8sZExOjv//+W8HBwbblcnBwUP369SUVfP0ib1wtEAAAANetc+fOWrZsmZYtW6Z9+/ZpxIgRSkhIKNA8/P395ebmpuXLlysuLs52Rbt69eppwYIF2rt3rzZu3KgBAwbIzc0tz/l06dJF4eHh6tWrl3766ScdPXpU69ev1zPPPKM//vgj19f0799fFotFw4YN0549e/TDDz9o6tSpdmNGjhypc+fO6YEHHtDmzZt1+PBhrVixQoMHD85RCK/k5OSkxx9/XBs3btSWLVs0aNAg3XTTTbaylZfOnTtrwYIF+u2337Rr1y5FRkbaXewiv8vp6uqqyMhI7dixQ7/99ptGjx6tPn362M5rK+j6Rd4oVwAAALhuQ4YMUWRkpAYOHKiOHTuqdu3aBdprJV06N+nNN9/U3LlzFRwcrJ49e0qS3n//fZ0/f16tWrXSQw89pNGjR8vf3z/P+VgsFv3www+65ZZbNHjwYN14443q16+fjh07poCAgFxf4+npqe+++067du1Sy5Yt9cwzz+Q4/C84OFi///67srOz1bVrVzVt2lRjxoyRj4+PHBzy/rXa3d1dTz31lPr3768OHTrI09NTixYtuub6mDhxojp27Kg777xTd9xxh3r16qU6deoUeDnr1q2re++9Vz169FDXrl3VrFkzvf3227bnC7p+kTeLUZibHJRzSUlJ8vb2VmJiory8vMyOAwAAyom0tDQdOXLE7l5GKBpBQUF6+eWXNXToULOj2Jk/f77GjBlT4L14KFlX+24WpBtwzhUAAADKrNTUVP3++++Ki4tT48aNzY6DCo7DAgEAAFBmzZs3T/369dOYMWNyXDodKGkcFpgLDgsEAADFgcMCgdKpqA4LZM8VAAAAABQByhUAAEAJ48AhoHQpqu8k5QoAAKCEODk5Sbp0EQYApcfl7+Tl72hhcbVAAACAEuLo6CgfHx/Fx8dLunT/I4vFYnIqoOIyDEOpqamKj4+Xj4+P3U2aC4NyBQAAUIICAwMlyVawAJjPx8fH9t28HpQrAACAEmSxWBQUFCR/f39lZmaaHQeo8JycnK57j9VllCsAAAATODo6FtkvdABKBy5oAQAAAABFgHIFAAAAAEWAcgUAAAAARYBzrnJx+SZiSUlJJicBAAAAYKbLnSA/NxqmXOXiwoULkqTq1aubnAQAAABAaXDhwgV5e3tfdYzFyE8Fq2CsVqv+/vtvVa5cmRv7FUBSUpKqV6+u48ePy8vLy+w4KEJs2/KLbVt+sW3LL7Zt+cW2LZ0Mw9CFCxcUHBwsB4ern1XFnqtcODg46IYbbjA7Rpnl5eXFPwjlFNu2/GLbll9s2/KLbVt+sW1Ln2vtsbqMC1oAAAAAQBGgXAEAAABAEaBcoci4uLjohRdekIuLi9lRUMTYtuUX27b8YtuWX2zb8ottW/ZxQQsAAAAAKALsuQIAAACAIkC5AgAAAIAiQLkCAAAAgCJAuQIAAACAIkC5QoFNmjRJFovF7tGgQQPb82lpaRo5cqSqVasmT09P3XfffYqLizMxMfLy66+/6q677lJwcLAsFouWLl1q97xhGHr++ecVFBQkNzc3denSRQcPHrQbc+7cOQ0YMEBeXl7y8fHRww8/rOTk5BJcCuTmWtt20KBBOb7H3bp1sxvDti19Jk+erLZt26py5cry9/dXr169tH//frsx+fk3OCYmRnfccYfc3d3l7++vCRMmKCsrqyQXBf+Qn21766235vjeDh8+3G4M27b0eeedd9SsWTPbjYHDw8P1448/2p7nO1u+UK5QKI0bN9apU6dsj3Xr1tmeGzt2rL777jt9+eWX+uWXX/T333/r3nvvNTEt8pKSkqLmzZvrrbfeyvX5N954Q2+++abeffddbdy4UR4eHoqIiFBaWpptzIABA/Tnn39q5cqV+v777/Xrr7/qkUceKalFQB6utW0lqVu3bnbf488++8zuebZt6fPLL79o5MiR2rBhg1auXKnMzEx17dpVKSkptjHX+jc4Oztbd9xxhzIyMrR+/Xp99NFHmj9/vp5//nkzFgn/k59tK0nDhg2z+96+8cYbtufYtqXTDTfcoNdee01btmzRH3/8oc6dO6tnz576888/JfGdLXcMoIBeeOEFo3nz5rk+l5CQYDg5ORlffvmlbdrevXsNSUZ0dHQJJURhSDK+/vpr289Wq9UIDAw0pkyZYpuWkJBguLi4GJ999plhGIaxZ88eQ5KxefNm25gff/zRsFgsxsmTJ0ssO67un9vWMAwjMjLS6NmzZ56vYduWDfHx8YYk45dffjEMI3//Bv/www+Gg4ODERsbaxvzzjvvGF5eXkZ6enrJLgDy9M9taxiG0bFjR+OJJ57I8zVs27KjSpUqxn//+1++s+UQe65QKAcPHlRwcLBq166tAQMGKCYmRpK0ZcsWZWZmqkuXLraxDRo0UI0aNRQdHW1WXBTCkSNHFBsba7ctvb29FRYWZtuW0dHR8vHxUZs2bWxjunTpIgcHB23cuLHEM6Ng1q5dK39/f9WvX18jRozQ2bNnbc+xbcuGxMRESVLVqlUl5e/f4OjoaDVt2lQBAQG2MREREUpKSrL9JR3m++e2vWzhwoXy9fVVkyZNNHHiRKWmptqeY9uWftnZ2fr888+VkpKi8PBwvrPlUCWzA6DsCQsL0/z581W/fn2dOnVKL774om6++Wbt3r1bsbGxcnZ2lo+Pj91rAgICFBsba05gFMrl7XXlP+aXf778XGxsrPz9/e2er1SpkqpWrcr2LuW6deume++9V7Vq1dLhw4f173//W927d1d0dLQcHR3ZtmWA1WrVmDFj1KFDBzVp0kSS8vVvcGxsbK7f68vPwXy5bVtJ6t+/v2rWrKng4GDt3LlTTz31lPbv368lS5ZIYtuWZrt27VJ4eLjS0tLk6empr7/+Wo0aNdL27dv5zpYzlCsUWPfu3W3/v1mzZgoLC1PNmjX1xRdfyM3NzcRkAPKrX79+tv/ftGlTNWvWTHXq1NHatWt12223mZgM+TVy5Ejt3r3b7pxXlA95bdsrz3ls2rSpgoKCdNttt+nw4cOqU6dOScdEAdSvX1/bt29XYmKivvrqK0VGRuqXX34xOxaKAYcF4rr5+Pjoxhtv1KFDhxQYGKiMjAwlJCTYjYmLi1NgYKA5AVEol7fXP69YdOW2DAwMVHx8vN3zWVlZOnfuHNu7jKldu7Z8fX116NAhSWzb0m7UqFH6/vvvtWbNGt1www226fn5NzgwMDDX7/Xl52CuvLZtbsLCwiTJ7nvLti2dnJ2dVbduXbVu3VqTJ09W8+bNNWvWLL6z5RDlCtctOTlZhw8fVlBQkFq3bi0nJyetXr3a9vz+/fsVExOj8PBwE1OioGrVqqXAwEC7bZmUlKSNGzfatmV4eLgSEhK0ZcsW25iff/5ZVqvV9h99lA0nTpzQ2bNnFRQUJIltW1oZhqFRo0bp66+/1s8//6xatWrZPZ+ff4PDw8O1a9cuu/K8cuVKeXl5qVGjRiWzIMjhWts2N9u3b5cku+8t27ZssFqtSk9P5ztbHpl9RQ2UPePHjzfWrl1rHDlyxPj999+NLl26GL6+vkZ8fLxhGIYxfPhwo0aNGsbPP/9s/PHHH0Z4eLgRHh5ucmrk5sKFC8a2bduMbdu2GZKM6dOnG9u2bTOOHTtmGIZhvPbaa4aPj4/xzTffGDt37jR69uxp1KpVy7h48aJtHt26dTNatmxpbNy40Vi3bp1Rr14944EHHjBrkfA/V9u2Fy5cMKKioozo6GjjyJEjxqpVq4xWrVoZ9erVM9LS0mzzYNuWPiNGjDC8vb2NtWvXGqdOnbI9UlNTbWOu9W9wVlaW0aRJE6Nr167G9u3bjeXLlxt+fn7GxIkTzVgk/M+1tu2hQ4eMl156yfjjjz+MI0eOGN98841Ru3Zt45ZbbrHNg21bOj399NPGL7/8Yhw5csTYuXOn8fTTTxsWi8X46aefDMPgO1veUK5QYH379jWCgoIMZ2dnIyQkxOjbt69x6NAh2/MXL140HnvsMaNKlSqGu7u7cc899xinTp0yMTHysmbNGkNSjkdkZKRhGJcux/7cc88ZAQEBhouLi3HbbbcZ+/fvt5vH2bNnjQceeMDw9PQ0vLy8jMGDBxsXLlwwYWlwpatt29TUVKNr166Gn5+f4eTkZNSsWdMYNmyY3WV+DYNtWxrltk0lGR9++KFtTH7+DT569KjRvXt3w83NzfD19TXGjx9vZGZmlvDS4ErX2rYxMTHGLbfcYlStWtVwcXEx6tata0yYMMFITEy0mw/btvQZMmSIUbNmTcPZ2dnw8/MzbrvtNluxMgy+s+WNxTAMo+T2kwEAAABA+cQ5VwAAAABQBChXAAAAAFAEKFcAAAAAUAQoVwAAAABQBChXAAAAAFAEKFcAAAAAUAQoVwAAAABQBChXAFAIX3/9tb744guzYwAAgFKEcgUABbRp0yaNGTNGN910k9lRrtvatWtlsViUkJBgdpRyj3Vd9EJDQzVz5kyzYwCADeUKQIU2aNAgWSwWvfbaa3bTly5dKovFkmN8YmKihg4dqq+//lo1atQoqZgohEGDBqlXr15mxyh1ylPJ27x5sx555JEineett96qMWPGFOk8AVQclCsAFZ6rq6tef/11nT9//ppjvb29tXPnTrVq1aoEkuUuIyPDtPcujzIzM82OUCqVhc+Zn5+f3N3dzY4BADaUKwAVXpcuXRQYGKjJkyfnOWbSpElq0aKF3bSZM2cqNDTU9vPlPSWvvvqqAgIC5OPjo5deeklZWVmaMGGCqlatqhtuuEEffvih3XyOHz+uPn36yMfHR1WrVlXPnj119OjRHPN95ZVXFBwcrPr160uSdu3apc6dO8vNzU3VqlXTI488ouTk5Ksu6w8//KAbb7xRbm5u6tSpk937XLZu3TrdfPPNcnNzU/Xq1TV69GilpKRcc9188MEHqlGjhjw9PfXYY48pOztbb7zxhgIDA+Xv769XXnnF9pqjR4/KYrFo+/bttmkJCQmyWCxau3atbdru3bvVvXt3eXp6KiAgQA899JDOnDlje/6rr75S06ZNbeugS5cuSklJ0aRJk/TRRx/pm2++kcVisc338vsuWrRIHTt2lKurqxYuXKizZ8/qgQceUEhIiNzd3dW0aVN99tlndsuZ13uV5LqWpG+++UatWrWSq6urateurRdffFFZWVm25y0Wi/773//qnnvukbu7u+rVq6dvv/3Wtt47deokSapSpYosFosGDRok6dIem1GjRmnMmDHy9fVVREREvrbBrbfeqtGjR+vJJ59U1apVFRgYqEmTJtllnj59upo2bSoPDw9Vr15djz32mN1ndf78+fLx8dH333+v+vXry93dXffff79SU1P10UcfKTQ0VFWqVNHo0aOVnZ1te90/DwtMSEjQ0KFD5efnJy8vL3Xu3Fk7duywPX/5s7pgwQKFhobK29tb/fr104ULFyRd+q798ssvmjVrlu1zc3m7/fLLL2rXrp1cXFwUFBSkp59+2m69A4AkyQCACiwyMtL4v/buPSiqso8D+HeXS7gsOxrSijruOho7S+LGIiMMeSmU7ebQMIxTmWPKGN4gBmUIZVjJRMlRTKOxZlKKNKtBskwHyghoGfEaBO6ysEJAQuFtdEsg2F//vHteDotcBJt33n6fv3iec577Wdgfe86z0dHRdOzYMfLy8qKWlhYiIiosLKS+vyKNRiPpdDpR2ZycHFKpVKK6fHx8aP369WSxWOjDDz8kAGQwGGj79u1ktVpp27Zt5OHhIbTT3d1NWq2WVq1aRdXV1XT58mV6+eWXSaPRUFdXl1CvXC6n5cuXU01NDdXU1JDdbid/f3+KiYmhn3/+mU6fPk3Tp0+nFStW3HOszc3N9NBDD1FycjJZLBb65JNPSKlUEgC6efMmERE1NDSQt7c35eTkkNVqJZPJRMHBwfTqq6/es16j0UhyuZxiY2OptraWvvrqK/L09CSDwUAJCQlksVjo4MGDBIDOnDlDRESNjY0EgC5duiTUc/PmTQJAJSUlQtrPz4/S0tLIbDbTxYsXafHixfTkk08SEdHVq1fJ3d2d9uzZQ42NjVRdXU25ubl0584dunPnDi1dupSefvppamtro7a2Nurq6hLaVavVVFBQQFeuXKGrV69Sa2sr7dq1iy5dukQ2m4327dtHbm5uVFlZOWRb/+Rcl5WVkUKhoLy8PLLZbFRcXExqtZq2bt0qnAOApk6dSkeOHKH6+npKTEwkuVxO169fp56eHiooKCAAVFdXR21tbXTr1i0iIlqwYAHJ5XJKSUkhi8VCFotlyDVwllMoFLR161ayWq300UcfkUQioeLiYuGcnJwc+v7776mxsZFOnz5NGo2G1q5dKxw/dOgQeXh40OLFi+nixYtUWlpKvr6+FBUVRUuXLqXa2lr6+uuvydPTk44ePSqUU6lUlJOTI6QXLVpES5YsoXPnzpHVaqWNGzeSr68vXb9+XXStOl83ZWVlNGnSJNq8eTMREd26dYvCw8Np9erVwnXT09NDra2tJJPJaN26dWQ2m6mwsJAmTpxIRqPxnmvFGPt34uCKMfav5gyuiIjCwsJo1apVRHT/wZVKpaLe3l4hT6PR0Lx584R0T08PeXt706effkpERPn5+aTRaMjhcAjndHV10bhx46ioqEioV6lUCsEWEdEHH3xAEyZMILvdLuR98803JJVKqb29fcCxpqWlUWBgoCgvNTVV9IY/Li6OXnvtNdE55eXlJJVK6e7duwPWazQaSSaT0e3bt4U8g8FAarXaZS527NhBRMMLrrZt20ZRUVGitlpaWoTA4MKFCwSAmpqaBuxX37V1cra7d+/eAcv09dxzz9HGjRuJiIZsq78HNdeRkZGUlZUlysvPzyd/f38hDYDS09OFtN1uJwB06tQpIiIqKSkR9cNpwYIFFBwcLMobag2c5Z544gnROaGhoZSamjrgGIiIvvjiC/L19RXShw4dIgDU0NAg5MXHx5NMJhMFsAaDgeLj44V03+CqvLycFAoFdXZ2itqaMWMGvf/++0Q08LWakpJCc+fOFc3D66+/Lqpj8+bNLq/T3NxcksvlomucMcbc/7nPyBhj7H9bdnY2nnrqKWzatOm+63jssccglf73jmulUolZs2YJaTc3N/j6+uL3338HAFRVVaGhoQE+Pj6iejo7O2Gz2YR0UFAQPD09hbTZbIZOp4O3t7eQFxERAYfDgbq6OiiVSpe+mc1mzJ07V5QXHh4uSldVVaG6uhqHDx8W8ogIDocDjY2N0Gq1A45brVaLxqBUKuHm5uYyF85xD0dVVRVKSkogl8tdjtlsNkRFRSEyMhJBQUEwGAyIiopCbGwsJkyYMGTdc+bMEaV7e3uRlZWFzz//HL/++iu6u7vR1dUlPM+j0+lG1NaDmuuqqiqYTCbRLZa9vb3o7OzEn3/+KfR39uzZwnFvb28oFIphzX1ISIhLe4OtQUBAgEt7AODv7y9q77vvvsOOHTtgsVhw+/Zt9PT0uPRZJpNhxowZQhmlUgm1Wi1qe7BrqKqqCna7Hb6+vqL8u3fvil5L/a/V/n0diNlsRnh4uGiTm4iICNjtdrS2tvLmNowxAQdXjDH2H/Pnz4fBYEBaWprwHIqTVCoFEYnyBtoIwcPDQ5SWSCQD5jkcDgCA3W5HSEiI6A22k5+fn/Bz3yDqQbLb7YiPj0diYqLLscHeQI503M6gq++c9p9Pu92OJUuWIDs726U9f39/uLm54dtvv0VFRQWKi4uxf/9+bNmyBZWVlZg+ffqg4+w/n7t27cI777yDvXv3Cs8GJSUlCZs6jKate7mfubbb7cjMzERMTIzLMS8vL+HnweZ+MP3nZag1GE57TU1NeP7557F27Vps374dDz/8MH788UfExcWhu7tbCK5Geg31Z7fb4e/vL3pmz2n8+PHD6itjjI0WB1eMMdbHzp078fjjjwubRjj5+fmhvb0dRCT897rvZgz3S6/X47PPPsMjjzwChUIx7HJarRZ5eXn4448/hDfEJpMJUqnUpe99yzg3NnA6c+aMS38uX76MmTNnjnAkI+MMHNva2hAcHAzAdT71ej0KCgqgVqvh7j7wnyuJRIKIiAhEREQgIyMDKpUKhYWFSE5Ohqenp2jzg8GYTCZER0fjlVdeAQA4HA5YrVYEBgYOq63+HtRc6/V61NXVjWp9nJ+ADmduhrMGQ7lw4QIcDgd2794tBNUP4gu49Xo92tvb4e7uLtpoZqQGum60Wi0KCgpEr3+TyQQfHx9MnTp1NN1mjP2f4d0CGWOsj6CgICxbtgz79u0T5S9cuBAdHR14++23YbPZkJubi1OnTo26vWXLlmHixImIjo5GeXk5Ghsb8cMPPyAxMRGtra2DlvPy8sKKFStQU1ODkpISJCQkYPny5QPeEggAa9asQX19PVJSUlBXV4cjR44gLy9PdE5qaioqKiqwYcMG/PTTT6ivr8fx48exYcOGUY+1r3HjxiEsLAw7d+6E2WxGaWkp0tPTReesX78eN27cwEsvvYRz587BZrOhqKgIK1euRG9vLyorK5GVlYXz58+jubkZx44dQ0dHh3A7nVqtRnV1Nerq6nDt2rVBt1x/9NFHhU+mzGYz4uPj8dtvvwnHh2qrvwc11xkZGfj444+RmZmJ2tpamM1mHD161GXuBqNSqSCRSHDixAl0dHQMusPkUGswHDNnzsRff/2F/fv348qVK8jPz8eBAweG3d/hWrRoEcLDw/HCCy+guLgYTU1NqKiowJYtW3D+/Plh16NWq1FZWYmmpiZcu3YNDocD69atQ0tLCxISEmCxWHD8+HEYjUYkJyeLbn1ljDH+jcAYY/28+eabLrcJabVavPfee8jNzYVOp8PZs2dH9WyWk0wmQ1lZGaZNm4aYmBhotVrExcWhs7Nz0E+yZDIZioqKcOPGDYSGhiI2NhaRkZF4991371lm2rRpKCgowJdffgmdTocDBw4gKytLdM7s2bNRWloKq9WKefPmITg4GBkZGZg8efKox9rfwYMH0dPTg5CQECQlJeGtt94SHZ88eTJMJhN6e3sRFRWFoKAgJCUlYfz48ZBKpVAoFCgrK8Ozzz6LgIAApKenY/fu3XjmmWcAAKtXr4ZGo8GcOXPg5+cHk8l0z76kp6dDr9fDYDBg4cKFmDRpkugLiIdqq78HNdcGgwEnTpxAcXExQkNDERYWhpycHKhUqqGmWzBlyhRkZmbijTfegFKpHDSYG2oNhkOn02HPnj3Izs7GrFmzcPjw4UG/9uB+SSQSnDx5EvPnz8fKlSsREBCAF198Eb/88ss9/+EwkE2bNsHNzQ2BgYHw8/NDc3MzpkyZgpMnT+Ls2bPQ6XRYs2YN4uLiRhTUMsb+HSTU/yECxhhjjDHGGGMjxp9cMcYYY4wxxtgY4OCKMcYYY4wxxsYAB1eMMcYYY4wxNgY4uGKMMcYYY4yxMcDBFWOMMcYYY4yNAQ6uGGOMMcYYY2wMcHDFGGOMMcYYY2OAgyvGGGOMMcYYGwMcXDHGGGOMMcbYGODgijHGGGOMMcbGAAdXjDHGGGOMMTYG/ga2rc7iRUWTZQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Puntaje de entrenamiento', color='blue')\n",
    "plt.plot(train_sizes, test_mean, label='Puntaje de prueba', color='green')\n",
    "plt.xlabel('Número de muestras de entrenamiento')\n",
    "plt.ylabel('Precisión')\n",
    "plt.title('Curva de Aprendizaje - Random Forest')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusión: posibilidad de persistencia de sobreajuste\n",
    "\n",
    "El hecho de que el puntaje de entrenamiento se mantenga estable en el 1 desde el principio da lugar a un posible sobreajuste. En modelos con muchos parámetros o muy feibles (como el caso de random forest muy produndos o con muchas características), el modelo podría estar **memorizando** los datos de entrenamiento en vez de aprender patrones generales. \n",
    "\n",
    "Random Forest, aunque generalmente es robusto, puede caer en este comportamiento si se ajusta sin restricciones (por ejemplo, sin un límite en la profundidad de los árboles o en el número de muestras mínimas por hoja). Si el modelo tiene una alta capacidad para ajustarse a los datos de entrenamiento, puede llegar a predecir perfectamente (puntaje 1) todas las muestras de entrenamiento\n",
    "\n",
    "Esto tiene sentido si se tiene en cuenta que este random forest no ha sido regularizado adecuadamente. Por tanto, a continuación se harán los siguientes ajustes al modelo, entre otros:\n",
    "\n",
    "- **Limitar profundidad máxima de los árboles (max_depth)**\n",
    "- **Aumentar el número mínimo de muestrar para dividir un nodo (min_samples_split)**: aumentar su valor fuerza a que haya un número mínimo de muestras para dividir un nodo. Esto ayuda a evitar que el modelo se ajuste a ruidos o detalles irrelevantes.\n",
    "- **Aumentar número mínimo de muestras por hoja (min_samples_split)**: este parámetro contorla el número mínimo de muestras en una hoja del árbol. Aumentarlo puede evitar que el modelo se ajuste a casos específicos o pequeños detalles en los datos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 23\u001b[0m\n\u001b[1;32m     12\u001b[0m random_search_rf \u001b[38;5;241m=\u001b[39m RandomizedSearchCV(\n\u001b[1;32m     13\u001b[0m     estimator\u001b[38;5;241m=\u001b[39mRandomForestClassifier(),\n\u001b[1;32m     14\u001b[0m     param_distributions\u001b[38;5;241m=\u001b[39mparam_distributions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m     n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     20\u001b[0m )\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# Ajustar el modelo\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m random_search_rf\u001b[38;5;241m.\u001b[39mfit(x_train, y_train)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Mejor estimador y parámetros encontrados\u001b[39;00m\n\u001b[1;32m     26\u001b[0m best_rf \u001b[38;5;241m=\u001b[39m random_search_rf\u001b[38;5;241m.\u001b[39mbest_estimator_\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1471\u001b[0m     )\n\u001b[1;32m   1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1019\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[1;32m   1014\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[1;32m   1015\u001b[0m     )\n\u001b[1;32m   1017\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[0;32m-> 1019\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_search(evaluate_candidates)\n\u001b[1;32m   1021\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[1;32m   1023\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1960\u001b[0m, in \u001b[0;36mRandomizedSearchCV._run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1958\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[1;32m   1959\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1960\u001b[0m     evaluate_candidates(\n\u001b[1;32m   1961\u001b[0m         ParameterSampler(\n\u001b[1;32m   1962\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_distributions, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_state\n\u001b[1;32m   1963\u001b[0m         )\n\u001b[1;32m   1964\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/model_selection/_search.py:965\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    958\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    959\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m candidates,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    960\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    961\u001b[0m             n_splits, n_candidates, n_candidates \u001b[38;5;241m*\u001b[39m n_splits\n\u001b[1;32m    962\u001b[0m         )\n\u001b[1;32m    963\u001b[0m     )\n\u001b[0;32m--> 965\u001b[0m out \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[1;32m    966\u001b[0m     delayed(_fit_and_score)(\n\u001b[1;32m    967\u001b[0m         clone(base_estimator),\n\u001b[1;32m    968\u001b[0m         X,\n\u001b[1;32m    969\u001b[0m         y,\n\u001b[1;32m    970\u001b[0m         train\u001b[38;5;241m=\u001b[39mtrain,\n\u001b[1;32m    971\u001b[0m         test\u001b[38;5;241m=\u001b[39mtest,\n\u001b[1;32m    972\u001b[0m         parameters\u001b[38;5;241m=\u001b[39mparameters,\n\u001b[1;32m    973\u001b[0m         split_progress\u001b[38;5;241m=\u001b[39m(split_idx, n_splits),\n\u001b[1;32m    974\u001b[0m         candidate_progress\u001b[38;5;241m=\u001b[39m(cand_idx, n_candidates),\n\u001b[1;32m    975\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_and_score_kwargs,\n\u001b[1;32m    976\u001b[0m     )\n\u001b[1;32m    977\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m (cand_idx, parameters), (split_idx, (train, test)) \u001b[38;5;129;01min\u001b[39;00m product(\n\u001b[1;32m    978\u001b[0m         \u001b[38;5;28menumerate\u001b[39m(candidate_params),\n\u001b[1;32m    979\u001b[0m         \u001b[38;5;28menumerate\u001b[39m(cv\u001b[38;5;241m.\u001b[39msplit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrouted_params\u001b[38;5;241m.\u001b[39msplitter\u001b[38;5;241m.\u001b[39msplit)),\n\u001b[1;32m    980\u001b[0m     )\n\u001b[1;32m    981\u001b[0m )\n\u001b[1;32m    983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    984\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    985\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo fits were performed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    986\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWas the CV iterator empty? \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    987\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWere there no candidates?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    988\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/utils/parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[1;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[1;32m     73\u001b[0m )\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(iterable_with_config)\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/joblib/parallel.py:2007\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   2001\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[1;32m   2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[1;32m   2003\u001b[0m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[1;32m   2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m   2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output)\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/joblib/parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1647\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m   1649\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1650\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m   1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m   1653\u001b[0m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m   1654\u001b[0m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m   1655\u001b[0m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[1;32m   1656\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/joblib/parallel.py:1760\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1755\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m-> 1760\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[1;32m   1761\u001b[0m         timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[1;32m   1762\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m0.01\u001b[39m)\n\u001b[1;32m   1763\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/aa/lib/python3.11/site-packages/joblib/parallel.py:768\u001b[0m, in \u001b[0;36mBatchCompletionCallBack.get_status\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    766\u001b[0m         \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n\u001b[0;32m--> 768\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_status\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout):\n\u001b[1;32m    769\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get the status of the task.\u001b[39;00m\n\u001b[1;32m    770\u001b[0m \n\u001b[1;32m    771\u001b[0m \u001b[38;5;124;03m    This function also checks if the timeout has been reached and register\u001b[39;00m\n\u001b[1;32m    772\u001b[0m \u001b[38;5;124;03m    the TimeoutError outcome when it is the case.\u001b[39;00m\n\u001b[1;32m    773\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    774\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m!=\u001b[39m TASK_PENDING:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'n_estimators': [100, 150, 200],\n",
    "    'max_depth': [5, 10, 20, 30],  # Menor profundidad para evitar sobreajuste\n",
    "    'min_samples_split': [2, 10, 20],  # Aumentar el valor para evitar ajustes excesivos\n",
    "    'min_samples_leaf': [1, 2, 4],  # Aumentar para evitar aprender patrones triviales\n",
    "    'max_features': ['sqrt', 'log2', 0.5],  # Limitar las características usadas por cada árbol\n",
    "    'oob_score': [True],  # Habilitar Out-of-Bag scoring para validación interna\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para RandomForest\n",
    "random_search_rf = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Ajustar el modelo\n",
    "random_search_rf.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_rf = random_search_rf.best_estimator_\n",
    "print(\"Mejores parámetros Random Forest:\", random_search_rf.best_params_)\n",
    "\n",
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_rf.predict(x_test)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy (Test) - Random Forest:\", accuracy_rf)\n",
    "print(\"Classification Report - Random Forest:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Comparación de la precisión entre modelos\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm, accuracy_rf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    best_rf, \n",
    "    x_train, \n",
    "    y_train, \n",
    "    cv=5, \n",
    "    scoring='accuracy', \n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Promedio de los puntajes de entrenamiento y prueba\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curva aprendizaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cSL58+XBycmLgwIEP/D0mvDZ06FCCgoKSbFO6dOlkj3/66adt/tAfN26czaIwSbk3VAQFBVGuXDmef/55Pv74YwYPHgyk/rOZ3O8goyQ1fkZ9LlIiqc84wGuvvZbkd97AMtMHlkB39OhRfvjhB9atW8eqVauYM2cOY8eO5Z133knfwkUkwyh8iUi28fzzz/Ppp5+yY8cOateu/cC2CYsO3L8CW1IrFz5Mhw4d6NevH0ePHmXFihXkzp2bVq1aWV//66+/+Oeff/j888+tlzOB5TLBlChevLh1VuteR48etfm5VKlSABQsWDBNS8MvW7YMFxcXli5dmugP2m3btvGf//yHs2fP2sw+JFXXP//8Q+7cuR+6EECpUqUwDIMSJUrwxBNPpLrepBQvXpyDBw9iGIZNwL7/vUrOV199xYwZM5gxYwYNGjRI9Po333xDo0aNWLBggc3+sLCwRLMv9ypZsiRgmc1L6+/m3tUHE/pLjeeee44GDRrw3nvv8frrr5MnT55H/mwmpXjx4mzatInbt2/bBOj7fwfFixdPcj9YVoUsUKCAw29HULx4cTZu3MitW7dsZr+OHDliff1BfH19yZs3L/Hx8Sn6vefJk4cOHTrQoUMHYmJieOmll5g0aRIjR47E3d092X80EpGsQ9/5EpFsY9iwYeTJk4eePXsSGhqa6PUTJ07w8ccfA5aZsgIFCiRaFv3e7/ekVNu2bXF2dubLL79k5cqVPP/88zZ/NCYEmXv/Jd4wDGstD9OyZUt27tzJrl27rPuuXr3KsmXLbNoFBQXh5eXFe++9R2xsbKJ+rl69+sBxli1bRr169ejQoQPt2rWzeSSs5vjll1/aHLNjxw6b7zqdO3eOb7/9lmbNmj10luSll17C2dmZd955J9EshWEYKb5s7F4tW7bk4sWLfPPNN9Z9kZGRKbqx9sGDB+nZsyevvfaa9ZYE93N2dk5U68qVKx/6fa2CBQvSsGFD5s2bx6VLlxK9/rDfTd26dWnatKn1kZbwBZZL265fv878+fOBR/9sJqVly5bExcUxd+5c6774+Hhmzpxp065w4cJUrVqVzz//3OYfQQ4ePMjPP/9My5Yt01yDvbRs2ZL4+HhmzZpls3/69OmYTCbrKpvJcXZ2pm3btqxatSrJWfl7f+/3f95dXV158sknMQzD+t9zwv+uJLdsv4hkfpr5EpFso1SpUixfvty6HHWXLl2oWLEiMTExbN++nZUrV9K1a1dr+549e/L+++/Ts2dPatSowa+//so///yT6nELFixIo0aNmDZtGrdu3aJDhw42r5crV45SpUoxdOhQLly4gJeXF6tWrUpyAYukDBs2jKVLl9K8eXPefPNN61LzxYsXt/m+k5eXF3PnzqVz58489dRTvPLKK/j6+nL27FnWrl1L3bp1E/0RmeCPP/7g+PHj9O/fP8nXixQpwlNPPcWyZcsYPny4dX/FihUJCgqyWWoeSNFlUqVKleLdd99l5MiRnD59mjZt2pA3b15OnTrFf//7X3r37s3QoUNT9B4l6NWrF7NmzaJLly7s3r2bwoULs3TpUnLnzv3QY7t16wZA/fr1+eKLL2xeq1OnDiVLluT5559nwoQJdOvWjTp16vDXX3+xbNmyFIWh2bNn88wzz1CpUiV69epFyZIlCQ0NZceOHZw/fz7RvcLSQ4sWLahYsSLTpk0jJCTkkT+bSWnVqhV169ZlxIgRnD59mieffJLVq1cn+d3Gjz76iBYtWlC7dm169OhhXWre29v7oZdVZoRWrVrRqFEjRo0axenTp6lSpQo///wz3377LQMHDrTONj/I+++/z5YtWwgMDKRXr148+eST3Lhxgz179rBx40Zu3LgBQLNmzfDz86Nu3boUKlSIw4cPM2vWLJ577jnrrFvC4jajRo3ilVdewcXFhVatWjl8hlBEUiFD11YUEckA//zzj9GrVy8jICDAcHV1NfLmzWvUrVvXmDlzps0y35GRkUaPHj0Mb29vI2/evMbLL79sXLlyJdml5q9evZrsmPPnzzcAI2/evMbdu3cTvX7o0CGjadOmhqenp1GgQAGjV69exv79+5Nd7v5+Bw4cMBo0aGC4u7sbRYoUMSZOnGgsWLAgyWWnt2zZYgQFBRne3t6Gu7u7UapUKaNr1642S8Lfb8CAAQZgnDhxItk248ePNwBj//79hmEY1mXLv/jiC6NMmTKGm5ubUa1aNZvl3A3j4e/fqlWrjGeeecbIkyePkSdPHqNcuXJGSEiIcfToUWubBg0aJLkMd1LLxZ85c8Z44YUXjNy5cxsFChQw3nzzTWPdunUPXWo+uWXH7/0dRUVFGUOGDDEKFy5seHh4GHXr1jV27NhhNGjQwGjQoIG1r+RuZXDixAmjS5cuhp+fn+Hi4mIUKVLEeP75541vvvkm6Tc9jYoXL24899xzSb62ePFim9pS+tkMDg428uTJk6i/hN/vva5fv2507tzZ8PLyMry9vY3OnTsbe/fuTfI92bhxo1G3bl3Dw8PD8PLyMlq1amUcOnQoyTHu/wwlV1Nyn5f7Peh9SnDr1i1j0KBBhr+/v+Hi4mKUKVPG+Oijjwyz2WzTLuG/h6SEhoYaISEhRtGiRQ0XFxfDz8/PaNKkifHpp59a28ybN8+oX7++kT9/fsPNzc0oVaqU8dZbbxnh4eE2fU2cONEoUqSI4eTkpGXnRbIgk2E44BupIiKS5ZlMJkJCQpKdTcvJTpw4QenSpVm6dKnN8vgiIpKz6TtfIiIidpbwva4HLcIhIiI5j77zJSIiYkcLFy5k4cKF5M6dm1q1ajm6HBERyUQ08yUiImJHvXv35saNG6xcuRIfHx9HlyMiIpmIvvMlIiIiIiKSATTzJSIiIiIikgEUvkRERERERDKAFtxII7PZzMWLF8mbNy8mk8nR5YiIiIiIiIMYhsGtW7fw9/fHySn5+S2FrzS6ePEiRYsWdXQZIiIiIiKSSZw7d47HH3882dcVvtIob968gOUN9vLycnA1IiIiIiLiKBERERQtWtSaEZKj8JVGCZcaenl5KXyJiIiIiMhDv46kBTdEREREREQygMKXiIiIiIhIBlD4EhERERERyQAKXyIiIiIiIhlA4UtERERERCQDKHyJiIiIiIhkAIUvERERERGRDKDwJSIiIiIikgEUvkRERERERDKAwpeIiIiIiEgGUPgSERERERHJAApfIiIiIiIiGUDhS0REREREJAMofImIiIiIiGQAh4avX3/9lVatWuHv74/JZGLNmjUPPWbr1q089dRTuLm5Ubp0aRYvXpyozezZswkICMDd3Z3AwEB27dpl83pUVBQhISHkz58fT09P2rZtS2hoqJ3OSkREREREJDGHhq87d+5QpUoVZs+enaL2p06d4rnnnqNRo0bs27ePgQMH0rNnT9avX29ts2LFCgYPHsy4cePYs2cPVapUISgoiCtXrljbDBo0iO+//56VK1fyyy+/cPHiRV566SW7n5+IiIiIiEgCk2EYhqOLADCZTPz3v/+lTZs2ybYZPnw4a9eu5eDBg9Z9r7zyCmFhYaxbtw6AwMBAnn76aWbNmgWA2WymaNGiDBgwgBEjRhAeHo6vry/Lly+nXbt2ABw5coTy5cuzY8cOatWqlaJ6IyIi8Pb2Jjw8HC8vrzSetYhcvAg7dzq6ChGR7CfeiCPGfJcY4y7R5rvW7VhzFDGG5edYI9rRZYo8kmolStCxYTVHl5HibJArA2t6ZDt27KBp06Y2+4KCghg4cCAAMTEx7N69m5EjR1pfd3JyomnTpuzYsQOA3bt3Exsba9NPuXLlKFas2APDV3R0NNHR//4PVEREhL1OSyTHiouDevXg5ElHVyIiks6cYiFXFLjchVx373uOSuG+u6nrwyne0Wctku7KH36djg0/cXQZKZalwtfly5cpVKiQzb5ChQoRERHB3bt3uXnzJvHx8Um2OXLkiLUPV1dXfHx8ErW5fPlysmNPnjyZd955xz4nIiIAfP+9JXjlyQNVqzq6GhHJKcymWMxOdzE7RWF2vmvZtnmOst3ndBezc9Q92w9o63yXeKe7GP/rO/5/xzg6CJni3XAye+Bkdscp3uN/2x44md3AMDm0NpFHUaJgKUeXkCpZKnw50siRIxk8eLD154iICIoWLerAikSyvv9dHcyAATB5smNrERHHiI2P5W7cXe7G3iUqLsq6fe9zVFxUivYlu/++vuMNxwYhN2c3PFw88MjlgYeLB+653K3bifYlt/8h+zxy/W///153MmmBa5HMIEuFLz8/v0SrEoaGhuLl5YWHhwfOzs44Ozsn2cbPz8/aR0xMDGFhYTazX/e2SYqbmxtubm72OxmRHO7wYdi8GZycoE8fR1cjImAbhFIaZJLdl8K2mS0I3RtaEoUc58T7k22bTEhSEBLJ2bJU+KpduzY//vijzb4NGzZQu3ZtAFxdXalevTqbNm2yLtxhNpvZtGkT/fv3B6B69eq4uLiwadMm2rZtC8DRo0c5e/astR8RSX8Ji5y2agXFizu2FpHM6P4g9EgzQyls6+gglBBOHjSLk2hfatret88tl5uCkIhkKIeGr9u3b3P8+HHrz6dOnWLfvn3ky5ePYsWKMXLkSC5cuMCSJUsA6NOnD7NmzWLYsGF0796dzZs38/XXX7N27VprH4MHDyY4OJgaNWpQs2ZNZsyYwZ07d+jWrRsA3t7e9OjRg8GDB5MvXz68vLwYMGAAtWvXTvFKhyLyaG7dgv/9Z01IiGNrEXkYwzCIM8c92uVw9wanFM4iZYYglKpL3R7hsjgFIRHJKRwavv7v//6PRo0aWX9O+E5VcHAwixcv5tKlS5w9e9b6eokSJVi7di2DBg3i448/5vHHH+ezzz4jKCjI2qZDhw5cvXqVsWPHcvnyZapWrcq6detsFuGYPn06Tk5OtG3blujoaIKCgpgzZ04GnLGIACxdaglgTzwBTZo4uhrJSgzDINYc+2iXw90bklIYqMyG2aHnfX94eeilbo9wWVzCMSaTFmEQEbG3THOfr6xG9/kSSRvDgAoVLN/5+vhjeOMNR1ckaZUQhNIUelK4UEJWCELpeVmch4sHbs5uCkIiIplctrzPl4hkfVu3WoJXnjwQHOzoarKPe4NQmi+HS+VCCZkhCKVqZieVs0BJtVcQEhGRR6HwJSIZKmGhjc6dwdvbsbWkl5QEoRTPDKWibVYLQqkJPQpCIiKSHSh8iUiGOX8e1qyxbGfUQhv3B6H0XDHu3r4zQxBKcbhJQxC6v72CkIiIyMMpfIlIhpk3D+LjoX59qFjRsi8yNpIj146kfvGEVMwiZZYglKIZnUdcKEFBSEREJPNS+BKRDBEdDZ9+atn+3233iI2Ppc6COuwP3Z9hdaR1xbi0LqGtICQiIiIJFL5EJEOsWgVXroC/P/zvHuh8tucz9ofux83ZjaLeRR8cZNK4QMK97RWERERExJEUvkQkQyQstNG7N7i4QER0BOO2jgNgSrMp9K/Z34HViYiIiKQ/3UpeRNLd3r2wfTvkymUJXwCTf5vM1cirlM1flterv+7YAkVEREQygMKXiKS7hFmvtm2hcGE4E3aG6TunA/Dhsx/i4uziwOpEREREMobCl4ikqxs3YPlyy3bC8vJvb36b6PhoGgY0pNUTrRxXnIiIiEgGUvgSkXS1aBHcvQuVK8Mzz8CuC7tY/pcljU1tNlULYIiIiEiOofAlIunGbIa5cy3bllkvg6E/DwWgc+XOPFX4KYfVJiIiIpLRFL5EJN2sXw8nToC3N7z6Kqw5sobfzv6Gey53JjWe5OjyRERERDKUwpeIpJtZsyzP3bqBi3sMwzYOA2BI7SEU9S7qwMpEREREMp7Cl4iki5Mn4aefLNv9+sHcP+dy/MZxCuUpxPC6wx1bnIiIiIgDKHyJSLqYOxcMA4KCoMDjN5nw6wQAJjSaQF63vA6uTkRERCTjKXyJiN1FRsKCBZbtkBB499d3uXH3BhV8K9C9WnfHFiciIiLiIApfImJ3X30FN29C8eJQttYJZu6aCcCUZlPI5ZTLwdWJiIiIOIbCl4jYlWH8u9BGv34weutIYs2xPFvyWYJKBTm2OBEREREHUvgSEbvauRP27gU3N6jYYjsrD63EhIkpzabohsoiIiKSoyl8iYhdzZ5tee7wisHEXUMA6F6tO5ULVXZgVSIiIiKOp/AlInYTGgpff23ZLvvS1+w8v5M8LnmY2GiiYwsTERERyQQUvkTEbj77DGJjoUatKOafGgHAsLrDKJy3sIMrExEREXE8hS8RsYu4OPjkE8t2qY4zOR12Gv+8/gypPcSxhYmIiIhkEgpfImIX330H589DvsevsS5yEgDvNnqXPK55HFyZiIiISOag8CUidpGw0EaJrhMIjw6nSqEqdKnSxbFFiYiIiGQiCl8i8sgOH4bNm8FU4B/2u8wFYGqzqTg7OTu4MhEREZHMQ+FLRB5ZwqxXodeGEWfE8VyZ52hSsoljixIRERHJZBS+ROSR3LoFS5YAxX/hss+3OJuc+ejZjxxdloiIiEimo/AlIo9k6VK4dduMW+vBAPSu3pvyvuUdXJWIiIhI5qPwJSJpZhgwaxZQaRnR+faQ1zUv4xuOd3RZIiIiIpmSwpeIpNnWrXD42F1MTd8GYOQzIymYp6BjixIRERHJpBS+RCTNZs8Gak/H8DpPMe9iDKw10NEliYiIiGRaCl8ikibnz8N/N4TCM5MBeK/xe3i4eDi4KhEREZHMS+FLRNJk3jww1x8Hbrep4V+DjpU6OrokERERkUxN4UtEUi06Guas/Buemg/AtGbTcDLpf05EREREHkR/LYlIqq1aBTdqvAVOZlo/8SL1itdzdEkiIiIimZ7Cl4ik2qQvN0CZn3AiFx82e9/R5YiIiIhkCQpfIpIq/7cnnkOPDwWgW8V+PJH/CQdXJCIiIpI1KHyJSKoM/vxz8DuAS7wPH7QY6+hyRERERLIMhS8RSbGzl2/zm8toAHqXHU3+3PkdXJGIiIhI1qHwJSIp1nPhFMh7CdfbJZnycn9HlyMiIiKSpSh8iUiKnA+/yMa7HwHQ9fH3cXdxc3BFIiIiIlmLwpeIpEj3L0Zj5IrE+WJtpvZo5+hyRERERLIchS8Reaj9l/ez4epiANrmnYqnp8mxBYmIiIhkQQpfIvJAhmEQ8u1QMBlw8GXefb22o0sSERERyZIUvkTkgX46/hO/X94Ica7Ui3mfMmUcXZGIiIhI1qTwJSLJijPHMWS95YbK/PEGb/Us4diCRERERLIwhS8RSdZnez7jyPXDEJmfomdG0bKloysSERERyboUvkQkSRHREYzdMtbyw9Zx9O/hg7OzY2sSERERycpyOboAEcmcPtj2AVcjr8L1Mrj+9Trd1zq6IhEREZGszeEzX7NnzyYgIAB3d3cCAwPZtWtXsm1jY2OZMGECpUqVwt3dnSpVqrBu3TqbNgEBAZhMpkSPkJAQa5uGDRsmer1Pnz7pdo4iWc258HNM2znN8sOGD+nUwZUCBRxbk4iIiEhW59DwtWLFCgYPHsy4cePYs2cPVapUISgoiCtXriTZfvTo0cybN4+ZM2dy6NAh+vTpw4svvsjevXutbf78808uXbpkfWzYsAGA9u3b2/TVq1cvm3Yffvhh+p2oSBbz9ua3iYqLwnSmPhxpzT3/diEiIiIiaWQyDMNw1OCBgYE8/fTTzJo1CwCz2UzRokUZMGAAI0aMSNTe39+fUaNG2cxitW3bFg8PD7744oskxxg4cCA//PADx44dw2Sy3Bi2YcOGVK1alRkzZqS59oiICLy9vQkPD8fLyyvN/YhkNv938f94ev7Tlh8+/ZOaj9fgjz8cW5OIiIhIZpbSbOCwma+YmBh2795N06ZN/y3GyYmmTZuyY8eOJI+Jjo7G3d3dZp+Hhwfbtm1LdowvvviC7t27W4NXgmXLllGgQAEqVqzIyJEjiYyMfGC90dHRRERE2DxEshvDMBjy8xAAch97DS7WoH9/BxclIiIikk04bMGNa9euER8fT6FChWz2FypUiCNHjiR5TFBQENOmTaN+/fqUKlWKTZs2sXr1auLj45Nsv2bNGsLCwujatavN/k6dOlG8eHH8/f05cOAAw4cP5+jRo6xevTrZeidPnsw777yTupMUyWK+Pfotv575FVeTO5E/TKJAAbjvil0RERERSaMstdrhxx9/TK9evShXrhwmk4lSpUrRrVs3Fi5cmGT7BQsW0KJFC/z9/W329+7d27pdqVIlChcuTJMmTThx4gSlSpVKsq+RI0cyePBg688REREULVrUDmclkjnExMcwbMMwAAqfGcSZ8GL0HAH3TTaLiIiISBo57LLDAgUK4OzsTGhoqM3+0NBQ/Pz8kjzG19eXNWvWcOfOHc6cOcORI0fw9PSkZMmSidqeOXOGjRs30rNnz4fWEhgYCMDx48eTbePm5oaXl5fNQyQ7mfd/8zh24xj53QpyZtkInJxAi4CKiIiI2I/DwperqyvVq1dn06ZN1n1ms5lNmzZRu3btBx7r7u5OkSJFiIuLY9WqVbRu3TpRm0WLFlGwYEGee+65h9ayb98+AAoXLpy6kxDJJsKiwnjnF8tltRWvvQPRXrRqBcWLO7gwERERkWzEoZcdDh48mODgYGrUqEHNmjWZMWMGd+7coVu3bgB06dKFIkWKMHnyZAD++OMPLly4QNWqVblw4QLjx4/HbDYzbNgwm37NZjOLFi0iODiYXLlsT/HEiRMsX76cli1bkj9/fg4cOMCgQYOoX78+lStXzpgTF8lkJv06iet3r1Mu35P831TLbLGWlxcRERGxL4eGrw4dOnD16lXGjh3L5cuXqVq1KuvWrbMuwnH27FmcnP6dnIuKimL06NGcPHkST09PWrZsydKlS/Hx8bHpd+PGjZw9e5bu3bsnGtPV1ZWNGzdag17RokVp27Yto0ePTtdzFcmsTt08xX92/QeARnEfMfdWLsqWhSZNHFyYiIiISDbj0Pt8ZWW6z5dkFx2+6cDXf39N0xJNOf/+zxw5bOLjj+GNNxxdmYiIiEjWkOnv8yUijrfj3A6+/vtrTJjo8NgUjhw2kScPBAc7ujIRERGR7EfhSySHuveGyl2rduWnxVUA6NwZvL0dWZmIiIhI9pSl7vMlIvbzzaFv2HF+B7ldctOv3LvU+tayXwttiIiIiKQPzXyJ5EDRcdEM3zgcgLfqvMW3X/gTHw8NGkDFig4uTkRERCSb0syXSA40a9csToWdorBnYd6o/hblO1j2a9ZLREREJP1o5kskh7keeZ13f3sXgHcbv8u67/Nw5Qr4+0ObNo6tTURERCQ708yXSA4z4ZcJhEWFUblQZYKrBFO/v2X/66+Di4tjaxMRERHJzjTzJZKDHLt+jDn/NweAKc9O4cB+Z7Zvh1y5oFcvBxcnIiIiks1p5kskBxm+cThx5jhalG7Bs6WepWdPy/62baFwYcfWJiIiIpLdaeZLJIf49cyv/PfIf3EyOfHRsx9x4wYsX255rX9/x9YmIiIikhMofInkAGbDbL2hcq+nelGhYAUWLYK7d6FyZahb18EFioiIiOQACl8iOcCXf33J/138PzxdPXmn4TuYzTDH8tUv+vcHk8mx9YmIiIjkBApfItnc3di7jNw0EoCRz4ykkGch1q2DkyfB2xs6dXJwgSIiIiI5hMKXSDY3Y+cMzkWc43GvxxlUaxAAs2dbXuvWDfLkcWBxIiIiIjmIwpdINnblzhUmb5sMwHuN38PDxYMTJ+Cnnyyv9+vnwOJEREREchiFL5FsbPzW8dyKuUX1wtV5tfKrAMydC4YBQUFQpoyDCxQRERHJQRS+RLKpw1cP8+nuTwGY2mwqTiYnIiNh4ULL6yEhDixOREREJAdS+BLJpt7a8BbxRjyty7amQUADAL76Cm7ehIAAaNnSsfWJiIiI5DQKXyLZ0KaTm1h7bC25nHLx4bMfApZLDWfNsrzety84OzuwQBEREZEcSOFLJJuJN8dbb6jct0Zfnsj/BAA7d8LeveDmBt27O7JCERERkZxJ4Uskm1l6YCn7Q/fj7ebN2AZjrfsTlpfv2BEKFHBQcSIiIiI5mMKXSDZyJ+YOozaPAmBUvVEUyG1JWaGh8PXXljZaaENERETEMRS+RLKRqTumcvHWRQJ8AhgQOMC6/7PPIDYWAgOhRg0HFigiIiKSgyl8iWQTl25d4sPfLYtrvN/kfdxzuQMQFweffGJpo1kvEREREcdR+BLJJsZsGcOd2DvUerwWL1d42br/u+/g/HnL97zat3dggSIiIiI5nMKXSDZwIPQAC/da7p48tdlUTCaT9bWEhTZ69QJ3d0dUJyIiIiKg8CWS5RmGwdCfh2Jg0P7J9tQpWsf62qFDsHkzODlBnz4OLFJEREREFL5Esrr1J9az4eQGXJxcmNxkss1rc+ZYnlu1gmLFHFCciIiIiFgpfIlkYXHmOIb+PBSAATUHUCpfKetrERHw+eeW7f79HVGdiIiIiNxL4UskC1u4dyF/X/2bfB75GF1/tM1rS5fC7dtQtiw0aeKgAkVERETESuFLJIu6FX2LMVvGADC2/lge83jM+pph/LvQRr9+cM/6GyIiIiLiIApfIlnUB79/wJU7VyidrzR9n+5r89rWrXD4MOTJA8HBjqlPRERERGwpfIlkQefCzzF1x1QAPmz6Ia7Orjavz5plee7cGby9M7o6EREREUmKwpdIFjR6y2ii4qKoV6webcq1sXnt/Hn49lvLdkhIxtcmIiIiIklT+BLJYvZc2sOS/UuAxDdUBpg3D+LjoUEDqFjRERWKiIiISFIUvkSyEMMwGPLzEAA6VerE00Wetnk9Oho+/dSyrVkvERERkcxF4UskC/n+n+/Zenorbs5uvNf4vUSvr1oFV66Avz+0aZPx9YmIiIhI8hS+RLKI2PhY3trwFgCDag2iuE/xRG0SFtp4/XVwccnI6kRERETkYRS+RLKIebvn8c/1f/DN7cvIeiMTvb53L+zYAblyQa9eDihQRERERB5I4UskCwiLCmP81vEAvNPwHbzcvBK1Sbipcrt2ULhwBhYnIiIiIimi8CWSBUz+bTLX716nXIFy9KqeeFrrxg1YtsyyrYU2RERERDInhS+RTO502Glm/DEDgI+e/YhcTrkStVm0CKKioHJlqFs3gwsUERERkRRR+BLJ5EZuGklMfAyNSzTmuTLPJXrdbIY5cyzb/fvDfbf9EhEREZFMQuFLJBP74/wffHXwK0yYkryhMsC6dXDyJHh7Q6dODihSRERERFJE4UskkzIMg8E/DwYguGowVf2qJtkuYaGNbt0gT54MKk5EREREUk3hSySTWnV4FdvPbccjlwfvNno3yTYnTsBPP1m2+/XLwOJEREREJNUUvkQyoZj4GIZvHA7A0DpDKeJVJMl2c+eCYUBQEJQpk5EVioiIiEhqKXyJZEKzd83m5M2T+Hn6MazusCTbREbCwoWW7f79M7A4EREREUkThS+RTObG3RtM/HUiABMbTcTT1TPJdl99BTdvQkAAtGiRgQWKiIiISJoofIlkMhN/mcjNqJtUKliJblW7JdnGMGDWLMt2377g7JyBBYqIiIhImih8iWQix28cZ/afluULpzSbgrNT0qlq507Yuxfc3aFHj4ysUERERETSyuHha/bs2QQEBODu7k5gYCC7du1Ktm1sbCwTJkygVKlSuLu7U6VKFdatW2fTZvz48ZhMJptHuXLlbNpERUUREhJC/vz58fT0pG3btoSGhqbL+YmkxvCNw4k1x9K8dHOalWqWbLuEWa9XXoH8+TOoOBERERF5JA4NXytWrGDw4MGMGzeOPXv2UKVKFYKCgrhy5UqS7UePHs28efOYOXMmhw4dok+fPrz44ovs3bvXpl2FChW4dOmS9bFt2zab1wcNGsT333/PypUr+eWXX7h48SIvvfRSup2nSEr8duY3Vh9ejZPJiY+e/SjZdqGhsHKlZTskJIOKExEREZFH5tDwNW3aNHr16kW3bt148skn+eSTT8idOzcLE5Zwu8/SpUt5++23admyJSVLlqRv3760bNmSqVOn2rTLlSsXfn5+1keBAgWsr4WHh7NgwQKmTZtG48aNqV69OosWLWL79u3s3LkzXc9XJDlmw8yQn4cA0KNaDyoWrJhs288+g9hYCAyEGjUyqkIREREReVQOC18xMTHs3r2bpk2b/luMkxNNmzZlx44dSR4THR2Nu7u7zT4PD49EM1vHjh3D39+fkiVL8uqrr3L27Fnra7t37yY2NtZm3HLlylGsWLFkx00YOyIiwuYhYi8rDq7gz4t/4unqyYRGE5JtFxcHn3xi2dasl4iIiEjW4rDwde3aNeLj4ylUqJDN/kKFCnH58uUkjwkKCmLatGkcO3YMs9nMhg0bWL16NZcuXbK2CQwMZPHixaxbt465c+dy6tQp6tWrx61btwC4fPkyrq6u+Pj4pHhcgMmTJ+Pt7W19FC1aNI1nLmIrKi6KkZtGAjC87nD8PP2Sbfvdd3D+PBQoAO3bZ1SFIiIiImIPDl9wIzU+/vhjypQpQ7ly5XB1daV///5069YNJ6d/T6NFixa0b9+eypUrExQUxI8//khYWBhff/31I409cuRIwsPDrY9z58496umIAPDxzo85E36GInmLMLj24Ae2nW1ZCJFevSwrHYqIiIhI1uGw8FWgQAGcnZ0TrTIYGhqKn1/S//Lv6+vLmjVruHPnDmfOnOHIkSN4enpSsmTJZMfx8fHhiSee4Pjx4wD4+fkRExNDWFhYiscFcHNzw8vLy+Yh8qiu3rnKe9veA+C9Ju+R2yV3sm0PHYLNm8HJCfr0yagKRURERMReHBa+XF1dqV69Ops2bbLuM5vNbNq0idq1az/wWHd3d4oUKUJcXByrVq2idevWyba9ffs2J06coHDhwgBUr14dFxcXm3GPHj3K2bNnHzquiL2N3zqeiOgIqvlV47XKrz2w7Zw5lucXXoBixTKgOBERERGxq1yOHHzw4MEEBwdTo0YNatasyYwZM7hz5w7dunUDoEuXLhQpUoTJkycD8Mcff3DhwgWqVq3KhQsXGD9+PGazmWHDhln7HDp0KK1ataJ48eJcvHiRcePG4ezsTMeOHQHw9vamR48eDB48mHz58uHl5cWAAQOoXbs2tWrVyvg3QXKsI9eOMG/3PACmNpuKkyn5fwuJiIDPP7dsa6ENERERkazJoeGrQ4cOXL16lbFjx3L58mWqVq3KunXrrItwnD171ub7XFFRUYwePZqTJ0/i6elJy5YtWbp0qc3iGefPn6djx45cv34dX19fnnnmGXbu3Imvr6+1zfTp03FycqJt27ZER0cTFBTEnIRpBZEMMmzDMOKNeFo90YpGJRo9sO3SpXD7NpQtC02aZFCBIiIiImJXJsMwDEcXkRVFRETg7e1NeHi4vv8lqbbl1BYaL2mMs8mZg/0OUq5AuWTbGgZUqACHD8N//gMDBmRgoSIiIiLyUCnNBllqtUOR7ODeGyr3qdHngcELYMsWS/DKkwe6dMmICkVEREQkPSh8iWSwpfuXsvfyXrzcvBjXYNxD2ycsL9+5M3h7p3NxIiIiIpJuFL5EMlBkbCSjNo8CYFS9Ufjm8X1g+3Pn4NtvLdtaaENEREQka1P4EslA03ZM48KtCxT3Ls4bgW88tP28eRAfDw0aQMWKGVCgiIiIiKQbhS+RDHL59mXe3/Y+AJObTMY9l/sD20dHw/z5lm3NeomIiIhkfQpfIhlk7Jax3Im9Q80iNXml4isPbb9qFVy5Av7+0KZN+tcnIiIiIulL4UskAxy8cpAFexcAMK3ZNEwm00OPmTXL8vz66+Dikp7ViYiIiEhGUPgSyQBDfx6K2TDTtnxb6har+9D2e/fCjh2QKxf06pUBBYqIiIhIulP4Ekln64+vZ/2J9bg4ufBB0w9SdEzC8vLt2kHhwulYnIiIiIhkGIUvkXQUb45n6IahAPSv2Z9S+Uo99JgbN2DZMsu2FtoQERERyT4UvkTS0aJ9izh45SCPuT/G6PqjU3bMIoiKgipVoO7Dr1AUERERkSxC4UskndyOuc2YLWMAGFN/DPk88j30GLMZ5syxbIeEQArW5RARERGRLELhSySdfPj7h1y+fZlSj5UipGbKrh9ctw5OngRvb+jUKZ0LFBEREZEMpfAlkg7OR5xnyvYpAHzQ9ANcnV1TdFzCQhvdu0OePOlVnYiIiIg4gsKXSDoYvXk0d+Pu8kyxZ3ip/EspOubECfjpJ8t2377pWJyIiIiIOITCl4id7b20lyX7lwAw5dkpKbqhMsDcuWAYEBQEZcqkZ4UiIiIi4ggKXyJ2ZBgGQ34egoHBKxVfIfDxwBQdFxkJCxdatvv3T8cCRURERMRhFL5E7GjtsbVsOb0FN2c3JjeZnOLjvvwSbt6EgABo0SL96hMRERERx1H4ErGT2PhY3trwFgBvBr5JgE9Aio4zjH8X2ujbF5yd06lAEREREXEohS8RO5m/Zz5Hrh2hQO4CvF3v7RQft3Mn7N0L7u7Qo0c6FigiIiIiDqXwJWIH4VHhjNs6DoDxDcbj7e6d4mNnzbI8v/IK5M+fHtWJiIiISGag8CViB5O3TeZa5DXK5i9L7+q9U3xcaCisXGnZ1kIbIiIiItmbwpfIIzoddpoZO2cA8NGzH+Hi7JLiYz/7DGJjITAQqldPpwJFREREJFNQ+BJ5RKM2jyI6PpqGAQ15/onnU3xcXBx88ollOyQknYoTERERkUxD4UvkEey6sIvlfy3HhImpzaam+IbKAN99B+fPg68vtG+fjkWKiIiISKag8CWSRgk3VAboXKUzTxV+KlXHJywv37OnZaVDEREREcneFL5E0ui/R/7LtrPb8MjlwaTGk1J17KFDsHkzODlBnz7pVKCIiIiIZCoKXyJpEBMfw/CNwwEYUnsIj3s9nqrj58yxPL/wAhQrZu/qRERERCQzUvgSSYM5f87h+I3jFMpTiGF1h6Xq2IgI+Pxzy7YW2hARERHJORS+RFLp5t2bTPhlAgATGk0gr1veVB2/dCncvg1ly0KTJulRoYiIiIhkRgpfIqn07q/vcjPqJhV8K9C9WvdUHWsY/y60ERICqVgcUURERESyOIUvkVQ4ceMEM3fNBGBKsynkcsqVquO3bIHDhyFPHujSJT0qFBEREZHMSuFLJBVGbBpBrDmWZqWa0bx081QfnzDr1aULeHvbuTgRERERydQUvkRS6Pezv/PNoW9wMjkx5dkpqT7+3Dn49lvLthbaEBEREcl5FL5EUuDeGyp3r9qdSoUqpbqPefMgPh4aNIAKFexdoYiIiIhkdgpfIimw4u8V/HHhD/K45GFCowmpPj46GubPt2z372/n4kREREQkS1D4EnmIqLgoRmwcAcCwusMonLdwqvtYtQquXAF/f2jd2t4VioiIiEhWoPAl8hAz/5jJmfAz+Of1Z0jtIWnqY9Ysy/Prr4OLix2LExEREZEsQ+FL5AGuRV5j0m+TAJjUeBJ5XPOkuo89e2DHDkvo6t3b3hWKiIiISFah8CXyAO9sfYfw6HCq+lWlS5W03ZgrYXn5tm3Bz8+OxYmIiIhIlqLwJZKMo9eO8snuTwCY2mwqTqbU/+dy4wYsX27Z1vLyIiIiIjmbwpdIMoZtHEacOY7nn3iexiUap6mPRYsgKgqqVIG6de1coIiIiIhkKQpfIknYenor3x39DmeTMx82/TBNfZjNMGeOZTskBEwmOxYoIiIiIlmOwpfIfcyG2XpD5d7Ve1Pet3ya+lm3Dk6eBG9v6NTJnhWKiIiISFak8CVyn2UHlrHn0h7yuuZlfMPxae4nYaGN7t0hT+oXSRQRERGRbEbhS+QekbGRvL35bQDervc2BfMUTFM/J07ATz9Ztvv2tVd1IiIiIpKVKXyJ3GP6jumcjzhPMe9iDKw1MM39zJ0LhgHNm0OZMvarT0RERESyLoUvkf+5fPsy7//+PgCTm0zGPZd7mvqJjISFCy3bWl5eRERERBIofIn8z7gt47gdc5un/Z/mlYqvpLmfL7+EmzchIABatLBffSIiIiKStSl8iQB/X/mbz/Z+BqT9hspgudQwYaGNfv3A2dleFYqIiIhIVqfwJQK8teEtzIaZF8u9SL3i9dLcz86dsHcvuLtbVjkUEREREUng8PA1e/ZsAgICcHd3JzAwkF27diXbNjY2lgkTJlCqVCnc3d2pUqUK69ats2kzefJknn76afLmzUvBggVp06YNR48etWnTsGFDTCaTzaNPnz7pcn6S+W04sYGfjv9ELqdcfND0g0fqa9Ysy/Mrr0D+/HYoTkRERESyjVxpOSg+Pp7FixezadMmrly5gtlstnl98+bNKepnxYoVDB48mE8++YTAwEBmzJhBUFAQR48epWDBxEt8jx49mi+++IL58+dTrlw51q9fz4svvsj27dupVq0aAL/88gshISE8/fTTxMXF8fbbb9OsWTMOHTpEnntuttSrVy8mTJhg/Tl37txpeSski4s3x1tvqBzydAhl8qd9acLQUFi50rLdv789qhMRERGR7MRkGIaR2oP69+/P4sWLee655yhcuDAmk8nm9enTp6eon8DAQJ5++mlm/W+6wGw2U7RoUQYMGMCIESMStff392fUqFGE3LOEXNu2bfHw8OCLL75IcoyrV69SsGBBfvnlF+rXrw9YZr6qVq3KjBkzUlRnUiIiIvD29iY8PBwvL6809yOOtWDPAnp+3xMfdx9OvHGCfB750tzXu+/CmDEQGGi5/FBEREREcoaUZoM0zXx99dVXfP3117Rs2TLNBcbExLB7925Gjhxp3efk5ETTpk3ZsWNHksdER0fj7m67/LeHhwfbtm1Ldpzw8HAA8uWz/aN62bJlfPHFF/j5+dGqVSvGjBnzwNmv6OhooqOjrT9HREQkf3KSJdyOuc3oLaMBGFN/zCMFr7g4mDfPsq3l5UVEREQkKWkKX66urpQuXfqRBr527Rrx8fEUKlTIZn+hQoU4cuRIkscEBQUxbdo06tevT6lSpdi0aROrV68mPj4+yfZms5mBAwdSt25dKlasaN3fqVMnihcvjr+/PwcOHGD48OEcPXqU1atXJ1vv5MmTeeedd9JwppJZTdk+hcu3L1PysZKEPP1oiem77+D8efD1hfbt7VSgiIiIiGQraVpwY8iQIXz88cek4YrFR/Lxxx9TpkwZypUrh6urK/3796dbt244OSV9GiEhIRw8eJCvvvrKZn/v3r0JCgqiUqVKvPrqqyxZsoT//ve/nDhxItmxR44cSXh4uPVx7tw5u56bZKyLty7y0faPAHi/yfu45XJ7pP4SFtro2dOy0qGIiIiIyP3SNPO1bds2tmzZwk8//USFChVwcXGxef1BM0gJChQogLOzM6GhoTb7Q0ND8fPzS/IYX19f1qxZQ1RUFNevX8ff358RI0ZQsmTJRG379+/PDz/8wK+//srjjz/+wFoCAwMBOH78OKVKlUqyjZubG25uj/YHumQeozePJjI2kjpF69DuyXaP1NehQ7BlCzg5gRbNFBEREZHkpCl8+fj48OKLLz7SwK6urlSvXp1NmzbRpk0bwHKZ4KZNm+j/kKXi3N3dKVKkCLGxsaxatYqXX37Z+pphGAwYMID//ve/bN26lRIlSjy0ln379gFQuHDhNJ+PZB37Lu9j8b7FgOWGyvcvGJNac+ZYnl94AYoVe8TiRERERCTbSlP4WrRokV0GHzx4MMHBwdSoUYOaNWsyY8YM7ty5Q7du3QDo0qULRYoUYfLkyQD88ccfXLhwgapVq3LhwgXGjx+P2Wxm2LBh1j5DQkJYvnw53377LXnz5uXy5csAeHt74+HhwYkTJ1i+fDktW7Ykf/78HDhwgEGDBlG/fn0qV65sl/OSzMswDIb+PBQDgw4VOlDr8VqP1F9EBHz+uWVbC22IiIiIyIOkKXwluHr1qvUGxmXLlsXX1zdVx3fo0IGrV68yduxYLl++TNWqVVm3bp11EY6zZ8/afJ8rKiqK0aNHc/LkSTw9PWnZsiVLly7Fx8fH2mbu3LmAZTn5ey1atIiuXbvi6urKxo0brUGvaNGitG3bltGjR6fhHZCs5sdjP7Lp1CZcnV2Z3GTyI/e3dCncvg1ly0KTJnYoUERERESyrTTd5+vOnTsMGDCAJUuWWG+w7OzsTJcuXZg5c2aOuGGx7vOV9cSZ46g8tzKHrx3mrTpv8eGzHz5Sf4YBFSrA4cPwn//AgAF2KlREREREspSUZoMUrXY4Y8YMNm3aZP158ODB/PLLL3z//feEhYURFhbGt99+yy+//MKQIUMevXqRdPDZns84fO0w+T3y83a9tx+5vy1bLMErTx7o0sUOBYqIiIhItpai8FWvXj169erF0qVLAVi1ahULFiygRYsWeHl54eXlRcuWLZk/fz7ffPNNuhYskhYR0RGM3TIWgHENxuHj7vPIfc6ebXnu0gW8vR+5OxERERHJ5lIUvqpXr84ff/zB8uXLAYiMjEx0c2SAggULEhkZad8KRezg/W3vczXyKk/kf4I+NR59Pfhz52DNGsu2FtoQERERkZRI8U2WfX19+fHHHwGoXbs248aNIyoqyvr63bt3eeedd6hdu7b9qxR5BGfDzzJ953QAPmz6IS7OLg854uHmzQOzGRo0sHzvS0RERETkYVK12mHC/ZA+/vhjgoKCePzxx6lSpQoA+/fvx93dnfXr19u/SpFH8Pamt4mKi6JB8Qa8UPaFR+4vOhrmz7dsP+SWdCIiIiIiVmlaar5ixYocO3aMZcuWceTIEQA6duzIq6++ioeHh10LFHkUf174k2V/LQPsc0NlgG++gStXwN8fWrd+5O5EREREJIdI832+cufOTa9evexZi4hdGYbB0A1DAXit8mtU969ul34TFtp4/XVwefQrGEVEREQkh0hx+Pruu+9o0aIFLi4ufPfddw9s+8ILj35pl8ij+vbot/x65lfcc7nzXuP37NLnnj2wY4cldPXubZcuRURERCSHSHH4atOmDZcvX6ZgwYK0adMm2XYmk4n4+Hh71CaSZjHxMQzbMAyAwbUGU9S7qF36TZj1atsW/Pzs0qWIiIiI5BApDl9msznJbZHM6JP/+4RjN45RME9BRjwzwi593rgB/7vbghbaEBEREZFUS/FS8w8TFhZmr65EHsnNuzd555d3AJjQcAJ53fLapd9FiyAqCqpUgTp17NKliIiIiOQgaQpfH3zwAStWrLD+3L59e/Lly0eRIkXYv3+/3YoTSYtJv03ixt0bPOn7JD2e6mGXPs1mmDPHsh0SAnZYNFFEREREcpg0ha9PPvmEokUt36HZsGEDGzduZN26dbRo0YK33nrLrgWKpMbJmyeZuWsmAFOenUIupzQv6Glj3To4eRJ8fKBTJ7t0KSIiIiI5TJr+Mr18+bI1fP3www+8/PLLNGvWjICAAAIDA+1aoEhqjNw0kpj4GJqWbErz0s3t1m/CQhvdukGePHbrVkRERERykDTNfD322GOcO3cOgHXr1tG0aVPAcl8lrXQojrLj3A6+/vtrTJiY8uwUu9xQGeDECfjpJ8t237526VJEREREcqA0zXy99NJLdOrUiTJlynD9+nVatGgBwN69eyldurRdCxRJCcMwGPzzYAC6Ve1GFb8qdut77lwwDGjeHMqUsVu3IiIiIpLDpCl8TZ8+nYCAAM6dO8eHH36Ip6cnAJcuXaJfv352LVAkJVYeWsnO8zvJ7ZKbiY0n2q3fyEhYsMCyHRJit25FREREJAdKU/hycXFh6NChifYPGjTokQsSSa3ouGhGbLTcy2tYnWH45/W3W99ffglhYRAQAP+b4BURERERSZMUh6/vvvuOFi1a4OLiwnfffffAti+88MIjFyaSUjN3zeRU2CkKexZmaJ3E/yiQVoYBs2ZZtvv1A2dnu3UtIiIiIjmQyTAMIyUNnZycuHz5MgULFsTJKfl1OkwmU45YdCMiIgJvb2/Cw8Px8vJydDk51vXI65T6TynCo8NZ8MICulfrbre+t2+HunXB3R3On4f8+e3WtYiIiIhkIynNBime+TKbzUluizjShF8mEB4dTuVClQmuEmzXvhOWl+/YUcFLRERERB5dmpaaF8kM/rn+D3P+bw4AU5tNxdnJftcFhobCypWWbS20ISIiIiL2kKbw9cYbb/Cf//wn0f5Zs2YxcODAR61JJEWGbxxOnDmOlmVa0rRkU7v2PX8+xMZCYCBUr27XrkVEREQkh0pT+Fq1ahV169ZNtL9OnTp88803j1yUyMP8cvoX1hxZg7PJmY+e/ciufcfFwbx5lu3+/e3atYiIiIjkYGkKX9evX8fb2zvRfi8vL65du/bIRYk8iNkwM+TnIQD0eqoXT/o+adf+v/vOssCGry+0b2/XrkVEREQkB0tT+CpdujTr1q1LtP+nn36iZMmSj1yUyIN8+deX7L60G09XT8Y3HG/3/hOWl+/ZE9zc7N69iIiIiORQabrJ8uDBg+nfvz9Xr16lcePGAGzatImpU6cyY8YMe9YnYuNu7F1GbhoJwMhnRlLIs5Bd+z90CLZsAScn6NPHrl2LiIiISA6XpvDVvXt3oqOjmTRpEhMnTgQgICCAuXPn0qVLF7sWKHKvGTtncC7iHEW9ijKo1iC79z/HsngiL7wAxYrZvXsRERERycFSfJPl5Fy9ehUPDw88PT3tVVOWoJssZ7wrd65Q+j+luRVzi6UvLuW1yq/Ztf+ICChSBG7fhg0boKl9F1AUERERkWwqpdkgzff5iouLY+PGjaxevZqE/Hbx4kVu376d1i5FHmjclnHcirlFDf8adKrUye79L11qCV5ly0KTJnbvXkRERERyuDRddnjmzBmaN2/O2bNniY6O5tlnnyVv3rx88MEHREdH88knn9i7TsnhDl09xKd7PgUsN1R2Mtn3/uCGAbNnW7ZDQsBksmv3IiIiIiJpm/l68803qVGjBjdv3sTDw8O6/8UXX2TTpk12K04kwVsb3sJsmGlTrg31i9e3e/9btsDhw+DpCcHBdu9eRERERCRtM1+//fYb27dvx9XV1WZ/QEAAFy5csEthIgk2ntzIj8d+JJdTLj5o+kG6jJEw69W5M+grfCIiIiKSHtI082U2m4mPj0+0//z58+TNm/eRixJJEG+OZ+jPQwHoW6MvT+R/wu5jnDsHa9ZYtkNC7N69iIiIiAiQxvDVrFkzm/t5mUwmbt++zbhx42jZsqW9ahNhyf4l7A/dj7ebN2MbjE2XMebNA7MZGjaEChXSZQgRERERkbRddjhlyhSaN2/Ok08+SVRUFJ06deLYsWMUKFCAL7/80t41Sg51J+YOozaPAmB0/dEUyF3A7mNER8P8+ZZtzXqJiIiISHpKU/gqWrQo+/fvZ8WKFezfv5/bt2/To0cPXn31VZsFOEQexZTtU7h0+xIlfEowoOaAdBnjm2/gyhXw94fWrdNlCBERERERIA3hKzY2lnLlyvHDDz/w6quv8uqrr6ZHXZLDXbx1kQ+3fwjA+03fxy2XW7qMk7DQRp8+4OKSLkOIiIiIiABp+M6Xi4sLUVFR6VGLiNXYLWOJjI2k1uO1aP9k+3QZY88e2LHDErp69UqXIURERERErNK04EZISAgffPABcXFx9q5HhAOhB1i4dyFguaGyKZ3ueJww69W2Lfj5pcsQIiIiIiJWafrO159//smmTZv4+eefqVSpEnny5LF5ffXq1XYpTnIewzAY+vNQDAzaP9meOkXrpMs4N27A8uWW7f7902UIEREREREbaQpfPj4+tG3b1t61iLDu+Do2nNyAq7Mr7zd9P93GWbgQoqKgShWokz75TkRERETERqrCl9ls5qOPPuKff/4hJiaGxo0bM378eK1wKHYRZ45j6AbLDZUH1BxAycdKpss4ZjPMnWvZDgmBdLqqUURERETERqq+8zVp0iTefvttPD09KVKkCP/5z38I0c2RxE4W7FnAoauHyOeRj1H1RqXbOOvWwcmT4OMDnTql2zAiIiIiIjZSFb6WLFnCnDlzWL9+PWvWrOH7779n2bJlmM3m9KpPcoiI6AjGbh0LwLgG43jM47F0G2vWLMtzt25w39cVRURERETSTarC19mzZ2nZsqX156ZNm2Iymbh48aLdC5Oc5cPfP+TKnSuUzleaPjX6pNs4J05YZr4A+vVLt2FERERERBJJVfiKi4vD3d3dZp+LiwuxsbF2LUpylnPh55i6YyoAHzb9EFdn13Qba+5cMAxo3hxKl063YUREREREEknVghuGYdC1a1fc3Nys+6KioujTp4/NcvNaal5SY9TmUUTFRVGvWD3alGuTbuNERsKCBZZtfVVRRERERDJaqsJXcHBwon2vvfaa3YqRnGf3xd0sPbAUgGlB09LthsoAX34JYWFQogS0aJFuw4iIiIiIJClV4WvRokXpVYfkQIZhMOTnIQC8WulVavjXSMex/l1oo29fcHZOt6FERERERJKUqu98idjTd0e/45czv+Dm7MZ7Td5L17F27IB9+8DdHbp3T9ehRERERESSpPAlDhEbH8uwjcMAGFRrEMW8i6XreLNnW547doT8+dN1KBERERGRJDk8fM2ePZuAgADc3d0JDAxk165dybaNjY1lwoQJlCpVCnd3d6pUqcK6hHXDU9FnVFQUISEh5M+fH09PT9q2bUtoaKjdz02SN2/3PP65/g++uX0ZWW9kuo4VGgorV1q2tdCGiIiIiDiKQ8PXihUrGDx4MOPGjWPPnj1UqVKFoKAgrly5kmT70aNHM2/ePGbOnMmhQ4fo06cPL774Inv37k1Vn4MGDeL7779n5cqV/PLLL1y8eJGXXnop3c9XLMKiwhi/dTwA7zR8By83r3Qdb/58iI2FwECoXj1dhxIRERERSZbJMAzDUYMHBgby9NNPM+t/KyGYzWaKFi3KgAEDGDFiRKL2/v7+jBo1ipB7pi/atm2Lh4cHX3zxRYr6DA8Px9fXl+XLl9OuXTsAjhw5Qvny5dmxYwe1atVKUe0RERF4e3sTHh6Ol1f6hofsZtiGYXy0/SPKFyjPgb4HyOWUqnVfUiUuDgIC4MIFWLoUtDiniIiIiNhbSrOBw2a+YmJi2L17N02bNv23GCcnmjZtyo4dO5I8Jjo6OtFNnj08PNi2bVuK+9y9ezexsbE2bcqVK0exYsWSHTdh7IiICJuHpN6pm6f4+I+PAfjo2Y/SNXgBfPutJXj5+kL79uk6lIiIiIjIAzksfF27do34+HgKFSpks79QoUJcvnw5yWOCgoKYNm0ax44dw2w2s2HDBlavXs2lS5dS3Ofly5dxdXXFx8cnxeMCTJ48GW9vb+ujaNGiqT1lAUZuGklMfAxNSjShZZmW6T5ewkIbvXrBPfcGFxERERHJcA5fcCM1Pv74Y8qUKUO5cuVwdXWlf//+dOvWDSen9D+NkSNHEh4ebn2cO3cu3cfMbnae38mKv1dgwsSUZlPS9YbKAIcOwZYt4OQEr7+erkOJiIiIiDyUw8JXgQIFcHZ2TrTKYGhoKH5+fkke4+vry5o1a7hz5w5nzpzhyJEjeHp6UrJkyRT36efnR0xMDGFhYSkeF8DNzQ0vLy+bh6TcvTdUDq4aTFW/quk+ZsKs1wsvQLH0XcleREREROShHBa+XF1dqV69Ops2bbLuM5vNbNq0idq1az/wWHd3d4oUKUJcXByrVq2idevWKe6zevXquLi42LQ5evQoZ8+efei4knarDq9i+7nt5HbJzbuN3k338SIiYMkSy3b//uk+nIiIiIjIQ6XvagcPMXjwYIKDg6lRowY1a9ZkxowZ3Llzh27dugHQpUsXihQpwuTJkwH4448/uHDhAlWrVuXChQuMHz8es9nMsGHDUtynt7c3PXr0YPDgweTLlw8vLy8GDBhA7dq1U7zSoaROdFw0wzcOB2Bo7aEU8SqS7mMuXQq3b0O5ctC4cboPJyIiIiLyUA4NXx06dODq1auMHTuWy5cvU7VqVdatW2ddMOPs2bM23+eKiopi9OjRnDx5Ek9PT1q2bMnSpUttFs94WJ8A06dPx8nJibZt2xIdHU1QUBBz5szJsPPOaWb/OZuTN0/i5+nHW3XfSvfxDOPfSw779YN0/mqZiIiIiEiKOPQ+X1mZ7vOVMtcjr1N6ZmnCosL4rNVn9HiqR7qPuXkzNGkCnp6WZeb16xERERGR9JTp7/MlOcPEXycSFhVGpYKV6Fq1a4aMmTDr1bmzgpeIiIiIZB4KX5Jujl0/xuw/LUloSrMpODs5p/uY587BmjWW7ZCQdB9ORERERCTFFL4k3YzYNII4cxzNSzenWalmGTLmvHlgNkPDhlChQoYMKSIiIiKSIgpfki5+O/Mbqw+vxsnkxJRnp2TImNHRMH++ZVuzXiIiIiKS2Sh8id2ZDbP1hso9q/WkQsGMmYL65hu4cgWKFIH/3fpNRERERCTTUPgSu/vq4Ff8efFPPF09mdBoQoaNm7DQxuuvg4tLhg0rIiIiIpIiCl9iV3dj7zJy00gARtQdQSHPQg85wj727IEdOyyhq1evDBlSRERERCRVFL7Erv7zx384G36Wx70eZ1DtQRk2bsKsV7t24OeXYcOKiIiIiKSYwpfYzdU7V3lv23sATGo8idwuuTNk3Bs3YPlyy7YW2hARERGRzErhS+xm/NbxRERH8FThp3it8msZNu7ChRAVBVWqQJ06GTasiIiIiEiqKHyJXRy+eph5u+cBMLXZVJxMGfPRMpth7lzLdv/+YDJlyLAiIiIiIqmm8CV2MWzjMOKNeF4o+wINAxpm2Ljr1sHJk+DjA506ZdiwIiIiIiKppvAlj2zzqc388M8P5HLKxYdNP8zQsWfNsjx36wa5M+YrZiIiIiIiaaLwJY8k3hxvvaFyn+p9KFugbIaNfeKEZeYLoF+/DBtWRERERCRNFL7kkXxx4Av2Xd6Hl5sXYxuMzdCx584Fw4DmzaF06QwdWkREREQk1RS+JM0iYyMZtXkUAKPqjcI3j2/GjR0JCxZYtrW8vIiIiIhkBQpfkmZTt0/lwq0LFPcuzhuBb2To2F9+CWFhUKIEtGiRoUOLiIiIiKSJwpekyaVbl/jg9w8AeL/p+7jncs+wsQ3j34U2+vYFZ+cMG1pEREREJM0UviRNxm4Zy53YOwQWCaRDhQ4ZOvaOHbBvH7i7Q/fuGTq0iIiIiEiaKXxJqv0V+hcL9y0EYFrQNEwZfGfj2bMtzx07Qv78GTq0iIiIiEiaKXxJqr214S3Mhpl2T7ajTtE6GTp2aCisXGnZ1kIbIiIiIpKVKHxJqqw/vp71J9bj4uTC+03ez/Dx58+H2FioVQuqV8/w4UVERERE0kzhS1Is3hzP0A1DAehfsz+l8pXK0PHj4uCTTyzbmvUSERERkaxG4UtSbOHehRy8cpDH3B9jdP3RGT7+t9/ChQvg6wvt22f48CIiIiIij0ThS1LkVvQtxmwZA8DYBmPJ55Evw2tIWGijVy9wc8vw4UVEREREHonCl6TIh79/SOidUErnK02/p/tl+PiHDsGWLeDkBK+/nuHDi4iIiIg8MoUveajzEeeZumMqAB80/QBXZ9cMryFh1uuFF6BYsQwfXkRERETkkSl8yUON3jyau3F3eabYM7xY7sUMHz8iApYssWz375/hw4uIiIiI2IXClzzQ3kt7WbLfknymNpua4TdUBli6FG7fhnLloHHjDB9eRERERMQuFL4kWYZhMOTnIRgYdKzYkZpFajqghn8vOQwJAQdkPxERERERu1D4kmT98M8PbDm9BTdnNyY3meyQGrZsgcOHwdMTunRxSAkiIiIiInah8CVJio2P5a0NbwEwsNZAivsUd0gds2ZZnjt3Bi8vh5QgIiIiImIXCl+SpE93f8rR60cpkLsAI58Z6ZAazp2z3FgZLJccioiIiIhkZQpfkkh4VDjjfxkPwDsN38Hb3dshdcybB2YzNGwIFSo4pAQREREREbtR+JJEJm+bzLXIa5TNX5ZeT/VySA3R0fDpp5ZtzXqJiIiISHag8CU2ToedZsbOGQB89OxHuDi7OKSOb76Bq1ehSBFo3dohJYiIiIiI2JXCl9h4e9PbRMdH0yigEc8/8bzD6khYXv7118HFMflPRERERMSuFL7EateFXXx58EtMmBx2Q2WAPXtgxw5L6OrlmKseRURERETsTuFLAMsNlQevHwxAlypdqFa4msNqSZj1atcO/PwcVoaIiIiIiF0pfAkAqw+v5vdzv+ORy4N3G7/rsDpu3IDlyy3bWmhDRERERLIThS8hJj6G4RuHAzCk9hAe93rcYbUsXAhRUVC1KtSp47AyRERERETsTuFLmPPnHE7cPEGhPIUYVneYw+owm2HuXMt2SAg46CtnIiIiIiLpQuErh7tx9wYTfpkAwMRGE8nrltdhtaxbBydPgo8PdOrksDJERERERNKFwlcO9+6v73Iz6iYVC1ake7XuDq1l1izLc/fukDu3Q0sREREREbE7ha8c7PiN48zaZUk8U56dgrOTs+NqOW6Z+QLo29dhZYiIiIiIpBuFrxxsxMYRxJpjCSoVRFDpIIfWMncuGAY0bw6lSzu0FBERERGRdKHwlUP9fvZ3Vh1ehZPJiY+e/cihtURGWlY5BOjf36GliIiIiIikG4WvHMgwDIb8PASA7lW7U6lQJYfW8+WXEBYGJUpYZr5ERERERLIjha8caMXfK/jjwh/kccnDxMYTHVqLYfy70EbfvuDsuK+diYiIiIikK4WvHCYqLooRG0cAMLzucPw8/Rxaz44dsG8fuLtbVjkUEREREcmuFL5ymP/88R/OhJ+hSN4iDKkzxNHlMHu25bljR8if37G1iIiIiIikJ4eHr9mzZxMQEIC7uzuBgYHs2rXrge1nzJhB2bJl8fDwoGjRogwaNIioqCjr6wEBAZhMpkSPkJAQa5uGDRsmer1Pnz7pdo6ZxdU7V5n02yQAJjWeRG4Xx95MKzQUVq60bN/z6xERERERyZZyOXLwFStWMHjwYD755BMCAwOZMWMGQUFBHD16lIIFCyZqv3z5ckaMGMHChQupU6cO//zzD127dsVkMjFt2jQA/vzzT+Lj463HHDx4kGeffZb27dvb9NWrVy8mTJhg/Tl3Drir74RfJhARHUFVv6p0rtLZ0eUwfz7ExkKtWlC9uqOrERERERFJXw4NX9OmTaNXr15069YNgE8++YS1a9eycOFCRowYkaj99u3bqVu3Lp06dQIss1wdO3bkjz/+sLbx9fW1Oeb999+nVKlSNGjQwGZ/7ty58fNz7PedMtLRa0f5ZPcnAExtNhUnk2MnPePi4BNLOZr1EhEREZEcwWF/gcfExLB7926aNm36bzFOTjRt2pQdO3YkeUydOnXYvXu39dLEkydP8uOPP9KyZctkx/jiiy/o3r07JpPJ5rVly5ZRoEABKlasyMiRI4mMjHxgvdHR0URERNg8spJhG4cRZ47j+Seep3GJxo4uh2+/hQsXwNcX7puUFBERERHJlhw283Xt2jXi4+MpVKiQzf5ChQpx5MiRJI/p1KkT165d45lnnsEwDOLi4ujTpw9vv/12ku3XrFlDWFgYXbt2TdRP8eLF8ff358CBAwwfPpyjR4+yevXqZOudPHky77zzTupOMpPYenor3x39DmeTs8NvqJwgYaGNXr3Azc2xtYiIiIiIZASHXnaYWlu3buW9995jzpw5BAYGcvz4cd58800mTpzImDFjErVfsGABLVq0wN/f32Z/7969rduVKlWicOHCNGnShBMnTlCqVKkkxx45ciSDBw+2/hwREUHRokXtdGbpx2yYrTdUfr3665QrUM7BFcHff8OWLeDkBK+/7uhqREREREQyhsPCV4ECBXB2diY0NNRmf2hoaLLfxRozZgydO3emZ8+egCU43blzh969ezNq1CicnP69ivLMmTNs3LjxgbNZCQIDAwE4fvx4suHLzc0Ntyw4RfPFgS/Yc2kPXm5ejG843tHlADBnjuW5dWsoVsyxtYiIiIiIZBSHfefL1dWV6tWrs2nTJus+s9nMpk2bqF27dpLHREZG2gQsAGdnZwAMw7DZv2jRIgoWLMhzzz330Fr27dsHQOHChVNzCpleZGwkb2+yXJL59jNv45vH9yFHpL+ICFiyxLKthTZEREREJCdx6GWHgwcPJjg4mBo1alCzZk1mzJjBnTt3rKsfdunShSJFijB58mQAWrVqxbRp06hWrZr1ssMxY8bQqlUrawgDS4hbtGgRwcHB5Mple4onTpxg+fLltGzZkvz583PgwAEGDRpE/fr1qVy5csadfAaYvmM6F25doJh3Md6s9aajywEswev2bShXDho7ft0PEREREZEM49Dw1aFDB65evcrYsWO5fPkyVatWZd26ddZFOM6ePWsz0zV69GhMJhOjR4/mwoUL+Pr60qpVKyZNmmTT78aNGzl79izdu3dPNKarqysbN260Br2iRYvStm1bRo8enb4nm8Eu377M+7+/D8DkJpNxz+Xu4IrAMP695DAkBO5bgFJEREREJFszGfdfrycpEhERgbe3N+Hh4Xh5eTm6nERe//51Pt3zKU/7P83Onjsdfl8vgM2boUkT8PS0LDOfCd82EREREZFUS2k2cPxf5GJ3B68c5LO9nwEwLWhapgheALNmWZ47d1bwEhEREZGcJ3P8VS529daGtzAbZl4q/xLPFHvG0eUAcO6c5cbKoIU2RERERCRnUvjKZn4+8TPrjq/DxcmFD5p+4OhyrObNA7MZGjaEChUcXY2IiIiISMZT+MpG4s3xDP15KAAhT4dQOl9pB1dkER0Nn35q2e7f37G1iIiIiIg4isJXNrJ432L+uvIXPu4+jGkwxtHlWH3zDVy9CkWKWG6sLCIiIiKSEyl8ZRO3Y24zeotlufwx9ceQzyOfgyv61+zZlufXX4dcDr25gYiIiIiI4yh8ZRMf/f4Rl29fpuRjJQl5OvOsaLFnD+zYAS4u0KuXo6sREREREXEcha9s4ELEBT7a/hEAHzT9ALdcbg6u6F8Js17t2oGfn2NrERERERFxJIWvbGD0ltHcjbtL3aJ1aVu+raPLsbp+HZYvt2xreXkRERERyekUvrK4/Zf38/m+zwGY2mwqJpPJwRX9a9EiiIqCqlWhTh1HVyMiIiIi4lha/iCLK+9bnqnNpvLP9X8IfDzQ0eVYxcfD3LmW7ZAQyESZUERERETEIRS+sjhXZ1cG1R7k6DISWbcOTp4EHx/o1MnR1YiIiIiIOJ4uO5R0kbDQRvfukDu3Y2sREREREckMFL7E7o4ft8x8AfTt69haREREREQyC4Uvsbu5c8EwoEULKF3a0dWIiIiIiGQOCl9iV5GRsHChZVvLy4uIiIiI/EvhS+zqyy8hLAxKlIDmzR1djYiIiIhI5qHwJXZjGDBrlmW7Xz9wdnZsPSIiIiIimYnCl9jNjh2wbx+4u1tWORQRERERkX8pfIndJCwv37Ej5Mvn2FpERERERDIbhS+xi9BQWLnSst2/v2NrERERERHJjBS+xC7mz4fYWKhVC556ytHViIiIiIhkPgpf8sji4uCTTyzbWl5eRERERCRpCl/yyL79Fi5cAF9faN/e0dWIiIiIiGROCl/yyBIW2ujVC9zcHFuLiIiIiEhmpfAlj+Tvv2HLFnBygtdfd3Q1IiIiIiKZl8KXPJI5cyzPrVtDsWKOrUVEREREJDNT+JI0i4iAJUss21poQ0RERETkwRS+JM2WLIHbt6FcOWjc2NHViIiIiIhkbgpfkiaG8e8lhyEhYDI5th4RERERkcxO4UvSZMsWOHwYPD2hSxdHVyMiIiIikvkpfEmazJplee7SBby8HFuLiIiIiEhWoPAlqXb2rOXGygD9+jm2FhERERGRrELhS1Jt3jwwm6FhQ6hQwdHViIiIiIhkDQpfkirR0TB/vmW7f3/H1iIiIiIikpUofEmqfPMNXL0KRYpYbqwsIiIiIiIpo/AlqZKw0Mbrr0OuXI6tRUREREQkK1H4khTbswd27gQXF+jVy9HViIiIiIhkLQpfkmKzZ1ue27UDPz/H1iIiIiIiktUofEmKXL8Oy5dbtrXQhoiIiIhI6il8SYosWgRRUVC1KtSu7ehqRERERESyHoUveaj4eJg717IdEgImk2PrERERERHJihS+5KHWrYOTJ8HHBzp1cnQ1IiIiIiJZk8KXPFTCQhvdu0Pu3I6tRUREREQkq1L4kgc6ftwy8wXQt69jaxERERERycoUvuSB5s4Fw4AWLaB0aUdXIyIiIiKSdSl8SbIiI2HhQst2SIhjaxERERERyeoUviRZy5dDWBiUKAHNmzu6GhERERGRrE3hS5JkGP8utNGvHzg7O7YeEREREZGsTuFLkrRjB+zbB+7ullUORURERETk0Sh8SZJmzbI8d+oE+fI5thYRERERkezA4eFr9uzZBAQE4O7uTmBgILt27Xpg+xkzZlC2bFk8PDwoWrQogwYNIioqyvr6+PHjMZlMNo9y5crZ9BEVFUVISAj58+fH09OTtm3bEhoami7nlxWFhsI331i2tdCGiIiIiIh9ODR8rVixgsGDBzNu3Dj27NlDlSpVCAoK4sqVK0m2X758OSNGjGDcuHEcPnyYBQsWsGLFCt5++22bdhUqVODSpUvWx7Zt22xeHzRoEN9//z0rV67kl19+4eLFi7z00kvpdp5Zzfz5EBsLtWrBU085uhoRERERkewhlyMHnzZtGr169aJbt24AfPLJJ6xdu5aFCxcyYsSIRO23b99O3bp16dSpEwABAQF07NiRP/74w6Zdrly58PPzS3LM8PBwFixYwPLly2ncuDEAixYtonz58uzcuZNatWrZ8xSznLg4+OQTy3b//o6tRUREREQkO3HYzFdMTAy7d++madOm/xbj5ETTpk3ZsWNHksfUqVOH3bt3Wy9NPHnyJD/++CMtW7a0aXfs2DH8/f0pWbIkr776KmfPnrW+tnv3bmJjY23GLVeuHMWKFUt2XIDo6GgiIiJsHtnRt9/ChQvg6wvt2jm6GhERERGR7MNhM1/Xrl0jPj6eQoUK2ewvVKgQR44cSfKYTp06ce3aNZ555hkMwyAuLo4+ffrYXHYYGBjI4sWLKVu2LJcuXeKdd96hXr16HDx4kLx583L58mVcXV3x8fFJNO7ly5eTrXfy5Mm88847aT/hLCJheflevcDNzbG1iIiIiIhkJw5fcCM1tm7dynvvvcecOXPYs2cPq1evZu3atUycONHapkWLFrRv357KlSsTFBTEjz/+SFhYGF9//fUjjT1y5EjCw8Otj3Pnzj3q6WQ6f/8NW7aAkxP06ePoakREREREsheHzXwVKFAAZ2fnRKsMhoaGJvt9rTFjxtC5c2d69uwJQKVKlbhz5w69e/dm1KhRODklzpI+Pj488cQTHD9+HAA/Pz9iYmIICwuzmf160LgAbm5uuGXzqaA5cyzPrVtD0aKOrUVEREREJLtx2MyXq6sr1atXZ9OmTdZ9ZrOZTZs2Ubt27SSPiYyMTBSwnJ2dATAMI8ljbt++zYkTJyhcuDAA1atXx8XFxWbco0ePcvbs2WTHzQkiImDJEsu2lpcXEREREbE/h652OHjwYIKDg6lRowY1a9ZkxowZ3Llzx7r6YZcuXShSpAiTJ08GoFWrVkybNo1q1aoRGBjI8ePHGTNmDK1atbKGsKFDh9KqVSuKFy/OxYsXGTduHM7OznTs2BEAb29vevToweDBg8mXLx9eXl4MGDCA2rVr5+iVDpcsgdu3oVw5+N8ikCIiIiIiYkcODV8dOnTg6tWrjB07lsuXL1O1alXWrVtnXYTj7NmzNjNdo0ePxmQyMXr0aC5cuICvry+tWrVi0qRJ1jbnz5+nY8eOXL9+HV9fX5555hl27tyJr6+vtc306dNxcnKibdu2REdHExQUxJyEa+5yIMP4d6GNkBAwmRxbj4iIiIhIdmQykrteTx4oIiICb29vwsPD8fLycnQ5j2TTJmjaFDw9LcvMZ/HTERERERHJUCnNBllqtUNJHwmzXl26KHiJiIiIiKQXha8c7uxZy42VAfr1c2wtIiIiIiLZmcJXDjdvHpjN0KgRVKjg6GpERERERLIvha8cLDoa5s+3bGt5eRERERGR9KXwlYN98w1cvQpFilhurCwiIiIiIulH4SsHmzXL8tynD+Ry6E0HRERERESyP4WvHGrPHti5E1xcoFcvR1cjIiIiIpL9ab4jh0pYXr5dO/jfPa1FREQkEzGbzcTExDi6DBEBXFxccHZ2fuR+FL5yoOvXYflyy3b//o6tRURERBKLiYnh1KlTmM1mR5ciIv/j4+ODn58fJpMpzX0ofOVAixZBVBRUrQq1azu6GhEREbmXYRhcunQJZ2dnihYtipOTviUi4kiGYRAZGcmVK1cAKFy4cJr7UvjKYeLjYc4cy3ZICDxCcBcREZF0EBcXR2RkJP7+/uTOndvR5YgI4OHhAcCVK1coWLBgmi9B1D+l5DDr1sGpU+DjA506OboaERERuV98fDwArq6uDq5ERO6V8I8hsbGxae5D4SuHSVhoo3t30D+miYiIZF6P8r0SEbE/e/w3qfCVgxw/Dj/9ZLnUsG9fR1cjIiIiIpKzKHzlIHPnWp6bN4fSpR1bi4iIiEhGCggIYMaMGXbt8/Tp05hMJvbt22fXfnOyhg0bMnDgQEeXkW4UvnKIyEhYuNCyHRLi2FpEREQk++natSsmkwmTyYSrqyulS5dmwoQJxMXF2W2MRwk7f/75J71797ZbLZlJdgqBq1evZuLEiXbts2vXrrRp08aufaaVVjvMIZYvh7AwKFnSMvMlIiIiYm/Nmzdn0aJFREdH8+OPPxISEoKLiwsjR450dGn4+vo6ugSHi4mJyfQLueTLl8/RJaQrzXzlAIbx70IbffuCHW7OLSIiIhnEMODOHcc8DCN1tbq5ueHn50fx4sXp27cvTZs25bvvvgOSvpysTZs2dO3a1fpzQEAA7733Ht27dydv3rwUK1aMTz/91Pp6iRIlAKhWrRomk4mGDRsCllmtZ599lgIFCuDt7U2DBg3Ys2ePzVj3X3YYFhZGz5498fX1xcvLi8aNG7N///4Hnt+uXbuoVq0a7u7u1KhRg7179yZqc/DgQVq0aIGnpyeFChWic+fOXLt27YH9btu2jXr16uHh4UHRokV54403uHPnziO/LwkzPpMmTcLf35+yZcsCcO7cOV5++WV8fHzIly8frVu35vTp09b+Eo6bMmUKhQsXJn/+/ISEhNis8rd06VJq1KhB3rx58fPzo1OnTtb7YAFs3boVk8nE+vXrqVatGh4eHjRu3JgrV67w008/Ub58eby8vOjUqRORkZHW4+7/nERHRzN06FCKFClCnjx5CAwMZOvWrdbXFy9ejI+PD+vXr6d8+fJ4enrSvHlzLl26BMD48eP5/PPP+fbbb60zswnH//XXXzRu3BgPDw/y589P7969uX379gN/V49K4SsH2LED9u0Dd3fLKociIiKSdURGgqenYx73/E2cJh4eHsTExKTqmKlTp1qDTb9+/ejbty9Hjx4FLOEHYOPGjVy6dInVq1cDcOvWLYKDg9m2bRs7d+6kTJkytGzZklu3biU7Tvv27a1BYPfu3Tz11FM0adKEGzduJNn+9u3bPP/88zz55JPs3r2b8ePHM3ToUJs2YWFhNG7cmGrVqvF///d/rFu3jtDQUF5++eVk6zhx4gTNmzenbdu2HDhwgBUrVrBt2zb69+//yO8LwKZNmzh69CgbNmzghx9+IDY2lqCgIPLmzctvv/3G77//bg0s9/6utmzZwokTJ9iyZQuff/45ixcvZvHixdbXY2NjmThxIvv372fNmjWcPn3aJkgnGD9+PLNmzWL79u3W0DdjxgyWL1/O2rVr+fnnn5k5c2ay70///v3ZsWMHX331FQcOHKB9+/Y0b96cY8eOWdtERkYyZcoUli5dyq+//srZs2etv5uhQ4fy8ssvWwPZpUuXqFOnDnfu3CEoKIjHHnuMP//8k5UrV7Jx48ZE77vdGZIm4eHhBmCEh4c7upSH6tjRMMAwund3dCUiIiLyMHfv3jUOHTpk3L171zAMw7h92/L/44543L6d8rqDg4ON1q1bG4ZhGGaz2diwYYPh5uZmDB061DAMw2jQoIHx5ptv2hzTunVrIzg42Ppz8eLFjddee836s9lsNgoWLGjMnTvXMAzDOHXqlAEYe/fufWAt8fHxRt68eY3vv//epu/p06cbhmEYv/32m+Hl5WVERUXZHFeqVClj3rx5SfY5b948I3/+/Nbfi2EYxty5c23qmThxotGsWTOb486dO2cAxtGjR5Pst0ePHkbv3r1t9v3222+Gk5OTday0vi/BwcFGoUKFjOjoaOu+pUuXGmXLljXMZrN1X3R0tOHh4WGsX7/eelzx4sWNuLg4a5v27dsbHTp0SPIcDMMw/vzzTwMwbt26ZRiGYWzZssUAjI0bN1rbTJ482QCMEydOWPe9/vrrRlBQkPXnez8nZ86cMZydnY0LFy7YjNWkSRNj5MiRhmEYxqJFiwzAOH78uPX12bNnG4UKFbJ5HxI+mwk+/fRT47HHHjNu3/MhX7t2reHk5GRcvnw5yXO8/7/Ne6U0G+g7X9lcaCh8841lWwttiIiIZD25c0M6Xwn1wLFT44cffsDT05PY2FjMZjOdOnVi/PjxqeqjcuXK1m2TyYSfn5/N5WxJCQ0NZfTo0WzdupUrV64QHx9PZGQkZ8+eTbL9/v37uX37Nvnz57fZf/fuXU6cOJHkMYcPH6Zy5cq4u7tb99WuXTtRv1u2bMHT0zPR8SdOnOCJJ55IspYDBw6wbNky6z7DMDCbzZw6dYry5csDaXtfACpVqmTzPa/9+/dz/Phx8ubNa9MuKirK5twrVKiA8z3fVSlcuDB//fWX9eeE2b/9+/dz8+ZNzGYzAGfPnuXJJ5+0tru37kKFCpE7d25Klixpsy9h5u5+f/31F/Hx8Ynet+joaJvfXe7cuSlVqpRNrQ97bw4fPkyVKlXIkyePdV/dunUxm80cPXqUQoUKPfD4tFL4yubmz4fYWKhVC556ytHViIiISGqZTHDP34eZWqNGjZg7dy6urq74+/uTK9e/f2o6OTlh3Pclsnu/Q5TAxcXF5meTyWT9wz45wcHBXL9+nY8//pjixYvj5uZG7dq1k73k8fbt2xQuXNjmu0MJfHx8HjjWg9y+fZtWrVrxwQcfJHqtcOHCyR7z+uuv88YbbyR6rVixYtbttLwvgE24SBivevXqNmEvwb2LkjxovIRL9oKCgli2bBm+vr6cPXuWoKCgRO/5vf2YTKZUncft27dxdnZm9+7dNkEQsAm4SfV5/2cts1D4ysbi4uCTTyzb6X35qoiIiEiePHkonczNRH19fa2LIADEx8dz8OBBGjVqlOL+E2Zw4uPjbfb//vvvzJkzh5YtWwKWBSUetMjFU089xeXLl8mVKxcBAQEpGrt8+fIsXbqUqKgo6+zXzp07E/W7atUqAgICbILngzz11FMcOnQo2fctJZJ7X5Ibb8WKFRQsWBAvL680jXfkyBGuX7/O+++/T9GiRQH4v//7vzT19SDVqlUjPj6eK1euUK9evTT34+rqmui9KV++PIsXL+bOnTvWgPr777/j5ORkXZgkPWjBjWzs22/hwgXw9YV27RxdjYiIiORkjRs3Zu3ataxdu5YjR47Qt29fwsLCUtVHwYIF8fDwsC5kER4eDkCZMmVYunQphw8f5o8//uDVV1/Fw8Mj2X6aNm1K7dq1adOmDT///DOnT59m+/btjBo1KtkQ0alTJ0wmE7169eLQoUP8+OOPTJkyxaZNSEgIN27coGPHjvz555+cOHGC9evX061bt2SD0fDhw9m+fTv9+/dn3759HDt2jG+//TZVCz8k974k5dVXX6VAgQK0bt2a3377jVOnTrF161beeOMNzp8/n6LxihUrhqurKzNnzuTkyZN89913dr83F8ATTzzBq6++SpcuXVi9ejWnTp1i165dTJ48mbVr16a4n4CAAA4cOMDRo0e5du0asbGxvPrqq7i7uxMcHMzBgwfZsmULAwYMoHPnzul2ySEofGVrs2ZZnnv1Ajc3x9YiIiIiOVv37t0JDg6mS5cuNGjQgJIlS6Zq1gsgV65c/Oc//2HevHn4+/vTunVrABYsWMDNmzd56qmn6Ny5M2+88QYFCxZMth+TycSPP/5I/fr16datG0888QSvvPIKZ86cSfYPb09PT77//nv++usvqlWrxqhRoxJdXujv78/vv/9OfHw8zZo1o1KlSgwcOBAfHx+cnJL+s7ty5cr88ssv/PPPP9SrV49q1aoxduxY/P39H/l9SUru3Ln59ddfKVasGC+99BLly5enR48eREVFpXgmzNfXl8WLF7Ny5UqefPJJ3n///URB1F4WLVpEly5dGDJkCGXLlqVNmzb8+eefNpdkPkyvXr0oW7YsNWrUwNfXl99//53cuXOzfv16bty4wdNPP027du1o0qQJsxL+gE4nJiOzXhCZyUVERODt7U14eHiap2zT099/Q8WK4OQEp0/D/2aERUREJJOLiori1KlTlChRwmZxB3k0hQsXZuLEifTs2dPRpUgW9aD/NlOaDfSdr2xqzhzLc+vWCl4iIiKSc0VGRvL7778TGhpKhQoVHF2O5HC67DAbioiAJUss21poQ0RERHKyTz/9lFdeeYWBAwcmWhpeJKNp5isbWrLEcj+Q8uUhlZdSi4iIiGQrAwcOZODAgY4uQwTQzFe2Yxgwe7Zlu18/y71BRERERETE8RS+spnNm+HIEfD0hC5dHF2NiIiIiIgkUPjKZhJmvbp0gUy4CKOIiIiISI6l8JWNnD1rubEyWC45FBERERGRzEPhKxuZNw/MZssiG1pJVUREREQkc1H4yiaio2H+fMt2SIhjaxERERERkcQUvrKJlSvh6lUoUsRyY2URERER+VdAQAAzZsywa5+nT5/GZDKxb98+u/abkcaPH0/VqlUdXUaOofCVTSQstNGnD+TS3dtEREQkg3Xt2hWTyYTJZMLV1ZXSpUszYcIE4uLi7DbGo4SdP//8k969e9utFpG00J/p2cDu3bBzJ7i4QK9ejq5GREREcqrmzZuzaNEioqOj+fHHHwkJCcHFxYWRI0c6ujR8fX0dXYLdxMTE4Orq6ugyJA0085UNJMx6tW8PhQo5thYRERGxL8MwuBNzxyEPwzBSVaubmxt+fn4UL16cvn370rRpU7777jsAGjZsyMCBA23at2nThq5du1p/DggI4L333qN79+7kzZuXYsWK8emnn1pfL1GiBADVqlXDZDLRsGFDwDKr9eyzz1KgQAG8vb1p0KABe/bssRnr/ssOw8LC6NmzJ76+vnh5edG4cWP279//wPPbtWsX1apVw93dnRo1arB3795EbQ4ePEiLFi3w9PSkUKFCdO7cmWvXriXb5+LFi/Hx8WHNmjWUKVMGd3d3goKCOHfunLVNwqWBn332GSVKlMDd3T3JcwKoWrUq48ePT/V5zps3j6JFi5I7d25efvllwsPDra+l5P2VlNHMVxZ3/Tp8+aVlWwttiIiIZD+RsZF4TvZ0yNi3R94mj2ueNB/v4eHB9evXU3XM1KlTmThxIm+//TbffPMNffv2pUGDBpQtW5Zdu3ZRs2ZNNm7cSIUKFayzP7du3SI4OJiZM2diGAZTp06lZcuWHDt2jLx58yY5Tvv27fHw8OCnn37C29ubefPm0aRJE/755x/y5cuXqP3t27d5/vnnefbZZ/niiy84deoUb775pk2bsLAwGjduTM+ePZk+fTp3795l+PDhvPzyy2zevDnZc46MjGTSpEksWbIEV1dX+vXrxyuvvMLvv/9ubXP8+HFWrVrF6tWrcXZ2TvH7mZLzPH78OF9//TXff/89ERER9OjRg379+rFs2bI0v7+SNIWvLC4yEtq2hePHoXZtR1cjIiIiYpmt27RpE+vXr2fAgAGpOrZly5b0+98NS4cPH8706dPZsmULZcuWtV46mD9/fvz8/KzHNG7c2KaPTz/9FB8fH3755Reef/75RGNs27aNXbt2ceXKFdzc3ACYMmUKa9as4Ztvvknyu2HLly/HbDazYMEC3N3dqVChAufPn6dv377WNrNmzaJatWq899571n0LFy6kaNGi/PPPPzzxxBNJnnNsbCyzZs0iMDAQgM8//5zy5ctbwyZYLjVcsmRJqi6fTOl5RkVFsWTJEooUKQLAzJkzee6555g6dSp+fn6pfn8leQpfWVzRovDFFxAfDyaTo6sRERERe8vtkpvbI287bOzU+OGHH/D09CQ2Nhaz2UynTp1sLoFLicqVK1u3TSYTfn5+XLly5YHHhIaGMnr0aLZu3cqVK1eIj48nMjKSs2fPJtl+//793L59m/z589vsv3v3LidOnEjymMOHD1O5cmXrJX8Ate/7l+/9+/ezZcsWPD0Tz1SeOHEi2fCVK1cunn76aevP5cqVw8fHh8OHD1vDV/HixVP9vbWUnmexYsWswSvhvMxmM0ePHsXPzy/V768kT+Erm0jF7LOIiIj8f3v3HVXFtf4N/HtQ2hER6QcLx0owIhE1yI9YIWAJC2NcxnIN9oYSovEqNjReRc2NLdeSrETxGjUmUWJiNyqoqKhERRRQCIhGsKMCAgLP+0cu83qkW0DJ97MWazGz98zsvZ8zRx5nZs9rRKVSPdetf1WpW7duWL16NQwMDGBnZ4faT0zBrKenV+wZssePHxfbh76+vs6ySqVCYWFhmcf18/PDnTt3sHz5ctjb28PQ0BBubm7Iy8srsX5mZiY0Gg3Cw8OLlZmZmZV5rLJkZmbCx8cHixYtKlam0Wieeb8AUKdO8c9AeWP6ovpZ2fGl0jH5IiIiIqIXok6dOmjevHmJZVZWVkhLS1OWCwoKEBsbi27dulV4/0XPeBUUFOisj4yMxKpVq9CrVy8AwNWrV8uc5MLFxQXp6emoXbs2tFpthY7t6OiIDRs2ICcnR7n6deLEiWL73bp1K7RarU7iWZ78/HycPn1aucqVkJCAjIwMODo6lrnd02P64MEDJCcn67SnIv1MTU3F9evXYWdnp/RLT08PDg4OACo/vlQ6znZIRERERC9d9+7dsXPnTuzcuRPx8fEYN24cMjIyKrUPa2trGBsbY8+ePbhx44YyI1+LFi2wYcMGxMXFISoqCoMHD4axsXGp+/H09ISbmxv69OmDffv2ISUlBceOHcOMGTNw+vTpErcZNGgQVCoVRo0ahYsXL2LXrl3497//rVPH398fd+/excCBA3Hq1CkkJSVh7969GDZsWLGE8Un6+vqYOHEioqKiEB0djaFDh6Jjx45KMlaa7t27Y8OGDThy5AjOnz8PPz8/nck4KtpPIyMj+Pn54dy5czhy5AgCAgLQv39/5bm6yo4vlY7JFxERERG9dMOHD4efnx8++ugjdOnSBU2bNq3UVS/gr2ejVqxYga+++gp2dnbw9fUFAHz77be4d+8eXFxcMGTIEAQEBMDa2rrU/ahUKuzatQudO3fGsGHD0LJlSwwYMABXrlyBTSnv7TExMcGvv/6K8+fPo23btpgxY0ax2wvt7OwQGRmJgoICeHl5wcnJCYGBgTAzM4OeXul/dqvVakydOhWDBg2Cu7s7TExMsGXLlnLHIygoCF26dMF7772H3r17o0+fPmjWrFml+9m8eXP07dsXvXr1gpeXF9q0aYNVq1Yp5ZUdXyqdSir7AgcC8Ndl3Xr16uH+/fswNTWt7uYQERFRDZGTk4Pk5GSd9znR89NoNJg3bx5GjhxZ3U3RERoaisDAwEpfBaSqV9a5WdHcgM98EREREVGNlZ2djcjISNy4cQNvvvlmdTeH/uZ42yERERER1Vhff/01BgwYgMDAwGJTwxNVNd52+Ix42yERERG9DLztkOjV9CJuO+SVLyIiIiIioipQ7cnXypUrodVqYWRkBFdXV5w8ebLM+suWLYODgwOMjY3RqFEjfPLJJ8jJyVHKQ0JC0KFDB9StWxfW1tbo06cPEhISdPbRtWtXqFQqnZ+xY8e+lP4RERERPQvenET0ankR52S1Jl9btmzBpEmTEBwcjN9//x3Ozs7w9vbGzZs3S6y/adMmTJs2DcHBwYiLi8O3336LLVu2YPr06UqdiIgI+Pv748SJE9i/fz8eP34MLy8vZGVl6exr1KhRSEtLU34WL178UvtKREREVBFF72nKy8ur5pYQ0ZOys7MB/PVetmdVrbMdLlmyBKNGjcKwYcMAAGvWrMHOnTuxdu1aTJs2rVj9Y8eOwd3dHYMGDQIAaLVaDBw4EFFRUUqdPXv26GwTGhoKa2trREdHo3Pnzsp6tVqtvDiOiIiI6FVRu3ZtqNVq3Lp1C/r6+mW+H4qIXj4RQXZ2Nm7evAkzMzOdF1lXVrUlX3l5eYiOjkZQUJCyTk9PD56enjh+/HiJ2/zf//0fvvvuO5w8eRJvv/02/vjjD+zatQtDhgwp9ThFbz43NzfXWb9x40Z89913sLW1hY+PD2bNmgW1Wl3qfnJzc5Gbm6ssP3jwoEL9JCIiIqoMlUoFjUaD5ORkXLlypbqbQ0T/Y2Zm9twXb6ot+bp9+zYKCgqKvUXcxsYG8fHxJW4zaNAg3L59G++88w5EBPn5+Rg7dqzObYdPKiwsRGBgINzd3dG6dWud/djb28POzg4xMTGYOnUqEhISsG3btlLbGxISgrlz5z5DT4mIiIgqx8DAAC1atOCth0SvCH19/ee64lXktXrJcnh4OBYsWIBVq1bB1dUViYmJ+PjjjzFv3jzMmjWrWH1/f3/Exsbi6NGjOutHjx6t/O7k5ASNRgMPDw8kJSWhWbNmJR47KCgIkyZNUpYfPHiARo0avaCeEREREenS09PjVPNENUy1JV+WlpaoVasWbty4obP+xo0bpV7OmzVrFoYMGYKRI0cC+CtxysrKwujRozFjxgyde6InTJiAHTt24PDhw2jYsGGZbXF1dQUAJCYmlpp8GRoawtDQsML9IyIiIiIielK1PcFpYGCAdu3a4cCBA8q6wsJCHDhwoNS3j2dnZxd76LTo8l/R1I8iggkTJiAsLAwHDx5EkyZNym3L2bNnAQAajeZZukJERERERFSuar3tcNKkSfDz80P79u3x9ttvY9myZcjKylJmP/zoo4/QoEEDhISEAAB8fHywZMkStG3bVrntcNasWfDx8VGSMH9/f2zatAnbt29H3bp1kZ6eDgCoV68ejI2NkZSUhE2bNqFXr16wsLBATEwMPvnkE3Tu3Blt2rSpnoEgIiIiIqIar1qTrw8//BC3bt3C7NmzkZ6ejrfeegt79uxRJuFITU3VudI1c+ZMqFQqzJw5E3/++SesrKzg4+OD+fPnK3VWr14N4K8XKT9p3bp1GDp0KAwMDPDbb78piV6jRo3wwQcfYObMmZVqe9GVNs56SERERET091aUE5T3ImaV8PXpz+TatWuccIOIiIiIiBRXr14tc74JJl/PqLCwENevX0fdunWhUqmquzmvjaJZIq9evQpTU9Pqbg69QIxtzcXY1lyMbc3F2NZcjO2rSUTw8OFD2NnZlfli9NdqqvlXiZ6eXrmzKFLpTE1N+YVRQzG2NRdjW3MxtjUXY1tzMbavnnr16pVbp9pmOyQiIiIiIvo7YfJFRERERERUBZh8UZUyNDREcHAwX1hdAzG2NRdjW3MxtjUXY1tzMbavN064QUREREREVAV45YuIiIiIiKgKMPkiIiIiIiKqAky+iIiIiIiIqgCTLyIiIiIioirA5IteuDlz5kClUun8vPHGG0p5Tk4O/P39YWFhARMTE3zwwQe4ceNGNbaYSnP48GH4+PjAzs4OKpUKP//8s065iGD27NnQaDQwNjaGp6cnLl++rFPn7t27GDx4MExNTWFmZoYRI0YgMzOzCntBJSkvtkOHDi12Hvfo0UOnDmP7agoJCUGHDh1Qt25dWFtbo0+fPkhISNCpU5Hv4dTUVPTu3RtqtRrW1taYMmUK8vPzq7Ir9JSKxLZr167Fzt2xY8fq1GFsXz2rV69GmzZtlBcnu7m5Yffu3Uo5z9mag8kXvRRvvvkm0tLSlJ+jR48qZZ988gl+/fVX/Pjjj4iIiMD169fRt2/famwtlSYrKwvOzs5YuXJlieWLFy/GihUrsGbNGkRFRaFOnTrw9vZGTk6OUmfw4MG4cOEC9u/fjx07duDw4cMYPXp0VXWBSlFebAGgR48eOufx5s2bdcoZ21dTREQE/P39ceLECezfvx+PHz+Gl5cXsrKylDrlfQ8XFBSgd+/eyMvLw7Fjx7B+/XqEhoZi9uzZ1dEl+p+KxBYARo0apXPuLl68WCljbF9NDRs2xMKFCxEdHY3Tp0+je/fu8PX1xYULFwDwnK1RhOgFCw4OFmdn5xLLMjIyRF9fX3788UdlXVxcnACQ48ePV1EL6VkAkLCwMGW5sLBQbG1t5fPPP1fWZWRkiKGhoWzevFlERC5evCgA5NSpU0qd3bt3i0qlkj///LPK2k5lezq2IiJ+fn7i6+tb6jaM7evj5s2bAkAiIiJEpGLfw7t27RI9PT1JT09X6qxevVpMTU0lNze3ajtApXo6tiIiXbp0kY8//rjUbRjb10f9+vXlm2++4Tlbw/DKF70Uly9fhp2dHZo2bYrBgwcjNTUVABAdHY3Hjx/D09NTqfvGG2+gcePGOH78eHU1l55BcnIy0tPTdWJZr149uLq6KrE8fvw4zMzM0L59e6WOp6cn9PT0EBUVVeVtpsoJDw+HtbU1HBwcMG7cONy5c0cpY2xfH/fv3wcAmJubA6jY9/Dx48fh5OQEGxsbpY63tzcePHig/E88Vb+nY1tk48aNsLS0ROvWrREUFITs7GyljLF99RUUFOD7779HVlYW3NzceM7WMLWruwFU87i6uiI0NBQODg5IS0vD3Llz0alTJ8TGxiI9PR0GBgYwMzPT2cbGxgbp6enV02B6JkXxevKLvmi5qCw9PR3W1tY65bVr14a5uTnj/Yrr0aMH+vbtiyZNmiApKQnTp09Hz549cfz4cdSqVYuxfU0UFhYiMDAQ7u7uaN26NQBU6Hs4PT29xHO7qIyqX0mxBYBBgwbB3t4ednZ2iImJwdSpU5GQkIBt27YBYGxfZefPn4ebmxtycnJgYmKCsLAwtGrVCmfPnuU5W4Mw+aIXrmfPnsrvbdq0gaurK+zt7fHDDz/A2Ni4GltGRBU1YMAA5XcnJye0adMGzZo1Q3h4ODw8PKqxZVQZ/v7+iI2N1XnulmqG0mL75HOXTk5O0Gg08PDwQFJSEpo1a1bVzaRKcHBwwNmzZ3H//n389NNP8PPzQ0RERHU3i14w3nZIL52ZmRlatmyJxMRE2NraIi8vDxkZGTp1bty4AVtb2+ppID2Tong9PdvSk7G0tbXFzZs3dcrz8/Nx9+5dxvs107RpU1haWiIxMREAY/s6mDBhAnbs2IFDhw6hYcOGyvqKfA/b2tqWeG4XlVH1Ki22JXF1dQUAnXOXsX01GRgYoHnz5mjXrh1CQkLg7OyM5cuX85ytYZh80UuXmZmJpKQkaDQatGvXDvr6+jhw4IBSnpCQgNTUVLi5uVVjK6mymjRpAltbW51YPnjwAFFRUUos3dzckJGRgejoaKXOwYMHUVhYqPxBQK+Ha9eu4c6dO9BoNAAY21eZiGDChAkICwvDwYMH0aRJE53yinwPu7m54fz58zoJ9v79+2FqaopWrVpVTUeomPJiW5KzZ88CgM65y9i+HgoLC5Gbm8tztqap7hk/qOaZPHmyhIeHS3JyskRGRoqnp6dYWlrKzZs3RURk7Nix0rhxYzl48KCcPn1a3NzcxM3NrZpbTSV5+PChnDlzRs6cOSMAZMmSJXLmzBm5cuWKiIgsXLhQzMzMZPv27RITEyO+vr7SpEkTefTokbKPHj16SNu2bSUqKkqOHj0qLVq0kIEDB1ZXl+h/yortw4cP5dNPP5Xjx49LcnKy/Pbbb+Li4iItWrSQnJwcZR+M7atp3LhxUq9ePQkPD5e0tDTlJzs7W6lT3vdwfn6+tG7dWry8vOTs2bOyZ88esbKykqCgoOroEv1PebFNTEyUzz77TE6fPi3Jycmyfft2adq0qXTu3FnZB2P7apo2bZpERERIcnKyxMTEyLRp00SlUsm+fftEhOdsTcLki164Dz/8UDQajRgYGEiDBg3kww8/lMTERKX80aNHMn78eKlfv76o1Wp5//33JS0trRpbTKU5dOiQACj24+fnJyJ/TTc/a9YssbGxEUNDQ/Hw8JCEhASdfdy5c0cGDhwoJiYmYmpqKsOGDZOHDx9WQ2/oSWXFNjs7W7y8vMTKykr09fXF3t5eRo0apTOFsQhj+6oqKa4AZN26dUqdinwPp6SkSM+ePcXY2FgsLS1l8uTJ8vjx4yruDT2pvNimpqZK586dxdzcXAwNDaV58+YyZcoUuX//vs5+GNtXz/Dhw8Xe3l4MDAzEyspKPDw8lMRLhOdsTaISEam662xERERERER/T3zmi4iIiIiIqAow+SIiIiIiIqoCTL6IiIiIiIiqAJMvIiIiIiKiKsDki4iIiIiIqAow+SIiIiIiIqoCTL6IiIiIiIiqAJMvIqKXJCwsDD/88EN1N4OIiIheEUy+iIhegpMnTyIwMBAdO3as7qY8t/DwcKhUKmRkZFR3U2o8jvWLp9VqsWzZsupuBhERACZfRETlGjp0KFQqFRYuXKiz/ueff4ZKpSpW//79+xg5ciTCwsLQuHHjqmomPYOhQ4eiT58+1d2MV05NSgJPnTqF0aNHv9B9du3aFYGBgS90n0T098Dki4ioAoyMjLBo0SLcu3ev3Lr16tVDTEwMXFxcqqBlJcvLy6u2Y9dEjx8/ru4mvJJeh8+ZlZUV1Gp1dTeDiAgAky8iogrx9PSEra0tQkJCSq0zZ84cvPXWWzrrli1bBq1WqywXXWlZsGABbGxsYGZmhs8++wz5+fmYMmUKzM3N0bBhQ6xbt05nP1evXkX//v1hZmYGc3Nz+Pr6IiUlpdh+58+fDzs7Ozg4OAAAzp8/j+7du8PY2BgWFhYYPXo0MjMzy+zrrl270LJlSxgbG6Nbt246xyly9OhRdOrUCcbGxmjUqBECAgKQlZVV7tisXbsWjRs3homJCcaPH4+CggIsXrwYtra2sLa2xvz585VtUlJSoFKpcPbsWWVdRkYGVCoVwsPDlXWxsbHo2bMnTExMYGNjgyFDhuD27dtK+U8//QQnJydlDDw9PZGVlYU5c+Zg/fr12L59O1QqlbLfouNu2bIFXbp0gZGRETZu3Ig7d+5g4MCBaNCgAdRqNZycnLB582adfpZ2rKocawDYvn07XFxcYGRkhKZNm2Lu3LnIz89XylUqFb755hu8//77UKvVaNGiBX755Rdl3Lt16wYAqF+/PlQqFYYOHQrgrys+EyZMQGBgICwtLeHt7V2hGHTt2hUBAQH45z//CXNzc9ja2mLOnDk6bV6yZAmcnJxQp04dNGrUCOPHj9f5rIaGhsLMzAw7duyAg4MD1Go1+vXrh+zsbKxfvx5arRb169dHQEAACgoKlO2evu0wIyMDI0eOhJWVFUxNTdG9e3ecO3dOKS/6rG7YsAFarRb16tXDgAED8PDhQwB/nWsRERFYvny58rkpiltERATefvttGBoaQqPRYNq0aTrjTkQEISKiMvn5+Ymvr69s27ZNjIyM5OrVqyIiEhYWJk9+jQYHB4uzs7POtkuXLhV7e3udfdWtW1f8/f0lPj5evv32WwEg3t7eMn/+fLl06ZLMmzdP9PX1lePk5eWJo6OjDB8+XGJiYuTixYsyaNAgcXBwkNzcXGW/JiYmMmTIEImNjZXY2FjJzMwUjUYjffv2lfPnz8uBAwekSZMm4ufnV2pfU1NTxdDQUCZNmiTx8fHy3XffiY2NjQCQe/fuiYhIYmKi1KlTR5YuXSqXLl2SyMhIadu2rQwdOrTU/QYHB4uJiYn069dPLly4IL/88osYGBiIt7e3TJw4UeLj42Xt2rUCQE6cOCEiIsnJyQJAzpw5o+zn3r17AkAOHTqkLFtZWUlQUJDExcXJ77//Lu+++65069ZNRESuX78utWvXliVLlkhycrLExMTIypUr5eHDh/Lw4UPp37+/9OjRQ9LS0iQtLU1yc3OV42q1Wtm6dav88ccfcv36dbl27Zp8/vnncubMGUlKSpIVK1ZIrVq1JCoqqtxjVeVYHz58WExNTSU0NFSSkpJk3759otVqZc6cOUodANKwYUPZtGmTXL58WQICAsTExETu3Lkj+fn5snXrVgEgCQkJkpaWJhkZGSIi0qVLFzExMZEpU6ZIfHy8xMfHlxuDou1MTU1lzpw5cunSJVm/fr2oVCrZt2+fUmfp0qVy8OBBSU5OlgMHDoiDg4OMGzdOKV+3bp3o6+vLu+++K7///rtERESIhYWFeHl5Sf/+/eXChQvy66+/ioGBgXz//ffKdvb29rJ06VJl2dPTU3x8fOTUqVNy6dIlmTx5slhYWMidO3d0PqtF583hw4fF1tZWpk+fLiIiGRkZ4ubmJqNGjVI+N/n5+XLt2jVRq9Uyfvx4iYuLk7CwMLG0tJTg4OBSY0VEfz9MvoiIylGUfImIdOzYUYYPHy4iz5582dvbS0FBgbLOwcFBOnXqpCzn5+dLnTp1ZPPmzSIismHDBnFwcJDCwkKlTm5urhgbG8vevXuV/drY2CjJmIjI119/LfXr15fMzExl3c6dO0VPT0/S09NL7GtQUJC0atVKZ93UqVN1EoIRI0bI6NGjdeocOXJE9PT05NGjRyXuNzg4WNRqtTx48EBZ5+3tLVqttthYhISEiEjFkq958+aJl5eXzrGuXr2qJA7R0dECQFJSUkps15OxLVJ03GXLlpW4zZN69+4tkydPFhEp91hPe1lj7eHhIQsWLNBZt2HDBtFoNMoyAJk5c6aynJmZKQBk9+7dIiJy6NAhnXYU6dKli7Rt21ZnXXkxKNrunXfe0anToUMHmTp1aol9EBH58ccfxcLCQllet26dAJDExERl3ZgxY0StVuskuN7e3jJmzBhl+cnk68iRI2Jqaio5OTk6x2rWrJl89dVXIlLyZ3XKlCni6uqqMw4ff/yxzj6mT59e7DxduXKlmJiY6HzGiejvrXbVXWMjInr9LVq0CN27d8enn376zPt48803oaf3/+/6trGxQevWrZXlWrVqwcLCAjdv3gQAnDt3DomJiahbt67OfnJycpCUlKQsOzk5wcDAQFmOi4uDs7Mz6tSpo6xzd3dHYWEhEhISYGNjU6xtcXFxcHV11Vnn5uams3zu3DnExMRg48aNyjoRQWFhIZKTk+Ho6Fhiv7VarU4fbGxsUKtWrWJjUdTvijh37hwOHToEExOTYmVJSUnw8vKCh4cHnJyc4O3tDS8vL/Tr1w/169cvd9/t27fXWS4oKMCCBQvwww8/4M8//0ReXh5yc3OV54mcnZ0rdayXNdbnzp1DZGSkzi2cBQUFyMnJQXZ2ttLeNm3aKOV16tSBqalphca+Xbt2xY5XVgxatmxZ7HgAoNFodI7322+/ISQkBPHx8Xjw4AHy8/OLtVmtVqNZs2bKNjY2NtBqtTrHLuszdO7cOWRmZsLCwkJn/aNHj3TOpac/q0+3tSRxcXFwc3PTmYTH3d0dmZmZuHbtGiffISIAAJMvIqJK6Ny5M7y9vREUFKQ8B1NET08PIqKzrqSJGvT19XWWVSpViesKCwsBAJmZmWjXrp3OH+BFrKyslN+fTLJepszMTIwZMwYBAQHFysr6A7Oy/S5Kyp4c06fHMzMzEz4+Pli0aFGx42k0GtSqVQv79+/HsWPHsG/fPnz55ZeYMWMGoqKi0KRJkzL7+fR4fv7551i+fDmWLVumPJsUGBioTDrxPMcqzbOMdWZmJubOnYu+ffsWKzMyMlJ+L2vsy/L0uJQXg4ocLyUlBe+99x7GjRuH+fPnw9zcHEePHsWIESOQl5enJF+V/Qw9LTMzExqNRueZwSJmZmYVaisR0fNg8kVEVEkLFy7EW2+9pUxqUcTKygrp6ekQEeV/v5+cLOJZubi4YMuWLbC2toapqWmFt3N0dERoaCiysrKUP5gjIyOhp6dXrO1PblM08UKREydOFGvPxYsX0bx580r2pHKKEsu0tDS0bdsWQPHxdHFxwdatW6HValG7dsn/pKlUKri7u8Pd3R2zZ8+Gvb09wsLCMGnSJBgYGOhMzlCWyMhI+Pr64h//+AcAoLCwEJcuXUKrVq0qdKynvayxdnFxQUJCwnPFp+gKakXGpiIxKE90dDQKCwvxxRdfKEn3y3hBuYuLC9LT01G7dm2diXAqq6TPjaOjI7Zu3apz/kdGRqJu3bpo2LDh8zSbiGoQznZIRFRJTk5OGDx4MFasWKGzvmvXrrh16xYWL16MpKQkrFy5Ert3737u4w0ePBiWlpbw9fXFkSNHkJycjPDwcAQEBODatWtlbmdkZAQ/Pz/Exsbi0KFDmDhxIoYMGVLiLYcAMHbsWFy+fBlTpkxBQkICNm3ahNDQUJ06U6dOxbFjxzBhwgScPXsWly9fxvbt2zFhwoTn7uuTjI2N0bFjRyxcuBBxcXGIiIjAzJkzder4+/vj7t27GDhwIE6dOoWkpCTs3bsXw4YNQ0FBAaKiorBgwQKcPn0aqamp2LZtG27duqXcrqfVahETE4OEhATcvn27zCnlW7RooVzZiouLw5gxY3Djxg2lvLxjPe1ljfXs2bPx3//+F3PnzsWFCxcQFxeH77//vtjYlcXe3h4qlQo7duzArVu3ypwhs7wYVETz5s3x+PFjfPnll/jjjz+wYcMGrFmzpsLtrShPT0+4ubmhT58+2LdvH1JSUnDs2DHMmDEDp0+frvB+tFotoqKikJKSgtu3b6OwsBDjx4/H1atXMXHiRMTHx2P79u0IDg7GpEmTdG6tJaK/N34bEBE9g88++6zYbUiOjo5YtWoVVq5cCWdnZ5w8efK5ng0rolarcfjwYTRu3Bh9+/aFo6MjRowYgZycnDKvhKnVauzduxd3795Fhw4d0K9fP3h4eOA///lPqds0btwYW7duxc8//wxnZ2esWbMGCxYs0KnTpk0bRERE4NKlS+jUqRPatm2L2bNnw87O7rn7+rS1a9ciPz8f7dq1Q2BgIP71r3/plNvZ2SEyMhIFBQXw8vKCk5MTAgMDYWZmBj09PZiamuLw4cPo1asXWrZsiZkzZ+KLL75Az549AQCjRo2Cg4MD2rdvDysrK0RGRpbalpkzZ8LFxQXe3t7o2rUrbG1tdV7QXN6xnvayxtrb2xs7duzAvn370KFDB3Ts2BFLly6Fvb19ecOtaNCgAebOnYtp06bBxsamzGSvvBhUhLOzM5YsWYJFixahdevW2LhxY5mvdXhWKpUKu3btQufOnTFs2DC0bNkSAwYMwJUrV0r9D4mSfPrpp6hVqxZatWoFKysrpKamokGDBti1axdOnjwJZ2dnjB07FiNGjKhU0ktENZ9Knn5AgYiIiIiIiF44XvkiIiIiIiKqAky+iIiIiIiIqgCTLyIiIiIioirA5IuIiIiIiKgKMPkiIiIiIiKqAky+iIiIiIiIqgCTLyIiIiIioirA5IuIiIiIiKgKMPkiIiIiIiKqAky+iIiIiIiIqgCTLyIiIiIioirw/wDyUQDk3P3KMAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Puntaje de entrenamiento', color='blue')\n",
    "plt.plot(train_sizes, test_mean, label='Puntaje de prueba', color='green')\n",
    "plt.xlabel('Número de muestras de entrenamiento')\n",
    "plt.ylabel('Precisión')\n",
    "plt.title('Curva de Aprendizaje - Random Forest')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Justificación de la corrección del sobreajuste (Overfitting)\n",
    "\n",
    "**Antes de los ajustes**:  \n",
    "El puntaje de entrenamiento aumentaba rápidamente hasta 1, mientras que el puntaje de prueba crecía lentamente, lo que indicaba un sobreajuste. El modelo estaba memorizando los datos de entrenamiento pero no generalizaba bien a los datos de prueba.\n",
    "\n",
    "**Después de los ajustes**:  \n",
    "Se ajustaron parámetros como la profundidad máxima (`max_depth`), el número mínimo de muestras para dividir (`min_samples_split`) y para ser hoja (`min_samples_leaf`). Estos cambios ayudaron a limitar la complejidad del modelo y evitar que los árboles se ajustaran demasiado a los datos de entrenamiento.\n",
    "\n",
    "**Resultados**:  \n",
    "- El puntaje de entrenamiento ya no comenzó desde 1. En cambio, comenzó en 0.825 y fue creciendo de manera progresiva hasta estabilizarse cerca de 1 a partir de la iteración 120.\n",
    "- El puntaje de prueba mejoró de forma más continua y estable, indicando que el modelo estaba aprendiendo patrones más generales que se aplican tanto a los datos de entrenamiento como a los de prueba.\n",
    "\n",
    "**Conclusión**:  \n",
    "El ajuste de parámetros corrigió el sobreajuste, logrando un modelo más equilibrado con mejor capacidad de generalización, lo que se refleja en una curva de aprendizaje más estable entre los datos de entrenamiento y prueba.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Obtener las importancias\n",
    "importances = best_rf.feature_importances_\n",
    "\n",
    "# Obtener los nombres de las características desde el DataFrame\n",
    "feature_names = data.columns  # Asumiendo que 'data' es tu DataFrame con las características\n",
    "\n",
    "# Ordenar las características por su importancia\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Gráfico de importancia de características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Importancia de Características - Random Forest\")\n",
    "plt.barh(range(len(importances)), importances[indices], align=\"center\")\n",
    "plt.yticks(range(len(importances)), [feature_names[i] for i in indices])  # Usando los nombres de las características\n",
    "plt.xlabel(\"Importancia\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusiones del modelo\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminación de las columnas nota 1,2,y 3 y promedio notas\n",
    "Con el objetivo de tener modelos que generalicen mas allá de las columnas de las notas, no se dejará ninguna columna de moficiaciones. De esta forma, se podrá evaluar el rendimiento de los modelos y sus capacidades de predicción a partir de variables que no se encuentren tan relaciondadas.\n",
    "\n",
    "Mantener 4 columnas con calificaciones cuando el objetivo es precisamente observar quienes serán becados en base a percentil de la nota no tiene mucho sentido, puesto que se podría hacer prácticamente con un análisis matemático muy simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>educacion_madre</th>\n",
       "      <th>educacion_padre</th>\n",
       "      <th>tiempo_viaje</th>\n",
       "      <th>tiempo_estudio</th>\n",
       "      <th>relacion_familiar</th>\n",
       "      <th>tiempo_libre</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>...</th>\n",
       "      <th>independizado_padres_True</th>\n",
       "      <th>ocupacion_madre_True</th>\n",
       "      <th>ocupacion_padre_True</th>\n",
       "      <th>razon_matricula_True</th>\n",
       "      <th>interes_educacion_superior_True</th>\n",
       "      <th>internet_casa_True</th>\n",
       "      <th>apoyo_escolar_True</th>\n",
       "      <th>apoyo_familiar_True</th>\n",
       "      <th>actividades_True</th>\n",
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       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>18.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>587 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     edad  educacion_madre  educacion_padre  tiempo_viaje  tiempo_estudio  \\\n",
       "0    15.0              4.0              3.0           1.0             3.0   \n",
       "2    18.0              2.0              2.0           2.0             3.0   \n",
       "3    17.0              3.0              2.0           1.0             1.0   \n",
       "4    18.0              4.0              4.0           1.0             2.0   \n",
       "5    19.0              4.0              2.0           2.0             2.0   \n",
       "..    ...              ...              ...           ...             ...   \n",
       "590  17.0              2.0              1.0           3.0             1.0   \n",
       "591  18.0              2.0              3.0           2.0             3.0   \n",
       "592  17.0              4.0              3.0           2.0             2.0   \n",
       "593  17.0              3.0              4.0           3.0             2.0   \n",
       "594  18.0              2.0              3.0           1.0             4.0   \n",
       "\n",
       "     relacion_familiar  tiempo_libre  salir_amigos  \\\n",
       "0                  5.0           4.0           3.0   \n",
       "2                  4.0           2.0           1.0   \n",
       "3                  5.0           5.0           5.0   \n",
       "4                  2.0           4.0           4.0   \n",
       "5                  5.0           4.0           4.0   \n",
       "..                 ...           ...           ...   \n",
       "590                3.0           2.0           2.0   \n",
       "591                3.0           2.0           3.0   \n",
       "592                5.0           5.0           4.0   \n",
       "593                5.0           4.0           5.0   \n",
       "594                4.0           5.0           5.0   \n",
       "\n",
       "     consumo_alcohol_entre_semana  consumo_alcohol_fin_de_semana  ...  \\\n",
       "0                             1.0                            1.0  ...   \n",
       "2                             1.0                            1.0  ...   \n",
       "3                             2.0                            4.0  ...   \n",
       "4                             1.0                            1.0  ...   \n",
       "5                             1.0                            1.0  ...   \n",
       "..                            ...                            ...  ...   \n",
       "590                           1.0                            2.0  ...   \n",
       "591                           2.0                            2.0  ...   \n",
       "592                           1.0                            1.0  ...   \n",
       "593                           2.0                            4.0  ...   \n",
       "594                           1.0                            3.0  ...   \n",
       "\n",
       "     independizado_padres_True  ocupacion_madre_True  ocupacion_padre_True  \\\n",
       "0                            1                     1                     1   \n",
       "2                            1                     1                     1   \n",
       "3                            1                     1                     1   \n",
       "4                            1                     1                     1   \n",
       "5                            1                     1                     1   \n",
       "..                         ...                   ...                   ...   \n",
       "590                          1                     1                     1   \n",
       "591                          1                     1                     1   \n",
       "592                          1                     1                     1   \n",
       "593                          1                     1                     1   \n",
       "594                          1                     1                     1   \n",
       "\n",
       "     razon_matricula_True  interes_educacion_superior_True  \\\n",
       "0                       1                                1   \n",
       "2                       1                                1   \n",
       "3                       1                                1   \n",
       "4                       1                                1   \n",
       "5                       1                                1   \n",
       "..                    ...                              ...   \n",
       "590                     1                                1   \n",
       "591                     1                                1   \n",
       "592                     1                                1   \n",
       "593                     1                                1   \n",
       "594                     1                                1   \n",
       "\n",
       "     internet_casa_True  apoyo_escolar_True  apoyo_familiar_True  \\\n",
       "0                     1                   0                    1   \n",
       "2                     1                   0                    0   \n",
       "3                     1                   0                    1   \n",
       "4                     1                   1                    1   \n",
       "5                     1                   0                    1   \n",
       "..                  ...                 ...                  ...   \n",
       "590                   1                   1                    1   \n",
       "591                   1                   0                    1   \n",
       "592                   1                   0                    0   \n",
       "593                   1                   1                    1   \n",
       "594                   1                   0                    1   \n",
       "\n",
       "     actividades_True  relacion_romantica_True  \n",
       "0                   1                        0  \n",
       "2                   0                        1  \n",
       "3                   1                        0  \n",
       "4                   1                        1  \n",
       "5                   1                        1  \n",
       "..                ...                      ...  \n",
       "590                 1                        1  \n",
       "591                 0                        1  \n",
       "592                 0                        0  \n",
       "593                 1                        1  \n",
       "594                 1                        1  \n",
       "\n",
       "[587 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eliminar las columnas 'nota1', 'nota2' y 'nota3' y promedio notas\n",
    "data = data.drop(\n",
    "    columns=[\n",
    "        'nota1', \n",
    "        'nota2', \n",
    "        'nota3',\n",
    "        'promedio_notas'\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((410, 27), (177, 27), (410,), (177,))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data = data[\"beca\"]\n",
    "x_data = data.drop(\"beca\", axis=1)\n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x_data, \n",
    "    y_data, \n",
    "    test_size=TEST_SPLIT_SIZE, \n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "# Imprimos las dimensiones para comprobar la proporpoción \n",
    "x_train.shape, x_test.shape, y_train.shape , y_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Accuracy Scores: [0.86585366 0.85365854 0.86585366 0.86585366 0.86585366]\n",
      "Mean CV Accuracy: 0.8634146341463416\n",
      "\n",
      "Accuracy (Test): 0.8757062146892656\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.93       159\n",
      "           1       0.30      0.17      0.21        18\n",
      "\n",
      "    accuracy                           0.88       177\n",
      "   macro avg       0.61      0.56      0.57       177\n",
      "weighted avg       0.85      0.88      0.86       177\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear un pipeline con escalado\n",
    "pipeline_rlo = make_pipeline(\n",
    "    StandardScaler(), \n",
    "    LogisticRegression(max_iter=500)\n",
    ")\n",
    "\n",
    "# Validación cruzada\n",
    "cv_scores = cross_val_score(\n",
    "    pipeline_rlo, \n",
    "    x_train,\n",
    "    y_train, \n",
    "    cv=5, \n",
    "    scoring='accuracy'\n",
    ")\n",
    "print(\"Cross-Validation Accuracy Scores:\", cv_scores)\n",
    "print(\"Mean CV Accuracy:\", cv_scores.mean())\n",
    "\n",
    "# Entrenamiento y predicción\n",
    "pipeline_rlo.fit(x_train, y_train)\n",
    "y_pred = pipeline_rlo.predict(x_test)\n",
    "\n",
    "# Evaluación en datos de prueba\n",
    "accuracy_rlo = accuracy_score(y_test, y_pred)\n",
    "print(\"\\nAccuracy (Test):\", accuracy_rlo)\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Matriz de confusión\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=pipeline_rlo[-1].classes_)\n",
    "disp.plot(cmap='Blues')\n",
    "disp.ax_.set_title(\"Matriz de Confusión - Regresión Logística (Con Ajustes)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ................C=0.001, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=0.001, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ....................C=10.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.01, gamma=auto, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=0.1, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ....................C=1.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.1s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ..................C=10.0, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=1000.0, gamma=scale, kernel=poly; total time=   0.1s\n",
      "[CV] END ...................C=10.0, gamma=scale, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.1s[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.1s\n",
      "\n",
      "[CV] END .................C=0.01, gamma=scale, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...............C=0.001, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..................C=0.01, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ...................C=0.001, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.2s\n",
      "[CV] END .................C=0.001, gamma=auto, kernel=linear; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END .....................C=1.0, gamma=auto, kernel=poly; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...................C=100.0, gamma=scale, kernel=rbf; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END .................C=1.0, gamma=scale, kernel=sigmoid; total time=   0.0s\n",
      "[CV] END ..................C=1000.0, gamma=auto, kernel=poly; total time=   0.1s\n",
      "[CV] END .................C=100.0, gamma=auto, kernel=linear; total time=   0.2s\n",
      "Mejores parámetros SVM: {'kernel': 'linear', 'gamma': 'scale', 'C': np.float64(0.001)}\n",
      "Accuracy (Test) - SVM: 0.8983050847457628\n",
      "Classification Report - SVM:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95       159\n",
      "           1       0.00      0.00      0.00        18\n",
      "\n",
      "    accuracy                           0.90       177\n",
      "   macro avg       0.45      0.50      0.47       177\n",
      "weighted avg       0.81      0.90      0.85       177\n",
      "\n",
      "El modelo con el mayor accuracy_score es el Modelo 2 con un valor de 0.8983.\n",
      "La diferencia con los otros modelos es: ['0.0226']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7a96a8398210>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'C': np.logspace(-3, 3, 7),\n",
    "    'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],\n",
    "    'gamma': ['scale', 'auto']\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para SVM\n",
    "random_search_svm = RandomizedSearchCV(\n",
    "    estimator=SVC(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Ajustar el modelo\n",
    "random_search_svm.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_svm = random_search_svm.best_estimator_\n",
    "print(\"Mejores parámetros SVM:\", random_search_svm.best_params_)\n",
    "\n",
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_svm.predict(x_test)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy (Test) - SVM:\", accuracy_svm)\n",
    "print(\"Classification Report - SVM:\\n\", classification_report(y_test, y_pred))\n",
    "# Comparacion de modelos \n",
    "# Comparar ambos accuracies\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm)\n",
    "\n",
    "# Matriz de confusión\n",
    "ConfusionMatrixDisplay.from_estimator(best_svm, x_test, y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.4s\n",
      "Mejores parámetros Random Forest: {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 30}\n",
      "Accuracy (Test) - Random Forest: 0.8870056497175142\n",
      "Classification Report - Random Forest:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94       159\n",
      "           1       0.00      0.00      0.00        18\n",
      "\n",
      "    accuracy                           0.89       177\n",
      "   macro avg       0.45      0.49      0.47       177\n",
      "weighted avg       0.81      0.89      0.84       177\n",
      "\n",
      "El modelo con el mayor accuracy_score es el Modelo 2 con un valor de 0.8983.\n",
      "La diferencia con los otros modelos es: ['0.0226', '0.0113']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'max_features': ['sqrt', 'log2']  # Usar 'sqrt' o 'log2' en lugar de 'auto'\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para RandomForest\n",
    "random_search_rf = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Ajustar el modelo\n",
    "random_search_rf.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_rf = random_search_rf.best_estimator_\n",
    "print(\"Mejores parámetros Random Forest:\", random_search_rf.best_params_)\n",
    "\n",
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_rf.predict(x_test)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy (Test) - Random Forest:\", accuracy_rf)\n",
    "print(\"Classification Report - Random Forest:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm, accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    best_rf, \n",
    "    x_train, \n",
    "    y_train, \n",
    "    cv=5, \n",
    "    scoring='accuracy', \n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Promedio de los puntajes de entrenamiento y prueba\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "# Graficar la curva de aprendizaje\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Puntaje de entrenamiento', color='blue')\n",
    "plt.plot(train_sizes, test_mean, label='Puntaje de prueba', color='green')\n",
    "plt.xlabel('Número de muestras de entrenamiento')\n",
    "plt.ylabel('Precisión')\n",
    "plt.title('Curva de Aprendizaje - Random Forest')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=0.5, min_samples_leaf=2, min_samples_split=2, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100, oob_score=True; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=150, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=0.5, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200, oob_score=True; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200, oob_score=True; total time=   0.2s\n",
      "Mejores parámetros Random Forest: {'oob_score': True, 'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 10}\n",
      "Accuracy (Test) - Random Forest: 0.8983050847457628\n",
      "Classification Report - Random Forest:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95       159\n",
      "           1       0.00      0.00      0.00        18\n",
      "\n",
      "    accuracy                           0.90       177\n",
      "   macro avg       0.45      0.50      0.47       177\n",
      "weighted avg       0.81      0.90      0.85       177\n",
      "\n",
      "El modelo con el mayor accuracy_score es el Modelo 2 con un valor de 0.8983.\n",
      "La diferencia con los otros modelos es: ['0.0226']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/santi/anaconda3/envs/aa/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# Parámetros para la búsqueda aleatoria\n",
    "param_distributions = {\n",
    "    'n_estimators': [100, 150, 200],\n",
    "    'max_depth': [5, 10, 20, 30],  # Menor profundidad para evitar sobreajuste\n",
    "    'min_samples_split': [2, 10, 20],  # Aumentar el valor para evitar ajustes excesivos\n",
    "    'min_samples_leaf': [1, 2, 4],  # Aumentar para evitar aprender patrones triviales\n",
    "    'max_features': ['sqrt', 'log2', 0.5],  # Limitar las características usadas por cada árbol\n",
    "    'oob_score': [True],  # Habilitar Out-of-Bag scoring para validación interna\n",
    "}\n",
    "\n",
    "# RandomizedSearchCV para RandomForest\n",
    "random_search_rf = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=20,  # Número de combinaciones aleatorias\n",
    "    cv=5,       # Validación cruzada\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Ajustar el modelo\n",
    "random_search_rf.fit(x_train, y_train)\n",
    "\n",
    "# Mejor estimador y parámetros encontrados\n",
    "best_rf = random_search_rf.best_estimator_\n",
    "print(\"Mejores parámetros Random Forest:\", random_search_rf.best_params_)\n",
    "\n",
    "# Evaluación con los datos de prueba\n",
    "y_pred = best_rf.predict(x_test)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy (Test) - Random Forest:\", accuracy_rf)\n",
    "print(\"Classification Report - Random Forest:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Comparación de la precisión entre modelos\n",
    "comparar_accuracy(accuracy_rlo, accuracy_svm, accuracy_rf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    best_rf, x_train, y_train, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Promedio de los puntajes de entrenamiento y prueba\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "# Graficar la curva de aprendizaje\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Puntaje de entrenamiento', color='blue')\n",
    "plt.plot(train_sizes, test_mean, label='Puntaje de prueba', color='green')\n",
    "plt.xlabel('Número de muestras de entrenamiento')\n",
    "plt.ylabel('Precisión')\n",
    "plt.title('Curva de Aprendizaje - Random Forest')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Obtener las importancias\n",
    "importances = best_rf.feature_importances_\n",
    "\n",
    "# Obtener los nombres de las características desde el DataFrame\n",
    "feature_names = data.columns  # Asumiendo que 'data' es tu DataFrame con las características\n",
    "\n",
    "# Ordenar las características por su importancia\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Gráfico de importancia de características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Importancia de Características - Random Forest\")\n",
    "plt.barh(range(len(importances)), importances[indices], align=\"center\")\n",
    "plt.yticks(range(len(importances)), [feature_names[i] for i in indices])  # Usando los nombres de las características\n",
    "plt.xlabel(\"Importancia\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitud de importances: 27\n",
      "Longitud de feature_names: 28\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Obtener los coeficientes del SVC\n",
    "# Para SVC multiclase, tomamos el valor absoluto promedio de los coeficientes\n",
    "importances = np.abs(best_svm.coef_).mean(axis=0)\n",
    "\n",
    "# Imprimir información de diagnóstico\n",
    "print(f\"Longitud de importances: {len(importances)}\")\n",
    "print(f\"Longitud de feature_names: {len(data.columns)}\")\n",
    "\n",
    "# Obtener los nombres de las características y asegurar que coincidan\n",
    "feature_names = np.array(data.columns[:len(importances)])\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Verificar que los índices están dentro del rango\n",
    "indices = indices[:len(feature_names)]\n",
    "n_features = len(indices)\n",
    "\n",
    "# Gráfico de importancia de características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Importancia de Características - SVC\")\n",
    "plt.barh(range(n_features), importances[indices], align=\"center\")\n",
    "plt.yticks(range(n_features), feature_names[indices])\n",
    "plt.xlabel(\"Magnitud del Coeficiente\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitud de importances: 27\n",
      "Longitud de feature_names: 28\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Longitud de importances: {len(importances)}\")\n",
    "print(f\"Longitud de feature_names: {len(data.columns)}\")\n",
    "\n",
    "# Obtener los nombres de las características y asegurar que coincidan\n",
    "feature_names = np.array(data.columns[:len(importances)])\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Verificar que los índices están dentro del rango\n",
    "indices = indices[:len(feature_names)]\n",
    "n_features = len(indices)\n",
    "\n",
    "# Gráfico de importancia de características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Importancia de Características - Regresión Logística\")\n",
    "plt.title(\"Importancia de Características - Random Forest\")\n",
    "plt.yticks(range(n_features), feature_names[indices])\n",
    "plt.xlabel(\"Magnitud del Coeficiente\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Persistencia modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear las carpetas si no existen\n",
    "directorios = [\n",
    "    'Persistencia',\n",
    "    'Persistencia/Regresion',\n",
    "    'Persistencia/Clasificacion',\n",
    "    'Persistencia/Clusterizacion',\n",
    "]\n",
    "\n",
    "for directorio in directorios:\n",
    "    if not os.path.exists(directorio):\n",
    "        os.makedirs(directorio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardar los modelos entrenados\n",
    "joblib.dump(pipeline_rlo, 'Persistencia/Clasificacion/modelo_regresion_logistica.pkl')\n",
    "joblib.dump(best_svm, 'Persistencia/Clasificacion/modelo_svm.pkl')\n",
    "joblib.dump(best_rf, 'Persistencia/Clasificacion/modelo_random_forest.pkl')\n",
    "\n",
    "# Guardar el conjunto de datos procesados\n",
    "data.to_csv('Persistencia/Clasificacion/datos_procesados.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aa",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}