CreditCard_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning: Detección de Fraude en Tarjetas de Crédito\n",
    "Este proyecto aborda el problema de detección de transacciones fraudulentas en tarjetas de crédito, utilizando modelos de Machine Learning y técnicas de balanceo de clases. \n",
    "Se emplean métodos de detección de anomalías y estrategias de muestreo para mejorar la identificación de fraudes en un conjunto de datos altamente desbalanceado.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis y evaluación del Dataset\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Importar Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix, roc_curve, auc\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Cargar y analizar el dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargar los datos\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.dropna(inplace=True)\n",
    "df.drop(['Time'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>2.239053e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>1.088850e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>-2.458826e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>-5.354257e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>-9.291738e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>4.539234e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>2.374514e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 V1            V2            V3            V4            V5  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15  9.604066e-16   \n",
       "std    1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00  1.380247e+00   \n",
       "min   -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 -1.137433e+02   \n",
       "25%   -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 -6.915971e-01   \n",
       "50%    1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02 -5.433583e-02   \n",
       "75%    1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01  6.119264e-01   \n",
       "max    2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01  3.480167e+01   \n",
       "\n",
       "                 V6            V7            V8            V9           V10  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15  2.239053e-15   \n",
       "std    1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00  1.088850e+00   \n",
       "min   -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01 -2.458826e+01   \n",
       "25%   -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01 -5.354257e-01   \n",
       "50%   -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02 -9.291738e-02   \n",
       "75%    3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01  4.539234e-01   \n",
       "max    7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01  2.374514e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Análisis de las clases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud_cases = df['Class'].value_counts()\n",
    "sns.barplot(x=fraud_cases.index, y=fraud_cases.values)\n",
    "plt.title('Distribución de Clases')\n",
    "plt.xlabel('Clase')\n",
    "plt.ylabel('Número de Casos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=df[\"V1\"], y=df[\"V2\"], hue=df[\"Class\"], alpha=0.5, palette={0: \"blue\", 1: \"red\"})\n",
    "plt.title(\"Distribución de transacciones por variables V1 y V2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset desbalanceado:**\n",
    "* Clase 0 (No Fraude): 284315 transacciones\n",
    "* Clase 1 (Fraude): 492 transacciones\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Preprocesamiento de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividir en train y test\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Class', axis=1), df['Class'], train_size=0.7, random_state=42)\n",
    "# Estandarizar datos\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Modelo de Regresión Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9992626663389628\n",
      "Recall: 0.625\n",
      "Precision: 0.8762886597938144\n",
      "F1-score: 0.7296137339055794\n"
     ]
    }
   ],
   "source": [
    "# Entrenar modelo base\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train_scaled, y_train)\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "# Evaluación\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('F1-score:', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Modelo de Regresión Logística\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 85295 TN (True Negatives) → **85295 transacciones normales detectadas** correctamente.\n",
    "* 12 FP (False Positives) → **12 transacciones normales** clasificadas como fraude.\n",
    "* 51 FN (False Negatives) → **51 fraudes no detectados** (el modelo falló en ellos).\n",
    "* 85 TP (True Positives) → **85 fraudes detectados** correctamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Evaluación de las métricas**\n",
    "* **Specificity (Especificidad) = 0.99986 (~99.99%)**: Detecta perfectamente los casos normales.\n",
    "* **F1-score = 0.729 (~72.96%)**: Un balance entre precisión y recall, el valor no es malo, pero tampoco ideal.\n",
    "* **Accuracy (Exactitud) = 0.99926 (~99.93%)** Métrica engañosa porque el dataset está desbalanceado, la **alta exactitud no significa que detecte bien los fraudes**.\n",
    "* **Recall (Sensibilidad) = 0.625 (~62.5%):** De cada 100 fraudes reales, detecta 62 y deja escapar 38.**Queremos maximizar el Recall.**\n",
    "* **Precision (Precisión) = 0.876 (~87.6%)** : Cuando el modelo predice \"Fraude\", acierta el 87.6% de las veces."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métodos de Detección de Anomalías\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Determinante de Covarianza Mínima**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta etapa, evaluaremos cuál es el mejor nivel de contaminación para maximizar el Recall, asegurando que el modelo detecte la mayor cantidad posible de fraudes sin generar demasiados falsos positivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lista para almacenar las métricas de cada iteración\n",
    "results = []\n",
    "\n",
    "# Iterar sobre diferentes niveles de contaminación\n",
    "for c in np.arange(0.01, 0.3, 0.01):\n",
    "    # Aplicar el modelo EllipticEnvelope con la contaminación actual\n",
    "    mcd = EllipticEnvelope(contamination=c, random_state=42)\n",
    "    mcd.fit(X_train_scaled)\n",
    "\n",
    "    # Detectar outliers y filtrar los datos\n",
    "    y_pred_elli = mcd.predict(X_train_scaled)\n",
    "    mask = y_pred_elli != -1  # Filtrar solo los datos no considerados outliers\n",
    "    X_train_elli, y_train_elli = X_train_scaled[mask], y_train[mask]\n",
    "\n",
    "    # Entrenar el modelo de Regresión Logística\n",
    "    model_mcd = LogisticRegression()\n",
    "    model_mcd.fit(X_train_elli, y_train_elli)\n",
    "\n",
    "    # Predecir en el conjunto de prueba\n",
    "    y_pred_mcd = model_mcd.predict(X_test_scaled)\n",
    "\n",
    "    # Calcular métricas\n",
    "    metrics = {\n",
    "        \"Contaminación\": c,\n",
    "        \"Accuracy\": accuracy_score(y_test, y_pred_mcd),\n",
    "        \"Recall\": recall_score(y_test, y_pred_mcd),\n",
    "        \"Precision\": precision_score(y_test, y_pred_mcd),\n",
    "        \"Specificity\": recall_score(y_test, y_pred_mcd, pos_label=0),  # Negativos predichos correctamente\n",
    "        \"F1-score\": f1_score(y_test, y_pred_mcd)\n",
    "    }\n",
    "\n",
    "    results.append(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Contaminación</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.999368</td>\n",
       "      <td>0.808824</td>\n",
       "      <td>0.797101</td>\n",
       "      <td>0.999672</td>\n",
       "      <td>0.802920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.998818</td>\n",
       "      <td>0.816176</td>\n",
       "      <td>0.593583</td>\n",
       "      <td>0.999109</td>\n",
       "      <td>0.687307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.997507</td>\n",
       "      <td>0.808824</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.997808</td>\n",
       "      <td>0.508083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.995833</td>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.230392</td>\n",
       "      <td>0.996319</td>\n",
       "      <td>0.345588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.994862</td>\n",
       "      <td>0.573529</td>\n",
       "      <td>0.169935</td>\n",
       "      <td>0.995534</td>\n",
       "      <td>0.262185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.994160</td>\n",
       "      <td>0.213235</td>\n",
       "      <td>0.068884</td>\n",
       "      <td>0.995405</td>\n",
       "      <td>0.104129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.994125</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.995393</td>\n",
       "      <td>0.097122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.993949</td>\n",
       "      <td>0.213235</td>\n",
       "      <td>0.066059</td>\n",
       "      <td>0.995194</td>\n",
       "      <td>0.100870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.993984</td>\n",
       "      <td>0.191176</td>\n",
       "      <td>0.060465</td>\n",
       "      <td>0.995264</td>\n",
       "      <td>0.091873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.994043</td>\n",
       "      <td>0.227941</td>\n",
       "      <td>0.071264</td>\n",
       "      <td>0.995264</td>\n",
       "      <td>0.108581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.994008</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.062791</td>\n",
       "      <td>0.995276</td>\n",
       "      <td>0.095406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.994008</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.062791</td>\n",
       "      <td>0.995276</td>\n",
       "      <td>0.095406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.993984</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.995252</td>\n",
       "      <td>0.095070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.974673</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.975805</td>\n",
       "      <td>0.032200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.974205</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.016822</td>\n",
       "      <td>0.975336</td>\n",
       "      <td>0.031634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.973749</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.016521</td>\n",
       "      <td>0.974879</td>\n",
       "      <td>0.031102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.973433</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.015880</td>\n",
       "      <td>0.974574</td>\n",
       "      <td>0.029915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.973749</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.016077</td>\n",
       "      <td>0.974891</td>\n",
       "      <td>0.030264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.973620</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.015996</td>\n",
       "      <td>0.974762</td>\n",
       "      <td>0.030120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.974123</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.016317</td>\n",
       "      <td>0.975266</td>\n",
       "      <td>0.030688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.973199</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.015737</td>\n",
       "      <td>0.974340</td>\n",
       "      <td>0.029661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.972765</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.973906</td>\n",
       "      <td>0.029203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.972929</td>\n",
       "      <td>0.257353</td>\n",
       "      <td>0.015576</td>\n",
       "      <td>0.974070</td>\n",
       "      <td>0.029375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.972871</td>\n",
       "      <td>0.272059</td>\n",
       "      <td>0.016401</td>\n",
       "      <td>0.973988</td>\n",
       "      <td>0.030936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.973046</td>\n",
       "      <td>0.242647</td>\n",
       "      <td>0.014778</td>\n",
       "      <td>0.974211</td>\n",
       "      <td>0.027860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.972368</td>\n",
       "      <td>0.242647</td>\n",
       "      <td>0.014404</td>\n",
       "      <td>0.973531</td>\n",
       "      <td>0.027194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.971899</td>\n",
       "      <td>0.242647</td>\n",
       "      <td>0.014157</td>\n",
       "      <td>0.973062</td>\n",
       "      <td>0.026753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.971911</td>\n",
       "      <td>0.242647</td>\n",
       "      <td>0.014163</td>\n",
       "      <td>0.973074</td>\n",
       "      <td>0.026764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.971197</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.014208</td>\n",
       "      <td>0.972347</td>\n",
       "      <td>0.026888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Contaminación  Accuracy    Recall  Precision  Specificity  F1-score\n",
       "0            0.01  0.999368  0.808824   0.797101     0.999672  0.802920\n",
       "1            0.02  0.998818  0.816176   0.593583     0.999109  0.687307\n",
       "2            0.03  0.997507  0.808824   0.370370     0.997808  0.508083\n",
       "3            0.04  0.995833  0.691176   0.230392     0.996319  0.345588\n",
       "4            0.05  0.994862  0.573529   0.169935     0.995534  0.262185\n",
       "5            0.06  0.994160  0.213235   0.068884     0.995405  0.104129\n",
       "6            0.07  0.994125  0.198529   0.064286     0.995393  0.097122\n",
       "7            0.08  0.993949  0.213235   0.066059     0.995194  0.100870\n",
       "8            0.09  0.993984  0.191176   0.060465     0.995264  0.091873\n",
       "9            0.10  0.994043  0.227941   0.071264     0.995264  0.108581\n",
       "10           0.11  0.994008  0.198529   0.062791     0.995276  0.095406\n",
       "11           0.12  0.994008  0.198529   0.062791     0.995276  0.095406\n",
       "12           0.13  0.993984  0.198529   0.062500     0.995252  0.095070\n",
       "13           0.14  0.974673  0.264706   0.017143     0.975805  0.032200\n",
       "14           0.15  0.974205  0.264706   0.016822     0.975336  0.031634\n",
       "15           0.16  0.973749  0.264706   0.016521     0.974879  0.031102\n",
       "16           0.17  0.973433  0.257353   0.015880     0.974574  0.029915\n",
       "17           0.18  0.973749  0.257353   0.016077     0.974891  0.030264\n",
       "18           0.19  0.973620  0.257353   0.015996     0.974762  0.030120\n",
       "19           0.20  0.974123  0.257353   0.016317     0.975266  0.030688\n",
       "20           0.21  0.973199  0.257353   0.015737     0.974340  0.029661\n",
       "21           0.22  0.972765  0.257353   0.015480     0.973906  0.029203\n",
       "22           0.23  0.972929  0.257353   0.015576     0.974070  0.029375\n",
       "23           0.24  0.972871  0.272059   0.016401     0.973988  0.030936\n",
       "24           0.25  0.973046  0.242647   0.014778     0.974211  0.027860\n",
       "25           0.26  0.972368  0.242647   0.014404     0.973531  0.027194\n",
       "26           0.27  0.971899  0.242647   0.014157     0.973062  0.026753\n",
       "27           0.28  0.971911  0.242647   0.014163     0.973074  0.026764\n",
       "28           0.29  0.971197  0.250000   0.014208     0.972347  0.026888"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title 2.Mostrar Dataframe con  Métricas para cada tipo de Contaminación\n",
    "from IPython.display import display\n",
    "\n",
    "# Convertir a DataFrame\n",
    "metrics_df = pd.DataFrame(results)\n",
    "\n",
    "# Mostrar la tabla en el notebook\n",
    "display(metrics_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **C = Contamination**: Cuántos datos sospechosos descartamos.\n",
    "* **Samples Used:** Cuántos datos quedan después de descartar los outliers.\n",
    "* **Accuracy (Exactitud):** Cuántas predicciones hicimos bien en total.\n",
    "* **Recall (Sensibilidad):** De todos los fraudes reales, ¿cuántos detectamos?\n",
    "* **Precision (Precisión):** De todas las transacciones que dijimos que eran fraudes, ¿cuántas realmente eran fraudes?\n",
    "* **Specificity:** De todas las transacciones normales, ¿cuántas detectamos correctamente?\n",
    "* **F1-score:** Un balance entre precisión y recall (si es alto, el modelo es bueno detectando fraudes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Encontrar el mejor F1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor contaminación para Determinante de Covarianza: 0.01\n"
     ]
    }
   ],
   "source": [
    "best_row = metrics_df.loc[metrics_df[\"F1-score\"].idxmax()]\n",
    "best_c = best_row[\"Contaminación\"] #mejor contaminación\n",
    "\n",
    "print(f\"Mejor contaminación para Determinante de Covarianza: {best_c:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Entrenar nuevamente con la mejor contaminación seleccionada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_mcd = EllipticEnvelope(contamination=best_c, random_state=42)\n",
    "best_mcd.fit(X_train_scaled)\n",
    "\n",
    "y_pred_best = best_mcd.predict(X_train_scaled)\n",
    "mask_best = y_pred_best != -1  # Filtrar datos sin outliers\n",
    "X_train_best, y_train_best = X_train_scaled[mask_best], y_train[mask_best]\n",
    "\n",
    "# Entrenar el mejor modelo de Regresión Logística\n",
    "best_model_mcd = LogisticRegression()\n",
    "best_model_mcd.fit(X_train_best, y_train_best)\n",
    "\n",
    "# Predecir en el conjunto de prueba\n",
    "y_pred_best_mcd = best_model_mcd.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Matriz de Confusión: MCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S+BYDAwM0atRIaV9dXV2pt0WhUOD58+dIT09HgwYNcrz2e/bsiTJlykjPmzdvDgB5as/OnTthZmaGNm3aKL1Wzs7OMDEx+eDr/fjxY0RERMDT0xNmZmZSeZs2beDo6Fhgx1HlPQ3k/b3w2WefwcnJSeopAN59JuzatQudOnVSuiZU+Yxt2bJltvPPSdY609LS8OzZM9jb28Pc3DzHelX57MnIyEDv3r3x8uVL7N27F8bGxmqdy4f06dMHx48fR2xsLI4dO4bY2NgP/p7KSV7eDx+i7vtTLpdL47YyMjLw7NkzmJiYoHr16iq/Dv8lao++tLS0hJubG7Zu3Yo3b94gIyMD3bt3zzX+wIEDmDNnDiIiIpCSkiKVq7q+xse6ht83ZMgQ3LlzB2fOnPlol/z9+/eho6OT7ZZO9erVlZ7Hx8cjISEBa9euxdq1a3Os68mTJ7kex9TUFMC/v8Q+JD4+Hm/evMnWBgBwcHCAQqHAP//8g5o1a0rl73fvZn7Yv3jx4qPHy6uAgAB4enqiYsWKcHZ2RocOHdC/f3+lxOh99+/fh62tbbYPZgcHB2l7Vjl1U5cpU+aj5xEbG6v03MzMLNdf2Hnx6tUrAP/+Qrl16xZu3rwJS0vLHOM/9LMH1Lt+crvuDQ0Ns7XDzMwMFSpUyPbeMjMzy9M1kNO+ZcqUwV9//aVUtnHjRixcuBCRkZFIS0v7YFvzc03eunULiYmJud4a/dDrnXlNVatWLdu29z+883McVd7Tme3K63uhZ8+e+N///oeHDx/ik08+wfHjx/HkyRP07NlTaV9VPmPz+jn69u1b+Pv7Y8OGDXj48KHSLe3ExMRs8ar8nKdOnYpjx47h4MGD2T5zC+r3RYcOHVC6dGls374dERERaNiwIezt7XHv3r0815HX90NO8vP+VCgUWLp0KVatWoXo6Gil6daaur1cGPI15aNPnz4YMmQIYmNj0b59e5ibm+cYd+rUKXz55Zdo0aIFVq1aBRsbG+jr62PDhg3YunWrSsdU5ZfH0qVLsW3bNvzyyy8FuviVQqEAAHzzzTfw9PTMMaZOnTq57l+jRg0AwNWrVwtlUa7cenNELmNgMuX2hs5pbYEePXqgefPm2Lt3L/744w8sWLAA8+fPx549ez56nzOv1D0PGxsbpecbNmzAgAED1G7HtWvXoKurK31QKxQK1K5dG4sWLcoxvmLFih+sT53rJ7frPrfXSN3XLq/7/vLLLxgwYAA6d+6MCRMmoHz58tDV1YW/vz/u3LlToO1RKBQoX748tmzZkuP23JI/VeXnOPb29tDT08PVq1cLpC1Z9ezZE1OmTMHOnTvh7e2NHTt2wMzMDO3atZNiVP2Mzevn6OjRo7FhwwZ4e3vD1dUVZmZmkMlk6NWrl3QdZ5XXn/O+ffswf/58zJ49W+k81DmXD5HL5ejatSs2btyIu3fvqrUOTGG8l/JS59y5czFt2jQMGjQIs2fPhoWFBXR0dODt7Z3ja68t8pV0dOnSBd9++y3Onj2r1P33vt27d8PQ0BBHjhyBXC6Xyjds2JAttqBWFj116hTGjx8Pb29v9O3bN0/72NnZQaFQ4M6dO0o9C1FRUUpxlpaWKF26NDIyMtSa+tm+fXvo6uril19++ehgUktLS5QqVSpbGwAgMjISOjo6H/0ll1eZf5UkJCQoled2W8bGxgYjRozAiBEj8OTJE9SvXx/ff/99rkmHnZ0djh49ipcvXyr9hRcZGSltLwjBwcFKz7P2AqkqJiYGJ06cgKurq9TmTz/9FFeuXEHr1q0/er3mtD2/189/wa5du1C1alXs2bNH6RynT5+udp25vZaffvopjh49iqZNm6rcY5V5Td26dSvbtvffU/k5TqlSpfDFF1/g2LFj+Oeffz76nlTlvVClShU0atQI27dvx6hRo7Bnzx507txZ6bNUlc9YVezatQuenp5YuHChVJacnJztM0IVf//9Nzw9PdG5c2f873//y7a9oM+lT58++Pnnn6Gjo6M08Pa/bteuXWjVqhXWr1+vVJ6QkIBy5coVUavyL18T/U1MTLB69WrMmDEDnTp1yjVOV1cXMplM6S/me/fu5bhSo7Gxcb4uaODdfdwePXqgWbNmWLBgQZ73y/xl+f7smyVLlig919XVRbdu3bB79+4c593Hx8d/8DgVK1bEkCFD8Mcff2D58uXZtisUCixcuBAPHjyArq4u2rZti99++02pSzAuLg5bt25Fs2bNpK7d/DI1NUW5cuVw8uRJpfJVq1YpPc/IyMjWtVq+fHnY2toqdYW+r0OHDsjIyMCKFSuUyhcvXgyZTFZgPSRubm5Kj/d7PvLq+fPn6N27NzIyMpRWKOzRowcePnyIn376Kds+b9++xevXr6XnOV3P+b1+/gsy/1LL+pfZuXPnEBYWpnadmff033+9evTogYyMDMyePTvbPunp6R/8vLCxsYGTkxM2btyodM0GBwdnW202P8cB3iVcQgj069dPuiWXVXh4ODZu3AhA9fdCz549cfbsWfz88894+vRptlsrqnzGqkJXVzfbX/TLly9Xe2XVV69eoUuXLvjkk0+wcePGHBPNgj6XVq1aYfbs2VixYoXSDKD/upxe+507d+Lhw4dF1KKCke8VtXLrHs7Kw8MDixYtQrt27dCnTx88efIEK1euhL29fbb7Ys7Ozjh69CgWLVoEW1tbVKlSJduU0I8ZM2YM4uPjMXHiRPz6669K2+rUqZPrrQ8nJyf07t0bq1atQmJiIpo0aYKQkBDcvn07W+y8efPw559/wsXFBUOGDIGjoyOeP3+OS5cu4ejRo3j+/PkH27hw4ULcuXMHY8aMwZ49e9CxY0eUKVMGMTEx2LlzJyIjI6WsfM6cOQgODkazZs0wYsQI6Onp4ccff0RKSgoCAgJUem0+ZvDgwZg3bx4GDx6MBg0a4OTJk/j777+VYl6+fIkKFSqge/fuqFu3LkxMTHD06FFcuHBB6S+i93Xq1AmtWrXCd999h3v37qFu3br4448/8Ntvv8Hb2zvbfV1N+vvvv/HLL79ACIGkpCRcuXIFO3fuxKtXr6RrN1O/fv2wY8cODBs2DH/++SeaNm2KjIwMREZGYseOHThy5AgaNGgAIPfrOb/XT1Hr2LEj9uzZgy5dusDDwwPR0dFYs2YNHB0dc/yFmxfOzs4A3r1/3d3doauri169eqFly5b49ttv4e/vj4iICLRt2xb6+vq4desWdu7ciaVLl35wPJm/vz88PDzQrFkzDBo0CM+fP8fy5ctRs2ZNpbbm9zhNmjTBypUrMWLECNSoUQP9+vVDtWrV8PLlSxw/fhy///475syZA0D190KPHj0wfvx4jB8/XlpWPStVPmNV0bFjR2zevBlmZmZwdHREWFgYjh49qvaYgpkzZ+LGjRuYOnUqfvvtN6Vtn376KVxdXQv8XHR0dDB16lS12luUOnbsiFmzZmHgwIFo0qQJrl69ii1btnxw3JxWUGWqS9Ypsx+S05TZ9evXi2rVqgm5XC5q1KghNmzYkONU18jISNGiRQthZGSU4+JgOS1R+349mdOccnp8bHGwt2/fijFjxoiyZcsKY2PjDy4OFhcXJ0aOHCkqVqwo9PX1hbW1tWjdurVYu3btB4+RKT09Xaxbt040b95cmJmZCX19fWFnZycGDhyYbTrtpUuXhLu7uzAxMRGlSpUSrVq1EmfOnFGKye3nk9NUsJymzArxbqqal5eXMDMzE6VLlxY9evQQT548UTr/lJQUMWHCBFG3bl1psbK6deuKVatWKdWV0+JLL1++FD4+PsLW1lbo6+uLatWqfXBxsPfZ2dkV+LLiWa8PHR0dYW5uLurVqyfGjh2b47RCId5N4Zs/f7604FSZMmWEs7OzmDlzpkhMTJTicruehcjb9ZP5s8tpYaTcfoaZCxq97/335YcWQ8rpWFl/lgqFQsydO1fY2dkJuVwu6tWrJw4cOJAtLqepiZnef0+lp6eL0aNHC0tLSyGTybJ9Nqxdu1Y4OzsLIyMjUbp0aVG7dm0xceJE8ejRo2x1v2/37t3CwcFByOVy4ejomOviYPk9jhBChIeHiz59+kjXeJkyZUTr1q3Fxo0blaZA5vW9kKlp06YCgBg8eHCO2/P6GZvbeytzW9afyYsXL8TAgQOlxRLd3d1FZGRktvdhXj97PrRwY9b68nouOcntfZGVKlNm8/J++NDiYO/L6/szOTlZjBs3TtjY2AgjIyPRtGlTERYWJlq2bClatmz5wfP7L5MJkYfRMERERET5VCy+2p6IiIj++5h0EBERkUYw6SAiIiKNYNJBREREGsGkg4iIiDSCSQcRERFpBJMOIiIi0oh8r0hanBjVG1XUTSAqdC8urPh4EJGWMyzE3275/V3x9nLJfQ8y6SAiIlKFjDcJ1MWkg4iISBUF9G3oJRGTDiIiIlWwp0NtfOWIiIhII9jTQUREpAreXlEbkw4iIiJV8PaK2ph0EBERqYI9HWpj0kFERKQK9nSojUkHERGRKtjToTama0RERKQR7OkgIiJSBW+vqI1JBxERkSp4e0VtTDqIiIhUwZ4OtTHpICIiUgV7OtTGdI2IiEgVMp38PfIoIyMD06ZNQ5UqVWBkZIRPP/0Us2fPhhBCihFCwM/PDzY2NjAyMoKbmxtu3bqlVM/z58/Rt29fmJqawtzcHF5eXnj16pVSzF9//YXmzZvD0NAQFStWREBAQLb27Ny5EzVq1IChoSFq166NQ4cOqfjCMekgIiL6T5o/fz5Wr16NFStW4ObNm5g/fz4CAgKwfPlyKSYgIADLli3DmjVrcO7cORgbG8Pd3R3JyclSTN++fXH9+nUEBwfjwIEDOHnyJIYOHSptT0pKQtu2bWFnZ4fw8HAsWLAAM2bMwNq1a6WYM2fOoHfv3vDy8sLly5fRuXNndO7cGdeuXVPpnGQia8pUwhnVG1XUTSAqdC8urCjqJhAVOsNCHDxg1HJWvvZ/e8IvT3EdO3aElZUV1q9fL5V169YNRkZG+OWXXyCEgK2tLcaNG4fx48cDABITE2FlZYXAwED06tULN2/ehKOjIy5cuIAGDRoAAIKCgtChQwc8ePAAtra2WL16Nb777jvExsbCwMAAADB58mTs27cPkZGRAICePXvi9evXOHDggNSWxo0bw8nJCWvWrMnzubOng4iISBU6snw9UlJSkJSUpPRISUnJdpgmTZogJCQEf//9NwDgypUrCA0NRfv27QEA0dHRiI2NhZubm7SPmZkZXFxcEBYWBgAICwuDubm5lHAAgJubG3R0dHDu3DkppkWLFlLCAQDu7u6IiorCixcvpJisx8mMyTxOnl86laKJiIhKunyO6fD394eZmZnSw9/fP9thJk+ejF69eqFGjRrQ19dHvXr14O3tjb59+wIAYmNjAQBWVlZK+1lZWUnbYmNjUb58eaXtenp6sLCwUIrJqY6sx8gtJnN7XnH2ChERkSryOXtlypQp8PX1VSqTy+XZ4nbs2IEtW7Zg69atqFmzJiIiIuDt7Q1bW1t4enrmqw1FhUkHERGRKvK5TodcLs8xyXjfhAkTpN4OAKhduzbu378Pf39/eHp6wtraGgAQFxcHGxsbab+4uDg4OTkBAKytrfHkyROletPT0/H8+XNpf2tra8TFxSnFZD7/WEzm9rzi7RUiIqL/oDdv3kBHR/nXtK6uLhQKBQCgSpUqsLa2RkhIiLQ9KSkJ586dg6urKwDA1dUVCQkJCA8Pl2KOHTsGhUIBFxcXKebkyZNIS0uTYoKDg1G9enWUKVNGisl6nMyYzOPkFZMOIiIiVchk+XvkUadOnfD999/j4MGDuHfvHvbu3YtFixahS5cu/98MGby9vTFnzhz8/vvvuHr1Kvr37w9bW1t07twZAODg4IB27dphyJAhOH/+PE6fPo1Ro0ahV69esLW1BQD06dMHBgYG8PLywvXr17F9+3YsXbpU6RbQ2LFjERQUhIULFyIyMhIzZszAxYsXMWqUarM+eXuFiIhIFRpaBn358uWYNm0aRowYgSdPnsDW1hbffvst/Pz+nXI7ceJEvH79GkOHDkVCQgKaNWuGoKAgGBoaSjFbtmzBqFGj0Lp1a+jo6KBbt25YtmyZtN3MzAx//PEHRo4cCWdnZ5QrVw5+fn5Ka3k0adIEW7duxdSpU/G///0P1apVw759+1CrVi2VzonrdGTBdTqoJOA6HVQSFOo6He4/5Gv/t0fGF1BLtA97OoiIiFTBL3xTG5MOIiIiVfAL39TGdI2IiIg0gj0dREREquDtFbUx6SAiIlIFb6+ojUkHERGRKtjToTYmHURERKpg0qE2Jh1ERESq4O0VtTFdIyIiIo1gTwcREZEqeHtFbUw6iIiIVMHbK2pj0kFERKQK9nSojUkHERGRKtjToTYmHURERCqQMelQG/uIiIiISCPY00FERKQC9nSoj0kHERGRKphzqE3rk47k5GQYGhoWdTOIiKiEYE+H+rRyTIdCocDs2bPxySefwMTEBHfv3gUATJs2DevXry/i1hERUXEmk8ny9SjJtDLpmDNnDgIDAxEQEAADAwOpvFatWli3bl0RtoyIiIo7Jh3q08qkY9OmTVi7di369u0LXV1dqbxu3bqIjIwswpYRERFRbrRyTMfDhw9hb2+frVyhUCAtLa0IWkRERCVFSe+tyA+t7OlwdHTEqVOnspXv2rUL9erVK4IWERFRiSHL56ME08qeDj8/P3h6euLhw4dQKBTYs2cPoqKisGnTJhw4cKCom0dERMUYezrUp5U9HV999RX279+Po0ePwtjYGH5+frh58yb279+PNm3aFHXziIioGONAUvVpZdIBAM2bN0dwcDCePHmCN2/eIDQ0FG3bti3qZhERUTGnyaSjcuXKOdYxcuRIAO/Wqho5ciTKli0LExMTdOvWDXFxcUp1xMTEwMPDA6VKlUL58uUxYcIEpKenK8UcP34c9evXh1wuh729PQIDA7O1ZeXKlahcuTIMDQ3h4uKC8+fPq/bCQYuTDiIiouLuwoULePz4sfQIDg4GAHz99dcAAB8fH+zfvx87d+7EiRMn8OjRI3Tt2lXaPyMjAx4eHkhNTcWZM2ewceNGBAYGws/PT4qJjo6Gh4cHWrVqhYiICHh7e2Pw4ME4cuSIFLN9+3b4+vpi+vTpuHTpEurWrQt3d3c8efJEpfORCSFEfl4QTSlTpkyeM8Tnz5+rdQyjeqPU2o9Im7y4sKKom0BU6AwLccRi2f7b8rX/s0291d7X29sbBw4cwK1bt5CUlARLS0ts3boV3bt3BwBERkbCwcEBYWFhaNy4MQ4fPoyOHTvi0aNHsLKyAgCsWbMGkyZNQnx8PAwMDDBp0iQcPHgQ165dk47Tq1cvJCQkICgoCADg4uKChg0bYsWKd58fCoUCFStWxOjRozF58uQ8t19rBpIuWbJE+v+zZ88wZ84cuLu7w9XVFQAQFhaGI0eOYNq0aUXUQiIiKhHyOSwjJSUFKSkpSmVyuRxyufyD+6WmpuKXX36Br68vZDIZwsPDkZaWBjc3NymmRo0aqFSpkpR0hIWFoXbt2lLCAQDu7u4YPnw4rl+/jnr16iEsLEypjswYb29v6bjh4eGYMmWKtF1HRwdubm4ICwtT6dy1Junw9PSU/t+tWzfMmjULo0b92zMxZswYrFixAkePHoWPj09RNJGIiEqA/A4G9ff3x8yZM5XKpk+fjhkzZnxwv3379iEhIQEDBgwAAMTGxsLAwADm5uZKcVZWVoiNjZVisiYcmdszt30oJikpCW/fvsWLFy+QkZGRY4yqC3Jq5ZiOI0eOoF27dtnK27Vrh6NHjxZBi4iIqKTI70DSKVOmIDExUemRtRchN+vXr0f79u1ha2urgbMsHFqZdJQtWxa//fZbtvLffvsNZcuWLYIWERFRSZHfpEMul8PU1FTp8bFbK/fv38fRo0cxePBgqcza2hqpqalISEhQio2Li4O1tbUU8/5slsznH4sxNTWFkZERypUrB11d3RxjMuvIK61MOmbOnIlJkyahU6dOmDNnDubMmYNOnTph8uTJ2bqsiIiItN2GDRtQvnx5eHh4SGXOzs7Q19dHSEiIVBYVFYWYmBhpvKOrqyuuXr2qNMskODgYpqamcHR0lGKy1pEZk1mHgYEBnJ2dlWIUCgVCQkKkmLzSmjEdWQ0YMAAODg5YtmwZ9uzZAwBwcHBAaGgoXFxcirh1RERUrGl4fS+FQoENGzbA09MTenr//to2MzODl5cXfH19YWFhAVNTU4wePRqurq5o3LgxAKBt27ZwdHREv379EBAQgNjYWEydOhUjR46UeleGDRuGFStWYOLEiRg0aBCOHTuGHTt24ODBg9KxfH194enpiQYNGqBRo0ZYsmQJXr9+jYEDB6p0LlqZdADvpu9s2bKlqJtBREQljKZXFT169ChiYmIwaNCgbNsWL14MHR0ddOvWDSkpKXB3d8eqVauk7bq6ujhw4ACGDx8OV1dXGBsbw9PTE7NmzZJiqlSpgoMHD8LHxwdLly5FhQoVsG7dOri7u0sxPXv2RHx8PPz8/BAbGwsnJycEBQVlG1z6MVqzTkdWMTExH9xeqVIlterlOh1UEnCdDioJCnOdDushu/K1f+xP3QuoJdpHK3s6MpeFzU1GRoYGW0NERCVJSf/+lPzQyqTj8uXLSs/T0tJw+fJlLFq0CN9//30RtYqIiEoCJh3q08qko27dutnKGjRoAFtbWyxYsEBp3XkiIiL6b9DKpCM31atXx4ULF4q6GUREVJyxo0NtWpl0JCUlKT0XQuDx48eYMWMGqlWrVkStIiKikoC3V9SnlUmHubl5th+6EAIVK1bEr7/+WkStIiKikoBJh/q0Mun4888/lZ7r6OjA0tIS9vb2SgunEBERFTQmHerTyt/QLVu2LOomEBERkYq0MunIdOPGDcTExCA1NVWp/MsvvyyiFhERUbHHjg61aWXScffuXXTp0gVXr16FTCZD5qKqmV1eXByscOjoyDB1WAf07tAQVmVN8Tg+EZv3n8O8n4KkmLUzv0G/Lxsr7ffH6Rv4atS7ZXkr2VhgytB2+LzhZ1Id2w5dwPx1R5CW/u7n9t23HTB1WIdsx3/9NgXlmowDAOjp6WDCoLb4pqMLbMub4+/7cZi69DcEn7lZWKdPlKv1P/2IkOA/EB19F3JDQzg51YO373hUrlJVinkaH49FCwNw9swZvH7zGpUrV8GQocPg1tb9AzXTfxFvr6hPK5OOsWPHokqVKggJCUGVKlVw/vx5PHv2DOPGjcMPP/xQ1M0rtsYNaIMh3ZtjiN9m3LjzGM41K+HHGd8g6dVbrNp2Qoo7cvo6vp3+i/Q8JTVd+n/1KlbQkelg1JxfceefeNS0t8XKab1hbCTHlMV7AQBLNh3Ful2nlI596McxCL9+X3o+Y0Qn9PZoiBGztyIqOg5tmjhg+8IhaDVgEa5EPSisl4AoRxcvnEfP3n1Rs3ZtZKRnYPnSRRg2xAt7fj+IUqVKAQC++98kvExKwtIVq1GmTBkcOrgfE8Z5Y+uO3XBwcCziMyBVMOlQn1YmHWFhYTh27BjKlSsHHR0d6OjooFmzZvD398eYMWOyrVhKBaNx3ao4cOIvBIVeBwDEPH6OHu0aoEFNO6W41NR0xD17mWMdwWduKvVG3Hv4DJ/ZlceQr5tLScfrt6l4/fbfW2a1P/sEjp/aYMz3/85M6tOxEeavO4IjoTcAAD/tDMUXLjUwtt8XGDR1U8GcMFEerV67Xun5rO/noVVzV9y8cR3ODRoCAK5cvozv/Kajdp06AIChw0bgl00bcfP6dSYdWoZJh/p0iroB6sjIyEDp0qUBAOXKlcOjR48AAHZ2doiKiirKphVrZ6/cRatG1WFfqTyAd8mAq1NV/HH6hlJc8wbVcD/EH1f2TsPS//WEhZnxB+s1NTHC86Q3uW4f2KUJ/r4Xh9OX70hlBvp6SE5NU4p7m5yKJvU+VfW0iArcq5fvkm5TMzOprG69ejgSdBiJCQlQKBQ4fOggUlJT0KBho6JqJqlJJpPl61GSaWVPR61atXDlyhVUqVIFLi4uCAgIgIGBAdauXYuqVat+vAJSyw8bgmFqYogre6ciI0NAV1eG6SsP4NfDF6WY4DM38duxK7j38BmqViiHmaM74bcVw9HScyEUiuxfaFy1YjkM79VS6uV4n9xADz3bN8DCDcFK5UfDbmLMN18g9NJt3P3nKVo1qo6vvnCCrm7JfkNT0VMoFAiYPxdO9eqjWrXPpPIFC5dg4jgftGjqAj09PRgaGmLx0hWoZGf3gdqIihetTDqmTp2K169fAwBmzZqFjh07onnz5ihbtiy2b9+epzpSUlKQkpKiVCYUGZDp6BZ4e4uL7m3ro1f7hhjwv424cecx6lT/BAvGd8fj+ERs2X8OALDzSLgUf/32I1y99RA3D8xEiwbVcPz830r12Vqa4fcVI7Hn6GVs2Hsmx2N+9UVdlC5liF/+v/5M4xfswqppvXFlzzQIIXD3wVNs+v0sPL9qnGM9RJoyd85M3Ll1C4GbtyqVr1y+FC9fJmHt+kCYm5fBn8eOYuI4b2zYtAXVPqteRK0ltfBvG7VpZdLh7v7vaG97e3tERkbi+fPnKFOmTJ67rvz9/TFz5kylMl2rhtC3YVdnbuZ6d8YPG4KlxOL67UeoZGOBCQPbSEnH++49fIb4Fy/xaUVLpaTDxtIMQT+Nxdm/7mLk7G25HnNA5yY4fOoanjxXHiPy9MUr9PD9CXIDPZQ1M8aj+ETMGfMVoh8+K4AzJVLP3DmzcPLEcfy88RdYWVtL5f/ExODXrb9g928HYG//7qsaqteogUvhF/Hrti2YNn1WUTWZ1FDSb5Hkh9aN6UhLS4Oenh6uXbumVG5hYaHShTBlyhQkJiYqPfSsnAu6ucWKkaEBFEKhVJahENDRyf0y+qS8OcqaGSP26b/fl2NraYYjP43F5ZsxGDr9F2nK8/vsbMuiZcNqCNwXlmv9KanpeBSfCD09HXRu7YQDx/9S8ayI8k8IgblzZuFYSDB++nkjKlSoqLQ9OfktAEBHpvxe0dHRhcjhtiP9t3FMh/q0rqdDX18flSpVyvdaHHK5HHK5XKmMt1Y+7NDJq5jk5Y5/Hr/AjTuP4VSjAsZ80wqb9p0FABgbGeC7bztgX0gEYp8moWrFcvh+bGfc+eepNGPF1tIMR9aNRczj55iyaC8sy5hI9b8/48Wzc2PEPk3CkdPXs7WlYS072JY3x5WoB/ikvDm++7YDdHRkWBR4tBBfAaKczZ09E4cPHcCS5atgXMoYT+PjAQAmpUvD0NAQlatURaVKdpg90w++4yfB3Nwcx44dxdmw01i+6scibj2pqoTnDfkiE7n9mfkftn79euzZswebN2+GhYVFgdVrVG9UgdVVHJmUkmP6iI748ou6sCxjgsfxidgRFI65aw8jLT0DhnJ97Fg0FHVrVIB5aSM8jk/E0bBIzFp1QLo98k0nF/w0q1+O9Wd9/WUyGf4+NAtbDpzHjJX7s8U2c7bHsv/1RJVPyuHVmxQcOX0d05b9jsfxiYVz8sXIiwsriroJxU7dmjmPyZg1xx9fdekKALh//x6WLlqIy5fD8ebNG1SqWAn9Bw5Cpy87a7ClJYdhIf5JXW1C0MeDPuDWgnYF1BLto5VJR7169XD79m2kpaXBzs4OxsbKUzIvXbqkVr1MOqgkYNJBJQGTjv8mrbu9AgCdO3cu6iYQEVEJxdsr6tOqpOPnn39G3759MX369KJuChERlVAlfTBofmjV7JUhQ4YgMfHfe/a2tra4d+9e0TWIiIhKHJksf4+STKt6Ot4ffvLy5UsoFIpcoomIiAqejk4JzxzyQauSDiIioqJW0nsr8kOrbq+8v7AKF1ohIqLi7OHDh/jmm29QtmxZGBkZoXbt2rh48d/vuxJCwM/PDzY2NjAyMoKbmxtu3bqlVMfz58/Rt29fmJqawtzcHF5eXnj16pVSzF9//YXmzZvD0NAQFStWREBAQLa27Ny5EzVq1IChoSFq166NQ4cOqXw+WpV0CCHw2WefwcLCAhYWFnj16hXq1asnPc98EBERFRZNrUj64sULNG3aFPr6+jh8+DBu3LiBhQsXokyZMlJMQEAAli1bhjVr1uDcuXMwNjaGu7s7kpOTpZi+ffvi+vXrCA4OxoEDB3Dy5EkMHTpU2p6UlIS2bdvCzs4O4eHhWLBgAWbMmIG1a9dKMWfOnEHv3r3h5eWFy5cvo3PnzujcuXO21cE/+tpp0zodGzduzFOcp6enWvVznQ4qCbhOB5UEhblOR+1pwR8P+oCrs9vkKW7y5Mk4ffo0Tp06leN2IQRsbW0xbtw4jB8/HgCQmJgIKysrBAYGolevXrh58yYcHR1x4cIFNGjQAAAQFBSEDh064MGDB7C1tcXq1avx3XffITY2FgYGBtKx9+3bh8jISABAz5498fr1axw4cEA6fuPGjeHk5IQ1a9bk+dy1akyHuskEERFRQcnvbf2cvuU8p6/m+P333+Hu7o6vv/4aJ06cwCeffIIRI0ZgyJAhAIDo6GjExsbCzc1N2sfMzAwuLi4ICwtDr169EBYWBnNzcynhAAA3Nzfo6Ojg3Llz6NKlC8LCwtCiRQsp4QDefbHq/Pnz8eLFC5QpUwZhYWHw9fVVap+7uzv27dun0rlr1e0VIiKiopbf2yv+/v4wMzNTevj7+2c7zt27d7F69WpUq1YNR44cwfDhwzFmzBip1z82NhYAYGVlpbSflZWVtC02Nhbly5dX2q6npwcLCwulmJzqyHqM3GIyt+eVVvV0EBERFbX8zl+YMmVKtl6D93s5AEChUKBBgwaYO3cugHdfAXLt2jWsWbNGa3v+2dNBRESkQXK5HKampkqPnJIOGxsbODo6KpU5ODggJiYGAGBtbQ0AiIuLU4qJi4uTtllbW+PJkydK29PT0/H8+XOlmJzqyHqM3GIyt+cVkw4iIiIVaGr2StOmTREVFaVU9vfff8POzg4AUKVKFVhbWyMkJETanpSUhHPnzsHV1RUA4OrqioSEBISHh0sxx44dg0KhgIuLixRz8uRJpKWlSTHBwcGoXr26NFPG1dVV6TiZMZnHySutTzqEENlWKiUiIiosmloG3cfHB2fPnsXcuXNx+/ZtbN26FWvXrsXIkSP/vx0yeHt7Y86cOfj9999x9epV9O/fH7a2ttIXozo4OKBdu3YYMmQIzp8/j9OnT2PUqFHo1asXbG1tAQB9+vSBgYEBvLy8cP36dWzfvh1Lly5VugU0duxYBAUFYeHChYiMjMSMGTNw8eJFjBql2qxPrU06Nm3ahNq1a8PIyAhGRkaoU6cONm/eXNTNIiKiYk5TPR0NGzbE3r17sW3bNtSqVQuzZ8/GkiVL0LdvXylm4sSJGD16NIYOHYqGDRvi1atXCAoKgqGhoRSzZcsW1KhRA61bt0aHDh3QrFkzpTU4zMzM8McffyA6OhrOzs4YN24c/Pz8lNbyaNKkiZT01K1bF7t27cK+fftQq1Yt1V47bVqnI9OiRYswbdo0jBo1Ck2bNgUAhIaGYuXKlZgzZw58fHzUqpfrdFBJwHU6qCQozHU6Gsz5M1/7X5zaqoBaon20cvbK8uXLsXr1avTv318q+/LLL1GzZk3MmDFD7aSDiIjoY/j1G+rTytsrjx8/RpMmTbKVN2nSBI8fPy6CFhEREdHHaGXSYW9vjx07dmQr3759O6pVq1YELSIiopJCUwNJiyOtvL0yc+ZM9OzZEydPnpTGdJw+fRohISE5JiNEREQFhbdX1KeVSUe3bt1w7tw5LF68WFr33cHBAefPn0e9evWKtnFERFSsMedQn1YmHQDg7OyMX375paibQUREJQx7OtSntUkHERFRUWDOoT6tSjp0dHQ+mmHKZDKkp6drqEVERESUV1qVdOzduzfXbWFhYVi2bBkUCoUGW0RERCUNb6+oT6uSjq+++ipbWVRUFCZPnoz9+/ejb9++mDVrVhG0jIiISgrmHOrTynU6AODRo0cYMmQIateujfT0dERERGDjxo3St+8REREVBk1990pxpHVJR2JiIiZNmgR7e3tcv34dISEh2L9/v8pfOkNERKQOJh3q06rbKwEBAZg/fz6sra2xbdu2HG+3EBERFaYSnjfki1YlHZMnT4aRkRHs7e2xceNGbNy4Mce4PXv2aLhlRERE9DFalXT079+/xHdNERFR0eLvIfVpVdIRGBhY1E0gIqISjjmH+rQq6SAiIipq7OlQH5MOIiIiFTDnUB+TDiIiIhXoMOtQm9at00FERETaiT0dREREKmBHh/qYdBAREamAA0nVx6SDiIhIBTrMOdTGpIOIiEgF7OlQH5MOIiIiFTDnUB9nrxAREf1HzZgxI9u31NaoUUPanpycjJEjR6Js2bIwMTFBt27dEBcXp1RHTEwMPDw8UKpUKZQvXx4TJkxAenq6Uszx48dRv359yOVy2Nvb57gC+MqVK1G5cmUYGhrCxcUF58+fV/l8mHQQERGpQJbPf6qqWbMmHj9+LD1CQ0OlbT4+Pti/fz927tyJEydO4NGjR+jatau0PSMjAx4eHkhNTcWZM2ewceNGBAYGws/PT4qJjo6Gh4cHWrVqhYiICHh7e2Pw4ME4cuSIFLN9+3b4+vpi+vTpuHTpEurWrQt3d3c8efJEtddOCCFUfgWKKaN6o4q6CUSF7sWFFUXdBKJCZ1iIgwe+XHshX/v/PrRhnmNnzJiBffv2ISIiItu2xMREWFpaYuvWrejevTsAIDIyEg4ODggLC0Pjxo1x+PBhdOzYEY8ePYKVlRUAYM2aNZg0aRLi4+NhYGCASZMm4eDBg7h27ZpUd69evZCQkICgoCAAgIuLCxo2bIgVK959figUClSsWBGjR4/G5MmT83w+7OkgIiJSwfu3O1R9pKSkICkpSemRkpKS6/Fu3boFW1tbVK1aFX379kVMTAwAIDw8HGlpaXBzc5Nia9SogUqVKiEsLAwAEBYWhtq1a0sJBwC4u7sjKSkJ169fl2Ky1pEZk1lHamoqwsPDlWJ0dHTg5uYmxeQVkw4iIiIVyGT5e/j7+8PMzEzp4e/vn+OxXFxcEBgYiKCgIKxevRrR0dFo3rw5Xr58idjYWBgYGMDc3FxpHysrK8TGxgIAYmNjlRKOzO2Z2z4Uk5SUhLdv3+Lp06fIyMjIMSazjrzi7BUiIiIV5Pe7V6ZMmQJfX1+lMrlcnmNs+/btpf/XqVMHLi4usLOzw44dO2BkZJSvdhQF9nQQERFpkFwuh6mpqdIjt6Tjfebm5vjss89w+/ZtWFtbIzU1FQkJCUoxcXFxsLa2BgBYW1tnm82S+fxjMaampjAyMkK5cuWgq6ubY0xmHXnFpIOIiEgF+b29kh+vXr3CnTt3YGNjA2dnZ+jr6yMkJETaHhUVhZiYGLi6ugIAXF1dcfXqVaVZJsHBwTA1NYWjo6MUk7WOzJjMOgwMDODs7KwUo1AoEBISIsXkFW+vEBERqUCTK5KOHz8enTp1gp2dHR49eoTp06dDV1cXvXv3hpmZGby8vODr6wsLCwuYmppi9OjRcHV1RePGjQEAbdu2haOjI/r164eAgADExsZi6tSpGDlypNS7MmzYMKxYsQITJ07EoEGDcOzYMezYsQMHDx6U2uHr6wtPT080aNAAjRo1wpIlS/D69WsMHDhQpfNh0kFERKQCTa5I+uDBA/Tu3RvPnj2DpaUlmjVrhrNnz8LS0hIAsHjxYujo6KBbt25ISUmBu7s7Vq1aJe2vq6uLAwcOYPjw4XB1dYWxsTE8PT0xa9YsKaZKlSo4ePAgfHx8sHTpUlSoUAHr1q2Du7u7FNOzZ0/Ex8fDz88PsbGxcHJyQlBQULbBpR/DdTqy4DodVBJwnQ4qCQpznY6eGy/na//tnvUKqCXahz0dREREKuBXr6iPA0mJiIhII9jTQUREpAJ+tb36mHQQERGpQIc5h9qYdBAREamAPR3qY9JBRESkAuYc6mPSQUREpAL2dKiPs1eIiIhII9jTQUREpAIOJFVfoScdXbt2zXPsnj17CrElRERE+cfbK+or9KTDzMyssA9BRESkMUw51FfoSceGDRsK+xBEREQao8OeDrVxICkRERFphMYHku7atQs7duxATEwMUlNTlbZdunRJ080hIiJSCTs61KfRno5ly5Zh4MCBsLKywuXLl9GoUSOULVsWd+/eRfv27TXZFCIiIrXIZLJ8PUoyjSYdq1atwtq1a7F8+XIYGBhg4sSJCA4OxpgxY5CYmKjJphAREalFJsvfoyTTaNIRExODJk2aAACMjIzw8uVLAEC/fv2wbds2TTaFiIhILToyWb4eJZlGkw5ra2s8f/4cAFCpUiWcPXsWABAdHQ0hhCabQkREpBb2dKhPo0nHF198gd9//x0AMHDgQPj4+KBNmzbo2bMnunTposmmEBERkYZpdPbK2rVroVAoAAAjR45E2bJlcebMGXz55Zf49ttvNdkUIiIitZT0waD5IRO8ryFJTi/qFhARUUEwLMQ/qUfvvZmv/Zd3cSiglmgfjS8OdurUKXzzzTdwdXXFw4cPAQCbN29GaGiopptCRESkMk6ZVZ9Gk47du3fD3d0dRkZGuHz5MlJSUgAAiYmJmDt3riabQkREpBYdWf4eJZlGk445c+ZgzZo1+Omnn6Cvry+VN23alKuREhGRVmDSoT6NJh1RUVFo0aJFtnIzMzMkJCRosilERESkYRpfp+P27dvZykNDQ1G1alVNNoWIiEgtRTWmY968eZDJZPD29pbKkpOTpdmgJiYm6NatG+Li4pT2i4mJgYeHB0qVKoXy5ctjwoQJSE9Xnjlx/Phx1K9fH3K5HPb29ggMDMx2/JUrV6Jy5cowNDSEi4sLzp8/r/I5aDTpGDJkCMaOHYtz585BJpPh0aNH2LJlC8aNG4fhw4drsilERERqKYrbKxcuXMCPP/6IOnXqKJX7+Phg//792LlzJ06cOIFHjx6ha9eu0vaMjAx4eHggNTUVZ86cwcaNGxEYGAg/Pz8pJjo6Gh4eHmjVqhUiIiLg7e2NwYMH48iRI1LM9u3b4evri+nTp+PSpUuoW7cu3N3d8eTJE5XOQ6NTZoUQmDt3Lvz9/fHmzRsAgFwux4QJEzBlyhQYGRlpqik54pRZIqLioTCnzE48GJWv/QM8qqsU/+rVK9SvXx+rVq3CnDlz4OTkhCVLliAxMRGWlpbYunUrunfvDgCIjIyEg4MDwsLC0LhxYxw+fBgdO3bEo0ePYGVlBQBYs2YNJk2ahPj4eBgYGGDSpEk4ePAgrl27Jh2zV69eSEhIQFBQEADAxcUFDRs2xIoVKwAACoUCFStWxOjRozF58uQ8n4tGezpkMhm+++47PH/+HNeuXcPZs2cRHx8PMzMzVKlSRZNNISIiUkt+v3slJSUFSUlJSo/M2Zw5GTlyJDw8PODm5qZUHh4ejrS0NKXyGjVqoFKlSggLCwMAhIWFoXbt2lLCAQDu7u5ISkrC9evXpZj363Z3d5fqSE1NRXh4uFKMjo4O3NzcpJg8v3YqRaspJSUFU6ZMQYMGDdC0aVMcOnQIjo6OuH79OqpXr46lS5fCx8dHE00hIiLKF518Pvz9/WFmZqb08Pf3z/FYv/76Ky5dupTj9tjYWBgYGMDc3Fyp3MrKCrGxsVJM1oQjc3vmtg/FJCUl4e3bt3j69CkyMjJyjMmsI680sgy6n58ffvzxR7i5ueHMmTP4+uuvMXDgQJw9exYLFy7E119/DV1dXU00hYiIqEhNmTIFvr6+SmVyuTxb3D///IOxY8ciODgYhoaGmmpeodJI0rFz505s2rQJX375Ja5du4Y6deogPT0dV65cKfGrsxERkXbJ768tuVyeY5LxvvDwcDx58gT169eXyjIyMnDy5EmsWLECR44cQWpqKhISEpR6O+Li4mBtbQ3g3azR92eZZM5uyRrz/oyXuLg4mJqawsjICLq6utDV1c0xJrOOvNLI7ZUHDx7A2dkZAFCrVi3I5XL4+Pgw4SAiIq2T3zEdedW6dWtcvXoVERER0qNBgwbo27ev9H99fX2EhIRI+0RFRSEmJgaurq4AAFdXV1y9elVplklwcDBMTU3h6OgoxWStIzMmsw4DAwM4OzsrxSgUCoSEhEgxeaWRno6MjAwYGBj8e1A9PZiYmGji0ERERAVKU38vly5dGrVq1VIqMzY2RtmyZaVyLy8v+Pr6wsLCAqamphg9ejRcXV3RuHFjAEDbtm3h6OiIfv36ISAgALGxsZg6dSpGjhwp9bYMGzYMK1aswMSJEzFo0CAcO3YMO3bswMGDB6Xj+vr6wtPTEw0aNECjRo2wZMkSvH79GgMHDlTpnDSSdAghMGDAAOkEk5OTMWzYMBgbGyvF7dmzRxPNISIiUtt/aSnzxYsXQ0dHB926dUNKSgrc3d2xatUqabuuri4OHDiA4cOHw9XVFcbGxvD09MSsWbOkmCpVquDgwYPw8fHB0qVLUaFCBaxbtw7u7u5STM+ePREfHw8/Pz/ExsbCyckJQUFB2QaXfoxG1unIaya0YcOGQm7Jh3GdDiKi4qEw1+mYFZx9ZW1V+LWxL6CWaB+N9HQUdTJBRERERU8jSQcREVFxwTkQ6mPSQUREpIL/0pgObcOkg4iISAUyMOtQF5MOIiIiFbCnQ31MOoiIiFTApEN9Gv2WWSIiIiq52NNBRESkAn6Fh/qYdBAREamAt1fUx6SDiIhIBezoUB+TDiIiIhWo8k2xpIxJBxERkQp4e0V9nL1CREREGsGeDiIiIhXw7or6mHQQERGpQIfLoKuNSQcREZEK2NOhPiYdREREKuBAUvUx6SAiIlIBp8yqj7NXiIiISCPY00FERKQCdnSoj0kHERGRCnh7RX1MOoiIiFTAnEN9TDqIiIhUwMGQ6mPSQUREpAIZuzrUxoSNiIiINII9HURERCpgP4f62NNBRESkAh2ZLF8PVaxevRp16tSBqakpTE1N4erqisOHD0vbk5OTMXLkSJQtWxYmJibo1q0b4uLilOqIiYmBh4cHSpUqhfLly2PChAlIT09Xijl+/Djq168PuVwOe3t7BAYGZmvLypUrUblyZRgaGsLFxQXnz59X6VwAJh1EREQqkeXzoYoKFSpg3rx5CA8Px8WLF/HFF1/gq6++wvXr1wEAPj4+2L9/P3bu3IkTJ07g0aNH6Nq1q7R/RkYGPDw8kJqaijNnzmDjxo0IDAyEn5+fFBMdHQ0PDw+0atUKERER8Pb2xuDBg3HkyBEpZvv27fD19cX06dNx6dIl1K1bF+7u7njy5Ilqr50QQqj4GhRbyekfjyEiov8+w0IcPLD10oN87d+tpiVSUlKUyuRyOeRyeZ72t7CwwIIFC9C9e3dYWlpi69at6N69OwAgMjISDg4OCAsLQ+PGjXH48GF07NgRjx49gpWVFQBgzZo1mDRpEuLj42FgYIBJkybh4MGDuHbtmnSMXr16ISEhAUFBQQAAFxcXNGzYECtWrAAAKBQKVKxYEaNHj8bkyZPzfO7s6SAiIlKBTCbL18Pf3x9mZmZKD39//48eNyMjA7/++itev34NV1dXhIeHIy0tDW5ublJMjRo1UKlSJYSFhQEAwsLCULt2bSnhAAB3d3ckJSVJvSVhYWFKdWTGZNaRmpqK8PBwpRgdHR24ublJMXnFgaREREQaNGXKFPj6+iqVfaiX4+rVq3B1dUVycjJMTEywd+9eODo6IiIiAgYGBjA3N1eKt7KyQmxsLAAgNjZWKeHI3J657UMxSUlJePv2LV68eIGMjIwcYyIjI/N+4mDSQUREpJL83iJQ5VYKAFSvXh0RERFITEzErl274OnpiRMnTuSzFUWDSQcREZEKNL04mIGBAezt7QEAzs7OuHDhApYuXYqePXsiNTUVCQkJSr0dcXFxsLa2BgBYW1tnm2WSObsla8z7M17i4uJgamoKIyMj6OrqQldXN8eYzDryimM6iIiIVKDJ2Ss5USgUSElJgbOzM/T19RESEiJti4qKQkxMDFxdXQEArq6uuHr1qtIsk+DgYJiamsLR0VGKyVpHZkxmHQYGBnB2dlaKUSgUCAkJkWLyij0dREREKtBkT8eUKVPQvn17VKpUCS9fvsTWrVtx/PhxHDlyBGZmZvDy8oKvry8sLCxgamqK0aNHw9XVFY0bNwYAtG3bFo6OjujXrx8CAgIQGxuLqVOnYuTIkdItnmHDhmHFihWYOHEiBg0ahGPHjmHHjh04ePCg1A5fX194enqiQYMGaNSoEZYsWYLXr19j4MCBKp0Pkw4iIiIVaPIWwZMnT9C/f388fvwYZmZmqFOnDo4cOYI2bdoAABYvXgwdHR1069YNKSkpcHd3x6pVq6T9dXV1ceDAAQwfPhyurq4wNjaGp6cnZs2aJcVUqVIFBw8ehI+PD5YuXYoKFSpg3bp1cHd3l2J69uyJ+Ph4+Pn5ITY2Fk5OTggKCso2uPRjuE5HFlyng4ioeCjMdTr2XHmcr/271rUpoJZoH/Z0EBERqYDfMqs+Jh1EREQqYMqhPiYdREREKmBHh/qYdBAREalAh30datP6dTpOnTqFb775Bq6urnj48CEAYPPmzQgNDS3ilhERUXEkk+XvUZJpddKxe/duuLu7w8jICJcvX5a+tS8xMRFz584t4tYRERFRVlqddMyZMwdr1qzBTz/9BH19fam8adOmuHTpUhG2jIiIiitZPv+VZFo9piMqKgotWrTIVm5mZoaEhATNN4iIiIq9kn6LJD+0uqfD2toat2/fzlYeGhqKqlWrFkGLiIiouNOBLF+Pkkyrk44hQ4Zg7NixOHfuHGQyGR49eoQtW7Zg/PjxGD58eFE3j4iIiiEOJFWfVt9emTx5MhQKBVq3bo03b96gRYsWkMvlGD9+PEaPHl3UzSMiomKopCcO+VEsvnslNTUVt2/fxqtXr+Do6AgTExO16uF3rxARFQ+F+d0rf9yMz9f+bR0sC6gl2kerezoyGRgYwNHRsaibQUREJUBJn4GSH1qXdHTt2jXPsXv27CnElhARUUmkw5xDbVqXdJiZmUn/F0Jg7969MDMzQ4MGDQAA4eHhSEhIUCk5ISIiyiv2dKhP65KODRs2SP+fNGkSevTogTVr1kBXVxcAkJGRgREjRsDU1LSomkhERMUYB5KqT6sHklpaWiI0NBTVq1dXKo+KikKTJk3w7NkzlerjQFIiouKhMAeS/hml2u+W97WqXraAWqJ9tHqdjvT0dERGRmYrj4yMhEKhKIIWERFRccdl0NWndbdXsho4cCC8vLxw584dNGrUCABw7tw5zJs3DwMHDizi1hEArP/pR4QE/4Ho6LuQGxrCyakevH3Ho3IV5RVjr0RcxvKli3H16l/Q1dFB9RoOWL12PQwNDYuo5US5C794AYE/r8fNG9cQHx+PxctW4ovWbtL2o8F/YOeOX3Hz+nUkJiZg+659qOHgoFRHSkoKFgbMQ9DhQ0hNTUWTps3w3bTpKFuunKZPh1TEgaTq0+qk44cffoC1tTUWLlyIx48fAwBsbGwwYcIEjBs3rohbRwBw8cJ59OzdFzVr10ZGegaWL12EYUO8sOf3gyhVqhSAdwnHiG8HY9DgbzH5u2nQ09VFVFQkdHS0uiOOirG3b9+gevXq6Ny1G3zHjspxe7169eHu3h4zp0/NsY4F8+fi1IkTWLBoCUqXLg3/72fDd+wobNzya2E3n/KppPdW5IdWj+nIKikpCQDyNYCUYzoK3/Pnz9GquSt+3vgLnBs0BAB807sHGrs2wagx3kXbOCI11K1ZPVtPR6aHDx+gQ9vW2Xo6Xr58ic+buWJewA9o494OABB99w46d+qAzVu3o05dJ001v9gqzDEdobde5Gv/ZtXKFFBLtE+x+VPS1NSUM1a0wKuXLwEApv8/9fnZs2e4+tcVWJQti/59e6FViyYY5PkNLoVfLMpmEhWqG9evIT09DS6uTaSyKlU/hY2NLa5ERBRdwyhPZPl8lGRafXulSpUqkH1g7tLdu3c12Br6GIVCgYD5c+FUrz6qVfsMAPDwwT8AgDUrV8B3wkRUr+GAA7/tw1CvAdj92wHY2VUuwhYTFY5nT59CX18/2x9KFmXL4unT/C2xTfRfptVJh7e3t9LztLQ0XL58GUFBQZgwYcIH901JSUFKSopSmdCVQy6XF3Qz6f/NnTMTd27dQuDmrVJZ5iyj7j16onOXbgAABwdHnDsXhn17dmOsD8fmENF/iw4X6lCbVicdY8eOzbF85cqVuHjxw93z/v7+mDlzplLZd9OmY6rfjIJqHmUxd84snDxxHD9v/AVW1tZSeTnLd198VPXTT5Xiq1T9FLGPH2m0jUSaUrZcOaSlpSEpKUmpt+P5s2coV67kfhmYtmDKob5iM6Yjq/bt22P37t0fjJkyZQoSExOVHhMmTdFQC0sOIQTmzpmFYyHB+OnnjahQoaLS9k8+qQDL8uVxLzpaqfz+vXuwsf1Ek00l0hjHmrWgp6eP82fDpLJ70Xfx+PEj1HVyKrqGUd5oaFCHv78/GjZsiNKlS6N8+fLo3LkzoqKilGKSk5MxcuRIlC1bFiYmJujWrRvi4uKUYmJiYuDh4YFSpUqhfPnymDBhAtLTlWdOHD9+HPXr14dcLoe9vT0CAwOztWflypWoXLkyDA0N4eLigvPnz+f9ZP5fsUw6du3aBQsLiw/GyOVyafBp5oO3Vgre3NkzcejA75gXsBDGpYzxND4eT+PjkZycDACQyWQYMNAL27ZsRvCRIMTcv48Vy5bgXvRddOnavYhbT5SzN69fI/LmTUTevAkAePjgASJv3sTjR+965xITEhB58ybu3rkDALh3LxqRN2/iafy78RqlS5dGl27d8EPAPJw/dxY3rl+D39T/oa5TPc5c0QKaWhzsxIkTGDlyJM6ePYvg4GCkpaWhbdu2eP36tRTj4+OD/fv3Y+fOnThx4gQePXqk9N1jGRkZ8PDwQGpqKs6cOYONGzciMDAQfn5+Ukx0dDQ8PDzQqlUrREREwNvbG4MHD8aRI0ekmO3bt8PX1xfTp0/HpUuXULduXbi7u+PJkyeqvXbaPGW2Xr16SgNJhRCIjY1FfHw8Vq1ahaFDh6pUH6fMFry6NavnWD5rjj++6vLvG2P9T2ux/dctSExMRPXqNeDtOx71nRtoqplEKrlw/hwGD+yfrfzLr7pg9tx5+G3vHvhNzd5zOmzEKAwfORrAv4uDHT50EKlp/7842NTp0i1Hyp/CnDJ7/m5ivvZvVNXs40E5iI+PR/ny5XHixAm0aNECiYmJsLS0xNatW9G9+7s/0iIjI+Hg4ICwsDA0btwYhw8fRseOHfHo0SNYWVkBANasWYNJkyYhPj4eBgYGmDRpEg4ePIhr165Jx+rVqxcSEhIQFBQEAHBxcUHDhg2xYsUKAO/G41WsWBGjR4/G5MmT83wOWp10vD8mQ0dHB5aWlvj8889Ro0YNletj0kFEVDz8l5OOup8YZpvIIJd/fCLD7du3Ua1aNVy9ehW1atXCsWPH0Lp1a7x48QLm5uZSnJ2dHby9veHj4wM/Pz/8/vvviMgyFTs6OhpVq1bFpUuXUK9ePbRo0QL169fHkiVLpJgNGzbA29sbiYmJSE1NRalSpbBr1y507txZivH09ERCQgJ+++23PJ+7Vg8knT59elE3gYiISpj8DiTNaSLD9OnTMWPGjFz3USgU8Pb2RtOmTVGrVi0AQGxsLAwMDJQSDgCwsrJCbGysFJPZw5F1e+a2D8UkJSXh7du3ePHiBTIyMnKMyen7zz5Eq5OOrJKTk5GamqpUxsXCiIiowOUz65gyZQp8fX2Vyj7WyzFy5Ehcu3YNoaGh+Tt4EdPqpOP169eYNGkSduzYkePX2GdkZBRBq4iIqDjL73ev5OVWSlajRo3CgQMHcPLkSVSoUEEqt7a2RmpqKhISEpR6O+Li4mD9/0sTWFtbZ5tlkjm7JWvM+zNe4uLiYGpqCiMjI+jq6kJXVzfHGOssSyDkhVbPXpk4cSKOHTuG1atXQy6XY926dZg5cyZsbW2xadOmom4eEREVQzJZ/h55JYTAqFGjsHfvXhw7dgxVqlRR2u7s7Ax9fX2EhIRIZVFRUYiJiYGrqysAwNXVFVevXlWaZRIcHAxTU1M4OjpKMVnryIzJrMPAwADOzs5KMQqFAiEhIVJMXmn1QNJKlSph06ZN+Pzzz2FqaopLly7B3t4emzdvxrZt23Do0CGV6uNAUiKi4qEwB5JeupeUr/3rV87brf8RI0Zg69at+O2331C9+r8zAc3MzGBkZAQAGD58OA4dOoTAwECYmppi9Oh3s6POnDkD4F2Pv5OTE2xtbREQEIDY2Fj069cPgwcPxty5cwG8G1haq1YtjBw5EoMGDcKxY8cwZswYHDx4EO7u7gDeTZn19PTEjz/+iEaNGmHJkiXYsWMHIiMjs431+BCtTjpMTExw48YNVKpUCRUqVMCePXvQqFEjREdHo3bt2nj16pVK9THpICIqHopD0pHbd4tt2LABAwYMAPBuPOO4ceOwbds2pKSkwN3dHatWrVK67XH//n0MHz4cx48fh7GxMTw9PTFv3jzo6f37Ih0/fhw+Pj64ceMGKlSogGnTpknHyLRixQosWLAAsbGxcHJywrJly+Di4qLSuWt10lGnTh0sX74cLVu2hJubG5ycnPDDDz9g2bJlCAgIwIMHD1Sqj0kHEVHxUKhJx/18Jh12JXeSg1aP6Rg4cCCuXLkCAJg8eTJWrlwJQ0ND+Pj4fPQL34iIiNShqRVJiyOt7ul43/379xEeHg57e3vUqVNH5f3Z00FEVDwUZk9HRMzLfO3vVKl0AbVE+2htT0daWhpat26NW7duSWV2dnbo2rWrWgkHERFRXmjo+96KJa1dp0NfXx9//fVXUTeDiIhKmpKeOeSD1vZ0AMA333yD9evXF3UziIiIKA+0tqcDANLT0/Hzzz/j6NGjcHZ2hrGxsdL2RYsWFVHLiIiouCrpg0HzQyuTjrt376Jy5cq4du0a6tevDwD4+++/lWJym99MRESUH/z1oj6tnL2iq6uLx48fo3z58gCAnj17YtmyZSqtipYTzl4hIioeCnP2yrUHqi08+b5aFUwKqCXaRyt7Ot7Pkw4fPozXr18XUWuIiKhEYU+H2rQy6XifFnbWEBGRluKYDvVp5ewVmUyWbcwGx3AQERH9t2llT4cQAgMGDIBcLgfw7gtvhg0blm32yp49e4qieUREVIzxb1z1aWXS4enpqfT8m2++KaKWEBFRScOcQ31aOXulsHD2ChFR8VCYs1duPs7fxAUHG+OPBxVTWtnTQUREVFQ4kFR9TDqIiIhUwDEd6tPK2StERESkfdjTQUREpAJ2dKiPSQcREZEqmHWojUkHERGRCjiQVH1MOoiIiFTAgaTqY9JBRESkAuYc6uPsFSIiItII9nQQERGpgl0damPSQUREpAIOJFUfb68QERGpQCbL30MVJ0+eRKdOnWBrawuZTIZ9+/YpbRdCwM/PDzY2NjAyMoKbmxtu3bqlFPP8+XP07dsXpqamMDc3h5eXF169eqUU89dff6F58+YwNDRExYoVERAQkK0tO3fuRI0aNWBoaIjatWvj0KFDqp0MmHQQERGpRJbPhypev36NunXrYuXKlTluDwgIwLJly7BmzRqcO3cOxsbGcHd3R3JyshTTt29fXL9+HcHBwThw4ABOnjyJoUOHStuTkpLQtm1b2NnZITw8HAsWLMCMGTOwdu1aKebMmTPo3bs3vLy8cPnyZXTu3BmdO3fGtWvXVDoffstsFvyWWSKi4qEwv2X23rPkjwd9QOWyhmrtJ5PJsHfvXnTu3BnAu14OW1tbjBs3DuPHjwcAJCYmwsrKCoGBgejVqxdu3rwJR0dHXLhwAQ0aNAAABAUFoUOHDnjw4AFsbW2xevVqfPfdd4iNjYWBgQEAYPLkydi3bx8iIyMBAD179sTr169x4MABqT2NGzeGk5MT1qxZk+dzYE8HERGRBqWkpCApKUnpkZKSonI90dHRiI2NhZubm1RmZmYGFxcXhIWFAQDCwsJgbm4uJRwA4ObmBh0dHZw7d06KadGihZRwAIC7uzuioqLw4sULKSbrcTJjMo+TV0w6iIiIVCDL5z9/f3+YmZkpPfz9/VVuR2xsLADAyspKqdzKykraFhsbi/Llyytt19PTg4WFhVJMTnVkPUZuMZnb84qzV4iIiFSQ3xVJp0yZAl9fX6UyuVyev0q1BJMOIiIiFeR3wqxcLi+QJMPa2hoAEBcXBxsbG6k8Li4OTk5OUsyTJ0+U9ktPT8fz58+l/a2trREXF6cUk/n8YzGZ2/OKt1eIiIhUoMkpsx9SpUoVWFtbIyQkRCpLSkrCuXPn4OrqCgBwdXVFQkICwsPDpZhjx45BoVDAxcVFijl58iTS0tKkmODgYFSvXh1lypSRYrIeJzMm8zh5xaSDiIhIJZqbNPvq1StEREQgIiICwLvBoxEREYiJiYFMJoO3tzfmzJmD33//HVevXkX//v1ha2srzXBxcHBAu3btMGTIEJw/fx6nT5/GqFGj0KtXL9ja2gIA+vTpAwMDA3h5eeH69evYvn07li5dqnQLaOzYsQgKCsLChQsRGRmJGTNm4OLFixg1apRqrxynzP6LU2aJiIqHwpwy++BFar72r1DG4ONB/+/48eNo1apVtnJPT08EBgZCCIHp06dj7dq1SEhIQLNmzbBq1Sp89tlnUuzz588xatQo7N+/Hzo6OujWrRuWLVsGExMTKeavv/7CyJEjceHCBZQrVw6jR4/GpEmTlI65c+dOTJ06Fffu3UO1atUQEBCADh06qHTuTDqyYNJBRFQ8FGbS8TAhf0nHJ+Z5TzqKGw4kJSIiUgG/eUV9TDqIiIhUUJCDQUsaJh1EREQq4LfMqo9JBxERkSqYc6iNU2aJiIhII9jTQUREpAJ2dKiPSQcREZEKOJBUfUw6iIiIVMCBpOpj0kFERKQK5hxqY9JBRESkAuYc6uPsFSIiItII9nQQERGpgANJ1cekg4iISAUcSKo+Jh1EREQqYE+H+jimg4iIiDSCPR1EREQqYE+H+tjTQURERBrBng4iIiIVcCCp+ph0EBERqYC3V9THpIOIiEgFzDnUx6SDiIhIFcw61MaBpERERKQR7OkgIiJSAQeSqo9JBxERkQo4kFR9TDqIiIhUwJxDfUw6iIiIVMGsQ21MOoiIiFTAMR3q4+wVIiIi0gj2dBAREamAA0nVJxNCiKJuBJVMKSkp8Pf3x5QpUyCXy4u6OUSFgtc50b+YdFCRSUpKgpmZGRITE2FqalrUzSEqFLzOif7FMR1ERESkEUw6iIiISCOYdBAREZFGMOmgIiOXyzF9+nQOrqNijdc50b84kJSIiIg0gj0dREREpBFMOoiIiEgjmHQQERGRRjDpoGItMDAQ5ubmRd0MIpUMGDAAnTt3LupmEBU4Jh0l1IABAyCTyTBv3jyl8n379kGWzy8WCAwMhEwmy/ZYt25dvuolKmyZ74v3H7dv3y7qphEVC/zCtxLM0NAQ8+fPx7fffosyZcoUaN2mpqaIiopSKjMzM8sWl5qaCgMDgwI9NlF+tGvXDhs2bFAqs7S0VHrO65ZIPezpKMHc3NxgbW0Nf3//D8bt3r0bNWvWhFwuR+XKlbFw4cKP1i2TyWBtba30MDIywowZM+Dk5IR169ahSpUqMDQ0BAAEBQWhWbNmMDc3R9myZdGxY0fcuXNHqu/48eOQyWRISEiQyiIiIiCTyXDv3j2pLDAwEJUqVUKpUqXQpUsXPHv2LFvbfvvtN9SvXx+GhoaoWrUqZs6cifT09I+eE5UMcrk827XbunVrjBo1Ct7e3ihXrhzc3d0BAIsWLULt2rVhbGyMihUrYsSIEXj16pVUV+b1ntWSJUtQuXJl6XlGRgZ8fX2la3/ixIl4fyUDhUIBf39/VKlSBUZGRqhbty527dpVaK8BUWFh0lGC6erqYu7cuVi+fDkePHiQY0x4eDh69OiBXr164erVq5gxYwamTZuGwMBAtY97+/Zt7N69G3v27EFERAQA4PXr1/D19cXFixcREhICHR0ddOnSBQqFIs/1njt3Dl5eXhg1ahQiIiLQqlUrzJkzRynm1KlT6N+/P8aOHYsbN27gxx9/RGBgIL7//nu1z4dKho0bN8LAwACnT5/GmjVrAAA6OjpYtmwZrl+/jo0bN+LYsWOYOHGiSvUuXLgQgYGB+PnnnxEaGornz59j7969SjH+/v7YtGkT1qxZg+vXr8PHxwfffPMNTpw4UWDnR6QRgkokT09P8dVXXwkhhGjcuLEYNGiQEEKIvXv3iqyXRZ8+fUSbNm2U9p0wYYJwdHTMte4NGzYIAMLY2Fh6WFlZCSGEmD59utDX1xdPnjz5YPvi4+MFAHH16lUhhBB//vmnACBevHghxVy+fFkAENHR0UIIIXr37i06dOigVE/Pnj2FmZmZ9Lx169Zi7ty5SjGbN28WNjY2H2wPlQyenp5CV1dX6drt3r27aNmypahXr95H99+5c6coW7as9Hz69Omibt26SjGLFy8WdnZ20nMbGxsREBAgPU9LSxMVKlSQ3p/JycmiVKlS4syZM0r1eHl5id69e6t+kkRFiGM6CPPnz8cXX3yB8ePHZ9t28+ZNfPXVV0plTZs2xZIlS5CRkQFdXd0c6yxdujQuXbokPdfR+bdTzc7OLts98lu3bsHPzw/nzp3D06dPpR6OmJgY1KpVK0/ncfPmTXTp0kWpzNXVFUFBQdLzK1eu4PTp00o9GxkZGUhOTsabN29QqlSpPB2Liq9WrVph9erV0nNjY2P07t0bzs7O2WKPHj0Kf39/REZGIikpCenp6SpdS4mJiXj8+DFcXFykMj09PTRo0EC6xXL79m28efMGbdq0Udo3NTUV9erVU/c0iYoEkw5CixYt4O7ujilTpmDAgAEFUqeOjg7s7e1z3GZsbJytrFOnTrCzs8NPP/0EW1tbKBQK1KpVC6mpqVJ9AJTudaelpancrlevXmHmzJno2rVrtm2Z40uoZDM2Ns7x2n3/ur137x46duyI4cOH4/vvv4eFhQVCQ0Ph5eWF1NRUlCpVCjo6OtnGZ6h63WaOETl48CA++eQTpW38PhfSNkw6CAAwb948ODk5oXr16krlDg4OOH36tFLZ6dOn8dlnn+Xay6GqZ8+eISoqCj/99BOaN28OAAgNDVWKyewZefz4sTTTJnM8SNa2njt3Tqns7NmzSs/r16+PqKioXBMiorwKDw+HQqHAwoULpaR4x44dSjGWlpaIjY2FEEKaip71ujUzM4ONjQ3OnTuHFi1aAADS09MRHh6O+vXrAwAcHR0hl8sRExODli1bauDMiAoPkw4CANSuXRt9+/bFsmXLlMrHjRuHhg0bYvbs2ejZsyfCwsKwYsUKrFq1qsCOXaZMGZQtWxZr166FjY0NYmJiMHnyZKUYe3t7VKxYETNmzMD333+Pv//+O9ssmjFjxqBp06b44Ycf8NVXX+HIkSNKt1YAwM/PDx07dkSlSpXQvXt36Ojo4MqVK7h27Vq2QadEH2Jvb4+0tDQsX74cnTp1Uhpgmunzzz9HfHw8AgIC0L17dwQFBeHw4cMwNTWVYsaOHYt58+ahWrVqqFGjBhYtWqQ0S6t06dIYP348fHx8oFAo0KxZMyQmJuL06dMwNTWFp6enpk6ZKP+KdkgJFZWsA0kzRUdHCwMDA/H+ZbFr1y7h6Ogo9PX1RaVKlcSCBQs+WPeGDRuUBm9mldPAOiGECA4OFg4ODkIul4s6deqI48ePCwBi7969UkxoaKioXbu2MDQ0FM2bNxc7d+5UGkgqhBDr168XFSpUEEZGRqJTp07ihx9+yNaWoKAg0aRJE2FkZCRMTU1Fo0aNxNq1az94TlQy5PS+EEKIli1birFjx2YrX7RokbCxsRFGRkbC3d1dbNq0KduA59WrV4uKFSsKY2Nj0b9/f/H9998rDSRNS0sTY8eOFaampsLc3Fz4+vqK/v37K7VDoVCIJUuWiOrVqwt9fX1haWkp3N3dxYkTJwru5Ik0gF9tT0RERBrBdTqIiIhII5h0EBERkUYw6SAiIiKNYNJBREREGsGkg4iIiDSCSQcRERFpBJMOIiIi0ggmHURUIJKTk/H999/j9u3bRd0UIvqPYtJBVMwMGDAAnTt3lp5//vnn8Pb2LpS6sxozZgxu377N77Uholzxu1eINGTAgAHYuHEjAEBfXx+VKlVC//798b///Q96eoX3VtyzZw/09fULpK6lS5dm+9ZUANiyZQvu3buHgwcPFshxiKh4YtJBpEHt2rXDhg0bkJKSgkOHDmHkyJHQ19fHlClTlOJSU1NhYGBQIMe0sLAokHqAd9+KmpO+ffuib9++BXYcIiqeeHuFSIPkcjmsra1hZ2eH4cOHw83NDb///rt02+L777+Hra0tqlevDgD4559/0KNHD5ibm8PCwgJfffUV7t27J9WXkZEBX19fmJubo2zZspg4cWK2noj3b6+kpKRg0qRJqFixIuRyOezt7bF+/Xpp+/Xr19GxY0eYmpqidOnSaN68Oe7cuQMg++2VlJQUjBkzBuXLl4ehoSGaNWuGCxcuSNuPHz8OmUyGkJAQNGjQAKVKlUKTJk0QFRVVgK8qEWkLJh1ERcjIyAipqakAgJCQEERFRSE4OBgHDhxAWloa3N3dUbp0aZw6dQqnT5+GiYkJ2rVrJ+2zcOFCBAYG4ueff0ZoaCieP3+OvXv3fvCY/fv3x7Zt27Bs2TLcvHkTP/74I0xMTAAADx8+RIsWLSCXy3Hs2DGEh4dj0KBBSE9Pz7GuiRMnYvfu3di4cSMuXboEe3t7uLu74/nz50px3333HRYuXIiLFy9CT08PgwYNyu9LR0TaqGi/5Jao5Mj6tekKhUIEBwcLuVwuxo8fLzw9PYWVlZVISUmR4jdv3iyqV68uFAqFVJaSkiKMjIzEkSNHhBBC2NjYiICAAGl7WlqaqFChgtLXomf9WvaoqCgBQAQHB+fYxilTpogqVaqI1NTUj57Dq1evhL6+vtiyZYu0PTU1Vdja2kpt+vPPPwUAcfToUSnm4MGDAoB4+/btR14xIipu2NNBpEEHDhyAiYkJDA0N0b59e/Ts2RMzZswAANSuXVtpHMeVK1dw+/ZtlC5dGiYmJjAxMYGFhQWSk5Nx584dJCYm4vHjx3BxcZH20dPTQ4MGDXI9fkREBHR1ddGyZctctzdv3jxPA0/v3LmDtLQ0NG3aVCrT19dHo0aNcPPmTaXYOnXqSP+3sbEBADx58uSjxyCi4oUDSYk0qFWrVli9ejUMDAxga2urNGvF2NhYKfbVq1dwdnbGli1bstVjaWmp1vGNjIzytV1dWZMYmUwGAFAoFIVyLCL672JPB5EGGRsbw97eHpUqVfroNNn69evj1q1bKF++POzt7ZUeZmZmMDMzg42NDc6dOyftk56ejvDw8FzrrF27NhQKBU6cOJHj9jp16uDUqVNIS0v76Ll8+umnMDAwwOnTp6WytLQ0XLhwAY6Ojh/dn4hKHiYdRP9Rffv2Rbly5fDVV1/h1KlTiI6OxvHjxzFmzBg8ePAAADB27FjMmzcP+/btQ2RkJEaMGIGEhIRc66xcuTI8PT0xaNAg7Nu3T6pzx44dAIBRo0YhKSkJvXr1wsWLF3Hr1i1s3rw5x9kmxsbGGD58OCZMmICgoCDcuHEDQ4YMwZs3b+Dl5VUorwkRaTcmHUT/UaVKlcLJkydRqVIldO3aFQ4ODvDy8kJycjJMTU0BAOPGjUO/fv3g6ekJV1dXlC5dGl26dPlgvatXr0b37t0xYsQI1KhRA0OGDMHr168BAGXLlsWxY8fw6tUrtGzZEs7Ozvjpp59yHeMxb948dOvWDf369UP9+vVx+/ZtHDlyBGXKlCnYF4OIigWZEDksL0hERERUwNjTQURERBrBpIOIiIg0gkkHERERaQSTDiIiItIIJh1ERESkEUw6iIiISCOYdBAREZFGMOkgIiIijWDSQURERBrBpIOIiIg0gkkHERERacT/ASkZi19FowtoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_conf_matrix = confusion_matrix(y_test, y_pred_best_mcd)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(best_conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Métricas de Evaluación: MCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9993679997191109\n",
      "Recall:  0.8088235294117647\n",
      "precision_score:  0.7971014492753623\n",
      "Especificidad:  0.9996717737114188\n",
      "F1-score:  0.8029197080291971\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", accuracy_score(y_test, y_pred_best_mcd))\n",
    "print(\"Recall: \", recall_score(y_test, y_pred_best_mcd))\n",
    "print(\"precision_score: \", precision_score(y_test, y_pred_best_mcd))\n",
    "print(\"Especificidad: \", recall_score(y_test, y_pred_best_mcd, pos_label=0)) # Negativos predichos correctamente\n",
    "print(\"F1-score: \", f1_score(y_test, y_pred_best_mcd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS:**\n",
    "\n",
    "* **Accuracy:** (de 99.92% a 99.93%), en optimas condiciones\n",
    "* **Recall mejoró:** (de 62.5% a 80.88%), lo que significa que el **modelo detecta muchos más fraudes.**\n",
    "* **F1-score aumentó** de 72.9% a 80.58%, lo que indica un mejor balance entre precisión y recall.\n",
    "* **La precision bajó** ligeramente (87.6% → 80.3%), lo que significa que el modelo está detectando más fraudes, pero con más falsos positivos.\n",
    "* **La especificidad bajó un poco**, pero sigue siendo casi perfecta (~99.97%).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Isolation Forest**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isolation Forest es un método de detección de anomalías basado en árboles de decisión. A diferencia de los enfoques tradicionales, no modela la distribución de los datos, sino que busca identificar observaciones anómalas basándose en su facilidad para ser aisladas en un espacio multidimensional.\n",
    "\n",
    "En esta etapa, aplicamos Isolation Forest para identificar transacciones fraudulentas como outliers y evaluar su desempeño en comparación con otros enfoques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_iforest = IsolationForest(contamination=0.01, random_state=42)\n",
    "best_iforest.fit(X_train_scaled)\n",
    "\n",
    "y_pred_best_iforest = best_iforest.predict(X_train_scaled)\n",
    "mask_best_iforest = y_pred_best_iforest == 1  # Filtrar datos sin outliers\n",
    "X_train_best_iforest, y_train_best_iforest = X_train_scaled[mask_best_iforest], y_train[mask_best_iforest]\n",
    "\n",
    "# Entrenar modelo de Regresión Logística\n",
    "best_model_iforest = LogisticRegression()\n",
    "best_model_iforest.fit(X_train_best_iforest, y_train_best_iforest)\n",
    "\n",
    "# Predecir en el conjunto de prueba\n",
    "y_pred_best_iforest_test = best_model_iforest.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_conf_matrix_iforest = confusion_matrix(y_test, y_pred_best_iforest_test)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(best_conf_matrix_iforest, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Métricas de Evaluación: Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.999204147794436\n",
      "Recall:  0.8088235294117647\n",
      "precision_score:  0.7236842105263158\n",
      "Especificidad:  0.9995076605671281\n",
      "F1-score:  0.7638888888888888\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", accuracy_score(y_test, y_pred_best_iforest_test))\n",
    "print(\"Recall: \", recall_score(y_test, y_pred_best_iforest_test))\n",
    "print(\"precision_score: \", precision_score(y_test, y_pred_best_iforest_test))\n",
    "print(\"Especificidad: \", recall_score(y_test, y_pred_best_iforest_test, pos_label=0)) # Negativos predichos correctamente\n",
    "print(\"F1-score: \", f1_score(y_test, y_pred_best_iforest_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS**\n",
    "\n",
    "* El **Recall se mantiene (80.88%)**, igual que con MCD.\n",
    "* Se detectan más fraudes, pero se generan más falsos positivos (baja precisión).\n",
    "* **La precisión bajó más** que con MCD: 80.3% → 72.3% . Ahora casi el 30% de los fraudes detectados son errores.\n",
    "* El **F1-score bajó** un poco (80.5% → 76.3%).\n",
    "* **Isolation Forest es útil, pero genera demasiados falsos positivos.**\n",
    "* **El modelo con MCD sigue siendo mejor** (balance entre Recall y Precisión).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estrategias de Balanceo de Clases\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Cost-Sensitive Training**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelo supervisado con penalización por desbalance. En modelos desbalanceados, el aprendizaje tiende a favorecer la clase mayoritaria. Cost-Sensitive Training ajusta los pesos de cada clase para que el modelo penalice más los errores en la clase minoritaria (fraudes). Esto permite mejorar el recall sin necesidad de modificar la distribución de los datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entrenar el modelo de Regresión Logística con penalización por desbalance\n",
    "cost_sensitive_model = LogisticRegression(class_weight=\"balanced\", random_state=42)\n",
    "cost_sensitive_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predecir en el conjunto de prueba\n",
    "y_pred_cost_sensitive = cost_sensitive_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix_cost_sensitive = confusion_matrix(y_test, y_pred_cost_sensitive)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix_cost_sensitive, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Métricas de : Cost-Sensitive Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.97424013669932\n",
      "Recall: 0.9264705882352942\n",
      "Precision: 0.054380664652567974\n",
      "Specificity: 0.9743162929185178\n",
      "F1-score: 0.10273134936812067\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_cost_sensitive))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_cost_sensitive))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_cost_sensitive))\n",
    "print(\"Specificity:\", recall_score(y_test, y_pred_cost_sensitive, pos_label=0))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_cost_sensitive))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS**\n",
    "* Detecta muchos fraudes (**alto Recall**), pero genera **demasiados falsos positivos.**\n",
    "* Baja precisión significa que muchas transacciones legítimas son clasificadas como fraude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Undersampling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplicar Undersampling\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "# Entrenar modelo\n",
    "model_under = LogisticRegression()\n",
    "model_under.fit(X_train_under, y_train_under)\n",
    "\n",
    "# Predecir\n",
    "y_pred_under = model_under.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix_under = confusion_matrix(y_test, y_pred_under)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix_under, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Métricas de : Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9607925751670704\n",
      "Recall: 0.9338235294117647\n",
      "Precision: 0.036620530565167245\n",
      "Specificity: 0.9608355703517882\n",
      "F1-score: 0.07047724750277469\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_under))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_under))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_under))\n",
    "print(\"Specificity:\", recall_score(y_test, y_pred_under, pos_label=0))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_under))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS**:\n",
    "* **Aumenta el Recall a 93.3%**, pero con una **precisión muy baja.**\n",
    "* Muchos falsos positivos, lo que no es ideal en un sistema de detección de fraudes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **OverSampling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Aplicar Oversampling\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_over, y_train_over = oversampler.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "# Entrenar modelo\n",
    "model_over = LogisticRegression()\n",
    "model_over.fit(X_train_over, y_train_over)\n",
    "\n",
    "# Predecir\n",
    "y_pred_over = model_over.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix_over = confusion_matrix(y_test, y_pred_over)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix_over, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Métricas de : Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.974006062521213\n",
      "Recall: 0.9264705882352942\n",
      "Precision: 0.05391527599486521\n",
      "Specificity: 0.9740818455695313\n",
      "F1-score: 0.101900525677315\n"
     ]
    }
   ],
   "source": [
    "# Evaluar el modelo\n",
    "print(\"Accuracy:\", accuracy_score(y_test,y_pred_over))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_over))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_over))\n",
    "print(\"Specificity:\", recall_score(y_test, y_pred_over, pos_label=0))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_over))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS:**\n",
    "* Resultados similares a Cost-Sensitive Training.\n",
    "* Se detectan muchos fraudes, pero con muchos falsos positivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **SMOTE:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Aplicar SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "# Entrenar modelo\n",
    "model_smote = LogisticRegression()\n",
    "model_smote.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Predecir\n",
    "y_pred_smote = model_smote.predict(X_test_scaled)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tRkZGaNKkidK2+vr6Um+LQqFAYmIiMjMz0bhx4zzP/V69eqFs2bLS65YtWwJAgdqze/duWFpa4rPPPlN6r1xcXGBmZvbO9/vRo0eIioqCl5cXLC0tpfLPPvsMzs7OhbYfVT7TQME/Cx999BEaNGgg9RQAb74T9uzZg86dOyudE6p8x7Zu3TrX8eclZ50ZGRl4+vQpnJycYGVllWe9qnz3ZGVloU+fPnj+/Dn27dsHU1NTtY7lXfr27YsTJ04gLi4Ox44dQ1xc3Dt/T+WlIJ+Hd1H38ymXy6VxW1lZWXj69CnMzMxQo0YNld+H/xK1R1/a2NjA3d0d27dvx6tXr5CVlYUePXrkG3/w4EHMmTMHUVFRSEtLk8pVvb/G+7qG3zZ06FDcvn0bZ8+efW+X/N9//w09Pb1cl3Rq1Kih9Prx48dISkrChg0bsGHDhjzrSkhIyHc/FhYWAP79JfYujx8/xqtXr3K1AQBq1aoFhUKBf/75B7Vr15bK3+7ezf6yf/bs2Xv3V1ALFy6El5cXKlWqBBcXF3Ts2BEDBgxQSoze9vfff8PBwSHXF3OtWrWk9Tnl1U1dtmzZ9x5HXFyc0mtLS8t8f2EXxIsXLwD8+wvl5s2buHHjBmxsbPKMf9fPHlDv/MnvvDc2Ns7VDktLS1SsWDHXZ8vS0rJA50Be25YtWxZ//vmnUtmWLVuwePFiREdHIyMj451t1eScvHnzJpKTk/O9NPqu9zv7nKpevXqudW9/eWuyH1U+09ntKuhnoVevXvjf//6HBw8e4IMPPsCJEyeQkJCAXr16KW2ryndsQb9HX79+jcDAQAQFBeHBgwdKl7STk5Nzxavyc54yZQqOHTuGQ4cO5frOLazfFx07doS5uTl27tyJqKgofPzxx3BycsK9e/cKXEdBPw950eTzqVAosHz5cqxZswZ3795Vmm6trcvLRUGjKR99+/bF0KFDERcXhw4dOsDKyirPuNOnT+Pzzz9Hq1atsGbNGtjb28PQ0BBBQUHYvn27SvtU5ZfH8uXLsWPHDvzwww+FevMrhUIBAPjqq6/g5eWVZ0y9evXy3b5mzZoAgCtXrhTJTbny680R+YyByZbfBzqvewv07NkTLVu2xL59+/Dbb79h0aJFWLBgAfbu3fve65wFpe5x2NvbK70OCgrCwIED1W7H1atXoa+vL31RKxQK1K1bF0uWLMkzvlKlSu+sT53zJ7/zPr/3SN33rqDb/vDDDxg4cCC6dOmC8ePHo0KFCtDX10dgYCBu375dqO1RKBSoUKECtm3bluf6/JI/VWmyHycnJxgYGODKlSuF0pacevXqhcmTJ2P37t3w8/PDrl27YGlpifbt20sxqn7HFvR7dNSoUQgKCoKfnx/c3NxgaWkJmUyG3r17S+dxTgX9Oe/fvx8LFizA7NmzlY5DnWN5F7lcjm7dumHLli24c+eOWveBKYrPUkHqnDdvHqZOnYrBgwdj9uzZsLa2hp6eHvz8/PJ873WFRklH165d8fXXX+P3339X6v57208//QRjY2McOXIEcrlcKg8KCsoVW1h3Fj19+jTGjRsHPz8/9OvXr0DbVKlSBQqFArdv31bqWYiJiVGKs7Gxgbm5ObKystSa+tmhQwfo6+vjhx9+eO9gUhsbG5QpUyZXGwAgOjoaenp67/0lV1DZf5UkJSUpled3Wcbe3h4jR47EyJEjkZCQgEaNGmHu3Ln5Jh1VqlTB0aNH8fz5c6W/8KKjo6X1hSE0NFTpdc5eIFXFxsbi5MmTcHNzk9r84Ycf4vLly2jbtu17z9e81mt6/vwX7NmzB9WqVcPevXuVjnH69Olq15nfe/nhhx/i6NGjaN68uco9Vtnn1M2bN3Ote/szpcl+ypQpg08//RTHjh3DP//8897PpCqfBUdHRzRp0gQ7d+6Er68v9u7diy5duih9l6ryHauKPXv2wMvLC4sXL5bKUlNTc31HqOKvv/6Cl5cXunTpgv/973+51hf2sfTt2xffffcd9PT0lAbe/tft2bMHbdq0webNm5XKk5KSUL58+WJqleY0muhvZmaGtWvXYsaMGejcuXO+cfr6+pDJZEp/Md+7dy/POzWamppqdEIDb67j9uzZEy1atMCiRYsKvF32L8u3Z98sW7ZM6bW+vj66d++On376Kc95948fP37nfipVqoShQ4fit99+w8qVK3OtVygUWLx4Me7fvw99fX20a9cOP//8s1KXYHx8PLZv344WLVpIXbuasrCwQPny5XHq1Cml8jVr1ii9zsrKytW1WqFCBTg4OCh1hb6tY8eOyMrKwqpVq5TKly5dCplMVmg9JO7u7krL2z0fBZWYmIg+ffogKytL6Q6FPXv2xIMHD7Bx48Zc27x+/RovX76UXud1Pmt6/vwXZP+llvMvs3PnziEiIkLtOrOv6b/9fvXs2RNZWVmYPXt2rm0yMzPf+X1hb2+PBg0aYMuWLUrnbGhoaK67zWqyH+BNwiWEQP/+/aVLcjlFRkZiy5YtAFT/LPTq1Qu///47vvvuOzx58iTXpRVVvmNVoa+vn+sv+pUrV6p9Z9UXL16ga9eu+OCDD7Bly5Y8E83CPpY2bdpg9uzZWLVqldIMoP+6vN773bt348GDB8XUosKh8R218usezsnT0xNLlixB+/bt0bdvXyQkJGD16tVwcnLKdV3MxcUFR48exZIlS+Dg4ABHR8dcU0LfZ/To0Xj8+DEmTJiAH3/8UWldvXr18r300aBBA/Tp0wdr1qxBcnIymjVrhrCwMNy6dStX7Pz583H8+HG4urpi6NChcHZ2RmJiIi5duoSjR48iMTHxnW1cvHgxbt++jdGjR2Pv3r3o1KkTypYti9jYWOzevRvR0dFSVj5nzhyEhoaiRYsWGDlyJAwMDLB+/XqkpaVh4cKFKr037zNkyBDMnz8fQ4YMQePGjXHq1Cn89ddfSjHPnz9HxYoV0aNHD9SvXx9mZmY4evQoLly4oPQX0ds6d+6MNm3a4JtvvsG9e/dQv359/Pbbb/j555/h5+eX67quNv3111/44YcfIIRASkoKLl++jN27d+PFixfSuZutf//+2LVrF4YPH47jx4+jefPmyMrKQnR0NHbt2oUjR46gcePGAPI/nzU9f4pbp06dsHfvXnTt2hWenp64e/cu1q1bB2dn5zx/4RaEi4sLgDefXw8PD+jr66N3795o3bo1vv76awQGBiIqKgrt2rWDoaEhbt68id27d2P58uXvHE8WGBgIT09PtGjRAoMHD0ZiYiJWrlyJ2rVrK7VV0/00a9YMq1evxsiRI1GzZk30798f1atXx/Pnz3HixAn88ssvmDNnDgDVPws9e/bEuHHjMG7cOOm26jmp8h2rik6dOmHr1q2wtLSEs7MzIiIicPToUbXHFMycORPXr1/HlClT8PPPPyut+/DDD+Hm5lbox6Knp4cpU6ao1d7i1KlTJ8yaNQuDBg1Cs2bNcOXKFWzbtu2d4+Z0gipTXXJOmX2XvKbMbt68WVSvXl3I5XJRs2ZNERQUlOdU1+joaNGqVSthYmKS583B8rpF7dv1ZE9zymt5383BXr9+LUaPHi3KlSsnTE1N33lzsPj4eOHj4yMqVaokDA0NhZ2dnWjbtq3YsGHDO/eRLTMzU2zatEm0bNlSWFpaCkNDQ1GlShUxaNCgXNNpL126JDw8PISZmZkoU6aMaNOmjTh79qxSTH4/n7ymguU1ZVaIN1PVvL29haWlpTA3Nxc9e/YUCQkJSseflpYmxo8fL+rXry/drKx+/fpizZo1SnXldfOl58+fC39/f+Hg4CAMDQ1F9erV33lzsLdVqVKl0G8rnvP80NPTE1ZWVqJhw4ZizJgxeU4rFOLNFL4FCxZIN5wqW7ascHFxETNnzhTJyclSXH7nsxAFO3+yf3Z53Rgpv59h9g2N3vb25/JdN0PKa185f5YKhULMmzdPVKlSRcjlctGwYUNx8ODBXHF5TU3M9vZnKjMzU4waNUrY2NgImUyW67thw4YNwsXFRZiYmAhzc3NRt25dMWHCBPHw4cNcdb/tp59+ErVq1RJyuVw4Ozvne3MwTfcjhBCRkZGib9++0jletmxZ0bZtW7FlyxalKZAF/Sxka968uQAghgwZkuf6gn7H5vfZyl6X82fy7NkzMWjQIOlmiR4eHiI6OjrX57Cg3z3vunFjzvoKeix5ye9zkZMqU2YL8nl4183B3lbQz2dqaqoYO3assLe3FyYmJqJ58+YiIiJCtG7dWrRu3fqdx/dfJhOiAKNhiIiIiDRUIh5tT0RERP99TDqIiIhIK5h0EBERkVYw6SAiIiKtYNJBREREWsGkg4iIiLSCSQcRERFphcZ3JC1JTBr6FncTiIpcQsSK9wcR6Thz46L7m1rT3xWv/1j1/qASikkHERGRKmS8SKAuJh1ERESqKKSnoZdGTDqIiIhUwZ4OtfGdIyIiIq1gTwcREZEqeHlFbUw6iIiIVMHLK2rjO0dERKQKmUyzpYCysrIwdepUODo6wsTEBB9++CFmz54NIYQUI4TAtGnTYG9vDxMTE7i7u+PmzZtK9SQmJqJfv36wsLCAlZUVvL298eLFC6WYP//8Ey1btoSxsTEqVaqEhQsX5mrP7t27UbNmTRgbG6Nu3br49ddfVXzjmHQQERGpRqan2VJACxYswNq1a7Fq1SrcuHEDCxYswMKFC7Fy5UopZuHChVixYgXWrVuHc+fOwdTUFB4eHkhNTZVi+vXrh2vXriE0NBQHDx7EqVOnMGzYMGl9SkoK2rVrhypVqiAyMhKLFi3CjBkzsGHDBinm7Nmz6NOnD7y9vfHHH3+gS5cu6NKlC65evaraWydypkylHG8ORqUBbw5GpUGR3hzMbZJG27+OmF+guE6dOsHW1habN2+Wyrp37w4TExP88MMPEELAwcEBY8eOxbhx4wAAycnJsLW1RXBwMHr37o0bN27A2dkZFy5cQOPGjQEAISEh6NixI+7fvw8HBwesXbsW33zzDeLi4mBkZAQAmDRpEvbv34/o6GgAQK9evfDy5UscPHhQakvTpk3RoEEDrFu3rsDHzp4OIiIiLUpLS0NKSorSkpaWliuuWbNmCAsLw19//QUAuHz5MsLDw9GhQwcAwN27dxEXFwd3d3dpG0tLS7i6uiIiIgIAEBERASsrKynhAAB3d3fo6enh3LlzUkyrVq2khAMAPDw8EBMTg2fPnkkxOfeTHZO9n4Ji0kFERKQKDS+vBAYGwtLSUmkJDAzMtZtJkyahd+/eqFmzJgwNDdGwYUP4+fmhX79+AIC4uDgAgK2trdJ2tra20rq4uDhUqFBBab2BgQGsra2VYvKqI+c+8ovJXl9QnL1CRESkCg2nzE6ePBkBAQFKZXK5PFfcrl27sG3bNmzfvh21a9dGVFQU/Pz84ODgAC8vL43aUFyYdBAREalCwymzcrk8zyTjbePHj5d6OwCgbt26+PvvvxEYGAgvLy/Y2dkBAOLj42Fvby9tFx8fjwYNGgAA7OzskJCQoFRvZmYmEhMTpe3t7OwQHx+vFJP9+n0x2esLipdXiIiIVKGlKbOvXr2Cnp7yr2l9fX0oFAoAgKOjI+zs7BAWFiatT0lJwblz5+Dm5gYAcHNzQ1JSEiIjI6WYY8eOQaFQwNXVVYo5deoUMjIypJjQ0FDUqFEDZcuWlWJy7ic7Jns/BcWkg4iISBVamjLbuXNnzJ07F4cOHcK9e/ewb98+LFmyBF27dn3TDJkMfn5+mDNnDn755RdcuXIFAwYMgIODA7p06QIAqFWrFtq3b4+hQ4fi/PnzOHPmDHx9fdG7d284ODgAAPr27QsjIyN4e3vj2rVr2LlzJ5YvX650CWjMmDEICQnB4sWLER0djRkzZuDixYvw9VVt1icvrxAREf0HrVy5ElOnTsXIkSORkJAABwcHfP3115g2bZoUM2HCBLx8+RLDhg1DUlISWrRogZCQEBgbG0sx27Ztg6+vL9q2bQs9PT10794dK1b8O3Xe0tISv/32G3x8fODi4oLy5ctj2rRpSvfyaNasGbZv344pU6bgf//7H6pXr479+/ejTp06Kh0T79ORA+/TQaUB79NBpUGR3qej9SyNtn99ctr7g0oo9nQQERGpQo8PfFMXkw4iIiJV8IFvamPSQUREpAo+2l5tTDqIiIhUwZ4OtfGdIyIiIq1gTwcREZEqeHlFbUw6iIiIVMHLK2pj0kFERKQK9nSojUkHERGRKtjToTYmHURERKpgT4famK4RERGRVrCng4iISBW8vKI2Jh1ERESq4OUVtTHpICIiUgV7OtTGpIOIiEgVTDrUxqSDiIhIFby8ojama0RERKQV7OkgIiJSBS+vqI1JBxERkSp4eUVtTDqIiIhUwZ4OtTHpICIiUgV7OtTGpIOIiEgFMiYdamMfEREREWkFezqIiIhUwJ4O9THpICIiUgVzDrXpfNKRmpoKY2Pj4m4GERGVEuzpUJ9OjulQKBSYPXs2PvjgA5iZmeHOnTsAgKlTp2Lz5s3F3DoiIirJZDKZRosqqlatmmcdPj4+AN784e3j44Ny5crBzMwM3bt3R3x8vFIdsbGx8PT0RJkyZVChQgWMHz8emZmZSjEnTpxAo0aNIJfL4eTkhODg4FxtWb16NapWrQpjY2O4urri/Pnzqr1x0NGkY86cOQgODsbChQthZGQkldepUwebNm0qxpYREVFJp82k48KFC3j06JG0hIaGAgC+/PJLAIC/vz8OHDiA3bt34+TJk3j48CG6desmbZ+VlQVPT0+kp6fj7Nmz2LJlC4KDgzFt2jQp5u7du/D09ESbNm0QFRUFPz8/DBkyBEeOHJFidu7ciYCAAEyfPh2XLl1C/fr14eHhgYSEBNXeOyGEUGmL/wAnJyesX78ebdu2hbm5OS5fvoxq1aohOjoabm5uePbsmVr1mjT0LeSWEv33JESsKO4mEBU5c+Oi+5vaovf3Gm2f8uMAtbf18/PDwYMHcfPmTaSkpMDGxgbbt29Hjx49AADR0dGoVasWIiIi0LRpUxw+fBidOnXCw4cPYWtrCwBYt24dJk6ciMePH8PIyAgTJ07EoUOHcPXqVWk/vXv3RlJSEkJCQgAArq6u+Pjjj7Fq1SoAb644VKpUCaNGjcKkSZMK3H6d7Ol48OABnJyccpUrFApkZGQUQ4uIiKi00LSnIy0tDSkpKUpLWlrae/ebnp6OH374AYMHD4ZMJkNkZCQyMjLg7u4uxdSsWROVK1dGREQEACAiIgJ169aVEg4A8PDwQEpKCq5duybF5KwjOya7jvT0dERGRirF6Onpwd3dXYopKJ1MOpydnXH69Olc5Xv27EHDhg2LoUVERFRqyDRbAgMDYWlpqbQEBga+d7f79+9HUlISBg4cCACIi4uDkZERrKyslOJsbW0RFxcnxeRMOLLXZ697V0xKSgpev36NJ0+eICsrK8+Y7DoKSidnr0ybNg1eXl548OABFAoF9u7di5iYGHz//fc4ePBgcTePiIhKME1nr0yePBkBAQFKZXK5/L3bbd68GR06dICDg4NG+y9OOtnT8cUXX+DAgQM4evQoTE1NMW3aNNy4cQMHDhzAZ599VtzNIyKiEkzTyytyuRwWFhZKy/uSjr///htHjx7FkCFDpDI7Ozukp6cjKSlJKTY+Ph52dnZSzNuzWbJfvy/GwsICJiYmKF++PPT19fOMya6joHQy6QCAli1bIjQ0FAkJCXj16hXCw8PRrl274m4WERGVcNqcvZItKCgIFSpUgKenp1Tm4uICQ0NDhIWFSWUxMTGIjY2Fm5sbAMDNzQ1XrlxRmmUSGhoKCwsLODs7SzE568iOya7DyMgILi4uSjEKhQJhYWFSTEHp5OUVIiKi0kKhUCAoKAheXl4wMPj317alpSW8vb0REBAAa2trWFhYYNSoUXBzc0PTpk0BAO3atYOzszP69++PhQsXIi4uDlOmTIGPj4/UuzJ8+HCsWrUKEyZMwODBg3Hs2DHs2rULhw4dkvYVEBAALy8vNG7cGE2aNMGyZcvw8uVLDBo0SKVj0Zmko2zZsgXOEBMTE4u4NUREVFpp+46kR48eRWxsLAYPHpxr3dKlS6Gnp4fu3bsjLS0NHh4eWLNmjbReX18fBw8exIgRI+Dm5gZTU1N4eXlh1qxZUoyjoyMOHToEf39/LF++HBUrVsSmTZvg4eEhxfTq1QuPHz/GtGnTEBcXhwYNGiAkJCTX4NL30Zn7dGzZskX6/9OnTzFnzhx4eHhIXTsRERE4cuQIpk6dCn9/f7X2wft0UGnA+3RQaVCU9+ko57VDo+2fbulTSC3RPTqTdOTUvXt3tGnTBr6+yknCqlWrcPToUezfv1+tepl0UGnApINKg6JMOsoP/FGj7Z8E9y6klugenRxIeuTIEbRv3z5Xefv27XH06NFiaBEREZUWxTGQtKTQyaSjXLly+Pnnn3OV//zzzyhXrlwxtIiIiEoLJh3q05mBpDnNnDkTQ4YMwYkTJ+Dq6goAOHfuHEJCQrBx48Zibh0RERHlRSeTjoEDB6JWrVpYsWIF9u7dCwCoVasWwsPDpSSEiIioSJTuzgqN6GTSAbx54t22bduKuxlERFTKlPZLJJrQyaQjNjb2nesrV66spZYQEVFpw6RDfTqZdFStWvWdP/SsrCwttoaIiEoTJh3q08mk448//lB6nZGRgT/++ANLlizB3Llzi6lVRERUGjDpUJ9OJh3169fPVda4cWM4ODhg0aJF6NatWzG0ioiIiN5FJ5OO/NSoUQMXLlwo7mYQEVFJxo4Otelk0pGSkqL0WgiBR48eYcaMGahevXoxtYqIiEoDXl5Rn04mHVZWVrl+6EIIVKpUCT/+qNk98YmIiN6FSYf6dDLpOH78uNJrPT092NjYwMnJCQYGOnlIRESkI5h0qE8nf0O3bt26uJtAREREKtLJpCPb9evXERsbi/T0dKXyzz//vJhaREREJR47OtSmk0nHnTt30LVrV1y5cgUymQxCCAD/dnnx5mBFQ09PhinDO6JPx49hW84Cjx4nY+uBc5i/MQQAYGCghxkjO8OjRW04ViyHlBepOHYuGlNX/IJHj5OlehrUrIg5Y7rApXZlZGUJ7A+LwsTFP+Hl6zfJ41edXbFxVv8821D500l4/OwFWrpUx2+bxuRaX9V9MuKfPi+Co6fSLGjzBhwPC8W9u3cglxujXoOGGOU3FlWrOkoxc2dNx/lzEXjyOAEmZcqgXv2GGO03FlUdqwEAkpKeYerkCbh5MwbJSUmwti6HVp98Cp/R/jAzMwMAHDv6G/bs/hF/xUQjIz0d1T50wrDhvnBr3qJYjpvyxssr6tPJpGPMmDFwdHREWFgYHB0dcf78eTx9+hRjx47Ft99+W9zNK7HGDvwMQ3u0xNBpW3H99iO41K6M9TO+QsqL11iz4yTKGBuhQa1KmL/xMP786wHKWpTBt+N7YPeyr9Gi30IAgL2NJQ6tG4U9v12C//xdsDA1xqLx3bFxVn/0Hb8ZALDnt0sIPXtdad8bZvaHsdwQj5+9UCqv+8UsPH/5WnqdkKi8nqgwXLp4AV/26gvn2nWQlZWF1SuXwne4N3bvPQiTMmUAALWca6ODZyfY2TkgJSUJ69euhs/wIfjl11Do6+tDT08Prdt8ihG+Y1C2bFn8808sFsybjZQ5yZg7/8331h+XLsK1aTP4jPKHubk5Dvy8D/6jRyL4hx9Rs5Zzcb4FlAOTDvXpZNIRERGBY8eOoXz58tDT04Oenh5atGiBwMBAjB49OtcdS6lwNK1fDQdP/omQ8GsAgNhHiejZvjEa164CAEh5kYpOI1YpbeM/fxfCt01AJbuy+CfuGTq0rIOMzCz4Be6SeqhGzd2Ji7v/h2qVyuPOP0+QmpaB1LQMqY7yZc3wSZOPMHxm7gf8PU58juQXr3OVExWmlWs3Kr2eMSsQn7Vpjhs3rqGRy8cAgG49ekrrHT74ACN9x6DPl13w6OEDVKxUGRYWlujRs48UY+/wAb7s2Qdbt3wnlY2d8D+l/fiM9sfJ42E4ffI4k47/ECYd6tMr7gaoIysrC+bm5gCA8uXL4+HDhwCAKlWqICYmpjibVqL9fvkO2jSpAafKFQAAdT/6AG4NquG3M9fz3cbC3AQKhQJJz98kBnIjA2RkZEkJBwC8TntzWaVZgw/zrKNfpyZ4lZqOfUejcq07t3MS7vw2FwfX+sKtfjV1D41IJS9evLmEZ2Fhmef6169e4Zef9+KDDyrC1s4uz5jHCQk4dixUSlryolAo8PLVK1hYWmncZio8MplMo6U008mejjp16uDy5ctwdHSEq6srFi5cCCMjI2zYsAHVqvEXT1H5NigUFmbGuLxvCrKyBPT1ZZi++iB+PHwxz3i5kQHmjP4Cu0Ii8fxlKgDgxPkYLAjoBv8BbbFq+wmYmhhhzugvAAB2Nnl/gXt1ccPOwxeVej/iniTDd84OXLoeC7mRAQZ2aYYjG8eg1YBFiIq+X8hHTvQvhUKBxQsDUb9BIzhV/0hp3e6d27Fi6WK8fv0KVao6YvX6zTA0NFKK+d/EsTh54hjSUlPRsnUbTJkxO999bd3yHV6/eoXP2rUvkmMh0jadTDqmTJmCly9fAgBmzZqFTp06oWXLlihXrhx27txZoDrS0tKQlpamVCYUWZDp6Rd6e0uKHu0aoXeHjzHwf1tw/fYj1KvxARaN64FHj5Ox7cA5pVgDAz38sNAbMpkMo+f9+zO5cScOQ6dtxfyx3TBr1OfIUiiwZsdJxD1JgVAocu3TtZ4jalWzh/eU75XKb/6dgJt/J0ivf798F9Uqlceofp/Ce+r3b1dDVGgWzJuF27dvYlNw7st9HTp2hmvTZnjy5DG2bgnCpPH+2LxlO+RyuRQTMH4Shg33wd9/38Pq5Uuw9Nv5mPTN9Fx1hfx6EBvXrcHi5atgXa5ckR4Tqah0d1ZoRCeTDg8PD+n/Tk5OiI6ORmJiIsqWLVvgrqvAwEDMnDlTqUzf9mMY2jcp1LaWJPP8uuDboFDsPhIJALh26yEq21tj/KDPlJIOAwM9bFvgjcr2ZdFh2EqplyPbzpCL2BlyERWszfHydRqEAEZ/9Snu3n+aa58Du7ohKvof/HHjn/e27+LVv9GsYd6XaIgKw4J5sxF+6iQ2fLcVtra5L5uYmZvDzNwclatURd169dGmRVMcP3YU7Tt4SjHly9ugfHkbVHWsBksLSwwZ9BWGDBuB8jYVpJgjhw9h9sypWLBoKVybNtPKsVHBlfZLJJrQuTEdGRkZMDAwwNWrV5XKra2tVToRJk+ejOTkZKXFwNalsJtbopgYG0EhlHsjshQCenr/nkbZCceHlW3gOXwVEpNf5ltfQuJzvHydjh4ejZCanoGw36OV1puaGKH7Z42wZX9EgdpXr0ZFxOWYmktUWIQQWDBvNk4cO4q1G4PwQcWKBdgGEBDIeOs+Qjllf57S0/+9dBhy+BBmTf8Gc+d/ixatPtG47VT4OKZDfTrX02FoaIjKlStrfC8OuVyu1OUJgJdW3uPXU1cw0dsD/zx6huu3H6FBzYoY/VUbfL//dwBvEo7ti4agYc1K6DZmHfT1ZLAt92bAb2LyK2RkvvmZDe/VCr9fvoMXr9LRtmlNzPPrgqkrf841C6WHhwsM9PWw41DuJwf79v0E9x4+xfXbj2BsZIhBXZvhk48/QqeRq3LFEmlqwbxZCDl8CIuXrUIZU1M8efIYAGBmZg5jY2Pcv/8PQo8cRlO35ihbtizi4+MR/N1GGMvlaN6iFQAg/PRJJD59CufadVCmjCnu3L6J5Uu/Rf0GjeDwwQcA3lxSmT51MsZNmIw6detJ+zGWG8Ps/wfPU/Er5XmDRmQi5zQCHbF582bs3bsXW7duhbW1daHVa9LQt9DqKonMysgxfWQnfP5pfdiUNcOjx8nYFRKJeRsOIyMzC5XtrRHz66w8t203ZDlOR94EAGya3R/tW9SBWRkjxNyLx7Lvw/JMLI4HB+Deg6cY9M2WXOsCvNwxuFtzOFSwxKvUDFy9+QDzNhzGqYs3C/egS6CEiBXF3QSd07h+rTzLp8+ah85fdMXjhATMnjkF0devIyUlBeXKlUNDl8YY8vVI6QZiF8+fw5pVy3Dnzm1kpKfD1tYObdp+hoGDh8LcwgIAMMx7AC5dzP1Z6PR5F8yYHVh0B1gCmRsXXUd+9fEhGm1/c1HpHRisk0lHw4YNcevWLWRkZKBKlSowNTVVWn/p0iW16mXSQaUBkw4qDUpK0vHgwQNMnDgRhw8fxqtXr+Dk5ISgoCA0btwYwJtLf9OnT8fGjRuRlJSE5s2bY+3atahevbpUR2JiIkaNGoUDBw5AT08P3bt3x/Lly6U74QLAn3/+CR8fH1y4cAE2NjYYNWoUJkyYoNSW3bt3Y+rUqbh37x6qV6+OBQsWoGPHjiodu85dXgGALl26FHcTiIiolNLW5ZVnz56hefPmaNOmDQ4fPgwbGxvcvHkTZcuWlWIWLlyIFStWYMuWLXB0dMTUqVPh4eGB69evw9jYGADQr18/PHr0CKGhocjIyMCgQYMwbNgwbN++HQCQkpKCdu3awd3dHevWrcOVK1cwePBgWFlZYdiwYQCAs2fPok+fPggMDESnTp2wfft2dOnSBZcuXUKdOnUKfEw61dPx3XffoV+/frnGYhQW9nRQacCeDioNirKno8bEIxptH7PA4/1BACZNmoQzZ87g9OnTea4XQsDBwQFjx47FuHHjAADJycmwtbVFcHAwevfujRs3bsDZ2RkXLlyQekdCQkLQsWNH3L9/Hw4ODli7di2++eYbxMXFwcjISNr3/v37ER39ZoB/r1698PLlSxw8eFDaf9OmTdGgQQOsW7euwMeuU7NXhg4diuTkf2cnODg44N69e8XXICIiKnVkMs2WtLQ0pKSkKC1v3zcKAH755Rc0btwYX375JSpUqICGDRti48Z/b8l/9+5dxMXFwd3dXSqztLSEq6srIiLezPqLiIiAlZWVlHAAgLu7O/T09HDu3DkpplWrVlLCAby5NUVMTAyePXsmxeTcT3ZM9n4KSqeSjrc7ZZ4/fw5FHjeUIiIiKip6ejKNlsDAQFhaWiotgYG5BwrfuXNHGp9x5MgRjBgxAqNHj8aWLW8G18fFxQEAbG1tlbaztbWV1sXFxaFChQpK6w0MDGBtba0Uk1cdOfeRX0z2+oLSyTEdRERExUXTMR2TJ09GQECAUllewwYUCgUaN26MefPmAXgzieLq1atYt24dvLy8NGtEMdGpno63b6zCG60QEZGukcvlsLCwUFrySjrs7e3h7Kz8dOFatWohNjYWAGD3/w8TjI+PV4qJj4+X1tnZ2SEhIUFpfWZmJhITE5Vi8qoj5z7yi7HL54GG+dGppEMIgY8++gjW1tawtrbGixcv0LBhQ+l19kJERFRUtHVH0ubNm+d6cvpff/2FKlWqAAAcHR1hZ2eHsLAwaX1KSgrOnTsHNzc3AICbmxuSkpIQGRkpxRw7dgwKhQKurq5SzKlTp5CR8e+dcUNDQ1GjRg1ppoybm5vSfrJjsvdTUDp1eSUoKKi4m0BERKWctjrY/f390axZM8ybNw89e/bE+fPnsWHDBmzYsOH/2yGDn58f5syZg+rVq0tTZh0cHKRbS9SqVQvt27fH0KFDsW7dOmRkZMDX1xe9e/eGg4MDAKBv376YOXMmvL29MXHiRFy9ehXLly/H0qVLpbaMGTMGrVu3xuLFi+Hp6Ykff/wRFy9elNpSUDo1ZbaoccoslQacMkulQVFOma037ahG2/85y/39Qf/v4MGDmDx5Mm7evAlHR0cEBARg6NCh0vrsm4Nt2LABSUlJaNGiBdasWYOPPvpIiklMTISvr6/SzcFWrFiR783Bypcvj1GjRmHixIlKbdm9ezemTJki3Rxs4cKFKt8cjElHDkw6qDRg0kGlQVEmHfWnh70/6B0uz2xbSC3RPTp1eYWIiKi4cf6C+nRqICkRERHpLvZ0EBERqYC3alCfzicd2UNSeBIQEZE28NeN+nT28sr333+PunXrwsTEBCYmJqhXrx62bt1a3M0iIqISTlv36SiJdLKnY8mSJZg6dSp8fX3RvHlzAEB4eDiGDx+OJ0+ewN/fv5hbSEREJVUpzxs0opNJx8qVK7F27VoMGDBAKvv8889Ru3ZtzJgxg0kHEREVmdLeW6EJnby88ujRIzRr1ixXebNmzfDo0aNiaBERERG9j04mHU5OTti1a1eu8p07d6J69erF0CIiIiotZDLNltJMJy+vzJw5E7169cKpU6ekMR1nzpxBWFhYnskIERFRYeHlFfXpZNLRvXt3nDt3DkuXLsX+/fsBvHmozfnz59GwYcPibRwREZVozDnUp5NJBwC4uLjghx9+KO5mEBFRKcOeDvXpbNJBRERUHJhzqE+nkg49Pb33ZpgymQyZmZlaahEREREVlE4lHfv27ct3XUREBFasWAGFQqHFFhERUWnDyyvq06mk44svvshVFhMTg0mTJuHAgQPo168fZs2aVQwtIyKi0oI5h/p08j4dAPDw4UMMHToUdevWRWZmJqKiorBlyxZUqVKluJtGREQlGJ+9oj6dSzqSk5MxceJEODk54dq1awgLC8OBAwdQp06d4m4aERGVAkw61KdTl1cWLlyIBQsWwM7ODjt27MjzcgsREVFRKuV5g0Z0KumYNGkSTExM4OTkhC1btmDLli15xu3du1fLLSMiIqL30amkY8CAAaW+a4qIiIoXfw+pT6eSjuDg4OJuAhERlXLMOdSnU0kHERFRcWNPh/qYdBAREamAOYf6mHQQERGpQI9Zh9p07j4dREREpJuYdBAREalAJtNsUcWMGTNy3VysZs2a0vrU1FT4+PigXLlyMDMzQ/fu3REfH69UR2xsLDw9PVGmTBlUqFAB48ePz/Vg1BMnTqBRo0aQy+VwcnLKc+LG6tWrUbVqVRgbG8PV1RXnz59X7WDApIOIiEgl2r4jae3atfHo0SNpCQ8Pl9b5+/vjwIED2L17N06ePImHDx+iW7du0vqsrCx4enoiPT0dZ8+exZYtWxAcHIxp06ZJMXfv3oWnpyfatGmDqKgo+Pn5YciQIThy5IgUs3PnTgQEBGD69Om4dOkS6tevDw8PDyQkJKj23gkhhMrvQAll0tC3uJtAVOQSIlYUdxOIipy5cdH9Td1h7TmNtj88wrXAsTNmzMD+/fsRFRWVa11ycjJsbGywfft29OjRAwAQHR2NWrVqISIiAk2bNsXhw4fRqVMnPHz4ELa2tgCAdevWYeLEiXj8+DGMjIwwceJEHDp0CFevXpXq7t27N5KSkhASEgIAcHV1xccff4xVq1YBABQKBSpVqoRRo0Zh0qRJBT4e9nQQERGpQNOejrS0NKSkpCgtaWlp+e7v5s2bcHBwQLVq1dCvXz/ExsYCACIjI5GRkQF3d3cptmbNmqhcuTIiIiIAABEREahbt66UcACAh4cHUlJScO3aNSkmZx3ZMdl1pKenIzIyUilGT08P7u7uUkxBMekgIiJSgaZjOgIDA2Fpaam0BAYG5rkvV1dXBAcHIyQkBGvXrsXdu3fRsmVLPH/+HHFxcTAyMoKVlZXSNra2toiLiwMAxMXFKSUc2euz170rJiUlBa9fv8aTJ0+QlZWVZ0x2HQXFKbNERERaNHnyZAQEBCiVyeXyPGM7dOgg/b9evXpwdXVFlSpVsGvXLpiYmBRpO4sCezqIiIhUINPwn1wuh4WFhdKSX9LxNisrK3z00Ue4desW7OzskJ6ejqSkJKWY+Ph42NnZAQDs7OxyzWbJfv2+GAsLC5iYmKB8+fLQ19fPMya7joJi0kFERKQCPZlmiyZevHiB27dvw97eHi4uLjA0NERYWJi0PiYmBrGxsXBzcwMAuLm54cqVK0qzTEJDQ2FhYQFnZ2cpJmcd2THZdRgZGcHFxUUpRqFQICwsTIopKF5eISIiUoE2n70ybtw4dO7cGVWqVMHDhw8xffp06Ovro0+fPrC0tIS3tzcCAgJgbW0NCwsLjBo1Cm5ubmjatCkAoF27dnB2dkb//v2xcOFCxMXFYcqUKfDx8ZF6V4YPH45Vq1ZhwoQJGDx4MI4dO4Zdu3bh0KFDUjsCAgLg5eWFxo0bo0mTJli2bBlevnyJQYMGqXQ8TDqIiIhUoM27oN+/fx99+vTB06dPYWNjgxYtWuD333+HjY0NAGDp0qXQ09ND9+7dkZaWBg8PD6xZs0baXl9fHwcPHsSIESPg5uYGU1NTeHl5YdasWVKMo6MjDh06BH9/fyxfvhwVK1bEpk2b4OHhIcX06tULjx8/xrRp0xAXF4cGDRogJCQk1+DS9+F9OnLgfTqoNOB9Oqg0KMr7dHTbHKnR9nu9XQqpJbqHYzqIiIhIK3h5hYiISAV8yKz6mHQQERGpQJsDSUsaJh1EREQqYM6hPiYdREREKtBj1qE2Jh1EREQqYMqhPs5eISIiIq1gTwcREZEKOJBUfUw6iIiIVKDp81NKMyYdREREKmBPh/qYdBAREamAOYf6mHQQERGpgD0d6uPsFSIiItIK9nQQERGpgANJ1VfkSUe3bt0KHLt3794ibAkREZHmeHlFfUWedFhaWhb1LoiIiLSGKYf6ijzpCAoKKupdEBERaQ2fvaI+DiQlIiIirdD6QNI9e/Zg165diI2NRXp6utK6S5cuabs5REREKmFHh/q02tOxYsUKDBo0CLa2tvjjjz/QpEkTlCtXDnfu3EGHDh202RQiIiK1yGQyjZbSTKtJx5o1a7BhwwasXLkSRkZGmDBhAkJDQzF69GgkJydrsylERERqkck0W0ozrSYdsbGxaNasGQDAxMQEz58/BwD0798fO3bs0GZTiIiI1KInk2m0lGZaTTrs7OyQmJgIAKhcuTJ+//13AMDdu3chhNBmU4iIiNTCng71aTXp+PTTT/HLL78AAAYNGgR/f3989tln6NWrF7p27arNphAREZGWaXX2yoYNG6BQKAAAPj4+KFeuHM6ePYvPP/8cX3/9tTabQkREpJbSPhhUEzLB6xqS1MzibgERERUG4yL8k3rUvhsabb+ya61Caonu0frNwU6fPo2vvvoKbm5uePDgAQBg69atCA8P13ZTiIiIVFZcU2bnz58PmUwGPz8/qSw1NVW6cmBmZobu3bsjPj5eabvY2Fh4enqiTJkyqFChAsaPH4/MTOW/sk+cOIFGjRpBLpfDyckJwcHBufa/evVqVK1aFcbGxnB1dcX58+dVPgatJh0//fQTPDw8YGJigj/++ANpaWkAgOTkZMybN0+bTSEiIlKLnkyzRR0XLlzA+vXrUa9ePaVyf39/HDhwALt378bJkyfx8OFDpQetZmVlwdPTE+np6Th79iy2bNmC4OBgTJs2TYq5e/cuPD090aZNG0RFRcHPzw9DhgzBkSNHpJidO3ciICAA06dPx6VLl1C/fn14eHggISFBpePQ6uWVhg0bwt/fHwMGDIC5uTkuX76MatWq4Y8//kCHDh0QFxenrabkiZdXiIhKhqK8vBLwS7RG2y/5vKZK8S9evECjRo2wZs0azJkzBw0aNMCyZcuQnJwMGxsbbN++HT169AAAREdHo1atWoiIiEDTpk1x+PBhdOrUCQ8fPoStrS0AYN26dZg4cSIeP34MIyMjTJw4EYcOHcLVq1elffbu3RtJSUkICQkBALi6uuLjjz/GqlWrAAAKhQKVKlXCqFGjMGnSpAIfi1Z7OmJiYtCqVatc5ZaWlkhKStJmU4iIiIpFWloaUlJSlJbsnv+8+Pj4wNPTE+7u7krlkZGRyMjIUCqvWbMmKleujIiICABAREQE6tatKyUcAODh4YGUlBRcu3ZNinm7bg8PD6mO9PR0REZGKsXo6enB3d1diikord+n49atW7nKw8PDUa1aNW02hYiISC2ajukIDAyEpaWl0hIYGJjnvn788UdcunQpz/VxcXEwMjKClZWVUrmtra105SAuLk4p4chen73uXTEpKSl4/fo1njx5gqysrDxjVL1CodUps0OHDsWYMWPw3XffQSaT4eHDh4iIiMDYsWOVri8RERH9V6k7LiPb5MmTERAQoFQml8tzxf3zzz8YM2YMQkNDYWxsrNlO/yO0mnRMmjQJCoUCbdu2xatXr9CqVSvI5XKMHz8eQ4YM0WZTiIiI1KLpbTrkcnmeScbbIiMjkZCQgEaNGkllWVlZOHXqFFatWoUjR44gPT0dSUlJSr0d8fHxsLOzA/DmCsPbs0yyZ7fkjHl7xkt8fDwsLCxgYmICfX196Ovr5xmTXUdBafXyikwmwzfffIPExERcvXoVv//+Ox4/fgxLS0s4OjpqsylERERq0dazV9q2bYsrV64gKipKWho3box+/fpJ/zc0NERYWJi0TUxMDGJjY+Hm5gYAcHNzw5UrV5RmmYSGhsLCwgLOzs5STM46smOy6zAyMoKLi4tSjEKhQFhYmBRTUFrp6UhLS8OMGTMQGhoq9Wx06dIFQUFB6Nq1K/T19eHv76+NphAREWlEW3+tm5ubo06dOkplpqamKFeunFTu7e2NgIAAWFtbw8LCAqNGjYKbmxuaNm0KAGjXrh2cnZ3Rv39/LFy4EHFxcZgyZQp8fHyk3pbhw4dj1apVmDBhAgYPHoxjx45h165dOHTokLTfgIAAeHl5oXHjxmjSpAmWLVuGly9fYtCgQSodk1aSjmnTpmH9+vVwd3fH2bNn8eWXX2LQoEH4/fffsXjxYnz55ZfQ19fXRlOIiIhKjKVLl0JPTw/du3dHWloaPDw8sGbNGmm9vr4+Dh48iBEjRsDNzQ2mpqbw8vLCrFmzpBhHR0ccOnQI/v7+WL58OSpWrIhNmzbBw8NDiunVqxceP36MadOmIS4uDg0aNEBISEiuwaXvo5X7dFSrVg3Lli3D559/jqtXr6JevXoYOHAgNm/e/J+6hz3v00FEVDIU5X06vjn8l0bbz+3wUSG1RPdopafj/v37cHFxAQDUqVMHcrkc/v7+/6mEg4iIqCBUGZdByrSSdGRlZcHIyOjfnRoYwMzMTBu7JiIiKlTMOdSnlaRDCIGBAwdKg1ZSU1MxfPhwmJqaKsXt3btXG80hIiJSm6b36SjNtJJ0eHl5Kb3+6quvtLFbIiKiQsfLK+rTStIRFBSkjd0QERHRf5hW70hKRESk69jRoT4mHURERCrgmA71MekgIiJSgQzMOtTFpIOIiEgF7OlQH5MOIiIiFTDpUJ9WnzJLREREpRd7OoiIiFTAR3ioj0kHERGRCnh5RX1MOoiIiFTAjg71MekgIiJSAW+Drj4mHURERCrg5RX1cfYKERERaQV7OoiIiFTAqyvqY9JBRESkAj3eBl1tTDqIiIhUwJ4O9THpICIiUgEHkqqPSQcREZEKOGVWfZy9QkRERFrBng4iIiIVsKNDfUw6iIiIVMDLK+pj0kFERKQC5hzq45gOIiIiFehpuKhi7dq1qFevHiwsLGBhYQE3NzccPnxYWp+amgofHx+UK1cOZmZm6N69O+Lj45XqiI2NhaenJ8qUKYMKFSpg/PjxyMzMVIo5ceIEGjVqBLlcDicnJwQHB+dqy+rVq1G1alUYGxvD1dUV58+fV/FomHQQERGpRCaTabSoomLFipg/fz4iIyNx8eJFfPrpp/jiiy9w7do1AIC/vz8OHDiA3bt34+TJk3j48CG6desmbZ+VlQVPT0+kp6fj7Nmz2LJlC4KDgzFt2jQp5u7du/D09ESbNm0QFRUFPz8/DBkyBEeOHJFidu7ciYCAAEyfPh2XLl1C/fr14eHhgYSEBNXeOyGEUGmLEiw18/0xRET032dchIMHtlz8R6PtvRpX0mh7a2trLFq0CD169ICNjQ22b9+OHj16AACio6NRq1YtREREoGnTpjh8+DA6deqEhw8fwtbWFgCwbt06TJw4EY8fP4aRkREmTpyIQ4cO4erVq9I+evfujaSkJISEhAAAXF1d8fHHH2PVqlUAAIVCgUqVKmHUqFGYNGlSgdvOng4iIiIVyDRc0tLSkJKSorSkpaW9d79ZWVn48ccf8fLlS7i5uSEyMhIZGRlwd3eXYmrWrInKlSsjIiICABAREYG6detKCQcAeHh4ICUlReotiYiIUKojOya7jvT0dERGRirF6Onpwd3dXYopKCYdREREKtCTyTRaAgMDYWlpqbQEBgbmu78rV67AzMwMcrkcw4cPx759++Ds7Iy4uDgYGRnByspKKd7W1hZxcXEAgLi4OKWEI3t99rp3xaSkpOD169d48uQJsrKy8ozJrqOgOHuFiIhIBZpOXpk8eTICAgKUyuRyeb7xNWrUQFRUFJKTk7Fnzx54eXnh5MmTGraieDDpICIiUoGmU2blcvk7k4y3GRkZwcnJCQDg4uKCCxcuYPny5ejVqxfS09ORlJSk1NsRHx8POzs7AICdnV2uWSbZs1tyxrw94yU+Ph4WFhYwMTGBvr4+9PX184zJrqOgeHmFiIhIBdqcvZIXhUKBtLQ0uLi4wNDQEGFhYdK6mJgYxMbGws3NDQDg5uaGK1euKM0yCQ0NhYWFBZydnaWYnHVkx2TXYWRkBBcXF6UYhUKBsLAwKaag2NNBRET0HzV58mR06NABlStXxvPnz7F9+3acOHECR44cgaWlJby9vREQEABra2tYWFhg1KhRcHNzQ9OmTQEA7dq1g7OzM/r374+FCxciLi4OU6ZMgY+Pj9TbMnz4cKxatQoTJkzA4MGDcezYMezatQuHDh2S2hEQEAAvLy80btwYTZo0wbJly/Dy5UsMGjRIpeNh0kFERKQCbV4iSEhIwIABA/Do0SNYWlqiXr16OHLkCD777DMAwNKlS6Gnp4fu3bsjLS0NHh4eWLNmjbS9vr4+Dh48iBEjRsDNzQ2mpqbw8vLCrFmzpBhHR0ccOnQI/v7+WL58OSpWrIhNmzbBw8NDiunVqxceP36MadOmIS4uDg0aNEBISEiuwaXvw/t05MD7dBARlQxFeZ+OXVEPNdq+ZwOHQmqJ7mFPBxERkQr46BX1MekgIiJSQWEMBi2tmHQQERGpgNM+1cf3joiIiLSCPR1EREQq4OUV9THpICIiUgFTDvUx6SAiIlIBOzrUx6SDiIhIBXrs61Cbzg8kPX36NL766iu4ubnhwYMHAICtW7ciPDy8mFtGREQlkUym2VKa6XTS8dNPP8HDwwMmJib4448/kJaWBgBITk7GvHnzirl1RERElJNOJx1z5szBunXrsHHjRhgaGkrlzZs3x6VLl4qxZUREVFLJNPxXmun0mI6YmBi0atUqV7mlpSWSkpK03yAiIirxSvslEk3odE+HnZ0dbt26las8PDwc1apVK4YWERFRSacHmUZLaabTScfQoUMxZswYnDt3DjKZDA8fPsS2bdswbtw4jBgxoribR0REJRAHkqpPpy+vTJo0CQqFAm3btsWrV6/QqlUryOVyjBs3DqNGjSru5hERUQlU2hMHTciEEKK4G6Gp9PR03Lp1Cy9evICzszPMzMzUqic1s5AbRkRExcK4CP+k/u3GY422b1fLppBaont0uqcjm5GREZydnYu7GUREVAqU9hkomtC5pKNbt24Fjt27d28RtoSIiEojPeYcatO5pMPS0lL6vxAC+/btg6WlJRo3bgwAiIyMRFJSkkrJCRERUUGxp0N9Opd0BAUFSf+fOHEievbsiXXr1kFfXx8AkJWVhZEjR8LCwqK4mkhERCUYB5KqT6cHktrY2CA8PBw1atRQKo+JiUGzZs3w9OlTlerjQFIiopKhKAeSHo9R7XfL29rUKFdILdE9On2fjszMTERHR+cqj46OhkKhKIYWERFRScfboKtP5y6v5DRo0CB4e3vj9u3baNKkCQDg3LlzmD9/PgYNGlTMraNskRcvIPi7zbhx/SoeP36MpStW49O27tJ6IQTWrFqBvXt24/nzFDRo2AjfTJuBKlWqFl+jid7hXed0RkYGVq1YhvDTp3D//j8wNzODq1szjPEfiwoVbJXqOXXyBNavXY2bf8XASC5H48YfY9nKNcVxSKQCDiRVn04nHd9++y3s7OywePFiPHr0CABgb2+P8ePHY+zYscXcOsr2+vUr1KhRA126dUfAGN9c64M2b8SObVsxe958fPBBRaxeuRwjhnlj3y+/Qi6XF0OLid7tXed0amoqom9cx7DhI1CjRk2kpKRgQeBcjPEdgR27/p1Rd/S3I5g5fSpG+fmjiWtTZGVm4datv7R9KKSG0t5boQmdHtORU0pKCgBoNICUYzqKXv3aNZT+KhRCwP2TlhgwcBC8BnkDAJ4/f45PWzXDrLnz0aGjZ3E2l+i93j6n83L1yp/o1/tLhIQeh72DAzIzM9Gh3acY4TMK3bp/qcXWlh5FOaYj/OYzjbZvUb1sIbVE9+j0mI6cLCwsOGNFBz24fx9PnjyGa9NmUpm5uTnq1quPPy//UYwtIyo8L168gEwmg/n/f0fduH4dCfHx0NPTQ8/uXdC2dQuM/HoIbt5kT4cukGm4lGY6fXnF0dERsnfMXbpz544WW0PqePLkze2Ey5VXHs1drlw5PHnypDiaRFSo0tLSsGzJt+jQ0VN6RMP9+/8AANatXoVxEybB4YMP8H1wEIYM7I9fDh2BpZVVMbaYqOjodE+Hn58fxowZIy0jR46Em5sbkpOTMWzYsHdum5aWhpSUFKUlLS1NSy0notIgIyMD4wPGQAiBb6bNlMrF/8+uGzJsONzbecC5dh3MmhsImUyG334LKa7mUgHpyWQaLQUVGBiIjz/+GObm5qhQoQK6dOmCmJgYpZjU1FT4+PigXLlyMDMzQ/fu3REfH68UExsbC09PT5QpUwYVKlTA+PHjkZmpPJ7gxIkTaNSoEeRyOZycnBAcHJyrPatXr0bVqlVhbGwMV1dXnD9/vuBv2v/T6Z6OMWPG5Fm+evVqXLx48Z3bBgYGYubMmUpl30ydjinTZhRW86gAypd/8+Cjp0+ewsamglT+9OlT1KhZs7iaRaSxjIwMjB/rh0cPH2Jj0BalB1GWt3lz3lf78EOpzMjICB9UrIS4/x8UT/9d2rpEcvLkSfj4+ODjjz9GZmYm/ve//6Fdu3a4fv06TE1NAQD+/v44dOgQdu/eDUtLS/j6+qJbt244c+YMgDc3zPT09ISdnR3Onj2LR48eYcCAATA0NMS8efMAAHfv3oWnpyeGDx+Obdu2ISwsDEOGDIG9vT08PDwAADt37kRAQADWrVsHV1dXLFu2DB4eHoiJiUGFChXyPoA86HRPR346dOiAn3766Z0xkydPRnJystIyfuJkLbWQsn1QsSLKl7fBuXMRUtmLFy9w5c/LqFe/YTG2jEh92QlH7N9/Y/3mYFhZKQ8cdK5dB0ZGRrh3767SNg8fPoC9vYO2m0uq0tKgjpCQEAwcOBC1a9dG/fr1ERwcjNjYWERGRgIAkpOTsXnzZixZsgSffvopXFxcEBQUhLNnz+L3338HAPz222+4fv06fvjhBzRo0AAdOnTA7NmzsXr1aqSnpwMA1q1bB0dHRyxevBi1atWCr68vevTogaVLl0ptWbJkCYYOHYpBgwbB2dkZ69atQ5kyZfDdd9+p9NbpdE9Hfvbs2QNra+t3xsjl8lzTMTl7pWi8evkSsbGx0usH9+8j+sYNWFpawt7BAf36D8DG9WtRpXIVfFDxzZRZmwoV3jkbgKg4veucLm9jg3H+o3HjxnWsXL0eiqwsPHn8ZuySpaUlDI2MYGZmhi979sba1SthZ2cPBwcHBAdtBgC082hfLMdEBafplNm0tLRcl/Pz+p30tuTkZACQfr9FRkYiIyMD7u7/flfWrFkTlStXRkREBJo2bYqIiAjUrVsXtrb/3iPGw8MDI0aMwLVr19CwYUNEREQo1ZEd4+fnBwBIT09HZGQkJk/+9w9zPT09uLu7IyIiAqrQ6aSjYcOGSgNJhRCIi4vD48ePsWYNb7DzX3Ht2lUMGTRAev3twkAAwOdfdMXsefMxyHsoXr9+jVkzpuH58xQ0bOSCNes38R4d9J/1rnN6uI8vThw/BgDo2f0Lpe02BX2Pj5u4AgD8x02AvoEBvpk8AWmpqahbrz42frcFFjkeakn/TZo+eyWvy/vTp0/HjBkz8t1GoVDAz88PzZs3R506dQAAcXFxMDIygtVbA49tbW0RFxcnxeRMOLLXZ697V0xKSgpev36NZ8+eISsrK8+YvO4K/i46nXR06dJF6bWenh5sbGzwySefoCbHA/xnfNzEFZevxeS7XiaTwWfUGPiMynuMDtF/zfvO6Xety2ZoaIix4ydi7PiJhdk00gGTJ09GQECAUtn7/sjy8fHB1atXER4eXpRNK3I6nXRMnz69uJtARESljKYDSQtyKSUnX19fHDx4EKdOnULFihWlcjs7O6SnpyMpKUmptyM+Ph52dnZSzNuzTLJnt+SMeXvGS3x8PCwsLGBiYgJ9fX3o6+vnGZNdR0GVmIGkqampuabAEhERFTotDSQVQsDX1xf79u3DsWPH4OjoqLTexcUFhoaGCAsLk8piYmIQGxsLNzc3AICbmxuuXLmChIQEKSY0NBQWFhZwdnaWYnLWkR2TXYeRkRFcXFyUYhQKBcLCwqSYgtLpno6XL19i4sSJ2LVrV56Psc/KyiqGVhERUUmmrWev+Pj4YPv27fj5559hbm4ujcGwtLSEiYkJLC0t4e3tjYCAAFhbW8PCwgKjRo2Cm5sbmjZtCgBo164dnJ2d0b9/fyxcuBBxcXGYMmUKfHx8pN6W4cOHY9WqVZgwYQIGDx6MY8eOYdeuXTh06JDUloCAAHh5eaFx48Zo0qQJli1bhpcvX6r8cFWdfvaKj48Pjh8/jtmzZ6N///5YvXo1Hjx4gPXr12P+/Pno16+fSvVx9goRUclQlM9eibynWU+6S9WCPbIjvztuBwUFYeDAgQDe9PKPHTsWO3bsQFpaGjw8PLBmzRqlyx5///03RowYgRMnTsDU1BReXl6YP38+DAz+fZNOnDgBf39/XL9+HRUrVsTUqVOlfWRbtWoVFi1ahLi4ODRo0AArVqyAq6urSseu00lH5cqV8f333+OTTz6BhYUFLl26BCcnJ2zduhU7duzAr7/+qlJ9TDqIiEqGokw6LmmYdDQqYNJREun0mI7ExERUq1YNwJsHviUmJgIAWrRogVOnThVn04iIiOgtOp10VKtWDXfvvrmjX82aNbFr1y4AwIEDB3LNWyYiIioUfMys2nQ66Rg0aBAuX74MAJg0aRJWr14NY2Nj+Pv7Y/z48cXcOiIiKolkGv4rzXR6TMfb/v77b0RGRsLJyQn16tVTeXuO6SAiKhmKckxHVOxzjbZvUNm8kFqie3S2pyMjIwNt27bFzZs3pbIqVaqgW7duaiUcREREBcGrK+rT2ft0GBoa4s8//yzuZhARUWlT2jMHDehsTwcAfPXVV9i8eXNxN4OIiIgKQGd7OgAgMzMT3333HY4ePQoXFxeYmpoqrV+yZEkxtYyIiEqq0j4YVBM6mXTcuXMHVatWxdWrV9GoUSMAwF9//aUUk9+d3IiIiDTBXy/q08nZK/r6+nj06BEqVKgAAOjVqxdWrFgBW1tbjerl7BUiopKhKGevXL3/QqPt61Q0K6SW6B6d7Ol4O086fPgwXr58WUytISKiUoU9HWrTyaTjbTrYWUNERDqKYzrUp5OzV2QyWa4xGxzDQURE9N+mkz0dQggMHDgQcrkcwJtH+w4fPjzX7JW9e/cWR/OIiKgE49+46tPJpMPLy0vp9VdffVVMLSEiotKGOYf6dHL2SlHh7BUiopKhKGev3Hik2cSFWvam7w8qoXSyp4OIiKi4cCCp+ph0EBERqYBjOtSnk7NXiIiISPewp4OIiEgF7OhQH5MOIiIiVTDrUBuTDiIiIhVwIKn6mHQQERGpgANJ1cekg4iISAXMOdTH2StERESkFezpICIiUgW7OtTGng4iIiIVyDT8p4pTp06hc+fOcHBwgEwmw/79+5XWCyEwbdo02Nvbw8TEBO7u7rh586ZSTGJiIvr16wcLCwtYWVnB29sbL168UIr5888/0bJlSxgbG6NSpUpYuHBhrrbs3r0bNWvWhLGxMerWrYtff/1VpWMBmHQQERGpRCbTbFHFy5cvUb9+faxevTrP9QsXLsSKFSuwbt06nDt3DqampvDw8EBqaqoU069fP1y7dg2hoaE4ePAgTp06hWHDhknrU1JS0K5dO1SpUgWRkZFYtGgRZsyYgQ0bNkgxZ8+eRZ8+feDt7Y0//vgDXbp0QZcuXXD16lXV3js+8O1ffOAbEVHJUJQPfLv3JPX9Qe9QtbyxWtvJZDLs27cPXbp0AfCml8PBwQFjx47FuHHjAADJycmwtbVFcHAwevfujRs3bsDZ2RkXLlxA48aNAQAhISHo2LEj7t+/DwcHB6xduxbffPMN4uLiYGRkBACYNGkS9u/fj+joaABAr1698PLlSxw8eFBqT9OmTdGgQQOsW7euwMfAng4iIiJVyDRb0tLSkJKSorSkpaWp3Iy7d+8iLi4O7u7uUpmlpSVcXV0REREBAIiIiICVlZWUcACAu7s79PT0cO7cOSmmVatWUsIBAB4eHoiJicGzZ8+kmJz7yY7J3k9BMekgIiLSosDAQFhaWiotgYGBKtcTFxcHALC1tVUqt7W1ldbFxcWhQoUKSusNDAxgbW2tFJNXHTn3kV9M9vqC4uwVIiIiFWh6R9LJkycjICBAqUwul2tUp65g0kFERKQCTe9IKpfLCyXJsLOzAwDEx8fD3t5eKo+Pj0eDBg2kmISEBKXtMjMzkZiYKG1vZ2eH+Ph4pZjs1++LyV5fULy8QkREpAINh3QUGkdHR9jZ2SEsLEwqS0lJwblz5+Dm5gYAcHNzQ1JSEiIjI6WYY8eOQaFQwNXVVYo5deoUMjIypJjQ0FDUqFEDZcuWlWJy7ic7Jns/BcWkg4iISAXanDL74sULREVFISoqCsCbwaNRUVGIjY2FTCaDn58f5syZg19++QVXrlzBgAED4ODgIM1wqVWrFtq3b4+hQ4fi/PnzOHPmDHx9fdG7d284ODgAAPr27QsjIyN4e3vj2rVr2LlzJ5YvX650CWjMmDEICQnB4sWLER0djRkzZuDixYvw9fVV7b3jlNl/ccosEVHJUJRTZu8/S9do+4pljd4f9P9OnDiBNm3a5Cr38vJCcHAwhBCYPn06NmzYgKSkJLRo0QJr1qzBRx99JMUmJibC19cXBw4cgJ6eHrp3744VK1bAzMxMivnzzz/h4+ODCxcuoHz58hg1ahQmTpyotM/du3djypQpuHfvHqpXr46FCxeiY8eOKh07k44cmHQQEZUMJSXpKGk4kJSIiEgFfLS9+ph0EBERqYA5h/qYdBAREamAPR3qY9JBRESkAk1vDlaaMekgIiJSBXMOtfE+HURERKQV7OkgIiJSATs61Mekg4iISAUcSKo+Jh1EREQq4EBS9THpICIiUgVzDrUx6SAiIlIBcw71cfYKERERaQV7OoiIiFTAgaTqY9JBRESkAg4kVR+TDiIiIhWwp0N9HNNBREREWsGeDiIiIhWwp0N97OkgIiIirWBPBxERkQo4kFR9TDqIiIhUwMsr6mPSQUREpALmHOpj0kFERKQKZh1q40BSIiIi0gr2dBAREamAA0nVx6SDiIhIBRxIqj4mHURERCpgzqE+Jh1ERESqYNahNiYdREREKuCYDvVx9goRERFpBXs6iIiIVMCBpOqTCSFEcTeCSqe0tDQEBgZi8uTJkMvlxd0coiLB85zoX0w6qNikpKTA0tISycnJsLCwKO7mEBUJnudE/+KYDiIiItIKJh1ERESkFUw6iIiISCuYdFCxkcvlmD59OgfXUYnG85zoXxxISkRERFrBng4iIiLSCiYdREREpBVMOoiIiEgrmHRQiRYcHAwrK6vibgaRSgYOHIguXboUdzOICh2TjlJq4MCBkMlkmD9/vlL5/v37IdPwwQLBwcGQyWS5lk2bNmlUL1FRy/5cvL3cunWruJtGVCLwgW+lmLGxMRYsWICvv/4aZcuWLdS6LSwsEBMTo1RmaWmZKy49PR1GRkaFum8iTbRv3x5BQUFKZTY2Nkqved4SqYc9HaWYu7s77OzsEBgY+M64n376CbVr14ZcLkfVqlWxePHi99Ytk8lgZ2entJiYmGDGjBlo0KABNm3aBEdHRxgbGwMAQkJC0KJFC1hZWaFcuXLo1KkTbt++LdV34sQJyGQyJCUlSWVRUVGQyWS4d++eVBYcHIzKlSujTJky6Nq1K54+fZqrbT///DMaNWoEY2NjVKtWDTNnzkRmZuZ7j4lKB7lcnuvcbdu2LXx9feHn54fy5cvDw8MDALBkyRLUrVsXpqamqFSpEkaOHIkXL15IdWWf7zktW7YMVatWlV5nZWUhICBAOvcnTJiAt+9koFAoEBgYCEdHR5iYmKB+/frYs2dPkb0HREWFSUcppq+vj3nz5mHlypW4f/9+njGRkZHo2bMnevfujStXrmDGjBmYOnUqgoOD1d7vrVu38NNPP2Hv3r2IiooCALx8+RIBAQG4ePEiwsLCoKenh65du0KhUBS43nPnzsHb2xu+vr6IiopCmzZtMGfOHKWY06dPY8CAARgzZgyuX7+O9evXIzg4GHPnzlX7eKh02LJlC4yMjHDmzBmsW7cOAKCnp4cVK1bg2rVr2LJlC44dO4YJEyaoVO/ixYsRHByM7777DuHh4UhMTMS+ffuUYgIDA/H9999j3bp1uHbtGvz9/fHVV1/h5MmThXZ8RFohqFTy8vISX3zxhRBCiKZNm4rBgwcLIYTYt2+fyHla9O3bV3z22WdK244fP144OzvnW3dQUJAAIExNTaXF1tZWCCHE9OnThaGhoUhISHhn+x4/fiwAiCtXrgghhDh+/LgAIJ49eybF/PHHHwKAuHv3rhBCiD59+oiOHTsq1dOrVy9haWkpvW7btq2YN2+eUszWrVuFvb39O9tDpYOXl5fQ19dXOnd79OghWrduLRo2bPje7Xfv3i3KlSsnvZ4+fbqoX7++UszSpUtFlSpVpNf29vZi4cKF0uuMjAxRsWJF6fOZmpoqypQpI86ePatUj7e3t+jTp4/qB0lUjDimg7BgwQJ8+umnGDduXK51N27cwBdffKFU1rx5cyxbtgxZWVnQ19fPs05zc3NcunRJeq2n92+nWpUqVXJdI7958yamTZuGc+fO4cmTJ1IPR2xsLOrUqVOg47hx4wa6du2qVObm5oaQkBDp9eXLl3HmzBmlno2srCykpqbi1atXKFOmTIH2RSVXmzZtsHbtWum1qakp+vTpAxcXl1yxR48eRWBgIKKjo5GSkoLMzEyVzqXk5GQ8evQIrq6uUpmBgQEaN24sXWK5desWXr16hc8++0xp2/T0dDRs2FDdwyQqFkw6CK1atYKHhwcmT56MgQMHFkqdenp6cHJyynOdqalprrLOnTujSpUq2LhxIxwcHKBQKFCnTh2kp6dL9QFQutadkZGhcrtevHiBmTNnolu3brnWZY8vodLN1NQ0z3P37fP23r176NSpE0aMGIG5c+fC2toa4eHh8Pb2Rnp6OsqUKQM9Pb1c4zNUPW+zx4gcOnQIH3zwgdI6Ps+FdA2TDgIAzJ8/Hw0aNECNGjWUymvVqoUzZ84olZ05cwYfffRRvr0cqnr69CliYmKwceNGtGzZEgAQHh6uFJPdM/Lo0SNppk32eJCcbT137pxS2e+//670ulGjRoiJick3ISIqqMjISCgUCixevFhKinft2qUUY2Njg7i4OAghpKnoOc9bS0tL2Nvb49y5c2jVqhUAIDMzE5GRkWjUqBEAwNnZGXK5HLGxsWjdurUWjoyo6DDpIABA3bp10a9fP6xYsUKpfOzYsfj4448xe/Zs9OrVCxEREVi1ahXWrFlTaPsuW7YsypUrhw0bNsDe3h6xsbGYNGmSUoyTkxMqVaqEGTNmYO7cufjrr79yzaIZPXo0mjdvjm+//RZffPEFjhw5onRpBQCmTZuGTp06oXLlyujRowf09PRw+fJlXL16NdegU6J3cXJyQkZGBlauXInOnTsrDTDN9sknn+Dx48dYuHAhevTogZCQEBw+fBgWFhZSzJgxYzB//nxUr14dNWvWxJIlS5RmaZmbm2PcuHHw9/eHQqFAixYtkJycjDNnzsDCwgJeXl7aOmQizRXvkBIqLjkHkma7e/euMDIyEm+fFnv27BHOzs7C0NBQVK5cWSxatOiddQcFBSkN3swpr4F1QggRGhoqatWqJeRyuahXr544ceKEACD27dsnxYSHh4u6desKY2Nj0bJlS7F7926lgaRCCLF582ZRsWJFYWJiIjp37iy+/fbbXG0JCQkRzZo1EyYmJsLCwkI0adJEbNiw4Z3HRKVDXp8LIYRo3bq1GDNmTK7yJUuWCHt7e2FiYiI8PDzE999/n2vA89q1a0WlSpWEqampGDBggJg7d67SQNKMjAwxZswYYWFhIaysrERAQIAYMGCAUjsUCoVYtmyZqFGjhjA0NBQ2NjbCw8NDnDx5svAOnkgL+Gh7IiIi0grep4OIiIi0gkkHERERaQWTDiIiItIKJh1ERESkFUw6iIiISCuYdBAREZFWMOkgIiIirWDSQUSFIjU1FXPnzsWtW7eKuylE9B/FpIOohBk4cCC6dOkivf7kk0/g5+dXJHXnNHr0aNy6dYvPtSGifPHZK0RaMnDgQGzZsgUAYGhoiMqVK2PAgAH43//+BwODovso7t27F4aGhoVS1/Lly3M9NRUAtm3bhnv37uHQoUOFsh8iKpmYdBBpUfv27REUFIS0tDT8+uuv8PHxgaGhISZPnqwUl56eDiMjo0LZp7W1daHUA7x5Kmpe+vXrh379+hXafoioZOLlFSItksvlsLOzQ5UqVTBixAi4u7vjl19+kS5bzJ07Fw4ODqhRowYA4J9//kHPnj1hZWUFa2trfPHFF7h3755UX1ZWFgICAmBlZYVy5cphwoQJuXoi3r68kpaWhokTJ6JSpUqQy+VwcnLC5s2bpfXXrl1Dp06dYGFhAXNzc7Rs2RK3b98GkPvySlpaGkaPHo0KFSrA2NgYLVq0wIULF6T1J06cgEwmQ1hYGBo3bowyZcqgWbNmiImJKcR3lYh0BZMOomJkYmKC9PR0AEBYWBhiYmIQGhqKgwcPIiMjAx4eHjA3N8fp06dx5swZmJmZoX379tI2ixcvRnBwML777juEh4cjMTER+/bte+c+BwwYgB07dmDFihW4ceMG1q9fDzMzMwDAgwcP0KpVK8jlchw7dgyRkZEYPHgwMjMz86xrwoQJ+Omnn7BlyxZcunQJTk5O8PDwQGJiolLcN998g8WLF+PixYswMDDA4MGDNX3riEgXFe9DbolKj5yPTVcoFCI0NFTI5XIxbtw44eXlJWxtbUVaWpoUv3XrVlGjRg2hUCiksrS0NGFiYiKOHDkihBDC3t5eLFy4UFqfkZEhKlasqPRY9JyPZY+JiREARGhoaJ5tnDx5snB0dBTp6envPYYXL14IQ0NDsW3bNml9enq6cHBwkNp0/PhxAUAcPXpUijl06JAAIF6/fv2ed4yIShr2dBBp0cGDB2FmZgZjY2N06NABvXr1wowZMwAAdevWVRrHcfnyZdy6dQvm5uYwMzODmZkZrK2tkZqaitu3byM5ORmPHj2Cq6urtI2BgQEaN26c7/6joqKgr6+P1q1b57u+ZcuWBRp4evv2bWRkZKB58+ZSmaGhIZo0aYIbN24oxdarV0/6v729PQAgISHhvfsgopKFA0mJtKhNmzZYu3YtjIyM4ODgoDRrxdTUVCn2xYsXcHFxwbZt23LVY2Njo9b+TUxMNFqvrpxJjEwmAwAoFIoi2RcR/Xexp4NIi0xNTeHk5ITKlSu/d5pso0aNcPPmTVSoUAFOTk5Ki6WlJSwtLWFvb49z585J22RmZiIyMjLfOuvWrQuFQoGTJ0/mub5evXo4ffo0MjIy3nssH374IYyMjHDmzBmpLCMjAxcuXICzs/N7tyei0odJB9F/VL9+/VC+fHl88cUXOH36NO7evYsTJ05g9OjRuH//PgBgzJgxmD9/Pvbv34/o6GiMHDkSSUlJ+dZZtWpVeHl5YfDgwdi/f79U565duwAAvr6+SElJQe/evXHx4kXcvHkTW7duzXO2iampKUaMGIHx48cjJCQE169fx9ChQ/Hq1St4e3sXyXtCRLqNSQfRf1SZMmVw6tQpVK5cGd26dUOtWrXg7e2N1NRUWFhYAADGjh2L/v37w8vLC25ubjA3N0fXrl3fWe/atWvRo0cPjBw5EjVr1sTQoUPx8uVLAEC5cuVw7NgxvHjxAq1bt4aLiws2btyY7xiP+fPno3v37ujfvz8aNWqEW7du4ciRIyhbtmzhvhlEVCLIhMjj9oJEREREhYw9HURERKQVTDqIiIhIK5h0EBERkVYw6SAiIiKtYNJBREREWsGkg4iIiLSCSQcRERFpBZMOIiIi0gomHURERKQVTDqIiIhIK5h0EBERkVb8H1PgN4XXldmhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix_smote = confusion_matrix(y_test, y_pred_smote)\n",
    "\n",
    "# Crear el gráfico\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix_smote, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Fraude\", \"Fraude\"], yticklabels=[\"No Fraude\", \"Fraude\"])\n",
    "\n",
    "# Etiquetas\n",
    "plt.xlabel(\"Predicción\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.title(\"Matriz de Confusión - Determinante de Covarianza Mínima\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Métricas de : Smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9725899137436653\n",
      "Recall: 0.9264705882352942\n",
      "Precision: 0.05126118795768918\n",
      "Specificity: 0.9726634391081623\n",
      "F1-score: 0.09714726291441789\n"
     ]
    }
   ],
   "source": [
    "# Evaluar el modelo\n",
    "print(\"Accuracy:\", accuracy_score(y_test,y_pred_smote))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_smote))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_smote))\n",
    "print(\"Specificity:\", recall_score(y_test, y_pred_smote, pos_label=0))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_smote))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANÁLISIS:**\n",
    "* Mejor que Undersampling, pero peor que Oversampling en términos de precisión.\n",
    "* Aumenta la detección de fraudes, pero con muchos falsos positivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CONCLUSIÓN**\n",
    "\n",
    "* **MCD** fue la mejor técnica con **Recall del 80.88%** y un buen equilibrio entre Recall y Precisión.\n",
    "* **Isolation Forest** también fue útil, pero con **más falsos positivos**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}