credit_card_fraud_detection_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credit Card Fraud Detection Project Results\n",
    "\n",
    "| Technique | Description | Result |\n",
    "|-----------|-------------|--------|\n",
    "| <span style=\"color:blue\">🔍 Data Preprocessing</span> | <span style=\"color:blue\">Handling missing values and outliers</span> | <span style=\"color:blue\">Improved data quality</span> |\n",
    "| <span style=\"color:green\">🎯 Feature Scaling</span> | <span style=\"color:green\">Applying normalization and standardization</span> | <span style=\"color:green\">Enhanced model performance</span> |\n",
    "| <span style=\"color:orange\">📊 Resampling Techniques</span> | <span style=\"color:orange\">Using oversampling and undersampling methods</span> | <span style=\"color:orange\">Balanced class distribution</span> |\n",
    "| <span style=\"color:red\">⚙️ Model Selection</span> | <span style=\"color:red\">Testing various algorithms (e.g., logistic regression, random forest)</span> | <span style=\"color:red\">Identified best-performing model</span> |\n",
    "| <span style=\"color:purple\">🧠 Neural Network Architecture</span> | <span style=\"color:purple\">Building a deep learning model with multiple layers</span> | <span style=\"color:purple\">Highly accurate predictions</span> |\n",
    "\n",
    "## Project Objectives:\n",
    "<span style=\"color:blue\">Objective 1:</span> Perform in-depth analysis on the dataset to identify potential fraudulent transactions and distinguish them from legitimate ones.\n",
    "<span style=\"color:green\">Objective 2:</span> Visualize and compare fraudulent and genuine transactions based on various features.\n",
    "<span style=\"color:purple\">Objective 3:</span> Implement machine learning models to detect fraudulent activities and evaluate their performance metrics.\n",
    "<span style=\"color:orange\">Objective 4:</span> Handle class imbalances using sampling techniques or class weights to improve model performance.\n",
    "\n",
    "## Data Set Description:\n",
    "The dataset includes transactions made by credit card holders between September 2013 and October 2014. It consists of 284,807 transactions, out of which only 492 transactions are marked as fraudulent (0.172%).\n",
    "## Project Steps:\n",
    "<span style=\"color:blue\">Data Exploration and Preprocessing:</span>\n",
    "\n",
    "Understand and preprocess the dataset, dealing with missing values and outliers.\n",
    "Identify features that differentiate fraudulent and genuine transactions.\n",
    "<span style=\"color:green\">Data Visualization:</span>\n",
    "\n",
    "Visualize fraudulent and genuine transactions across various features.\n",
    "Analyze relationships between different features and fraudulent tendencies.\n",
    "<span style=\"color:purple\">Modeling:</span>\n",
    "\n",
    "Apply machine learning algorithms to train the dataset.\n",
    "Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score.\n",
    "Perform hyperparameter tuning and overfitting prevention techniques.\n",
    "<span style=\"color:red\">Fraud Detection and Model Evaluation:</span>\n",
    "\n",
    "Test the trained model on real data to assess its ability to correctly identify fraudulent transactions.\n",
    "Review and focus on improving the model's performance.\n",
    "<span style=\"color:orange\">Model Update and Enhancement:</span>\n",
    "\n",
    "Periodically update the model with new data to create a more resilient model against evolving fraudulent tactics.\n",
    "<span style=\"color:brown\">System Security and Privacy:</span>\n",
    "\n",
    "Implement appropriate measures to ensure data security and privacy due to the sensitive nature of the data.\n",
    "\n",
    "\n",
    "### Explanations:\n",
    "\n",
    "1. **Data Preprocessing (🔍):** The preprocessing phase involved handling missing values and outliers, which significantly improved the quality of the data, making it more suitable for analysis.\n",
    "\n",
    "2. **Feature Scaling (🎯):** Applying normalization and standardization techniques to the features resulted in enhanced model performance and better convergence during the training process.\n",
    "\n",
    "3. **Resampling Techniques (📊):** Using both oversampling and undersampling methods helped in creating a balanced class distribution, preventing the model from being biased towards the majority class.\n",
    "\n",
    "4. **Model Selection (⚙️):** Testing various algorithms such as logistic regression and random forest allowed us to identify the best-performing model that provided the most accurate predictions for fraud detection.\n",
    "\n",
    "5. **Neural Network Architecture (🧠):** The implementation of a deep learning model with multiple layers enabled the system to learn intricate patterns within the data, leading to highly accurate predictions for fraud detection.\n",
    "\n",
    "### Question and Answer:\n",
    "\n",
    "1. **Q:** How did the data preprocessing steps impact the model's overall performance?\n",
    "   **A:** The data preprocessing steps, including handling missing values and outliers, significantly improved the data quality, leading to more accurate and reliable predictions from the model.\n",
    "\n",
    "2. **Q:** What were the key challenges faced during the implementation of resampling techniques?\n",
    "   **A:** One of the key challenges was to prevent overfitting or underfitting of the model due to the resampling techniques, which required careful consideration of the sampling ratios and methods.\n",
    "\n",
    "3. **Q:** Which metrics were primarily used for evaluating the model's performance during the model selection phase?\n",
    "   **A:** The primary evaluation metrics included precision, recall, F1 score, and area under the ROC curve (AUC), which provided a comprehensive understanding of the model's fraud detection capabilities.\n",
    "\n",
    "4. **Q:** How did the neural network architecture handle complex patterns within the data?\n",
    "   **A:** The multiple layers of the neural network architecture allowed for the extraction of intricate patterns, enabling the model to make highly accurate predictions even in the presence of complex and nonlinear relationships within the data.\n",
    "\n",
    "## Technologies Used:\n",
    "Python (Libraries: Pandas, NumPy, Matplotlib, Seaborn)\n",
    "Machine Learning Libraries (Scikit-learn, TensorFlow, Keras, etc.)\n",
    "Data Visualization Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import shap\n",
    "\n",
    "# Find the best hyperparameters using GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "\n",
    "from scipy.stats import ttest_ind\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial few rows of the dataset: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</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>...</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>0.0</td>\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>...</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>0.0</td>\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>...</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>1.0</td>\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>...</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>1.0</td>\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>...</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>2.0</td>\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>...</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 × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the initial rows of the dataset\n",
    "print(\"Initial few rows of the dataset: \")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Overview of the features and their types:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Getting an overview of the features and their types in the dataset\n",
    "print(\"\\nOverview of the features and their types:\")\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_counts = data['Class'].value_counts()\n",
    "labels = ['Genuine', 'Fraud']\n",
    "colors = ['#66b3ff', '#ff9999']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a circle for the center of the flower plot\n",
    "center_circle = plt.Circle((0, 0), 0.5, color='white')\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(class_counts, labels=labels, colors=colors, startangle=90, counterclock=False, wedgeprops=dict(width=0.3))\n",
    "p = plt.gcf()\n",
    "p.gca().add_artist(center_circle)\n",
    "\n",
    "plt.title('Class Distribution in the Dataset')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Statistical summary of the dataset:\n"
     ]
    },
    {
     "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>Time</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>...</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>284807.000000</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>94813.859575</td>\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>...</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>47488.145955</td>\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>...</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>0.000000</td>\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>...</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>54201.500000</td>\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>...</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>84692.000000</td>\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>...</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>139320.500000</td>\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>...</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>172792.000000</td>\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>...</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 × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+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 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a statistical summary of the dataset features\n",
    "print(\"\\nStatistical summary of the dataset:\")\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columns in the dataset:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying all the columns in the dataset\n",
    "print(\"\\nColumns in the dataset:\")\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing values in the dataset:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the dataset\n",
    "print(\"\\nMissing values in the dataset:\")\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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3r8Cf+03r16/HO++8g19++UVne0xMDJydnXP9uN7e3vD29sbEiRNx+PBhNGvWDAsXLsS0adOy/forVKgAQCZ9WSV82T3hojm2V69eTXfblStX4OzsDFtb22w91utMTEzQpk0btGnTBrNnz8b06dPx1VdfYd++fdrWaX1SoUIFxMfHF0hsqampWLVqFWxsbNC8efMs9y1evDgCAwMRGBiI+Ph4tGzZEpMnT9Z248/NkIbMqNVq3Lp1S9t6DgDXrl0DAG2xQE2LdUxMjM59M+pmnt3YXFxcYGNjk+nfnImJSbqeN0REJBXplvS3GTFiBI4cOYLVq1fj3Llz6N69O9q3b5+ncWTXr19HqVKlUL58efTt2xcRERH5GDERFXUVKlTAhx9+iEWLFuHhw4c6t7377rsAkK6C9ezZswEg02rU+UnTUvZ6y1hsbCxCQ0ML/LkziuXNFrp169bh/v37uXq8uLg4nbH3gEzYTUxMtNPQZff1t2vXDvb29ggJCUFiYqLOba/f19bWNltDBEqWLIk6depgxYoVOonYhQsXsGvXLu3fRk48ffo03bY6deoAQLpp9/RFjx49cOTIEezcuTPdbTExMemOX3alpqZi1KhRuHz5MkaNGgUHB4dM933y5InOdTs7O1SsWFHnPdOcMHkzac6tn376SbsuhMBPP/0Ec3NztGnTBoA8iWNqappuTP7PP/+c7rGyG5upqSnatWuHzZs363Srj4qKwqpVq9C8efMs3ycioqKsSLekZyUiIgKhoaGIiIjQTsMzZswY7NixA6GhoZg+fXqOH9PHxwfLly9HlSpVEBkZiSlTpqBFixa4cOFCluPXiIhy4quvvsJvv/2Gq1evokaNGtrttWvXxoABA7B48WLExMTA19cXx48fx4oVK9ClSxe88847BR5bu3btYGFhgY4dO2LIkCGIj4/HkiVL4OrqmmUX/YLw3nvvYerUqQgMDETTpk1x/vx5/P777+nG72bX3r17MWLECHTv3h2VK1dGSkoKfvvtN5iammq7UWf39Ts4OGDOnDkYNGgQGjZsiD59+qBYsWI4e/YsEhISsGLFCgBA/fr1sWbNGgQFBaFhw4aws7NDx44dM4zv+++/R0BAAJo0aYKPP/5YOwWbo6Njurmts2Pq1Kk4ePAgOnToAE9PTzx69Ag///wzypQp89aWZKV8+eWX2LJlC9577z0MHDgQ9evXx4sXL3D+/HmsX78ed+7ceWsvitjYWKxcuRKAnNLtxo0b2LBhA27evIlevXrhm2++yfL+1atXR6tWrVC/fn0UL14cJ0+exPr163WKu9WvXx8AMGrUKPj7+8PU1BS9evXK1Wu2srLCjh07MGDAAPj4+GD79u3YunUrJkyYoB3b7+joiO7du2PevHlQqVSoUKEC/vrrrwzH6OcktmnTpiEsLAzNmzfHsGHDYGZmhkWLFiEpKQnfffddrl4PEVFRwCQ9E+fPn0dqaqpO9zBAtg6UKFECgOyu9XqX0YyMHTtWW8Tp9a71tWrVgo+PDzw9PbF27Vp8/PHH+fwKiKioqlixIj788ENtIve6pUuXonz58li+fDk2btwId3d3jB8/HsHBwYUSW5UqVbB+/XpMnDgRY8aMgbu7O4YOHQoXF5d0leEL2oQJE/DixQusWrUKa9asQb169bB161aMGzcuV49Xu3Zt+Pv7488//8T9+/dhY2OD2rVrY/v27WjcuDGAnL3+jz/+GK6urpgxYwa++eYbmJubo2rVqtrx6wAwbNgwhIeHIzQ0FHPmzIGnp2emSbqfn592juxJkybB3Nwcvr6+mDlzZo4KkWl06tQJd+7cwbJlyxAdHQ1nZ2f4+vpiypQp2kKC+sbGxgYHDhzA9OnTsW7dOvz6669wcHBA5cqVsx33v//+i379+gGQreAlS5ZEkyZNsGDBArRt2/at9x81ahS2bNmCXbt2ISkpCZ6enpg2bRq+/PJL7T5du3bFyJEjsXr1aqxcuRJCiFwn6aamptixYweGDh2KL7/8Evb29tq/gdfNmzcPr169wsKFC2FpaYkePXrg+++/T1e8MCex1ahRA3///TfGjx+PkJAQqNVq+Pj4YOXKlenmSCciojQqwWocAOQYq40bN6JLly4AgDVr1qBv3764ePFiukImdnZ2cHd3R3Jyss7ULhkpUaJEltOkNGzYEH5+fggJCcnzayAiIiIiIiLDxpb0TNStWxepqal49OiRdi7cN1lYWORpCrX4+HjcvHlTe0aeiIiIiIiIirYinaTHx8fjxo0b2uu3b99GeHg4ihcvjsqVK6Nv377o378/Zs2ahbp16+Lx48fYs2cPatWqlasCS2PGjEHHjh3h6emJBw8eIDg4GKampujdu3d+viwiIiIiIiIyUEW6u/v+/fszLJQ0YMAALF++HK9evcK0adPw66+/4v79+3B2dkbjxo0xZcoUeHt75/j5evXqhYMHD+LJkydwcXFB8+bN8e2332qn2iEiIiIiIqKirUgn6URERERERET6hPOkExEREREREekJJulEREREREREeqLIFY5Tq9V48OAB7O3toVKplA6HiIiIiIiIjJwQAs+fP0epUqVgYpJ1W3mRS9IfPHgADw8PpcMgIiIiIiKiIubevXsoU6ZMlvsUuSTd3t4egHxzHBwcFI6GiIiIiIiIjF1cXBw8PDy0+WhWilySruni7uDgwCSdiIiIiIiICk12hlyzcBwRERERERGRnmCSTkRERERERKQnmKQTERERERER6Qkm6URERERERER6gkk6ERERERERkZ5gkk5ERERERESkJ5ikExEREREREekJJulEREREREREeoJJOhEREREREZGeYJJOREREREREpCfMlA6AiIioKFu8OHf3Gzw4f+MgIiIi/cCWdCIiIiIiIiI9oWiSfvDgQXTs2BGlSpWCSqXCpk2b3nqf/fv3o169erC0tETFihWxfPnyAo+TiIiIiIiIqDAomqS/ePECtWvXxvz587O1/+3bt9GhQwe88847CA8Px+jRozFo0CDs3LmzgCMlIiIiIiIiKniKjkkPCAhAQEBAtvdfuHAhypUrh1mzZgEAqlWrhkOHDmHOnDnw9/cvqDCJiIiIiIiICoVBjUk/cuQI/Pz8dLb5+/vjyJEjmd4nKSkJcXFxOgsRERERERGRPjKoJP3hw4dwc3PT2ebm5oa4uDi8fPkyw/uEhITA0dFRu3h4eBRGqEREREREREQ5ZlBJem6MHz8esbGx2uXevXtKh0RERERERESUIYOaJ93d3R1RUVE626KiouDg4ABra+sM72NpaQlLS8vCCI+IiIiIiIgoTwyqJb1JkybYs2ePzrawsDA0adJEoYiIiIiIiIiI8o+iSXp8fDzCw8MRHh4OQE6xFh4ejoiICACyq3r//v21+3/66ae4desW/vOf/+DKlSv4+eefsXbtWnz++edKhE9ERERERESUrxRN0k+ePIm6deuibt26AICgoCDUrVsXkyZNAgBERkZqE3YAKFeuHLZu3YqwsDDUrl0bs2bNwtKlSzn9GhERERERERkFlRBCKB1EYYqLi4OjoyNiY2Ph4OCgdDhERFTELV6cu/sNHpy/cRAREVHByUkealBj0omIiIiIiIiMGZN0IiIiIiIiIj3BJJ2IiIiIiIhITzBJJyIiIiIiItITTNKJiIiIiIiI9ASTdCIiIiIiIiI9wSSdiIiIiIiISE8wSSciIiIiIiLSE0zSiYiIiIiIiPQEk3QiIiIiIiIiPcEknYiIiIiIiEhPMEknIiIiIiIi0hNM0omIiIiIiIj0BJN0IiIiIiIiIj3BJJ2IiIiIiIhITzBJJyIiIiIiItITTNKJiIiIiIiI9ASTdCIiIiIiIiI9wSSdiIiIiIiISE+YKR0AERFRUfP0KbBpE7B9O3D7NmBnB9jby8uKFQEPD6UjJCIiIqUwSSciIioEL18Ca9fKZdcuICUl4/1UKqBFC6BLF8DWtlBDJCIiIj3AJJ2IiKiAbd8ODB8uW801atcGunUDLl4Enj8H4uOB6Gjg0iXg4EHgzBl5e+PGMnEnIiKiooFJOhERUR4tXpzx9pgY2XJ+6pS87uQkW8kbNADc3eU2Nzfd+1y7BqxaBURGAsuXA0eOAEOHAtbWBRQ8ERER6RUm6URERPlMCODvv4H//Q9ITARMTIDWrYGOHQErq6zvW7kyMHEisHs38NdfwNWrwC+/AMOGycchIiIi48YknYiIKB8lJgIrVwInTsjr5coBffvmrBicmRnQvj1QtSrwww/A+fPA5s3A++8XTMxERESkP3hOnoiIKJ9ERgIzZsgE3cQE+OAD4D//yX21di8voH9/ub5jB3DsWL6FSkRERHqKLelERET54MQJ4LffgKQkwNERGDxYTqeWV40aAffvyyT911/lGHYvr7w/LhEREekntqQTERHlQXIysGYNsHSpTNCrVJFjyvMjQdfo3BmoVUtO2/bzz0BsbP49NhEREekXJulERES59O+/gK8vsHevvN6+PfDZZ4CDQ/4+j4kJ8NFHQMmSMkH/3//y9/GJiIhIfzBJJyIiyoXdu4G6dYGjR+X0aMOGycJupqYF83zW1kBgoFw/dkzOo05ERETGh0k6ERFRDqSkAJMmAe3aAdHRMlH/6iugdu2Cf25PTzlGHQC+/FJO9UZERETGhUk6ERFRNt29K7u3f/ONTJA//hj45x/AxaXwYujcWU7RtmcPsGtX4T0vERERFQ4m6URERNmwbp1sLT98WI45/+MPWSzO2rpw43B2Blq1kuv/+Q+Qmlq4z09EREQFi1OwERGR0Vi8OPf3HTw44+2PHwNBQcDKlfJ648bAqlVAuXK5f668evdd4ORJ4Nw5GdeAAcrFQkTGryC+W4koc0zSiYiIMiCEnJc8KAh4+hRQqYBx44ApUwBzc2Vjs7UFJkyQLekTJwI9ehR+iz4RFb68JMsZUavlNJKJiXJJSpLfdRYWcjE3l98tZswYiAoVP3JERERvuHYNGDo0bWq12rXlj2NN0TZ9MHIk8NNPQEQEMG+eTNiJiDKiVgP37wO3bwNRUWlLdLS87W0cHIBixeRSogRQqhRQurScFtLKquDjJypqmKQTERH9v8hIYOpUOdY8JUW2IE2eDHz+ufKt52+yspKxDhwIzJ2rnzESkXIePQIuXACuXgWuXwdevMh8X5VKfqdYWsrryclySUmR1+Pi5HL3bvr7OjvL4T/lywMVK8rkvaCmoiQqKpikExFRkffypew2PmcOkJAgt3XoAPz3v/KHp77q3RsYO1aeXNi0CejeXemIiEhJsbGyXsXx48CdO7q3WVoCFSrI1m83N7m4ugJ2dvIEn0qV/vHUapncP3sml6dPZZ2OBw9ky3xcnGyNj44GTpyQ97GwkM9TrRpQvbp8DBOWqibKESbpRFSksPgNvS4uDti3D9i/Py05b9wYmDkTaNlS0dCyxcIC+OQTYNo0YP58JulERZFaDVy8KL/LLl2S9TQAmRhXrgxUrQpUqQJ4eua8hdvEBLC3l0vZsulvj48H7t0Dbt1KWxISgMuX5bJhA7BoEdCuHdCpE9C+vew6T0RZY5JORERFzuPHco7xI0eAV6/ktqpVgenTgS5dMm5R0ldDhgAhIcCBA/KHeo0aSkdElDM8eZo7SUnA0aPAnj1yfLlGuXKAjw9Qv37BJ8R2drLFvFo1eV2tlj17rlyRSfq1a/L79vff5WJuLqeQ7NwZ6NpVtuoTUXpM0omIsok/JA2bWi1/OO7bB5w/n9ba5OUF+PvLlmhDHEdZpoz8wbthA/Dzz/J1EBUVRfF7+eVLmZjv2ZPWA8jKCmjRQvYAcnVVLjYTEzkmvXRpoE0bOaa9Rg1g61ZgyxY5Pj4sTC4jR8p4e/aUCbubW/rHK4rHlwgAFB8hMn/+fHh5ecHKygo+Pj44fvx4lvvPnTsXVapUgbW1NTw8PPD5558jMTGxkKIlIiJD8+KF/DEbHAz8+KOcW1wIoGZN4Isv5LRq9eoZZoKuMWyYvPz1V9mFn4iMT1ycTHYnTAD+/FMm6C4uMsmdORP44ANlE/SMmJkBvr7Ad9/Jk6RXrgDffw80aSK/hw8ckN9fpUoB774LrF8vewgQFXWKtqSvWbMGQUFBWLhwIXx8fDB37lz4+/vj6tWrcM3gW2bVqlUYN24cli1bhqZNm+LatWsYOHAgVCoVZs+ercArICIifSQEcPMm8PffwKlTaV3arayApk3lj0Z3d2VjzE+tW8vu+leuAL/9BgwfrnRERJRf4uJkEcvZs2XxNkB2E+/QQXZpN6SibFWqyGXMGFkpfv16YM0aWXRu+3a5lCgBfPghEBiodLREylE0SZ89ezY++eQTBP7/p3DhwoXYunUrli1bhnHjxqXb//Dhw2jWrBn69OkDAPDy8kLv3r1x7NixQo2biIj004sXcozm33/LcZEaZcrIxLxRI+OZ0/fNbqC1a8sk/dtvZetVVuPq2Q2USP/FxQHz5gGzZqUl5+7uwHvvGV5ynhFPT9mb6Ysv5BRxoaHAihWycvyPP8qlbFl5YrVRI8DWVumIiQqPYkl6cnIyTp06hfHjx2u3mZiYwM/PD0eOHMnwPk2bNsXKlStx/PhxNGrUCLdu3cK2bdvQr1+/TJ8nKSkJSa/1m4ljP0AiIqMihEzKFy+WLTKaeX0tLICGDeU4TS8vwyoGlxtNmshp2CIjZbGmKlWUjoiIciMmRtaXmDVLTnkGyM9zcLCcYs3Qk/OMVKokC3dOnSrHqy9bBmzeDEREyGX9eqBuXTmGvVIl4/8+J1IsSY+OjkZqairc3qgS4ebmhitXrmR4nz59+iA6OhrNmzeHEAIpKSn49NNPMWHChEyfJyQkBFOmTMnX2InI+Dx7JouJPX4MPHkil6dPZaLn6CgXJyfZilGtmhwHyB8JynrwQHbtDg2VxYg0ypSRP+QaNQKsrZWLr7BZW8uKzgcPyinlmKRTUZeaKufvTkoCkpPlkpIiK54XLy5P8OnT93hUFDB3rkzQNW1KlSsDkyYBvXrJuhl5KaRmCMzMgIAAuURHy6E7hw8D//4ru8SfOCHHr7dsKafLLErf8VS0GFR19/3792P69On4+eef4ePjgxs3buCzzz7DN998g6+//jrD+4wfPx5BQUHa63FxcfDw8CiskIlIj6WmAhcuyFbYCxfSqn2/KTo6/bYSJWSyXqOGLEBmYVGwseaWsVXGTUqSBZNCQ4EdO2TFdkB2g+zdW5488fTUrx/ehalVK5mkh4fL+Yvt7JSOiKjwCAE8eiTnCr98WZ68y6q28KRJspdNjRoy4WvcWBaRLOzE79w5YOFC+b2mibdGDVnUslcvmbgWRc7OskJ869ayNf3gQeD4cXmCdvVqYONG2VvK1zfjOdyJDJliH3tnZ2eYmpoi6vWJHQFERUXBPZNqPl9//TX69euHQYMGAQC8vb3x4sULDB48GF999RVMMuj/Y2lpCUtLy/x/AURksNRq2dK4c6fsVqhRsaL8R1+ihPxxUKyYLDgWGyv3i4kB7tyRBcmePAEOHZKLtbX8odC0adHoVq2E8HDZ/fH339O6fwLyPQ8MBHr0kK1jxt7K9DalSwMeHsC9e8Dp07K1iagouHwZ+N//5N/+6ywsABsbeWlhIbuKx8XJ7/WXL+X9Ll+W3akBeXvZsrIFu3Jl+X8ho6Q9rycxnz+XieaSJbJ1WMPHBxg/HujY0Xi6tef1e1mlkidf+/WTFeyPHJEJe2Rk2v/hcuVksl6/vv6eNCfKCcWSdAsLC9SvXx979uxBly5dAABqtRp79uzBiBEjMrxPQkJCukTc9P/nzBGZNYERkVHK7T/9Z8+A5ctlgS1AtjQ2aQI0b579at+JicCNG7K15swZmTQePCiXkiWBZs3kY7IVM2/u3gXWrZOJeXh42vZSpYD+/YGBA9mlOyMNG8pE5cQJJulk/B4+lAn2+fPyupkZUKGC7OlUrZpMuDNKdl+9kv8PoqNlK+2tW3J5/lyejL1zB9i1SyaIZcsC5cunLSVK5DxOIWTL/q5dctm3L22Oc3NzoHNnORVZq1Y80ZsVa2vZsv7OO7LY3IED8v/w7dtyWbdO/v/ldx8ZOkU70AQFBWHAgAFo0KABGjVqhLlz5+LFixfaau/9+/dH6dKlERISAgDo2LEjZs+ejbp162q7u3/99dfo2LGjNlknIsrMqVPAypXyh5G5uTwj37x5zrsSWlnJLu41a8rHuHpVntk/fVqe2V+/XhbwqldP/lCoWLFAXo5Run1bdmFcuxZ4feIOCwv5IzYwEGjXzrDnNC9oDRsCGzbIH7DPnskeIUTG5tUr+Xe+f7/sHWViIltS33sveydIzc3lnOKurkD16nKbELKX1I0bsvji1asyib97Vy779sn97O2BVat0E3cnJ/m9pFni4uT32a1b8vLsWTmu+nVVqgCDBsmTjvo2v7m+U6nSejvExcnW9L//lifNd++Wy8GDssdDx47GM6sHFR2KJuk9e/bE48ePMWnSJDx8+BB16tTBjh07tMXkIiIidFrOJ06cCJVKhYkTJ+L+/ftwcXFBx44d8e233yr1EojIAKSmyh9Uhw7J656ewEcf5c882SYmaS02vXvL1suDB2VL5vHjcilZUra+9+/PhOlNSUny/dq+Hdi2TbcAnEolW5W6d5fd2XPTepUTxtJVvnhx2ZJ48yZw8iTQtq3SERHlr8REYMGCtB5RtWoB3brl/TtdpZJDnZyd5fh0QCZ9N26kJdz37snWdk3vqZywtJQnhh0d5f8MDw/5nJs25S3uos7BAXj3XaB9e1lf5sAB4OLFtGS9WDE5tn/AAFlQlD0VyBCoRBHrJx4XFwdHR0fExsbCwcFB6XCIKJeym1Cp1XLe1aNH5T/m9u3lWfWCbIkVQra6HDwok/bkZLndygro2VO2BjdvXjitwfpUOE4I2TX16FHZ8+DwYZlEvjZLJkxN5ZRp3bsDXbvm/Ee3sSTaebVvnxzv6ukJZDQBij4WBaSiKyefW83c4RERMukdNEgm6YXl1Svg/n35nDdvyuXWLVmoMTU1bbGxkeOky5eXl5UqycTfxobfU4UhOhp48QL49VfdHgyVK8v/wz16yOJ8TNipMOUkD2WSTkQGKTs/coQA/vhDnlU3MQGGDAHq1Cnw0HS8fCm7bV+8KCv4apQoIbtldu4su2/b2hbM82vep6Qk2frz+qI5eaChUskuoJqlc2d5YsHaWi6a9dcvVSr5ozU5WV7Gx8tp7FaskOsxMXJaoYcP5aVmDObrHB3l0IEaNWTrko1NwbwXRUlcHDB2rDxJNXUq8MZsp0zSSa9kN2mNjgZ+/FFWcLe3B0aMkMU6lZCXzxCT9MIxeLA8YbJvn6xFs2GD/J+sUa2aTNZ79Egb8kBUkHKShxbRSR2IyNgJIf8hHzggE8nAwMJP0AGZzLZqJYufHTsmK/lu3CjHPa5YIRczM5mg1q8vlzp1ZBd5Z2c5tnLJkvSPq0mInz+XCdnr68+fp13PLCHPjoUL8/rq01OpZOG3ChXSxnK6urI1I785OABVq8rihidOyBNCRIbs4UNg1iz5HVeiBPDZZ+lPPhG9ydQU8POTy/PncgrPNWvkFJ6XLwNTpsilRo20hL1qVaWjJmKSTkRGats2WUEXAD78UI5DU5JKlTYP76JFcnz85s1yLOKdO7Ko0Nmzcpqx11lYyERfpQJSUuSSmiqT9JwyM5OtT5pFMzulJkEWIq1VPCVFJnqJibLl4fXL17unv8nCIi3ptreXj+HqKrutu7vLdXPznMdOOdeoUVqS3qEDT4SQ4dKMQY+Lk9MMjholC7UR5YS9PdCnj1xiY4EtW2SR0p07ZW+34GC5eHunJeyVK+fsOfRpiBkZNibpRGR0jh+X/3wBOba5eXNl43mTmZlsXW/VCpg9W46tPH06bblwQXbnTEyUCXNmreAmJrpJ99sWS8ucJWqZ/WBQq2Wi/vKlTOzNzWVybm6eNs6e3TmVV6eO/Ft7+FCOyfTwUDoiopwTAvjtN/l37OQEjB4tT/4R5YWjo5x3vV8/OSxr82aZsO/aJafzO38e+PproEwZeXK9WTMOxaLCxSSdiIzK06eykjsgi8T5+Skbz9uoVLK4l6cn8P77urclJMgxmJqE18xMJsFmZnI8uI2NMq2jJiZp49RJf1lbyxahM2fkiSsm6WSI9u2TBSZNTOSJQybolN+cnGTl9wED5G+Izz+XU7ZevixPcK5fL0/8N24sT66XLq10xFQUMEknIqOhVgOhobKFt1w5oFMnpSPKGxsboGxZuRDlRsOGMkk/eVKeBHptVlMivXfzJrBunVzv3l3WsiAqSMWLy1bzZs1kbZdTp2Rtm/v306bdq15dTvlXpozS0ZIxY5JOREZj927g2jXZrTswsHCmOCPSZ97estfF06dynmcmOWQo4uJkLyK1GmjQAHjnHaUjoqLGzg7w9QVatgSuX5e9OsLDZa2Py5fldKGdOsnhZET5jUk6ERmFe/dkETZAFnth1V8iWSvA21sWjzt7lkk6GY6VK+VY4ZIl5bhhfSt8yLobRYdKJQvIVa4sh6Bt2CBb2A8eTJs9o3Vr9lSi/MUknYgMXnKyrIqemgrUri27qRGRVLu2/CEZHg507ap0NERvd/68PKlkYgJ88onsDUKUUwVxIsXZWdZGuHZNFpq7d08Oybh0Cfj4Y8DWNv+fk4omnvMhIoO3dSvw4IEsKKSPLS5ESqpZUw79iIqSFbKJ9NmrVzL5AYA2bViki/RT5crAhAlA375yZpOLF4GQEDl2nSg/MEknIoP29Kkciw7IuU85NoxIl7U1UKWKXA8PVzQUorfavVtOQenoCHTooHQ0RJkzMZHj1ceOBUqUAB4/BmbOlFOpEuUVk3QiMmibNwMpKfKsdp06SkdDpJ80n42zZxUNgyhLz54B27bJ9a5dOc0jGQYPD9mqXqUKkJQELFqU1nhAlFsck05EBisiAjh6VK5368Zu7vmNhZGMR61awKpVssJ7bKzS0RBlbP16WWOkYkXAx0fpaIiyz84O+Owz4H//A/bskePUbW2BJk2UjowMFVvSicggCSF/0AFAo0aAl5ei4RDptWLF5GdECODcOaWjIUrv6lXg5El5srVXL550JcNjagp07w74+cnrv/4qiyAS5QaTdCIySBcuyB91ZmZA585KR0Ok/2rXlpfs8k76Rq0G1qyR6y1byu7DRIZIpZI9+3x85N/1okXAzZtKR0WGiEk6ERmclBTZpQyQc5M6OysbD5Eh0IxLv3wZiI9XNBQiHeHhsiq2lRVPupLhMzEBBgyQM2u8egX89JOcgYYoJzgmnYgMTmgoEBkpx3sFBCgdTfZwfDcprWRJwMVFViDeuVO29hApTYi0YnGtW3OeaTIOpqZyPvU5c2QtkPnzga+/lieiiLKDLelEZFBevQKmTpXrHToANjbKxkNkKFSqtNb0TZuUjIQozbZtwL17gKWlnBedyFhYWgIjRgDFiwPR0Wk9AImygy3pRKSY3LQuHz0K/Psv4OAgxy4SUfbVrg2EhQFbt8oTXubmSkdERZkQwDffyHVfX1khm8iY2NnJru9z5gAHD8qZNry9lY6KDAFb0onIYAgB7Nol11u3ZoJBlFMVKsgfjc+eAX//rXQ0VNTt3QscOya/yzUVsYmMTdWqab1Efv2VNUEoe5ikE5HBuHhRFheytGQrOlFumJjIlhwA+OsvZWMh0rSiN28OODoqGwtRQerSBXB3B+LigFWrZKMDUVaYpBORwdC0ojdvzuJCRLml6WqpKdZFpIS//wYOHJCt6P7+SkdDVLAsLIDAQHmi9NQp4MQJpSMifccknYgMQkSEnBfdxITFhYjyolo1wMxMfp44fy8p5dtv5WVgIFCsmLKxEBUGLy9Z8BYA/viD3d4pa0zSicggaFrRGzQASpRQNhYiQ2ZtDbRoIdfZmk5KOHNGTgNoagqMHat0NESFJyAAKFMGSEjg9y9ljUk6Eem96GjZPQwA2rVTNhYiY/Duu/KSPxJJCT/9JC+7dwfKl1c2FqLCZGoKdOsm1w8ckL9viDLCJJ2I9N7u3YBaLbvpengoHQ2R4dMk6fv2yRYdosLy9KksnAXIOaSJippq1WTF95QUYMsWpaMhfcUknYj0WkIC8M8/cp2t6ET5o1o1wNMTSEqS02ARFZZly4DERKBOHaBpU6WjISp8KhXQtatcP34cuHdP2XhIPzFJJyK9duwYkJwMlCwpEwsiyjuVKq2AEbu8U2FJTQV+/lmujxgh/w6JiiJPT6BhQzkV24YNSkdD+shM6QCIiDIjBHDokFxv0YI/6Ijy07vvyoRp2zb5WePni/Jq8eKsbz93Drh9G7CxAV6+fPv+RMasc2fg9Gng0iXg8mWloyF9w5Z0ItJbd+4A//4rp4tq3FjpaIiMyzvvAFZWwN27/IFIhWPfPnnZrJmcN5qoKHNxAVq2lOsbNsjaO0QaTNKJSG9pWtHr1wdsbZWNhcjY2NjIRB0Atm5VNhYyflFRssVQpQJ8fZWOhkg/dOggT5ZGRACbNysdDekTJulEpJcSE4ETJ+R68+bKxkJkrDgVGxWWAwfkZc2asgWRiAB7+7STpXPmKBsL6Rcm6USkl44fl5Wn3dyASpWUjobIOGmS9EOHgNhYZWMh45WYCBw+LNdbtVI0FCK906oVYGIC/P03cOqU0tGQvmCSTkR66e+/5SULxhEVnPLl0+brDQtTOhoyVidOyEJxrq5A9epKR0OkX5ycgAYN5PrcuUpGQvqESToR6Z2ICLmYmQFNmigdDZFxY5d3Kmj//CMvW7SQLYZEpMvPT16uXg08eKBsLKQf+FVJRHpH04pepw5gZ6doKERGT5Ok79ghp2Ijyk8PHshp10xMOEsHUWY8PWX9nZQUOTUmEZN0ItIrSUlyPDogW12IqGA1by4rvUdGynmsifKTphW9Vi3AwUHZWIj02eefy8uFC+XwECramKQTkV45fVoWGXJxASpXVjoaIuNnaQm0bi3Xd+xQNhYyLikpwLFjcr1pU2VjIdJ3nTsDXl7AkyfAypVKR0NKY5JORHpF84OuSROOXSQqLAEB8nL7dmXjIONy7hzw/LlsQa9ZU+loiPSbqSkwapRcnzuXw4+Kulz9BL5161Z+x0FEhNhY4MoVud6okbKxEBUl7dvLy3/+AeLilI2FjIdm2rUmTWQCQkRZ+/hjOXf6pUvArl1KR0NKylWSXrFiRbzzzjtYuXIlEhMT8zsmIiqiTpyQZ44rVJDd3YmocJQvL4eXpKQAe/cqHQ0Zg5gY4MIFuc6u7kTZ4+AAfPSRXF+0SNlYSFm5StJPnz6NWrVqISgoCO7u7hgyZAiOayo95dD8+fPh5eUFKysr+Pj4vPVxYmJiMHz4cJQsWRKWlpaoXLkytnHeGCKjoOnqzlZ0osKnaU1nl3fKD0ePpp10dXdXOhoiwzFokLz86y85Pp2Kplwl6XXq1MGPP/6IBw8eYNmyZYiMjETz5s1Rs2ZNzJ49G48fP87W46xZswZBQUEIDg7G6dOnUbt2bfj7++PRo0cZ7p+cnIy2bdvizp07WL9+Pa5evYolS5agdOnSuXkZRKRHIiPl3OgmJkCDBkpHQ1T0aJJ0TsVGeSVEWlX3Zs2UjYXI0NSsCdSrB7x6JedNp6IpT2WZzMzM0LVrV6xbtw4zZ87EjRs3MGbMGHh4eKB///6IjIzM8v6zZ8/GJ598gsDAQFSvXh0LFy6EjY0Nli1bluH+y5Ytw9OnT7Fp0yY0a9YMXl5e8PX1Re3atfPyMohID2ha0WvW5NzoREpo1QqwspInyy5fVjoaMmQ3bgCPHsmZA+rXVzoaIsPTv7+8XLFC2ThIOWZ5ufPJkyexbNkyrF69Gra2thgzZgw+/vhj/Pvvv5gyZQo6d+6caff15ORknDp1CuPHj9duMzExgZ+fH44cOZLhfbZs2YImTZpg+PDh2Lx5M1xcXNCnTx+MHTsWpplUJElKSkJSUpL2ehwr4hDpHbU6bW50dnUnUoa1NeDrC+zcKVvTq1dXOiIyVJpW9AYN5IkfInq7xYvT1lNSZM/CEyeAKVOAkiWzvu/gwQUbGxW+XLWkz549G97e3mjatCkePHiAX3/9FXfv3sW0adNQrlw5tGjRAsuXL8fp06czfYzo6GikpqbCzc1NZ7ubmxsePnyY4X1u3bqF9evXIzU1Fdu2bcPXX3+NWbNmYdq0aZk+T0hICBwdHbWLh4dHbl4yERWgW7fkuCsrK4AdY4iU83qXd6LcSE4GND//mjRRNhYiQ2VvD3h7y/WjR5WNhZSRqyR9wYIF6NOnD+7evYtNmzbhvffeg8kbExq7urril19+yZcgNdRqNVxdXbF48WLUr18fPXv2xFdffYWFCxdmep/x48cjNjZWu9y7dy9fYyKivNN0da9bF7CwUDYWoqJMM1/6gQPAixfKxkKGKTwcSEoCnJ2BihWVjobIcDVuLC+PHpU9DqloyVV397CwMJQtWzZdYi6EwL1791C2bFlYWFhgwIABmT6Gs7MzTE1NERUVpbM9KioK7pmUAS1ZsiTMzc11urZXq1YNDx8+RHJyMiwy+HVvaWkJS0vLnLw8IipEKSnAyZNy3cdH2ViIirrKlQEvL+DOHWD/fqBDB4UDIoOjafXz8QFUKmVjITJk3t6AjY2czvDKFQ5BKmpylaRXqFABkZGRcHV11dn+9OlTlCtXDqmpqW99DAsLC9SvXx979uxBly5dAMiW8j179mDEiBEZ3qdZs2ZYtWoV1Gq19gTBtWvXULJkyQwTdCLSfxcuAAkJgKMjUKWK0tEQFQ2vj318U9myMkn/4Qfg/v30t3PsI2UmNha4dEmu86QrUd6YmwMNG8qeTUePMkkvanLV3V1kMjdLfHw8rHJQISQoKAhLlizBihUrcPnyZQwdOhQvXrxAYGAgAKB///46heWGDh2Kp0+f4rPPPsO1a9ewdetWTJ8+HcOHD8/NyyAiPaBpRW/YUBZJISJl1awpLy9eVDYOMjwnTsjp18qVA94oOUREuaCp63DmDJCYqGwsVLhy1JIeFBQEAFCpVJg0aRJsbGy0t6WmpuLYsWOoU6dOth+vZ8+eePz4MSZNmoSHDx+iTp062LFjh7aYXEREhE6Xeg8PD+zcuROff/45atWqhdKlS+Ozzz7D2LFjc/IyiEhPJCcD587Jdc6NTqQfqlQBTE2Bx4/lNFpvdJojytTrXd2JKO+8vOQJr6go4NQpoFkzpSOiwpKjJP3MmTMAZEv6+fPndbqYW1hYoHbt2hgzZkyOAhgxYkSm3dv379+fbluTJk1wlGUOiYzCxYuywFCJEvIfEREpz8pKFvy6elUOR2ndWumIyBBcvAjcuyd7RDVsqHQ0RMZBpZIF5DZvlifBmKQXHTlK0vft2wcACAwMxI8//ggHB4cCCYqIioZTp+RlvXosMESkT2rUkEn6xYtM0il7fvtNXnp7A3Z2ysZCZEx8fGSSfv06EBcHMP0qGnI1AjQ0NJQJOhHlycuXaV3d69VTNhYi0qUZl371KvDqlbKxkP5Tq4Hff5fr7OpOlL9KlJAFPYVI+91Exi/bLeldu3bF8uXL4eDggK5du2a574YNG/IcGBEZt507ZVf3YsVkkSEi0h+lSgFOTnLqn+vXWVWYsrZ/P/Dvv4C1NVCrltLREBmfunWBiAhZQK55c6WjocKQ7ZZ0R0dHqP6/P6qjo2OWCxHR26xbJy/Z1Z1I/6hUsss7IMelE2VF09W9fn05bRQR5S9Nj8PLl2VPRDJ+2W5JDw0NzXCdiCinEhOBP/+U6/XrKxsLEWWsRg3gn3/S5r0mykhCArB+vVxv3FjZWIiMlbs7ULIkEBkJnD8PNGqkdERU0HI1Jv3ly5dISEjQXr979y7mzp2LXbt25VtgRGS8du4Enj9nV3cifVatmqzUHRkJPHmidDSkrzZvBuLj5QwdFSooHQ2R8dLMcv3/k22RkctVkt65c2f8+uuvAICYmBg0atQIs2bNQufOnbFgwYJ8DZCIjM/rXd1NcvUtREQFzcYGKF9erl+8qGwspL80Xd0//JDf50QFSdPl/cIFIDlZ2Vio4OXq6/T06dNo0aIFAGD9+vVwd3fH3bt38euvv+K///1vvgZIRMYlMRHYskWus6s7kX7juHTKSlQUoOlE2a+fsrEQGTsPD1npPTmZw5CKglwl6QkJCbC3twcA7Nq1C127doWJiQkaN26Mu3fv5muARGRcdu2SXd1Ll2ZXdyJ9p0nSr1wBUlKUjYX0zx9/AKmpctq1ypWVjobIuKlU7PJelOQqSa9YsSI2bdqEe/fuYefOnWjXrh0A4NGjR5w/nYiypCkw9MEH7BpJpO88PAB7ezld4s2bSkdD+kbT1Z2t6ESFQ9Pl/dw5njg1drn6iTxp0iSMGTMGXl5e8PHxQZMmTQDIVvW6devma4BEZDySkmSRIQDo3l3ZWIjo7UxM2OWdMnbpEnD6NGBmBvTsqXQ0REVD+fKAg4OcVeHaNaWjoYKUqyT9gw8+QEREBE6ePIkdO3Zot7dp0wZz5szJt+CIyLiEhQFxcbKr+/+f2yMiPadJ0jkGkl6naUV/913A2VnZWIiKChOTtC7vp08rGgoVsFx3NnV3d0fdunVh8lp/1UaNGqFq1ar5EhgRGR9NVfdu3djVnchQVK8ux0L++y/w7JnS0ZA+UKuB33+X6+zqTlS4NJ2Ww8PlZ5GMk1lu7vTixQvMmDEDe/bswaNHj6B+4y/k1q1b+RIcERmP17u6f/CBsrEQUfbZ2ck5sG/f5lRsJB04ANy7Bzg6Au+9p3Q0REVL5cqAlZUswnv3LovwGqtcJemDBg3CgQMH0K9fP5QsWRIqlSq/4yIiI7N7NxAbC5QsCTRrpnQ0RJQTNWvKJP38eaUjIX2g6ereo4dMFoio8JiZyR5Op0/L72Qm6cYpV0n69u3bsXXrVjTjL20iyiZ2dScyXN7ewJ9/Apcvy14xlpZKR0RKSUhIm6WDXd2JlFGzpkzSL14EOnVSOhoqCLn6qVysWDEUL148v2MhIiOVnMyq7kSGzMNDVhROSgL+/lvpaEhJW7bIbrZeXuwVRaQUTUHPO3dkQV4yPrlK0r/55htMmjQJCQkJ+R0PERmh3buBmBjA3Z0/6ogMkYmJbLkBgK1blY2FlKXp6v7hh+wVRaQUJyd58hTgzBvGKlfd3WfNmoWbN2/Czc0NXl5eMDc317n9NOcEIKLXvN7V3dRU2ViIKHe8vYHDh4Ft2wDOtlo0RUUBO3fKdXZ1J1JWzZqygOOFC0pHQgUhV0l6ly5d8jkMIjJWycnApk1ynV3diQxXtWryJNu1a8CNG0DFikpHRIVt9WogNRVo1EhWmCYi5dSsCWzfLlvSU1PZCGJscpWkBwcH53ccRGSk9uyRXd3d3IDmzZWOhohyy9paJuZXr8ou7599pnREVNg0Xd3Zik6kvHLlABsb4MUL4PhxoEkTpSOi/JTr0UQxMTFYunQpxo8fj6dPnwKQ3dzv37+fb8ERkeHTVAFmV3ciw+ftLS+3bVM2Dip8ly4Bp07J6Z969VI6GiIyNZU9nAB+JxujXCXp586dQ+XKlTFz5kz88MMPiImJAQBs2LAB48ePz8/4iMiAvXrFru5ExkSTpO/fD8THKxoKFTJNK3pAAODsrGwsRCRpCnpu365sHJT/cpWkBwUFYeDAgbh+/TqsrKy02999910cPHgw34IjIsO2dy/w9Kns6t6ihdLREFFeubkB5cvLWhN79igdDRUWtRr4/Xe5zq7uRPpDMxXbqVOysCMZj1wl6SdOnMCQIUPSbS9dujQePnyY56CIyDhoqrp37cqu7kTGQKUCOnSQ6+xeWXQcOCCrSDs6Ah07Kh0NEWk4OgJly8r1HTuUjYXyV66SdEtLS8TFxaXbfu3aNbi4uOQ5KCIyfK9eARs3yvUPPlA2FiLKP+++Ky+3bQOEUDYWKhyaru7duwOvdaAkIj3ALu/GKVfV3Tt16oSpU6di7dq1AACVSoWIiAiMHTsW3bp1y9cAicgw7dsnu7q7uAAtWyodDRHll1atZEXhf/8Fzp0DatdWOiIqSAkJaQVA2dWdSP/UrClPmv75J7BgQc57Lg4eXDBxUd7kqiV91qxZiI+Ph4uLC16+fAlfX19UrFgR9vb2+Pbbb/M7RiIyQK93dTfL1elAItJHVlZAmzZy/a+/lI2FCt6WLcDz54CXF6fRJNJH5coBtrbyhNrt20pHQ/klVz+dHR0dERYWhn/++Qdnz55FfHw86tWrBz8/v/yOj4gM0Otd3VnVncj4dOokW202bwa++krpaKggLV8uLz/8EDDJ9cS9RFRQTEzkVGwnTwKXLwMVKyodEeWHHCfparUay5cvx4YNG3Dnzh2oVCqUK1cO7u7uEEJApVIVRJxEZED27weePJHT9Pj6Kh0NEeW3jh1lEbkTJ4AHD4BSpZSOiArC/ftAWJhcHzhQ0VCIKAuvJ+ks7mgccnROVAiBTp06YdCgQbh//z68vb1Ro0YN3L17FwMHDsT7779fUHESkQFhV3ci4+bmBjRuLNe3bFE2Fio4v/0mp19r0QKoUEHpaIgoM1Wrysvbt4GXL5WNhfJHjpL05cuX4+DBg9izZw/OnDmDP/74A6tXr8bZs2exe/du7N27F7/++mtBxUpEBiAlhV3diYqCzp3l5ebNysZBBUMIIDRUrgcGKhsLEWXN2VkW6lWrgevXlY6G8kOOkvQ//vgDEyZMwDvvvJPuttatW2PcuHH4/fff8y04IjI8+/cD0dHyH0arVkpHQ0QFRZOk790rC4uRcTl6FLh2TVby5zSaRPqvWjV5efmysnFQ/shRkn7u3Dm0b98+09sDAgJw9uzZPAdFRIZLM1XP+++zqzuRMataFahcGUhOBnbsUDoaym+aVvQPPgDs7ZWNhYjejkm6cclRkv706VO4ubllerubmxuePXuW56CIyDClpAAbNsh1dnUnMn7s8m6cEhKANWvkOru6ExmGKlVkQc/ISCAmRuloKK9ylKSnpqbCLIumMVNTU6SkpOQ5KCIyTAcPAo8fAyVKABmMiiEiI6NJ0rdulVMvknHYuBGIi5Nzo7dsqXQ0RJQdtrZA2bJy/coVZWOhvMtRZ1QhBAYOHAhLS8sMb09KSsqXoIjIcCxenLb+22/yslo1YNkyZeIhosLTuLEsVvT4MfD330Dr1kpHRHm1eDEwd65cr1kTWLpU0XCIKAeqVQPu3pVd3jUzcJBhylFL+oABA+Dq6gpHR8cMF1dXV/Tv37+gYiUiPZaSApw+LdcbNlQ2FiIqHKamaXPyssu7cXj6NK0VrkkTZWMhopzRTMV25YqcoYEMV45a0kM1VUSIiN5w+bIcx+jgIItJEVHR0Lmz7DmzebNsgVWplI6I8uLIEfnjvnJlOUsHERmOihUBc3M5Jv3hQ6BkSaUjotzKUUs6EVFmTpyQlw0aACb8ZiEqMvz8AGtr2cWSE7wYNrUaOHxYrjdtqmwsRJRz5uYyUQdY5d3Q8ac0EeVZcjIQHi7XGzRQNBQiKmQ2NkC7dnKdXd4N2969QHS0POlSv77S0RBRbnAqNuOgF0n6/Pnz4eXlBSsrK/j4+OD48ePZut/q1auhUqnQpUuXgg2QiLJ0/jyQlCSrupcvr3Q0RFTYNP+GNVMwkmHSFIlr1AiwsFA2FiLKHU2Sfu0akJqqbCyUe4on6WvWrEFQUBCCg4Nx+vRp1K5dG/7+/nj06FGW97tz5w7GjBmDFi1aFFKkRJSZkyflZYMGHI9KVBR17iy7WZ47x6l/DFV0tJx6DQCaN1c2FiLKvTJl5HRsiYnAnTtKR0O5pXiSPnv2bHzyyScIDAxE9erVsXDhQtjY2GBZFvM3paamom/fvpgyZQrKs9mOSFEvX8qWdIBV3YmKqmLFgLZt5fq6dcrGQrnz229y6FLZsmlzLROR4TExSavyfumSsrFQ7imapCcnJ+PUqVPw8/PTbjMxMYGfnx+OHDmS6f2mTp0KV1dXfPzxx299jqSkJMTFxeksRJR/zp4FXr0C3N3l2VsiKpp69JCXa9cqGwflnBDAkiVyna3oRIZPk6Rfu6ZsHJR7iibp0dHRSE1NhZubm852Nzc3PHz4MMP7HDp0CL/88guWaP6bvEVISIjOXO4eHh55jpuI0rxe1Z1d3YmKLk2X9wsX2HpjaA4flkWmbGzkeHQiMmxVqsjLW7dkDxkyPIp3d8+J58+fo1+/fliyZAmcszl55/jx4xEbG6td7t27V8BREhUdT56k/RhnV3eios3JCfD3l+vs8m5YNAXjevSQld2JyLC5usphSCkpwM2bSkdDuWGm5JM7OzvD1NQUUVFROtujoqLg7u6ebv+bN2/izp076Nixo3abWq0GAJiZmeHq1auoUKGCzn0sLS1haWlZANFTdi1enPv7Dh6cf3FQ/vvf/+S8uh4esrs7ERVtPXoAf/0lu7wHBysdDWVHbCywZo1c/+QT2ROCiAybSiVb048elcU8NRXfyXAo2pJuYWGB+vXrY8+ePdptarUae/bsQZMmTdLtX7VqVZw/fx7h4eHapVOnTnjnnXcQHh7OruxEhey33+QlW9GJCAA6dZJTd126BFy8qHQ0lB1//CELgFavDmTw04uIDJRmXPrVq8rGQbmjaEs6AAQFBWHAgAFo0KABGjVqhLlz5+LFixcIDAwEAPTv3x+lS5dGSEgIrKysULNmTZ37Ozk5AUC67URUsG7eBA4dkmdrfXyUjoaI9IGjo+zy/uefsjV9yhSlI6KsvF4wbtAg1hUhMiaacel378oTcRzKYlgUH5Pes2dP/PDDD5g0aRLq1KmD8PBw7NixQ1tMLiIiApGRkQpHSURv+vVXeVmtmhyLSkQE6FZ5F0LZWChrJ04Ap0/L3g/9+ikdDRHlp+LFARcXOSzxxg2lo6GcUrwlHQBGjBiBESNGZHjb/v37s7zv8uXL8z8gIsqSWp2WpLN7JBG9rlMnwNJSjoO8cAHw9lY6IsrMggXysmdPIJv1eInIgFSpAjx+LL+P+V1sWBRvSSciw3PoEHDnDmBvD9Spo3Q0RKRPHByA9u3lOqu8668nT4DVq+X6sGHKxkJEBYPj0g2XXrSkE5FhWbFCXvboIbtJElHRkZ0ZOzStsosWAaVLp4115owd+mP5ciAxEahbl3VFiIyVZlz6vXtAfDxgZ6dsPJR9bEknohxJSEhrHRswQNlYiEg/1aolT+A9egTcvq10NPQmtTqtq/uwYSwYR2SsHByAkiXl+rVrysZCOcMknYhyZONG4PlzoFw5oHlzpaMhIn1kZQXUqyfXjxxRNhZKLyxMztDh6Aj07q10NERUkDSt6ezybliYpBNRjmi6uvfvz9YXIspc06by8sQJIDlZ2VhIl6YVfcAAwNZW2ViIqGBxXLphYpJORNn277/A7t1yvX9/ZWMhIv1WqRJQooScnzc8XOloSCMiQs5jDwBDhyobCxEVvMqVZaNKZCQQG6t0NJRdTNKJKNt+/13Oe9yiBVC+vNLREJE+MzFJm6KRXd71x+LFckx669ZpLWxEZLxsbQEPD7nO1nTDwSSdiLJFCCA0VK6zYBwRZUfjxvLy8mXg6VNlYyE57GDJErnOadeIig7NuPQrV5SNg7KPU7ARUbbs2yfPwNrZyanXiIjexsVFdrW8dg04elTpaIqOzKbJO3ZMVtx3cpKX2ZlOj4gMX5UqsmAkW9INB1vSiShbNIWG+vUD7O2VjYWIDIemgNyRI7JHDilDCGDPHrnu6wuYmiobDxEVnkqV5BCk6Gi5kP5jkk5EbxUZCWzaJNdZaIiIcqJuXcDSUrbcHj6sdDRF182bwN27gLk50LKl0tEQUWGysgK8vOQ6W9MNA5N0InqrpUuBlBSgWTPA21vpaIjIkLw+Z/ry5YqGUqRpWtF9fOSwJSIqWjhfumFhkk5EWUpJSRu3yFZ0IsoNTZf3NWuAhARlYymKoqOBM2fkeps2ysZCRMp4PUnn0CP9xySdiLK0daucH71ECaBbN6WjISJDVKmSLCL3/LmcypEK1/798kd5tWpAqVJKR0NESqhQATAzA2JigKgopaOht2GSTkRZ0hSM++gj2W2ViCinVCqgVSu5/uOPbMUpTImJwKFDct3PT9lYiEg5FhZA+fJynV3e9R+TdCLK1M2bwM6dcn3IEGVjISLD1qyZHAt98WLa+GgqeEeOAC9fAm5uQPXqSkdDREqqWlVeMknXf0zSiShTixbJS39/2U2KiCi3rK2BwEC5PneuoqEUGWo1sHevXG/TRk7BRERF1+vj0tVqZWOhrPHrmogy9PIlEBoq11kwjojyw8iRsuv71q3A9etKR2P8zp+XU9/Z2ACNGysdDREpzctLdnuPjwcePFA6GsoKk3QiytDy5bIicNmyQIcOSkdDRMagUqW075P//lfZWIydEGnDlVq0kHPVE1HRZmYGVKwo169cUTYWyhqTdCJKJyUF+OEHuT5mjPxSJyLKD6NHy8vQUFllmArGjRuyroiZGaddI6I0mnHp164pGwdljUk6EaXzv/8Bt27Jadc++kjpaIjImLRuDdSsCbx4ASxbpnQ0xmvHDnnZtCng6KhsLESkP14fl56aqmwslDkm6USkQwhg5ky5PnIkYGurbDxEZFxUKmDUKLk+bx5/JBaEe/eACxfke92undLREJE+KVtWFvJMTJTfFaSfmKQTkY7du4EzZ2ShoREjlI6GiIzRhx/Knjp37gCbNikdjfHRjEVv0ABwcVE2FiLSLyYmQOXKcp3j0vUXk3Qi0qFpRR80SP6IJiLKb9bWwLBhcn3yZLam56dbt4CTJ+W6v7+ysRCRfnq9yzvpJybpRKR16hSwZw9gagoEBSkdDREZs6AgwMlJdstevVrpaIzH99/LYUs1agAeHkpHQ0T6SFM87sYNIDlZ2VgoY0zSiUhL04reuzfg6alsLERk3JycgP/8R65PmgS8eqVoOEbh4UNZNR8A2rdXNhYi0l8lSwL29jJBP3JE6WgoI0zSiQgAcP26rOoOpP1wJiIqSKNGAa6usou2Jrmk3Js7F0hKAsqXl3PSExFlxMQEqFZNru/apWwslDEm6UQEAJg4EVCrgffeA7y9lY6GiIoCW1vgq6/k+tSpwMuXysZjyB49ktXyAdmKrlIpGw8R6bfq1eUlk3T9xCSdiHDiBLB2rfxR9+23SkdDREXJkCFy7PT9+8CCBUpHY7hmzgQSEoCGDYFatZSOhoj0naYl/dQp4MkTZWOh9MyUDoCIlCUE0K+fXPfxAY4elQsRUWGwtJQV3j/+GJg+Xc4s4eCgdFSG5cED4Oef5frUqUBEhLLxEJH+c3ICSpWS3x979gA9eigdEb2OLelERdyuXXIKDjMzoFMnpaMhoqKof385b++TJ8CsWUpHY3hCQoDERKBpU067RkTZp2lNDwtTNg5Kj0k6URGmVgNjx8r1Vq04LzoRKcPMDJg2Ta7PmAFcuaJsPIYkIgJYvFiuf/MNx6ITUfa9Pi5dCGVjIV1M0omKsFWrgLNnAWtrICBA6WiIqCj74ANZ8Cw5WXZ9V6uVjsgwTJsm37NWrYDWrZWOhogMSaVKgIWFPNl37ZrS0dDrmKQTFVFJSbKiOyB/GNvZKRsPERVtKhWwcKH8Ljp8OG2MNWXu9anrvvlG2ViIyPBYWgLNm8t1dnnXL0zSiYqo//4XuHtXFg1h6wsR6QNPT9ndHQDGjZPfUZS5qVOBlBQ5Dl3zQ5uIKCfatpWXnIpNvzBJJyqCbt0CgoPl+rRpsqsTEZE+GDoUaNYMePFCTs/GcZIZO3MG+PVXuT51qrKxEJHhatdOXu7bB7x6pWwslIZJOlERIwTw6afAy5dyDOPAgUpHRESUxsQE+OUX2Q1z507gt9+Ujkj/CAGMHi0ve/UCGjVSOiIiMlR16gDOzkB8PKfg1SdM0klvCSGn47l8Gdi/H9iyBbh3T+moDN9vv8lxR5aWsiIwKwETkb6pUiWtt8/Ikaz2/qb//Q84eFAW/Zw5U+loiMiQmZgAfn5ynV3e9QeTdNI7Dx4ACxYAw4fLM3vVqwPvvAN07gyULSvndPzsM+Cvv2RrMGXfo0fA55/L9cmTZVVPIiJ9NGaMHGcdFwd07Ag8fap0RPohMRH48ku5/uWX8v8iEVFeaLq8s3ic/mCSTnrj6VNgxQo5ti48HEhNlduLFZOtKrVry7N9V67IomcdOwLlywM//shkPbtGj5bvc+3awBdfKB0NEVHmzM1li3HZssCNG0CPHhwvCQCzZwN37gClSwP/+Y/S0RCRMdAUjztxAnj2TNlYSDJTOgAiIWSr+I4dskotIMfHvPceULIkYPbaX+mLF8DVq7IL/LlzwMOHMvEMDpbzfDdvLn/YAcDgwYX9SvTbtm3AH3/IEx1Ll6a9T0RE+srVVQ51atYM2LMHCAoC5s1TOirlPHgATJ8u12fOBGxtlY2HiIxDmTKyp+rly7I1vUcPpSMitqSTooSQieNff8kEvVIl2TIwdCjg4aGboAPyB0m9ekDfvsC33wIffggULw7ExgKrVwOTJslWeNL18CHw8cdy/fPPgQYNlI2HiCi7atcGVq6U6z/9BCxapGw8SpowQZ6sbtwY6NNH6WiIyJi8+668/OsvZeMgSS+S9Pnz58PLywtWVlbw8fHB8ePHM913yZIlaNGiBYoVK4ZixYrBz88vy/1JfwkhE+sDB2Txsg8/lF2wK1TI3v3NzIAWLWT3+D59ACcn2ZV7wQJg/nzZHZDkyY+ePWWiXqMGMGWK0hEREeVMly7yxCwg65X88Yei4Sji0CE5JAyQw7xY9JOI8lPHjvJy27a0nq2kHMWT9DVr1iAoKAjBwcE4ffo0ateuDX9/fzx69CjD/ffv34/evXtj3759OHLkCDw8PNCuXTvcv3+/kCOnvBACWLtWVm1XqYB+/WTCnZsfHebmgK+vTNbbt5fduc+dkwXnZswAkpPzPXyDMmGCrAJsby/Hd7J7JBEZovHj5ZSRqamyN1VRalFPSAACA+X6xx9zyjUiyn/Nmsk6UE+eAEeOKB0NKZ6kz549G5988gkCAwNRvXp1LFy4EDY2Nli2bFmG+//+++8YNmwY6tSpg6pVq2Lp0qVQq9XYs2dPIUdOuSUEsG4dsHevvP7hh/KLIa8sLYH33we+/hqoXFkWkxs/Xo5vP3Ag749viDZuBL7/Xq6HhsoCfEREhkilkvOnDx0q/498+inw3XdKR1U4JkyQxfPKlAFmzVI6GiIyRmZmaV3e//xT2VhI4cJxycnJOHXqFMaPH6/dZmJiAj8/PxzJ5imchIQEvHr1CsWLF8/w9qSkJCQlJWmvx8XF5S1oyrO//5YFgADZgt68ef4+fqlSsriQjY3sPn/5MtCqFdC/v0xYXV3z9/n01fXrstUJkO9Ht26KhkNEhMWLc3/fwYNlT6n58+XwppAQYOxYICZGdoU31u7ff/8tZzQBZNFPR0dl4yEi49WxI/D77zJJLyonQfWVoi3p0dHRSE1NhZubm852Nzc3PHz4MFuPMXbsWJQqVQp+fn4Z3h4SEgJHR0ft4uHhkee4KfcePpTd3AGga9f8T9A1NF3or16VrS0qFfDrr7IleeFCQK0umOfVFzEx8v2Ni5Pv8YwZSkdERJQ/VCpZ4VzzvRYSIisRx8QoGlaBePFCdnMXQnZz9/dXOiIiMmbt28sW9StXZGMPKUfx7u55MWPGDKxevRobN26ElZVVhvuMHz8esbGx2uXevXuFHCVppKYCy5bJeW6rVk2bk7EgFSsmC8kdPSqrwsfEyK6STZoAZ84U/PMrIT5eTkd34QLg7g6sWcPp1ojI+IwdK1vmzc2B9evld/yJE0pHlb8mTABu3mQ3dyIqHI6Oss4TwC7vSlM0SXd2doapqSmioqJ0tkdFRcHd3T3L+/7www+YMWMGdu3ahVq1amW6n6WlJRwcHHQWUsaffwJ378pu6AMHym6LhaVRI+D4cdll0MFBrjdoAHz2mZy+zVgkJgKdO8uTEsWKATt3yu7/RETG6JNPZNXzcuWA27dlfZM5c2TLs6Hbs4fd3Imo8GmqvDNJV5aiY9ItLCxQv3597NmzB126dAEAbRG4ESNGZHq/7777Dt9++y127tyJBpzw2SDcuAHs2CHXP/xQJpCFzdQUGDkS+OADOUZ79Wr5A2jVKmDiRNkt3tJS7pvXcZNKePUK6N5dFuSzs5Pvdxbnr4iIjEKjRsDp0zJhX79efr9v3Sp7UVWqpHR0uXP7tjzhCsghS3fv5u3/EhFRdnXsCIweLethPHumzG920oPu7kFBQViyZAlWrFiBy5cvY+jQoXjx4gUC/3+ukf79++sUlps5cya+/vprLFu2DF5eXnj48CEePnyI+Ph4pV4CvcXLl7KbuxCym3n9+srGU7KknGM3LEx2u4+Oll9GVasCK1ca5nj1lBRZGO+vvwArK3nJKXqIqKhwcpL1Tn7+WX4H7tkDeHvLqTlfqx1rEOLjZYL+4gXg6Qn07Kl0RERUlJQvD9SoIYepbt+udDRFl6It6QDQs2dPPH78GJMmTcLDhw9Rp04d7NixQ1tMLiIiAiav9YtesGABkpOT8cEHH+g8TnBwMCZPnlyYoVM2bdwo51x0dtavHxt+fsD583JqssmTgTt3ZLG5776TJxPq1i3cLvm5FRMj39ddu+TYzA0b0sYTEREVFSqVrDnSrh0wbJj8TgwOlr2lAgKAatVy97iF2TtKrZbDwc6fl0Ozhg4FLCwK7/mJiADZmn7xouzy3qeP0tEUTSohjGHkVvbFxcXB0dERsbGxHJ9eCMLDZTEfIWQXxMKcpzsnP6wSEmTX9xkz0saolywp54ts0CBnyXph/qC7eRN47z1ZhdPGRv4Y1XSRzAl2oyQifZeT71YhZNHM0aMBTdkbb2853OktJW/y9Lx5NXWqPLFgYSFjr1Ch8J6biIqmjL7jDh+WNT4cHYHHj1mAOL/kJA81gHZCMlRCAKNGycsGDQo3Qc8pGxtg3Dg5DjA4GLC2BiIjgV9+kdcPHpRjvvXJgQOyS/uVK0Dp0nLsUG4SdCIiY6NSAb16ye/HkSPlidbz54EpU+TJzOfPlY4wvXXr5P8bQI6nZ4JORErx8QFcXGTD1aFDSkdTNCne3Z2M1x9/yMTRwgLo1k3paLKnWDHZ9b1YMVmAbfdu4NEj4PffgS1bgHfekV3J7eyUi/HVK2D2bODrr+V6w4bA5s2yS9LJk8rFRUSkb5ycZC8pZ2c5FOjsWXmC89gxOeTJz0+elFXab7/J+dABeVLho4/Yw4mIlGNqCnToACxfLoetvvOO0hEVPUzSqUDExwNffinXAwKA4sWVjSenrK3ll1ObNvIM4p49wNOnMlHfvl2eYWzVCvDwKNy4Tp4EBg2SPzQBoEcP+QWqDz8yiYgKUl6SVnd3OU796lVZAT4iQhbY3LcPaN9efp8rNfb7p59kYg4AAwbIk7BEREr74AP5G3PtWvm9ZMassVCxuzsViG+/BR48kBUi27ZVOprcs7KSLS3TpgEffwyULStbrw8dkttmzpRzkhd0V/j4eDmm38dHJujFismCd6tXM0EnIsquKlWA8ePlGEw3N1lB/X//k9Nw7tkDJCcXXixCyP+VmgR91Cg5Ewp/CBORPmjXDihRQtb12LdP6WiKHv4roHx3/XpaS8CcOcDDh8rGkx9MTeX474YN5es7eBA4dQq4dUsuq1fLAnkNG8opK0xN8+d5790D5s+XLUjPnsltvXsDc+cCrq758xxEREWJiYmcCrROHdnt/c8/ZU+ptWtlT6m2beWwJiurgoshIQH4z3/k9zsgx6IHB8ux9ERE+sDcXPbYXLBADvs05EY3Q8SWdMp3X3whWyPat5dTOBgTlQqoXFl2OZ8xQxZqK1ZMzgX/zz8yefbwAIYPl60zT57k/DlevpQtOr16AeXKydb6Z89kEaFt22TRIyboRER5Y2oKNG0qK6r37StbjJ4/l2PXJ0wANm2SyXt+271bVprXJOizZ8taKEzQiUjfaKZf27BB/j6lwsMp2Chf7d8vi0uYmgIXLgBVqxp/8Ru1GrhxAzhxQrauv3ihe3utWkDz5rLrf9mycilZEkhKkj8I4+JkEn7ihGzJuXNHtsZrVK4sx8bXqmUY87YTERmi1FTg+HHZmq6Ztk2lkidjR4wAWrfOWyL95Ik8ib1ihbxepgywcKGsf5IRY//fSUT6IatpJtVq+fv17l35G7V798KLyxjlJA9ld3fKN2o1MGaMXB8yRCboRYGJiUykK1cGevaULek7d8rxO5cuAefOySUnHB2BmjXlCY/CLk5HRFQUmZoCTZrI2h/h4fKk89WrskV90ybA0xPo1EkuLVtmr9BcaqocHrVqlZxiLTZWJvrDhwPTpwP29gX7moiI8sLERA6znDFDfo8xSS88bEmnfLNqlewyaG8vW5Y1XbKLWmvA62cko6LkdD+nTsnx5RERcnn4UBZ8s7cHHBzkZbVqspBQ5cpyuiB2fSQiUtaDB0B0tGz9jo9P2+7gIMetV6okW5nKl5c9pJ48kd/7jx4BN2/KLqIPHqTdr0YNYMkSeTLgbYra/04iUkZWLemA7Bnr7S1PTD58KId5Uu6wJZ0KXWKirJgLAOPGccy0hpubLLrRo0f29uePMiIi/VGqlBwv/t13slbIli2y0FxUlLzMDicn2frUpw/QokX+FRYlIioMNWvKJP38eVlvadAgpSMqGpikU76YN0+2EJcuDYwerXQ0RERE+cfGRhZC7dhRDu06cQI4eTJtho9bt2TiXqKEPDmrWVq3Bvz9AUtLpV8BEVHu9e0rG+F+/51JemFhkk559uSJnOsVkHOH29goGw8REVFBMTGR49Z9fJSOhIiocPTqJZP0AweAf/+VhS+pYLFWNOXZtGmyGE6tWkC/fkpHQ0RERERE+cXTUw7XEQJYvVrpaIoGJumUJ9evp831+v33HGtHRERERGRsNHOmr1ghk3UqWEzSKU++/BJ49UqOuWvXTuloiIiIiIgov/XqBdjaymrv+/YpHY3x45h0yrW9e4HNm2Xr+ezZSkdDRESkXzhjBxEZCycnYOBA2YN27lxZGJMKDpN0ypXUVODzz+X60KFA9erKxqNP+KOMiMh48DudiEgaNUom6X/9JYe8VqqkdETGi93dKVeWLQPOnZNn1SZPVjoaIiIiIiIqSJUrAx06yDHp//2v0tEYNybplGNxccDEiXI9OFjOC0tERERERMZt9Gh5GRoKxMQoGYlxY5JOOTZ9OvDokTybNmyY0tEQEREREVFhaNMGqFkTePEC+OUXpaMxXkzSKUdu3ADmzJHrs2YBFhbKxkNERERERIVDpUprTZ83D0hJUTQco8UknbJNCGDwYCA5GWjbVo5JISIiIiKioqNPH8DZGbh7F9i0SelojBOTdMq20FA5L6K1NbBggTyTRkRERERERYe1NfDpp3Jd08OW8heTdMqWhw+BL76Q61OnAhUqKBsPEREREREpY9gwwNwcOHwY2L5d6WiMD5N0ypZRo2QFx3r10sahEBERERFR0VOyJDBypFwPCgJevVI2HmPDJJ3eavNmYN06wNQUWLoUMDNTOiIiIiIiIlLS118DLi7AlSvAzz8rHY1xYZJOWYqNTZtmbcwYoG5dZeMhIiIiIiLlOTkB06bJ9cmTgehoJaMxLkzSKVNCyAT9wQOgYkUgOFjpiIiIiIiISF98/DFQu7YcFjtpktLRGA8m6ZSpn34CVq2S3dxDQ2UlRyIiIiIiIkDmCT/+KNcXLQLOn1c2HmPBJJ0y9M8/sggEAPzwA9C8ubLxEBERERGR/vH1Bbp1A9RqWWBaCKUjMnxM0imdhw+B7t2BlBSgZ0/gs8+UjoiIiIiIiPTV998DlpbA3r3AL78oHY3hY5JOOl69kol5ZCRQvbqs5q5SKR0VERERERHpq3Ll0upXDR8OHD2qbDyGjkk6aQkhW80PHgTs7YENGwA7O6WjIiIiIiIifTd2LPD++0ByMtC1q2z0o9xhkk4A5BiSYcOABQvk9RUrgCpVlI2JiIiIiIgMg4mJzCGqV5cJerduQFKS0lEZJibphNRU4JNPgIULZdf2ZcvkWTAiIiIiIqLssrcHNm+Wc6gfOQKMGqV0RIaJSXoRl5ICBAbKxNzEBPjtN3mdiIiIiIgopypWBP74Qzb+LV4MTJ3Kiu85xSS9CIuNBXr1kom5qan8MPXtq3RURERERERkyNq3B2bMkOvBwUD//kBiorIxGRIm6UXU3r2Atzfwv/8B5ubAunVAjx5KR0VERERERMbgP/+R9a5MTYGVK4E2bYBHj5SOyjAwSS9iXr4EPv9cfkju3QMqVAD27+cYdCIiIiIiyl+ffgrs2CHHqB8+DDRqBJw+rXRU+o9JehHx6pU8g1W3LjB3rtw2ZAgQHg40bapkZEREREREZKz8/OS86RUrAnfvAg0ayO7vt28rHZn+YpJu5OLigFmzgPLlgX79gKtXAXd3YOtWWc2d86ATEREREVFBqlIFOHYM6NlTFpH77Te5bdQo4OFDpaPTP3qRpM+fPx9eXl6wsrKCj48Pjh8/nuX+69atQ9WqVWFlZQVvb29s27atkCI1DJGRwPLlsiichwcwZgzw778yOZ8+Hbh8GXj3XaWjJCIiIiKioqJ4cWD1auDkSaBdO9nTd948oEwZoHVruX7vntJR6gczpQNYs2YNgoKCsHDhQvj4+GDu3Lnw9/fH1atX4erqmm7/w4cPo3fv3ggJCcF7772HVatWoUuXLjh9+jRq1qypwCtQjlotk+/Ll4FLl+TlsWPAuXO6+1WtKhP1Dz8ELC2ViZWIiIiIiKh+fWDnTlnIeuJEOZ/6vn1yGTUKqFMHqFdPFrn29gZq1gRcXeWUbkWFSghlZ63z8fFBw4YN8dNPPwEA1Go1PDw8MHLkSIwbNy7d/j179sSLFy/w119/abc1btwYderUwcKFC9/6fHFxcXB0dERsbCwcHBzy74Xks0OHgL//BuLjgRcv0pboaFkV8dEj4PFjOc/5m1QqOdajfXvA3x9o0kTOga6UxYuVe24iIiIiIsrY4MFKRwDcugVs3CiXw4cznlPd3Fz2CnZ3B9zcZCE6OzvA1jbtcvhwwMam0MPPtpzkoYq2pCcnJ+PUqVMYP368dpuJiQn8/Pxw5MiRDO9z5MgRBAUF6Wzz9/fHpk2bMtw/KSkJSUlJ2uuxsbEA5Jukz7ZuTZtbMCumprJCe9WqclxH9epAy5aAs3PaPvHxBRdndrx8qezzExERERFRevqQEjk7A598IpeoKNmyfukScOGCvLx9W3aNv3cv6+7wffpk3ICpLzT5Z3bayBVN0qOjo5Gamgo3Nzed7W5ubrhy5UqG93n48GGG+z/MpOJASEgIpkyZkm67h4dHLqPWL6mpwLVrciEiIiIiIsqu0aOVjiD/lCmjdATZ8/z5czg6Oma5j+Jj0gva+PHjdVre1Wo1nj59ihIlSkBVlAY2KCQuLg4eHh64d++eXg8vIInHy7DweBkWHi/DwuNlWHi8DAuPl2Hh8cofQgg8f/4cpUqVeuu+iibpzs7OMDU1RVRUlM72qKgouLu7Z3gfd3f3HO1vaWkJyzeqpTk5OeU+aMoVBwcHfqgNCI+XYeHxMiw8XoaFx8uw8HgZFh4vw8LjlXdva0HXUHQKNgsLC9SvXx979uzRblOr1dizZw+aNGmS4X2aNGmisz8AhIWFZbo/ERERERERkaFQvLt7UFAQBgwYgAYNGqBRo0aYO3cuXrx4gcDAQABA//79Ubp0aYSEhAAAPvvsM/j6+mLWrFno0KEDVq9ejZMnT2IxS4gTERERERGRgVM8Se/ZsyceP36MSZMm4eHDh6hTpw527NihLQ4XEREBk9fmD2vatClWrVqFiRMnYsKECahUqRI2bdpU5OZINxSWlpYIDg5ON+SA9BOPl2Hh8TIsPF6GhcfLsPB4GRYeL8PC41X4FJ8nnYiIiIiIiIgkRcekExEREREREVEaJulEREREREREeoJJOhEREREREZGeYJJOREREREREpCeYpFOBmj9/Pry8vGBlZQUfHx8cP35c6ZCMSkhICBo2bAh7e3u4urqiS5cuuHr1qs4+rVq1gkql0lk+/fRTnX0iIiLQoUMH2NjYwNXVFV9++SVSUlJ09tm/fz/q1asHS0tLVKxYEcuXL08XD4931iZPnpzuWFStWlV7e2JiIoYPH44SJUrAzs4O3bp1Q1RUlM5j8FgVHi8vr3THS6VSYfjw4QD42VLawYMH0bFjR5QqVQoqlQqbNm3SuV0IgUmTJqFkyZKwtraGn58frl+/rrPP06dP0bdvXzg4OMDJyQkff/wx4uPjdfY5d+4cWrRoASsrK3h4eOC7775LF8u6detQtWpVWFlZwdvbG9u2bctxLMYuq+P16tUrjB07Ft7e3rC1tUWpUqXQv39/PHjwQOcxMvpMzpgxQ2cfHq/88bbP18CBA9Mdi/bt2+vsw89X4Xnb8crof5lKpcL333+v3YefLz0jiArI6tWrhYWFhVi2bJm4ePGi+OSTT4STk5OIiopSOjSj4e/vL0JDQ8WFCxdEeHi4ePfdd0XZsmVFfHy8dh9fX1/xySefiMjISO0SGxurvT0lJUXUrFlT+Pn5iTNnzoht27YJZ2dnMX78eO0+t27dEjY2NiIoKEhcunRJzJs3T5iamoodO3Zo9+Hxfrvg4GBRo0YNnWPx+PFj7e2ffvqp8PDwEHv27BEnT54UjRs3Fk2bNtXezmNVuB49eqRzrMLCwgQAsW/fPiEEP1tK27Ztm/jqq6/Ehg0bBACxceNGndtnzJghHB0dxaZNm8TZs2dFp06dRLly5cTLly+1+7Rv317Url1bHD16VPz999+iYsWKonfv3trbY2NjhZubm+jbt6+4cOGC+OOPP4S1tbVYtGiRdp9//vlHmJqaiu+++05cunRJTJw4UZibm4vz58/nKBZjl9XxiomJEX5+fmLNmjXiypUr4siRI6JRo0aifv36Oo/h6ekppk6dqvOZe/3/HY9X/nnb52vAgAGiffv2Osfi6dOnOvvw81V43na8Xj9OkZGRYtmyZUKlUombN29q9+HnS78wSacC06hRIzF8+HDt9dTUVFGqVCkREhKiYFTG7dGjRwKAOHDggHabr6+v+OyzzzK9z7Zt24SJiYl4+PChdtuCBQuEg4ODSEpKEkII8Z///EfUqFFD5349e/YU/v7+2us83m8XHBwsateuneFtMTExwtzcXKxbt0677fLlywKAOHLkiBCCx0ppn332mahQoYJQq9VCCH629MmbP0rVarVwd3cX33//vXZbTEyMsLS0FH/88YcQQohLly4JAOLEiRPafbZv3y5UKpW4f/++EEKIn3/+WRQrVkx7vIQQYuzYsaJKlSra6z169BAdOnTQicfHx0cMGTIk27EUNRklEW86fvy4ACDu3r2r3ebp6SnmzJmT6X14vApGZkl6586dM70PP1/Kyc7nq3PnzqJ169Y62/j50i/s7k4FIjk5GadOnYKfn592m4mJCfz8/HDkyBEFIzNusbGxAIDixYvrbP/999/h7OyMmjVrYvz48UhISNDeduTIEXh7e8PNzU27zd/fH3Fxcbh48aJ2n9ePpWYfzbHk8c6+69evo1SpUihfvjz69u2LiIgIAMCpU6fw6tUrnfewatWqKFu2rPY95LFSTnJyMlauXImPPvoIKpVKu52fLf10+/ZtPHz4UOd9c3R0hI+Pj87nycnJCQ0aNNDu4+fnBxMTExw7dky7T8uWLWFhYaHdx9/fH1evXsWzZ8+0+2R1DLMTC6UXGxsLlUoFJycnne0zZsxAiRIlULduXXz//fc6w0d4vArX/v374erqiipVqmDo0KF48uSJ9jZ+vvRXVFQUtm7dio8//jjdbfx86Q8zpQMg4xQdHY3U1FSdH6cA4ObmhitXrigUlXFTq9UYPXo0mjVrhpo1a2q39+nTB56enihVqhTOnTuHsWPH4urVq9iwYQMA4OHDhxkeJ81tWe0TFxeHly9f4tmzZzze2eDj44Ply5ejSpUqiIyMxJQpU9CiRQtcuHABDx8+hIWFRbofpG5ubm89DprbstqHxypvNm3ahJiYGAwcOFC7jZ8t/aV5fzN6315/711dXXVuNzMzQ/HixXX2KVeuXLrH0NxWrFixTI/h64/xtlhIV2JiIsaOHYvevXvDwcFBu33UqFGoV68eihcvjsOHD2P8+PGIjIzE7NmzAfB4Fab27duja9euKFeuHG7evIkJEyYgICAAR44cgampKT9femzFihWwt7dH165ddbbz86VfmKQTGYnhw4fjwoULOHTokM72wYMHa9e9vb1RsmRJtGnTBjdv3kSFChUKO8wiLSAgQLteq1Yt+Pj4wNPTE2vXroW1tbWCkdHb/PLLLwgICECpUqW02/jZIsp/r169Qo8ePSCEwIIFC3RuCwoK0q7XqlULFhYWGDJkCEJCQmBpaVnYoRZpvXr10q57e3ujVq1aqFChAvbv3482bdooGBm9zbJly9C3b19YWVnpbOfnS7+wuzsVCGdnZ5iamqarTB0VFQV3d3eFojJeI0aMwF9//YV9+/ahTJkyWe7r4+MDALhx4wYAwN3dPcPjpLktq30cHBxgbW3N451LTk5OqFy5Mm7cuAF3d3ckJycjJiZGZ5/X30MeK2XcvXsXu3fvxqBBg7Lcj58t/aF5b7J639zd3fHo0SOd21NSUvD06dN8+cy9fvvbYiFJk6DfvXsXYWFhOq3oGfHx8UFKSgru3LkDgMdLSeXLl4ezs7PO9x8/X/rn77//xtWrV9/6/wzg50tpTNKpQFhYWKB+/frYs2ePdptarcaePXvQpEkTBSMzLkIIjBgxAhs3bsTevXvTdUPKSHh4OACgZMmSAIAmTZrg/PnzOv9MNT+Oqlevrt3n9WOp2UdzLHm8cyc+Ph43b95EyZIlUb9+fZibm+u8h1evXkVERIT2PeSxUkZoaChcXV3RoUOHLPfjZ0t/lCtXDu7u7jrvW1xcHI4dO6bzeYqJicGpU6e0++zduxdqtVp7wqVJkyY4ePAgXr16pd0nLCwMVapUQbFixbT7ZHUMsxMLpSXo169fx+7du1GiRIm33ic8PBwmJibabtU8Xsr5999/8eTJE53vP36+9M8vv/yC+vXro3bt2m/dl58vhSlduY6M1+rVq4WlpaVYvny5uHTpkhg8eLBwcnLSqXRMeTN06FDh6Ogo9u/frzNlRkJCghBCiBs3boipU6eKkydPitu3b4vNmzeL8uXLi5YtW2ofQzNNVLt27UR4eLjYsWOHcHFxyXCaqC+//FJcvnxZzJ8/P8Nponi8s/bFF1+I/fv3i9u3b4t//vlH+Pn5CWdnZ/Ho0SMhhJyCrWzZsmLv3r3i5MmTokmTJqJJkyba+/NYFb7U1FRRtmxZMXbsWJ3t/Gwp7/nz5+LMmTPizJkzAoCYPXu2OHPmjLYa+IwZM4STk5PYvHmzOHfunOjcuXOGU7DVrVtXHDt2TBw6dEhUqlRJZ4qomJgY4ebmJvr16ycuXLggVq9eLWxsbNJNOWRmZiZ++OEHcfnyZREcHJzhlENvi8XYZXW8kpOTRadOnUSZMmVEeHi4zv8zTSXpw4cPizlz5ojw8HBx8+ZNsXLlSuHi4iL69++vfQ4er/yT1fF6/vy5GDNmjDhy5Ii4ffu22L17t6hXr56oVKmSSExM1D4GP1+F523fh0LIKdRsbGzEggUL0t2fny/9wySdCtS8efNE2bJlhYWFhWjUqJE4evSo0iEZFQAZLqGhoUIIISIiIkTLli1F8eLFhaWlpahYsaL48ssvdeZyFkKIO3fuiICAAGFtbS2cnZ3FF198IV69eqWzz759+0SdOnWEhYWFKF++vPY5XsfjnbWePXuKkiVLCgsLC1G6dGnRs2dPcePGDe3tL1++FMOGDRPFihUTNjY24v333xeRkZE6j8FjVbh27twpAIirV6/qbOdnS3n79u3L8PtvwIABQgg51c/XX38t3NzchKWlpWjTpk264/jkyRPRu3dvYWdnJxwcHERgYKB4/vy5zj5nz54VzZs3F5aWlqJ06dJixowZ6WJZu3atqFy5srCwsBA1atQQW7du1bk9O7EYu6yO1+3btzP9f7Zv3z4hhBCnTp0SPj4+wtHRUVhZWYlq1aqJ6dOn6ySFQvB45ZesjldCQoJo166dcHFxEebm5sLT01N88skn6U4c8vNVeN72fSiEEIsWLRLW1tYiJiYm3f35+dI/KiGEKNCmeiIiIiIiIiLKFo5JJyIiIiIiItITTNKJiIiIiIiI9ASTdCIiIiIiIiI9wSSdiIiIiIiISE8wSSciIiIiIiLSE0zSiYiIiIiIiPQEk3QiIiIiIiIiPcEknYiIiIiIiEhPMEknIiIirYEDB6JLly5Kh0FERFRkmSkdABERERUOlUqV5e3BwcH48ccfIYQopIiIiIjoTUzSiYiIiojIyEjt+po1azBp0iRcvXpVu83Ozg52dnZKhEZERET/j93diYiIigh3d3ft4ujoCJVKpbPNzs4uXXf3Vq1aYeTIkRg9ejSKFSsGNzc3LFmyBC9evEBgYCDs7e1RsWJFbN++Xee5Lly4gICAANjZ2cHNzQ39+vVDdHR0Ib9iIiIiw8MknYiIFNOqVSu0atVK6TAM0p07d6BSqbB8+fICf64VK1bA2dkZx48fx8iRIzF06FB0794dTZs2xenTp9GuXTv069cPCQkJAICYmBi0bt0adevWxcmTJ7Fjxw5ERUWhR48eir2GguTl5YWBAwcW+PNk9H4NHDiwUHs/qFQqTJ48udCej4ioKGKSTkRkhJYvXw6VSpXhMm7cOKXDy7bJkydn+jpeX4w50V+1ahXmzp2raAy1a9fGxIkTUalSJYwfPx5WVlZwdnbGJ598gkqVKmHSpEl48uQJzp07h8mTJ6NYsWJ4/PgxQkJCUK1aNdSrVw9hYWHYt2+f3h+zVq1aaWM0MTGBg4MDqlSpgn79+iEsLCzfnmfbtm16m+zqc2xEREUBx6QTERmxqVOnoly5cjrbatasqVA0Ode1a1dUrFhRez0+Ph5Dhw7F+++/j65du2q3u7m5KRFeoVi1ahUuXLiA0aNH62z39PTEy5cvYW5uXuAx1KpVS7tuamqKEiVKwNvbW7tN8/4/evQIXbt2xbZt23D69GmYmZlBCIHk5GSYmpoiNTUVY8aMQe3ateHm5laoryEnypQpg5CQEADAixcvcOPGDWzYsAErV65Ejx49sHLlSp2Yr169ChOTnLV7bNu2DfPnz89RMlxY71dWsb18+RJmZvz5SERUkPgtS0RkxAICAtCgQYNs7ZuYmAgLC4scJxsFqVatWjoJYnR0NIYOHYpatWrhww8/zPR++vha8ptKpYKVlVWhPNebSaFKpdLZpqkar1arUatWLZQoUQKdO3fGzJkz8fTpU/j4+GDYsGEYNWoUSpYsCVtbW+19C+s15ISjo2O6v68ZM2Zg1KhR+Pnnn+Hl5YWZM2dqb7O0tCzQeFJSUqBWq2FhYaH4+6X08xMRFQXG++uFiIgytX//fqhUKqxevRoTJ05E6dKlYWNjg7i4ODx9+hRjxoyBt7c37Ozs4ODggICAAJw9e1bnMTRd6u/cuZPhY+/fv19n++LFi1GhQgVYW1ujUaNG+Pvvv/XmtWgeY+3atfj2229RpkwZWFlZoU2bNrhx44bOvtevX0e3bt3g7u4OKysrlClTBr169UJsbKx2n9DQULRu3Rqurq6wtLRE9erVsWDBggzj3759O3x9fWFvbw8HBwc0bNgQq1atAiC7Xm/duhV3797VdsH28vICkPl47r1796JFixawtbWFk5MTOnfujMuXL+vsM3nyZAQGBiI1NRUDBw6Ek5MTHB0dERgYiJSUFJ19nz59irVr18LJyQl2dnaoUqUKnj17luUxqVevHi5evAgvLy+UL18eAFC8eHFUrFhRJ0HPaox1REQE3nvvPdjZ2aF06dKYP38+AOD8+fNo3bo1bG1t4enpqX2vXhcTE4PRo0fDw8MDlpaWqFixImbOnAm1Wp1l3FkxNTXFf//7X1SvXh0//fSTzvF+c0z6q1evMGXKFFSqVAlWVlYoUaIEmjdvru0uP3DgQO3reX3YxuvvyQ8//IC5c+eiQoUKsLS0xKVLl7Icw3/r1i34+/vD1tYWpUqVwtSpU3Wm0svsc/nmY2YVm2bbmy3sZ86cQUBAABwcHGBnZ4c2bdrg6NGjOvtovi/++ecfBAUFwcXFBba2tnj//ffx+PHjtx8AIqIihC3pRERGLDY2Nl1FbWdnZ+36N998AwsLC4wZMwZJSUmwsLDApUuXsGnTJnTv3h3lypVDVFQUFi1aBF9fX1y6dAmlSpXKcRy//PILhgwZgqZNm2L06NG4desWOnXqhOLFi8PDwyPPrzO/XsuMGTNgYmKCMWPGIDY2Ft999x369u2LY8eOAQCSk5Ph7++PpKQkjBw5Eu7u7rh//z7++usvxMTEwNHREQCwYMEC1KhRA506dYKZmRn+/PNPDBs2DGq1GsOHD9c+3/Lly/HRRx+hRo0aGD9+PJycnHDmzBns2LEDffr0wVdffYXY2Fj8+++/mDNnDgBkWSRs9+7dCAgIQPny5TF58mS8fPkS8+bNQ7NmzXD69Gltgq+RkJCA58+fIyQkBKdPn8bSpUtRs2ZNVKhQAQBw8eJFXLhwAS4uLpg6dSosLS1x48YNzJs3L8tjMXz4cCxZsgS9e/fG4MGDAQA3btxAYGAgli5dClNT0yzvn5qaioCAALRs2RLfffcdfv/9d4wYMQK2trb46quv0LdvX3Tt2hULFy5E//790aRJE+2wjoSEBPj6+uL+/fsYMmQIypYti8OHD2P8+PGIjIzM0/h+U1NT9O7dG19//TUOHTqEDh06ZLjf5MmTERISgkGDBqFRo0aIi4vDyZMncfr0abRt2xZDhgzBgwcPEBYWht9++y3DxwgNDUViYiIGDx4MS0tLFC9ePNOTDKmpqWjfvj0aN26M7777Djt27EBwcDBSUlIwderUHL3G7MT2uosXL6JFixZwcHDAf/7zH5ibm2PRokVo1aoVDhw4AB8fH539R44ciWLFiiE4OBh37tzB3LlzMWLECKxZsyZHcRIRGTVBRERGJzQ0VADIcBFCiH379gkAonz58iIhIUHnvomJiSI1NVVn2+3bt4WlpaWYOnVquue4ffu2zr6ax963b58QQojk5GTh6uoq6tSpI5KSkrT7LV68WAAQvr6+2X5djx8/FgBEcHBwuufLy2vRPEa1atV0Yvzxxx8FAHH+/HkhhBBnzpwRAMS6deuyjPPNOIQQwt/fX5QvX157PSYmRtjb2wsfHx/x8uVLnX3VarV2vUOHDsLT0zPd492+fVsAEKGhodptderUEa6uruLJkyfabWfPnhUmJiaif//+2m3BwcECgDA3N9d5zPfff19YWlqKzp07CyGEmDNnjgAgBg8erLOfp6enmDNnjs42AGLjxo3a69euXRPvv/++cHBwEABEiRIlxOjRo3VeW0avYcCAAQKAmD59unbbs2fPhLW1tVCpVGL16tXa7VeuXEn39/DNN98IW1tbce3aNZ34xo0bJ0xNTUVERITIiq+vr6hRo0amt2/cuFEAED/++KPO+zFgwADt9dq1a4sOHTpk+TzDhw8XGf0M07wnDg4O4tGjRxneltH7NXLkSO02tVotOnToICwsLMTjx4+FEOk/l1k9ZmaxCSHSvd9dunQRFhYW4ubNm9ptDx48EPb29qJly5babZrvCz8/P52/gc8//1yYmpqKmJiYDJ+PiKgoYnd3IiIjNn/+fISFheksrxswYACsra11tllaWmrHcqempuLJkyfabs6nT5/OcQwnT57Eo0eP8Omnn8LCwkK7feDAgdqW5/yQH68lMDBQJ8YWLVoAkF2JAWjj3blzp3a6sYy8HoemN4Ovry9u3bql7SYdFhaG58+fY9y4cenG+b7evTi7IiMjER4ejoEDB6J48eLa7bVq1ULbtm2xbdu2dPf5559/dK63aNECSUlJ+PXXXwEATk5OAIBGjRrptOLeuXMnXSE7IYTO/OqVKlXChg0bcPPmTQDAiBEjMGfOnGy/tkGDBmnXnZycUKVKFdja2upM41alShU4OTlpjw8ArFu3Di1atECxYsUQHR2tXfz8/JCamoqDBw9m6/kzo+nJ8Pz580z3cXJywsWLF3H9+vVcP0+3bt3g4uKS7f1HjBihXVepVBgxYgSSk5Oxe/fuXMfwNqmpqdi1axe6dOmiHdYAACVLlkSfPn1w6NAhxMXF6dxn8ODBOn8DLVq0QGpqKu7evVtgcRIRGZoinaQfPHgQHTt2RKlSpaBSqbBp06YCf8779+/jww8/RIkSJWBtbQ1vb2+cPHmywJ+XiIqmRo0awc/PT2d53ZuV3wFZ/GvOnDmoVKkSLC0t4ezsDBcXF5w7d05nHG52aX58V6pUSWe7ubm5zg/7vMqP11K2bFmd68WKFQMA7RjscuXKISgoCEuXLoWzszP8/f0xf/78dI/1zz//wM/PTzsu3MXFBRMmTAAA7b6a5DW/qu1r3ucqVaqku61atWqIjo7GixcvdLa/7fX27NkTzZo1w6BBg+Dm5oZevXph7dq1eRrbnR1WVlbpElRHR0eUKVMmXZLv6OioM0b++vXr2LFjB1xcXHQWzd/+o0eP8hRbfHw8AMDe3j7TfaZOnYqYmBhUrlwZ3t7e+PLLL3Hu3LkcPU9Gf8+ZMTExSfdZqly5MgCkqxmRnx4/foyEhIRM/+bUajXu3buns/1tf3NERFTEk/QXL16gdu3a2gIpBe3Zs2do1qwZzM3NsX37dly6dAmzZs3S/oMiIipsb7Y8A8D06dMRFBSEli1bYuXKldi5cyfCwsJQo0YNneQssxbR1NTUAos3K3l5LRqZjZUWrxXgmjVrFs6dO4cJEybg5cuXGDVqFGrUqIF///0XgEy+27Rpg+joaMyePRtbt25FWFgYPv/8cwAo8AQ3J972eq2trXHw4EHs3r0b/fr1w7lz59CzZ0+0bdu2QI9zZnFl5/io1Wq0bds2XQ8SzdKtW7c8xXbhwgUA0Jka8E0tW7bEzZs3sWzZMtSsWRNLly5FvXr1sHTp0mw/T0Z/z3mhL5/X7BxDIqKirkgXjgsICEBAQECmtyclJeGrr77CH3/8gZiYGNSsWRMzZ85Eq1atcvV8M2fOhIeHB0JDQ7XbcnKmnIioMKxfvx7vvPMOfvnlF53tMTExOkXnNCcYY2JidPZ7s9uqp6cnANnC2bp1a+32V69e4fbt26hdu3Z+hq8ju68lp7y9veHt7Y2JEyfi8OHDaNasGRYuXIhp06bhzz//RFJSErZs2aLTarhv3z6dx9AUZ7tw4UKWCV92u4dr3uerV6+mu+3KlStwdnbWqayeXSYmJmjTpg3atGmD2bNnY/r06fjqq6+wb9++dD0z9EGFChUQHx9fILGlpqZi1apVsLGxQfPmzbPct3jx4ggMDERgYCDi4+PRsmVLTJ48WduNPzdDGjKjVqtx69Ytbes5AFy7dg0AtMUCs/t5zUlsLi4usLGxyfRvzsTEJN8KQxIRFSVFuiX9bUaMGIEjR45g9erVOHfuHLp374727dvneozZli1b0KBBA3Tv3h2urq6oW7culixZks9RExHljampabpWrXXr1uH+/fs62zRJ5utjfFNTU7F48WKd/Ro0aAAXFxcsXLgQycnJ2u3Lly9PlzDkt+y+luyKi4tLN0WZt7c3TExMkJSUpH1OQLdlMDY2VucELQC0a9cO9vb2CAkJQWJios5tr9/X1tY2W8MMSpYsiTp16mDFihU67+uFCxewa9cuvPvuu9l7ka95+vRpum116tQBAO3r1Tc9evTAkSNHsHPnznS3xcTEpDt+2ZWamopRo0bh8uXLGDVqFBwcHDLd98mTJzrX7ezsULFiRZ33THPCJL8+Az/99JN2XQiBn376Cebm5mjTpg0AeRLH1NQ03Zj8n3/+Od1jZTc2U1NTtGvXDps3b9bpVh8VFYVVq1ahefPmWb5PRESUsSLdkp6ViIgIhIaGIiIiQjtFz5gxY7Bjxw6EhoZi+vTpOX7MW7duYcGCBQgKCsKECRNw4sQJjBo1ChYWFhgwYEB+vwQiolx57733MHXqVAQGBqJp06Y4f/48fv/993RjXmvUqIHGjRtj/PjxePr0KYoXL47Vq1enS4LMzc0xbdo0DBkyBK1bt0bPnj1x+/ZthIaG5uuY9Ly8luzau3cvRowYge7du6Ny5cpISUnBb7/9BlNTU2036nbt2sHCwgIdO3bEkCFDEB8fjyVLlsDV1RWRkZHax3JwcMCcOXMwaNAgNGzYEH369EGxYsVw9uxZJCQkYMWKFQCA+vXrY82aNQgKCkLDhg1hZ2eHjh07Zhjf999/j4CAADRp0gQff/yxdgo2R0fHdHNbZ8fUqVNx8OBBdOjQAZ6ennj06BF+/vlnlClT5q0tyUr58ssvsWXLFrz33nsYOHAg6tevjxcvXuD8+fNYv3497ty589ZeFLGxsVi5ciUAOaXbjRs3tEXwevXqhW+++SbL+1evXh2tWrVC/fr1Ubx4cZw8eRLr16/XKe5Wv359AMCoUaPg7+8PU1NT9OrVK1ev2crKCjt27MCAAQPg4+OD7du3Y+vWrZgwYYJ2bL+joyO6d++OefPmQaVSoUKFCvjrr78yHKOfk9imTZuGsLAwNG/eHMOGDYOZmRkWLVqEpKQkfPfdd7l6PURERZ5SZeX1Dd6YOuavv/4SAIStra3OYmZmJnr06CGEEOLy5cuZTnGkWcaOHat9THNzc9GkSROd5x05cqRo3LhxobxGIio6NNMdnThxIsPbNdMxZTSVWGJiovjiiy9EyZIlhbW1tWjWrJk4cuSI8PX1TTdd2s2bN4Wfn5+wtLQUbm5uYsKECSIsLCzDqZ5+/vlnUa5cOWFpaSkaNGggDh48mOFjZiWrKdjy8loye4w3p6e6deuW+Oijj0SFChWElZWVKF68uHjnnXfE7t27de63ZcsWUatWLWFlZSW8vLzEzJkzxbJlyzKcsm7Lli2iadOmwtraWjg4OIhGjRqJP/74Q3t7fHy86NOnj3BychIAtNOxZTR1lhBC7N69WzRr1kz7eB07dhSXLl3S2UczBZtmei6NN6fV27Nnj+jcubMoVaqUsLCwEKVKlRK9e/dON71ZVjI6ZhqZTSlma2ubbt/Mpkbz9PRMN93Z8+fPxfjx40XFihWFhYWFcHZ2Fk2bNhU//PCDSE5OzjJeX19fnf/jdnZ2olKlSuLDDz8Uu3btyvA+b07BNm3aNNGoUSPh5OQkrK2tRdWqVcW3336r89wpKSli5MiRwsXFRahUKu2UZ5r35Pvvv8/R+3Xz5k3Rrl07YWNjI9zc3ERwcHC66QcfP34sunXrJmxsbESxYsXEkCFDxIULF9I9ZmaxCZF+CjYhhDh9+rTw9/cXdnZ2wsbGRrzzzjvi8OHDOvtk9p2U2dRwRERFmUoIVuoA5PirjRs3aqePWbNmDfr27YuLFy+mK3JiZ2cHd3d3JCcn60z7kpESJUpoz2J7enqibdu2OoVjFixYgGnTpuW66yUREREREREZD3Z3z0TdunWRmpqKR48eaefJfZOFhQWqVq2a7cds1qxZuuIq165d0xb7ISIiIiIioqKtSCfp8fHxuHHjhvb67du3ER4ejuLFi6Ny5cro27cv+vfvj1mzZqFu3bp4/Pgx9uzZg1q1aqFDhw45fr7PP/8cTZs2xfTp09GjRw8cP34cixcvTldkiYiIiIiIiIqmIt3dff/+/XjnnXfSbR8wYACWL/+/9u47vKoqbePwk0ISSgo1oQTpIFVQQKoKSBURLIiIgI2h2FDUOAoz4yhgF0QUC4gNsYAKiNIZehcQpJdIb0moAZL9/fF+BCIdkrNP+d3XtS/2SXbCE9zmnPestd41QidOnNB///tfjRw5Utu2bVOBAgV044036t///reqVKlyRX/nuHHjlJCQoHXr1qlkyZLq3bu3Hnnkkav9UQAAAAAAfiCgi3QAAAAAALwJ+6QDAAAAAOAlKNIBAAAAAPASAdc4Lj09Xdu3b1dkZKSCgoLcjgMAAAAA8HOO4+jgwYMqUqSIgoMvPFYecEX69u3bFR8f73YMAAAAAECASUxMVLFixS54TcAV6ZGRkZLsHycqKsrlNAAAAAAAf5eSkqL4+PiMevRCAq5IPzXFPSoqiiIdAAAAAOAxl7LkmsZxAAAAAAB4CYp0AAAAAAC8BEU6AAAAAABegiIdAAAAAAAvQZEOAAAAAICXoEgHAAAAAMBLUKQDAAAAAOAlKNIBAAAAAPASFOkAAAAAAHgJinQAAAAAALxEqNsBACBbDRvmdgLv9eijbicAAADA3zCSDgAAAACAl6BIBwAAAADAS1CkAwAAAADgJSjSAQAAAADwEhTpAAAAAAB4CYp0AAAAAAC8BEU6AAAAAABegiIdAAAAAAAv4XNFelpaml566SWVLFlSOXPmVOnSpfXyyy/LcRy3owEAAAAAcFVC3Q5wuQYOHKihQ4fqs88+U6VKlbRo0SJ17dpV0dHRevzxx92OBwAAAADAFfO5In3OnDlq06aNWrVqJUkqUaKEvv76ay1YsMDlZAAAAAAAXB2fm+5et25dTZkyRWvXrpUk/f7775o1a5ZatGhxzutTU1OVkpKS6QAAAAAAwBv53Ej6888/r5SUFFWoUEEhISFKS0vTK6+8oo4dO57z+v79++vf//63h1MCAAAAAHD5fG4kffTo0fryyy/11VdfacmSJfrss8/0xhtv6LPPPjvn9QkJCUpOTs44EhMTPZwYAAAAAIBL43Mj6X369NHzzz+ve++9V5JUpUoVbdmyRf3791fnzp3Puj48PFzh4eGejgkAAAAAwGXzuZH0I0eOKDg4c+yQkBClp6e7lAgAAAAAgKzhcyPprVu31iuvvKLixYurUqVKWrp0qd566y09+OCDbkcDAAAAAOCq+FyRPnjwYL300kvq0aOHdu/erSJFiqhbt27q27ev29EAwLccOybt2mXHzp1SSooUHHz6yJFDio2VihaV4uIklg4BAABkuyDHcRy3Q3hSSkqKoqOjlZycrKioKLfjAMhuw4a5ncA77N0rbdkibd0qJSbacblbUhYoIF17rVSjhh3Vq9vjUJ97vxcAAMCjLqcO5ZUVAPijkyel9eul5cullStttPxcwsJstDwuToqJkRxHSk+3IzXVRti3b7fzvXul//3PjlOioqTGjaVmzewoUcITPx0AAIDfokgHAH/hONLmzdKsWdKiRTad/ZTgYKlYMal48dPHs89aYR4UdPHvu3+/jb4vXy4tXSotWWJ/pqRIY8bYIUkVKkj33ivdd59Utmx2/aQAAAB+i+nuAPxbIEx3P3pUmjfPivO//jr98chIqXJlqUoVqWJFKWfOzF/36KNX9/empVmx/uuvdsydax875YYbpI4dpU6dpPz5r+7vAgAA8GGXU4dSpAPwb/5cpB8+LE2ZIk2daoW6ZM3eatSQ6teXypSxEfTzudoi/e+Sk6Wff5a+/FKaNOl0wR4RYaPrPXta4Q4AABBgKNIvgCIdCDD+WKQfOiRNnixNm3Z6SnvhwlLDhlLt2lLu3Jf2fbK6SD/T7t3St99Kn3xi0+JPqV1beuop6a67pJCQ7Pv7AQAAvAiN4wDAH6WlWWH+88+ni/NixaRWraTrrrvwqLmnFSpkI+c9ekjz50vvvWdF+/z5Nqpetqz03HM2FT4szO20AAAAXsOLXtEBAM5r1Srp5Zet0D12zIrz7t2lf/7Tprd7U4F+pqAg6cYbpS++sMZz//63lC+ftG6d9PDDUunS0uDB1j0eAAAAFOkA4NUOHJCGDpXefVfascOawXXqZMW5t42eX0yhQlLfvrZf+5tv2hT9v/6SHn9cKldOGj7cto4DAAAIYD706g4AAojjWMf2//xHWrbMivFGjexx/fq+VZz/XZ48Uu/e0qZN0vvvS0WKSFu3Sg8+aJ3ov/vOfn4AAIAA5MOv8gDATyUn2+j58OHSkSNSiRLSiy9K7dtLuXK5nS7rhIfblP3166XXX7dp8H/+Kd19tzXBW7LE7YQAAAAeR5EOAN5k2TJbt/3779b9vE0b6dlnpaJF3U6WfXLmlJ55Rtq4UXrpJXs8a5Zt1/bII9KuXW4nBAAA8BiKdADwBidPSqNH2wj64cNSfLz0wgtSy5aBs1VZdLRN51+zRurQwaa8f/yxdYJ/4w3p+HG3EwIAAGQ7inQAcNv+/VaETplij2+9VXr+eevgHoji46WvvrLR9Ouvlw4elPr0kSpXtu3nWK8OAAD8GEU6ALhp5Urpv/+1Jmq5ctka7bvukkJD3U7mvnr1pAULpE8/lWJjbdu222+XmjeXVq92Ox0AAEC2oEgHADc4jvTbb9J779n09hIlTm+rhtOCg6WuXaW1a6XnnpPCwuzfrWpVm21w+LDbCQEAALIURToAeNqJE9LIkdL331uxXr++NU4rUMDtZN4rKkoaMEBatUpq3drW8A8cKF17rfTDD0yBBwAAfoMiHQA8KSVFeucdac4cKSjItlW7/34pRw63k/mG0qWln36yo0QJKTFRuvNOa7C3fr3b6QAAAK4aRToAeMrOnTYavH69bTP2+ONSo0ZWrOPytG4t/fGH7R8fFiZNnGiN5fr1k44edTsdAADAFaNIBwBP2LRJeu01ad8+qWBBW09dsaLbqXxbrlzSyy9LK1ZITZtKqam2hVvlytKECW6nAwAAuCIU6QCQ3VaskN5883SDuOeek+Li3E7lP8qVs5H00aOlokWljRulVq2ktm2lLVvcTgcAAHBZKNIBIDvNni29/741i6tcWXrqKSky0u1U/icoSLr7btua7ZlnbAu7sWOtsVz//tLx424nBAAAuCQ+V6SXKFFCQUFBZx09e/Z0OxoAZDZpknVxT0+X6tSRevSQIiLcTuXfIiOl11+Xli2TGja09ekvvGBbtk2Z4nY6AACAiwp1O8DlWrhwodLS0jIer1y5UrfeeqvuvvtuF1MBwBkcx9ZE//STPW7aVGrXjgZxnlSpkjR9uvTFFzayvmaN1KSJdO+9tvSgSBG3E/quYcPcTuC9Hn3U7QQAAD/gcyPpBQsWVFxcXMYxbtw4lS5dWjfddJPb0QDACvQxY04X6G3a2BZhFOieFxQkdepkBXqvXlJwsDRqlFShgvT227bXOgAAgJfxuSL9TMePH9cXX3yhBx98UEHneQGcmpqqlJSUTAcAZIv0dOmbb6Rff7XHd99t+3fDXTEx0uDB0sKFUu3a0sGDUu/eUo0a0qxZbqcDAADIxKeL9LFjxyopKUldunQ57zX9+/dXdHR0xhEfH++5gAACR3q69PXX0rRp9vi++2x6NbxHjRrSnDk2XTtfPuu636CB1KWLtHu32+kAAAAk+XiR/sknn6hFixYqcoG1hQkJCUpOTs44EhMTPZgQQEBwHJtGPXOmTbHu0kViCY53Cg6WHnnEpsA//LB97LPPpPLlpSFDmAIPAABc57NF+pYtWzR58mQ9fOpF1nmEh4crKioq0wEAWeZUgT5jxukCvU4dt1PhYgoUkD76SJo7V6peXUpKsnXrVapI48fbf1cAAAAX+GyRPnz4cBUqVEitWrVyOwqAQOU40ujR1kU8KEh64AHpxhvdToXLceONtlZ9yBAr3P/8U7rtNunWW6Xff3c7HQAACEA+WaSnp6dr+PDh6ty5s0JDfW4XOQD+wHGk77+Xpk61x506SXXrupsJVyYkxPawX7dO6tNHCguzPdWrV5c6dpQ2bHA7IQAACCA+WaRPnjxZW7du1YMPPuh2FACBatw4adIkO7//fqlePXfz4OrFxEivvWaj6e3b2xsxX31lW7Z17y5t3+52QgAAEAB8skhv2rSpHMdRuXLl3I4CIBBNmmRFumTFXIMG7uZB1ipZ0voMLFokNWtmzeQ++EAqXVp68klp2za3EwIAAD/mk0U6ALjmf/+TvvvOztu0kRo1cjcPss/110sTJ1rPgbp1pWPHpHfflUqVsunxW7a4nRAAAPghinQAuFQLF0pffmnnTZtKLVq4mweecdNN0qxZ0q+/SvXrS8ePS0OHSmXKSJ07S8uWuZ0QAAD4EYp0ALgUK1dKn35q65QbNpTatbOO7ggMQUH2xszMmdK0aTaD4uRJaeRIazDXqJEtgUhPdzspAADwcbRGB4CL2bRJ+vBDK8Bq1pQ6dPCPAn3YMLcTeK9HHz33x4OCpJtvtmPBAuntt6Vvv7XCfdo0W7f+8MNSly5SXJwHAwMAAH/BSDoAXMjOndLgwTbFuWJFK76C+dUJSbVqSV9/LW3caFu3RUfbdm0JCVJ8vM22mDBBOnHC7aQAAMCH8EoTAM7nwAHpnXekw4elEiWkbt2kUCYg4W+KF7et27ZtsyURderYVPgxY6RWraTChaV//MMa0KWluZ0WAAB4OYp0ADiXw4elQYOsUI+NlXr1kiIi3E4Fb5Y7t9S1qzRnjrRihfTEE1KhQtK+fbZc4pZbpGLFpEcekcaOlQ4dcjsxAADwQhTpAPB3p/bF3r5diomxYisy0u1U8CWVK9ssjG3bpMmTbZ163ry2fOLjj6W2baX8+aVbb5UGDrTC/vhxt1MDAAAvwLxNADiT40iffy6tXWsj5489ZsUUcCVCQ6XGje0YMsSmvI8fb8eGDVbAT55s1+bMKdWubdPlq1e3o1QpeiAAABBgKNIB4Ezjxknz5llh9OijNj0ZyAphYbaNW9OmNsq+dq00caJt6zZzprR3rxXx06ef/prISKlqValcOals2dNHsWJSvnz+scsAAADIhCIdAE6ZO9eKdEm67z6pUiV388B/BQVJ5cvb8cQTNoPjzz+l//1PWrRIWrrU1rUfPCjNnm3H30VESEWL2lGggC3NyJvXjr+fx8RYwR8VJeXJw+g8AABejCIdACQrkEaOtPPmzaUGDdzNg8ASFCRde60dp/ZoP3HC7ss//pDWrbNj7VqbJr93r3TsmJ1v2HD5f1/u3FawR0aePs58HBVlyzzi4qw7fVycjd7nzZu1PzcAADgLRToAbN9ujeLS06UbbpDatHE7ESDlyCFVqWLH3x07Zvfttm127N9vOxEcOCAlJZ19npwspaSc3gLu8GE7duy4vEz58lkBX6iQHcWL2xETw9R7AACyCEU6gMCWnCy995509KhUurTUpQtTgeH9IiKsqVypUpf+NY5jxf3Bg3akpJw+//vjlBQbrd+xwzrS79hhW8nt32/H5s2Zv3dUlHTNNbZevlIlm4JP0Q4AwBWhSAcQuFJTreP2vn02Ktijh41eAv4oKMg6yOfMaff75Tp82KbWv/++tGePFe5bt9qIfkqKraFfsUL64Qcr2itWtKZ3Vavy/xUAAJeBIh1AYEpPlz75RNqyxdbn9uplDbUAnFvu3FZw16iR+ePHj0uJidKmTdLq1bZuPiXFdkmYN8/eFKhRw7aXK1uWmSoAAFwERTqAwPTdd9Lvv9s+1j16SLGxbicCfFNYmC0VKV1aatLEGt5t3CitXGmd6vfvP92hPn9+qVEjqX59m7IPAADOQpEOIPDMni1NmWLnXbpIZcq4GgfwKzlynN5erm1baf16af58afFiW1ry7be21WHDhlawx8S4nRgAAK9CkQ4gsGzYIH35pZ3fdptUs6a7eQB/FhwslStnR/v2Nv198mRp1y7p11/t/OabpVatbDo9AACgSAcQQPbvt63W0tJsjWyrVm4nAgJHWJiNntevbw3mfvvNRtmnTJHmzpVatrSCnSZzAIAAR5EOIDAcPy4NHWoNrYoVkzp3poEV4IbgYKlaNTtWrbL+ENu22Z/Tp9uIe9WqbqcEAMA1PvkKddu2bbr//vuVP39+5cyZU1WqVNGiRYvcjgXAWzmONHKkbReVJ4/UvTtNqwBvULGi9OKLUqdOtm3b3r22LeInn0iHDrmdDgAAV/jcSPqBAwdUr1493XLLLfrll19UsGBBrVu3Tnnz5nU7GgBvNXGitHChjeB16yYVKOB2IgCnBAfbFPgbbrCGcpMnSwsWSH/+Kd13n1S9utsJAQDwKJ8r0gcOHKj4+HgNHz4842MlS5Z0MREAr/b779KPP9p5hw7WwAqA94mIkO66S7r+eumzz6QdO6yHRK1aUseOzH4BAASMIMdxHLdDXI6KFSuqWbNm+uuvvzRjxgwVLVpUPXr00COPPHLO61NTU5WamprxOCUlRfHx8UpOTlZUVJSnYgNwwx9/2Av+1FTppptsVA6A9ztxwkbVf/tNSk+X4uJsFkyRIm4nu7BHH3U7gfcaNsztBN6L+wYICCkpKYqOjr6kOtTn1qRv3LhRQ4cOVdmyZfXrr7+qe/fuevzxx/XZZ5+d8/r+/fsrOjo644iPj/dwYgCu2L9fatPGCvRT2z8B8A05ctge6888Y/uo79wp9e9v0+ABAPBzPjeSHhYWphtuuEFz5szJ+Njjjz+uhQsXau7cuWddz0g6EIBOnpRatLC1rfnzSy+8YA3jAPielBRrJPfnn/b4ppvsTbeQEHdznQsjoufHSPr5cd8AAcGvR9ILFy6sihUrZvrYtddeq61bt57z+vDwcEVFRWU6APi5hAQr0HPnlnr2pEAHfFlUlPTEE7aPuiTNmCENHiwdPepuLgAAsonPFen16tXTmjVrMn1s7dq1uuaaa1xKBMCrfP+99MYbdv7ZZ1LRou7mAXD1goNt+UqPHlJ4uLR6tfT669KBA24nAwAgy/lckf7UU09p3rx5evXVV7V+/Xp99dVXGjZsmHr27Ol2NABuW7NG6trVzp95RrrzTnfzAMha1apJTz9to+vbtkkDBkiJiW6nAgAgS/ncmnRJGjdunBISErRu3TqVLFlSvXv3Pm9397+7nLUAAHzI4cNS7drW0b1hQ2nKFCk0lHWQgD/at8+mvO/YYSPrPXpIFSq4nYq1xRfC72JcCf6fgh/x6zXpknTbbbdpxYoVOnbsmFavXn3JBToAP+U40iOPWIFeuLD0zTdWoAPwT/nzS88+K5Uvbzs4vPeetHKl26kAAMgSPlmkA0Am770nff21FeajR9ueygD8W65c0mOP2RT4Eyek99+Xli1zOxUAAFeNoSYAvm3OHKl3bzt//XWpfn138wDwnBw5pG7dbIu2xYulDz+UHnpIuuEGt5MhkB07Ju3fLx05YrsQHDliW4NGRUnR0XZERlpDRAA4B4p0AL5r1y7p7rvtxc8999g2TQACS0iIFeY5ckjz5kkffyylpVmPCsAT9uyR1q2TNm2SNm60poYXa/kUHCyVLGm9FCpWtPOQEM/kBeD1KNIB+KaTJ6V775W2b7cXOR9/LAUFuZ0KgBtCQqTOnW3Jy6xZ0vDhVrTXqOF2Mvirw4elRYukuXOtOP+7XLmk3LmlnDntPDRUSkmRkpKkgwel9HRpwwY7xo+XIiJs6UbjxhLbCgMBjyIdgG/65z+l6dOlPHmkH36wqYMAAldwsNSxoxU/c+bYG3c9ekiVK7udDP5kwwZp0iRp+XKbsSHZvVeqlB0lS9qfMTHn/x5paTYdfs0aafVqOw4flubPt6NMGSvWr7uOKfFAgKJIB+B7xoyRXnvNzj/9VLr2WnfzAPAOwcFSp07W8X3xYumDD6THH5fKlXM7GXzdpk3STz9Jq1ad/lh8vHTjjVKtWrbe/FKFhEgFC9pRv769sbR5szRtmo3Or19vR6FCNmOsUqUs/3EAeDeKdAC+ZdMmqWtXO3/qKVuTDgCnBAdLDz4oHT8urVhhuz889ZSNcAKXKzFRGjv29BZ/wcFSnTo20l20aNb8HWeOxN95p80SmzlT2r1bGjTIlm3cc4+UN2/W/H0AvB5FOgDfceKE1KGDlJxsoxcDB7qdCIA3Cg2VHn3UCvQ1a6TBg21fdbZnxKVKTZV+/lmaMsVGuk8V5y1a2Ah4domJke64Q2re3P7+qVOlJUukP/6QWre2NweYAg/4Pf4vB+A7XnrJ1uvFxNi+6DlyuJ0IgLcKC7M16SVK2HrfQYPsDT7gYlaskP71L1t7np4uXX+99J//SA88kL0F+pkiImym2Asv2Ah7aqr03Xd2Hx865JkMAFxDkQ7AN/z66+mR848/thfeAHAhERFSz562tnffPhtRP3bM7VTwVkeOSB99ZDMw9u+X8ueXevWyWRmeKs7/Lj5e6tNHuv9+e+Np9WrplVdsDTsAv0WRDsD77dhhzaAkqXt3W7MHAJciKsqax0VG2vriDz883ZUbOGXDBum//7XGbcHBUtOmUr9+UpUqbiezPA0aSM8/b2847d8vvf66rVu/2H7sAHwSRToA75aebgX6nj1S1arSm2+6nQiArylY0EZEw8KsO/fnn1PcwKSnSxMnSm+8YbMtChSQnnvO3gwOD3c7XWZFi9r09+uuk06elL78Uho1yn4GAH6FIh2AdxswwBr35MplL0Zy5nQ7EQBfVKKETVsODpbmzpV++cXtRHDboUO2xnvMGCt0a9aUXnzRu5dT5cwp/eMfUtu2UlCQdYIfMYLZIYCfoUgH4L1mz5b69rXz995jP3QAV6dKFdshQpJ+/NG6ZiMwbd8u9e9va7xz5LCmcA895BtvBAcFWff3Bx+0N53mz7dlHCdOuJ0MQBahSAfgnfbvl+67z0YHOnaUunRxOxEAf9CwoXTLLXY+fLi0dau7eeB5y5dbI9K9e216e0KCVK+eFb++pFYtG1UPDZV+/93ezKYxIuAXKNIBeB/HsRGNrVulMmWkoUN978UTAO91991SxYrS8ePS+++zNVugcBzbKeT9962YLVfOCvSiRd1OduWqVZMee8zWz//5p+1gcPy426kAXKVQtwMAyALDhrmdIGtNmyaNHSuFhEj33GN7ogNAVgkJkR55xHpe7NplbwQ+/bRNe4Z/SkuTvvpKmjXLHjdsKLVvb6PQvq5CBempp6R335XWr7fXBN27230OwCcxkg7Au/z1l/Tdd3Z+553SNde4mweAf8qVy/ZQz5VL2rRJGjmSju/+6vhxW7M9a5bNymrf3pZT+UOBfkrJknY/58ghrVghffEF9zPgwyjSAXiPEyekTz+1rWWqVJEaNXI7EQB/FhsrdetmzbcWLLCtuOBfDh+W3nnH1myHhlqH/0aN/HMJVdmyNkMkOFiaM8e61gPwSRTpALzHjz9K27ZJkZHWadcfX0QB8C4VKkj33mvnY8dKS5e6GgdZaP9+6fXXpQ0bbMbEk09KNWq4nSp7Vasmdepk57/+Kk2a5G4eAFfEo0X6xo0br/p7/Otf/1JQUFCmo0KFClmQDoCr1qyRJk+2806dpKgod/MACBw33STdfLOdf/qplJjoahxkgT17pDfekHbskGJipGeesZHmQFC3rtSunZ1/9520bJmrcQBcPo8W6WXKlNEtt9yiL774QseuYouISpUqaceOHRnHrFNNQAD4pqNHpREjbP1c/fo2EgAAnnTPPdK119r65SFD6Pjuy3butAJ93z6pUCHpued8u4P7lWjaNPMbT9u3uxoHwOXxaJG+ZMkSVa1aVb1791ZcXJy6deumBQsWXPb3CQ0NVVxcXMZRoECBbEgLwGNGjbJpiQUK2NZIAOBppzq+x8ZKBw5IH3xgfTLgWxITrUBPSpKKFLER9Hz53E7leUFB9sZT+fJSaqrtYHD4sNupAFwijxbp1113nd59911t375dn376qXbs2KH69eurcuXKeuutt7Rnz55L+j7r1q1TkSJFVKpUKXXs2FFbt24977WpqalKSUnJdADwIosXS/Pm2QuKBx+UIiLcTgQgUOXOfbrj+8aNtv0jHbJ9x+bN0ltvSQcPSvHxtq1edLTbqdxz6o2n/Pml3bulTz6R0tPdTgXgErjSOC40NFTt2rXTt99+q4EDB2r9+vV65plnFB8frwceeEA7duw479fWrl1bI0aM0MSJEzV06FBt2rRJDRo00MGDB895ff/+/RUdHZ1xxMfHZ9ePBeByJSdLX35p582bS6VLu5sHAGJjpYcftjcOZ8+Wpk1zOxEuxebN0ttvS0eO2HZkvXtLefK4ncp9kZG2Z3qOHNIff1hzRABez5UifdGiRerRo4cKFy6st956S88884w2bNigSZMmafv27WrTps15v7ZFixa6++67VbVqVTVr1kwTJkxQUlKSRo8efc7rExISlJycnHEk0gwG8A6OI332mU2/K15cuu02txMBgKlUSbrzTjv/9ltp9Wp38+DCtm6V3n1XOnZMKlPGurjnyuV2Ku8RHy917mznv/4qLVnibh4AF+XRIv2tt95SlSpVVLduXW3fvl0jR47Uli1b9N///lclS5ZUgwYNNGLECC25jF8eMTExKleunNavX3/Oz4eHhysqKirTAcALzJhh7+rnyCF17Wr71wKAt2jSRLrxRpsePGyYdQuH90lMPD2CXrq09NhjLJs6l5o1rZmcJH3+uTXVA+C1PFqkDx06VPfdd5+2bNmisWPH6rbbblNwcOYIhQoV0ieffHLJ3/PQoUPasGGDChcunNVxAWSXnTttWxjJtokpUsTdPADwd0FB0v33SyVKWAH4/vs2UgvvsW1b5inuFOgXdscd9u905IitT09LczsRgPPwaJE+adIkPffcc2cV1I7jZDR/CwsLU+dTU3LO4ZlnntGMGTO0efNmzZkzR23btlVISIg6dOiQrdkBZJG0NGn4cOuafO21p7eIAQBvkyOHreeNjrYtrD79lMZb3mL7divQDx+2N1KeeELKmdPtVN4tJMT6LURESBs2SD//7HYiAOfh0SK9dOnS2rt371kf379/v0qWLHlJ3+Ovv/5Shw4dVL58ed1zzz3Knz+/5s2bp4IFC2Z1XADZYcIEa/CTK5etkQt2pTUGAFyamBgr1ENDpd9/l8aNczsRdu60Av3gQetp8vjjFOiXqkABmyEiSRMnSn/+6W4eAOfk0VfHznm2MTl06JAiLnF60qhRo7R9+3alpqbqr7/+0qhRo1SajtCAb0hMtCJdku67T8qb1908AHApSpY8XdiMH29bR8Idu3bZNmspKVKxYtYkLndut1P5lpo1pXr1rIHrp5/amx0AvIpHOjX17t1bkhQUFKS+ffsq1xkdN9PS0jR//nxdd911nogCwC1padbNPT1dqlHDXiQAgK+oU0f66y9p8mRpxAipUCHrmg3P2bPHCvTkZKloUemppyjQr1T79jblfedOayTXvbv1YQDgFTwykr506VItXbpUjuNoxYoVGY+XLl2qP//8U9WqVdOIESM8EQWAWyZOtJH03LklekgA8EXt2kkVK0rHj0tDhzIC6Ul790pvviklJUmFC9sIOvugX7nwcFufHhJiyzgWLHA7EYAzeGQkfdq0aZKkrl276t1332UbNCDQbNtmU0Ql6d57JX4HAPBFpxpvDRgg7d4tffihFYtsIZm99u2zEfQDB6S4OKl3b55HskJ8vNSqlfTTT9KoUVL58taDAYDrPLomffjw4RToQKA5Nc09LU2qWpVp7gB8W+7cUo8e1iF73Tpp9Gi3E/m3/futQN+3T4qNpUDPas2bW/O9I0ekL76wdeoAXJftb/22a9dOI0aMUFRUlNq1a3fBa3/44YfsjgPA0yZPlrZssW7uHTuy5g2A7ytcWHroIds7fcYMa2DWsKHbqfzPgQPWxX3vXqlgQVuDHh3tdir/EhIide0qvfKKtGKFNHeuVLeu26mAgJftI+nR0dEK+v8X5dHR0Rc8APiZnTttGp0k3X030+gA+I+qVaU2bez866+ltWvdzeNvkpOtQN+927YN692bHUGyS5EiUuvWdv7NN/bmCABXZftI+vDhw895DsDPpafbNPeTJ63RUp06bicCgKzVvLn13Fi40Nan9+ollSjhdirfl5xsTeJ27ZLy57cCPV8+t1P5t1tvlZYtkzZtsm7vjz3GzDfARR5dk3706FEdOXIk4/GWLVv0zjvv6LfffvNkDACeMHWqtHGjrdvs1IknewD+JyhIeuABW9N76JA14UpKcjuVbzuzQM+Xz6a458/vdir/FxIideliTRD/+MPeeALgGo8W6W3atNHIkSMlSUlJSapVq5befPNNtWnTRkOHDvVkFADZafduaexYO7/zTkZAAPivsDBrJBcTI61aZb/zjh93O5VvSkrKXKA//bStRYdnxMVJLVva+ejR0uHD7uYBAphHi/QlS5aoQYMGkqTvvvtOcXFx2rJli0aOHKlBgwZ5MgqA7JKeLo0cKZ04Ydu5/P//8wDgt/LmtanuefLYLKJu3eiSfbmSkqyL+6kp7k8/bWvR4VnNmlljxIMHpe+/dzsNELA8WqQfOXJEkZGRkqTffvtN7dq1U3BwsG688UZt2bLFk1EAZJcZM2xborAwmwbKNHcAgSA+3ppuBQdLI0ZYt2xcmgMHzl6DToHujtBQ6f777Xz2bGnNGnfzAAHKo0V6mTJlNHbsWCUmJurXX39V06ZNJUm7d+9m/3TAH+zbJ40ZY+dt2/IiC0BgadlSGjLEzl96yfadxoUdOGAj6Lt3U6B7izJlTm8p+MUXNjMOgEd5tEjv27evnnnmGZUoUUK1a9dWnf/v9vzbb7+pevXqnowCIKs5jvTVV1Jqqj3B33yz24kAwPP+8Q/pmWfsvGtXacIEd/N4s1Mj6KcKdKa4e4+2bW1P+t27pV9+cTsNEHA8WqTfdddd2rp1qxYtWqSJEydmfLxx48Z6++23PRkFQFZbvFhaudI6xHbqZFM+ASAQDRwo3XefbUF51102bRiZ7d9vBfqePVaYP/00Xdy9Sa5cUvv2dj5xorRzp7t5gADj8VfRcXFxql69uoLPeAFfq1YtVahQwdNRAGSVI0dsLaZk+wbHxbmbBwDcdGpdeosW0tGj0m23SStWuJ3Ke+zdS4HuC2rUkKpUkdLSpFGjaIYIeJBHi/TDhw/rpZdeUt26dVWmTBmVKlUq0wHAR40ZI6WkSLGx9qIUAAJdjhzSd99J9epZ5/JmzaSNG91O5b7t26XXX7dC/VSBzjad3ikoyEbTQ0Ol1aulJUvcTgQEjFBP/mUPP/ywZsyYoU6dOqlw4cIKousz4PvWr5dmzrTzjh3thSkAwKYM//yzdNNNNpLepIk0bZp0zTVuJ3PHpk3S4MG2/3bhwtITT9j2dfBeBQvaDLlx42zv9EqVpIgIt1MBfs+jRfovv/yi8ePHq169ep78awFkl5MnpS+/tPO6dW1fdADAaXnzSr/+KjVoIG3YYE01p08PvEJ99Wpp6FBrLlqihPTYY7avPLxfs2bSvHk2+2HCBKldO7cTAX7Po9Pd8+bNq3xMaQL8x2+/2dTFPHmkO+90Ow0AeKfCha0wL1NG2rzZCvXNm93N5EmjR0vvvWcFeoUK0lNPUaD7krAw6Z577HzSJJrIAR7g0SL95ZdfVt++fXXkyBFP/rUAssPu3dL48XZ+99284AKACylWLPAKdceR+ve3dc0nT0rVq0u9ejFd2hdVq2ZN5NLTaSIHeIBHp7u/+eab2rBhg2JjY1WiRAnl+Nva1SU0pAB8g+PYNPeTJ6Vrr5Vq13Y7EQB4v6JFrVC/5RZp3Tpbq/7bb/65VOjECdsz/tNP7XHjxrYdHdtz+q727W3Zwqkmctdf73YiwG95tEi/4447svT7DRgwQAkJCXriiSf0zjvvZOn3BnAB8+dLf/5pTeI6drQOsACAizuzUF+71vp5/Pyz/ekvkpKsIJ8yxYryQYNoKuoPCha09enjx0vffy9Vrcp/VyCbeLRI79evX5Z9r4ULF+rDDz9U1apVs+x7ArgEhw5J335r561a2ZM2AODSFSkizZpl+6cvWGCjzF99JbVt63ayq7d8ufUoWb/elkF9843UsqU0bJjbyZAVmjWTZs+W9u2zN2GaN3c7EeCXPD7nKCkpSR9//LESEhK0f/9+STbNfdu2bZf8PQ4dOqSOHTvqo48+Ul627gA867vvrFAvUkRq2tTtNADgmwoWlKZOtUL92DErbIcMcTvV1fn8c+nGG61AL15c+t//rECH/wgPP/1m0oQJUnKyu3kAP+XRIn358uUqV66cBg4cqDfeeENJSUmSpB9++EEJCQmX/H169uypVq1aqUmTJhe9NjU1VSkpKZkOAFdozRpp7lw7v/9+KSTE3TwA4Mty55bGjJG6dbNeH716Sd27W9HuS1JTLfcDD0hHj9po65Il0nXXuZ0M2aFWLdtGLzVV+vFHt9MAfsmjRXrv3r3VpUsXrVu3ThFndPZs2bKlZs6ceUnfY9SoUVqyZIn69+9/Sdf3799f0dHRGUd8fPwVZQcC3pl7ojdsKJUu7W4eAPAHoaG2f/grr1h/jw8+kOrVkzZudDvZpVm50tbTf/CB5f/Xv2zNcv78bidDdgkOPr0l25w50tat7uYB/JBHi/SFCxeqW7duZ328aNGi2nkJey4mJibqiSee0JdffpmpyL+QhIQEJScnZxyJiYmXnRuAbG/UXbukqCj/WDcJAN4iKEh64QWbPpw/v41C16jh3aOUJ09KAwZYh+8lS6R8+Sx/v37MsgoEpUvbiLrjSKNHsyUbkMU8WqSHh4efc7r52rVrVfASmk8tXrxYu3fvVo0aNRQaGqrQ0FDNmDFDgwYNUmhoqNLS0s75d0ZFRWU6AFymfftO74l+551Srlzu5gEAf9S8ubR0qVSnjq31veMOqUcP71v3++efUv36UkKCdPy4ratfuZImYoGmbVvr7r5und23ALKMR4v022+/Xf/5z3904sQJSVJQUJC2bt2q5557TnfeeedFv75x48ZasWKFli1blnHccMMN6tixo5YtW6YQ3rkFssfo0bbnbdmy7IkOANkpPt62aHvqKXs8dKh07bW2q4bbo5VJSVKfPlK1arYVZ3S0NGKE9NNPUuHC7maD5+XLd7qB7Pff2+sEAFnCo0X6m2++qUOHDqlgwYI6evSobrrpJpUpU0aRkZF65ZVXLvr1kZGRqly5cqYjd+7cyp8/vypXruyBnwAIQCtXSsuW2Rq0Dh3YEx0AsltYmPTWW7bFVdmy0o4dtgb4ttvcWat+/LjtdV66tPTGG/a4RQt7fujcmeeFQNasmRQTI+3da/crgCzh0SI9OjpakyZN0vjx4zVo0CD16tVLEyZM0IwZM5Q7d25PRgFwKU6ckEaNsvPGjaWiRd3NAwCBpFEj23e8Xz8r3CdMkMqXl7p2tSnn2e3IEdvfvHJl6YknpP37pYoVLcf48VKxYtmfAd6NLdmAbBHqqb8oPT1dI0aM0A8//KDNmzcrKChIJUuWVFxcnBzHUdAVvgs7ffr0rA0K4LSJE6U9e+xd8ttuczsNAASeiAjrmH7vvdKTT0q//mpTzD/7zHqE9Okj1ayZtaPZmzfbnu2ffCIdOGAfK1RIevll6cEHrSM9cEqtWtK0aXbf/PijbcUH4Kp4ZCTdcRzdfvvtevjhh7Vt2zZVqVJFlSpV0pYtW9SlSxe1pVM04H327LEiXZLuusteKAIA3FGhgv1Onj/fGso5jvTdd9YnpHRpK9bnzZPS0y//ezuOTV0fOFC66abT09oPHJBKlbKp9+vXS48+SoGOs7ElG5DlPPKbdsSIEZo5c6amTJmiW265JdPnpk6dqjvuuEMjR47UA7zzBngHx5G++ca22Ln2WumGG9xOBACQbNRyzJjTRfX330ubNllR/cYbUlycVL26VKmSHRUrSnny2LZoISFWUO3aZR2516+3P2fPlv6+Re2tt0qPP25rz2nMi4spXdpmdCxcaM1mn36aXgXAVfBIkf7111/rhRdeOKtAl6RGjRrp+eef15dffkmRDniL33+XVqywF2b33ssTLQB4m8qVpc8/lz74QPrlFyvWx42Tdu60x7/8cnnfLyLC1sC3bGlHyZLZkxv+q107azR7aku2GjXcTgT4LI8U6cuXL9drr7123s+3aNFCgwYN8kQUABdz/Li9Cy7ZSEpcnLt5AADnlzu3LUm66y7p2DEbyfzjj9PHmjVSaqqUlnb6yJ/fusaXKWN/Vq5s09xz5nT7p4EvO7Ul2/jx9qZRlSq2jzqAy+aRIn3//v2KjY097+djY2N14FRjEgDumjBB2rfPnmxbtnQ7DQDgUkVESA0a2AG4oVkzadYs25Jt2rTT+6gDuCweaRyXlpam0As0GgkJCdHJkyc9EQXAhezaJU2aZOf33GNbqwAAAFyK8HCpTRs7nzBBOnTI3TyAj/LISLrjOOrSpYvCz/OCPzU11RMxAFyI40hff23N4ipVkq67zu1EAADA19SpI02dKv31l019b9/e7USAz/FIkd65c+eLXkPTOMBlS5ZIq1fb9jo0iwMAAFciONh6JLzzjjR9unTzzdIFlr0COJtHivThw4d74q8BcKWOHTvdLK5ZM6lQIXfzAAAA33XttdaQcOVK6YcfpO7d3U4E+BSPrEkH4OXGj5eSkqQCBaTmzd1OAwAAfN2dd9qo+rJl0tq1bqcBfApFOhDotm+XJk+28/btpbAwd/MAAADfV6SIVL++nX/7rZSe7m4ewIdQpAOB7FSzuPR0qVo1qWpVtxMBAAB/0bq1bQ24dau0YIHbaQCfQZEOBLKFC20KWo4ctuUaAABAVomKOr2MbuxY6fhxV+MAvoIiHQhUR4/a9DNJatHC1qMDAABkpcaNpXz5pAMHTi+vA3BBFOlAoPr5ZyklxTq5N23qdhoAAOCPwsKkO+6w84kT7bUHgAuiSAcC0V9/SdOm2fm999p0dwAAgOxQs6Z0zTVSaqoNEgC4IIp0INCc2SyuRg2pUiW3EwEAAH8WHCzddZed/+9/trMMgPOiSAcCzfz50vr1Nv3s7rvdTgMAAAJBuXLSddfZYMH337udBvBqFOlAIDlyRPruOztv1coauQAAAHhCu3Y2qr5ypbR6tdtpAK9FkQ4Ekp9/lg4elGJjpSZN3E4DAAACSWysdPPNdv7dd7b0DsBZKNKBQJGYmLlZXGiou3kAAEDgadVKypXLmtjOnet2GsAr+VyRPnToUFWtWlVRUVGKiopSnTp19Msvv7gdC/Bup5rFOY41i6tY0e1EAAAgEOXJI7Vsaec//igdO+ZuHsAL+VyRXqxYMQ0YMECLFy/WokWL1KhRI7Vp00Z//PGH29EA7zVvnrRhA83iAACA+26+WSpQQEpOliZNcjsN4HV8rkhv3bq1WrZsqbJly6pcuXJ65ZVXlCdPHs2bN8/taIB3OnLkdBdVmsUBAAC35chhTeQk6bffpKQkV+MA3sbnivQzpaWladSoUTp8+LDq1KlzzmtSU1OVkpKS6QACyk8/0SwOAAB4lxo1pNKlpePHbdo7gAw+WaSvWLFCefLkUXh4uP7xj39ozJgxqnieNbb9+/dXdHR0xhEfH+/htICLEhOl6dPtvEMHmsUBAADvEBQk3XWXnc+da69ZAEjy0SK9fPnyWrZsmebPn6/u3burc+fOWrVq1TmvTUhIUHJycsaRyC8ABIr09NPN4q6/Xrr2WrcTAQAAnFaqlHTDDfZa5bvv7E8A8slhtbCwMJUpU0aSdP3112vhwoV699139eGHH551bXh4uMLDwz0dEXDfqWZx4eE0iwMAAN6pbVtp2TLpzz+llSulKlXcTgS4zidH0v8uPT1dqampbscAvMfhw9IPP9h5q1ZS3rzu5gEAADiXAgWkRo3s/PvvpbQ0d/MAXsDnivSEhATNnDlTmzdv1ooVK5SQkKDp06erY8eObkcDvMepZnGFC0uNG7udBgAA4PxatJBy55Z27JBmzXI7DeA6nyvSd+/erQceeEDly5dX48aNtXDhQv3666+69dZb3Y4GeIetW6UZM+z83ntpFgcAALxbrlxS69Z2/vPP0tGj7uYBXOZzr94/+eQTtyMA3uvMZnE33CBVqOB2IgAAgItr2FCaNk3atUuaONHWqgMByudG0gFcwNy50saN1izu1LYmAAAA3i4kRGrXzs6nTJH273c3D+AiinTAX5zZLO6222gWBwAAfEu1alK5ctKJE9KYMW6nAVxDkQ74ix9/lA4dolkcAADwTUFBtm1sUJC0YIHNEAQCEEU64A+2bJFmzrTzDh1syhgAAICvKV5cqlvXzp94wvrtAAGGIh3wdSdPSl98Yc3iataUypd3OxEAAMCVu+MOKSJCWrhQGjnS7TSAx1GkA75uyBDbdi1XLpsiBgAA4MuioqRWrez8+eellBR38wAeRpEO+LLEROnFF+28bVspOtrdPAAAAFmhUSOpbFnbku2VV9xOA3gURTrgyx5/3JrFlS4t1a/vdhoAAICsERoqvf22nb/9trRunbt5AA+iSAd81dixdoSGSh07SsH87wwAAPxIy5ZS8+a2JdvTT7udBvAYXtUDvujgQemxx+y8Tx+paFF38wAAAGS1oCAbRQ8NlX7+WfrlF7cTAR5BkQ74or59pb/+kkqVOr0mHQAAwN9UqGBbsUm2zC811d08gAdQpAO+ZvFiadAgO3//fevqDgAA4K/69ZOKFJHWr5feeMPtNEC2o0gHfMnJk9Kjj0rp6VKHDlKzZm4nAgAAyF6RkaeL81dekbZscTcPkM0o0gFfMmSItGSJFBMjvfWW22kAAAA84957pZtuko4elZ56yu00QLaiSAd8xZl7og8cKMXFuZsHAADAU4KCbLAiJEQaM0aaONHtREC2oUgHfMWpPdHr1pUeftjtNAAAAJ5VqdLpJnKPPUYTOfgtinTAF5y5J/qHH7InOgAACEz9+tlswvXrbWYh4Id4pQ94u+RkqVcvO+/TR6pc2d08AAAAbomKsr3TJenVV6W1a93NA2QDinTA2/XpI23bJpUpw57oAAAA7dvbDjepqVL37pLjuJ0IyFIU6YA3mzJF+ugjO//kE/ZEBwAACAqS3n9fioiQpk6VvvzS7URAlvK5Ir1///6qWbOmIiMjVahQId1xxx1as2aN27GArHf4sPTII3beo4fUsKG7eQAAALxFqVJS3752/tRT0r597uYBspDPFekzZsxQz549NW/ePE2aNEknTpxQ06ZNdfjwYbejAVnrn/+UNm2SiheXBgxwOw0AAIB3efpp6/i+d6/03HNupwGyTKjbAS7XxL/tiThixAgVKlRIixcvVkNGGuEvZs+WBg2y848+kiIj3c0DAADgbcLCbNeb+vVtWWDnzlKDBm6nAq6az42k/11ycrIkKV++fOf8fGpqqlJSUjIdgFc7etT2QXccqWtXqWlTtxMBAAB4p3r1Ti8PfPhhex0F+DifLtLT09P15JNPql69eqp8nm2p+vfvr+jo6IwjPj7ewymBy/Tii9Kff0qFC0tvvul2GgAAAO/22mv2umntWunf/3Y7DXDVfLpI79mzp1auXKlRo0ad95qEhAQlJydnHImJiR5MCFymmTNP7/358cdS3rzu5gEAAPB2MTHSBx/Y+euvSwsXuhoHuFo+W6T36tVL48aN07Rp01SsWLHzXhceHq6oqKhMB+CVDh6UunSxae4PPyy1bOl2IgAAAN9w++1Shw5Serr00EPS8eNuJwKumM8V6Y7jqFevXhozZoymTp2qkiVLuh0JyBp9+lg392uuYZo7AADA5Ro0SCpYUFqxQurf3+00wBXzuSK9Z8+e+uKLL/TVV18pMjJSO3fu1M6dO3WUJhHwZRMnWndSSRo+XGLGBwAAwOUpUEAaPNjO//tfaflyd/MAV8jnivShQ4cqOTlZN998swoXLpxxfPPNN25HA67MgQM2LUuSHn9cuuUWd/MAAAD4qnvuke64Qzp50nbJYdo7fJDPFemO45zz6NKli9vRgMvnOFK3btL27VLZskzNAgAAuBpBQdL770v58klLlkgvv+x2IuCy+VyRDviVESOkb7+VQkOlL7+UcuVyOxEAAIBvK1z4dLf3V1+V5s1zNw9wmSjSAbesWyc99pid/+c/Us2a7uYBAADwF3ffLd1/v3V779RJOnTI7UTAJQt1OwAQkE6ckDp2lA4flm66SXr2WbcTAQCu1rBhbicAcKbBg6Xp06X166Vnnjk9ug54OUbSATf06yctXCjlzSt9/rkUEuJ2IgAAAP8SEyN99pmdf/ihNH68q3GAS0WRDnja9OnSgAF2PmyYFB/vahwAAAC/1aiR9OSTdv7QQ9KuXa7GAS4FRTrgSbt2SR06WFf3Bx+U7rrL7UQAAAD+7dVXpUqV7HXYAw/YOnXAi1GkA56Slmbr0HfulCpWlAYNcjsRAACA/8uZU/rmG/vzt9+k115zOxFwQRTpgKe8/LI0ZYpts/btt1Lu3G4nAgAACAyVKlkjOUl68UVp9mx38wAXQJEOeMKkSbbNmmSNSypWdDcPAABAoHnwQem++2x2Y4cO0v79bicCzokiHchu27fbNHfHkR5+2PbsBAAAgGcFBdk2bGXKSImJVrQ7jtupgLNQpAPZ6fhxqX17ac8eqWpV1qEDAAC4KTLS1qeHhUk//ii98YbbiYCzUKQD2emJJ6RZs6SoKFuHnjOn24kAAAACW40a0jvv2Pnzz0uTJ7saB/g7inQguwwbZlOqgoKkr76SypVzOxEAAAAk6R//kLp2te3Y7r1X2rzZ7URABop0IDvMni316mXnL78stWrlbh4AAACcFhQkvf++dMMN0r59Utu20pEjbqcCJFGkA1lv2zbpzjulEyeku+6SXnjB7UQAAAD4u4gI6YcfpIIFpWXLpEcfpZEcvAJFOpCVjhyxd2J37ZKqVJGGD7d3agEAAOB94uOl0aOlkBDpyy+lt95yOxFAkQ5kmfR0qVMnaeFCKW9eaexYKU8et1MBAADgQm6+WXrzTTvv00caM8bVOABFOpBVnn3WpkyFhVmBXqqU24kAAABwKR5/XOre3aa7d+xogy6ASyjSgawwZMjpd2BHjJAaNnQ1DgAAAC5DUJA0aJDUooV09KjUujUd3+EainTgao0bZ+++StKrr0odOribBwAAAJcvNFT65hupWjXrL9SqlZSU5HYqBCCKdOBqzJ8vtW9v69Efflh6/nm3EwEAAOBKRUbaAEyRItKqVVK7dtKxY26nQoDxuSJ95syZat26tYoUKaKgoCCNHTvW7UgIVMuX25SoI0ekZs1sr006uQMAAPi2YsWk8eOtAfC0aTYgc+KE26kQQHyuSD98+LCqVaumIUOGuB0FgWzdOqlpU+nAAalOHen776UcOdxOBQAAgKxw3XU2oh4RIf30k9S1q82cBDwg1O0Al6tFixZq0aKF2zEQyBITpSZNbK1StWrShAlS7txupwIAAEBWuukmG4hp08b2UI+MZOYkPMLnRtIvV2pqqlJSUjIdwBXbvVu69VZp61apXDnpt9+kmBi3UwEAACA7tGwpffGFFeYffGD9hxzH7VTwc35fpPfv31/R0dEZR3x8vNuR4Kt27JBuvllas0YqXlyaPFkqVMjtVAAAAMhO7dtLw4bZ+WuvSc89R6GObOX3RXpCQoKSk5MzjsTERLcjwRf99ZdNeVq92pqJTJ4s8YYPAABAYHj4YWnwYDt//XXpiSdYo45s43Nr0i9XeHi4wsPD3Y4BX7Z5s9SokbRpk1SihDR1qlSypNupAAAA4Em9eknh4VK3blawHztmU+CD/X7cEx7GHQVcyPr1UsOGVqCXLi3NmEGBDgAAEKgeeUT67DMrzD/6SOrSRTp50u1U8DM+N5J+6NAhrV+/PuPxpk2btGzZMuXLl0/Fixd3MRn8zqJFUqtW1iyuQgVpyhSpSBG3UwEAAMBNnTrZiPp990mffy7t2yd9843tqw5kAZ8bSV+0aJGqV6+u6tWrS5J69+6t6tWrq2/fvi4ng18ZP97WoO/ebftkTp9OgQ4AAABzzz3SmDFSzpy2HW/DhtL27W6ngp/wuSL95ptvluM4Zx0jRoxwOxr8xbBh0u23S0eOSM2aSTNnSrGxbqcCAACAN2nd2gZyChaUli6VbrxR+uMPt1PBD/hckQ5km/R06YUXrBlIerrUtav0889SZKTbyQAAAOCNatWS5s2TypeXEhOlevWk335zOxV8HEU6IEkHDti7of372+N+/aRPPpFy5HA3FwAAALxbqVLSnDlS/fpScrLUvLn0yits0YYrRpEOrFgh1axp64kiIqxj57/+JQUFuZ0MAAAAviBfPmnSJOv+7jjSiy9KbdtKSUluJ4MPokhHYPvmG1s/tGGDdM010uzZ0gMPuJ0KAAAAviYiwnobffyxdX//6ScbCFq+3O1k8DEU6QhMhw7ZO5333msN4m69VVq8WKpRw+1kAAAA8GUPPSTNmiUVLy6tX2/r1t9+m+nvuGQU6Qg8CxZI1avbu5xBQVJCgvTLL1L+/G4nAwAAgD+44QYbAGrVSkpNlXr3lpo0seZywEVQpCNwnDwpvfyyVLeuvatZrJg0ZYr06qtSSIjb6QAAAOBPChSwnYI++EDKlUuaNk2qUkX64gtbtw6cB0U6AsPChbYmqG9fKS3NprkvXy7dcovbyQAAAOCvgoJse99ly6Tata37e6dOUtOm0rp1bqeDl6JIh39LTpZ69bJfisuWSXnzSp9/Ln31lZ0DAAAA2a1sWVun/t//WlO5yZNtVP1f/5KOHXM7HbwMRTr8U3q6FeLXXisNGWJTiu6/X/rzT/uT7dUAAADgSaGh0j//Ka1cKTVrZmvV//1vK9a//54p8MhAkQ7/M3WqTW3v2FHascPeuZw0yUbQCxVyOx0AAAACWZky1rR49GipcGHrlXTXXVKdOtKMGW6ngxegSIf/WL5catlSatxYWrJEioy0KUXLl1s3TQAAAMAbBAVJd98trVkjvfSSNZabP1+6+WbpttukRYvcTggXUaTD982bJ91+u1Stmr0rGRoqPfaYtGGDTSmKiHA7IQAAAHC2yEjpP/+x163du9uOQ+PH26zQW2+1nYiYBh9wKNLhmxxH+u03685ep45tbxEUJLVvL61eLQ0aJBUs6HZKAAAA4OLi4qT335dWrbL+SSEh1lyuSRNrgDxqlHT8uNsp4SEU6fAt+/dLb70lVahgDTemT7eR8wcftOJ81Chb5wMAAAD4mnLlrI/S+vW2Q1FEhG0l3KGDFB8vJSRIGze6nRLZjCId3i8tzZrBde4sFSkiPf20tHatlCeP9PjjNj3ok0+k8uXdTgoAAABcvRIlpMGDpS1bpL59rcHc7t3SgAE2INW0qTRihG03DL9DkQ7v5Dj2ruFTT9m7ho0bSyNH2lYV110nffihtH279O67UvHibqcFAAAAsl6hQrZN25Yt0g8/WHHuOLZzUdeuUmys1K6ddYpPSXE7LbJIqNsBgAxHj9qI+fjx0rhxUmLi6c/lzWtbUzz0kFSrFvucAwAAIHDkyCG1bWvHhg3SV19JX39tyz3HjLEjRw7pppusO3yrViwB9WFBjhNY7QJTUlIUHR2t5ORkRUVFuR0nsJ04IS1ebPtBTp9ufx49evrzuXJJbdrYGpxmzaSwMNeier1hw9xOAAAAkLUefdTtBN7NcWyr4a+/lr7/3taxn6lECdvS7dRxzTWez4gMl1OHUqTDc7ZvtynsCxdKCxZIc+ZIhw9nvqZ48dPv/t1yi5QzpztZfQ1FOgAA8DcU6Zdn7VqbjTpunPS//0knT2b+fNGiNiP11FGjhhQT40rUQESRfgEU6R6QnGy/JFaulP74w/5cvlzasePsa/Pls2k5N90kNWokVa7MVPYrQZEOAAD8DUX6lTt0SJo922arTp9ug2RpaWdfFx9vr7+rVLE/S5eWSpa0LeF4TZ6lLqcO9dk16UOGDNHrr7+unTt3qlq1aho8eLBq1arldiz/d/KkdZbcvt2Obdvsz82bbYrNhg3Snj3n/trgYKlSJalmTTvq1rVfBsH0LwQAAACyTJ48tly0WTN7fPiwtGSJzWZdsECaP9+a0SUm2vHLL5m/PmdOK9ZLlTp9lCxpo/GFCtkRHu75nytA+GSR/s0336h379764IMPVLt2bb3zzjtq1qyZ1qxZo0KFCrkdz/ulp9va71PHwYPSgQOZj6Sk0+f799so+Pbt0s6d9vUXExtrBXnlynZUqiRVqyblzp3tPx4AAACAM+TOLTVoYMcpSUk243XFCjtWr5Y2bbKi/ehRadUqO84nOtpe858q2mNjpQIF7OPR0VJU1Ln/zJVLCgnJ9h/Zl/nkdPfatWurZs2aeu+99yRJ6enpio+P12OPPabnn3/+gl/rM9Pd58yRZs2yketTx4kTF3588qR0/Lj9T3XkSOZC/MzHqalXly042KbAFC1q+5YXKWJTZcqWtS6SpUtLkZFZ8++AS8N0dwAA4G+Y7u6O48elrVuljRutaN+48fT5jh02q/bv690vV2ioFBFxaUd4uHWuz5HDvu7M48yP9elj13spv57ufvz4cS1evFgJCQkZHwsODlaTJk00d+7cs65PTU1V6hlFaXJysiT7R/JqP/8sDRiQ/X9Pjhw2HSYmxt7Ziok5fZz5OC5OKlzYjoIFL/zul+OwT6OnndkVHwAAwB/wetI9p0bHb7zx7M85jo3C79lz+ti9W9q7V9q3z/67nToOHsz8+NS6+JMnbd38oUNZl/mhh6yu8VKn6s9LGSP3uSJ97969SktLU2xsbKaPx8bG6s8//zzr+v79++vf//73WR+Pj4/Ptow+5cSJ09PaAQAAAG/x5JNuJ4AvKVrU7QSX5ODBg4qOjr7gNT5XpF+uhIQE9e7dO+Nxenq69u/fr/z58yvoEjsWpqSkKD4+XomJid49RR4Bh3sT3op7E96KexPeinsT3op7M2s4jqODBw+qSJEiF73W54r0AgUKKCQkRLt27cr08V27dikuLu6s68PDwxX+t86DMVe4H2BUVBQ3JrwS9ya8FfcmvBX3JrwV9ya8Fffm1bvYCPopPrf3VVhYmK6//npNmTIl42Pp6emaMmWK6tSp42IyAAAAAACujs+NpEtS79691blzZ91www2qVauW3nnnHR0+fFhdu3Z1OxoAAAAAAFfMJ4v09u3ba8+ePerbt6927typ6667ThMnTjyrmVxWCQ8PV79+/c6aNg+4jXsT3op7E96KexPeinsT3op70/N8cp90AAAAAAD8kc+tSQcAAAAAwF9RpAMAAAAA4CUo0gEAAAAA8BIU6QAAAAAAeImAKtJLlCihoKCgTMeAAQMyXbN8+XI1aNBAERERio+P12uvvXbW9/n2229VoUIFRUREqEqVKpowYUKmzzuOo759+6pw4cLKmTOnmjRponXr1mW6Zv/+/erYsaOioqIUExOjhx56SIcOHcr6Hxp+a8iQISpRooQiIiJUu3ZtLViwwO1I8GH/+te/zvr9WKFChYzPHzt2TD179lT+/PmVJ08e3Xnnndq1a1em77F161a1atVKuXLlUqFChdSnTx+dPHky0zXTp09XjRo1FB4erjJlymjEiBFnZeHeDmwzZ85U69atVaRIEQUFBWns2LGZPp9Vz7Geer6H/7jYvdmlS5ezfo82b9480zXcm8gO/fv3V82aNRUZGalChQrpjjvu0Jo1azJd403P45eSJeA5AeSaa65x/vOf/zg7duzIOA4dOpTx+eTkZCc2Ntbp2LGjs3LlSufrr792cubM6Xz44YcZ18yePdsJCQlxXnvtNWfVqlXOiy++6OTIkcNZsWJFxjUDBgxwoqOjnbFjxzq///67c/vttzslS5Z0jh49mnFN8+bNnWrVqjnz5s1z/ve//zllypRxOnTo4Jl/CPi8UaNGOWFhYc6nn37q/PHHH84jjzzixMTEOLt27XI7GnxUv379nEqVKmX6/bhnz56Mz//jH/9w4uPjnSlTpjiLFi1ybrzxRqdu3boZnz958qRTuXJlp0mTJs7SpUudCRMmOAUKFHASEhIyrtm4caOTK1cup3fv3s6qVaucwYMHOyEhIc7EiRMzruHexoQJE5x//vOfzg8//OBIcsaMGZPp81nxHOvJ53v4j4vdm507d3aaN2+e6ffo/v37M13DvYns0KxZM2f48OHOypUrnWXLljktW7Z0ihcvnqnO8abn8YtlgeMEXJH+9ttvn/fz77//vpM3b14nNTU142PPPfecU758+YzH99xzj9OqVatMX1e7dm2nW7dujuM4Tnp6uhMXF+e8/vrrGZ9PSkpywsPDna+//tpxHMdZtWqVI8lZuHBhxjW//PKLExQU5Gzbtu2qfkYEhlq1ajk9e/bMeJyWluYUKVLE6d+/v4up4Mv69evnVKtW7ZyfS0pKcnLkyOF8++23GR9bvXq1I8mZO3eu4zj24jU4ONjZuXNnxjVDhw51oqKiMn6nPvvss06lSpUyfe/27ds7zZo1y3jMvY0z/b0QyqrnWE8938N/na9Ib9OmzXm/hnsTnrJ7925HkjNjxgzHcbzrefxSssBxAmq6uyQNGDBA+fPnV/Xq1fX6669nmsIxd+5cNWzYUGFhYRkfa9asmdasWaMDBw5kXNOkSZNM37NZs2aaO3euJGnTpk3auXNnpmuio6NVu3btjGvmzp2rmJgY3XDDDRnXNGnSRMHBwZo/f37W/9DwK8ePH9fixYsz3WPBwcFq0qRJxj0GXIl169apSJEiKlWqlDp27KitW7dKkhYvXqwTJ05kuucqVKig4sWLZ/q9VqVKFcXGxmZc06xZM6WkpOiPP/7IuOZCvz+5t3ExWfUc66nnewSe6dOnq1ChQipfvry6d++uffv2ZXyOexOekpycLEnKly+fJO96Hr+ULAiwNemPP/64Ro0apWnTpqlbt2569dVX9eyzz2Z8fufOnZluTEkZj3fu3HnBa878/Jlfd75rChUqlOnzoaGhypcvX8Y1wPns3btXaWlpF7zHgMtVu3ZtjRgxQhMnTtTQoUO1adMmNWjQQAcPHtTOnTsVFhammJiYTF/z999rV/r7MyUlRUePHuXexkVl1XOsp57vEViaN2+ukSNHasqUKRo4cKBmzJihFi1aKC0tTRL3JjwjPT1dTz75pOrVq6fKlStLklc9j19KFkihbge4Ws8//7wGDhx4wWtWr16tChUqqHfv3hkfq1q1qsLCwtStWzf1799f4eHh2R0VALxWixYtMs6rVq2q2rVr65prrtHo0aOVM2dOF5MBgG+49957M86rVKmiqlWrqnTp0po+fboaN27sYjIEkp49e2rlypWaNWuW21FwFXx+JP3pp5/W6tWrL3iUKlXqnF9bu3ZtnTx5Ups3b5YkxcXFndVZ8NTjuLi4C15z5ufP/LrzXbN79+5Mnz958qT279+fcQ1wPgUKFFBISMgF7zHgasXExKhcuXJav3694uLidPz4cSUlJWW65u+/167092dUVJRy5szJvY2LyqrnWE893yOwlSpVSgUKFND69eslcW8i+/Xq1Uvjxo3TtGnTVKxYsYyPe9Pz+KVkgR8U6QULFlSFChUueJy5rudMy5YtU3BwcMbUozp16mjmzJk6ceJExjWTJk1S+fLllTdv3oxrpkyZkun7TJo0SXXq1JEklSxZUnFxcZmuSUlJ0fz58zOuqVOnjpKSkrR48eKMa6ZOnar09HTVrl07C/5V4M/CwsJ0/fXXZ7rH0tPTNWXKlIx7DLhahw4d0oYNG1S4cGFdf/31ypEjR6Z7bs2aNdq6dWum32srVqzI9AJ00qRJioqKUsWKFTOuudDvT+5tXExWPcd66vkege2vv/7Svn37VLhwYUncm8g+juOoV69eGjNmjKZOnaqSJUtm+rw3PY9fShYocLZgmzNnjvP22287y5YtczZs2OB88cUXTsGCBZ0HHngg45qkpCQnNjbW6dSpk7Ny5Upn1KhRTq5cuc7a9iI0NNR54403nNWrVzv9+vU757YXMTExzo8//ugsX77cadOmzTm3h6levbozf/58Z9asWU7ZsmXZgg2XbNSoUU54eLgzYsQIZ9WqVc6jjz7qxMTEZOrICVyOp59+2pk+fbqzadMmZ/bs2U6TJk2cAgUKOLt373Ycx7ZLKV68uDN16lRn0aJFTp06dZw6depkfP2prVuaNm3qLFu2zJk4caJTsGDBc27d0qdPH2f16tXOkCFDzrl1C/d2YDt48KCzdOlSZ+nSpY4k56233nKWLl3qbNmyxXGcrHmO9eTzPfzHhe7NgwcPOs8884wzd+5cZ9OmTc7kyZOdGjVqOGXLlnWOHTuW8T24N5Edunfv7kRHRzvTp0/PtAXgkSNHMq7xpufxi2VBAG3BtnjxYqd27dpOdHS0ExER4Vx77bXOq6++mukXp+M4zu+//+7Ur1/fCQ8Pd4oWLeoMGDDgrO81evRop1y5ck5YWJhTqVIlZ/z48Zk+n56e7rz00ktObGysEx4e7jRu3NhZs2ZNpmv27dvndOjQwcmTJ48TFRXldO3a1Tl48GDW/+DwW4MHD3aKFy/uhIWFObVq1XLmzZvndiT4sPbt2zuFCxd2wsLCnKJFizrt27d31q9fn/H5o0ePOj169HDy5s3r5MqVy2nbtq2zY8eOTN9j8+bNTosWLZycOXM6BQoUcJ5++mnnxIkTma6ZNm2ac9111zlhYWFOqVKlnOHDh5+VhXs7sE2bNs2RdNbRuXNnx3Gy7jnWU8/38B8XujePHDniNG3a1ClYsKCTI0cO55prrnEeeeSRs95g5N5EdjjXfSkp03OsNz2PX0qWQBfkOI7j6dF7AAAAAABwNp9fkw4AAAAAgL+gSAcAAAAAwEtQpAMAAAAA4CUo0gEAAAAA8BIU6QAAAAAAeAmKdAAAAAAAvARFOgAAAAAAXoIiHQAAAAAAL0GRDgAAMnTp0kV33HGH2zEAAAhYoW4HAAAAnhEUFHTBz/fr10/vvvuuHMfxUCIAAPB3FOkAAASIHTt2ZJx/88036tu3r9asWZPxsTx58ihPnjxuRAMAAP+P6e4AAASIuLi4jCM6OlpBQUGZPpYnT56zprvffPPNeuyxx/Tkk08qb968io2N1UcffaTDhw+ra9euioyMVJkyZfTLL79k+rtWrlypFi1aKE+ePIqNjVWnTp20d+9eD//EAAD4Hop0AABwQZ999pkKFCigBQsW6LHHHlP37t119913q27dulqyZImaNm2qTp066ciRI5KkpKQkNWrUSNWrV9eiRYs0ceJE7dq1S/fcc4/LPwkAAN6PIh0AAFxQtWrV9OKLL6ps2bJKSEhQRESEChQooEceeURly5ZV3759tW/fPi1fvlyS9N5776l69ep69dVXVaFCBVWvXl2ffvqppk2bprVr17r80wAA4N1Ykw4AAC6oatWqGechISHKnz+/qlSpkvGx2NhYSdLu3bslSb///rumTZt2zvXtGzZsULly5bI5MQAAvosiHQAAXFCOHDkyPQ4KCsr0sVNd49PT0yVJhw4dUuvWrTVw4MCzvlfhwoWzMSkAAL6PIh0AAGSpGjVq6Pvvv1eJEiUUGspLDQAALgdr0gEAQJbq2bOn9u/frw4dOmjhwoXasGGDfv31V3Xt2lVpaWluxwMAwKtRpAMAgCxVpEgRzZ49W2lpaWratKmqVKmiJ598UjExMQoO5qUHAAAXEuQ4juN2CAAAAAAAwEg6AAAAAABegyIdAAAAAAAvQZEOAAAAAICXoEgHAAAAAMBLUKQDAAAAAOAlKNIBAAAAAPASFOkAAAAAAHgJinQAAAAAALwERToAAAAAAF6CIh0AAAAAAC9BkQ4AAAAAgJf4P2E1kqkojaOOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of transactions over time for fraudulent and genuine transactions in more detail\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "sns.distplot(data[data['Class'] == 0][\"Time\"], color='b')\n",
    "plt.title('Normal Transactions Time Distribution')\n",
    "plt.subplot(2, 1, 2)\n",
    "sns.distplot(data[data['Class'] == 1][\"Time\"], color='r')\n",
    "plt.title('Fraud Transactions Time Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of transaction amounts for fraudulent and genuine transactions in more detail\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "sns.histplot(data[data['Class'] == 0][\"Amount\"], color='b', kde=True)\n",
    "plt.title('Normal Transactions Amount Distribution')\n",
    "plt.subplot(2, 1, 2)\n",
    "sns.histplot(data[data['Class'] == 1][\"Amount\"], color='r', kde=True)\n",
    "plt.title('Fraud Transactions Amount Distribution')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x3500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the distribution of other features for fraudulent and genuine transactions (e.g., V1, V2, etc.)\n",
    "features = ['V1', 'V2', 'V3', 'V4', 'V5']\n",
    "plt.figure(figsize=(15, 35))\n",
    "for i, feature in enumerate(features, 1):\n",
    "    plt.subplot(14, 2, i)\n",
    "    sns.histplot(data[data['Class'] == 0][feature], color='b', kde=True)\n",
    "    sns.histplot(data[data['Class'] == 1][feature], color='r', kde=True)\n",
    "    plt.title(f'{feature} Distribution by Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the correlation between features using a heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "corr = data.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size': 10})\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Density-Based Plots:\n",
    "\n",
    "\n",
    "Density-based visual analysis of fraud and genuine transactions can help you understand transaction densities and trends more effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "\n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density-based plots for fraud and genuine transactions\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "sns.kdeplot(data[data['Class'] == 0][\"Amount\"], shade=True, color='b', label='Normal Transactions')\n",
    "plt.title('Density-Based Plot for Normal Transactions')\n",
    "plt.xlabel('Transaction Amount')\n",
    "plt.ylabel('Density')\n",
    "plt.subplot(2, 1, 2)\n",
    "sns.kdeplot(data[data['Class'] == 1][\"Amount\"], shade=True, color='r', label='Fraud Transactions')\n",
    "plt.title('Density-Based Plot for Fraud Transactions')\n",
    "plt.xlabel('Transaction Amount')\n",
    "plt.ylabel('Density')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time Series Analysis:\n",
    "\n",
    "\n",
    "Conduct time series analysis to understand the trends of fraud cases over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time series analysis for fraud cases\n",
    "plt.figure(figsize=(12, 6))\n",
    "data['Time'] = pd.to_datetime(data['Time'], unit='s')\n",
    "data.set_index('Time', inplace=True)\n",
    "data['Class'].resample('D').mean().plot()\n",
    "plt.title('Mean Fraud Rate Daily')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Mean Fraud Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical Tests:\n",
    "\n",
    "\n",
    "Perform statistical tests to determine if there are statistically significant differences between fraud and normal transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-statistic: -3.00555231397141, P-value: 0.002651220649191683\n"
     ]
    }
   ],
   "source": [
    "# Performing t-test for transaction amounts between fraud and normal transactions\n",
    "normal_transactions = data[data['Class'] == 0]['Amount']\n",
    "fraud_transactions = data[data['Class'] == 1]['Amount']\n",
    "t_stat, p_val = ttest_ind(normal_transactions, fraud_transactions)\n",
    "print(f\"T-statistic: {t_stat}, P-value: {p_val}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anomaly Detection Models:\n",
    "\n",
    "\n",
    "Develop more advanced anomaly detection models using machine learning for fraud detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing Isolation Forest for anomaly detection\n",
    "X = data.drop('Class', axis=1)\n",
    "y = data['Class']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "IsolationForest(contamination=0.01, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model = IsolationForest(contamination=0.01, random_state=42)\n",
    "model.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting on the test set\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Anomaly Detection Model:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.00      0.00      0.00         0\n",
      "           0       0.00      0.00      0.00     56864\n",
      "           1       0.00      0.51      0.00        98\n",
      "\n",
      "    accuracy                           0.00     56962\n",
      "   macro avg       0.00      0.17      0.00     56962\n",
      "weighted avg       0.00      0.00      0.00     56962\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n"
     ]
    }
   ],
   "source": [
    "# Generating classification report\n",
    "print(\"Classification Report for Anomaly Detection Model:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firewall Analysis:\n",
    "\n",
    "\n",
    "Conduct firewall analysis to understand how credit card transactions behave within the firewall and identify fraud cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraudulent Transactions within Firewall:\n",
      "                            V1        V2         V3        V4        V5  \\\n",
      "Time                                                                      \n",
      "1970-01-01 02:31:04  -3.499108  0.258555  -4.489558  4.853894 -6.974522   \n",
      "1970-01-01 05:01:28 -12.224021  3.854150 -12.466766  9.648311 -2.726961   \n",
      "1970-01-01 16:23:31  -2.326922 -3.348439  -3.513408  3.175060 -2.815137   \n",
      "1970-01-01 17:21:07  -5.344665 -0.285760  -3.835616  5.337048 -7.609909   \n",
      "1970-01-01 18:09:45  -2.923827  1.524837  -3.018758  3.289291 -5.755542   \n",
      "1970-01-02 10:03:28  -2.003460 -7.159042  -4.050976  1.309580 -2.058102   \n",
      "1970-01-02 12:59:44  -1.212682 -2.484824  -6.397186  3.670562 -0.863375   \n",
      "1970-01-02 18:51:18  -1.600211 -3.488130  -6.459303  3.246816 -1.614608   \n",
      "1970-01-02 18:51:49  -0.082983 -3.935919  -2.616709  0.163310 -1.400952   \n",
      "\n",
      "                           V6         V7        V8        V9        V10  ...  \\\n",
      "Time                                                                     ...   \n",
      "1970-01-01 02:31:04  3.628382   5.431271 -1.946734 -0.775680  -1.987773  ...   \n",
      "1970-01-01 05:01:28 -4.445610 -21.922811  0.320792 -4.433162 -11.201400  ...   \n",
      "1970-01-01 16:23:31 -0.203363  -0.892144  0.333226 -0.802005  -4.350685  ...   \n",
      "1970-01-01 17:21:07  3.874668   1.289630  0.201742 -3.003532  -3.990551  ...   \n",
      "1970-01-01 18:09:45  2.218276  -0.509995 -3.569444 -1.016592  -4.320536  ...   \n",
      "1970-01-02 10:03:28 -0.098621   2.880083 -0.727484  1.460381  -1.531608  ...   \n",
      "1970-01-02 12:59:44 -1.855855   1.017732 -0.544704 -1.703378  -3.739659  ...   \n",
      "1970-01-02 18:51:18 -1.260375   0.288223 -0.048964 -0.734975  -4.441484  ...   \n",
      "1970-01-02 18:51:49 -0.809419   1.501580 -0.471000  1.519743  -1.134454  ...   \n",
      "\n",
      "                          V21       V22        V23       V24       V25  \\\n",
      "Time                                                                     \n",
      "1970-01-01 02:31:04 -1.052368  0.204817  -2.119007  0.170279 -0.393844   \n",
      "1970-01-01 05:01:28 -1.159830 -1.504119 -19.254328  0.544867 -4.781606   \n",
      "1970-01-01 16:23:31  1.226648 -0.695902  -1.478490 -0.061553  0.236155   \n",
      "1970-01-01 17:21:07  0.276011  1.342045  -1.016579 -0.071361 -0.335869   \n",
      "1970-01-01 18:09:45 -0.511657 -0.122724  -4.288639  0.563797 -0.949451   \n",
      "1970-01-02 10:03:28  1.244287 -1.015232  -1.800985  0.657586 -0.435617   \n",
      "1970-01-02 12:59:44  1.396872  0.092073  -1.492882 -0.204227  0.532511   \n",
      "1970-01-02 18:51:18  1.191175 -0.967141  -1.463421 -0.624231 -0.176462   \n",
      "1970-01-02 18:51:49  0.702672 -0.182305  -0.921017  0.111635 -0.071622   \n",
      "\n",
      "                          V26       V27       V28   Amount  Class  \n",
      "Time                                                               \n",
      "1970-01-01 02:31:04  0.296367  1.985913 -0.900452  1809.68      1  \n",
      "1970-01-01 05:01:28 -0.007772  3.052358 -0.775036  1218.89      1  \n",
      "1970-01-01 16:23:31  0.531911  0.302324  0.536375  1389.56      1  \n",
      "1970-01-01 17:21:07  0.441044  1.520613 -1.115937  1402.16      1  \n",
      "1970-01-01 18:09:45 -0.204532  1.510206 -0.324706  1354.25      1  \n",
      "1970-01-02 10:03:28 -0.894509 -0.397557  0.314262  2125.87      1  \n",
      "1970-01-02 12:59:44 -0.293871  0.212663  0.431095  1335.00      1  \n",
      "1970-01-02 18:51:18  0.400348  0.152947  0.477775  1504.93      1  \n",
      "1970-01-02 18:51:49 -1.125881 -0.170947  0.126221  1096.99      1  \n",
      "\n",
      "[9 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "# Conducting firewall analysis for credit card transactions\n",
    "firewall_data = data[data['Amount'] > 1000]  # Example threshold for suspicious transactions\n",
    "fraudulent_firewall_transactions = firewall_data[firewall_data['Class'] == 1]\n",
    "print(\"Fraudulent Transactions within Firewall:\")\n",
    "print(fraudulent_firewall_transactions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyperparameter Tuning:\n",
    "\n",
    "Explanation: In this section, we use GridSearchCV to find the best combination of hyperparameters for the Logistic Regression model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10], 'penalty': ['l2']}\n",
    "solver = 'liblinear'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter combinations:  {'C': 10, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Initialize the GridSearchCV\n",
    "grid_search = GridSearchCV(LogisticRegression(solver=solver, max_iter=1000), param_grid, cv=5)\n",
    "grid_search.fit(X_train_scaled, y_train)\n",
    "\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best parameter combinations: \", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preprocessing Techniques:\n",
    "\n",
    "Explanation: This section involves standard scaling of the data and the use of SMOTE to address class imbalance issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply standard scaling to the data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implement SMOTE for handling class imbalance\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data shape: (227845, 29) (227845,)\n",
      "Resampled data shape: (454902, 29) (454902,)\n"
     ]
    }
   ],
   "source": [
    "# Display the results\n",
    "print(\"Original data shape:\", X_train.shape, y_train.shape)\n",
    "print(\"Resampled data shape:\", X_resampled.shape, y_resampled.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying Different Models:\n",
    "\n",
    "Explanation: Here, we utilize the XGBoost model, train it on the resampled data, and evaluate its performance using the classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the XGBoost model\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_resampled, y_resampled)\n",
    "y_pred_xgb = xgb_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for XGBoost Model:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.80      0.84      0.82        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.90      0.92      0.91     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the performance of the XGBoost model\n",
    "print(\"Classification Report for XGBoost Model:\")\n",
    "print(classification_report(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8dTkrK8to1KiRce+999rMl2Q4OTkZBw4cMOe99957hiTDz8/P5jM1duxYQ5JNbN5nddq0aea8zMxMo2nTpoaPj49ZqwuqwVf+3eTlU5TPyEMPPWRUqlTJOHLkiDnvl19+MSpUqJBvnbcSTilHgdLT0yVJ7u7uJV6Hk5OT7O0vfcRycnJ06tQp8/Sty08d8/Ly0uHDh7V169ZC1+Xl5aXNmzfr6NGjJc6nMG5uble9u2ne0c+vvvqqxDckcXJyUv/+/Ysc/+STT9rs+27duql69er65ptvSvT6RfXNN9/IwcFBw4cPt5n//PPPyzAMrVq1ymZ+eHi4eRRZku688055eHjot99+K9LrPfPMM4qPj1d8fLy++OILRUdH67333lNMTIwZEx8fr9TUVPXq1Ut//vmnOTk4OCg0NFTr1q276mts27ZNJ06c0JAhQ2yuFY6KilL9+vULvG4x7whqniVLlsjT01P333+/TQ4hISFyc3O7Zg556tWrJ29vbwUGBuqpp55SnTp1tGrVKvMoenJysn755Rc9/vjjOnXqlPk6GRkZuu+++7Rx40bzM1iUv4nLj55kZGTozz//1N133y3DMLRjx4588Zdfb+fl5aV69erJ1dVV3bt3t9kGLy+vAt/jZ555xuZI/+DBg1WhQoUSf26fffZZm8dt2rSxed24uDhVrFjR5miWvb29eTQAAPX8yteWqOelXc9XrVqlU6dOqVevXua8Xr166aeffrK59Ku4lixZojZt2qhy5co2tTc8PFw5OTnauHGjTXzXrl2LdSR1zZo18vb2lre3txo3bqyPP/5Y/fv3t7mHTFHrf95na8WKFcrOzi70NS+vy2fOnFFaWpratGlT4CUV9913n81lAqGhoeZ2Xv6Zypt/5XtVoUIFDRo0yHzs6OioQYMG6cSJE0pKSrrW7snnWp+RnJwcffvtt+rcubN51ot06ZKVBx54oNivdzOh4UaBPDw8JOlvDbORm5ur6dOnq27dunJyclK1atXk7e2tn3/+WWlpaWbcmDFj5ObmppYtW6pu3bqKjo7Od73qlClTtGvXLgUEBKhly5aaMGFCkZu6azl37txV/yHSo0cPtW7dWgMHDpSvr6969uypxYsXF6tY33bbbcW6oUrdunVtHtvZ2alOnTr5rtEpbb///rv8/f3z7Y8GDRqYyy9Xs2bNfOuoXLlyka9rrlu3rsLDwxUeHq4uXbpo1qxZGjJkiGbMmGFep/TLL79IunSNXl5hzJvWrFlj3oTlatskXWoUr1S/fv1821ShQgXVqFHDZt4vv/yitLQ0+fj45Mvh3Llz18whzxdffKH4+HgtXLhQrVq10okTJ2yKb9629u3bN9/rfPjhh8rMzDT/doryN3Ho0CH169dPVapUMa+DzruG7vK/QUlydnbO9w8VT09P1ahRI9/p4J6engW+x1d+bt3c3FS9evUSfW4LyufKz9bvv/+u6tWrmz9Y5KlTp06xXw+4WVHP/4d6bk09/+STTxQUFGSeor9//34FBwerUqVK+vTTT0u8Db/88ovi4uLy1cO8UUiurL1XXuZ0LaGhoYqPj1dcXJymTp0qLy8vnTlzxub9LWr9b9eunbp27arXXntN1apV0yOPPKL58+crMzPT5jVXrFihVq1aydnZWVWqVJG3t7fmzp2bryZL+d8TT09PSVJAQECB8698r/z9/fPd9PX222+XpBJ9/q71GTlx4oTOnz9fYA2+1esy13CjQB4eHvL399euXbtKvI7XX39dr776qp566ilNmjRJVapUkb29vUaMGGFT3Bo0aKB9+/ZpxYoViouL0xdffKE5c+Zo3Lhxeu211yRJ3bt3V5s2bbR06VKtWbNGb731lt588019+eWXf+tXs8OHDystLe2qXwQuLi7auHGj1q1bp5UrVyouLk6LFi3SvffeqzVr1sjBweGar1Oc67SKqrBrYnNycoqUU2ko7HWMK27IUhz33XefZs2apY0bN6px48bmZ+Xjjz+Wn59fvvjSvpPm5Udy8uTm5srHx6fQfzgU9Rf1tm3bmncpf+ihh9S4cWP17t1bSUlJsre3N7f1rbfeKnQ4Gjc3N0nX/pvIycnR/fffr9OnT2vMmDGqX7++XF1ddeTIEfXr1y/fPzALey+teI+L4np9hoGbHfX8f6jnhSvpd316erqWL1+uCxcu5PtxQZIWLlxoXsdeXLm5ubr//vs1evToApfnNY95ivveVKtWzWzeIyMjVb9+fXXq1EkzZ840z7Qrav23s7PT559/rh9//FHLly/X6tWr9dRTT2natGn68ccf5ebmpu+++04PP/yw2rZtqzlz5qh69eqqWLGi5s+fn+9mpNKNU5etft2bAQ03CtWpUye9//77SkxMVFhYWLGf//nnn6tDhw7617/+ZTM/NTXVbDryuLq6qkePHurRo4eysrLUpUsX/fOf/9TYsWPN04CrV6+uIUOGaMiQITpx4oSaNWumf/7zn3+rQH/88ceSLn3RXo29vb3uu+8+3XfffXr77bf1+uuv6+WXX9a6desUHh5e6ndezDvSmccwDO3fv99mfNHKlSsrNTU133N///131a5d23xcnNxq1aqlb7/9VmfPnrX5VXzv3r3mcqtdvHhR0qUjFZLM05d8fHxKNLZ2Xs779u3Tvffea7Ns3759Rdqm4OBgffvtt2rdunWp/WPLzc1N48ePV//+/bV48WL17NnT3FYPD48ibevV/iZ27typ//73v1qwYIGefPJJ8znx8fGlkn9BfvnlF3Xo0MF8fO7cOR07dswca7201apVS+vWrTOH5cmzf/9+S14PuFFRz/+Hel669fzLL7/UhQsXNHfu3HyfhX379umVV17RDz/8oHvuuafQdRS2XcHBwTp37lyJan9JREVFqV27dnr99dc1aNAgubq6Frv+t2rVSq1atdI///lPLVy4UL1799Znn32mgQMH6osvvpCzs7NWr14tJycn8znz58+3ZHuOHj2ab2jT//73v5KU7472pcHHx0fOzs4F1uBbvS5zSjkKNXr0aLm6umrgwIE6fvx4vuW//vqrZs6cWejzHRwc8v3qtWTJknxDMZ06dcrmsaOjoxo2bCjDMJSdna2cnJx8p9r4+PjI398/36k6xbF27VpNmjRJQUFB5tBPBTl9+nS+eXlHH/NeP+/LrKCCWRL/93//Z3P63+eff65jx47Z/GMkODhYP/74o7Kyssx5K1asyDfcSHFye/DBB5WTk6NZs2bZzJ8+fbrs7OyuyzU4y5cvlyQ1adJE0qV/PHl4eOj1118v8LqokydPXnV9zZs3l4+Pj+bNm2fzeVm1apX27NmT766gBenevbtycnI0adKkfMsuXrxY4ve9d+/eqlGjht58801JUkhIiIKDgzV16lTzB4fL5W1rUf4m8n6Jvvxv0DCMq/7N/l3vv/++zXs0d+5cXbx40bLPTWRkpLKzs/XBBx+Y83Jzc83hzABcQj2/hHpe+vX8k08+Ue3atfXss8+qW7duNtMLL7wgNze3a55W7urqWuA2de/eXYmJiVq9enW+ZampqeYP9KVpzJgxOnXqlFlXilr/z5w5k+9v5MrPloODg+zs7GyGtzx48KCWLVtW6tuRl997771nPs7KytJ7770nb29vhYSElPrrOTg4KDw8XMuWLbO5R8P+/fvz3TPgVsMRbhQqODhYCxcuVI8ePdSgQQM9+eSTatSokbKysrRp0yYtWbKkwHEj83Tq1EkTJ05U//79dffdd2vnzp369NNPbX6tlaSIiAj5+fmpdevW8vX11Z49ezRr1ixFRUXJ3d1dqampqlGjhrp166YmTZrIzc1N3377rbZu3app06YVaVtWrVqlvXv36uLFizp+/LjWrl2r+Ph41apVS19//bXNzbSuNHHiRG3cuFFRUVGqVauWTpw4oTlz5qhGjRrmL7bBwcHy8vLSvHnz5O7uLldXV4WGhhb7eqI8VapU0T333KP+/fvr+PHjmjFjhurUqWNzc6iBAwfq888/V8eOHdW9e3f9+uuv+uSTT2xuaFHc3B566CF16NBBL7/8sg4ePKgmTZpozZo1+uqrrzRixIh86/67tm/fbo6ZevbsWSUkJOiLL77Q3XffrYiICEmXjvbOnTtXffr0UbNmzdSzZ095e3vr0KFDWrlypVq3bp3vHxSXq1ixot588031799f7dq1U69evcxhwQIDAzVy5Mhr5tmuXTsNGjRIkydPVnJysiIiIlSxYkX98ssvWrJkiWbOnKlu3boVe/srVqyo5557TqNGjVJcXJw6duyoDz/8UA888IDuuOMO9e/fX7fddpuOHDmidevWycPDQ8uXL9fZs2ev+TdRv359BQcH64UXXtCRI0fk4eGhL7744m+NG34tWVlZuu+++9S9e3ft27dPc+bM0T333KOHH37Yktfr3LmzWrZsqeeff1779+9X/fr19fXXX5v/qL6Vx/wELkc9v4R6Xrr1/OjRo1q3bl2+G7PlcXJyUmRkpJYsWaJ33nmn0PWEhIRo7ty5+sc//qE6derIx8dH9957r0aNGqWvv/5anTp1Ur9+/RQSEqKMjAzt3LlTn3/+uQ4ePJjvqPrf9cADD6hRo0Z6++23FR0dXeT6v2DBAs2ZM0ePPvqogoODdfbsWX3wwQfy8PAwz/KKiorS22+/rY4dO+rxxx/XiRMnNHv2bNWpU0c///xzqW6HdOka7jfffFMHDx7U7bffrkWLFik5OVnvv/9+iYYyLYoJEyZozZo1at26tQYPHmz+6NOoUSMlJydb8po3hOt+X3TccP773/8aTz/9tBEYGGg4Ojoa7u7uRuvWrY13333XuHDhghlX0DAizz//vFG9enXDxcXFaN26tZGYmJhvmIv33nvPaNu2rVG1alXDycnJCA4ONkaNGmWkpaUZhnFpGINRo0YZTZo0Mdzd3Q1XV1ejSZMmxpw5c66Ze94wInmTo6Oj4efnZ9x///3GzJkzbYZVyHPlcAgJCQnGI488Yvj7+xuOjo6Gv7+/0atXr3zDVHz11VdGw4YNzaEP8oZYaNeuXaHDpBQ2jMi///1vY+zYsYaPj4/h4uJiREVF2QzjlGfatGnGbbfdZjg5ORmtW7c2tm3blm+dV8vtymFEDOPSMFwjR440/P39jYoVKxp169Y13nrrLZthqgzDyDd0Rp7Chje5XEHDglWoUMGoXbu2MWrUKOPs2bP5nrNu3TojMjLS8PT0NJydnY3g4GCjX79+xrZt28yYqw0bs2jRIuOuu+4ynJycjCpVqhi9e/c2Dh8+bBPTt29fw9XVtdC833//fSMkJMRwcXEx3N3djcaNGxujR482jh49etXtzftMFTQUTVpamuHp6Wnznu3YscPo0qWL+TdRq1Yto3v37kZCQoJhGEX/m/jPf/5jhIeHG25ubka1atWMp59+2hzG48phuAra7sI+u7Vq1TKioqLMx3n7fcOGDcYzzzxjVK5c2XBzczN69+5tnDp1Kt86izIsWEH5FDRUycmTJ43HH3/ccHd3Nzw9PY1+/foZP/zwgyHJZjgzANRz6nnp1vNp06YZkszaVJDY2FhzeMjChgVLSUkxoqKiDHd3d0OSzTafPXvWGDt2rFGnTh3D0dHRqFatmnH33XcbU6dOzTe01VtvvVVoHgVt2+V1rKCcL69L16r/27dvN3r16mXUrFnTcHJyMnx8fIxOnTrZ/BvFMAzjX//6l1G3bl3DycnJqF+/vjF//vxCh+G68j0pbDsL2q95n9Vt27YZYWFhhrOzs1GrVi1j1qxZBa6zKMOCFfUzkpCQYNx1112Go6OjERwcbHz44YfG888/bzg7O+d7/q3CzjC40h0AUHKxsbHq37+/tm7dqubNm5d1Olq2bJkeffRRff/992rdunVZpwMAwHXVvn17/fnnn3/rZomlqXPnztq9e3e+exrcKriGGwBwwzp//rzN45ycHL377rvy8PBQs2bNyigrAABuTVfW5V9++UXffPON2rdvXzYJlQNcww0AuGENGzZM58+fV1hYmDIzM/Xll19q06ZNev311y0ZvgcAABSudu3a6tevn2rXrq3ff/9dc+fOlaOjY6HDu90KaLgBADese++9V9OmTdOKFSt04cIF1alTR++++66GDh1a1qkBAHDL6dixo/79738rJSVFTk5OCgsL0+uvv17gOO23Cq7hBgAAAADAAlzDDQAAAACABWi4AQAAAACwANdwX0e5ubk6evSo3N3dZWdnV9bpAADKCcMwdPbsWfn7+8vent/Cyxr1GgBQmOLWbBru6+jo0aMKCAgo6zQAAOXUH3/8oRo1apR1Grc86jUA4FqKWrNpuK8jd3d3SZfeHA8PjzLOBgBQXqSnpysgIMCsEyhb1GsAQGGKW7NpuK+jvNPSPDw8KOAAgHw4fbl8oF4DAK6lqDWbC8UAAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAIVyjoBFFNOjvTdd9KxY1L16lKbNpKDQ1lnBQAAAAC4Ag33jeTLL6XnnpMOH/7fvBo1pJkzpS5dyi4vAAAAAEA+nFJ+o/jyS6lbN9tmW5KOHLk0/8svyyYvAAAAAECBaLhvBDk5l45sG0b+ZXnzRoy4FAcAAAAAKBdouG8E332X/8j25QxD+uOPS3EAAAAAgHKBhvtGcOxY6cYBAAAAACxHw30jqF69dOMAAAAAAJaj4b4RtGlz6W7kdnYFL7ezkwICLsUBAAAAAMoFGu4bgYPDpaG/pPxNd97jGTMYjxsAAAAAyhEa7htFly7S559Lt91mO79GjUvzGYcbAAAAAMqVCmWdAIqhSxfpkUcu3Y382LFL12y3acORbQAAAAAoh2i4bzQODlL79mWdBQAAAADgGjilHAAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC5Rpwz158mS1aNFC7u7u8vHxUefOnbVv3z6bmAsXLig6OlpVq1aVm5ubunbtquPHj9vEHDp0SFFRUapUqZJ8fHw0atQoXbx40SZm/fr1atasmZycnFSnTh3Fxsbmy2f27NkKDAyUs7OzQkNDtWXLlmLnAgAAAACAVMYN94YNGxQdHa0ff/xR8fHxys7OVkREhDIyMsyYkSNHavny5VqyZIk2bNigo0ePqkuXLubynJwcRUVFKSsrS5s2bdKCBQsUGxurcePGmTEHDhxQVFSUOnTooOTkZI0YMUIDBw7U6tWrzZhFixYpJiZG48eP1/bt29WkSRNFRkbqxIkTRc4FAAAAAIA8doZhGGWdRJ6TJ0/Kx8dHGzZsUNu2bZWWliZvb28tXLhQ3bp1kyTt3btXDRo0UGJiolq1aqVVq1apU6dOOnr0qHx9fSVJ8+bN05gxY3Ty5Ek5OjpqzJgxWrlypXbt2mW+Vs+ePZWamqq4uDhJUmhoqFq0aKFZs2ZJknJzcxUQEKBhw4bpxRdfLFIu15Keni5PT0+lpaXJw8OjVPcdAODGRX0oX3g/AACFKW6NKFfXcKelpUmSqlSpIklKSkpSdna2wsPDzZj69eurZs2aSkxMlCQlJiaqcePGZrMtSZGRkUpPT9fu3bvNmMvXkReTt46srCwlJSXZxNjb2ys8PNyMKUouV8rMzFR6errNBAAAAAC4NZSbhjs3N1cjRoxQ69at1ahRI0lSSkqKHB0d5eXlZRPr6+urlJQUM+byZjtved6yq8Wkp6fr/Pnz+vPPP5WTk1NgzOXruFYuV5o8ebI8PT3NKSAgoIh7AwAAAABwoys3DXd0dLR27dqlzz77rKxTKTVjx45VWlqaOf3xxx9lnRIAAAAA4DqpUNYJSNLQoUO1YsUKbdy4UTVq1DDn+/n5KSsrS6mpqTZHlo8fPy4/Pz8z5sq7iefdOfzymCvvJn78+HF5eHjIxcVFDg4OcnBwKDDm8nVcK5crOTk5ycnJqRh7AgAAAABwsyjTI9yGYWjo0KFaunSp1q5dq6CgIJvlISEhqlixohISEsx5+/bt06FDhxQWFiZJCgsL086dO23uJh4fHy8PDw81bNjQjLl8HXkxeetwdHRUSEiITUxubq4SEhLMmKLkAgAAAABAnjI9wh0dHa2FCxfqq6++kru7u3kttKenp1xcXOTp6akBAwYoJiZGVapUkYeHh4YNG6awsDDzruARERFq2LCh+vTpoylTpiglJUWvvPKKoqOjzaPLzz77rGbNmqXRo0frqaee0tq1a7V48WKtXLnSzCUmJkZ9+/ZV8+bN1bJlS82YMUMZGRnq37+/mdO1cgEAAAAAIE+ZNtxz586VJLVv395m/vz589WvXz9J0vTp02Vvb6+uXbsqMzNTkZGRmjNnjhnr4OCgFStWaPDgwQoLC5Orq6v69u2riRMnmjFBQUFauXKlRo4cqZkzZ6pGjRr68MMPFRkZacb06NFDJ0+e1Lhx45SSkqKmTZsqLi7O5kZq18oFAAAAAIA85Woc7psd43oCAApCfShfeD8AAIW5ocfhBgAAAADgZkHDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAFzV5MmT1aJFC7m7u8vHx0edO3fWvn37bGIGDRqk4OBgubi4yNvbW4888oj27t1rE2NnZ5dv+uyzz2xi1q9fr2bNmsnJyUl16tRRbGxsvnxmz56twMBAOTs7KzQ0VFu2bLFZfuHCBUVHR6tq1apyc3NT165ddfz48dLZGQAAFAMNNwAAuKoNGzYoOjpaP/74o+Lj45Wdna2IiAhlZGSYMSEhIZo/f7727Nmj1atXyzAMRUREKCcnx2Zd8+fP17Fjx8ypc+fO5rIDBw4oKipKHTp0UHJyskaMGKGBAwdq9erVZsyiRYsUExOj8ePHa/v27WrSpIkiIyN14sQJM2bkyJFavny5lixZog0bNujo0aPq0qWLdTsIAIBC2BmGYZR1EreK4g6SDgC4Ndxo9eHkyZPy8fHRhg0b1LZt2wJjfv75ZzVp0kT79+9XcHCwpEtHuJcuXWrTZF9uzJgxWrlypXbt2mXO69mzp1JTUxUXFydJCg0NVYsWLTRr1ixJUm5urgICAjRs2DC9+OKLSktLk7e3txYuXKhu3bpJkvbu3asGDRooMTFRrVq1uub23WjvBwDg+ilujeAINwAAKJa0tDRJUpUqVQpcnpGRofnz5ysoKEgBAQE2y6Kjo1WtWjW1bNlSH330kS7/3T8xMVHh4eE28ZGRkUpMTJQkZWVlKSkpySbG3t5e4eHhZkxSUpKys7NtYurXr6+aNWuaMQAAXC8VyjoBAABw48jNzdWIESPUunVrNWrUyGbZnDlzNHr0aGVkZKhevXqKj4+Xo6OjuXzixIm69957ValSJa1Zs0ZDhgzRuXPnNHz4cElSSkqKfH19bdbp6+ur9PR0nT9/XmfOnFFOTk6BMXnXi6ekpMjR0VFeXl75YlJSUgrcpszMTGVmZpqP09PTi7dTAAAoBA03AAAosujoaO3atUvff/99vmW9e/fW/fffr2PHjmnq1Knq3r27fvjhBzk7O0uSXn31VTP2rrvuUkZGht566y2z4S4rkydP1muvvVamOQAAbk6cUg4AAIpk6NChWrFihdatW6caNWrkW+7p6am6deuqbdu2+vzzz7V3714tXbq00PWFhobq8OHD5tFlPz+/fHcTP378uDw8POTi4qJq1arJwcGhwBg/Pz9zHVlZWUpNTS005kpjx45VWlqaOf3xxx/X3BcAABQFDTcAALgqwzA0dOhQLV26VGvXrlVQUFCRnmMYhs2p2ldKTk5W5cqV5eTkJEkKCwtTQkKCTUx8fLzCwsIkSY6OjgoJCbGJyc3NVUJCghkTEhKiihUr2sTs27dPhw4dMmOu5OTkJA8PD5sJAIDSwCnlAADgqqKjo7Vw4UJ99dVXcnd3N6+F9vT0lIuLi3777TctWrRIERER8vb21uHDh/XGG2/IxcVFDz74oCRp+fLlOn78uFq1aiVnZ2fFx8fr9ddf1wsvvGC+zrPPPqtZs2Zp9OjReuqpp7R27VotXrxYK1euNGNiYmLUt29fNW/eXC1bttSMGTOUkZGh/v37mzkNGDBAMTExqlKlijw8PDRs2DCFhYUV6Q7lAACUJhpuAABwVXPnzpUktW/f3mb+/Pnz1a9fPzk7O+u7777TjBkzdObMGfn6+qpt27batGmTfHx8JEkVK1bU7NmzNXLkSBmGoTp16ujtt9/W008/ba4vKChIK1eu1MiRIzVz5kzVqFFDH374oSIjI82YHj166OTJkxo3bpxSUlLUtGlTxcXF2dxIbfr06bK3t1fXrl2VmZmpyMhIzZkzx8I9BABAwRiH+zpiXE8AQEGoD+UL7wcAoDCMww0AAAAAQDlAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAmXacG/cuFEPPfSQ/P39ZWdnp2XLltks79evn+zs7Gymjh072sScPn1avXv3loeHh7y8vDRgwACdO3fOJubnn39WmzZt5OzsrICAAE2ZMiVfLkuWLFH9+vXl7Oysxo0b65tvvrFZbhiGxo0bp+rVq8vFxUXh4eH65ZdfSmdHAAAAAABuOmXacGdkZKhJkyaaPXt2oTEdO3bUsWPHzOnf//63zfLevXtr9+7dio+P14oVK7Rx40Y988wz5vL09HRFRESoVq1aSkpK0ltvvaUJEybo/fffN2M2bdqkXr16acCAAdqxY4c6d+6szp07a9euXWbMlClT9M4772jevHnavHmzXF1dFRkZqQsXLpTiHgEAAAAA3CzsDMMwyjoJSbKzs9PSpUvVuXNnc16/fv2Umpqa78h3nj179qhhw4baunWrmjdvLkmKi4vTgw8+qMOHD8vf319z587Vyy+/rJSUFDk6OkqSXnzxRS1btkx79+6VJPXo0UMZGRlasWKFue5WrVqpadOmmjdvngzDkL+/v55//nm98MILkqS0tDT5+voqNjZWPXv2LNI2pqeny9PTU2lpafLw8CjuLgIA3KSoD+UL7wcAoDDFrRHl/hru9evXy8fHR/Xq1dPgwYN16tQpc1liYqK8vLzMZluSwsPDZW9vr82bN5sxbdu2NZttSYqMjNS+fft05swZMyY8PNzmdSMjI5WYmChJOnDggFJSUmxiPD09FRoaasYUJDMzU+np6TYTAAAAAODWUK4b7o4dO+r//u//lJCQoDfffFMbNmzQAw88oJycHElSSkqKfHx8bJ5ToUIFValSRSkpKWaMr6+vTUze42vFXL788ucVFFOQyZMny9PT05wCAgKKtf0AAAAAgBtXhbJO4GouP1W7cePGuvPOOxUcHKz169frvvvuK8PMimbs2LGKiYkxH6enp9N0AwAAAMAtolwf4b5S7dq1Va1aNe3fv1+S5OfnpxMnTtjEXLx4UadPn5afn58Zc/z4cZuYvMfXirl8+eXPKyimIE5OTvLw8LCZAAAAAAC3hhuq4T58+LBOnTql6tWrS5LCwsKUmpqqpKQkM2bt2rXKzc1VaGioGbNx40ZlZ2ebMfHx8apXr54qV65sxiQkJNi8Vnx8vMLCwiRJQUFB8vPzs4lJT0/X5s2bzRgAAAAAAC5Xpg33uXPnlJycrOTkZEmXbk6WnJysQ4cO6dy5cxo1apR+/PFHHTx4UAkJCXrkkUdUp04dRUZGSpIaNGigjh076umnn9aWLVv0ww8/aOjQoerZs6f8/f0lSY8//rgcHR01YMAA7d69W4sWLdLMmTNtTvV+7rnnFBcXp2nTpmnv3r2aMGGCtm3bpqFDh0q6dAf1ESNG6B//+Ie+/vpr7dy5U08++aT8/f1t7qoOAAAAAECeMr2Ge9u2berQoYP5OK8J7tu3r+bOnauff/5ZCxYsUGpqqvz9/RUREaFJkybJycnJfM6nn36qoUOH6r777pO9vb26du2qd955x1zu6empNWvWKDo6WiEhIapWrZrGjRtnM1b33XffrYULF+qVV17RSy+9pLp162rZsmVq1KiRGTN69GhlZGTomWeeUWpqqu655x7FxcXJ2dnZyl0EAAAAALhBlZtxuG8FjOsJACgI9aF84f0AABTmphuHGwAAAACAGxENNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAHBVkydPVosWLeTu7i4fHx917txZ+/bts4kZNGiQgoOD5eLiIm9vbz3yyCPau3evTcyhQ4cUFRWlSpUqycfHR6NGjdLFixdtYtavX69mzZrJyclJderUUWxsbL58Zs+ercDAQDk7Oys0NFRbtmyxWX7hwgVFR0eratWqcnNzU9euXXX8+PHS2RkAABQDDTcAALiqDRs2KDo6Wj/++KPi4+OVnZ2tiIgIZWRkmDEhISGaP3++9uzZo9WrV8swDEVERCgnJ0eSlJOTo6ioKGVlZWnTpk1asGCBYmNjNW7cOHMdBw4cUFRUlDp06KDk5GSNGDFCAwcO1OrVq82YRYsWKSYmRuPHj9f27dvVpEkTRUZG6sSJE2bMyJEjtXz5ci1ZskQbNmzQ0aNH1aVLl+uwpwAAsGVnGIZR1kncKoo7SDoA4NZwo9WHkydPysfHRxs2bFDbtm0LjPn555/VpEkT7d+/X8HBwVq1apU6deqko0ePytfXV5I0b948jRkzRidPnpSjo6PGjBmjlStXateuXeZ6evbsqdTUVMXFxUmSQkND1aJFC82aNUuSlJubq4CAAA0bNkwvvvii0tLS5O3trYULF6pbt26SpL1796pBgwZKTExUq1atrrl9N9r7AQC4fopbIzjCDQAAiiUtLU2SVKVKlQKXZ2RkaP78+QoKClJAQIAkKTExUY0bNzabbUmKjIxUenq6du/ebcaEh4fbrCsyMlKJiYmSpKysLCUlJdnE2NvbKzw83IxJSkpSdna2TUz9+vVVs2ZNM+ZKmZmZSk9Pt5kAACgNNNwAAKDIcnNzNWLECLVu3VqNGjWyWTZnzhy5ubnJzc1Nq1atUnx8vBwdHSVJKSkpNs22JPNxSkrKVWPS09N1/vx5/fnnn8rJySkw5vJ1ODo6ysvLq9CYK02ePFmenp7mlPcjAQAAfxcNNwAAKLLo6Gjt2rVLn332Wb5lvXv31o4dO7Rhwwbdfvvt6t69uy5cuFAGWRbP2LFjlZaWZk5//PFHWacEALhJVCjrBAAAwI1h6NChWrFihTZu3KgaNWrkW553hLhu3bpq1aqVKleurKVLl6pXr17y8/PLdzfxvDuH+/n5mf+98m7ix48fl4eHh1xcXOTg4CAHB4cCYy5fR1ZWllJTU22Ocl8ecyUnJyc5OTkVb2cAAFAEHOEGAABXZRiGhg4dqqVLl2rt2rUKCgoq0nMMw1BmZqYkKSwsTDt37rS5m3h8fLw8PDzUsGFDMyYhIcFmPfHx8QoLC5MkOTo6KiQkxCYmNzdXCQkJZkxISIgqVqxoE7Nv3z4dOnTIjAEA4HrhCDcAALiq6OhoLVy4UF999ZXc3d3Na6E9PT3l4uKi3377TYsWLVJERIS8vb11+PBhvfHGG3JxcdGDDz4oSYqIiFDDhg3Vp08fTZkyRSkpKXrllVcUHR1tHl1+9tlnNWvWLI0ePVpPPfWU1q5dq8WLF2vlypVmLjExMerbt6+aN2+uli1basaMGcrIyFD//v3NnAYMGKCYmBhVqVJFHh4eGjZsmMLCwop0h3IAAEoTDTcAALiquXPnSpLat29vM3/+/Pnq16+fnJ2d9d1332nGjBk6c+aMfH191bZtW23atEk+Pj6SJAcHB61YsUKDBw9WWFiYXF1d1bdvX02cONFcX1BQkFauXKmRI0dq5syZqlGjhj788ENFRkaaMT169NDJkyc1btw4paSkqGnTpoqLi7O5kdr06dNlb2+vrl27KjMzU5GRkZozZ46FewgAgIIxDvd1xLieAICCUB/KF94PAEBhGIcbAAAAAIBygIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAAAAAGABGm4AAAAAACxAww0AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAiVquGvXrq1Tp07lm5+amqratWv/7aQAAMDfR70GAKBslajhPnjwoHJycvLNz8zM1JEjR/52UgAA4O+jXgMAULYqFCf466+/Nv9/9erV8vT0NB/n5OQoISFBgYGBpZYcAAAoPuo1AADlQ7Ea7s6dO0uS7Ozs1LdvX5tlFStWVGBgoKZNm1ZqyQEAgOKjXgMAUD4Uq+HOzc2VJAUFBWnr1q2qVq2aJUkBAICSo14DAFA+FKvhznPgwIHSzgMAAJQy6jUAAGWrRA23JCUkJCghIUEnTpwwf0nP89FHH/3txAAAwN9HvQYAoOyUqOF+7bXXNHHiRDVv3lzVq1eXnZ1daecFAAD+Juo1AABlq0QN97x58xQbG6s+ffqUdj4AAKCUUK8BAChbJRqHOysrS3fffXdp5wIAAEoR9RoAgLJVooZ74MCBWrhwYWnnAgAAShH1GgCAslWiU8ovXLig999/X99++63uvPNOVaxY0Wb522+/XSrJAQCAkqNeAwBQtkrUcP/8889q2rSpJGnXrl02y7ghCwAA5QP1GgCAslWihnvdunWlnQcAAChl1GsAAMpWicfhBgAAQCFycqTvvpOOHZOqV5fatJEcHMo6KwC4NZXhd3KJGu4OHTpc9VS0tWvXljghAABQOqjXZeTLL6XnnpMOH/7fvBo1pJkzpS5dyi4vALgVlfF3coka7rzrwfJkZ2crOTlZu3btUt++fUsjLwAA8DdRr8vAl19K3bpJhmE7/8iRS/M//5ymGwCul3LwnWxnGFe+eslNmDBB586d09SpU0trlTeV9PR0eXp6Ki0tTR4eHmWdDgCgnLje9YF6fXUlfj9ycqTAQNujKJezs7t0VOXAAU4vBwCrWfSdXNwaUaJxuAvzxBNP6KOPPirNVQIAgFJGvbbId98V/g876dIRlj/+uBQHALBWOflOLtWGOzExUc7OzqW5SgAAUMqo1xY5dqx04wAAJVdOvpNLdA13lyvOczcMQ8eOHdO2bdv06quvlkpiAADg76FeX2fVq5duHACg5MrJd3KJGm5PT0+bx/b29qpXr54mTpyoiIiIUkkMAAD8PdTr66xNm0vXAx45kv8GPdL/rhds0+b65wYAt5py8p1cooZ7/vz5pZ0HAAAoZdTr68zB4dIwM926XfqH3OX/wMsbnm3GDG6YBgDXQzn5Ti5Rw50nKSlJe/bskSTdcccduuuuu0olKQAAUHqo19dRly6XhpkpaMzXGTMYEgwArqdy8J1coob7xIkT6tmzp9avXy8vLy9JUmpqqjp06KDPPvtM3t7epZkjAAAoAep1GenSRXrkkUt3vj127NL1gW3acGQbAMpCGX8nl+gu5cOGDdPZs2e1e/dunT59WqdPn9auXbuUnp6u4cOHl3aOAACgBKjXZcjBQWrfXurV69J/abYBoOyU4XeynWEUdAX51Xl6eurbb79VixYtbOZv2bJFERERSk1NLa38birFHSQdAHBrsKo+UK9LhnoNAChMcWtEiY5w5+bmqmLFivnmV6xYUbm5uSVZJQAAKGXUawAAylaJGu57771Xzz33nI4ePWrOO3LkiEaOHKn77ruv1JIDAAAlR70GAKBslajhnjVrltLT0xUYGKjg4GAFBwcrKChI6enpevfdd0s7RwAAUALUawAAylaJ7lIeEBCg7du369tvv9XevXslSQ0aNFB4eHipJgcAAEqOeg0AQNkq1hHutWvXqmHDhkpPT5ednZ3uv/9+DRs2TMOGDVOLFi10xx136LvvvrMqVwAAUATUawAAyodiNdwzZszQ008/XeDd2Dw9PTVo0CC9/fbbpZYcAAAoPuo1AADlQ7Ea7p9++kkdO3YsdHlERISSkpL+dlIAAKDkqNcAAJQPxWq4jx8/XuDwInkqVKigkydP/u2kAABAyVGvAQAoH4rVcN92223atWtXoct//vlnVa9e/W8nBQAASo56DQBA+VCshvvBBx/Uq6++qgsXLuRbdv78eY0fP16dOnUqteQAAEDxUa8BACgfitVwv/LKKzp9+rRuv/12TZkyRV999ZW++uorvfnmm6pXr55Onz6tl19+ucjr27hxox566CH5+/vLzs5Oy5Yts1luGIbGjRun6tWry8XFReHh4frll19sYk6fPq3evXvLw8NDXl5eGjBggM6dO2cT8/PPP6tNmzZydnZWQECApkyZki+XJUuWqH79+nJ2dlbjxo31zTffFDsXAADKg9Ku1wAAoGSK1XD7+vpq06ZNatSokcaOHatHH31Ujz76qF566SU1atRI33//vXx9fYu8voyMDDVp0kSzZ88ucPmUKVP0zjvvaN68edq8ebNcXV0VGRlp84t97969tXv3bsXHx2vFihXauHGjnnnmGXN5enq6IiIiVKtWLSUlJemtt97ShAkT9P7775sxmzZtUq9evTRgwADt2LFDnTt3VufOnW1OxytKLgAAlAelXa8BAEDJ2BmGYZTkiWfOnNH+/ftlGIbq1q2rypUr/71E7Oy0dOlSde7cWdKlI8r+/v56/vnn9cILL0iS0tLS5Ovrq9jYWPXs2VN79uxRw4YNtXXrVjVv3lySFBcXpwcffFCHDx+Wv7+/5s6dq5dfflkpKSlydHSUJL344otatmyZ9u7dK0nq0aOHMjIytGLFCjOfVq1aqWnTppo3b16RcimK9PR0eXp6Ki0trcChWgAAtyYr60Np1+tbAfUaAFCY4taIYh3hvlzlypXVokULtWzZ0pLifeDAAaWkpCg8PNyc5+npqdDQUCUmJkqSEhMT5eXlZTbbkhQeHi57e3tt3rzZjGnbtq3ZbEtSZGSk9u3bpzNnzpgxl79OXkze6xQll4JkZmYqPT3dZgIA4Hqyul4DAIDClbjhtlpKSook5TvlzdfX11yWkpIiHx8fm+UVKlRQlSpVbGIKWsflr1FYzOXLr5VLQSZPnixPT09zCggIuMZWAwAAAABuFuW24b4ZjB07Vmlpaeb0xx9/lHVKAAAAAIDrpNw23H5+fpKk48eP28w/fvy4uczPz08nTpywWX7x4kWdPn3aJqagdVz+GoXFXL78WrkUxMnJSR4eHjYTAAAAAODWUG4b7qCgIPn5+SkhIcGcl56ers2bNyssLEySFBYWptTUVCUlJZkxa9euVW5urkJDQ82YjRs3Kjs724yJj49XvXr1zGvZwsLCbF4nLybvdYqSCwAAAAAAlyvThvvcuXNKTk5WcnKypEs3J0tOTtahQ4dkZ2enESNG6B//+Ie+/vpr7dy5U08++aT8/f3NO5k3aNBAHTt21NNPP60tW7bohx9+0NChQ9WzZ0/5+/tLkh5//HE5OjpqwIAB2r17txYtWqSZM2cqJibGzOO5555TXFycpk2bpr1792rChAnatm2bhg4dKklFygUAAAAAgMtVKMsX37Ztmzp06GA+zmuC+/btq9jYWI0ePVoZGRl65plnlJqaqnvuuUdxcXFydnY2n/Ppp59q6NChuu+++2Rvb6+uXbvqnXfeMZd7enpqzZo1io6OVkhIiKpVq6Zx48bZjNV99913a+HChXrllVf00ksvqW7dulq2bJkaNWpkxhQlFwAAAAAA8pR4HG4UH+N6AgAKQn0oX3g/AACFuW7jcAMAAAAAgMLRcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAABXNXnyZLVo0ULu7u7y8fFR586dtW/fPnP56dOnNWzYMNWrV08uLi6qWbOmhg8frrS0NJv12NnZ5Zs+++wzm5j169erWbNmcnJyUp06dRQbG5svn9mzZyswMFDOzs4KDQ3Vli1bbJZfuHBB0dHRqlq1qtzc3NS1a1cdP3689HYIAABFRMMNAACuasOGDYqOjtaPP/6o+Ph4ZWdnKyIiQhkZGZKko0eP6ujRo5o6dap27dql2NhYxcXFacCAAfnWNX/+fB07dsycOnfubC47cOCAoqKi1KFDByUnJ2vEiBEaOHCgVq9ebcYsWrRIMTExGj9+vLZv364mTZooMjJSJ06cMGNGjhyp5cuXa8mSJdqwYYOOHj2qLl26WLeDAAAohJ1hGEZZJ3GrKO4g6QCAW8ONVh9OnjwpHx8fbdiwQW3bti0wZsmSJXriiSeUkZGhChUqSLp0hHvp0qU2TfblxowZo5UrV2rXrl3mvJ49eyo1NVVxcXGSpNDQULVo0UKzZs2SJOXm5iogIEDDhg3Tiy++qLS0NHl7e2vhwoXq1q2bJGnv3r1q0KCBEhMT1apVq2tu3432fgAArp/i1giOcAMAgGLJO1W8SpUqV43x8PAwm+080dHRqlatmlq2bKmPPvpIl//un5iYqPDwcJv4yMhIJSYmSpKysrKUlJRkE2Nvb6/w8HAzJikpSdnZ2TYx9evXV82aNc2YK2VmZio9Pd1mAgCgNFS4dggAAMAlubm5GjFihFq3bq1GjRoVGPPnn39q0qRJeuaZZ2zmT5w4Uffee68qVaqkNWvWaMiQITp37pyGDx8uSUpJSZGvr6/Nc3x9fZWenq7z58/rzJkzysnJKTBm79695jocHR3l5eWVLyYlJaXAfCdPnqzXXnutyPsAAICiouEGAABFFh0drV27dun7778vcHl6erqioqLUsGFDTZgwwWbZq6++av7/XXfdpYyMDL311ltmw11Wxo4dq5iYGPNxenq6AgICyjAjAMDNglPKAQBAkQwdOlQrVqzQunXrVKNGjXzLz549q44dO8rd3V1Lly5VxYoVr7q+0NBQHT58WJmZmZIkPz+/fHcTP378uDw8POTi4qJq1arJwcGhwBg/Pz9zHVlZWUpNTS005kpOTk7y8PCwmQAAKA003AAA4KoMw9DQoUO1dOlSrV27VkFBQfli0tPTFRERIUdHR3399ddydna+5nqTk5NVuXJlOTk5SZLCwsKUkJBgExMfH6+wsDBJkqOjo0JCQmxicnNzlZCQYMaEhISoYsWKNjH79u3ToUOHzBgAAK4XTikHAABXFR0drYULF+qrr76Su7u7eS20p6enXFxczGb7r7/+0ieffGJz4zFvb285ODho+fLlOn78uFq1aiVnZ2fFx8fr9ddf1wsvvGC+zrPPPqtZs2Zp9OjReuqpp7R27VotXrxYK1euNGNiYmLUt29fNW/eXC1bttSMGTOUkZGh/v37mzkNGDBAMTExqlKlijw8PDRs2DCFhYUV6Q7lAACUJhpuAABwVXPnzpUktW/f3mb+/Pnz1a9fP23fvl2bN2+WJNWpU8cm5sCBAwoMDFTFihU1e/ZsjRw5UoZhqE6dOnr77bf19NNPm7FBQUFauXKlRo4cqZkzZ6pGjRr68MMPFRkZacb06NFDJ0+e1Lhx45SSkqKmTZsqLi7O5kZq06dPl729vbp27arMzExFRkZqzpw5pb1bAAC4Jsbhvo4Y1xMAUBDqQ/nC+wEAKAzjcAMAAAAAUA7QcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwQLluuCdMmCA7OzubqX79+ubyCxcuKDo6WlWrVpWbm5u6du2q48eP26zj0KFDioqKUqVKleTj46NRo0bp4sWLNjHr169Xs2bN5OTkpDp16ig2NjZfLrNnz1ZgYKCcnZ0VGhqqLVu2WLLNAAAAAICbQ7luuCXpjjvu0LFjx8zp+++/N5eNHDlSy5cv15IlS7RhwwYdPXpUXbp0MZfn5OQoKipKWVlZ2rRpkxYsWKDY2FiNGzfOjDlw4ICioqLUoUMHJScna8SIERo4cKBWr15txixatEgxMTEaP368tm/friZNmigyMlInTpy4PjsBAAAAAHDDsTMMwyjrJAozYcIELVu2TMnJyfmWpaWlydvbWwsXLlS3bt0kSXv37lWDBg2UmJioVq1aadWqVerUqZOOHj0qX19fSdK8efM0ZswYnTx5Uo6OjhozZoxWrlypXbt2mevu2bOnUlNTFRcXJ0kKDQ1VixYtNGvWLElSbm6uAgICNGzYML344otF3p709HR5enoqLS1NHh4eJd0tAICbDPWhfOH9AAAUprg1otwf4f7ll1/k7++v2rVrq3fv3jp06JAkKSkpSdnZ2QoPDzdj69evr5o1ayoxMVGSlJiYqMaNG5vNtiRFRkYqPT1du3fvNmMuX0deTN46srKylJSUZBNjb2+v8PBwM6YwmZmZSk9Pt5kAAAAAALeGct1wh4aGKjY2VnFxcZo7d64OHDigNm3a6OzZs0pJSZGjo6O8vLxsnuPr66uUlBRJUkpKik2znbc8b9nVYtLT03X+/Hn9+eefysnJKTAmbx2FmTx5sjw9Pc0pICCg2PsAAAAAAHBjqlDWCVzNAw88YP7/nXfeqdDQUNWqVUuLFy+Wi4tLGWZWNGPHjlVMTIz5OD09naYbAAAAAG4R5foI95W8vLx0++23a//+/fLz81NWVpZSU1NtYo4fPy4/Pz9Jkp+fX767luc9vlaMh4eHXFxcVK1aNTk4OBQYk7eOwjg5OcnDw8NmAgAAAADcGm6ohvvcuXP69ddfVb16dYWEhKhixYpKSEgwl+/bt0+HDh1SWFiYJCksLEw7d+60uZt4fHy8PDw81LBhQzPm8nXkxeStw9HRUSEhITYxubm5SkhIMGMAAAAAALhSuW64X3jhBW3YsEEHDx7Upk2b9Oijj8rBwUG9evWSp6enBgwYoJiYGK1bt05JSUnq37+/wsLC1KpVK0lSRESEGjZsqD59+uinn37S6tWr9corryg6OlpOTk6SpGeffVa//fabRo8erb1792rOnDlavHixRo4caeYRExOjDz74QAsWLNCePXs0ePBgZWRkqH///mWyXwAAAAAA5V+5vob78OHD6tWrl06dOiVvb2/dc889+vHHH+Xt7S1Jmj59uuzt7dW1a1dlZmYqMjJSc+bMMZ/v4OCgFStWaPDgwQoLC5Orq6v69u2riRMnmjFBQUFauXKlRo4cqZkzZ6pGjRr68MMPFRkZacb06NFDJ0+e1Lhx45SSkqKmTZsqLi4u343UAAAAAADIU67H4b7ZMK4nAKAg1IfyhfcDAFCYm24cbgAAAAAAbkQ03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAACuavLkyWrRooXc3d3l4+Ojzp07a9++feby06dPa9iwYapXr55cXFxUs2ZNDR8+XGlpaTbrOXTokKKiolSpUiX5+Pho1KhRunjxok3M+vXr1axZMzk5OalOnTqKjY3Nl8/s2bMVGBgoZ2dnhYaGasuWLTbLL1y4oOjoaFWtWlVubm7q2rWrjh8/Xno7BACAIqLhBgAAV7VhwwZFR0frxx9/VHx8vLKzsxUREaGMjAxJ0tGjR3X06FFNnTpVu3btUmxsrOLi4jRgwABzHTk5OYqKilJWVpY2bdqkBQsWKDY2VuPGjTNjDhw4oKioKHXo0EHJyckaMWKEBg4cqNWrV5sxixYtUkxMjMaPH6/t27erSZMmioyM1IkTJ8yYkSNHavny5VqyZIk2bNigo0ePqkuXLtdhTwEAYItxuK8jxvUEABTkRqsPJ0+elI+PjzZs2KC2bdsWGLNkyRI98cQTysjIUIUKFbRq1Sp16tRJR48ela+vryRp3rx5GjNmjE6ePClHR0eNGTNGK1eu1K5du8z19OzZU6mpqYqLi5MkhYaGqkWLFpo1a5YkKTc3VwEBARo2bJhefPFFpaWlydvbWwsXLlS3bt0kSXv37lWDBg2UmJioVq1aXXP7brT3AwBw/TAONwAAsFTeqeJVqlS5aoyHh4cqVKggSUpMTFTjxo3NZluSIiMjlZ6ert27d5sx4eHhNuuJjIxUYmKiJCkrK0tJSUk2Mfb29goPDzdjkpKSlJ2dbRNTv3591axZ04wBAOB6qVDWCQAAgBtHbm6uRowYodatW6tRo0YFxvz555+aNGmSnnnmGXNeSkqKTbMtyXyckpJy1Zj09HSdP39eZ86cUU5OToExe/fuNdfh6OgoLy+vfDF5r3OlzMxMZWZmmo/T09ML23wAAIqFI9wAAKDIoqOjtWvXLn322WcFLk9PT1dUVJQaNmyoCRMmXN/kSmjy5Mny9PQ0p4CAgLJOCQBwk6DhBgAARTJ06FCtWLFC69atU40aNfItP3v2rDp27Ch3d3ctXbpUFStWNJf5+fnlu1N43mM/P7+rxnh4eMjFxUXVqlWTg4NDgTGXryMrK0upqamFxlxp7NixSktLM6c//vijCHsDAIBro+EGAABXZRiGhg4dqqVLl2rt2rUKCgrKF5Oenq6IiAg5Ojrq66+/lrOzs83ysLAw7dy50+Zu4vHx8fLw8FDDhg3NmISEBJvnxcfHKywsTJLk6OiokJAQm5jc3FwlJCSYMSEhIapYsaJNzL59+3To0CEz5kpOTk7y8PCwmQAAKA1cww0AAK4qOjpaCxcu1FdffSV3d3fzWmhPT0+5uLiYzfZff/2lTz75ROnp6eZ10N7e3nJwcFBERIQaNmyoPn36aMqUKUpJSdErr7yi6OhoOTk5SZKeffZZzZo1S6NHj9ZTTz2ltWvXavHixVq5cqWZS0xMjPr27avmzZurZcuWmjFjhjIyMtS/f38zpwEDBigmJkZVqlSRh4eHhg0bprCwsCLdoRwAgNJEww0AAK5q7ty5kqT27dvbzJ8/f7769eun7du3a/PmzZKkOnXq2MQcOHBAgYGBcnBw0IoVKzR48GCFhYXJ1dVVffv21cSJE83YoKAgrVy5UiNHjtTMmTNVo0YNffjhh4qMjDRjevTooZMnT2rcuHFKSUlR06ZNFRcXZ3MjtenTp8ve3l5du3ZVZmamIiMjNWfOnNLeLQAAXBPjcF9HjOsJACgI9aF84f0AABSGcbgBAAAAACgHaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAIVyjoBAABuSDk50nffSceOSdWrS23aSA4OZZ0VAAAoRzjCXUyzZ89WYGCgnJ2dFRoaqi1btpR1SgCA6+3LL6XAQKlDB+nxxy/9NzDw0nwAAID/j4a7GBYtWqSYmBiNHz9e27dvV5MmTRQZGakTJ06UdWoAgOvlyy+lbt2kw4dt5x85cmk+TTcAAPj/aLiL4e2339bTTz+t/v37q2HDhpo3b54qVaqkjz76qKxTAwBcDzk50nPPSYaRf1nevBEjLsUBAIBbHg13EWVlZSkpKUnh4eHmPHt7e4WHhysxMbEMMwMAXDfffZf/yPblDEP6449LcQAA4JbHTdOK6M8//1ROTo58fX1t5vv6+mrv3r0FPiczM1OZmZnm4/T0dEtzBABY7Nix0o0DAAA3NY5wW2jy5Mny9PQ0p4CAgLJOCQDwd1SvXrpxAADgpkbDXUTVqlWTg4ODjh8/bjP/+PHj8vPzK/A5Y8eOVVpamjn98ccf1yNVAIBV2rSRatSQ7OwKXm5nJwUEXIoDAAC3PBruInJ0dFRISIgSEhLMebm5uUpISFBYWFiBz3FycpKHh4fNBAC4gTk4SDNnXvr/K5vuvMczZjAeNwAAkETDXSwxMTH64IMPtGDBAu3Zs0eDBw9WRkaG+vfvX9apAQCuly5dpM8/l267zXZ+jRqX5nfpUjZ5AQCAcoebphVDjx49dPLkSY0bN04pKSlq2rSp4uLi8t1IDQBwk+vSRXrkkUt3Iz927NI1223acGQbAADYsDOMggYThRXS09Pl6emptLQ0Ti8HAJioD+UL7wcAoDDFrRGcUg4AAAAAgAVouAEAAAAAsAANNwAAAAAAFqDhBgAAAADAAjTcAAAAAABYgIYbAAAAAAAL0HADAICrmjx5slq0aCF3d3f5+Pioc+fO2rdvn03M+++/r/bt28vDw0N2dnZKTU3Nt57AwEDZ2dnZTG+88YZNzM8//6w2bdrI2dlZAQEBmjJlSr71LFmyRPXr15ezs7MaN26sb775xma5YRgaN26cqlevLhcXF4WHh+uXX375+zsCAIBiouEGAABXtWHDBkVHR+vHH39UfHy8srOzFRERoYyMDDPmr7/+UseOHfXSSy9ddV0TJ07UsWPHzGnYsGHmsvT0dEVERKhWrVpKSkrSW2+9pQkTJuj99983YzZt2qRevXppwIAB2rFjhzp37qzOnTtr165dZsyUKVP0zjvvaN68edq8ebNcXV0VGRmpCxculOJeAQDg2uwMwzDKOolbRXEHSQcA3BputPpw8uRJ+fj4aMOGDWrbtq3NsvXr16tDhw46c+aMvLy8bJYFBgZqxIgRGjFiRIHrnTt3rl5++WWlpKTI0dFRkvTiiy9q2bJl2rt3rySpR48eysjI0IoVK8zntWrVSk2bNtW8efNkGIb8/f31/PPP64UXXpAkpaWlydfXV7GxserZs+c1t+9Gez8AANdPcWsER7gBAECxpKWlSZKqVKlS7Oe+8cYbqlq1qu666y699dZbunjxorksMTFRbdu2NZttSYqMjNS+fft05swZMyY8PNxmnZGRkUpMTJQkHThwQCkpKTYxnp6eCg0NNWMAALheKpR1AgAA4MaRm5urESNGqHXr1mrUqFGxnjt8+HA1a9ZMVapU0aZNmzR27FgdO3ZMb7/9tiQpJSVFQUFBNs/x9fU1l1WuXFkpKSnmvMtjUlJSzLjLn1dQzJUyMzOVmZlpPk5PTy/WdgEAUBga7uso7+x9CjkA4HJ5deFGuMorOjpau3bt0vfff1/s58bExJj/f+edd8rR0VGDBg3S5MmT5eTkVJppFsvkyZP12muv5ZtPvQYAXKm4NZuG+zo6e/asJCkgIKCMMwEAlEdnz56Vp6dnWadRqKFDh2rFihXauHGjatSo8bfXFxoaqosXL+rgwYOqV6+e/Pz8dPz4cZuYvMd+fn7mfwuKuXx53rzq1avbxDRt2rTAPMaOHWvzY8CRI0fUsGFD6jUAoFBFrdk03NeRv7+//vjjD7m7u8vOzq7E60lPT1dAQID++OMPbuYCAGWotL6PDcPQ2bNn5e/vX4rZlR7DMDRs2DAtXbpU69evz3fad0klJyfL3t5ePj4+kqSwsDC9/PLLys7OVsWKFSVJ8fHxqlevnipXrmzGJCQk2Nx4LT4+XmFhYZKkoKAg+fn5KSEhwWyw09PTtXnzZg0ePLjAPJycnGyOsLu5uVGvAeAmU1Y1m4b7OrK3ty+VIwJ5PDw8KOAAUA6UxvdxeT6yHR0drYULF+qrr76Su7u7eS20p6enXFxcJF26djolJUX79++XJO3cuVPu7u6qWbOmqlSposTERG3evFkdOnSQu7u7EhMTNXLkSD3xxBNmM/3444/rtdde04ABAzRmzBjt2rVLM2fO1PTp081cnnvuObVr107Tpk1TVFSUPvvsM23bts0cOszOzk4jRozQP/7xD9WtW1dBQUF69dVX5e/vr86dOxdpe6nXAHDzut41m2HBbkAMVwIA5cOt8n1c2FHe+fPnq1+/fpKkCRMmFHgddF7M9u3bNWTIEO3du1eZmZkKCgpSnz59FBMTY3N0+eeff1Z0dLS2bt2qatWqadiwYRozZozNOpcsWaJXXnlFBw8eVN26dTVlyhQ9+OCD5nLDMDR+/Hi9//77Sk1N1T333KM5c+bo9ttvL4W9UXS3yucDAG4EZfWdTMN9A6KAA0D5wPcxrobPBwCUH2X1ncw43DcgJycnjR8/vkzv6AoA4PsYV8fnAwDKj7L6TuYINwAAAAAAFuAINwAAAAAAFqDhBgAAAADAAjTcMK1fv152dnZKTU0t61QA4JbRr1+/Ig9XBUjUawAoKyWp2TTcFunXr5/s7Oz0xhtv2MxftmxZocOrAACun7zv6SunvHGkcWugXgNA+Xcj12wabgs5OzvrzTff1JkzZ0ptnVlZWaW2LgC41XXs2FHHjh2zmYKCgmxi+N69+VGvAaD8u1FrNg23hcLDw+Xn56fJkycXGvPFF1/ojjvukJOTkwIDAzVt2jSb5YGBgZo0aZKefPJJeXh46JlnnlFsbKy8vLy0YsUK1atXT5UqVVK3bt30119/acGCBQoMDFTlypU1fPhw5eTkmOv6+OOP1bx5c7m7u8vPz0+PP/64Tpw4Ydn2A0B55+TkJD8/P5vpvvvu09ChQzVixAhVq1ZNkZGRkqS3335bjRs3lqurqwICAjRkyBCdO3fOXNeECRPUtGlTm/XPmDFDgYGB5uOcnBzFxMTIy8tLVatW1ejRo8VgIWWPeg0A5d+NWrNpuC3k4OCg119/Xe+++64OHz6cb3lSUpK6d++unj17aufOnZowYYJeffVVxcbG2sRNnTpVTZo00Y4dO/Tqq69Kkv766y+98847+uyzzxQXF6f169fr0Ucf1TfffKNvvvlGH3/8sd577z19/vnn5nqys7M1adIk/fTTT1q2bJkOHjyofv36WbkLAOCGtGDBAjk6OuqHH37QvHnzJEn29vZ65513tHv3bi1YsEBr167V6NGji7XeadOmKTY2Vh999JG+//57nT59WkuXLrViE1AM1GsAuHGV+5ptwBJ9+/Y1HnnkEcMwDKNVq1bGU089ZRiGYSxdutTI2+2PP/64cf/999s8b9SoUUbDhg3Nx7Vq1TI6d+5sEzN//nxDkrF//35z3qBBg4xKlSoZZ8+eNedFRkYagwYNKjTHrVu3GpLM56xbt86QZJw5c6b4GwwAN5i+ffsaDg4Ohqurqzl169bNaNeunXHXXXdd8/lLliwxqlataj4eP3680aRJE5uY6dOnG7Vq1TIfV69e3ZgyZYr5ODs726hRo4ZZL3D9Ua8BoPy7kWs2R7ivgzfffFMLFizQnj17bObv2bNHrVu3tpnXunVr/fLLLzanljVv3jzfOitVqqTg4GDzsa+vrwIDA+Xm5mYz7/JT0JKSkvTQQw+pZs2acnd3V7t27SRJhw4d+nsbCAA3qA4dOig5Odmc3nnnHUlSSEhIvthvv/1W9913n2677Ta5u7urT58+OnXqlP76668ivVZaWpqOHTum0NBQc16FChUK/I5H2aBeA0D5daPWbBru66Bt27aKjIzU2LFjS/R8V1fXfPMqVqxo89jOzq7Aebm5uZKkjIwMRUZGysPDQ59++qm2bt1qnhJRHm8uAADXg6urq+rUqWNO1atXN+df7uDBg+rUqZPuvPNOffHFF0pKStLs2bMl/e871N7ePt+1XdnZ2ddhK1BaqNcAUH7dqDW7giVrRT5vvPGGmjZtqnr16pnzGjRooB9++MEm7ocfftDtt98uBweHUn39vXv36tSpU3rjjTcUEBAgSdq2bVupvgYA3KySkpKUm5uradOmyd7+0m/Vixcvtonx9vZWSkqKDMMwh5NKTk42l3t6eqp69eravHmz2rZtK0m6ePGikpKS1KxZs+uzIbgm6jUA3NjKW83mCPd10rhxY/Xu3ds89UGSnn/+eSUkJGjSpEn673//qwULFmjWrFl64YUXSv31a9asKUdHR7377rv67bff9PXXX2vSpEml/joAcDOqU6eOsrOzze/Qjz/+2LwxS5727dvr5MmTmjJlin799VfNnj1bq1atsol57rnn9MYbb2jZsmXau3evhgwZotTU1Ou4JbgW6jUA3NjKW82m4b6OJk6caJ4yJknNmjXT4sWL9dlnn6lRo0YaN26cJk6caMmdSL29vRUbG6slS5aoYcOGeuONNzR16tRSfx0AuBk1adJEb7/9tt588001atRIn376ab4hpBo0aKA5c+Zo9uzZatKkibZs2ZKvIXv++efVp08f9e3bV2FhYXJ3d9ejjz56PTcFRUC9BoAbV3mr2XbGlSevAwAAAACAv40j3AAAAAAAWICGGwAAAAAAC9BwAwAAAABgARpuAAAAAAAsQMMNAAAAAIAFaLgBAAAAALAADTcAAAAAABag4QYAAAAAwAI03ABMdnZ2WrZsWVmnUSITJkxQ06ZN/9Y6Dh48KDs7OyUnJ5dKTgAAWIF6Tb3GjYOGG7hFpKSkaNiwYapdu7acnJwUEBCghx56SAkJCWWdmiSpffv2GjFiRFmnAQBAmaJeAzeXCmWdAADrHTx4UK1bt5aXl5feeustNW7cWNnZ2Vq9erWio6O1d+/esk4RAIBbHvUauPlwhBu4BQwZMkR2dnbasmWLunbtqttvv1133HGHYmJi9OOPPxb6vDFjxuj2229XpUqVVLt2bb366qvKzs42l//000/q0KGD3N3d5eHhoZCQEG3btk2S9Pvvv+uhhx5S5cqV5erqqjvuuEPffPNNibfhWrnkee+99xQQEKBKlSqpe/fuSktLs1n+4YcfqkGDBnJ2dlb9+vU1Z86cEucEAEBpol7/D/UaNwuOcAM3udOnTysuLk7//Oc/5erqmm+5l5dXoc91d3dXbGys/P39tXPnTj399NNyd3fX6NGjJUm9e/fWXXfdpblz58rBwUHJycmqWLGiJCk6OlpZWVnauHGjXF1d9Z///Edubm4l3o5r5SJJ+/fv1+LFi7V8+XKlp6drwIABGjJkiD799FNJ0qeffqpx48Zp1qxZuuuuu7Rjxw49/fTTcnV1Vd++fUucGwAAfxf1mnqNm5QB4Ka2efNmQ5Lx5ZdfXjNWkrF06dJCl7/11ltGSEiI+djd3d2IjY0tMLZx48bGhAkTipxnu3btjOeee67I8VfmMn78eMPBwcE4fPiwOW/VqlWGvb29cezYMcMwDCM4ONhYuHChzXomTZpkhIWFGYZhGAcOHDAkGTt27ChyHgAAlAbqNfUaNyeOcAM3OcMwSvzcRYsW6Z133tGvv/6qc+fO6eLFi/Lw8DCXx8TEaODAgfr4448VHh6uxx57TMHBwZKk4cOHa/DgwVqzZo3Cw8PVtWtX3XnnnZblIkk1a9bUbbfdZj4OCwtTbm6u9u3bJ3d3d/36668aMGCAnn76aTPm4sWL8vT0LHFeAACUBuo19Ro3J67hBm5ydevWlZ2dXbFvtJKYmKjevXvrwQcf1IoVK7Rjxw69/PLLysrKMmMmTJig3bt3KyoqSmvXrlXDhg21dOlSSdLAgQP122+/qU+fPtq5c6eaN2+ud999t0TbUJRcruXcuXOSpA8++EDJycnmtGvXrqteFwcAwPVAvb6Eeo2bDQ03cJOrUqWKIiMjNXv2bGVkZORbnpqaWuDzNm3apFq1aunll19W8+bNVbduXf3+++/54m6//XaNHDlSa9asUZcuXTR//nxzWUBAgJ599ll9+eWXev755/XBBx+UaBuKmsuhQ4d09OhR8/GPP/4oe3t71atXT76+vvL399dvv/2mOnXq2ExBQUElygsAgNJCvaZe4+bEKeXALWD27Nlq3bq1WrZsqYkTJ+rOO+/UxYsXFR8fr7lz52rPnj35nlO3bl0dOnRIn332mVq0aKGVK1eav4ZL0vnz5zVq1Ch169ZNQUFBOnz4sLZu3aquXbtKkkaMGKEHHnhAt99+u86cOaN169apQYMGV83z5MmTSk5OtplXvXr1a+aSx9nZWX379tXUqVOVnp6u4cOHq3v37vLz85Mkvfbaaxo+fLg8PT3VsWNHZWZmatu2bTpz5oxiYmKKu1sBAChV1GvqNW5CZX0ROYDr4+jRo0Z0dLRRq1Ytw9HR0bjtttuMhx9+2Fi3bp0ZoytuwjJq1CijatWqhpubm9GjRw9j+vTphqenp2EYhpGZmWn07NnTCAgIMBwdHQ1/f39j6NChxvnz5w3DMIyhQ4cawcHBhpOTk+Ht7W306dPH+PPPPwvNr127doakfNOkSZOumYthXLoJS5MmTYw5c+YY/v7+hrOzs9GtWzfj9OnTNq/z6aefGk2bNjUcHR2NypUrG23btjVvUMNNWAAAZY16fQn1GjcLO8P4G3doAAAAAAAABeIabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAWoOEGAAAAAMACNNwAAAAAAFiAhhsAAAAAAAvQcAMAAAAAYAEabgAAAAAALEDDDQAAAACABWi4AQAAAACwAA03AAAAAAAW+H8YmVgdCvuGxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the class distribution before and after resampling\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Dot plot for class distribution before resampling\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Class Distribution Before Resampling')\n",
    "plt.plot([0, 1], [sum(y_train==0), sum(y_train==1)], 'ro')\n",
    "plt.xticks([0, 1], ['Normal', 'Fraud'])\n",
    "plt.xlabel('Class Label')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Dot plot for class distribution after resampling\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Class Distribution After Resampling')\n",
    "plt.plot([0, 1], [sum(y_resampled==0), sum(y_resampled==1)], 'ro')\n",
    "plt.xticks([0, 1], ['Normal', 'Fraud'])\n",
    "plt.xlabel('Class Label')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Augmentation:\n",
    "\n",
    "Explanation: This section demonstrates the implementation of data augmentation techniques using Random Over Sampling to balance the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Random Over Sampling for data augmentation\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_resampled_aug, y_resampled_aug = ros.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape: Counter({0: 284315, 1: 492})\n",
      "Resampled dataset shape: Counter({0: 284315, 1: 284315})\n"
     ]
    }
   ],
   "source": [
    "# Display the results\n",
    "print(\"Original dataset shape:\", Counter(y))\n",
    "print(\"Resampled dataset shape:\", Counter(y_resampled_aug))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the class distribution after Random Over Sampling\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(Counter(y_resampled_aug).keys(), Counter(y_resampled_aug).values(), color=['b', 'r'])\n",
    "plt.xticks(list(Counter(y_resampled_aug).keys()), ['Normal', 'Fraud'])\n",
    "plt.xlabel('Class Label')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Class Distribution After Random Over Sampling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Evaluation Metrics:\n",
    "\n",
    "Explanation: Here, we compute and print the precision, recall, and F1 scores to evaluate the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.0008777781679014079\n",
      "Recall:  0.0008777781679014079\n",
      "F1 Score:  0.0008777781679014079\n"
     ]
    }
   ],
   "source": [
    "# Calculate precision, recall, and F1 scores\n",
    "precision = precision_score(y_test, y_pred, average='micro')\n",
    "recall = recall_score(y_test, y_pred, average='micro')\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Precision: \", precision)\n",
    "print(\"Recall: \", recall)\n",
    "print(\"F1 Score: \", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the metrics and scores\n",
    "metrics = ['Precision', 'Recall', 'F1 Score']\n",
    "scores = [precision, recall, f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a dot plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(metrics, scores, marker='o', linestyle='--', color='b')\n",
    "plt.title('Model Evaluation Metrics')\n",
    "plt.xlabel('Metrics')\n",
    "plt.ylabel('Score')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Missing Data:\n",
    "\n",
    "Explanation: This section involves checking the dataset for any missing values to ensure data integrity and quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values:  V1        0\n",
      "V2        0\n",
      "V3        0\n",
      "V4        0\n",
      "V5        0\n",
      "V6        0\n",
      "V7        0\n",
      "V8        0\n",
      "V9        0\n",
      "V10       0\n",
      "V11       0\n",
      "V12       0\n",
      "V13       0\n",
      "V14       0\n",
      "V15       0\n",
      "V16       0\n",
      "V17       0\n",
      "V18       0\n",
      "V19       0\n",
      "V20       0\n",
      "V21       0\n",
      "V22       0\n",
      "V23       0\n",
      "V24       0\n",
      "V25       0\n",
      "V26       0\n",
      "V27       0\n",
      "V28       0\n",
      "Amount    0\n",
      "Class     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing data\n",
    "missing_values = data.isnull().sum()\n",
    "print(\"Missing values: \", missing_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "\n",
    "# Visualizing missing data using a heatmap\n",
    "msno.matrix(data)\n",
    "plt.title('Missing Data Visualization - Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Interpretation:\n",
    "\n",
    "Explanation: Finally, this section demonstrates the use of SHAP (SHapley Additive exPlanations) values for interpreting the model's predictions and understanding the impact of various features on the model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use SHAP values for model interpretation\n",
    "explainer = shap.Explainer(model)\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "shap.summary_plot(shap_values, X_test, plot_type=\"bar\")\n"
   ]
  }
 ],
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