heart_attack_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the dataset:\n",
      "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
      "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
      "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
      "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
      "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
      "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
      "\n",
      "   caa  thall  output  \n",
      "0    0      1       1  \n",
      "1    0      2       1  \n",
      "2    0      2       1  \n",
      "3    0      2       1  \n",
      "4    0      2       1  \n",
      "\n",
      "Dataset information:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trtbps    303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "# Assuming your CSV file is named 'heart_attack_data.csv' and is in the same directory\n",
    "df = pd.read_csv('data/heart.csv')\n",
    "\n",
    "# Display first few rows and basic information about the dataset\n",
    "print(\"First few rows of the dataset:\")\n",
    "print(df.head())\n",
    "print(\"\\nDataset information:\")\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Separate features (X) and target variable (y)\n",
    "# Replace 'target' with your actual target column name\n",
    "X = df.drop('output', axis=1)  \n",
    "y = df['output']\n",
    "\n",
    "# Split the data into training and testing sets -- random_state is the seed\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Accuracy: 0.8524590163934426\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.86      0.85        29\n",
      "           1       0.87      0.84      0.86        32\n",
      "\n",
      "    accuracy                           0.85        61\n",
      "   macro avg       0.85      0.85      0.85        61\n",
      "weighted avg       0.85      0.85      0.85        61\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[25  4]\n",
      " [ 5 27]]\n"
     ]
    }
   ],
   "source": [
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Initialize and train the logistic regression model\n",
    "model = LogisticRegression(random_state=42)\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"\\nModel Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), \n",
    "            annot=True, \n",
    "            fmt='d', \n",
    "            cmap='Blues',\n",
    "            xticklabels=['No Heart Disease', 'Heart Disease'],\n",
    "            yticklabels=['No Heart Disease', 'Heart Disease'])  # Removed xticklabels_position\n",
    "plt.title('Confusion Matrix Heatmap')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "# Move x-axis labels to the top\n",
    "plt.gca().xaxis.set_label_position('top')  # Move x-axis label to the top\n",
    "plt.gca().xaxis.tick_top()  # Move the x-axis ticks to the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Feature importance analysis\n",
    "feature_importance = pd.DataFrame({\n",
    "    'Feature': X.columns,\n",
    "    'Importance': abs(model.coef_[0])\n",
    "})\n",
    "feature_importance = feature_importance.sort_values('Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=feature_importance, x='Importance', y='Feature')\n",
    "plt.title('Feature Importance in Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Key Metrics Summary:\n",
      "Overall Accuracy: 0.852\n",
      "Precision (Heart Disease): 0.871\n",
      "Recall (Heart Disease): 0.844\n",
      "F1-Score (Heart Disease): 0.857\n"
     ]
    }
   ],
   "source": [
    "# 3. Calculate and display key metrics\n",
    "precision = classification_report(y_test, y_pred, output_dict=True)\n",
    "print(\"\\nKey Metrics Summary:\")\n",
    "print(f\"Overall Accuracy: {accuracy_score(y_test, y_pred):.3f}\")\n",
    "print(f\"Precision (Heart Disease): {precision['1']['precision']:.3f}\")\n",
    "print(f\"Recall (Heart Disease): {precision['1']['recall']:.3f}\")\n",
    "print(f\"F1-Score (Heart Disease): {precision['1']['f1-score']:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TBg0aoGbNmti9ezeGDRsG4PUwB1tbW80v8djYWLx48QLr1q3DunXrcnWMypUrvzXzG2/+UUtKStL62vXfciqOq1WrlmXd6tWrw9/fH4Bur/PbcqempmL+/PnYvHkzHj58qDW92n8LPF39t8h5U7zEx8cDgGZO1qpVq2qtZ2homONX8P9maWkJ4J/XUB+53uzz/PnzmDlzJoKDg/Hy5Uut9RMSEmBlZaV5nNP7ITf7uHv3LgCgbt26Oj2HN3T9fOT2vbtw4UIMGTIESqUSLi4u6Nq1KwYPHgxnZ2edM775QyevzxFAjtP+OTk5Yf369VCr1bh79y7mzp2L2NjYLH9EWFhYvLMY/e9n0dLSUpNd16zvKujz+t7NrezOs6GhIXr16oWdO3ciPT0dCoUCAQEBePXqlVbh+z6/c4kAFr5UzDRr1gxNmjQB8LpXsk2bNujfvz8iIiJQqlQpzfyZkyZNyrYXDMha6LyNWq1GvXr18MMPP2S7XKlUvnV7T09PzJ07F3FxcbCwsEBgYCD69eun6WF8k3fgwIFZxgK/Ub9+fa3HuentBV6PgT1w4ACuXr2Ktm3bZrvO1atXASBXvXD/lpfXObvc48aNw+bNm/HVV1+hZcuWsLKygkwmQ9++fXOcCzW3cprKKqciRlc1a9YEAISHh6Nhw4a53u5due7evYuOHTuiZs2a+OGHH6BUKmFsbIyjR49i6dKlWV6X7F5XXfeRV7p+PnL73u3Tpw9cXV2xf/9+nDhxAosWLcL333+PgIAAfPjhh++dO7fKlCkD4J8/lv7L3Nxca2x869at0bhxY0ydOhUrVqzQtNeqVQthYWG4f/9+lj983vjvZ7FmzZq4fPkyoqOj3/l75t/i4+Oz/cP133R97+ZUSKtUqmzbczrPffv2xdq1a3Hs2DF4eHjA398fNWvWRIMGDTTrvO/vXCIWvlRsyeVyzJ8/H+3bt8ePP/4IHx8fTY+QkZGR1j9I2alSpQquXbv2znWuXLmCjh075uqr3//y9PTE7NmzsW/fPpQrVw6JiYmaizgAoGzZsrCwsIBKpXpnXl19/PHHmD9/PrZu3Zpt4atSqbBz507Y2NigdevWWsv+97//ZVn/9u3bmp5QXV7nt9m7dy+GDBmCJUuWaNrS0tLw4sULrfXy8tq/y5ubEdy5cwft27fXtGdmZiIqKirLHxz/9eGHH0Iul2P79u16vUjo0KFDSE9PR2BgoFaRpMtXvLndR5UqVQAA165de+sfhDm9/u/7+XgbBwcHfPHFF/jiiy/w9OlTNG7cGHPnztUUvrk93pv36rs+69l5UyDeu3cvV+vXr18fAwcOxNq1azFp0iTNa//xxx/j559/xtatWzF9+vQs2yUmJuLgwYOoWbOm5jx069YNP//8M7Zv344pU6bk6viZmZmIjo5G9+7d37qeru9dGxubLJ9JADrfya5t27ZwcHDA7t270aZNG/z666+YNm2a1jr5+Z6ikoFjfKlYa9euHZo1a4Zly5YhLS0NdnZ2aNeuHdauXYvHjx9nWT82Nlbz/7169cKVK1ewf//+LOu96X3r06cPHj58iPXr12dZJzU1VTM7QU5q1aqFevXqYffu3di9ezccHBy0ilC5XI5evXph37592f7D/O+8umrVqhU6deqEzZs3Z3tnqGnTpuH27dv45ptvsvTQHDhwQGuM7qVLl3Dx4kVN0aHL6/w2crk8Sw/sypUrs/QkmZubA0C2//jmVZMmTVCmTBmsX78emZmZmvYdO3bk2MP3b0qlEiNGjMCJEyewcuXKLMvVajWWLFmCBw8e6JTrTY/wf4d9bN68We/76Ny5MywsLDB//nykpaVpLfv3tubm5tkOPXnfz0d2VCpVlmPZ2dmhfPnySE9Pf2em/ypbtizatm2LTZs24f79+1rL3tX77+joCKVSqdNdzL755hu8evVKq8fy008/Re3atbFgwYIs+1Kr1Rg9ejTi4+Mxc+ZMrW3q1auHuXPnIjg4OMtxkpKSshSNN27cQFpaGlq1avXWjLq+d6tUqYKEhARNrzQAPH78ONvfnW9jYGCATz/9FIcOHcK2bduQmZmpNcwByJ/3FJUs7PGlYu/rr79G79694efnh1GjRmHVqlVo06YN6tWrhxEjRsDZ2RlPnjxBcHAwHjx4oLml59dff625I9hnn30GFxcXPH/+HIGBgVizZg0aNGiAQYMGwd/fH6NGjcLp06fRunVrqFQq3Lp1C/7+/ggKCtIMvciJp6cnfH19YWJigmHDhsHAQPvv0QULFuD06dNo3rw5RowYgdq1a+P58+cIDQ3FyZMn8fz58zy/Nlu3bkXHjh3Ro0cP9O/fH66urkhPT0dAQADOnDkDT09PfP3111m2q1q1Ktq0aYPRo0cjPT0dy5YtQ5kyZfDNN99o1snt6/w2H3/8MbZt2wYrKyvUrl0bwcHBOHnypOYr5jcaNmwIuVyO77//HgkJCVAoFOjQoQPs7Ozy/NoYGxtj1qxZGDduHDp06IA+ffogKioKfn5+qFKlSq56m5YsWYK7d+9i/PjxCAgIwMcffwwbGxvcv38fe/bswa1bt7R6+HOjc+fOMDY2Rrdu3TBy5EgkJydj/fr1sLOzy/aPjPfZh6WlJZYuXYrhw4ejadOm6N+/P2xsbHDlyhW8fPkSW7ZsAQC4uLhg9+7dmDBhApo2bYpSpUqhW7duevl8/FdSUhIqVKiATz/9VHOb3pMnT+LPP//U+mYgp0zZWbFiBdq0aYPGjRvj888/R+XKlREVFYUjR44gLCzsrXl69OiB/fv352rsLPB6qELXrl2xYcMGzJgxA2XKlIGxsTH27t2Ljh07ok2bNlp3btu5cydCQ0MxceJErfeKkZERAgIC0KlTJ7Rt2xZ9+vRB69atYWRkhOvXr2u+rfn3dGy//PILzMzM8MEHH7wzpy7v3b59+2Ly5Mno2bMnxo8fj5cvX2L16tWoXr26zhehenp6YuXKlZg5cybq1auXZVrC/HhPUQlT8BNJEOlfTjewEOL1nYGqVKkiqlSpopku6+7du2Lw4MHC3t5eGBkZCUdHR/Hxxx+LvXv3am377NkzMXbsWOHo6KiZKH3IkCFaU4tlZGSI77//XtSpU0coFAphY2MjXFxcxOzZs0VCQoJmvf9OZ/bG//73P80k++fOncv2+T158kSMGTNGKJVKYWRkJOzt7UXHjh3FunXrNOu8maZrz549Or12SUlJYtasWaJOnTrC1NRUWFhYiNatWws/P78s0zn9+wYWS5YsEUqlUigUCuHq6iquXLmSZd+5eZ3fdu7i4+PF0KFDha2trShVqpRwd3cXt27dyva1XL9+vXB2dhZyuTxXN7D47+uU040NVqxYISpVqiQUCoVo1qyZOH/+vHBxcRFdunTJxav7+i5XGzZsEK6ursLKykoYGRmJSpUqiaFDh2pNF5XTndvevD7/vmlHYGCgqF+/vjAxMRFOTk7i+++/F5s2bcqy3psbWGQnt/t4s26rVq2EqampsLS0FM2aNRM///yzZnlycrLo37+/sLa2znIDi9x+PvD/NzbIDv41nVl6err4+uuvRYMGDYSFhYUwNzcXDRo0yHLzjZwy5XSer127Jnr27Cmsra2FiYmJqFGjhpgxY0a2ef4tNDRUAMgyvVZON7AQQogzZ85kmaJNCCGePn0qJkyYIKpWrSoUCoWwtrYWnTp10kxhlp34+Hjh6+sr6tWrJ8zMzISJiYmoW7eumDJlinj8+LHWus2bNxcDBw5853N6I7fvXSGEOHHihKhbt64wNjYWNWrUENu3b3/rDSxyolarhVKpFADEd999l+06uX1PEWVHJoSeruQgomIvKioKlStXxqJFizBp0iSp40hCrVajbNmy+OSTT7L9upVKno4dO6J8+fLYtm2b1FFyFBYWhsaNGyM0NFSniy2JihuO8SUiykFaWlqWcZ5bt27F8+fP0a5dO2lCUaEzb9487N69W+eLuQrSggUL8Omnn7LopRKPY3yJiHLwxx9/wNvbG71790aZMmUQGhqKjRs3om7duujdu7fU8aiQaN68OTIyMqSO8Va7du2SOgJRocDCl4goB05OTlAqlVixYgWeP3+O0qVLY/DgwViwYIHWXd+IiKho4BhfIiIiIioROMaXiIiIiEoEFr5EREREVCKUuDG+arUajx49goWFBW93SERERFQICSGQlJSE8uXLZ7mx0/socYXvo0ePoFQqpY5BRERERO8QHR2NChUq6G1/Ja7wtbCwAPD6hbS0tJQ4DRERERH9V2JiIpRKpaZu05cSV/i+Gd5gaWnJwpeIiIioENP3sFRe3EZEREREJQILXyIiIiIqEVj4EhEREVGJwMKXiIiIiEoEFr5EREREVCKw8CUiIiKiEoGFLxERERGVCCx8iYiIiKhEYOFLRERERCUCC18iIiIiKhFY+BIRERFRicDCl4iIiIhKBBa+RERERFQisPAlIiIiohKBhS8RERERlQiSFr6//fYbunXrhvLly0Mmk+HAgQPv3ObMmTNo3LgxFAoFqlatCj8/v3zPSURERERFn6SFb0pKCho0aIBVq1blav179+7ho48+Qvv27REWFoavvvoKw4cPR1BQUD4nJSIiIqKizlDKg3/44Yf48MMPc73+mjVrULlyZSxZsgQAUKtWLZw7dw5Lly6Fu7t7fsUkIiIiogKiVgtcv/40X/YtaeGrq+DgYHTq1Emrzd3dHV999VWO26SnpyM9PV3zODExMb/iEVFJFbEHuOALZCRJnYSIqEh7nGCKoVvccPZ26XzZf5EqfGNiYlCuXDmttnLlyiExMRGpqakwNTXNss38+fMxe/bsgopIRCXRBV/g+S2pUxARFWkHr9XA8D3dEZdiDiAtX45RpArfvJgyZQomTJigeZyYmAilUilhIiIqdt709MoMAHMHabMQERUxKemGmLi3Bdb+XkvTZmeRiqf58CVakSp87e3t8eTJE622J0+ewNLSMtveXgBQKBRQKBQFEY+ISjpzB2DkA6lTEBEVGSEhjzBgQAAiIp5p2jw8auKHH9zg7Lxc78crUvP4tmzZEqdOndJq++WXX9CyZUuJEhERERGRrlQqNb7//hxatNioKXrNzIywbt3HCAjogzJlzPLluJIWvsnJyQgLC0NYWBiA19OVhYWF4f79+wBeD1MYPHiwZv1Ro0YhMjIS33zzDW7duoWffvoJ/v7+8Pb2liI+EREREeVBWlomNmy4jMxMNQDAxcUBly+PxIgRLpDJZPl2XEkL37/++guNGjVCo0aNAAATJkxAo0aN4OvrCwB4/PixpggGgMqVK+PIkSP45Zdf0KBBAyxZsgQbNmzgVGZERERERYi5uTF27vwExsZyTJnSBhcuDEP16mXy/bgyIYTI96MUIomJibCyskJCQgIsLS2ljkNExcHaCkDyQ6CUI8f4EhFlIykpHYmJ6XB01K69Hj5MzNIG5F+9VqTG+BIRERFR0RIcHI2GDdeiT5+9mqENb2RX9OYnFr5EREREpHeZmWrMnn0Grq6bERkZjwsXovH99+ckzVSkpjMjIiIiosIvMjIeAwcGIDj4n+FfrVop0b9/PQlTsfAlIiIiIj0RQmDbtqsYO/YokpIyAAByuQwzZ7phyhRXGBpKO9iAhS8RERERvbf4+FSMGnUE/v7XNW3OzjbYseMTtGhRQcJk/2DhS0SFU8Qe4ILvP7cDLsxSHkudgIhIUomJ6WjYcC3u30/QtHl5NcSKFV1gYVF47qDLwpeICqcLvsDzW1Kn0I2xhdQJiIgkYWmpQM+eNbF8+UXY2Jhg7dqP0bt3HaljZcHCl4gKpzc9vTIDwNxB2iy5YWwBtJ4jdQoiIsksWNAJaWmZmDbNFUqlldRxssXCl4gKN3MH3hSCiKgQEUJg/fpQyOUyDBvWWNNuYmKINWs+ljDZu7HwJSIiIqJciY1NwYgRh3DwYARMTQ3RqpUStWqVlTpWrvEGFkRERET0TidO3EX9+mtw8GAEACA1NROHD9+WOJVu2ONLRERERDlKS8vElCknsWzZRU2bra0ZNm3qjm7dakiYTHcsfImIiIgoW+HhTzBgQADCw59q2rp0qYrNm3vA3r6UhMnyhoUvEREREWkRQmDlykv45ptfkJ6uAgAoFHIsWvQBxo5tBplMJnHCvGHhS0RERERakpMzsGRJsKborV+/HHbs+AR169pJnOz98OI2IiIiItJiYaHA9u09IZfL4O3dAhcvDi/yRS/AHl8iIiKiEi8lJQMpKa9gZ2euaXN1rYTbt8fB2dlGwmT6xR5fIiIiohIsJOQRXFzWoV+/fVCrhday4lT0Aix8iYiIiEoklUqN778/hxYtNiIi4hl+/fUeli4NljpWvuJQByJ9iNgDXPAFMpKkTlJ8pDyWOgERUbEVHZ2AwYMP4MyZKE2bi4tDkZuXV1csfIn04YIv8PyW1CmKJ2MLqRMQERUr/v7XMXLkYbx4kQYAkMkAH582mDWrHYyN5RKny18sfIn04U1Pr8wAMHeQNktxYmwBtJ4jdQoiomIhMTEd48cfw5YtVzRtSqUltm3rCTc3J+mCFSAWvkT6ZO4AjHwgdQoiIiItCQlpaNx4HSIj4zVtnp51sHr1R7CxMZUwWcHixW1ERERExZyVlQk6dHACAFhYGGPrVg/8/HOvElX0AuzxJSIiIioRli7tgtTUTHz7bftiN01ZbrHwJSIiIipGhBDYtu0qjIwM0K9fPU17qVLG2L79EwmTSY+FLxEREVExER+filGjjsDf/zpKlTJGs2aOqFKltNSxCg2O8SUiIiIqBs6ciUL9+mvg738dAJCcnIG9e29InKpwYY8vERERURGWkaGCr+9pLFx4HuL/7zhsbW2Cdes+Ru/edaQNV8iw8CUiIiIqoiIi4tC/fwBCQ/+522W7dk7YutUDSqWVhMkKJxa+REREREWMEALr1oXA2zsIqamZAAAjIwPMndsBEye2goGBTOKEhRMLXyIiIqIiJiEhHbNmndUUvTVqlMHOnb3QuDHvHvo2vLiNiIiIqIixtjaBn18PAMCoUS4IDR3JojcX2ONL+hGxB7jgC2QkSZ1EGimP370OERFRHqWlZeLly1coXfqfO625u1fFtWujUaeOnYTJihYWvqQfF3yB57ekTiE9YwupExARUTETHv4E/fsHoFIlKxw61A8y2T/jd1n06oaFL+nHm55emQFgXkK/ajG2AFrPkToFEREVE2q1wMqVFzF58kmkp6tw7dpTrFnzF0aPbip1tCKLhS/pl7kDMPKB1CmIiIiKtMePkzB06EEEBd3VtNWvXw6urpUkTFX0sfAlIiIiKkQOHryF4cMPIS7upabN27sF5s3rCBMTlm7vg68eERERUSGQkpKBiRNPYO3aEE2bg0MpbNnigQ8+qCJhsuKDhS8RERGRxOLjU9Gy5UZERDzTtHl41MT69d1ga2smYbLihfP4EhEREUnMxsYULi7lAQBmZkZYv74bAgL6sOjVM/b4EhERERUCq1Z1RWrqKyxY0AnVq5eROk6xxMKXiIiIqID5+1+HQiFHjx41NW3W1iYICPCUMFXxx8KXiIiIqIAkJqZj/Phj2LLlCmxsTHD1anlUqGApdawSg2N8iYiIiApAcHA0GjZcgy1brgAA4uPTsH37VYlTlSzs8SUiIiLKR5mZanz33W/47rvfoFIJAICFhTFWreqKgQPrS5yuZGHhS0RERJRPIiPjMXBgAIKD/7mraatWSmzf3hOVK9tImKxkYuFLREREpGdCCGzdegVjxx5DcnIGAEAul8HX1w1Tp7rC0JCjTaXAwpeIiIhIz+Lj0zBx4glN0evsbIMdOz5BixYVJE5WsvHPDSIiIiI9K13aFBs2dAcAeHk1RFjYSBa9hQB7fImIiIjeU0aGCunpmbCwUGjaPDxq4q+/RmjuyEbSY48vERER0XuIiIhDy5YbMXz4IQghtJax6C1cWPgSERER5YEQAmvX/oVGjdYiNPQx/P2vY9s2zstbmHGoAxEREZGOYmNTMHz4IQQGRmjaatQog7p17SRMRe/CwpeIiIhIB0FBd+DldRAxMcmatlGjXLBkiTvMzIwkTEbvwsKXiIiIKBfS0jIxZcpJLFt2UdNma2uGTZu6o1u3GhImo9xi4UtERET0Ds+fp6JdOz+Ehz/VtHXpUhWbN/eAvX0pCZORLnhxGxEREdE72NiYwNn59S2GFQo5VqzogqNH+7PoLWLY40tERET0DjKZDBs2dEdqagCWLOnMi9iKKBa+RERERP8RGBgBhUIOd/eqmjZbWzMEBQ2UMBW9Lw51ICIiIvp/KSkZGDXqMHr02IXBgw/g6dMUqSORHrHwJSIiIgIQEvIIjRuvw9q1IQCAp09TsGnTZYlTkT5xqAMRERGVaCqVGosXX8D06aeRmakGAJiZGWHZMncMH95Y4nSkTyx8iYiIqMSKjk7AoEH7cfbs35o2FxcH7NzZC9Wrl5EwGeUHFr5ERERUIvn7X8fIkYfx4kUaAEAmA3x82mDWrHYwNpZLnI7yAwtfIiIiKnHi4l5ixIhDSExMBwAolZbYtq0n3NycpA1G+YoXtxEREVGJY2trhtWrPwIAeHrWwZUro1j0lgDs8SUiIqJiLzNTjYwMFczMjDRt/fvXQ4UKlnB1rQiZTCZhOioo7PElIiKiYi0yMh5t227G2LFHsyxr27YSi94ShIUvERERFUtCCGzdegUNGqxBcPADbN4chj17rksdiyTEoQ5ERERU7MTHp2LUqCPw9/+n0HV2toFSaSVhKpIaC18iIiIqVs6cicKgQfvx4EGips3LqyFWrOgCCwuFhMlIaix8iYiIqFjIyFDB1/c0Fi48DyFet9nYmGDt2o/Ru3cdacNRocDCl4iIiIq8Z89eonPn7QgNfaxpa9/eCVu39kSFCpYSJqPChBe3ERERUZFnY2MKW1szAICRkQEWLuyEkycHs+glLSx8iYiIqMgzMJDBz68H2rSpiD/+GI6vv24NAwNOU0baONSBiIiIipwTJ+7CxMQQbdtW0rQ5OFjg99+HSpiKCjvJe3xXrVoFJycnmJiYoHnz5rh06dJb11+2bBlq1KgBU1NTKJVKeHt7Iy0trYDSEhERkZTS0jLh7X0c7u7bMWBAAOLjU6WOREWIpIXv7t27MWHCBMycOROhoaFo0KAB3N3d8fTp02zX37lzJ3x8fDBz5kzcvHkTGzduxO7duzF16tQCTk5EREQFLTz8CZo1W49lyy4CAB48SMS6dSESp6KiRNLC94cffsCIESMwdOhQ1K5dG2vWrIGZmRk2bdqU7foXLlxA69at0b9/fzg5OaFz587o16/fO3uJiYiIqOhSqwWWL/8DTZuuR3j4684xhUKOFSu64JtvWkucjooSyQrfjIwMhISEoFOnTv+EMTBAp06dEBwcnO02rVq1QkhIiKbQjYyMxNGjR9G1a9ccj5Oeno7ExEStHyIiIioaHj9OQteuO/DVV0FIT1cBAOrVs8Nff32OceOaQybjBWyUe5Jd3BYXFweVSoVy5cpptZcrVw63bt3Kdpv+/fsjLi4Obdq0gRACmZmZGDVq1FuHOsyfPx+zZ8/Wa3YiIiLKfwcP3sLw4YcQF/dS0+bt3QLz5nWEiQmvzyfdSX5xmy7OnDmDefPm4aeffkJoaCgCAgJw5MgRzJkzJ8dtpkyZgoSEBM1PdHR0ASYmIiKivIiNTcGAAQGaotfBoRSCggbihx/cWfRSnkn2zrG1tYVcLseTJ0+02p88eQJ7e/tst5kxYwYGDRqE4cOHAwDq1auHlJQUfP7555g2bRoMDLLW8QqFAgoF78tNRERUlJQta45ly7pgxIhD6NGjBjZs6K65QQVRXknW42tsbAwXFxecOnVK06ZWq3Hq1Cm0bNky221evnyZpbiVy+UAAPHmptxERERU5KhUaqSnZ2q1DRvWCMeODcD+/Z4sekkvJB3qMGHCBKxfvx5btmzBzZs3MXr0aKSkpGDo0NeTTw8ePBhTpkzRrN+tWzesXr0au3btwr179/DLL79gxowZ6Natm6YAJiIioqIlOjoBnTptw6RJJ7TaZTIZunSpygvYSG8kHSTj6emJ2NhY+Pr6IiYmBg0bNsTx48c1F7zdv39fq4d3+vTpkMlkmD59Oh4+fIiyZcuiW7dumDt3rlRPgYiIiN6Dv/91jBx5GC9epOHMmSh8+GE1dO1aTepYVEzJRAkbI5CYmAgrKyskJCTA0tJS6jjFx9oKQPJDoJQjMPKB1GmIiKiQS0xMx/jxx7BlyxVNm1JpiR07PoGra6W3bEklQX7Va7wskoiIiApUcHA0Bg7cj8jIeE2bp2cdrF79EWxsTCVMRsUdC18iIiIqEJmZasyd+xvmzPkNKtXrL5wtLIyxalVXDBxYn2N5Kd+x8CUiIqJ89+zZS3Tr9jOCg/8ZDteqlRLbt/dE5co2EiajkqRI3cCCiIiIiiZraxMYGr4uO+RyGWbPboezZ71Y9FKBYuFLRERE+U4uN8C2bT3RuLEDzp37DL6+bppCmKigcKgDERER6d3Zs1EwNTVCs2aOmrZKlazx118jOJaXJMM/tYiIiEhvMjJUmDLlJNq334J+/fYhKSldazmLXpISC18iIiLSi4iIOLRsuRELFpyHEEBkZDxWr/5L6lhEGhzqQERERO9FCIH160Px1VfHkZqaCQAwMjLA3LkdMHFiK4nTEf2DhS8RERHlWWxsCkaMOISDByM0bTVqlMHOnb3QuLGDhMmIsmLhS0RERHkSFHQHXl4HEROTrGkbNcoFS5a4w8zMSMJkRNlj4UtEREQ6e/IkGR4eu5GW9npog62tGTZt6o5u3WpInIwoZ7y4jYiIiHRWrlwpLFjQEQDg7l4F4eGjWfRSocceXyIiInontVpApVLDyEiuaRs3rjkqVLBEz561YGDAacqo8GPhW5hF7AEu+AIZSVInebeUx1InICKifPL4cRK8vA6iYcNy+P77DzTtBgYy9OpVW8JkRLph4VuYXfAFnt+SOoVujC2kTkBERHp08OAtDBsWiGfPUvHLL3fh7l4VHTpUljoWUZ6w8C3M3vT0ygwA8yIwJYyxBdB6jtQpiIhID1JSMjBx4gmsXRuiaStXrpSEiYjeHwvfosDcARj5QOoURERUQoSEPEL//gG4ffuZpq1HjxrYsKE7bG3NJExG9H5Y+BIREREAQKVSY/HiC5g+/TQyM9UAADMzIyxb5o7hwxtDJuMFbFS0sfAlIiIixMW9RO/ee3DmTJSmzcXFATt39kL16mWkC0akR5zHl4iIiGBlpUBycgYAQCYDpkxpgwsXhrHopWKFhS8RERHByEiOHTs+Qa1atjh9egjmzesIY2P5uzckKkI41IGIiKgECg6OhpmZERo0sNe0Va9eBteufcGbUVCxxR5fIiKiEiQzU43Zs8/A1XUz+vXbh5cvX2ktZ9FLxRkLXyIiohIiMjIebdtuxqxZZ6FSCdy8GYeffvpT6lhEBYZDHYiIiIo5IQS2bbuKsWOPIinp9QVscrkMM2e64auvWkicjqjgsPAlIiIqxuLjUzFq1BH4+1/XtFWpYoPt2z9BixYVJExGVPBY+BIRERVTZ85EYdCg/XjwIFHTNnRoQyxf3gUWFgoJkxFJg4UvERFRMfT4cRLc3bcjI0MFALCxMcHatR+jd+86Eicjkg4vbiMiIiqGHBwsMHOmGwCgfXsnXL06mkUvlXjs8SUiIioGhBBQqwXk8n/6tCZPbg2l0hIDBtTnNGVEYI8vERFRkRcbm4KePXfju+9+02qXyw0waFADFr1E/489vkREREVYUNAdeHkdRExMMg4fvo3OnaugZUul1LGICiUWvkREREVQWlompkw5iWXLLmrabGxMNfP0ElFWLHyJiIiKmPDwJxgwIADh4U81be7uVeDn5wF7+1ISJiMq3Fj4EhERFRFqtcDKlRcxefJJpKe/nqZMoZBj4cIPMHZsM47lJXoHFr5ERERFwLNnLzFgQACCgu5q2urVs8POnb1Qt66dhMmIig7O6kBERFQEmJsb4+HDJM1jb+8WuHRpBIteIh2w8CUiIioCTEwMsXPnJ6hc2RpBQQPxww/uMDHhF7dEuuAnhoiIqBAKCXkEc3Nj1Kxpq2mrV68cbt8eB0ND9lsR5QU/OURERIWISqXG99+fQ4sWG9Gv3z6kp2dqLWfRS5R3/PQQEREVEtHRCejYcSt8fE4hM1ONsLAY/PTTn1LHIio2ONSBiIioEPD3v46RIw/jxYs0AIBMBvj4tMGYMc0kTkZUfLDwJSIiklBiYjrGjz+GLVuuaNqUSkts29YTbm5O0gUjKoZY+BIREUkkODgaAwfuR2RkvKbN07MOVq/+CDY2phImIyqeWPgSERFJ4OHDRLRrtwUZGa/vwGZhYYxVq7pi4MD6kMl4Bzai/MCL24iIiCTg6GiJSZNaAgBatVLiypVRGDSoAYteonzEHl8iIqICIIQAAK3CdtasdqhY0QrDhjXmNGVEBYCfMiIionwWH5+Kvn33YcmSYK12IyM5Ro5swqKXqICwx5eIiCgfnTkThUGD9uPBg0Ts338THTtWRqNGDlLHIiqR+CcmERFRPsjIUMHH5yQ6dNiCBw8SAQClShkjJiZZ4mREJRd7fImIiPQsIiIO/fsHIDT0saatfXsnbN3aExUqWEqYjKhkY+FLRESkJ0IIrFsXAm/vIKSmZgIAjIwMMHduB0yc2AoGBpyxgUhK71X4pqWlwcTERF9ZiIiIiqznz1MxdOhBBAZGaNpq1CiDnTt7oXFjjuklKgx0HuOrVqsxZ84cODo6olSpUoiMjAQAzJgxAxs3btR7QCIioqJAoZDj1q04zePRo5sgNHQki16iQkTnwve7776Dn58fFi5cCGNjY0173bp1sWHDBr2GIyIiKirMzY2xY8cnKF/eAoGBffHTTx/BzMxI6lhE9C86F75bt27FunXrMGDAAMjlck17gwYNcOvWLb2GIyIiKqzCw58gMjJeq61Jk/KIjByPbt1qSJSKiN5G58L34cOHqFq1apZ2tVqNV69e6SUUERFRYaVWCyxf/geaNl2PAQMCkJmp1lquUPC6caLCSufCt3bt2vj999+ztO/duxeNGjXSSygiIqLC6PHjJHz44Q589VUQ0tNV+OOPB1i9+k+pYxFRLun8Z6mvry+GDBmChw8fQq1WIyAgABEREdi6dSsOHz6cHxmJiIgkd/DgLQwbFohnz1I1bd7eLTBihIuEqYhIFzr3+Pbo0QOHDh3CyZMnYW5uDl9fX9y8eROHDh3CBx98kB8ZiYiIJJOSkoFRow7Dw2O3puh1cCiFoKCB+OEHd5iYcGgDUVGRp0+rq6srfvnlF31nISIiKlRCQh6hf/8A3L79TNPm4VET69d3g62tmYTJiCgvdO7xdXZ2xrNnz7K0v3jxAs7OznoJRUREJLXo6AS0arVJU/SamRlh/fpuCAjow6KXqIjSufCNioqCSqXK0p6eno6HDx/qJRQREZHUlEorfPFFEwCAi4sDLl8eieHDG0Mm422HiYqqXA91CAwM1Px/UFAQrKysNI9VKhVOnToFJycnvYYjIiIqSEIIrcJ2/vxOqFjRCmPGNIOxsfwtWxJRUZDrwtfDwwMAIJPJMGTIEK1lRkZGcHJywpIlS/QajoiIqCAkJqZj/PhjaNbMEV980VTTbmJiCG/vlhImIyJ9ynXhq1a/nqC7cuXK+PPPP2Fra5tvoYiIiApKcHA0BgwIwL17L7B793W0b++EWrXKSh2LiPKBzmN87927x6KXiIiKvMxMNWbNOgNX1824d+8FAMDIyAB378a/fUMiKrLyNJ1ZSkoKzp49i/v37yMjI0Nr2fjx4/USjIiIKL9ERsZj4MAABAc/0LS1aqXE9u09UbmyjYTJiCg/6Vz4Xr58GV27dsXLly+RkpKC0qVLIy4uDmZmZrCzs2PhS0REhZYQAlu3XsHYsceQnPy640Yul8HX1w1Tp7rC0FDnL0KJqAjR+RPu7e2Nbt26IT4+Hqampvjjjz/w999/w8XFBYsXL86PjERERO/txYs09O27D15eBzVFr7OzDc6d+wy+vm4seolKAJ0/5WFhYZg4cSIMDAwgl8uRnp4OpVKJhQsXYurUqfmRkYiI6L3JZMDFi/8MbfDyaoiwsJFo0aKChKmIqCDpXPgaGRnBwOD1ZnZ2drh//z4AwMrKCtHR0fpNR0REpCdWVibYtq0nbG3N4O//KTZv7gELC4XUsYioAOk8xrdRo0b4888/Ua1aNbi5ucHX1xdxcXHYtm0b6tatmx8ZiYiIdBYREQdzc2NUqGCpaXN1rYSoqC9hbm4sYTIikorOPb7z5s2Dg4MDAGDu3LmwsbHB6NGjERsbi7Vr1+o9IBERkS6EEFi79i80arQWgwfvh1ottJaz6CUquXTu8W3SpInm/+3s7HD8+HG9BiIiIsqr2NgUDB9+CIGBEQCA06ejsG5dCEaNavKOLYmoJNDbJayhoaH4+OOP9bU7IiIinQQF3UH9+ms0RS8AjBrlgsGDG0iYiogKE50K36CgIEyaNAlTp05FZGQkAODWrVvw8PBA06ZNNbc11sWqVavg5OQEExMTNG/eHJcuXXrr+i9evMCYMWPg4OAAhUKB6tWr4+jRozofl4iIioe0tEx4ex9Hly47EBOTDACwtTVDYGBfrF79MczMjCROSESFRa6HOmzcuBEjRoxA6dKlER8fjw0bNuCHH37AuHHj4OnpiWvXrqFWrVo6HXz37t2YMGEC1qxZg+bNm2PZsmVwd3dHREQE7OzssqyfkZGBDz74AHZ2dti7dy8cHR3x999/w9raWqfjEhFR8RAe/gQDBgQgPPypps3dvQr8/Dxgb19KwmREVBjJhBDi3asB9evXx6BBg/D1119j37596N27N1q0aAF/f39UqJC3ORCbN2+Opk2b4scffwQAqNVqKJVKjBs3Dj4+PlnWX7NmDRYtWoRbt27ByChvf8EnJibCysoKCQkJsLS0fPcGUlpbAUh+CJRyBEY+ePf6REQlyN9/v0CNGj8iPV0FAFAo5Fi48AOMHdsMBgYyidMR0fvIr3ot10Md7t69i969ewMAPvnkExgaGmLRokV5LnozMjIQEhKCTp06/RPGwACdOnVCcHBwttsEBgaiZcuWGDNmDMqVK4e6deti3rx5UKlUOR4nPT0diYmJWj9ERFT0VapkrRm/W6+eHf7663OMH9+cRS8R5SjXQx1SU1NhZmYGAJDJZFAoFJppzfIiLi4OKpUK5cqV02ovV64cbt26le02kZGR+PXXXzFgwAAcPXoUd+7cwRdffIFXr15h5syZ2W4zf/58zJ49O885iYio8Fq61B2VKllh4sRWMDHReaIiIiphdPotsWHDBpQq9XrMVGZmJvz8/GBra6u1zvjx4/WX7j/UajXs7Oywbt06yOVyuLi44OHDh1i0aFGOhe+UKVMwYcIEzePExEQolcp8y0hERPqXkpKBiRNPoEWLCvDyaqhpNzc3xrRpbaULRkRFSq4L34oVK2L9+vWax/b29ti2bZvWOjKZLNeFr62tLeRyOZ48eaLV/uTJE9jb22e7jYODA4yMjCCXyzVttWrVQkxMDDIyMmBsnHVScoVCAYWCt6QkIiqqQkIeYcCAAEREPMOOHeFwda2IKlVKSx2LiIqgXBe+UVFRej2wsbExXFxccOrUKXh4eAB43aN76tQpjB07NtttWrdujZ07d0KtVsPA4PXw5Nu3b8PBwSHbopeIiIoulUqNxYsvYPr008jMfD1dplotcO3aUxa+RJQneruBRV5MmDAB69evx5YtW3Dz5k2MHj0aKSkpGDp0KABg8ODBmDJlimb90aNH4/nz5/jyyy9x+/ZtHDlyBPPmzcOYMWOkegpERJQPoqMT0LHjVvj4nNIUvS4uDrh8eSR69KgpcToiKqokvRLA09MTsbGx8PX1RUxMDBo2bIjjx49rLni7f/++pmcXAJRKJYKCguDt7Y369evD0dERX375JSZPnizVUyAiIj3z97+OkSMP48WLNACATAb4+LTBrFntYGwsf8fWREQ5y/U8vsUF5/ElIiqckpLSMW7cMWzZckXTplRaYtu2nnBzc5IuGBEVuPyq1zj3CxERFQrp6SqcOHFX89jTsw5Wr/4INjamEqYiouJE0jG+REREb9jammHLFg9YWiqwdasHfv65F4teItKrPBW+d+/exfTp09GvXz88ffr6/ujHjh3D9evX9RqOiIiKr8jIeDx5kqzV9sEHVfD3319h0KAGkMl4BzYi0i+dC9+zZ8+iXr16uHjxIgICApCc/PqX1pUrV3K8iQQREdEbQghs2RKGBg3W4LPPAvHfS02srU0kSkZExZ3Oha+Pjw++++47/PLLL1pz53bo0AF//PGHXsMREVHxEh+fir5998HL6yCSkzNw9Oj/sHlzmNSxiKiE0PnitvDwcOzcuTNLu52dHeLi4vQSioiIip8zZ6IwaNB+PHiQqGnz8mqI3r1rS5iKiEoSnXt8ra2t8fjx4yztly9fhqOjo15CERFR8ZGRoYKPz0l06LBFU/Ta2JjA3/9TbN7cAxYWvK08ERUMnXt8+/bti8mTJ2PPnj2QyWRQq9U4f/48Jk2ahMGDB+dHRiIiKqJu3YrDgAEBCA39p8OkfXsnbN3aExUqFPK51Imo2NG58H1zi2ClUgmVSoXatWtDpVKhf//+mD59en5kLBoi9gAXfIGMJP3tMyVrzzoRUVERGRmPxo3XIjU1EwBgZGSAuXM7YOLEVjAw4IwNRFTw8nzntvv37+PatWtITk5Go0aNUK1aNX1nyxf5due2zbWA57f0t79/K10TGHozf/ZNRJSPBg4MwI4d4ahRowx27uyFxo0dpI5EREVAoblz27lz59CmTRtUrFgRFStW1FuQIu9NT6/MADDX4y92Ywug9Rz97Y+IqACtWtUVlSpZYdq0tjAzM5I6DhGVcDr3+BobG8PR0RH9+vXDwIEDUbt20boaN996fNdWAJIfAqUcgZEP9LdfIqIiIC0tE1OmnESrVkr07l1H6jhEVMTlV72m86wOjx49wsSJE3H27FnUrVsXDRs2xKJFi/DgAYs9IqKSKDz8CZo1W49lyy7i888PIzo6QepIRETZ0rnwtbW1xdixY3H+/HncvXsXvXv3xpYtW+Dk5IQOHTrkR0YiIiqE1GqB5cv/QNOm6xEe/vr29ampr/DXX48kTkZElD2dx/j+W+XKleHj44MGDRpgxowZOHv2rL5yERFRIfb4cRKGDj2IoKC7mrZ69eywc2cv1K1rJ2EyIqKc6dzj+8b58+fxxRdfwMHBAf3790fdunVx5MgRfWYjIqJC6ODBW6hff41W0evt3QKXLo1g0UtEhZrOPb5TpkzBrl278OjRI3zwwQdYvnw5evToATMzs/zIR0REhURKSgYmTjyBtWtDNG0ODqXg5+eBzp2rSJiMiCh3dC58f/vtN3z99dfo06cPbG1t8yMTEREVQomJ6di37585xT08amL9+m6wtWXHBxEVDToXvufPn8+PHEREVMg5OFhgw4Zu6N8/AMuXd8GwYY0gk/EObERUdOSq8A0MDMSHH34IIyMjBAYGvnXd7t276yUYERFJKzo6Aebmxihd2lTT1qNHTdy79yXs7MwlTEZElDe5Knw9PDwQExMDOzs7eHh45LieTCaDSqXSVzYiIpKIv/91jBx5GJ06OcPf/1Otnl0WvURUVOVqVge1Wg07OzvN/+f0w6KXiKhoS0xMh5fXAXh67sWLF2nYu/cGdu4MlzoWEZFe6Dyd2datW5Genp6lPSMjA1u3btVLKCIiKnjBwdFo2HANtmy5omnz9KyDrl2rSZiKiEh/dC58hw4dioSErLejTEpKwtChQ/USioiICk5mphqzZ5+Bq+tm3Lv3AgBgYWGMrVs98PPPvWBjY/r2HRARFRE6z+oghMj2Kt4HDx7AyspKL6GIiKhgREbGY+DAAAQHP9C0tWqlxPbtPVG5so2EyYiI9C/XhW+jRq+nrZHJZOjYsSMMDf/ZVKVS4d69e+jSpUu+hCQiIv27c+c5Gjdei6SkDACAXC6Dr68bpk51haFhnm/sSURUaOW68H0zm0NYWBjc3d1RqlQpzTJjY2M4OTmhV69eeg9IRET5o0oVG3Ts6IwDB27B2dkGO3Z8ghYtKkgdi4go3+S68J05cyYAwMnJCZ6enjAxMcm3UERElP9kMhnWr++GSpWsMGdOe1hYKKSORESUr3T+LmvIkCEseomIipiMDBV8fE7iyJHbWu22tmZYtqwLi14iKhFy1eNbunRp3L59G7a2trCxsXnrLSqfP3+ut3BERPT+IiLi0L9/AEJDH2Pz5jBcvToK5cqVeveGRETFTK4K36VLl8LCwkLz/7w3OxFR4SeEwLp1IfD2DkJqaiYAID4+FefPR+OTT2pJnI6IqODlqvAdMmSI5v+9vLzyKwsREelJbGwKhg8/hMDACE1bjRplsHNnLzRu7CBhMiIi6eg8xjc0NBTh4f/cvvLgwYPw8PDA1KlTkZGRoddwRESku6CgO6hff41W0Tt6dBOEho5k0UtEJZrOhe/IkSNx+/briyMiIyPh6ekJMzMz7NmzB998843eAxIRUe6kpWXC2/s4unTZgZiYZACvL14LDOyLn376CGZmRhInJCKSls6F7+3bt9GwYUMAwJ49e+Dm5oadO3fCz88P+/bt03c+IiLKpadPU7B5c5jmcZcuVREePhrdutWQLhQRUSGic+ErhIBarQYAnDx5El27dgUAKJVKxMXF6TcdERHlWsWKVli9+iMoFHKsWNEFR4/2h709Z28gInoj1zeweKNJkyb47rvv0KlTJ5w9exarV68GANy7dw/lypXTe0AiIsre48dJMDc3hqXlP3Pw9utXD23aVIRSaSVhMiKiwknnHt9ly5YhNDQUY8eOxbRp01C1alUAwN69e9GqVSu9ByQioqwOHryF+vXXYPz4Y1mWseglIsqeTAgh9LGjtLQ0yOVyGBkV7osnEhMTYWVlhYSEBFhaWupvx2srAMkPgVKOwMgH+tsvEdG/pKRkYOLEE1i7NkTTtndvb/TqVVvCVERE+pVf9ZrOQx3eCAkJwc2bNwEAtWvXRuPGjfUWioiIsgoJeYT+/QNw+/YzTZuHR024uTlJF4qIqAjRufB9+vQpPD09cfbsWVhbWwMAXrx4gfbt22PXrl0oW7asvjMSEZVoKpUaixdfwPTpp5GZ+friYjMzIyxf3gXDhjXi3TSJiHJJ5zG+48aNQ3JyMq5fv47nz5/j+fPnuHbtGhITEzF+/Pj8yEhEVGJFRyegY8et8PE5pSl6XVwccPnySAwf3phFLxGRDnTu8T1+/DhOnjyJWrX+uc977dq1sWrVKnTu3Fmv4YiISrLbt5+hefMNePEiDQAgkwE+Pm0wa1Y7GBvLJU5HRFT06Nzjq1ars72AzcjISDO/LxERvb+qVUujeXNHAIBSaYnTp4dg3ryOLHqJiPJI58K3Q4cO+PLLL/Ho0SNN28OHD+Ht7Y2OHTvqNRwRUUlmYCDD5s098PnnjXHlyihexEZE9J50Lnx//PFHJCYmwsnJCVWqVEGVKlVQuXJlJCYmYuXKlfmRkYio2MvMVGP27DP49dd7Wu0ODhZYu7YbbGxMJUpGRFR86DzGV6lUIjQ0FKdOndJMZ1arVi106tRJ7+GIiEqCyMh4DBwYgODgB3B0tMDVq6NRujQLXSIifdOp8N29ezcCAwORkZGBjh07Yty4cfmVi4io2BNCYNu2qxg79iiSkjIAADExyTh9+h5vSEFElA9yXfiuXr0aY8aMQbVq1WBqaoqAgADcvXsXixYtys98RETFUnx8KkaNOgJ//+uaNmdnG+zY8QlatKggYTIiouIr12N8f/zxR8ycORMREREICwvDli1b8NNPP+VnNiKiYunMmSjUr79Gq+j18mqIsLCRLHqJiPJRrgvfyMhIDBkyRPO4f//+yMzMxOPHj/MlGBFRcZORocKUKSfRocMWPHiQCACwtjaBv/+n2Ly5BywsFBInJCIq3nI91CE9PR3m5uaaxwYGBjA2NkZqamq+BCMiKm4ePEjEypWXIMTrx+3aOWHrVg8olVbSBiMiKiF0urhtxowZMDMz0zzOyMjA3LlzYWX1zy/tH374QX/piIiKEWdnGyxf3gWjRx/B3LkdMHFiKxgY8JbDREQFJdeFb9u2bREREaHV1qpVK0RGRmoe857xRET/iIt7CTMzI5iZ/XO3y88+awQ3NydUrVpawmRERCVTrgvfM2fO5GMMIqLiJSjoDry8DuKTT2pi1aqPNO0ymYxFLxGRRHS+cxsREeUsLS0T3t7H0aXLDsTEJOOnn/7CkSO3pY5FRETIw53biIgoe+HhTzBgQADCw59q2rp0qQoXl/ISpiIiojdY+BIRvSe1WmDlyouYPPkk0tNVAACFQo5Fiz7A2LHNeP0DEVEhwcKXiOg9PH6chKFDDyIo6K6mrV49O+zc2Qt169pJmIyIiP6LhS8RUR5FRMShTZvNiIt7qWnz9m6BefM6wsSEv16JiAqbPF3c9vvvv2PgwIFo2bIlHj58CADYtm0bzp07p9dwRESFWdWqpVG7dlkAgINDKQQFDcQPP7iz6CUiKqR0Lnz37dsHd3d3mJqa4vLly0hPTwcAJCQkYN68eXoPSERUWMnlBti2rScGDaqPq1dHo3PnKlJHIiKit9C58P3uu++wZs0arF+/HkZG/0zK3rp1a4SGhuo1HBFRYaFSqfH99+dw4UK0VnvFilbYurUnbG3NctiSiIgKC52/j4uIiEDbtm2ztFtZWeHFixf6yEREVKhERydg0KD9OHv2b1SubI2wsFGwtFRIHYuIiHSkc4+vvb097ty5k6X93LlzcHZ21ksoIqLCwt//OurXX4OzZ/8GAERFvcCJE3ffsRURERVGOhe+I0aMwJdffomLFy9CJpPh0aNH2LFjByZNmoTRo0fnR0YiogKXmJgOL68D8PTcixcv0gAASqUlTp8egk8/rS1xOiIiygudhzr4+PhArVajY8eOePnyJdq2bQuFQoFJkyZh3Lhx+ZGRiKhABQdHY+DA/YiMjNe0eXrWwerVH8HGxlTCZERE9D5kQgiRlw0zMjJw584dJCcno3bt2ihVqpS+s+WLxMREWFlZISEhAZaWlvrb8doKQPJDoJQjMPKB/vZLRAUmM1ONuXN/w5w5v0Glev2r0cLCGKtWdcXAgfV5BzYiogKSX/VaniebNDY2Ru3a/LqPiIqPu3efY/78c5qit1UrJbZv74nKlW0kTkZERPqgc+Hbvn37t/Z6/Prrr+8ViIhIKjVq2GLhwg8wYUIQfH3dMHWqKwwN83SfHyIiKoR0LnwbNmyo9fjVq1cICwvDtWvXMGTIEH3lIiLKd/HxqTAzM4JC8c+vwnHjmqFDh8qoW9dOwmRERJQfdC58ly5dmm37rFmzkJyc/N6BiIgKwpkzURg0aD/69q2DRYs6a9plMhmLXiKiYkpv3+ENHDgQmzZt0tfuiIjyRUaGClOmnESHDlvw4EEiFi8OxqlTkVLHIiKiApDni9v+Kzg4GCYmJvraHRGR3kVExKF//wCEhj7WtLVv74QaNWwlTEVERAVF58L3k08+0XoshMDjx4/x119/YcaMGXoLRkSkL0IIrFsXAm/vIKSmZgIAjIwMMHduB0yc2AoGBpymjIioJNC58LWystJ6bGBggBo1auDbb79F586dc9iKiEgasbEpGD78EAIDIzRtNWqUwc6dvdC4sYOEyYiIqKDpVPiqVCoMHToU9erVg40N57UkosItIiIO7dptQUzMPxfejh7dBIsXd4aZmZGEyYiISAo6Fb5yuRydO3fGzZs39Vr4rlq1CosWLUJMTAwaNGiAlStXolmzZu/cbteuXejXrx969OiBAwcO6HbQTTUBUz3Oz5ny+N3rEFGBcna2gVJpiZiYZNjammHTpu7o1q2G1LGIiEgiOg91qFu3LiIjI1G5cmW9BNi9ezcmTJiANWvWoHnz5li2bBnc3d0REREBO7ucpxSKiorCpEmT4OrqmrcDpzwGVHkM/TbGFvmwUyLKCyMjOXbs+AQ+PqewalVX2NsXjVurExFR/pAJIYQuGxw/fhxTpkzBnDlz4OLiAnNzc63lut5PuXnz5mjatCl+/PFHAIBarYZSqcS4cePg4+OT7TYqlQpt27bFZ599ht9//x0vXrzIdY+v5t7Pc2WwLFNep6zvZGwBtJ4DVP9Uv/slondSqwV+/PESXF0rolEjjt0lIirKNPVaQoLOteXb5LrH99tvv8XEiRPRtWtXAED37t21bl0shIBMJoNKlftu1IyMDISEhGDKlCmaNgMDA3Tq1AnBwcFvzWJnZ4dhw4bh999/f+sx0tPTkZ6ernmcmJj4+n/M7IGRD3KdlYgKr8ePkzB06EEEBd1FzZq2CAn5nGN4iYgoi1wXvrNnz8aoUaNw+vRpvR08Li4OKpUK5cqV02ovV64cbt26le02586dw8aNGxEWFparY8yfPx+zZ89+36hEVEgdPHgLw4cfQlzcSwDArVtxOHbsf+jVq7bEyYiIqLDJdeH7ZkSEm5tbvoV5l6SkJAwaNAjr16+HrW3uJpyfMmUKJkyYoHmcmJgIpVKZXxGJqICkpGRg4sQTWLs2RNPm4FAKfn4e6Ny5ioTJiIiosNLp4rZ/D23QB1tbW8jlcjx58kSr/cmTJ7C3t8+y/t27dxEVFYVu3bpp2tRqNQDA0NAQERERqFJF+x88hUIBhUKh19xEJK2QkEfo3z8At28/07R5eNTE+vXdYGtrJmEyIiIqzHQqfKtXr/7O4vf58+e53p+xsTFcXFxw6tQpeHh4AHhdyJ46dQpjx47Nsn7NmjURHh6u1TZ9+nQkJSVh+fLl7MklKuZUKjUWLbqAGTNOIzPz9R+9ZmZGWLbMHcOHN9b7H+dERFS86FT4zp49O8ud297XhAkTMGTIEDRp0gTNmjXDsmXLkJKSgqFDhwIABg8eDEdHR8yfPx8mJiaoW7eu1vbW1tYAkKWdiIqfW7fitIpeFxcH7NzZC9Wrl5E4GRERFQU6Fb59+/Z969y6eeHp6YnY2Fj4+voiJiYGDRs2xPHjxzUXvN2/fx8GBnq80QQRFVl16thhzpz2mDr1FHx82mDWrHYwNpZLHYuIiIqIXM/jK5fL8fjxY70XvgVNMy/cUgdYfvVI6jhE9BZJSekwNTWCoeE/f/yqVGpcvhyDJk30PA83EREVGvk1j2+uu1J1vM8FEdF7CQ6ORsOGa/Hdd79ptcvlBix6iYgoT3Jd+KrV6iLf20tEhV9mphqzZ5+Bq+tmREbGY86c33DhQrTUsYiIqBjQaYwvEVF+ioyMx8CBAQgO/ueuii1aVICDQykJUxERUXHBwpeIJCeEwLZtVzF27FEkJWUAAORyGXx93TB1qqvWGF8iIqK8YuFLRJKKj0/F6NFHsHv3dU2bs7MNduz4BC1aVJAwGRERFTcsfIlIMhERcfjgg22Ijk7UtHl5NcSKFV1gYcE7LhIRkX7x+0MikkylStawtjYBANjYmMDf/1Ns3tyDRS8REeULFr5EJBkTE0Ps3NkLXbtWw9Wro9G7dx2pIxERUTHGwpeICoQQAuvWheDGjVit9rp17XDkSH9UqKC/CcqJiIiyw8KXiPJdbGwKPDx2Y+TIw+jffx/S0zOljkRERCUQC18iyldBQXdQv/4aBAZGAACuXHmCw4dvS5yKiIhKIha+RJQv0tIy8dVXx9Glyw7ExCQDAGxtzRAY2Be9etWWOB0REZVEnM6MiPQuPPwJ+vcPwLVrTzVt7u5V4OfnAXt73oWNiIikwcKXiPRGrRZYufIiJk8+ifR0FQBAoZBj4cIPMHZsMxgYyCROSEREJRkLXyLSm/DwJ5gw4QTUagEAqFfPDjt39kLdunYSJyMiIuIYXyLSowYN7DF1ahsAgLd3C1y6NIJFLxERFRrs8SWiPHv58hVMTAy1hjD4+rqhc+cqcHWtJGEyIiKirNjjS0R5EhLyCI0arcWSJRe02o2M5Cx6iYioUGLhS0Q6UanU+P77c2jRYiNu336GadN+RWjoY6ljERERvROHOhBRrkVHJ2DQoP04e/ZvTVv9+uVQqpSxhKmIiIhyh4UvEeWKv/91jBx5GC9epAEAZDLAx6cNZs1qB2NjucTpiIiI3o2FLxG9VWJiOsaPP4YtW65o2pRKS2zb1hNubk7SBSMiItIRC18iylFERBy6dt2JyMh4TZunZx2sWfMxrK1NJExGRESkOxa+RJSjChUsYWj4+hpYCwtjrFrVFQMH1odMxjuwERFR0cNZHYgoR+bmxti58xO0a+eEK1dGYdCgBix6iYioyGLhS0QAACEEtm69grt3n2u1u7iUx6+/DkblyjYSJSMiItIPFr5EhPj4VPTtuw9DhhzAgAEBePVKpbWcvbxERFQcsPAlKuHOnIlC/fpr4O9/HQBw8eJDHD58W+JURERE+sfCl6iEyshQwcfnJDp02IIHDxIBADY2Jtizpzd69qwlcToiIiL946wORCVQREQc+vcP0LrVcPv2Tti6tScqVLCUMBkREVH+YeFLVIIIIbBuXQi8vYOQmpoJADAyMsDcuR0wcWIrGBhwLC8RERVfLHyJSpDLl2MwatQRzeMaNcpg585eaNzYQcJUREREBYNjfIlKkMaNHTBhQgsAwOjRTRAaOpJFLxERlRjs8SUqxtLTM2FsLNeajmzevI7o0qUqPvigioTJiIiICh57fImKqfDwJ2jSZD1Wr/5Lq12hMGTRS0REJRILX6JiRq0WWL78DzRtuh7Xrj3FxIkncONGrNSxiIiIJMehDkTFyOPHSRg69CCCgu5q2qpVKy1hIiIiosKDhS9RMXHw4C0MH34IcXEvNW3e3i0wb15HmJjwo05ERMR/DYmKuJSUDEyceAJr14Zo2hwcSsHPzwOdO3MsLxER0RssfImKsNu3n6Fbt59x+/YzTZuHR02sX98NtrZmEiYjIiIqfFj4EhVh5cqZIyNDBQAwMzPC8uVdMGxYI63py4iIiOg1zupAVIRZWZlg+/aeaN7cEZcvj8Tw4Y1Z9BIREeWAhS9REbJnz3VERydotbVuXRHBwcNQvXoZiVIREREVDSx8iYqAxMR0eHkdQJ8+ezF48AGoVGqt5ezlJSIiejcWvkSFXHBwNBo1WostW64AAM6cicLhw7clTkVERFT0sPAlKqQyM9WYPfsMXF03IzIyHgBgYWGMrVs90L17DYnTERERFT2c1YGoEIqMjMfAgQEIDn6gaWvVSont23uicmUbCZMREREVXSx8iQoRIQS2bbuKsWOPIikpAwAgl8vg6+uGqVNdYWjIL2mIiIjyioUvUSHy11+PMGTIAc1jZ2cb7NjxCVq0qCBdKCIiomKC3UdEhUjTpo4YOdIFAODl1RBhYSNZ9BIREekJe3yJJPTqlQqGhgZa05EtWdIZXbtW4wVsREREesYeXyKJRETEoUWLjZppyt4wNzdm0UtERJQPWPgSFTAhBNau/QuNGq1FaOhjjBt3DHfuPJc6FhERUbHHoQ5EBSg2NgXDhx9CYGCEps3R0QKpqa8kTEVERFQysPAlKiBBQXfg5XUQMTHJmrZRo1ywZIk7zMyMJExGRERUMrDwJcpnaWmZmDLlJJYtu6hps7U1w6ZN3dGtG8fyEhERFRQWvkT56M6d5/jkk90ID3+qaevSpSo2b+4Be/tSEiYjIiIqeVj4EuUjGxsTPHuWCgBQKORYtOgDjB3bTGv6MiIiIioYnNWBKB+VKWMGP78eaNCgHP7663OMG9ecRS8REZFE2ONLpEeHDkWgaVNHrWEMH3xQBSEhlSGX8+9MIiIiKfFfYiI9SEnJwKhRh9G9+y589tlBCCG0lrPoJSIikh7/NSZ6TyEhj9C48TqsXRsCADh27A4OH74tcSoiIiL6Lxa+RHmkUqnx/ffn0KLFRty+/QwAYGZmhPXru+Hjj6tLnI6IiIj+i2N8ifIgOjoBgwbtx9mzf2vaXFwcsHNnL1SvXkbCZERERJQTFr5EOtq9+xpGjTqCFy/SAAAyGeDj0wazZrWDsbFc4nRERESUExa+RDr4448H6Nt3n+axUmmJbdt6ws3NSbpQRERElCsc40ukgxYtKmDQoPoAAE/POrhyZRSLXiIioiKCPb5Eb6FWCxgYaN9w4scfu+Kjj6qhT586vBkFERFREcIeX6IcREbGo02bTfD3v67VbmmpgKdnXRa9RERERQx7fIn+QwiBbduuYuzYo0hKysDNm4fRsmUFKJVWUkcjIiKi98AeX6J/iY9PRd+++zBkyAEkJWUAAEqXNsWzZ6kSJyMiIqL3xR5fov935kwUBg3ajwcPEjVtXl4NsWJFF1hYKCRMRkRERPrAwpdKvIwMFXx9T2PhwvMQ4nWbtbUJ1q37GL1715E2HBEREekNC18q0SIj49G79x6Ehj7WtLVr54StWz04ppeIiKiY4RhfKtFMTQ1x/34CAMDIyAALF3bCqVODWfQSEREVQyx8qURzcLDAxo3dUbOmLf74Yzi+/rp1lnl7iYiIqHjgUAcqUU6ejESjRvYoU8ZM09a9ew18+GFVGBnJJUxGRERE+a1Q9PiuWrUKTk5OMDExQfPmzXHp0qUc112/fj1cXV1hY2MDGxsbdOrU6a3rEwFAWlomvL2P44MPtmHkyMMQb65i+38seomIiIo/yQvf3bt3Y8KECZg5cyZCQ0PRoEEDuLu74+nTp9muf+bMGfTr1w+nT59GcHAwlEolOnfujIcPHxZwcioqwsOfoFmz9Vi27CIAYN++mzh+/I7EqYiIiKigycR/u74KWPPmzdG0aVP8+OOPAAC1Wg2lUolx48bBx8fnndurVCrY2Njgxx9/xODBg9+5fmJiIqysrJCw1AGWXz167/xUeKnVAitXXsTkySeRnq4CACgUcixa9AHGjm3GWw4TEREVUpp6LSEBlpaWetuvpGN8MzIyEBISgilTpmjaDAwM0KlTJwQHB+dqHy9fvsSrV69QunTpbJenp6cjPT1d8zgxMTHb9ah4efw4CUOHHkRQ0F1NW716dti5sxfq1rWTMBkRERFJRdKhDnFxcVCpVChXrpxWe7ly5RATE5OrfUyePBnly5dHp06dsl0+f/58WFlZaX6USuV756bCLTAwAvXrr9Eqer29W+DSpREseomIiEowycf4vo8FCxZg165d2L9/P0xMTLJdZ8qUKUhISND8REdHF3BKKkjnz99Hjx67EBf3EgBgb18KQUED8cMP7jAx4SQmREREJZmkha+trS3kcjmePHmi1f7kyRPY29u/ddvFixdjwYIFOHHiBOrXr5/jegqFApaWllo/VHy1aqVEz541AQA9etRAePhodO5cReJUREREVBhIWvgaGxvDxcUFp06d0rSp1WqcOnUKLVu2zHG7hQsXYs6cOTh+/DiaNGlSEFGpkPrvtZkymQzr13fD5s09sH+/J2xtzXLYkoiIiEoayYc6TJgwAevXr8eWLVtw8+ZNjB49GikpKRg6dCgAYPDgwVoXv33//feYMWMGNm3aBCcnJ8TExCAmJgbJyclSPQWSSHR0Ajp02IrDh29rtZcpYwYvr4actYGIiIi0SD7o0dPTE7GxsfD19UVMTAwaNmyI48ePay54u3//PgwM/qnPV69ejYyMDHz66ada+5k5cyZmzZpVkNFJQv7+1zFy5GG8eJGG69ef4urV0bC3LyV1LCIiIirEJJ/Ht6BxHt+iLTExHePHH8OWLVc0bUqlJQ4c6IvGjR0kTEZERET6Uizn8SXSRXBwNAYMCMC9ey80bZ6edbB69UewsTGVLhgREREVCSx8qdDLzFTju+9+w3ff/QaV6vUXFBYWxli1qisGDqzPsbxERESUKyx8qVCLinqB/v33ITj4gaatVSsltm/vicqVbSRMRkREREWN5LM6EL2NgYEMN27EAgDkchlmz26Hs2e9WPQSERGRzlj4UqFWsaIV1qz5GM7ONjh37jP4+rrB0JBvWyIiItIdKwgqVH7//W8kJqZrtfXtWxfXr3+BFi0qSJSKiIiIigMWvlQoZGSo4ONzEm5ufhg37liW5SYmHI5ORERE74eFL0kuIiIOLVtuxPffn4cQwNatV3DixF2pYxEREVExw240kowQAuvWhcDbOwipqZkAACMjA8yd2wGdOjlLnI6IiIiKGxa+JInY2BQMH34IgYERmrYaNcpg585evAMbERER5QsWvlTggoLuwMvrIGJikjVto0c3weLFnWFmZiRhMiIiIirOWPhSgfr997/RpcsOzWNbWzNs2tQd3brVkDAVERERlQS8uI0KVJs2FdGlS1UAQJcuVREePppFLxERERUI9vhSgZLJZNi8uQf277+JUaOaQCaTSR2JiIiISgj2+FK+iYlJxkcf7cSpU5Fa7fb2pTB6dFMWvURERFSg2ONL+SIwMALDhgUiLu4lrlyJwZUro1CmjJnUsYiIiKgEY48v6VVKSgZGjTqMHj12IS7uJQBArRaIinohbTAiIiIq8djjS3oTEvIIAwYEICLimabNw6Mm1q/vBltb9vYSERGRtFj40ntTqdRYvPgCpk8/jcxMNQDAzMwIy5d3wbBhjTiWl4iIiAoFFr70Xh48SMSgQftx5kyUps3FxQE7d/ZC9eplpAtGRERE9B8c40vvJTX1Ff788yEAQCYDpkxpgwsXhrHoJSIiokKHhS+9l2rVymDFig+hVFri9OkhmDevI4yN5VLHIiIiIsqChS/p5NKlh3j58pVW29ChDXHjxhi4uTlJE4qIiIgoF1j4Uq5kZqoxe/YZtGq1EZMmndBaJpPJUKqUsUTJiIiIiHKHhS+9U2RkPNq23YxZs85CpRJYvfovnD59T+pYRERERDrhrA6UIyEEtm27irFjjyIpKQMAIJfL4OvrBlfXShKnIyIiItINC1/KVnx8KkaPPoLdu69r2pydbbBjxydo0aKChMmIiIiI8oaFL2Vx9mwUBg3aj+joRE2bl1dDrFjRBRYWCgmTEREREeUdC1/ScvZsFNq33wIhXj+2sTHB2rUfo3fvOtIGIyIiInpPvLiNtLRpUxFt274ev9u+vROuXh3NopeIiIiKBfb4kha53ADbtvXEnj038NVXLWBgIJM6EhEREZFesMe3BIuNTUGvXv44f/6+VrtSaYUJE1qy6CUiIqJihT2+JVRQ0B14eR1ETEwyQkMf48qVUbC05IVrREREVHyxx7eESUvLxFdfHUeXLjsQE5MMAEhOzsDt288kTkZERESUv9jjW4KEhz9B//4BuHbtqaatS5eq2Ly5B+ztS0mYjIiIiCj/sfAtAdRqgZUrL2Ly5JNIT1cBABQKORYt+gBjxzaDTMaxvERERFT8sfAt5h4/TsLQoQcRFHRX01avnh127uyFunXtJExGREREVLA4xreYe/48FWfORGkee3u3wKVLI1j0EhERUYnDwreYq1PHDosWfQB7+1IIChqIH35wh4kJO/qJiIio5GHhW8xcuRKD9PRMrbaxY5vhxo0v0LlzFYlSEREREUmPhW8xoVKp8f3359CkyXpMm/ar1jKZTAYbG1OJkhEREREVDix8i4Ho6AR07LgVPj6nkJmpxpIlwTh37v67NyQiIiIqQTjYs4jz97+OkSMP48WLNACATAb4+LRBs2aOEicjIiIiKlxY+BZRiYnpGD/+GLZsuaJpUyotsW1bT7i5OUkXjIiIiKiQYuFbBAUHR2PgwP2IjIzXtHl61sHq1R9xLC8RERFRDlj4FjFnzkShU6etUKkEAMDCwhirVnXFwIH1eQc2IiIiorfgxW1FTOvWSri4lAcAtGqlxJUrozBoUAMWvURERETvwB7fIsbISI4dOz7B7t3XMHlyGxga8m8XIiIiotxg4VuIxcenYuzYY5gwoYWmlxcAqlYtjWnT2kqYjIio+BNCIDMzEyqVSuooRMWSkZER5HJ5gR6ThW8hdeZMFAYN2o8HDxIREvIIoaEjYWZmJHUsIqISISMjA48fP8bLly+ljkJUbMlkMlSoUAGlSpUqsGOy8C1kMjJU8PU9jYULz0O8vn4NT5+m4Pr1p2jalHPzEhHlN7VajXv37kEul6N8+fIwNjbmdRREeiaEQGxsLB48eIBq1aoVWM8vC99CJCIiDv37ByA09LGmrX17J2zd2hMVKlhKmIyIqOTIyMiAWq2GUqmEmZmZ1HGIiq2yZcsiKioKr169YuFbkgghsG5dCLy9g5CamgkAMDIywNy5HTBxYisYGLCngYiooBkY8OJhovwkxTcpLHwlFhubguHDDyEwMELTVqNGGezc2QuNGztImIyIiIioeGHhK7Ho6EQcPfo/zePRo5tg8eLOvJCNiIiISM/4PY7EGjd2wHfftYetrRkCA/vip58+YtFLRERUwCIiImBvb4+kpCSpoxQbLVq0wL59+6SOoYWFbwG7dSsOr15pzwk5aVIrXL/+Bbp1qyFRKiIiKg68vLwgk8kgk8lgZGSEypUr45tvvkFaWlqWdQ8fPgw3NzdYWFjAzMwMTZs2hZ+fX7b73bdvH9q1awcrKyuUKlUK9evXx7fffovnz5/n8zMqOFOmTMG4ceNgYWGRZVnNmjWhUCgQExOTZZmTkxOWLVuWpX3WrFlo2LChVltMTAzGjRsHZ2dnKBQKKJVKdOvWDadOndLX08jWnj17ULNmTZiYmKBevXo4evToO7dZtWoVatWqBVNTU9SoUQNbt27VWh4QEIAmTZrA2toa5ubmaNiwIbZt26a1zvTp0+Hj4wO1Wq3X5/M+WPgWELVaYPnyP9Cw4Rp8991vWsvkcgPY2ZlLlIyIiIqTLl264PHjx4iMjMTSpUuxdu1azJw5U2udlStXokePHmjdujUuXryIq1evom/fvhg1ahQmTZqkte60adPg6emJpk2b4tixY7h27RqWLFmCK1euZCl08lNGRka+7fv+/fs4fPgwvLy8siw7d+4cUlNT8emnn2LLli15PkZUVBRcXFzw66+/YtGiRQgPD8fx48fRvn17jBkz5j3Sv92FCxfQr18/DBs2DJcvX4aHhwc8PDxw7dq1HLdZvXo1pkyZglmzZuH69euYPXs2xowZg0OHDmnWKV26NKZNm4bg4GBcvXoVQ4cOxdChQxEUFKRZ58MPP0RSUhKOHTuWb89PZ6KESUhIEABEwlKHAjvmo0eJwt19mwBmCWCWMDCYLS5efFBgxyciotxLTU0VN27cEKmpqVJH0dmQIUNEjx49tNo++eQT0ahRI83j+/fvCyMjIzFhwoQs269YsUIAEH/88YcQQoiLFy8KAGLZsmXZHi8+Pj7HLNHR0aJv377CxsZGmJmZCRcXF81+s8v55ZdfCjc3N81jNzc3MWbMGPHll1+KMmXKiHbt2ol+/fqJPn36aG2XkZEhypQpI7Zs2SKEEEKlUol58+YJJycnYWJiIurXry/27NmTY04hhFi0aJFo0qRJtsu8vLyEj4+POHbsmKhevXqW5ZUqVRJLly7N0j5z5kzRoEEDzeMPP/xQODo6iuTk5Czrvu11fF99+vQRH330kVZb8+bNxciRI3PcpmXLlmLSpElabRMmTBCtW7d+67EaNWokpk+frtU2dOhQMXDgwGzXf9tnTVOvJSS89Zi64sVt+ezgwVsYPvwQ4uL+ufvP+PHNUL9+OQlTERGRzrY3AVKyftWdr8ztgYF/5Xnza9eu4cKFC6hUqZKmbe/evXj16lWWnl0AGDlyJKZOnYqff/4ZzZs3x44dO1CqVCl88cUX2e7f2to62/bk5GS4ubnB0dERgYGBsLe3R2hoqM5feW/ZsgWjR4/G+fPnAQB37txB7969kZycrLnbV1BQEF6+fImePXsCAObPn4/t27djzZo1qFatGn777TcMHDgQZcuWhZubW7bH+f3339GkSZMs7UlJSdizZw8uXryImjVrIiEhAb///jtcXV11eh7Pnz/H8ePHMXfuXJibZ/2GN6fXEQB27NiBkSNHvnX/x44dyzFTcHAwJkyYoNXm7u6OAwcO5Li/9PR0mJiYaLWZmpri0qVLePXqFYyMtK9FEkLg119/RUREBL7//nutZc2aNcOCBQvemr8gsfDNJykpGZg48QTWrg3RtNnbl8KWLR7o3LmKhMmIiChPUmKA5IdSp3inw4cPo1SpUsjMzER6ejoMDAzw448/apbfvn0bVlZWcHDIOmWmsbExnJ2dcfv2bQDA//73Pzg7O2cpdN5l586diI2NxZ9//onSpUsDAKpWrarzc6lWrRoWLlyoeVylShWYm5tj//79GDRokOZY3bt3h4WFBdLT0zFv3jycPHkSLVu2BAA4Ozvj3LlzWLt2bY6F799//51t4btr1y5Uq1YNderUAQD07dsXGzdu1LnwvXPnDoQQqFmzpk7bAUD37t3RvHnzt67j6JjznV1jYmJQrpx2Z1u5cuWyHa/8hru7OzZs2AAPDw80btwYISEh2LBhA169eoW4uDjNeychIQGOjo5IT0+HXC7HTz/9hA8++EBrX+XLl0d0dDTUanWhmBubhW8+CAl5hP79A3D79jNNW48eNbBhQ3fY2vIuQERERZK5fZE4Zvv27bF69WqkpKRg6dKlMDQ0RK9evfJ0eCFEnrYLCwtDo0aNNEVvXrm4uGg9NjQ0RJ8+fbBjxw4MGjQIKSkpOHjwIHbt2gXgdYH58uXLLMVXRkYGGjVqlONxUlNTs/RwAsCmTZswcOBAzeOBAwfCzc0NK1euzPYiuJzk9XUEAAsLC52OpQ8zZsxATEwMWrRoASEEypUrhyFDhmDhwoVaxauFhQXCwsKQnJyMU6dOYcKECXB2dka7du0065iamkKtViM9PR2mpqYF+jyyw8JXz3799R7c3bcjM/P11zlmZkZYtswdw4c35r3eiYiKsvcYclCQzM3NNb2rmzZtQoMGDbBx40YMGzYMAFC9enUkJCTg0aNHKF++vNa2GRkZuHv3Ltq3b69Z99y5c9l+vf027ypwDAwMshSDr169yva5/NeAAQPg5uaGp0+f4pdffoGpqSm6dOkC4PUQCwA4cuRIll5QhUKRYx5bW1vEx8drtd24cQN//PEHLl26hMmTJ2vaVSoVdu3ahREjRgAALC0tkZCQkGWfL168gJWVFYDXPdcymQy3bt3KMUNO3neog729PZ48eaLV9uTJE9jb5/xHlampKTZt2oS1a9fiyZMncHBwwLp162BhYYGyZctq1jMwMNC81xo2bIibN29i/vz5WoXv8+fPYW5uXiiKXoCzOuhd69ZK1K79+k3h4uKAy5dHYsQIFxa9RERU4AwMDDB16lRMnz4dqampAIBevXrByMgIS5YsybL+mjVrkJKSgn79+gEA+vfvj+TkZPz000/Z7v/FixfZttevXx9hYWE5TndWtmxZPH78WKstLCwsV8+pVatWUCqV2L17N3bs2IHevXtrivLatWtDoVDg/v37qFq1qtaPUqnMcZ+NGjXCjRs3tNo2btyItm3b4sqVKwgLC9P8TJgwARs3btSsV6NGDYSEhPx3lwgNDUX16tUBvJ4Bwd3dHatWrUJKSkqWdXN6HYHXQx3+ffzsfrIbpvFGy5Yts0yX9ssvv2iGgryNkZERKlSoALlcjl27duHjjz9+63CFNz27/3bt2rW39rYXOL1eKlcEFMSsDteuPRHTpp0S6emZ+XYMIiLKH8VtVodXr14JR0dHsWjRIk3b0qVLhYGBgZg6daq4efOmuHPnjliyZIlQKBRi4sSJWtt/8803Qi6Xi6+//lpcuHBBREVFiZMnT4pPP/00x9ke0tPTRfXq1YWrq6s4d+6cuHv3rti7d6+4cOGCEEKI48ePC5lMJrZs2SJu374tfH19haWlZZZZHb788sts9z9t2jRRu3ZtYWhoKH7//fcsy8qUKSP8/PzEnTt3REhIiFixYoXw8/PL8XULDAwUdnZ2IjPz9b/bGRkZomzZsmL16tVZ1r1x44YAIK5duyaEEOL8+fPCwMBAfPfdd+LGjRsiPDxcTJ06VRgaGorw8HDNdnfv3hX29vaidu3aYu/eveL27dvixo0bYvny5aJmzZo5Zntf58+fF4aGhmLx4sXi5s2bYubMmcLIyEgrm4+Pjxg0aJDmcUREhNi2bZu4ffu2uHjxovD09BSlS5cW9+7d06wzb948ceLECXH37l1x48YNsXjxYmFoaCjWr1+vdXw3Nzfx7bffZptNilkdWPi+177SxPDhB8W1a0/0kIyIiAqD4lb4CiHE/PnzRdmyZbWm0jp48KBwdXUV5ubmwsTERLi4uIhNmzZlu9/du3eLtm3bCgsLC2Fubi7q168vvv3227dOwxUVFSV69eolLC0thZmZmWjSpIm4ePGiZrmvr68oV66csLKyEt7e3mLs2LG5LnzfFJ+VKlUSarVaa5larRbLli0TNWrUEEZGRqJs2bLC3d1dnD17Nsesr169EuXLlxfHjx8XQgixd+9eYWBgIGJiYrJdv1atWsLb21vzOCgoSLRu3VrY2Nhopl7L7niPHj0SY8aMEZUqVRLGxsbC0dFRdO/eXZw+fTrHbPrg7+8vqlevLoyNjUWdOnXEkSNHtJYPGTJE67W/ceOGaNiwoTA1NRWWlpaiR48e4tatW1rbTJs2TVStWlWYmJgIGxsb0bJlS7Fr1y6tdR48eCCMjIxEdHR0trmkKHxlQrzHiOsiKDExEVZWVkhY6gDLrx7leT/BwdEYOHA/IiPjUb9+OVy6NBwKBYdMExEVdWlpabh37x4qV66c7QVPVDytWrUKgYGBWjdgoPczefJkxMfHY926ddkuf9tnTVOvJSTA0tJSb5k4xldHmZlqzJ59Bq6umxEZ+Xog/L178bh69ck7tiQiIqLCauTIkWjbti2SkpKkjlJs2NnZYc6cOVLH0MIuSh1ERsZj4MAABAc/0LS1aqXE9u09UbmyjYTJiIiI6H0YGhpi2rRpUscoViZOnCh1hCxY+OaCEALbtl3F2LFHkZT0+l7hcrkMvr5umDrVFYaG7DgnIiIiKuxY+L5DfHwqRo8+gt27r2vanJ1tsGPHJ2jRooKEyYiIiIhIFyx83+HmzTjs2fPP3H5eXg2xYkUXWFjkPBE2EREVfSXs2m+iAifFZ4zf0b9Dq1ZKTJvmCmtrE/j7f4rNm3uw6CUiKsbe3Azh5cuXEichKt4yMt4MH5UX2DHZ4/sf9+7Fo2JFK8jl//xNMGNGW4wc6QJHR/1Np0FERIWTXC6HtbU1nj59CgAwMzPj3TeJ9EytViM2NhZmZmYwNCy4cpSF7/8TQmDduhB4ewdh5kw3TJ7cRrPMyEjOopeIqASxt7cHAE3xS0T6Z2BggIoVKxboH5YsfAHExqZg+PBDCAyMAABMn34anTtXQaNGDhInIyIiKchkMjg4OMDOzg6vXr2SOg5RsWRsbAwDg4IddVviC9+goDvw8jqImJhkTdvw4Y1Qo4athKmIiKgwkMvlBTr+kIjyV6G4uG3VqlVwcnKCiYkJmjdvjkuXLr11/T179qBmzZowMTFBvXr1cPToUZ2PmfZKjq++Oo4uXXZoil5bWzMEBvbF6tUfw8zMKE/PhYiIiIgKJ8kL3927d2PChAmYOXMmQkND0aBBA7i7u+c4rurChQvo168fhg0bhsuXL8PDwwMeHh64du2aTsdt98NHWL78ouZxly5VER4+Gt261Xiv50NEREREhZNMSDxRYfPmzdG0aVP8+OOPAF5f5adUKjFu3Dj4+PhkWd/T0xMpKSk4fPiwpq1FixZo2LAh1qxZ887jJSYmwsrKCoAPABMoFHIsWvQBxo5txqt2iYiIiAqBN/VaQkICLC31N8GApGN8MzIyEBISgilTpmjaDAwM0KlTJwQHB2e7TXBwMCZMmKDV5u7ujgMHDmS7fnp6OtLT0zWPExIS3ixB7dplsXFjD9SuXRZJSUnv9VyIiIiISD8SExMB6P8mF5IWvnFxcVCpVChXrpxWe7ly5XDr1q1st4mJicl2/ZiYmGzXnz9/PmbPnp3NkqW4cQNo2XJinrITERERUf569uzZ/39Trx/FflaHKVOmaPUQv3jxApUqVcL9+/f1+kJS4ZSYmAilUono6Gi9flVChRPPd8nC812y8HyXLAkJCahYsSJKly6t1/1KWvja2tpCLpfjyZMnWu1PnjzRTB7+X/b29jqtr1AooFBkvcWwlZUVPzgliKWlJc93CcLzXbLwfJcsPN8li77n+ZV0VgdjY2O4uLjg1KlTmja1Wo1Tp06hZcuW2W7TsmVLrfUB4JdffslxfSIiIiIioBAMdZgwYQKGDBmCJk2aoFmzZli2bBlSUlIwdOhQAMDgwYPh6OiI+fPnAwC+/PJLuLm5YcmSJfjoo4+wa9cu/PXXX1i3bp2UT4OIiIiICjnJC19PT0/ExsbC19cXMTExaNiwIY4fP665gO3+/fta3dytWrXCzp07MX36dEydOhXVqlXDgQMHULdu3VwdT6FQYObMmdkOf6Dih+e7ZOH5Lll4vksWnu+SJb/Ot+Tz+BIRERERFQTJ79xGRERERFQQWPgSERERUYnAwpeIiIiISgQWvkRERERUIhTLwnfVqlVwcnKCiYkJmjdvjkuXLr11/T179qBmzZowMTFBvXr1cPTo0QJKSvqgy/lev349XF1dYWNjAxsbG3Tq1Omd7w8qXHT9fL+xa9cuyGQyeHh45G9A0itdz/eLFy8wZswYODg4QKFQoHr16vydXoToer6XLVuGGjVqwNTUFEqlEt7e3khLSyugtPQ+fvvtN3Tr1g3ly5eHTCbDgQMH3rnNmTNn0LhxYygUClStWhV+fn66H1gUM7t27RLGxsZi06ZN4vr162LEiBHC2tpaPHnyJNv1z58/L+RyuVi4cKG4ceOGmD59ujAyMhLh4eEFnJzyQtfz3b9/f7Fq1Spx+fJlcfPmTeHl5SWsrKzEgwcPCjg55YWu5/uNe/fuCUdHR+Hq6ip69OhRMGHpvel6vtPT00WTJk1E165dxblz58S9e/fEmTNnRFhYWAEnp7zQ9Xzv2LFDKBQKsWPHDnHv3j0RFBQkHBwchLe3dwEnp7w4evSomDZtmggICBAAxP79+9+6fmRkpDAzMxMTJkwQN27cECtXrhRyuVwcP35cp+MWu8K3WbNmYsyYMZrHKpVKlC9fXsyfPz/b9fv06SM++ugjrbbmzZuLkSNH5mtO0g9dz/d/ZWZmCgsLC7Fly5b8ikh6lJfznZmZKVq1aiU2bNgghgwZwsK3CNH1fK9evVo4OzuLjIyMgopIeqTr+R4zZozo0KGDVtuECRNE69at8zUn6V9uCt9vvvlG1KlTR6vN09NTuLu763SsYjXUISMjAyEhIejUqZOmzcDAAJ06dUJwcHC22wQHB2utDwDu7u45rk+FR17O93+9fPkSr169QunSpfMrJulJXs/3t99+Czs7OwwbNqwgYpKe5OV8BwYGomXLlhgzZgzKlSuHunXrYt68eVCpVAUVm/IoL+e7VatWCAkJ0QyHiIyMxNGjR9G1a9cCyUwFS1/1muR3btOnuLg4qFQqzV3f3ihXrhxu3bqV7TYxMTHZrh8TE5NvOUk/8nK+/2vy5MkoX758lg8TFT55Od/nzp3Dxo0bERYWVgAJSZ/ycr4jIyPx66+/YsCAATh69Cju3LmDL774Aq9evcLMmTMLIjblUV7Od//+/REXF4c2bdpACIHMzEyMGjUKU6dOLYjIVMByqtcSExORmpoKU1PTXO2nWPX4EuliwYIF2LVrF/bv3w8TExOp45CeJSUlYdCgQVi/fj1sbW2ljkMFQK1Ww87ODuvWrYOLiws8PT0xbdo0rFmzRupolA/OnDmDefPm4aeffkJoaCgCAgJw5MgRzJkzR+poVIgVqx5fW1tbyOVyPHnyRKv9yZMnsLe3z3Ybe3t7ndanwiMv5/uNxYsXY8GCBTh58iTq16+fnzFJT3Q933fv3kVUVBS6deumaVOr1QAAQ0NDREREoEqVKvkbmvIsL59vBwcHGBkZQS6Xa9pq1aqFmJgYZGRkwNjYOF8zU97l5XzPmDEDgwYNwvDhwwEA9erVQ0pKCj7//HNMmzYNBgbs2ytOcqrXLC0tc93bCxSzHl9jY2O4uLjg1KlTmja1Wo1Tp06hZcuW2W7TsmVLrfUB4JdffslxfSo88nK+AWDhwoWYM2cOjh8/jiZNmhREVNIDXc93zZo1ER4ejrCwMM1P9+7d0b59e4SFhUGpVBZkfNJRXj7frVu3xp07dzR/4ADA7du34eDgwKK3kMvL+X758mWW4vbNHz2vr5ei4kRv9Zpu190Vfrt27RIKhUL4+fmJGzduiM8//1xYW1uLmJgYIYQQgwYNEj4+Ppr1z58/LwwNDcXixYvFzZs3xcyZMzmdWRGi6/lesGCBMDY2Fnv37hWPHz/W/CQlJUn1FEgHup7v/+KsDkWLruf7/v37wsLCQowdO1ZERESIw4cPCzs7O/Hdd99J9RRIB7qe75kzZwoLCwvx888/i8jISHHixAlRpUoV0adPH6meAukgKSlJXL58WVy+fFkAED/88IO4fPmy+Pvvv4UQQvj4+IhBgwZp1n8zndnXX38tbt68KVatWsXpzN5YuXKlqFixojA2NhbNmjUTf/zxh2aZm5ubGDJkiNb6/v7+onr16sLY2FjUqVNHHDlypIAT0/vQ5XxXqlRJAMjyM3PmzIIPTnmi6+f731j4Fj26nu8LFy6I5s2bC4VCIZydncXcuXNFZmZmAaemvNLlfL969UrMmjVLVKlSRZiYmAilUim++OILER8fX/DBSWenT5/O9t/jN+d4yJAhws3NLcs2DRs2FMbGxsLZ2Vls3rxZ5+PKhOD3AURERERU/BWrMb5ERERERDlh4UtEREREJQILXyIiIiIqEVj4EhEREVGJwMKXiIiIiEoEFr5EREREVCKw8CUiIiKiEoGFLxERERGVCCx8iYgA+Pn5wdraWuoYeSaTyXDgwIG3ruPl5QUPD48CyUNEVBix8CWiYsPLywsymSzLz507d6SOBj8/P00eAwMDVKhQAUOHDsXTp0/1sv/Hjx/jww8/BABERUVBJpMhLCxMa53ly5fDz89PL8fLyaxZszTPUy6XQ6lU4vPPP8fz58912g+LdCLKD4ZSByAi0qcuXbpg8+bNWm1ly5aVKI02S0tLREREQK1W48qVKxg6dCgePXqEoKCg9963vb39O9exsrJ67+PkRp06dXDy5EmoVCrcvHkTn332GRISErB79+4COT4RUU7Y40tExYpCoYC9vb3Wj1wuxw8//IB69erB3NwcSqUSX3zxBZKTk3Pcz5UrV9C+fXtYWFjA0tISLi4u+OuvvzTLz507B1dXV5iamkKpVGL8+PFISUl5azaZTAZ7e3uUL18eH374IcaPH4+TJ08iNTUVarUa3377LSpUqACFQoGGDRvi+PHjmm0zMjIwduxYODg4wMTEBJUqVcL8+fO19v1mqEPlypUBAI0aNYJMJkO7du0AaPeirlu3DuXLl4dardbK2KNHD3z22WeaxwcPHkTjxo1hYmICZ2dnzJ49G5mZmW99noaGhrC3t4ejoyM6deqE3r1745dfftEsV6lUGDZsGCpXrgxTU1PUqFEDy5cv1yyfNWsWtmzZgoMHD2p6j8+cOQMAiI6ORp8+fWBtbY3SpUujR48eiIqKemseIqI3WPgSUYlgYGCAFStW4Pr169iyZQt+/fVXfPPNNzmuP2DAAFSoUAF//vknQkJC4OPjAyMjIwDA3bt30aVLF/Tq1QtXr17F7t27ce7cOYwdO1anTKamplCr1cjMzMTy5cuxZMkSLF68GFevXoW7uzu6d++O//3vfwCAFStWIDAwEP7+/oiIiMCOHTvg5OSU7X4vXboEADh58iQeP36MgICALOv07t0bz549w+nTpzVtz58/x/HjxzFgwAAAwO+//197dx/SZNfHAfz7zDB1zsBKcoQF6YZQVstVahHZi4uM4RItBYXMRPMFzSjCtBFaFioUvQiiko00g0gyNfrDWgvCXlTI3LJmLyRBBork0tx5/ojGvVKr++bhuXHfD+yPc67zO9fvXP7z83guZ0RSUhJycnLQ09ODyspK1NbWori4+LfX2N/fj7a2Nri7uzv67HY7Fi5ciMbGRvT09KCwsBBHjhzB1atXAQD5+fmIi4uDRqPBwMAABgYGEB4ejvHxcURFRUEmk8FoNMJkMsHb2xsajQZjY2O/nRMRuTBBRDRDJCcnCzc3NyGVSh2f2NjYScc2NjaKuXPnOto1NTVizpw5jrZMJhO1tbWTxqakpIh9+/Y59RmNRiGRSMTo6OikMT/Ob7FYhEKhEKGhoUIIIeRyuSguLnaKUavVIiMjQwghRFZWloiMjBR2u33S+QGI69evCyGEsFqtAoB4+vSp05jk5GSh1Wodba1WK/bs2eNoV1ZWCrlcLiYmJoQQQmzatEmUlJQ4zVFXVyf8/f0nzUEIIYqKioREIhFSqVR4eHgIAAKAKC8vnzJGCCH2798vdu7cOWWu3++tVCqdnsGXL1+Ep6enaGtrm3Z+IiIhhOAZXyKaUTZu3IgLFy442lKpFMC33c8TJ06gt7cXw8PD+Pr1K2w2Gz5//gwvL6+f5snLy8PevXtRV1fn+HP9kiVLAHw7BtHd3Q2DweAYL4SA3W6H1WpFcHDwpLkNDQ3B29sbdrsdNpsN69atQ1VVFYaHh/H+/XtEREQ4jY+IiEBXVxeAb8cUtmzZAqVSCY1Gg+joaGzduvUfPavExESkpqbi/PnzmD17NgwGA3bt2gWJROJYp8lkctrhnZiYmPa5AYBSqURTUxNsNhsuX76Mzs5OZGVlOY05d+4cqqur8ebNG4yOjmJsbAwrVqyYNt+uri709fVBJpM59dtsNrx8+fJvPAEicjUsfIloRpFKpQgMDHTq6+/vR3R0NNLT01FcXAxfX1/cv38fKSkpGBsbm7SAO3bsGBISEtDc3IyWlhYUFRWhvr4eMTExGBkZQVpaGrKzs3+KCwgImDI3mUyGJ0+eQCKRwN/fH56engCA4eHhX65LpVLBarWipaUFd+7cQVxcHDZv3oxr1679MnYqO3bsgBACzc3NUKvVMBqNqKiocFwfGRmBXq+HTqf7KdbDw2PKed3d3R0/g5MnT2L79u3Q6/U4fvw4AKC+vh75+fkoKytDWFgYZDIZTp8+jYcPH06b78jICFatWuX0C8d3/5YXGIno342FLxHNeI8fP4bdbkdZWZljN/P7edLpKBQKKBQK5ObmYvfu3aipqUFMTAxUKhV6enp+KrB/RSKRTBrj4+MDuVwOk8mEDRs2OPpNJhNWr17tNC4+Ph7x8fGIjY2FRqPBp0+f4Ovr6zTf9/O0ExMT0+bj4eEBnU4Hg8GAvr4+KJVKqFQqx3WVSgWz2fzH6/xRQUEBIiMjkZ6e7lhneHg4MjIyHGN+3LF1d3f/KX+VSoWGhgb4+fnBx8fnH+VERK6JL7cR0YwXGBiI8fFxnD17Fq9evUJdXR0uXrw45fjR0VFkZmaivb0dr1+/hslkQkdHh+MIw6FDh/DgwQNkZmais7MTL168wI0bN/745ba/OnjwIEpLS9HQ0ACz2YzDhw+js7MTOTk5AIDy8nJcuXIFvb29sFgsaGxsxIIFCyb90g0/Pz94enqitbUVHz58wNDQ0JT3TUxMRHNzM6qrqx0vtX1XWFiIS5cuQa/X49mzZ3j+/Dnq6+tRUFDwR2sLCwtDSEgISkpKAABBQUF49OgR2traYLFYcPToUXR0dDjFLF68GN3d3TCbzfj48SPGx8eRmJiIefPmQavVwmg0wmq1or29HdnZ2Xj37t0f5UREromFLxHNeMuXL0d5eTlKS0uxdOlSGAwGp38F9iM3NzcMDg4iKSkJCoUCcXFx2LZtG/R6PQAgJCQEd+/ehcViwfr167Fy5UoUFhZCLpf/7Ryzs7ORl5eHAwcOYNmyZWhtbUVTUxOCgoIAfDsmcerUKYSGhkKtVqO/vx+3bt1y7GD/1axZs3DmzBlUVlZCLpdDq9VOed/IyEj4+vrCbDYjISHB6VpUVBRu3ryJ27dvQ61WY+3ataioqMCiRYv+eH25ubmoqqrC27dvkZaWBp1Oh/j4eKxZswaDg4NOu78AkJqaCqVSidDQUMyfPx8mkwleXl64d+8eAgICoNPpEBwcjJSUFNhsNu4AE9Fv+Y8QQvy/kyAiIiIi+l/jji8RERERuQQWvkRERETkElj4EhEREZFLYOFLRERERC6BhS8RERERuQQWvkRERETkElj4EhEREZFLYOFLRERERC6BhS8RERERuQQWvkRERETkElj4EhEREZFL+C8DoyEvN80xFgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. ROC Curve\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, \n",
    "         label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}