{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cce0a340",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# Importing necessary libraries for model development and evaluation\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, classification_report,  confusion_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a08cc3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>General_Health</th>\n",
       "      <th>Checkup</th>\n",
       "      <th>Exercise</th>\n",
       "      <th>Heart_Disease</th>\n",
       "      <th>Skin_Cancer</th>\n",
       "      <th>Other_Cancer</th>\n",
       "      <th>Depression</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Arthritis</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_Category</th>\n",
       "      <th>Height_(cm)</th>\n",
       "      <th>Weight_(kg)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking_History</th>\n",
       "      <th>Alcohol_Consumption</th>\n",
       "      <th>Fruit_Consumption</th>\n",
       "      <th>Green_Vegetables_Consumption</th>\n",
       "      <th>FriedPotato_Consumption</th>\n",
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       "      <td>0.409059</td>\n",
       "      <td>0.491063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   General_Health  Checkup  Exercise  Heart_Disease  Skin_Cancer  \\\n",
       "0             0.0      2.0       0.0            0.0          0.0   \n",
       "1             3.0      1.0       0.0            1.0          0.0   \n",
       "2             3.0      1.0       1.0            0.0          0.0   \n",
       "3             0.0      1.0       1.0            1.0          0.0   \n",
       "4             2.0      1.0       0.0            0.0          0.0   \n",
       "\n",
       "   Other_Cancer  Depression  Diabetes  Arthritis  Sex  Age_Category  \\\n",
       "0           0.0         0.0         0        1.0  0.0          10.0   \n",
       "1           0.0         0.0         1        0.0  0.0          10.0   \n",
       "2           0.0         0.0         1        0.0  0.0           8.0   \n",
       "3           0.0         0.0         1        0.0  1.0          11.0   \n",
       "4           0.0         0.0         0        0.0  1.0          12.0   \n",
       "\n",
       "   Height_(cm)  Weight_(kg)       BMI  Smoking_History  Alcohol_Consumption  \\\n",
       "0     0.000000     0.000000  0.000000              1.0             0.000000   \n",
       "1     0.348837     0.295420  0.326529              0.0             0.000000   \n",
       "2     0.302326     0.409059  0.491063              0.0             0.133333   \n",
       "3     0.697674     0.459064  0.340504              0.0             0.000000   \n",
       "4     0.953488     0.409059  0.202016              1.0             0.000000   \n",
       "\n",
       "   Fruit_Consumption  Green_Vegetables_Consumption  FriedPotato_Consumption  \n",
       "0           0.250000                      0.125000                  0.09375  \n",
       "1           0.250000                      0.000000                  0.03125  \n",
       "2           0.100000                      0.023438                  0.12500  \n",
       "3           0.250000                      0.234375                  0.06250  \n",
       "4           0.066667                      0.031250                  0.00000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(308774, 19)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Read the dataset\n",
    "dataset_path = \"cvd_data_preprocessed.csv\"\n",
    "data = pd.read_csv(dataset_path)\n",
    "display(data.head(n=5))\n",
    "df_copy = data.copy()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "afc4acaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Defining the features (X) and the target (y)\n",
    "X = data.drop(\"Heart_Disease\", axis=1)\n",
    "y = data[\"Heart_Disease\"]\n",
    "\n",
    "# Performing the train-test split\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": 4,
   "id": "5b03c725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(308774, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4637d942",
   "metadata": {},
   "source": [
    "# Training and comparing models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a06c29b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "F1 Score: 0.11427560587298777\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.56      0.06      0.11      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.74      0.53      0.54     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "==========================================================\n",
      "Model: Decision Tree\n",
      "F1 Score: 0.21409779551846142\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.92     56677\n",
      "         1.0       0.20      0.23      0.21      5078\n",
      "\n",
      "    accuracy                           0.86     61755\n",
      "   macro avg       0.56      0.57      0.57     61755\n",
      "weighted avg       0.87      0.86      0.86     61755\n",
      "\n",
      "==========================================================\n",
      "Model: Random Forest\n",
      "F1 Score: 0.07496360989810771\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.49      0.04      0.07      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.71      0.52      0.52     61755\n",
      "weighted avg       0.89      0.92      0.88     61755\n",
      "\n",
      "==========================================================\n",
      "Model: XGBoost\n",
      "F1 Score: 0.09355472237100518\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.50      0.05      0.09      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.71      0.52      0.53     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining the function to apply models\n",
    "def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
    "    # Fit the model\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print('==========================================================')\n",
    "    \n",
    "    # Compute ROC curve and ROC area\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    return accuracy, precision, recall, f1, fpr, tpr, roc_auc\n",
    "\n",
    "# Defining the models\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(random_state=42, max_iter=500)),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier(random_state=42)),\n",
    "    (\"Random Forest\", RandomForestClassifier(random_state=42)),\n",
    "    (\"XGBoost\", XGBClassifier(random_state=42))\n",
    "]\n",
    "\n",
    "# Applying the models and storing the results\n",
    "results = []\n",
    "roc_curves = []\n",
    "\n",
    "for name, model in models:\n",
    "    accuracy, precision, recall, f1, fpr, tpr, roc_auc = apply_model(model, X_train, y_train, X_test, y_test, name)\n",
    "    results.append((name, accuracy, precision, recall, f1))\n",
    "    roc_curves.append((name, fpr, tpr, roc_auc))\n",
    "\n",
    "# Plotting the ROC curves for each model\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve comparison')\n",
    "for name, fpr, tpr, roc_auc in roc_curves:\n",
    "    plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "26c22024",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "F1 Score: 0.11427560587298777\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.56      0.06      0.11      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.74      0.53      0.54     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "========================================================\n",
      "Model: Decision Tree\n",
      "F1 Score: 0.21409779551846142\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.92     56677\n",
      "         1.0       0.20      0.23      0.21      5078\n",
      "\n",
      "    accuracy                           0.86     61755\n",
      "   macro avg       0.56      0.57      0.57     61755\n",
      "weighted avg       0.87      0.86      0.86     61755\n",
      "\n",
      "========================================================\n",
      "Model: Random Forest\n",
      "F1 Score: 0.07496360989810771\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.49      0.04      0.07      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.71      0.52      0.52     61755\n",
      "weighted avg       0.89      0.92      0.88     61755\n",
      "\n",
      "========================================================\n",
      "Model: XGBoost\n",
      "F1 Score: 0.09355472237100518\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.50      0.05      0.09      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.71      0.52      0.53     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "========================================================\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OHz8e48ePR15eHn755RfMnTsX3t7e+OOPP9C8efMKx7//d86Drl27Jj4PhkgKVk500KxZsyAIAoKCglBUVFRhe3FxMXbv3g0AeOGFFwAAW7du1YiJi4tDcnKyOLpfDuVl7LNnz2qsL29LZfT19eHh4YE1a9YAAH777beHxvbp0weHDh0Sk5Fyn3/+OUxNTWtsOmOTJk0wffp0+Pj4wN/f/6FxCoUCBgYGGoNt8/PzsWXLlgqxclWjSktLMXbsWCgUCuzfvx8RERFYtWoVvv3220fuZ2RkhC5dumgsUp7bAtz7/ty5c6dCMvb555+L24F7XWUA8NVXX2nERUdHa3U+hUKBZ555BsuWLUODBg003jtVvb9t2rQRu+0enFFUXePGjYOXlxc2btwodiF6e3sjKSkJTk5OFe57ly5dxOSkV69euH37Nvbv369xTG3ujbe3N27cuIHS0tJKz1VZElqVn0OVSoVBgwZh9uzZKCoqwvnz5ys9v7OzM5o0aYIvvvhCo6swLy8PO3bsEGfwEEnByokO8vT0xLp16xAcHAx3d3e8+eabaN++PYqLi3H69Gl88skncHV1hY+PD5ydnTFhwgSsWrUKenp6GDRokDhbx8HBAe+8845s7Ro8eDAsLS0RGBiIDz74AAYGBoiMjERqaqpG3Pr163Ho0CEMGTIEzZo1Q0FBgTgqv2/fvg89/ty5c8W++zlz5sDS0hLbtm3D3r17sXDhQqjVatmu5UEff/zxY2OGDBmCpUuXwtfXFxMmTMCNGzewePHiSqd7l//1/9VXX6Fly5YwNjau0jiRB82dOxe//vorDhw4ADs7O0ydOhVHjhxBYGAgOnXqBEdHR62P+Sh//fUXvvnmmwrrXVxc8Oqrr2LNmjXw9/dHSkoK3NzccPToUYSHh2Pw4MHi93bgwIHo3r07pk6ditzcXLi7uyM2NlZMYu4fp/CgPXv2YO3atXjxxRfRsmVLCIKAb7/9Frdu3UK/fv3EODc3N/z888/YvXs3GjduDHNz84dWhtasWQMfHx907doV77zzDpo1a4arV6/ihx9+wLZt26p1nxYsWAAPDw98+OGH+PTTT/HBBx/g4MGD6NatG0JCQuDs7IyCggKkpKRg3759WL9+PZo2bQp/f38sW7YMr7zyCubPn49WrVph//79+OGHHx57b8qNGTMG27Ztw+DBg/H222/jueeeg6GhIdLS0nD48GEMGzYML730UpV+DoOCgmBiYoLu3bujcePGyMjIQEREBNRqNZ599tlKz6+np4eFCxdi3Lhx8Pb2xsSJE1FYWIhFixbh1q1bVfpZInqs2hyNS4+WmJgo+Pv7C82aNROMjIwElUoldOrUSZgzZ46QmZkpxpWWlgoLFiwQ2rRpIxgaGgrW1tbCK6+8IqSmpmocr1evXkL79u0rnMff319o3ry5xjo8ZObGqVOnhG7dugkqlUpo0qSJMHfuXOHTTz/VmHERGxsrvPTSS0Lz5s0FpVIpWFlZCb169RK+//77Cue4f+aGIAjCuXPnBB8fH0GtVgtGRkbCM888ozGDQRD+mz3w9ddfa6wvn13xYPyD7p+t8yiVzbjZtGmT4OzsLCiVSqFly5ZCRESE8Nlnn2lcvyAIQkpKitC/f3/B3NxcACDe34e1/f5t5bMiDhw4IOjp6VW4Rzdu3BCaNWsmPPvss0JhYeEjr0EbAB66lLfhxo0bwhtvvCE0btxYMDAwEJo3by7MmjVLKCgo0DjWzZs3hfHjxwsNGjQQTE1NhX79+gknTpwQAAgrVqwQ4x6crfP7778LY8eOFZycnAQTExNBrVYLzz33nBAZGalx/MTERKF79+6CqampAED8Pj1sxlNsbKwwaNAgQa1WC0qlUnBychLeeeedR96P8vfTokWLKt3+8ssvCwYGBsKlS5cEQRCErKwsISQkRHB0dBQMDQ0FS0tLwd3dXZg9e7Zw584dcb+rV68Kw4cPF8zMzARzc3NhxIgRwr59+wQAwnfffSfG+fv7CyqVqtJzFxcXC4sXLxaeeeYZwdjYWDAzMxPatm0rTJw4Ufjzzz/Fa37cz2FUVJTQu3dvwdbWVjAyMhLs7e2FUaNGacwGe9g93bVrl+Dh4SEYGxsLKpVK6NOnj3Ds2DGNmPLZOllZWRrrH/y+Ez1IIQgSh/ATEVVB+bMxjh07hm7dutV2c3RKeHg4/ve//+Hq1au1+pEORLqC3TpEJLsvv/wS//zzD9zc3KCnp4cTJ05g0aJF6NmzZ71PTMoH0bZt2xbFxcU4dOgQVq5ciVdeeYWJCdH/Y3JCRLIzNzdHdHQ05s+fj7y8PDRu3BgBAQGYP39+bTet1pmammLZsmVISUlBYWEhmjVrhpkzZ+J///tfbTeNSGewW4eIiIh0CqcSExERkU5hckJEREQ6hckJERER6RQOiK2CsrIyXLt2Debm5lo9dpyIiHSDIAi4ffs27O3tq/Swu+oqKCio9Mne2jIyMqr2h1U+DZicVMG1a9cqfFIuERHVPampqTU2ZbugoAAm5lZAyd3HBz+GnZ0dLl++XG8TFCYnVVD+mSRGLv5Q6BvVcmuIasbVnxfXdhOIaszt3Fy0cnSQ/BlTj1JUVASU3IXSxR+Q8n9FaREyLkShqKiIyQk9XHlXjkLfiMkJPbUsLCxquwlENe6JdM0bGEv6v0JQcDgokxMiIiI5KQBISYI4tJHJCRERkawUevcWKfvXc7wDREREpFNYOSEiIpKTQiGxW4f9OkxOiIiI5MRuHcl4B4iIiEinsHJCREQkJ3brSMbkhIiISFYSu3XYqcE7QERERLqFlRMiIiI5sVtHMiYnREREcuJsHcl4B4iIiEinsHJCREQkJ3brSMbkhIiISE7s1pGMyQkREZGcWDmRjOkZERER6RRWToiIiOTEbh3JmJwQERHJSaGQmJywW4fpGREREekUVk6IiIjkpKe4t0jZv55jckJERCQnjjmRjHeAiIiIdAorJ0RERHLic04kY3JCREQkJ3brSMY7QERERDqFlRMiIiI5sVtHMiYnREREcmK3jmRMToiIiOTEyolkTM+IiIhIp7ByQkREJCd260jG5ISIiEhO7NaRjOkZERER6RRWToiIiGQlsVuHdQMmJ0RERLJit45kTM+IiIhIpzA5ISIikpNC8d+MnWot2lVOwsLCoFAoNBY7OztxuyAICAsLg729PUxMTODl5YXz589rHKOwsBCTJ0+GtbU1VCoVhg4dirS0NI2Y7Oxs+Pn5Qa1WQ61Ww8/PD7du3dKIuXr1Knx8fKBSqWBtbY2QkBAUFRVpd//A5ISIiEhekhKT6o1Xad++PdLT08Xl3Llz4raFCxdi6dKlWL16NeLi4mBnZ4d+/frh9u3bYkxoaCh27tyJ6OhoHD16FHfu3IG3tzdKS0vFGF9fXyQmJiImJgYxMTFITEyEn5+fuL20tBRDhgxBXl4ejh49iujoaOzYsQNTp07V+no45oSIiKiOMzAw0KiWlBMEAcuXL8fs2bMxfPhwAEBUVBRsbW3xxRdfYOLEicjJycFnn32GLVu2oG/fvgCArVu3wsHBAT/++CMGDBiA5ORkxMTE4MSJE/Dw8AAAbNy4EZ6enrh48SKcnZ1x4MABXLhwAampqbC3twcALFmyBAEBAfjoo49gYWFR5eth5YSIiEhO5QNipSwAcnNzNZbCwsKHnvLPP/+Evb09HB0dMWbMGPz9998AgMuXLyMjIwP9+/cXY5VKJXr16oXjx48DABISElBcXKwRY29vD1dXVzEmNjYWarVaTEwAoGvXrlCr1Roxrq6uYmICAAMGDEBhYSESEhK0uoVMToiIiOQkU7eOg4ODOL5DrVYjIiKi0tN5eHjg888/xw8//ICNGzciIyMD3bp1w40bN5CRkQEAsLW11djH1tZW3JaRkQEjIyM0bNjwkTE2NjYVzm1jY6MR8+B5GjZsCCMjIzGmqtitQ0REJCeZphKnpqZqdIUolcpKwwcNGiR+7ebmBk9PTzg5OSEqKgpdu3b9/0NqtkcQhArrHvRgTGXx1YmpClZOiIiIdJCFhYXG8rDk5EEqlQpubm74888/xXEoD1YuMjMzxSqHnZ0dioqKkJ2d/ciY69evVzhXVlaWRsyD58nOzkZxcXGFisrjMDkhIiKSUy3M1rlfYWEhkpOT0bhxYzg6OsLOzg4HDx4UtxcVFeHIkSPo1q0bAMDd3R2GhoYaMenp6UhKShJjPD09kZOTg1OnTokxJ0+eRE5OjkZMUlIS0tPTxZgDBw5AqVTC3d1dq2tgtw4REZGcnvATYqdNmwYfHx80a9YMmZmZmD9/PnJzc+Hv7w+FQoHQ0FCEh4ejdevWaN26NcLDw2FqagpfX18AgFqtRmBgIKZOnQorKytYWlpi2rRpcHNzE2fvtGvXDgMHDkRQUBA2bNgAAJgwYQK8vb3h7OwMAOjfvz9cXFzg5+eHRYsW4ebNm5g2bRqCgoK0mqkDMDkhIiKq09LS0jB27Fj8+++/aNSoEbp27YoTJ06gefPmAIAZM2YgPz8fwcHByM7OhoeHBw4cOABzc3PxGMuWLYOBgQFGjRqF/Px89OnTB5GRkdDX1xdjtm3bhpCQEHFWz9ChQ7F69Wpxu76+Pvbu3Yvg4GB0794dJiYm8PX1xeLFi7W+JoUgCEJ1b0h9kZubC7VaDaVbEBT6RrXdHKIakR23+vFBRHVUbm4ubK3UyMnJ0fqveG3OoVarYeyzGgpDk2ofRyjOR8Hut2q0rbqOlRMiIiIZlT9CXsIB5GtMHcUBsURERKRTWDkhIiKSk+L/Fyn713NMToiIiGTEbh3p2K1DREREOoWVEyIiIhmxciIdkxMiIiIZMTmRjskJERGRjJicSMcxJ0RERKRTWDkhIiKSE6cSS8bkhIiISEbs1pGO3TpERESkU1g5ISIikpFCAYmVE/naUlcxOSEiIpKRAhK7dZidsFuHiIiIdAsrJ0RERDLigFjpmJwQERHJiVOJJWO3DhEREekUVk6IiIjkJLFbR2C3DpMTIiIiOUkdcyJtps/TgckJERGRjJicSMcxJ0RERKRTWDkhIiKSE2frSMbkhIiISEbs1pGO3TpERESkU1g5ISIikhErJ9IxOSEiIpIRkxPp2K1DREREOoWVEyIiIhmxciIdkxMiIiI5cSqxZOzWISIiIp3CygkREZGM2K0jHZMTIiIiGTE5kY7JCRERkYyYnEjHMSdERESkU1g5ISIikhNn60jG5ISIiEhG7NaRjt06REREpFPqZOUkMjISoaGhuHXrVm03hQDMDBqMdycM1lh3/UYu2g58T3zdpoUtwia/iO6dW0GhUOD3v9Px2qxNSLueXeF4X694E327tce4aZ9g35GzAACHxpaYHjgQPbu0gY2VBTL+zcH2/XFYsukHFJeUivtmx62ucLwpEdHY/O1RuS6XqFJLN/+APYfP4M8r12GsNMRzHVoi7K1haN3CVowJDtuCL/ee1Nivi2sLHNw8DQCQnZOHiE/24vCJ3/HP9WxYNjDDEK8OeO8Nb6jNTJ7o9VD1sXIiXa0mJwEBAYiKiqqw/s8//0SrVq1qoUVUXcl/XcOLk1aJr0tLBfHrFk2ssX/jFGz9/jgiNuxFbl4+nFvYoaCouMJx3hzbG4JQYTXatLCFnp4e3omIxt9pWXBxssfy98bC1ESJOSt2asQGz9uCn2IviK9z7xTIcIVEj3b8t0t4/eWe6OTSHCWlpZi/bjeGT16NE9v/B5WJUozr4+mCNXNeEV8bGeqLX6dn5SAjKwcfvP0S2ra0Q2r6TUz5OBoZWTmIWvD6E70eqj4FJCYnHHRS+5WTgQMHYvPmzRrrGjVqVEutoeoqKS1D5o3blW57P9gHB4+fx9xV34nrrvxzo0Kca+smmDTuBbzgvxAXYyI0tv0Um4yfYpM19m/VzAavjexRITnJuZ3/0LYQ1ZRvVk3SeL1mzito3X8WEpNT0b3zf39sKY0MYGttUekxXFrZ4/OFQeJrx6aN8L83fTBxzucoKSmFgYF+pfsRPW1qfcyJUqmEnZ2dxrJixQq4ublBpVLBwcEBwcHBuHPnzkOPcebMGfTu3Rvm5uawsLCAu7s74uPjxe3Hjx9Hz549YWJiAgcHB4SEhCAvL+9JXF690dKhES7s+wiJu8Lw2Ufj0byJFYB75cl+3dvj0tVMfLNyEv74IQIHN0/D4F4dNPY3URpi4/wATF+4vcqJhYWZCbJz7lZYv3D6y7h08GP8FDUd44c/zxIp1Yryil1DC1ON9UcT/kTr/u+iy4h5eHv+F8i6+ej3e+6dApirjJmY1CHl3TpSlvqu1pOTyujp6WHlypVISkpCVFQUDh06hBkzZjw0fty4cWjatCni4uKQkJCAd999F4aGhgCAc+fOYcCAARg+fDjOnj2Lr776CkePHsVbb731pC7nqZdwPgVvzt2CkZPX4O3wL2FjZYEfPpuKhmoVGlmawVxljFD/fvgp9gKGT16NvT+fwZaFr6PbfX9Nhk8ZgVNnL2P/L+eqdM4WTawxYXQvbP72V43189ftxvhZm/DipFX49kACPgx9CVPG95f1eokeRxAEzF62A107OsGllb24vm83F3zyoT++WxuCD98ejt8uXMHQN1eisJIuTgC4eesOFn22HwHDuz+pppMcFDIs9Vytd+vs2bMHZmZm4utBgwbh66+/Fl87Ojriww8/xJtvvom1a9dWeoyrV69i+vTpaNu2LQCgdevW4rZFixbB19cXoaGh4raVK1eiV69eWLduHYyNjSscr7CwEIWFheLr3NxcSdf4tPvx+H/jO/AXEHf2Mn7bFYaxQzzw7YEEAMD+I+ew7svDAICkP/7Bcx1a4rXhz+P4b5cwqKcbenRpg16vfFyl89lZq/HNymDs+vE0tnwXq7FtyaYfxK+T/vgHADDj9UEa64lq2vSF23H+0jXs3/iOxvrh/d3Fr11a2aOTSzN08JmDA0fPw+eFjhqxuXfyMfqd9XB2bIyZQZoDzomedrWenPTu3Rvr1q0TX6tUKhw+fBjh4eG4cOECcnNzUVJSgoKCAuTl5UGlUlU4xpQpU/D6669jy5Yt6Nu3L15++WU4OTkBABISEnDp0iVs27ZNjBcEAWVlZbh8+TLatWtX4XgRERGYN29eDVxt/XC3oAgXLl2Dk0Mj3Lh1B8Ulpfj9crpGzB+XM9C1Y0sAQI8ubeDY1BophxZpxHy+4HXEJv4FnzdWiOvsrNX4fn0I4s5dRmj4l49tS/y5FFiYmaCRpfljy+dEcpixaDv2/3IO+z4JRRPbho+MtbNWw6GxJf5KzdJYfzuvACND1kJlosTWRUEwZJdOncLZOtLVereOSqVCq1atxKWoqAiDBw+Gq6srduzYgYSEBKxZswYAUFxceekzLCwM58+fx5AhQ3Do0CG4uLhg5857gyTLysowceJEJCYmisuZM2fw559/ignMg2bNmoWcnBxxSU1NrZmLf0oZGRqgTQtbZNzIQXFJKU5fuILWzW01Ypya2SA1/d404uVRB/C8bwR6vvKxuADAe8t2YNIHW8V9GjdSY/f6t3H291RM+mArhMqm9Tygg3NT5BcUIed2voxXSFSRIAiYvnA79hw+g+/XhaB5E+vH7nPz1h38cz0bdvcNkM29k48Rk1fDyFAfXyydCGOlYU02m2oAx5xIV+uVkwfFx8ejpKQES5YsgZ7evdxp+/btj92vTZs2aNOmDd555x2MHTsWmzdvxksvvYTOnTvj/PnzWk1NViqVUCqVjw8kAMAHb7+EmF/PIS0jG40ammFa4ECYq4wRvefe8xxWbvkRm8Jfw/HTl/Br/B/o6+mCgT1cxYpI5o3blQ6CTcvIxtVr92b12FnfS0zSrmfj/RU7Yd3wv67A8n0H9nCFjZUF4s5dRn5BMXp0aY3/BfsgatcxFBWX1PRtoHpu2oLt+OaHeHyxeALMTI1x/d973cEWZsYwMTbCnbuFWPDJXvi80BF21mpcTb+BD9bshlUDMwzxegbAvYrJiMlrcLegCBs+8MftOwW4/f8Da60bmkFfv9b/nqQqUCjuLVL2r+90LjlxcnJCSUkJVq1aBR8fHxw7dgzr169/aHx+fj6mT5+OkSNHwtHREWlpaYiLi8OIESMAADNnzkTXrl0xadIkBAUFQaVSITk5GQcPHsSqVaseelyquiY2DfDp/PGwaqDCv9l3EJ+Ugv6vLUFqxr3KyN6fz2JKRDTeCeiPj6eOxKWrmXh15qc4cebvKp+jd9e2cGpmA6dmNriw7yONbQ2fvTe4ubikFIEje2B+6HDo6Slw5Z8bCN+wF59+/Yt8F0v0EJt23Buc7X1fNyRwb0qxr09X6OspcOGva4jedwo5t/Nha22BHu5tsCn8NZir7o19O/P7VcQnpQAAOr+k2bV85rt5aGZvVfMXQqQDFEJVauM1JCAgALdu3cKuXbs01i9btgyLFi3CrVu30LNnT4wbNw6vvvoqsrOz0aBBA40nxBYVFcHf3x/Hjh3D9evXYW1tjeHDh2PRokXiYNe4uDjMnj0bsbGxEAQBTk5OGD16NN57771KWlVRbm4u1Go1lG5BUOgbyX0biHRCZU/XJXpa5ObmwtZKjZycHFhYVP6cGTnOoVar0XLyN9BTVhwfWVVlhXn4e9XIGm2rrqvV5KSuYHJC9QGTE3qaPdHkJOQb6EtITkoL8/D3yvqdnLADk4iIiHSKzo05ISIiqss4lVg6JidEREQy4mwd6ditQ0RERDqFlRMiIiIZ6ekpoKdX/fKHIGHfpwUrJ0RERDIq79aRslRXREQEFAqF+HlywL2nF4eFhcHe3h4mJibw8vLC+fPnNfYrLCzE5MmTYW1tDZVKhaFDhyItLU0jJjs7G35+flCr1VCr1fDz88OtW7c0Yq5evQofHx+oVCpYW1sjJCQERUVFWl8HkxMiIqKnQFxcHD755BN06NBBY/3ChQuxdOlSrF69GnFxcbCzs0O/fv1w+/Z/T+YODQ3Fzp07ER0djaNHj+LOnTvw9vZGaWmpGOPr64vExETExMQgJiYGiYmJ8PPzE7eXlpZiyJAhyMvLw9GjRxEdHY0dO3Zg6tSpWl8LkxMiIiIZ1cZn69y5cwfjxo3Dxo0b0bDhfx84KQgCli9fjtmzZ2P48OFwdXVFVFQU7t69iy+++AIAkJOTg88++wxLlixB37590alTJ2zduhXnzp3Djz/+CABITk5GTEwMPv30U3h6esLT0xMbN27Enj17cPHiRQDAgQMHcOHCBWzduhWdOnVC3759sWTJEmzcuBG5ublaXQ+TEyIiIhnJ1a2Tm5ursRQWFj70nJMmTcKQIUPQt29fjfWXL19GRkYG+vfvL65TKpXo1asXjh8/DgBISEhAcXGxRoy9vT1cXV3FmNjYWKjVanh4eIgxXbt2hVqt1ohxdXWFvb29GDNgwAAUFhYiISFBq3vI5ISIiEhGclVOHBwcxPEdarUaERERlZ4vOjoav/32W6XbMzIyAAC2tpqfDG9raytuy8jIgJGRkUbFpbIYGxubCse3sbHRiHnwPA0bNoSRkZEYU1WcrUNERKSDUlNTNR5fr1QqK415++23ceDAAfHz5CrzYFeRIAiP7T56MKay+OrEVAUrJ0RERDKSq3JiYWGhsVSWnCQkJCAzMxPu7u4wMDCAgYEBjhw5gpUrV8LAwECsZDxYucjMzBS32dnZoaioCNnZ2Y+MuX79eoXzZ2VlacQ8eJ7s7GwUFxdXqKg8DpMTIiIiGT3JqcR9+vTBuXPnkJiYKC5dunTBuHHjkJiYiJYtW8LOzg4HDx4U9ykqKsKRI0fQrVs3AIC7uzsMDQ01YtLT05GUlCTGeHp6IicnB6dOnRJjTp48iZycHI2YpKQkpKenizEHDhyAUqmEu7u7VveQ3TpERER1lLm5OVxdXTXWqVQqWFlZietDQ0MRHh6O1q1bo3Xr1ggPD4epqSl8fX0BAGq1GoGBgZg6dSqsrKxgaWmJadOmwc3NTRxg265dOwwcOBBBQUHYsGEDAGDChAnw9vaGs7MzAKB///5wcXGBn58fFi1ahJs3b2LatGkICgrS+tOVmZwQERHJSAGJH/wHeZ8QO2PGDOTn5yM4OBjZ2dnw8PDAgQMHYG5uLsYsW7YMBgYGGDVqFPLz89GnTx9ERkZCX19fjNm2bRtCQkLEWT1Dhw7F6tWrxe36+vrYu3cvgoOD0b17d5iYmMDX1xeLFy/Wus0KQRAECddcL+Tm5kKtVkPpFgSFvlFtN4eoRmTHrX58EFEdlZubC1srNXJycrT+K16bc6jVanSY9T30jVXVPk5pQR7ORgyt0bbqOo45ISIiIp3Cbh0iIiIZVfcpr/fvX98xOSEiIpKR1A/vY27Cbh0iIiLSMaycEBERyYjdOtIxOSEiIpIRu3WkY3JCREQkI1ZOpOOYEyIiItIprJwQERHJSWK3jswPiK2TmJwQERHJiN060rFbh4iIiHQKKydEREQy4mwd6ZicEBERyYjdOtKxW4eIiIh0CisnREREMmK3jnRMToiIiGTEbh3p2K1DREREOoWVEyIiIhmxciIdkxMiIiIZccyJdExOiIiIZMTKiXQcc0JEREQ6hZUTIiIiGbFbRzomJ0RERDJit4507NYhIiIincLKCRERkYwUkNitI1tL6i4mJ0RERDLSUyigJyE7kbLv04LdOkRERKRTWDkhIiKSEWfrSMfkhIiISEacrSMdkxMiIiIZ6SnuLVL2r+845oSIiIh0CisnREREclJI7Jph5YTJCRERkZw4IFY6dusQERGRTmHlhIiISEaK//8nZf/6jskJERGRjDhbRzp26xAREZFOYeWEiIhIRnwIm3RVSk5WrlxZ5QOGhIRUuzFERER1HWfrSFel5GTZsmVVOphCoWByQkRERJJUKTm5fPlyTbeDiIjoqaCnUEBPQvlDyr5Pi2oPiC0qKsLFixdRUlIiZ3uIiIjqtPJuHSlLfad1cnL37l0EBgbC1NQU7du3x9WrVwHcG2vy8ccfy95AIiKiuqR8QKyUpb7TOjmZNWsWzpw5g59//hnGxsbi+r59++Krr76StXFERERU/2g9lXjXrl346quv0LVrV43szsXFBX/99ZesjSMiIqprOFtHOq2Tk6ysLNjY2FRYn5eXx1IUERHVexwQK53W3TrPPvss9u7dK74uT0g2btwIT09P+VpGRERE9ZLWlZOIiAgMHDgQFy5cQElJCVasWIHz588jNjYWR44cqYk2EhER1RmK/1+k7F/faV056datG44dO4a7d+/CyckJBw4cgK2tLWJjY+Hu7l4TbSQiIqozOFtHump9to6bmxuioqLkbgsRERFR9ZKT0tJS7Ny5E8nJyVAoFGjXrh2GDRsGAwN+jiAREdVveop7i5T96zuts4mkpCQMGzYMGRkZcHZ2BgD88ccfaNSoEb7//nu4ubnJ3kgiIqK6gp9KLJ3WY05ef/11tG/fHmlpafjtt9/w22+/ITU1FR06dMCECRNqoo1ERERUj2hdOTlz5gzi4+PRsGFDcV3Dhg3x0Ucf4dlnn5W1cURERHURix/SaF05cXZ2xvXr1yusz8zMRKtWrWRpFBERUV3F2TrSVSk5yc3NFZfw8HCEhITgm2++QVpaGtLS0vDNN98gNDQUCxYsqOn2EhER6bTyAbFSFm2sW7cOHTp0gIWFBSwsLODp6Yn9+/eL2wVBQFhYGOzt7WFiYgIvLy+cP39e4xiFhYWYPHkyrK2toVKpMHToUKSlpWnEZGdnw8/PD2q1Gmq1Gn5+frh165ZGzNWrV+Hj4wOVSgVra2uEhISgqKhIuwtCFbt1GjRooJHJCYKAUaNGiesEQQAA+Pj4oLS0VOtGEBERUfU0bdoUH3/8sdh7ERUVhWHDhuH06dNo3749Fi5ciKVLlyIyMhJt2rTB/Pnz0a9fP1y8eBHm5uYAgNDQUOzevRvR0dGwsrLC1KlT4e3tjYSEBOjr6wMAfH19kZaWhpiYGADAhAkT4Ofnh927dwO4N5N3yJAhaNSoEY4ePYobN27A398fgiBg1apVWl2TQijPLB5Bmye/9urVS6sG1AW5ublQq9VQugVBoW9U280hqhHZcatruwlENSY3Nxe2Vmrk5OTAwsKixs6hVqvh+9lxGJmaVfs4RXfv4IvAbpLaamlpiUWLFuG1116Dvb09QkNDMXPmTAD3qiS2trZYsGABJk6ciJycHDRq1AhbtmzB6NGjAQDXrl2Dg4MD9u3bhwEDBiA5ORkuLi44ceIEPDw8AAAnTpyAp6cnfv/9dzg7O2P//v3w9vZGamoq7O3tAQDR0dEICAhAZmamVtdSpcrJ05hwEBER1QS5Hl+fm5ursV6pVEKpVD5y39LSUnz99dfIy8uDp6cnLl++jIyMDPTv31/jOL169cLx48cxceJEJCQkoLi4WCPG3t4erq6uOH78OAYMGIDY2Fio1WoxMQGArl27Qq1W4/jx43B2dkZsbCxcXV3FxAQABgwYgMLCQiQkJKB3795VvgfVfmra3bt3cfXq1Qp9SR06dKjuIYmIiOj/OTg4aLyeO3cuwsLCKo09d+4cPD09UVBQADMzM+zcuRMuLi44fvw4AMDW1lYj3tbWFleuXAEAZGRkwMjISGMWbnlMRkaGGGNjY1PhvDY2NhoxD56nYcOGMDIyEmOqSuvkJCsrC+PHj9cYbHM/jjkhIqL6TE+hgJ6EGTfl+6ampmp0hTyqauLs7IzExETcunULO3bsgL+/v8aQjAdnAAmC8NhZQQ/GVBZfnZiq0HoqcWhoKLKzs3HixAmYmJggJiYGUVFRaN26Nb7//nttD0dERPRUUSikLwDE2Tfly6OSEyMjI7Rq1QpdunRBREQEnnnmGaxYsQJ2dnYAUKFykZmZKVY57OzsUFRUhOzs7EfGVPYYkaysLI2YB8+TnZ2N4uLiChWVx9E6OTl06BCWLVuGZ599Fnp6emjevDleeeUVLFy4EBEREdoejoiIiGQmCAIKCwvh6OgIOzs7HDx4UNxWVFSEI0eOoFu3bgAAd3d3GBoaasSkp6cjKSlJjPH09EROTg5OnTolxpw8eRI5OTkaMUlJSUhPTxdjDhw4AKVSCXd3d63ar3W3Tl5entjvZGlpiaysLLRp0wZubm747bfftD0cERHRU+VJf7bOe++9h0GDBsHBwQG3b99GdHQ0fv75Z8TExEChUCA0NBTh4eFo3bo1WrdujfDwcJiamsLX1xcAoFarERgYiKlTp8LKygqWlpaYNm0a3Nzc0LdvXwBAu3btMHDgQAQFBWHDhg0A7k0l9vb2Fj9nr3///nBxcYGfnx8WLVqEmzdvYtq0aQgKCtJ61pHWyYmzszMuXryIFi1aoGPHjtiwYQNatGiB9evXo3HjxtoejoiI6Klyf9dMdffXxvXr1+Hn54f09HSo1Wp06NABMTEx6NevHwBgxowZyM/PR3BwMLKzs+Hh4YEDBw6IzzgBgGXLlsHAwACjRo1Cfn4++vTpg8jISPEZJwCwbds2hISEiLN6hg4ditWr/3sEgb6+Pvbu3Yvg4GB0794dJiYm8PX1xeLFi7W/B1V5zsn9tm3bhuLiYgQEBOD06dMYMGAAbty4ASMjI0RGRopzpJ8mfM4J1Qd8zgk9zZ7kc04Cok5Ifs5JpH/XGm2rrtO6cjJu3Djx606dOiElJQW///47mjVrBmtra1kbR0REVNfINVunPqv2c07KmZqaonPnznK0hYiIqM570t06T6MqJSdTpkyp8gGXLl1a7cYQERHVdU96QOzTqErJyenTp6t0MN5QIiIikqpKycnhw4druh11wrm94TCvp4OTiIioavRQjYeIPbB/fSd5zAkRERH9h9060jFBIyIiIp3CygkREZGMFApAj7N1JGFyQkREJCM9icmJlH2fFuzWISIiIp1SreRky5Yt6N69O+zt7XHlyhUAwPLly/Hdd9/J2jgiIqK6pnxArJSlvtM6OVm3bh2mTJmCwYMH49atWygtLQUANGjQAMuXL5e7fURERHVKebeOlKW+0zo5WbVqFTZu3IjZs2drfFphly5dcO7cOVkbR0RERPWP1gNiL1++jE6dOlVYr1QqkZeXJ0ujiIiI6ip+to50WldOHB0dkZiYWGH9/v374eLiIkebiIiI6qzyTyWWstR3WldOpk+fjkmTJqGgoACCIODUqVP48ssvERERgU8//bQm2khERFRn8PH10mmdnIwfPx4lJSWYMWMG7t69C19fXzRp0gQrVqzAmDFjaqKNREREVI9U6yFsQUFBCAoKwr///ouysjLY2NjI3S4iIqI6iWNOpJP0hFhra2u52kFERPRU0IO0cSN6YHaidXLi6Oj4yAfE/P3335IaRERERPWb1slJaGioxuvi4mKcPn0aMTExmD59ulztIiIiqpPYrSOd1snJ22+/Xen6NWvWID4+XnKDiIiI6jJ+8J90ss1YGjRoEHbs2CHX4YiIiKiekjQg9n7ffPMNLC0t5TocERFRnaRQQNKAWHbrVCM56dSpk8aAWEEQkJGRgaysLKxdu1bWxhEREdU1HHMindbJyYsvvqjxWk9PD40aNYKXlxfatm0rV7uIiIiontIqOSkpKUGLFi0wYMAA2NnZ1VSbiIiI6iwOiJVOqwGxBgYGePPNN1FYWFhT7SEiIqrTFDL8q++0nq3j4eGB06dP10RbiIiI6rzyyomUpb7TesxJcHAwpk6dirS0NLi7u0OlUmls79Chg2yNIyIiovqnysnJa6+9huXLl2P06NEAgJCQEHGbQqGAIAhQKBQoLS2Vv5VERER1BMecSFfl5CQqKgoff/wxLl++XJPtISIiqtMUCsUjP4OuKvvXd1VOTgRBAAA0b968xhpDREREpNWYE2ZzREREj8ZuHem0Sk7atGnz2ATl5s2bkhpERERUl/EJsdJplZzMmzcParW6ptpCREREpF1yMmbMGNjY2NRUW4iIiOo8PYVC0gf/Sdn3aVHl5ITjTYiIiB6PY06kq/ITYstn6xARERHVpCpXTsrKymqyHURERE8HiQNi+dE61Xh8PRERET2cHhTQk5BhSNn3acHkhIiISEacSiyd1p9KTERERFSTWDkhIiKSEWfrSMfkhIiISEZ8zol07NYhIiIincLKCRERkYw4IFY6JidEREQy0oPEbh1OJWa3DhEREekWVk6IiIhkxG4d6ZicEBERyUgP0rol2KXBe0BEREQ6hpUTIiIiGSkUCigk9M1I2fdpweSEiIhIRgpI+2Bhpibs1iEiIpJV+RNipSzaiIiIwLPPPgtzc3PY2NjgxRdfxMWLFzViBEFAWFgY7O3tYWJiAi8vL5w/f14jprCwEJMnT4a1tTVUKhWGDh2KtLQ0jZjs7Gz4+flBrVZDrVbDz88Pt27d0oi5evUqfHx8oFKpYG1tjZCQEBQVFWl1TUxOiIiI6rAjR45g0qRJOHHiBA4ePIiSkhL0798feXl5YszChQuxdOlSrF69GnFxcbCzs0O/fv1w+/ZtMSY0NBQ7d+5EdHQ0jh49ijt37sDb2xulpaVijK+vLxITExETE4OYmBgkJibCz89P3F5aWoohQ4YgLy8PR48eRXR0NHbs2IGpU6dqdU0KQRAECfekXsjNzYVarcYfV7NgbmFR280hqhENVEa13QSiGpObmwtbKzVycnJgUUO/x8v/r/jk5wswNTOv9nHu3rmNCV4u1W5rVlYWbGxscOTIEfTs2ROCIMDe3h6hoaGYOXMmgHtVEltbWyxYsAATJ05ETk4OGjVqhC1btmD06NEAgGvXrsHBwQH79u3DgAEDkJycDBcXF5w4cQIeHh4AgBMnTsDT0xO///47nJ2dsX//fnh7eyM1NRX29vYAgOjoaAQEBCAzM7PK18PKCRERkYzKn3MiZZEiJycHAGBpaQkAuHz5MjIyMtC/f38xRqlUolevXjh+/DgAICEhAcXFxRox9vb2cHV1FWNiY2OhVqvFxAQAunbtCrVarRHj6uoqJiYAMGDAABQWFiIhIaHK18ABsURERDooNzdX47VSqYRSqXzkPoIgYMqUKXj++efh6uoKAMjIyAAA2NraasTa2triypUrYoyRkREaNmxYIaZ8/4yMDNjY2FQ4p42NjUbMg+dp2LAhjIyMxJiqYOWEiIhIRuVTiaUsAODg4CAOPFWr1YiIiHjsud966y2cPXsWX375ZaXtup8gCI+dtvxgTGXx1Yl5HFZOiIiIZCTXE2JTU1M1xmg8rmoyefJkfP/99/jll1/QtGlTcb2dnR2Ae1WNxo0bi+szMzPFKoednR2KioqQnZ2tUT3JzMxEt27dxJjr169XOG9WVpbGcU6ePKmxPTs7G8XFxRUqKo/CygkREZEOsrCw0FgelpwIgoC33noL3377LQ4dOgRHR0eN7Y6OjrCzs8PBgwfFdUVFRThy5IiYeLi7u8PQ0FAjJj09HUlJSWKMp6cncnJycOrUKTHm5MmTyMnJ0YhJSkpCenq6GHPgwAEolUq4u7tX+dpZOSEiIpLRk35C7KRJk/DFF1/gu+++g7m5uTi2Q61Ww8TEBAqFAqGhoQgPD0fr1q3RunVrhIeHw9TUFL6+vmJsYGAgpk6dCisrK1haWmLatGlwc3ND3759AQDt2rXDwIEDERQUhA0bNgAAJkyYAG9vbzg7OwMA+vfvDxcXF/j5+WHRokW4efMmpk2bhqCgIK1mHjE5ISIiktGTfkLsunXrAABeXl4a6zdv3oyAgAAAwIwZM5Cfn4/g4GBkZ2fDw8MDBw4cgLn5f1Oely1bBgMDA4waNQr5+fno06cPIiMjoa+vL8Zs27YNISEh4qyeoUOHYvXq1eJ2fX197N27F8HBwejevTtMTEzg6+uLxYsXa3VNfM5JFfA5J1Qf8Dkn9DR7ks85ifz1d8nPOQno0bZG26rrWDkhIiKSET/4TzomJ0RERDKSa7ZOfcbkhIiISEasnEjHBI2IiIh0CisnREREMnrSs3WeRkxOiIiIZCT1w/vYq8NuHSIiItIxrJwQERHJSA8K6EnonJGy79OCyQkREZGM2K0jHbt1iIiISKewckJERCQjxf//k7J/fcfkhIiISEbs1pGO3TpERESkU1g5ISIikpFC4mwdduswOSEiIpIVu3WkY3JCREQkIyYn0nHMCREREekUVk6IiIhkxKnE0jE5ISIikpGe4t4iZf/6jt06REREpFNYOSEiIpIRu3WkY3JCREQkI87WkY7dOkRERKRTWDkhIiKSkQLSumZYOGFyQkREJCvO1pGO3TpERESkU1g5oRq3dtuPWLRxH8aP6IE5k18CADh6Tak09t03vDFxzAsAgDFvr8HJM39pbPfu3RGr5r4qvn5+9If453q2RswbY1/AzInecl4C0WN99s2v2LTjV6Sm3wQAtG1ph+mBg9Cve3sUl5Ri/rrdOHjsPK78cwMWZsbo9VxbzH1rKBo3aiAeIzT8Sxw5dREZ/+ZAZaLEcx0cETZ5GNq0sKulq6Lq4Gwd6XQqOVE8Zoiyv78/IiMjn0xjSBZnfr+KL3efQFunxhrrT+0I03j986nfMXPhVxjU8xmN9WO8u2LK+IHia6XSsMI53nltIMYO6Sq+NjVRytByIu3Y2zTA3LeGoWVTawDAl3tPYty0T3Bk67uwt22As7+nYnrgILi2boJbt+/ivaU74Dt1Aw5/PlM8Rse2Dnh54LNwsGuI7Ny7+PiTvRj+1hqc+W4e9PVZ6K4rOFtHOp1KTtLT08Wvv/rqK8yZMwcXL14U15mYmGjEFxcXw9Cw4n9WpBvy7hYidP42REwbhdVbDmpsa2RlofH64NEkeHZqhWb2VhrrTZSGFWIfZGaifGwMUU0b1NNN4/X7wUOxacdRxCddhp9TN+xcM1lj+4JpL6NPwCKkZtyEg50lACBg+PPi9mb2Vpj9pg96+EbgavoNODZtVPMXQbJQQNqgVuYmOjbmxM7OTlzUajUUCoX4uqCgAA0aNMD27dvh5eUFY2NjbN26FWFhYejYsaPGcZYvX44WLVporNu8eTPatWsHY2NjtG3bFmvXrn1yF1ZPzVmxAy90bYfnu7R5ZFzWzds4fOICRg1+rsK27378DZ2Hvo/+AQvw0drvceduQYWY9V8eQqeh/8PgwMVYveUgiopLZLsGouooLS3DjgPxuJtfhGfdHCuNyb2TD4VCAbWZSaXb8/IL8cXuE2hub4Umtg1rsrlEOkenKidVMXPmTCxZsgSbN2+GUqnEJ5988th9Nm7ciLlz52L16tXo1KkTTp8+jaCgIKhUKvj7+1eILywsRGFhofg6NzdX1muoD3b/dBrn/0jDd+vfeWzsjh/ioDJVYmCPDhrrh/XrDAc7KzSyNMcflzOwcONeJP91DVuXvCHGjB/ZE+1bN4Ha3BRnkq9i0ca9SE2/iQUzRst+TUSPc/7SPxjw2hIUFJVAZaLElkVBaNuycYW4gsJizFvzHUYO6AKLB5KTT7/+BWGrdiEvvwhtWthi55q3YGRY535V12t6UEBPQt+MHmsndS85CQ0NxfDhw7Xa58MPP8SSJUvE/RwdHXHhwgVs2LCh0uQkIiIC8+bNk6W99dG1zGzMW70Tny+aWOkYkQd9ve8UhvV1rxA71ttT/Nq5ZWO0aGqNoROXIemPNLi2aQoACHy5lxjTzskeanMTBM+NwrsTvdFQrZLpioiqpnVzW/yybRZybt/F94cSERy2BXs2vK2RoBSXlCJw9maUlQlYPHNUhWO8POhZ9PZoi4x/c7F6648YP2sTYj6dAuMq/CyRbmC3jnR1Ljnp0qWLVvFZWVlITU1FYGAggoKCxPUlJSVQq9WV7jNr1ixMmfLfbJLc3Fw4ODhUr8H1UNLFNNzIvoOhE5aJ60rLynDq7N/4fOcxXDy4UBzcd+rs3/g7NROr5vo99riubZrC0EAfKWlZYnLyoE4uzQEAKf/8y+SEnjgjQwO0dLg3NqSTS3OcvnAV66N/xvL3xgK4l5iMn/UZrly7ge/XTq5QNQEAtZkJ1GYmcGpmg2fdWsDxhRnY8/MZjByg3e8+orqsziUnKpXmfzh6enoQBEFjXXFxsfh1WVkZgHtdOx4eHhpx+vr6lZ5DqVRCqeSMj+rq5t4aMZuma6ybsSAaLZvZ4I2xL2jMOti+9yTc2jSFS6smjz3uH5czUFxS+sjBr+f//AcAYMMBsqQDBEFAUdG9MVDliclfV7Owe30ILBuYaX0MqiNYOpGsziUnD2rUqBEyMjIgCII4FTkxMVHcbmtriyZNmuDvv//GuHHjaqmV9YuZqTGcH+hnNzE2QkMLU431t/MKsO/IGcx+c2iFY1z5519892MCvDxcYKlW4c8rGfho7fdo37oJurjeG2D42/kUnL5wBZ4dW8HczBhnfk/F/DW70Ld7ew4gpCfugzXfo283FzS1bYjbdwvw7YEEHP3tT3yzMhglJaXwn/kpzvyeiuhlb6C0VMD1f++NZWuoNoWRoQFS0v7FtwcT8ELXdrBqaIb0zFtY8fmPMDY2RL/u7Wv56kgbfM6JdHU+OfHy8kJWVhYWLlyIkSNHIiYmBvv374eFxX9/OYeFhSEkJAQWFhYYNGgQCgsLER8fj+zsbI3uG3qydh86DUEQ4NOnU4Vthob6OPbbn9i841fczS9E40YN0NvTBW/79xcrL0aGBthz6DRWRP6AouISNLG1xJghXTFx7AtP+lKIkHXzNt6Y+zmu/5sLCzNjtG/VBN+sDEZvj3a4eu0G9v9yDgDQc9zHGvvtXh+C593bQKk0QGziX1gf/TNu5d5FI0tzdOvUCj98OhWNLM1r45KIao1CeLBPREdERkYiNDQUt27dAgCkpKTA0dERp0+frjB1eP369QgPD8fNmzcxYsQIODs745NPPkFKSooY88UXX2DRokW4cOECVCoV3NzcEBoaipdeeumxbcnNzYVarcYfV7NgbsHuAno6NVAZ1XYTiGpMbm4ubK3UyMnJ0fjjVe5zqNVq/JR4FWbm1T/Hndu56NOxWY22VdfpbHKiS5icUH3A5ISeZk8yOTkkQ3LyQj1PTnTqIWxEREREdX7MCRERkU7hbB3JmJwQERHJiLN1pGNyQkREJCN+KrF0HHNCREREOoWVEyIiIhlxyIl0TE6IiIjkxOxEMnbrEBERkU5h5YSIiEhGnK0jHZMTIiIiGXG2jnTs1iEiIiKdwsoJERGRjDgeVjomJ0RERHJidiIZu3WIiIhIp7ByQkREJCPO1pGOyQkREZGMOFtHOiYnREREMuKQE+k45oSIiKgO++WXX+Dj4wN7e3soFArs2rVLY7sgCAgLC4O9vT1MTEzg5eWF8+fPa8QUFhZi8uTJsLa2hkqlwtChQ5GWlqYRk52dDT8/P6jVaqjVavj5+eHWrVsaMVevXoWPjw9UKhWsra0REhKCoqIira+JyQkREZGcFDIsWsjLy8MzzzyD1atXV7p94cKFWLp0KVavXo24uDjY2dmhX79+uH37thgTGhqKnTt3Ijo6GkePHsWdO3fg7e2N0tJSMcbX1xeJiYmIiYlBTEwMEhMT4efnJ24vLS3FkCFDkJeXh6NHjyI6Oho7duzA1KlTtbsgAApBEASt96pncnNzoVar8cfVLJhbWNR2c4hqRAOVUW03gajG5ObmwtZKjZycHFjU0O/x8v8rTiRfg5l59c9x53Yuurazr1ZbFQoFdu7ciRdffBHAvaqJvb09QkNDMXPmTAD3qiS2trZYsGABJk6ciJycHDRq1AhbtmzB6NGjAQDXrl2Dg4MD9u3bhwEDBiA5ORkuLi44ceIEPDw8AAAnTpyAp6cnfv/9dzg7O2P//v3w9vZGamoq7O3tAQDR0dEICAhAZmamVtfCygkREdFT6vLly8jIyED//v3FdUqlEr169cLx48cBAAkJCSguLtaIsbe3h6urqxgTGxsLtVotJiYA0LVrV6jVao0YV1dXMTEBgAEDBqCwsBAJCQlatZsDYomIiGQk12yd3NxcjfVKpRJKpVKrY2VkZAAAbG1tNdbb2triypUrYoyRkREaNmxYIaZ8/4yMDNjY2FQ4vo2NjUbMg+dp2LAhjIyMxJiqYuWEiIhIRnINOXFwcBAHn6rVakRERFS/TQ9kS4IgVFj3oAdjKouvTkxVsHJCRESkg1JTUzXGaWhbNQEAOzs7APeqGo0bNxbXZ2ZmilUOOzs7FBUVITs7W6N6kpmZiW7duokx169fr3D8rKwsjeOcPHlSY3t2djaKi4srVFQeh5UTIiIiOclUOrGwsNBYqpOcODo6ws7ODgcPHhTXFRUV4ciRI2Li4e7uDkNDQ42Y9PR0JCUliTGenp7IycnBqVOnxJiTJ08iJydHIyYpKQnp6elizIEDB6BUKuHu7q5Vu1k5ISIiktGTfnz9nTt3cOnSJfH15cuXkZiYCEtLSzRr1gyhoaEIDw9H69at0bp1a4SHh8PU1BS+vr4AALVajcDAQEydOhVWVlawtLTEtGnT4Obmhr59+wIA2rVrh4EDByIoKAgbNmwAAEyYMAHe3t5wdnYGAPTv3x8uLi7w8/PDokWLcPPmTUybNg1BQUFazzpickJERFSHxcfHo3fv3uLrKVOmAAD8/f0RGRmJGTNmID8/H8HBwcjOzoaHhwcOHDgAc3NzcZ9ly5bBwMAAo0aNQn5+Pvr06YPIyEjo6+uLMdu2bUNISIg4q2fo0KEaz1bR19fH3r17ERwcjO7du8PExAS+vr5YvHix1tfE55xUAZ9zQvUBn3NCT7Mn+ZyT+D/SJT/npEubxjXaVl3HygkREZGM+Nk60jE5ISIikhOzE8k4W4eIiIh0CisnREREMnrSs3WeRkxOiIiI5CTx8fXMTditQ0RERDqGlRMiIiIZcTysdExOiIiI5MTsRDJ26xAREZFOYeWEiIhIRpytIx2TEyIiIhkpJM7WkTTT5ynBbh0iIiLSKaycEBERyYjjYaVjckJERCQnZieSMTkhIiKSEQfESscxJ0RERKRTWDkhIiKSkQISZ+vI1pK6i8kJERGRjDjkRDp26xAREZFOYeWEiIhIRnwIm3RMToiIiGTFjh2p2K1DREREOoWVEyIiIhmxW0c6JidEREQyYqeOdOzWISIiIp3CygkREZGM2K0jHZMTIiIiGfGzdaRjckJERCQnDjqRjGNOiIiISKewckJERCQjFk6kY3JCREQkIw6IlY7dOkRERKRTWDkhIiKSEWfrSMfkhIiISE4cdCIZu3WIiIhIp7ByQkREJCMWTqRjckJERCQjztaRjt06REREpFNYOSEiIpKVtNk67NhhckJERCQrdutIx24dIiIi0ilMToiIiEinsFuHiIhIRuzWkY7JCRERkYz4+Hrp2K1DREREOoWVEyIiIhmxW0c6JidEREQy4uPrpWO3DhEREekUVk6IiIjkxNKJZExOiIiIZMTZOtKxW4eIiIh0CisnREREMuJsHemYnBAREcmIQ06kY3JCREQkJ2YnknHMCREREekUVk6IiIhkxNk60jE5ISIikhEHxErH5KQKBEEAANy5fbuWW0JUc/RKjWq7CUQ15nZuLoD/fp/XpNz/P1dt7f80YHJSBbf/Pynp3L5lLbeEiIikuH37NtRqdY0c28jICHZ2dmjt6CD5WHZ2djAyqr9/MCiEJ5FG1nFlZWW4du0azM3NoWC97YnIzc2Fg4MDUlNTYWFhUdvNIZIV399PniAIuH37Nuzt7aGnV3NzQQoKClBUVCT5OEZGRjA2NpahRXUTKydVoKenh6ZNm9Z2M+olCwsL/vKmpxbf309WTVVM7mdsbFyvkwq5cCoxERER6RQmJ0RERKRTmJyQTlIqlZg7dy6USmVtN4VIdnx/Ez0aB8QSERGRTmHlhIiIiHQKkxMiIiLSKUxOiIiISKcwOSGdEhkZiQYNGtR2M4iIqBYxOaEaERAQAIVCUWG5dOlSbTeNSFaVvc/vXwICAmq7iUR1Dp8QSzVm4MCB2Lx5s8a6Ro0a1VJriGpGenq6+PVXX32FOXPm4OLFi+I6ExMTjfji4mIYGho+sfYR1UWsnFCNUSqVsLOz01hWrFgBNzc3qFQqODg4IDg4GHfu3HnoMc6cOYPevXvD3NwcFhYWcHd3R3x8vLj9+PHj6NmzJ0xMTODg4ICQkBDk5eU9icsjAgCN97darYZCoRBfFxQUoEGDBti+fTu8vLxgbGyMrVu3IiwsDB07dtQ4zvLly9GiRQuNdZs3b0a7du1gbGyMtm3bYu3atU/uwohqEZMTeqL09PSwcuVKJCUlISoqCocOHcKMGTMeGj9u3Dg0bdoUcXFxSEhIwLvvviv+1Xnu3DkMGDAAw4cPx9mzZ/HVV1/h6NGjeOutt57U5RBVycyZMxESEoLk5GQMGDCgSvts3LgRs2fPxkcffYTk5GSEh4fj/fffR1RUVA23lqj2sVuHasyePXtgZmYmvh40aBC+/vpr8bWjoyM+/PBDvPnmmw/9i/Dq1auYPn062rZtCwBo3bq1uG3RokXw9fVFaGiouG3lypXo1asX1q1bxw/fIp0RGhqK4cOHa7XPhx9+iCVLloj7OTo64sKFC9iwYQP8/f1roplEOoPJCdWY3r17Y926deJrlUqFw4cPIzw8HBcuXEBubi5KSkpQUFCAvLw8qFSqCseYMmUKXn/9dWzZsgV9+/bFyy+/DCcnJwBAQkICLl26hG3btonxgiCgrKwMly9fRrt27Wr+IomqoEuXLlrFZ2VlITU1FYGBgQgKChLXl5SUPJFP1iWqbUxOqMaoVCq0atVKfH3lyhUMHjwYb7zxBj788ENYWlri6NGjCAwMRHFxcaXHCAsLg6+vL/bu3Yv9+/dj7ty5iI6OxksvvYSysjJMnDgRISEhFfZr1qxZjV0XkbYeTLz19PTw4CeH3P8zUFZWBuBe146Hh4dGnL6+fg21kkh3MDmhJyY+Ph4lJSVYsmQJ9PTuDXfavn37Y/dr06YN2rRpg3feeQdjx47F5s2b8dJLL6Fz5844f/68RgJEVBc0atQIGRkZEAQBCoUCAJCYmChut7W1RZMmTfD3339j3LhxtdRKotrD5ISeGCcnJ5SUlGDVqlXw8fHBsWPHsH79+ofG5+fnY/r06Rg5ciQcHR2RlpaGuLg4jBgxAsC9QYZdu3bFpEmTEBQUBJVKheTkZBw8eBCrVq16UpdFpDUvLy9kZWVh4cKFGDlyJGJiYrB//35YWFiIMWFhYQgJCYGFhQUGDRqEwsJCxMfHIzs7G1OmTKnF1hPVPM7WoSemY8eOWLp0KRYsWABXV1ds27YNERERD43X19fHjRs38Oqrr6JNmzYYNWoUBg0ahHnz5gEAOnTogCNHjuDPP/9Ejx490KlTJ7z//vto3Ljxk7okompp164d1q5dizVr1uCZZ57BqVOnMG3aNI2Y119/HZ9++ikiIyPh5uaGXr16ITIyEo6OjrXUaqInRyE82PFJREREVItYOSEiIiKdwuSEiIiIdAqTEyIiItIpTE6IiIhIpzA5ISIiIp3C5ISIiIh0CpMTIiIi0ilMTojqiLCwMHTs2FF8HRAQgBdffPGJtyMlJQUKhULjcesPatGiBZYvX17lY0ZGRqJBgwaS26ZQKLBr1y7JxyGi2sXkhEiCgIAAKBQKKBQKGBoaomXLlpg2bRry8vJq/NwrVqxAZGRklWKrklAQEekKfrYOkUQDBw7E5s2bUVxcjF9//RWvv/468vLysG7dugqxxcXFMDQ0lOW8arValuMQEekaVk6IJFIqlbCzs4ODgwN8fX0xbtw4sWuhvCtm06ZNaNmyJZRKJQRBQE5ODiZMmAAbGxtYWFjghRdewJkzZzSO+/HHH8PW1hbm5uYIDAxEQUGBxvYHu3XKysqwYMECtGrVCkqlEs2aNcNHH30EAOLnsXTq1AkKhQJeXl7ifps3b0a7du1gbGyMtm3bYu3atRrnOXXqFDp16gRjY2N06dIFp0+f1voeLV26FG5ublCpVHBwcEBwcDDu3LlTIW7Xrl1o06YNjI2N0a9fP6Smpmps3717N9zd3WFsbIyWLVti3rx5KCkp0bo9RKTbmJwQyczExATFxcXi60uXLmH79u3YsWOH2K0yZMgQZGRkYN++fUhISEDnzp3Rp08f3Lx5EwCwfft2zJ07Fx999BHi4+PRuHHjCknDg2bNmoUFCxbg/fffx4ULF/DFF1/A1tYWwL0EAwB+/PFHpKen49tvvwUAbNy4EbNnz8ZHH32E5ORkhIeH4/3330dUVBQAIC8vD97e3nB2dkZCQgLCwsIqfEBdVejp6WHlypVISkpCVFQUDh06hBkzZmjE3L17Fx999BGioqJw7Ngx5ObmYsyYMeL2H374Aa+88gpCQkJw4cIFbNiwAZGRkWICRkRPEYGIqs3f318YNmyY+PrkyZOClZWVMGrUKEEQBGHu3LmCoaGhkJmZKcb89NNPgoWFhVBQUKBxLCcnJ2HDhg2CIAiCp6en8MYbb2hs9/DwEJ555plKz52bmysolUph48aNlbbz8uXLAgDh9OnTGusdHByEL774QmPdhx9+KHh6egqCIAgbNmwQLC0thby8PHH7unXrKj3W/Zo3by4sW7bsodu3b98uWFlZia83b94sABBOnDghrktOThYACCdPnhQEQRB69OghhIeHaxxny5YtQuPGjcXXAISdO3c+9LxEVDdwzAmRRHv27IGZmRlKSkpQXFyMYcOGYdWqVeL25s2bo1GjRuLrhIQE3LlzB1ZWVhrHyc/Px19//QUASE5OxhtvvKGx3dPTE4cPH660DcnJySgsLESfPn2q3O6srCykpqYiMDAQQUFB4vqSkhJxPEtycjKeeeYZmJqaarRDW4cPH0Z4eDguXLiA3NxclJSUoKCgAHl5eVCpVAAAAwMDdOnSRdynbdu2aNCgAZKTk/Hcc88hISEBcXFxGpWS0tJSFBQU4O7duxptJKK6jckJkUS9e/fGunXrYGhoCHt7+woDXsv/8y1XVlaGxo0b4+eff65wrOpOpzUxMdF6n7KyMgD3unY8PDw0tunr6wMABEGoVnvud+XKFQwePBhvvPEGPvzwQ1haWuLo0aMIDAzU6P4C7k0FflD5urKyMsybNw/Dhw+vEGNsbCy5nUSkO5icEEmkUqnQqlWrKsd37twZGRkZMDAwQIsWLSqNadeuHU6cOIFXX31VXHfixImHHrN169YwMTHBTz/9hNdff73CdiMjIwD3Kg3lbG1t0aRJE/z9998YN25cpcd1cXHBli1bkJ+fLyZAj2pHZeLj41FSUoIlS5ZAT+/eMLft27dXiCspKUF8fDyee+45AMDFixdx69YttG3bFsC9+3bx4kWt7jUR1U1MToiesL59+8LT0xMvvvgiFixYAGdnZ1y7dg379u3Diy++iC5duuDtt9+Gv78/unTpgueffx7btm3D+fPn0bJly0qPaWxsjJkzZ2LGjBkwMjJC9+7dkZWVhfPnzyMwMBA2NjYwMTFBTEwMmjZtCmNjY6jVaoSFhSEkJAQWFhYYNGgQCgsLER8fj+zsbEyZMgW+vr6YPXs2AgMD8b///Q8pKSlYvHixVtfr5OSEkpISrFq1Cj4+Pjh27BjWr19fIc7Q0BCTJ0/GypUrYWhoiLfeegtdu3YVk5U5c+bA29sbDg4OePnll6Gnp4ezZ8/i3LlzmD9/vvbfCCLSWZytQ/SEKRQK7Nu3Dz179sRrr72GNm3aYMyYMUhJSRFn14wePRpz5szBzJkz4e7ujitXruDNN9985HHff/99TJ06FXPmzEG7du0wevRoZGZmArg3nmPlypXYsGED7O3tMWzYMADA66+/jk8//RSRkZFwc3NDr169EBkZKU49NjMzw+7du3HhwgV06tQJs2fPxoIFC7S63o4dO2Lp0qVYsGABXF1dsW3bNkRERFSIMzU1xcyZM+Hr6wtPT0+YmJggOjpa3D5gwADs2bMHBw8exLPPPouuXbti6dKlaN68uVbtISLdpxDk6FQmIiIikgkrJ0RERKRTmJwQERGRTmFyQkRERDqFyQkRERHpFCYnREREpFOYnBAREZFOYXJCREREOoXJCREREekUJidERESkU5icEBERkU5hckJEREQ6hckJERER6ZT/A6vpXYieKWihAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, roc_auc_score, confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "# Defining the function to apply models\n",
    "def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
    "    # Fit the model\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(\"========================================================\")\n",
    "\n",
    "    # Compute ROC curve and ROC area\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    return accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm\n",
    "\n",
    "# Defining the models\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(random_state=42, max_iter=500)),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier(random_state=42)),\n",
    "    (\"Random Forest\", RandomForestClassifier(random_state=42)),\n",
    "    (\"XGBoost\", XGBClassifier(random_state=42))\n",
    "]\n",
    "\n",
    "# Applying the models and storing the results\n",
    "results = []\n",
    "roc_curves = []\n",
    "confusion_matrices = []\n",
    "\n",
    "for name, model in models:\n",
    "    accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm = apply_model(model, X_train, y_train, X_test, y_test, name)\n",
    "    results.append((name, accuracy, precision, recall, f1))\n",
    "    roc_curves.append((name, fpr, tpr, roc_auc))\n",
    "    confusion_matrices.append((name, cm))\n",
    "\n",
    "# Plotting the ROC curves for each model\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve comparison')\n",
    "for name, fpr, tpr, roc_auc in roc_curves:\n",
    "    plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "# Draw confusion matrices for each model\n",
    "for name, cm in confusion_matrices:\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.title(f'Confusion Matrix - {name}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed040a8a",
   "metadata": {},
   "source": [
    "### KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3de72dc4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: k-Nearest Neighbors (KNN)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95     56677\n",
      "         1.0       0.33      0.11      0.17      5078\n",
      "\n",
      "    accuracy                           0.91     61755\n",
      "   macro avg       0.63      0.55      0.56     61755\n",
      "weighted avg       0.88      0.91      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Assuming X_train and X_test are numpy arrays\n",
    "X_train_knn = np.ascontiguousarray(X_train)\n",
    "X_test_knn = np.ascontiguousarray(X_test)\n",
    "\n",
    "# Create a KNN classifier with specified number of neighbors\n",
    "k = 5\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "knn_classifier.fit(X_train_knn, y_train)\n",
    "\n",
    "# Get the predicted labels for the test data\n",
    "y_pred = knn_classifier.predict(X_test_knn)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: k-Nearest Neighbors (KNN)\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f20fcde",
   "metadata": {},
   "source": [
    "### After using PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "89eb678e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "F1 Score: 0.11352785145888593\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.56      0.06      0.11      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.74      0.53      0.54     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "========================================================\n",
      "Model: Decision Tree\n",
      "F1 Score: 0.2180935486882644\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.93     56677\n",
      "         1.0       0.21      0.23      0.22      5078\n",
      "\n",
      "    accuracy                           0.86     61755\n",
      "   macro avg       0.57      0.58      0.57     61755\n",
      "weighted avg       0.87      0.86      0.87     61755\n",
      "\n",
      "========================================================\n",
      "Model: Random Forest\n",
      "F1 Score: 0.12211221122112212\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.99      0.95     56677\n",
      "         1.0       0.38      0.07      0.12      5078\n",
      "\n",
      "    accuracy                           0.91     61755\n",
      "   macro avg       0.65      0.53      0.54     61755\n",
      "weighted avg       0.88      0.91      0.89     61755\n",
      "\n",
      "========================================================\n",
      "Model: XGBoost\n",
      "F1 Score: 0.0903387703889586\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.50      0.05      0.09      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.71      0.52      0.52     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dkzoio/dg5+PSwyY5OVmMGjVKtGzZUpibmwutViu6du0qpk+fXuVpnbNmzRLu7u7CzMxM2NvbixdffPG2zzm51ahRo6TncFRCNat1hBDi8OHDonv37kKr1YrmzZuLGTNmiE8++cRglUNCQoJ47rnnRKtWrYRGoxF2dnbC399ffPfdd1WuUd1zTgIDA4VOpxPm5uaic+fOYtWqVQYxlatavvrqK4P2tLQ0AaBK/K1uXq1zJ9WtuPnss8+Eh4eH0Gg0ok2bNiImJkZ6iNjNqzzOnTsn+vXrJ6ytrat9zsmtfb95X+VqnZ07dwq1Wl3lPbp8+bJo2bKleOyxx0RxcfEd76EmysvLhZ+fn3BychKXL1+usr+kpER07txZtGrVSuj1eoN93333nQgMDBSOjo7C1NRUWFtbiy5duogJEyZUeebMrat11Gq1cHZ2FgMGDBB79+6tct3K55xotVphaWkpfH19xZYtW6rE1eQzM2/ePNG9e3dhb28vzM3NRcuWLUVoaKg4d+6cQdzUqVOFs7OzUKvVfM4JPTRUQtRwOjwRERHRfcA5J0RERGRUmJwQERGRUWFyQkREREaFyQkREREZFSYnREREZFSYnBAREZFR4UPYaqCiogIXL16EtbV1rR7hTURExkEIgatXr8LZ2Rlqdd39Xl5UVFTtU7Jry9zcXPr6jYaIyUkNXLx4sco3iBIRUf2Tnp6OFi1a1Mm5i4qKYGltB5TJ/+4jJycnpKWlNdgEhclJDVQ+Ctq84yioTMwfcG+I6saFvXMfdBeI6sxVvR7tXF3q9NH+JSUlQNl1aDqOAuT8rCgvQdbp1SgpKWFyQrdXOZSjMjFnckIPLRsbmwfdBaI6d1+G5k0tZP2sECpOB2VyQkREpCQVADlJEKc2MjkhIiJSlEp9Y5NzfAPHd4CIiIiMCisnRERESlKpZA7rcFyHyQkREZGSOKwjG98BIiIiMiqsnBARESmJwzqyMTkhIiJSlMxhHQ5q8B0gIiIi48LKCRERkZI4rCMbkxMiIiIlcbWObHwHiIiIyKiwckJERKQkDuvIxuSEiIhISRzWkY3JCRERkZJYOZGN6RkREREZFVZOiIiIlMRhHdmYnBARESlJpZKZnHBYh+kZERERGRVWToiIiJSkVt3Y5BzfwDE5ISIiUhLnnMjGd4CIiIiMCisnRERESuJzTmRjckJERKQkDuvIxneAiIiIjAorJ0RERErisI5sTE6IiIiUxGEd2ZicEBERKYmVE9mYnhEREZFRYeWEiIhISRzWkY3JCRERkZI4rCMb0zMiIiIyKqycEBERKUrmsA7rBkxOiIiIFMVhHdmYnhEREZFRYXJCRESkJJXqnxU797TVrnISFRUFlUplsDk5OUn7hRCIioqCs7MzLC0t0bNnT5w6dcrgHMXFxRg3bhzs7e2h1WoxePBgZGRkGMTk5uYiODgYOp0OOp0OwcHByMvLM4i5cOECAgMDodVqYW9vj4iICJSUlNTu/QOTEyIiImXJSkzubb7KI488gszMTGk7efKktG/27NmYP38+li5disTERDg5OaFv3764evWqFBMZGYlNmzYhLi4O+/fvx7Vr1zBo0CCUl5dLMUFBQUhOTkZ8fDzi4+ORnJyM4OBgaX95eTkGDhyIgoIC7N+/H3Fxcdi4cSMmTJhQ6/vhnBMiIqJ6ztTU1KBaUkkIgYULF2LatGkYMmQIAGD16tVwdHTE+vXr8corryA/Px+ffvop1qxZgz59+gAA1q5dCxcXF/zwww8ICAhAamoq4uPjcfDgQfj4+AAAVq5cCT8/P5w5cwYeHh7YuXMnTp8+jfT0dDg7OwMA5s2bh5CQELz//vuwsbGp8f2wckJERKSkygmxcjYAer3eYCsuLr7tJX///Xc4OzvD1dUVL7zwAs6ePQsASEtLQ1ZWFvr16yfFajQa+Pv748CBAwCApKQklJaWGsQ4OzvD09NTiklISIBOp5MSEwDw9fWFTqcziPH09JQSEwAICAhAcXExkpKSavUWMjkhIiJSkkLDOi4uLtL8Dp1Oh5iYmGov5+Pjg88//xzff/89Vq5ciaysLHTv3h2XL19GVlYWAMDR0dHgGEdHR2lfVlYWzM3N0aRJkzvGODg4VLm2g4ODQcyt12nSpAnMzc2lmJrisA4REZGSFFpKnJ6ebjAUotFoqg0fMGCA9GcvLy/4+fmhbdu2WL16NXx9ff8+pWF/hBBV2m51a0x18fcSUxOsnBARERkhGxsbg+12ycmttFotvLy88Pvvv0vzUG6tXGRnZ0tVDicnJ5SUlCA3N/eOMZcuXapyrZycHIOYW6+Tm5uL0tLSKhWVu2FyQkREpKQHsFrnZsXFxUhNTUWzZs3g6uoKJycn7Nq1S9pfUlKCffv2oXv37gAAb29vmJmZGcRkZmYiJSVFivHz80N+fj4OHz4sxRw6dAj5+fkGMSkpKcjMzJRidu7cCY1GA29v71rdA4d1iIiIlHSfnxA7ceJEBAYGomXLlsjOzsZ7770HvV6PUaNGQaVSITIyEtHR0XBzc4Obmxuio6NhZWWFoKAgAIBOp0NoaCgmTJgAOzs72NraYuLEifDy8pJW73To0AH9+/dHWFgYVqxYAQAYM2YMBg0aBA8PDwBAv3790LFjRwQHB2POnDm4cuUKJk6ciLCwsFqt1AGYnBAREdVrGRkZGDFiBP766y80bdoUvr6+OHjwIFq1agUAmDx5MgoLCxEeHo7c3Fz4+Phg586dsLa2ls6xYMECmJqaYtiwYSgsLETv3r0RGxsLExMTKWbdunWIiIiQVvUMHjwYS5culfabmJhg27ZtCA8PR48ePWBpaYmgoCDMnTu31vekEkKIe31DGgq9Xg+dTgeNVxhUJuYPujtEdSI3cendg4jqKb1eD0c7HfLz82v9W3xtrqHT6WARuBQqM8t7Po8oLUTRltfrtK/GjpUTIiIiBVU+Ql7GCZTrTD3FCbFERERkVFg5ISIiUpLq703O8Q0ckxMiIiIFcVhHPg7rEBERkVFh5YSIiEhBrJzIx+SEiIhIQUxO5GNyQkREpCAmJ/JxzgkREREZFVZOiIiIlMSlxLIxOSEiIlIQh3Xk47AOERERGRVWToiIiBSkUkFm5US5vtRXTE6IiIgUpILMYR1mJxzWISIiIuPCygkREZGCOCFWPiYnRERESuJSYtk4rENERERGhZUTIiIiJckc1hEc1mFyQkREpCS5c07krfR5ODA5ISIiUhCTE/k454SIiIiMCisnRERESuJqHdmYnBARESmIwzrycViHiIiIjAorJ0RERApi5UQ+JidEREQKYnIiH4d1iIiIyKiwckJERKQgVk7kY3JCRESkJC4llo3DOkRERGRUWDkhIiJSEId15GNyQkREpCAmJ/IxOSEiIlIQkxP5OOeEiIiIjAorJ0REREriah3ZmJwQEREpiMM68nFYh4iIiIxKvaycxMbGIjIyEnl5eQ+6KwRgStjT+O+Ypw3aLl3Wo33/t6TX7q0dETXuWfR4tB1UKhV+PZuJl6d+hoxLuVXO99Wi19Cn+yMYOfFjbN93AgDg0swWk0L748lu7nCws0HWX/nYsCMR8z77HqVl5dKxXTu2xIzXn0GX9i4QAjh6+jxmLNmMlN/+r47unuiG+au+x9Y9x/H7+Uuw0Jjh8U5tEPX6M3Br7VhtfGT0F1i96RdEv/k8XgvqJbWnZeTg7UWbcDD5LEpKy9DbrwNmTfw3HOxs7tetkEysnMj3QJOTkJAQrF69ukr777//jnbt2j2AHtG9Sv3zIp4du0R6XV4upD+3bm6PHSvHY+13BxCzYhv0BYXwaO2EopLSKud5bUQvCFGlGe6tHaFWq/FmTBzOZuSgY1tnLHxrBKwsNZi+aBMAoJGVBhsXj8X2n05i4qwvYWqixn/HDMTGxWPxyMD/oay8QvkbJ/rbgaN/YPS/n0TXjq1QVl6O95ZtwZBxS3Fww/+gtdQYxG7bexxJKefQrKnOoL2gsBhDXv8Qnm7N8e2ycQCA6OXbMGL8CuxaNQFqNYvd9YEKMpMTTjp58JWT/v37Y9WqVQZtTZs2fUC9oXtVVl6B7MtXq933dnggdh04hRlLvpXazv/f5Spxnm7NMXbkU3hq1GyciY8x2PdjQip+TEg1OL5dSwe8PPQJKTlp18oRTXRaxKzYiv+7lAcAmL1yB36JewstnGxx7v/+knubRLf19ZKxBq8/nP4i3PpNRXJqOno8+s8vWxez8zB5zlf4evFYDH9zmcExh46fxYXMy9i3dgpsGllK53HtPRk/Jf6Gnj7t6/5GiIzAA0/DNRoNnJycDLZFixbBy8sLWq0WLi4uCA8Px7Vr1257juPHj6NXr16wtraGjY0NvL29ceTIEWn/gQMH8OSTT8LS0hIuLi6IiIhAQUHB/bi9BqONS1Oc3v4+kjdH4dP3X0Kr5nYAbpQn+/Z4BH9cyMbXi8fit+9jsGvVRDzt38ngeEuNGVa+F4JJszfcNsm5lU0jS+TmX5de/3H+Ev7KvYoXB3eHmakJLDRmePEZP6T+eRHpWVeUu1miGtBfKwIANLGxktoqKirw6ozPMe7F3ujQtlmVY4pLyqBSqaAx/+f3Ro25KdRqFQ4e/7PuO02KqBzWkbM1dA88OamOWq3G4sWLkZKSgtWrV2P37t2YPHnybeNHjhyJFi1aIDExEUlJSfjvf/8LMzMzAMDJkycREBCAIUOG4MSJE/jyyy+xf/9+vP766/frdh56SafO4bUZazB03Id4I/oLONjZ4PtPJ6CJToumto1grbVA5Ki++DHhNIaMW4pte49jzezR6H7Tb5PR45/H4RNp2PHTyRpds3Vze4wZ7o9V3/wstV27XozAVxdh2IDHkLl/ATL2zcNTvh0w7I1lKOeQDt1HQghMW7ARvl3aomM7Z6l94epdMDVR45UXelZ73GNerWFlYY6oJd/ielEJCgqLMX3xZlRUCGT9pb9PvSfZVApsDdwDH9bZunUrGjVqJL0eMGAAvvrqK+m1q6srZs6ciddeew0fffRRtee4cOECJk2ahPbtb5Q83dzcpH1z5sxBUFAQIiMjpX2LFy+Gv78/li1bBgsLiyrnKy4uRnFxsfRar+c/Cnfyw4HT/7z4E0g8kYajm6MwYqAPvtmZBADYse8kln2xBwCQ8tv/4fFObfDykH/hwNE/MOBJLzzRzR3+L35Qo+s52evw9eJwbP7hGNZ8myC1W2jMsOTtF3Ho+FmM/t8qmKjVeP3F3tiw6DU8NWoOioqrznEhqguTZm/AqT8uYsfKN6W25NQLWBG3F3vXTrntb8b2TawR+0EoJnzwJVZ8uQ9qtQrP9/NG5/YuMOF8E2pAHnhy0qtXLyxb9s+4q1arxZ49exAdHY3Tp09Dr9ejrKwMRUVFKCgogFarrXKO8ePHY/To0VizZg369OmDf//732jbti0AICkpCX/88QfWrVsnxQshUFFRgbS0NHTo0KHK+WJiYvDOO+/Uwd02DNeLSnD6j4to69IUl/OuobSsHL+mZRrE/JaWBd8ubQAAT3Rzh2sLe5zbPccg5vNZo5GQ/CcCX10ktTnZ6/Dd8ggknkxDZPQXBvFDA7qhZTNb9Ht5HsTfs2rD/heLtN2z8fSTnfDNrqS6uF0iA5PnbMCOn05i+8eRaO7YRGpPOPYncnKvwStwutRWXl6B/y36Bsvi9uDEd+8CAJ7y7YBjm6NwOe8aTE3U0FlbwSNgKlr1s7vv90L3hqt15HvgyYlWqzVYmXP+/Hk8/fTTePXVVzFz5kzY2tpi//79CA0NRWlp9b/5RkVFISgoCNu2bcOOHTswY8YMxMXF4bnnnkNFRQVeeeUVREREVDmuZcuW1Z5v6tSpGD9+vPRar9fDxcVF5p02HOZmpnBv7YiE5D9QWlaOY6fPw62V4XLKti0dkJ55YxnxwtU7sebbAwb7D8RNw1sLNiL+5xSprVlTHb5b9gaO/3oBY99dKyUglSwtzFEhhEH7jdeAWs3/2aluCSEwec5X2Lb3OLYsfwOtmtsb7B/+9GPwf9zDoG1oxIcYNuBxjAz0rXI+u8Y3Kso/JZ5BTu41DHjCq+46T4piciLfA09ObnXkyBGUlZVh3rx50rK5DRs23PU4d3d3uLu7480338SIESOwatUqPPfcc3j00Udx6tSpWi1N1mg00Gg0dw8kAMC7bzyH+J9PIiMrF02bNMLE0P6w1logbushAMDiNT/gs+iXceDYH/j5yG/o49cR/Z/wlCoi2ZevVjsJNiMrFxcu3ljV42Svw5blbyDjUi7eXrQJ9k3+GQqsPHbvoV/xbsSzmDtlGD7+uyQeOaofysvL8fOR3+r6baAGbuKsDfj6+yNYP3cMGllZ4NLfc0RsGlnA0sIcto0bwbZxI4NjTE1N4GhnY/AslHXfJcDd1Qn2TRrh8Ik0TJ3/NcJH9Lrt81LI+KhUNzY5xzd0RpectG3bFmVlZViyZAkCAwPxyy+/YPny5beNLywsxKRJkzB06FC4uroiIyMDiYmJeP755wEAU6ZMga+vL8aOHYuwsDBotVqkpqZi165dWLJkyW3PSzXX3KExPnnvJdg11uKv3Gs4knIO/V6eh/SsG5WRbXtPYHxMHN4M6YcPJgzFHxey8Z8pn+Dg8bM1vkYv3/Zo29IBbVs64PT29w32NXnsxuTm389fwojxKzAlbAB2fjYBFRUCJ37LwNCIj3DpMucNUd36bOONydmDbhqGBG4sBQ6qpjJyO7+fz8a7H36HXP11tHS2xYSXAhAe9JSifSUydipxa238PgoJCUFeXh42b95s0L5gwQLMmTMHeXl5ePLJJzFy5Ej85z//QW5uLho3bmzwhNiSkhKMGjUKv/zyCy5dugR7e3sMGTIEc+bMkSa7JiYmYtq0aUhISIAQAm3btsXw4cPx1ltvVdOrqvR6PXQ6HTReYVCZmCv9NhAZhdzEpQ+6C0R1Rq/Xw9FOh/z8fNjY1M3Tdit/VrQZ9zXUmqrzI2uqorgAZ5cMrdO+GrsHmpzUF0xOqCFgckIPs/uanER8DRMZyUl5cQHOLm7YyQnXphEREZFRMbo5J0RERPUZV+vIx+SEiIhIQVytIx+HdYiIiMiosHJCRESkILVaJevBj4IPjWTlhIiISEmVwzpytnsVExMDlUolfZ8ccOPpxVFRUXB2doalpSV69uyJU6dOGRxXXFyMcePGwd7eHlqtFoMHD0ZGRoZBTG5uLoKDg6HT6aDT6RAcHIy8vDyDmAsXLiAwMBBarRb29vaIiIhASUlJre+DyQkREdFDIDExER9//DE6depk0D579mzMnz8fS5cuRWJiIpycnNC3b19cvfrPk7kjIyOxadMmxMXFYf/+/bh27RoGDRqE8vJyKSYoKAjJycmIj49HfHw8kpOTERwcLO0vLy/HwIEDUVBQgP379yMuLg4bN27EhAkTan0vTE6IiIgUVLlaR85WW9euXcPIkSOxcuVKNGnyzxdOCiGwcOFCTJs2DUOGDIGnpydWr16N69evY/369QCA/Px8fPrpp5g3bx769OmDrl27Yu3atTh58iR++OEHAEBqairi4+PxySefwM/PD35+fli5ciW2bt2KM2fOAAB27tyJ06dPY+3atejatSv69OmDefPmYeXKldDra/eUbiYnREREClJqWEev1xtsxcXFt73m2LFjMXDgQPTp08egPS0tDVlZWejXr5/UptFo4O/vjwMHbnzhalJSEkpLSw1inJ2d4enpKcUkJCRAp9PBx8dHivH19YVOpzOI8fT0hLOzsxQTEBCA4uJiJCXV7lvhmZwQEREpSKnKiYuLizS/Q6fTISYmptrrxcXF4ejRo9Xuz8rKAgA4Ohp+caSjo6O0LysrC+bm5gYVl+piHBwcqpzfwcHBIObW6zRp0gTm5uZSTE1xtQ4REZERSk9PN3h8vUajqTbmjTfewM6dO6Xvk6vOrUNFQoi7Dh/dGlNd/L3E1AQrJ0RERApSqnJiY2NjsFWXnCQlJSE7Oxve3t4wNTWFqakp9u3bh8WLF8PU1FSqZNxaucjOzpb2OTk5oaSkBLm5uXeMuXTpUpXr5+TkGMTcep3c3FyUlpZWqajcDZMTIiIiBd3PpcS9e/fGyZMnkZycLG3dunXDyJEjkZycjDZt2sDJyQm7du2SjikpKcG+ffvQvXt3AIC3tzfMzMwMYjIzM5GSkiLF+Pn5IT8/H4cPH5ZiDh06hPz8fIOYlJQUZGZmSjE7d+6ERqOBt7d3rd5DDusQERHVU9bW1vD09DRo02q1sLOzk9ojIyMRHR0NNzc3uLm5ITo6GlZWVggKCgIA6HQ6hIaGYsKECbCzs4OtrS0mTpwILy8vaYJthw4d0L9/f4SFhWHFihUAgDFjxmDQoEHw8PAAAPTr1w8dO3ZEcHAw5syZgytXrmDixIkICwur9bcrMzkhIiJSkAoyv/gPyj4hdvLkySgsLER4eDhyc3Ph4+ODnTt3wtraWopZsGABTE1NMWzYMBQWFqJ3796IjY2FiYmJFLNu3TpERERIq3oGDx6MpUuXSvtNTEywbds2hIeHo0ePHrC0tERQUBDmzp1b6z6rhBBCxj03CHq9HjqdDhqvMKhMzB90d4jqRG7i0rsHEdVTer0ejnY65Ofn1/q3+NpcQ6fTodPU72Biob3n85QXFeBEzOA67aux45wTIiIiMioc1iEiIlLQvT7l9ebjGzomJ0RERAqS++V9zE04rENERERGhpUTIiIiBXFYRz4mJ0RERArisI58TE6IiIgUxMqJfJxzQkREREaFlRMiIiIlyRzWUfgBsfUSkxMiIiIFcVhHPg7rEBERkVFh5YSIiEhBXK0jH5MTIiIiBXFYRz4O6xAREZFRYeWEiIhIQRzWkY/JCRERkYI4rCMfh3WIiIjIqLByQkREpCBWTuRjckJERKQgzjmRj8kJERGRglg5kY9zToiIiMiosHJCRESkIA7ryMfkhIiISEEc1pGPwzpERERkVFg5ISIiUpAKMod1FOtJ/cXkhIiISEFqlQpqGdmJnGMfFhzWISIiIqPCygkREZGCuFpHPiYnRERECuJqHfmYnBARESlIrbqxyTm+oeOcEyIiIjIqrJwQEREpSSVzaIaVEyYnRERESuKEWPk4rENERERGhZUTIiIiBan+/k/O8Q0dkxMiIiIFcbWOfBzWISIiIqPCygkREZGC+BA2+WqUnCxevLjGJ4yIiLjnzhAREdV3XK0jX42SkwULFtToZCqViskJERERyVKj5CQtLa2u+0FERPRQUKtUUMsof8g59mFxzxNiS0pKcObMGZSVlSnZHyIionqtclhHztbQ1To5uX79OkJDQ2FlZYVHHnkEFy5cAHBjrskHH3ygeAeJiIjqk8oJsXK2hq7WycnUqVNx/Phx7N27FxYWFlJ7nz598OWXXyraOSIiImp4ar2UePPmzfjyyy/h6+trkN117NgRf/75p6KdIyIiqm+4Wke+WicnOTk5cHBwqNJeUFDAUhQRETV4nBArX62HdR577DFs27ZNel2ZkKxcuRJ+fn7K9YyIiIgapFpXTmJiYtC/f3+cPn0aZWVlWLRoEU6dOoWEhATs27evLvpIRERUb6j+3uQc39DVunLSvXt3/PLLL7h+/Tratm2LnTt3wtHREQkJCfD29q6LPhIREdUbXK0j3z19t46XlxdWr16tdF+IiIiI7i05KS8vx6ZNm5CamgqVSoUOHTrgmWeegakpv0eQiIgaNrXqxibn+Iau1tlESkoKnnnmGWRlZcHDwwMA8Ntvv6Fp06b47rvv4OXlpXgniYiI6gt+K7F8tZ5zMnr0aDzyyCPIyMjA0aNHcfToUaSnp6NTp04YM2ZMXfSRiIiIGpBaV06OHz+OI0eOoEmTJlJbkyZN8P777+Oxxx5TtHNERET1EYsf8tS6cuLh4YFLly5Vac/Ozka7du0U6RQREVF9xdU68tUoOdHr9dIWHR2NiIgIfP3118jIyEBGRga+/vprREZGYtasWXXdXyIiIqNWOSFWzlYby5YtQ6dOnWBjYwMbGxv4+flhx44d0n4hBKKiouDs7AxLS0v07NkTp06dMjhHcXExxo0bB3t7e2i1WgwePBgZGRkGMbm5uQgODoZOp4NOp0NwcDDy8vIMYi5cuIDAwEBotVrY29sjIiICJSUltbsh1HBYp3HjxgaZnBACw4YNk9qEEACAwMBAlJeX17oTREREdG9atGiBDz74QBq9WL16NZ555hkcO3YMjzzyCGbPno358+cjNjYW7u7ueO+999C3b1+cOXMG1tbWAIDIyEhs2bIFcXFxsLOzw4QJEzBo0CAkJSXBxMQEABAUFISMjAzEx8cDAMaMGYPg4GBs2bIFwI2VvAMHDkTTpk2xf/9+XL58GaNGjYIQAkuWLKnVPalEZWZxB7V58qu/v3+tOlAf6PV66HQ6aLzCoDIxf9DdIaoTuYlLH3QXiOqMXq+Ho50O+fn5sLGxqbNr6HQ6BH16AOZWje75PCXXr2F9aHdZfbW1tcWcOXPw8ssvw9nZGZGRkZgyZQqAG1USR0dHzJo1C6+88gry8/PRtGlTrFmzBsOHDwcAXLx4ES4uLti+fTsCAgKQmpqKjh074uDBg/Dx8QEAHDx4EH5+fvj111/h4eGBHTt2YNCgQUhPT4ezszMAIC4uDiEhIcjOzq7VvdSocvIwJhxERER1QanH1+v1eoN2jUYDjUZzx2PLy8vx1VdfoaCgAH5+fkhLS0NWVhb69etncB5/f38cOHAAr7zyCpKSklBaWmoQ4+zsDE9PTxw4cAABAQFISEiATqeTEhMA8PX1hU6nw4EDB+Dh4YGEhAR4enpKiQkABAQEoLi4GElJSejVq1eN34N7fmra9evXceHChSpjSZ06dbrXUxIREdHfXFxcDF7PmDEDUVFR1caePHkSfn5+KCoqQqNGjbBp0yZ07NgRBw4cAAA4OjoaxDs6OuL8+fMAgKysLJibmxuswq2MycrKkmIcHByqXNfBwcEg5tbrNGnSBObm5lJMTdU6OcnJycFLL71kMNnmZpxzQkREDZlapYJaxoqbymPT09MNhkLuVDXx8PBAcnIy8vLysHHjRowaNcpgSsatK4CEEHddFXRrTHXx9xJTE7VeShwZGYnc3FwcPHgQlpaWiI+Px+rVq+Hm5obvvvuutqcjIiJ6qKhU8jcA0uqbyu1OyYm5uTnatWuHbt26ISYmBp07d8aiRYvg5OQEAFUqF9nZ2VKVw8nJCSUlJcjNzb1jTHWPEcnJyTGIufU6ubm5KC0trVJRuZtaJye7d+/GggUL8Nhjj0GtVqNVq1Z48cUXMXv2bMTExNT2dERERKQwIQSKi4vh6uoKJycn7Nq1S9pXUlKCffv2oXv37gAAb29vmJmZGcRkZmYiJSVFivHz80N+fj4OHz4sxRw6dAj5+fkGMSkpKcjMzJRidu7cCY1GA29v71r1v9bDOgUFBdK4k62tLXJycuDu7g4vLy8cPXq0tqcjIiJ6qNzv79Z56623MGDAALi4uODq1auIi4vD3r17ER8fD5VKhcjISERHR8PNzQ1ubm6Ijo6GlZUVgoKCAAA6nQ6hoaGYMGEC7OzsYGtri4kTJ8LLywt9+vQBAHTo0AH9+/dHWFgYVqxYAeDGUuJBgwZJ37PXr18/dOzYEcHBwZgzZw6uXLmCiRMnIiwsrNarjmqdnHh4eODMmTNo3bo1unTpghUrVqB169ZYvnw5mjVrVtvTERERPVRuHpq51+Nr49KlSwgODkZmZiZ0Oh06deqE+Ph49O3bFwAwefJkFBYWIjw8HLm5ufDx8cHOnTulZ5wAwIIFC2Bqaophw4ahsLAQvXv3RmxsrPSMEwBYt24dIiIipFU9gwcPxtKl/zyCwMTEBNu2bUN4eDh69OgBS0tLBAUFYe7cubV/D2rynJObrVu3DqWlpQgJCcGxY8cQEBCAy5cvw9zcHLGxsdIa6YcJn3NCDQGfc0IPs/v5nJOQ1QdlP+ckdpRvnfbV2NW6cjJy5Ejpz127dsW5c+fw66+/omXLlrC3t1e0c0RERPWNUqt1GrJ7fs5JJSsrKzz66KNK9IWIiKjeu9/DOg+jGiUn48ePr/EJ58+ff8+dISIiqu/u94TYh1GNkpNjx47V6GR8Q4mIiEiuGiUne/bsqet+1Asnt0XDuoFOTiIioppR4x4eInbL8Q2d7DknRERE9A8O68jHBI2IiIiMCisnREREClKpADVX68jC5ISIiEhBapnJiZxjHxYc1iEiIiKjck/JyZo1a9CjRw84Ozvj/PnzAICFCxfi22+/VbRzRERE9U3lhFg5W0NX6+Rk2bJlGD9+PJ5++mnk5eWhvLwcANC4cWMsXLhQ6f4RERHVK5XDOnK2hq7WycmSJUuwcuVKTJs2zeDbCrt164aTJ08q2jkiIiJqeGo9ITYtLQ1du3at0q7RaFBQUKBIp4iIiOorfreOfLWunLi6uiI5OblK+44dO9CxY0cl+kRERFRvVX4rsZytoat15WTSpEkYO3YsioqKIITA4cOH8cUXXyAmJgaffPJJXfSRiIio3uDj6+WrdXLy0ksvoaysDJMnT8b169cRFBSE5s2bY9GiRXjhhRfqoo9ERETUgNzTQ9jCwsIQFhaGv/76CxUVFXBwcFC6X0RERPUS55zIJ+sJsfb29kr1g4iI6KGghrx5I2owO6l1cuLq6nrHB8ScPXtWVoeIiIioYat1chIZGWnwurS0FMeOHUN8fDwmTZqkVL+IiIjqJQ7ryFfr5OSNN96otv3DDz/EkSNHZHeIiIioPuMX/8mn2IqlAQMGYOPGjUqdjoiIiBooWRNib/b111/D1tZWqdMRERHVSyoVZE2I5bDOPSQnXbt2NZgQK4RAVlYWcnJy8NFHHynaOSIiovqGc07kq3Vy8uyzzxq8VqvVaNq0KXr27In27dsr1S8iIiJqoGqVnJSVlaF169YICAiAk5NTXfWJiIio3uKEWPlqNSHW1NQUr732GoqLi+uqP0RERPWaSoH/Grpar9bx8fHBsWPH6qIvRERE9V5l5UTO1tDVes5JeHg4JkyYgIyMDHh7e0Or1Rrs79Spk2KdIyIiooanxsnJyy+/jIULF2L48OEAgIiICGmfSqWCEAIqlQrl5eXK95KIiKie4JwT+WqcnKxevRoffPAB0tLS6rI/RERE9ZpKpbrjd9DV5PiGrsbJiRACANCqVas66wwRERFRreacMJsjIiK6Mw7ryFer5MTd3f2uCcqVK1dkdYiIiKg+4xNi5atVcvLOO+9Ap9PVVV+IiIiIapecvPDCC3BwcKirvhAREdV7apVK1hf/yTn2YVHj5ITzTYiIiO6Oc07kq/ETYitX6xARERHVpRpXTioqKuqyH0RERA8HmRNi+dU69/D4eiIiIro9NVRQy8gw5Bz7sGByQkREpCAuJZav1t9KTERERFSXWDkhIiJSEFfryMfkhIiISEF8zol8HNYhIiIio8LKCRERkYI4IVY+JidEREQKUkPmsA6XEnNYh4iIiIwLKydEREQK4rCOfExOiIiIFKSGvGEJDmnwPSAiIiIjw8oJERGRglQqFVQyxmbkHPuwYHJCRESkIBXkfbEwUxMO6xARESmq8gmxcrbaiImJwWOPPQZra2s4ODjg2WefxZkzZwxihBCIioqCs7MzLC0t0bNnT5w6dcogpri4GOPGjYO9vT20Wi0GDx6MjIwMg5jc3FwEBwdDp9NBp9MhODgYeXl5BjEXLlxAYGAgtFot7O3tERERgZKSklrdE5MTIiKiemzfvn0YO3YsDh48iF27dqGsrAz9+vVDQUGBFDN79mzMnz8fS5cuRWJiIpycnNC3b19cvXpViomMjMSmTZsQFxeH/fv349q1axg0aBDKy8ulmKCgICQnJyM+Ph7x8fFITk5GcHCwtL+8vBwDBw5EQUEB9u/fj7i4OGzcuBETJkyo1T2phBBCxnvSIOj1euh0Ovx2IQfWNjYPujtEdaKx1vxBd4Gozuj1ejja6ZCfnw+bOvp3vPJnxcd7T8OqkfU9n+f6tasY07PjPfc1JycHDg4O2LdvH5588kkIIeDs7IzIyEhMmTIFwI0qiaOjI2bNmoVXXnkF+fn5aNq0KdasWYPhw4cDAC5evAgXFxds374dAQEBSE1NRceOHXHw4EH4+PgAAA4ePAg/Pz/8+uuv8PDwwI4dOzBo0CCkp6fD2dkZABAXF4eQkBBkZ2fX+H5YOSEiIlJQ5XNO5Gxy5OfnAwBsbW0BAGlpacjKykK/fv2kGI1GA39/fxw4cAAAkJSUhNLSUoMYZ2dneHp6SjEJCQnQ6XRSYgIAvr6+0Ol0BjGenp5SYgIAAQEBKC4uRlJSUo3vgRNiiYiIjJBerzd4rdFooNFo7niMEALjx4/Hv/71L3h6egIAsrKyAACOjo4GsY6Ojjh//rwUY25ujiZNmlSJqTw+KysLDg4OVa7p4OBgEHPrdZo0aQJzc3MppiZYOSEiIlJQ5VJiORsAuLi4SBNPdTodYmJi7nrt119/HSdOnMAXX3xRbb9uJoS467LlW2Oqi7+XmLth5YSIiEhBSj0hNj093WCOxt2qJuPGjcN3332Hn376CS1atJDanZycANyoajRr1kxqz87OlqocTk5OKCkpQW5urkH1JDs7G927d5diLl26VOW6OTk5Buc5dOiQwf7c3FyUlpZWqajcCSsnRERERsjGxsZgu11yIoTA66+/jm+++Qa7d++Gq6urwX5XV1c4OTlh165dUltJSQn27dsnJR7e3t4wMzMziMnMzERKSooU4+fnh/z8fBw+fFiKOXToEPLz8w1iUlJSkJmZKcXs3LkTGo0G3t7eNb53Vk6IiIgUdL+fEDt27FisX78e3377LaytraW5HTqdDpaWllCpVIiMjER0dDTc3Nzg5uaG6OhoWFlZISgoSIoNDQ3FhAkTYGdnB1tbW0ycOBFeXl7o06cPAKBDhw7o378/wsLCsGLFCgDAmDFjMGjQIHh4eAAA+vXrh44dOyI4OBhz5szBlStXMHHiRISFhdVq5RGTEyIiIgXd7yfELlu2DADQs2dPg/ZVq1YhJCQEADB58mQUFhYiPDwcubm58PHxwc6dO2Ft/c+S5wULFsDU1BTDhg1DYWEhevfujdjYWJiYmEgx69atQ0REhLSqZ/DgwVi6dKm038TEBNu2bUN4eDh69OgBS0tLBAUFYe7cubW6Jz7npAb4nBNqCPicE3qY3c/nnMT+/Kvs55yEPNG+Tvtq7Fg5ISIiUhC/+E8+JidEREQKUmq1TkPG5ISIiEhBrJzIxwSNiIiIjAorJ0RERAq636t1HkZMToiIiBQk98v7OKrDYR0iIiIyMqycEBERKUgNFdQyBmfkHPuwYHJCRESkIA7ryMdhHSIiIjIqrJwQEREpSPX3f3KOb+iYnBARESmIwzrycViHiIiIjAorJ0RERApSyVytw2EdJidERESK4rCOfExOiIiIFMTkRD7OOSEiIiKjwsoJERGRgriUWD4mJ0RERApSq25sco5v6DisQ0REREaFlRMiIiIFcVhHPiYnRERECuJqHfk4rENERERGhZUTIiIiBakgb2iGhRMmJ0RERIriah35OKxDRERERoWVE6pzH637AXNWbsdLzz+B6eOeAwAUXC/GrI+3Ytf+FOTqC9DCyRYhzz+BF5/pAQDI0xdgwarv8fORM8jMzoOtTou+//LE+JcHwKaRpcH5dyecxuLPd+LXPy/CykKDxzu3wfKZL933+6SGbf6q77F1z3H8fv4SLDRmeLxTG0S9/gzcWjtKMeFRa/DFtkMGx3XzbI1dqyYCAHLzCxDz8TbsOfgr/u9SLmwbN8LAnp3w1quDoLvlc0/Gi6t15DOq5ER1lynKo0aNQmxs7P3pDCni+K8X8MWWg2jftplB+8wPN+PgsT+wYNpItHCyxU9HzmD6go1wsNOh3788cekvPbIv5+Ot1wbDrZUj/u9SLqbN/xqX/tJj2bsh0nl27DuOqXM3YNLogfB7tB2EAM6czbzPd0kEHDj6B0b/+0l07dgKZeXleG/ZFgwZtxQHN/wPWkuNFNfbryM+nP6i9NrczET6c2ZOPrJy8vHuG8+hfRsnpGdewfgP4pCVk4/Vs0bf1/uhe8fVOvIZVXKSmfnPD5Uvv/wS06dPx5kzZ6Q2S0vD3xxKS0thZmZ23/pHtVNwvRiR761DzMRhWLpml8G+Y6fOY0j/x+DbtR0AICjQD19sScDJM+no9y9PeLRphmXv/lP9aNXcHhNHD8D499ehrKwcpqYmKCsrx7tLNmPqq4EYPtBXim3b0uH+3CDRTb5eMtbg9YfTX4Rbv6lITk1Hj0fbSe0ac1M42ttUe46O7Zzx+eww6bVri6b432uBeGX659LnnoyfCvImtTI3MbI5J05OTtKm0+mgUqmk10VFRWjcuDE2bNiAnj17wsLCAmvXrkVUVBS6dOlicJ6FCxeidevWBm2rVq1Chw4dYGFhgfbt2+Ojjz66fzfWQE1ftBFP+XbAv7q5V9nXzcsVP/5yClk5eRBCIOHY70hLz8GTj3nc9nxXrxWhkZWF9A90yu8ZyPorH2q1GgNHz8PjQ2YgZPLH+C0tq87uiaim9NeKAABNbKwM2vcn/Q63fv9Ft+ffwRvvrUfOlat3PY+11oKJCTUoRlU5qYkpU6Zg3rx5WLVqFTQaDT7++OO7HrNy5UrMmDEDS5cuRdeuXXHs2DGEhYVBq9Vi1KhRVeKLi4tRXFwsvdbr9YreQ0Ow5cdjOPVbBr5d/ma1+2dEPIepczfA79/vwtREDbVahZhJw/FYpzbVxufmF2DJml0YEegntaVfvAIAWBj7Pf4XPhgtnGzxyYa9eOGND7F77X/R2Ear/I0R1YAQAtMWbIRvl7bo2M5Zau/TvSOe6dMVLk62OH/xMqKXb8Xg1xZj75rJ0JhXrQJfybuGOZ/uQMiQHvez+ySTGiqoZYzNqFk7qX/JSWRkJIYMGVKrY2bOnIl58+ZJx7m6uuL06dNYsWJFtclJTEwM3nnnHUX62xBdzM7FO0s34fM5r0CjqX7YLXbjzzh2+jxWRoeiuWMTHD7+5405J7Y2VSotVwuK8PJ/V8KtlSPeCAmQ2itEBQBg7It9MMC/MwBg9pQR6P7vd7B973EEDe5eR3dIdGeTZm/AqT8uYsdKw+R8SD9v6c8d2zmja8eW6BQ4HTv3n0LgU10MYvXXCjH8zeXwcG2GKWFP349uk0I4rCNfvUtOunXrVqv4nJwcpKenIzQ0FGFh/4zllpWVQafTVXvM1KlTMX78eOm1Xq+Hi4vLvXW4AUo5k4HLudcweMwCqa28ogKHT5zF55t+wYlt72PuJ9uxfOZLeMqvIwCgQ1tnnP7jIlZ+uccgObl2vQghkz+G1lKDFTNfgtlNpW0Huxvj9m6t/lkNoTE3hYuzHf4vO6+O75KoepPnbMCOn05i+8eRaO7Y5I6xTvY6uDSzxZ/pOQbtVwuKMDTiI2gtNVg7J8zgc0/UENS75ESrNSzVq9VqCCEM2kpLS6U/V1Tc+O165cqV8PHxMYgzMan+f3iNRgONRlPtPrq77t5uiP9skkHb5FlxaNPSAa+OeArlFQKlZeVQ3/KkIRMTFSpu+ru8WlCEUZNWwNzMFCujQ6tUYTzdXWBuZoqz6dnScFBpWTkysq7c9YcCkdKEEJg85yts23scW5a/gVbN7e96zJW8a/i/S7lwummCrP5aIYZGfAhzM1Osn/8KLG5TfSQjxtKJbPUuOblV06ZNkZWVBSGEtBQ5OTlZ2u/o6IjmzZvj7NmzGDly5APqZcPSyMoCHm0Mlw5bWpijiY2V1O7TuS1ilm2BhbkZmjs1waHkP/HN90fwv7HPALhRMfnPxOUoLC7Fgmkjca2gCNcKbkwwtG3cCCYmalhrLTBysB8WrvoezRyaoLljE3wctwcAMLBn5/t4x0TAxFkb8PX3R7B+7hg0srLApb9uzFWzaWQBSwtzXLtejFkfb0PgU13gZK/DhczLePfDLbBr3Ej6vF4tKMLz4z7E9aISrHh3FK5eK8LVvyfW2je58bkn48fnnMhX75OTnj17IicnB7Nnz8bQoUMRHx+PHTt2wMbmn99EoqKiEBERARsbGwwYMADFxcU4cuQIcnNzDYZv6P5ZMj0Ys1duQ+T7a5Gnv47mjraYOPppjPx7nkjKmQwkp14AAPQcGW1w7M9f/A8tmtkCAKa+NhgmJiYYH70OxcWl6NyhFdbPD4fO2nCFBFFd+2zjzwCAQa8uMmj/cPqLCAr0hYlahdN/XkTc9sPIv1oIR3sbPOHtjs+iX4a11gLAjecCHUk5BwB49DnDeW/Hv30HLZ3t6v5GiIyAStw6JmIkYmNjERkZiby8PADAuXPn4OrqimPHjlVZOrx8+XJER0fjypUreP755+Hh4YGPP/4Y586dk2LWr1+POXPm4PTp09BqtfDy8kJkZCSee+65u/ZFr9dDp9Phtws5sLap/vkERPVdY635g+4CUZ3R6/VwtNMhPz/f4JdXpa+h0+nwY/IFNLK+92tcu6pH7y4t67Svxs5okxNjwuSEGgImJ/Qwu5/JyW4FkpOnGnhywgFMIiIiMir1fs4JERGRUeFqHdmYnBARESmIq3XkY3JCRESkIH4rsXycc0JERERGhZUTIiIiBXHKiXxMToiIiJTE7EQ2DusQERGRUWHlhIiISEFcrSMfkxMiIiIFcbWOfBzWISIiIqPCygkREZGCOB9WPiYnRERESmJ2IhuHdYiIiMiosHJCRESkIK7WkY/JCRERkYK4Wkc+JidEREQK4pQT+TjnhIiIqB776aefEBgYCGdnZ6hUKmzevNlgvxACUVFRcHZ2hqWlJXr27IlTp04ZxBQXF2PcuHGwt7eHVqvF4MGDkZGRYRCTm5uL4OBg6HQ66HQ6BAcHIy8vzyDmwoULCAwMhFarhb29PSIiIlBSUlLre2JyQkREpCSVAlstFBQUoHPnzli6dGm1+2fPno358+dj6dKlSExMhJOTE/r27YurV69KMZGRkdi0aRPi4uKwf/9+XLt2DYMGDUJ5ebkUExQUhOTkZMTHxyM+Ph7JyckIDg6W9peXl2PgwIEoKCjA/v37ERcXh40bN2LChAm1uyEAKiGEqPVRDYxer4dOp8NvF3JgbWPzoLtDVCcaa80fdBeI6oxer4ejnQ75+fmwqaN/xyt/VhxMvYhG1vd+jWtX9fDt4HxPfVWpVNi0aROeffZZADeqJs7OzoiMjMSUKVMA3KiSODo6YtasWXjllVeQn5+Ppk2bYs2aNRg+fDgA4OLFi3BxccH27dsREBCA1NRUdOzYEQcPHoSPjw8A4ODBg/Dz88Ovv/4KDw8P7NixA4MGDUJ6ejqcnZ0BAHFxcQgJCUF2dnat7oWVEyIioodUWloasrKy0K9fP6lNo9HA398fBw4cAAAkJSWhtLTUIMbZ2Rmenp5STEJCAnQ6nZSYAICvry90Op1BjKenp5SYAEBAQACKi4uRlJRUq35zQiwREZGClFqto9frDdo1Gg00Gk2tzpWVlQUAcHR0NGh3dHTE+fPnpRhzc3M0adKkSkzl8VlZWXBwcKhyfgcHB4OYW6/TpEkTmJubSzE1xcoJERGRgpSacuLi4iJNPtXpdIiJibn3Pt2SLQkhqrTd6taY6uLvJaYmWDkhIiIyQunp6QbzNGpbNQEAJycnADeqGs2aNZPas7OzpSqHk5MTSkpKkJuba1A9yc7ORvfu3aWYS5cuVTl/Tk6OwXkOHTpksD83NxelpaVVKip3w8oJERGRkhQqndjY2Bhs95KcuLq6wsnJCbt27ZLaSkpKsG/fPinx8Pb2hpmZmUFMZmYmUlJSpBg/Pz/k5+fj8OHDUsyhQ4eQn59vEJOSkoLMzEwpZufOndBoNPD29q5Vv1k5ISIiUtD9fnz9tWvX8Mcff0iv09LSkJycDFtbW7Rs2RKRkZGIjo6Gm5sb3NzcEB0dDSsrKwQFBQEAdDodQkNDMWHCBNjZ2cHW1hYTJ06El5cX+vTpAwDo0KED+vfvj7CwMKxYsQIAMGbMGAwaNAgeHh4AgH79+qFjx44IDg7GnDlzcOXKFUycOBFhYWG1XnXE5ISIiKgeO3LkCHr16iW9Hj9+PABg1KhRiI2NxeTJk1FYWIjw8HDk5ubCx8cHO3fuhLW1tXTMggULYGpqimHDhqGwsBC9e/dGbGwsTExMpJh169YhIiJCWtUzePBgg2ermJiYYNu2bQgPD0ePHj1gaWmJoKAgzJ07t9b3xOec1ACfc0INAZ9zQg+z+/mckyO/Zcp+zkk392Z12ldjx8oJERGRgvjdOvIxOSEiIlISsxPZuFqHiIiIjAorJ0RERAq636t1HkZMToiIiJQk8/H1zE04rENERERGhpUTIiIiBXE+rHxMToiIiJTE7EQ2DusQERGRUWHlhIiISEFcrSMfkxMiIiIFqWSu1pG10uchwWEdIiIiMiqsnBARESmI82HlY3JCRESkJGYnsjE5ISIiUhAnxMrHOSdERERkVFg5ISIiUpAKMlfrKNaT+ovJCRERkYI45UQ+DusQERGRUWHlhIiISEF8CJt8TE6IiIgUxYEduTisQ0REREaFlRMiIiIFcVhHPiYnRERECuKgjnwc1iEiIiKjwsoJERGRgjisIx+TEyIiIgXxu3XkY3JCRESkJE46kY1zToiIiMiosHJCRESkIBZO5GNyQkREpCBOiJWPwzpERERkVFg5ISIiUhBX68jH5ISIiEhJnHQiG4d1iIiIyKiwckJERKQgFk7kY3JCRESkIK7WkY/DOkRERGRUWDkhIiJSlLzVOhzYYXJCRESkKA7ryMdhHSIiIjIqTE6IiIjIqHBYh4iISEEc1pGPyQkREZGC+Ph6+TisQ0REREaFlRMiIiIFcVhHPiYnRERECuLj6+XjsA4REREZFVZOiIiIlMTSiWxMToiIiBTE1TrycViHiIiIjAorJ0RERAriah35mJwQEREpiFNO5GNyQkREpCRmJ7JxzgkREREZFVZOiIiIFMTVOvIxOSEiIlIQJ8TKx+SkBoQQAIBrV68+4J4Q1R11ufmD7gJRnbmq1wP459/zuqT/+1oP6viHAZOTGrj6d1Ly6CNtHnBPiIhIjqtXr0Kn09XJuc3NzeHk5AQ3VxfZ53JycoK5ecP9hUEl7kcaWc9VVFTg4sWLsLa2hor1tvtCr9fDxcUF6enpsLGxedDdIVIUP9/3nxACV69ehbOzM9TqulsLUlRUhJKSEtnnMTc3h4WFhQI9qp9YOakBtVqNFi1aPOhuNEg2Njb8x5seWvx83191VTG5mYWFRYNOKpTCpcRERERkVJicEBERkVFhckJGSaPRYMaMGdBoNA+6K0SK4+eb6M44IZaIiIiMCisnREREZFSYnBAREZFRYXJCRERERoXJCRmV2NhYNG7c+EF3g4iIHiAmJ1QnQkJCoFKpqmx//PHHg+4akaKq+5zfvIWEhDzoLhLVO3xCLNWZ/v37Y9WqVQZtTZs2fUC9IaobmZmZ0p+//PJLTJ8+HWfOnJHaLC0tDeJLS0thZmZ23/pHVB+xckJ1RqPRwMnJyWBbtGgRvLy8oNVq4eLigvDwcFy7du225zh+/Dh69eoFa2tr2NjYwNvbG0eOHJH2HzhwAE8++SQsLS3h4uKCiIgIFBQU3I/bIwIAg8+3TqeDSqWSXhcVFaFx48bYsGEDevbsCQsLC6xduxZRUVHo0qWLwXkWLlyI1q1bG7StWrUKHTp0gIWFBdq3b4+PPvro/t0Y0QPE5ITuK7VajcWLFyMlJQWrV6/G7t27MXny5NvGjxw5Ei1atEBiYiKSkpLw3//+V/qt8+TJkwgICMCQIUNw4sQJfPnll9i/fz9ef/31+3U7RDUyZcoUREREIDU1FQEBATU6ZuXKlZg2bRref/99pKamIjo6Gm+//TZWr15dx70levA4rEN1ZuvWrWjUqJH0esCAAfjqq6+k166urpg5cyZee+212/5GeOHCBUyaNAnt27cHALi5uUn75syZg6CgIERGRkr7Fi9eDH9/fyxbtoxfvkVGIzIyEkOGDKnVMTNnzsS8efOk41xdXXH69GmsWLECo0aNqotuEhkNJidUZ3r16oVly5ZJr7VaLfbs2YPo6GicPn0aer0eZWVlKCoqQkFBAbRabZVzjB8/HqNHj8aaNWvQp08f/Pvf/0bbtm0BAElJSfjjjz+wbt06KV4IgYqKCqSlpaFDhw51f5NENdCtW7daxefk5CA9PR2hoaEICwuT2svKyu7LN+sSPWhMTqjOaLVatGvXTnp9/vx5PP3003j11Vcxc+ZM2NraYv/+/QgNDUVpaWm154iKikJQUBC2bduGHTt2YMaMGYiLi8Nzzz2HiooKvPLKK4iIiKhyXMuWLevsvohq69bEW61W49ZvDrn5/4GKigoAN4Z2fHx8DOJMTEzqqJdExoPJCd03R44cQVlZGebNmwe1+sZ0pw0bNtz1OHd3d7i7u+PNN9/EiBEjsGrVKjz33HN49NFHcerUKYMEiKg+aNq0KbKysiCEgEqlAgAkJydL+x0dHdG8eXOcPXsWI0eOfEC9JHpwmJzQfdO2bVuUlZVhyZIlCAwMxC+//ILly5ffNr6wsBCTJk3C0KFD4erqioyMDCQmJuL5558HcGOSoa+vL8aOHYuwsDBotVqkpqZi165dWLJkyf26LaJa69mzJ3JycjB79mwMHToU8fHx2LFjB2xsbKSYqKgoREREwMbGBgMGDEBxcTGOHDmC3NxcjB8//gH2nqjucbUO3TddunTB/PnzMWvWLHh6emLdunWIiYm5bbyJiQkuX76M//znP3B3d8ewYcMwYMAAvPPOOwCATp06Yd++ffj999/xxBNPoGvXrnj77bfRrFmz+3VLRPekQ4cO+Oijj/Dhhx+ic+fOOHz4MCZOnGgQM3r0aHzyySeIjY2Fl5cX/P39ERsbC1dX1wfUa6L7RyVuHfgkIiIieoBYOSEiIiKjwuSEiIiIjAqTEyIiIjIqTE6IiIjIqDA5ISIiIqPC5ISIiIiMCpMTIiIiMipMTojqiaioKHTp0kV6HRISgmefffa+9+PcuXNQqVQGj1u/VevWrbFw4cIanzM2NhaNGzeW3TeVSoXNmzfLPg8RPVhMTohkCAkJgUqlgkqlgpmZGdq0aYOJEyeioKCgzq+9aNEixMbG1ii2JgkFEZGx4HfrEMnUv39/rFq1CqWlpfj5558xevRoFBQUYNmyZVViS0tLYWZmpsh1dTqdIuchIjI2rJwQyaTRaODk5AQXFxcEBQVh5MiR0tBC5VDMZ599hjZt2kCj0UAIgfz8fIwZMwYODg6wsbHBU089hePHjxuc94MPPoCjoyOsra0RGhqKoqIig/23DutUVFRg1qxZaNeuHTQaDVq2bIn3338fAKTvY+natStUKhV69uwpHbdq1Sp06NABFhYWaN++PT766COD6xw+fBhdu3aFhYUFunXrhmPHjtX6PZo/fz68vLyg1Wrh4uKC8PBwXLt2rUrc5s2b4e7uDgsLC/Tt2xfp6ekG+7ds2QJvb29YWFigTZs2eOedd1BWVlbr/hCRcWNyQqQwS0tLlJaWSq//+OMPbNiwARs3bpSGVQYOHIisrCxs374dSUlJePTRR9G7d29cuXIFALBhwwbMmDED77//Po4cOYJmzZpVSRpuNXXqVMyaNQtvv/02Tp8+jfXr18PR0RHAjQQDAH744QdkZmbim2++AQCsXLkS06ZNw/vvv4/U1FRER0fj7bffxurVqwEABQUFGDRoEDw8PJCUlISoqKgqX1BXE2q1GosXL0ZKSgpWr16N3bt3Y/LkyQYx169fx/vvv4/Vq1fjl19+gV6vxwsvvCDt//777/Hiiy8iIiICp0+fxooVKxAbGyslYET0EBFEdM9GjRolnnnmGen1oUOHhJ2dnRg2bJgQQogZM2YIMzMzkZ2dLcX8+OOPwsbGRhQVFRmcq23btmLFihVCCCH8/PzEq6++arDfx8dHdO7cudpr6/V6odFoxMqVK6vtZ1pamgAgjh07ZtDu4uIi1q9fb9A2c+ZM4efnJ4QQYsWKFcLW1lYUFBRI+5ctW1btuW7WqlUrsWDBgtvu37Bhg7Czs5Ner1q1SgAQBw8elNpSU1MFAHHo0CEhhBBPPPGEiI6ONjjPmjVrRLNmzaTXAMSmTZtue10iqh8454RIpq1bt6JRo0YoKytDaWkpnnnmGSxZskTa36pVKzRt2lR6nZSUhGvXrsHOzs7gPIWFhfjzzz8BAKmpqXj11VcN9vv5+WHPnj3V9iE1NRXFxcXo3bt3jfudk5OD9PR0hIaGIiwsTGovKyuT5rOkpqaic+fOsLKyMuhHbe3ZswfR0dE4ffo09Ho9ysrKUFRUhIKCAmi1WgCAqakpunXrJh3Tvn17NG7cGKmpqXj88ceRlJSExMREg0pJeXk5ioqKcP36dYM+ElH9xuSESKZevXph2bJlMDMzg7Ozc5UJr5U/fCtVVFSgWbNm2Lt3b5Vz3etyWktLy1ofU1FRAeDG0I6Pj4/BPhMTEwCAEOKe+nOz8+fP4+mnn8arr76KmTNnwtbWFvv370doaKjB8BdwYynwrSrbKioq8M4772DIkCFVYiwsLGT3k4iMB5MTIpm0Wi3atWtX4/hHH30UWVlZMDU1RevWrauN6dChAw4ePIj//Oc/UtvBgwdve043NzdYWlrixx9/xOjRo6vsNzc3B3Cj0lDJ0dERzZs3x9mzZzFy5Mhqz9uxY0esWbMGhYWFUgJ0p35U58iRIygrK8O8efOgVt+Y5rZhw4YqcWVlZThy5Agef/xxAMCZM2eQl5eH9u3bA7jxvp05c6ZW7zUR1U9MTojusz59+sDPzw/PPvssZs2aBQ8PD1y8eBHbt2/Hs88+i27duuGNN97AqFGj0K1bN/zrX//CunXrcOrUKbRp06bac1pYWGDKlCmYPHkyzM3N0aNHD+Tk5ODUqVMIDQ2Fg4MDLC0tER8fjxYtWsDCwgI6nQ5RUVGIiIiAjY0NBgwYgOLiYhw5cgS5ubkYP348goKCMG3aNISGhuJ///sfzp07h7lz59bqftu2bYuysjIsWbIEgYGB+OWXX7B8+fIqcWZmZhg3bhwWL14MMzMzvP766/D19ZWSlenTp2PQoEFwcXHBv//9b6jVapw4cQInT57Ee++9V/u/CCIyWlytQ3SfqVQqbN++HU8++SRefvlluLu744UXXsC5c+ek1TXDhw/H9OnTMWXKFHh7e+P8+fN47bXX7njet99+GxMmTMD06dPRoUMHDB8+HNnZ2QBuzOdYvHgxVqxYAWdnZzzzzDMAgNGjR+OTTz5BbGwsvLy84O/vj9jYWGnpcaNGjbBlyxacPn0aXbt2xbRp0zBr1qxa3W+XLl0wf/58zJo1C56enli3bh1iYmKqxFlZWWHKlCkICgqCn58fLC0tERcXJ+0PCAjA1q1bsWvXLjz22GPw9fXF/Pnz0apVq1r1h4iMn0ooMahMREREpBBWToiIiMioMDkhIiIio8LkhIiIiIwKkxMiIiIyKkxOiIiIyKgwOSEiIiKjwuSEiIiIjAqTEyIiIjIqTE6IiIjIqDA5ISIiIqPC5ISIiIiMCpMTIiIiMir/D0pa5O9Fr4UyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, roc_auc_score, confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "# Importing PCA from sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Defining the function to apply models\n",
    "def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
    "    # Fit the model\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(\"========================================================\")\n",
    "\n",
    "    # Compute ROC curve and ROC area\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    return accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm\n",
    "\n",
    "# Defining the models\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(random_state=42, max_iter=500)),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier(random_state=42)),\n",
    "    (\"Random Forest\", RandomForestClassifier(random_state=42)),\n",
    "    (\"XGBoost\", XGBClassifier(random_state=42))\n",
    "]\n",
    "\n",
    "# Applying PCA to reduce the dimensionality of the data\n",
    "pca = PCA(n_components=15) # You can change the number of components as per your requirement\n",
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "\n",
    "# Calculating the explained variance ratio for each component\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "# Plotting the cumulative sum of the explained variance ratio as a function of the number of components\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio.cumsum(), marker='o', linestyle='--')\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance ratio')\n",
    "plt.title('Elbow point for choosing n_components')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Applying the models and storing the results\n",
    "results = []\n",
    "roc_curves = []\n",
    "confusion_matrices = []\n",
    "\n",
    "for name, model in models:\n",
    "    accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm = apply_model(model, X_train_pca, y_train, X_test_pca, y_test, name)\n",
    "    results.append((name, accuracy, precision, recall, f1))\n",
    "    roc_curves.append((name, fpr, tpr, roc_auc))\n",
    "    confusion_matrices.append((name, cm))\n",
    "\n",
    "# Plotting the ROC curves for each model\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve comparison')\n",
    "for name, fpr, tpr, roc_auc in roc_curves:\n",
    "    plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "# Draw confusion matrices for each model\n",
    "for name, cm in confusion_matrices:\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.title(f'Confusion Matrix - {name}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3301e249",
   "metadata": {},
   "source": [
    "### After balancing out the samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37259bd8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "F1 Score: 0.3294769431318234\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "============================================\n",
      "Model: Decision Tree\n",
      "F1 Score: 0.20566466002171124\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.93      0.93     56677\n",
      "         1.0       0.21      0.21      0.21      5078\n",
      "\n",
      "    accuracy                           0.87     61755\n",
      "   macro avg       0.57      0.57      0.57     61755\n",
      "weighted avg       0.87      0.87      0.87     61755\n",
      "\n",
      "============================================\n",
      "Model: Random Forest\n",
      "F1 Score: 0.06522920818988948\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.41      0.04      0.07      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.66      0.52      0.51     61755\n",
      "weighted avg       0.88      0.92      0.88     61755\n",
      "\n",
      "============================================\n",
      "Model: XGBoost\n",
      "F1 Score: 0.0969241773962804\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.53      0.05      0.10      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.72      0.52      0.53     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "============================================\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EviHfhy+//DJefvlllJaW4ocffsCCBQvg6+uL3377De3atatT/60/c2535coV8X4wRNpgz4kOevvttyEIAoKDg1FZWVlnf1VVFb7++msAwNNPPw0A2LFjhyQmKSkJ6enp4ux+OdR2Y//yyy+S8tq21EdfXx+enp748MMPAQA//fTTHWMHDRqEw4cPi8lIrU8//RSmpqZNtpyxdevWmDVrFvz8/DBp0qQ7xikUChgYGEgm25aVlWH79u11YuXqjaqpqcELL7wAhUKBgwcPIioqCmvXrsUXX3xx1+OMjIzQp08fyabNfVuAm1+fkpKSOsnYp59+Ku4Hbg6VAcCuXbskcTExMRqdT6FQoEePHli5ciVatmwp+ew09P3t3LmzOGx3+4qixpo4cSIGDBiAzZs3i0OIvr6+SEtLg7Ozc533vU+fPmJy0r9/fxQXF+PgwYOSOjV5b3x9fXH16lXU1NTUe676ktCGfB+qVCr4+Phg3rx5qKysxNmzZ+s9v4uLC1q3bo3PPvtMMlRYWlqKPXv2iCt4iLTBnhMd5O3tjQ0bNiAkJAQeHh54/fXX0a1bN1RVVeH06dP46KOP4ObmBj8/P7i4uGDy5MlYu3Yt9PT04OPjI67WcXR0xJtvvilbu5555hlYWloiKCgI7777LgwMDBAdHY3MzExJ3MaNG3H48GGMGDECbdu2RXl5uTgrf/DgwXesf8GCBeLY/fz582FpaYmdO3di//79WLJkCdRqtWzXcrsPPvjgnjEjRozAihUr4O/vj8mTJ+Pq1atYtmxZvcu9a//637VrFzp06ABjY+MGzRO53YIFC/Djjz8iLi4OdnZ2CA8Px7FjxxAUFIRevXrByclJ4zrv5s8//8T//ve/OuWurq546aWX8OGHH2LSpEnIyMiAu7s74uPjERkZiWeeeUb82g4fPhz9+vVDeHg4ioqK4OHhgYSEBDGJuXWewu2++eYbrF+/HqNHj0aHDh0gCAK++OILFBQUYMiQIWKcu7s7jh49iq+//hr29vYwMzO7Y8/Qhx9+CD8/P3h5eeHNN99E27ZtcenSJXz77bfYuXNno96nxYsXw9PTE++99x4+/vhjvPvuuzh06BD69u2L0NBQuLi4oLy8HBkZGThw4AA2btyINm3aYNKkSVi5ciVefPFFvP/+++jYsSMOHjyIb7/99p7vTa3nn38eO3fuxDPPPIM33ngDjz/+OAwNDXH58mUcOXIEo0aNwrPPPtug78Pg4GCYmJigX79+sLe3R3Z2NqKioqBWq/HYY4/Ve349PT0sWbIEEydOhK+vL6ZMmYKKigosXboUBQUFDfpeIrqnBzkbl+4uNTVVmDRpktC2bVvByMhIUKlUQq9evYT58+cLubm5YlxNTY2wePFioXPnzoKhoaFgbW0tvPjii0JmZqakvv79+wvdunWrc55JkyYJ7dq1k5ThDis3Tp06JfTt21dQqVRC69athQULFggff/yxZMVFQkKC8Oyzzwrt2rUTlEqlYGVlJfTv31/46quv6pzj1pUbgiAIZ86cEfz8/AS1Wi0YGRkJPXr0kKxgEIR/Vw/897//lZTXrq64Pf52t67WuZv6Vtx88skngouLi6BUKoUOHToIUVFRwpYtWyTXLwiCkJGRIQwdOlQwMzMTAIjv753afuu+2lURcXFxgp6eXp336OrVq0Lbtm2Fxx57TKioqLjrNWgCwB232jZcvXpVeO211wR7e3vBwMBAaNeunfD2228L5eXlkrquXbsmvPzyy0LLli0FU1NTYciQIUJiYqIAQFi9erUYd/tqnV9//VV44YUXBGdnZ8HExERQq9XC448/LkRHR0vqT01NFfr16yeYmpoKAMSv051WPCUkJAg+Pj6CWq0WlEql4OzsLLz55pt3fT9qP09Lly6td/9zzz0nGBgYCH/88YcgCIKQl5cnhIaGCk5OToKhoaFgaWkpeHh4CPPmzRNKSkrE4y5duiSMGTNGaNGihWBmZiaMHTtWOHDggABA+PLLL8W4SZMmCSqVqt5zV1VVCcuWLRN69OghGBsbCy1atBC6dOkiTJkyRfj999/Fa77X9+G2bduEgQMHCra2toKRkZHg4OAgjB8/XrIa7E7v6b59+wRPT0/B2NhYUKlUwqBBg4Tjx49LYmpX6+Tl5UnKb/+6E91OIQhaTuEnImqA2ntjHD9+HH379n3QzdEpkZGR+M9//oNLly490Ec6EOkKDusQkew+//xz/P3333B3d4eenh4SExOxdOlSPPXUU80+MamdRNulSxdUVVXh8OHDWLNmDV588UUmJkT/j8kJEcnOzMwMMTExeP/991FaWgp7e3sEBgbi/ffff9BNe+BMTU2xcuVKZGRkoKKiAm3btsWcOXPwn//850E3jUhncFiHiIiIdAqXEhMREZFOYXJCREREOoXJCREREekUTohtgBs3buDKlSswMzPT6LbjRESkGwRBQHFxMRwcHBp0s7vGKi8vr/fO3poyMjJq9MMqHwVMThrgypUrdZ6US0RED5/MzMwmW7JdXl4OEzMroPr6vYPvwc7ODhcuXGi2CQqTkwaofSaJkeskKPSNHnBriJrG2rXyPeqASNeUlZbgjRGPa/2MqbuprKwEqq9D6ToJ0OZ3RU0lss9tQ2VlJZMTurPaoRyFvhGTE3pkmbRouh/aRLrivgzNGxhr9btCUHA6KJMTIiIiOSkAaJMEcWojkxMiIiJZKfRubtoc38zxHSAiIiKdwp4TIiIiOSkUWg7rcFyHPSdERERyqh3W0WZrpKioKCgUCoSFhYllgiAgIiICDg4OMDExwYABA3D27FnJcRUVFZg+fTqsra2hUqkwcuRIXL58WRKTn5+PgIAAqNVqqNVqBAQEoKCgQBJz6dIl+Pn5QaVSwdraGqGhoY267wuTEyIiokdAUlISPvroI3Tv3l1SvmTJEqxYsQLr1q1DUlIS7OzsMGTIEBQXF4sxYWFh2Lt3L2JiYhAfH4+SkhL4+vqipqZGjPH390dqaipiY2MRGxuL1NRUBAQEiPtramowYsQIlJaWIj4+HjExMdizZw/Cw8M1vhYmJ0RERHKqHdbRZtNQSUkJJk6ciM2bN8PCwkIsFwQBq1atwrx58zBmzBi4ublh27ZtuH79Oj777DMAQGFhIbZs2YLly5dj8ODB6NWrF3bs2IEzZ87gu+++AwCkp6cjNjYWH3/8Mby9veHt7Y3Nmzfjm2++wfnz5wEAcXFxOHfuHHbs2IFevXph8ODBWL58OTZv3oyioiKNrofJCRERkay0HdLR/Ffz1KlTMWLECAwePFhSfuHCBWRnZ2Po0KFimVKpRP/+/XHixAkAQEpKCqqqqiQxDg4OcHNzE2MSEhKgVqvh6ekpxnh5eUGtVkti3Nzc4ODgIMYMGzYMFRUVSElJ0eh6OCGWiIhIB93e26BUKqFUKuvExcTE4KeffkJSUlKdfdnZ2QAAW1tbSbmtrS0uXrwoxhgZGUl6XGpjao/Pzs6GjY1NnfptbGwkMbefx8LCAkZGRmJMQ7HnhIiISE4yDes4OjqKk0/VajWioqLqnCozMxNvvPEGduzYcddb3d9+Z1xBEO55t9zbY+qLb0xMQ7DnhIiISE4y3YQtMzMT5ubmYnF9vSYpKSnIzc2Fh4eHWFZTU4MffvgB69atE+eDZGdnw97eXozJzc0Vezns7OxQWVmJ/Px8Se9Jbm4u+vbtK8bk5OTUOX9eXp6knpMnT0r25+fno6qqqk6Pyr2w54SIiEgHmZubS7b6kpNBgwbhzJkzSE1NFbc+ffpg4sSJSE1NRYcOHWBnZ4dDhw6Jx1RWVuLYsWNi4uHh4QFDQ0NJTFZWFtLS0sQYb29vFBYW4tSpU2LMyZMnUVhYKIlJS0tDVlaWGBMXFwelUilJnhqCPSdERERyuo83YTMzM4Obm5ukTKVSwcrKSiwPCwtDZGQkOnXqhE6dOiEyMhKmpqbw9/cHAKjVagQFBSE8PBxWVlawtLTEzJkz4e7uLk6w7dq1K4YPH47g4GBs2rQJADB58mT4+vrCxcUFADB06FC4uroiICAAS5cuxbVr1zBz5kwEBwdLeoAagskJERGRnHTs2TqzZ89GWVkZQkJCkJ+fD09PT8TFxcHM7N8nka9cuRIGBgYYP348ysrKMGjQIERHR0NfX1+M2blzJ0JDQ8VVPSNHjsS6devE/fr6+ti/fz9CQkLQr18/mJiYwN/fH8uWLdO4zQpBEAQtrrlZKCoqglqthtI9WKvHYBPpss0fv/Wgm0DUZMpKijF5gCsKCws1/iu+ocTfFV6zoTCoOwTTUEJ1BSoSlzRpW3Ud55wQERGRTuGwDhERkZx0bFjnYcTkhIiISE4KhZbJCZ9KzPSMiIiIdAp7ToiIiOSkp7i5aXN8M8fkhIiISE6cc6I1vgNERESkU9hzQkREJKf7eIfYRxWTEyIiIjlxWEdrfAeIiIhIp7DnhIiISE4c1tEakxMiIiI5cVhHa0xOiIiI5MSeE60xPSMiIiKdwp4TIiIiOXFYR2tMToiIiOTEYR2tMT0jIiIincKeEyIiIllpOazDfgMmJ0RERLLisI7WmJ4RERGRTmHPCRERkZwUCi1X67DnhMkJERGRnLiUWGt8B4iIiEinsOeEiIhITpwQqzUmJ0RERHLisI7WmJwQERHJiT0nWmN6RkRERDqFPSdERERy4rCO1picEBERyYnDOlpjekZEREQ6hT0nREREMlIoFFCw50QrTE6IiIhkxOREexzWISIiIp3CnhMiIiI5Kf5/0+b4Zo7JCRERkYw4rKM9DusQERE9xDZs2IDu3bvD3Nwc5ubm8Pb2xsGDB8X9gYGBYsJUu3l5eUnqqKiowPTp02FtbQ2VSoWRI0fi8uXLkpj8/HwEBARArVZDrVYjICAABQUFkphLly7Bz88PKpUK1tbWCA0NRWVlpcbXxOSEiIhIRrcnAo3ZNNGmTRt88MEHSE5ORnJyMp5++mmMGjUKZ8+eFWOGDx+OrKwscTtw4ICkjrCwMOzduxcxMTGIj49HSUkJfH19UVNTI8b4+/sjNTUVsbGxiI2NRWpqKgICAsT9NTU1GDFiBEpLSxEfH4+YmBjs2bMH4eHhGr+HHNYhIiKS0f0e1vHz85O8XrRoETZs2IDExER069YNAKBUKmFnZ1fv8YWFhdiyZQu2b9+OwYMHAwB27NgBR0dHfPfddxg2bBjS09MRGxuLxMREeHp6AgA2b94Mb29vnD9/Hi4uLoiLi8O5c+eQmZkJBwcHAMDy5csRGBiIRYsWwdzcvMHXxJ4TIiIiGd3vnpNb1dTUICYmBqWlpfD29hbLjx49ChsbG3Tu3BnBwcHIzc0V96WkpKCqqgpDhw4VyxwcHODm5oYTJ04AABISEqBWq8XEBAC8vLygVqslMW5ubmJiAgDDhg1DRUUFUlJSNLoO9pwQERHpoKKiIslrpVIJpVJZb+yZM2fg7e2N8vJytGjRAnv37oWrqysAwMfHB8899xzatWuHCxcu4J133sHTTz+NlJQUKJVKZGdnw8jICBYWFpI6bW1tkZ2dDQDIzs6GjY1NnfPa2NhIYmxtbSX7LSwsYGRkJMY0FJMTIiIiOcm0lNjR0VFSvGDBAkRERNR7iIuLC1JTU1FQUIA9e/Zg0qRJOHbsGFxdXTFhwgQxzs3NDX369EG7du2wf/9+jBkz5o7NEARB0otTX49OY2IagskJERGRjOSac5KZmSmZp3GnXhMAMDIyQseOHQEAffr0QVJSElavXo1NmzbVibW3t0e7du3w+++/AwDs7OxQWVmJ/Px8Se9Jbm4u+vbtK8bk5OTUqSsvL0/sLbGzs8PJkycl+/Pz81FVVVWnR+VeOOeEiIhIB9UuDa7d7pac3E4QBFRUVNS77+rVq8jMzIS9vT0AwMPDA4aGhjh06JAYk5WVhbS0NDE58fb2RmFhIU6dOiXGnDx5EoWFhZKYtLQ0ZGVliTFxcXFQKpXw8PBo+IWDPSdERESyUijqH95oeAWahc+dOxc+Pj5wdHREcXExYmJicPToUcTGxqKkpAQREREYO3Ys7O3tkZGRgblz58La2hrPPvssAECtViMoKAjh4eGwsrKCpaUlZs6cCXd3d3H1TteuXTF8+HAEBweLvTGTJ0+Gr68vXFxcAABDhw6Fq6srAgICsHTpUly7dg0zZ85EcHCwRit1ACYnREREslJAy2EdDbOTnJwcBAQEICsrC2q1Gt27d0dsbCyGDBmCsrIynDlzBp9++ikKCgpgb2+PgQMHYteuXTAzMxPrWLlyJQwMDDB+/HiUlZVh0KBBiI6Ohr6+vhizc+dOhIaGiqt6Ro4ciXXr1on79fX1sX//foSEhKBfv34wMTGBv78/li1bpvk7IAiCoPFRzUxRURHUajWU7sFQ6Bs96OYQNYnNH7/1oJtA1GTKSooxeYArCgsLNf4rvqFqf1e0HL8ZCiPTRtcjVF5Hwe7gJm2rrmPPCRERkYz4bB3tMTkhIiKSE59KrDWu1iEiIiKdwp4TIiIiOWk5rCNwWIfJCRERkZy0nXOi3UqfRwOTEyIiIhkxOdEe55wQERGRTmHPCRERkZy4WkdrTE6IiIhkxGEd7XFYh4iIiHQKe06IiIhkxJ4T7TE5ISIikhGTE+1xWIeIiIh0CntOiIiIZMSeE+0xOSEiIpITlxJrjcM6REREpFPYc0JERCQjDutoj8kJERGRjJicaI/JCRERkYyYnGiPc06IiIhIp7DnhIiISE5craM1JidEREQy4rCO9jisQ0RERDrloew5iY6ORlhYGAoKCh50U6gebwYOxfypI7Hh8yOYu2IPAMB3YA8EPvsEenZ1hFXLFnhyYhTSfvtbPKaluSnenjwCA726oLWtBa4VlGD/0V8QufEbFJWWi3E/f7kQbR2sJOdbtS0OC9d9Jb6OCh8Lrx7O6Opsj98ycvDUxA+a+IqpOfjtt0v4NvYkLl7MQWFhCUKmjkGvXp3F/Z988g0STqRJjnHq4IC5c1+qU5cgCFiz+r9IS/tLUs8//xTgm29O4NdfL6KosBQtW7aAp1c3jBjRFwYG+pI6jh//BYfikpCTcw2mpsbw8HCB/8ShTXDlpCn2nGjvgSYngYGB2LZtW53y33//HR07dnwALSJt9XJti0mj+yLtt8uScpWxEU7+8ie+/P4nrPnPxDrH2bdSw66VGvNX78Wvf2XD0d4SK956Hnat1Ah8a4skdtHGb/DpvuPi69LrFZL9Ciiw8+tEeHRrh26dWst4ddScVVRUoY2jLfr1644NG/bWG+Pm1gGBLz8jvjbQ16837rtDSfWWZ2dfg3BDQEDAcNjYWODvv/Pw6baDqKyownPjnxbj4uJO4VDcKYx7biCcnBxQVVWNf/IKGn9xJCsFtExOOOnkwfecDB8+HFu3bpWUtWrV6gG1hrShMjHCR+8G4o3IzzHzleGSfbsO3vxh7GhvWe+x6X9mYdKcj8XXGX//g/c3fI1N774EfX091NTcEPeVXC9H7tXiO7bjreX/AwBYtXyGyQnJxt3dGe7uzneNMTDQh1rd4q4xmZk5OHQoCfP+Mwkzw9dJ9rm5dYCbWwfxdatWLZGTfQ1Hj/4kJielpeX4ct8PmDZ9HLp2bS/Gtm7Nn5v06Hjgc06USiXs7Owk2+rVq+Hu7g6VSgVHR0eEhISgpKTkjnX8/PPPGDhwIMzMzGBubg4PDw8kJyeL+0+cOIGnnnoKJiYmcHR0RGhoKEpLS+/H5TUrS2dPQNzxNBw7dV6W+sxbGKO4tFySmADAGy8NwZ+HFuOHnW8h/OVhMDSo/69Tovvt/PlLmPHmGsybtwmfbjuIoiLpz5mKiips/ugr+PsPuWcSU+t6WQVUKhPx9blzF3DjhoD8/GK885/NmDXrQ2zcuA/XrhXJei3UeLXDOtpszd0DT07qo6enhzVr1iAtLQ3btm3D4cOHMXv27DvGT5w4EW3atEFSUhJSUlLw1ltvwdDQEABw5swZDBs2DGPGjMEvv/yCXbt2IT4+HtOmTbtfl9MsjBnigR5dHPHuh1/dO7gBLNQqzAryQfQXxyXlG2OOImjeVox8fTU27z6G114YgGVzJshyTiJtuLs549VgP4TPfAHjn3saFzKysHzZ56iqqhZjdu/6Hs7OrdHzlrkqd5Obm48jh1PQv38vseyfvAIIgoCDBxIw4flBeO210bheWoaVK2JQXV0j+3VRIyhk2Jq5Bz6s880336BFi3//gvDx8cF///tf8bWTkxPee+89vP7661i/fn29dVy6dAmzZs1Cly5dAACdOnUS9y1duhT+/v4ICwsT961Zswb9+/fHhg0bYGxsXKe+iooKVFT8O4+hqIh/kdxNa9uWiAofi7HTP0RFZfW9D7gHM5Uxdq18DecvZGHx5gOSfRs+PyL+/+wfV1BQVIZPl7yKiHVfIr+QvWH04Dz2eFfx/61bt0K79vZ4a856nPnlT/T2cEFq6u/49deLeGf+yw2qr6CgGKtX7YaHhwuefKqHWH5DEFBTcwPPvzAE3bo5AQCCJ49C+Iy1+PXXi5JhIaKH1QNPTgYOHIgNGzaIr1UqFY4cOYLIyEicO3cORUVFqK6uRnl5OUpLS6FSqerUMWPGDLz66qvYvn07Bg8ejOeeew7OzjfHhlNSUvDHH39g586dYrwgCLhx4wYuXLiArl271qkvKioKCxcubIKrfTT16NIWNlbmOPLpv71bBgb66NvLGcHPPQXbfmG4cUNoUF0tTJX435oQlJZV4MVZm1F925DO7ZLTLgAAOrSxRgqTE9IhLVu2gJWVGjm5+QCAX3+9iLy8fLwRulISt2H9XnTq1AazZv87UbygoBjLln6ODs4OCHjJR1rv/w8H2dv/u2rNzMwULVqYcGhHR3C1jvYeeHKiUqkkK3MuXryIZ555Bq+99hree+89WFpaIj4+HkFBQaiqqqq3joiICPj7+2P//v04ePAgFixYgJiYGDz77LO4ceMGpkyZgtDQ0DrHtW3btt763n77bcyYMUN8XVRUBEdHRy2v9NH1Q9J59H1+kaRs3fwX8XtGDlZ/eqjBiYmZyhj/WzMVlVXV8J+xqUG9MN1dbn5dcv7hD2XSLSUlZbh2rQhq9c0/qHx8vPDkkz0kMRELtmDChEHo3uPfn4H5+cVYtuwztGtnh5dfHgE9PekvKueObQAAOTnXYGlpDgAoLSlDSUkZrKzUTXlJ1EBMTrT3wJOT2yUnJ6O6uhrLly+Hnt7NKTG7d+++53GdO3dG586d8eabb+KFF17A1q1b8eyzz6J37944e/asRkuTlUollEplo6+huSm5XoH0P7MkZdfLKnGtsFQsb2luijZ2FrC3vvnDs1M7WwBA7tUi5F4tRgtTJfasnQpTYyNMmb8NZi2MYdbi5pDbP/kluHFDwGPuTujj1h4/pvyGopJy9HZti0VvjsWBY7/gck6+eG6nNtZQmSpha2UOY6Uh3DrfXLFz/q9sVHFMnhqpvLwSubn/fs7+ySvApUs5UKmMoVKZ4Ouv4tHbwwVqtQpX/ynE3r0/oIWZKXr3vjm/RK1uUe8kWEsrc7Rq1RJAbY/JZ7C0NMdzzz2N4uLrYlztsXZ2lujZsxNiPv8OAS8Nh4mJEl/sOQo7eyu4uNT/BxfdXwrFzU2b45s7nUtOnJ2dUV1djbVr18LPzw/Hjx/Hxo0b7xhfVlaGWbNmYdy4cXBycsLly5eRlJSEsWPHAgDmzJkDLy8vTJ06FcHBwVCpVEhPT8ehQ4ewdu3a+3VZzZ7PU+5YvyBAfP1J5CsAgA8+OoDFmw+gR5e2eMz95vj56X0RkmO7j5yPzKxrqKiswrNDemNOsA+MDA2QmX0Nn+47gTWfHpLEr/nPRDzh8e+8ox93vi2ph6gxLmZkYdmyz8XXu3cfBgB493XDiy8Ow+W/85CQkIbr18uhVreAS5e2mDxlFIyNG/6HztmzF5Cbm4/c3HzMnvWhZN/mj98S//9KkC927foea9f8FwqFAp07t0VY2Pg6N2ojelgpBEFoWJ97EwgMDERBQQH27dsnKV+5ciWWLl2KgoICPPXUU5g4cSJeeukl5Ofno2XLlpI7xFZWVmLSpEk4fvw4cnJyYG1tjTFjxmDp0qXiZNekpCTMmzcPCQkJEAQBzs7OmDBhAubOndugdhYVFUGtVkPpHgyFvpHcbwORTrj1lx/Ro6aspBiTB7iisLAQ5ubmTXKO2t8VHab/D3rKuvMjG+pGRSn+WjuuSduq6x5ocvKwYHJCzQGTE3qU3dfkJPR/0NciOampKMVfa5p3cqKT9zkhIiKihtmwYQO6d+8Oc3NzmJubw9vbGwcPHhT3C4KAiIgIODg4wMTEBAMGDMDZs2cldVRUVGD69OmwtraGSqXCyJEjcfmy9DEk+fn5CAgIgFqthlqtRkBAQJ1n3F26dAl+fn5QqVSwtrZGaGgoKisrNb4mJidEREQyut93iG3Tpg0++OADJCcnIzk5GU8//TRGjRolJiBLlizBihUrsG7dOiQlJcHOzg5DhgxBcfG/jwEJCwvD3r17ERMTg/j4eJSUlMDX1xc1Nf8uIvD390dqaipiY2MRGxuL1NRUBAT8O5ewpqYGI0aMQGlpKeLj4xETE4M9e/YgPDxc8/eQwzr3xmEdag44rEOPsvs5rNMxbI/Wwzp/rBqrVVstLS2xdOlSvPLKK3BwcEBYWBjmzJkD4GYvia2tLRYvXowpU6agsLAQrVq1wvbt2zFhws07bl+5cgWOjo44cOAAhg0bhvT0dLi6uiIxMRGenp4AgMTERHh7e+PXX3+Fi4sLDh48CF9fX2RmZsLBwQEAEBMTg8DAQOTm5mp0Lew5ISIiekTU1NQgJiYGpaWl8Pb2xoULF5CdnY2hQ4eKMUqlEv3798eJEycA3LxZaVVVlSTGwcEBbm5uYkxCQgLUarWYmACAl5cX1Gq1JMbNzU1MTABg2LBhqKioQEpKikbXoXNLiYmIiB5menqKOjfP04Tw/8fe/uiUu92D68yZM/D29kZ5eTlatGiBvXv3wtXVVUwcbG1tJfG2tra4ePEiACA7OxtGRkawsLCoE5OdnS3G2NjY1DmvjY2NJOb281hYWMDIyEiMaSj2nBAREcmo9iZs2mwA4OjoKE4+VavViIqKuuM5XVxckJqaisTERLz++uuYNGkSzp07d0ubpMmSIAj3nNtye0x98Y2JaQj2nBAREemgzMxMyTyNu9253MjISLwTep8+fZCUlITVq1eL80yys7Nhb28vxufm5oq9HHZ2dqisrER+fr6k9yQ3Nxd9+/YVY3JycuqcNy8vT1LPyZMnJfvz8/NRVVVVp0flXthzQkREJCO5VuvULg2u3TR5rIogCKioqICTkxPs7Oxw6NC/d9KurKzEsWPHxMTDw8MDhoaGkpisrCykpaWJMd7e3igsLMSpU6fEmJMnT6KwsFASk5aWhqysfx9nEhcXB6VSCQ8PD43eQ/acEBERyeh+P1tn7ty58PHxgaOjI4qLixETE4OjR48iNjYWCoUCYWFhiIyMRKdOndCpUydERkbC1NQU/v7+AAC1Wo2goCCEh4fDysoKlpaWmDlzJtzd3TF48GAAQNeuXTF8+HAEBwdj06ZNAIDJkyfD19cXLi4uAIChQ4fC1dUVAQEBWLp0Ka5du4aZM2ciODhY41VHTE6IiIhkdL+fSpyTk4OAgABkZWVBrVaje/fuiI2NxZAhQwAAs2fPRllZGUJCQpCfnw9PT0/ExcXBzMxMrGPlypUwMDDA+PHjUVZWhkGDBiE6Ohr6+v8+r2nnzp0IDQ0VV/WMHDkS69atE/fr6+tj//79CAkJQb9+/WBiYgJ/f38sW7ZM8/eA9zm5N97nhJoD3ueEHmX38z4nrrP3aX2fk3NLRjfr29ez54SIiEhG97vn5FHE5ISIiEhG93vOyaOIq3WIiIhIp7DnhIiISEYKaDmsA3adMDkhIiKSEYd1tMdhHSIiItIp7DkhIiKSEVfraI/JCRERkYw4rKM9DusQERGRTmHPCRERkYw4rKM9JidEREQy4rCO9picEBERyYg9J9rjnBMiIiLSKew5ISIikpOWwzq8QSyTEyIiIllxWEd7HNYhIiIincKeEyIiIhlxtY72mJwQERHJiMM62uOwDhEREekU9pwQERHJiMM62mNyQkREJCMO62iPwzpERESkU9hzQkREJCP2nGiPyQkREZGMOOdEe0xOiIiIZMSeE+1xzgkRERHpFPacEBERyYjDOtpjckJERCQjDutoj8M6REREpFPYc0JERCQjBbQc1pGtJQ8vJidEREQy0lMooKdFdqLNsY8KDusQERGRTmHPCRERkYy4Wkd7TE6IiIhkxNU62mNyQkREJCM9xc1Nm+ObO845ISIieohFRUXhscceg5mZGWxsbDB69GicP39eEhMYGCj26NRuXl5ekpiKigpMnz4d1tbWUKlUGDlyJC5fviyJyc/PR0BAANRqNdRqNQICAlBQUCCJuXTpEvz8/KBSqWBtbY3Q0FBUVlZqdE1MToiIiOSkQJ1EQJNN07XEx44dw9SpU5GYmIhDhw6huroaQ4cORWlpqSRu+PDhyMrKErcDBw5I9oeFhWHv3r2IiYlBfHw8SkpK4Ovri5qaGjHG398fqampiI2NRWxsLFJTUxEQECDur6mpwYgRI1BaWor4+HjExMRgz549CA8P1+iaOKxDREQko/s9ITY2NlbyeuvWrbCxsUFKSgqeeuopsVypVMLOzq7eOgoLC7FlyxZs374dgwcPBgDs2LEDjo6O+O677zBs2DCkp6cjNjYWiYmJ8PT0BABs3rwZ3t7eOH/+PFxcXBAXF4dz584hMzMTDg4OAIDly5cjMDAQixYtgrm5eYOuiT0nREREOqioqEiyVVRUNOi4wsJCAIClpaWk/OjRo7CxsUHnzp0RHByM3NxccV9KSgqqqqowdOhQsczBwQFubm44ceIEACAhIQFqtVpMTADAy8sLarVaEuPm5iYmJgAwbNgwVFRUICUlpcHXzuSEiIhIRgoZ/gGAo6OjOLdDrVYjKirqnucWBAEzZszAE088ATc3N7Hcx8cHO3fuxOHDh7F8+XIkJSXh6aefFhOe7OxsGBkZwcLCQlKfra0tsrOzxRgbG5s657SxsZHE2NraSvZbWFjAyMhIjGkIDusQERHJSK7VOpmZmZJhEKVSec9jp02bhl9++QXx8fGS8gkTJoj/d3NzQ58+fdCuXTvs378fY8aMuWN9giBIljbXt8y5MTH3wp4TIiIiHWRubi7Z7pWcTJ8+HV999RWOHDmCNm3a3DXW3t4e7dq1w++//w4AsLOzQ2VlJfLz8yVxubm5Yk+InZ0dcnJy6tSVl5cnibm9hyQ/Px9VVVV1elTuhskJERGRjLRZqdOYG7gJgoBp06bhiy++wOHDh+Hk5HTPY65evYrMzEzY29sDADw8PGBoaIhDhw6JMVlZWUhLS0Pfvn0BAN7e3igsLMSpU6fEmJMnT6KwsFASk5aWhqysLDEmLi4OSqUSHh4eDb6mBg3rrFmzpsEVhoaGNjiWiIjoUXO/V+tMnToVn332Gb788kuYmZmJPRdqtRomJiYoKSlBREQExo4dC3t7e2RkZGDu3LmwtrbGs88+K8YGBQUhPDwcVlZWsLS0xMyZM+Hu7i6u3unatSuGDx+O4OBgbNq0CQAwefJk+Pr6wsXFBQAwdOhQuLq6IiAgAEuXLsW1a9cwc+ZMBAcHN3ilDtDA5GTlypUNqkyhUDA5ISIiuo82bNgAABgwYICkfOvWrQgMDIS+vj7OnDmDTz/9FAUFBbC3t8fAgQOxa9cumJmZifErV66EgYEBxo8fj7KyMgwaNAjR0dHQ19cXY3bu3InQ0FBxVc/IkSOxbt06cb++vj7279+PkJAQ9OvXDyYmJvD398eyZcs0uiaFIAiCpm9Ec1NUVAS1Wg2lezAU+kYPujlETWLzx2896CYQNZmykmJMHuCKwsJCjf6C10Tt7wrftUdhaNKi0fVUlZXgm+kDmrStuq7Rc04qKytx/vx5VFdXy9keIiKih1rtsI42W3OncXJy/fp1BAUFwdTUFN26dcOlS5cA3Jxr8sEHH8jeQCIioofJ/Z4Q+yjSODl5++238fPPP+Po0aMwNjYWywcPHoxdu3bJ2jgiIiJqfjS+Cdu+ffuwa9cueHl5SbI7V1dX/Pnnn7I2joiI6GFzv1frPIo0Tk7y8vLqvX1taWkpu6KIiKjZ01MooKfF70Ntjn1UaDys89hjj2H//v3i69qEpPbJhERERETa0LjnJCoqCsOHD8e5c+dQXV2N1atX4+zZs0hISMCxY8eaoo1EREQPDcX/b9oc39xp3HPSt29fHD9+HNevX4ezszPi4uJga2uLhIQEjW5NS0RE9Cjiah3tNeqpxO7u7ti2bZvcbSEiIiJqXHJSU1ODvXv3Ij09HQqFAl27dsWoUaNgYNCo6oiIiB4ZeoqbmzbHN3caZxNpaWkYNWoUsrOzxQf9/Pbbb2jVqhW++uoruLu7y95IIiKih4W2QzMc1mnEnJNXX30V3bp1w+XLl/HTTz/hp59+QmZmJrp3747Jkyc3RRuJiIioGdG45+Tnn39GcnIyLCwsxDILCwssWrQIjz32mKyNIyIiehix80M7GvecuLi4ICcnp055bm4uOnbsKEujiIiIHlZcraO9BvWcFBUVif+PjIxEaGgoIiIi4OXlBQBITEzEu+++i8WLFzdNK4mIiB4SnBCrvQYlJy1btpRkcoIgYPz48WKZIAgAAD8/P9TU1DRBM4mIiKi5aFBycuTIkaZuBxER0SOBq3W016DkpH///k3dDiIiokcCb1+vvUbfNe369eu4dOkSKisrJeXdu3fXulFERETUfGmcnOTl5eHll1/GwYMH693POSdERNSc6SkU0NNiaEabYx8VGi8lDgsLQ35+PhITE2FiYoLY2Fhs27YNnTp1wldffdUUbSQiInpoKBTab82dxj0nhw8fxpdffonHHnsMenp6aNeuHYYMGQJzc3NERUVhxIgRTdFOIiIiaiY07jkpLS2FjY0NAMDS0hJ5eXkAbj6p+KeffpK3dURERA8Z3oRNe426Q+z58+cBAD179sSmTZvw999/Y+PGjbC3t5e9gURERA8TDutoT+NhnbCwMGRlZQEAFixYgGHDhmHnzp0wMjJCdHS03O0jIiKiZkbj5GTixIni/3v16oWMjAz8+uuvaNu2LaytrWVtHBER0cOGq3W01+j7nNQyNTVF79695WgLERHRQ0/boRnmJg1MTmbMmNHgClesWNHoxhARET3sePt67TUoOTl9+nSDKuMbSkRERNrig/80cOnoMpibmz/oZhA1icLrVQ+6CURNprjI5L6dSw+NWAp72/HNndZzToiIiOhfHNbRHhM0IiIi0insOSEiIpKRQgHocbWOVpicEBERyUhPy+REm2MfFRzWISIiIp3SqORk+/bt6NevHxwcHHDx4kUAwKpVq/Dll1/K2jgiIqKHzf1+8F9UVBQee+wxmJmZwcbGBqNHjxafgVdLEARERETAwcEBJiYmGDBgAM6ePSuJqaiowPTp02FtbQ2VSoWRI0fi8uXLkpj8/HwEBARArVZDrVYjICAABQUFkphLly7Bz88PKpUK1tbWCA0NRWVlpUbXpHFysmHDBsyYMQPPPPMMCgoKUFNTAwBo2bIlVq1apWl1REREj5TaYR1tNk0cO3YMU6dORWJiIg4dOoTq6moMHToUpaWlYsySJUuwYsUKrFu3DklJSbCzs8OQIUNQXFwsxoSFhWHv3r2IiYlBfHw8SkpK4OvrK/6eBwB/f3+kpqYiNjYWsbGxSE1NRUBAgLi/pqYGI0aMQGlpKeLj4xETE4M9e/YgPDxco2tSCIIgaHKAq6srIiMjMXr0aJiZmeHnn39Ghw4dkJaWhgEDBuCff/7RqAEPg6KiIqjVauRcLeR9TuiRxfuc0KOsuKgInRytUVjYdD/Ha39XTN+VDKVpi0bXU3G9BGsn9Gl0W/Py8mBjY4Njx47hqaeegiAIcHBwQFhYGObMmXPzHBUVsLW1xeLFizFlyhQUFhaiVatW2L59OyZMmAAAuHLlChwdHXHgwAEMGzYM6enpcHV1RWJiIjw9PQEAiYmJ8Pb2xq+//goXFxccPHgQvr6+yMzMhIODAwAgJiYGgYGByM3NbfD1aNxzcuHCBfTq1atOuVKplGRpREREzVHts3W02YCbyc6tW0VFRYPOX1hYCACwtLQEcPP3dnZ2NoYOHSrGKJVK9O/fHydOnAAApKSkoKqqShLj4OAANzc3MSYhIQFqtVpMTADAy8sLarVaEuPm5iYmJgAwbNgwVFRUICUlpcHvocbJiZOTE1JTU+uUHzx4EK6urppWR0RE9EipfSqxNhsAODo6inM71Go1oqKi7nluQRAwY8YMPPHEE3BzcwMAZGdnAwBsbW0lsba2tuK+7OxsGBkZwcLC4q4xNjY2dc5pY2Mjibn9PBYWFjAyMhJjGkLjpcSzZs3C1KlTUV5eDkEQcOrUKXz++eeIiorCxx9/rGl1REREjxS5bl+fmZkpGQZRKpX3PHbatGn45ZdfEB8fX2ff7RNtBUG45+Tb22Pqi29MzL1onJy8/PLLqK6uxuzZs3H9+nX4+/ujdevWWL16NZ5//nlNqyMiIqJ6mJubazTnZPr06fjqq6/www8/oE2bNmK5nZ0dgJu9Gvb29mJ5bm6u2MthZ2eHyspK5OfnS3pPcnNz0bdvXzEmJyenznnz8vIk9Zw8eVKyPz8/H1VVVXV6VO6mUcldcHAwLl68iNzcXGRnZyMzMxNBQUGNqYqIiOiRIteck4YSBAHTpk3DF198gcOHD8PJyUmy38nJCXZ2djh06JBYVllZiWPHjomJh4eHBwwNDSUxWVlZSEtLE2O8vb1RWFiIU6dOiTEnT55EYWGhJCYtLQ1ZWVliTFxcHJRKJTw8PBp8TVrdIdba2lqbw4mIiB45evh33khjj9fE1KlT8dlnn+HLL7+EmZmZOLdDrVbDxMQECoUCYWFhiIyMRKdOndCpUydERkbC1NQU/v7+YmxQUBDCw8NhZWUFS0tLzJw5E+7u7hg8eDAAoGvXrhg+fDiCg4OxadMmAMDkyZPh6+sLFxcXAMDQoUPh6uqKgIAALF26FNeuXcPMmTMRHBysUS+QxsmJk5PTXceN/vrrL02rJCIiokbasGEDAGDAgAGS8q1btyIwMBAAMHv2bJSVlSEkJAT5+fnw9PREXFwczMzMxPiVK1fCwMAA48ePR1lZGQYNGoTo6Gjo6+uLMTt37kRoaKi4qmfkyJFYt26duF9fXx/79+9HSEgI+vXrBxMTE/j7+2PZsmUaXZPG9zlZvXq15HVVVRVOnz6N2NhYzJo1C2+99ZZGDXgY8D4n1BzwPif0KLuf9zmZvecnKFVa3OektARLxvZu0rbqOo17Tt544416yz/88EMkJydr3SAiIqKHGR/8pz3ZHvzn4+ODPXv2yFUdERERNVNaTYi91f/+9z/xbnRERETNlUIBrSbEanHoI0Pj5KRXr16SCbGCICA7Oxt5eXlYv369rI0jIiJ62DRmOfDtxzd3Gicno0ePlrzW09NDq1atMGDAAHTp0kWudhEREVEzpVFyUl1djfbt22PYsGHiHeeIiIjoX5wQqz2NJsQaGBjg9ddfb/CTEYmIiJobhQz/mjuNV+t4enri9OnTTdEWIiKih15tz4k2W3On8ZyTkJAQhIeH4/Lly/Dw8IBKpZLs7969u2yNIyIiouanwcnJK6+8glWrVmHChAkAgNDQUHGfQqEQH4dcU1MjfyuJiIgeEpxzor0GJyfbtm3DBx98gAsXLjRle4iIiB5qCoXirs+ga8jxzV2Dk5PaR/C0a9euyRpDREREpNGcE2ZzREREd8dhHe1plJx07tz5ngnKtWvXtGoQERHRw4x3iNWeRsnJwoULoVarm6otRERERJolJ88//zxsbGyaqi1EREQPPT2FQqsH/2lz7KOiwckJ55sQERHdG+ecaK/Bd4itXa1DRERE1JQa3HNy48aNpmwHERHRo0HLCbF8tE4jbl9PREREd6YHBfS0yDC0OfZRweSEiIhIRlxKrD2Nn0pMRERE1JTYc0JERCQjrtbRHpMTIiIiGfE+J9rjsA4RERHpFPacEBERyYgTYrXH5ISIiEhGetByWIdLiTmsQ0RERLqFPSdEREQy4rCO9picEBERyUgP2g1LcEiD7wERERHpGPacEBERyUihUEChxdiMNsc+KpicEBERyUgB7R4szNSEyQkREZGseIdY7XHOCREREekUJidEREQyU2ixaeqHH36An58fHBwcoFAosG/fPsn+wMBAcR5M7ebl5SWJqaiowPTp02FtbQ2VSoWRI0fi8uXLkpj8/HwEBARArVZDrVYjICAABQUFkphLly7Bz88PKpUK1tbWCA0NRWVlpcbXxOSEiIhIRrX3OdFm00RpaSl69OiBdevW3TFm+PDhyMrKErcDBw5I9oeFhWHv3r2IiYlBfHw8SkpK4Ovri5qaGjHG398fqampiI2NRWxsLFJTUxEQECDur6mpwYgRI1BaWor4+HjExMRgz549CA8P1+yCwDknREREDzUfHx/4+PjcNUapVMLOzq7efYWFhdiyZQu2b9+OwYMHAwB27NgBR0dHfPfddxg2bBjS09MRGxuLxMREeHp6AgA2b94Mb29vnD9/Hi4uLoiLi8O5c+eQmZkJBwcHAMDy5csRGBiIRYsWwdzcvMHXxJ4TIiIiGd0+hNKYDQCKiookW0VFRaPbdPToUdjY2KBz584IDg5Gbm6uuC8lJQVVVVUYOnSoWObg4AA3NzecOHECAJCQkAC1Wi0mJgDg5eUFtVotiXFzcxMTEwAYNmwYKioqkJKSolF7mZwQERHJSE+GDQAcHR3F+R1qtRpRUVGNao+Pjw927tyJw4cPY/ny5UhKSsLTTz8tJjvZ2dkwMjKChYWF5DhbW1tkZ2eLMTY2NnXqtrGxkcTY2tpK9ltYWMDIyEiMaSgO6xAREemgzMxMyVCIUqlsVD0TJkwQ/+/m5oY+ffqgXbt22L9/P8aMGXPH4wRBkNwQrr6bwzUmpiHYc0JERCQjuYZ1zM3NJVtjk5Pb2dvbo127dvj9998BAHZ2dqisrER+fr4kLjc3V+wJsbOzQ05OTp268vLyJDG395Dk5+ejqqqqTo/KvTA5ISIikpE2y4i1vbtsQ1y9ehWZmZmwt7cHAHh4eMDQ0BCHDh0SY7KyspCWloa+ffsCALy9vVFYWIhTp06JMSdPnkRhYaEkJi0tDVlZWWJMXFwclEolPDw8NGojh3WIiIgeYiUlJfjjjz/E1xcuXEBqaiosLS1haWmJiIgIjB07Fvb29sjIyMDcuXNhbW2NZ599FgCgVqsRFBSE8PBwWFlZwdLSEjNnzoS7u7u4eqdr164YPnw4goODsWnTJgDA5MmT4evrCxcXFwDA0KFD4erqioCAACxduhTXrl3DzJkzERwcrNFKHYDJCRERkazu94P/kpOTMXDgQPH1jBkzAACTJk3Chg0bcObMGXz66acoKCiAvb09Bg4ciF27dsHMzEw8ZuXKlTAwMMD48eNRVlaGQYMGITo6Gvr6+mLMzp07ERoaKq7qGTlypOTeKvr6+ti/fz9CQkLQr18/mJiYwN/fH8uWLdP8PRAEQdD4qGamqKgIarUaOVcLNc7+iB4WhderHnQTiJpMcVEROjlao7Cw6X6O1/6u2B5/HqYtzO59wB1cLylGwBMuTdpWXceeEyIiIhnd756TRxEnxBIREZFOYc8JERGRjLRdccN+EyYnREREsmrMw/tuP76547AOERER6RT2nBAREclIDwroaTE4o82xjwomJ0RERDLisI72OKxDREREOoU9J0RERDJS/P8/bY5v7picEBERyYjDOtrjsA4RERHpFPacEBERyUih5WodDuswOSEiIpIVh3W0x+SEiIhIRkxOtMc5J0RERKRT2HNCREQkIy4l1h6TEyIiIhnpKW5u2hzf3HFYh4iIiHQKe06IiIhkxGEd7TE5ISIikhFX62iPwzpERESkU9hzQkREJCMFtBuaYccJkxMiIiJZcbWO9jisQ0RERDqFyQk1ieM//YHn39yIrj5zYfHYNOw/+rNkvyAI+OCj/ejqMxf2T7wJ3ymrkP5nliQmLPJz9BodAfsn3kTHIW/BP3wTfsvIrvd8FZVVeNI/ChaPTcOZ85eb7LqI6vPhju/Qvv+bWLh2r1gmCAJWbo3F42MWwGXIbEx4Yx1+u5BV7/GCIGDSrE1o3/9NfPvjmXpjKiqr4RO0FO37v4mzv//dJNdB8lDI8K+506nkRKFQ3HULDAx80E2kBrpeVgG3zq2xZNb4evev/vQ7rP/sCJbMGo/vo2fBxsocY6atRXFpuRjTs4sj1s1/ESd3/wd71k6FIAgYM+1D1NTcqFPfgjVfwq6Vusmuh+hOfk6/hM+/TkAXZwdJ+cbPD2PL7qN4N2wsvtr0JlpZmuPF8I0ouV5ep44t/z0GxT2WaERt/Aq2VvyMPwxqV+toszV3OpWcZGVliduqVatgbm4uKVu9erUkvqqq6gG1lO5lSL9u+M/rfvB7umedfYIgYOPnRzDj5WHwe7onXDs6YENEAK6XV+F/3yaLcYFjnkC/3h3R1sEKPbo4Yt7rfvg7Jx+Xsq5K6jt0/CyOnEzHe28829SXRSRRer0CYe/vwAezxkNtZiKWC4KAT/57DFMDhmD4U93h0sEey9/2R1lFJb787idJHef++Btbdh/DkjnP3/E8RxLT8WPSecwLGdlk10LyUciwNXc6lZzY2dmJm1qthkKhEF+Xl5ejZcuW2L17NwYMGABjY2Ps2LEDERER6Nmzp6SeVatWoX379pKyrVu3omvXrjA2NkaXLl2wfv36+3dhJHHx76vIuVqEp726iGVKI0P0690Rp375q95jSssq8NnXiWjnYIXWthZiee7VIoRFfo6NC1+CqbFRk7ed6FbvrPofBnp3xRN9XCTlmVlXkXetGE/eUq40MoBnj45ISbsglpWVVyL03e1YGDYGNlbm9Z4j71ox3l62CyvnTYSxkp9xah4eutU6c+bMwfLly7F161YolUp89NFH9zxm8+bNWLBgAdatW4devXrh9OnTCA4OhkqlwqRJk+rEV1RUoKKiQnxdVFQk6zU0dzlXb76frSzNJOU2lmbIzL4mKfv4vz8gYu0+lJZVonN7W+z9cBqMDG9+bAVBQMjCHXh5zBPo5doOl65Ie1SImtJX3/+Es7/9jS83vVlnX961YgB1P+OtLFrgck6++Prddfvg4dYeQ59wr/ccgiBgZtRnmDiyL7p3aYvMrGv1xpFu0YMCelqMzeix7+ThS07CwsIwZswYjY557733sHz5cvE4JycnnDt3Dps2bao3OYmKisLChQtlaS/d2e1j7IJQ994Az/k8hoGeXZD9TxHW7fgOL7/9CWI/ngFjpSE+2nUMxaXleDNw6P1sNhGu5Obj3bV78emy12CsNLxj3O2/nwTh38/9oeNpSPjpd+z/eOYdj4/e8yNKrpcjZOJgWdpN94e2QzNMTR7C5KRPnz4axefl5SEzMxNBQUEIDg4Wy6urq6FW1z+57O2338aMGTPE10VFRXB0dGxcg6kO2//vvs69WgQ763+/Bnn5xWhlJf1LU93CBOoWJnBua4PH3NvD6enZ+Obozxg3rA9+SP4NyWkXYNsvTHLMwElL8NzwPtgQ8VKTXws1T2fOX8Y/+SXwm7xCLKupuYFTP/+FT/fG4/D2twEAuVeLYXPLJNZ/CkpgbdECAHDip99x8cpVdPedK6n79flb8Vj3Dti1ehpOnP4dp89dROchsyQxI6eswKjBvbFi7sSmukSiB+qhS05UKpXktZ6eHgRBkJTdOlH2xo2bKzs2b94MT09PSZy+vn6951AqlVAqlXI0l+rRrrUVbK3MceTkr+jucjPpq6yqxvGf/kDE9FF3PVYQBFRWVgMAPpg5DvNe8xX3Zf9TiLHTP8QnkS/Do1v7Jms/UT+PTvh262xJ2awPPodzWxu85j8IbR2s0MrSDPHJ5+HWuQ2Am5/xkz//gbem+AEAXvcfhOdHeEnqGPbyErwzdTQG9+sGAIgIHYOZQc+I+3OuFuKlmZuwbsFL6Nm1XVNeImmDXSdae+iSk9u1atUK2dnZEARB7C5NTU0V99va2qJ169b466+/MHEi/8q4X0quV+BCZp74+uKVqzhz/jJaqk3haGeJ114YiBVb4+DsaIMOjq2wIvpbmBobYtywmz1jGZf/wReHUvC0V1dYWbRAVm4BVn/6HYyNDTHk/39wO9pZSs7ZwvRmQunUupVk0iyR3FqYGsOlg72kzMTECC3VKrH8lef648Od36F9m1ZwatMKH+74DiZKI4wa3BsAYGNlXu8kWAdbCzjaWwFAnc+xqcnNz3hbB2vY27SU+7JIJnwqsfYe+uRkwIAByMvLw5IlSzBu3DjExsbi4MGDMDf/95s+IiICoaGhMDc3h4+PDyoqKpCcnIz8/HzJ8A3JJzX9IvxeWyO+nrfyCwDACyM8sT4iAG+8NBjlFZWYuXgXCoqvw6Nbe+xZOw1mKmMAgFJpgITUP7Ex5igKiq6jlaUZ+vbqiG8/Dq8zyZBIF732wtMor6jCOyv/h8KSMvTs2g7bl72GFqbGD7ppRDpPIdw+JqIjoqOjERYWhoKCAgBARkYGnJyccPr06TpLhzdu3IjIyEhcu3YNY8eOhYuLCz766CNkZGSIMZ999hmWLl2Kc+fOQaVSwd3dHWFhYXj22XvfG6OoqAhqtRo5VwslSQ/Ro6TwOu8bRI+u4qIidHK0RmFh0/0cr/1d8X3qJbQwa/w5SoqLMKhn2yZtq67Tqfuc3CowMFBMTACgffv2EAShTmICAK+99houXbqEkpISbNu2DXPnzpUkJgDg7++P06dPo6KiAteuXcOxY8calJgQERFp4n7fhO2HH36An58fHBwcoFAosG/fPsl+QRAQEREBBwcHmJiYYMCAATh79qwkpqKiAtOnT4e1tTVUKhVGjhyJy5eljwLJz89HQEAA1Go11Go1AgICJL+nAeDSpUvw8/ODSqWCtbU1QkNDUVlZqeEV6XByQkRERPdWWlqKHj16YN26dfXuX7JkCVasWIF169YhKSkJdnZ2GDJkCIqLi8WYsLAw7N27FzExMYiPj0dJSQl8fX1RU1Mjxvj7+yM1NRWxsbGIjY1FamoqAgICxP01NTUYMWIESktLER8fj5iYGOzZswfh4eEaX5PODuvoEg7rUHPAYR16lN3PYZ3DP2s/rPN0j8YN6ygUCuzduxejR48GcLPXxMHBAWFhYZgzZw6Am70ktra2WLx4MaZMmYLCwkK0atUK27dvx4QJEwAAV65cgaOjIw4cOIBhw4YhPT0drq6uSExMFFe+JiYmwtvbG7/++itcXFxw8OBB+Pr6IjMzEw4ON581FRMTg8DAQOTm5mp0Lew5ISIikpFcTyUuKiqSbLfeubyhLly4gOzsbAwd+u/NKpVKJfr3748TJ04AAFJSUlBVVSWJcXBwgJubmxiTkJAAtVotuSWHl5cX1Gq1JMbNzU1MTABg2LBhqKioQEpKikbtZnJCREQkI7meSuzo6CjO71Cr1YiKitK4LdnZ2QBu3lbjVra2tuK+7OxsGBkZwcLC4q4xNjY2deq3sbGRxNx+HgsLCxgZGYkxDfXQLyUmIiJ6FGVmZkqGQrS5OWjdx4UIdcpud3tMffGNiWkI9pwQERHJSK7VOubm5pKtMcmJnZ0dANTpucjNzRV7Oezs7FBZWYn8/Py7xuTk5NSpPy8vTxJz+3ny8/NRVVVVp0flXpicEBERyel+ryW+CycnJ9jZ2eHQoUNiWWVlJY4dO4a+ffsCADw8PGBoaCiJycrKQlpamhjj7e2NwsJCnDp1Sow5efIkCgsLJTFpaWnIysoSY+Li4qBUKuHh4aFRuzmsQ0RE9BArKSnBH3/8Ib6+cOECUlNTYWlpibZt2yIsLAyRkZHo1KkTOnXqhMjISJiamsLf3x8AoFarERQUhPDwcFhZWcHS0hIzZ86Eu7s7Bg+++UTsrl27Yvjw4QgODsamTZsAAJMnT4avry9cXFwAAEOHDoWrqysCAgKwdOlSXLt2DTNnzkRwcLDGq46YnBAREcnofj9bJzk5GQMHDhRf1z6WZdKkSYiOjsbs2bNRVlaGkJAQ5Ofnw9PTE3FxcTAz+/dRICtXroSBgQHGjx+PsrIyDBo0CNHR0ZIH5O7cuROhoaHiqp6RI0dK7q2ir6+P/fv3IyQkBP369YOJiQn8/f2xbNkyzd8D3ufk3nifE2oOeJ8TepTdz/uc/Jh2Wev7nDzp1oa3ryciIiLSFRzWISIikpG2c1plnA/70GJyQkREJCdmJ1rjsA4RERHpFPacEBERyeh+r9Z5FDE5ISIiktGtz8dp7PHNHZMTIiIiGXHKifY454SIiIh0CntOiIiI5MSuE60xOSEiIpIRJ8Rqj8M6REREpFPYc0JERCQjrtbRHpMTIiIiGXHKifY4rENEREQ6hT0nREREcmLXidaYnBAREcmIq3W0x2EdIiIi0insOSEiIpIRV+toj8kJERGRjDjlRHtMToiIiOTE7ERrnHNCREREOoU9J0RERDLiah3tMTkhIiKSk5YTYpmbcFiHiIiIdAx7ToiIiGTE+bDaY3JCREQkJ2YnWuOwDhEREekU9pwQERHJiKt1tMfkhIiISEa8fb32OKxDREREOoU9J0RERDLifFjtMTkhIiKSE7MTrTE5ISIikhEnxGqPc06IiIhIp7DnhIiISEYKaLlaR7aWPLzYc0JERCQjhQybJiIiIqBQKCSbnZ2duF8QBERERMDBwQEmJiYYMGAAzp49K6mjoqIC06dPh7W1NVQqFUaOHInLly9LYvLz8xEQEAC1Wg21Wo2AgAAUFBRo2NqGYXJCRET0kOvWrRuysrLE7cyZM+K+JUuWYMWKFVi3bh2SkpJgZ2eHIUOGoLi4WIwJCwvD3r17ERMTg/j4eJSUlMDX1xc1NTVijL+/P1JTUxEbG4vY2FikpqYiICCgSa6HwzpEREQyehA3YTMwMJD0ltQSBAGrVq3CvHnzMGbMGADAtm3bYGtri88++wxTpkxBYWEhtmzZgu3bt2Pw4MEAgB07dsDR0RHfffcdhg0bhvT0dMTGxiIxMRGenp4AgM2bN8Pb2xvnz5+Hi4tL4y+4Huw5ISIikpU8AztFRUWSraKi4o5n/P333+Hg4AAnJyc8//zz+OuvvwAAFy5cQHZ2NoYOHSrGKpVK9O/fHydOnAAApKSkoKqqShLj4OAANzc3MSYhIQFqtVpMTADAy8sLarVajJETkxMiIiId5OjoKM7vUKvViIqKqjfO09MTn376Kb799lts3rwZ2dnZ6Nu3L65evYrs7GwAgK2treQYW1tbcV92djaMjIxgYWFx1xgbG5s657axsRFj5MRhHSIiIhnJNayTmZkJc3NzsVypVNYb7+PjI/7f3d0d3t7ecHZ2xrZt2+Dl5fX/dUobJAhCnbLb3R5TX3xD6mkM9pwQERHJSK7VOubm5pLtTsnJ7VQqFdzd3fH777+L81Bu793Izc0Ve1Ps7OxQWVmJ/Pz8u8bk5OTUOVdeXl6dXhk5MDkhIiJ6hFRUVCA9PR329vZwcnKCnZ0dDh06JO6vrKzEsWPH0LdvXwCAh4cHDA0NJTFZWVlIS0sTY7y9vVFYWIhTp06JMSdPnkRhYaEYIycO6xAREcnofq/WmTlzJvz8/NC2bVvk5ubi/fffR1FRESZNmgSFQoGwsDBERkaiU6dO6NSpEyIjI2Fqagp/f38AgFqtRlBQEMLDw2FlZQVLS0vMnDkT7u7u4uqdrl27Yvjw4QgODsamTZsAAJMnT4avr6/sK3UAJidERESyut/P1rl8+TJeeOEF/PPPP2jVqhW8vLyQmJiIdu3aAQBmz56NsrIyhISEID8/H56enoiLi4OZmZlYx8qVK2FgYIDx48ejrKwMgwYNQnR0NPT19cWYnTt3IjQ0VFzVM3LkSKxbt67R13k3CkEQhCap+RFSVFQEtVqNnKuFkslJRI+SwutVD7oJRE2muKgInRytUVjYdD/Ha39X/Jb5D8y0OEdxURE6N3FbdR3nnBAREZFO4bAOERGRjBrzfJzbj2/umJwQERHJ6EHcvv5Rw2EdIiIi0insOSEiIpLR/V6t8yhickJERCQnTjrRGod1iIiISKew54SIiEhG7DjRHpMTIiIiGXG1jvY4rENEREQ6hT0nREREstJutQ4HdpicEBERyYrDOtrjsA4RERHpFCYnREREpFM4rENERCQjDutoj8kJERGRjHj7eu1xWIeIiIh0CntOiIiIZMRhHe0xOSEiIpIRb1+vPQ7rEBERkU5hzwkREZGc2HWiNSYnREREMuJqHe1xWIeIiIh0CntOiIiIZMTVOtpjckJERCQjTjnRHpMTIiIiOTE70RrnnBAREZFOYc8JERGRjLhaR3tMToiIiGTECbHaY3LSAIIgAACKi4oecEuImk7x9aoH3QSiJlNcXAzg35/nTalIy98V2h7/KGBy0gC1H+qOTo4PuCVERKSN4uJiqNXqJqnbyMgIdnZ26CTD7wo7OzsYGRnJ0KqHk0K4H2nkQ+7GjRu4cuUKzMzMoGB/231RVFQER0dHZGZmwtzc/EE3h0hW/Hzff4IgoLi4GA4ODtDTa7q1IOXl5aisrNS6HiMjIxgbG8vQoocTe04aQE9PD23atHnQzWiWzM3N+cObHln8fN9fTdVjcitjY+NmnVTIhUuJiYiISKcwOSEiIiKdwuSEdJJSqcSCBQugVCofdFOIZMfPN9HdcUIsERER6RT2nBAREZFOYXJCREREOoXJCREREekUJiekU6Kjo9GyZcsH3QwiInqAmJxQkwgMDIRCoaiz/fHHHw+6aUSyqu9zfusWGBj4oJtI9NDhHWKpyQwfPhxbt26VlLVq1eoBtYaoaWRlZYn/37VrF+bPn4/z58+LZSYmJpL4qqoqGBoa3rf2ET2M2HNCTUapVMLOzk6yrV69Gu7u7lCpVHB0dERISAhKSkruWMfPP/+MgQMHwszMDObm5vDw8EBycrK4/8SJE3jqqadgYmICR0dHhIaGorS09H5cHhEASD7farUaCoVCfF1eXo6WLVti9+7dGDBgAIyNjbFjxw5ERESgZ8+eknpWrVqF9u3bS8q2bt2Krl27wtjYGF26dMH69evv34URPUBMTui+0tPTw5o1a5CWloZt27bh8OHDmD179h3jJ06ciDZt2iApKQkpKSl46623xL86z5w5g2HDhmHMmDH45ZdfsGvXLsTHx2PatGn363KIGmTOnDkIDQ1Feno6hg0b1qBjNm/ejHnz5mHRokVIT09HZGQk3nnnHWzbtq2JW0v04HFYh5rMN998gxYtWoivfXx88N///ld87eTkhPfeew+vv/76Hf8ivHTpEmbNmoUuXboAADp16iTuW7p0Kfz9/REWFibuW7NmDfr3748NGzbw4VukM8LCwjBmzBiNjnnvvfewfPly8TgnJyecO3cOmzZtwqRJk5qimUQ6g8kJNZmBAwdiw4YN4muVSoUjR44gMjIS586dQ1FREaqrq1FeXo7S0lKoVKo6dcyYMQOvvvoqtm/fjsGDB+O5556Ds7MzACAlJQV//PEHdu7cKcYLgoAbN27gwoUL6Nq1a9NfJFED9OnTR6P4vLw8ZGZmIigoCMHBwWJ5dXX1fXmyLtGDxuSEmoxKpULHjh3F1xcvXsQzzzyD1157De+99x4sLS0RHx+PoKAgVFVV1VtHREQE/P39sX//fhw8eBALFixATEwMnn32Wdy4cQNTpkxBaGhonePatm3bZNdFpKnbE289PT3c/uSQW78Hbty4AeDm0I6np6ckTl9fv4laSaQ7mJzQfZOcnIzq6mosX74ceno3pzvt3r37nsd17twZnTt3xptvvokXXngBW7duxbPPPovevXvj7NmzkgSI6GHQqlUrZGdnQxAEKBQKAEBqaqq439bWFq1bt8Zff/2FiRMnPqBWEj04TE7ovnF2dkZ1dTXWrl0LPz8/HD9+HBs3brxjfFlZGWbNmoVx48bByckJly9fRlJSEsaOHQvg5iRDLy8vTJ06FcHBwVCpVEhPT8ehQ4ewdu3a+3VZRBobMGAA8vLysGTJEowbNw6xsbE4ePAgzM3NxZiIiAiEhobC3NwcPj4+qKioQHJyMvLz8zFjxowH2HqipsfVOnTf9OzZEytWrMDixYvh5uaGnTt3Iioq6o7x+vr6uHr1Kl566SV07twZ48ePh4+PDxYuXAgA6N69O44dO4bff/8dTz75JHr16oV33nkH9vb29+uSiBqla9euWL9+PT788EP06NEDp06dwsyZMyUxr776Kj7++GNER0fD3d0d/fv3R3R0NJycnB5Qq4nuH4Vw+8AnERER0QPEnhMiIiLSKUxOiIiISKcwOSEiIiKdwuSEiIiIdAqTEyIiItIpTE6IiIhIpzA5ISIiIp3C5IToIREREYGePXuKrwMDAzF69Oj73o6MjAwoFArJ7dZv1759e6xatarBdUZHR6Nly5Zat02hUGDfvn1a10NEDxaTEyItBAYGQqFQQKFQwNDQEB06dMDMmTNRWlra5OdevXo1oqOjGxTbkISCiEhX8Nk6RFoaPnw4tm7diqqqKvz444949dVXUVpaig0bNtSJraqqgqGhoSznVavVstRDRKRr2HNCpCWlUgk7Ozs4OjrC398fEydOFIcWaodiPvnkE3To0AFKpRKCIKCwsBCTJ0+GjY0NzM3N8fTTT+Pnn3+W1PvBBx/A1tYWZmZmCAoKQnl5uWT/7cM6N27cwOLFi9GxY0colUq0bdsWixYtAgDxeSy9evWCQqHAgAEDxOO2bt2Krl27wtjYGF26dMH69esl5zl16hR69eoFY2Nj9OnTB6dPn9b4PVqxYgXc3d2hUqng6OiIkJAQlJSU1Inbt28fOnfuDGNjYwwZMgSZmZmS/V9//TU8PDxgbGyMDh06YOHChaiurta4PUSk25icEMnMxMQEVVVV4us//vgDu3fvxp49e8RhlREjRiA7OxsHDhxASkoKevfujUGDBuHatWsAgN27d2PBggVYtGgRkpOTYW9vXydpuN3bb7+NxYsX45133sG5c+fw2WefwdbWFsDNBAMAvvvuO2RlZeGLL74AAGzevBnz5s3DokWLkJ6ejsjISLzzzjvYtm0bAKC0tBS+vr5wcXFBSkoKIiIi6jygriH09PSwZs0apKWlYdu2bTh8+DBmz54tibl+/ToWLVqEbdu24fjx4ygqKsLzzz8v7v/222/x4osvIjQ0FOfOncOmTZsQHR0tJmBE9AgRiKjRJk2aJIwaNUp8ffLkScHKykoYP368IAiCsGDBAsHQ0FDIzc0VY77//nvB3NxcKC8vl9Tl7OwsbNq0SRAEQfD29hZee+01yX5PT0+hR48e9Z67qKhIUCqVwubNm+tt54ULFwQAwunTpyXljo6OwmeffSYpe++99wRvb29BEARh06ZNgqWlpVBaWiru37BhQ7113apdu3bCypUr77h/9+7dgpWVlfh669atAgAhMTFRLEtPTxcACCdPnhQEQRCefPJJITIyUlLP9u3bBXt7e/E1AGHv3r13PC8RPRw454RIS9988w1atGiB6upqVFVVYdSoUVi7dq24v127dmjVqpX4OiUlBSUlJbCyspLUU1ZWhj///BMAkJ6ejtdee02y39vbG0eOHKm3Denp6aioqMCgQYMa3O68vDxkZmYiKCgIwcHBYnl1dbU4nyU9PR09evSAqamppB2aOnLkCCIjI3Hu3DkUFRWhuroa5eXlKC0thUqlAgAYGBigT58+4jFdunRBy5YtkZ6ejscffxwpKSlISkqS9JTU1NSgvLwc169fl7SRiB5uTE6ItDRw4EBs2LABhoaGcHBwqDPhtfaXb60bN27A3t4eR48erVNXY5fTmpiYaHzMjRs3ANwc2vH09JTs09fXBwAIgtCo9tzq4sWLeOaZZ/Daa6/hvffeg6WlJeLj4xEUFCQZ/gJuLgW+XW3ZjRs3sHDhQowZM6ZOjLGxsdbtJCLdweSESEsqlQodO3ZscHzv3r2RnZ0NAwMDtG/fvt6Yrl27IjExES+99JJYlpiYeMc6O3XqBBMTE3z//fd49dVX6+w3MjICcLOnoZatrS1at26Nv/76CxMnTqy3XldXV2zfvh1lZWViAnS3dtQnOTkZ1dXVWL58OfT0bk5z2717d5246upqJCcn4/HHHwcAnD9/HgUFBejSpQuAm+/b+fPnNXqviejhxOSE6D4bPHgwvL29MXr0aCxevBguLi64cuUKDhw4gNGjR6NPnz544403MGnSJPTp0wdPPPEEdu7cibNnz6JDhw711mlsbIw5c+Zg9uzZMDIyQr9+/ZCXl4ezZ88iKCgINjY2MDExQWxsLNq0aQNjY2Oo1WpEREQgNDQU5ubm8PHxQUVFBZKTk5Gfn48ZM2bA398f8+bNQ1BQEP7zn/8gIyMDy5Yt0+h6nZ2dUV1djbVr18LPzw/Hjx/Hxo0b68QZGhpi+vTpWLNmDQwNDTFt2jR4eXmJycr8+fPh6+sLR0dHPPfcc9DT08Mvv/yCM2fO4P3339f8C0FEOourdYjuM4VCgQMHDuCpp57CK6+8gs6dO+P5559HRkaGuLpmwoQJmD9/PubMmQMPDw9cvHgRr7/++l3rfeeddxAeHo758+eja9eumDBhAnJzcwHcnM+xZs0abNq0CQ4ODhg1ahQA4NVXX8XHH3+M6OhouLu7o3///oiOjhaXHrdo0QJff/01zp07h169emHevHlYvHixRtfbs2dPrFixAosXL4abmxt27tyJqKioOnGmpqaYM2cO/P394e3tDRMTE8TExIj7hw0bhm+++QaHDh3CY489Bi8vL6xYsQLt2rXTqD1EpPsUghyDykREREQyYc8JERER6RQmJ0RERKRTmJwQERGRTmFyQkRERDqFyQkRERHpFCYnREREpFOYnBAREZFOYXJCREREOoXJCREREekUJidERESkU5icEBERkU5hckJEREQ65f8AehhJ8PvI73kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "# Define the function to apply models\n",
    "def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
    "    # Fit the model on the training data\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(\"============================================\")\n",
    "\n",
    "    # Compute ROC curve and ROC area\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    return accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm\n",
    "\n",
    "# Define the models with class_weight='balanced'\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier(class_weight='balanced', random_state=42)),\n",
    "    (\"Random Forest\", RandomForestClassifier(class_weight='balanced', random_state=42)),\n",
    "    (\"XGBoost\", XGBClassifier(scale_pos_weight=1, eval_metric='logloss', random_state=42)),\n",
    "]\n",
    "\n",
    "# Applying the models and storing the results\n",
    "results = []\n",
    "roc_curves = []\n",
    "confusion_matrices = []\n",
    "\n",
    "for name, model in models:\n",
    "    accuracy, precision, recall, f1, fpr, tpr, roc_auc, cm = apply_model(model, X_train, y_train, X_test, y_test, name)\n",
    "    results.append((name, accuracy, precision, recall, f1))\n",
    "    roc_curves.append((name, fpr, tpr, roc_auc))\n",
    "    confusion_matrices.append((name, cm))\n",
    "\n",
    "# Plotting the ROC curves for each model\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve comparison')\n",
    "for name, fpr, tpr, roc_auc in roc_curves:\n",
    "    plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "# Draw confusion matrices for each model\n",
    "for name, cm in confusion_matrices:\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.title(f'Confusion Matrix - {name}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e281282d",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning for Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46eee5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.1, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.32413002218453657\n",
      "Model: Logistic Regression with hyperparameter tuning\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "# Create a Logistic Regression classifier object\n",
    "logreg_classifier = LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Define the parameter grid for the Logistic Regression classifier\n",
    "param_grid = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10],\n",
    "    'solver': ['liblinear', 'saga']\n",
    "}\n",
    "\n",
    "# Create a grid search object with F1-score as the scoring metric and n_jobs=-1\n",
    "grid_search = GridSearchCV(logreg_classifier, param_grid, scoring='f1', cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the training data using grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "\n",
    "# Get the best model from the grid search\n",
    "best_logreg_model = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred = best_logreg_model.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(\"Model: Logistic Regression with hyperparameter tuning\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d1e2fb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "15 fits failed out of a total of 100.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\base.py\", line 1151, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1168, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 66, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:976: UserWarning: One or more of the test scores are non-finite: [0.83386061 0.83385672 0.83386013 0.83386438 0.83386005 0.83381042\n",
      " 0.83386065 0.83386055 0.83383393 0.83386125        nan        nan\n",
      " 0.83386061 0.83386055        nan 0.83386017 0.83386056 0.83386026\n",
      " 0.83386065 0.83386061]\n",
      "  warnings.warn(\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'solver': 'liblinear', 'penalty': 'l2', 'l1_ratio': 0.9, 'C': 0.1}\n",
      "0.8338643768850357\n",
      "Model: Logistic Regression with hyperparameter tuning\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "# Create a Logistic Regression classifier object\n",
    "logreg_classifier = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "\n",
    "# Define the parameter distribution for the Logistic Regression classifier\n",
    "param_dist = {\n",
    "    'penalty': ['l1', 'l2', 'elasticnet'],\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10],\n",
    "    'solver': ['liblinear', 'saga'],\n",
    "    'l1_ratio': [0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "}\n",
    "\n",
    "# Create a randomized search object with roc_auc as the scoring metric and n_iter=20\n",
    "random_search = RandomizedSearchCV(logreg_classifier, param_dist, scoring='roc_auc', n_iter=20, cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the training data using random search\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(random_search.best_params_)\n",
    "print(random_search.best_score_)\n",
    "\n",
    "# Get the best model from the random search\n",
    "best_logreg_model = random_search.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred = best_logreg_model.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(\"Model: Logistic Regression with hyperparameter tuning\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c600f4c",
   "metadata": {},
   "source": [
    "## Tuned Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "93d522f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)\n",
    "threshold = 0.5\n",
    "# The more we increase the threshold the more likely it'll predict negative value\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ce4cfdde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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xY4bG563tOu/XxuDgYJSWlsLW1va+Y00e5EHvl1u3biE3N7febZXTg36+1d0gkZGR4gdk9RIaGgoA4tTf69evw9bWtsY5altXH3W9r7m5uRAEQZIkVatt3d2mT5+OhQsXIiEhAX5+frC0tESfPn2QlJRUr3bn5OTA3t5eklBpwsTEBImJiUhMTMT3338PHx8ffP311/j000/FmOqfU7du3Wr8nLZt2yb5GQG134d73Zu777cm74nly5dj2rRp2LVrF3r16gULCwsMGTIEf/zxB4Db/+5++ukn+Pr6Yv78+ejSpQuaN2+OsLAwFBYW3vOeVL/X7h4/ZG1tDQMDg0fyO5m01+jHnOjr66NPnz7Yu3cvrly58sCpttVv7IyMjBqxV69elXWmhLGxMYDbA+juHENR27Mfnn/+eTz//POoqqpCUlISVqxYgfDwcNjY2OCVV16p9fiWlpbIyMiosf7q1asA0GCzPoKDgzFz5kysWbNG8hff3WJjY2FoaIgffvhBvBcAsGvXLo3PWdvA4nvJyMjAhAkT0KlTJ5w9exaRkZFYvny5xucEpO+Xu129ehV6enowNzevd1vvx9jYGPn5+TXW1/fZIdXvh+nTp2Po0KG1xjg7OwO4fd13j+kBUOu6+qjrfRUEAQqFotbxJXVpi4GBASZPnozJkycjLy8PBw4cwPvvvw9fX1+kp6ffczzXvTRv3hzx8fG4detWvRIUPT09cQAscHs2oYeHB2bPno1Ro0bBwcFB/Dl98803cHR0vOexqu+hJvfm7vemJu8JlUqF2bNnY/bs2cjKyhKrKAEBAfjtt98AAI6Ojli/fj2A27Pftm/fjqioKJSXl99zzJelpSWOHz8u/qyrZWdno7KykrPXHlONvnIC3P6HJQgCQkJCUF5eXmN7RUUFvv/+ewBA7969AQCbN2+WxCQmJiItLQ19+vSRrV3Vo+VPnz4tWV/dltro6+vD09MTn332GQDg119/vWdsnz59cPDgQTEZqfbVV1+hSZMmDTb1uEWLFpgyZQoCAgIwevToe8YpFAoYGBhIuhpKSkqwadOmGrFy/eVTVVWFV199FQqFAnv37sXcuXPFwY714ezsjBYtWmDr1q2SGUbFxcXYsWOHONOkIbRu3Rq///67ZHDj9evX6z0rxNnZGe3atcOpU6fQtWvXWpfqcnmvXr3w008/ST74qqqqsG3bNu0u6o621OW+qlQqdO3aFbt27ZL82y4qKtJ4hkazZs0wfPhwTJgwATdu3BBny2lSQfTz80NpaalsD8dTKpX47LPPUFpaik8++QQA4OvrCwMDA/z111/3/DkBt++hra1tjVk1ly9frvN7RJP3xJ1sbGwQHByMV199FefPn6/RtQkA7du3xwcffAB3d/cH/h4rKiqq8UfLV199JW6nx0+jr5wAgLe3N1avXo3Q0FB4eHjg7bffxtNPP42KigqcPHkSn3/+Odzc3BAQEABnZ2eMHTsWK1asgJ6eHvz8/MTZOg4ODnj33Xdla9fAgQNhYWGBMWPG4KOPPoKBgQFiYmKQnp4uiVuzZg0OHjyIQYMGoVWrVigtLcWXX34JAOjbt+89jz9r1iz88MMP6NWrF2bOnAkLCwts2bIFu3fvxvz58yUP85LbnWXoexk0aBAWL16MwMBAjB07FtevX8fChQtrne7t7u6O2NhYbNu2DW3atIGxsXG9xonMmjUL//vf/7Bv3z7Y2toiIiICR44cwZgxY9C5c2eNnyOhp6eH+fPnY9SoUfD398e4ceNQVlaGBQsWIC8vr073ob6CgoKwdu1avPbaawgJCcH169cxf/58jR8ydqe1a9fCz88Pvr6+CA4ORosWLXDjxg2kpaXh119/xX/+8x8AwAcffIDvvvsOvXv3xsyZM9GkSRN89tlnGs8++v7772v9cBs+fHid7+tHH32EQYMGwdfXF++88w6qqqqwYMECNG3a9IFPfg4ICICbmxu6du2K5s2b49KlS1i6dCkcHR3Rrl07ABDfZ8uWLcPo0aNhaGgIZ2fnWtv96quvYsOGDRg/fjzOnz+PXr164datWzh+/DhcXFzuWeW8n549e2LgwIHYsGED3nvvPTg5OeGjjz7CjBkz8Pfff2PAgAEwNzdHVlYWTpw4IVYw9PT0MHv2bIwbNw7Dhw/Hm2++iby8PMyePRt2dnZ1ruzU9T3h6ekJf39/dOzYEebm5khLS8OmTZvERPL06dOYOHEiXn75ZbRr1w5GRkY4ePAgTp8+jffee++e53/99dfx2WefYfTo0bh48SLc3d0RHx+P6OhoDBw48L6/A0mHPcrRuLomJSVFGD16tNCqVSvByMhIUKlUQufOnYWZM2cK2dnZYlxVVZUwb948oX379oKhoaFgZWUlvPbaa0J6errkeD179hSefvrpGue5e7aEINQ+W0cQbs+Y6N69u6BSqYQWLVoIs2bNEr744gvJ7IBjx44JL730kuDo6CgolUrB0tJS6Nmzp/Ddd9/VOMeds3UEQRDOnDkjBAQECGq1WjAyMhKeeeYZYcOGDZKY2mahCMK/I/jvjr/bnbN17qe2GTdffvml4OzsLCiVSqFNmzbC3LlzhfXr19eYHXHx4kWhf//+gqmpqQBAvL/3avud26pn6+zbt0/Q09OrcY+uX78utGrVSujWrZtkxse9jlfbuXbt2iV4enoKxsbGgkqlEvr06SP88ssvkpgHzVC5n3vtu3HjRsHFxUUwNjYWXF1dhW3btt1zts6CBQtqHLe298ypU6eEESNGCNbW1oKhoaFga2sr9O7dW1izZo0k7pdffhG8vLwEpVIp2NraClOmTBE+//xzjWbr3GupVpf7KgiCsHPnTsHd3V0wMjISWrVqJXz66adCWFiYYG5uLom7e7bOokWLhO7duwtWVlbivmPGjBEuXrwo2W/69OmCvb29oKenJ3lP1TZjqqSkRJg5c6bQrl07wcjISLC0tBR69+4tHD169L73pHq2Tm3OnDkj6OnpCW+88Ybk3vTq1UswMzMTlEql4OjoKAwfPlw4cOCAZN/PP/9ceOqppwQjIyOhffv2wpdffim8+OKLQufOncWY+71HBKFu74n33ntP6Nq1q2Bubi7+e3733XeFa9euCYIgCFlZWUJwcLDQoUMHQaVSCU2bNhU6duwoLFmyRDKLrLZ7ev36dWH8+PGCnZ2dYGBgIDg6OgrTp08XSktLJXH3+j1798+dHj2FINxREyUiagQqKirQqVMntGjRotE89r2u8vLy0L59ewwZMgSff/75o24ONVLs1iGiJ96YMWPQr18/2NnZITMzE2vWrEFaWtoDZ7Q96TIzMzFnzhz06tULlpaWuHTpEpYsWYLCwkK88847j7p51IgxOSGiJ15hYSEiIyORk5MDQ0NDdOnSBXv27Gn04xGUSiUuXryI0NBQ3LhxQxwIv2bNGjz99NOPunnUiLFbh4iIiHQKpxITERGRTmFyQkRERDqFyQkRERHpFA6IrYNbt27h6tWrMDU1le3R4kRE9PAIgoDCwkKtvtuoLkpLS2t90rimjIyMJF/b0dgwOamDq1ev1vjmXiIievykp6c/8DvU6qu0tBQmppZAZc3H8WvK1tYWFy5caLQJCpOTOqh+DLWR62go9I0eEE30eFqxQr6vXiDSNSXFRXhn0LO1fq2AXMrLy4HKm1C6jga0+ayoKkfmuY0oLy9nckL3Vt2Vo9A3YnJCTyyTpg33S5tIVzyUrnkDY60+KwQFh4MyOSEiIpKTAoA2SRCHNjI5ISIikpVC7/aizf6NHO8AERER6RRWToiIiOSkUGjZrcN+HVZOiIiI5FTdraPNUk9z586FQqFAeHi4uE4QBERFRcHe3h4mJibw8fHB2bNnJfuVlZVh0qRJsLKygkqlwuDBg3HlyhVJTG5uLoKCgqBWq6FWqxEUFIS8vDxJzOXLlxEQEACVSgUrKyuEhYXV67kvTE6IiIieAImJifj888/RsWNHyfr58+dj8eLFWLlyJRITE2Fra4t+/fqhsLBQjAkPD8fOnTsRGxuL+Ph4FBUVwd/fH1VVVWJMYGAgUlJSEBcXh7i4OKSkpCAoKEjcXlVVhUGDBqG4uBjx8fGIjY3Fjh07EBERofG1MDkhIiKSU3W3jjaLhoqKijBq1CisW7cO5ubm4npBELB06VLMmDEDQ4cOhZubGzZu3IibN29i69atAID8/HysX78eixYtQt++fdG5c2ds3rwZZ86cwYEDBwAAaWlpiIuLwxdffAFvb294e3tj3bp1+OGHH3D+/HkAwL59+3Du3Dls3rwZnTt3Rt++fbFo0SKsW7cOBQUFGl0PkxMiIiJZadulo/lH84QJEzBo0CD07dtXsv7ChQvIzMxE//79xXVKpRI9e/bE0aNHAQDJycmoqKiQxNjb28PNzU2MOXbsGNRqNTw9PcUYLy8vqNVqSYybmxvs7e3FGF9fX5SVlSE5OVmj6+GAWCIiIh10d7VBqVRCqVTWiIuNjcWvv/6KxMTEGtsyMzMBADY2NpL1NjY2uHTpkhhjZGQkqbhUx1Tvn5mZCWtr6xrHt7a2lsTcfR5zc3MYGRmJMXXFygkREZGcZOrWcXBwEAefqtVqzJ07t8ap0tPT8c4772Dz5s33fdT93U/GFQThgU/LvTumtvj6xNQFKydERERykukhbOnp6TAzMxNX11Y1SU5ORnZ2Njw8PMR1VVVV+Pnnn7Fy5UpxPEhmZibs7OzEmOzsbLHKYWtri/LycuTm5kqqJ9nZ2ejevbsYk5WVVeP8OTk5kuMcP35csj03NxcVFRU1KioPwsoJERGRDjIzM5MstSUnffr0wZkzZ5CSkiIuXbt2xahRo5CSkoI2bdrA1tYW+/fvF/cpLy/HkSNHxMTDw8MDhoaGkpiMjAykpqaKMd7e3sjPz8eJEyfEmOPHjyM/P18Sk5qaioyMDDFm3759UCqVkuSpLlg5ISIiktNDfAibqakp3NzcJOtUKhUsLS3F9eHh4YiOjka7du3Qrl07REdHo0mTJggMDAQAqNVqjBkzBhEREbC0tISFhQUiIyPh7u4uDrB1cXHBgAEDEBISgrVr1wIAxo4dC39/fzg7OwMA+vfvD1dXVwQFBWHBggW4ceMGIiMjERISIqkA1QWTEyIiIjnp2HfrTJ06FSUlJQgNDUVubi48PT2xb98+mJr++03kS5YsgYGBAUaMGIGSkhL06dMHMTEx0NfXF2O2bNmCsLAwcVbP4MGDsXLlSnG7vr4+du/ejdDQUPTo0QMmJiYIDAzEwoULNW6zQhAEQYtrbhQKCgqgVquhdA/R6muwiXTZui/ee9RNIGowJUWFGOvjivz8fI3/iq8r8bPCayoUBjW7YOpKqCxDWcL8Bm2rruOYEyIiItIp7NYhIiKSk4516zyOmJwQERHJSaHQMjnhtxIzPSMiIiKdwsoJERGRnPQUtxdt9m/kmJwQERHJiWNOtMY7QERERDqFlRMiIiI5PcQnxD6pmJwQERHJid06WuMdICIiIp3CygkREZGc2K2jNSYnREREcmK3jtaYnBAREcmJlROtMT0jIiIincLKCRERkZzYraM1JidERERyYreO1pieERERkU5h5YSIiEhWWnbrsG7A5ISIiEhW7NbRGtMzIiIi0imsnBAREclJodBytg4rJ0xOiIiI5MSpxFrjHSAiIiKdwsoJERGRnDggVmtMToiIiOTEbh2tMTkhIiKSEysnWmN6RkRERDqFlRMiIiI5sVtHa0xOiIiI5MRuHa0xPSMiIiKdwsoJERGRjBQKBRSsnGiFyQkREZGMmJxoj906REREpFNYOSEiIpKT4v8XbfZv5JicEBERyYjdOtpjtw4REdFjbPXq1ejYsSPMzMxgZmYGb29v7N27V9weHBwsJkzVi5eXl+QYZWVlmDRpEqysrKBSqTB48GBcuXJFEpObm4ugoCCo1Wqo1WoEBQUhLy9PEnP58mUEBARApVLBysoKYWFhKC8v1/iamJwQERHJ6O5EoD6LJlq2bIlPP/0USUlJSEpKQu/evfHiiy/i7NmzYsyAAQOQkZEhLnv27JEcIzw8HDt37kRsbCzi4+NRVFQEf39/VFVViTGBgYFISUlBXFwc4uLikJKSgqCgIHF7VVUVBg0ahOLiYsTHxyM2NhY7duxARESExveQ3TpEREQyetjdOgEBAZLXc+bMwerVq5GQkICnn34aAKBUKmFra1vr/vn5+Vi/fj02bdqEvn37AgA2b94MBwcHHDhwAL6+vkhLS0NcXBwSEhLg6ekJAFi3bh28vb1x/vx5ODs7Y9++fTh37hzS09Nhb28PAFi0aBGCg4MxZ84cmJmZ1fmaWDkhIiKS0cOunNypqqoKsbGxKC4uhre3t7j+8OHDsLa2Rvv27RESEoLs7GxxW3JyMioqKtC/f39xnb29Pdzc3HD06FEAwLFjx6BWq8XEBAC8vLygVqslMW5ubmJiAgC+vr4oKytDcnKyRtfBygkREZEOKigokLxWKpVQKpW1xp45cwbe3t4oLS1F06ZNsXPnTri6ugIA/Pz88PLLL8PR0REXLlzAhx9+iN69eyM5ORlKpRKZmZkwMjKCubm55Jg2NjbIzMwEAGRmZsLa2rrGea2trSUxNjY2ku3m5uYwMjISY+qKyQkREZGcZJpK7ODgIFk9a9YsREVF1bqLs7MzUlJSkJeXhx07dmD06NE4cuQIXF1dMXLkSDHOzc0NXbt2haOjI3bv3o2hQ4fesxmCIEiqOLVVdOoTUxdMToiIiGQk15iT9PR0yTiNe1VNAMDIyAhPPfUUAKBr165ITEzEsmXLsHbt2hqxdnZ2cHR0xB9//AEAsLW1RXl5OXJzcyXVk+zsbHTv3l2MycrKqnGsnJwcsVpia2uL48ePS7bn5uaioqKiRkXlQTjmhIiISAdVTw2uXu6XnNxNEASUlZXVuu369etIT0+HnZ0dAMDDwwOGhobYv3+/GJORkYHU1FQxOfH29kZ+fj5OnDghxhw/fhz5+fmSmNTUVGRkZIgx+/btg1KphIeHR90vHKycEBERyUqhqL17o+4H0Cz8/fffh5+fHxwcHFBYWIjY2FgcPnwYcXFxKCoqQlRUFIYNGwY7OztcvHgR77//PqysrPDSSy8BANRqNcaMGYOIiAhYWlrCwsICkZGRcHd3F2fvuLi4YMCAAQgJCRGrMWPHjoW/vz+cnZ0BAP3794erqyuCgoKwYMEC3LhxA5GRkQgJCdFopg7A5ISIiEhWCmjZraNhdpKVlYWgoCBkZGRArVajY8eOiIuLQ79+/VBSUoIzZ87gq6++Ql5eHuzs7NCrVy9s27YNpqam4jGWLFkCAwMDjBgxAiUlJejTpw9iYmKgr68vxmzZsgVhYWHirJ7Bgwdj5cqV4nZ9fX3s3r0boaGh6NGjB0xMTBAYGIiFCxdqfgcEQRA03quRKSgogFqthtI9BAp9o0fdHKIGse6L9x51E4gaTElRIcb6uCI/P1/jv+LrqvqzotmIdVAYNan3cYTym8jbHtKgbdV1rJwQERHJiN+toz0mJ0RERHLitxJrjbN1iIiISKewckJERCQnLbt1BHbrMDkhIiKSk7ZjTrSb6fNkYHJCREQkIyYn2uOYEyIiItIprJwQERHJibN1tMbkhIiISEbs1tEeu3WIiIhIp7ByQkREJCNWTrTH5ISIiEhGTE60x24dIiIi0imsnBAREcmIlRPtMTkhIiKSE6cSa43dOkRERKRTWDkhIiKSEbt1tMfkhIiISEZMTrTH5ISIiEhGTE60xzEnREREpFNYOSEiIpITZ+tojckJERGRjNitoz126xAREZFOeSwrJzExMQgPD0deXt6jbgrV4t3g/pg5YTBWf30I7y/eAQDw7/UMgl96Dp1cHGDZrCmeHzUXqb//I+7TzKwJpo8dhF5eHdDCxhw38oqw+/BpRK/5AQXFpWLcqW9no5W9peR8Szfuw+yV30nWverviQmBvdG2lTXyi0rw3U8nMXXBfxrwqulJ9/vvl/Fj3HFcupSF/PwihE4Yis6d24vbv/zyBxw7mirZx6mNPd5///UaxxIEAcuX/QepqX9LjnPtWh5++OEofvvtEgryi9GsWVN4ej2NQYO6w8BAX9w/9uv9+OOPK7h69Rps7Swxa9abDXTVVB+snGjvkSYnwcHB2LhxY431f/zxB5566qlH0CLSVmfXVhg9pDtSf78iWa8yNsLx03/h259+xfIPRtXYz665GrbN1Zi5bCd++zsTDnYWWPzeK7Btrkbwe+slsXPW/ICvdv0ivi6+WSbZHhrYGxNG9cas5buQlHoRxkoDtG5hJeNVUmNUVlaBlg426NGjI1av3llrjJtbGwS/MVB8baCvX2vcgf2Jta7PzLwB4ZaAoKABsLY2xz//5OCrjXtRXlaBl0f0FuMEAXjuuY74+8JVXLmSo8VVUUNQQMvkhINOHn3lZMCAAdiwYYNkXfPmzR9Ra0gbKhMjfP5RMN6J/hqRbw6QbNu29/YvYwc7i1r3TfsrA6OnfSG+vvjPNXyy+nus/eh16Ovroarqlrit6GYpsq8X1noctakJZrztj1cnr8HPib+L63/7O7Pe10UEAO7ubeHu3va+MQYG+lCrm943Jj09C/v3J2LGB6MRGbFSss3NrQ3c3NqIr5s3b4aszBs4fPhXSXLyamA/AEDhtzeZnNAT6ZGPOVEqlbC1tZUsy5Ytg7u7O1QqFRwcHBAaGoqioqJ7HuPUqVPo1asXTE1NYWZmBg8PDyQlJYnbjx49ihdeeAEmJiZwcHBAWFgYiouLH8blNSoLpo7Evl9SceTEeVmOZ9bUGIXFpZLEBADeeb0f/to/Dz9veQ8Rb/jC8I5ydy/PDtBTKGDXvBkStn+A1B8+xpfRb6KFTTNZ2kR0P+fPX8bkd5djxoy1+GrjXhQUSH/PlJVVYN3n3yEwsN8Dk5hqN0vKoFKZNERzqYFUd+toszR2jzw5qY2enh6WL1+O1NRUbNy4EQcPHsTUqVPvGT9q1Ci0bNkSiYmJSE5OxnvvvQdDQ0MAwJkzZ+Dr64uhQ4fi9OnT2LZtG+Lj4zFx4sSHdTmNwtB+HnimgwM++uy7BwfXgblahSlj/BDz318k69fEHsaYGRsw+O1lWLf9CMa/6oOF00aK21u3sIKengKT3+iP9xfvQPB762GuboL/rpwoSWKI5Obu1hZvhQQgIvJVjHi5Ny5czMCihV+joqJSjNm+7Se0bdsCne4Yq3I/2dm5OHQwGT17dm6oZlNDUMiwNHKPvFvnhx9+QNOm//4F4efnh//859+Bi05OTvj444/x9ttvY9WqVbUe4/Lly5gyZQo6dOgAAGjXrp24bcGCBQgMDER4eLi4bfny5ejZsydWr14NY2PjGscrKytDWdm/4xgKCgq0usYnXQubZpgbMQzDJn2GsvLKB+/wAKYqY2xbMh7nL2Rg3ro9km2rvz4k/v/ZP68ir6AEX81/C1Erv0VufjH0FAoYGRrgvYXf4NDx3wAAb82Iwfm4aDzftT0OJqRp3T6i2nR71kX8/xYtmsOxtR3em7YKZ07/hS4ezkhJ+QO//XYJH858o07Hy8srxLKl2+Hh4YznX3imoZpNpJMeeXLSq1cvrF69WnytUqlw6NAhREdH49y5cygoKEBlZSVKS0tRXFwMlUpV4xiTJ0/GW2+9hU2bNqFv3754+eWX0bbt7b7h5ORk/Pnnn9iyZYsYLwgCbt26hQsXLsDFxaXG8ebOnYvZs2c3wNU+mZ7p0ArWlmY49NW/1S0DA31079wWIS+/AJse4bh1S6jTsZo2UeKb5aEoLinDa1PWofKuLp27JaVeAAC0aWmF5PxiZF6/nUiev/DvGJPreUW4nleElrbmml4aUb01a9YUlpZqZGXnAgB+++0ScnJy8U7YEknc6lU70a5dS0yZ+u9A8by8Qixc8DXatLVH0Ot+D7XdpD3O1tHeI09OVCqVZGbOpUuXMHDgQIwfPx4ff/wxLCwsEB8fjzFjxqCioqLWY0RFRSEwMBC7d+/G3r17MWvWLMTGxuKll17CrVu3MG7cOISFhdXYr1WrVrUeb/r06Zg8ebL4uqCgAA4ODlpe6ZPr58Tz6P7KHMm6lTNfwx8Xs7Dsq/11TkxMVcb4ZvkElFdUInDy2jpVYTo63/65ZF27nZQcP/U3AOApR2tczc4DcHuasmWzpkjPuFHXSyLSWlFRCW7cKIBaffsPKj8/Lzz/vLQCEjVrPUaO7IOOz/z7OzA3txALF26Fo6Mt3nhjEPT0+EH1uGFyor1HnpzcLSkpCZWVlVi0aBH09G4Pidm+ffsD92vfvj3at2+Pd999F6+++io2bNiAl156CV26dMHZs2c1mpqsVCqhVCrrfQ2NTdHNMqT9lSFZd7OkHDfyi8X1zcyaoKWtOeys1ACAdo42AIDs6wXIvl6Ipk2U2LFiApoYG2HczI0wbWoM06a3u9yu5Rbh1i0B3dyd0NWtNf6X/DsKikrRxbUV5rw7DHuOnMaVrNt/nf51ORu7D5/CpxHDER79NQqLSzFzwmD8fikL/0v6HUT1VVpajuz/r4IAwLWcPFy+nAWVyhgqlQm+/y4eXTycoVarcP1aPnbu/BlNTZugS5fb40vU6qa1DoK1sDRD8+bNAFRXTLbCwsIML7/cG4WFN8W4O/fNzspFaVk58guKUVFeicuXswAA9vZWkueh0KOhUNxetNm/sdO55KRt27aorKzEihUrEBAQgF9++QVr1qy5Z3xJSQmmTJmC4cOHw8nJCVeuXEFiYiKGDRsGAJg2bRq8vLwwYcIEhISEQKVSIS0tDfv378eKFSse1mU1en4vuGPVrCDx9ZfRtx8a9ennezBv3R4806EVurk7AQBO7oqS7Ntx8EykZ9xAWXkFXurXBdNC/GBkaID0zBv4atdRLP9qvyT+7ahNmPPuUGxb8jZu3RLwy8k/8HLYZw/sIiK6n0sXM7Bw4dfi6+3bDwIAvLu74bXXfHHlnxwcO5aKmzdLoVY3hXOHVhg77kUYG9f9D52zZy8gOzsX2dm5mDrlM8m2dV+8J/7/xo178Pvv6eLrjz+6/TiGuZ+Oh5VVs/pcHpFOUQiCULeaewMIDg5GXl4edu3aJVm/ZMkSLFiwAHl5eXjhhRcwatQovP7668jNzUWzZs0kT4gtLy/H6NGj8csvvyArKwtWVlYYOnQoFixYIA52TUxMxIwZM3Ds2DEIgoC2bdti5MiReP/99+vUzoKCAqjVaijdQ6DQN5L7NhDphDs//IieNCVFhRjr44r8/HyYmZk1yDmqPyvaTPoGesqa4yPr6lZZMf5eMbxB26rrHmly8rhgckKNAZMTepI91OQk7Bvoa5GcVJUV4+/ljTs50cnnnBAREVHdrF69Gh07doSZmRnMzMzg7e2NvXv3itsFQUBUVBTs7e1hYmICHx8fnD17VnKMsrIyTJo0CVZWVlCpVBg8eDCuXJF+DUlubi6CgoKgVquhVqsRFBRU4zvuLl++jICAAKhUKlhZWSEsLAzl5eUaXxOTEyIiIhk97CfEtmzZEp9++imSkpKQlJSE3r1748UXXxQTkPnz52Px4sVYuXIlEhMTYWtri379+qGw8N+vAQkPD8fOnTsRGxuL+Ph4FBUVwd/fH1VVVWJMYGAgUlJSEBcXh7i4OKSkpCAo6N+xhFVVVRg0aBCKi4sRHx+P2NhY7NixAxEREZrfQ3brPBi7dagxYLcOPckeZrfOU+E7tO7W+XPpMK3aamFhgQULFuDNN9+Evb09wsPDMW3aNAC3qyQ2NjaYN28exo0bh/z8fDRv3hybNm3CyJG3n7h99epVODg4YM+ePfD19UVaWhpcXV2RkJAAT09PAEBCQgK8vb3x22+/wdnZGXv37oW/vz/S09Nhb28PAIiNjUVwcDCys7M1uhZWToiIiJ4QVVVViI2NRXFxMby9vXHhwgVkZmaif//+YoxSqUTPnj1x9OhRALcfVlpRUSGJsbe3h5ubmxhz7NgxqNVqMTEBAC8vL6jVakmMm5ubmJgAgK+vL8rKypCcnKzRdejcVGIiIqLHmZ6eQquH5wn/v+/dX51yv2dwnTlzBt7e3igtLUXTpk2xc+dOuLq6iomDjY2NJN7GxgaXLl0CAGRmZsLIyAjm5uY1YjIzM8UYa2vrGue1traWxNx9HnNzcxgZGYkxdcXKCRERkYyqH8KmzQIADg4O4uBTtVqNuXPn3vOczs7OSElJQUJCAt5++22MHj0a586du6NN0mRJEIQHjm25O6a2+PrE1AUrJ0RERDooPT1dMk7jfk8uNzIyEp+E3rVrVyQmJmLZsmXiOJPMzEzY2dmJ8dnZ2WKVw9bWFuXl5cjNzZVUT7Kzs9G9e3cxJisrq8Z5c3JyJMc5fvy4ZHtubi4qKipqVFQehJUTIiIiGck1W6d6anD1osnXqgiCgLKyMjg5OcHW1hb79//7JO3y8nIcOXJETDw8PDxgaGgoicnIyEBqaqoY4+3tjfz8fJw4cUKMOX78OPLz8yUxqampyMj49+tM9u3bB6VSCQ8PD43uISsnREREMnrY363z/vvvw8/PDw4ODigsLERsbCwOHz6MuLg4KBQKhIeHIzo6Gu3atUO7du0QHR2NJk2aIDAwEACgVqsxZswYREREwNLSEhYWFoiMjIS7uzv69u0LAHBxccGAAQMQEhKCtWvXAgDGjh0Lf39/ODs7AwD69+8PV1dXBAUFYcGCBbhx4wYiIyMREhKi8awjJidEREQyetjfSpyVlYWgoCBkZGRArVajY8eOiIuLQ79+/QAAU6dORUlJCUJDQ5GbmwtPT0/s27cPpqam4jGWLFkCAwMDjBgxAiUlJejTpw9iYmKgr//vF0lu2bIFYWFh4qyewYMHY+XKleJ2fX197N69G6GhoejRowdMTEwQGBiIhQsXan4P+JyTB+NzTqgx4HNO6En2MJ9z4jp1l9bPOTk3f0ijfnw9KydEREQyetiVkycRkxMiIiIZPewxJ08iztYhIiIincLKCRERkYwU0LJbByydMDkhIiKSEbt1tMduHSIiItIprJwQERHJiLN1tMfkhIiISEbs1tEeu3WIiIhIp7ByQkREJCN262iPyQkREZGM2K2jPSYnREREMmLlRHscc0JEREQ6hZUTIiIiOWnZrcMHxDI5ISIikhW7dbTHbh0iIiLSKaycEBERyYizdbTH5ISIiEhG7NbRHrt1iIiISKewckJERCQjdutoj8kJERGRjNitoz126xAREZFOYeWEiIhIRqycaI/JCRERkYw45kR7TE6IiIhkxMqJ9jjmhIiIiHQKKydEREQyYreO9picEBERyYjdOtpjtw4RERHpFFZOiIiIZKSAlt06srXk8cXkhIiISEZ6CgX0tMhOtNn3ScFuHSIiItIprJwQERHJiLN1tMfkhIiISEacraM9JidEREQy0lPcXrTZv7HjmBMiIqLH2Ny5c9GtWzeYmprC2toaQ4YMwfnz5yUxwcHBYkWnevHy8pLElJWVYdKkSbCysoJKpcLgwYNx5coVSUxubi6CgoKgVquhVqsRFBSEvLw8Sczly5cREBAAlUoFKysrhIWFoby8XKNrYnJCREQkJwVqJAKaLJrOJT5y5AgmTJiAhIQE7N+/H5WVlejfvz+Ki4slcQMGDEBGRoa47NmzR7I9PDwcO3fuRGxsLOLj41FUVAR/f39UVVWJMYGBgUhJSUFcXBzi4uKQkpKCoKAgcXtVVRUGDRqE4uJixMfHIzY2Fjt27EBERIRG18RuHSIiIhk97AGxcXFxktcbNmyAtbU1kpOT8cILL4jrlUolbG1taz1Gfn4+1q9fj02bNqFv374AgM2bN8PBwQEHDhyAr68v0tLSEBcXh4SEBHh6egIA1q1bB29vb5w/fx7Ozs7Yt28fzp07h/T0dNjb2wMAFi1ahODgYMyZMwdmZmZ1uiZWToiIiJ4g+fn5AAALCwvJ+sOHD8Pa2hrt27dHSEgIsrOzxW3JycmoqKhA//79xXX29vZwc3PD0aNHAQDHjh2DWq0WExMA8PLyglqtlsS4ubmJiQkA+Pr6oqysDMnJyXW+BlZOiIiIZKT4//+02R8ACgoKJOuVSiWUSuV99xUEAZMnT8Zzzz0HNzc3cb2fnx9efvllODo64sKFC/jwww/Ru3dvJCcnQ6lUIjMzE0ZGRjA3N5ccz8bGBpmZmQCAzMxMWFtb1zintbW1JMbGxkay3dzcHEZGRmJMXTA5ISIikpFcs3UcHBwk62fNmoWoqKj77jtx4kScPn0a8fHxkvUjR44U/9/NzQ1du3aFo6Mjdu/ejaFDh97zeIIgSKY21zbNuT4xD8LkhIiISAelp6dLxmg8qGoyadIkfPfdd/j555/RsmXL+8ba2dnB0dERf/zxBwDA1tYW5eXlyM3NlVRPsrOz0b17dzEmKyurxrFycnLEaomtrS2OHz8u2Z6bm4uKiooaFZX74ZgTIiIiGWkzU+fOB7iZmZlJlnslJ4IgYOLEifjvf/+LgwcPwsnJ6YFtvH79OtLT02FnZwcA8PDwgKGhIfbv3y/GZGRkIDU1VUxOvL29kZ+fjxMnTogxx48fR35+viQmNTUVGRkZYsy+ffugVCrh4eFR53tYp8rJ8uXL63zAsLCwOscSERE9aR72bJ0JEyZg69at+Pbbb2FqaiqO7VCr1TAxMUFRURGioqIwbNgw2NnZ4eLFi3j//fdhZWWFl156SYwdM2YMIiIiYGlpCQsLC0RGRsLd3V2cvePi4oIBAwYgJCQEa9euBQCMHTsW/v7+cHZ2BgD0798frq6uCAoKwoIFC3Djxg1ERkYiJCSkzjN1gDomJ0uWLKnTwRQKBZMTIiKih2j16tUAAB8fH8n6DRs2IDg4GPr6+jhz5gy++uor5OXlwc7ODr169cK2bdtgamoqxi9ZsgQGBgYYMWIESkpK0KdPH8TExEBfX1+M2bJlC8LCwsRZPYMHD8bKlSvF7fr6+ti9ezdCQ0PRo0cPmJiYIDAwEAsXLtTomhSCIAia3ojGpqCgAGq1Gkr3ECj0jR51c4gaxLov3nvUTSBqMCVFhRjr44r8/HyN/oLXRPVnhf+KwzA0aVrv41SUFOGHST4N2lZdV+8xJ+Xl5Th//jwqKyvlbA8REdFjrbpbR5ulsdM4Obl58ybGjBmDJk2a4Omnn8bly5cB3B5r8umnn8reQCIioseJXANiGzONk5Pp06fj1KlTOHz4MIyNjcX1ffv2xbZt22RtHBERETU+Gj/nZNeuXdi2bRu8vLwk2Z2rqyv++usvWRtHRET0uHnYs3WeRBonJzk5ObU+vra4uJilKCIiavT0FAroafF5qM2+TwqNu3W6deuG3bt3i6+rE5LqbyYkIiIi0obGlZO5c+diwIABOHfuHCorK7Fs2TKcPXsWx44dw5EjRxqijURERI8Nxf8v2uzf2GlcOenevTt++eUX3Lx5E23btsW+fftgY2ODY8eOafRoWiIioicRZ+tor15f/Ofu7o6NGzfK3RYiIiKi+iUnVVVV2LlzJ9LS0qBQKODi4oIXX3wRBgb8kmMiImrc9BS3F232b+w0ziZSU1Px4osvIjMzU/yin99//x3NmzfHd999B3d3d9kbSURE9LjQtmuG3Tr1GHPy1ltv4emnn8aVK1fw66+/4tdff0V6ejo6duyIsWPHNkQbiYiIqBHRuHJy6tQpJCUlwdzcXFxnbm6OOXPmoFu3brI2joiI6HHE4od2NK6cODs7Iysrq8b67OxsPPXUU7I0ioiI6HHF2Traq1PlpKCgQPz/6OhohIWFISoqCl5eXgCAhIQEfPTRR5g3b17DtJKIiOgxwQGx2qtTctKsWTNJJicIAkaMGCGuEwQBABAQEICqqqoGaCYRERE1FnVKTg4dOtTQ7SAiInoicLaO9uqUnPTs2bOh20FERPRE4OPrtVfvp6bdvHkTly9fRnl5uWR9x44dtW4UERERNV4aJyc5OTl44403sHfv3lq3c8wJERE1ZnoKBfS06JrRZt8nhcZTicPDw5Gbm4uEhASYmJggLi4OGzduRLt27fDdd981RBuJiIgeGwqF9ktjp3Hl5ODBg/j222/RrVs36OnpwdHREf369YOZmRnmzp2LQYMGNUQ7iYiIqJHQuHJSXFwMa2trAICFhQVycnIA3P6m4l9//VXe1hERET1m+BA27dXrCbHnz58HAHTq1Alr167FP//8gzVr1sDOzk72BhIRET1O2K2jPY27dcLDw5GRkQEAmDVrFnx9fbFlyxYYGRkhJiZG7vYRERFRI6NxcjJq1Cjx/zt37oyLFy/it99+Q6tWrWBlZSVr44iIiB43nK2jvXo/56RakyZN0KVLFznaQkRE9NjTtmuGuUkdk5PJkyfX+YCLFy+ud2OIiIged3x8vfbqlJycPHmyTgfjDSUiIiJt8Yv/NHD58EKYmZk96mYQNYj8mxWPuglEDaawwOShnUsP9ZgKe9f+jZ3WY06IiIjoX+zW0R4TNCIiItIprJwQERHJSKEA9DhbRytMToiIiGSkp2Vyos2+Twp26xAREZFOqVdysmnTJvTo0QP29va4dOkSAGDp0qX49ttvZW0cERHR4+Zhf/Hf3Llz0a1bN5iamsLa2hpDhgwRvwOvmiAIiIqKgr29PUxMTODj44OzZ89KYsrKyjBp0iRYWVlBpVJh8ODBuHLliiQmNzcXQUFBUKvVUKvVCAoKQl5eniTm8uXLCAgIgEqlgpWVFcLCwlBeXq7RNWmcnKxevRqTJ0/GwIEDkZeXh6qqKgBAs2bNsHTpUk0PR0RE9ESp7tbRZtHEkSNHMGHCBCQkJGD//v2orKxE//79UVxcLMbMnz8fixcvxsqVK5GYmAhbW1v069cPhYWFYkx4eDh27tyJ2NhYxMfHo6ioCP7+/uLnPAAEBgYiJSUFcXFxiIuLQ0pKCoKCgsTtVVVVGDRoEIqLixEfH4/Y2Fjs2LEDERERGl2TQhAEQZMdXF1dER0djSFDhsDU1BSnTp1CmzZtkJqaCh8fH1y7dk2jBjwOCgoKoFarkXU9n885oScWn3NCT7LCggK0c7BCfn7D/R6v/qyYtC0JyiZN632csptFWDGya73bmpOTA2traxw5cgQvvPACBEGAvb09wsPDMW3atNvnKCuDjY0N5s2bh3HjxiE/Px/NmzfHpk2bMHLkSADA1atX4eDggD179sDX1xdpaWlwdXVFQkICPD09AQAJCQnw9vbGb7/9BmdnZ+zduxf+/v5IT0+Hvb09ACA2NhbBwcHIzs6u8/VoXDm5cOECOnfuXGO9UqmUZGlERESNUfV362izALeTnTuXsrKyOp0/Pz8fAGBhYQHg9ud2ZmYm+vfvL8YolUr07NkTR48eBQAkJyejoqJCEmNvbw83Nzcx5tixY1Cr1WJiAgBeXl5Qq9WSGDc3NzExAQBfX1+UlZUhOTm5zvdQ4+TEyckJKSkpNdbv3bsXrq6umh6OiIjoiVL9rcTaLADg4OAgju1Qq9WYO3fuA88tCAImT56M5557Dm5ubgCAzMxMAICNjY0k1sbGRtyWmZkJIyMjmJub3zfG2tq6xjmtra0lMXefx9zcHEZGRmJMXWg8lXjKlCmYMGECSktLIQgCTpw4ga+//hpz587FF198oenhiIiInihyPb4+PT1d0g2iVCofuO/EiRNx+vRpxMfH19h290BbQRAeOPj27pja4usT8yAaJydvvPEGKisrMXXqVNy8eROBgYFo0aIFli1bhldeeUXTwxEREVEtzMzMNBpzMmnSJHz33Xf4+eef0bJlS3G9ra0tgNtVDTs7O3F9dna2WOWwtbVFeXk5cnNzJdWT7OxsdO/eXYzJysqqcd6cnBzJcY4fPy7Znpubi4qKihoVlfupV3IXEhKCS5cuITs7G5mZmUhPT8eYMWPqcygiIqInilxjTupKEARMnDgR//3vf3Hw4EE4OTlJtjs5OcHW1hb79+8X15WXl+PIkSNi4uHh4QFDQ0NJTEZGBlJTU8UYb29v5Ofn48SJE2LM8ePHkZ+fL4lJTU1FRkaGGLNv3z4olUp4eHjU+Zq0ekKslZWVNrsTERE9cfTw77iR+u6viQkTJmDr1q349ttvYWpqKo7tUKvVMDExgUKhQHh4OKKjo9GuXTu0a9cO0dHRaNKkCQIDA8XYMWPGICIiApaWlrCwsEBkZCTc3d3Rt29fAICLiwsGDBiAkJAQrF27FgAwduxY+Pv7w9nZGQDQv39/uLq6IigoCAsWLMCNGzcQGRmJkJAQjapAGicnTk5O9+03+vvvvzU9JBEREdXT6tWrAQA+Pj6S9Rs2bEBwcDAAYOrUqSgpKUFoaChyc3Ph6emJffv2wdTUVIxfsmQJDAwMMGLECJSUlKBPnz6IiYmBvr6+GLNlyxaEhYWJs3oGDx6MlStXitv19fWxe/duhIaGokePHjAxMUFgYCAWLlyo0TVp/JyTZcuWSV5XVFTg5MmTiIuLw5QpU/Dee+9p1IDHAZ9zQo0Bn3NCT7KH+ZyTqTt+hVKlxXNOioswf1iXBm2rrtO4cvLOO+/Uuv6zzz5DUlKS1g0iIiJ6nPGL/7Qn2xf/+fn5YceOHXIdjoiIiBoprQbE3umbb74Rn0ZHRETUWCkU0GpArBa7PjE0Tk46d+4sGRArCAIyMzORk5ODVatWydo4IiKix019pgPfvX9jp3FyMmTIEMlrPT09NG/eHD4+PujQoYNc7SIiIqJGSqPkpLKyEq1bt4avr6/4xDkiIiL6FwfEak+jAbEGBgZ4++236/zNiERERI2NQob/GjuNZ+t4enri5MmTDdEWIiKix1515USbpbHTeMxJaGgoIiIicOXKFXh4eEClUkm2d+zYUbbGERERUeNT5+TkzTffxNKlSzFy5EgAQFhYmLhNoVCIX4dcVVUlfyuJiIgeExxzor06JycbN27Ep59+igsXLjRke4iIiB5rCoXivt9BV5f9G7s6JyfVX8Hj6OjYYI0hIiIi0mjMCbM5IiKi+2O3jvY0Sk7at2//wATlxo0bWjWIiIjoccYnxGpPo+Rk9uzZUKvVDdUWIiIiIs2Sk1deeQXW1tYN1RYiIqLHnp5CodUX/2mz75OizskJx5sQERE9GMecaK/OT4itnq1DRERE1JDqXDm5detWQ7aDiIjoyaDlgFh+tU49Hl9PRERE96YHBfS0yDC02fdJweSEiIhIRpxKrD2Nv5WYiIiIqCGxckJERCQjztbRHpMTIiIiGfE5J9pjtw4RERHpFFZOiIiIZMQBsdpjckJERCQjPWjZrcOpxOzWISIiIt3CygkREZGM2K2jPSYnREREMtKDdt0S7NLgPSAiIiIdw8oJERGRjBQKBRRa9M1os++TgskJERGRjBTQ7ouFmZowOSEiIpIVnxCrPY45ISIiIp3C5ISIiEhmCi0WTf38888ICAiAvb09FAoFdu3aJdkeHBwsjoOpXry8vCQxZWVlmDRpEqysrKBSqTB48GBcuXJFEpObm4ugoCCo1Wqo1WoEBQUhLy9PEnP58mUEBARApVLBysoKYWFhKC8v1/iamJwQERHJqPo5J9osmiguLsYzzzyDlStX3jNmwIAByMjIEJc9e/ZItoeHh2Pnzp2IjY1FfHw8ioqK4O/vj6qqKjEmMDAQKSkpiIuLQ1xcHFJSUhAUFCRur6qqwqBBg1BcXIz4+HjExsZix44diIiI0OyCwDEnREREjzU/Pz/4+fndN0apVMLW1rbWbfn5+Vi/fj02bdqEvn37AgA2b94MBwcHHDhwAL6+vkhLS0NcXBwSEhLg6ekJAFi3bh28vb1x/vx5ODs7Y9++fTh37hzS09Nhb28PAFi0aBGCg4MxZ84cmJmZ1fmaWDkhIiKS0d1dKPVZAKCgoECylJWV1btNhw8fhrW1Ndq3b4+QkBBkZ2eL25KTk1FRUYH+/fuL6+zt7eHm5oajR48CAI4dOwa1Wi0mJgDg5eUFtVotiXFzcxMTEwDw9fVFWVkZkpOTNWovkxMiIiIZ6cmwAICDg4M4vkOtVmPu3Ln1ao+fnx+2bNmCgwcPYtGiRUhMTETv3r3FZCczMxNGRkYwNzeX7GdjY4PMzEwxxtrausaxra2tJTE2NjaS7ebm5jAyMhJj6ordOkRERDooPT1d0hWiVCrrdZyRI0eK/+/m5oauXbvC0dERu3fvxtChQ++5nyAIkgfC1fZwuPrE1AUrJ0RERDKSq1vHzMxMstQ3ObmbnZ0dHB0d8ccffwAAbG1tUV5ejtzcXElcdna2WAmxtbVFVlZWjWPl5ORIYu6ukOTm5qKioqJGReVBmJwQERHJSJtpxNo+XbYurl+/jvT0dNjZ2QEAPDw8YGhoiP3794sxGRkZSE1NRffu3QEA3t7eyM/Px4kTJ8SY48ePIz8/XxKTmpqKjIwMMWbfvn1QKpXw8PDQqI3s1iEiInqMFRUV4c8//xRfX7hwASkpKbCwsICFhQWioqIwbNgw2NnZ4eLFi3j//fdhZWWFl156CQCgVqsxZswYREREwNLSEhYWFoiMjIS7u7s4e8fFxQUDBgxASEgI1q5dCwAYO3Ys/P394ezsDADo378/XF1dERQUhAULFuDGjRuIjIxESEiIRjN1ACYnREREsnrYX/yXlJSEXr16ia8nT54MABg9ejRWr16NM2fO4KuvvkJeXh7s7OzQq1cvbNu2DaampuI+S5YsgYGBAUaMGIGSkhL06dMHMTEx0NfXF2O2bNmCsLAwcVbP4MGDJc9W0dfXx+7duxEaGooePXrAxMQEgYGBWLhwoeb3QBAEQeO9GpmCggKo1WpkXc/XOPsjelzk36x41E0gajCFBQVo52CF/PyG+z1e/VmxKf48mjQ1ffAO93CzqBBBzzk3aFt1HSsnREREMnrYlZMnEQfEEhERkU5h5YSIiEhG2s64Yd2EyQkREZGs6vPlfXfv39ixW4eIiIh0CisnREREMtKDAnpadM5os++TgskJERGRjNitoz126xAREZFOYeWEiIhIRor//0+b/Rs7JidEREQyYreO9titQ0RERDqFlRMiIiIZKbScrcNuHSYnREREsmK3jvaYnBAREcmIyYn2OOaEiIiIdAorJ0RERDLiVGLtMTkhIiKSkZ7i9qLN/o0du3WIiIhIp7ByQkREJCN262iPyQkREZGMOFtHe+zWISIiIp3CygkREZGMFNCua4aFEyYnREREsuJsHe2xW4eIiIh0Cisn1CB++fVPrNh0AKd+u4zMawXYvCAEg3yeEbcLgoB56/Zg485fkFdYAo+nHbFg6ki4tLUTY8Kjv8aRE+eReS0fKhMlnu3ohKhJL6J9a1sAQHzy7wgYv7zW8/8UMwVdnnZs2Isk+n+fbT6ABet2443hL2DWpJcA3H6PL435EV9/fwz5hSXo5NoKH4cPQ3snuxr7C4KA4Kmf48iJ37D2kzfh+7y7uG3lpv04eOwczv35DwwN9XFm99yHdl1UP5ytoz2dqpwoFIr7LsHBwY+6iVRHN0vK4Na+BeZPGVHr9mVfHcCqrYcwf8oI/BQzBdaWZhg6cQUKi0vFmE4dHLBy5ms4vv0D7FgxAYIgYOjEz1BVdQsA8GzHNvhtb7Rkef3F7mhlb4nOrq0eynUSnUq7jK+/P4YObe0l69d8fRDrtx/GR+HD8N3ad9HcwgyvRaxB0c3SGsdY/58jUNxjikZ5RSUG+jyD117s0SDtJ/lVz9bRZmnsdCo5ycjIEJelS5fCzMxMsm7ZsmWS+IqKikfUUnqQfj2exgdvByCgd6ca2wRBwJqvD2HyG74I6N0Jrk/ZY3VUEG6WVuCbH5PEuOChz6FHl6fQyt4Sz3RwwIy3A/BPVi4uZ1wHABgZGsDGykxcLJqpsPd/Z/BagNc9f9ETyan4ZhnCP9mMT6eMgNrURFwvCAK+/M8RTAjqhwEvdIRzGzssmh6IkrJyfHvgV8kxzv35D9ZvP4L5016p9RyT3/TDWyN84NymZsWFdJNChqWx06nkxNbWVlzUajUUCoX4urS0FM2aNcP27dvh4+MDY2NjbN68GVFRUejUqZPkOEuXLkXr1q0l6zZs2AAXFxcYGxujQ4cOWLVq1cO7MJK49M91ZF0vQG+vDuI6pZEhenR5CidO/13rPsUlZdj6fQIc7S3Rwsa81pi9P5/G9bwivOrv1SDtJrrbh0u/QS9vFzzX1VmyPj3jOnJuFOL5O9YrjQzg+cxTSE69IK4rKS1H2EebMDt8KKwtzR5au4l03WM35mTatGlYtGgRNmzYAKVSic8///yB+6xbtw6zZs3CypUr0blzZ5w8eRIhISFQqVQYPXp0jfiysjKUlZWJrwsKCmS9hsYu6/rt+9ncwlSy3trCFOmZNyTrvvjPz4hasQvFJeVo39oGOz+bCCPD2t+2m749ht5eLmhpW3vyQiSn7376FWd//wffrn23xracG4UAar7Hm5s3xZWsXPH1Ryt3wcOtNfo/5w56cuhBAT0tqrd6rJ08fslJeHg4hg4dqtE+H3/8MRYtWiTu5+TkhHPnzmHt2rW1Jidz587F7NmzZWkv3dvdXS+CUHMg2Mt+3dDLswMyrxVg5eYDeGP6l4j7YjKMlYaSuH+ycnEwIQ0b5r7Z4O0mupqdi49W7MRXC8fXeC/e6e7PJ0H4932//5dUHPv1D+z+IrIhm0qPgLZdM0xNHsPkpGvXrhrF5+TkID09HWPGjEFISIi4vrKyEmq1utZ9pk+fjsmTJ4uvCwoK4ODgUL8GUw02/1++zr5eAFurf38GObmFaG4p/UtT3dQE6qYmaNvKGt3cW8Op91T8cPgUhvtK3wdbv0+AhVoFvxc6NvwFUKN35vwVXMstQsDYxeK6qqpbOHHqb3y1Mx4HN00HAGRfL4S15b/v8Wt5RbAybwoAOPrrH7h09To6+r8vOfbbMzegW8c22LZs4kO4EiLd9NglJyqVSvJaT08PgiBI1t05UPbWrdszO9atWwdPT09JnL6+fq3nUCqVUCqVcjSXauHYwhI2lmY4dPw3dHS+nfSVV1Til1//RNSkF++7ryAIKC+vrLFuy/cJeGXgszA0qP1nSiSnHh7t8OOGqZJ1Uz79Gm1bWWN8YB+0srdEcwtTxCedh1v7lgBuv8ePn/oT740LAAC8HdgHrwySjo/yfWM+PpwwBH17PP1wLoQaBksnWnvskpO7NW/eHJmZmRAEQSyXpqSkiNttbGzQokUL/P333xg1atQjamXjU3SzDBfSc8TXl65ex5nzV9BM3QQOthYY/2ovLN6wD20drNHGoTkWx/yIJsaGYkXk4pVr+O/+ZPT2coGleVNkZOdh2VcHYGxsiH53/eL+OfF3XLp6Ha+92P2hXiM1Xk2bGNeYPWNiYoRmapW4/s2Xe+KzLQfQumVzOLVsjs82H4CJ0ggv9u0CALC2NKt1EKy9jTkc7CzF1/9k5SKv4CauZuXiVpWAs3/8AwBo3cIKqib8I0oX8Tkn2nvskxMfHx/k5ORg/vz5GD58OOLi4rB3716Ymf37jz4qKgphYWEwMzODn58fysrKkJSUhNzcXEn3DcknJe2S5AFpM5b8FwDw6iBPrIoKwjuv90VpWTki521DXuFNeDzdGjtWTISpyhgAoFQa4FjKX1gTexh5BTfR3MIU3Ts/hR+/iKgxyHDTd0fxbMc2cHayfXgXSPQA41/tjdKyCny45BvkF5Wgk4sjNi0cj6ZNjDU6zuIv92JHXKL4etBbCwEAXy+dAO/OT8naZiJdoRDu7hPRETExMQgPD0deXh4A4OLFi3BycsLJkydrTB1es2YNoqOjcePGDQwbNgzOzs74/PPPcfHiRTFm69atWLBgAc6dOweVSgV3d3eEh4fjpZdeemBbCgoKoFarkXU9X5L0ED1J8m/yuUH05CosKEA7Byvk5zfc7/Hqz4qfUi6jqWn9z1FUWIA+nVo1aFt1nU495+ROwcHBYmICAK1bt4YgCDUSEwAYP348Ll++jKKiImzcuBHvv/++JDEBgMDAQJw8eRJlZWW4ceMGjhw5UqfEhIiISBMP+yFsP//8MwICAmBvbw+FQoFdu3ZJtguCgKioKNjb28PExAQ+Pj44e/asJKasrAyTJk2ClZUVVCoVBg8ejCtXrkhicnNzERQUBLVaDbVajaCgIMnnNABcvnwZAQEBUKlUsLKyQlhYGMrLyzW8Ih1OToiIiOjBiouL8cwzz2DlypW1bp8/fz4WL16MlStXIjExEba2tujXrx8KCwvFmPDwcOzcuROxsbGIj49HUVER/P39UVVVJcYEBgYiJSUFcXFxiIuLQ0pKCoKCgsTtVVVVGDRoEIqLixEfH4/Y2Fjs2LEDERERGl+Tznbr6BJ261BjwG4depI9zG6dg6e079bp/Uz9unUUCgV27tyJIUOGALhdNbG3t0d4eDimTZsG4HaVxMbGBvPmzcO4ceOQn5+P5s2bY9OmTRg5ciQA4OrVq3BwcMCePXvg6+uLtLQ0uLq6IiEhQZz5mpCQAG9vb/z2229wdnbG3r174e/vj/T0dNjb3/6uqdjYWAQHByM7O1uja2HlhIiISEYKGf4Dbic7dy53Prm8ri5cuIDMzEz0799fXKdUKtGzZ08cPXoUAJCcnIyKigpJjL29Pdzc3MSYY8eOQa1WSx7J4eXlBbVaLYlxc3MTExMA8PX1RVlZGZKTkzVqN5MTIiIiGcn1rcQODg7i+A61Wo25c+dq3JbMzEwAtx+rcScbGxtxW2ZmJoyMjGBubn7fGGtr6xrHt7a2lsTcfR5zc3MYGRmJMXX12E8lJiIiehKlp6dLukK0eThoza8LER747e13x9QWX5+YumDlhIiISEZyzdYxMzOTLPVJTmxtbz//6e7KRXZ2tljlsLW1RXl5OXJzc+8bk5WVVeP4OTk5kpi7z5Obm4uKiooaFZUHYXJCREQkp4c9l/g+nJycYGtri/3794vrysvLceTIEXTvfvup2h4eHjA0NJTEZGRkIDU1VYzx9vZGfn4+Tpw4IcYcP34c+fn5kpjU1FRkZGSIMfv27YNSqYSHh4dG7Wa3DhER0WOsqKgIf/75p/j6woULSElJgYWFBVq1aoXw8HBER0ejXbt2aNeuHaKjo9GkSRMEBgYCANRqNcaMGYOIiAhYWlrCwsICkZGRcHd3R9++fQEALi4uGDBgAEJCQrB27VoAwNixY+Hv7w9nZ2cAQP/+/eHq6oqgoCAsWLAAN27cQGRkJEJCQjSedcTkhIiISEYP+7t1kpKS0KtXL/F19deyjB49GjExMZg6dSpKSkoQGhqK3NxceHp6Yt++fTA1/ferQJYsWQIDAwOMGDECJSUl6NOnD2JiYiRfkLtlyxaEhYWJs3oGDx4sebaKvr4+du/ejdDQUPTo0QMmJiYIDAzEwoULNb8HfM7Jg/E5J9QY8Dkn9CR7mM85+V/qFa2fc/K8W0s+vp6IiIhIV7Bbh4iISEbajmmVcTzsY4vJCRERkZyYnWiN3TpERESkU1g5ISIiktHDnq3zJGJyQkREJKM7vx+nvvs3dkxOiIiIZMQhJ9rjmBMiIiLSKaycEBERyYmlE60xOSEiIpIRB8Rqj906REREpFNYOSEiIpIRZ+toj8kJERGRjDjkRHvs1iEiIiKdwsoJERGRnFg60RqTEyIiIhlxto722K1DREREOoWVEyIiIhlxto72mJwQERHJiENOtMfkhIiISE7MTrTGMSdERESkU1g5ISIikhFn62iPyQkREZGctBwQy9yE3TpERESkY1g5ISIikhHHw2qPyQkREZGcmJ1ojd06REREpFNYOSEiIpIRZ+toj8kJERGRjPj4eu2xW4eIiIh0CisnREREMuJ4WO0xOSEiIpITsxOtMTkhIiKSEQfEao9jToiIiEinsHJCREQkIwW0nK0jW0seX6ycEBERyUghw6KJqKgoKBQKyWJraytuFwQBUVFRsLe3h4mJCXx8fHD27FnJMcrKyjBp0iRYWVlBpVJh8ODBuHLliiQmNzcXQUFBUKvVUKvVCAoKQl5enoatrRsmJ0RERI+5p59+GhkZGeJy5swZcdv8+fOxePFirFy5EomJibC1tUW/fv1QWFgoxoSHh2Pnzp2IjY1FfHw8ioqK4O/vj6qqKjEmMDAQKSkpiIuLQ1xcHFJSUhAUFNQg18NuHSIiIhk9ioewGRgYSKol1QRBwNKlSzFjxgwMHToUALBx40bY2Nhg69atGDduHPLz87F+/Xps2rQJffv2BQBs3rwZDg4OOHDgAHx9fZGWloa4uDgkJCTA09MTALBu3Tp4e3vj/PnzcHZ2rv8F14KVEyIiIlnJ07FTUFAgWcrKyu55xj/++AP29vZwcnLCK6+8gr///hsAcOHCBWRmZqJ///5irFKpRM+ePXH06FEAQHJyMioqKiQx9vb2cHNzE2OOHTsGtVotJiYA4OXlBbVaLcbIickJERGRDnJwcBDHd6jVasydO7fWOE9PT3z11Vf48ccfsW7dOmRmZqJ79+64fv06MjMzAQA2NjaSfWxsbMRtmZmZMDIygrm5+X1jrK2ta5zb2tpajJETu3WIiIhkJFe3Tnp6OszMzMT1SqWy1ng/Pz/x/93d3eHt7Y22bdti48aN8PLy+v9jShskCEKNdXe7O6a2+Locpz5YOSEiIpKRXLN1zMzMJMu9kpO7qVQquLu7448//hDHodxd3cjOzharKba2tigvL0dubu59Y7KysmqcKycnp0ZVRg5MToiIiJ4gZWVlSEtLg52dHZycnGBra4v9+/eL28vLy3HkyBF0794dAODh4QFDQ0NJTEZGBlJTU8UYb29v5Ofn48SJE2LM8ePHkZ+fL8bIid06REREMnrYs3UiIyMREBCAVq1aITs7G5988gkKCgowevRoKBQKhIeHIzo6Gu3atUO7du0QHR2NJk2aIDAwEACgVqsxZswYREREwNLSEhYWFoiMjIS7u7s4e8fFxQUDBgxASEgI1q5dCwAYO3Ys/P39ZZ+pAzA5ISIiktXD/m6dK1eu4NVXX8W1a9fQvHlzeHl5ISEhAY6OjgCAqVOnoqSkBKGhocjNzYWnpyf27dsHU1NT8RhLliyBgYEBRowYgZKSEvTp0wcxMTHQ19cXY7Zs2YKwsDBxVs/gwYOxcuXKel/n/SgEQRAa5MhPkIKCAqjVamRdz5cMTiJ6kuTfrHjUTSBqMIUFBWjnYIX8/Ib7PV79WfF7+jWYanGOwoICtG/gtuo6jjkhIiIincJuHSIiIhnV5/tx7t6/sWNyQkREJKNH8fj6Jw27dYiIiEinsHJCREQko4c9W+dJxOSEiIhIThx0ojV26xAREZFOYeWEiIhIRiycaI/JCRERkYw4W0d77NYhIiIincLKCRERkay0m63Djh0mJ0RERLJit4722K1DREREOoXJCREREekUdusQERHJiN062mNyQkREJCM+vl577NYhIiIincLKCRERkYzYraM9JidEREQy4uPrtcduHSIiItIprJwQERHJiaUTrTE5ISIikhFn62iP3TpERESkU1g5ISIikhFn62iPyQkREZGMOOREe0xOiIiI5MTsRGscc0JEREQ6hZUTIiIiGXG2jvaYnBAREcmIA2K1x+SkDgRBAAAUFhQ84pYQNZzCmxWPuglEDaawsBDAv7/PG1KBlp8V2u7/JGByUgfVb+qnnBwecUuIiEgbhYWFUKvVDXJsIyMj2Nraop0MnxW2trYwMjKSoVWPJ4XwMNLIx9ytW7dw9epVmJqaQsF620NRUFAABwcHpKenw8zM7FE3h0hWfH8/fIIgoLCwEPb29tDTa7i5IKWlpSgvL9f6OEZGRjA2NpahRY8nVk7qQE9PDy1btnzUzWiUzMzM+Mubnlh8fz9cDVUxuZOxsXGjTirkwqnEREREpFOYnBAREZFOYXJCOkmpVGLWrFlQKpWPuilEsuP7m+j+OCCWiIiIdAorJ0RERKRTmJwQERGRTmFyQkRERDqFyQnplJiYGDRr1uxRN4OIiB4hJifUIIKDg6FQKGosf/7556NuGpGsanuf37kEBwc/6iYSPXb4hFhqMAMGDMCGDRsk65o3b/6IWkPUMDIyMsT/37ZtG2bOnInz58+L60xMTCTxFRUVMDQ0fGjtI3ocsXJCDUapVMLW1layLFu2DO7u7lCpVHBwcEBoaCiKiorueYxTp06hV69eMDU1hZmZGTw8PJCUlCRuP3r0KF544QWYmJjAwcEBYWFhKC4ufhiXRwQAkve3Wq2GQqEQX5eWlqJZs2bYvn07fHx8YGxsjM2bNyMqKgqdOnWSHGfp0qVo3bq1ZN2GDRvg4uICY2NjdOjQAatWrXp4F0b0CDE5oYdKT08Py5cvR2pqKjZu3IiDBw9i6tSp94wfNWoUWrZsicTERCQnJ+O9994T/+o8c+YMfH19MXToUJw+fRrbtm1DfHw8Jk6c+LAuh6hOpk2bhrCwMKSlpcHX17dO+6xbtw4zZszAnDlzkJaWhujoaHz44YfYuHFjA7eW6NFjtw41mB9++AFNmzYVX/v5+eE///mP+NrJyQkff/wx3n777Xv+RXj58mVMmTIFHTp0AAC0a9dO3LZgwQIEBgYiPDxc3LZ8+XL07NkTq1ev5pdvkc4IDw/H0KFDNdrn448/xqJFi8T9nJyccO7cOaxduxajR49uiGYS6QwmJ9RgevXqhdWrV4uvVSoVDh06hOjoaJw7dw4FBQWorKxEaWkpiouLoVKpahxj8uTJeOutt7Bp0yb07dsXL7/8Mtq2bQsASE5Oxp9//oktW7aI8YIg4NatW7hw4QJcXFwa/iKJ6qBr164axefk5CA9PR1jxoxBSEiIuL6ysvKhfLMu0aPG5IQajEqlwlNPPSW+vnTpEgYOHIjx48fj448/hoWFBeLj4zFmzBhUVFTUeoyoqCgEBgZi9+7d2Lt3L2bNmoXY2Fi89NJLuHXrFsaNG4ewsLAa+7Vq1arBrotIU3cn3np6erj7m0Pu/Ddw69YtALe7djw9PSVx+vr6DdRKIt3B5IQemqSkJFRWVmLRokXQ07s93Gn79u0P3K99+/Zo37493n33Xbz66qvYsGEDXnrpJXTp0gVnz56VJEBEj4PmzZsjMzMTgiBAoVAAAFJSUsTtNjY2aNGiBf7++2+MGjXqEbWS6NFhckIPTdu2bVFZWYkVK1YgICAAv/zyC9asWXPP+JKSEkyZMgXDhw+Hk5MTrly5gsTERAwbNgzA7UGGXl5emDBhAkJCQqBSqZCWlob9+/djxYoVD+uyiDTm4+ODnJwczJ8/H8OHD0dcXBz27t0LMzMzMSYqKgphYWEwMzODn58fysrKkJSUhNzcXEyePPkRtp6o4XG2Dj00nTp1wuLFizFv3jy4ublhy5YtmDt37j3j9fX1cf36dbz++uto3749RowYAT8/P8yePRsA0LFjRxw5cgR//PEHnn/+eXTu3Bkffvgh7OzsHtYlEdWLi4sLVq1ahc8++wzPPPMMTpw4gcjISEnMW2+9hS+++AIxMTFwd3dHz549ERMTAycnp0fUaqKHRyHc3fFJRERE9AixckJEREQ6hckJERER6RQmJ0RERKRTmJwQERGRTmFyQkRERDqFyQkRERHpFCYnREREpFOYnBA9JqKiotCpUyfxdXBwMIYMGfLQ23Hx4kUoFArJ49bv1rp1ayxdurTOx4yJiUGzZs20bptCocCuXbu0Pg4RPVpMToi0EBwcDIVCAYVCAUNDQ7Rp0waRkZEoLi5u8HMvW7YMMTExdYqtS0JBRKQr+N06RFoaMGAANmzYgIqKCvzvf//DW2+9heLiYqxevbpGbEVFBQwNDWU5r1qtluU4RES6hpUTIi0plUrY2trCwcEBgYGBGDVqlNi1UN0V8+WXX6JNmzZQKpUQBAH5+fkYO3YsrK2tYWZmht69e+PUqVOS43766aewsbGBqakpxowZg9LSUsn2u7t1bt26hXnz5uGpp56CUqlEq1atMGfOHAAQv4+lc+fOUCgU8PHxEffbsGEDXFxcYGxsjA4dOmDVqlWS85w4cQKdO3eGsbExunbtipMnT2p8jxYvXgx3d3eoVCo4ODggNDQURUVFNeJ27dqF9u3bw9jYGP369UN6erpk+/fffw8PDw8YGxujTZs2mD17NiorKzVuDxHpNiYnRDIzMTFBRUWF+PrPP//E9u3bsWPHDrFbZdCgQcjMzMSePXuQnJyMLl26oE+fPrhx4wYAYPv27Zg1axbmzJmDpKQk2NnZ1Uga7jZ9+nTMmzcPH374Ic6dO4etW7fCxsYGwO0EAwAOHDiAjIwM/Pe//wUArFu3DjNmzMCcOXOQlpaG6OhofPjhh9i4cSMAoLi4GP7+/nB2dkZycjKioqJqfEFdXejp6WH58uVITU3Fxo0bcfDgQUydOlUSc/PmTcyZMwcbN27EL7/8goKCArzyyivi9h9//BGvvfYawsLCcO7cOaxduxYxMTFiAkZETxCBiOpt9OjRwosvvii+Pn78uGBpaSmMGDFCEARBmDVrlmBoaChkZ2eLMT/99JNgZmYmlJaWSo7Vtm1bYe3atYIgCIK3t7cwfvx4yXZPT0/hmWeeqfXcBQUFglKpFNatW1drOy9cuCAAEE6ePClZ7+DgIGzdulWy7uOPPxa8vb0FQRCEtWvXChYWFkJxcbG4ffXq1bUe606Ojo7CkiVL7rl9+/btgqWlpfh6w4YNAgAhISFBXJeWliYAEI4fPy4IgiA8//zzQnR0tOQ4mzZtEuzs7MTXAISdO3fe87xE9HjgmBMiLf3www9o2rQpKisrUVFRgRdffBErVqwQtzs6OqJ58+bi6+TkZBQVFcHS0lJynJKSEvz1118AgLS0NIwfP16y3dvbG4cOHaq1DWlpaSgrK0OfPn3q3O6cnBykp6djzJgxCAkJEddXVlaK41nS0tLwzDPPoEmTJpJ2aOrQoUOIjo7GuXPnUFBQgMrKSpSWlqK4uBgqlQoAYGBggK5du4r7dOjQAc2aNUNaWhqeffZZJCcnIzExUVIpqaqqQmlpKW7evClpIxE93picEGmpV69eWL16NQwNDWFvb19jwGv1h2+1W7duwc7ODocPH65xrPpOpzUxMdF4n1u3bgG43bXj6ekp2aavrw8AEAShXu2506VLlzBw4ECMHz8eH3/8MSwsLBAfH48xY8ZIur+A21OB71a97tatW5g9ezaGDh1aI8bY2FjrdhKR7mByQqQllUqFp556qs7xXbp0QWZmJgwMDNC6detaY1xcXJCQkIDXX39dXJeQkHDPY7Zr1w4mJib46aef8NZbb9XYbmRkBOB2paGajY0NWrRogb///hujRo2q9biurq7YtGkTSkpKxATofu2oTVJSEiorK7Fo0SLo6d0e5rZ9+/YacZWVlUhKSsKzzz4LADh//jzy8vLQoUMHALfv2/nz5zW610T0eGJyQvSQ9e3bF97e3hgyZAjmzZsHZ2dnXL16FXv27MGQIUPQtWtXvPPOOxg9ejS6du2K5557Dlu2bMHZs2fRpk2bWo9pbGyMadOmYerUqTAyMkKPHj2Qk5ODs2fPYsyYMbC2toaJiQni4uLQsmVLGBsbQ61WIyoqCmFhYTAzM4Ofnx/KysqQlJSE3NxcTJ48GYGBgZgxYwbGjBmDDz74ABcvXsTChQs1ut62bduisrISK1asQEBAAH755ResWbOmRpyhoSEmTZqE5cuXw9DQEBMnToSXl5eYrMycORP+/v5wcHDAyy+/DD09PZw+fRpnzpzBJ598ovkPgoh0FmfrED1kCoUCe/bswQsvvIA333wT7du3xyuvvIKLFy+Ks2tGjhyJmTNnYtq0afDw8MClS5fw9ttv3/e4H374ISIiIjBz5ky4uLhg5MiRyM7OBnB7PMfy5cuxdu1a2Nvb48UXXwQAvPXWW/jiiy8QExMDd3d39OzZEzExMeLU46ZNm+L777/HuXPn0LlzZ8yYMQPz5s3T6Ho7deqExYsXY968eXBzc8OWLVswd+7cGnFNmjTBtGnTEBgYCG9vb5iYmCA2Nlbc7uvrix9++AH79+9Ht27d4OXlhcWLF8PR0VGj9hCR7lMIcnQqExEREcmElRMiIiLSKUxOiIiISKcwOSEiIiKdwuSEiIiIdAqTEyIiItIpTE6IiIhIpzA5ISIiIp3C5ISIiIh0CpMTIiIi0ilMToiIiEinMDkhIiIincLkhIiIiHTK/wEsf+w3iCKpBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "threshold = 0.5\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(f'Confusion Matrix for Tuned Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f751fd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.77      0.86     56677\n",
      "         1.0       0.23      0.74      0.35      5078\n",
      "\n",
      "    accuracy                           0.77     61755\n",
      "   macro avg       0.60      0.76      0.60     61755\n",
      "weighted avg       0.91      0.77      0.82     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "threshold = 0.555\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(f'Confusion Matrix for Tuned Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90503586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.69      0.81     56677\n",
      "         1.0       0.20      0.83      0.32      5078\n",
      "\n",
      "    accuracy                           0.71     61755\n",
      "   macro avg       0.59      0.76      0.56     61755\n",
      "weighted avg       0.91      0.71      0.77     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "threshold = 0.46\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(f'Confusion Matrix for Tuned Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da666caf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.77      0.86     56677\n",
      "         1.0       0.23      0.74      0.35      5078\n",
      "\n",
      "    accuracy                           0.77     61755\n",
      "   macro avg       0.60      0.76      0.60     61755\n",
      "weighted avg       0.91      0.77      0.82     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import numpy as np\n",
    "\n",
    "threshold = 0.555\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(f'Confusion Matrix for Tuned Logistic Regression')\n",
    "plt.show()\n",
    "\n",
    "# Calculate the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Calculate Youden's J statistic for each threshold\n",
    "youden_j = tpr - fpr\n",
    "\n",
    "# Find the index of the threshold that maximizes Youden's J\n",
    "best_threshold_index = np.argmax(youden_j)\n",
    "\n",
    "# Plot only the best ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr[best_threshold_index], tpr[best_threshold_index], color='darkorange', lw=2, label=f'Best ROC curve (threshold = {thresholds[best_threshold_index]:.2f}, J = {youden_j[best_threshold_index]:.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 (Best Curve)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Annotate the best threshold value on the plot\n",
    "plt.annotate(f'Threshold = {thresholds[best_threshold_index]:.2f}', (fpr[best_threshold_index], tpr[best_threshold_index]), textcoords=\"offset points\", xytext=(-15,10), ha='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7382179",
   "metadata": {},
   "source": [
    "### With best threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4296ad12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.69      0.81     56677\n",
      "         1.0       0.20      0.83      0.32      5078\n",
      "\n",
      "    accuracy                           0.71     61755\n",
      "   macro avg       0.59      0.76      0.56     61755\n",
      "weighted avg       0.91      0.71      0.77     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "threshold = 0.46\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Get the probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"False\", \"True\"])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(f'Confusion Matrix for Tuned Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "671c5743",
   "metadata": {},
   "source": [
    "### Using Gridsearch and cross validation to check for best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb885216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'solver': 'liblinear'}\n",
      "0.10738332290856571\n",
      "Model: Logistic regression with random undersampling and grid search\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "# Create a logistic regression classifier object\n",
    "logreg_classifier = LogisticRegression(random_state=42)\n",
    "\n",
    "# Define the parameter grid for the logistic regression classifier\n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10],\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'solver': ['liblinear', 'saga']\n",
    "}\n",
    "\n",
    "# Create a grid search object with F1-score as the scoring metric and n_jobs=-1\n",
    "grid_search = GridSearchCV(logreg_classifier, param_grid, scoring='f1', cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "\n",
    "# Get the best model from the grid search\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Create an instance of SMOTE and TomekLinks resampling\n",
    "resampling = SMOTE(sampling_strategy='minority')\n",
    "tomek = TomekLinks(sampling_strategy='majority')\n",
    "\n",
    "# Resample the training data using SMOTE\n",
    "X_train_resampled, y_train_resampled = resampling.fit_resample(X_train, y_train)\n",
    "\n",
    "# Undersample the training data using TomekLinks after SMOTE\n",
    "X_train_resampled, y_train_resampled = tomek.fit_resample(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Fit the best model on the resampled training data\n",
    "best_model.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic Regression with SMOTE and TomekLinks resampling\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5886fa89",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression with random undersampling\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the library\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)\n",
    "threshold = 0.5 # Adjust the threshold according to your needs\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "# Create an instance of the RandomUnderSampler with a 1:1 ratio\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "\n",
    "# Resample the training data\n",
    "X_train_resampled, y_train_resampled = rus.fit_resample(X_train, y_train)\n",
    "\n",
    "# Fit the model on the resampled training data\n",
    "log_reg.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1] # Use log_reg instead of best_model\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression with random undersampling\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72fbf2fc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.640406</td>\n",
       "      <td>0.113071</td>\n",
       "      <td>0.030447</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.001</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 0.001, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.072329</td>\n",
       "      <td>0.075375</td>\n",
       "      <td>0.068448</td>\n",
       "      <td>0.073471</td>\n",
       "      <td>0.071111</td>\n",
       "      <td>0.072147</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.869163</td>\n",
       "      <td>0.080196</td>\n",
       "      <td>0.027139</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.001</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 0.001, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.072329</td>\n",
       "      <td>0.075375</td>\n",
       "      <td>0.068448</td>\n",
       "      <td>0.073471</td>\n",
       "      <td>0.071111</td>\n",
       "      <td>0.072147</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.592992</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.026704</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>0.001</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 0.001, 'solver': 'liblinear'}</td>\n",
       "      <td>0.091631</td>\n",
       "      <td>0.090032</td>\n",
       "      <td>0.088504</td>\n",
       "      <td>0.099842</td>\n",
       "      <td>0.083657</td>\n",
       "      <td>0.090733</td>\n",
       "      <td>0.005278</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.422102</td>\n",
       "      <td>0.062028</td>\n",
       "      <td>0.027198</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>0.001</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 0.001, 'solver': 'sag'}</td>\n",
       "      <td>0.072329</td>\n",
       "      <td>0.075375</td>\n",
       "      <td>0.068448</td>\n",
       "      <td>0.073471</td>\n",
       "      <td>0.071111</td>\n",
       "      <td>0.072147</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.833016</td>\n",
       "      <td>0.076175</td>\n",
       "      <td>0.027013</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>0.001</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 0.001, 'solver': 'saga'}</td>\n",
       "      <td>0.072329</td>\n",
       "      <td>0.075375</td>\n",
       "      <td>0.068448</td>\n",
       "      <td>0.073471</td>\n",
       "      <td>0.071111</td>\n",
       "      <td>0.072147</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.796245</td>\n",
       "      <td>0.044705</td>\n",
       "      <td>0.027328</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.01</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 0.01, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.096730</td>\n",
       "      <td>0.107717</td>\n",
       "      <td>0.095713</td>\n",
       "      <td>0.102968</td>\n",
       "      <td>0.103060</td>\n",
       "      <td>0.101238</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.429598</td>\n",
       "      <td>0.176245</td>\n",
       "      <td>0.027600</td>\n",
       "      <td>0.000492</td>\n",
       "      <td>0.01</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 0.01, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.108145</td>\n",
       "      <td>0.095735</td>\n",
       "      <td>0.102968</td>\n",
       "      <td>0.103060</td>\n",
       "      <td>0.101336</td>\n",
       "      <td>0.004565</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.820637</td>\n",
       "      <td>0.015657</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.01</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 0.01, 'solver': 'liblinear'}</td>\n",
       "      <td>0.094742</td>\n",
       "      <td>0.107790</td>\n",
       "      <td>0.095668</td>\n",
       "      <td>0.110342</td>\n",
       "      <td>0.101527</td>\n",
       "      <td>0.102014</td>\n",
       "      <td>0.006263</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.900707</td>\n",
       "      <td>0.047489</td>\n",
       "      <td>0.027265</td>\n",
       "      <td>0.001211</td>\n",
       "      <td>0.01</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 0.01, 'solver': 'sag'}</td>\n",
       "      <td>0.096730</td>\n",
       "      <td>0.108145</td>\n",
       "      <td>0.095713</td>\n",
       "      <td>0.102968</td>\n",
       "      <td>0.103060</td>\n",
       "      <td>0.101323</td>\n",
       "      <td>0.004579</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.864034</td>\n",
       "      <td>0.067794</td>\n",
       "      <td>0.026647</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>0.01</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 0.01, 'solver': 'saga'}</td>\n",
       "      <td>0.096730</td>\n",
       "      <td>0.107717</td>\n",
       "      <td>0.095713</td>\n",
       "      <td>0.102968</td>\n",
       "      <td>0.103060</td>\n",
       "      <td>0.101238</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.114847</td>\n",
       "      <td>0.142962</td>\n",
       "      <td>0.027824</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 0.1, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.099593</td>\n",
       "      <td>0.115350</td>\n",
       "      <td>0.098849</td>\n",
       "      <td>0.109772</td>\n",
       "      <td>0.109595</td>\n",
       "      <td>0.106632</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.308469</td>\n",
       "      <td>0.428432</td>\n",
       "      <td>0.028419</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 0.1, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.099593</td>\n",
       "      <td>0.115324</td>\n",
       "      <td>0.098849</td>\n",
       "      <td>0.109772</td>\n",
       "      <td>0.109595</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>0.006393</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.031003</td>\n",
       "      <td>0.044209</td>\n",
       "      <td>0.027948</td>\n",
       "      <td>0.001635</td>\n",
       "      <td>0.1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 0.1, 'solver': 'liblinear'}</td>\n",
       "      <td>0.098754</td>\n",
       "      <td>0.114132</td>\n",
       "      <td>0.099256</td>\n",
       "      <td>0.110690</td>\n",
       "      <td>0.107806</td>\n",
       "      <td>0.106127</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.781400</td>\n",
       "      <td>0.208467</td>\n",
       "      <td>0.026971</td>\n",
       "      <td>0.000981</td>\n",
       "      <td>0.1</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 0.1, 'solver': 'sag'}</td>\n",
       "      <td>0.099570</td>\n",
       "      <td>0.115350</td>\n",
       "      <td>0.098849</td>\n",
       "      <td>0.109772</td>\n",
       "      <td>0.109595</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>0.006405</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.473608</td>\n",
       "      <td>0.057494</td>\n",
       "      <td>0.026157</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.1</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 0.1, 'solver': 'saga'}</td>\n",
       "      <td>0.099593</td>\n",
       "      <td>0.115350</td>\n",
       "      <td>0.098849</td>\n",
       "      <td>0.109772</td>\n",
       "      <td>0.109595</td>\n",
       "      <td>0.106632</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.109343</td>\n",
       "      <td>0.134400</td>\n",
       "      <td>0.027859</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 1, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.774360</td>\n",
       "      <td>0.407104</td>\n",
       "      <td>0.027645</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 1, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.099548</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.099707</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107127</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.126992</td>\n",
       "      <td>0.008989</td>\n",
       "      <td>0.026804</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 1, 'solver': 'liblinear'}</td>\n",
       "      <td>0.099525</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.099707</td>\n",
       "      <td>0.110096</td>\n",
       "      <td>0.109944</td>\n",
       "      <td>0.107173</td>\n",
       "      <td>0.006621</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.622794</td>\n",
       "      <td>0.200193</td>\n",
       "      <td>0.026571</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>1</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 1, 'solver': 'sag'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.295044</td>\n",
       "      <td>0.428108</td>\n",
       "      <td>0.027220</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>1</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 1, 'solver': 'saga'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.155465</td>\n",
       "      <td>0.131700</td>\n",
       "      <td>0.028204</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>10</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 10, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.477817</td>\n",
       "      <td>0.582595</td>\n",
       "      <td>0.027461</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>10</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 10, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100586</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.109620</td>\n",
       "      <td>0.107305</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.175925</td>\n",
       "      <td>0.119870</td>\n",
       "      <td>0.028203</td>\n",
       "      <td>0.003830</td>\n",
       "      <td>10</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 10, 'solver': 'liblinear'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107383</td>\n",
       "      <td>0.006434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.444312</td>\n",
       "      <td>0.647966</td>\n",
       "      <td>0.028285</td>\n",
       "      <td>0.001447</td>\n",
       "      <td>10</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 10, 'solver': 'sag'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11.699894</td>\n",
       "      <td>0.243812</td>\n",
       "      <td>0.027691</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>10</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 10, 'solver': 'saga'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.305755</td>\n",
       "      <td>0.136009</td>\n",
       "      <td>0.028301</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>100</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 100, 'solver': 'newton-cg'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.818915</td>\n",
       "      <td>0.508003</td>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>100</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 100, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.099955</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100158</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107383</td>\n",
       "      <td>0.006435</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.124524</td>\n",
       "      <td>0.008480</td>\n",
       "      <td>0.028174</td>\n",
       "      <td>0.001598</td>\n",
       "      <td>100</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 100, 'solver': 'liblinear'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.537760</td>\n",
       "      <td>0.125567</td>\n",
       "      <td>0.027388</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>100</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 100, 'solver': 'sag'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11.616767</td>\n",
       "      <td>0.391239</td>\n",
       "      <td>0.027214</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>100</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 100, 'solver': 'saga'}</td>\n",
       "      <td>0.099977</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>0.100135</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>0.110042</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0        1.640406      0.113071         0.030447        0.004711   0.001   \n",
       "1        0.869163      0.080196         0.027139        0.000554   0.001   \n",
       "2        0.592992      0.010029         0.026704        0.001436   0.001   \n",
       "3        1.422102      0.062028         0.027198        0.001580   0.001   \n",
       "4        1.833016      0.076175         0.027013        0.001119   0.001   \n",
       "5        1.796245      0.044705         0.027328        0.000387    0.01   \n",
       "6        1.429598      0.176245         0.027600        0.000492    0.01   \n",
       "7        0.820637      0.015657         0.028531        0.001243    0.01   \n",
       "8        1.900707      0.047489         0.027265        0.001211    0.01   \n",
       "9        1.864034      0.067794         0.026647        0.000950    0.01   \n",
       "10       2.114847      0.142962         0.027824        0.000216     0.1   \n",
       "11       2.308469      0.428432         0.028419        0.000742     0.1   \n",
       "12       1.031003      0.044209         0.027948        0.001635     0.1   \n",
       "13       2.781400      0.208467         0.026971        0.000981     0.1   \n",
       "14       5.473608      0.057494         0.026157        0.001037     0.1   \n",
       "15       2.109343      0.134400         0.027859        0.000794       1   \n",
       "16       2.774360      0.407104         0.027645        0.000957       1   \n",
       "17       1.126992      0.008989         0.026804        0.001221       1   \n",
       "18       4.622794      0.200193         0.026571        0.001413       1   \n",
       "19      10.295044      0.428108         0.027220        0.001493       1   \n",
       "20       2.155465      0.131700         0.028204        0.001470      10   \n",
       "21       2.477817      0.582595         0.027461        0.000821      10   \n",
       "22       1.175925      0.119870         0.028203        0.003830      10   \n",
       "23       5.444312      0.647966         0.028285        0.001447      10   \n",
       "24      11.699894      0.243812         0.027691        0.002637      10   \n",
       "25       2.305755      0.136009         0.028301        0.001276     100   \n",
       "26       2.818915      0.508003         0.027429        0.000533     100   \n",
       "27       1.124524      0.008480         0.028174        0.001598     100   \n",
       "28       5.537760      0.125567         0.027388        0.001358     100   \n",
       "29      11.616767      0.391239         0.027214        0.001549     100   \n",
       "\n",
       "   param_solver                               params  split0_test_score  \\\n",
       "0     newton-cg  {'C': 0.001, 'solver': 'newton-cg'}           0.072329   \n",
       "1         lbfgs      {'C': 0.001, 'solver': 'lbfgs'}           0.072329   \n",
       "2     liblinear  {'C': 0.001, 'solver': 'liblinear'}           0.091631   \n",
       "3           sag        {'C': 0.001, 'solver': 'sag'}           0.072329   \n",
       "4          saga       {'C': 0.001, 'solver': 'saga'}           0.072329   \n",
       "5     newton-cg   {'C': 0.01, 'solver': 'newton-cg'}           0.096730   \n",
       "6         lbfgs       {'C': 0.01, 'solver': 'lbfgs'}           0.096774   \n",
       "7     liblinear   {'C': 0.01, 'solver': 'liblinear'}           0.094742   \n",
       "8           sag         {'C': 0.01, 'solver': 'sag'}           0.096730   \n",
       "9          saga        {'C': 0.01, 'solver': 'saga'}           0.096730   \n",
       "10    newton-cg    {'C': 0.1, 'solver': 'newton-cg'}           0.099593   \n",
       "11        lbfgs        {'C': 0.1, 'solver': 'lbfgs'}           0.099593   \n",
       "12    liblinear    {'C': 0.1, 'solver': 'liblinear'}           0.098754   \n",
       "13          sag          {'C': 0.1, 'solver': 'sag'}           0.099570   \n",
       "14         saga         {'C': 0.1, 'solver': 'saga'}           0.099593   \n",
       "15    newton-cg      {'C': 1, 'solver': 'newton-cg'}           0.099977   \n",
       "16        lbfgs          {'C': 1, 'solver': 'lbfgs'}           0.099548   \n",
       "17    liblinear      {'C': 1, 'solver': 'liblinear'}           0.099525   \n",
       "18          sag            {'C': 1, 'solver': 'sag'}           0.099977   \n",
       "19         saga           {'C': 1, 'solver': 'saga'}           0.099977   \n",
       "20    newton-cg     {'C': 10, 'solver': 'newton-cg'}           0.099977   \n",
       "21        lbfgs         {'C': 10, 'solver': 'lbfgs'}           0.099977   \n",
       "22    liblinear     {'C': 10, 'solver': 'liblinear'}           0.099977   \n",
       "23          sag           {'C': 10, 'solver': 'sag'}           0.099977   \n",
       "24         saga          {'C': 10, 'solver': 'saga'}           0.099977   \n",
       "25    newton-cg    {'C': 100, 'solver': 'newton-cg'}           0.099977   \n",
       "26        lbfgs        {'C': 100, 'solver': 'lbfgs'}           0.099955   \n",
       "27    liblinear    {'C': 100, 'solver': 'liblinear'}           0.099977   \n",
       "28          sag          {'C': 100, 'solver': 'sag'}           0.099977   \n",
       "29         saga         {'C': 100, 'solver': 'saga'}           0.099977   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.075375           0.068448           0.073471   \n",
       "1            0.075375           0.068448           0.073471   \n",
       "2            0.090032           0.088504           0.099842   \n",
       "3            0.075375           0.068448           0.073471   \n",
       "4            0.075375           0.068448           0.073471   \n",
       "5            0.107717           0.095713           0.102968   \n",
       "6            0.108145           0.095735           0.102968   \n",
       "7            0.107790           0.095668           0.110342   \n",
       "8            0.108145           0.095713           0.102968   \n",
       "9            0.107717           0.095713           0.102968   \n",
       "10           0.115350           0.098849           0.109772   \n",
       "11           0.115324           0.098849           0.109772   \n",
       "12           0.114132           0.099256           0.110690   \n",
       "13           0.115350           0.098849           0.109772   \n",
       "14           0.115350           0.098849           0.109772   \n",
       "15           0.116592           0.100135           0.109748   \n",
       "16           0.116592           0.099707           0.109748   \n",
       "17           0.116592           0.099707           0.110096   \n",
       "18           0.116592           0.100135           0.109748   \n",
       "19           0.116592           0.100135           0.109748   \n",
       "20           0.116592           0.100135           0.109748   \n",
       "21           0.116592           0.100586           0.109748   \n",
       "22           0.116592           0.100135           0.110169   \n",
       "23           0.116592           0.100135           0.109748   \n",
       "24           0.116592           0.100135           0.109748   \n",
       "25           0.116592           0.100135           0.109748   \n",
       "26           0.116592           0.100158           0.110169   \n",
       "27           0.116592           0.100135           0.109748   \n",
       "28           0.116592           0.100135           0.109748   \n",
       "29           0.116592           0.100135           0.109748   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.071111         0.072147        0.002322               27  \n",
       "1            0.071111         0.072147        0.002322               27  \n",
       "2            0.083657         0.090733        0.005278               26  \n",
       "3            0.071111         0.072147        0.002322               27  \n",
       "4            0.071111         0.072147        0.002322               27  \n",
       "5            0.103060         0.101238        0.004453               24  \n",
       "6            0.103060         0.101336        0.004565               22  \n",
       "7            0.101527         0.102014        0.006263               21  \n",
       "8            0.103060         0.101323        0.004579               23  \n",
       "9            0.103060         0.101238        0.004453               24  \n",
       "10           0.109595         0.106632        0.006400               16  \n",
       "11           0.109595         0.106627        0.006393               19  \n",
       "12           0.107806         0.106127        0.006153               20  \n",
       "13           0.109595         0.106627        0.006405               18  \n",
       "14           0.109595         0.106632        0.006400               16  \n",
       "15           0.110042         0.107299        0.006400                4  \n",
       "16           0.110042         0.107127        0.006595               15  \n",
       "17           0.109944         0.107173        0.006621               14  \n",
       "18           0.110042         0.107299        0.006400                4  \n",
       "19           0.110042         0.107299        0.006400                4  \n",
       "20           0.110042         0.107299        0.006400                4  \n",
       "21           0.109620         0.107305        0.006267                3  \n",
       "22           0.110042         0.107383        0.006434                1  \n",
       "23           0.110042         0.107299        0.006400                4  \n",
       "24           0.110042         0.107299        0.006400                4  \n",
       "25           0.110042         0.107299        0.006400                4  \n",
       "26           0.110042         0.107383        0.006435                2  \n",
       "27           0.110042         0.107299        0.006400                4  \n",
       "28           0.110042         0.107299        0.006400                4  \n",
       "29           0.110042         0.107299        0.006400                4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Convert the cv_results_ dictionary into a data frame\n",
    "cv_results_df = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "# Display the data frame\n",
    "cv_results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0cfecc",
   "metadata": {},
   "source": [
    "## Undersampling Majority values and Oversampling minority values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "380e9792",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression with random undersampling and best parameters\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the library\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Create a logistic regression object with the best parameters\n",
    "log_reg = LogisticRegression(C=10, solver='liblinear', random_state=42, max_iter=500)\n",
    "threshold = 0.5\n",
    "\n",
    "# Create an instance of the RandomUnderSampler with a 1:1 ratio\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "\n",
    "# Resample the training data\n",
    "X_train_resampled, y_train_resampled = rus.fit_resample(X_train, y_train)\n",
    "\n",
    "# Fit the model on the resampled training data\n",
    "log_reg.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Calculate predicted probabilities\n",
    "y_pred_proba = log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression with random undersampling and best parameters\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "204dc075",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic regression with SMOTE and F2-score\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "F2-score: 0.509\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the libraries\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import fbeta_score\n",
    "\n",
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(random_state=42, max_iter=500)\n",
    "threshold = 0.5\n",
    "\n",
    "# Create an instance of SMOTE with a 1:1 ratio\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Resample the training data using SMOTE\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Fit the model on the resampled training data\n",
    "log_reg.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make predictions based on the threshold\n",
    "y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "f2 = fbeta_score(y_test, y_pred, beta=2) # Use F2-score to give more weight to recall\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Logistic regression with SMOTE and F2-score\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(f\"F2-score: {f2:.3f}\")\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de786acc",
   "metadata": {},
   "source": [
    "### Hyperparamter tuning using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fe52dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'solver': 'liblinear', 'penalty': 'l1', 'C': 10.0}\n",
      "0.32412827833255753\n",
      "Model: Logistic Regression with hyperparameter tuning\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "import numpy as np\n",
    "\n",
    "# Create a Logistic Regression classifier object\n",
    "logreg_classifier = LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Define the parameter distribution for the Logistic Regression classifier\n",
    "param_dist = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': np.logspace(-3, 1, 10), # sample from a logarithmic scale\n",
    "    'solver': ['liblinear', 'saga']\n",
    "}\n",
    "\n",
    "# Create a random search object with F1-score as the scoring metric and n_iter=20\n",
    "random_search = RandomizedSearchCV(logreg_classifier, param_dist, scoring='f1', cv=5, n_iter=20, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the training data using random search\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(random_search.best_params_)\n",
    "print(random_search.best_score_)\n",
    "\n",
    "# Get the best model from the random search\n",
    "best_logreg_model = random_search.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred = best_logreg_model.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(\"Model: Logistic Regression with hyperparameter tuning\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15700e0c",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning for decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5af852a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "0.21202895809586844\n",
      "Model: Decision Tree with hyperparameter tuning\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.92     56677\n",
      "         1.0       0.20      0.23      0.21      5078\n",
      "\n",
      "    accuracy                           0.86     61755\n",
      "   macro avg       0.56      0.57      0.57     61755\n",
      "weighted avg       0.87      0.86      0.86     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "# Create a Decision Tree classifier object\n",
    "tree_classifier = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Define the parameter grid for the Decision Tree classifier\n",
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Create a grid search object with F1-score as the scoring metric and n_jobs=-1\n",
    "grid_search = GridSearchCV(tree_classifier, param_grid, scoring='f1', cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the training data using grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "\n",
    "# Get the best model from the grid search\n",
    "best_tree_model = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred = best_tree_model.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(\"Model: Decision Tree with hyperparameter tuning\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f6ed75",
   "metadata": {},
   "source": [
    "# Ensembling technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a7c0220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Voting classifier with soft voting\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.99      0.96     56677\n",
      "         1.0       0.44      0.10      0.16      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.68      0.54      0.56     61755\n",
      "weighted avg       0.88      0.92      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the library\n",
    "from sklearn.ensemble import VotingClassifier # Import VotingClassifier\n",
    "\n",
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(C=10, solver='liblinear', random_state=42, max_iter=500)\n",
    "threshold = 0.5\n",
    "\n",
    "# Create other base classifiers\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Create a voting classifier with soft voting\n",
    "voting_clf = VotingClassifier(estimators=[('lr', log_reg), ('dt', dt), ('rf', rf), ('gb', gb)], voting='soft')\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions based on the weighted average of probabilities\n",
    "y_pred = voting_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Voting classifier with soft voting\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9975e92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Voting classifier with weighted voting and SMOTE\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.94      0.94     56677\n",
      "         1.0       0.31      0.32      0.31      5078\n",
      "\n",
      "    accuracy                           0.89     61755\n",
      "   macro avg       0.63      0.63      0.63     61755\n",
      "weighted avg       0.89      0.89      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "# Import the libraries\n",
    "from sklearn.ensemble import VotingClassifier # Import VotingClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(random_state=42, max_iter=500)\n",
    "threshold = 0.5\n",
    "\n",
    "# Create other base classifiers\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Create a voting classifier with weighted voting\n",
    "voting_clf = VotingClassifier(estimators=[('lr', log_reg), ('dt', dt), ('rf', rf), ('gb', gb)], voting='soft', weights=[1, 1, 2, 3])\n",
    "\n",
    "# Create an instance of SMOTE with a 1:1 ratio\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Resample the training data using SMOTE\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Fit the model on the resampled training data\n",
    "voting_clf.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make predictions based on the weighted average of probabilities\n",
    "y_pred = voting_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Voting classifier with weighted voting and SMOTE\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e5c73c",
   "metadata": {},
   "source": [
    "## Ensembling technique using tuned Logistic regression and gradient boosting models (with balanced sampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19b46a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1395: UserWarning: `use_label_encoder` is deprecated in 1.7.0.\n",
      "  warnings.warn(\"`use_label_encoder` is deprecated in 1.7.0.\")\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1395: UserWarning: `use_label_encoder` is deprecated in 1.7.0.\n",
      "  warnings.warn(\"`use_label_encoder` is deprecated in 1.7.0.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Voting classifier with soft voting\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.54      0.05      0.09      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.73      0.52      0.53     61755\n",
      "weighted avg       0.89      0.92      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "# Create a logistic regression object with class weight and a lower threshold\n",
    "log_reg = LogisticRegression(C=10, solver='liblinear', random_state=42, max_iter=500)\n",
    "\n",
    "# Create other base classifiers\n",
    "xgb = XGBClassifier(eval_metric='logloss', use_label_encoder=False, random_state=42)\n",
    "\n",
    "# Create a voting classifier with soft voting\n",
    "voting_clf = VotingClassifier(estimators=[('lr', log_reg), ('xgb', xgb)], voting='soft')\n",
    "\n",
    "# Fit the model on the scaled training data\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions based on the weighted average of probabilities\n",
    "y_pred = voting_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Model: Voting classifier with soft voting\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('==========================================================')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbc5164",
   "metadata": {},
   "source": [
    "## Ensembling technique using logistic regression and random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c72c40ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Ensemble (Voting Classifier)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.92      0.93     56677\n",
      "         1.0       0.33      0.42      0.37      5078\n",
      "\n",
      "    accuracy                           0.88     61755\n",
      "   macro avg       0.64      0.67      0.65     61755\n",
      "weighted avg       0.90      0.88      0.89     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create individual models\n",
    "log_reg = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "random_forest = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Create a Voting Classifier that combines the models\n",
    "voting_classifier = VotingClassifier(estimators=[('lr', log_reg), ('rf', random_forest)], voting='soft')\n",
    "\n",
    "# Fit the Voting Classifier on the scaled training data\n",
    "voting_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Get predictions\n",
    "y_pred_ensemble = voting_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics for the ensemble\n",
    "accuracy_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
    "precision_ensemble = precision_score(y_test, y_pred_ensemble)\n",
    "recall_ensemble = recall_score(y_test, y_pred_ensemble)\n",
    "f1_ensemble = f1_score(y_test, y_pred_ensemble)\n",
    "\n",
    "# Print the results for the ensemble\n",
    "print(\"Model: Ensemble (Voting Classifier)\")\n",
    "print(classification_report(y_test, y_pred_ensemble))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate and display the confusion matrix for the ensemble\n",
    "cm_ensemble = confusion_matrix(y_test, y_pred_ensemble)\n",
    "disp_ensemble = ConfusionMatrixDisplay(confusion_matrix=cm_ensemble, display_labels=[\"False\", \"True\"])\n",
    "disp_ensemble.plot(cmap=plt.cm.Blues)\n",
    "plt.title(\"Confusion Matrix for Ensemble (Voting Classifier)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143f49d0",
   "metadata": {},
   "source": [
    "# Checking the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a700e6c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest for feature importance\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Get feature importances\n",
    "importances = rf.feature_importances_\n",
    "\n",
    "# Get feature names\n",
    "feature_names = list(X_train.columns)\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.bar(range(X_train.shape[1]), importances[indices])\n",
    "plt.xticks(range(X_train.shape[1]), np.array(feature_names)[indices], rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dccdb35",
   "metadata": {},
   "source": [
    "# Boosting using Logistic regression model to increase better sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3bfac3fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Boosted (AdaBoostClassifier with Logistic Regression)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.00      0.00      0.00      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.46      0.50      0.48     61755\n",
      "weighted avg       0.84      0.92      0.88     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a logistic regression base estimator\n",
    "log_reg_base = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', random_state=42, max_iter=500)\n",
    "\n",
    "# Create an AdaBoostClassifier with logistic regression as the base estimator\n",
    "ada_boost = AdaBoostClassifier(estimator=log_reg_base, n_estimators=1000, random_state=42)\n",
    "\n",
    "# Fit the AdaBoostClassifier on the scaled training data\n",
    "ada_boost.fit(X_train, y_train)\n",
    "\n",
    "# Get predictions\n",
    "y_pred_boosted = ada_boost.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics for the boosted model\n",
    "accuracy_boosted = accuracy_score(y_test, y_pred_boosted)\n",
    "precision_boosted = precision_score(y_test, y_pred_boosted)\n",
    "recall_boosted = recall_score(y_test, y_pred_boosted)\n",
    "f1_boosted = f1_score(y_test, y_pred_boosted)\n",
    "\n",
    "# Print the results for the boosted model\n",
    "print(\"Model: Boosted (AdaBoostClassifier with Logistic Regression)\")\n",
    "print(classification_report(y_test, y_pred_boosted))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate and display the confusion matrix for the boosted model\n",
    "cm_boosted = confusion_matrix(y_test, y_pred_boosted)\n",
    "disp_boosted = ConfusionMatrixDisplay(confusion_matrix=cm_boosted, display_labels=[\"False\", \"True\"])\n",
    "disp_boosted.plot(cmap=plt.cm.Blues)\n",
    "plt.title(\"Confusion Matrix for Boosted Model (AdaBoost with Logistic Regression)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9f23d257",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Bagged (BaggingClassifier with Logistic Regression)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.73      0.83     56677\n",
      "         1.0       0.21      0.80      0.33      5078\n",
      "\n",
      "    accuracy                           0.73     61755\n",
      "   macro avg       0.59      0.76      0.58     61755\n",
      "weighted avg       0.91      0.73      0.79     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a logistic regression base estimator\n",
    "log_reg_base = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', class_weight='balanced', random_state=42, max_iter=500)\n",
    "\n",
    "# Create a BaggingClassifier with logistic regression as the base estimator\n",
    "bagging = BaggingClassifier(base_estimator=log_reg_base, n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the BaggingClassifier on the scaled training data\n",
    "bagging.fit(X_train, y_train)\n",
    "\n",
    "# Get predictions\n",
    "y_pred_bagged = bagging.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics for the bagged model\n",
    "accuracy_bagged = accuracy_score(y_test, y_pred_bagged)\n",
    "precision_bagged = precision_score(y_test, y_pred_bagged)\n",
    "recall_bagged = recall_score(y_test, y_pred_bagged)\n",
    "f1_bagged = f1_score(y_test, y_pred_bagged)\n",
    "\n",
    "# Print the results for the bagged model\n",
    "print(\"Model: Bagged (BaggingClassifier with Logistic Regression)\")\n",
    "print(classification_report(y_test, y_pred_bagged))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate and display the confusion matrix for the bagged model\n",
    "cm_bagged = confusion_matrix(y_test, y_pred_bagged)\n",
    "disp_bagged = ConfusionMatrixDisplay(confusion_matrix=cm_bagged, display_labels=[\"False\", \"True\"])\n",
    "disp_bagged.plot(cmap=plt.cm.Blues)\n",
    "plt.title(\"Confusion Matrix for Bagged Model (Bagging with Logistic Regression)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c60d6ce",
   "metadata": {},
   "source": [
    "## Balanced boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b34b99df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Boosted (AdaBoostClassifier with Logistic Regression and Class Weights)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     56677\n",
      "         1.0       0.00      0.00      0.00      5078\n",
      "\n",
      "    accuracy                           0.92     61755\n",
      "   macro avg       0.46      0.50      0.48     61755\n",
      "weighted avg       0.84      0.92      0.88     61755\n",
      "\n",
      "==========================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\ziyan\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: 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",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a logistic regression base estimator without class weight\n",
    "log_reg_base = LogisticRegression(C=0.1, penalty='l1', solver='liblinear', random_state=42, max_iter=500)\n",
    "\n",
    "# Create an AdaBoostClassifier with logistic regression as the base estimator and class weight\n",
    "ada_boost = AdaBoostClassifier(base_estimator=log_reg_base, n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the AdaBoostClassifier on the scaled training data with class weights\n",
    "ada_boost.fit(X_train, y_train, sample_weight=y_train.map({0: 1, 1: 5}))  # Adjust the class weight as needed\n",
    "\n",
    "# Get predictions\n",
    "y_pred_boosted = ada_boost.predict(X_test)\n",
    "\n",
    "# Evaluate the performance metrics for the boosted model\n",
    "accuracy_boosted = accuracy_score(y_test, y_pred_boosted)\n",
    "precision_boosted = precision_score(y_test, y_pred_boosted, zero_division=1)  # Set zero_division parameter to handle warnings\n",
    "recall_boosted = recall_score(y_test, y_pred_boosted, zero_division=1)\n",
    "f1_boosted = f1_score(y_test, y_pred_boosted, zero_division=1)\n",
    "\n",
    "# Print the results for the boosted model\n",
    "print(\"Model: Boosted (AdaBoostClassifier with Logistic Regression and Class Weights)\")\n",
    "print(classification_report(y_test, y_pred_boosted))\n",
    "print('==========================================================')\n",
    "\n",
    "# Calculate and display the confusion matrix for the boosted model\n",
    "cm_boosted = confusion_matrix(y_test, y_pred_boosted)\n",
    "disp_boosted = ConfusionMatrixDisplay(confusion_matrix=cm_boosted, display_labels=[\"False\", \"True\"])\n",
    "disp_boosted.plot(cmap=plt.cm.Blues)\n",
    "plt.title(\"Confusion Matrix for Boosted Model (AdaBoost with Logistic Regression and Class Weights)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f13ddf",
   "metadata": {},
   "source": [
    "### Cross validation to compare models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f740d155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression (K-Fold Cross-Validation)\n",
      "Avg F1 Score: 0.4554\n",
      "Avg Accuracy: 0.4600\n",
      "Avg Precision: 0.4883\n",
      "Avg Recall: 0.4418\n",
      "Avg ROC AUC: 0.4127\n",
      "==========================================================\n",
      "Model: Decision Tree (K-Fold Cross-Validation)\n",
      "Avg F1 Score: 0.5844\n",
      "Avg Accuracy: 0.5700\n",
      "Avg Precision: 0.5934\n",
      "Avg Recall: 0.5894\n",
      "Avg ROC AUC: 0.5722\n",
      "==========================================================\n",
      "Model: Random Forest (K-Fold Cross-Validation)\n",
      "Avg F1 Score: 0.5864\n",
      "Avg Accuracy: 0.5400\n",
      "Avg Precision: 0.5555\n",
      "Avg Recall: 0.6403\n",
      "Avg ROC AUC: 0.5511\n",
      "==========================================================\n",
      "Model: XGBoost (K-Fold Cross-Validation)\n",
      "Avg F1 Score: 0.6251\n",
      "Avg Accuracy: 0.5800\n",
      "Avg Precision: 0.5768\n",
      "Avg Recall: 0.6935\n",
      "Avg ROC AUC: 0.5933\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Define the number of folds for cross-validation\n",
    "num_folds = 5\n",
    "kf = KFold(n_splits=num_folds, shuffle=True, random_state=42)\n",
    "\n",
    "# Sample data (replace with your actual data)\n",
    "X = np.random.rand(100, 10)\n",
    "y = np.random.randint(2, size=100)\n",
    "\n",
    "# Defining the models\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier(class_weight='balanced', random_state=42)),\n",
    "    (\"Random Forest\", RandomForestClassifier(class_weight='balanced', random_state=42)),\n",
    "    (\"XGBoost\", XGBClassifier(scale_pos_weight=1, eval_metric='logloss', random_state=42)),\n",
    "]\n",
    "\n",
    "# Applying k-fold cross-validation for each model\n",
    "for name, model in models:\n",
    "    accuracy_scores = []\n",
    "    precision_scores = []\n",
    "    recall_scores = []\n",
    "    f1_scores = []\n",
    "    roc_aucs = []\n",
    "\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "        accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
    "        precision_scores.append(precision_score(y_test, y_pred))\n",
    "        recall_scores.append(recall_score(y_test, y_pred))\n",
    "        f1_scores.append(f1_score(y_test, y_pred))\n",
    "        roc_aucs.append(roc_auc_score(y_test, y_pred_proba))\n",
    "\n",
    "    avg_accuracy = np.mean(accuracy_scores)\n",
    "    avg_precision = np.mean(precision_scores)\n",
    "    avg_recall = np.mean(recall_scores)\n",
    "    avg_f1 = np.mean(f1_scores)\n",
    "    avg_roc_auc = np.mean(roc_aucs)\n",
    "\n",
    "    print(f\"Model: {name} (K-Fold Cross-Validation)\")\n",
    "    print(f\"Avg F1 Score: {avg_f1:.4f}\")\n",
    "    print(f\"Avg Accuracy: {avg_accuracy:.4f}\")\n",
    "    print(f\"Avg Precision: {avg_precision:.4f}\")\n",
    "    print(f\"Avg Recall: {avg_recall:.4f}\")\n",
    "    print(f\"Avg ROC AUC: {avg_roc_auc:.4f}\")\n",
    "    print('==========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66953fdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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