code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49ea88fa",
   "metadata": {},
   "source": [
    "# Detection of Myocardial Infarction using PTB Diagnostic ECG database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8f5a619",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61070783",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndata = pd.read_csv(r\"path\",header=None)\n",
    "#This is the normal ptbdb where the last column for all rows is 0 meaning these patients were healthy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fde0f60",
   "metadata": {},
   "outputs": [
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ndata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4c82689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4046, 188)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    "ndata.shape"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17ed79db",
   "metadata": {},
   "outputs": [],
   "source": [
    "abdata = pd.read_csv(r\"path\",header=None)\n",
    "#The last column is observed to be 1 for all rows meaning these patients were infected with MI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcdcd888",
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       "[5 rows x 188 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "abdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f278803d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10506, 188)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "abdata.shape"
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  {
   "cell_type": "markdown",
   "id": "d1e7a951",
   "metadata": {},
   "source": [
    "#### Sampling frequency = 125Hz\n",
    "#### This indicates that the time scale = 1/125 = 0.008seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c23d41",
   "metadata": {},
   "source": [
    "## Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "37ff377a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the time scale\n",
    "time_scale = 0.008  #seconds\n",
    "\n",
    "# Generate time axis based on the time scale\n",
    "time_axis = np.arange(ndata.shape[1] - 1) * time_scale\n",
    "\n",
    "\n",
    "random_row_index = np.random.randint(0, ndata.shape[0])\n",
    "\n",
    "# Plot Normal ECG signal with adjusted time scale\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(time_axis, ndata.iloc[random_row_index, :-1])\n",
    "plt.title(\"Normal ECG Signal\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.show()\n",
    "\n",
    "# Plot MI ECG signal with adjusted time scale\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(time_axis, abdata.iloc[random_row_index, :-1])\n",
    "plt.title(\"MI ECG Signal\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "839bfb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate average ECG waveform for normal subjects\n",
    "avg_ndata = np.mean(ndata, axis=0)\n",
    "\n",
    "# Calculate average ECG waveform for MI subjects\n",
    "avg_abdata = np.mean(abdata, axis=0)\n",
    "\n",
    "# Plotting average ECG waveforms\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.plot(avg_ndata, label='Normal Subjects', color='blue')\n",
    "plt.plot(avg_abdata, label='MI Subjects', color='red')\n",
    "plt.title('Average ECG Waveforms')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc2b914e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "<p>5 rows × 188 columns</p>\n",
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      ],
      "text/plain": [
       "            0         1         2         3         4         5         6    \\\n",
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       "\n",
       "            7         8         9    ...  178  179  180  181  182  183  184  \\\n",
       "13047  0.170674  0.160853  0.162547  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1392   0.184501  0.203875  0.213413  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "9452   0.092417  0.093207  0.093602  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "9390   0.411330  0.385468  0.418719  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "11643  0.337255  0.334454  0.334454  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       185  186  187  \n",
       "13047  0.0  0.0  1.0  \n",
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       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concatenated_data = pd.concat([ndata, abdata],ignore_index=True)\n",
    "shuffled_data = concatenated_data.sample(frac=1, random_state=0) # shuffle the data \n",
    "shuffled_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2d3b809a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14552, 188)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = shuffled_data\n",
    "all_data.reset_index(drop=True, inplace=True)\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ac498367",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.rename(columns={ndata.columns[-1]: 'Status'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c50f73fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 188 columns</p>\n",
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       "          0         1         2         3         4         5         6  \\\n",
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       "3  0.977832  0.843596  0.881773  0.698276  0.543103  0.407635  0.427340   \n",
       "4  1.000000  0.849860  0.496919  0.426891  0.336695  0.331092  0.349020   \n",
       "\n",
       "          7         8         9  ...  178  179  180  181  182  183  184  185  \\\n",
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       "\n",
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       "4  0.0     1.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "da3aec89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming the last column is named 'Status' where 0 represents normal and 1 represents infected\n",
    "\n",
    "# Count occurrences of each category\n",
    "status_counts = all_data['Status'].value_counts()\n",
    "label = ['Normal', 'MI']\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(status_counts, labels=label, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'lightcoral'])\n",
    "plt.title(\"Subjects's condition\")\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dfd0b233",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = all_data.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a8d85921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14552, 187)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "95c0a89a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution after resampling: {0.0: 10506, 1.0: 10506}\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Extract the features and labels\n",
    "X = all_data.iloc[:, :-1]\n",
    "y = all_data.iloc[:, -1]\n",
    "\n",
    "# Apply SMOTE to balance the class distribution\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
    "\n",
    "# Check the class distribution after resampling\n",
    "unique, counts = np.unique(y_resampled, return_counts=True)\n",
    "print(\"Class distribution after resampling:\", dict(zip(unique, counts)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f5a54df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of PCA features after SMOTE: (21012, 37)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=0.95)  # Retain 95% of variance\n",
    "pca.fit(X_resampled)\n",
    "\n",
    "# Transform the data\n",
    "pca_features = pca.transform(X_resampled)\n",
    "\n",
    "# Print the shape of PCA features\n",
    "print(\"Shape of PCA features after SMOTE:\", pca_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4e7f7221",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca_features, y_resampled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d359dddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16809, 37)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d0f12bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN classifier: 0.9469426600047585\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhi Patil\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Create and train KNN classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "\n",
    "# Evaluate accuracy\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(\"Accuracy of KNN classifier:\", accuracy_knn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0d95e7",
   "metadata": {},
   "source": [
    "#### 1.n=5 -->acc = 94.69\n",
    "#### 2.n=7 -->acc = 93.81\n",
    "#### 3. n =8 -->acc = 92.88"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "22188e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier: 0.9412324530097549\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Create and train SVM classifier\n",
    "#svm = SVC(kernel='linear', random_state=42)\n",
    "#svm = SVC(kernel='sigmoid', gamma='scale', coef0=0.0, random_state=42)\n",
    "svm = SVC(kernel='poly', degree=3, gamma='scale', coef0=1,probability=True, random_state=42)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# Evaluate accuracy\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "print(\"Accuracy of SVM classifier:\", accuracy_svm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5eb86e",
   "metadata": {},
   "source": [
    "#### kernel = linear  acc - 0.78\n",
    "#### kernel = sigmoid acc - 0.58\n",
    "#### kernel = polynomial with deg 3 acc - 0.94 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3b168c8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest classifier: 0.9628836545324768\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create and train Random Forest classifier\n",
    "rf = RandomForestClassifier(n_estimators=135, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "# Evaluate accuracy\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(\"Accuracy of Random Forest classifier:\", accuracy_rf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed33389",
   "metadata": {},
   "source": [
    "#### n_estimators 90 - 96\n",
    "####                      100 - 96.05\n",
    "####                       120 - 96.12\n",
    "####                       130 - 96.14\n",
    "####                        135 - 96.28\n",
    "####                        140 - 96.12.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "04b1ed5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhi Patil\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "knn_predictions = knn.predict(X_test)\n",
    "rf_predictions = rf.predict(X_test)\n",
    "svm_predictions = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "940f2157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Metrics:\n",
      "Accuracy: 0.9628836545324768\n",
      "Precision: 0.9747524752475247\n",
      "Recall: 0.9493731918997107\n",
      "F1 Score: 0.9618954567659991\n",
      "\n",
      "KNN Metrics:\n",
      "Accuracy: 0.9469426600047585\n",
      "Precision: 0.9848088004190676\n",
      "Recall: 0.9064609450337512\n",
      "F1 Score: 0.9440120512176751\n",
      "\n",
      "SVM Metrics:\n",
      "Accuracy: 0.9412324530097549\n",
      "Precision: 0.9672634271099744\n",
      "Recall: 0.9117647058823529\n",
      "F1 Score: 0.9386944651278232\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "# Random Forest Evaluation\n",
    "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
    "rf_precision = precision_score(y_test, rf_predictions)\n",
    "rf_recall = recall_score(y_test, rf_predictions)\n",
    "rf_f1 = f1_score(y_test, rf_predictions)\n",
    "\n",
    "\n",
    "print(\"Random Forest Metrics:\")\n",
    "print(\"Accuracy:\", rf_accuracy)\n",
    "print(\"Precision:\", rf_precision)\n",
    "print(\"Recall:\", rf_recall)\n",
    "print(\"F1 Score:\", rf_f1)\n",
    "\n",
    "\n",
    "knn_accuracy = accuracy_score(y_test, knn_predictions)\n",
    "knn_precision = precision_score(y_test, knn_predictions)\n",
    "knn_recall = recall_score(y_test, knn_predictions)\n",
    "knn_f1 = f1_score(y_test, knn_predictions)\n",
    "\n",
    "\n",
    "print(\"\\nKNN Metrics:\")\n",
    "print(\"Accuracy:\", knn_accuracy)\n",
    "print(\"Precision:\", knn_precision)\n",
    "print(\"Recall:\", knn_recall)\n",
    "print(\"F1 Score:\", knn_f1)\n",
    "\n",
    "\n",
    "\n",
    "svm_accuracy = accuracy_score(y_test, svm_predictions )\n",
    "svm_precision = precision_score(y_test,svm_predictions )\n",
    "svm_recall = recall_score(y_test, svm_predictions )\n",
    "svm_f1 = f1_score(y_test, svm_predictions )\n",
    "\n",
    "\n",
    "print(\"\\nSVM Metrics:\")\n",
    "print(\"Accuracy:\", svm_accuracy)\n",
    "print(\"Precision:\", svm_precision)\n",
    "print(\"Recall:\", svm_recall)\n",
    "print(\"F1 Score:\", svm_f1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "90c9236c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Plot confusion matrices side by side with different colors\n",
    "\n",
    "# Confusion matrix for KNN\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_knn), annot=True, fmt='d', cmap='Blues',cbar=False)\n",
    "plt.title(\"Confusion Matrix for KNN\")\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "# Confusion matrix for SVM\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_svm), annot=True, fmt='d', cmap='Greens',cbar=False)\n",
    "plt.title(\"Confusion Matrix for SVM\")\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "# Confusion matrix for Random Forest\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_rf), annot=True, fmt='d', cmap='Oranges',cbar=False)\n",
    "plt.title(\"Confusion Matrix for Random Forest\")\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4c3c3ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nj4+Py4OlO57TJWyZkHQA0k5ARhxkJUD2b2M8sy9C8i+QcrjgYz3Lt4SqgyCgcu52L9/fQ6ojnAb8oS0AUz6/zG7lx7bdYuHoqeXUq98Xc0k8f8VMWnYa2bacXvnd53az+fRmTFedwYMJB1nyy5KbC6D7iqrKorX/wn4A4tPib7Bljqu/HwYGjSo2ItAnkJTsFAD63dafeuUb5Brvdt1/fxsjdyVY/fFxi81GlG8r7Dav38fV2cBqveq2DWzWwj/2xy/rtY5hNUhJTcfPNxCbzZTnmPZLNbGlh2K15uxXELuv89jSgh3C43h55QxZdcaX2Xztx0wmGydOHKF+/Tr4+Zld9rx//CpMmCvNDh48yOLYxViyLPj7+zN48GBq1arF8uXLXZZlCvMcHhVm27Vrx9KluX/ErF69mpYtW5bMkOgOhgHZl38Loom/X3h0JZxefT/7IiTt/y2oFqDnxjsEQm4D33LgWwZ8Qn/rffztX9/yEHEXBFV3+sss6ax2K+mWgn9EUxAJ6Qkcv3wcq93KptObCPYNvu72i39ZTNXQqtfd5oczP5CclUy5gHIAHLt8rMjqLax6oXdgGMAfeur+eD9vm8El20kam4fn2+uWb+/cVW02w4phmAhNboPNDkFJLfIGvN+CGxnlMSXVdAS2K1+n/3D/Z8/pSHU5ZwW5oghazn7OK7177mCx2Fm+/Gf69q2Fj09RjOIUZ8nIyGDJkiVkZWVRtWpVhg0bRtmyZYv1dT9uDbOpqakcOXLEcf/48ePs2rWL8uXLU716dZ577jl+/fVXPv30UwAmT57Mu+++y9SpU5k0aRJbtmxh1qxZzJ8/310vwfNkxMOlnyDrfE5oNQwwrDk9qilH4OIPkHWh8Mf1LQ+h9SAgEvwq/hZOy+Z8BdeGMg0hoIr+JC6Ao5eOcib5DLvid7E9bjup2akEeAfk2ib6QDTVy1THy5T3N1NKdgrxqQXr4SsuLmdevu7joafuxmQNdARBq98FzBdvx3f3I9iTInMFOVsBe+VyM/HLTVX+u223uL8rXB1y3BGybuV5wcr27Vu58862+Pt739TzqldO5MYCAgIYNGgQp0+fplu3bpjNxf+PD7eG2e3bt9O1a1fH/StjWx944AHmzp1LXFwcp06dcjweFRXF8uXLeeqpp3jvvfeoXLky77zzjqbluh5rBmT8Csk/Q9xqOPpxzrCAGzEH/tZ7etXX1fd9y4JP2Zye1rK354TYYvBb4krP2B97rm7lKzPTxPbtlUlMzHl9V9ozLFnYrgQoG2RYM4iz7s/Vm3aW7djtkGac55z3j/hbw6/qkbNxuux8gjPq5wzlMyAt8OcCv9ajl48655t4I+cbgncmmLPh+F3X2dAA/yQ41v36x/POgLMtwfpbYM8KhYt1f3/cbib5GgNF0gpXOT4+Bt7epmLVi+aq5zWbi8Vb9KZZLAYZGRdp185AH8SJFK39+/fj5+dHnTp1AKhfvz7169d3c1UF59Yw26VLF8cFXPmZO3dunrbOnTvz008/ObGqEsCww4XNcOgdOL0w71jV0PoQVCOnN9Vkxmb34nxyOGlUJ8nckkRTU7JtATmhzZKThwsbAK+Md3P1l+3ql2qy53xdLeg81FwPgRfAuOqvzWqbwfACuzc0XABJNXLuX+3qrrdK+6/9/S/kXCipAdcIsBfqQ8Wf4XhXON4NskJyP+5lhTNtr33gC43A8nuPrtcfA1M+AcqcXwi6aoydj9kXn6vDoG8BglQt94c7w7CwcuVy+vbtqyFJIiK/sVgsrFq1ih07dhAQEMDDDz9MSEjIjXcsZtwaZsUJTn4FPz0FGWd/bzMH5oTXih2g6mCo3BdMJqxW+OwzeOkluKoDvHgJPQP1FucNlldU3QreWdAgJie8XRFxvUs7CqDiwZveNdAejq8R+lsvmMFlryM05h4spmTKeFUh3Fwv56pUM5i87ASZQ6jq3wDzb1euVvKvSpXAWr+HsTtvPeB5eq/crSrGQ71ERNwiISGB6Ohozp07B0DLli0JCgpyc1U3R2G2JLCmwdnlcPwz+PW3C+S8g6D6KKj3OJRtkivJGAbERMPf/gY//9YpWK4cVKpU9B+Tpnofx+J9GbPZ4Hv+SaBXOby8TDlhzpQzvcnOzIWU966Kl8kLkymnVBNwNKOQPfCFDLAD6w3E39vfcf/45eMMqT8Es5eZbFs27au1z/n2Wq388MMPtGnTBm/v398ykcGRVAmtkuuYgT6B158aSERExM327NnDsmXLsFgsBAUFMWTIEGrXru3usm6afut6quwkiF8Nl/fA0ZmQeeWCHxPUnggt38mZT/UqhgGxsfDXv8KOHTltYWHw3HMwZQoE5L7G6Jrshp03N7/J8cvHHfP7rjm+Bn9vf/ac2/NbFaZck7s7btp++/qD1OyEGz7v0AZD820/fvk4A+sNJCwgjPoVfh/jY/Yy0yS8CWbT75/5m0wmyviVuea8xPmxWCxkHMiga82u+ohaREQ8lt1uZ9myZezcuROAmjVrMnToUI8cWnA1hVlPZBiwsiWk/j4TBEE1oPpIqD0hZ1aBP9i6NSe0rl+fcz84GKZOhT/9CW60dkRKVgpf7f+Krw58xeqjqwtW4h9W46oSUgWr3cq5tHP8vevf82yfYcmgY42OedorBFagZeWWBXpOERERuTavq+Zm69y5M506dcrV5qkUZj1RVsLvQbbacIjsCVEP5KxK9Qf79sHzz8OSJTn3fX1zemH/+leoWDH/w28+vZlvj33Li+tfvGEpL3Z+0TGBe2JmIl1qdqFSUCVqlauV83xmX8ccoiIiIuJ6VqvVMUyuT58+NGvWjOrVS86c7gqznsaSDNsezLldrjl0XJDvZseOwYsvwrx5OR25Xl4wbhy88AJc/f/v5tObeXH9i2RZs9h4auN1n9rf25+m4U15o8cbtIhsQZCvZw4UFxERKQ2ys7NZvnw5qampjB49GpPJhI+PT4kKsqAw61lsWRB7JyTuBS8faPqPPJvExcGrr8JHH/2+pOOIEfD3v0Pt26y8uflN/v3lv2kS3oS95/ZyIf3aCyQ0rNiQ28rfxj+6/YPbwm7DN5+eXxERESl+zp07R3R0NAkJCZhMJs6cOUO1atXcXZZTKMx6knNrfwuyftB9PVT4fY7Ry5fh9dfhnXcgIyOnrVcv+Mc/oPkddnr/rzexX8Q6tl97fG2uQ7eu0po/tfsTEcER1ChTg+plqhfqIikRERFxP8Mw+Omnn1i5ciVWq5WQkBCGDRtWYoMsKMx6DsMOe1/OuV17giPIpqXB22/DG29AUlLOw+3awbRp0LkzWO1WzK/kvQJ/bLOx9KrdCy+TF3dF3UVYYJirXomIiIg4QVZWFsuWLWPfvn0A1KlThyFDhhAYGOjmypxLYdZT/PJfuPgDeAdD4/8jKws+/jhnSMFv8x1z++05PbH9+/8+rewfe2DXP7CeTjU6qddVRESkhImOjubIkSOYTCbuuusu2rdvXyp+3yvMFneZCbDvZTg8AwD77a/y2VeRvPQSnDiRs0mtWvDKK3D33TmLF0DOxwwTl0xk9q7ZjkMZL1576WARERHxbN26dePSpUsMHjy4RA8r+COF2eLMboXvBkHCZgCOmx+k//DHOXAg5+HIyJxVvCZMyJly64pzqeeo8986pGanOtoebvmwKysXERERJ8vMzOT06dPcdtttAERGRvLII4+UiLljC0NhtriypMKPUyBhM3ZzKFNjvuDtBb0BE+XKwbPPwqOPwh+HwcSlxFH5rcq52jaM3UCnGp1cV7uIiIg41dmzZ4mOjiYpKYkJEyZQuXLO7/7SFmRBYbZ4MgxY18vRI/vmdx/y9oI+BAbCU0/Bn/8MZcvmv+uwr4Y5breIbMHiuxdTJbSKC4oWERERZzMMgx9++IHY2FjsdjtlrxUIShGF2eIoYWtOkDV5cyTiC/4yYxheXvDjj9Cw4fV33XJmCwB1ytdh+4PbXVCsiIiIuEJGRgZLlizh559/BqBBgwYMHDgQf39/N1fmXgqzxdHxT3L+rXEPU/+Z09N67703DrKLfl7kuP2ndn9yUnEiIiLiamfOnHEMKzCbzfTs2ZNWrVqVitkKbkRhtrhJ/gVOzAPgkG0sS5fmLEX7f/93412HfDnEcXtUo1HOqlBERERc7OTJkyQlJVGuXDlGjBhBZGSku0sqNhRmi5NLO2HjELCmQoV2PPNmFwDuuQfq1bv2bhabhQr/quC4/2H/DykXUM7JxYqIiIirtG/fHoCWLVvi5+fn5mqKl9J3yVtx9etyWN0O0k5CcB32llvE4iVemEzX75U9dvkYvq/6kpyV7Gh7sMWDLihYREREnOXUqVP873//Izs7GwCTyUSHDh0UZPOhMFtc7H0B7FkQfhf0+oEXXqsE5CyEUL9+/rusOLyC2u/UztV28ZmLzq5UREREnMQwDDZu3MjcuXM5evQoGzdudHdJxZ6GGRQH1jS49FPO7XafsutgeRYtylmS9m9/y3+XHWd30Pfzvo771ctU58CUAwT5Bjm/XhERESlyaWlpfP311xw9ehSAJk2a0LFjRzdXVfwpzBYHxz8FDAioDIGVeeWVnOZRo6BBg7yb2w07LT9u6bj/3z7/ZUqrKXiZ1NEuIiLiiU6cOEFMTAypqal4e3vTt29fmjVrptkKCkBh1t0MA36ennO7wZ/ZvRu+/vr6vbKbT2923J7QfAKPtn7U+XWKiIiIU+zZs4dFixZhGAYVK1Zk+PDhVKpUyd1leQyFWXdLPggph8DLD2pP4JW7c5pHjrz2vLLLDy933H6/3/suKFJEREScJSoqioCAAOrWrUufPn3w9fV1d0keRWHW3c5/l/NvpY7sORjKwoXX75VNyUph2vfTAJg9cDY+Zh8XFSoiIiJF5eLFi4SFhQEQEhLC5MmTCQkJcXNVnkmDLN0tYWvOv2Ft+fvfc26OGAGNGuW/+aMrfh9ScEfkHU4uTkRERIqS3W5n3bp1vPfeexw4cMDRriB789Qz607WNDi7DIDjaR2Ijs5pzq9X1jAMVhxZwae7P3W0NY1o6ooqRUREpAgkJyezcOFCTp48CeQsUdvwRmvVyw0pzLrT0VmQdRGCa/Ps9B4ADB8OjRvn3XTgFwNZdmiZ4/6Su5e4qkoRERG5RUeOHOHrr78mPT0dX19fBgwYQOP8fuFLoSnMuovdCgf/DcCvZZ5hQbQZgBdeyLvp53s/zxVkX+nyCgPqDXBJmSIiInLzbDYb69atY9OmTQBEREQwfPhwx3hZuXUKs+6SuBvST4FPWf7ywRgMA4YNg9tvz71ZljWL0QtHO+4ffuwwdcrXcXGxIiIicjNOnjzpCLKtWrWiZ8+eeHsrfhUlfTfd5bcLv1L82/H5l/5A/r2y3x771nH7g34fKMiKiIh4kFq1anHnnXcSGRmp8bFOotkM3OXijwB8u7MVhgFDh0KTJnk36z+/v+P2Qy0fclV1IiIichNsNhtr164lOTnZ0XbXXXcpyDqRembd4fxGOP4JANv2hAPw+ON5N7t6cYQWkS1cUpqIiIjcnMTERKKjo/n11185deoUDzzwgJajdQGFWXfYOhYAS/DtfLjybkwmaJFPVu33eT/H7Y3jNrqoOBERESmsgwcPsmTJEjIzM/H396dt27YKsi6iMOsOGfEAbDEv5HJaeerVg+Dg3JvEHIhx3B7ZaCQBPgGurFBEREQKwGq1Ehsby7Zt2wCoWrUqw4YNo2zZsu4trBRRmHWjfftzvv3Nm+d9bN2JdY7bcwbNcVVJIiIiUkDJycl88cUXxMXFAdC+fXu6deuG2Wx2c2Wli8KsG+3/bRW7O/6wKm1qdirv/fgeAHdF3UWgT6CLKxMREZEb8ff3x2q1EhAQwODBg6lbt667SyqVFGZdzbCDYQFgz56cv9yu7pnNtGZyx4e/p9tHWz/q0vJERETk2qxWK2azGZPJhK+vL6NGjcLHx4fQ0FB3l1ZqaWouV8uIB7sFw2Rm654IIHeYXX9iPYcvHQagaXhTBtcf7IYiRURE5I8SEhKYOXOmYxEEgLCwMAVZN1PPrKulHgMgy6saVpsP1arB1Svazd4523H72zHf/nFvERERcYM9e/awbNkyLBYLaWlptG7dGl9fX3eXJSjMul7acQAuZNQC8l78tfLISgDqhtWlQmAFl5YmIiIiuVksFlasWMHOnTsBqFmzJkOHDlWQLUYUZl0t/QwAJy5UB3Jf/PXqd6+Skp0CwJSWU1xemoiIiPzuwoULREdHc/78eQA6d+5Mp06d8PLSKM3iRGHW1QwbAPHnc/6iu9Izm5SZxN/W/c2xWZeaXVxdmYiIiPwmKyuL2bNnk5mZSXBwMEOHDiUqKsrdZUk+FGZdLesiABcu5p5jdt/5fY5N5g+bT9OIpi4vTURERHL4+fnRtWtXfvnlF4YMGULwH1c3kmJDYdaVDDuczlnZ69u9dxEWBlWr5jz09g9vOza7u/Hd7qhORESkVDt37hyGYRARkTPbUKtWrWjVqpWWpS3mNOjDhUwXt0D6abKMMizf3ZfmzeHK+2PBgQUA9Kzd040VioiIlD6GYbBjxw5mzpzJV199RVZWFgAmk0lB1gOoZ9aFTOfXA7AnoQ9ZFn/HxV+GYTi2Gd9svBsqExERKZ2ysrJYtmwZ+/blDPcLCwvDZrO5uSopDIVZV7LkzFTwy6lqwO/jZX9O+NmxSeeanV1eloiISGkUHx/PggULuHTpEiaTiW7dutGhQwf1xnoYhVk3OH8h598rYTbTmul4LCI4wg0ViYiIlB6GYbB9+3ZWrVqFzWYjNDSU4cOHU61aNXeXJjdBYdYNrFYICoLbbsvdXiWkinsKEhERKWUOHTqEzWajbt26DBo0iMDAQHeXJDdJYdZNmjaFK3MuX8q45N5iREREShGTycTgwYM5cOAALVu21LACD6fZDNzk6pW/nlr1FADn0s65qRoREZGSyzAMtm7dyrJlyxxtQUFBmnarhFDPrCvZswEwDBPNmv3eHBkSyd7ze6kaWtU9dYmIiJRQGRkZLFmyhJ9/zrnYulGjRlrJq4RRmHUhU9JeAH6Jq0er395HS35ZwuqjqwF4pcsr7ipNRESkxDlz5gzR0dEkJSVhNpvp2bMnNWvWdHdZUsQUZl3IZEkE4MylqkRG5rS9+t2rjserhOoCMBERkVtlGAZbtmxhzZo12O12ypUrx/Dhw6lcubK7SxMnUJh1IbsdzL/djoyEz3Z/xo9nfwTgmfbP0LVmV/cVJyIiUkIsWbKEXbt2ATnDCvr374+/v797ixKnUZh1IYslJ8z6+kKZMvDn2D87Hnui7RMahC4iIlIEGjVqxL59++jVqxctWrTQ79cSTmHWhazWnH/LlwOTCcoHlOd82nn+edc/qRyijz5ERERuhmEYXLx4kQoVKgBQp04dnnjiCYKDg91cmbiCpuZyIYsl59/y5XPeeFeWsW1bta0bqxIREfFcaWlpzJs3j5kzZ3L58mVHu4Js6aGeWReyWEzgkxNmN5zc4Gg3e5mvs5eIiIjk58SJE8TExJCamoq3tzfnz5+nXLly7i5LXExh1oWsVhxh9vjl4472VpVbua8oERERD2O329m4cSMbNmzAMAwqVKjAiBEjqFSpkrtLEzdQmHUhL3saAGXK+/LZvvkAdK/VHT9vP3eWJSIi4jFSU1NZuHAhx4/ndAo1a9aMPn364Ovr6+bKxF0UZl3Ey7BQMeAYAN5l6xIRHAGACV1hKSIiUlBbt27l+PHj+Pj40K9fP5o2beruksTNFGZdJMg4i9nLRlJ6KGUiIyFnZVt61e7l3sJEREQ8SJcuXUhJSaFjx46O2QukdNNsBi4SbI8Dcpayjaxs4vClw26uSEREpPhLTk5m9erV2O12ALy9vRkyZIiCrDioZ9ZFDHvOJLNpWUE0iIStZ7YCkG3LdmdZIiIixdaRI0f4+uuvSU9Px8/Pj86dO7u7JCmGFGZdJCPdB3xybodVsDvaW1Ru4aaKREREiiebzca6devYtGkTABERETRu3NjNVUlxpTDrIqlp3lA2ZynbY4m/DzFoGq6B6yIiIlckJSURExPD6dOnAWjZsiW9evXC21uRRfKn/zNcJC3V1xFmbYbN0R4eHO6+okRERIqRY8eOER0dTUZGBn5+fgwYMIBGjRq5uywp5hRmXSQtLedb7ecLZ1POAhAWEObOkkRERIqV4OBgLBYLkZGRDB8+nPLly7u7JPEACrMukpqaM5mzrx/sObcHgIsZF91ZkoiIiNtlZ2c7FjyoVKkSY8aMITIyUsMKpMA0NZeLpKXlXP3l6ws+Xjm3O9fQVZkiIlJ6/fzzz7z99tuO8bEA1apVU5CVQtH/LS6Slv77MIPNZzYDOFYBExERKU2sViuxsbFs27YNyFnVq1q1am6uSjyV23tmZ8yYQVRUFP7+/rRo0YKNGzded/t58+bRtGlTAgMDiYyMZNy4cVy8WPw/rk+7apjB8sPLAYhPjXdnSSIiIi536dIlZs+e7Qiy7dq1Y+jQoW6uSjyZW8Psl19+yZNPPsnzzz/Pzp076dixI3369OHUqVP5bv/9998zZswYJkyYwP79+1mwYAE//vgjEydOdHHlhWfJyplb1sfXh+SsZAAG1hvozpJERERc6sCBA3z44YfExcUREBDAPffcQ8+ePTGbze4uTTyYW8PsW2+9xYQJE5g4cSINGjRg+vTpVKtWjffffz/f7bdu3UrNmjV5/PHHiYqK4s477+Shhx5i+/btLq68cAwDQrwTAPAOiSDQJxCAXrV7ubMsERERl0lJSWHRokVkZ2dTrVo1HnroIerWrevusqQEcNuY2ezsbHbs2MGzzz6bq71nz55s3rw5333at2/P888/z/Lly+nTpw/nz58nOjqafv36XfN5srKyyMrKctxPTs7pFbVYLFgsliJ4JTd2/ryF8NBzAPiU+X1eWV+Tr8tqkJt35RzpXHkunUPPp3Po2SwWC8HBwdStW5cKFSrQqVMnvLy8dD49iKvfg4V5HreF2YSEBGw2G+HhuRcNCA8PJz4+/7Gk7du3Z968eYwaNYrMzEysVisDBw7kv//97zWfZ9q0abz88st52levXk1gYOCtvYgCOn06hCD/NACOnYzDZstZNGHdunWE+2nRBE8RGxvr7hLkFukcej6dQ8+SmJhISEgIZrMZk8lEQEAA6enprFy50t2lyU1y1XswPT29wNu6fTYDk8mU675hGHnarjhw4ACPP/44L7zwAr169SIuLo6nn36ayZMnM2vWrHz3ee6555g6darjfnJyMtWqVaNnz56EhoYW3Qu5jtWrbZzdPR+AuvXqwi857V27dqVm2ZouqUFunsViITY2lh49euDj4+PucuQm6Bx6Pp1Dz3LlfJ04cYIGDRrQq1cvvv32W3r27Knz56Fc/R688kl6QbgtzFaoUAGz2ZynF/b8+fN5emuvmDZtGh06dODpp58GoEmTJgQFBdGxY0deffVVIiMj8+zj5+eHn59fnnYfHx+XvaEuXPg9nGfbs8myZbm8Brl1Ol+eT+fQ8+kcFn8XLlwgOjqa8+fPA1CxYkXHOdP583yuOoeFeQ63XQDm6+tLixYt8nRXx8bG0r59+3z3SU9Px8srd8lXroA0DMM5hRaB9HQTXqac2QziUuIc7VVCqrirJBERkSK3a9cuPv74Y86fP09QUBD3338/Xbt2veYnriJFwa3DDKZOncr9999Py5YtadeuHR999BGnTp1i8uTJQM4QgV9//ZVPP/0UgAEDBjBp0iTef/99xzCDJ598ktatW1O5cmV3vpTrMgyoE34EgJ+SfvtLNbAiPmb9dSoiIp4vOzub5cuXs3v3bgCioqIYOnQowcHBbq5MSgO3htlRo0Zx8eJFXnnlFeLi4mjcuDHLly+nRo0aAMTFxeWac3bs2LGkpKTw7rvv8qc//YmyZcvSrVs3Xn/9dXe9hAIxDINmNXYBsOpSTpgN9tUbXERESgaLxcLRo0cxmUx06dKFO++8M88nqSLO4vYLwKZMmcKUKVPyfWzu3Ll52h577DEee+wxJ1dVtAJNZykTmIzV7s0nx3NWPNEcsyIiUlIEBQUxfPhwDMOgZs2a7i5HShm3h9nSwMvIueAr2+6DBSsAj7d53J0liYiI3LSsrCy++eYb6tSpQ5MmTQAcn6qKuJrCrAsZppz5ZZtHNKdBxQZurkZERKTw4uPjWbBgAZcuXeLw4cPUq1cv31mDRFxFYdYFrky0YJBz4/Clw26sRkREpPAMw2D79u2sWrUKm81GaGgow4YNU5AVt1OYdYErYfbKxCSTW0x2Wy0iIiKFlZmZydKlSzlw4AAAdevWZdCgQS5bSVPkehRm3UDz7YmIiKfIzs7mo48+4vLly3h5edG9e3fatm2r32VSbCjMukAxXs9BRETkunx9fWnQoAH79+9n+PDhVK1a1d0lieSiMCsiIiK5ZGRkYLFYCA0NBaBbt27ceeedBAQEuLkykbw0o7ELqGdWREQ8xZkzZ/jwww/56quvsNlyZuExm80KslJsqWfWBa6EWTs29xYiIiJyDYZhsGXLFtasWYPdbsfLy4uUlBTKli3r7tJErkth1gUcPbMmOwDJWcnuK0ZEROQP0tPTWbx4MYcOHQKgYcOGDBgwAH9/fzdXJnJjCrMu8MdhBqNvH+2eQkRERP7g1KlTxMTEkJycjNlspnfv3rRo0UKzFYjHUJh1gT+G2fbV2runEBERkasYhsGqVatITk6mfPnyjBgxgoiICHeXJVIoCrNuoL92RUSkODCZTAwdOpRNmzbRq1cvreYlHkmzGbiAZjMQEZHi4sSJE2zdutVxPywsjIEDByrIisdSz6yIiEgpYLfb2bhxIxs2bMAwDCIjI6lRo4a7yxK5ZQqzLqCeWRERcafU1FQWLlzI8ePHAWjatCmRkZFurkqkaCjMuoDCrIiIuMuxY8dYuHAhaWlp+Pj40LdvX5o1a+buskSKjMKsiIhICbVx40bWrl0LQKVKlRg+fDgVK1Z0c1UiRUth1gXUMysiIu4QFBQEQPPmzenTpw8+Pj5urkik6CnMuoDCrIiIuEp2dja+vr5AToitUKEC1atXd3NVIs6jqblcQGFWRESczW638+233zJjxgwyMjKAnHlkFWSlpFPPrAsozIqIiDMlJSURExPD6dOnAThw4AAtWrRwc1UirqEwKyIi4sEOHTrEokWLyMjIwM/PjwEDBtCoUSN3lyXiMgqzIiIiHshms7FmzRq2bNkCQGRkJMOHD6d8+fJurkzEtRRmXUDDDEREpKitX7/eEWRbt25Njx498PbWr3UpffR/vQsozIqISFFr3749hw8fpnPnzjRo0MDd5Yi4jWYzcAGFWRERuVVWq5Xdu3dj/PZLJSAggIceekhBVko99cy6gMKsiIjcisuXL7NgwQLi4uKwWq2OmQpMJpObKxNxP4VZERGRYuzAgQMsWbKErKwsAgICCAkJcXdJIsWKwqwLqGdWREQKy2q1smrVKrZv3w5AtWrVGDZsGGXKlHFzZSLFi8KsCyjMiohIYVy8eJHo6Gji4+MB6NChA127dsVsNru5MpHiR2HWBRRmRUSkMJKTk4mPjycwMJAhQ4ZQp04dd5ckUmwpzIqIiBQDhmE4LuiKiopi8ODBREVFERoa6ubKRIo3Tc0lIiLiZhcuXGDOnDlcvHjR0da0aVMFWZECUJh1gT8OMzChqVRERCTHrl27+Pjjjzl9+jQrVqxwdzkiHkfDDFzg6jBbu1xtzQsoIiJkZ2ezfPlydu/eDfw+tEBECkdh1gWuDrOVgiq5rxARESkWzp8/z4IFC0hISMBkMtG5c2c6duyIl5c+MBUpLIVZF7g6zAb4BLivEBERcbszZ87wySefYLVaCQ4OZtiwYdSsWdPdZYl4LIVZF7g6zHar2c19hYiIiNtFRkYSERGBn58fQ4YMISgoyN0liXg0hVkREREnu3DhAuXLl8dsNmM2m7n33nvx9/fXNRQiRUCDc0RERJzEMAy2b9/Ohx9+yNq1ax3tAQEBCrIiRUQ9sy6gFcBEREqfrKwsli5dyv79+wFISEjAbrfrIi+RIqYwKyIiUsTOnj1LdHQ0ly9fxsvLi7vuuot27dqpN1bECRRmXUA9syIipYNhGGzbto3Y2FhsNhtlypRh+PDhVK1a1d2liZRYCrMiIiJFJCUlhbVr12Kz2ahfvz4DBw4kIEBTMoo4k8KsC6hnVkSkdAgNDWXAgAGkpaXRunVrDSsQcQGFWRdQmBURKZkMw2Dr1q1EREQQFRUFQOPGjd1clUjpojDrAgqzIiIlT0ZGBosWLeLQoUMEBwczZcoUDSkQcQOFWRdQmBURKVlOnz5NdHQ0ycnJmM1mOnXqhL+/v7vLEimVFGZdQGFWRKRkMAyDTZs2sXbtWgzDoHz58owYMYKIiAh3lyZSainMioiIFIDFYuGrr77iyJEjQM7Y2P79++Pn5+fmykRKN4VZF1DPrIiI5/P29sbf3x9vb2969+7NHXfcodkKRIoBhVkREZFrsNvtWK1WfH19MZlM9O/fn44dO1KpUiV3lyYiv9EC0S6gnlkREc+TmprKvHnz+PrrrzF++0Hu5+enICtSzKhn1hUMu7srEBGRQjh+/DgLFy4kNTUVHx8fEhISqFixorvLEpF8KMy6QJDPJQAu29xciIiIXJfdbmfDhg189913AFSsWJERI0YoyIoUYwqzLhDqcw6AeIVZEZFiKyUlhYULF3LixAkAmjdvTp8+ffDx8XFvYSJyXQqzLhDiEw9AvNXNhYiISL4Mw+CLL77g7Nmz+Pj40L9/f5o0aeLuskSkAG7qAjCr1cq3337Lhx9+SEpKCgBnz54lNTW1SIsrKUJ91TMrIlKcmUwmevfuTWRkJA899JCCrIgHKXTP7MmTJ+nduzenTp0iKyuLHj16EBISwhtvvEFmZiYffPCBM+r0aGV8f++ZNbu5FhERyZGcnEx8fDx169YFoFq1akyaNElzx4p4mEL3zD7xxBO0bNmSy5cvExAQ4GgfMmQIa9asKdLiSoogn4sAXNSkBiIixcLhw4f54IMPWLBgAefPn3e0K8iKeJ5C98x+//33bNq0CV9f31ztNWrU4Ndffy2ywkoSEznzE9o036yIiFvZbDbWrl3L5s2bAYiMjMTbW5ePiHiyQr+D7XY7NlvewZ9nzpwhJCSkSIoqabRogoiI+yUmJhITE8OZM2cAaN26NT169FCYFfFwhR5m0KNHD6ZPn+64bzKZSE1N5cUXX6Rv375FWZuIiEiR+Pnnn/nwww85c+YMfn5+jBw5kj59+ijIipQAhX4X/+c//6Fr1640bNiQzMxM7r33Xg4fPkyFChWYP3++M2oUERG5JXFxcWRmZlKlShWGDRtGuXLl3F2SiBSRQofZypUrs2vXLr744gt27NiB3W5nwoQJjB49OtcFYfI7DTMQEXE9wzAcF3R17tyZoKAgWrRogdmseWVESpJCh9nvvvuO9u3bM27cOMaNG+dot1qtfPfdd3Tq1KlICxQRESmsAwcO8OOPPzJ69Gi8vb3x8vKidevW7i5LRJyg0GNmu3btyqVLl/K0JyUl0bVr1yIpSkRE5GZYrVaWL1/OggULOHHiBNu2bXN3SSLiZIXumb36Y5urXbx4kaCgoCIpSkREpLAuXrxIdHQ08fE5C9V06NCBNm3auLkqEXG2AofZoUOHAjmzF4wdOxY/Pz/HYzabjT179tC+ffuir7AE0JhZERHn2rdvH0uXLiU7O5vAwEAGDx7Mbbfd5u6yRMQFChxmy5QpA+T0zIaEhOS62MvX15e2bdsyadKkoq9QRETkOjZv3kxsbCwA1atXZ9iwYYSGhrq5KhFxlQKH2Tlz5gBQs2ZN/vznP2tIgYiIFAsNGzbk+++/p2XLlnTp0gUvr0JfDiIiHqzQY2ZffPFFZ9QhIiJSYHFxcURGRgJQtmxZHnvsMU0PKVJK3dSfr9HR0YwcOZK2bdtyxx135PoqrBkzZhAVFYW/vz8tWrRg48aN190+KyuL559/nho1auDn50ft2rWZPXv2zbwMl9GYWRGRopGdnc3ixYv56KOPOHz4sKNdQVak9Cp0mH3nnXcYN24clSpVYufOnbRu3ZqwsDCOHTtGnz59CnWsL7/8kieffJLnn3+enTt30rFjR/r06cOpU6euuc/IkSNZs2YNs2bN4pdffmH+/PnUr1+/sC9DREQ8TEZGBnPnzmXXrl2YTCYSEhLcXZKIFAOFHmYwY8YMPvroI+655x4++eQTnnnmGWrVqsULL7yQ7/yz1/PWW28xYcIEJk6cCMD06dNZtWoV77//PtOmTcuz/cqVK9mwYQPHjh2jfPnyQM4YXhERKbkMw2D37t0cOnQIwzAIDg5m2LBh+vkvIsBNhNlTp045puAKCAggJSUFgPvvv5+2bdvy7rvvFug42dnZ7Nixg2effTZXe8+ePdm8eXO++yxZsoSWLVvyxhtv8NlnnxEUFMTAgQP5+9//fs2PmLKyssjKynLcT05OBsBisWCxWApU6626epiBzW5z2fNK0bhyvnTePJfOoefKzs5m5cqV7Nu3D8jpwBg0aBBBQUE6nx5E70HP5+pzWJjnKXSYjYiI4OLFi9SoUYMaNWqwdetWmjZtyvHjxzEKMTg0ISEBm81GeHh4rvbw8HDHhNd/dOzYMb7//nv8/f35+uuvSUhIYMqUKVy6dOma42anTZvGyy+/nKd99erVBAYGFrjeWxGSmem4feiXQyxPWu6S55WidWXqH/FcOoeeJzExkRMnTgAQGRlJmTJl2LBhg3uLkpum96Dnc9U5TE9PL/C2hQ6z3bp1Y+nSpdxxxx1MmDCBp556iujoaLZv3+5YWKEw/ria2LVWGAOw2+2YTCbmzZvnmPf2rbfeYvjw4bz33nv59s4+99xzTJ061XE/OTmZatWq0bNnT5fNQ/jjL/9z3K5bry59O/R1yfNK0bBYLMTGxtKjRw98fHzcXY7cBJ1Dz7Z+/Xpq1KjBwYMHdQ49lN6Dns/V5/DKJ+kFUegw+9FHH2G32wGYPHky5cuX5/vvv2fAgAFMnjy5wMepUKECZrM5Ty/s+fPn8/TWXhEZGUmVKlUcQRagQYMGGIbBmTNn8l3txc/PL9dqZVf4+Pi45Q1l9jLrjeyh3PX/jBQdncPiLysri2+//ZYuXbo45jPv0aMHFouFgwcP6hx6OJ0/z+eqc1iY5yj0bAZeXl54e/+egUeOHMk777zD448/zoULFwp8HF9fX1q0aJGnuzo2Nvaay+J26NCBs2fPkpqa6mg7dOgQXl5eVK1atZCvxHU0NZeIyI3FxcXx4Ycfsn37dpYsWeLuckTEQxTJMinx8fE89thj1KlTp1D7TZ06lZkzZzJ79mwOHjzIU089xalTpxw9vM899xxjxoxxbH/vvfcSFhbGuHHjOHDgAN999x1PP/0048eP1xyDIiIeyjAMtm3bxqxZs7h8+TJlypThzjvvdHdZIuIhChxmExMTGT16NBUrVqRy5cq888472O12XnjhBWrVqsXWrVsLvXjBqFGjmD59Oq+88grNmjXju+++Y/ny5dSoUQPI+Sv96jlng4ODiY2NJTExkZYtWzJ69GgGDBjAO++8U6jnFRGR4iEzM5MFCxawYsUKbDYb9erV46GHHqJatWruLk1EPESBx8z+9a9/5bvvvuOBBx5g5cqVPPXUU6xcuZLMzExWrFhB586db6qAKVOmMGXKlHwfmzt3bp62+vXr62pIEZESICEhgXnz5pGYmIiXlxc9evSgTZs217wIWEQkPwUOs9988w1z5syhe/fuTJkyhTp16lC3bl2mT5/uxPJKBo2ZFRHJKyQkBC8vL8qWLcvw4cOpUqWKu0sSEQ9U4DB79uxZGjZsCECtWrXw9/d3rNwlIiJSEFlZWfj6+mIymfDz8+Oee+4hODgYf39/d5cmIh6qwGNm7XZ7rmkSzGazY9oUERGRGzl9+jQzZsxg27ZtjrYKFSooyIrILSlwz6xhGIwdO9YxZ2tmZiaTJ0/OE2gXLlxYtBWKiIhHMwyDzZs3s2bNGgzDYMeOHbRs2RKz2ezu0kSkBChwmH3ggQdy3b/vvvuKvJiSSmNmRaS0SktLY9GiRRw5cgSAxo0b079/fwVZESkyBQ6zc+bMcWYdIiJSwpw8eZKYmBhSUlLw9vamd+/e3HHHHZqtQESKVKGXsxUREbmRlJQUPvvsM2w2G2FhYYwYMeKaS5WLiNwKhVkRESlyISEhdOnShQsXLtCvXz98fX3dXZKIlFAKsy6gMbMiUhocP36coKAgKlWqBECHDh0ANKxARJyqwFNziYiI5Mdut7N+/Xo+/fRToqOjyc7OBnJCrIKsiDibemZFROSmpaSksHDhQk6cOAFAlSpVFGBFxKVuqmf2s88+o0OHDlSuXJmTJ08CMH36dBYvXlykxYmISPF19OhRPvzwQ06cOIGPjw9Dhgxh0KBBuRbYERFxtkKH2ffff5+pU6fSt29fEhMTsdlsAJQtW5bp06cXdX0lgsbMikhJYrfbWbt2Lf/73/9IS0sjPDycBx98kCZNmri7NBEphQodZv/73//y8ccf8/zzz+ea9Lply5bs3bu3SIsTEZHi6dSpUwC0aNGCCRMmUKFCBTdXJCKlVaHHzB4/fpzmzZvnaffz8yMtLa1IihIRkeLHMAxMJhNeXl4MGzaMU6dO0ahRI3eXJSKlXKF7ZqOioti1a1ee9hUrVtCwYcOiqElERIoRm81GbGwsK1eudLSFhIQoyIpIsVDontmnn36aRx55hMzMTAzDYNu2bcyfP59p06Yxc+ZMZ9To8TRmVkQ8VVJSEtHR0Zw5cwaA5s2bExER4eaqRER+V+gwO27cOKxWK8888wzp6ence++9VKlShbfffpu7777bGTWKiIgb/PLLLyxatIjMzEz8/PwYOHCggqyIFDs3Nc/spEmTmDRpEgkJCdjtdsdqLyIi4vmuDCv44YcfAKhcuTLDhw+nXLlybq5MRCSvQo+Zffnllzl69CgAFSpUUJAtAA0zEBFPYRgG8+fPdwTZtm3bMn78eAVZESm2Ch1mY2JiqFu3Lm3btuXdd9/lwoULzqhLRETcwGQy0aJFC/z9/bn77rvp1atXrmkYRUSKm0KH2T179rBnzx66devGW2+9RZUqVejbty+ff/456enpzqhRREScyGq1cu7cOcf9Bg0a8MQTT1CvXj03ViUiUjA3tZxto0aNeO211zh27Bjr1q0jKiqKJ598UhcGXINGGYhIcXXp0iVmzZrFp59+SnJysqPd39/fjVWJiBTcTV0AdrWgoCACAgLw9fUlJSWlKGoqeZRmRaQY2rdvH0uXLiU7O5uAgAASExMJDQ11d1kiIoVyU2H2+PHjfP7558ybN49Dhw7RqVMnXnrpJUaMGFHU9YmISBGzWCysWrWKHTt2AFC9enWGDRumICsiHqnQYbZdu3Zs27aN22+/nXHjxjnmmRURkeIvISGB6OhoxxjZjh070qVLF7y8bmrUmYiI2xU6zHbt2pWZM2dqGcNC0CgDESkufvjhB86dO0dQUBBDhgyhdu3a7i5JROSWFDrMvvbaa86oo2RTmhWRYqJHjx7Y7Xa6dOlCSEiIu8sREbllBQqzU6dO5e9//ztBQUFMnTr1utu+9dZbRVKYiIjcuvPnz7Njxw569+6NyWTC19eXAQMGuLssEZEiU6Awu3PnTiwWi+O2FI46ZkXE1QzDYNeuXSxfvhyr1Uq5cuVo27atu8sSESlyBQqz69aty/e2iIgUP9nZ2XzzzTfs2bMHgNq1a3P77be7uSoREeco9OWr48ePz3c+2bS0NMaPH18kRZU46poVERc5d+4cH330EXv27MFkMtGtWzdGjx5NUFCQu0sTEXGKQofZTz75hIyMjDztGRkZfPrpp0VSlIiIFN6+ffuYOXMmFy9eJCQkhAceeICOHTtiMpncXZqIiNMUeDaD5ORkDMPAMAxSUlJyLXVos9lYvnw5lSpVckqRnk4dsyLiCuXLl8cwDOrUqcOQIUMIDAx0d0kiIk5X4DBbtmxZTCYTJpOJunXr5nncZDLx8ssvF2lxJYZx9U1FWxEpOpmZmY7OhcqVKzNhwgQiIiLUGysipUaBw+y6deswDINu3boRExND+fLlHY/5+vpSo0YNKleu7JQiS5Ky/mXdXYKIlACGYfDjjz+ydu1aHnjgASIjIwEc/4qIlBYFDrOdO3cG4Pjx41SvXl1/9d+kVpVbubsEEfFwmZmZLF26lAMHDgCwa9cuhVgRKbUKFGb37NlD48aN8fLyIikpib17915z2yZNmhRZcSWFgYK/iBSNX3/9lejoaBITE/Hy8qJHjx60adPG3WWJiLhNgcJss2bNiI+Pp1KlSjRr1gyTyYRh5B37aTKZsNlsRV6kx9MwWRG5RYZh8MMPPxAbG4vdbqds2bIMHz6cKlWquLs0ERG3KlCYPX78OBUrVnTcFhER1zp48CCrVq0CoEGDBgwcODDXrDIiIqVVgcJsjRo18r0tBaOOWRG5VQ0aNKBevXrUqlWLVq1a6boFEZHf3NSiCd98843j/jPPPEPZsmVp3749J0+eLNLiRERKK8Mw2LFjBxaLBcgZxjVq1Chat26tICsicpVCh9nXXnuNgIAAALZs2cK7777LG2+8QYUKFXjqqaeKvMASQV2zIlII6enpzJ8/n2XLlrF8+XJHu0KsiEheBZ6a64rTp09Tp04dABYtWsTw4cN58MEH6dChA126dCnq+koELZQgIgV18uRJYmJiSElJwdvbm6pVq2IYhoKsiMg1FDrMBgcHc/HiRapXr87q1asdvbH+/v5kZGQUeYElgdU/3nG7SqiuPBaRvAzD4Pvvv3csUBMWFsaIESMIDw93d2kiIsVaocNsjx49mDhxIs2bN+fQoUP069cPgP3791OzZs2irq9EsHknO25XDa3qxkpEpDhKS0vj66+/5ujRo0DOfN39+vXD19fXzZWJiBR/hR4z+95779GuXTsuXLhATEwMYWFhAOzYsYN77rmnyAssCWwBvwJQPaCemysRkeLIZrMRFxeHt7c3AwcOZPDgwQqyIiIFVOie2bJly/Luu+/maX/55ZeLpKCSyNfwA8DHHOrmSkSkuLh6HGxoaCgjRowgMDCQSpUqubkyERHPUugwC5CYmMisWbM4ePAgJpOJBg0aMGHCBMqUKVPU9ZUIviY7ABX867i5EhEpDlJTU1m4cCGtWrWiQYMGABqmJSJykwo9zGD79u3Url2b//znP1y6dImEhAT+85//ULt2bX766Sdn1OjxfLxywqzt5v52EJES5NixY3zwwQccP36clStXaglwEZFbVOh09dRTTzFw4EA+/vhjvL1zdrdarUycOJEnn3yS7777rsiL9HRXembtmN1ciYi4i91uZ/369WzcuBGA8PBwhg8fjtmsnwsiIrei0GF2+/btuYIsgLe3N8888wwtW7Ys0uJKCt/femat6pkVKZWSk5OJiYnh1KlTALRo0YJevXrh4+Pj5spERDxfodNVaGgop06don79+rnaT58+TUhISJEVVpL4/NYzazOpB0aktElLS+PDDz8kPT0dX19fBgwYQOPGjd1dlohIiVHoMDtq1CgmTJjAm2++Sfv27TGZTHz//fc8/fTTmprrGnwdY2YVZkVKm6CgIBo1asTp06cZPny4YzpDEREpGoUOs2+++SYmk4kxY8ZgtVoB8PHx4eGHH+af//xnkRdYElwZM6sLwERKh6SkJLy8vByfVvXs2RMg1/AsEREpGoX+yerr68vbb7/NtGnTOHr0KIZhUKdOHQIDA51RX4ngmM1AwwxESrxffvmFRYsWER4ezpgxY/Dy8lKIFRFxogJPzZWens4jjzxClSpVqFSpEhMnTiQyMpImTZooyN6AhhmIlHw2m41Vq1bxxRdfkJmZicViISMjw91liYiUeAXuLnjxxReZO3cuo0ePxt/fn/nz5/Pwww+zYMECZ9ZXImhqLpGS7fLly8TExPDrrzlLV7dt25bu3btr2i0RERcocJhduHAhs2bN4u677wbgvvvuo0OHDthsNv3ALiB74deoEJFi7uDBgyxevJisrCz8/f0ZPHgw9erVc3dZIiKlRoHD7OnTp+nYsaPjfuvWrfH29ubs2bNUq1bNKcWVNL8twy4iJYTNZmPdunVkZWVRtWpVhg0bRtmyZd1dlohIqVLgMGuz2fD19c29s7e3Y0YDEZHSxmw2M3z4cPbu3UuXLl30KZWIiBsUOMwahsHYsWPx8/NztGVmZjJ58mSCgoIcbQsXLizaCksQ9cyKeL79+/eTlpZG69atAahUqRJ33XWXm6sSESm9ChxmH3jggTxt9913X5EWIyJSXFksFlatWsWOHTswmUxUq1aNyMhId5clIlLqFTjMzpkzx5l1lA7qmRXxSAkJCURHR3Pu3DkA7rzzTsLDw91clYiIwE0smiAiUprs2bOHZcuWYbFYCAoKYsiQIdSuXdvdZYmIyG8UZl1IY2ZFPMs333zD9u3bAahZsyZDhw51LFErIiLFg8KsiMg1VKhQAYDOnTvTqVMnvLw0V7SISHGjMCsicpWMjAwCAgKAnPm0a9SoQUREhJurEhGRa1E3gwtpmIFI8ZWdnc2iRYuYOXMmWVlZAJhMJgVZEZFi7qbC7GeffUaHDh2oXLkyJ0+eBGD69OksXry4SIsrKUwY7i5BRK7j3LlzfPzxx+zevZvLly9z/Phxd5ckIiIFVOgw+/777zN16lT69u1LYmIiNpsNgLJlyzJ9+vSirq9E8PqtR9ZQR7hIsWIYBjt27GDmzJkkJCQQEhLCAw88QP369d1dmoiIFFCh09V///tfPv74Y55//vlcSze2bNmSvXv3FmlxJYWPyQ6A3aQwK1JcZGVlsXDhQpYtW4bVaqVOnTpMnjyZGjVquLs0EREphEJfAHb8+HGaN2+ep93Pz4+0tLQiKaqk8fG6Ema1brtIcbF69Wr27duHyWTirrvuon379pg0sF1ExOMUuqswKiqKXbt25WlfsWIFDRs2LHQBM2bMICoqCn9/f1q0aMHGjRsLtN+mTZvw9vamWbNmhX5OV/M25YyZtWuYgUix0a1bN6pWrcq4cePo0KGDgqyIiIcqdM/s008/zSOPPEJmZiaGYbBt2zbmz5/PtGnTmDlzZqGO9eWXX/Lkk08yY8YMOnTowIcffkifPn04cOAA1atXv+Z+SUlJjBkzhrvuusuxvGSxZbc5xszaUc+siLvYbDZ27txJ69atAQgKCmL8+PEKsSIiHq7QYXbcuHFYrVaeeeYZ0tPTuffee6lSpQpvv/02d999d6GO9dZbbzFhwgQmTpwI5MyIsGrVKt5//32mTZt2zf0eeugh7r33XsxmM4sWLSrsS3Atw+q4qWEGIu4RFxfHL7/8wt69ewkICOD2228HUJAVESkBbmrRhEmTJjFp0iQSEhKw2+1UqlSp0MfIzs5mx44dPPvss7nae/bsyebNm6+535w5czh69Cj/+9//ePXVV2/4PFlZWY45IwGSk5MBsFgsWCyWQtddaNZ0fK7ctBuueU4pUlfOmc6d5zEMgx9//JG1a9dit9spU6YMoaGhOpceSO9Dz6bz5/lcfQ4L8zy3tALYlaUeb0ZCQgI2m43w8PBc7eHh4cTHx+e7z+HDh3n22WfZuHEj3t4FK33atGm8/PLLedpXr15NYGBg4QsvJB8jlb6/3T564hTLly93+nOKc8TGxrq7BCkEq9XK6dOnSUpKAqBMmTJUq1aNXbt25TvuXzyD3oeeTefP87nqHKanpxd420KH2aioqOt+NHfs2LFCHe+PxzIMI9/j22w27r33Xl5++WXq1q1b4OM/99xzTJ061XE/OTmZatWq0bNnT0JDQwtV603JugBLcm7WiKpN3759r7+9FDsWi4XY2Fh69OiBj4/PjXcQt/v1119ZtGgRSUlJmM1munTpQkJCAj179tQ59FB6H3o2nT/P5+pzeOWT9IIodJh98sknc923WCzs3LmTlStX8vTTTxf4OBUqVMBsNufphT1//nye3lqAlJQUtm/fzs6dO3n00UcBsNvtGIaBt7c3q1evplu3bnn28/Pzw8/PL0+7j4+Pa95Qv/WSWw0we3vrTezBXPb/jNwyi8VCUlIS5cqVY8SIEVSoUIHly5frHJYAOoeeTefP87nqHBbmOQodZp944ol829977z22b99e4OP4+vrSokULYmNjGTJkiKM9NjaWQYMG5dk+NDQ0z6IMM2bMYO3atURHRxMVFVXg53ap3y4As2hFWxGnuvpTndtuu42hQ4dSt25d/Pz8NE5PRKQEK7KJT/v06UNMTEyh9pk6dSozZ85k9uzZHDx4kKeeeopTp04xefJkIGeIwJgxY3IK9fKicePGub4qVaqEv78/jRs3JigoqKheStGy5/wStQLowmkRpzh16hQffPABiYmJjrbbb789309lRESkZLmlC8CuFh0dTfny5Qu1z6hRo7h48SKvvPIKcXFxNG7cmOXLlzuWk4yLi+PUqVNFVaJ7/BZmLYayrEhRMwyD77//nnXr1mEYBuvWrcv1SY+IiJR8hQ6zzZs3z3WBlmEYxMfHc+HCBWbMmFHoAqZMmcKUKVPyfWzu3LnX3fell17ipZdeKvRzutRvwwysBmhKS5Gik5aWxtdff83Ro0cBaNKkCf369XNzVSIi4mqFDrODBw/Odd/Ly4uKFSvSpUsX6tevX1R1lRxXwqybyxApSU6cOEFMTAypqal4e3vTt29fmjVrpkUQRERKoUKFWavVSs2aNenVqxcRERHOqqlkMXKu/LLrAjCRInH48GHmz5+PYRhUrFiR4cOH39TCLSIiUjIUKsx6e3vz8MMPc/DgQWfVU6Kp00jk1kVFRREeHk5ERAR9+vTB19fX3SWJiIgbFXqYQZs2bdi5c6fjIi0REWc7c+YMlStXxsvLC29vb8aOHauZCkREBLiJMDtlyhT+9Kc/cebMGVq0aJFnSqwmTZoUWXEljXpmRQrHbrezfv16Nm7cSOfOnenSpQuAgqyIiDgUOMyOHz+e6dOnM2rUKAAef/xxx2Mmk8kxYbnNZiv6KksKhVmRAktOTmbhwoWcPHkSgNTU1Gsudy0iIqVXgcPsJ598wj//+U+OHz/uzHpKNLuhoC9SEEeOHOHrr78mPT0dX19fBgwYQOPGjd1dloiIFEMFDrPGb1fla6zszasZWsfdJYgUazabjXXr1rFp0yYAIiIiGD58OGFhYW6uTEREiqtCjZnVx3u3pkZIlLtLECnWLl++zA8//ABAq1at6NmzJ97eRbZQoYiIlECF+i1Rt27dGwbaS5cu3VJBJZn+FhC5vgoVKtC/f398fHxo2LChu8sREREPUKgw+/LLL1OmTBln1SIipYzNZmPt2rXUr1+fatWqAdC0aVM3VyUiIp6kUGH27rvv1ko7t0A9syK/S0xMJDo6ml9//ZX9+/fz6KOPakiBiIgUWoF/c2i8rIgUlYMHD7JkyRIyMzPx9/end+/eCrIiInJTCj2bgdw8/T0gpZ3VaiU2NpZt27YBULVqVYYNG0bZsmXdW5iIiHisAodZu93uzDpKBYVZKc0yMjL47LPPiIuLA6B9+/Z069YNs9ns5spERMST6XM9F1KYldLM39+f0NBQEhMTGTx4MHXr1nV3SSIiUgIozDqZYfy+iq3CrJQ2VqsVu92Or68vJpOJQYMGYbFYCA0NdXdpIiJSQni5u4BSRWFWSpGLFy8yc+ZMli5d6hhzHxAQoCArIiJFSj2zTmagDCulz969e1m2bBnZ2dmkpKSQkpKiECsiIk6hMOtkV08CoVArJZ3FYmHFihXs3LkTgJo1azJ06FBCQkLcXJmIiJRUCrNOlivMKs1KCXbhwgWio6M5f/48AJ07d6ZTp054eWk0k4iIOI/CrJMpzEppYLfbmT9/PpcvXyY4OJihQ4cSFRXl7rJERKQUUJh1IYVZKam8vLwYMGAAmzZtYvDgwQQHB7u7JBERKSUUZp1NC6dJCXXu3DmSkpIc88VGRUVRs2ZNLX0tIiIupTDrZFdnWf2Ol5LAMAx27tzJihUr8PLy4sEHHyQsLAxAQVZERFxOYdbJ7Pbf46x+z4uny8rK4ptvvmHv3r0A1KlTB39/fzdXJSIipZnCrJOdSjrFlUU7Q3w1z6Z4rvj4eBYsWMClS5cwmUzcddddtG/fXr2xIiLiVgqzTpZhzXDcrhxS2Y2ViNy87du3s3LlSmw2G6GhoQwfPpxq1aq5uywRERGFWZcxNNemeK5Lly5hs9moW7cugwYNIjAw0N0liYiIAAqzzqd5ZsVDGYbhGEJw1113ERERwe23365hBSIiUqyou9DJtGiCeBrDMNi6dSuffPIJNpsNALPZTJMmTRRkRUSk2FHPrJNpai7xJBkZGSxZsoSff/4ZgH379tG0aVM3VyUiInJtCrNOpp5Z8RRnzpwhOjqapKQkzGYzPXv2pEmTJu4uS0RE5LoUZl1IYVaKI8Mw2LJlC2vWrMFut1OuXDmGDx9O5cqafUNERIo/hVknM7ScrRRzsbGxbNmyBYBGjRoxYMAA/Pz83FyViIhIwSjMupB6ZqU4uuOOO9i9ezddu3alRYsWushLREQ8isKss2nMrBQzhmFw+vRpqlevDkCFChV44okn8PX1dXNlIiIihaepuZxMsxlIcZKWlsa8efOYO3cuJ06ccLQryIqIiKdSz6wLKcyKO504cYKYmBhSU1Px9vYmJSXF3SWJiIjcMoVZJ9MFYOJudrudjRs3smHDBgzDoEKFCowYMYJKlSq5uzQREZFbpjDrZJpnVtwpNTWVhQsXcvz4cQCaNWtGnz59NKxARERKDIVZZ1OYFTc6fPgwx48fx8fHh379+mk1LxERKXEUZp1MPbPiTs2aNePy5cvcfvvtVKxY0d3liIiIFDnNZiBSgqSkpLBw4UIyMjIAMJlMdOvWTUFWRERKLPXMipQQR44c4euvvyY9PR2AoUOHurkiERER51OYdTJNZiDOZrfbWbt2LZs2bQIgIiKCzp07u7kqERER11CYdTJNzSXOlJSURExMDKdPnwagZcuW9OrVC29vvbVFRKR00G88Z1OYFSc5c+YMn3/+ORkZGfj5+TFw4EAaNmzo7rJERERcSmHWydQzK84SFhaGj48P5cqVY/jw4ZQrV87dJYmIiLicwqyIB0lLSyMwMBCTyURAQABjxoyhTJkyGlYgIiKllqbmcjL1zEpROXjwIO+++y47d+50tIWFhSnIiohIqaYwK1LMWa1WVqxYwVdffUVmZiZ79+7F0F9JIiIigIYZOJ0ih9yKS5cuER0dTVxcHADt2rXjrrvuwqTl5ERERACFWadTB5rcrP3797N06VKysrIICAhg8ODB1K1b191liYiIFCsKsyLF0MWLF4mJicEwDKpVq8awYcMoU6aMu8sSEREpdhRmnU09s3ITwsLC6NSpEzabja5du+LlpeHtIiIi+VGYdTINM5CC2rt3L5UrVyYsLAyALl26uLcgERERD6DuHidTlpUbsVgsLFmyhIULFxIdHY3VanV3SSIiIh5DPbNOdjTpZzq7uwgpti5cuEB0dDTnz58HoG7duhpSICIiUggKs06WnJWYc8Nkd2sdUvzs2rWL5cuXY7FYCAoKYujQodSqVcvdZYmIiHgUhVknc8wHag10byFSbFgsFr755ht2794NQFRUFEOHDiU4ONjNlYmIiHgehVln06BZ+QMvLy8SEhIwmUx06dKFO++8U0MLREREbpLCrJMpywrgWH7WZDJhNpsZPnw4iYmJ1KxZ072FiYiIeDiFWREny8rK4ptvviEkJIQePXoAULZsWcqWLevewkREREoAhVknU89s6RYfH8+CBQu4dOkSXl5etGrVSiFWRESkCCnMijiBYRhs376dVatWYbPZCA0NZdiwYQqyIiIiRUxh1sm0Aljpk5mZydKlSzlw4ACQM3fsoEGDCAzUjBYiIiJFTWFWpAgZhsHcuXM5d+4cXl5edO/enbZt2/4+RZuIiIgUKc0H5GzqmS1VTCYT7du3p0yZMowbN4527dopyIqIiDiRemadTWG2xMvIyCApKYmIiAgAmjRpQoMGDfDx8XFzZSIiIiWfwqyTKcuWbGfOnCE6OhqbzcbkyZMJCgoCUJAVERFxEYVZkZtgGAZbtmxhzZo12O12ypUrR1pamiPMioiIiGsozDqbumZLnPT0dBYvXsyhQ4cAaNiwIQMGDMDf39/NlYmIiJQ+br8AbMaMGURFReHv70+LFi3YuHHjNbdduHAhPXr0oGLFioSGhtKuXTtWrVrlwmoLT1m2ZDl16hQffvghhw4dwmw207dvX4YPH64gKyIi4iZuDbNffvklTz75JM8//zw7d+6kY8eO9OnTh1OnTuW7/XfffUePHj1Yvnw5O3bsoGvXrgwYMICdO3e6uHIprbZv305ycjLly5dn4sSJtGrVSrMViIiIuJFbhxm89dZbTJgwgYkTJwIwffp0Vq1axfvvv8+0adPybD99+vRc91977TUWL17M0qVLad68uStKLjT1zJYs/fr1IygoiC5duuDn5+fuckREREo9t4XZ7OxsduzYwbPPPpurvWfPnmzevLlAx7Db7aSkpFC+fPlrbpOVlUVWVpbjfnJyMgAWiwWLxXITlReO3WZ33HbF80nROnnyJPv378cwDCwWCz4+PnTr1g3Q+fQkV86Vzpnn0jn0bDp/ns/V57Awz+O2MJuQkIDNZiM8PDxXe3h4OPHx8QU6xr///W/S0tIYOXLkNbeZNm0aL7/8cp721atXu2R50bNnz0Jkzu3Y2FinP58UDcMwOHfunOP/xWrVqun8lQA6h55P59Cz6fx5Pledw/T09AJv6/bZDP443tAwjAKNQZw/fz4vvfQSixcvplKlStfc7rnnnmPq1KmO+8nJyVSrVo2ePXsSGhp684UX0M/RaxxjDXr06KH5Rz1AamoqS5YscQTZRo0a4eXlpfPnwSwWC7GxsTqHHkzn0LPp/Hk+V5/DK5+kF4TbwmyFChUwm815emHPnz+fp7f2j7788ksmTJjAggUL6N69+3W39fPzy3dso4+Pj0tOhsnk9VuYNVz2nHLzjh07xsKFC0lLS8PHx4e+ffvSqFEjli9frvNXAugcej6dQ8+m8+f5XHUOC/McbpvNwNfXlxYtWuTpro6NjaV9+/bX3G/+/PmMHTuWzz//nH79+jm7TClFtm7dymeffUZaWhqVKlVi0qRJNGvWzN1liYiIyHW4dZjB1KlTuf/++2nZsiXt2rXjo48+4tSpU0yePBnIGSLw66+/8umnnwI5QXbMmDG8/fbbtG3b1tGrGxAQQJkyZdz2OqRkqFKlCiaTiWbNmtGnTx/1HoiIiHgAt4bZUaNGcfHiRV555RXi4uJo3Lgxy5cvp0aNGgDExcXlmnP2ww8/xGq18sgjj/DII4842h944AHmzp3r6vILxNDcXMVaamoqwcHBQM5FXlOmTKFChQpurkpEREQKyu0XgE2ZMoUpU6bk+9gfA+r69eudX5CTaFr94sVut7N27Vq2bdvGxIkTHRcRKsiKiIh4FreH2ZJOPbPFT1JSEjExMZw+fRqAQ4cOXXdGDBERESm+FGalVDl06BCLFi0iIyMDPz8/BgwYQKNGjdxdloiIiNwkhVknU8ds8WCz2VizZg1btmwBIDIykuHDh1939TgRcT+bzeZYsdHb25vMzExsNpu7y5JC0vnzfM44h76+vnh53frEWgqzUirs3LnTEWRbt25Njx498PbW//4ixZVhGMTHx5OYmOi4HxERwenTpwu0sI4ULzp/ns8Z59DLy4uoqCh8fX1v6Tj6be5kJsPQ1V/FwB133MHRo0dp0qQJDRo0cHc5InIDV4JspUqVCAwMxDAMx+wjRdGTI65lt9t1/jxcUZ9Du93O2bNniYuLo3r16rcUkBVmnS5noIHNUKJ1JZvNxtatW2nTpg3e3t54eXkxatQod5clIgVgs9kcQTYsLAzI+cWXnZ2Nv7+/wpAH0vnzfM44hxUrVuTs2bNYrdZbmttdYdbJrvTM2tU96zKXL18mOjqas2fPkpSURN++fd1dkogUgsViASAwMNDNlYiIM10ZXmCz2RRmizPTbz2zdvXMusSBAwdYsmQJWVlZBAQEUKdOHXeXJCI3SWMrRUq2onqPK8w6mSPMurmOks5qtbJq1Sq2b98O5KzmNWzYMC1zLCIiUsIpzDqZl6GeWWe7dOkSCxYsID4+HoAOHTrQtWtXzGazmysTERERZ9MobKez//ZfhVlnMZlMXL58mcDAQEaPHk337t0VZEVEgE6dOvH555+7u4wSIysri+rVq7Njxw53lyJXUZh1OvXMOoPd/vvAjXLlyjFq1CgeeughjZEVEbcaO3YsgwcPztUWHR2Nv78/b7zxBgAvvfQSJpOJyZMn59pu165dmEwmTpw4AcCJEycwmUxUqlSJlJSUXNs2a9aMl1566bq1LFu2jPj4eO6+++48j7322muYzWb++c9/5nnspZdeolmzZnnaExMTMZlMrF+/Pld7TEwMXbp0oUyZMgQHB9OkSRNeeeUVLl26dN36bkVWVhaPPfYYFSpUICgoiIEDB3LmzJnr7pOSksKTTz5JjRo1CAgIoH379vz444+5tjl37hxjx46lcuXKBAYG0rt3bw4fPux43M/Pjz//+c/85S9/ccrrkpujMOtkXgqzRe7ChQt89NFHHDlyxNEWFRVFaGioG6sSEclr5syZjB49mnfffZdnnnnG0e7v78+sWbM4dOjQDY+RkpLCm2++Wejnfueddxg3bly+0yjNmTOHZ555htmzZxf6uFd7/vnnGTVqFK1atWLFihXs27ePf//73+zevZvPPvvslo59PU8++SRff/01X3zxBd9//z2pqan079//uitTTZw4kdjYWD777DP27t1Lz5496d69O7/++iuQsyjA4MGDOXbsGIsXL2bnzp3UqFGD7t27k5aW5jjO6NGj2bhxIwcPHnTa65PCUZh1NkMXgBWl3bt38/HHH3Pu3DliY2MxDC0YLFIaGAakpbnn62Z/zLzxxhs8+uijfP7550ycODHXY/Xq1aNr16783//93w2P89hjj/HWW29x/vz5Aj93QkIC3377LQMHDszz2IYNG8jIyOCVV14hLS2N7777rsDHvdq2bdt47bXX+Pe//82//vUv2rdvT82aNenRowcxMTE88MADN3XcG0lKSmLWrFn8+9//pnv37jRv3pz//e9/7N27l2+//TbffTIyMoiJieGNN96gU6dO1KlTh5deeomoqCjef/99AA4fPszWrVt5//33adWqFfXq1WPGjBmkpqYyf/58x7HCwsJo3759rjZxL4VZJ9PUXEUjOzubxYsXs2jRIiwWC1FRUdx///2aukeklEhPh6pVyxIa6kVwMC79Sk8vfL3PPvssf//731m2bBnDhg3Ld5t//vOfxMTE5Pmo+4/uuece6tSpwyuvvFLg5//+++8JDAzMd8XDWbNmcc899+Dj48M999zDrFmzCnzcq82bN4/g4GCmTJmS7+Nly5a95r6NGjUiODj4ml+NGjW65r47duzAYrHQs2dPR1vlypVp3Lgxmzdvzncfq9WKzWbD398/V3tAQADff/89kDN0Aci1jdlsxtfX17HNFa1bt2bjxo3XrFFcS7MZOJljNgNdAHbTzp8/z4IFC0hISMBkMtG5c2c6duyoVWREpFhasWIFixcvZs2aNXTr1u2a291xxx2MHDmSZ599ljVr1lxzO5PJxD//+U8GDBjAU089Re3atW9Yw4kTJwgPD8/zczI5OZmYmBhH6Lvvvvvo0KED//3vfws9VOvw4cPUqlXrpia7X758uWNxjPxc75jx8fH4+vpSrly5XO3h4eGOWW3+KCQkhHbt2vH3v/+dBg0aEB4ezvz58/nhhx+47bbbAKhfvz41atTgueee48MPPyQoKIi33nqL+Ph44uLich2vSpUqjrHN4n4Ks05mujKbgT4NvymXL1/m448/xmq1EhwczLBhw6hZs6a7yxIRFwsMhDNnEgkNDXX5H7KFXYisSZMmJCQk8MILL9CqVStCQkKuue2rr75KgwYNWL16NZUqVbrmdr169eLOO+/kb3/7W4FmJ8jIyMjTCwnw+eefU6tWLZo2bQrkXEhWq1YtvvjiCx588MECvLrfGYZx05+O1ahR46b2u54b1fPZZ58xfvx4qlSpgtls5o477uDee+/lp59+AnICdExMDBMmTKB8+fKYzWa6d+9Onz598hwrICCA9JvpshenUNeWk10ZZmDTMIObUq5cORo3bkzt2rWZPHmygqxIKWUyQVCQe74Km9eqVKnChg0biIuLo3fv3nlmIrha7dq1mTRpEs8+++wNrwH45z//yZdffsnOnTtvWEOFChW4fPlynvbZs2ezf/9+vL29HV/79+/PNdQgNDSUpKSkPPsmJiYCOBajqVu3LkePHr1uD+u13Mowg4iICLKzs/O8vvPnzxMeHn7N/WrXrs2GDRtITU3l9OnTbNu2zTFs7YoWLVqwa9cuEhMTiYuLY+XKlVy8eDHXNpAzv3nFihUL/brFOdQz63TGb/9VmC2o+Ph4QkJCCAoKAqBfv36YzWaNjxURj1G9enU2bNhA165d6dmzJ6tWrbrmx/gvvPACtWvX5osvvrjuMVu3bs3QoUN59tlnb/j8zZs3Jz4+nsuXLzs+jt+7dy/bt29n/fr1lC9f3rFtYmIinTp1Yt++fTRu3Jj69etz5swZ4uPjiYiIcGz3448/4uXl5ZgC8d577+Wdd95hxowZPPHEE3lqSExMvOa42VsZZtCiRQt8fHyIjY1l5MiRAMTFxbFv3z7H9GfXExQURFBQEJcvX2bVqlX57nMlsB8+fJjt27fz97//Pdfj+/bto3nz5jd8LnENhVkn09RcBWcYBjt27GDlypVERUVx7733YjKZ8PbW/6Yi4nmqVq3K+vXrcwXa/JbYDg8PZ+rUqfzrX/+64TH/8Y9/0KhRoxv+XGzevDkVK1Zk06ZN9O/fH8i58Kt169Z06tQpz/bt2rVj1qxZ/Oc//6Fnz540aNCAu+++m3/84x9UrlyZPXv28Oc//5nJkyc7hk20adOGZ555hj/96U/8+uuvDBkyhMqVK3PkyBE++OAD7rzzznxDLtzaMIMyZcowYcIE/vSnPxEWFkb58uX585//zO2330737t0d2911110MGTKERx99FIBVq1ZhGAb16tXjyJEjPP3009SrV49x48Y59lmwYAEVK1akevXq7N27lyeeeILBgwfnutgMYOPGjXkCrriPhhk4mUlTcxVIZmYmMTExfPPNN9hsNkwm0019dCUiUpxcGXKQmJhIjx49HB/V/9HTTz9NcHDwDY9Xt25dxo8fT2Zm5nW3M5vNjB8/nnnz5gE5M8L873//u+bMCsOGDeN///sf2dnZeHt7s3r1amrVqsXo0aNp1KgRzz77LBMnTuStt97Ktd/rr7/O559/zg8//ECvXr1o1KgRU6dOpUmTJk6bmgvgP//5D4MHD2bkyJF06NCBwMBAli5dmmv1x6NHj5KQkOC4n5SUxCOPPEL9+vUZM2YMd955J6tXr87VCxwXF8f9999P/fr1efzxx7n//vvzTMG1ZcsWkpKSGD58uNNenxSOyShlE3UmJydTpkwZkpKSXDLJ/vtz7+Nh33l8d7ES7SafuamrPku6s2fPEh0dzeXLl/Hy8uKuu+6iXbt2xWZYgcViYfny5fTt21fnz0PpHHqWzMxMjh8/TlRUlOMiJrvdTnJyslsuAPNU586do1GjRuzYscMpF1wVRkk6fyNGjKB58+b89a9/dXcpLuWMc5jfe/2KwuQ1fX7rZI5hBhozm4dhGGzbto3Y2FhsNhtlypRh+PDhVK1a1d2liYh4vPDwcGbNmsWpU6fcHmZLiqysLJo2bcpTTz3l7lLkKgqzTqapua7NYrHwww8/YLPZqFevHoMGDSIgIMDdZYmIlBiDBg1ydwklip+fX4FWbRPXUph1MseYWV0Aloevry/Dhw/n1KlTtGnTptgMKxARERHPoTDrZCYNM3AwDIOtW7fi4+NDy5YtgZwlCCtXruzmykRERMRTKcw6mUlTcwE5q9EsWrSIQ4cOYTabqVWrVq55DkVERERuhsKsk/3eM1t6nT59mujoaJKTkzGbzfTq1SvPmtoiIiIiN0Nh1slKc8+sYRhs2rSJtWvXYhgG5cuXZ8SIEblWlBERERG5FQqzTmYycvpkS9tytoZh8MUXX3Do0CEAGjduTP/+/fHz83NzZSIiIlKSePbMxR7gSs+srZT1zJpMJqpWrYq3tzcDBgxg6NChCrIiIi42a9asPEuxyq3585//zOOPP+7uMuQqCrNO5lWKxsza7XZSU1Md9++8804efvhh7rjjDk27JSKlwvnz53nooYeoXr06fn5+RERE0KtXL7Zs2UJ2djYVKlTg1VdfzXffadOmUaFCBbKzs5k7dy4mk4kGDRrk2e6rr77CZDJRs2bN69aSlZXFCy+8wN/+9rc8j505cwZfX1/q16+f57ETJ05gMpnYtWtXnscGDx7M2LFjc7UdOXKEcePGUbVqVfz8/IiKiuKee+5h+/bt163vVsXExNCwYUP8/Pxo2LAhX3/99Q33+eqrr2jWrBmBgYHUqFGDf/3rX3m2mTdvHk2bNiUwMJDIyEjGjRvHxYsXHY8/88wzzJkzh+PHjxfp65GbpzDrdKVjzGxqairz5s3j008/xWKxADm9s5qxQERKk2HDhrF7924++eQTDh06xJIlS+jSpQuXLl3C19eX++67j7lz55LfSvJz5szh/vvvx9fXF4CgoCDOnz/Pli1bcm03e/ZsqlevfsNaYmJiCA4OpmPHjnkemzt3LiNHjiQ9PZ1Nmzbd5KuF7du306JFCw4dOsSHH37IgQMH+Prrr6lfvz5/+tOfbvq4N7JlyxZGjRrF/fffz+7du7n//vsZOXIkP/zwwzX3WbFiBaNHj2by5Mns27ePGTNm8NZbb/Huu+86tvn+++8ZM2YMEyZMYP/+/SxYsIAff/yRiRMnOrapVKkSPXv25IMPPnDa65PC0ZhZJysNy9keP36cmJgY0tLS8PHxIS4urkA/aEVECsowDNIsaZizzUW2LnxBBfoEFujTpcTERL7//nvWr19P586dAahRowatW7d2bDNhwgTefvttvvvuO8c2ABs3buTw4cNMmDDB0ebt7c29997L7NmzadeuHZDTo7p+/Xqeeuop5s+ff916vvjiCwYOHJin3TAM5syZw4wZM6hatSqzZs2iQ4cON3x9+R1n7Nix3HbbbWzcuDHXeWnWrBlPPPFEoY9ZUNOnT6dHjx4899xzADz33HNs2LCB6dOnX/P78tlnnzF48GAmT54MQK1atfjLX/7C66+/ziOPPILJZGLr1q3UrFnTMYwgKiqKhx56iDfeeCPXsQYOHMjf/vY3Xn/9dae9Rik4hVmnywmz+fwR7vHsdjsbNmzgu+++A6BixYqMGDGCihUrurkyESlp0i3pVJ1R1S3PnfpcKkG+QTfcLjg4mODgYBYtWkTbtm3zvU7g9ttvp1WrVsyZMydXmJ09ezatW7emcePGubafMGECnTp14u233yYwMJC5c+fSu3dvwsPDb1jPxo0bGT16dJ72devWkZ6eTvfu3alatSpt2rTh7bffJiQk5IbHvNquXbvYv38/n3/+eb5/YJQtW/aa+7722mu89tpr1z3+ihUr8u1Vhpye2aeeeipXW69evZg+ffo1j5eVlUVgYGCutoCAAM6cOcPJkyepWbMm7du35/nnn2f58uX06dOH8+fPEx0dTb9+/XLt17p1a06fPs3JkyepUaPGdV+HOJ+GGThZSe2ZTUlJ4bPPPnME2ebNmzNp0iQFWREptby9vZk7dy6ffPIJZcuWpUOHDvz1r39lz549ubYbP3480dHRjmsMUlNTWbBgQa5e2SuaNWtG7dq1iY6OxjAM5s6dy/jx429YS2JiIomJifmusDhr1izuvvtuzGYzjRo1ok6dOnz55ZeFfr2HDx8GyHfc7Y1MnjyZXbt2XffrykqR+YmPj88T6MPDw4mPj7/mPr169WLhwoWsWbMGu93OoUOHHOE3Li4OgPbt2zNv3jxGjRqFr68vERERlC1blv/+97+5jlWlShUgZ3yxuJ96Zp3tty7ZktYxu2LFCk6cOIGPjw/9+/enSZMm7i5JREqwQJ9Azkw5Q2hIqFuGGRTUsGHD6NevHxs3bmTLli2sXLmSN954g5kzZzounLrnnnuYOnUqX375JRMmTODLL7/EMAzuvvvufI85fvx45syZQ/Xq1UlNTaVv3765xnnmJyMjAwB/f/9c7YmJiSxcuJDvv//e0Xbfffcxe/bsXONCC+LKuN+bucC3fPnyt3xNxR+f1zCM69YyadIkjh49Sv/+/bFYLISGhvLEE0/w0ksvYTabAThw4ACPP/44L7zwAr169SIuLo6nn36ayZMnM2vWLMexAgICAEhPT7+l1yBFQ2FWbkrv3r3JzMykb9++VKhQwd3liEgJZzKZCPIJIsg3yOVhtrD8/f3p0aMHPXr04IUXXmDixIm8+OKLjjBbpkwZhg8fzpw5c5gwYQJz5sxh+PDhhIaG5nu80aNH88wzz/DSSy8xZswYvL1v/Ks7LCwMk8nE5cuXc7V//vnnZGZm0qZNG0ebYRjY7XYOHDhAw4YNKVOmDABJSUl5jpuYmOj4WL1u3boAHDx4kGbNmt2wpqvd6jCDiIiIPL2w58+fv+7wC5PJxOuvv85rr71GfHw8FStWZM2aNQCOmSGmTZtGhw4dePrppwFo0qQJQUFBdOzYkVdffZXIyEgALl26BKBPI4uJ4v0TQYqN5ORktm3b5rgfGhrKmDFjFGRFRG6gYcOGpKWl5WqbMGECmzZtYtmyZWzatCnfIQZXlC9fnoEDB7Jhw4YCDTEA8PX1pWHDhhw4cCBX+6xZs/jTn/6U6+P83bt307VrV2bPng1AuXLlqFixIj/++GOufTMyMti/fz/16tUDcoZANGzYkH//+9/Y7XknoExMTLxmfbc6zKBdu3bExsbmalu9ejXt27e/7vcFwGw2U6VKFXx9fZk/fz7t2rWjUqVKQE5P6x//WLrSa3v1DBT79u3Dx8eHRo0a3fD5xPnUM+tkJWF4weHDh/n666/JyMggNDT0psZHiYiUdBcvXmTEiBGMHz+eJk2aEBISwvbt23njjTcYNGhQrm07d+5MnTp1GDNmDHXq1KFTp07XPfbcuXOZMWMGYWFhBa6nV69efP/99zz55JNAzgVbP/30E/Pmzcvzc/yee+7h+eefZ9q0afj4+PDnP/+Z1157jfDwcNq3b8/ly5d5/fXX8fb25r777gNyejrnzJlD9+7d6dSpE3/961+pX78+qampLF26lNWrV7Nhw4Z8a7vVYQZPPPEEnTp14vXXX2fQoEEsXryYb7/9NtfwiXfffZevv/7a0fuakJBAdHQ0Xbp0ITMzkzlz5rBgwYJcNQ4YMIBJkybx/vvvO4YZPPnkk7Ru3TrX+OONGzfSsWNHx3ADcS/1zDqbB6dZm81GbGwsn3/+ORkZGURGRjr+ehURkdyCg4Np06YN//nPf+jUqRONGzfmb3/7G5MmTcp3jOv48eO5fPlygXpbAwICChVkIWeM6PLlyx3DBWbNmkXDhg3z7ZAYPHgwly5dYunSpUDOKlevvvoqb775Jk2bNmXw4MEYhsHGjRtzDYdo3bo127dvp3bt2kyaNIkGDRowcOBA9u/ff92ZBW5V+/bt+eKLL5gzZw5NmjRh7ty5fPnll7mGTyQkJHD06NFc+33yySe0bNmSDh06sH//ftavX59r6rSxY8c65p5t3LgxI0aMoF69eixcuDDXcebPn8+kSZOc9vqkcExGfjM3l2DJycmUKVOGpKSka45PKkqfz+zHvYHL+fxsTUY8cQgfHx+nP2dRSExMJCYmhjNnzgA5P7B69OhRoLFaJY3FYmH58uX07dvXY86f5KZz6FkyMzM5fvw4UVFRjguY7HY7ycnJhIa6/gIwTzZy5EiaN2/umI/VXUrS+fvmm294+umn2bNnT6n6neiMc5jfe/2KwuS10nMWpMAOHTrE119/TWZmJn5+fgwaNCjfJRVFRKR4+9e//sWSJUvcXUaJkpaWxpw5c0pVkC3udCaczBO7va1WK5mZmVSpUoVhw4ZRrlw5d5ckIiI3oUaNGjz22GPuLqNEGTlypLtLkD9QmHU2D0mzdrvd8bFBw4YNGTlyJHXr1nVcxSkiIiJSHHn2wBUP4AlZ9sCBA7z33nukpKQ42ho0aKAgKyIiIsWewqyLFMfFbK1WK9988w0LFizg0qVLbNq0yd0liYiIiBSKhhmUUhcvXiQ6OtqxgkqHDh3o2rWrm6sSERERKRyF2VJo3759LF26lOzsbAIDAxk8eDC33Xabu8sSERERKTSFWWcrZoNmd+/ezaJFiwCoXr06w4YNc8l8uyIiIiLOoDDrZI4sW0wGzTZo0IBNmzZRv359unTp4vGTV4uIiEjppiRTChw9epQrC735+vry4IMP0q1bNwVZEZESpmbNmk5dRrakys7Opk6dOroQugidP3+eihUr8uuvvzr9uZRmSrDs7GwWL17M//73PzZv3uxo16olIiLOMXbsWEwmEyaTCW9vb6pXr87DDz/M5cuX3V2aU7300kuO133117fffuvWmpo1a1agbT/66CNq1KhBhw4d8jz24IMPYjab+eKLL/I8NnbsWAYPHpynfdeuXZhMJk6cOOFoMwyDjz76iDZt2hAcHEzZsmVp2bIl06dPJz09vaAvq9AuX77M/fffT5kyZShTpgz3338/iYmJ193n3LlzjB07lsqVKxMYGEjv3r05fPhwrm2OHj3KkCFDqFixIqGhoYwcOZJz5845Hq9UqRL3338/L774ojNeVi4KsyXU+fPnmTlzpuMNZbfb3V2SiEip0Lt3b+Li4jhx4gQzZ85k6dKlTJkyxd1lOV2jRo2Ii4vL9dWpU6ebOlZ2dnYRV3d9//3vf5k4cWKe9vT0dL788kuefvppZs2adUvPcf/99/Pkk08yaNAg1q1bx65du/jb3/7G4sWLWb169S0d+3ruvfdedu3axcqVK1m5ciW7du3i/vvvv+b2hmEwePBgjh07xuLFi9m5cyc1atSgZ8+epKWlATlL+vbs2ROTycTatWvZtGkT2dnZDBgwIFfeGDduHPPmzXP6H3MKs87m4gvADMNg586dfPzxx1y4cIHg4GDGjBlDx44dXVuIiEhRMgywprnnyyjcD3I/Pz8iIiKoWrUqPXv2ZNSoUbnCis1mY8KECURFRREQEEC9evV4++23cx3jSo/fm2++SWRkJGFhYTzyyCNYLBbHNufPn2fAgAEEBAQQFRXFvHnz8tRy6tQpBg0aRHBwcL69Z1d6L2fPnk316tUJDg7m4Ycfxmaz8cYbbxAREUGlSpX4xz/+ccPX7e3tTURERK4vX19fAPbu3cvAgQMJCgoiLCyMBx98kNTU1Dyvd9q0aVSuXJm6desC8OuvvzJq1CjKlStHWFgYgwYNytXbuX79elq3bk1QUBBly5alQ4cOnDx5krlz5/Lyyy+ze/duRy/x3Llz8637p59+4siRI/Tr1y/PYwsWLKBhw4Y899xzbNq0KddzF8ZXX33FvHnzmD9/Pn/9619p1aoVNWvWZNCgQaxdu9ZpU2MePHiQlStXMnPmTNq1a0e7du34+OOPWbZsGb/88ku++xw+fJitW7fy/vvv06pVK+rVq8eMGTNITU0lJiYGwPG9mDt3Lrfffju33347c+bM4ccff2Tt2rWOY91+++1ERETw9ddfO+X1XaHPm53syo9AV1z/lZ2dzbJly9i7dy8AtWvXZsiQIQQFBbng2UVEnMiWTtnVVd3z3CNTwfvmfo4eO3aMlStX4uPj42iz2+1UrVqVr776igoVKrB582YefPBBIiMjGTlypGO7devWERkZybp16zhy5AijRo2iWbNmTJo0CcgJgKdPn2bt2rX4+vry+OOPc/78ecf+V3rYgoKC2LBhA1arlSlTpjBq1CjWr1/v2O7o0aOsWLGClStXcvToUYYPH87x48epW7cuGzZsYPPmzYwfP5677rqLtm3bFvp7kJ6eTt++fWnRogU//PADCQkJTJw4kUcffTRXwFyzZg2hoaHExsZiGAbp6el07dqVjh078t133+Ht7c2rr75K79692bNnD15eXgwePJhJkyYxf/58srOz2bZtGyaTiVGjRrFv3z5WrlzpGOpQpkyZfOv77rvvqFu3br4z+8yaNYv77ruPMmXK0LdvX+bMmcPLL79c6O/BvHnzqFevHoMGDcrzmMlkumZtAMHBwdc9dseOHVmxYkW+j23ZsoUyZcrQpk0bR1vbtm0pU6YMmzdvpl69enn2ycrKAsDf39/RZjab8fX1ZevWrTz66KNkZWVhMpnw8/NzbOPv74+Xlxfff/893bt3d7S3bt2ajRs3Mn78+Ou+jluhMOtsLuyZvXjxIvv378dkMtG1a1fuvPNOTKZiMo2CiEgpsWzZMoKDg7HZbGRmZgLw1ltvOR738fHJFYiioqLYvHkzX331Va4wW65cOd59913MZjP169enX79+rFmzhkmTJnHo0CFWrFjB1q1bHUFl1qxZNGjQwLH/t99+y549ezh+/DjVqlUD4LPPPqNRo0b8+OOPtGrVCsgJ17NnzyYkJISGDRvStWtXfvnlF5YvX46Xlxf16tXj9ddfZ/369dcNs3v37s0VvBo2bMi2bduYN28eGRkZvP/++0RGRuLl5cW7777LgAEDeP311wkPDwcgKCiImTNnOnpzZ8+ejZeXFzNnznT8LpszZw5ly5Zl/fr1tGzZkqSkJPr370/t2rUBcr3+4OBgR2/x9Zw4cYLKlSvnab/SQ7lw4UIA7rvvPh5//HFefPHFQl9Affjw4XyDY0Hs2rXruo8HBARc87H4+HgqVaqUp71SpUqORZP+qH79+tSoUYPnnnuODz/8kKCgIN566y3i4+Mdvfpt27YlKCiIv/zlL7z22msYhsFf/vIX7HY7cXFxuY5XpUoVdu7ceYNXeWsUZkuQyMhI+vfvT1hYGNWrV3d3OSIiRcccSGLPM4SGhrp+JhZzYKE279q1K++//z7p6enMnDmTQ4cO8dhjj+Xa5oMPPmDmzJmcPHmSjIwMsrOz81ys1KhRI8xms+N+ZGSk45O3gwcP4u3tTcuWLR2P169fn7JlyzruHzx4kGrVqjmCLOQEzLJly3Lw4EFHmK1ZsyYhISGObcLDwzGbzbm+z+Hh4bl6ffNTr149lixZ4rh/pdfu4MGDNG3aNNenhB06dMBut/PLL784wuztt9/uCLIAO3bs4MiRI7lqA8jMzOTo0aP07NmTsWPH0qtXL3r06EH37t0ZOXIkkZGR163zjzIyMnL1Ql4xa9YsevXqRYUKFQDo27cvEyZM4Ntvv6Vnz56Feg7DMG66c6lOnTo3td8V+T3v9erx8fEhJiaGCRMmUL58ecxmM927d6d3795YrVYAKlasyIIFC3j44Yd555138PLy4p577uGOO+7I9f8s5IRtZ17gBgqzHi0rK4vly5fTtm1bx5u3efPmbq5KRMQJTKacj/q9g6CYTysYFBTkCCDvvPMOXbt25eWXX+bvf/87kDN+8qmnnuLf//437dq1IyQkhH/961/88MMPuY5z9dAEINfFvFemW7xeQLpWYPlje37Pc73nvhZfX998g9f1gtPV7X8cEme322nRokW+Y4ErVqwI5PTUPv7446xcuZIvv/yS//u//yM2NrZQwyEqVKjg+CPhCpvNxqeffkp8fHyuGYBsNhuzZs1yhNnQ0FBOnjyZ55hXZgu4Mnygbt26HDx4sMA1Xe1WhhlERETkGiN9xYULFxx/ROSnRYsW7Nq1i6SkJLKzs6lYsSJt2rTh9ttvd2zTs2dPjh49SkJCAt7e3pQtW5aIiAiioqJyHevSpUuO8+UsCrNO9vuiCUU73iAuLo4FCxZw+fJlzp49y8MPP6x5Y0VEiqEXX3yRPn368PDDD1O5cmU2btxI+/btc81wcPTo0UIds0GDBlitVrZv307r1q0B+OWXX3JNudSwYUNOnTrF6dOnHb2zBw4cICkpKdfH8c7WsGFDPvnkE9LS0hzjUjdt2oSXl5fjQq/83HHHHXz55ZdUqlTpuitVNm/enObNm/Pcc8/Rrl07Pv/8c9q2bYuvry82m+2G9TVv3pz3338/V+hevnw5KSkp7Ny5M1dP488//8zo0aO5ePEiYWFh1K9fn/nz55OZmZmrd/fHH3+kYsWKlCtXDsiZUeDuu+9m8eLFecbNGoZBcnLyNcfN3sowg3bt2pGUlMS2bdsc/5/88MP/t3fvcTGm/R/APzPNdG7Khs4HIXLIIVtkV7JJm0OUaukhhxDaCGt59mfLWrycErsO+9jUgyKWPF4r1FLJYVFYEgptZWV7WB100FTX7w/P3Gs0UqmZJt/36zWvl7nv677v7zXfpr6uue5rLqGkpASOjo4Nnhf4uxjPyclBeno6vvzyy3ptJCPXZ86cQVFREcaNGye1PzMzE8OHD3/rtd4FVT+trYXnzDLGcPnyZURGRuLZs2fQ1dXFuHHjqJAlhJA2avjw4ejduzfWrFkD4OXHxunp6Th16hSys7OxYsUKXLlypUnn7NGjB9zc3DBr1ixcunQJGRkZCAgIkCpsXFxcYGtrCz8/P1y9ehWXL1/G1KlT4eTkJDU9obX5+flBXV0d8+bNQ2ZmJpKTk/H5559jypQpDY4O+vn5oWPHjvDw8EBaWhpyc3ORmpqKBQsW4OHDh8jNzcXy5ctx8eJF5OXlITExEdnZ2VyhbmlpidzcXFy/fh1Pnjzhbmx6nbOzM8rLy3Hr1i1uW2RkJEaPHo1+/fqhT58+3MPLywudOnXCvn37uBgFAgGmTJmC9PR03L9/H/v27cPatWvxxRdfcOfz8fGBr68vJk2ahLVr1yI9PR15eXn4+eef4eLiguTk5De+Dt26dWvwYWJi8sZjbWxsuJ+TX3/9Fb/++itmzZqFMWPGSM3h7dmzp9SKA4cOHUJKSgq3PNfIkSPh4eGBESNGcG2ioqLw66+/cn329vZGSEiI1HkrKiqQkZHR5GkZTUUVkBKpqqrCoUOHcOLECdTW1qJHjx6YM2eO1HwoQgghbc+iRYuwa9cuFBQUIDAwEJ6envD19YWDgwOePn3arHVoo6KiYGZmBicnJ3h6emL27NlSN/vweDwcPXoUHTp0wLBhw+Di4gIrKyvExcW1ZNfeSlNTEydOnMCzZ8/g4OCAiRMn4pNPPsH333//1uPOnj0Lc3NzeHp6wsbGBjNmzEBlZSVEIhE0NTVx584deHl5wdraGrNnz0ZQUBDmzJkDAPDy8oKbmxucnZ3RqVMn7N+/X+Z19PX14enpyU1n+PPPP3H8+HF4eXnVa8vj8eDp6cmtOaurq4u0tDRu5Yh+/fph/fr1WLVqFRYvXix1XGxsLMLDwxEfHw8nJyfY2toiLCwMHh4eGDVqVLNe28aIiYlB37594erqCldXV9ja2mLv3r1Sbe7evYuSkhLueWFhIaZMmYKePXsiODgYU6ZMQWxsbL1jxo8fDxsbG3zzzTf46quvsHHjRqk2//nPf2Bubt7qy4PyGGviAnpKTjKUX1JS0uDHFi3l3ztGw183AQceW8Dr85x685Aaq7S0FFFRUSguLgafz8fIkSPh4OBAqxXIgVgsRkJCAtzd3ZudP6JYlEPlUlVVhdzcXHTp0oX76Laurg6lpaWKuQGMvLO2nr+bN2/CxcVF5g1n5KXm5NDe3h4LFy7E5MmTZe6X9V6XaEq9RnNmlYSOjg4++OADAMDEiRMb/FiBEEIIIY3Xt29frF+/Hr///rvUTU6k+YqKijBx4kRMmjSp1a9FxWwre5dh78rKSggEAgiFQvB4PHh5eYHP58tcQoQQQgghzefv76/oENqVzp07Y+nSpXK5Vtsb629vmlnNFhQUYOfOnVLLbWhqalIhSwghhBDyChqZbWMYY7hw4QJOnz4Nxhjy8vLqLflBCCGEEEJeomK2DSkvL8fRo0dx7949AECfPn0wZswYqe8+JoQQQgghf6Nito3Iy8vD4cOHUVZWBoFAADc3NwwcOJBWKyCEEEIIaQAVs62sMVNmxWIxDh06hPLycujr68Pb27vBhaQJIYQQQshLVMy2Mskqvg2NrwqFQnh4eCAzMxOjR4+GqqqqXGIjhBBCCFF2VMwqSG5uLmpqatC9e3cAQPfu3bl/E0IIIYSQxqGlueSsrq4OKSkp2LNnD44cOSL19XGEEELIu7C0tERERISiw2izoqOjoaenp+gwSAtTeDG7fft27mvM7OzskJaW1mD71NRU2NnZQV1dHVZWVti5c6ecIn1HPOD58+fYu3cvUlNTAQA9e/aEpqamggMjhBDSUqZNmwYejwcejweBQABzc3PMnTsXz549U3Rora60tBQrVqxA7969oaGhAX19fXz44YdYv359m+m/r68vsrOzFR0GaWEKnWYQFxeHhQsXYvv27Rg6dCh++OEHfPrpp8jKyoK5uXm99rm5uXB3d8esWbOwb98+nD9/HvPmzUOnTp3g5eWlgB40wv/mzJbXGOPHH39ERUUFhEIhxowZA1tbW8XGRgghpMW5ubkhKioKNTU1yMrKwowZM1BcXIz9+/crOrRW89dff+Gjjz5CaWkpVq1aBTs7O6iqquLevXuIjY3F/v378Y9//EPRYUJDQwMaGhqKDoO0MIWOzIaHh2PmzJkICAiAjY0NIiIiYGZmhh07dshsv3PnTpibmyMiIgI2NjYICAjAjBkzsHHjRjlH3nh1DDj9ZAQKKlxRUVEBAwMDzJ49mwpZQghphurq6jc+ampqGt1WLBY3qm1zqKmpwdDQEKampnB1dYWvry8SExO5/bW1tZg5cya6dOkCDQ0N9OjRA1u2bJE6x7Rp0zB+/Hhs3LgRRkZG0NfXx/z586XiLioqwtixY6GhoYEuXbogJiamXiz5+fnw8PCAtrY2RCIRfHx88Oeff3L7w8LC0L9/f+zevRvm5ubQ1tbG3LlzUVtbi/Xr18PQ0BCdO3fG6tWrG+zzP//5T+Tn5+PSpUuYPn06bG1t0bNnT4wZMwaxsbGYO3cu15bH4+Ho0aNSx+vp6SE6Opp7/scff8DX1xcdOnSAvr4+PDw88Pvvv3P7U1JSYG9vDy0tLejp6WHo0KHIy8sDAPz2229wdnaGjo4ORCIR7OzskJ6eDqD+NANJ//fu3QtLS0vo6uris88+Q1lZGdemrKwMfn5+0NLSgpGRETZv3ozhw4dj4cKFDb4mRH4UNjJbXV2NjIwMLFu2TGq7q6srLly4IPOYixcvwtXVVWrbqFGjEBkZCbFYDKFQWO+YFy9e4MWLF9zz0tJSAC+Xw3r9l1nrYKiq0wDAQ79+/eDq6gqhUCina5OWIMkV5Ux5UQ6Vi1gsBmMMdXV1qKurA/Dy2xEBYN26dW88rlu3bpg0aRL3fOPGjW/MuYWFBaZOnco937JlCyoqKuq1W7FiRZNiZ4xxsQPAgwcPcPLkSQiFQm5bTU0NTExMcODAAXTs2BEXLlxAYGAgDAwM4OPjw50nOTkZhoaGOH36NO7du4dJkybB1tYWs2bNAgD4+/vj4cOH+OWXX6CqqoqFCxeiqKiIuz5jDOPHj4eWlhaSk5NRU1ODoKAg+Pr64syZM9x17t+/j4SEBCQkJOD+/fvw8fHBgwcPYG1tjeTkZFy4cAEBAQFwdnbG4MGD6/W5rq4OcXFx8PPzg5GREdfPN70+kmNebyfZVlFRAWdnZ3z00UdISUmBQCDA6tWr4ebmhuvXr4PP52P8+PEICAhATEwMqqurcfnyZa7ffn5+6N+/P7Zt2wYVFRVcv34dKioqUtd89efq/v37iI+Px7Fjx/Ds2TN89tlnWLt2Lb799lsAQEhICM6fP4+jR4/CwMAAoaGhuHr1Kvr169dgX9sbSe5e/fl+V5KfU7FYDBUVFal9Tfl9rbBi9smTJ6itra23nqqBgQEeP34s85jHjx/LbF9TU4MnT57AyMio3jFr167FypUr621PTEyUy3zVv55roYdeEnQEKuDx+iMpKanVr0laB+VO+VEOlYNAIIChoSGeP3/epNHRmpoabsAC+PuP77u0fbVNY4jFYhw/fhwikQi1tbWoqqoCAKxevVrqXIsWLeL+PXbsWKSmpmL//v1wc3PjzqOrq4vVq1dDRUUFxsbGcHV1xalTp+Dr64t79+7h5MmTSEpKQu/evQEAmzdvhoODA6qqqlBaWork5GTcuHED169fh6mpKQBg27ZtGDJkCFJSUjBw4EC8ePECdXV12Lx5M3R0dGBqaoqPP/4Yd+7cwf79+8Hn8+Hl5YV169bh1KlT6NWrV70+FxUVobi4GBYWFlJ9HD58OPeNlpKBJ8mIZ2VlZb3XXxL3vn37AACbNm3ivjgoIiIClpaWSEhIwIABA1BSUgJnZ2d06tQJADBhwgQuX/n5+Zg/fz6MjY25a0v2VVVVgTHGXVvS/y1btkBHRwfm5ubw9vZGUlISli5dirKyMuzZswe7du3Chx9+yMXSq1cvVFdXN/nnoz14ddT6XVVXV6OyshJnz56t98mKrP9cvonCl+Z6/RuuGGMNfuuVrPaytkssX75c6pdGaWkpzMzM4OrqCpFI1NywG83d3R1isRh/JSVh5MiRMkePSdsmFouRRPlTapRD5VJVVYWCggJoa2tDXV0dwMvf9WVlZVi6dOkbf9/z+XwIBH//WVu8ePEbr8Hj8aR+FoKDg2W2a+q630KhEMOHD8f27dtRUVGByMhIZGdnY8mSJVKx7dy5E7t370ZeXh4qKytRXV2N/v37c3+XhEIh+vTpgw4dOnDHmJmZITMzEyKRCAUFBRAIBHBycuJGtAYNGgQ9PT2oq6tDJBIhPz8fZmZmUgWovb099PT0kJ+fj+HDh0NNTQ2WlpYwMTHh2hgbG0NVVVXq43gjIyOUlpbK/LtZWVkJ4OV81Ff3Hz16FNXV1Vi2bBlXqOjo6Mhsy+PxuLizsrLw4MEDmJmZSV2nqqoKhYWFGD9+PPz9/eHl5QUXFxe4uLjA29ubG9AKCQlBcHAwDh8+jE8++QQTJ05E165dAQDq6urg8XjctWX139LSEj///DNEIhFyc3MhFovh5OTEHSMSidCjRw+oqqrKpY5oKyTvQR0dnRb7dtKqqipoaGhg2LBh3Htdoin/UVBYMduxY0eoqKjUG4UtKip647dfGRoaymwvEAigr68v8xg1NTWoqanV2y4UCuX+R00R1yQth/Kn/CiHyqG2thY8Hg98Ph98/stbOyQfa6qpqXHb3ub1P44t1bYhPB4P2trasLa2BgB89913cHZ2xqpVq7Bq1SoAwMGDB7F48WJs2rQJQ4YMgY6ODjZs2IBLly5xfePxeFBVVZXqK5/PR11dHfh8PldMqKio1Hs9JK/d6/+WYIxxx0mK+tev8/q1eTweGGMyX3sDAwPo6enh7t27UvstLS0BvCz+JKsZSFZ6eD0usVjM5ZsxBjs7O5lzgDt16gQ+n4/o6GgsWLAAJ0+exMGDB7FixQokJSVh8ODBWLlyJfz8/HD8+HGcOHECYWFhOHDgACZMmMBd89XXR1b/3/Y6SwbeGvuz2B5I3oMt2e9XfwZf/93clN/VCsuCqqoq7Ozs6n3sl5SUBEdHR5nHDBkypF77xMREDBo0iP5AEUIIaZNCQ0OxceNGPHr0CACQlpYGR0dHzJs3DwMGDEC3bt1w//79Jp3TxsYGNTU13I1NAHD37l0UFxdzz3v16oX8/HwUFBRw27KyslBSUgIbG5t369Qr+Hw+fHx8sG/fPvzxxx9vbd+pUycUFhZyz3NycqQ+Uh44cCBycnLQuXNndOvWTeqhq6vLtRswYACWL1+OCxcuoE+fPoiNjeX2WVtbIyQkBImJifD09ERUVFSz+ta1a1cIhUJcvnyZ21ZaWoqcnJxmnY+0DoX+l2LRokX48ccfsXv3bty+fRshISHIz89HYGAggJdTBF6doB8YGIi8vDwsWrQIt2/fxu7duxEZGYklS5YoqguEEEJIg4YPH47evXtjzZo1AF7eqJaeno5Tp04hOzsbK1aswJUrV5p0zh49esDNzQ2zZs3CpUuXkJGRgYCAAKllp1xcXGBraws/Pz9cvXoVly9fxtSpU+Hk5IRBgwa1aB/XrFkDExMTODg4YPfu3bhx4wZ3Y9XFixelbu4ZMWIEvv/+e1y9ehXp6ekIDAyUGpDy8/NDx44d4eHhgbS0NOTm5iI1NRULFizAw4cPkZubi+XLl+PixYvIy8tDYmIisrOzYWNjg8rKSgQFBSElJQV5eXk4f/48rly50uziXUdHB/7+/vjiiy+QnJyMW7duYcaMGVKjtkTxFFrM+vr6IiIiAt988w369++Ps2fPIiEhARYWFgCAwsJC5Ofnc+27dOmChIQEpKSkoH///li1ahW2bt3adteYJYQQQvBy8GbXrl0oKChAYGAgPD094evrCwcHBzx9+hTz5s1r8jmjoqJgZmYGJycneHp6Yvbs2ejcuTO3X7IEVocOHTBs2DC4uLjAysoKcXFxLdk1AIC+vj5XLG/YsAH29vbo27cvwsLC4Ovri3/9619c202bNsHMzAzDhg3D5MmTsWTJEqkbsjU1NXH27FmYm5vD09MTNjY2mDFjBiorKyESiaCpqYk7d+7Ay8sL1tbWmD17NoKCgjBnzhyoqKjg6dOnmDp1KqytreHj44NPP/1U5o3gjRUeHo4hQ4ZgzJgxcHFxwdChQ2FjY9NiU1PIu+Oxhm73bIdKS0uhq6uLkpISuU3cFovFSEhIgLu7O02HUEKUP+VHOVQuVVVVyM3N5b4dEng5X09yA9L7NE+xvWhP+SsvL4eJiQk2bdqEmTNnKjocuWmNHMp6r0s0pV5T+GoGhBBCCCFt1bVr13Dnzh3Y29ujpKQE33zzDQDAw8NDwZERCSpmCSGEEEIasHHjRty9e5e7eT0tLQ0dO3ZUdFjkf6iYJYQQQgh5gwEDBiAjI0PRYZAGKPfEFUIIIYQQ8l6jYpYQQkib9J7dn0zIe6el3uNUzBJCCGlTJCtONOW72Qkhyqe6uhoApNYhbg6aM0sIIaRNUVFRgZ6eHoqKigC8XHeUMYbq6mpUVVUp/dJO76O6ujrKn5Jr6RzW1dXhv//9LzQ1NSEQvFs5SsUsIYSQNsfQ0BAAuIKWMYbKykpoaGjQNy8pIcqf8muNHPL5fJibm7/z+aiYJYQQ0ubweDwYGRmhc+fOEIvFEIvFOHv2LIYNG0ZffKGEKH/KrzVyqKqq2iKjvFTMEkIIabNUVFS4R01NDdTV1akYUkKUP+XXlnNIE1cIIYQQQojSomKWEEIIIYQoLSpmCSGEEEKI0nrv5sxKFugtLS2V2zXFYjEqKipQWlra5uaZkLej/Ck/yqHyoxwqN8qf8pN3DiV1WmO+WOG9K2bLysoAAGZmZgqOhBBCCCGENKSsrAy6uroNtuGx9+z7Auvq6vDo0SPo6OjIba270tJSmJmZoaCgACKRSC7XJC2H8qf8KIfKj3Ko3Ch/yk/eOWSMoaysDMbGxm9dvuu9G5nl8/kwNTVVyLVFIhG9iZUY5U/5UQ6VH+VQuVH+lJ88c/i2EVkJugGMEEIIIYQoLSpmCSGEEEKI0qJiVg7U1NQQGhoKNTU1RYdCmoHyp/woh8qPcqjcKH/Kry3n8L27AYwQQgghhLQfNDJLCCGEEEKUFhWzhBBCCCFEaVExSwghhBBClBYVs4QQQgghRGlRMdsCtm/fji5dukBdXR12dnZIS0trsH1qairs7Oygrq4OKysr7Ny5U06RkjdpSg6PHDmCkSNHolOnThCJRBgyZAhOnTolx2iJLE19H0qcP38eAoEA/fv3b90AyVs1NYcvXrzAV199BQsLC6ipqaFr167YvXu3nKIlr2tq/mJiYtCvXz9oamrCyMgI06dPx9OnT+UULXnd2bNnMXbsWBgbG4PH4+Ho0aNvPabN1DOMvJMDBw4woVDIdu3axbKystiCBQuYlpYWy8vLk9n+wYMHTFNTky1YsIBlZWWxXbt2MaFQyH766Sc5R04kmprDBQsWsHXr1rHLly+z7Oxstnz5ciYUCtnVq1flHDmRaGoOJYqLi5mVlRVzdXVl/fr1k0+wRKbm5HDcuHHMwcGBJSUlsdzcXHbp0iV2/vx5OUZNJJqav7S0NMbn89mWLVvYgwcPWFpaGuvduzcbP368nCMnEgkJCeyrr75ihw8fZgBYfHx8g+3bUj1Dxew7sre3Z4GBgVLbevbsyZYtWyaz/dKlS1nPnj2lts2ZM4cNHjy41WIkDWtqDmXp1asXW7lyZUuHRhqpuTn09fVl//d//8dCQ0OpmFWwpubwxIkTTFdXlz19+lQe4ZG3aGr+NmzYwKysrKS2bd26lZmamrZajKTxGlPMtqV6hqYZvIPq6mpkZGTA1dVVarurqysuXLgg85iLFy/Waz9q1Cikp6dDLBa3WqxEtubk8HV1dXUoKyvDBx980Bohkrdobg6joqJw//59hIaGtnaI5C2ak8Njx45h0KBBWL9+PUxMTGBtbY0lS5agsrJSHiGTVzQnf46Ojnj48CESEhLAGMOff/6Jn376CaNHj5ZHyKQFtKV6RiDXq7UzT548QW1tLQwMDKS2GxgY4PHjxzKPefz4scz2NTU1ePLkCYyMjFotXlJfc3L4uk2bNqG8vBw+Pj6tESJ5i+bkMCcnB8uWLUNaWhoEAvo1qGjNyeGDBw9w7tw5qKurIz4+Hk+ePMG8efPw119/0bxZOWtO/hwdHRETEwNfX19UVVWhpqYG48aNw3fffSePkEkLaEv1DI3MtgAejyf1nDFWb9vb2svaTuSnqTmU2L9/P8LCwhAXF4fOnTu3VnikERqbw9raWkyePBkrV66EtbW1vMIjjdCU92FdXR14PB5iYmJgb28Pd3d3hIeHIzo6mkZnFaQp+cvKykJwcDC+/vprZGRk4OTJk8jNzUVgYKA8QiUtpK3UMzQk8Q46duwIFRWVev/zLCoqqve/FQlDQ0OZ7QUCAfT19VstViJbc3IoERcXh5kzZ+LQoUNwcXFpzTBJA5qaw7KyMqSnp+PatWsICgoC8LIwYoxBIBAgMTERI0aMkEvs5KXmvA+NjIxgYmICXV1dbpuNjQ0YY3j48CG6d+/eqjGTvzUnf2vXrsXQoUPxxRdfAABsbW2hpaWFjz/+GN9++y19SqkE2lI9QyOz70BVVRV2dnZISkqS2p6UlARHR0eZxwwZMqRe+8TERAwaNAhCobDVYiWyNSeHwMsR2WnTpiE2NpbmeClYU3MoEolw8+ZNXL9+nXsEBgaiR48euH79OhwcHOQVOvmf5rwPhw4dikePHuH58+fctuzsbPD5fJiamrZqvERac/JXUVEBPl+6BFFRUQHw9+geadvaVD0j91vO2hnJciSRkZEsKyuLLVy4kGlpabHff/+dMcbYsmXL2JQpU7j2kqUsQkJCWFZWFouMjKSluRSsqTmMjY1lAoGAbdu2jRUWFnKP4uJiRXXhvdfUHL6OVjNQvKbmsKysjJmamrKJEyeyW7dusdTUVNa9e3cWEBCgqC6815qav6ioKCYQCNj27dvZ/fv32blz59igQYOYvb29orrw3isrK2PXrl1j165dYwBYeHg4u3btGre8WluuZ6iYbQHbtm1jFhYWTFVVlQ0cOJClpqZy+/z9/ZmTk5NU+5SUFDZgwACmqqrKLC0t2Y4dO+QcMXldU3Lo5OTEANR7+Pv7yz9wwmnq+/BVVMy2DU3N4e3bt5mLiwvT0NBgpqambNGiRayiokLOUROJpuZv69atrFevXkxDQ4MZGRkxPz8/9vDhQzlHTSSSk5Mb/NvWlusZHmM0nk8IIYQQQpQTzZklhBBCCCFKi4pZQgghhBCitKiYJYQQQgghSouKWUIIIYQQorSomCWEEEIIIUqLillCCCGEEKK0qJglhBBCCCFKi4pZQgghhBCitKiYJYQQANHR0dDT01N0GM1maWmJiIiIBtuEhYWhf//+comHEELkhYpZQki7MW3aNPB4vHqPe/fuKTo0REdHS8VkZGQEHx8f5Obmtsj5r1y5gtmzZ3PPeTwejh49KtVmyZIlOH36dItc701e76eBgQHGjh2LW7duNfk8yvyfC0KI/FAxSwhpV9zc3FBYWCj16NKli6LDAgCIRCIUFhbi0aNHiI2NxfXr1zFu3DjU1ta+87k7deoETU3NBttoa2tDX1//na/1Nq/28/jx4ygvL8fo0aNRXV3d6tcmhLx/qJglhLQrampqMDQ0lHqoqKggPDwcffv2hZaWFszMzDBv3jw8f/78jef57bff4OzsDB0dHYhEItjZ2SE9PZ3bf+HCBQwbNgwaGhowMzNDcHAwysvLG4yNx+PB0NAQRkZGcHZ2RmhoKDIzM7mR4x07dqBr165QVVVFjx49sHfvXqnjw8LCYG5uDjU1NRgbGyM4OJjb9+o0A0tLSwDAhAkTwOPxuOevTjM4deoU1NXVUVxcLHWN4OBgODk5tVg/Bw0ahJCQEOTl5eHu3btcm4bykZKSgunTp6OkpIQb4Q0LCwMAVFdXY+nSpTAxMYGWlhYcHByQkpLSYDyEkPaNillCyHuBz+dj69atyMzMxL///W+cOXMGS5cufWN7Pz8/mJqa4sqVK8jIyMCyZcsgFAoBADdv3sSoUaPg6emJGzduIC4uDufOnUNQUFCTYtLQ0AAAiMVixMfHY8GCBVi8eDEyMzMxZ84cTJ8+HcnJyQCAn376CZs3b8YPP/yAnJwcHD16FH379pV53itXrgAAoqKiUFhYyD1/lYuLC/T09HD48GFuW21tLQ4ePAg/P78W62dxcTFiY2MBgHv9gIbz4ejoiIiICG6Et7CwEEuWLAEATJ8+HefPn8eBAwdw48YNeHt7w83NDTk5OY2OiRDSzjBCCGkn/P39mYqKCtPS0uIeEydOlNn24MGDTF9fn3seFRXFdHV1uec6OjosOjpa5rFTpkxhs2fPltqWlpbG+Hw+q6yslHnM6+cvKChggwcPZqampuzFixfM0dGRzZo1S+oYb29v5u7uzhhjbNOmTcza2ppVV1fLPL+FhQXbvHkz9xwAi4+Pl2oTGhrK+vXrxz0PDg5mI0aM4J6fOnWKqaqqsr/++uud+gmAaWlpMU1NTQaAAWDjxo2T2V7ibflgjLF79+4xHo/H/vjjD6ntn3zyCVu+fHmD5yeEtF8CxZbShBDSspydnbFjxw7uuZaWFgAgOTkZa9asQVZWFkpLS1FTU4OqqiqUl5dzbV61aNEiBAQEYO/evXBxcYG3tze6du0KAMjIyMC9e/cQExPDtWeMoa6uDrm5ubCxsZEZW0lJCbS1tcEYQ0VFBQYOHIgjR45AVVUVt2/flrqBCwCGDh2KLVu2AAC8vb0REREBKysruLm5wd3dHWPHjoVA0Pxf435+fhgyZAgePXoEY2NjxMTEwN3dHR06dHinfuro6ODq1auoqalBamoqNmzYgJ07d0q1aWo+AODq1atgjMHa2lpq+4sXL+QyF5gQ0jZRMUsIaVe0tLTQrVs3qW15eXlwd3dHYGAgVq1ahQ8++ADnzp3DzJkzIRaLZZ4nLCwMkydPxvHjx3HixAmEhobiwIEDmDBhAurq6jBnzhypOasS5ubmb4xNUuTx+XwYGBjUK9p4PJ7Uc8YYt83MzAx3795FUlISfvnlF8ybNw8bNmxAamqq1Mf3TWFvb4+uXbviwIEDmDt3LuLj4xEVFcXtb24/+Xw+l4OePXvi8ePH8PX1xdmzZwE0Lx+SeFRUVJCRkQEVFRWpfdra2k3qOyGk/aBilhDS7qWnp6OmpgabNm0Cn//yVoGDBw++9Thra2tYW1sjJCQEkyZNQlRUFCZMmICBAwfi1q1b9Yrmt3m1yHudjY0Nzp07h6lTp3LbLly4IDX6qaGhgXHjxmHcuHGYP38+evbsiZs3b2LgwIH1zicUChu1SsLkyZMRExMDU1NT8Pl8jB49mtvX3H6+LiQkBOHh4YiPj8eECRMalQ9VVdV68Q8YMAC1tbUoKirCxx9//E4xEULaD7oBjBDS7nXt2hU1NTX47rvv8ODBA+zdu7fex96vqqysRFBQEFJSUpCXl4fz58/jypUrXGH55Zdf4uLFi5g/fz6uX7+OnJwcHDt2DJ9//nmzY/ziiy8QHR2NnTt3IicnB+Hh4Thy5Ah341N0dDQiIyORmZnJ9UFDQwMWFhYyz2dpaYnTp0/j8ePHePbs2Ruv6+fnh6tXr2L16tWYOHEi1NXVuX0t1U+RSISAgACEhoaCMdaofFhaWuL58+c4ffo0njx5goqKClhbW8PPzw9Tp07FkSNHkJubiytXrmDdunVISEhoUkyEkHZEkRN2CSGkJfn7+zMPDw+Z+8LDw5mRkRHT0NBgo0aNYnv27GEA2LNnzxhj0jccvXjxgn322WfMzMyMqaqqMmNjYxYUFCR109Ply5fZyJEjmba2NtPS0mK2trZs9erVb4xN1g1Nr9u+fTuzsrJiQqGQWVtbsz179nD74uPjmYODAxOJRExLS4sNHjyY/fLLL9z+128AO3bsGOvWrRsTCATMwsKCMVb/BjCJDz/8kAFgZ86cqbevpfqZl5fHBAIBi4uLY4y9PR+MMRYYGMj09fUZABYaGsoYY6y6upp9/fXXzNLSkgmFQmZoaMgmTJjAbty48caYCCHtG48xxhRbThNCCCGEENI8NM2AEEIIIYQoLSpmCSGEEEKI0qJilhBCCCGEKC0qZgkhhBBCiNKiYpYQQgghhCgtKmYJIYQQQojSomKWEEIIIYQoLSpmCSGEEEKI0qJilhBCCCGEKC0qZgkhhBBCiNKiYpYQQgghhCit/wfuHnQ0Xuw5KAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fpr_knn, tpr_knn, thresholds_knn = roc_curve(y_test, knn.predict_proba(X_test)[:, 1])\n",
    "auc_knn = roc_auc_score(y_test, knn.predict_proba(X_test)[:, 1])\n",
    "\n",
    "\n",
    "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_test, svm.predict_proba(X_test)[:, 1])\n",
    "auc_svm = roc_auc_score(y_test, svm.predict_proba(X_test)[:, 1])\n",
    "\n",
    "\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "auc_rf = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_knn, tpr_knn, label=f'KNN (AUC = {auc_knn:.2f})', color='blue')\n",
    "plt.plot(fpr_svm, tpr_svm, label=f'SVM (AUC = {auc_svm:.2f})', color='green')\n",
    "plt.plot(fpr_rf, tpr_rf, label=f'Random Forest (AUC = {auc_rf:.2f})', color='orange')\n",
    "\n",
    "# Plot random guessing (baseline) line\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random Guessing')\n",
    "\n",
    "plt.title('ROC Curves')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}