02-e-ModelTraining-ComparisonAndBestModelSelection.ipynb

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   "source": [
    "## Model Comparison and Evaluation: Random Forest, Decision Tree, Naive Bayes, and SVM"
   ]
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    "Four models (Decision Tree, Random Forest, SVM, Naive Bayes) were trained using their best hyperparameters and the top 10 features identified. The Random Forest model outperformed the others, achieving the highest metrics and proving to be the most effective for phishing detection."
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   "source": [
    "### Importing Libraries for Model Evaluation"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
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   "source": [
    "### Loading the dataset\r\n"
   ]
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   "source": [
    "df = pd.read_csv(\"Data/Phishing_URL_Dataset.csv\")"
   ]
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   "source": [
    "The top 10 features selected for phishing detection include URLSimilarityIndex, LineOfCode, NoOfExternalRef, and HasSocialNet, among others. These features were chosen based on their significance in improving model accuracy and performance."
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   "source": [
    "# List of features to be used\n",
    "selected_features = [\n",
    "    \"URLSimilarityIndex\",\n",
    "    \"LineOfCode\",\n",
    "    \"NoOfExternalRef\",\n",
    "    \"NoOfSelfRef\",\n",
    "    \"NoOfCSS\",\n",
    "    \"NoOfImage\",\n",
    "    \"HasSocialNet\",\n",
    "    \"HasCopyrightInfo\",\n",
    "    \"HasDescription\",\n",
    "    \"NoOfJS\"\n",
    "]"
   ]
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   "source": [
    "### Defining Features and Target Variables\r\n"
   ]
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   "source": [
    "#Features\n",
    "X = df[selected_features]\n",
    "#Target\n",
    "y = df[\"label\"]"
   ]
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   "source": [
    "### Splitting Data into Training and Testing Sets\r\n"
   ]
  },
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   "source": [
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
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    "Best hyperparameters for each model were identified during individual model training, and detailed information can be found in the respective model training files. The parameters were fine-tuned to achieve optimal performance across all models."
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   "source": [
    "### Random Forest Model training with Best Hyperparameters"
   ]
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       "RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200)"
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    "\n",
    "rf_model = RandomForestClassifier(n_estimators=200, max_depth=20, min_samples_split=5, min_samples_leaf=1)\n",
    "rf_model.fit(X_train, y_train)"
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   "source": [
    "### Decision Tree Model training with Best Hyperparameters"
   ]
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       "DecisionTreeClassifier(max_depth=5)"
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     "execution_count": 8,
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   "source": [
    "dt_model = DecisionTreeClassifier(criterion='gini', max_depth=5, min_samples_leaf=1, min_samples_split=2)\n",
    "dt_model.fit(X_train, y_train)"
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   "source": [
    "### SVM Model training with Best Hyperparameters"
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       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, kernel='linear')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model = SVC(C=0.1, kernel='linear', gamma='scale') \n",
    "svm_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "afddd9db24e4423eb88e76864caea017",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Naive Bayes Model training with Best Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": "45b95f5c48fc49b19a683f20f60a8ecc",
    "deepnote_cell_type": "code",
    "execution_context_id": "6f9cb295-3667-4ca0-9920-4e481b8dcddb",
    "execution_millis": 12,
    "execution_start": 1733222125803,
    "source_hash": "c461e50c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model = GaussianNB(var_smoothing = 1e-09)\n",
    "nb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "5baabc8d8c524728af8e79cfe10538f3",
    "deepnote_cell_type": "text-cell-p",
    "formattedRanges": []
   },
   "source": [
    "The models evaluated include Random Forest, Decision Tree, SVM, and Naive Bayes, each trained and tested to determine the best performing model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cell_id": "29ed0c253b4e4ffc9d59a93f90ff7640",
    "deepnote_cell_type": "code",
    "execution_context_id": "6f9cb295-3667-4ca0-9920-4e481b8dcddb",
    "execution_millis": 2,
    "execution_start": 1733222132805,
    "source_hash": "3af53179"
   },
   "outputs": [],
   "source": [
    "# Evaluate models\n",
    "models = {\n",
    "    \"Random Forest\": rf_model,\n",
    "    \"Decision Tree\": dt_model,\n",
    "    \"SVM\": svm_model,\n",
    "    \"Naive Bayes\": nb_model\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "e77d5dee94df4c55b23448926228bdfd",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Model Evaluation and Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "28d95741bf9d4f58b3f26a20d1edf78e",
    "deepnote_cell_type": "text-cell-p",
    "formattedRanges": []
   },
   "source": [
    "The following code evaluates the performance of four models (Random Forest, Decision Tree, SVM, and Naive Bayes) on test data, calculating accuracy, precision, recall, F1-score, and confusion matrices. The results are then organized into a DataFrame for easy comparison of all models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "f41cf204591e4f90a77535b9b6852a47",
    "deepnote_cell_type": "code",
    "execution_context_id": "6f9cb295-3667-4ca0-9920-4e481b8dcddb",
    "execution_millis": 953,
    "execution_start": 1733222136579,
    "source_hash": "765c6b5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Precision    Recall  F1-Score  \\\n",
      "0  Random Forest  0.999915   0.999852  1.000000  0.999926   \n",
      "1  Decision Tree  0.999873   0.999815  0.999963  0.999889   \n",
      "2            SVM  0.999830   0.999703  1.000000  0.999852   \n",
      "3    Naive Bayes  0.994678   0.999925  0.990764  0.995323   \n",
      "\n",
      "             Confusion Matrix  \n",
      "0    [[20196, 4], [0, 26959]]  \n",
      "1    [[20195, 5], [1, 26958]]  \n",
      "2    [[20192, 8], [0, 26959]]  \n",
      "3  [[20198, 2], [249, 26710]]  \n"
     ]
    }
   ],
   "source": [
    "results = {\n",
    "    \"Model\": [],\n",
    "    \"Accuracy\": [],\n",
    "    \"Precision\": [],\n",
    "    \"Recall\": [],\n",
    "    \"F1-Score\": [],\n",
    "    \"Confusion Matrix\": []\n",
    "}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    # Predict on test data\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate 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",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # Append results\n",
    "    results[\"Model\"].append(model_name)\n",
    "    results[\"Accuracy\"].append(accuracy)\n",
    "    results[\"Precision\"].append(precision)\n",
    "    results[\"Recall\"].append(recall)\n",
    "    results[\"F1-Score\"].append(f1)\n",
    "    results[\"Confusion Matrix\"].append(cm)\n",
    "    \n",
    "# Convert results to a DataFrame for easier comparison\n",
    "import pandas as pd\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Print the comparison of all models\n",
    "print(results_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "c155e977a55b47d3a98ff392e60e25da",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Visualization of Confusion Matrices for Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "7a7e4810d2f34287afbce03cda88c05e",
    "deepnote_cell_type": "text-cell-p",
    "formattedRanges": []
   },
   "source": [
    "Confusion matrices were plotted for all models to visualize prediction performance on legitimate and phishing cases. This helps in understanding classification strengths and areas needing improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cell_id": "f84dd281a6b4495da6d22c9d10313b00",
    "deepnote_cell_type": "code",
    "execution_context_id": "6f9cb295-3667-4ca0-9920-4e481b8dcddb",
    "execution_millis": 1877,
    "execution_start": 1733222161086,
    "source_hash": "b594e008"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 391
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 391
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 391
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hzJkzcHJyqvI8gIpfUR+6f/8+tLW1oaKi8uUnUYVhw4bhjz/+gFAorHIQv9KhQ4fg4OCAnTt3cspzcnKgra3NvuabqPkoKCjAqFGjYGVlhe+//x7+/v4YMGAAe0VUTRkbG+PmzZsoLy/ntB7u37/Prq8tZmZm+Omnn7Bt2zbY2dmJrD906BA8PT0REBDAlr179473QOqQIUOwfv165OXlYf/+/TAxMUHHjh05xwcAXV3dKj9zX6K0tBQA2G6tf/75B0VFRTh69CinRVtd19XIkSPh4+OD9PR09tLpypZvZewMw8DU1BTNmjX7ZDzW1tawtrbG/Pnz2Ys4goKC8Ntvv33JaUodie5WAoBffvkFKioqGDt2LDIzM0XWp6SksP2dffr0AQCRK4rWrl0LALV6vb6ZmRlyc3Nx8+ZNtiw9PZ1tHlfKzs4W2bbyZrAPL6+tZGBgABsbG4SGhnK+HG7fvo3IyEj2POuCg4MDli1bhk2bNlX5C7OSjIyMSKvk4MGD+O+//zhllUmsNq4WmTNnDtLS0hAaGoq1a9fCxMQEnp6e1b6Pn9KnTx9kZGRg//79bFlpaSk2btwIVVVVdOvW7Ytjft/8+fNRUlICf39/kXVVvZ8bN24UaYlWx93dHUVFRQgNDcXJkydFbmZ0dnaGuro6VqxYUeWlo+93CdXUsWPHAABt2rQB8L9W7Pvnk5ubi+Dg4Cq3Hzp0KAQCAaZNm4bHjx9zrlICKq6kk5GRwZIlS0TeI4Zh8OrVKwAVY5SViaqStbU1hELhZ39GpJnEtxzMzMywZ88euLu7o0WLFpw7pOPj43Hw4EF4eXkBqPhwenp64vfff0dOTg66deuGy5cvIzQ0FP3796/2MsnP4eHhgTlz5mDAgAGYOnUq3r59i61bt6JZs2acAcSlS5ciLi4OLi4uMDY2RlZWFrZs2QIjIyN2sLAqq1evRu/evWFvb48xY8agsLAQGzduhIaGxke7e76UUCjE/PnzP1mvb9++WLp0KUaNGoXvv/8et27dwu7du9G0aVNOPTMzM2hqaiIoKAhqampQUVGBnZ1djfvvY2JisGXLFixatIi9tDY4OBjdu3fHggULqvzC/ZTx48dj27Zt8PLyQmJiIkxMTHDo0CFcuHAB69at430hBF+VrYcP76MAKt7PsLAwaGhowMrKCgkJCTh9+jQaNmzIa9+2trYwNzfHvHnzUFRUxOlSAiq6aLdu3YoRI0bA1tYWHh4e0NHRQVpaGiIiItCpUyds2rTpk8c5d+4ce7d8dnY2jh49irNnz8LDwwPNmzcHUHHhgLy8PPr164cJEyYgPz8f27dvh66ubpVjLjo6OujVqxcOHjwITU1NkR9xZmZm+O233+Dr64snT56gf//+UFNTQ2pqKo4cOYLx48dj1qxZiImJweTJkzF48GA0a9YMpaWlCAsLg4yMDAYOHMjrfSTvEdt1UjX04MEDZty4cYyJiQkjLy/PqKmpMZ06dWI2btzIvHv3jq1XUlLCLFmyhDE1NWXk5OSYxo0bM76+vpw6DFP1pY0MI3oJZXWXsjIMw0RGRjKtWrVi5OXlGUtLS+bPP/8UuZQ1OjqacXV1ZQwNDRl5eXnG0NCQGTp0KPPgwQORY3x4uefp06eZTp06MUpKSoy6ujrTr18/5u7du5w6lcf78FLZ4OBgBgCTmppa7XvKMNxLWatT3aWsM2fOZAwMDBglJSWmU6dOTEJCQpWXoP7999+MlZUVIysryznPbt26MS1btqzymO/vJy8vjzE2NmZsbW2ZkpISTr0ZM2YwQqGQSUhI+Og5VPf3zszMZEaNGsVoa2sz8vLyjLW1tcjf4WOfgZoe7+HDh4yMjIzIJZevX79m41BVVWWcnZ2Z+/fvM8bGxoynpydb78NLWd83b948BgBjbm5ebVxnzpxhnJ2dGQ0NDUZRUZExMzNjvLy8mKtXr370fKq6lFVeXp5p3rw5s3z5cqa4uJhT/+jRo0zr1q0ZRUVFxsTEhFm1ahXzxx9/VPuZrLz0dvz48dXG8NdffzGdO3dmVFRUGBUVFaZ58+aMt7c3k5yczDAMwzx+/JgZPXo0Y2ZmxigqKjJaWlqMg4MDc/r06Y+eG6magGFqMGJJCCF14O+//0b//v0RFxfHubyciA8lB0KI2PXt2xf37t3Do0ePavUiBvL5JH7MgRBSf+3btw83b95EREQE1q9fT4lBglDLgRAiNgKBAKqqqnB3d0dQUNAX3W9Bahf9JQghYkO/TSWXxN/nQAgh5Ouj5EAIIUQEJQdCCCEi6uWYg+n0iE9XIvXGvTVfbxpTIn6KX/itpdSW/1zwhdc/fdd4fVUvkwMhhFRLQB0mfFByIIRIF2HtPCa/vqPkQAiRLnSjHS+UHAgh0oW6lXih5EAIkS7UcuCFkgMhRLpQy4EXSg6EEOlCLQdeKDkQQqQLtRx4oeRACJEu1HLghZIDIUS6UMuBF3qXCCHSRSjDf6kBPz8/tG/fHmpqatDV1UX//v2RnJzMqdO9e3cIBALOMnHiRE6dtLQ0uLi4QFlZGbq6upg9ezZKS0s5dWJjY2FrawsFBQWYm5sjJCREJJ7NmzfDxMQEioqKsLOzw+XLl2t0PpQcCCHSRSDkv9TA2bNn4e3tjYsXLyIqKgolJSXo2bMnCgoKOPXGjRuH9PR0dvH392fXlZWVwcXFBcXFxYiPj0doaChCQkKwcOFCtk5qaipcXFzg4OCApKQkTJ8+HWPHjsWpU6fYOvv374ePjw8WLVqEa9euoU2bNnB2dkZWVhb/t6k+zgRHD96TLvTgPenyxQ/ec1jGu27hmQWffZwXL15AV1cXZ8+eRdeuXQFUtBxsbGywbt26Krc5ceIE+vbti+fPn0NPTw8AEBQUhDlz5uDFixeQl5fHnDlzEBERgdu3b7PbeXh4ICcnBydPngQA2NnZoX379ti0qeLBgeXl5WjcuDGmTJmCX3/9lVf81HIghEiXGrQcioqKkJeXx1mKiop4HSY3NxcAoKWlxSnfvXs3tLW10apVK/j6+uLt27fsuoSEBFhbW7OJAQCcnZ2Rl5eHO3fusHWcnJw4+3R2dkZCQgIAoLi4GImJiZw6QqEQTk5ObB0+KDkQQqSLQMB78fPzg4aGBmfx8/P75CHKy8sxffp0dOrUCa1atWLLhw0bhj///BNnzpyBr68vwsLC8NNPP7HrMzIyOIkBAPs6IyPjo3Xy8vJQWFiIly9foqysrMo6lfvgg65WIoRIlxqMJfj6+sLHx4dTpqCg8MntvL29cfv2bZw/f55TPn78ePbf1tbWMDAwgKOjI1JSUmBmZsY7rq+BkgMhRLrU4D4HBQUFXsngfZMnT8axY8cQFxcHIyOjj9a1s7MDADx69AhmZmbQ19cXuaooMzMTAKCvr8/+f2XZ+3XU1dWhpKQEGRkZyMjIVFmnch98ULcSIUS61NHVSgzDYPLkyThy5AhiYmJgamr6yW2SkpIAAAYGBgAAe3t73Lp1i3NVUVRUFNTV1WFlZcXWiY6O5uwnKioK9vb2AAB5eXm0a9eOU6e8vBzR0dFsHT6o5UAIkS51dIe0t7c39uzZg7///htqamps/76GhgaUlJSQkpKCPXv2oE+fPmjYsCFu3ryJGTNmoGvXrmjdujUAoGfPnrCyssKIESPg7++PjIwMzJ8/H97e3mwLZuLEidi0aRN++eUXjB49GjExMThw4AAiIv53laaPjw88PT3x3XffoUOHDli3bh0KCgowatQo3udDyYEQIl3qaCa4rVu3Aqi4XPV9wcHB8PLygry8PE6fPs1+UTdu3BgDBw7E/Pnz2boyMjI4duwYJk2aBHt7e6ioqMDT0xNLly5l65iamiIiIgIzZszA+vXrYWRkhB07dsDZ2Zmt4+7ujhcvXmDhwoXIyMiAjY0NTp48KTJI/TF0nwP55tF9DtLli+9z6LOed93C49O+7GDfMGo5EEKkCz14jxdKDoQQ6UIP3uNFYt6lnJwc7NixA76+vsjOzgYAXLt2Df/995+YIyOE1Ct1dLVSfSMRLYebN2/CyckJGhoaePLkCcaNGwctLS0cPnwYaWlp2LVrl7hDJITUF9StxItEpEYfHx94eXnh4cOHUFRUZMv79OmDuLg4MUZGCKl3qOXAi0S0HK5cuYJt27aJlDdq1KhGzwIhhJBPopYDLxKRHBQUFJCXlydS/uDBA+jo6IghIkJIvSXlLQK+JOJd+vHHH7F06VKUlJQAAAQCAdLS0jBnzhwMHDhQzNERQuoTgVDIe5FmEnH2AQEByM/Ph66uLgoLC9GtWzeYm5tDTU0Ny5cvF3d4hJB65MNpOj+2SDOJ6FbS0NBAVFQULly4gBs3biA/Px+2trYiE1oQQsgXk+7vfN4kIjns2rUL7u7u6NSpEzp16sSWFxcXY9++fRg5cqQYoyOE1CfS3iLgSyK6lUaNGsVOqfe+N2/e1OgpgoQQ8inUrcSPRLQcGIap8g/x7NkzaGhoiCEiQkh9Je1f+nyJNTm0bduWzdCOjo6Qlf1fOGVlZUhNTUWvXr3EGCEhpL6h5MCPWJND//79AVTMhuTs7AxVVVV2nby8PExMTOhSVkJI7aLcwItYk8OiRYsAACYmJnB3d+c8OoMQQuoCtRz4kYgxB09PT3GHQAiREpQc+JGI5FBWVobAwEAcOHAAaWlpKC4u5qyvfIQ3IYR8KaGU3/nMl0S8S0uWLMHatWvh7u6O3Nxc+Pj4wM3NDUKhEIsXLxZ3eISQ+kRQg0WKSURy2L17N7Zv346ZM2dCVlYWQ4cOxY4dO7Bw4UJcvHhR3OERQuoRus+BH4lIDhkZGbC2tgYAqKqqsjfE9e3bFxEREeIMjRBSz1By4EcikoORkRHS09MBAGZmZoiMjARQMc+DgoKCOEMjhNQzlBz4kYjkMGDAAERHRwMApkyZggULFsDCwgIjR47E6NGjxRwdIaReoTEHXiTiaqWVK1ey/3Z3d0eTJk2QkJAACwsL9OvXT4yREULqG2lvEfAlEcnhQ/b29rC3txd3GISQeoiSAz8SkxyeP3+O8+fPIysrC+Xl5Zx1U6dOFVNUhJD6hpIDPxKRHEJCQjBhwgTIy8ujYcOGnD+eQCCg5EAIqTUCISUHPiQiOSxYsAALFy6Er68v3b1ICKlT1HLgRyKSw9u3b+Hh4UGJgRBS5yg58CMR38ZjxozBwYMHxR0GIUQK0H0O/EhEy8HPzw99+/bFyZMnYW1tDTk5Oc76tWvXiimyujPJyQzOrfVhpquKdyVluPbkNVb9cx+PswrYOvKyQsx3bYG+toaQlxUi7v4LLDx4Gy/z//dgwkVuVmhnqoVmBqpIycyHy+rzIsdysTHAzz+YwVRHFdn5Rdh17il+P/OYU8e1nSEm9DCDiY4K3hSWIPb+C/j9fQ85b0vq7k0gNbZz+zZER0UiNfUxFBQVYWPTFtN9ZsHEtKm4Q/t2SPd3Pm8SkxxOnToFS0tLABAZkK6P7My0EHb+KW6m5UBWKMAsl+bYNbEDflgZh8LiMgDAggFWcLDShXfINbwpLMGSQa2wdXQ7DN6QwNnXwUv/wsZYE80N1USO062FDgJH2GDxX3dwLvkFzPXU4OdujXclZdh1/ikAoJ1pAwQMt8Fv4Xdx+nYm9DUU8dsQa/i5t8ak4MS6fzMIb1evXIb70OFoaW2NstIybFy/FhPHjcHhoxFQVlYWd3jfhPr6nVLbJKJbKSAgAH/88Qfu3buH2NhYnDlzhl1iYmLEHV6d8Np2BX9dfoaHGfm49/wNZu+5gUZayrA2qpgzW01RFkPsGmN5+F0kPHyF28/yMHvPDXzXVAs2xprsfpYcvouw80+R9uptlccZ8F0jRN3KxJ74NPz7qhBn7mZh6+kUTHA0Y+vYmjTAs+y3CIl7gmfZhbia+hp749PQxpjm75Y0W3/fCdcBbjA3t4Bl8+ZYunwl0tOf497dO+IO7ZtRV91Kfn5+aN++PdTU1KCrq4v+/fsjOTmZU+fdu3fw9vZGw4YNoaqqioEDByIzM5NTJy0tDS4uLlBWVoauri5mz56N0tJSTp3Y2FjY2tpCQUEB5ubmCAkJEYln8+bNMDExgaKiIuzs7HD58uUanY9EJAcFBQV06tRJ3GGIlZpSRSMu521Fl1GrxhqQlxXi/IOXbJ3HWQX4L/stbE0a8N6vvKwQRSVlnLJ3JWUwbKCERlpKAIBrT17DQFMJ3VvoAAC0VeXRu40+Yu+++KJzInUv/80bAIC6BiVyvuoqOZw9exbe3t64ePEioqKiUFJSgp49e6Kg4H9dxTNmzMA///yDgwcP4uzZs3j+/Dnc3NzY9WVlZXBxcUFxcTHi4+MRGhqKkJAQLFy4kK2TmpoKFxcXODg4ICkpCdOnT8fYsWNx6tQpts7+/fvh4+ODRYsW4dq1a2jTpg2cnZ2RlZXF+3wkIjlMmzYNGzdu/Kxti4qKkJeXx1mY0m+rn1wgqOhCuvI4Gw8y8gEAOmoKKCotw5tC7i+Gl2+KoaPO/2GE5+6/gHNrfXxv0RACAWCqo4KxDhX907r/v5/E1NeYEXYdGz1t8SCgN6789gPevCvFwkO3a+kMSV0oLy+H/6oVsGlrCwuLZuIO55tRV8nh5MmT8PLyQsuWLdGmTRuEhIQgLS0NiYkVXbO5ubnYuXMn1q5dix49eqBdu3YIDg5GfHw8OzVBZGQk7t69iz///BM2Njbo3bs3li1bhs2bN7OToAUFBcHU1BQBAQFo0aIFJk+ejEGDBiEwMJCNZe3atRg3bhxGjRoFKysrBAUFQVlZGX/88Qfv85GI5HD58mWEhoaiadOm6NevH9zc3DjLx/j5+UFDQ4Oz5Fw98JUirx1LB7WCpYEapoZer/V97034F7vOP8XOce3xYE1vHJ7+Pf659hwAwDAVdcz1VLHQrSU2nnqIH9ech2fQJRhpKeG3Ida1Hg+pPSt+W4KUhw/hvybw05UJSyAU8F6q+vFZVFTE6ziVUw9oaWkBABITE1FSUgInJye2TvPmzdlnyQFAQkICrK2toaenx9ZxdnZGXl4e7ty5w9Z5fx+VdSr3UVxcjMTERE4doVAIJycntg4fEpEcNDU14ebmhm7dukFbW1vky/5jfH19kZuby1k0vxvylSL/cksGtkQPK10M3XQRGbnv2PIXb4qgICvDdjdV0laTx4s8fh/OSqv+uY+Wc06i89IYdFh4GjfScgAAaS8rxil+djJDYupr/H7mMe6nv0Hc/ZdYcPA23Ds2rlErhXw9K35birizsdgeHAo9fX1xh/NNqUnLoaofn35+fp88Rnl5OaZPn45OnTqhVatWACrmrZGXl4empianrp6eHjIyMtg67yeGyvWV6z5WJy8vD4WFhXj58iXKysqqrFO5Dz4k4mql4ODgz95WQUFBZM4HgaxcNbUly5KBLdHTWh9DNyXgWXYhZ93tf3NRXFqOThbaOHmz4g/aVFcFjbSUce3J6xofq5wBMnMrksqPtoZITH2N7IKKZqqivAzKyhlO/bL/b1bQdR2ShWEY+C1fhpjoKOwMCYORUWNxh/TNqUlvka+vL3x8fDhlfOaY8fb2xu3bt3H+vOil5d8KiUgO0mjpoFZwbWeI8TuuIr+oDNpqFR+4N+9KUFRSjjfvSnHg0r+Y378Fct6WIP9dCRYPbIXE1NdIeprD7sdYWxnKCrLQUVOAopwMWjRSBwA8yniDkjIGDVTk0LuNAS4+egUFORkM7mCEPm0M4LHpf83L6DtZ8HO3xvBOTRB3/wV01RWxYIAVkp6+RlYNWymkbq1YtgQnjh/Duo1boKKsgpcvKi4aUFVTg6Kiopij+zbUZCyhqh+fnzJ58mQcO3YMcXFxMDIyYsv19fVRXFyMnJwcTushMzMT+v/f+tPX1xe5qqjyaqb363x4hVNmZibU1dWhpKQEGRkZyMjIVFlHvwatTLElB1tbW0RHR6NBgwZo27btR/9g165d+4qRfR0jOhsDAPZN4T6afNaeG/jr8jMAwLIjd8GUt8DWUbb/fxPcSyz4YJB4pUdrdDRvyL4+PrsLAKDz0hj89/+tkYEdjDDXtQUEAK49ycHQzRdxIy2X3eavy8+gqiCLkZ1NMM/VCnmFJUh4+Aor/7lX6+dNvsyB/XsBAGO8RnDKl/7mB9cBHx+fIxXq6jYHhmEwZcoUHDlyBLGxsTA1NeWsb9euHeTk5BAdHY2BAwcCAJKTk5GWlsZOUWBvb4/ly5cjKysLurq6AICoqCioq6vDysqKrXP8+HHOvqOioth9yMvLo127doiOjkb//v0BVHRzRUdHY/LkybzPR8AwDPPparVvyZIlmD17NpSVlbF48eKPJodFixbVaN+m02neaWlyb42LuEMgX5HiF/6ktZxz6tOV/l/yKmfedX/++Wfs2bMHf//9N3tDLwBoaGhASanisvFJkybh+PHjCAkJgbq6OqZMmQIAiI+PB1BxKauNjQ0MDQ3h7++PjIwMjBgxAmPHjsWKFSsAVFzK2qpVK3h7e2P06NGIiYnB1KlTERERAWfninj3798PT09PbNu2DR06dMC6detw4MAB3L9/X2QsojpiSw51iZKDdKHkIF2+NDk0/5V/cri/kn9yqO4HbnBwMLy8vABU3AQ3c+ZM7N27F0VFRXB2dsaWLVs43T1Pnz7FpEmTEBsbCxUVFXh6emLlypWQlf3ficfGxmLGjBm4e/cujIyMsGDBAvYYlTZt2oTVq1cjIyMDNjY22LBhA+zs7PifjyQkh6ZNm+LKlSto2LAhpzwnJwe2trZ4/PhxNVtWjZKDdKHkIF2+NDlYzY3kXffuip5fdrBvmEQMSD958gRlZWUi5UVFRXj27JkYIiKE1Ff0aCV+xJocjh49yv771KlTnHsaysrKEB0dLTKoQwghX0JIM8HxItbkUDmSLhAI4OnpyVknJycHExMTBAQEiCEyQkh9RU9l5UesyaG8vBwAYGpqiitXrkBbW1uc4RBCpAAlB34kYswhNTVV3CEQQqQE5QZ+xJYcNmzYgPHjx0NRUREbNmz4aN2pU6d+pagIIfUdtRz4EVtyCAwMxPDhw6GoqMh51OyHBAIBJQdCSK2h3MCP2JLD+11J1K1ECPlaqOXAj0SMORBCyNdCuYEfiUgOHz4St5JAIICioiLMzc3h6urKTppBCCGfi1oO/EhEcrh+/TquXbuGsrIy9oFVDx48gIyMDJo3b44tW7Zg5syZOH/+PPtkQkII+RyUG/iRiJngXF1d4eTkhOfPnyMxMRGJiYl49uwZfvjhBwwdOhT//fcfunbtihkzZog7VELIN04oFPBepJlEJIfVq1dj2bJlUFdXZ8s0NDSwePFi+Pv7Q1lZGQsXLmQn6iaEkM9Vk2lCpZlEJIfc3FxkZWWJlL948QJ5eXkAKuaZLi4u/tqhEULqGYGA/yLNJCI5uLq6YvTo0Thy5AiePXuGZ8+e4ciRIxgzZgz7/KXLly+jWbNm4g2UEPLNo5YDPxIxIL1t2zbMmDEDHh4eKC0tBQDIysrC09OTvUGuefPm2LFjhzjDJITUA1L+nc+bRCQHVVVVbN++HYGBgezEPk2bNoWqqipbx8bGRkzREULqE2lvEfAlEd1KlTIyMpCeng4LCwuoqqpCAiapI4TUM9StxI9EJIdXr17B0dERzZo1Q58+fZCeng4AGDNmDGbOnCnm6Agh9QkNSPMjEclhxowZkJOTQ1paGpSVldlyd3d3nDx5UoyREULqG2o58CMRYw6RkZE4deoUjIyMOOUWFhZ4+vSpmKIihNRH0n5zG18SkRwKCgo4LYZK2dnZUFBQEENEhJD6SsobBLxJRLdSly5dsGvXLva1QCBAeXk5/P390b17d/EFRgipd4QCAe9FmklEy8Hf3x+Ojo64evUqiouL8csvv+DOnTvIzs7GhQsXxB0eIaQekfLvfN4kouXQqlUrPHjwAJ07d4arqysKCgrg5uaGy5cvY9WqVeIOjxBSj9CAND8S0XIAKh60N2/ePE7ZjRs3sHPnTvz+++9iiooQUt/QeDQ/EpMcCCHka5D2FgFflBwIIVKFcgM/lBwIIVJFAMoOfIg1Obi5uX10fU5OztcJhBAiNWRo0IEXsSYHDQ2NT64fOXLkV4qGECINqFuJH7Emh+DgYHEenhAihaT95ja+eN3ncPToUd4LIYRIsrp8KmtcXBz69esHQ0NDCAQChIeHc9Z7eXmJ3EvRq1cvTp3s7GwMHz4c6urq0NTUxJgxY5Cfn8+pc/PmTXTp0gWKiopo3Lgx/P39RWI5ePAgmjdvDkVFRVhbW+P48eM1OhdeLYfKqTo/RSAQoKysrEYBEELI11SXl7IWFBSgTZs2GD16dLVjqr169eL0mnz4/Ljhw4cjPT0dUVFRKCkpwahRozB+/Hjs2bMHAJCXl4eePXvCyckJQUFBuHXrFkaPHg1NTU2MHz8eABAfH4+hQ4fCz88Pffv2xZ49e9C/f39cu3YNrVq14nUuAqYezqhjOj1C3CGQr+jeGhdxh0C+IsUv7AwfHHKNd92DXraffRyBQIAjR45wflx7eXkhJydHpEVR6d69e7CyssKVK1fw3XffAQBOnjyJPn364NmzZzA0NMTWrVsxb948ZGRkQF5eHgDw66+/Ijw8HPfv3wdQMd1BQUEBjh07xu67Y8eOsLGxQVBQEK/4JeLxGYQQ8rXU5MF7RUVFyMvL4yxFRUVfdPzY2Fjo6urC0tISkyZNwqtXr9h1CQkJ0NTUZBMDADg5OUEoFOLSpUtsna5du7KJAQCcnZ2RnJyM169fs3WcnJw4x3V2dkZCQgLvOD8rBxcUFODs2bNIS0tDcXExZ93UqVM/Z5eEEPJV1KRTyc/PD0uWLOGULVq0CIsXL/6sY/fq1Qtubm4wNTVFSkoK5s6di969eyMhIQEyMjLIyMiArq4uZxtZWVloaWkhIyMDQMV0yqamppw6enp67LoGDRogIyODLXu/TuU++Khxcrh+/Tr69OmDt2/foqCgAFpaWnj58iWUlZWhq6tLyYEQItFqMubg6+sLHx8fTtmXzDHj4eHB/tva2hqtW7eGmZkZYmNj4ejo+Nn7rQs17laaMWMG+vXrh9evX0NJSQkXL17E06dP0a5dO6xZs6YuYiSEkFojIxTwXhQUFKCurs5ZanMCsqZNm0JbWxuPHj0CAOjr6yMrK4tTp7S0FNnZ2dDX12frZGZmcupUvv5Uncr1fNQ4OSQlJWHmzJkQCoWQkZFBUVEReynV3Llza7o7Qgj5quryUtaaevbsGV69egUDAwMAgL29PXJycpCYmMjWiYmJQXl5Oezs7Ng6cXFxKCkpYetERUXB0tISDRo0YOtER0dzjhUVFQV7e3vesdU4OcjJyUEorNhMV1cXaWlpACruZv73339rujtCCPmq6nI+h/z8fCQlJSEpKQkAkJqaiqSkJKSlpSE/Px+zZ8/GxYsX8eTJE0RHR8PV1RXm5uZwdnYGALRo0QK9evXCuHHjcPnyZVy4cAGTJ0+Gh4cHDA0NAQDDhg2DvLw8xowZgzt37mD//v1Yv349p/tr2rRpOHnyJAICAnD//n0sXrwYV69exeTJk3mfS43HHNq2bYsrV67AwsIC3bp1w8KFC/Hy5UuEhYXxvn6WEELEpS4frXT16lU4ODiwryu/sD09PbF161bcvHkToaGhyMnJgaGhIXr27Illy5Zxuqp2796NyZMnw9HREUKhEAMHDsSGDRvY9RoaGoiMjIS3tzfatWsHbW1tLFy4kL3HAQC+//577NmzB/Pnz8fcuXNhYWGB8PDwGn1H1/g+h6tXr+LNmzdwcHBAVlYWRo4cifj4eFhYWOCPP/5AmzZtarK7OkH3OUgXus9BunzpfQ6j9t3iXTfYw/rLDvYNq/Hb/P71t7q6ujh58mStBkQIIXWJnqzED83nQAiRKvTgPX5qnBxMTU0/OlDz+PHjLwqIEELqEuUGfmqcHKZPn855XVJSguvXr+PkyZOYPXt2bcVFCCF1guaQ5qfGyWHatGlVlm/evBlXr1794oAIIaQuUW7gp9YevNe7d2/89ddftbU7QgipEzW5Q1qa1dqA9KFDh6ClpVVbuyOEkDpB3Ur8fNZNcO+/uQzDICMjAy9evMCWLVtqNbjPdWd1H3GHQL6iBu353/VJvn2F1zd90fY0TwE/NU4Orq6unOQgFAqho6OD7t27o3nz5rUaHCGE1DZqOfBT4+Twuc8xJ4QQSSDlQwm81biFJSMjI/JIWQB49eoVZGRkaiUoQgipK0IB/0Wa1bjlUN2jmIqKijjT1hFCiCSibiV+eCeHyqcCCgQC7NixA6qqquy6srIyxMXF0ZgDIUTiSXuLgC/eySEwMBBARcshKCiI04UkLy8PExMTBAUF1X6EhBBSi6jhwA/v5JCamgoAcHBwwOHDh9kZhwgh5FsiS9mBlxqPOZw5c6Yu4iCEkK+CcgM/Nb5aaeDAgVi1apVIub+/PwYPHlwrQRFCSF0RCgS8F2lW4+QQFxeHPn1E70Du3bs34uLiaiUoQgipKwIB/0Wa1bhbKT8/v8pLVuXk5JCXl1crQRFCSF2hq5X4qXHLwdraGvv37xcp37dvH6ysrGolKEIIqSvUrcRPjVsOCxYsgJubG1JSUtCjRw8AQHR0NPbs2YNDhw7VeoCEEFKbpPw7n7caJ4d+/fohPDwcK1aswKFDh6CkpIQ2bdogJiaGHtlNCJF41K3Ez2fN5+Di4gIXFxcAQF5eHvbu3YtZs2YhMTERZWVltRogIYTUJgEoO/Dx2Y82j4uLg6enJwwNDREQEIAePXrg4sWLtRkbIYTUOlkh/0Wa1ajlkJGRgZCQEOzcuRN5eXkYMmQIioqKEB4eToPRhJBvAj14jx/eubFfv36wtLTEzZs3sW7dOjx//hwbN26sy9gIIaTW0SO7+eHdcjhx4gSmTp2KSZMmwcLCoi5jIoSQOkMNB354txzOnz+PN2/eoF27drCzs8OmTZvw8uXLuoyNEEJqHd3nwA/v5NCxY0ds374d6enpmDBhAvbt2wdDQ0OUl5cjKioKb968qcs4CSGkVlC3Ej81Ho9XUVHB6NGjcf78edy6dQszZ87EypUroaurix9//LEuYiSEkFpDz1bi54su1rK0tIS/vz+ePXuGvXv31lZMhBBSZ4QQ8F6k2WfdBPchGRkZ9O/fH/3796+N3RFCSJ2R9hYBX1J+mwchRNrICgW8l5qKi4tDv379YGhoCIFAgPDwcM56hmGwcOFCGBgYQElJCU5OTnj48CGnTnZ2NoYPHw51dXVoampizJgxyM/P59S5efMmunTpAkVFRTRu3Bj+/v4isRw8eBDNmzeHoqIirK2tcfz48RqdCyUHQohUqcsxh4KCArRp0wabN2+ucr2/vz82bNiAoKAgXLp0CSoqKnB2dsa7d+/YOsOHD8edO3cQFRWFY8eOIS4uDuPHj2fX5+XloWfPnjA2NkZiYiJWr16NxYsX4/fff2frxMfHY+jQoRgzZgyuX7/O9uzcvn2b//vEMAxT87dAsr0tqXenRD6iYYcp4g6BfEWF1zd90fY7L6fxrjumQ5PPPo5AIMCRI0fY7naGYWBoaIiZM2di1qxZAIDc3Fzo6ekhJCQEHh4euHfvHqysrHDlyhV89913AICTJ0+iT58+ePbsGQwNDbF161bMmzcPGRkZ7Nw6v/76K8LDw3H//n0AgLu7OwoKCnDs2DE2no4dO8LGxgZBQUG84qeWAyFEqtSk5VBUVIS8vDzOUlRU9FnHTU1NRUZGBpycnNgyDQ0N2NnZISEhAQCQkJAATU1NNjEAgJOTE4RCIS5dusTW6dq1K2fSNWdnZyQnJ+P169dsnfePU1mn8jh8UHIghEgVYQ0WPz8/aGhocBY/P7/POm5GRgYAQE9Pj1Oup6fHrsvIyICuri5nvaysLLS0tDh1qtrH+8eork7lej5q5WolQgj5VtTkwXu+vr7w8fHhlCkoKNR2SBKJkgMhRKrUZJxZQUGh1pKBvr4+ACAzMxMGBgZseWZmJmxsbNg6WVlZnO1KS0uRnZ3Nbq+vr4/MzExOncrXn6pTuZ4P6lYihEgVcT1bydTUFPr6+oiOjmbL8vLycOnSJdjb2wMA7O3tkZOTg8TERLZOTEwMysvLYWdnx9aJi4tDSUkJWycqKgqWlpZo0KABW+f941TWqTwOHxLRcsjLy6uyXCAQQEFBgTPwQgghX6Iu74HLz8/Ho0eP2NepqalISkqClpYWmjRpgunTp+O3336DhYUFTE1NsWDBAhgaGrJXNLVo0QK9evXCuHHjEBQUhJKSEkyePBkeHh4wNDQEAAwbNgxLlizBmDFjMGfOHNy+fRvr169HYGAge9xp06ahW7duCAgIgIuLC/bt24erV69yLnf9FIlIDpqamh/tBzQyMoKXlxcWLVoEoZAaO4SQz1eXd0hfvXoVDg4O7OvK8QpPT0+EhITgl19+QUFBAcaPH4+cnBx07twZJ0+ehKKiIrvN7t27MXnyZDg6OkIoFGLgwIHYsGEDu15DQwORkZHw9vZGu3btoK2tjYULF3Luhfj++++xZ88ezJ8/H3PnzoWFhQXCw8PRqlUr3uciEfc57Nq1C/PmzYOXlxc6dOgAALh8+TJCQ0Mxf/58vHjxAmvWrMHs2bMxd+7cT+6P7nOQLnSfg3T50vsc9l//j3dd97aNvuhY3zKJaDmEhoYiICAAQ4YMYcv69esHa2trbNu2DdHR0WjSpAmWL1/OKzkQQkh1aJpQfiSijyY+Ph5t27YVKW/bti1700bnzp2Rlsb/zkZCCKmKoAaLNJOI5NC4cWPs3LlTpHznzp1o3LgxAODVq1fsSDwhhHwugUDAe5FmEtGttGbNGgwePBgnTpxA+/btAVQM7Ny/fx+HDh0CAFy5cgXu7u7iDJMQUg9IxC/ib4BEJIcff/wR9+/fx7Zt2/DgwQMAQO/evREeHg4TExMAwKRJk8QYISGkvpD2FgFfEpEcgIobRFauXCnuMAgh9RylBn4kJjnk5OTg8uXLyMrKQnl5OWfdyJEjxRQVIaS+oYYDPxKRHP755x8MHz4c+fn5UFdX5zT7BAIBJQdCSK2R9rmh+ZKIsZmZM2di9OjRyM/PR05ODl6/fs0u2dnZ4g6PEFKPiOvZSt8aiWg5/Pfff5g6dSqUlZXFHQohpJ6T8u983iSi5eDs7IyrV6+KOwxCiBQQQsB7kWYS0XJwcXHB7NmzcffuXVhbW0NOTo6z/scffxRTZISQ+oZaDvxIxIP3PvakVYFAgLKyshrtjx68J13owXvS5UsfvBd57wXvuj1b6HzRsb5lEtFy+PDSVUIIqSsCKe8u4ksikgMhhHwtQsoNvIgtOWzYsAHjx4+HoqIiZyKLqkydOvUrRUUIqe+o5cCP2MYcTE1NcfXqVTRs2BCmpqbV1hMIBHj8+HGN9k1jDtKFxhyky5eOOZxJfsW7roNlwy861rdMbC2H1NTUKv9N/mfn9m2IOR2FJ6mPoaCoiDY2bTFtxkyYmDYVqcswDCZPGo/48+ewdv0mODg6sesuXUzAlo3r8ejhAygpKaOfa394T50OWVnqVfxaZo3uif492qCZiR4Ki0pw6cZjzFv/Nx4+zeLUs2ttisXefdHe2gRlZeW4+eA/9Pt5M94VlaBLOwtE7phW5f47D/dH4t00KMjLYuM8D7Rt0QTNTfVw4txtDPHZLlK/SzsLrJrpBiszfTzLyMHKHSfx5z+X6uTcJY0MXa7EC307SLBrV6/AfegwtGxljdLSMmxaH4hJ48fi8N/HoPTBDYO7w0KrfNpk8v37mDJpPMaMn4hlfquQlZmJFUsXo6ysDD6z53ylMyFdbM0RtD8OiXeeQlZWBksm98OxrZPR1u03vH1XDKAiMfy96WesCY6Ez6qDKC0rR+tmjVBeXtESvnjjMUycfDn7XfhzXzh0sETi3YqJsGSEQhQWlWDL3lj0d7SpMhZjw4Y4snEidhw6j1HzQuDQwRJbFw5Dxss8nE64V3dvgoSgbiV+JCI5lJWVISQkBNHR0VU+eC8mJkZMkYnX5m07OK+XLPeDY9fvcffuHbT7rj1bnnz/HsJCg7F7/yH80L0LZ5vIk8dh0cwSEyZ5AwCaNDHGtJmzMGfmDEz42RsqKqp1fyIErpO3cF6PX/Qn/o1ZibZWjXHhWgoAwH+mG7bsi8Wa4Ci23vsti5LSMmS+esO+lpUVom/31ti67yxb9vZdMaat2A8AsLdpCk01JZFYxg3qjCf/vcKva48AAJJTM/F9WzNMGe4gHcmBcgMvEnGH9LRp0zBt2jSUlZWhVatWaNOmDWchFfLzK74YNDQ02LLCwkL4/jILv85bCG1t0Wuyi0uKoaCgwClTUFBEUVER7t25U7cBk2qpqyoCAF7nvgUA6DRQRYfWpniRnY8zIT54cnoFIndMw/c2ol2Ilfp2a42GGioI+/tijY5t18YUZy4lc8qi4u/BrnX1Y3/1CU0Tyo9EtBz27duHAwcOoE+fPjXetqioCEVFRZyyMqG8yBfit668vBxrVq6ATVtbmFs0Y8sD/P3QxqYtHHo4Vrnd9993xp6wXThx/Bh6OvfGq5cv8XtQxa/YFy/53wxEao9AIMDqWYMQfz0Fd1PSAQCmRtoAgHkT+sA38AhuJj/D8L4dcHzbFLQbvAIpaaJ/K8/+9ohKuIf/snJqdHy9hurIzH7DKcvKzoOGmhIUFeTwrqjk807sGyHtD9TjSyJaDvLy8jA3N/+sbf38/KChocFZ1qzyq+UIxc/vt6V49OghVq5ey5bFnonB5UuXMPtX32q3s+/UGdNnzsaKpYthZ9sarn17oXOXrgAAoUAi/vxSZ53vELQ0N8DIX4PZMuH/X3y/86/zCDt6ETeSn+GXgMN48CQLnq72IvtopKuJH+xbIDQ84avFXV9Qy4Efifh2mDlzJtavX4/PuarW19cXubm5nGXWnOq/LL9FK5cvxbmzsdj+xy7o6euz5VcuXcSzf9PQ1b4DvmvTEt+1aQkAmDVjKsZ6jWDrjfAchbiEKzgeFYMz5xLQ3aGilWFk1PjrnghB4JzB6NOlFZzHbeD84k9/kQcAuPc4g1M/OTUDjfUbiOxnhGtHvMotwLGzN2scQ+arPOhpqXHKdLXUkfumsN63GgBQduBJbN1Kbm5unNcxMTE4ceIEWrZsKfLgvcOHD1e7HwUFBZEupPpynwPDMFi1Yhliok9je/AuNDIy4qwfNXYcBgwcxCkbPOBHzPzlV3Tr3oNTLhAIoKurBwA4eSIC+voGaG5lVbcnQDgC5wzGjz3aoOe49Xj6nHut/dPnr/A8KwfNTHQ55ebGuoi8cFdkXyN/7Ig9xy6jtLTmj565dCMVzp1bcsocOzbHpZvScUk5Xa3Ej9iSw/uDqgAwYMAAMUUiufx+W4oTx48hcMNmqKio4OX/jxGoqqpBUVER2to6VQ5CGxgYchJJ6B878X3nzhAKhYg+HYXgHdvhHxAIGRmZr3Yu0m6d7xC49/4Og2f8jvyCd9BrWPHLPTf/HftrPTD0NOZPdMGtB//hRvIz/NTPDpYmehg2eydnX907NIOpkTaCj8RXeazmTfUhLyuDBhoqUFNWQOtmjQAANx/8BwDYfug8Jnp0xfJprgj9+yK6t2+GgT+0xYCpQXV1+hKFhhz4kYinsta2+tJyaNuqeZXlS35bgR/7u1W5rm2r5iI3wY0f7Yl79+6ipLgYzSybY/wkb3bcoT74Fu6Qru6u3nELwzg3n80a9QMmDOmKBhrKuPXgP8xbF474JO4TAkJWeKGJQQP0GBVY5T7vRyyBsaHonb1KbSez/+7SzgL+s9zQoqk+/svMgd/2b+cmuC+9Q/pKai7vuu1NNT5dqZ6SiORQWFgIhmHYmeCePn2KI0eOwMrKCj179qzx/upLciD8fAvJgdSeL00OV1PzeNf9zlT9i471LZOIAWlXV1fs2rULAJCTk4MOHTogICAArq6u2Lp1q5ijI4TUJwIB/0WaSURyuHbtGrp0qbiz99ChQ9DX18fTp0+xa9euTz6xlRBCaoIuVuJHIm6Ce/v2LdTUKgboIiMj4ebmBqFQiI4dO+Lp06dijo4QUq9I+7c+TxLRcjA3N0d4eDj+/fdfnDp1ih1nyMrKgrq69Pb5EUJqn6AG/5NmEpEcFi5ciFmzZsHExAR2dnawt6+4IzQyMhJt27YVc3SEkPqExhz4kYjkMGjQIKSlpeHq1as4efIkW+7o6IjAwKov1yOEkM9RV2MOixcvhkAg4CzNm//vcvR3797B29sbDRs2hKqqKgYOHIjMzEzOPtLS0uDi4gJlZWXo6upi9uzZKC0t5dSJjY2Fra0tFBQUYG5ujpCQkBpGyo9EjDkAgL6+PvTfezQEAHTo0EFM0RBC6q06bBG0bNkSp0+fZl+/P6HWjBkzEBERgYMHD0JDQwOTJ0+Gm5sbLly4AKBi6gIXFxfo6+sjPj4e6enpGDlyJOTk5LBixQoAFROjubi4YOLEidi9ezeio6MxduxYGBgYwNnZuVbPRWz3Obi5uSEkJATq6uoij9L40Mcen1EVus9ButB9DtLlS+9zuPlvPu+6lrpyIk99ruqRPUBFyyE8PBxJSUki63Jzc6Gjo4M9e/Zg0KCKR97cv38fLVq0QEJCAjp27IgTJ06gb9++eP78OfT0Kh51ExQUhDlz5uDFixeQl5fHnDlzEBERgdu3b7P79vDwQE5ODqfXpTaIrVtJQ0ODnbnsw6eqfrgQQkhtEQr4L1U99dnPr/qnPj98+BCGhoZo2rQphg8fjrS0ihn6EhMTUVJSAien/z25oHnz5mjSpAkSEiqerJuQkABra2s2MQCAs7Mz8vLycOf/515JSEjg7KOyTuU+apPYupWCg4Or/DchhNSpGnQr+fr6wsfHh1NW3VwxdnZ2CAkJgaWlJdLT07FkyRJ06dIFt2/fRkZGBuTl5aGpqcnZRk9PDxkZFU/izcjI4CSGyvWV6z5WJy8vD4WFhVBSEp3573NJzJgDIYR8DTW5RLW6LqSq9O7dm/1369atYWdnB2NjYxw4cKBWv7S/Fom4WikzMxMjRoyAoaEhZGVlISMjw1kIIaS2fK1LWTU1NdGsWTM8evQI+vr6KC4uRk5ODqdOZmYmeyGOvr6+yNVLla8/VUddXb3WE5BEtBy8vLyQlpaGBQsWwMDAgB2LIISQ2va1vl3y8/ORkpKCESNGoF27dpCTk0N0dDQGDhwIAEhOTkZaWhp7X5e9vT2WL1+OrKws6OpWzOsRFRUFdXV1WP3/3Cv29vY4fvw45zhRUVHsPmqTRCSH8+fP49y5c7CxsRF3KISQ+q6OssOsWbPQr18/GBsb4/nz51i0aBFkZGQwdOhQaGhoYMyYMfDx8YGWlhbU1dUxZcoU2Nvbo2PHjgCAnj17wsrKCiNGjIC/vz8yMjIwf/58eHt7s11bEydOxKZNm/DLL79g9OjRiImJwYEDBxAREVHr5yMRyaFx48afNUUoIYTUVF09FuPZs2cYOnQoXr16BR0dHXTu3BkXL16Ejk7FhFyBgYEQCoUYOHAgioqK4OzsjC1btrDby8jI4NixY5g0aRLs7e2hoqICT09PLF26lK1jamqKiIgIzJgxA+vXr4eRkRF27NhR6/c4ABIyn0NkZCQCAgKwbds2mJiYfPH+6D4H6UL3OUiXL73PITnjLe+6lvrKX3Ssb5nYWg4NGjTgjC0UFBTAzMwMysrKInNIZ2dnf+3wCCH1FI1o8iO25LBu3TpxHZoQIsXoghd+xJYcPD09UVZWhjVr1uDo0aMoLi6Go6MjFi1a9E1eE0wI+TZQbuBHrPc5rFixAnPnzoWqqioaNWqE9evXw9vbW5whEULqOZoJjh+xJoddu3Zhy5YtOHXqFMLDw/HPP/9g9+7dKC8vF2dYhJD6jLIDL2JNDmlpaejTpw/72snJCQKBAM+fPxdjVISQ+oxmguNHrPc5lJaWQlFRkVMmJyeHkpISMUVECKnvaMyBH7EmB4Zh4OXlxXmw1bt37zBx4kSoqKiwZTWdz4EQQqpDuYEfsSYHT09PkbKffvpJDJEQQqQGZQdexJocaB4HQsjXJu1jCXxJxLOVCCHkaxFSbuCFkgMhRKrQgDQ/lBwIIVKGsgMflBwIIVKFWg78UHIghEgVyg38UHIghEgVajnwQ8mBECJV6FJWfig5EEKkC+UGXig5EEKkCuUGfig5EEKkipAGHXih5EAIkS6UG3ih5EAIkSqUG/ih5EAIkSrUq8QPJQdCiFShS1n5oeRACJEq1HLgR6xzSBNCCJFM1HIghEgVajnwQ8mBECJVaMyBH0oOhBCpQi0Hfig5EEKkCiUHfig5EEKkCnUr8UPJgRAiVajlwA8lB0KIVKHcwA8lB0KIdKHswAslB0KIVKExB34oORBCpAqNOfAjYBiGEXcQ5MsVFRXBz88Pvr6+UFBQEHc4pI7R35vUNUoO9UReXh40NDSQm5sLdXV1cYdD6hj9vUldowfvEUIIEUHJgRBCiAhKDoQQQkRQcqgnFBQUsGjRIhqclBL09yZ1jQakCSGEiKCWAyGEEBGUHAghhIig5EAIIUQEJQcJZ2JignXr1n20zuLFi2FjY/NV4iGfJyQkBJqamh+t4+Xlhf79+/PaH5+6fD47hFSHksMXqsl/0J/jypUrGD9+PPtaIBAgPDycU2fWrFmIjo6usxgqURL6OC8vLwgEAggEAsjLy8Pc3BxLly5FaWkpr+3Xr1+PkJCQWovnw88OITVBD96TcDo6Op+so6qqClVV1a8QDfmUXr16ITg4GEVFRTh+/Di8vb0hJycHAwODT26roaFRq7Hw+ewQUh1qOdSh27dvo3fv3lBVVYWenh5GjBiBly9fsuvfvHmD4cOHQ0VFBQYGBggMDET37t0xffp0ts77XQMmJiYAgAEDBkAgELCvP/xFX9maWbFiBfT09KCpqcn+gp09eza0tLRgZGSE4OBgTrxz5sxBs2bNoKysjKZNm2LBggUoKSkBUNEtsmTJEty4cYP9dVz5KzcnJwdjx46Fjo4O1NXV0aNHD9y4caNW38tvhYKCAvT19WFsbIxJkybByckJR48eZdefOnUKLVq0gKqqKnr16oX09HR23Yet0EOHDsHa2hpKSkpo2LAhnJycUFBQwDnemjVrYGBggIYNG8Lb25v9ewGi3UoCgQA7duzAgAEDoKysDAsLC05sAHD06FFYWFhAUVERDg4OCA0NhUAgQE5OTu28QeSbQcmhjuTk5KBHjx5o27Ytrl69ipMnTyIzMxNDhgxh6/j4+ODChQs4evQooqKicO7cOVy7dq3afV65cgUAEBwcjPT0dPZ1VWJiYvD8+XPExcVh7dq1WLRoEfr27YsGDRrg0qVLmDhxIiZMmIBnz56x26ipqSEkJAR3797F+vXrsX37dgQGBgIA3N3dMXPmTLRs2RLp6elIT0+Hu7s7AGDw4MHIysrCiRMnkJiYCFtbWzg6OiI7O/uL3sP6QElJCcXFxQCAt2/fYs2aNQgLC0NcXBzS0tIwa9asKrdLT0/H0KFDMXr0aNy7dw+xsbFwc3PD+7clnTlzBikpKThz5gxCQ0MREhLyyW6pJUuWYMiQIbh58yb69OmD4cOHs3+n1NRUDBo0CP3798eNGzcwYcIEzJs3r3beCPLtYcgX8fT0ZFxdXUXKly1bxvTs2ZNT9u+//zIAmOTkZCYvL4+Rk5NjDh48yK7PyclhlJWVmWnTprFlxsbGTGBgIPsaAHPkyBHOfhctWsS0adOGE5OxsTFTVlbGlllaWjJdunRhX5eWljIqKirM3r17qz231atXM+3atav2OAzDMOfOnWPU1dWZd+/eccrNzMyYbdu2Vbvv+uj9z0J5eTkTFRXFKCgoMLNmzWKCg4MZAMyjR4/Y+ps3b2b09PSq3D4xMZEBwDx58qTaYxkbGzOlpaVs2eDBgxl3d3f2dVWfnfnz57Ov8/PzGQDMiRMnGIZhmDlz5jCtWrXiHGfevHkMAOb169c1ei/It4/GHOrIjRs3cObMmSrHAlJSUlBYWIiSkhJ06NCBLdfQ0IClpWWtHL9ly5YQCv/XMNTT00OrVq3Y1zIyMmjYsCGysrLYsv3792PDhg1ISUlBfn4+SktLP/k46Bs3biA/Px8NGzbklBcWFiIlJaVWzuVbcuzYMaiqqqKkpATl5eUYNmwYFi9ejIMHD0JZWRlmZmZsXQMDA877/742bdrA0dER1tbWcHZ2Rs+ePTFo0CA0aNCArdOyZUvIyMhw9nfr1q2Pxte6dWv23yoqKlBXV2djSE5ORvv27Tn13/98EulCyaGO5Ofno1+/fli1apXIOgMDAzx69KhOjy8nJ8d5LRAIqiwrLy8HACQkJGD48OFYsmQJnJ2doaGhgX379iEgIOCjx8nPz4eBgQFiY2NF1n3q0s36yMHBAVu3boW8vDwMDQ0hK/u//8Sqev+Zap5eIyMjg6ioKMTHxyMyMhIbN27EvHnzcOnSJZiamla7v8q/Z3U+ZxsinSg51BFbW1v89ddfMDEx4XxBVGratCnk5ORw5coVNGnSBACQm5uLBw8eoGvXrtXuV05ODmVlZbUeb3x8PIyNjTl9zE+fPuXUkZeXFzm2ra0tMjIyICsryw6QSzMVFRWYm5vXyr4EAgE6deqETp06YeHChTA2NsaRI0fg4+NTK/v/kKWlJY4fP84p+9i4FqnfaEC6FuTm5iIpKYmzjB8/HtnZ2Rg6dCiuXLmClJQUnDp1CqNGjUJZWRnU1NTg6emJ2bNn48yZM7hz5w7GjBkDoVAIwUcmuTUxMUF0dDQyMjLw+vXrWjsHCwsLpKWlYd++fUhJScGGDRtw5MgRkWOnpqYiKSkJL1++RFFREZycnGBvb4/+/fsjMjIST548QXx8PObNm4erV6/WWnzS5tKlS1ixYgWuXr2KtLQ0HD58GC9evECLFi3q7JgTJkzA/fv3MWfOHDx48AAHDhxgB7g/9pkk9RMlh1oQGxuLtm3bcpZly5bhwoULKCsrQ8+ePWFtbY3p06dDU1OTHQtYu3Yt7O3t0bdvXzg5OaFTp05o0aIFFBUVqz1WQEAAoqKi0LhxY7Rt27bWzuHHH3/EjBkzMHnyZNjY2CA+Ph4LFizg1Bk4cCB69eoFBwcH6OjoYO/evRAIBDh+/Di6du2KUaNGoVmzZvDw8MDTp0+hp6dXa/FJG3V1dcTFxaFPnz5o1qwZ5s+fj4CAAPTu3bvOjmlqaopDhw7h8OHDaN26NbZu3cq2JOnR4NKHHtktQQoKCtCoUSMEBARgzJgx4g6HECxfvhxBQUH4999/xR0K+cpozEGMrl+/jvv376NDhw7Izc3F0qVLAQCurq5ijoxIqy1btqB9+/Zo2LAhLly4gNWrV2Py5MniDouIASUHMVuzZg2Sk5MhLy+Pdu3a4dy5c9DW1hZ3WERKPXz4EL/99huys7PRpEkTzJw5E76+vuIOi4gBdSsRQggRQQPShBBCRFByIIQQIoKSAyGEEBGUHAghhIig5EAIIUQEJQfyTflwQpwPJ0f6WmJjY2kSHFKvUXIgteJL50/+XIcPH8ayZct41aUvdEL4o5vgSK2pbv7kD2+iKi4uhry8fK0cU0tLq1b2QwjhopYDqTXVzZ9c2RW0fPlyGBoashMa/fvvvxgyZAg0NTWhpaUFV1dXPHnyhN1fWVkZfHx8oKmpiYYNG+KXX34Rmf/gw26loqIizJkzB40bN4aCggLMzc2xc+dOPHnyBA4ODgCABg0aQCAQwMvLCwBQXl4OPz8/mJqaQklJCW3atMGhQ4c4xzl+/DiaNWsGJSUlODg4cOIkpD6i5EDqzPvzJ0dHRyM5ORlRUVE4duwYSkpK4OzsDDU1NZw7dw4XLlyAqqoqevXqxW4TEBCAkJAQ/PHHHzh//jyys7NFHiP+oZEjR2Lv3r3YsGED7t27h23btkFVVRWNGzfGX3/9BaBixrP09HSsX78eAODn54ddu3YhKCgId+7cwYwZM/DTTz/h7NmzACqSmJubG/r164ekpCSMHTsWv/76a129bYRIBnHOUUrqj4/Nn+zp6cno6ekxRUVFbP2wsDDG0tKSKS8vZ8uKiooYJSUl5tSpUwzDMIyBgQHj7+/Pri8pKWGMjIw4c3Z369aNnXM7OTmZAcBERUVVGeOZM2dE5kN+9+4do6yszMTHx3Pqjhkzhhk6dCjDMAzj6+vLWFlZcdbPmTOH5lYm9RqNOZBaU938yd7e3rC2tuaMM9y4cQOPHj2CmpoaZx/v3r1DSkoKcnNzkZ6eDjs7O3adrKwsvvvuu2qn1kxKSoKMjAy6devGO+ZHjx7h7du3+OGHHzjlxcXF7HwZ9+7d48QBAPb29ryPQci3iJIDqTUfmz9ZRUWFUzc/Px/t2rXD7t27Rfajo6PzWcdXUlKq8Tb5+fkAgIiICDRq1Iizjia4IdKMkgOpNTWZP9nW1hb79++Hrq4u1NXVq6xjYGCAS5cusXNql5aWIjExEba2tlXWt7a2Rnl5Oc6ePQsnJyeR9ZUtl/fnwbaysoKCggLS0tKqbXG0aNECR48e5ZRdvHjx0ydJyDeMBqSJWAwfPhza2tpwdXXFuXPnkJqaitjYWEydOhXPnj0DAEybNg0rV65EeHg47t+/j59//vmj9yiYmJjA09MTo0ePRnh4OLvPAwcOAACMjY0hEAhw7NgxvHjxAvn5+VBTU8OsWbMwY8YMhIaGIiUlBdeuXcPGjRsRGhoKAJg4cSIePnyI2bNnIzk5GXv27GHnViakvqLkQMRCWVkZcXFxaNKkCdzc3NCiRQuMGTMG7969Y1sSM2fOxIgRI+Dp6Ql7e3uoqalhwIABH93v1q1bMWjQIPz8889o3rw5xo0bh4KCAgBAo0aNsGTJEvz666/Q09NjZzhbtmwZFixYAD8/P7Ro0QK9evVCREQETE1NAQBNmjTBX3/9hfDwcLRp0wZBQUFYsWJFHb47hIgfTfZDCCFEBLUcCCGEiKDkQAghRAQlB0IIISIoORBCCBFByYEQQogISg6EEEJEUHIghBAigpIDIYQQEZQcCCGEiKDkQAghRAQlB0IIISL+D344MrHfRoduAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 391
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot confusion matrix\n",
    "def plot_confusion_matrix(model, X_test, y_test, model_name):\n",
    "    # Predict the target variable on the test set\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # Generate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # Plot confusion matrix using Seaborn heatmap\n",
    "    plt.figure(figsize=(4, 3))\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=['Legitimate', 'Phishing'], yticklabels=['Legitimate', 'Phishing'])\n",
    "    plt.title(f'Confusion Matrix for {model_name}')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.show()\n",
    "\n",
    "# Example usage for Random Forest, Decision Tree, SVM, and Naive Bayes\n",
    "plot_confusion_matrix(rf_model, X_test, y_test, 'Random Forest')\n",
    "plot_confusion_matrix(dt_model, X_test, y_test, 'Decision Tree')\n",
    "plot_confusion_matrix(svm_model, X_test, y_test, 'SVM')\n",
    "plot_confusion_matrix(nb_model, X_test, y_test, 'Naive Bayes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "d461bc915d8d47e0befa97cfcf1bf5a7",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Identifying the Best Model Based on Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cell_id": "8a9779c2b45f4c378abcbce4671b50e7",
    "deepnote_cell_type": "code",
    "execution_context_id": "6f9cb295-3667-4ca0-9920-4e481b8dcddb",
    "execution_millis": 1,
    "execution_start": 1733222172707,
    "source_hash": "69738b98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best Model based on Accuracy: Random Forest\n",
      "\n",
      "Best Model based on Precision: Naive Bayes\n",
      "\n",
      "Best Model based on Recall: Random Forest\n",
      "\n",
      "Best Model based on F1-Score: Random Forest\n"
     ]
    }
   ],
   "source": [
    "# Identify the best model based on performance metrics\n",
    "best_model_accuracy = results_df.loc[results_df['Accuracy'].idxmax(), 'Model']\n",
    "print(f\"\\nBest Model based on Accuracy: {best_model_accuracy}\")\n",
    "best_model_Precision = results_df.loc[results_df['Precision'].idxmax(), 'Model']\n",
    "print(f\"\\nBest Model based on Precision: {best_model_Precision}\")\n",
    "best_model_Recall = results_df.loc[results_df['Recall'].idxmax(), 'Model']\n",
    "print(f\"\\nBest Model based on Recall: {best_model_Recall}\")\n",
    "best_model_F1 = results_df.loc[results_df['F1-Score'].idxmax(), 'Model']\n",
    "print(f\"\\nBest Model based on F1-Score: {best_model_F1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "e99df9c904814e7fad2bb2e9161e66fd",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Model Evaluation Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "a16848ff5dd24ecc86a3693223b56c92",
    "deepnote_cell_type": "text-cell-p",
    "formattedRanges": []
   },
   "source": [
    "The Random Forest model demonstrated exceptional performance, achieving the highest accuracy (99.99%), precision, recall, and F1-Score among all models, with no false negatives and only four false positives. Decision Tree and SVM also showed high accuracy but slightly more errors in predictions. Naive Bayes, while efficient, had comparatively lower performance with higher false negatives. Based on the evaluation, Random Forest is the most reliable and effective model for phishing URL detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cell_id": "8727220f20014c58a1c697466f7a73fc",
    "deepnote_cell_type": "code",
    "execution_context_id": "d4f41fac-da8c-4b97-b323-d5306b2cf1a8",
    "execution_millis": 549,
    "execution_start": 1733219073217,
    "source_hash": "c61ac798"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 547,
       "width": 936
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Extract the feature importances\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "# Get the feature names (assuming X_train is a pandas DataFrame with column names)\n",
    "feature_names = X_train.columns\n",
    "\n",
    "# Sort the features by importance (highest to lowest)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the top 10 important features\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Feature Importances - Random Forest Model\")\n",
    "plt.barh(range(10), importances[indices[:10]], align=\"center\")\n",
    "plt.yticks(range(10), [feature_names[i] for i in indices[:10]])\n",
    "plt.xlabel(\"Importance\")\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to display most important feature on top\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "deepnote_notebook_id": "36c08df2037d44ac9614d20fd5a5b928",
  "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}