diff --git "a/main.ipynb" "b/main.ipynb"
new file mode 100644--- /dev/null
+++ "b/main.ipynb"
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import string\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
+    "from scipy.stats import mode\n",
+    "import joblib\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download NLTK stopwords\n",
+    "# nltk.download('stopwords')\n",
+    "stop_words = set(stopwords.words('english'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load dataset\n",
+    "df = pd.read_csv(\"news.csv\")  # Replace with actual file name\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6335 entries, 0 to 6334\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   Unnamed: 0  6335 non-null   int64 \n",
+      " 1   title       6335 non-null   object\n",
+      " 2   text        6335 non-null   object\n",
+      " 3   label       6335 non-null   object\n",
+      "dtypes: int64(1), object(3)\n",
+      "memory usage: 198.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Keep only relevant columns\n",
+    "df = df[[\"text\", \"label\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6335 entries, 0 to 6334\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column  Non-Null Count  Dtype \n",
+      "---  ------  --------------  ----- \n",
+      " 0   text    6335 non-null   object\n",
+      " 1   label   6335 non-null   object\n",
+      "dtypes: object(2)\n",
+      "memory usage: 99.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_text(text):\n",
+    "    text = text.lower()\n",
+    "    text = re.sub(f\"[{string.punctuation}]\", \"\", text)  # Remove punctuation\n",
+    "    text = \" \".join([word for word in text.split() if word not in stop_words])  # Remove stopwords\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess text\n",
+    "df[\"text\"] = df[\"text\"].astype(str).apply(clean_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert text to TF-IDF features\n",
+    "vectorizer = TfidfVectorizer(max_features=5000)\n",
+    "X = vectorizer.fit_transform(df[\"text\"])\n",
+    "# Convert labels to numeric\n",
+    "df[\"label\"] = df[\"label\"].map({\"FAKE\": 0, \"REAL\": 1})  # Ensure labels are integers\n",
+    "y = np.array(df[\"label\"])  # Convert to NumPy array\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into train and test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train Logistic Regression\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)\n",
+    "y_pred_logreg = logreg.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train Naïve Bayes\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(X_train, y_train)\n",
+    "y_pred_nb = nb.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train Unsupervised Models without Labels\n",
+    "X_unsupervised = vectorizer.transform(df[\"text\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train K-Means Clustering on Unlabeled Data\n",
+    "kmeans = KMeans(n_clusters=2, random_state=42)\n",
+    "kmeans.fit(X_unsupervised)\n",
+    "kmeans_labels = kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train Hierarchical Clustering on Unlabeled Data\n",
+    "hierarchical = AgglomerativeClustering(n_clusters=2)\n",
+    "hierarchical_labels = hierarchical.fit_predict(X_unsupervised.toarray())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_model(y_true, y_pred, model_name):\n",
+    "    accuracy = accuracy_score(y_true, y_pred)\n",
+    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
+    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
+    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
+    "    \n",
+    "    # Generate classification report\n",
+    "    report = classification_report(y_true, y_pred)\n",
+    "    \n",
+    "    # Create text output for the image\n",
+    "    text_output = f\"{model_name} Performance:\\n\"\n",
+    "    text_output += f\"Accuracy: {accuracy:.4f}\\n\"\n",
+    "    text_output += f\"Precision: {precision:.4f}\\n\"\n",
+    "    text_output += f\"Recall: {recall:.4f}\\n\"\n",
+    "    text_output += f\"F1 Score: {f1:.4f}\\n\\n\"\n",
+    "    text_output += f\"Classification Report:\\n{report}\"\n",
+    "\n",
+    "    # Plot and save as an image\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.text(0.01, 0.99, text_output, fontsize=12, ha=\"left\", va=\"top\", family=\"monospace\")\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.savefig(f\"{model_name}_classification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Evaluate Supervised Models\n",
+    "evaluate_model(y_test, y_pred_logreg, \"Logistic Regression\")\n",
+    "evaluate_model(y_test, y_pred_nb, \"Naïve Bayes\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['naive_bayes_model.pkl']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save Logistic Regression model\n",
+    "joblib.dump(logreg, \"logistic_regression_model.pkl\")\n",
+    "\n",
+    "# Save Naive Bayes model\n",
+    "joblib.dump(nb, \"naive_bayes_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['vectorizer.pkl']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(vectorizer, \"vectorizer.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Predictions\n",
+    "y_pred_logreg = logreg.predict(X_test)\n",
+    "\n",
+    "# Compute confusion matrix\n",
+    "cm_logreg = confusion_matrix(y_test, y_pred_logreg)\n",
+    "\n",
+    "# Display confusion matrix\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_logreg, display_labels=[\"FAKE\", \"REAL\"])\n",
+    "disp.plot(cmap=\"Blues\")\n",
+    "plt.title(\"Confusion Matrix - Logistic Regression\")\n",
+    "plt.savefig(\"logisticRegression.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Predictions\n",
+    "y_pred_nb = nb.predict(X_test)\n",
+    "\n",
+    "# Compute confusion matrix\n",
+    "cm_nb = confusion_matrix(y_test, y_pred_nb)\n",
+    "\n",
+    "# Display confusion matrix\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_nb, display_labels=[\"FAKE\", \"REAL\"])\n",
+    "disp.plot(cmap=\"Oranges\")\n",
+    "plt.title(\"Confusion Matrix - Naive Bayes\")\n",
+    "plt.savefig(\"naiveBayes.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Bayes and Logistic Regression model saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the trained model\n",
+    "joblib.dump(nb, \"naive_bayes_model.pkl\")\n",
+    "print(\"Naive Bayes and Logistic Regression model saved successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def map_clusters_to_labels(y_true, y_clusters):\n",
+    "    labels = np.zeros_like(y_clusters)\n",
+    "    unique_clusters = np.unique(y_clusters)\n",
+    "    \n",
+    "    # Ensure y_true is a NumPy integer array\n",
+    "    y_true = np.array(y_true, dtype=int)  \n",
+    "    \n",
+    "    for cluster in unique_clusters:\n",
+    "        mask = (y_clusters == cluster)\n",
+    "        if np.any(mask):  # Ensure there are valid values in the cluster\n",
+    "            most_common_label = mode(y_true[mask], keepdims=True)[0][0]  # Extract numeric mode\n",
+    "            labels[mask] = most_common_label\n",
+    "\n",
+    "    return labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans_mapped_labels = map_clusters_to_labels(y, kmeans_labels)\n",
+    "hierarchical_mapped_labels = map_clusters_to_labels(y, hierarchical_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kSFW0aFGZ3N6bdJ5CFegLL7ygWrZs6Rg81wogDx8+LMFZeHi4VHcOHjxYJmHH+ezbt8+j48uaNauUmP3444/q3r17Ln+LSd9RIoeq0ris9nXJsWvXLqkyRTBmSZ8+vQS9eM1bOF4r2MV0Y3Fno8B1u3z5sgTRuJa4Zr1791b58+d3G5B74/z58/IT054RERGRl7SP9OnTRyYst2AS8zfffNMlzb1793SJEiV0SEiIvnr1qstrp0+f9jidp5OwI2369On1tm3b4h0XXLlyRRZnx44d0+nSpZNJ4T09vqVLl8o+nSd6v3Dhgs6YMaMeOnRovOOzjjG52VK+fHmZbB4aN26sq1SpIhPbd+jQQRcoUMDj7X333Xe6e/fuevbs2Xru3LlybXGMzZo1c0l39+5d3aNHD50hQwbHeZQrV04fPHgwwW3PmzdP0q1evdrWseAcsmfPrmNjYz0+DyIiIvofn7UBRJs/lIhZ8DvWoZTNgk4JKDFDQ/7s2bO7/L1ziY7ddN5o0qSJql69uss6q1MEqn0tKLVDaRZ6n6LjA3o4e3p8KF1EFejcuXNVs2bNZJ1VIoiODe6UK1dO+aI6PiAgQH5H6SZ6BKNDBqqFE6qWT0xkZKQsFrQhxDVBJw1Ug4eFhcl6VNei5BHn1qZNGykFRueTdu3aSWmw1UHIW999950sY8eOTfa2iIiITOaTKmAER2iThbZzqDLFgt//+usvqQp0TgcVKlRIcnt20nnD6onqzoMHDyS4KFu2rFSfIshB9SWqHZ0DJ7vHh4AIgRPaHCIAs6p/a9euLUObuINq07jVzZ5CoHfnzh1Hz1zkQWBgoASGztXCyYEqYHCu5h82bJhcvy+//FLaH6LaF20iDxw4IMFicuzcuVOmP/vHP/4h1cpERETk5wDQ6kTRt29fadyPBe3AAKWAqe3+/fsJvpYrV64EX0NpFXqYovPB119/Lb1psSAQ9HbGvE6dOkkJHLaDTiYrV65UHTt2VCmpUKFC0iEDUEKJNnuAcQDxmi8UKVJEfuLcLGj/iNJAq/QRcC8gUE6oI40dMTEx0oMZ2/nPf/6T7DaSREREpvNJAIggD6Vm6BjhvGCdcwCIcd9gz549iW7Pbjor0HDujIBSPG8HPEZVLQIYDD2CEixU4aJjw6VLl7w6PggNDZVeydg2qn9xfN4Ow2IXOnug9NW51BL7RQcQvOYLJ06ckJ8oIbWcOnXKbfCNdegg5I1r166pVq1ayViGixcvls41RERE5OcA0Br+BcESSmmcF6zDa0gDNWvWlBIhVBPGnc3BOWizmw69bq02eRYMc2JVf3oKbQERaDibNm1avF68do/PghK/BQsWSGBZv359x3EnVEWdWDW1Hbj2uOYIOi0IxC9cuCBt8zzdb2xsbLx148aNc7SpdA6MMUgzgjbLoUOHpErbm8AT1x1Vvgg2MbC4uyFliIiIyHPJ7gSCNmAogXvqqafivYZgBzNBIA3Gy0OANXHiRJmlolatWjJIMT7U169fLyVEaB8HdtPVrVtXqmfffvttCRJwHAh6vO0g0LZtWxknsGfPnqpGjRrqjz/+kMDNefYLT47POQBE+zhMd4brkRhfjG+HoAzXHm3lTp8+LUHtJ598oqpVq6aeffZZj/eLbeF6oPMMSuBQnY3r0qVLFzl/C6r9u3fvLvcCpnBDCeSECRNk/8gjZ1iPktXdu3fL/2fNmiXXD1X01gDiaFLw888/y3lg5g/nKeUwfuOTTz6Z7GtFRERkJO2D4V+wGQyXEtfJkyflNaRxFh0dLcOUBAUFyRIWFuYyVIon6TZt2qSrVaums2bNquvUqaO3bt2a6DAwgwcPTvBcMFQKhnspUqSIbC88PFzv2LFDBwcH61atWnl1fJZKlSrJ8Cjnzp1LcP/WMfpidB4MN9OlSxedM2dOObbIyEgdExPj1X4HDBggQ8tgO5kyZZLhb4YNG+YYPsfZ/Pnzdd26dXWOHDl0YGCgXJ/NmzfHS4c8svbpvGC9BdffXRos7vKXiIiI7EmHf/wdhJoA1cZ58uSR0jMiIiKiR2ImEEoYpnzbtm2b6ty5s78PhYiIiEixBDAF/fnnn+r333+XaeXOnDnjGI+PiIiIyJ9YApiCvv/+e+kMgV7JGAKGwR8RERGlBSwBJCIiIjIMSwCJiIiIDMMAkIiIiMgwxgWAGOjZl3PJ+np7RERERA9FADhz5kwJgqwlKChIValSRY0YMcIxDRylTX///beKiopSuXPnVjlz5pQ5kL2dSznufWAtDRo0cNtBBjOLZMmSReYTRmcZTFXnDHMou9seFucp6OymIyIiIh9NBefsX//6l8wHe/nyZfXDDz+od955R+bpnTNnjkorBg0apAYMGJBmt5faMJ0d8ujdd9+VKduGDx+uWrRoobZs2SJT3nkDw944T59XsGBBl9cxP3RkZKSqV6+eGjVqlDp58qT8DaaF27Rpk0qf/n/fS8aMGeMyrzAcPXpUvf/++y6Bnd10RERE9H+0D8yYMUOm5/rtt98c6+7fv69r1aol60+dOuWL3ZCPLV++XPJn5syZjnWLFy+WdXPnzvX6Pjhy5Eii6Ro1aqSLFi0qU+9Zpk+fLn+7cOHCRP92yJAhOl26dPr48eM+SUdERGSiFGsDiFIcq+oPpTHOUDWHtnM//fSTqly5slQDBgcHqyVLljjSnD9/XnXv3l0VKlRIXq9Ro4bL687Wrl2rmjZtKlWYWLDf5cuXu6R57LHHXKoGE7J3717Vrl07VaBAARm3r1KlSnKscdnd3p49e1TLli2lWhxLq1at1L59+9xWnaLUDSVySIfrMX36dJWSFi1apDJnzizVvhaU/uXNm1ctXLjQ6+1iZKErV67IT3d27dqlwsLCVEBAgGPd008/LT8TymPL7Nmz1VNPPaWKFy/uk3REREQmStFOIIcOHZKfCCjiQrDzwgsvSHCEKryIiAhHoIjgoX79+lKN3LNnT/XZZ59JG7W2bduq6OjoeEFMo0aN1PHjx6XKeeTIkapo0aJqypQpLulQ1Thr1iwJsBKCAZsRAP3222/q7bfflmrJZs2auQ2G7GwPbenCw8NlNpCBAwfKgvPGutjY2Hjpu3btqooUKSLVsJg3+OWXX5Yp5NxJKvC0A4FYSEiIBNjOgTuCcrzmrapVqzqC8VdffVXduHHD5fVbt2657BOyZs3qCMATsnnzZnXgwAHVqVOnRPdvNx0REZGxfFGMaFX9/fLLL/r8+fP64MGDetiwYVIFV6lSpXjpkTZ9+vR627ZtLuvv3bsnPwcNGqQzZcqkd+3a5VKlXLlyZd2gQQOX9CVKlNAhISH66tWrLts6ffq022MdPHiw7N+d7du3y2tTp051WX/37t0Ezz2x7Vmvbdy40bFu3bp1sg5VlHGvX79+/RzrUHWJ6+eczhnSJzf7ypcvryMiIuT3xo0b6ypVqki1bIcOHXSBAgU83t53332nu3fvrmfPni1VyN26dZNjbNasmUu6atWq6Ro1arisW7VqlaStWLFigtvv1auX3BexsbGJHofddERERKbyaScQlOLF/f+kSZPcpkXjfPQCdWZ1Opg/f76qXbu2VP86l5SFhoaqGTNmqPv370tadF5Ayd/48eNV9uzZXbZVuHBhj4/fmqptw4YNUhpnVVFmzOjdZUJpZZkyZdSTTz7pWIdqSXSUwWsffPCBS3rn0kRUXaIjBTpIuFOuXDmVXOihbZ0jSl/RI/ju3btSLYxSOk+hYwcWS4cOHeQcUFqKanpU+8JLL72kevfuLVXruM4nTpyQkt5cuXIl2GscxzV37lyp6ndXouxpOiIiIpP5NACcOHGiVCmiDRuG5ojb+9NZ+fLlE606RiCA4UHcQRUxqoSPHDki/69QoYIPjv5/7frQ7hDVxwhC0Uu1cePGMkQJ9uepmJgYVaJEiXjrse7UqVPx1iPgdZYtWzaplnYnbjtCbyDQs7a/fft2CawRBOPax62i9RaqgBEArlmzxhEA9ujRQ6rZhwwZIguqsvv06SPV3RcvXnS7naVLl8qXgaSqde2mIyIiMplPA8AnnnhC1apVy1ZalPYkBAEB2t7169fP7etxS/t86YsvvpAAZdmyZbLgGKZOnSoBkq+CooRYw5+kFgScZ8+ejXdN0XYxbjDqLbRpBJQuWjDczFdffaU+/vhjdfjwYVWyZEkJilEymlAwj/aWCE7RQScxdtMRERGZzKcBoK+g2vTmzZvxqpTjQsBg9bRFSZ2voMcxFnTaQAeUvn37qpUrV0oPXk+gGhpV1HEdO3bMcez+hM4eKO107pTx4MED6QDSvHlzn+wD1bvgrjQXnXWwAAJBVEN369YtXjqMK7l48WKpIreq6d2xm46IiMh0aXIqOHyAr1u3Tm3cuDHBgAJq1qwpbeXGjh2rrl696pLOm9ksULV87949l3VWoOZNO0AMR4PABoMbW3BeCHTczY7hCVShJ1aNbkfr1q2luhdt5pyrUDEjR5s2bTzer7uezePGjZOfzgMyuxseBu0h0a6zY8eO8V6bN2+eBKlJVevaTUdERGS6NFkCiOFcMFUYSgBRHYtqQXSGQClcjhw5JEgBBAxod4iAEVXPKD3C+H3r169X169fl4AAdu7cKYv1uzVOnFX1aY1Bt2rVKumcgI4M6GSBQAgdTFA9iQ4oFrvbe+211+T48H+0cQMMLYNjxGvJsX//fpVcCMow3A7O+fTp01I1+8knn6hq1aqpZ5991uP9YlsoOUXnHgzrsmLFCrVgwQLVpUsXl6YBKAFFXmFYH1yvH3/8Uf38889S4uqucwuqddGhA80CEmM3HRERkfFSaiaQxCAthkhJDIbwwHAexYoV0wEBAbp48eI6MjJSr1ixIl7a6OhoGc4kKChIlrCwML1s2bJ4w7G4W0qWLOlId/jwYR0VFaVLlSqlM2fOrAsVKiT73L9/v8v+7G4Pdu/erZs3b64DAwNlwe979uyxNYMGtoWhVBK6hr7IvgsXLuguXbronDlzyrXD+cbExCSYPrH9DhgwQIaWwXYwDAuG58FwQNbwPpaLFy/qFi1a6Lx588p1xvA+kydP1g8ePIi3zaNHj8pwOD169Ej0POymIyIiIq3T4R9/B6FEREREZHgbQCIiIiJKOQwAiYiIiAzDAJCIiIjIMAwAiYiIiAzDAJCIiIjIMAwAiYiIiAxjRAD4z3/+U+YXtpbo6OhkpSMiIiJSpgeApUqVcgRNmE3iscceUy+//LI6deqUSgsw6wZmicBME75IlxL+/vtvFRUVpXLnzq1y5sypnnvuOa+ms4OZM2e6BLLW4m76OUxN17BhQ9lvvnz5VNOmTdXmzZtd0ty9e1cNHTpUXsNMLAkFx7/99pt68cUXJf+zZcumQkJCVP/+/eNN00dERET+5ZOBoBEAIoDo27evBAs7duxQkydPVnny5FG7du2S6bnSAgQtCHZWr16d6Fy8dtP5EvazdetW9e6770oQPXz4cFWyZEm1ZcsWmfLO0wDwhRdekGnnENRZChYs6DIn7/bt21WdOnVU1apVZWo25N2kSZMkcP/9998dc/5eunRJ8hfzIhcuXFjmaHZ3bZ5//nl5DT8R/O3du1emwnv88cclqPRmPmUiIiLyPZ99IhctWlSqUC0oBcIcs1999ZV6++23fbWbRxLmzF2zZo0EbgjEoGLFiqp169bqhx9+UB06dPBqu5iDGMF5QmbMmCGleb/88ouU7EGLFi0k8MNczIMGDZJ1QUFB6vjx46p48eKyHkGeO2+99ZbMiewc6GEeZcyDvHDhQtW+fXuvzoOIiIgekjaAKEGDv/76y2X9+fPnVffu3VWhQoVUlixZVI0aNdSSJUvcbmPt2rVS7YgqUSwocVq+fLnj9YsXL6p+/fqpKlWqSJCCICYiIiLBACWtWrRokcqcObNU+1oQiKHkFIGTt1C4e+XKFfnpztmzZyUPrOAPChQoEC8dSiAR/CUFpYlxS/mQH7B//34vzoCIiIgeqgDQav/nXP2LYKR+/fpSqtWzZ0/12WefSdVi27Zt47UpQ1DUqFEjKXl655131MiRI6WUccqUKY40hw8fVtOnT1fh4eFS3Tl48GB18uRJ1bhxY7Vv3z6VGqz2dcmBanJUmSIYs6RPn15VrlxZXvMWqnat4PnVV19VN27ccHkd1+3y5csSRONa4pqh1DZ//vzSHtEXEPADqo6JiIjoEasCRvux2NhYdf/+fWn7hWpflBx17NjRkWbEiBESaGzbtk1VqlRJ1iEQrFatmhoyZIijTRm28frrr6vg4GBpi5Y9e3ZZ/8orr6iYmBjH9sqVKycBIkr/LJGRkVLt+eWXX6pPP/1UPQzOnDmjihUr5igxQ9CEDhUojduzZ4/H2wsMDJRS1rCwMGlPiBJWtMk8cuSIWrZsmSMdrifaa44ZM0aNGjXKcU03bdrkOJ7kQptC5F+bNm18sj0iIiJKQwEgqmZRcuRcHYhgwwr0YP78+ap27dpS/Ytg0RIaGirt0RD4IWhEZwgEduPHj3cEfxbnkiTnwO/evXtSmoXep+j4gGAnNSBgSq7bt2+rgIAA+f3o0aPSIxgBNaqFb9265fH2EARjsaANIa4JgjxUqyMwBFTXouQRnTYQoN28eVM6n7Rr107aJCa38853330ny9ixY9NMRyAiIiLyYQCIgO+jjz6SIAIdAdBL1LkHKhw6dEiCHedA0RmqiFElbAVvFSpUSHSfDx48kCBxwoQJ8jcIIC3eBE7e8EVVMwK9O3fuOHrm4jxQiodr5VwtnByoAkYAiMDOCgCHDRsmJYMHDhxwBKCoPkcHHqT9+OOPvd7fzp071UsvvaT+8Y9/SLUyERERPYIBIII9q8F/q1at1JNPPqm6du0qAQ3aswHayjVr1kzanLkTt7QvKSitwph9nTt3luDTKmVCtbMPRrdJNSgRRYeMuNcA4wDiNV8oUqSI/ETpogXtKREMWsEfoLMHAu/kdKRBNT16MGM7//nPf5LdRpKIiIh8K0UGZkPAhw4ZCATnzZvn6N1apkwZKSG0AsWEYLw5QPs3lEglZO7cuRLAoMTRgqpTjFvnjhXooLo4MXbT+Qo6eyAYQ6mlVeKH0k10AGnevLlP9nHixAn56Vz6io46zqWmFqy7fv26V/u5du2a5DvaHi5evFhlzZo1GUdNRERED1UvYAxjUrZsWSmlszzzzDMy84S70iUrQIGaNWtKSRTajsWdRcJ5dgy0F0Sg4WzatGkJBm5Wx4aDBw8meux20wHGzLMGTPYWSstQ3YuA1rJ06VJ14cKFBDtPJLZf5/aVlnHjxslP54GgEWivWrVKgjbnanpUayMo9RSuO6p8kZdo/+luSBkiIiJ6hGYCQWcPlPg4QwCHQYB//vlnGc8PbfyeeOIJ6eDRo0cPqSLEsC0rV66UsegQ9DgPA4OAET2BMTgygon169dLyRRKFQE9hz/88EPZFsYT/OOPP9SCBQukFBBtEuMeD2A99vnee+/JPmvVquU2kLKbzqreTM5lxN9iSBZUl1szgXzyyScSBKM3sLsZNBLbL64rrkf16tWlBA4DTeO6dOnSRapkLVOnTpXewhguBlO4oQQS7SkRQGIGEucOPFiPktXdu3erOXPmSHoEkLly5ZIe2/Dmm29KoIk2f8hnZ8hHNAsgIiKiNED7QMmSJXWrVq3irb98+bLOnj27bty4sWNdbGys7tWrly5WrJgOCAjQxYsX15GRkXrFihXx/j46OlpHRETooKAgWcLCwvSyZcscr9++fVv3799fFylSRGfNmlWHh4frHTt26ODgYLfHAwcPHpR0mTNnRuSkR48enax0eM0Xl/HChQu6S5cuOmfOnHKuuCYxMTEJpk9svwMGDNDly5eX7WTKlEmHhIToYcOG6Xv37sVLO3/+fF23bl2dI0cOHRgYKNd78+bNbvPY2qfzgvUWXC93abB069bN62tDREREvuWTEkAiIiIienikWBtAIiIiIkqbGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhfBIAHj16VGamcLdkz57dkQ4zdAwdOlRmBcHsGng9Ojo6Wfv+9ddfVbNmzWR72Bdmr3jrrbcSnA/YFH///beKiopSuXPnVjlz5pT5mJ2n0fPEzJkz3eZtgwYN4qXFsJKTJ09WVapUkVlIMINL27ZtXaabw8wwmC+4aNGiMvcxZgnB7CGY+s5ZStwvREREpFT8OcaSoWPHjqply5Yu65zn6sU0bh988IFMIYa5Zt3NCewJTJOGIART0b3//vsqT548aseOHWrWrFnqpZdekmnKTIVp9LZu3eqYWg5zMmN+ZkzxhjmUvTF69GiVL18+x/8LFiwYL83AgQNlGrvIyEgJ6hD4YQq/mzdvOr4MII9wTL169ZJtYO7giRMnypSA27Ztk6AwJe4XIiIi+j++mE7kyJEjMt3XiBEjEk2HqciOHz8uv8+bN0/+ZvXq1V7v9+mnn9b58+fXf//9d7wp6K5cuaJNtXz5crm2M2fOdKxbvHixrJs7d67H25sxY4b8LfI5MXv37tUZMmTQAwcO9HgfCxculH3gvkip+4WIiIj+J1XbAKLkqXjx4j7b3p49e6TKN25JH6oLg4KC4qVfu3atVCeiShQLSg+XL18eb5soxcTfY0FV5b59+9zuH1WSH374ofrpp5+khMqqzlyyZIkjzfnz51X37t1VoUKF5PUaNWq4vJ4SFi1apDJnzizVvhaU/uXNm1ctXLjQ6+2ievfKlSvy051vv/1WSvZQ6gjO1b5JKVy4sPx0rqb29f1CRERE/+PTAPDGjRsqNjbWZbl165ZKKQgaUGWINoh2gqJGjRqp48ePq3feeUeNHDlS2qBNmTLFkQbBR3h4uPr999+lKhMLqkyxDufiDl5/4YUXJGgcM2aMioiIcBwPgqX69eurH374QfXs2VN99tln0iYPbeISastmta9Ljl27dqmQkBBHVSqkT59eglS85q2qVas6gudXX31V8tvZ5s2bZR8IMtH2DwE0AjgEhu5cvnxZnT17VqqIX3/9dTnv0NBQr4+PiIiIbNI+rAJ2t4wePdrt3/iiSu/777+XbWTLlk1HRkbqadOm6QsXLsRLh6rEEiVK6JCQEH316lWX106fPu34ffDgwbK9jRs3OtatW7dO1g0ZMiTedrE+ffr0etu2bfH2B4MGDdKZMmXSu3btcrx2//59XblyZd2gQQO352Rdt+QoX768joiIkN8bN26sq1Spom/fvq07dOigCxQo4PH2vvvuO929e3c9e/ZsqULu1q2bHGOzZs1c0lWqVEmXKlVKBwUF6U8//VTShoWF6XTp0umtW7fG226dOnUc55s7d249adKkBI+BVcBERES+49NOIKjqRON/Z+XKlVMp5dlnn1VLly5Vw4YNk1K2efPmqddee0317dtXeo9anR3QGQIlf+PHj3fplexc9QgolStTpox68sknHeueeuop6YSA19AhIa4mTZqo6tWru6yz9jt//nxVu3Ztqf51LkFEKdeMGTPU/fv343XI8MX1un37tgoICJDfURqJHsHoUYtqYW9KZJGnzvnaoUMH6QwyatQoqVYPCwuT9SgRxP7QoQP5ACgZLVKkiBoxYkS8kkDkB64LOoUsWLBA0hEREVHK82kAWLZsWakCTU3NmzeXBW3tVqxYocaNGycBIap30csUjhw5Ij8rVKiQ6LZiYmJUiRIl4q3HulOnTrn9m/Llyye4vUOHDkkwlj9/frevo4oYVcLOEmpv6AkEenfu3JHft2/fLoFmYGCgHItztXByoAoYAeCaNWscAaAVdLZv396RDgE3At6dO3fG2waCY6t9IqrKsR0E2vidiIiIHpIA0J8QZHXq1ElKBVGK99133zkCwJSU2FAzaNOGMQr79evn9vW4pZG+ghJHtK2Luw+0ccRrvmCV1qF00TkPEMDGDXgxPA9KYRNTr149GRJm2rRpDACJiIhS2CMTADqXfqEDBErzLKjCtXr4Nm7cOMG/RXUwqorjOnbsmGMbnkAgivHvUrtUFB0x0LkF1b1Wid+DBw+kAwhKS30BY/eBc7BXsWJFtW7dOnXmzBkpgbWgdNZO9S5KLa3AlYiIiFLOQz0VHIINVG86s9qUIfiy1KxZU3qjjh07Vl29etUlvfOwIxgW5vDhw2rTpk0u+0C7NnezXtgZjBl/724AYyuAclelnFi1sh2tW7eW6t65c+c61qGtJGbaaNOmjcf7ddcDGlXtVhtIixVcfvPNN4512OeGDRtUrVq1HOusKnlnGI4HaZN77kRERJS0dOgJopIJARJKyNDQP6HqTsuECRNkmrbdu3erOXPmqBdffFH+FlWpGArEEwg4Dhw4oJ5//nkZfw+lR9OnT5fjwXRjzqV9GAYGARnSdevWTYYpwfAjmG0CnUesYPDxxx+XIVP69OnjmP0CVbk4XudZMADrBw8eLGMBJtTG74knnpBSxR49ekgbxJMnT8qMFxirEEFZXNYQMMnJFvwthq5B+z9rJhDMzoEgGLOnZMyY0aP94rgxfiE6u2B6N7S1RKeNLl26qP/85z+OdChlxPkiAMf1Q9vJqVOnqv3796s///xT2ogC8hu/o3ocQ8rgNZRYYtuoKsbMLilxvxAREdH/Sc2ZQKBkyZJuh4vBek/98ssvumPHjrp06dI6S5YsunDhwrpFixYuw7g4i46OluFRMEwJFgxRsmzZMpc0u3fv1s2bN9eBgYGy4Pc9e/a43R6OG0PHJCY2Nlb36tVLFytWTAcEBOjixYvLkDUrVqxIcJu+yBYMh9OlSxedM2dOOVfsMyYmJsH0ie13wIABMrQMtoNhbTCczrBhwxzD3Tg7d+6c7tq1q86TJ4/OnDmzrlu3bryhWzBETGhoqMzigmuC/IuKitLHjh1L0fuFiIiI/scnJYBERERE9PB4qNsAEhEREZHnGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGYYBIBEREZFhGAASERERGSZVA8B//vOfKl26dI4lOjpapVUffvihHGNSTp06pVq3bq1y5swp6aOiopK1PX9J68dHREREaTQA/Pnnn1XDhg1Vjhw5VK5cudRTTz2lFixY4Hj9tddeU7NmzVIDBw5Uj4q3335b/frrr+pf//qXnFuPHj1UWvHTTz+pMWPGqIdVqVKlHF8WMmXKpB577DH18ssvS9D9MHrY84OIiB4d6bTW2hcbmjlzpnrxxRdVzZo1VdeuXVXGjBklIEyfPr2aP3++S1qU/CFQXL16tWrQoIFKi+7duydLlixZEk1XsGBB1bFjxyQ/2O1uz5dQGolrffTo0STT+uP47ASAuXPnVn379lV3795VO3bsUJMnT1Z58uRRu3btUnnz5lUPE0/yg4iIKCVl9MVGzp49q3r37q2efPJJtWbNGgn+4NVXX1UnT55UDyOcg3UeiTl//ryUdvpqe/6SVo+vaNGi0nTAglJA3GtfffWVlL4SERGRn6qAZ8+era5du6YGDRoUL4goVqyYx9u7ePGi6tevn6pSpYoKCgqSKuWIiAi1cePGeGn37t2r2rVrpwoUKKACAwNVpUqVpD2bt+kQYDi3U0yotNN6HQWoQ4YMcfw/bhtAO9uzrF27VjVt2lTaE2JB6ejy5cs9vi7WvhAkHTt2zGX/OHZvjm/Pnj2qZcuWsl8srVq1Uvv27XN7XbZs2aKeeeYZSRccHKymT5+ufAUlx/DXX3/FC8S7d++uChUqJKWYNWrUUEuWLHHbzhHXq2rVqpIOJdbr16/36nwt2Ca2jSreypUry3Zx3tb+PckPIiKi1OCTIh8ELvgwCw8P98Xm1OHDhyVoQMnPG2+8oS5fvqymTp2qGjdurP744w9Vvnx5SXfnzh3VokUL+YnSIFQN7t+/Xy1cuNAluLObDkaNGqWuXr0q1dY//vij2+MLCwuT9n7QpUsXCXbat28v/8cHv6fbg0WLFsl2EJC98847EqiiunDKlCkSFHpyXaxjw98i8B09erRjP6GhoR4f37lz5yRvkcdW+83PPvtM1u3evVvly5fPJT2aAOCYhg8frmbMmCHt9qpXry5BWXJZ7f+cq3+vXLmi6tevL0EgSgdx7X744QfVtm1b9csvv8RrZoC86tSpkwTrkyZNkntj586dqnTp0l6dLyDoHTt2rASh2A7yw6rq9SQ/LFYw7qMWGkRERK60D1SuXFnnz5/fdvrVq1fjU01+unPlyhVZnB07dkynS5dO9+/f37Fu+/btsp2pU6e6pL17967L/+2mczZ48GD5m6QgDdImJbHt3bt3T5coUUKHhIToq1evurx2+vRpj6+LpVu3brpkyZJJHltSx2e9tnHjRse6devWybohQ4Y41s2YMUPW9evXz7Hu+PHjcnzO6ezCsTdt2lSfP39enzlzRu6Xxx9/XGfIkEHv2rXLkW7QoEE6U6ZMLuvu378v92WDBg3inccHH3zgWHfkyBGdPn163bt3b4/P14L12Ma2bdvi5au3+YFt+ujtSUREFI9PqoCvX7/u084DVrUboGPChQsXVLZs2aTk5ciRI450qMqFDRs2SOmeJW41tN10/rJ161Z1/PhxKb3Knj27y2uFCxf2+Lr4Gkoiy5QpI208LejhjZIud0P5oCTTUrx4cTk+b9uCogo8f/78UrWL6l9cn2XLlkkVvgWll7Vr15Y0sbGxsqC6HKVrqO69f/++yzbRace5o8kTTzwhbVe9PV9o0qSJlHI6y5Ahg/JWuXLlZCEiIkoJPgkAEWDdunVL+cqDBw+kOq1s2bISWCKAQBCAKj7n/aC6FFVuaEeF19FmC1Waf//9t8v27KbzFyt4q1Chgk+ui6/FxMSoEiVKxFuPde6GZEEg5gxBqnPg7Yk6deqoFStWSHV9hw4dpBo8bhXsoUOHJNDDtXBevvjiC9kvqoidISiN207V+Tw8PV+wqt99Be0NE2pzSERElCYCwJIlS0qpy40bN3yxOWk71qdPH/nw//rrryUAwIIP/rhtovAhjxK0//f//p90REEnCZTcxA2I7KZLyzy5Lv6EoX98BeeGji5t2rRR3377rZTCoY0hgmHn9nLNmjVzXI+4S9xSVXcCAgKSdZx2eoITERGlFT6pA0UD/MWLF0s1GhrU2/2wRTWmO3PnzpWOFuhdbME4cJcuXXKbHp0LsKDBPhrrY9y4lStXSs9Nb9KlNqvzAXqeovNEQjy9Lr6a2QPV0Kiijgs9Wq1jT63AcvDgwZJf8+bNU88995ysR3XtzZs3JVC048SJEy4ldqiedi4VTKnz5UwrRESUVvikqAa9UlHN99FHH8UL6ty1/bKGhjl48KDb7aHtFGZ+cDZt2rR420bVXtx11ge0c/s+u+n8BUORIABB9S565DpDj1RPr4sF7QVRMpvQ63ahFy2qXjdt2uRYt27dOunlmtoDeeMLBqrAURrq3OYQx+NumCAEe3GhJNGCc0APXuce7Cl1vp7kBwJUX1crExERWXwS/RQpUkSGtsA0aGh4b80EYo1hF3cmELSlQsP7oUOHSlUexrOrVauW4wMPw3dgeJaePXtKiR2G1MCUcnHbfq1atUo6TkRGRkqDeXSKGD9+vGzfeXgNu+kwFAgW63ewSttQjfj00097dF3sbg+B3cSJEyWQwXXo1q2bDGWC8enQwQalXZ5cFwvODeeJ9o/YF0peMU4dBlf25PgwhR+OD/9HFTQgv3GMeC01oRStV69echy4vzBEDobN+f7776UEEPcg2lLiiwdKd3FvLV261GUbmE0EzQAQdH/++ecqc+bMcn9YUup8k8oPZximiIiIKMVoH1q8eLGuX7++DgwM1Dlz5tShoaF6/vz5btMePHhQh4eH68yZM8twF6NHj3a8dvv2bRnWpEiRIjpr1qySbseOHTo4OFi3atXKke7w4cM6KipKlypVSrZTqFAhHRkZqffv3++yL7vprOE/3C0JDd+R2DAwnm4vOjpaR0RE6KCgIFnCwsL0smXLPL4uzkOh9O3bVxcsWFCGYsF+MVSLN8e3e/du3bx5c8lbLPh9z549LmmsYWAwtIozbAtDoHgKf+fuvC5fvqyzZ8+uGzdu7FgXGxure/XqpYsVK6YDAgJ08eLFJY9XrFgR73zXrFmjK1WqJPdC9erV5brHZed8PR0KKKn8iLtNDgNDREQpxWdzAROldSg9xawtvOWJiMh0vuuuSUREREQPBQaARERERIZhAEhERERkGLYBJCIiIjIMSwCJiIiIDMMAkIiIiMgwDACJkhg6xpdTuPl6e5RyMMMR8spaoqOjk5UuJZ06dUq1bt1a5cyZU44hKioq1Y+BiB4uDACJiNzArC+zZs2SucN9kS4lvf322+rXX39V//rXv+RYMCOO83zhmHUJs+ZgZhx/Balktp9++kmNGTPG34dBTtgJhCgRmLcXS5YsWdLk9ijlIVhq2LChWr16daJzQdtNlxIKFiyoOnbs6PYD9tKlSyp37twy/3nhwoVlzmx/HCOZDaXSeI9gTnVKG1gCSJQIzGnty2DN19sjgvPnz6tcuXK5fS0oKEgdP35cHT58WL311lupfmxElDYxAKRUZ7WDQ0lE1apVJSCqWbOmWr9+vdv0SIu/QRVC5cqVJX1wcLBasmSJywdg9+7dVaFCheT1GjVquLzubO3atVIdhvZSWFASsnz5cpc0jz32mEu7roTs3btXtWvXThUoUEAFBgaqSpUqybHGZXd7e/bsUS1btpQPbSytWrVS+/btc0kzc+ZM2caWLVvUM888I+lwPaZPn64eRqmdvxcvXlT9+vVTVapUkWuHatGIiAi5Hx8m1n2ABRU5mObQ+r9zG8AMGTKo4sWL+3Tfdu97u+83O/d9St0v/mDn+ln5G7fErFSpUi75a/d56slz15f5Yd2TX331lTp27JjLcxDnSP6T0Y/7JsO1b99ederUSR5mkyZNUi1atFA7d+6Uqqq4EOyMHTtWHup4/Y8//nA8GK9cuaLq168vD/3evXvLQ/WHH35Qbdu2Vb/88otLVdeiRYskaEJA9s4770haVEtMmTJFPqQso0aNUlevXlXz589XP/74o9vjv3PnjhwzfqINVp48edT+/fvVwoUL4z3M7Wzv3LlzKjw8XB6MVnuyzz77TNbt3r1b5cuXzyV9165dVePGjdXw4cPVjBkz1Msvv6yqV68uH3YPm9TMX5SEIVhG54033nhDXb58WU2dOlWuJfZbvnx59TAICwuT9n7QpUsXOW+8pwAfwCnFk/veTn54et/78n7xB0+uX0o8T5NK5+v8sO5R5DkC39GjRzv+LjQ01OvzJR9AG0Ci1DR48GC0O9UffPCBY92RI0d0+vTpde/eveOlR1q8tm3bNpf19+7dk5+DBg3SmTJl0rt27XK8dv/+fV25cmXdoEEDl/QlSpTQISEh+urVqy7bOn36dKLH6s727dvltalTp7qsv3v3bpLnnthrGzdudKxbt26drBsyZIhj3YwZM2Rdv379HOuOHz+u06VL55LuYZHa+XvlyhVZnB07dkyuX//+/eMd3+rVq+UY8TMxdtOlBOwX909S5s2bl+xjtHvf280Pu/e9r+8Xf7F7/az3OZ6NzkqWLKm7devm8fPU03S+yg8LjhnHTmkHq4DJb9Bo3bla44knnlBr1qxxm7ZJkyZSuuUMVVuAUrXatWtLdU9sbKwsqObDt0tUd9y/f1/Sbd26VdpCoVQge/bsLttC43hPoeoGNmzYIN/mndv5eQMlI2XKlFFPPvmkY91TTz0l36jd9dpEyYoFVXz4Zn7y5En1MErN/LWqtQAdci5cuKCyZcsm1+/IkSOpcLYPN7v3vd388PS+99X94i++fm54+jxNKp2v84PSLlYBk9/EbZdUrFgx6Z3oTmLVcocOHVK3b99W+fPnd/s6qoTQC9L6cK9QoYLyBVRrocoDVRv40KlXr55UI7744ouyP0/FxMSoEiVKxFuPdRjnLS58wDlDEOP8gfIwSc38ffDggRo/fryaMGGC/I1zQHDr1i1lMlwLVJ06w3V3/jC3e9/bzQ9P73tf3S/+4uvnhqfP06TS+To/KO1iAEhpSkBAgNv1CfVwBLRVadasmTTsdydu6YMvffHFFzLm2rJly2TBMaA92fbt21O8t2/69I9OAX5q5i/aTKJtU+fOndVHH32k8ubN6ygZMX1UrBMnTsRrM4ZADiVFaeW+T8vPA7uSc/08KcFM6HnqbTpv8oPSLgaA5NcPG+dvjqi+9Ka3Iqorbt68KT05E2N9sKGHG75x+wo6XWBBUIHG0n379lUrV66UnnOeQLUYqsziQs85dx1jTOHr/J07d650oJg9e7bLYMkYLy+xD0dUFyfGbrq0DKXKK1asiLfOm/vebn74+r63e7/4W1LXz7qfbty44VJ6jU4ayXmeJpUupZ5DnAEp7Xl0ihDoofPtt986fkePMfQkQ08zT6Et3Lp169wO44GHnQVDHuBBh95q6JHrLKGHamJQlRT3w956QHrTnge9E9FDddOmTY51OC9cG3/3XPQnX+cvqjMzZcrk8vq0adMSDNxQRQYHDx5M9DjtpkvLUPqEwMl5iVsiZfe+t5sfvr7v7d4v/mL3+hUtWtTRltKCnsIJNfOw+zxNKl1KPYfQ7hbtMR/mL0iPGpYAkt9MnjxZXbt2TT4kPv/8c5U5c2ZpMO4pDC/x/fffy4cVqlXQ5gjfavFtGmO8LV261PHBP3HiRPmAqFWrlurWrZsMEYFxsK5fv67mzZsn6TAkAhbrd7BKi1B99PTTT8vvq1atkuONjIxU5cqVk84EaFuGtjLOwxvY3R6mFMPx4f99+vSRdRgyAceI10zl6/zFcCAYbqNnz55SAoMhKxYsWOB2eAtAfqKhPKZTQwkM9ontx233ZDedP6C9I0o4MYyHNTQHrguq7l5//XWPtmX3vrebH76+7+3eL/5i9/rVrVtX7kkMFYPAFSWBKL22mix4+zxNKl1KPYdwbjhPtH/EtlHCiXEDrUCX/MDf3ZDJPNYwA2vWrNGVKlXSmTNn1tWrV9fR0dFeD3ERGxure/XqpYsVK6YDAgJ08eLFdWRkpF6xYkW8tNhPRESEDgoKkiUsLEwvW7Ys3vG5W5yHMTh8+LCOiorSpUqVknMoVKiQ7HP//v1uzzep7cHu3bt18+bNdWBgoCz4fc+ePV4ND/GwSO38vX37tgz3UqRIEZ01a1YdHh6ud+zYoYODg3WrVq3c7v/gwYOSDvmM4x09enSy0qX2NcS9Yef+s8PufW83P+ze93bP1dP7JbV5cv02bdqkq1WrJvdpnTp19NatWxMcBiap56knz11f54c1FE/fvn11wYIFZcgl/B2eZeQ/nAuYUh1KXzBrAW89IqLUeZ7yuUtxsQ0gERERkWEYABIREREZhgEgERERkWHYBpCIiIjIMCwBJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAAJCIiIjIMA0AiIiIiwzAANNzff/+toqKiVO7cuVXOnDnVc889p86dO+fvwzLy+s2cOVOlS5cu3tKgQYN4abXWavLkyapKlSoqa9asqkCBAqpt27bq2rVrymS+zI9SpUq5zQ8sTZo0cUn7/fffq+rVq6ssWbKo/PnzqxdffFFduHBBmc7Xzxc7970n7yOT/Pzzz6pVq1aqaNGicp8GBwer3r17x7tP7969q4YOHaqaNm2qcuTIIdcuOjra7Ta9eQ7169dPtvn666/7/BzJMxk9TE+PmGeeeUZt3bpVvfvuuypTpkxq+PDhqkWLFmrLli0qQ4YM/j48I6/f6NGjVb58+Rz/L1iwYLw0AwcOVJ988omKjIyUhzgeuOvXr1c3b95U2bNnV6byZX6MGTMm3gfZ0aNH1fvvv+8SAK5evVryoV69emrUqFHq5MmTkoe7d+9WmzZtUunTm/s929fvD0/uezvvI5Ps2LFD8qBXr15yLU6cOKEmTpyoVq5cqbZt2yZBIVy/fl198MEHqnTp0qpy5cpq48aNCW7T0+fQ4cOH1ZQpU1L0PMkDmoy1fPlyjVtg5syZjnWLFy+WdXPnzvXrsZl4/WbMmCF/e+TIkUTT7d27V2fIkEEPHDjQq+N+VKXG/TxkyBCdLl06ffz4cce6Ro0a6aJFi+rbt2871k2fPl32u3DhQm0qX+eH3fve7vuItNyfuFbz5s1zrLt3757j/sZ6vL569WqfPIeeffZZ/cYbb8g2e/Xq5aOzIG+Z+9WU1KJFi1TmzJmlWsaCb+d58+ZVCxcu9OuxmXz9UK1y5coV+enOt99+K9/kUaoCplf7pub9PHv2bPXUU0+p4sWLO9bt2rVLhYWFqYCAAMe6p59+Wn4uWbJEmcrX+eHpfZ/U+4iUKly4sPx0rpZHyazz/e2r/EDJIKqh33vvvWQfN/kGA0CD4YMrJCTEUfQPqK5CsT9eI/9cv6pVq0p7KSyvvvqqunHjhsvrmzdvln3gQxRtboKCguSBjQeyyVL6fsZ1P3DggOrUqZPL+lu3brnsE9AeCvbu3atM5ev88PS+T+p9ZKrLly+rs2fPSkCGdnhojxcaGpqi+YEg/O2331ZvvfWWpKW0gQGgwc6cOeNoFxMRESEPzDt37sgbFK9R6l6/wMBA1b17dzVp0iQ1d+5c1b59e2lgjZ/OTp06pc6fP6969uyp+vfvL2nLlCmjOnfuLG15TJXS9/OsWbOkxAPtnZyhMT3aVzn79ddf5SfyyVS+zg+7973d95GpmjVrpgoVKqTq16+v/vrrL/X555+ratWqebwdT55DX3/9tbT/QwcQSkO8rjymh17p0qV1y5Yt5ffg4GCdJ08efe3aNd2lSxedI0cOfx9empca169v377SXmbNmjWOdWXKlJF1EydOdKy7evWqDgoK0s8//7w2VUrmx507d3S+fPl0q1at4r02fvx4yY/BgwfrQ4cO6ejoaB0SEqJz5colx2EqX+dHcu57d+8jU23ZskUvWbJEDxs2TNetW1cvWLAgwbSJtQG0mx83btzQxYsX1yNHjnSsYxvAtIElgAZD+xx8I4ft27fLNzR8e759+3a8Ki3yz/VD1RWsWbPGsc5qa+ZcooEed6jG2blzpzJVSubH0qVLVWxsbLzqX+jRo4fq2rWrGjJkiJQGNmzYUIbbQIlXtmzZlKl8nR/Jue/dvY9MVbt2bWmLOWDAADVy5Ejpqb1u3boUyw/0jEfMh97HlLYwADQYqgHQFsR646KtjNUgGK+R/69fkSJFHOOpWTDOnPNPS548eYwewzEl8wPVvwhe2rVrF+81VAt/9dVXMvzL2rVrZaiYzz77TB07dkwVK1ZMmcrX+ZGc+97d+4iUDF2Eavpp06alSH6gvSGG/sGXJHyBwnsEizXcDH7HuIPkHwwADYYGvGgDgkbslgcPHkgDbbxG/r9+GKsr7kO2YsWK8jNuOyq0x7E+6EyUUvmBD7HFixfLALcIAhOCAXbRrqpEiRJS2oVA8IknnlCm8nV+JOe+d/c+ov9BKa0VqPs6PxBwo3cwxs5EBxFrsQbsxu9x289S6mEAaLDWrVtLdQwa7zpXdWFk+DZt2vj12B7V61e+fHlZ3ME35LjGjRsnP50HHm7evLn8/OabbxzrsM8NGzaoWrVqKVP5Oj8s8+bNkyDGXfUvuBtmBAPpYjiNjh07KlP5Oj/s3vd230emOXLkSLx1y5cvl2uY1HvA2/xA6SKGA4q7AJpJ4PeyZcsm46woOdKhIWCytkAPLWR9eHi4tM+xRurHqO74Vvbbb7+pjBk5UYyvrx+GXLD+Nq4KFSqoGjVqyJRiGEZkxYoVasGCBapLly7qP//5j0spCkqW8M25T58+UuI0depUtX//fvXnn38a+0D1dX5YsE3M6hETEyPbjAslfd26dZMSQlR1/vjjjzLeGWZJ+Pe//61M5ev8sHvf230fmQYze+AaoRcwquNxzTArB64RZmvB1IeWCRMmqEuXLsl9P2fOHJnaEH+fK1cuxxRuyXkOIZ/RJhD7IT/ydy8U8q8LFy5Ir7ycOXNK763IyEgdExPj78N6ZK8f3nIJve0GDBigy5cvL9vJlCmT9CRFTz2MzB/XuXPndNeuXaVnZebMmaU3n7ueeqbxZX7A0aNHZeaPHj16JJjm4sWLukWLFjpv3rySF5UrV9aTJ0/WDx480KbzdX7Yue89eR+Z5NNPP9WhoaE6f/78OiAgQHppR0VF6WPHjsVLW7JkSUdeOC9Y74vnEHsBpw0sASQiIiIyDNsAEhERERmGASARERGRYRgAEhERERmGASARERGRYRgAEhERERmGASARERGRYRgAEhERERmGAaDBMAn30KFDVdOmTVWOHDlkdPbo6Gh/H9ZDBXNdRkVFqdy5c8vo+s8991ySE9MnBsNyTp48WVWpUkVG6C9QoIDMMIH5NBPSr18/yTtrhH6T+TI/MJsHpqvCHL9ZsmRRwcHBqnfv3jLdVVzr1q1TDRs2lP3my5dP3lObN29WpvP1+8POfY8ZLbDe3WLyVHB272e7nwt2rzM/Z9IuzvVlsOvXr8ucpZjiB5Ozb9y40d+H9NB55plnZBola6qr4cOHqxYtWqgtW7bIXLCewvRhmC4rMjJSHs4I/NavX69u3rwp04zFdfjwYZnOiXyfH5jiCtvAlFWY0/TEiRNq4sSJauXKlWrbtm3yIQqY6iwiIkJVrVpVffTRR/KBN2nSJNW4cWP1+++/ezXP6qPC1+8PO/f9mDFj4n1hwnR977//vtEBoN372e7ngt3rzM+ZNMzfU5GQ/2BqpOPHj8vv8+bNk+l5OJ2YfcuXL5drNnPmTMe6xYsXy7q5c+d6vL29e/fqDBky6IEDB9r+m2effVa/8cYbnFopBfLDnYULF8r28H6x4PpjGqzLly871u3bt0/SDR06VJsqJfPD0/t+yJAhMqWf9byjhO/n5HwuuLvO/JxJu1gFbDB8A8fE7OSdRYsWqcyZM0u1lgWlG3nz5lULFy70eHvffvutfENHaQkkVu0LKBlEtc57773nxdE/enydH+4ULlxYfjpXY549e1ZKT1C9ZUHVvelSKj+8ue9nz56tnnrqKT7vbNzPyflccHed+TmTdjEAJPLSrl27VEhIiKPqBNKnTy/VHHjNU2gzhr/FhyMCiKCgIHlwIjB011bw7bffVm+99RaDjRTKD8vly5clyEPggfZmaMMUGhrqeD08PFzSoE0aqib37dsn1ff58+eX9m+mSon88Oa+x/vqwIEDqlOnTl7t81GT1P3sLV7nhw8DQCIvnTlzRtrSgNUG7M6dO/LBhNc8derUKXX+/HnVs2dP1b9/fzV37lxVpkwZ1blzZ2mj4+zrr7+WYANBB6VMfliaNWumChUqpOrXr6/++usv9fnnn6tq1ao5Xn/llVdUjx49pE0UGtZXqFBB2v5t2rRJFStWTJkqJfLDm/t+1qxZUrKOdrWU9P3sLV7nhw87gRB56fbt2yogIMDR+Bk9HtEBANVet27d8nh7N27ckO2gYfZrr70m61q2bKmKFCmiRowY4SgJRIcQdBZBVbFztaPpfJ0flvHjx6vY2FhpRL9gwQLJD2cZM2aUkq7nn39etWnTRvIHnR3atWun1qxZI1WeJvJ1fnhz32N/+CKFHqim5oOn97M3eJ0fTgwAibyEDzKUaFg9Qe/fv68CAwPlg8+52ssu68Oyffv2jnXo+YvqmZ07dzrWjRo1SqrC0JuPUi4/LLVr13a0X0OpSVhYmAxjgd9h2LBhMnQPqr+sPEQP4Mcee0zy6uOPP1Ym8nV+eHPfL126VIIdVkvav5+9wev8cGIVMJGXUI2CtjRWoIZxzqwG1XjNU2gz5vzTkidPHkcjbbTfQekSqhzxwD158qQs1nAL+B3fxk3k6/xwp169elKtOW3aNMc6DEeCD1Er+AO03URVsMlDXvgyP7y971EtiaATpbFk7372Bq/zw4kBIJGX0JgdbWicq7MePHggDdzxmqcqVqwoP+O2j0K7QKuaBtVo6B2MsbYQZFgLzJw5U35H1Y6JfJ0fCUGplhXYWG03UboVF9YhODGVL/PDm/seQePixYtlIHUEJ2TvfvYUr/PDiwEgkZdat24t1Vlo++JcFYKR9dEWzB0MCpzQwMDNmzeXn998841jHba1YcMGVatWLfk/vq1jeI24C2CUf/xetmxZZSJf58eRI0firVu+fLlsz/lvMMDtqlWrXIbtOXTokPQG9mXgaXJ+eHPfz5s3T4JPVkt6dj97itf54ZUOgwH6+yDIfyZMmKAuXbqkdu/erebMmaNefPFF+UDLlSsXpxZLAt46GAIE7ZusmQ4wiwdKI3777TfpHBAXhlyw/jYulI488cQTUpLRp08fVaJECTV16lS1f/9+9eeffyYa2GG7aBuF/DSVr/MD7wNcc/SaRPUl8gDVvZiiD7NbYCosQB51795derni/YMPQ+QDqiox40WlSpWUiXydH57e99g3nmsxMTGyb9PZvZ89/Vywc535OZNG+XskavKvkiVLysjscResp6RduHBBd+nSRefMmVMHBQXpyMhIHRMTk2B66/om5Ny5c7pr1646T548MrtE3bp1bY2az5lAfJ8fn376qQ4NDdX58+fXAQEBunTp0joqKkofO3YsXtr58+dLXuXIkUMHBgbqiIgIvXnzZm06X78/7N73R48elRkpevTo4fWxP2o8uZ/tfi7Yvc78nEmbWAJIREREZBi2ASQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiIiIsMwACQiIiIyDANAIiJKEX///beKiopSuXPnVjlz5lTPPfecOnfunFfbmjlzpkqXLl28pUGDBvHSrlu3TjVs2FD2my9fPtW0aVO1efNmZTqttZo8ebKqUqWKypo1qypQoIBq27atunbtmiPNzz//rFq1aqWKFi2qsmTJooKDg1Xv3r3VhQsXXLZlN50n+UapK2Mq74+IiAzxzDPPqK1bt6p3331XZcqUSQ0fPly1aNFCbdmyRWXIkMGrbY4ePVqCOkvBggVdXt++fbuKiIhQVatWVR999JG6e/eumjRpkmrcuLH6/fffVfny5ZWpBg4cqD755BMVGRkpwRoCv/Xr16ubN2+q7NmzS5odO3ZIXvXq1Uuu7YkTJ9TEiRPVypUr1bZt2yTY8ySd3XwjP9BEREQ+tnz5co2PmJkzZzrWLV68WNbNnTvX4+3NmDFD/vbIkSOJpnvjjTd05syZ9eXLlx3r9u3bJ387dOhQbaq9e/fqDBky6IEDB3r8twsXLpTrN2/ePI/T2c03Sn1GVAFfvHhR9evXT4q9g4KCVI4cOeQb4saNG92mX7t2rVQZoMoCC4qqly9f7nE6q+j76NGjLn9XqlQpqRaJC2k//PBD9dNPP6nKlSs7itWXLFni8/PANz78/SuvvBLv76ZPny7Hgm94RETeWLRokcqcObNU+1pQ+pc3b161cOHCZFVjXrlyRX66c/bsWXl24vlmQVWn6b799lspsUNpLDhX+yalcOHC8jOp6vvE0iWVb5T6jAgADx8+LEFNeHi4FEMPHjxYnTx5UqoE9u3bF++h1ahRI3X8+HH1zjvvqJEjR0obhylTpniVzlOoGnnhhRdUy5Yt1ZgxYyTAswJIX54H2n88/fTT6scff1T37t1z+dt58+apihUrShUKEZE3du3apUJCQlyqAtOnTy9fbvGat/Bcsr7Uvvrqq+rGjRsur+P5ePnyZfmyjGcmno2o7syfP7/bL96mQBtIXHsE3wiIUYhQvHhxCQzdwTVEMI0q4tdff10KBUJDQ71Ol1S+kR9oA1y5ckUWZ8eOHdPp0qXT/fv3d6y7d++eLlGihA4JCdFXr151SX/69GmP0yVU9F2yZEndrVu3eMeJtOnTp9fbtm1zWY/9pcR5LF26VPa5bNkyx7oLFy7ojBkzGl1VQkTJV758eR0RESG/N27cWFepUkXfvn1bd+jQQRcoUMDj7X333Xe6e/fuevbs2VKFjGconl/NmjVzSXf37l3do0cPqe7E61jKlSunDx48qE1WqVIlXapUKR0UFKQ//fRTuYZhYWHy+bF169Z46evUqeO4frlz59aTJk1yu92k0tnNN0p9RnQCwTcdC0q78I0lW7Zs0iD1yJEjjtfQWBklZuPHj3c0iI1btO1JOm80adJEVa9e3WWd1Vja1+eB0kV8E5w7d65q1qyZrLNKBJ9//vlknQcRme327dsqICBAfkctBnoEo0MGqoVv3brl8fbQcQGLpUOHDvLsGzVqlDR3CQsLk/UZM2aUkkc8w9q0aSPNXdD5pF27dmrNmjVSBW0ilLghH9BR47XXXpN1qGkqUqSIGjFiRLySQHx+xMbGSlOgBQsWSDp3kkpnN98o9RlRBfzgwQM1duxYVbZsWamOwM2H6oDz58+7PIisIKpChQqJbs9uOm8k1kPN1+eBByXemGhziAezVf1bu3Zt9dhjj/nsnIjIPAj07ty54+iZi+rYwMBACQzj9hD1FqoSAYGdZdiwYfKc/PLLL6X9Iap90fb5wIEDEnSYygrG27dv71iHAgJU1+7cuTNeenwOoM3mgAEDpAkRenRjeB1v0yWVb5T6jAgA8e2vT58+qk6dOurrr79WK1askAUBlD8apN6/fz/B13LlypWq59GpUyf5Zo7toJMJuvB37NjRq20REVkKFSokbcOsQANtv6wOAnjNF6zSJjzDLGjnjFIlK+ABtHXDF+KEOsyZAIUFzj8tefLkSbJzR7169WTYlmnTpvkknbt8o9RnRBUwqjjxQJg9e7ZjHUq8Ll265JKudOnS8nPPnj3SsSIhdtNZDyDnxq4oxfN2IFRfnwfg2x96JWPbMTExcnwooiciSg50OEAwhtoJq8QPzxd0AGnevLlP9oGx5+IGNadOnXL7JRvrrl+/rkyFjn0omTtz5ox0CLSgBimh6l1nKM21AvrkpnOXb5T6jCgBRBs6dH93hm8ocXu/1qxZU74povrg6tWrLq85B21201lvMrTJs6AHllUt4u/zsKDED203EFjWr1/f5eFAROSN1q1bS3Uvvlxali5dKjNFoG1eQk1gEmoGg3ZmcY0bN87Rdtr5C/CqVatchjk5dOiQ9AZGUGoqK+j+5ptvHOuQFxs2bFC1atVyrHNuT25BFTrSOueN3XR2841SnxElgJjqBuPr9ezZU9WoUUP98ccfEvA4j0puBVhoIIs2DHhDdOvWTTpJoHs7vjmifZwn6erWrSv7ePvtt+UbD0oC8TD0thGyr8/DOQBEu5no6GgZMZ+IKLnw4Y4vlBiC5fTp0/LlFbNQVKtWTT377LNu/2b//v0Jbg/bwnMPneQwjBWareD516VLF5cABsO/dO/eXT311FPqxRdflBLICRMmyP7xLDYVPj9QOIDZQFAQUKJECTV16lQpGUX7PQuGD0M7c3QMRLX9n3/+KSW5qCpGEyRP09nNN/IDbQAMPYBhUooUKaKzZs2qw8PD9Y4dO3RwcLBu1apVvPTR0dEyfAG6y2NBV3nnoVI8Sbdp0yZdrVo12S+6y6O7fWLDwAwePDjVz8MaIgDDJpw7dy7B/RMReQLDSnXp0kXnzJlTnkGRkZE6JiYmwfTWcCLuDBgwQIaWwXYyZcokw1wNGzbMMUyWs/nz5+u6devqHDly6MDAQHkObt68WZsOz/euXbvqPHnyyGwpuEarV692SYMhYkJDQ3X+/Pl1QECALl26tI6KipIhx7xJ50m+UepKh3/8EXhS2oJvhvjmhm9nRERE9Ggzog0gJQ7jN2Hy7s6dO/v7UIiIiCgVsATQYGiz8fvvv8u0cugZZo3TRURERI82lgAa7Pvvv5dG0uiVjBlAGPwRERGZgSWARERERIZhCSARERGRYRgAEhERERmGASARERGRYRgAEiUDJjOPiopSuXPnltHwn3vuOa/negY0yZ08ebKqUqWKjJqPGVwwgr/ztFYpsd9HhS+vy8yZM1W6dOniLQ0aNIiXFnOsNmzYUPaLmXmaNm2qNm/erEznr/yw+z4yjZ3r8vPPP6tWrVrJlKCYwzk4OFhmc8EUb87spvMk3yh1GTEVHFFKwXR7mOv53Xfflammhg8frlq0aKG2bNkiU/J5CtM0YbqsyMhIeZjiwYwp/G7evKmyZ8+eYvt9VKTEdcEwSc7TLRYsWNDl9e3bt6uIiAhVtWpV9dFHH6m7d+/KlIqNGzeWYZYSmtvWBP7ID0/eR6axc10wLizyqlevXnJtMY0pphZduXKljBeLYM+TdJ7kG6WyVJ55hOiRsXz5cpm2aubMmY51ixcvlnVz5871eHt79+6V6fgGDhyYqvt9VPj6usyYMUP+9siRI4mme+ONN2RarcuXLzvW7du3T/526NCh2lT+yg+77yPTJOe6LFy4UK79vHnzPE5nN98o9T20VcBHjx6VYmR8o8G3iooVK6qNGzfKRON58+aVb+CWixcvygThKPYOCgpSOXLkkG/sSO/O2rVrpQoHVRZYUFS9fPnyeOmw/w8//FD99NNPqnLlyo5i8CVLljjS7NmzR7Vs2VL2iwVF5vv27fPqnO2cx5UrV+Q4Bg0aFO/vsS4gIEBdunTJsW7NmjUySTf+BiUYmzZtcpwXJW7RokUqc+bMUq1lQekG7r+FCxd6vL1vv/1WvlGjtAQSqq7y9X4fFSl1XVBthvdVQiNmnT17Vt4/eD9aULVmOn/lh933kWmSc10KFy4sP5Oqvk8sXVL5RqnvoQ0ALZi79r333pOAsFGjRqpdu3aqXr16EiihKgYww8X06dNVeHi4FEMPHjxYnTx5Uqpo4gZjeGhhO8ePH1fvvPOOGjlypLRxmDJlitv9oyrjhRdekCBvzJgxEpDhWKw3AfaJaiAEqliQHutiY2M9Plc752EFhQhK48Jgz0ibK1cu+f+xY8fkuPEg+Pjjj1WzZs2kaoDs2bVrlwoJCXGp6kifPr18GcBrnkKbMfwtPhwRQCDIL168uDy4U3K/j4qUui74YmR9GXz11VfVjRs3XF7H+/Hy5cvyzMF7FO9FVK/lz59f2r+Zyl/5Yfd9ZBpPrwvuaXy5QRXx66+/LgUDoaGhXqdLKt/ID/RDCsXJOPyvv/5a/t+sWTNdtmxZ+f3XX3+V11DkDVeuXJHF2bFjx3S6dOl0//79Hevu3bunS5QooUNCQvTVq1dd0p8+fTreMWAf6dOn19u2bXNZj+3A4MGDJc3GjRsdr61bt07WDRkyxONztnseX375pezjwIEDjnV//fWXrJs6dapjXZ8+fXTGjBn18ePHHetwXEiHY6fElS9fXkdERMjvjRs31lWqVNG3b9/WHTp00AUKFPB4e5UqVdKlSpXSQUFB+tNPP5VqsrCwMMnfrVu3pth+HxW+vi7fffed7t69u549e7bkRbdu3eS9gWeNs7t37+oePXpI9Rpex1KuXDl98OBBbTJ/5Yfd95FpPL0uderUcdzPuXPn1pMmTXK73aTS2c03Sn0PfQC4YsUK+X+nTp10vXr1XIId58DL+WEdGxurz58/r/Pnz6//8Y9/OF7bvHmz/N348eNtHUNSN3F4eLguU6ZMvPWlS5fWDRs21MmR2HlcuHBBAju8yS3Dhw+XDyiktzz++OO6UaNGLtu12i4xAEwa8rFly5bye3BwsM6TJ4++du2a7tKli86RI4fH28O9gms/ceJExzp8EcED+/nnn0+x/T4qUuO69O3bV/JozZo1LutHjRqlO3furOfMmSNtnhD84P2F96ip/JUfdt9HpvH0umzZskUvWbJEDxs2TNetW1cvWLDA7XbtprPzPqLU9dBXAWfM+L+OzGjb4Pw7YI5bePDggRo7dqwqW7asVEegzSCqZ86fP69u3brl2NaRI0fkZ4UKFWzvP7EefjExMapEiRLx1mPdqVOnlKfsnkeePHmk3SKqfC34vX79+i69sNBrK+7xoUqA7EH7JuseQ09QVP9hPuXbt2/H6wFnB9pnQvv27R3r0DMP1Sk7d+5Msf0+KlLjuqDqymo7axk2bJi8L7/88ktp74ZqX7QZPnDggBo1apQylb/yw+77yDSeXpfatWtLm80BAwZIUyj06MZwR96mSyrfKPU99AFgYqzGphh6oE+fPqpOnTrq66+/lnaDWBAMJbdBqtWeLjV4ch54k6PNx5kzZyQQxe/PPvusrSCT7ClUqJC0fbEepGjbYrX9xGueQjDv/NM5oHduVO3r/T4qUuO6FClSxDG+nQXtg8PCwhwfsNYXKXyRTKijmQn8lR9230emSc51Qbt6DNsybdo0n6Rzl2+U+h7pANAyd+5ceUDPnj1bvqGjkwQabjv3hoXSpUs7eu76AnpEoTNJXOh8gY4lKXUegG9hsGDBAlmc1zl/SMU9PnQqIXvQoPqvv/5yKX1FAI0G7njNU+jJDgjanaGE13pgpsR+HxWpcV1Qah73QxSl+ffv34+XFuuuX7+uTOWv/LD7PjJNcq8LSnOtgD656dzlG6U+IwJADDhqVQtb8A3l3r17Lutq1qwpQRGqc65everymjffHFENi2oPDK1iQdE4egl7Mwq63fMAfMNG0T56A2OpW7duvKCzSZMmMuSNcxA4Z84cj4/LVK1bt5bqLATmlqVLl8pI+G3atEmwyUBCzQaaN28uP7/55hvHOmxrw4YNqlatWsnarwl8nR/ueuqPGzfO8d5x/uK4atUql2E1Dh06JL2BTQ7I/ZUfdt9HprF7XaymUM7QpAFpnfPGbjq7+UZ+oB/yTiCrV6+W/6NnETpduHvtww8/lP+jp94XX3yhe/bsqQsXLqzz5cunW7VqFW8gS3SWQE/gf//739JrFtt27mRhSaqzxNmzZ2Uf6PH28ccfy4IOG/i/c2cMuzw5Dxg9erQOCAiQZeTIkfFeP3r0qM6WLZs00EYjdvQkthoKsxNI0h48eKDr168vjaiRtyNGjNB58+bV1apVk0467li95dy5f/++rlmzpnTg6devnx43bpyuXLmy5B86NiVnvybwdX6gIwc6l2E7EyZM0O3atZO06MTgbMqUKbK+atWqeuzYsdLhqnjx4jpr1qx6165d2lT+yg+77yPT2L0u6CncpEkT+czA59+bb74p9zI68TgP5mw3nd18o9RnRACIoQcQ3BQpUkRuUKTbsWOHBD7uAqfo6GgZvgAPLizoKr9s2bJ46ewESrt379bNmzfXgYGBsuD3PXv2eHXOnp4HhoixHqiHDx92u01cI3xwYSaD6tWrS48u02cw8AR6XONBljNnTrlXIiMjdUxMTILpE/uAg3PnzumuXbvKQxR5gl511n2cnP2awpf5MWDAAPnwwnYyZcokXwrR09Ea5snZ/PnzJa/QuxXvczw/MKqA6fyVH3bfR6axc10wekRoaKgUViA4RG/uqKgo+TzxJp0n+UapKx3+8UfJI6VNaB+Ctouff/65o6cWERERPVqMaANICYs7Gvt///tf+YmexkRERPRo+t/AeWSskiVLqg4dOkhjdXQGsaazq1Gjhr8PjYiIiFIIq4AN99JLL0kPxtOnT8s4XRgqZsSIES4T2xMREdGjhQEgERERkWHYBpCIiIjIMAwAiYiIiAyT5gPADz/8UKVLly5Z25g5c6ZsAzNwpAU4FpwXERERkT+k+QDQHzB1GnrDEiUFk5lHRUWp3LlzSycazNGcnAnn0SR38uTJqkqVKipr1qyqQIECqm3bti7TjKXEfh8Vvrwu1hfHuIu7aRwxxWPDhg1lv/ny5VNNmzZVmzdvVqbzV37YfR+Zxs51+fnnn1WrVq1k6tAsWbKo4OBg1bt3b5nizZnddJ7kG6WuND8MzKBBg9SAAQNSPQCMjo5Wffr0SdX90sMHvaa3bt2q3n33XZmnefjw4apFixZqy5YtMnezpwYOHKg++eQTFRkZKQ9TPJjXr1+vbt68qbJnz55i+31UpMR1GT16tAR1loIFC7q8vn37dhk6qWrVquqjjz5Sd+/eVZMmTVKNGzdWv//+e4Jz25rAH/nhyfvINHauy44dOySvevXqJdf2xIkTauLEiWrlypVq27ZtEux5ks6TfKNUpg0wY8YMmV7IeX7CxGBauZIlS6bY8XCu3UfD8uXLJS9nzpzpWLd48WJZN3fuXI+3t3fvXpmHeuDAgam630eFr6+L3efGG2+8IdNqXb582bFu3759xk+p6K/8sPs+Mk1yrsvChQvl2s+bN8/jdJ5+/lLqSXYVMIp/8Q0gIW+++aYqVKiQ4//nz59X3bt3l3X4hoABh5csWRLv7x577DGX4uKErFmzRlWvXl22hW/gmzZtSrCNHaoe8I00KChIiqunT5/u8rq1r6+++kodO3bMZf8oxnZm9zxWr14tryENit3xbctbFy9eVP369ZPt4BwwVh9KHjZu3OhIc+XKFdkXSk7jwrqAgAB16dIlr64fuVq0aJHKnDmzVGtZULqRN29etXDhQo+39+2338o3apSWQELVVb7e76Mipa4LvrPhfZXQiFlnz56V94/z2JmoWjOdv/LD7vvINMm5LpgeFJKqvk8sXVL5Rqkv2QHgE088IcW9CUHxP9IAMr9+/frqhx9+UD179lSfffaZtA1BGwRUuTobNWqUmjVrlgRsCUGQ1rJlS7mRP/74Y9WsWTMp2k5I165dVZEiRaQaIk+ePOrll192OXbsDwuOEUXV1v+xhIWFOdLZPY+9e/fK8d26dUuK3VEl9OyzzypvHT58WILW8PBwKU4fPHiwOnnypGx33759ksYKClGNHdePP/4oaXPlyuXV9SNXu3btUiEhIS5VHenTp5dZVfCap9BmDH+LD0cEEAjyixcvLg/ulNzvoyKlrgu+GKH9GhbMjx13+kS8Hy9fvixfzvAexXsR1Wv58+eX9m+m8ld+2H0fmcbT64J7Gl9uUGjx+uuvS8FAaGio1+mSyjfyg+QWIQ4bNkxny5ZN37t3T/5/6dIlWeD+/fs6e/bs+qOPPpL/Dxo0SGfKlEnv2rXL8fdIU7lyZd2gQQO320dVaUKH2adPH50xY0Z9/Phxx7ohQ4bEq2K1iqD79evnWIe/SZcunaT3tArY7nlERUXpgIAAHRMT41iH4ndvq4CvXLkii7Njx47JefTv39+x7ssvv5R9HDhwwLHur7/+knVTp071+PqRe+XLl9cRERHye+PGjXWVKlX07du3dYcOHXSBAgU83l6lSpV0qVKldFBQkP7000+lmiwsLEzyd+vWrSm230eFr6/Ld999p7t3765nz54teYHnAt4bzZo1c0l39+5d3aNHD6lew+tYypUrpw8ePKhN5q/8sPs+Mo2n16VOnTqO+zl37tx60qRJbrebVDq7+UapL9kB4MqVKyUz//zzT/l/rVq1dO3atR1tDvAa2oJAxYoVdWhoqD5//rzLgocnAiUriLQbAD7++OO6UaNGLuustjfuAsANGza4pM2fP79+5ZVXPA4A7Z4H3mzWA9Cye/dunwRY+NCJjY2V/eI8/vGPfzheu3DhggR2eJNbhg8fLh9QSO/p9SP3SpcurVu2bCm/BwcH6zx58uhr167pLl266Bw5cni8vTJlysi1nzhxomPd1atX5YH9/PPPp9h+HxWpcV369u0rebRmzRqX9aNGjdKdO3fWc+bMkecNgh+8v/AeNZW/8sPu+8g0nl6XLVu26CVLlkghT926dfWCBQvcbtduOjvvI0pdye4FXLt2bSnWR1UqipNR7WlVk2IdioORBg4dOqRu374tVSPu4G9QlWoXeh1Z27bgGBLi3BYRsmXLpu7cuaM8Zfc8ML9u3K7uJUqUUN568OCBGj9+vJowYYI6cuSIun//vuM1VDNbUL2N/aLKt3///rIOv1tV295eP3KF9k3W/YOeoMiPwMBAuTfi9oCzA+0zoX379o516JmH6pSdO3em2H4fFalxXVB1heYpaDtrNQsZNmyYDK1x4MABRx6iqQXaMSMtmleYyF/5Yfd9ZBpPr4v12YB2m/jswPVFEyf87k26pPKNUl+yA0C0I8AwBwj2EHjUrVtXGnmuXbtW1qENiNXmDMEg2pmhrYw7vuiejyApIQhUfcHuefj6wxhtF9GNv3PnzjLcBBpTQ8eOHeM1rMWbHO0xzpw5I6+h/ce4ceOSdf0o/hcKtH2Je++iAXTcLxt24AsF2o/F/WKB9xXa0qbUfh8VqXFd0IbYGt/OMmXKFPkQsz5grS9SFSpUcOmgZRp/5Yfd95FpknNd6tWrJ8O2TJs2LdHAzm46d/lGD+k4gFZHkIwZM8o3X8A3AKyzOoBAmTJlZLwhdFLwBTxkjx8/7rIOnSKSK6mZR+yeB0r74h5P3OP1xNy5c+WDZvbs2Y51GHPMuVevBZ1nEAAuWLDAERzG7VCTUtfPFGhQjQ9/lL5awT4CaDRwb968ucfbq1ixogwojKAdveude5xbD8yU2O+jIjWuC0rNwflD9NSpUy6l8Rasu379ujKVv/LD7vvINMm9LijNtQL65KZzl2+U+nxSJFanTh0p4seQJwgAERitWrVK/fHHH/KaBQEIbkB334qtG8ITTZo0kZJG5yBmzpw5yhelmrGxserevXtuX7d7HrgWOD684Sxff/2118eFgVPRjd8Zvmm5O058w0bRPnoDY0HJrPObPiWvnylat24t1VkIzC1Lly6VkfDbtGnj9m9QWp7QwMDWh+I333zjWIdtbdiwQdWqVStZ+zWBr/MDz4C4rFJ0vHcspUuXlued87AaaCaC0hYEQabyV37YfR+Zxu51QfOiuJYvXy5pnfPGbjq7+UZ+4IuGhOhBhE3lypVLOkCgRyx6A2EdGohaMFAqesdlzZpVeqB+8cUX+v3335cOFc2bN3ek27Fjh541a5YszzzzjGzH+v+PP/7oSHf06FHpgYwGxmiEjZ6wVkNXd51A4g5EiY4e6PAR17fffivpX3jhBWnQunTpUn3y5EmPz+Pw4cOSpkKFCnrMmDGStmDBgl53svjwww/lb9HZBPvs2bOnLly4sM6XL59u1apVvPSjR4+WTilYRo4cGe91u9eP3Hvw4IGuX7++NKL++OOP9YgRI3TevHl1tWrVpJOOO1ZvOXfwvqlZs6Z04EGP9XHjxknPcuQfenEnZ78m8HV+oCNHp06dZDsTJkzQ7dq1k7ToxOBsypQpsr5q1ap67Nix0uGqePHi8t53HinANP7KD7vvI9PYvS7ovNikSRP5zMCoEW+++abcy+jE4/wZajed3Xyj1OeTABBvZmR827ZtHesQuGF0fHT7d4Zecb169dLFihWTGw8PysjISL1ixYp4PX/dLXF7565evVoevNhX9erVJeCMOwK/pwEg3ijopYRgDV3k8bfYhqfnYR0fHng4PgyDgJ7I3gZYuJYI0ooUKSLXOzw8XIJlBHDuAkAMEWNdNwSj7ti5fpQw9LjGgyxnzpzyQYd7wHnYH08+4ODcuXO6a9eu8hBFnqBXHfIoufs1hS/zY8CAAfLhhe1g2KeQkBDp6ehutIL58+dLXqF3a2BgoPT+37x5szadv/LD7vvINHauC0aPQGEGRpfAZxt6c2NIM3yeeJPOk3yj1JUO/6hHCKpbMRr5559/Lj2NyDO8fkRERI8+33SL9aO4o4n/97//lZ/ObQ8pYbx+RERE5vFJL2B/KlmypOrQoYM0tkZnhjFjxkgnFMy/S0nj9SMiIjLPQ18F/NJLL0kPPAy6jDkG0UN3xIgRLhOzU8J4/YiIiMzz0AeARERERGRYG0AiIiIi8gwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiMgwDQCIiIiLDMAAkIiIiUmb5/5YhKwjotbRfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "evaluate_model(y, kmeans_mapped_labels, \"K-Means Clustering\")\n",
+    "evaluate_model(y, hierarchical_mapped_labels, \"Hierarchical Clustering\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apply K-Means clustering only on the test set\n",
+    "kmeans_test_labels = kmeans.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure y_test is a NumPy array\n",
+    "y_test = np.array(y_test)\n",
+    "\n",
+    "# Map cluster labels\n",
+    "kmeans_mapped_labels = map_clusters_to_labels(y_test, kmeans_test_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute confusion matrix\n",
+    "cm_kmeans = confusion_matrix(y_test, kmeans_mapped_labels)\n",
+    "\n",
+    "# Display confusion matrix\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_kmeans, display_labels=[\"FAKE\", \"REAL\"])\n",
+    "disp.plot(cmap=\"Greens\")\n",
+    "plt.title(\"Confusion Matrix - K-Means Clustering\")\n",
+    "plt.savefig(\"KMeans.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert sparse matrix to dense array\n",
+    "X_test_dense = X_test.toarray()\n",
+    "# Predict clusters only on the test set\n",
+    "hierarchical_test_labels = hierarchical.fit_predict(X_test_dense)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure y_test is a NumPy array\n",
+    "y_test = np.array(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map predicted clusters to true labels\n",
+    "hierarchical_mapped_labels = map_clusters_to_labels(y_test, hierarchical_test_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute confusion matrix\n",
+    "cm_hierarchical = confusion_matrix(y_test, hierarchical_mapped_labels)\n",
+    "\n",
+    "# Display confusion matrix\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_hierarchical, display_labels=[\"FAKE\", \"REAL\"])\n",
+    "disp.plot(cmap=\"Purples\")\n",
+    "plt.title(\"Confusion Matrix - Hierarchical Clustering\")\n",
+    "plt.savefig(\"hierarchical.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['hierarchical_model.pkl']"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save K-Means model\n",
+    "joblib.dump(kmeans, \"kmeans_model.pkl\")\n",
+    "\n",
+    "# Save Hierarchical Clustering model\n",
+    "joblib.dump(hierarchical, \"hierarchical_model.pkl\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}