fake_news_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the datasets \n",
    "\n",
    "# Load datasets from Kaggle \n",
    "real_news = pd.read_csv('data/True.csv')   # Source: https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets\n",
    "fake_news = pd.read_csv('data/Fake.csv')   # Source: https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets\n",
    "\n",
    "\n",
    "# Load datasets that contain both real and fake news in combined format\n",
    "news_dataset_2 = pd.read_csv('data/Kaggle2.csv')            # Source: https://www.kaggle.com/c/fake-news/data?select=test.csv\n",
    "news_dataset_3 = pd.read_csv('data/fake_or_real_news.csv')  # Source: https://www.kaggle.com/datasets/nopdev/real-and-fake-news-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Real News:\n"
     ]
    },
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       "                                               title  \\\n",
       "0  As U.S. budget fight looms, Republicans flip t...   \n",
       "1  U.S. military to accept transgender recruits o...   \n",
       "2  Senior U.S. Republican senator: 'Let Mr. Muell...   \n",
       "3  FBI Russia probe helped by Australian diplomat...   \n",
       "4  Trump wants Postal Service to charge 'much mor...   \n",
       "\n",
       "                                                text       subject  \\\n",
       "0  WASHINGTON (Reuters) - The head of a conservat...  politicsNews   \n",
       "1  WASHINGTON (Reuters) - Transgender people will...  politicsNews   \n",
       "2  WASHINGTON (Reuters) - The special counsel inv...  politicsNews   \n",
       "3  WASHINGTON (Reuters) - Trump campaign adviser ...  politicsNews   \n",
       "4  SEATTLE/WASHINGTON (Reuters) - President Donal...  politicsNews   \n",
       "\n",
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       "3  December 30, 2017   \n",
       "4  December 29, 2017   "
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of kaggle real news dataset\n",
    "print(\"Real News:\")\n",
    "real_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fake News:\n"
     ]
    },
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       "                                               title  \\\n",
       "0   Donald Trump Sends Out Embarrassing New Year’...   \n",
       "1   Drunk Bragging Trump Staffer Started Russian ...   \n",
       "2   Sheriff David Clarke Becomes An Internet Joke...   \n",
       "3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
       "4   Pope Francis Just Called Out Donald Trump Dur...   \n",
       "\n",
       "                                                text subject  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...    News   \n",
       "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
       "2  On Friday, it was revealed that former Milwauk...    News   \n",
       "3  On Christmas day, Donald Trump announced that ...    News   \n",
       "4  Pope Francis used his annual Christmas Day mes...    News   \n",
       "\n",
       "                date  \n",
       "0  December 31, 2017  \n",
       "1  December 31, 2017  \n",
       "2  December 30, 2017  \n",
       "3  December 29, 2017  \n",
       "4  December 25, 2017  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of kaggle fake news dataset\n",
    "print(\"Fake News:\")\n",
    "fake_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second Kaggle News Dataset:\n"
     ]
    },
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       "   id                                              title              author  \\\n",
       "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
       "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
       "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
       "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
       "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \n",
       "\n",
       "                                                text  label  \n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
       "1  Ever get the feeling your life circles the rou...      0  \n",
       "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1  "
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     "metadata": {},
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   ],
   "source": [
    "# Display the first few rows of second kaggle news dataset \n",
    "print(\"Second Kaggle News Dataset:\")\n",
    "news_dataset_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Third Kaggle News Dataset:\n"
     ]
    },
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       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  "
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of third kaggle news dataset\n",
    "print(\"Third Kaggle News Dataset:\")\n",
    "news_dataset_3.head()"
   ]
  },
  {
   "cell_type": "code",
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      "text/plain": [
       "                                               title  \\\n",
       "0  Kurdish leader Barzani resigns after independe...   \n",
       "1  White House Press Secretary Responds To Questi...   \n",
       "2   Thousands Of Doctors Agree: Single-Payer Is T...   \n",
       "3  Senate Intelligence committee leaders confirm ...   \n",
       "4  McConnell rules out action this year on high c...   \n",
       "\n",
       "                                                text       subject  \\\n",
       "0  ERBIL/BAGHDAD Iraq (Reuters) - Iraqi Kurdish l...     worldnews   \n",
       "1  Report: Friend Has Been Going By Middle Name T...           NaN   \n",
       "2  Despite recent assertions from certain Democra...          News   \n",
       "3  WASHINGTON (Reuters) - The Republican and Demo...  politicsNews   \n",
       "4  WASHINGTON (Reuters) - Republican U.S. Senate ...  politicsNews   \n",
       "\n",
       "                date  label       id author  Unnamed: 0  \n",
       "0  October 29, 2017       0      NaN    NaN         NaN  \n",
       "1                NaN      1  13953.0    NaN         NaN  \n",
       "2        May 7, 2016      1      NaN    NaN         NaN  \n",
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       "4    March 20, 2016       0      NaN    NaN         NaN  "
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     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a 'label' column to indicate real (0) or fake (1) news\n",
    "real_news['label'] = 0\n",
    "fake_news['label'] = 1\n",
    "\n",
    "# Format third dataset to match other two\n",
    "news_dataset_3['label'] = news_dataset_3['label'].replace({'REAL': 0, 'FAKE': 1})\n",
    "\n",
    "# Combine the datasets\n",
    "combined_df = pd.concat([real_news, fake_news, news_dataset_2, news_dataset_3], ignore_index=True)\n",
    "\n",
    "# Shuffle the combined dataset\n",
    "combined_df = combined_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "\n",
    "# Display the first few rows of combined dataset\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Dataset Information:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 72033 entries, 0 to 72032\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   title       71475 non-null  object \n",
      " 1   text        71994 non-null  object \n",
      " 2   subject     44898 non-null  object \n",
      " 3   date        44898 non-null  object \n",
      " 4   label       72033 non-null  int64  \n",
      " 5   id          20800 non-null  float64\n",
      " 6   author      18843 non-null  object \n",
      " 7   Unnamed: 0  6335 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Display dataset information\n",
    "print(\"Combined Dataset Information:\")\n",
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "title           558\n",
       "text             39\n",
       "subject       27135\n",
       "date          27135\n",
       "label             0\n",
       "id            51233\n",
       "author        53190\n",
       "Unnamed: 0    65698\n",
       "dtype: int64"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(\"Missing Values:\")\n",
    "combined_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of real and fake news\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.countplot(x='label', data=combined_df)\n",
    "plt.title('Distribution of Real and Fake News')\n",
    "plt.xlabel('News Type')\n",
    "plt.ylabel('Count')\n",
    "# Set the labels for the x-axis ticks\n",
    "ax.set_xticklabels(['Real News', 'Fake News'])  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of some of the article categories\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(x='subject', data=combined_df)\n",
    "plt.title('Distribution of Article Categories')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "text     0\n",
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Focus only on the 'text' and 'label' columns for the model\n",
    "combined_df = combined_df[['text', 'label']].dropna(subset=['text'])\n",
    "\n",
    "# Check again for missing values now that features/columns match in each df\n",
    "print(\"Missing Values:\")\n",
    "combined_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 9376 duplicate rows from the combined dataset.\n"
     ]
    }
   ],
   "source": [
    "# Remove duplicates from the dataset\n",
    "duplicates = combined_df.duplicated().sum()\n",
    "combined_df.drop_duplicates(inplace=True)\n",
    "print(f\"Removed {duplicates} duplicate rows from the combined dataset.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Real News Word Count Statistics:\n",
      "count    34568.000000\n",
      "mean       579.664979\n",
      "std        560.842455\n",
      "min          0.000000\n",
      "25%        244.000000\n",
      "50%        429.000000\n",
      "75%        785.000000\n",
      "max      14641.000000\n",
      "Name: word_count, dtype: float64\n",
      "\n",
      "Fake News Word Count Statistics:\n",
      "count    28050.000000\n",
      "mean       509.810410\n",
      "std        660.248519\n",
      "min          0.000000\n",
      "25%        243.000000\n",
      "50%        380.000000\n",
      "75%        565.000000\n",
      "max      24234.000000\n",
      "Name: word_count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Word Count Analysis\n",
    "combined_df['word_count'] = combined_df['text'].apply(lambda x: len(x.split()))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(data=combined_df, x='word_count', hue='label', kde=True, bins=30)\n",
    "plt.title('Word Count Distribution')\n",
    "plt.xlabel('Word Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend(title='News Type', labels=['Real', 'Fake']) \n",
    "plt.show()\n",
    "\n",
    "print(\"Real News Word Count Statistics:\")\n",
    "print(combined_df[combined_df['label'] == 0]['word_count'].describe()) \n",
    "print(\"\\nFake News Word Count Statistics:\")\n",
    "print(combined_df[combined_df['label'] == 1]['word_count'].describe()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    # Convert text to lowercase\n",
    "    text = text.lower()\n",
    "\n",
    "    # Remove HTML tags and URLs\n",
    "    text = re.sub(r'<.*?>|https?://\\S+|www\\.\\S+', '', text)\n",
    "\n",
    "    # Remove text within square brackets\n",
    "    text = re.sub(r'\\[.*?\\]', '', text)\n",
    "\n",
    "    # Remove text within parentheses\n",
    "    text = re.sub(r'\\(.*?\\)', '', text)\n",
    "\n",
    "    # Remove anything that is not a letter or a space\n",
    "    text = re.sub(r'[^a-z\\s]', '', text)\n",
    "\n",
    "    # Remove extra whitespace\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "\n",
    "    # Remove \"Reuters\" from the dataset\n",
    "    text = re.sub(r'\\bReuters\\b', '', text)\n",
    "\n",
    "    # Tokenize the text\n",
    "    words = text.split()\n",
    "\n",
    "    stopwords_set = {\n",
    "        'i', 'me', 'my', 'myself', 'we', 'our', \n",
    "        'ours', 'ourselves', 'you', 'your', 'yours', \n",
    "        'yourself', 'yourselves', 'he', 'him', 'his', \n",
    "        'himself', 'she', 'her', 'hers', 'herself', \n",
    "        'it', 'its', 'itself', 'they', 'them', 'their', \n",
    "        'theirs', 'themselves', 'what', 'which', 'who', \n",
    "        'whom', 'this', 'that', 'these', 'those', 'am', \n",
    "        'is', 'are', 'was', 'were', 'be', 'been', 'being', \n",
    "        'have', 'has', 'had', 'having', 'do', 'does', \n",
    "        'did', 'doing', 'a', 'an', 'the', 'and', 'but', \n",
    "        'if', 'or', 'because', 'as', 'until', 'while', \n",
    "        'of', 'at', 'by', 'for', 'with', 'about', 'against', \n",
    "        'between', 'into', 'through', 'during', 'before', \n",
    "        'after', 'above', 'below', 'to', 'from', 'up', \n",
    "        'down', 'in', 'out', 'on', 'off', 'over', 'under', \n",
    "        'again', 'further', 'then', 'once', 'here', 'there', \n",
    "        'when', 'where', 'why', 'how', 'all', 'any', 'both', \n",
    "        'each', 'few', 'more', 'most', 'other', 'some', 'such', \n",
    "        'no', 'nor', 'not', 'only', 'own', 'same', 'so', \n",
    "        'than', 'too', 'very', 's', 't', 'can', 'will', 'just', \n",
    "        'don', 'should', 'now'}\n",
    "\n",
    "    # Remove stopwords from the text\n",
    "    filtered_words = [word for word in words if word not in stopwords_set]\n",
    "\n",
    "    # Join the filtered words back into a single string\n",
    "    filtered_text = ' '.join(filtered_words)\n",
    "\n",
    "    return filtered_text\n",
    "\n",
    "# Apply the preprocessing function to each article text in the DataFrame\n",
    "combined_df['text'] = combined_df['text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Perform train-test split\n",
    "X = combined_df['text']\n",
    "y = combined_df['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the training data\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "\n",
    "# Transform the test data\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Initialize the Logistic Regression model\n",
    "lr_model = LogisticRegression()\n",
    "\n",
    "# Train the model\n",
    "lr_model.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test data\n",
    "y_pred_lr = lr_model.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9470909090909091\n",
      "Recall: 0.9348528356066045\n",
      "F1 Score: 0.9409320809248556\n",
      "Accuracy: 0.9477802618971575\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "\n",
    "# Calculate metrics for Logistic Regression\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_lr))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_lr))\n",
    "print(\"F1 Score:\", f1_score(y_test, y_pred_lr))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Compute confusion matrix for Logistic Regression\n",
    "cm = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "# Labels for the axes\n",
    "labels = ['Real News', 'Fake News']\n",
    "\n",
    "# Plot the confusion matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix - Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.9874894879625812\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Compute ROC AUC score\n",
    "y_scores = lr_model.predict_proba(X_test_tfidf)[:, 1]\n",
    "roc_auc = roc_auc_score(y_test, y_scores)\n",
    "print(\"ROC AUC Score:\", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initialize the Random Forest classifier\n",
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "# Train the model\n",
    "rf_model.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test data\n",
    "y_pred_rf = rf_model.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9370932754880694\n",
      "Recall: 0.8528356066044508\n",
      "F1 Score: 0.8929813022643991\n",
      "Accuracy: 0.9090546151389333\n"
     ]
    }
   ],
   "source": [
    "# Calculate metrics for Random Forest\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_rf))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_rf))\n",
    "print(\"F1 Score:\", f1_score(y_test, y_pred_rf))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FQgEDAwPo6+tLbRkZGVi3bl2BWG31OuXl5aFHjx5QKBT4448/EBISgvnz50u9ES9iZGRU4C/4V7mvStOmTdGrVy/s3LlTGjJ7+gv0+RVgS5YsKfb5n2revDkePXqE3377Ta19/fr1ao9dXV1RoUIFrF+/Xm0IKj09HVu2bJFW3sipRYsWSEtLK1Corl27Vtr/MqampmjevDlOnDiB2rVrF/g6NmjQoNCezjJlyqBLly4YPHgw7t+/L616e5WeQyJtYg/JW8zLywuLFi3CoEGDUL9+fQwcOBA1a9ZETk4OTpw4gaVLl6JWrVro1KkTXF1d0b9/f8yfPx96enpo164drly5gokTJ6JixYoYMWKE1vJq3749rKys0LdvX0ydOhUGBgZYvXo1rl+/rha3ePFi7N+/Hx06dEClSpWQmZmJlStXAgBatmz5wvMHBQXh999/R/PmzTFp0iRYWVkhLCwMO3fuxIwZM6BSqbT2XJ73ww8/aIzp0KEDZs+eDX9/f/Tv3x/37t3DzJkzC12a7e7ujg0bNmDjxo1wcnKCsbFxkeZ9PC8oKAgHDx7E7t27YW9vj1GjRiEyMhJ9+/aFh4eH9JeyLn333XfYuHEjJk6ciL1798Lb2xtly5bFV199haCgIBgaGiIsLAwnT5585Wv06tULoaGh6NWrF77//nu4uLhg165d+PPPP9Xi9PT0MGPGDPTs2RMdO3bEgAEDkJWVhR9//BEPHjwo0tdR13r16oWFCxeid+/euHLlCtzd3XHo0CEEBwejffv2L/0eeGru3Llo0qQJmjZtioEDB6JKlSp49OgRLl26hB07dmD//v0AgE6dOqFWrVpo0KABbGxscPXqVcyZMweVK1eGi4sLAEjvu7lz56J3794wNDSEq6vra934j6hY5J1TS9oQFxcnevfuLSpVqiSMjIyEmZmZ8PDwEJMmTRLJyclSXF5enpg+fbqoVq2aMDQ0FOXKlROfffaZuH79utr5fHx8RM2aNQtcp3fv3qJy5cpqbShklY0QQhw5ckR4e3sLMzMzUaFCBREUFCSWL1+uNos/OjpafPTRR6Jy5cpCqVQKa2tr4ePjI3777bcC13h2lY0QQpw+fVp06tRJqFQqYWRkJOrUqaO25FOI/1aj/O9//1NrT0hIKLBEtDDPrrJ5mcJWyqxcuVK4uroKpVIpnJycREhIiFixYkWBVQxXrlwRrVu3FhYWFgKA9Pq+KPdn9z1dZbN7926hp6dX4DW6d++eqFSpkmjYsKHIysp66XMojhd9zYUQYsyYMQKAiIyMFEIIERUVJby8vISpqamwsbERX375pTh+/HiB1793797CzMyswPkKW7F148YN8cknnwhzc3NhYWEhPvnkExEVFVXo13T79u3C09NTGBsbCzMzM9GiRQvx999/F3qNZ5e+vyynF31/PK9y5cqiQ4cOL425d++e+Oqrr0T58uWFgYGBqFy5shg/frzIzMxUi3vZa56QkCC++OILUaFCBWFoaChsbGyEt7e3mDZtmhQza9Ys4e3tLcqVKyeMjIxEpUqVRN++fcWVK1fUzjV+/Hjh4OAg9PT0Cl3JRaRLCiGKMKWeiIiISIc4h4SIiIhkx4KEiIiIZMeChIiIiGTHgoSIiIhkx4KEiIiIZMeChIiIiGTHgoSIiIhkVyrv1GriMUTuFIjeSCmxC+ROgeiNY1wCvwm19Xsp40Tp/R5mDwkRERHJrlT2kBAREb1RFPz7XxMWJERERLr2/59+TS/GgoSIiEjX2EOiEV8hIiIikh17SIiIiHSNQzYasSAhIiLSNQ7ZaMRXiIiIiGTHHhIiIiJd45CNRixIiIiIdI1DNhrxFSIiIiLZsYeEiIhI1zhkoxELEiIiIl3jkI1GfIWIiIhIduwhISIi0jUO2WjEgoSIiEjXOGSjEQsSIiIiXWMPiUYs2YiIiEh27CEhIiLSNQ7ZaMSChIiISNdYkGjEV4iIiIhkxx4SIiIiXdPjpFZNWJAQERHpGodsNOIrRERERLJjDwkREZGu8T4kGrEgISIi0jUO2WjEV4iIiIhkxx4SIiIiXeOQjUYsSIiIiHSNQzYasSAhIiLSNfaQaMSSjYiIiGTHHhIiIiJd45CNRnyFiIiIdE2h0M5WTDdv3sRnn30Ga2trmJqaom7dujh27Ji0XwiByZMnw8HBASYmJvD19cXZs2fVzpGVlYWhQ4eiXLlyMDMzg5+fH27cuKEWk5KSgoCAAKhUKqhUKgQEBODBgwfFypUFCRERUSmUkpKC999/H4aGhvjjjz9w7tw5zJo1C2XKlJFiZsyYgdmzZ2PBggWIjY2Fvb09WrVqhUePHkkxgYGB2LZtGzZs2IBDhw4hLS0NHTt2RF5enhTj7++PuLg4hIeHIzw8HHFxcQgICChWvgohhHjtZ/2GMfEYIncKRG+klNgFcqdA9MYxLoHJCybt52rlPBm7hhc59uuvv8bff/+NgwcPFrpfCAEHBwcEBgZi3LhxAJ70htjZ2WH69OkYMGAAUlNTYWNjg3Xr1qFbt24AgFu3bqFixYrYtWsX2rRpg/j4eNSoUQMxMTHw9PQEAMTExMDLywvnz5+Hq6trkfJlDwkREZGuaWnIJisrCw8fPlTbsrKyCr3kb7/9hgYNGuDTTz+Fra0tPDw8sGzZMml/QkICkpKS0Lp1a6lNqVTCx8cHUVFRAIBjx44hJydHLcbBwQG1atWSYqKjo6FSqaRiBAAaN24MlUolxRQFCxIiIqK3REhIiDRP4+kWEhJSaOzly5exaNEiuLi44M8//8RXX32FYcOGYe3atQCApKQkAICdnZ3acXZ2dtK+pKQkGBkZoWzZsi+NsbW1LXB9W1tbKaYouMqGiIhI17S0ymb8+PEYOXKkWptSqSw0Nj8/Hw0aNEBwcDAAwMPDA2fPnsWiRYvQq1ev/1J7brKsEKJA2/OejyksvijneRZ7SIiIiHRNoaeVTalUwtLSUm17UUFSvnx51KhRQ63Nzc0N165dAwDY29sDQIFejOTkZKnXxN7eHtnZ2UhJSXlpzO3btwtc/86dOwV6X16GBQkREVEp9P777+PChQtqbRcvXkTlypUBAI6OjrC3t8eePXuk/dnZ2YiMjIS3tzcAoH79+jA0NFSLSUxMxJkzZ6QYLy8vpKam4siRI1LM4cOHkZqaKsUUBYdsiIiIdE2GW8ePGDEC3t7eCA4ORteuXXHkyBEsXboUS5cu/f+UFAgMDERwcDBcXFzg4uKC4OBgmJqawt/fHwCgUqnQt29fjBo1CtbW1rCyssLo0aPh7u6Oli1bAnjS69K2bVv069cPS5YsAQD0798fHTt2LPIKG4AFCRERke7JcKfWhg0bYtu2bRg/fjymTp0KR0dHzJkzBz179pRixo4di4yMDAwaNAgpKSnw9PTE7t27YWFhIcWEhobCwMAAXbt2RUZGBlq0aIHVq1dDX19figkLC8OwYcOk1Th+fn5YsKB4txngfUiI3iG8DwlRQSVyH5LOS7Vynozt/bVynjcR55AQERGR7DhkQ0REpGv8cD2NWJAQERHpmgyTWt82LNmIiIhIduwhISIi0rHi3LH0XcWChIiISMdYkGjGIRsiIiKSHXtIiIiIdI0dJBqxICEiItIxDtloxiEbIiIikh17SIiIiHSMPSSasSAhIiLSMRYkmrEgISIi0jEWJJpxDgkRERHJ7o0rSPLy8hAXF4eUlBS5UyEiItIOhZa2Ukz2giQwMBArVqwA8KQY8fHxQb169VCxYkVERETImxwREZEWKBQKrWylmewFyebNm1GnTh0AwI4dO5CQkIDz588jMDAQEyZMkDk7IiIiKgmyFyR3796Fvb09AGDXrl349NNPUa1aNfTt2xenT5+WOTsiIqLXxx4SzWQvSOzs7HDu3Dnk5eUhPDwcLVu2BAA8fvwY+vr6MmdHRET0+liQaCb7st/PP/8cXbt2Rfny5aFQKNCqVSsAwOHDh1G9enWZsyMiIqKSIHtBMnnyZNSqVQvXr1/Hp59+CqVSCQDQ19fH119/LXN2REREr6+0925og+wFyePHj9GlS5cC7b1795YhGyIiIh1gPaKR7AVJmTJl0KBBA/j6+sLHxwdNmjSBmZmZ3GkRERFRCZK9IImMjERkZCQiIiKwYMECZGZmol69elKB0q5dO7lTJCIiei0cstFMIYQQcifxVF5eHmJjY7F48WKEhYUhPz8feXl5xT6PiccQHWRH9PZLiV0gdwpEbxzjEvjT3ObzjVo5z51V3bRynjeR7D0kAHD+/HlERERIPSU5OTno1KkTfHx85E6NiIjotbGHRDPZCxJ7e3vk5OTggw8+gK+vL7755hu4u7vLnRYRERGVINlvjGZvb4+0tDRcu3YN165dw40bN5CWliZ3WkRERNrDD9fTSPaCJC4uDrdv38aECROQm5uLiRMnwsbGBp6enrwPCRERlQq8U6tmb9Sk1vv37yMiIgK//vor1q9fz0mtRFrGSa1EBZXEpFa7L/+nlfPcXv6pVs7zJpJ9Dsm2bdsQERGBiIgInD17FtbW1mjatClCQ0PRvHlzudMjIiJ6baW9d0MbZC9IBgwYgGbNmqFfv37w9fVFrVq15E6JiIhIq1iQaCZ7QZKcnCx3CkRERCQz2Se1AsC///6Lb7/9Fj169JAKlPDwcJw9e1bmzIiIiF4fJ7VqJntBEhkZCXd3dxw+fBhbt26VlvyeOnUKQUFBMmdHRESkBVz2q5HsBcnXX3+NadOmYc+ePTAyMpLamzdvjujoaBkzIyIiopIi+xyS06dPY/369QXabWxscO/ePRkyIiIi0q7SPtyiDbL3kJQpUwaJiYkF2k+cOIEKFSrIkBEREZF2cQ6JZrIXJP7+/hg3bhySkpKgUCiQn5+Pv//+G6NHj0avXr3kTo+IiOi1sSDRTPaC5Pvvv0elSpVQoUIFpKWloUaNGmjWrBm8vb3x7bffyp0eERERlQDZ55AYGhoiLCwMU6dOxYkTJ5Cfnw8PDw+4uLjInRoREZF2lO7ODa2QvSB5qmrVqqhatarcaRAREWldaR9u0QbZCpKpU6cWKW7SpEk6zoSIiIjkJltBsm3bthfuUygUuHDhAjIzM1mQvAEcbFSYNvxDtH6/JkyUhvjnWjIGTgnDifjrUoyrox2mDe+MpvWcoaenQPy/ifhs3EpcT0oBAMyf0B0feLqivI0KaRlZiDmZgG/n/oqLV25L5/jfnAGoU60CbKwskPLwMQ4cvoBv5/2KxDupJf6ciYpr04b12LTxF9y6eRMAUNXZBQMGDkKTpj4AgL17dmPzpo2IP3cGDx48wMbN21HdzU3tHNevXcOsmdMRd/wYsrOz8X6Tpvj6m4mwLleuxJ8PaRd7SDSTrSA5ceJEoe1xcXH4+uuvcebMGfTr16+Es6LnlbEwwf7VIxEZ+w86D/kJyfcfwaliOTx4lCHFOL5XDvtWjsSa7VGYtmgnUtMyUN3RHplZOVLMifjr2PBHLK4npsBKZYoJX3XA7z8NRvWOQcjPFwCAv2Iv4scVfyLpbiocbMsgZMRHWP9jXzTvM7vEnzdRcdna2WP4iNGoWKkSAGDHr9sxfMhgbNyyDc7OLsjIeIy6Hh5o3aYtpgQVnLD/+PFjfNX/C1RzrY5lK9cAABbOn4uhg7/Cz79sgp6e7GsQ6DWwINHsjZlDkpCQgIkTJ2Ljxo34+OOPcfbsWU5sfQOM+rwVbiSlYMDkn6W2a4n31WKmDOmEPw+dxYS5v0ptV26q39Ru5da/1Y6fsnAHYjd9g8oO1ki4cRcAMD/swDMxKZi5ag82ze4HAwM95Obma/V5EWmbb/MP1B4PHT4Cmzb8glMn4+Ds7IJOfp0BADdv3ij0+LgTx3Hr5k1s3Lwd5ubmAICp00LQ1LsRjhyOQWMvb53mTyQ32Uvuu3fvYujQoahevToSExMRFRWFjRs3shh5Q3Twccfxc9cQNuMLXN0XguhfxuHzj/77wahQKNC2SU38cy0Zvy0cjKv7QvDX2tHo5Fv7hec0NTZCL7/GSLhxFzf+f0jneWUtTdG9XQPEnExgMUJvnby8PPyxaycyMh6jTh2PIh2TnZ0NhUKh9hEaRkol9PT0cOL4MV2lSiWE9yHRTLaCJD09HVOmTEHVqlURFRWFHTt2YN++fWjYsKFcKVEhHCuUQ79Pm+LStTvwG7QQyzcfwqyxXeDfsREAwNbKHBZmxhj9eSvsiTqHTgMX4LcDJ7Fh1pdoUt9Z7Vz9P22KO3/Pwr3o2WjlXQMdBi5ATm6eWsy0YR/ibtQs3IqcgYrlrfDpiKUl9lyJXtc/Fy+gcQMPNPRwx/dTgxA6byGqOjtrPhBA7Tp1YWJigjmzfkRGRgYeP36M2TNnID8/H3fu3NFx5qRzMny43uTJkwsUNPb29tJ+IQQmT54MBwcHmJiYwNfXF2fPnlU7R1ZWFoYOHYpy5crBzMwMfn5+uHFDvZcvJSUFAQEBUKlUUKlUCAgIwIMHD4qXLGQsSKpWrYoZM2Zg4MCBWLlyJezt7XHq1KkCmyZZWVl4+PCh2iby8zQeR0Wjp6dA3PnrCFqwAycv3MCKLX9j1bYo9P+06f/vf/IW+j3iNOaHHcCpizcxc9Ue7Dp4Fv26NFE714Y/YtG4xw9o2TcUl67fwc/Tv4DSSH3UMHTtXjTuPh0dvlqAvLx8LP8uoGSeKJEWVKniiE1btmPd+o34tFsPTPxmHP69dKlIx1pZWeHH2XMRGXkAXg090KRxA6SlPYJbjZrQ5/wRekU1a9ZEYmKitJ0+fVraN2PGDMyePRsLFixAbGws7O3t0apVKzx69EiKCQwMxLZt27BhwwYcOnQIaWlp6NixI/Ly/vs96+/vj7i4OISHhyM8PBxxcXEICCj+z27Z5pAkJycDePKC/PjjjxBCSPsUCgWEEFAoFGpPujAhISGYMmWKWpu+XUMYlm+k/aTfQUl3HyL+cpJa2/mEJHRuURcAcDclDTk5eYi/rP55RBcuJ8Hbw0mt7WFaJh6mZeLfa3dw5NQVJP41Ax9+UAebwv/rjr73IB33HqTj0rVkXEhIwqU/p8GztiMOn0rQzRMk0iJDIyNUqlwZAFCzljvOnjmNsJ/XYtLkot3mwPv9JtgZvhcpKfehr28AS0tLfNDsfVRo954u06YSINdwi4GBgVqvyFNCCMyZMwcTJkzAxx9/DABYs2YN7OzssH79egwYMACpqalYsWIF1q1bh5YtWwIAfv75Z1SsWBF79+5FmzZtEB8fj/DwcMTExMDT0xMAsGzZMnh5eeHChQtwdXUtcq6yld0JCQnSdvny5UIfX758WeN5xo8fj9TUVLXNwK5+CTyDd0N03GVUq2yr1uZSyVaa2JqTm4dj566iWmU79ZjKtriWWPj8kKcUUMDI8MU18dPv35fFEL3JhBDIyc4u9nFly1rB0tISh2Oicf/+vQITZuntI9cckn/++QcODg5wdHRE9+7dpd+rCQkJSEpKQuvWraVYpVIJHx8fREVFAQCOHTuGnJwctRgHBwfUqlVLiomOjoZKpZKKEQBo3LgxVCqVFFNUsv2kr/z/f0W8LqVSCaVSqdam0NPXyrkJmP/zfhxYPQpjvmiNLXuOo2HNKvjik/cx5LtfpJjQNXuxbvoXOHT8EiKPXkRr7xpo36wW2vSbCwCoUsEaXdrUx77oeNxNSYODbRmM6tMSGVk5+PPQk/HKBjUro0Gtyog68S8ePHqMKhXKYdLADvj32h32jtBbYd6c2WjStBns7O3xOD0d4X/swtHYI/hpyXIAQOqDB0hMTMSdO096h69cefK+LleuHMrZ2AAAtm/bAienqihb1gonT57AjJBgfNarD6o4OhV+UXpraKuDJCsrC1lZWWpthf0eBABPT0+sXbsW1apVw+3btzFt2jR4e3vj7NmzSEp60vNtZ6f+x6SdnR2uXr0KAEhKSoKRkRHKli1bIObp8UlJSbC1Vf+jFQBsbW2lmKLin570UsfOXUO3UcswdagfvunfDldu3sOYH7dgwx9HpZjfDpzC0O83YMwXrTFrbBdcvJqMHmOWIyruSSWelZ2L9z2qYoi/L8pamiL53iMcOn4JzfvMwp2UNABARlYOPvygDr79qgPMTIyQdDcVu6Pi0evrVcjOyZXluRMVx717dzHh67G4cycZ5hYWqFbNFT8tWQ4v7/cBABEH9mPSt+Ol+HGjRwAAvho0BAMHDwUAXElIwLzQ2UhNTYVDhQr4sv9XCOjdp8SfC725CpumEBQUhMmTJxeIbdeunfR/d3d3eHl5oWrVqlizZg0aN24MoOBQ0tPpEi/zfExh8UU5z/MU4tnJG6WEiccQuVMgeiOlxC6QOwWiN45xCfxp7jImXCvnOTOteZF7SArTqlUrODs7Y8yYMahatSqOHz8OD4//lqZ/+OGHKFOmDNasWYP9+/ejRYsWuH//vlovSZ06ddC5c2dMmTIFK1euxMiRIwusqilTpgxCQ0Px+eefF/m5ceo2ERGRjikU2tmUSiUsLS3VtqIWI1lZWYiPj0f58uXh6OgIe3t77NmzR9qfnZ2NyMhIeHs/uddU/fr1YWhoqBaTmJiIM2fOSDFeXl5ITU3FkSNHpJjDhw8jNTVViikqDtkQERGVQqNHj0anTp1QqVIlJCcnY9q0aXj48CF69+4NhUKBwMBABAcHw8XFBS4uLggODoapqSn8/f0BACqVCn379sWoUaNgbW0NKysrjB49Gu7u7tKqGzc3N7Rt2xb9+vXDkiVLAAD9+/dHx44di7XCBmBBQkREpHNyLPu9ceMGevTogbt378LGxgaNGzdGTEyMtKhk7NixyMjIwKBBg5CSkgJPT0/s3r0bFhYW0jlCQ0NhYGCArl27IiMjAy1atMDq1auhr//f4pGwsDAMGzZMWo3j5+eHBQuKPzwsyxwSDw+PIn9xjh8/Xuzzcw4JUeE4h4SooJKYQ1L96z+1cp7zP7TRynneRLL0kHTu3FmOyxIREdEbSpaCJCgoSI7LEhERyUJPr3R/MJ42cA4JERGRjpXyD+rVCtkLkry8PISGhmLTpk24du0asp+7zfL9+/dlyoyIiIhKiuz3IZkyZQpmz56Nrl27IjU1FSNHjsTHH38MPT29Qu88R0RE9LaR67Ns3iayFyRhYWFYtmwZRo8eDQMDA/To0QPLly/HpEmTEBMTI3d6REREr01bN0YrzWQvSJKSkuDu7g4AMDc3R2pqKgCgY8eO2Llzp5ypERERaQV7SDSTvSB57733kJiYCABwdnbG7t27AQCxsbFFvh0uERERvd1kL0g++ugj7Nu3DwAwfPhwTJw4ES4uLujVqxe++OILmbMjIiJ6fewh0Uz2VTY//PCD9P8uXbrgvffeQ1RUFJydneHn5ydjZkRERNpRymsJrZC9IHle48aN0bhxY7nTICIiohIk+5ANAKxbtw7vv/8+HBwccPXqVQDAnDlz8Ouvv8qcGRER0evjkI1mshckixYtwsiRI9G+fXs8ePAAeXl5AIAyZcpgzpw58iZHRESkBVz2q5nsBcn8+fOxbNkyTJgwQe3jjBs0aIDTp0/LmBkRERGVFNnnkCQkJMDDw6NAu1KpRHp6ugwZERERaVdpH27RBtl7SBwdHREXF1eg/Y8//kCNGjVKPiEiIiIt45CNZrL3kIwZMwaDBw9GZmYmhBA4cuQIfvnlF4SEhGD58uVyp0dEREQlQPaC5PPPP0dubi7Gjh2Lx48fw9/fHxUqVMDcuXPRvXt3udMjIiJ6bRyy0Uz2ggQA+vXrh379+uHu3bvIz8+Hra0tAODmzZuoUKGCzNkRERG9HtYjmsk+h+RZ5cqVg62tLZKSkjB06FA4OzvLnRIREdFr431INJOtIHnw4AF69uwJGxsbODg4YN68ecjPz8ekSZPg5OSEmJgYrFy5Uq70iIiIqATJNmTzzTff4K+//kLv3r0RHh6OESNGIDw8HJmZmfjjjz/g4+MjV2pERERaVco7N7RCtoJk586dWLVqFVq2bIlBgwbB2dkZ1apV491ZiYio1Cntwy3aINuQza1bt6T7jDg5OcHY2BhffvmlXOkQERGRjGTrIcnPz4ehoaH0WF9fH2ZmZnKlQ0REpDPsINFMtoJECIE+ffpAqVQCADIzM/HVV18VKEq2bt0qR3pERERawyEbzWQrSHr37q32+LPPPpMpEyIiIpKbbAXJqlWr5Lo0ERFRiWIHiWZvxJ1aiYiISjMO2Wj2Rt2plYiIiN5N7CEhIiLSMfaQaMaChIiISMdYj2jGgoSIiEjH2EOiGeeQEBERkezYQ0JERKRj7CDRjAUJERGRjnHIRjMO2RAREZHs2ENCRESkY+wg0YwFCRERkY7psSLRiEM2REREJDv2kBAREekYO0g0Y0FCRESkY1xloxkLEiIiIh3TYz2iEeeQEBERkezYQ0JERKRjHLLRjAUJERGRjrEe0YxDNkRERCQ79pAQERHpmALsItGEPSREREQ6pqfQzvY6QkJCoFAoEBgYKLUJITB58mQ4ODjAxMQEvr6+OHv2rNpxWVlZGDp0KMqVKwczMzP4+fnhxo0bajEpKSkICAiASqWCSqVCQEAAHjx4UKz8WJAQERGVcrGxsVi6dClq166t1j5jxgzMnj0bCxYsQGxsLOzt7dGqVSs8evRIigkMDMS2bduwYcMGHDp0CGlpaejYsSPy8vKkGH9/f8TFxSE8PBzh4eGIi4tDQEBAsXJkQUJERKRjCoVCK9urSEtLQ8+ePbFs2TKULVtWahdCYM6cOZgwYQI+/vhj1KpVC2vWrMHjx4+xfv16AEBqaipWrFiBWbNmoWXLlvDw8MDPP/+M06dPY+/evQCA+Ph4hIeHY/ny5fDy8oKXlxeWLVuG33//HRcuXChynixIiIiIdEyh0M6WlZWFhw8fqm1ZWVkvvfbgwYPRoUMHtGzZUq09ISEBSUlJaN26tdSmVCrh4+ODqKgoAMCxY8eQk5OjFuPg4IBatWpJMdHR0VCpVPD09JRiGjduDJVKJcUUBQsSIiKit0RISIg0T+PpFhIS8sL4DRs24Pjx44XGJCUlAQDs7OzU2u3s7KR9SUlJMDIyUutZKSzG1ta2wPltbW2lmKLgKhsiIiId09PSjUjGjx+PkSNHqrUplcpCY69fv47hw4dj9+7dMDY2fuE5nx8KEkJoHB56Pqaw+KKc51nsISEiItIxbQ3ZKJVKWFpaqm0vKkiOHTuG5ORk1K9fHwYGBjAwMEBkZCTmzZsHAwMDqWfk+V6M5ORkaZ+9vT2ys7ORkpLy0pjbt28XuP6dO3cK9L68DAsSIiIiHZNjUmuLFi1w+vRpxMXFSVuDBg3Qs2dPxMXFwcnJCfb29tizZ490THZ2NiIjI+Ht7Q0AqF+/PgwNDdViEhMTcebMGSnGy8sLqampOHLkiBRz+PBhpKamSjFFwSEbIiKiUsjCwgK1atVSazMzM4O1tbXUHhgYiODgYLi4uMDFxQXBwcEwNTWFv78/AEClUqFv374YNWoUrK2tYWVlhdGjR8Pd3V2aJOvm5oa2bduiX79+WLJkCQCgf//+6NixI1xdXYucLwsSIiIiHXtTP8tm7NixyMjIwKBBg5CSkgJPT0/s3r0bFhYWUkxoaCgMDAzQtWtXZGRkoEWLFli9ejX09fWlmLCwMAwbNkxajePn54cFCxYUKxeFEEJo52m9OUw8hsidAtEbKSW2eD8giN4FxiXwp3m3NSe0cp6NvT20cp43EeeQEBERkew4ZENERKRjb+iIzRuFBQkREZGOvept398lHLIhIiIi2bGHhIiISMf02EGiUZEKkt9++63IJ/Tz83vlZIiIiEojDtloVqSCpHPnzkU6mUKhQF5e3uvkQ0RERO+gIhUk+fn5us6DiIio1GIHiWacQ0JERKRjHLLR7JUKkvT0dERGRuLatWvIzs5W2zds2DCtJEZERFRacFKrZsUuSE6cOIH27dvj8ePHSE9Ph5WVFe7evQtTU1PY2tqyICEiIqJiK/Z9SEaMGIFOnTrh/v37MDExQUxMDK5evYr69etj5syZusiRiIjoraZQKLSylWbFLkji4uIwatQo6OvrQ19fH1lZWahYsSJmzJiBb775Rhc5EhERvdUUWtpKs2IXJIaGhlKVZmdnh2vXrgEAVCqV9H8iIiKi4ij2HBIPDw8cPXoU1apVQ/PmzTFp0iTcvXsX69atg7u7uy5yJCIieqvplfLhFm0odg9JcHAwypcvDwD47rvvYG1tjYEDByI5ORlLly7VeoJERERvO4VCO1tpVuwekgYNGkj/t7Gxwa5du7SaEBEREb17eGM0IiIiHSvtK2S0odgFiaOj40tf2MuXL79WQkRERKUN6xHNil2QBAYGqj3OycnBiRMnEB4ejjFjxmgrLyIiInqHFLsgGT58eKHtCxcuxNGjR187ISIiotKGq2w0K/Yqmxdp164dtmzZoq3TERERlRpcZaOZ1ia1bt68GVZWVto6HRERUanBSa2avdKN0Z59YYUQSEpKwp07d/DTTz9pNTkiIiJ6NxS7IPnwww/VChI9PT3Y2NjA19cX1atX12pyryohMlTuFIjeSJ7f7ZM7BaI3zskpLXR+Da3NjyjFil2QTJ48WQdpEBERlV4cstGs2EWbvr4+kpOTC7Tfu3cP+vr6WkmKiIiI3i3F7iERQhTanpWVBSMjo9dOiIiIqLTRYweJRkUuSObNmwfgSbfT8uXLYW5uLu3Ly8vDX3/99cbMISEiInqTsCDRrMgFSWjok4miQggsXrxYbXjGyMgIVapUweLFi7WfIREREZV6RS5IEhISAADNmzfH1q1bUbZsWZ0lRUREVJpwUqtmxZ5DcuDAAV3kQUREVGpxyEazYq+y6dKlC3744YcC7T/++CM+/fRTrSRFRERE75ZiFySRkZHo0KFDgfa2bdvir7/+0kpSREREpQk/y0azYg/ZpKWlFbq819DQEA8fPtRKUkRERKUJP+1Xs2L3kNSqVQsbN24s0L5hwwbUqFFDK0kRERGVJnpa2kqzYveQTJw4EZ988gn+/fdffPDBBwCAffv2Yf369di8ebPWEyQiIqLSr9gFiZ+fH7Zv347g4GBs3rwZJiYmqFOnDvbv3w9LS0td5EhERPRW44iNZsUuSACgQ4cO0sTWBw8eICwsDIGBgTh58iTy8vK0miAREdHbjnNINHvlIan9+/fjs88+g4ODAxYsWID27dvj6NGj2syNiIiI3hHF6iG5ceMGVq9ejZUrVyI9PR1du3ZFTk4OtmzZwgmtREREL8AOEs2K3EPSvn171KhRA+fOncP8+fNx69YtzJ8/X5e5ERERlQp6Cu1spVmRe0h2796NYcOGYeDAgXBxcdFlTkRERPSOKXIPycGDB/Ho0SM0aNAAnp6eWLBgAe7cuaPL3IiIiEoFPYVCK1tpVuSCxMvLC8uWLUNiYiIGDBiADRs2oEKFCsjPz8eePXvw6NEjXeZJRET01uKt4zUr9iobU1NTfPHFFzh06BBOnz6NUaNG4YcffoCtrS38/Px0kSMRERGVcq91J1pXV1fMmDEDN27cwC+//KKtnIiIiEoVTmrVTCu3xtfX10fnzp3x22+/aeN0REREpYpCS/+KY9GiRahduzYsLS1haWkJLy8v/PHHH9J+IQQmT54MBwcHmJiYwNfXF2fPnlU7R1ZWFoYOHYpy5crBzMwMfn5+uHHjhlpMSkoKAgICoFKpoFKpEBAQgAcPHhT7NSrtn9VDREQkOzl6SN577z388MMPOHr0KI4ePYoPPvgAH374oVR0zJgxA7Nnz8aCBQsQGxsLe3t7tGrVSm1OaGBgILZt24YNGzbg0KFDSEtLQ8eOHdXuyu7v74+4uDiEh4cjPDwccXFxCAgIKPZrpBBCiGIf9YZLepgjdwpEb6Q2s/6SOwWiN87JKS10fo0f9v+rlfN8/UHV1zreysoKP/74I7744gs4ODggMDAQ48aNA/CkN8TOzg7Tp0/HgAEDkJqaChsbG6xbtw7dunUDANy6dQsVK1bErl270KZNG8THx6NGjRqIiYmBp6cnACAmJgZeXl44f/48XF1di5wbe0iIiIh0TO45JHl5ediwYQPS09Ph5eWFhIQEJCUloXXr1lKMUqmEj48PoqKiAADHjh1DTk6OWoyDgwNq1aolxURHR0OlUknFCAA0btwYKpVKiimqV/pwPSIiIio6hZbW7GZlZSErK0utTalUQqlUFhp/+vRpeHl5ITMzE+bm5ti2bRtq1KghFQt2dnZq8XZ2drh69SoAICkpCUZGRihbtmyBmKSkJCnG1ta2wHVtbW2lmKJiDwkREdFbIiQkRJo8+nQLCQl5Ybyrqyvi4uIQExODgQMHonfv3jh37py0//lCSQihsXh6Pqaw+KKc53nsISEiItIxbS3ZHT9+PEaOHKnW9qLeEQAwMjKCs7MzAKBBgwaIjY3F3LlzpXkjSUlJKF++vBSfnJws9ZrY29sjOzsbKSkpar0kycnJ8Pb2lmJu375d4Lp37twp0PuiCXtIiIiIdExbd2pVKpXSMt6n28sKkucJIZCVlQVHR0fY29tjz5490r7s7GxERkZKxUb9+vVhaGioFpOYmIgzZ85IMV5eXkhNTcWRI0ekmMOHDyM1NVWKKSr2kBAREZVC33zzDdq1a4eKFSvi0aNH2LBhAyIiIhAeHg6FQoHAwEAEBwfDxcUFLi4uCA4OhqmpKfz9/QEAKpUKffv2xahRo2BtbQ0rKyuMHj0a7u7uaNmyJQDAzc0Nbdu2Rb9+/bBkyRIAQP/+/dGxY8dirbABWJAQERHpnBwfjHf79m0EBAQgMTERKpUKtWvXRnh4OFq1agUAGDt2LDIyMjBo0CCkpKTA09MTu3fvhoWFhXSO0NBQGBgYoGvXrsjIyECLFi2wevVq6OvrSzFhYWEYNmyYtBrHz88PCxYsKHa+vA8J0TuE9yEhKqgk7kMy71CCVs4zrImjVs7zJuIcEiIiIpIdh2yIiIh0TIYRm7cOCxIiIiId0yvmB+O9i1iQEBER6Rh7SDTjHBIiIiKSHXtIiIiIdExbd2otzViQEBER6Zgc9yF523DIhoiIiGTHHhIiIiIdYweJZixIiIiIdIxDNppxyIaIiIhkxx4SIiIiHWMHiWYsSIiIiHSMwxGa8TUiIiIi2bGHhIiISMcUHLPRiAUJERGRjrEc0YwFCRERkY5x2a9mnENCREREsmMPCRERkY6xf0QzFiREREQ6xhEbzThkQ0RERLJjDwkREZGOcdmvZixIiIiIdIzDEZrJ/hpdv34dN27ckB4fOXIEgYGBWLp0qYxZERERUUmSvSDx9/fHgQMHAABJSUlo1aoVjhw5gm+++QZTp06VOTsiIqLXp1AotLKVZrIXJGfOnEGjRo0AAJs2bUKtWrUQFRWF9evXY/Xq1fImR0REpAUKLW2lmewFSU5ODpRKJQBg79698PPzAwBUr14diYmJcqZGREREJUT2gqRmzZpYvHgxDh48iD179qBt27YAgFu3bsHa2lrm7IiIiF4fh2w0k70gmT59OpYsWQJfX1/06NEDderUAQD89ttv0lAOERHR20xPS1tpJvuyX19fX9y9excPHz5E2bJlpfb+/fvD1NRUxsyIiIi0o7T3bmiD7AXXsmXLcPnyZbViBACqVKkCW1tbmbIiIiKikiR7QTJr1iy4urrCwcEBPXr0wJIlS3D+/Hm50yIiItIarrLRTPaC5Pz587h16xZmzZoFlUqF0NBQ1KxZE/b29ujevbvc6REREb02hUI7W2km+xwSALC3t0ePHj3g5+eHQ4cOYcOGDfj555+xefNmuVMjIiKiEiB7QfLHH38gMjISEREROHnyJGrWrIlmzZphy5YtaNq0qdzpERERvTa9Uj/g8vpkL0g6dOgAGxsbjBo1Cn/++SdUKpXcKREREWlVaR9u0QbZ55DMnj0b77//Pn788Ue4urqiW7duWLRoEeLj4+VOjYiIiEqI7AVJYGAgtm7dijt37mDPnj1o2rQp9u7dizp16qB8+fJyp0dERPTaFFr6V5rJPmTz1IkTJxAREYEDBw7g4MGDyM/Px3vvvSd3WkRERK+NQzaayd5D4ufnBysrKzRs2BBhYWGoVq0a1q1bh/v37yM2Nlbu9IiIiKgEyN5DUq1aNfTv3x/NmjWDpaWl3OkQERFpHVfZaCZ7QTJz5kzp/5mZmTA2NpYxGyIiIu3jkI1msg/Z5Ofn47vvvkOFChVgbm6Oy5cvAwAmTpyIFStWyJwdERHR6+OdWjWTvSCZNm0aVq9ejRkzZsDIyEhqd3d3x/Lly2XMjIiIiEqK7AXJ2rVrsXTpUvTs2RP6+vpSe+3atfkhe0REVCpw2a9mss8huXnzJpydnQu05+fnIycnR4aMiIiItEuvdNcSWiF7D0nNmjVx8ODBAu3/+9//4OHhIUNGREREVNJk7yEJCgpCQEAAbt68ifz8fGzduhUXLlzA2rVr8fvvv8udHhER0Wsr7cMt2iB7D0mnTp2wceNG7Nq1CwqFApMmTUJ8fDx27NiBVq1ayZ0eERHRa5NjlU1ISAgaNmwICwsL2NraonPnzrhw4YJajBACkydPhoODA0xMTODr64uzZ8+qxWRlZWHo0KEoV64czMzM4Ofnhxs3bqjFpKSkICAgACqVCiqVCgEBAXjw4EGx8pW9IAGANm3aIDIyEmlpaXj8+DEOHTqE1q1by50WERHRWysyMhKDBw9GTEwM9uzZg9zcXLRu3Rrp6elSzIwZMzB79mwsWLAAsbGxsLe3R6tWrfDo0SMpJjAwENu2bcOGDRtw6NAhpKWloWPHjsjLy5Ni/P39ERcXh/DwcISHhyMuLg4BAQHFylchhBCv/7TfLEkPORmWqDBtZv0ldwpEb5yTU1ro/BoRF+5r5Ty+rlavfOydO3dga2uLyMhINGvWDEIIODg4IDAwEOPGjQPwpDfEzs4O06dPx4ABA5CamgobGxusW7cO3bp1AwDcunULFStWxK5du9CmTRvEx8ejRo0aiImJgaenJwAgJiYGXl5eOH/+PFxdXYuUn2xzSBwdHaHQ0P+kUCjw77//llBGREREuqGtVTZZWVnIyspSa1MqlVAqlRqPTU1NBQBYWT0pahISEpCUlKQ2IqFUKuHj44OoqCgMGDAAx44dQ05OjlqMg4MDatWqhaioKLRp0wbR0dFQqVRSMQIAjRs3hkqlQlRU1JtfkAQGBr5w35UrV7BkyZICLzoREdG7LCQkBFOmTFFrCwoKwuTJk196nBACI0eORJMmTVCrVi0AQFJSEgDAzs5OLdbOzg5Xr16VYoyMjFC2bNkCMU+PT0pKgq2tbYFr2traSjFFIVtBMnz48AJt9+/fx3fffYdFixbB09MT06dPlyEzepnc3FysXvYT9oTvxP17d2FtbYO2HT9Er74DoKenh9zcHCxfNB8xfx9E4s0bMDM3R/1GjTFgyAiUs/nvDZudnY2f5s7E/j93ISsrC/UaemLEuG9ha2cv47MjejVfNK2M4S2d8XP0NfwY/g+AFw8DzN79D9b8fQ0AsLxPPTR0VP9BH376NsZtPgMAcChjjP4+jmjkWBbW5ka48ygLO08lYdlfV5CbV+pG20s1ba2yGT9+PEaOHKnWVpTekSFDhuDUqVM4dOhQwdyeG60QQmgcwXg+prD4opznWbIv+wWAjIwMzJ49Gz/++COqVKmCrVu3on379nKnRYX4Ze0K/LZlE8ZP/h5VnJxxIf4sfpj6LczNzdGlRwAyMzNx8fw59Oo7AM4urnj06CEWzJ6Ob0YNwdK1m6TzzJ/9A6IPRmLS9z/CskwZ/DTnR4wfMRhL121Su2Mv0ZuupoMFutSvgAtJj9TaP/hR/f5KTZytMflDN+w9l6zWvvnoTfx04LL0OCvnv4mCVcqZQk8BfLfjPK7dfwxnW3ME+bnBxFAfs3df0sGzIV3R1ufQFHV45llDhw7Fb7/9hr/++gvvvfee1G5v/+QPwKSkJJQvX15qT05OlnpN7O3tkZ2djZSUFLVekuTkZHh7e0sxt2/fLnDdO3fuFOh9eRlZV9nk5eVh8eLFcHJywvLlyzF//nycOHGCxcgb7Ozpk3jfpzm8mvigvEMF+LZojYae3jgf/2SZmLm5BWYvXI4PWrVFpSqOqOleB8NGj8eF+HO4nZQIAEhLe4Rdv27FoOGj0cDTC9Vc3fDt1B9w+d9/cOxIjJxPj6hYTIz0EfJJLUz5LR4PM3LV9t1Ly1bbfKvbIPZKCm6mZKrFZebkqcWlZf1XkERduo9J2+MR/e993EzJROSFu1gTdRUt3Ap2j9ObTaGlrTiEEBgyZAi2bt2K/fv3w9HRUW2/o6Mj7O3tsWfPHqktOzsbkZGRUrFRv359GBoaqsUkJibizJkzUoyXlxdSU1Nx5MgRKebw4cNITU2VYopCtoJk06ZNcHNzQ1BQEL7++mtcuHABAQEBxereoZLnXqcejscexvWrVwAAly6ex+mTx9H4/WYvPCY9LQ0KhQLm5hYAgIvx55Cbm4uGjf97o5azsYVjVWecOXVCp/kTadM3HVzx1z93cfhyykvjrMyM0LSaNbYdv1VgX/va9ogY2xRbB3tiZGtnmBq9vIfQXGmA1AyuJCTNBg8ejJ9//hnr16+HhYUFkpKSkJSUhIyMDABPhlkCAwMRHByMbdu24cyZM+jTpw9MTU3h7+8PAFCpVOjbty9GjRqFffv24cSJE/jss8/g7u6Oli1bAgDc3NzQtm1b9OvXDzExMYiJiUG/fv3QsWPHIk9oBWQcsunevTtMTEzQo0cPXL16FV9//XWhcbNnz37peQqbcZyVpVfsLi0qGv/efZGe9ggBn3aCnp4+8vPz8OXAYWjZpvBeraysLCxdGIqWbdrDzNwcAHDv3l0YGhrCwlKlFlvWyhr3793T+XMg0oa2tezgVt4C/ktjNcb61bXH46w87Iu/o9a+63QSbqZk4F5aNpxtzTGsZVVUszfHV2vjCj3Pe2VN0MOzImb9+Y82ngKVID0Z/thetGgRAMDX11etfdWqVejTpw8AYOzYscjIyMCgQYOQkpICT09P7N69GxYWFlJ8aGgoDAwM0LVrV2RkZKBFixZYvXq12vB6WFgYhg0bJq3G8fPzw4IFC4qVr2wFSbNmzTQu6y1Kb0lhM45Hff0tRo+f9No5UkH79/yB3X/8jonTpqOKkzMuXTyPBbOno5yNLdp2/FAtNjc3B1MnjEF+vsCIcRM1nvvJBChdZU6kPXaWSoxtVw1frT2B7Nx8jfGdPRyw63RSgditx/7rMbmUnI6r9x5jw1eNUL28Bc4nqs9JsbEwwk8BdbHn7O1Ce1rozSbHj7ai3GZMoVBg8uTJL12lY2xsjPnz52P+/PkvjLGyssLPP//8KmlKZCtIIiIitHKewmYcp2S9ETegLZUWzZ2Fnr2/RIvWT3pEqjpXw+3ERIStXq5WkOTm5iBo/Cgk3rqB0J9WSr0jAGBtXQ45OTl49DBVrZfkQcp91Kpdt8SeC9GrquFgAWtzI/wyoKHUZqCvh/qVy6B7o/fQ8LsDyP//3wUelcrA0cYMY/93RuN54xMfISc3H5WtTNQKEhsLIyzvUw+nrqdi6o7zWn8+RG+CN2KVzesobMbxY96pVWeysjKheO4OP3p6esgX//3l97QYuXntGuYsXglVmTJq8dXcasDAwACxh6PxQau2AIB7d+8g4d9L+GroKJ0/B6LXdfhyCj5ZqD4Be0rnGrhyNx2rDl2VihEA+KheeZy9+RAXb6dpPK+zrRkMDfRwJy1barO1UGJ5n3o4l/gQk7afQ+m7t/Y7gr2/Gr31BQmVLO8mvvh51TLY2ZdHFSdn/HMhHpvWr0V7v48APLlPyaRxI3Hx/Dn8ELoQeXn5uHf3LgDAUqWCoaEhzM0t0P7Dj/HTnB+hUpWBhUqFRXNmwqmqC+o3aizn0yMqksfZebiUnK7WlpGdhwePc9TazZT6aF3TrtA5H++VNUGH2vY4+M9dPHicAycbM4xq44L4Ww8Rd+0BgP/vGfm8HpJSMzH7z0soa2YkHX/vmaKF3nz8tF/NWJBQsQwf8w1WLJ6P0OnTkJJyH+XK2cDv40/R+8uBAIA7ybfx918HAAB9e3ZRO3bO4pXwqN8IADBkxDjo6xtg8jejkJX55MZoIUELeA8SKlXa1npyD4Y/The8W2VOXj4aOZWFf+OKMDXSR1JqJg7+cw+LIy5LPSxeVa1R2doUla1NsWd0E7Xj6wTt03n+RCWJH65H9A7hh+sRFVQSH6535HKqVs7TyEmlOegtxR4SIiIiHeOAjWZvxHKUgwcP4rPPPoOXlxdu3rwJAFi3bl2h99wnIiKi0kf2gmTLli1o06YNTExMcOLECekmZ48ePUJwcLDM2REREWmBHPeOf8vIXpBMmzYNixcvxrJly2BoaCi1e3t74/jx4zJmRkREpB0KLf0rzWSfQ3LhwgU0a1bwc1AsLS3x4MGDkk+IiIhIy3gXas1k7yEpX748Ll0q+DHahw4dgpOTkwwZERERUUmTvSAZMGAAhg8fjsOHD0OhUODWrVsICwvD6NGjMWjQILnTIyIiem2cQqKZ7EM2Y8eORWpqKpo3b47MzEw0a9YMSqUSo0ePxpAhQ+ROj4iI6PWV9mpCC2QvSLKzs/H9999jwoQJOHfuHPLz81GjRg2Ym5vj7t27KFeunNwpEhERkY7JPmTTtWtX5Ofnw9TUFA0aNECjRo1gbm6O27dvw9fXV+70iIiIXhtX2Wgme0GSmJiIvn37Fmjz9fVF9erVZcqKiIhIexQK7WylmewFya5du3DkyBGMGDECAHDz5k34+vrC3d0dmzZtkjk7IiIiKgmyzyGxtrbGn3/+iSZNnnyS5c6dO1GvXj2EhYVBT0/2eomIiOi1lfLODa2QvSABgPfeew979uxBkyZN0KpVK6xbtw6K0t43RURE7w7+StNIloKkbNmyhRYcjx8/xo4dO2BtbS213b9/vyRTIyIiIhnIUpDMmTNHjssSERHJorSvkNEGWQqS3r17y3FZIiIiWXAWgmZvxBySpzIyMpCTk6PWZmlpKVM2RERE2sF6RDPZl7Gkp6djyJAhsLW1hbm5OcqWLau2ERERUekne0EyduxY7N+/Hz/99BOUSiWWL1+OKVOmwMHBAWvXrpU7PSIiotfHT9fTSPYhmx07dmDt2rXw9fXFF198gaZNm8LZ2RmVK1dGWFgYevbsKXeKREREr4WTWjWTvYfk/v37cHR0BPBkvsjTZb5NmjTBX3/9JWdqREREVEJkL0icnJxw5coVAECNGjWk28Xv2LEDZcqUkS8xIiIiLeFn2Wgme0Hy+eef4+TJkwCA8ePHS3NJRowYgTFjxsicHRER0evjFBLNZJtDcvnyZTg6OkofqgcAzZs3x/nz53H06FFUrVoVderUkSs9IiIiKkGy9ZC4uLjgzp070uNu3brh9u3bqFSpEj7++GMWI0REVHqwi0Qj2QoSIYTa4127diE9PV2mbIiIiHRHoaV/pZnsc0iIiIiIZJtDolAoCnzib2GfAExERPS24683zWQrSIQQ6NOnD5RKJQAgMzMTX331FczMzNTitm7dKkd6REREWsN6RDPZCpLnP/H3s88+kykTIiIiHWNFopFsBcmqVavkujQRERG9YWT/LBsiIqLSrrSvkNEGFiREREQ6xkmtmnHZLxEREcmOPSREREQ6xg4SzViQEBER6RorEo04ZENERESyYw8JERGRjnGVjWYsSIiIiHSMq2w045ANERERyY49JERERDrGDhLNWJAQERHpGisSjThkQ0REpGMKLf0rrr/++gudOnWCg4MDFAoFtm/frrZfCIHJkyfDwcEBJiYm8PX1xdmzZ9VisrKyMHToUJQrVw5mZmbw8/PDjRs31GJSUlIQEBAAlUoFlUqFgIAAPHjwoFi5siAhIiIqpdLT01GnTh0sWLCg0P0zZszA7NmzsWDBAsTGxsLe3h6tWrXCo0ePpJjAwEBs27YNGzZswKFDh5CWloaOHTsiLy9PivH390dcXBzCw8MRHh6OuLg4BAQEFCtXhRBCvNrTfHMlPcyROwWiN1KbWX/JnQLRG+fklBY6v8a1+1laOU8lK+UrH6tQKLBt2zZ07twZwJPeEQcHBwQGBmLcuHEAnvSG2NnZYfr06RgwYABSU1NhY2ODdevWoVu3bgCAW7duoWLFiti1axfatGmD+Ph41KhRAzExMfD09AQAxMTEwMvLC+fPn4erq2uR8mMPCRERkY4ptLRlZWXh4cOHaltW1qsVOwkJCUhKSkLr1q2lNqVSCR8fH0RFRQEAjh07hpycHLUYBwcH1KpVS4qJjo6GSqWSihEAaNy4MVQqlRRTFCxIiIiI3hIhISHSPI2nW0hIyCudKykpCQBgZ2en1m5nZyftS0pKgpGREcqWLfvSGFtb2wLnt7W1lWKKgqtsiIiIdExbN0YbP348Ro4cqdamVL76MA7wZCjnWUKIAm3Pez6msPiinOdZ7CEhIiLSOe0M2iiVSlhaWqptr1qQ2NvbA0CBXozk5GSp18Te3h7Z2dlISUl5aczt27cLnP/OnTsFel9ehgUJERHRO8jR0RH29vbYs2eP1JadnY3IyEh4e3sDAOrXrw9DQ0O1mMTERJw5c0aK8fLyQmpqKo4cOSLFHD58GKmpqVJMUXDIhoiISMfk+iybtLQ0XLp0SXqckJCAuLg4WFlZoVKlSggMDERwcDBcXFzg4uKC4OBgmJqawt/fHwCgUqnQt29fjBo1CtbW1rCyssLo0aPh7u6Oli1bAgDc3NzQtm1b9OvXD0uWLAEA9O/fHx07dizyChuABQkREZHOyXWj1qNHj6J58+bS46fzT3r37o3Vq1dj7NixyMjIwKBBg5CSkgJPT0/s3r0bFhYW0jGhoaEwMDBA165dkZGRgRYtWmD16tXQ19eXYsLCwjBs2DBpNY6fn98L733yIrwPCdE7hPchISqoJO5DcutBtlbO41DGSCvneROxh4SIiEjH5BqyeZuwICEiItKxV/kcmncNCxIiIiJdYz2iEZf9EhERkezYQ0JERKRj7CDRjAUJERGRjnFSq2YcsiEiIiLZsYeEiIhIx7jKRjMWJERERLrGekQjDtkQERGR7NhDQkREpGPsINGMBQkREZGOcZWNZhyyISIiItmxh4SIiEjHuMpGMxYkREREOsYhG804ZENERESyY0FCREREsuOQDRERkY5xyEYzFiREREQ6xkmtmnHIhoiIiGTHHhIiIiId45CNZixIiIiIdIz1iGYcsiEiIiLZsYeEiIhI19hFohELEiIiIh3jKhvNOGRDREREsmMPCRERkY5xlY1mLEiIiIh0jPWIZixIiIiIdI0ViUacQ0JERESyYw8JERGRjnGVjWYsSIiIiHSMk1o145ANERERyU4hhBByJ0GlU1ZWFkJCQjB+/HgolUq50yF6Y/B7g6ggFiSkMw8fPoRKpUJqaiosLS3lTofojcHvDaKCOGRDREREsmNBQkRERLJjQUJERESyY0FCOqNUKhEUFMRJe0TP4fcGUUGc1EpERESyYw8JERERyY4FCREREcmOBQkRERHJjgUJvZbJkyejbt26cqdBpFOrV69GmTJl5E6DqFRjQVJK9enTBwqFAgqFAgYGBqhUqRIGDhyIlJSUEs3jypUrUCgUsLW1xaNHj9T21a1bF5MnTy7RfOjd9ez3xLPbpUuXZMnH19cXCoUCGzZsUGufM2cOqlSpIktORHJiQVKKtW3bFomJibhy5QqWL1+OHTt2YNCgQbLk8ujRI8ycOVOWaxM99fR74tnN0dFRtnyMjY3x7bffIicnR7YciN4ULEhKMaVSCXt7e7z33nto3bo1unXrht27d6vFrFq1Cm5ubjA2Nkb16tXx008/qe0fN24cqlWrBlNTUzg5OWHixImv9MNz6NChmD17NpKTk18Yk52djbFjx6JChQowMzODp6cnIiIiAABCCNjY2GDLli1SfN26dWFrays9jo6OhqGhIdLS0gA8GU6qVKkSlEolHBwcMGzYsGLnTaXL0++JZzd9fX3Mnj0b7u7uMDMzQ8WKFTFo0CDpfVSYe/fuoVGjRvDz80NmZiaEEJgxYwacnJxgYmKCOnXqYPPmzRrz6dGjB1JTU7Fs2bKXxu3YsQP169eHsbExnJycMGXKFOTm5gIARo0ahU6dOkmxc+bMgUKhwM6dO6U2V1dXLFmyBAAQERGBRo0awczMDGXKlMH777+Pq1evasyVSNdYkLwjLl++jPDwcBgaGkpty5Ytw4QJE/D9998jPj4ewcHBmDhxItasWSPFWFhYYPXq1Th37hzmzp2LZcuWITQ0tNjX79GjB5ydnTF16tQXxnz++ef4+++/sWHDBpw6dQqffvop2rZti3/++QcKhQLNmjWTCpSUlBScO3cOOTk5OHfuHIAnP2jr168Pc3NzbN68GaGhoViyZAn++ecfbN++He7u7sXOm94Nenp6mDdvHs6cOYM1a9Zg//79GDt2bKGxN27cQNOmTVG9enVs3bpV6uVYtWoVFi1ahLNnz2LEiBH47LPPEBkZ+dLrWlpa4ptvvsHUqVORnp5eaMyff/6Jzz77DMOGDcO5c+ewZMkSrF69Gt9//z2AJ0M/Bw8eRH5+PgAgMjIS5cqVk66dlJSEixcvwsfHB7m5uejcuTN8fHxw6tQpREdHo3///lAoFK/60hFpj6BSqXfv3kJfX1+YmZkJY2NjAUAAELNnz5ZiKlasKNavX6923HfffSe8vLxeeN4ZM2aI+vXrS4+DgoJEnTp1XhifkJAgAIgTJ06I8PBwYWhoKC5duiSEEKJOnToiKChICCHEpUuXhEKhEDdv3lQ7vkWLFmL8+PFCCCHmzZsnatWqJYQQYvv27aJBgwbi448/FgsXLhRCCNG6dWsxbtw4IYQQs2bNEtWqVRPZ2dkve5noHfLs98TTrUuXLoXGbtq0SVhbW0uPV61aJVQqlbhw4YKoVKmSGDp0qMjPzxdCCJGWliaMjY1FVFSU2jn69u0revTo8cJ8fHx8xPDhw0VmZqaoXLmymDp1qhBCiNDQUFG5cmUprmnTpiI4OFjt2HXr1ony5csLIYR48OCB0NPTE0ePHhX5+fnC2tpahISEiIYNGwohhFi/fr2ws7MTQghx7949AUBEREQU5SUjKlEG8pZDpEvNmzfHokWL8PjxYyxfvhwXL17E0KFDAQB37tzB9evX0bdvX/Tr1086Jjc3FyqVSnq8efNmzJkzB5cuXUJaWhpyc3Nf+ePS27RpgyZNmmDixIlYv3692r7jx49DCIFq1aqptWdlZcHa2hrAk78Ehw8fjrt37yIyMhK+vr6oVKkSIiMj0b9/f0RFRSEwMBAA8Omnn2LOnDlwcnJC27Zt0b59e3Tq1AkGBnzLv8uefk88ZWZmBgA4cOAAgoODce7cOTx8+BC5ubnIzMxEenq6FJORkYEmTZqgR48emDt3rnSOc+fOITMzE61atVK7VnZ2Njw8PDTmpFQqMXXqVAwZMgQDBw4ssP/YsWOIjY2VekQAIC8vD5mZmXj8+DFUKhXq1q2LiIgIGBoaQk9PDwMGDEBQUBAePXqEiIgI+Pj4AACsrKzQp08ftGnTBq1atULLli3RtWtXlC9fvhivIpFucMimFDMzM4OzszNq166NefPmISsrC1OmTAEAqXt32bJliIuLk7YzZ84gJiYGABATE4Pu3bujXbt2+P3333HixAlMmDAB2dnZr5zTDz/8gI0bN+LEiRNq7fn5+dDX18exY8fU8omPj5d++NeqVQvW1taIjIyUChIfHx9ERkYiNjZW+oUBABUrVsSFCxewcOFCmJiYYNCgQWjWrBknD77jnn5PPN3Kly+Pq1evon379qhVqxa2bNmCY8eOYeHChQCg9n5RKpVo2bIldu7ciRs3bkjtT7+Xdu7cqfbePXfuXJHmkQDAZ599hipVqmDatGkF9uXn52PKlClq5z59+jT++ecfGBsbA3hSrEdERCAyMhI+Pj4oW7Ysatasib///hsRERHw9fWVzrdq1SpER0fD29sbGzduRLVq1aTveSI58c/Fd0hQUBDatWuHgQMHwsHBARUqVMDly5fRs2fPQuP//vtvVK5cGRMmTJDaXnfyW6NGjfDxxx/j66+/Vmv38PBAXl4ekpOT0bRp00KPfTqP5Ndff8WZM2fQtGlTWFhYICcnB4sXL0a9evVgYWEhxZuYmMDPzw9+fn4YPHgwqlevjtOnT6NevXqv9RyodDl69Chyc3Mxa9Ys6Ok9+Rtt06ZNBeL09PSwbt06+Pv744MPPkBERAQcHBxQo0YNKJVKXLt2TeqJKC49PT2EhITg448/LtBLUq9ePVy4cAHOzs4vPN7X1xcrVqyAgYEBWrZsCQDw8fHBhg0bpPkjz/Lw8ICHhwfGjx8PLy8vrF+/Ho0bN36l3Im0hQXJO8TX1xc1a9ZEcHAwFixYgMmTJ2PYsGGwtLREu3btkJWVhaNHjyIlJQUjR46Es7Mzrl27hg0bNqBhw4bYuXMntm3b9tp5fP/996hZs6ba8Em1atXQs2dP9OrVC7NmzYKHhwfu3r2L/fv3w93dHe3bt5eew4gRI+Dh4SENHTVr1gxhYWEYOXKkdL7Vq1cjLy8Pnp6eMDU1xbp162BiYoLKlSu/dv5UulStWhW5ubmYP38+OnXqhL///huLFy8uNFZfXx9hYWHo0aOHVJTY29tj9OjRGDFiBPLz89GkSRM8fPgQUVFRMDc3R+/evYuUR4cOHeDp6YklS5bAzs5Oap80aRI6duyIihUr4tNPP4Wenh5OnTqF06dPSz0qzZo1w6NHj7Bjxw6pzdfXF5988glsbGxQo0YNAEBCQgKWLl0KPz8/ODg44MKFC7h48SJ69er1Oi8hkXbIPYmFdKN3797iww8/LNAeFhYmjIyMxLVr16THdevWFUZGRqJs2bKiWbNmYuvWrVL8mDFjhLW1tTA3NxfdunUToaGhQqVSSfuLM6n1Wf379xcApEmtQgiRnZ0tJk2aJKpUqSIMDQ2Fvb29+Oijj8SpU6ekmNOnTwsAYvTo0VJbaGioACB+//13qW3btm3C09NTWFpaCjMzM9G4cWOxd+9eDa8alWYv+p4QQojZs2eL8uXLCxMTE9GmTRuxdu1aAUCkpKQIIf6b1PpUTk6O+Pjjj4Wbm5u4ffu2yM/PF3PnzhWurq7C0NBQ2NjYiDZt2ojIyMgX5vN0UuuzoqKiBAC1Sa1CCBEeHi68vb2FiYmJsLS0FI0aNRJLly5Vi6lfv76wsbGRJtveu3dPKBQKtYm7SUlJonPnzqJ8+fLCyMhIVK5cWUyaNEnk5eW9/MUjKgEKIYSQsyAiIiIi4qRWIiIikh0LEiIiIpIdCxIiIiKSHQsSIiIikh0LEiIiIpIdCxIiIiKSHQsSIiIikh0LEqJSaPLkyahbt670uE+fPujcuXOJ53HlyhUoFArExcWV+LWJ6O3CgoSoBPXp0wcKhQIKhQKGhoZwcnLC6NGjkZ6ertPrzp07F6tXry5SLIsIIpIDP8uGqIS1bdsWq1atQk5ODg4ePIgvv/wS6enpWLRokVpcTk4ODA0NtXJNlUqllfMQEekKe0iISphSqYS9vT0qVqwIf39/9OzZE9u3b5eGWVauXAknJycolUoIIZCamor+/fvD1tYWlpaW+OCDD3Dy5Em1c/7www+ws7ODhYUF+vbti8zMTLX9zw/Z5OfnY/r06XB2doZSqUSlSpXw/fffAwAcHR0BPPlEWIVCUeCj693c3GBsbIzq1avjp59+UrvOkSNH4OHhAWNjYzRo0AAnTpzQ4itHRKUZe0iIZGZiYoKcnBwAwKVLl7Bp0yZs2bIF+vr6AJ58CqyVlRV27doFlUqFJUuWoEWLFrh48SKsrKywadMmBAUFYeHChWjatCnWrVuHefPmwcnJ6YXXHD9+PJYtW4bQ0FA0adIEiYmJOH/+PIAnRUWjRo2wd+9e1KxZE0ZGRgCAZcuWISgoCAsWLICHhwdOnDiBfv36wczMDL1790Z6ejo6duyIDz74AD///DMSEhIwfPhwHb96RFRqyPzhfkTvlOc/cfbw4cPC2tpadO3aVQQFBQlDQ0ORnJws7d+3b5+wtLQUmZmZauepWrWqWLJkiRBCCC8vL/HVV1+p7ff09FT7FOZnr/vw4UOhVCrFsmXLCs3xRZ/QXLFiRbF+/Xq1tu+++054eXkJIYRYsmSJsLKyEunp6dL+RYsWFXouIqLncciGqIT9/vvvMDc3h7GxMby8vNCsWTPMnz8fAFC5cmXY2NhIsceOHUNaWhqsra1hbm4ubQkJCfj3338BAPHx8fDy8lK7xvOPnxUfH4+srCy0aNGiyDnfuXMH169fR9++fdXymDZtmloederUgampaZHyICJ6FodsiEpY8+bNsWjRIhgaGsLBwUFt4qqZmZlabH5+PsqXL4+IiIgC5ylTpswrXd/ExKTYx+Tn5wN4Mmzj6emptu/p0JIQ4pXyISICWJAQlTgzMzM4OzsXKbZevXpISkqCgYEBqlSpUmiMm5sbYmJi0KtXL6ktJibmhed0cXGBiYkJ9u3bhy+//LLA/qdzRvLy8qQ2Ozs7VKhQAZcvX0bPnj0LPW+NGjWwbt06ZGRkSEXPy/IgInoWh2yI3mAtW7aEl5cXOnfujD///BNXrlxBVFQUvv32Wxw9ehQAMHz4cKxcuRIrV67ExYsXERQUhLNnz77wnMbGxhg3bhzGjh2LtWvX4t9//0VMTAxWrFgBALC1tYWJiQnCw8Nx+/ZtpKamAnhys7WQkBDMnTsXFy9exOnTp7Fq1SrMnj0bAODv7w89PT307dsX586dw65duzBz5kwdv0JEVFqwICF6gykUCuzatQvNmjXDF198gWrVqqF79+64cuUK7OzsAADdunXDpEmTMG7cONSvXx9Xr17FwIEDX3reiRMnYtSoUZg0aRLc3NzQrVs3JCcnAwAMDAwwb948LFmyBA4ODvjwww8BAF9++SWWL1+O1atXw93dHT4+Pli9erW0TNjc3Bw7duzAuXPn4OHhgQkTJmD69Ok6fHWIqDRRCA78EhERkczYQ0JERESyY0FCREREsmNBQkRERLJjQUJERESyY0FCREREsmNBQkRERLJjQUJERESyY0FCREREsmNBQkRERLJjQUJERESyY0FCREREsmNBQkRERLL7PyUwi2UgUV+sAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix for Random Forest\n",
    "cm = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Labels for the axes\n",
    "labels = ['Real News', 'Fake News']\n",
    "\n",
    "# Plot the confusion matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix - Random Forest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.9747010471558847\n"
     ]
    }
   ],
   "source": [
    "# Compute ROC AUC score for Random Forest\n",
    "y_scores_rf = rf_model.predict_proba(X_test_tfidf)[:, 1]  \n",
    "roc_auc_rf = roc_auc_score(y_test, y_scores_rf)  \n",
    "print(\"ROC AUC Score:\", roc_auc_rf)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting XGBOOST 'pip install xgboost'\n",
    "import xgboost as xgb\n",
    "\n",
    "# Initialize the Extreme Gradient Boost Model\n",
    "gb_model = xgb.XGBClassifier()\n",
    "\n",
    "# Train the model\n",
    "gb_model.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make Predictions on data\n",
    "\n",
    "y_pred_gb = gb_model.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9512992912956569\n",
      "Recall: 0.9395190236898779\n",
      "F1 Score: 0.9453724604966139\n",
      "Accuracy: 0.9516927499201533\n"
     ]
    }
   ],
   "source": [
    "# Calculate metrics for Extreme Gradient Boosting\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_gb))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_gb))\n",
    "print(\"F1 Score:\", f1_score(y_test, y_pred_gb))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "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 for Extreme Gradient Boosting\n",
    "cm = confusion_matrix(y_test, y_pred_gb)\n",
    "\n",
    "# Labels for the axes\n",
    "labels = ['Real News', 'Fake News']\n",
    "\n",
    "# Plot the confusion matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix - Extreme Gradient Boosting')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.9898802923668152\n"
     ]
    }
   ],
   "source": [
    "# Compute ROC AUC score for Extreme Gradient Boost\n",
    "y_scores_gb = gb_model.predict_proba(X_test_tfidf)[:, 1]  \n",
    "roc_auc_gb = roc_auc_score(y_test, y_scores_gb)  \n",
    "print(\"ROC AUC Score:\", roc_auc_gb)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def predict(text):\n",
    "    # Preprocess text\n",
    "    preprocessed = preprocess_text(text)\n",
    "    \n",
    "    # Vectorize preprocessed text\n",
    "    vect_text = tfidf_vectorizer.transform([preprocessed])\n",
    "    \n",
    "    # Predict with Logistic Regression\n",
    "    lr_pred = lr_model.predict(vect_text)\n",
    "    # Predict with Random Forest\n",
    "    rf_pred = rf_model.predict(vect_text)\n",
    "    # Predict with XGB\n",
    "    gb_pred = gb_model.predict(vect_text)\n",
    "    \n",
    "    # Calculate probabilities with Logistic Regression\n",
    "    lr_prob = lr_model.predict_proba(vect_text)[0]\n",
    "    # Calculate probabilities with Random Forest\n",
    "    rf_prob = rf_model.predict_proba(vect_text)[0]\n",
    "    # Calculate probabilities with XGB\n",
    "    gb_prob = gb_model.predict_proba(vect_text)[0]\n",
    "\n",
    "    # Weights for each model: Logistic Regression, Random Forest, XGB\n",
    "    weights = [0.2, 0.2, 0.6]\n",
    "\n",
    "    # Calculate weighted probabilities\n",
    "    weighted_prob_real = lr_prob[0] * weights[0] + rf_prob[0] * weights[1] + gb_prob[0] * weights[2]\n",
    "    weighted_prob_fake = lr_prob[1] * weights[0] + rf_prob[1] * weights[1] + gb_prob[1] * weights[2]\n",
    "\n",
    "    # Print predictions and probabilities for each model\n",
    "    print(f\"LR Prediction: {'Fake' if lr_pred[0] == 1 else 'Real'}\")\n",
    "    print(f\"LR Probabilities: Real: {lr_prob[0]:.2f}, Fake: {lr_prob[1]:.2f}\")\n",
    "    print(f\"RF Prediction: {'Fake' if rf_pred[0] == 1 else 'Real'}\")\n",
    "    print(f\"RF Probabilities: Real: {rf_prob[0]:.2f}, Fake: {rf_prob[1]:.2f}\")\n",
    "    print(f\"XGB Prediction: {'Fake' if gb_pred[0] == 1 else 'Real'}\")\n",
    "    print(f\"XGB Probabilities: Real: {gb_prob[0]:.2f}, Fake: {gb_prob[1]:.2f}\")\n",
    "\n",
    "    # Ensemble output\n",
    "    print(f\"Ensemble Probabilities: Real: {weighted_prob_real:.2f}, Fake: {weighted_prob_fake:.2f}\")\n",
    "\n",
    "    # Bar chart\n",
    "    models = [\"Logistic Regression\", \"Random Forest\", \"XGBoost\", \"Ensemble\"]\n",
    "    prob_real = [lr_prob[0], rf_prob[0], gb_prob[0], weighted_prob_real]\n",
    "    prob_fake = [lr_prob[1], rf_prob[1], gb_prob[1], weighted_prob_fake]\n",
    "    bar_width = 0.35\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    index = np.arange(len(models))\n",
    "\n",
    "    bar1 = ax.bar(index, prob_real, bar_width, label='Real', color='blue')\n",
    "    bar2 = ax.bar(index + bar_width, prob_fake, bar_width, label='Fake', color='red')\n",
    "\n",
    "    # Labels and Layout\n",
    "    ax.set_title('Probability of Authenticity by Model')\n",
    "    ax.set_xlabel('Model')\n",
    "    ax.set_ylabel('Probability')\n",
    "    ax.set_xticks(index + bar_width / 2)\n",
    "    ax.set_xticklabels(models)\n",
    "    ax.legend()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Prediction: Real\n",
      "LR Probabilities: Real: 0.84, Fake: 0.16\n",
      "RF Prediction: Real\n",
      "RF Probabilities: Real: 0.72, Fake: 0.28\n",
      "XGB Prediction: Real\n",
      "XGB Probabilities: Real: 0.88, Fake: 0.12\n",
      "Ensemble Probabilities: Real: 0.84, Fake: 0.16\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Real news article example test\n",
    "# Source: https://www.cnn.com/2024/03/19/politics/supreme-court-allows-texas-to-begin-enforcing-controversial-immigration-law/index.html\n",
    "\n",
    "text_1 = \"\"\"\n",
    "\n",
    "The Supreme Court on Tuesday cleared the way for Texas to immediately begin enforcing a controversial immigration law that allows state officials to arrest and detain people they suspect of entering the country illegally.\n",
    "\n",
    "The court's three liberals dissented.\n",
    "\n",
    "Legal challenges to the law are ongoing at a federal appeals court, but the decision hands a significant yet temporary win to Texas, which has been battling the Biden administration over immigration policy.\n",
    "\n",
    "The court had been blocking the law from taking effect, issuing an indefinite pause on the proceedings a day earlier, which was wiped away by Tuesday's order.\n",
    "\n",
    "Senate Bill 4, signed into law by Republican Gov. Greg Abbott in December, makes entering Texas illegally a state crime and allows state judges to order immigrants to be deported. Immigration enforcement, generally, is a function of the federal government.\n",
    "\n",
    "The law immediately raised concerns among immigration advocates of increased racial profiling as well as detentions and attempted deportations by state authorities in Texas, where Latinos represent 40 percent of the population.\n",
    "\"\"\"\n",
    "\n",
    "predict(text_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Prediction: Fake\n",
      "LR Probabilities: Real: 0.25, Fake: 0.75\n",
      "RF Prediction: Fake\n",
      "RF Probabilities: Real: 0.45, Fake: 0.55\n",
      "XGB Prediction: Fake\n",
      "XGB Probabilities: Real: 0.16, Fake: 0.84\n",
      "Ensemble Probabilities: Real: 0.23, Fake: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fake news article example test\n",
    "\n",
    "text_2 = \"\"\"\n",
    "In a groundbreaking announcement that has shocked the global community, researchers from the International Space Agency (ISA) have confirmed what has long been a subject of folklore and whimsy: the moon is, in fact, made of cheese. This astonishing revelation comes after decades of speculation and myth, turning what was once a fanciful idea into undeniable fact.\n",
    "\n",
    "The ISA, a consortium of space agencies from over 60 countries, shared their findings at a press conference held at their headquarters in Geneva. According to Dr. Helena Fontaine, the chief of lunar research at the ISA, recent samples collected from the moon's surface during the highly secretive \"Apollo 20\" mission reveal that the moon is predominantly composed of a substance strikingly similar to aged Gouda.\n",
    "\n",
    "\"Through extensive analysis, we have discovered that the moon's surface is not made of rock, but rather a dense form of cheese that closely resembles Gouda in both taste and texture,\" Dr. Fontaine explained. \"This discovery not only challenges our understanding of the moon but also opens up new avenues for the dairy industry.\"\n",
    "\"\"\"\n",
    "\n",
    "predict(text_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Prediction: Fake\n",
      "LR Probabilities: Real: 0.21, Fake: 0.79\n",
      "RF Prediction: Fake\n",
      "RF Probabilities: Real: 0.29, Fake: 0.71\n",
      "XGB Prediction: Fake\n",
      "XGB Probabilities: Real: 0.14, Fake: 0.86\n",
      "Ensemble Probabilities: Real: 0.19, Fake: 0.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fake news GPT 3 Generation\n",
    "text_3 = \"\"\"\n",
    "In a recent study published in the esteemed Journal of Virology, researchers from the renowned Institute of Extraterrestrial Studies (IES) have delved into an intriguing possibility: could there be a connection between the common cold and extraterrestrial life?\n",
    "\n",
    "Led by Dr. Emily Chang, a respected virologist at the forefront of interstellar research, the team embarked on an ambitious project to analyze the genetic makeup of various strains of the common cold virus. Their goal was to uncover any potential links to microbial organisms found in meteorite samples collected from outer space.\n",
    "\n",
    "After months of meticulous analysis, the researchers made a compelling discovery. Certain genetic sequences within the common cold virus bore striking similarities to microbial DNA extracted from meteorite samples. While the findings are preliminary, they suggest a tantalizing possibility: some strains of the common cold may have origins beyond Earth.\n",
    "\"\"\"\n",
    "predict(text_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Prediction: Fake\n",
      "LR Probabilities: Real: 0.28, Fake: 0.72\n",
      "RF Prediction: Fake\n",
      "RF Probabilities: Real: 0.43, Fake: 0.57\n",
      "XGB Prediction: Fake\n",
      "XGB Probabilities: Real: 0.20, Fake: 0.80\n",
      "Ensemble Probabilities: Real: 0.26, Fake: 0.74\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fake medical news from GPT 3\n",
    "text_4 = \"\"\"\n",
    "In a surprising turn of events, medical professionals across urban areas are reporting a sharp rise in headache cases, prompting concern among public health officials. The sudden surge in headaches has left experts scrambling for answers and has sparked a wave of speculation about potential causes.\n",
    "According to Dr. Emily Watson, a neurologist at City General Hospital, \"We've seen a significant uptick in patients presenting with severe headaches over the past few weeks. Many of these cases are more intense and persistent than what we typically encounter.\"\n",
    "While the exact cause of this headache epidemic remains unclear, some experts are pointing fingers at environmental factors such as air pollution and electromagnetic radiation from electronic devices. Dr. Watson warns, \"We know that exposure to certain pollutants and electromagnetic fields can have adverse effects on health, including headaches. It's possible that these factors are contributing to the current situation.\"\n",
    "However, not all medical professionals agree on the potential causes. Dr. Michael Johnson, a leading researcher in headache disorders, suggests that stress and lifestyle factors may play a significant role. \"In today's fast-paced world, many people are under immense stress, which can trigger headaches. Additionally, factors such as poor sleep, dehydration, and excessive screen time can all contribute to headaches,\" Dr. Johnson explains.\n",
    "\"\"\"\n",
    "predict(text_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pretrained_models/tfidf_vectorizer.pkl']"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "import os\n",
    "if not os.path.exists('pretrained_models'):\n",
    "    os.makedirs('pretrained_models')\n",
    "    \n",
    "# Save the trained models for easy access in UI \n",
    "joblib.dump(lr_model, 'pretrained_models/lr_model.pkl')\n",
    "joblib.dump(rf_model, 'pretrained_models/rf_model.pkl')\n",
    "joblib.dump(gb_model, 'pretrained_models/gb_model.pkl')\n",
    "\n",
    "# Save the vectorizer\n",
    "joblib.dump(tfidf_vectorizer, 'pretrained_models/tfidf_vectorizer.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}