Fake_News_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3544f971",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for data handling, vectorization, and classification\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import numpy as np\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79de78a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0\n",
      "8476     FAKE\n",
      "10294    FAKE\n",
      "3608     REAL\n",
      "10142    FAKE\n",
      "875      REAL\n",
      "Name: label, dtype: object\n"
     ]
    },
    {
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       "      <th>8476</th>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3608</th>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>875</th>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4490</th>\n",
       "      <td>State Department says it can't find emails fro...</td>\n",
       "      <td>The State Department told the Republican Natio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>8062</th>\n",
       "      <td>The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...</td>\n",
       "      <td>The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8622</th>\n",
       "      <td>Anti-Trump Protesters Are Tools of the Oligarc...</td>\n",
       "      <td>Anti-Trump Protesters Are Tools of the Oligar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>In Ethiopia, Obama seeks progress on peace, se...</td>\n",
       "      <td>ADDIS ABABA, Ethiopia —President Obama convene...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4330</th>\n",
       "      <td>Jeb Bush Is Suddenly Attacking Trump. Here's W...</td>\n",
       "      <td>Jeb Bush Is Suddenly Attacking Trump. Here's W...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "<p>7795 rows × 139 columns</p>\n",
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      ],
      "text/plain": [
       "                                                        title  \\\n",
       "Unnamed: 0                                                      \n",
       "8476                             You Can Smell Hillary’s Fear   \n",
       "10294       Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "3608              Kerry to go to Paris in gesture of sympathy   \n",
       "10142       Bernie supporters on Twitter erupt in anger ag...   \n",
       "875          The Battle of New York: Why This Primary Matters   \n",
       "...                                                       ...   \n",
       "4490        State Department says it can't find emails fro...   \n",
       "8062        The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...   \n",
       "8622        Anti-Trump Protesters Are Tools of the Oligarc...   \n",
       "4021        In Ethiopia, Obama seeks progress on peace, se...   \n",
       "4330        Jeb Bush Is Suddenly Attacking Trump. Here's W...   \n",
       "\n",
       "                                                         text Unnamed: 4  \\\n",
       "Unnamed: 0                                                                 \n",
       "8476        Daniel Greenfield, a Shillman Journalism Fello...        NaN   \n",
       "10294       Google Pinterest Digg Linkedin Reddit Stumbleu...        NaN   \n",
       "3608        U.S. Secretary of State John F. Kerry said Mon...        NaN   \n",
       "10142       — Kaydee King (@KaydeeKing) November 9, 2016 T...        NaN   \n",
       "875         It's primary day in New York and front-runners...        NaN   \n",
       "...                                                       ...        ...   \n",
       "4490        The State Department told the Republican Natio...        NaN   \n",
       "8062        The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...        NaN   \n",
       "8622         Anti-Trump Protesters Are Tools of the Oligar...        NaN   \n",
       "4021        ADDIS ABABA, Ethiopia —President Obama convene...        NaN   \n",
       "4330        Jeb Bush Is Suddenly Attacking Trump. Here's W...        NaN   \n",
       "\n",
       "           Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10  \\\n",
       "Unnamed: 0                                                                      \n",
       "8476              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "10294             NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "3608              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "10142             NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "875               NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "...               ...        ...        ...        ...        ...         ...   \n",
       "4490              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "8062              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "8622              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "4021              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "4330              NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "\n",
       "           Unnamed: 11  ... Unnamed: 131 Unnamed: 132 Unnamed: 133  \\\n",
       "Unnamed: 0              ...                                          \n",
       "8476               NaN  ...          NaN          NaN          NaN   \n",
       "10294              NaN  ...          NaN          NaN          NaN   \n",
       "3608               NaN  ...          NaN          NaN          NaN   \n",
       "10142              NaN  ...          NaN          NaN          NaN   \n",
       "875                NaN  ...          NaN          NaN          NaN   \n",
       "...                ...  ...          ...          ...          ...   \n",
       "4490               NaN  ...          NaN          NaN          NaN   \n",
       "8062               NaN  ...          NaN          NaN          NaN   \n",
       "8622               NaN  ...          NaN          NaN          NaN   \n",
       "4021               NaN  ...          NaN          NaN          NaN   \n",
       "4330               NaN  ...          NaN          NaN          NaN   \n",
       "\n",
       "           Unnamed: 134 Unnamed: 135 Unnamed: 136 Unnamed: 137 Unnamed: 138  \\\n",
       "Unnamed: 0                                                                    \n",
       "8476                NaN          NaN          NaN          NaN          NaN   \n",
       "10294               NaN          NaN          NaN          NaN          NaN   \n",
       "3608                NaN          NaN          NaN          NaN          NaN   \n",
       "10142               NaN          NaN          NaN          NaN          NaN   \n",
       "875                 NaN          NaN          NaN          NaN          NaN   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "4490                NaN          NaN          NaN          NaN          NaN   \n",
       "8062                NaN          NaN          NaN          NaN          NaN   \n",
       "8622                NaN          NaN          NaN          NaN          NaN   \n",
       "4021                NaN          NaN          NaN          NaN          NaN   \n",
       "4330                NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "           Unnamed: 139 Unnamed: 140  \n",
       "Unnamed: 0                            \n",
       "8476                NaN          NaN  \n",
       "10294               NaN          NaN  \n",
       "3608                NaN          NaN  \n",
       "10142               NaN          NaN  \n",
       "875                 NaN          NaN  \n",
       "...                 ...          ...  \n",
       "4490                NaN          NaN  \n",
       "8062                NaN          NaN  \n",
       "8622                NaN          NaN  \n",
       "4021                NaN          NaN  \n",
       "4330                NaN          NaN  \n",
       "\n",
       "[7795 rows x 139 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Load dataset from CSV file\n",
    "frame = pd.read_csv('fake_or_real_news.csv', low_memory=False)\n",
    "\n",
    "# Handle missing values by replacing them with 'NaN'\n",
    "frame[pd.isnull(frame)] = 'NaN'\n",
    "\n",
    "# Set index to an unnamed column\n",
    "frame = frame.set_index(\"Unnamed: 0\")\n",
    "\n",
    "# Drop rows with missing values to clean the data\n",
    "data = frame.dropna()\n",
    "\n",
    "# Define the target variable 'y' as the 'label' column\n",
    "y = frame.label\n",
    "print(y.head())  # Preview target labels\n",
    "\n",
    "# Drop the 'label' column from the feature set\n",
    "frame.drop(\"label\", axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a124627",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Split the dataset into training and testing sets (33% test size)\n",
    "X_train, X_test, y_train, y_test = train_test_split(frame['text'], y, test_size=0.33, random_state=53)\n",
    "\n",
    "# Initialize the CountVectorizer to extract features from text data\n",
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "\n",
    "# Fit and transform the training data with the CountVectorizer\n",
    "count_train = count_vectorizer.fit_transform(X_train)\n",
    "\n",
    "# Transform the test data using the fitted CountVectorizer\n",
    "count_test = count_vectorizer.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93b65b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 29800)\t0.8571635611136458\n",
      "  (0, 9861)\t0.5150442985792322\n",
      "  (1, 53698)\t0.3252194831690456\n",
      "  (1, 51612)\t0.26647361529450964\n",
      "  (1, 51399)\t0.16972462507900363\n",
      "  (1, 50153)\t0.2170986869908999\n",
      "  (1, 49815)\t0.35462739813612415\n",
      "  (1, 48725)\t0.23709994521942462\n",
      "  (1, 43438)\t0.23951824561821972\n",
      "  (1, 42084)\t0.17785034337851724\n",
      "  (1, 36866)\t0.18850320244955115\n",
      "  (1, 34102)\t0.24525561824733755\n",
      "  (1, 30695)\t0.22350216188740737\n",
      "  (1, 23720)\t0.21501828013147872\n",
      "  (1, 16783)\t0.18319780321364093\n",
      "  (1, 15658)\t0.18510678940400643\n",
      "  (1, 13357)\t0.21042893734999282\n",
      "  (1, 13347)\t0.18408672424956707\n",
      "  (1, 8622)\t0.22511386415030168\n",
      "  (1, 8621)\t0.19977415027038287\n",
      "  (1, 5067)\t0.21876715403247657\n",
      "  (2, 55871)\t0.04993492033278148\n",
      "  (2, 55618)\t0.015828039602249735\n",
      "  (2, 55392)\t0.023927378610970534\n",
      "  (2, 55375)\t0.061308624894574275\n",
      "  :\t:\n",
      "  (2572, 10373)\t0.0618424552031402\n",
      "  (2572, 10362)\t0.2120095864152846\n",
      "  (2572, 9416)\t0.09094804528682138\n",
      "  (2572, 8867)\t0.0976414452462832\n",
      "  (2572, 8806)\t0.059702777611783955\n",
      "  (2572, 8569)\t0.0976414452462832\n",
      "  (2572, 8233)\t0.045706779830728426\n",
      "  (2572, 8207)\t0.16927938069790469\n",
      "  (2572, 7874)\t0.08195475645369507\n",
      "  (2572, 7766)\t0.049070315303083804\n",
      "  (2572, 6453)\t0.3904878044583051\n",
      "  (2572, 6329)\t0.04055047543800001\n",
      "  (2572, 5546)\t0.04602220561308489\n",
      "  (2572, 4591)\t0.06673822386090446\n",
      "  (2572, 4463)\t0.07263386401977387\n",
      "  (2572, 3993)\t0.05425788045177211\n",
      "  (2572, 2532)\t0.11815856901547173\n",
      "  (2572, 2489)\t0.07394622577164768\n",
      "  (2572, 2480)\t0.06020773696383299\n",
      "  (2572, 2360)\t0.08693286751446784\n",
      "  (2572, 2353)\t0.04214730034634754\n",
      "  (2572, 2266)\t0.05712273725053262\n",
      "  (2572, 2192)\t0.0629596110201529\n",
      "  (2572, 2046)\t0.03400411478678429\n",
      "  (2572, 1069)\t0.06822177807438923\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Initialize the TfidfVectorizer for feature extraction using TF-IDF scores\n",
    "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
    "\n",
    "# Fit and transform the training data with the TfidfVectorizer\n",
    "tfidf_train = tfidf_vectorizer.fit_transform(X_train)\n",
    "\n",
    "# Transform the test data using the fitted TfidfVectorizer\n",
    "tfidf_test = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "# Print the transformed TF-IDF test set\n",
    "print(tfidf_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c02afa57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Convert count vectorized training data into DataFrame for comparison with TF-IDF\n",
    "count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names_out())\n",
    "\n",
    "# Convert TF-IDF vectorized training data into DataFrame for comparison\n",
    "tfidf_df = pd.DataFrame(tfidf_train.A, columns=tfidf_vectorizer.get_feature_names_out())\n",
    "\n",
    "# Compare differences in feature names extracted by CountVectorizer and TfidfVectorizer\n",
    "difference = set(count_df.columns) - set(tfidf_df.columns)\n",
    "print(count_df.equals(tfidf_df))  # Check if both DataFrames are equal\n",
    "\n",
    "# Preview CountVectorizer DataFrame\n",
    "count_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "346470f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define a function to plot confusion matrix\n",
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    # Normalize the confusion matrix if requested\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46f11b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes (TF-IDF) accuracy:   0.792\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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P/VzNzCrEzQJmZhVQB7nVydXMckauuZqZlV29zOfq5GpmOeNBBGZmFeHeAmZm5eZ+rmZm5Ze1udZ+dnVyNbPccXI1M6uAOsitTq5mlj+uuZqZlZlUG6/OLsbJ1cxypw4qrk6uZpY/DXWQXZ1czSx36iC3OrmaWb7IE7eYmVVGHTzPcnI1s/yp694Cki4GYlH7I+KEikRkZm2aAFHHyRV4ptWiMDMrUK6Kq6TlgauBAWSVxf8CXgVuAlYH3gIOioiPUvnTgSOAecAJEXF/S6+9yOQaEdc1CXLZiPi8pRcyMyuJyjqf60XAfRHxHUkdgE7AGcCDEXGepNOA04CfSOoPDAE2AFYG/i5pnYiY15ILNxQrIGkbSROBl9P6xpIubcnFzMxKIRVfip9DXYHtgWsAIuLLiPg3MBhorDxeB+ybPg8GRkTE7IiYBLwObNnSeyiaXIELgd2BD1KAz6eAzczKTkC7BhVdgB6SnilYjmxyqjWB94FrJT0n6WpJywIrRcQ0gPRnz1S+DzC54PgpaVuLlNRbICImN6mmt6iabGZWihKbBWZExObN7G8PbAocHxFPSbqIrAlgkZddyLZFPtQvppSa62RJ2wIhqYOkU0lNBGZm5VZKk0CJTbJTgCkR8VRav4Us2b4nqXd2LfUGpheUX6Xg+L7A1JbeRynJ9SjgWLLq8b+AgWndzKwiGqSiSzER8S5Z5XDdtGlnYCIwChiWtg0D7kyfRwFDJHWUtAbQDxjb0nso2iwQETOAoS29gJnZ4ipjL9fjgRtST4E3gcPJKpUjJR0BvAMcCBAREySNJEvAc4FjW9pTAEpIrpLWJOvOsDVZ+8MTwEkR8WZLL2pm1pxydcWKiPHAwtpld15E+eHA8HJcu5Rmgb8CI4HeZH2/bgZuLMfFzcyakor3FGhXA8NjS0muioi/RMTctFzPEjxBMzMrpkwPtKqqubkFuqePD6dRDCPIkup3gXtaITYza6PqfcrBcWTJtPEu/7tgXwDnViooM2u7RJ1PORgRa7RmIGZmjeq95rqApAFAf2Dpxm0R8edKBWVmbVvtp9bSumKdBexIllzvBfYAHgOcXM2s7CRqojdAMaX0FvgOWZ+wdyPicGBjoGNFozKzNk1p2sHmlrwrpVlgZkTMlzQ3TeE1nWy2GTOziqiB3FlUKcn1mTSb91VkPQg+YwnG25qZNUeUNndA3pUyt8Ax6ePlku4DukbEC5UNy8zarBoZJFBMc4MINm1uX0Q8W5mQymP93l14/OcLHT5sOdVti+OqHYItptmTplXkvLXQplpMczXX3zazL4BvlTkWM7PsTQT1nFwjYqfWDMTMrFEd9MQqbRCBmVlrcnI1MyuzbNar2s+uTq5mljv1UHMtOkJLmUMlnZnWV5XU4nd5m5kVUw/zuZYy/PVSYBvg4LT+KXBJxSIyszZNQHup6JJ3pTQLbBURm0p6DiAiPkov+zIzq4gayJ1FlZJc50hqR3q1i6QVgfkVjcrM2iyV+OrsvCulWeD3wO1AT0nDyaYb/J+KRmVmbVo9tLmWMrfADZLGkU07KGDfiHi54pGZWZtVD70FSpkse1XgC+Cuwm0R8U4lAzOztknUx2TZpbS53sNXLypcGlgDeBXYoIJxmVlbpTZSc42IDQvX02xZ/72I4mZmS0x18BatxR6hFRHPStqiEsGYmdX9q7UbSTq5YLUB2BR4v2IRmVmb1yaSK9Cl4PNcsjbYWysTjplZG5i4JQ0e6BwRP2qleMysjcterV3tKJZcc695aR8Rc5t73YuZWSXUwwit5mquY8naV8dLGgXcDHzeuDMibqtwbGbWBrWZB1pAd+ADsndmNfZ3DcDJ1cwqog4qrs0m156pp8BLfJVUG0VFozKzNkw01EE/1+aajdsBndPSpeBz42JmVnaivBO3SGon6TlJd6f17pIekPRa+rNbQdnTJb0u6VVJuy/JfTRXc50WEecsycnNzBaboH15G11PBF4Guqb104AHI+I8Sael9Z9I6g8MIRvavzLwd0nrRMS8lly0uZpr7dfLzazmlLPmKqkvsBdwdcHmwcB16fN1wL4F20dExOyImAS8DrT4lVbNJdedW3pSM7Ml0ZAmzG5uKdGFwI/5+gT/K0XENID0Z8+0vQ8wuaDclLStZfewqB0R8WFLT2pmtiRKrLn2kPRMwXLk18+hvYHpETGu1MsuZFuLH9771dpmliuitFekADMiYvNm9m8HfFvSnmTTpXaVdD3wnqTeETFNUm9geio/BVil4Pi+wNTFDH+BOhhkZmZ1RdncAsWWYiLi9IjoGxGrkz2oeigiDgVGAcNSsWHAnenzKGCIpI6S1gD6kQ2mahHXXM0sVwS0q+wogvOAkZKOAN4BDgSIiAmSRgITySapOralPQXAydXMcqjcqTUixgBj0ucPWMQD+4gYDgwvxzWdXM0sd+p9+KuZWRWU1qaad06uZpYri9FbINecXM0sd1xzNTMrN9X/ZNlmZq3OzQJmZhXiZgEzswqo/dTq5GpmOVQHFVcnVzPLl1YY/toqnFzNLGeE6qBhwMnVzHKnDiquTq5mli9ZV6zaz65OrmaWL4v5dte8cnI1s9xxcjUzK7N66S1QD6PM6tbkyZPZfZedGLjh+my68Qb84fcXAXDrLTez6cYb0KlDA+OeeabKUbZNl581lLcf/BXP3HzGgm3dunbi7suO48U7z+Tuy45j+S7LANB9uWW578oTeP/x3/K7nxy4oHznTh15csRpC5bJD53Hb049oNXvJY9Uwn955+SaY+3bt+e883/L+Bdf5v8ee5IrLr+ElydOZIMNBjBi5G1845vbVzvENusvdz3J4GMv+dq2Uw/flTFjX2XDwecwZuyrnHr4bgDMmj2Hcy69m9N/d/vXyn/2xWy2HnLeguWdaR9yx0PjW+sWcq3Et7/mmpNrjvXu3ZtNNt0UgC5durDeeuszdeq/WG/99Vln3XWrHF3b9vizb/Dhx198bdveO27E9Xc9BcD1dz3FPjttBMAXs77kH+PfZNbsOYs831qrrkjP7l14/Nk3Khd0DamHmqvbXGvE22+9xfjxz7HFlltVOxRbhJ4rdOHdGZ8A8O6MT1ixe5eSjz1o0GbcMvrZSoVWUwQ05D93FlWxmqukeZLGS3pJ0l2Slk/bV5c0M+1rXP5fwXGbSApJuzc532eVijXvPvvsMw4+6AB+89sL6dq1a7XDsQo4cPfNGHmf288zpdRb8599K9ksMDMiBkbEAOBD4NiCfW+kfY3Lnwv2HQw8lv5s8+bMmcPBBx3Adw8eyr777V/tcKwZ0z/4lF49sl9+vXp05f0PPy3puA3X6UP7du147uXJlQyvdiiruRZb8q612lyfAPoUK6RsEsfvAN8DdpO0dIXjyrWI4KgfHMG6663PiSedXO1wrIh7/u9FDt0na7Y5dJ+tuHvMCyUdd9Ag11oLZc0CKrrkXcXbXCW1I3tH+DUFm9eSNL5g/fiIeBTYDpgUEW9IGgPsCdy2GNc6EjgSYJVVV13CyKvvH48/zl9v+AsDBmzIVpsNBOAXv/wfZs+ezck/PJ4Z77/P/oP3YqONB3LXvfdXN9g25rpffY9vbtaPHst35vX7zuXcy+/lf699gOt//V8M23cbJk/7iKE//up/+Vfu+QVdll2aDku1Z5+dNmLvYy7hlTffBeCAXTdl3+Mvq9at5FL+U2dxiojKnFiaB7wIrA6MA3aLiHmSVgfuTs0FTY+5BBgfEVdJ+jZwWEQcmPZ9FhGdS73+ZpttHo8/5dpALem2xXHVDsEW0+xXRzL/i+llzYXrb7hJXHvHw0XLbbN2t3ERsXk5r11OFW9zBVYDOvD1Ntf/kGq4BwBnSnoLuBjYQ1Lpj1zNrC74gVYJIuJj4ATgVElLNVN0F+D5iFglIlaPiNWAW4F9Kx2jmeWLBxGUKCKeA54HhqRNazXpinUCWe+A25sceitwSPrcSdKUgsVPeMzqVD0k14o90GraPhoR+xSsLlPiOUYBo9JnjyYzawMENfG1vxiP0DKzfKmRmmkxTq5mljt1kFudXM0sh+oguzq5mlnO1EZXq2KcXM0sV+plViwnVzPLnzpIru7eZGa5U44RWpJWkfSwpJclTZB0YtreXdIDkl5Lf3YrOOZ0Sa9LerXptKeLy8nVzHKnTIMI5gKnRMT6wNbAsZL6A6cBD0ZEP+DBtE7aNwTYABgEXJqG5beIk6uZ5Y5KWIqJiGkR8Wz6/CnwMtnUp4OB61Kx6/hqiP1gYEREzI6IScDrwJYtvQe3uZpZvghUWtW0h6TCqe+ujIgrF3rKbDa+TYCngJUiYhpkCVhSz1SsD/BkwWFTKGEe6kVxcjWzXBElf+2fUcqUg5I6k81T8sOI+KSZxL2wHS2ek9XNAmaWO+VoFgBIM/HdCtwQEY0T778nqXfa3xuYnrZPAVYpOLwvMLWl9+Dkamb5U4bsml4bdQ3wckRcULBrFDAsfR4G3FmwfYikjpLWAPoBY1t6C24WMLPcKdMIre2Aw4AXC14rdQZwHjBS0hHAO8CBABExQdJIYCJZT4NjI2JeSy/u5GpmuVOOWbEi4jEWXcfdeRHHDAeGL/nVnVzNLIc85aCZWZl5smwzs0rwZNlmZpVRB7nVydXMcqgOsquTq5nljCfLNjMrO0+WbWZWKU6uZmbl52YBM7MKcFcsM7MKqIPc6uRqZjnjQQRmZuWXTZZd+9nVydXMcqf2U6uTq5nlUB1UXJ1czSx/3BXLzKwSaj+3OrmaWb5IHv5qZlYRbhYwM6uE2s+tTq5mlj91kFudXM0sf9wVy8ys7DxZtplZ2WXDX6sdxZJzcjWz3HFyNTOrADcLmJmVm6ccNDMrP+GuWGZmlVEH2dXJ1cxyp6EO2gWcXM0sd2o/tTq5mlke1UF2dXI1s9xxVywzszKrlxFaiohqx1ARkt4H3q52HBXQA5hR7SBssdTzz2y1iFixnCeUdB/Z31kxMyJiUDmvXU51m1zrlaRnImLzasdhpfPPrG1qqHYAZmb1yMnVzKwCnFxrz5XVDsAWm39mbZDbXM3MKsA1VzOzCnByNTOrACfXGidpc0krVDsOM/s6J9caJml34CqgT7VjsdJI9TD2yErh5FqjJA0CfgWcFBEvSOomqUu147KiVgCQ5H97dc4/4BokaSOyGuu5ETFG0irAX4FNqhuZLYoyPYG3JX07IuY7wdY3/3BrjKTVgcnAP4EVJW0M3ATcGxGPVDM2W7TITAcOB66VtGdjgpXUrtrxWfk5udYQSWsAIyLiI+BI4NvALcCoiLi4oNwekso6mYaVR0SMBI4ARkjaKyLmAwEgaR9Je1c1QCsbJ9fasjQQkjpExBtkCXYiME9SdwBJBwPnA25/zQFJgyT9XNI2jdsi4g6yGuwISXunGux/A5cDr1QpVCszz+daAyRtALwBvAfMiogvJTVExFRJJwKXkiXYWWT/aL8bEW9WMWT7yg7AUcAgSROAPwCTIuLW1HPgT5LuBrYE9oyI16sYq5WRk2vOSeoEHEtWa/018LGkdhExDyAi3kq1nj8DPYGDImJi1QK2pkYBawPHA6cBQ4D+kk6OiFskfUjWtPOtiHi+inFamXlugZxLtZv+ZDXS9YE1geHAXLKHWksBXwKfkNVq/1WlUC2RtB4wOyImpfVRwMSIOE3SIcD1wEPAFOAiYEJEfFm1gK0inFxrQOqy0x84GRgG/A34nCyxrgQsC+wdEVOqFqQBIGlP4OfAYY1f8SX1A34AvAqcAXwfmApsC4xpTMJWX5xcc0jS9sBvgZ8Cb0XEP1OCbfxHuixwbHoQshRARMypWsAGLBgxdzZwdkTcL6kzWU+ADmQPq/YG9mjsMidJ4X+Adcu9BfKpL1kTwHbANZKGAt0j4lWyh1cB/FXS0hExx4m1+iRtSPaN4qcpsa4F3AGslbrODSeruS74duHEWt+cXHNEUu/08T7gZWA62dfIQcAFkk5IvQCuSvu7VSVQW6BgroC3gNuBg9JAjyuB+9PQ5IaIeAF4FNjJgwbaBifXnJC0F3CnpN4R8SFwFjAwIh4lqxHtAgyTdC+wFXB+REyrXsSWdACIiE+BoUBnsm5zd0TEb1JinS9pINkbYO9r7Olh9c1trjmQJmH5KTA8Iu6T1B5YkawJ4O9kXbFOjIgHJB0GjI6I96oXsQFI2g04GngeeCEibpO0LFn7aruIOCSVO4LsQeRBEfFu1QK2VuXkWmVpZNUMYP+IuCO11f08Ir4n6Qzgl8DQiLixqoHa16RfiL/gq/7FK5N9m3gtzU7W2DY+mmwQwVER8VK14rXW50EEVRYRH0raBzhX0pvA74B70+7fA73Ivmb66XJOpF+I9wKDI+IuSX3JHlitALwWEZ+m2upNwBXAFh7Y0fa45poTqSZ0L3BGRJyXul6J7OHV/Ij4flUDtK9JbeTnA9tExCepLbwTMJ5s1rI/kv38OrptvG1ycs0RSbsCFwNbRcTHadtSQF93NM8fSXuQfbu4j6zr3BVAd7JBAs8DJ0fEJ9WL0KrJyTVn0j/YC8lqRB9WORwrQtIuZO2qvRsfMqZvHd0jYkZVg7OqclesnImIvwE/Bv7umerzLyL+DuwFPCRppbRtvhOrueaaU5I6R8Rn1Y7DSiNpMFnf5M3TBNjWxjm5mpWJfyFaISdXM7MKcJuemVkFOLmamVWAk6uZWQU4uZqZVYCTaxsmaZ6k8ZJeknRzehliS8/1J0nfSZ+vltS/mbI7Stq2Bdd4S1KPUrc3KbNYT/ElnS3p1MWN0ayRk2vbNjMiBkbEALKXHB5VuLOlkzpHxPeLTFSyI9n7o8zqlpOrNXoUWDvVKh+W9FfgRUntJP1G0tOSXkiv8UaZP0iaKOkesmn3SPvGSNo8fR4k6VlJz0t6MM3SfxRwUqo1f1PSipJuTdd4WtJ26dgVJI2W9JykK8gmQmmWpDskjZM0QdKRTfb9NsXyoKQV07a1JN2XjnlU2ZtbzZaYpxw00uTce5BNQAKwJTAgIialBPVxRGwhqSPwuKTRwCbAusCGZG+gnUg2E1TheVckm9Vr+3Su7mmKxcuBzyLif1O5vwK/i4jHJK0K3E82EcpZwGMRcU6ahepryXIR/itdYxngaUm3RsQHZC91fDYiTpF0Zjr3cWSvYzkqzcO6Fdk8rN9qwV+j2dc4ubZty0ganz4/ClxD9nV9bMEsXLsBGzW2pwLLkb2FdnvgxvTKkqmSHlrI+bcGHmk8VzMT0ewC9NeC11HRNU04vT2wfzr2HkkflXBPJ0jaL31eJcX6ATCfbH5VgOuB25S9nXVb4OaCa3cs4RpmRTm5tm0zI2Jg4YaUZD4v3AQcHxH3Nym3J9lM+81RCWUga57aJiJmLiSWkocQStqRLFFvExFfSBoDLL2I4pGu+++mfwdm5eA2VyvmfuDoNK8sktZJ74l6BBiS2mR7Azst5NgngB0krZGO7Z62fwp0KSg3muwrOqncwPTxEbKX/jVOxVjsbbfLAR+lxLoeWc25UQPQWPs+hKy54RNgkqQD0zUkaeMi1zAriZOrFXM1WXvqs5JeIpsQuj3Za6RfA14ELgP+r+mBEfE+WTvpbZKe56uv5XcB+zU+0AJOADZPD8wm8lWvhV8A20t6lqx54p0isd4HtJf0AnAu8GTBvs+BDSSNI2tTPSdtHwockeKbAAwu4e/ErChP3GJmVgGuuZqZVYCTq5lZBTi5mplVgJOrmVkFOLmamVWAk6uZWQU4uZqZVcD/B4Hv1IFBmrw1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Train the Naive Bayes classifier using the TF-IDF features\n",
    "clf = MultinomialNB()\n",
    "clf.fit(tfidf_train, y_train)\n",
    "\n",
    "# Make predictions using the TF-IDF test data\n",
    "pred = clf.predict(tfidf_test)\n",
    "\n",
    "# Calculate and print the accuracy of the model\n",
    "score = accuracy_score(y_test, pred)\n",
    "print(\"Naive Bayes (TF-IDF) accuracy:   %0.3f\" % score)\n",
    "\n",
    "# Generate confusion matrix and plot it\n",
    "cm = confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abc05fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes (CountVectorizer) accuracy:   0.846\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Train the Naive Bayes classifier using CountVectorizer features\n",
    "clf = MultinomialNB()\n",
    "clf.fit(count_train, y_train)\n",
    "\n",
    "# Make predictions using the CountVectorizer test data\n",
    "pred = clf.predict(count_test)\n",
    "\n",
    "# Calculate and print the accuracy of the model\n",
    "score = accuracy_score(y_test, pred)\n",
    "print(\"Naive Bayes (CountVectorizer) accuracy:   %0.3f\" % score)\n",
    "\n",
    "# Generate confusion matrix and plot it\n",
    "cm = confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08fe2521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passive Aggressive accuracy:   0.880\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Train a Passive Aggressive Classifier using the TF-IDF features\n",
    "linear_clf = PassiveAggressiveClassifier()\n",
    "linear_clf.fit(tfidf_train, y_train)\n",
    "\n",
    "# Make predictions using the TF-IDF test data\n",
    "pred = linear_clf.predict(tfidf_test)\n",
    "\n",
    "# Calculate and print the accuracy of the Passive Aggressive Classifier\n",
    "score = accuracy_score(y_test, pred)\n",
    "print(\"Passive Aggressive accuracy:   %0.3f\" % score)\n",
    "\n",
    "# Generate confusion matrix and plot it\n",
    "cm = confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}