fake_news_detector.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\anaconda3\\lib\\site-packages (1.16.5)\n",
      "Requirement already satisfied: pandas in d:\\anaconda3\\lib\\site-packages (0.25.1)\n",
      "Requirement already satisfied: sklearn in d:\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in d:\\anaconda3\\lib\\site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in d:\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied: scikit-learn in d:\\anaconda3\\lib\\site-packages (from sklearn) (0.21.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\meghaj singh\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
      "Requirement already satisfied: scipy>=0.17.0 in d:\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.3.1)\n",
      "Requirement already satisfied: joblib>=0.11 in d:\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy pandas sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#necessary imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the data\n",
    "df=pd.read_csv('news/news.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3608</td>\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>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>875</td>\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>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape and head of the data\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    FAKE\n",
       "1    FAKE\n",
       "2    REAL\n",
       "3    FAKE\n",
       "4    REAL\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=df.label\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(df['text'],labels,test_size=0.2,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#inittializing a tfidfVecotrizer\n",
    "tfidf_vectorizer=TfidfVectorizer(stop_words='english',max_df=0.7)\n",
    "\n",
    "#fit and transform train set, transform test set\n",
    "tfidf_train=tfidf_vectorizer.fit_transform(x_train)\n",
    "tfidf_test=tfidf_vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.82%\n"
     ]
    }
   ],
   "source": [
    "#intialize a PassiveAggressiveClassifier\n",
    "\n",
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_train,y_train)\n",
    "\n",
    "y_pred=pac.predict(tfidf_test)\n",
    "score=accuracy_score(y_test,y_pred)\n",
    "print(f'Accuracy: {round(score*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[590,  48],\n",
       "       [ 43, 586]], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build confusion matrix\n",
    "confusion_matrix(y_test,y_pred,labels=['FAKE','REAL'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this model, gives us an accuracy of 98.82% with :- <br> 589 True Positives<br>587 True Negatives<br>42 False Positives<br>49 False Negatives"
   ]
  }
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