TH_FAKE_NEWS_PREDICTION.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d250b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FAke_NEws_DEtection_MOdule\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": 3,
   "id": "875e02b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the data\n",
    "df=pd.read_csv('//Users/vishal/Downloads/news.csv')\n",
    "\n",
    "#Get shape and head\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "734c3620",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#THUNDERSHARP - Get the labels\n",
    "labels=df.label\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2facf965",
   "metadata": {},
   "outputs": [],
   "source": [
    "#THUNDERSHARP - Split the dataset\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": 7,
   "id": "c76b97f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' TfidfVectorizer will stop words from the English language \n",
    "Stop words are the most common words in a language that are to \n",
    "be filtered out before processing the natural language data.\n",
    "And a TfidfVectorizer turns a collection of raw documents into \n",
    "a matrix of TF-IDF features.'''\n",
    "\n",
    "#THUNDERSHARP - Initialize a TfidfVectorizer\n",
    "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n",
    "\n",
    "#THUNDERSHARP - 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": 8,
   "id": "44e424d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.74%\n"
     ]
    }
   ],
   "source": [
    "'''Now, we’ll initialize a PassiveAggressiveClassifier.We’ll fit this on tfidf_train and y_train.\n",
    "\n",
    "   Then, we’ll predict on the test set from the TfidfVectorizer and calculate the accuracy with \n",
    "   accuracy_score() from sklearn.metrics.'''\n",
    "\n",
    "#THUNDERSHARP - Initialize a PassiveAggressiveClassifier\n",
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_train,y_train)\n",
    "\n",
    "#THUNDERSHARP - Predict on the test set and calculate accuracy\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": 9,
   "id": "3b744c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[588,  50],\n",
       "       [ 42, 587]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' We got an accuracy of 92.74% with this model.\n",
    "    Finally, let’s print out a confusion matrix to\n",
    "    gain insight into the number of false and true\n",
    "    negatives and positives.\n",
    "\n",
    "'''\n",
    "\n",
    "#THUNDERSHARP - Build confusion matrix\n",
    "confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6bbc566a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' So with this model, we have 588 true positives,\\n    587 true negatives\\n    42 false positives,\\n    and 50 false negatives.\\n\\n'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' So with this model, we have 588 true positives,\n",
    "    587 true negatives\n",
    "    42 false positives,\n",
    "    and 50 false negatives.\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae9b97f6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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