Fake_news_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fake news Detection"
   ]
  },
  {
   "attachments": {
    "I-Newspaper2.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![I-Newspaper2.jpg](attachment:I-Newspaper2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing required library\n",
    "Here I am going to importing some of the required library, if extra library is required to install It will be install later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting fake and real dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fake = pd.read_csv(\"Fake.csv\")\n",
    "df_true = pd.read_csv(\"True.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 30, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fake.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>As U.S. budget fight looms, Republicans flip t...</td>\n",
       "      <td>WASHINGTON (Reuters) - The head of a conservat...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S. military to accept transgender recruits o...</td>\n",
       "      <td>WASHINGTON (Reuters) - Transgender people will...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior U.S. Republican senator: 'Let Mr. Muell...</td>\n",
       "      <td>WASHINGTON (Reuters) - The special counsel inv...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FBI Russia probe helped by Australian diplomat...</td>\n",
       "      <td>WASHINGTON (Reuters) - Trump campaign adviser ...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 30, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trump wants Postal Service to charge 'much mor...</td>\n",
       "      <td>SEATTLE/WASHINGTON (Reuters) - President Donal...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               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",
       "                 date  \n",
       "0  December 31, 2017   \n",
       "1  December 29, 2017   \n",
       "2  December 31, 2017   \n",
       "3  December 30, 2017   \n",
       "4  December 29, 2017   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_true.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inserting a column called \"class\" for fake and real news dataset to categories fake and true news. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fake[\"class\"] = 0\n",
    "df_true[\"class\"] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing last 10 rows from both the dataset, for manual testing  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((23481, 5), (21417, 5))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fake.shape, df_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fake_manual_testing = df_fake.tail(10)\n",
    "for i in range(23480,23470,-1):\n",
    "    df_fake.drop([i], axis = 0, inplace = True)\n",
    "df_true_manual_testing = df_true.tail(10)\n",
    "for i in range(21416,21406,-1):\n",
    "    df_true.drop([i], axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((23471, 5), (21407, 5))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fake.shape, df_true.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging the manual testing dataframe in single dataset and save it in a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-3aaf8ec2aad1>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_fake_manual_testing[\"class\"] = 0\n",
      "<ipython-input-9-3aaf8ec2aad1>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_true_manual_testing[\"class\"] = 1\n"
     ]
    }
   ],
   "source": [
    "df_fake_manual_testing[\"class\"] = 0\n",
    "df_true_manual_testing[\"class\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23471</th>\n",
       "      <td>Seven Iranians freed in the prisoner swap have...</td>\n",
       "      <td>21st Century Wire says This week, the historic...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 20, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23472</th>\n",
       "      <td>#Hashtag Hell &amp; The Fake Left</td>\n",
       "      <td>By Dady Chery and Gilbert MercierAll writers ...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 19, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23473</th>\n",
       "      <td>Astroturfing: Journalist Reveals Brainwashing ...</td>\n",
       "      <td>Vic Bishop Waking TimesOur reality is carefull...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 19, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23474</th>\n",
       "      <td>The New American Century: An Era of Fraud</td>\n",
       "      <td>Paul Craig RobertsIn the last years of the 20t...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 19, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23475</th>\n",
       "      <td>Hillary Clinton: ‘Israel First’ (and no peace ...</td>\n",
       "      <td>Robert Fantina CounterpunchAlthough the United...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 18, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23476</th>\n",
       "      <td>McPain: John McCain Furious That Iran Treated ...</td>\n",
       "      <td>21st Century Wire says As 21WIRE reported earl...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 16, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23477</th>\n",
       "      <td>JUSTICE? Yahoo Settles E-mail Privacy Class-ac...</td>\n",
       "      <td>21st Century Wire says It s a familiar theme. ...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 16, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23478</th>\n",
       "      <td>Sunnistan: US and Allied ‘Safe Zone’ Plan to T...</td>\n",
       "      <td>Patrick Henningsen  21st Century WireRemember ...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 15, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23479</th>\n",
       "      <td>How to Blow $700 Million: Al Jazeera America F...</td>\n",
       "      <td>21st Century Wire says Al Jazeera America will...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 14, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23480</th>\n",
       "      <td>10 U.S. Navy Sailors Held by Iranian Military ...</td>\n",
       "      <td>21st Century Wire says As 21WIRE predicted in ...</td>\n",
       "      <td>Middle-east</td>\n",
       "      <td>January 12, 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "23471  Seven Iranians freed in the prisoner swap have...   \n",
       "23472                      #Hashtag Hell & The Fake Left   \n",
       "23473  Astroturfing: Journalist Reveals Brainwashing ...   \n",
       "23474          The New American Century: An Era of Fraud   \n",
       "23475  Hillary Clinton: ‘Israel First’ (and no peace ...   \n",
       "23476  McPain: John McCain Furious That Iran Treated ...   \n",
       "23477  JUSTICE? Yahoo Settles E-mail Privacy Class-ac...   \n",
       "23478  Sunnistan: US and Allied ‘Safe Zone’ Plan to T...   \n",
       "23479  How to Blow $700 Million: Al Jazeera America F...   \n",
       "23480  10 U.S. Navy Sailors Held by Iranian Military ...   \n",
       "\n",
       "                                                    text      subject  \\\n",
       "23471  21st Century Wire says This week, the historic...  Middle-east   \n",
       "23472   By Dady Chery and Gilbert MercierAll writers ...  Middle-east   \n",
       "23473  Vic Bishop Waking TimesOur reality is carefull...  Middle-east   \n",
       "23474  Paul Craig RobertsIn the last years of the 20t...  Middle-east   \n",
       "23475  Robert Fantina CounterpunchAlthough the United...  Middle-east   \n",
       "23476  21st Century Wire says As 21WIRE reported earl...  Middle-east   \n",
       "23477  21st Century Wire says It s a familiar theme. ...  Middle-east   \n",
       "23478  Patrick Henningsen  21st Century WireRemember ...  Middle-east   \n",
       "23479  21st Century Wire says Al Jazeera America will...  Middle-east   \n",
       "23480  21st Century Wire says As 21WIRE predicted in ...  Middle-east   \n",
       "\n",
       "                   date  class  \n",
       "23471  January 20, 2016      0  \n",
       "23472  January 19, 2016      0  \n",
       "23473  January 19, 2016      0  \n",
       "23474  January 19, 2016      0  \n",
       "23475  January 18, 2016      0  \n",
       "23476  January 16, 2016      0  \n",
       "23477  January 16, 2016      0  \n",
       "23478  January 15, 2016      0  \n",
       "23479  January 14, 2016      0  \n",
       "23480  January 12, 2016      0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fake_manual_testing.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21407</th>\n",
       "      <td>Mata Pires, owner of embattled Brazil builder ...</td>\n",
       "      <td>SAO PAULO (Reuters) - Cesar Mata Pires, the ow...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21408</th>\n",
       "      <td>U.S., North Korea clash at U.N. forum over nuc...</td>\n",
       "      <td>GENEVA (Reuters) - North Korea and the United ...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21409</th>\n",
       "      <td>U.S., North Korea clash at U.N. arms forum on ...</td>\n",
       "      <td>GENEVA (Reuters) - North Korea and the United ...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21410</th>\n",
       "      <td>Headless torso could belong to submarine journ...</td>\n",
       "      <td>COPENHAGEN (Reuters) - Danish police said on T...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21411</th>\n",
       "      <td>North Korea shipments to Syria chemical arms a...</td>\n",
       "      <td>UNITED NATIONS (Reuters) - Two North Korean sh...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 21, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21412</th>\n",
       "      <td>'Fully committed' NATO backs new U.S. approach...</td>\n",
       "      <td>BRUSSELS (Reuters) - NATO allies on Tuesday we...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21413</th>\n",
       "      <td>LexisNexis withdrew two products from Chinese ...</td>\n",
       "      <td>LONDON (Reuters) - LexisNexis, a provider of l...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21414</th>\n",
       "      <td>Minsk cultural hub becomes haven from authorities</td>\n",
       "      <td>MINSK (Reuters) - In the shadow of disused Sov...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21415</th>\n",
       "      <td>Vatican upbeat on possibility of Pope Francis ...</td>\n",
       "      <td>MOSCOW (Reuters) - Vatican Secretary of State ...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21416</th>\n",
       "      <td>Indonesia to buy $1.14 billion worth of Russia...</td>\n",
       "      <td>JAKARTA (Reuters) - Indonesia will buy 11 Sukh...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "21407  Mata Pires, owner of embattled Brazil builder ...   \n",
       "21408  U.S., North Korea clash at U.N. forum over nuc...   \n",
       "21409  U.S., North Korea clash at U.N. arms forum on ...   \n",
       "21410  Headless torso could belong to submarine journ...   \n",
       "21411  North Korea shipments to Syria chemical arms a...   \n",
       "21412  'Fully committed' NATO backs new U.S. approach...   \n",
       "21413  LexisNexis withdrew two products from Chinese ...   \n",
       "21414  Minsk cultural hub becomes haven from authorities   \n",
       "21415  Vatican upbeat on possibility of Pope Francis ...   \n",
       "21416  Indonesia to buy $1.14 billion worth of Russia...   \n",
       "\n",
       "                                                    text    subject  \\\n",
       "21407  SAO PAULO (Reuters) - Cesar Mata Pires, the ow...  worldnews   \n",
       "21408  GENEVA (Reuters) - North Korea and the United ...  worldnews   \n",
       "21409  GENEVA (Reuters) - North Korea and the United ...  worldnews   \n",
       "21410  COPENHAGEN (Reuters) - Danish police said on T...  worldnews   \n",
       "21411  UNITED NATIONS (Reuters) - Two North Korean sh...  worldnews   \n",
       "21412  BRUSSELS (Reuters) - NATO allies on Tuesday we...  worldnews   \n",
       "21413  LONDON (Reuters) - LexisNexis, a provider of l...  worldnews   \n",
       "21414  MINSK (Reuters) - In the shadow of disused Sov...  worldnews   \n",
       "21415  MOSCOW (Reuters) - Vatican Secretary of State ...  worldnews   \n",
       "21416  JAKARTA (Reuters) - Indonesia will buy 11 Sukh...  worldnews   \n",
       "\n",
       "                   date  class  \n",
       "21407  August 22, 2017       1  \n",
       "21408  August 22, 2017       1  \n",
       "21409  August 22, 2017       1  \n",
       "21410  August 22, 2017       1  \n",
       "21411  August 21, 2017       1  \n",
       "21412  August 22, 2017       1  \n",
       "21413  August 22, 2017       1  \n",
       "21414  August 22, 2017       1  \n",
       "21415  August 22, 2017       1  \n",
       "21416  August 22, 2017       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_true_manual_testing.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_manual_testing = pd.concat([df_fake_manual_testing,df_true_manual_testing], axis = 0)\n",
    "df_manual_testing.to_csv(\"manual_testing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging the main fake and true dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 30, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 29, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Racist Alabama Cops Brutalize Black Boy While...</td>\n",
       "      <td>The number of cases of cops brutalizing and ki...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fresh Off The Golf Course, Trump Lashes Out A...</td>\n",
       "      <td>Donald Trump spent a good portion of his day a...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 23, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Trump Said Some INSANELY Racist Stuff Inside ...</td>\n",
       "      <td>In the wake of yet another court decision that...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 23, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Former CIA Director Slams Trump Over UN Bully...</td>\n",
       "      <td>Many people have raised the alarm regarding th...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 22, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WATCH: Brand-New Pro-Trump Ad Features So Muc...</td>\n",
       "      <td>Just when you might have thought we d get a br...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 21, 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               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",
       "5   Racist Alabama Cops Brutalize Black Boy While...   \n",
       "6   Fresh Off The Golf Course, Trump Lashes Out A...   \n",
       "7   Trump Said Some INSANELY Racist Stuff Inside ...   \n",
       "8   Former CIA Director Slams Trump Over UN Bully...   \n",
       "9   WATCH: Brand-New Pro-Trump Ad Features So Muc...   \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",
       "5  The number of cases of cops brutalizing and ki...    News   \n",
       "6  Donald Trump spent a good portion of his day a...    News   \n",
       "7  In the wake of yet another court decision that...    News   \n",
       "8  Many people have raised the alarm regarding th...    News   \n",
       "9  Just when you might have thought we d get a br...    News   \n",
       "\n",
       "                date  class  \n",
       "0  December 31, 2017      0  \n",
       "1  December 31, 2017      0  \n",
       "2  December 30, 2017      0  \n",
       "3  December 29, 2017      0  \n",
       "4  December 25, 2017      0  \n",
       "5  December 25, 2017      0  \n",
       "6  December 23, 2017      0  \n",
       "7  December 23, 2017      0  \n",
       "8  December 22, 2017      0  \n",
       "9  December 21, 2017      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_marge = pd.concat([df_fake, df_true], axis =0 )\n",
    "df_marge.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'text', 'subject', 'date', 'class'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_marge.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"title\",  \"subject\" and \"date\" columns is not required for detecting the fake news, so I am going to drop the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_marge.drop([\"title\", \"subject\",\"date\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text     0\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randomly shuffling the dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13731</th>\n",
       "      <td>Wow! This lady who is a DNC Platform Committee...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12889</th>\n",
       "      <td>Smug liberal newspapers and tv networks have p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17074</th>\n",
       "      <td>The answer is anyone but YOU. Did you know tha...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5048</th>\n",
       "      <td>(Reuters) - A government watchdog group, Publi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16096</th>\n",
       "      <td>Tucker takes on the co-director of Popular Res...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  class\n",
       "13731  Wow! This lady who is a DNC Platform Committee...      0\n",
       "12889  Smug liberal newspapers and tv networks have p...      0\n",
       "17074  The answer is anyone but YOU. Did you know tha...      0\n",
       "5048   (Reuters) - A government watchdog group, Publi...      1\n",
       "16096  Tucker takes on the co-director of Popular Res...      0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)\n",
    "df.drop([\"index\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['text', 'class'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wow! This lady who is a DNC Platform Committee...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smug liberal newspapers and tv networks have p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The answer is anyone but YOU. Did you know tha...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Reuters) - A government watchdog group, Publi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tucker takes on the co-director of Popular Res...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  class\n",
       "0  Wow! This lady who is a DNC Platform Committee...      0\n",
       "1  Smug liberal newspapers and tv networks have p...      0\n",
       "2  The answer is anyone but YOU. Did you know tha...      0\n",
       "3  (Reuters) - A government watchdog group, Publi...      1\n",
       "4  Tucker takes on the co-director of Popular Res...      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a function to convert the text in lowercase, remove the extra space, special chr., ulr and links."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordopt(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub('\\[.*?\\]', '', text)\n",
    "    text = re.sub(\"\\\\W\",\" \",text) \n",
    "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
    "    text = re.sub('<.*?>+', '', text)\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub('\\n', '', text)\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"text\"] = df[\"text\"].apply(wordopt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining dependent and independent variable as x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[\"text\"]\n",
    "y = df[\"class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the dataset into training set and testing set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert text to vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorization = TfidfVectorizer()\n",
    "xv_train = vectorization.fit_transform(x_train)\n",
    "xv_test = vectorization.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LogisticRegression()\n",
    "LR.fit(xv_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr=LR.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9885026737967915"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(xv_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      5883\n",
      "           1       0.99      0.99      0.99      5337\n",
      "\n",
      "    accuracy                           0.99     11220\n",
      "   macro avg       0.99      0.99      0.99     11220\n",
      "weighted avg       0.99      0.99      0.99     11220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT = DecisionTreeClassifier()\n",
    "DT.fit(xv_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt = DT.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9945632798573975"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT.score(xv_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99      5883\n",
      "           1       0.99      0.99      0.99      5337\n",
      "\n",
      "    accuracy                           0.99     11220\n",
      "   macro avg       0.99      0.99      0.99     11220\n",
      "weighted avg       0.99      0.99      0.99     11220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(random_state=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBC = GradientBoostingClassifier(random_state=0)\n",
    "GBC.fit(xv_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_gbc = GBC.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9955436720142602"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBC.score(xv_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00      5883\n",
      "           1       0.99      1.00      1.00      5337\n",
      "\n",
      "    accuracy                           1.00     11220\n",
      "   macro avg       1.00      1.00      1.00     11220\n",
      "weighted avg       1.00      1.00      1.00     11220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_gbc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(random_state=0)\n",
    "RFC.fit(xv_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rfc = RFC.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9915329768270945"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.score(xv_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      5883\n",
      "           1       0.99      0.99      0.99      5337\n",
      "\n",
      "    accuracy                           0.99     11220\n",
      "   macro avg       0.99      0.99      0.99     11220\n",
      "weighted avg       0.99      0.99      0.99     11220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_rfc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Testing With Manual Entry\n",
    "\n",
    "### News"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_lable(n):\n",
    "    if n == 0:\n",
    "        return \"Fake News\"\n",
    "    elif n == 1:\n",
    "        return \"Not A Fake News\"\n",
    "    \n",
    "def manual_testing(news):\n",
    "    testing_news = {\"text\":[news]}\n",
    "    new_def_test = pd.DataFrame(testing_news)\n",
    "    new_def_test[\"text\"] = new_def_test[\"text\"].apply(wordopt) \n",
    "    new_x_test = new_def_test[\"text\"]\n",
    "    new_xv_test = vectorization.transform(new_x_test)\n",
    "    pred_LR = LR.predict(new_xv_test)\n",
    "    pred_DT = DT.predict(new_xv_test)\n",
    "    pred_GBC = GBC.predict(new_xv_test)\n",
    "    pred_RFC = RFC.predict(new_xv_test)\n",
    "\n",
    "    return print(\"\\n\\nLR Prediction: {} \\nDT Prediction: {} \\nGBC Prediction: {} \\nRFC Prediction: {}\".format(output_lable(pred_LR[0]), \n",
    "                                                                                                              output_lable(pred_DT[0]), \n",
    "                                                                                                              output_lable(pred_GBC[0]), \n",
    "                                                                                                              output_lable(pred_RFC[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paul Craig RobertsIn the last years of the 20th century fraud entered US foreign policy in a new way.  On false pretenses Washington dismantled Yugoslavia and Serbia in order to advance an undeclared agenda. In the 21st century this fraud multiplied many times. Afghanistan, Iraq, Somalia, and Libya were destroyed, and Iran and Syria would also have been destroyed if the President of Russia had not prevented it.  Washington is also behind the current destruction of Yemen, and Washington has enabled and financed the Israeli destruction of Palestine.  Additionally, Washington operated militarily within Pakistan without declaring war, murdering many women, children, and village elders under the guise of  combating terrorism.  Washington s war crimes rival those of any country in history.I have documented these crimes in my columns and books (Clarity Press). Anyone who still believes in the purity of Washington s foreign policy is a lost soul  Russia and China now have a strategic alliance that is too strong for Washington. Russia and China will prevent Washington from further encroachments on their security and national interests. Those countries important to Russia and China will be protected by the alliance.  As the world wakes up and sees the evil that the West represents, more countries will seek the protection of Russia and China.America is also failing on the economic front.  My columns and my book, The Failure of Laissez Faire Capitalism, which has been published in English, Chinese, Korean, Czech, and German, have shown how Washington has stood aside, indeed cheering it on, while the short-term profit interests of management, shareholders, and Wall Street eviscerated the American economy, sending manufacturing jobs, business know-how, and technology, along with professional tradeable skill jobs, to China, India, and other countries, leaving America with such a hollowed out economy that the median family income has been falling for years. Today 50% of 25 year-old Americans are living with their parents or grandparents because they cannot find employment sufficient to sustain an independent existance.This brutal fact is covered up by the presstitute US media, a source of fantasy stories of America s economic recovery.The facts of our existence are so different from what is reported that I am astonished. As a former professor of economics, Wall Street Journal editor and Assistant Secretary of the Treasury for Economic Policy, I am astonished at the corruption that rules in the financial sector, the Treasury, the financial regulatory agencies, and the Federal Reserve.  In my day, there would have been indictments and prison sentences of bankers and high government officials.In America today there are no free financial markets.  All the markets are rigged by the Federal Reserve and the Treasury. The regulatory agencies, controlled by those the agencies are supposed to regulate, turn a blind eye, and even if they did not, they are helpless to enforce any law, because private interests are more powerful than the law.Even the government s statistical agencies have been corrupted. Inflation measures have been concocted in order to understate inflation. This lie not only saves Washington from paying Social Security cost-of-living adjustments and frees the money for more wars, but also by understating inflation, the government can create real GDP growth by counting inflation as real growth, just as the government creates 5% unemployment by not counting any discouraged workers who have looked for jobs until they can no longer afford the cost of looking and give up.  The official unemployment rate is 5%, but no one can find a job.  How can the unemployment rate be 5% when half of 25-year olds are living with relatives because they cannot afford an independent existence?  As John Williams (shadowfacts) reports, the unemployment rate that includes those Americans who have given up looking for a job because there are no jobs to be found is 23%.The Federal Reserve, a tool of a small handful of banks, has succeeded in creating the illusion of an economic recovery since June, 2009, by printing trillions of dollars that found their way not into the economy but into the prices of financial assets.  Artificially booming stock and bond markets are the presstitute financial media s  proof  of a booming economy.The handful of learned people that America has left, and it is only a small handful, understand that there has been no recovery from the previous recession and that a new downturn is upon us.  John Williams has pointed out that US industrial production, when properly adjusted for inflation, has never recovered its 2008 level, much less its 2000 peak, and has again turned down.The American consumer is exhausted, overwhelmed by debt and lack of income growth. The entire economic policy of America is focused on saving a handful of NY banks, not on saving the American economy.Economists and other Wall Street shills will dismiss the decline in industrial production as America is now a service economy. Economists pretend that these are high-tech services of the New Economy, but in fact waitresses, bartenders, part time retail clerks, and ambulatory health care services have replaced manufacturing and engineering jobs at a fraction of the pay, thus collapsing effective aggregate demand in the US. On occasions when neoliberal economists recognize problems, they blame them on China.It is unclear that the US economy can be revived. To revive the US economy would require the re-regulation of the financial system and the recall of the jobs and US GDP that offshoring gave to foreign countries. It would require, as Michael Hudson demonstrates in his new book, Killing the Host, a revolution in tax policy that would prevent the financial sector from extracting economic surplus and capitalizing it in debt obligations paying interest to the financial sector.The US government, controlled as it is by corrupt economic interests, would never permit policies that impinged on executive bonuses and Wall Street profits.  Today US capitalism makes its money by selling out the American economy and the people dependent upon it.In  freedom and democracy  America, the government and the economy serve interests totally removed from the interests of the American people. The sellout of the American people is protected by a huge canopy of propaganda provided by free market economists and financial presstitutes paid to lie for their living.When America fails, so will Washington s vassal states in Europe, Canada, Australia, and Japan.  Unless Washington destroys the world in nuclear war, the world will be remade, and the corrupt and dissolute West will be an insignificant part of the new world.Dr. Paul Craig Roberts was Assistant Secretary of the Treasury for Economic Policy and associate editor of the Wall Street Journal. He was columnist for Business Week, Scripps Howard News Service, and Creators Syndicate. He has had many university appointments. His internet columns have attracted a worldwide following. Roberts  latest books are The Failure of Laissez Faire Capitalism and Economic Dissolution of the West, How America Was Lost, and The Neoconservative Threat to World Order. READ MORE NWO NEWS AT: 21st Century Wire NWO Files\n",
      "\n",
      "\n",
      "LR Prediction: Fake News \n",
      "DT Prediction: Fake News \n",
      "GBC Prediction: Fake News \n",
      "RFC Prediction: Fake News\n"
     ]
    }
   ],
   "source": [
    "news = str(input())\n",
    "manual_testing(news)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}