ML mini project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5bc880d",
   "metadata": {},
   "source": [
    "# Fake news Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbef8ae",
   "metadata": {},
   "source": [
    "### Importing Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0982a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #for importing dataset\n",
    "import numpy as nm #for doing mathematical operations \n",
    "from sklearn.model_selection import train_test_split as ttp #to break dataset for training and testing\n",
    "from sklearn.metrics import classification_report # to generate classification report\n",
    "import re #for data cleaning purpose\n",
    "import string #to deal with text data\n",
    "import matplotlib.pyplot as plt #for visualizing dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11af2114",
   "metadata": {},
   "source": [
    "### Reading two  data files \n",
    "### Download this dataset from\n",
    "https://www.kaggle.com/code/therealsampat/fake-news-detection/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93f94905",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_true=pd.read_csv(\"True.csv\")\n",
    "data_fake=pd.read_csv(\"Fake.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1094f7b",
   "metadata": {},
   "source": [
    "#### Printing top 5 values from both Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7f99bdf",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_true.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07370358",
   "metadata": {},
   "outputs": [
    {
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       "<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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fake.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec3e23c",
   "metadata": {},
   "source": [
    "#### Checking size of Datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08e820ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21417, 4), (23481, 4))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_true.shape, data_fake.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de50bde4",
   "metadata": {},
   "source": [
    "### Adding column name \"class\" in both datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "831dc51f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_true[\"class\"]=1\n",
    "data_fake[\"class\"]=0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953b0d92",
   "metadata": {},
   "source": [
    "###  Taking last 10 values for manual testing from both Datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7705ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_true_manual_testing = data_true.tail(10)\n",
    "for i in range(21416,21406,-1):\n",
    "    data_true.drop([i],axis=0, inplace=True)#removing those 10 values from original dataset\n",
    "    \n",
    "data_fake_manual_testing = data_fake.tail(10)\n",
    "for i in range(21416,21406,-1):\n",
    "    data_fake.drop([i],axis=0, inplace=True)#removing those 10 values from original dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3584dd7",
   "metadata": {},
   "source": [
    "### merging these two datasets in single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e8acddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_manual_testing = pd.concat([data_fake_manual_testing, data_true_manual_testing], axis=0)\n",
    "data_manual_testing.to_csv(\"manual_testing.csv \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742f02fa",
   "metadata": {},
   "source": [
    "### Merging main two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c74ca170",
   "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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merge = pd.concat([data_fake,data_true],axis=0)\n",
    "data_merge.head(10)#displaying first 10 values after above operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc2d9e7",
   "metadata": {},
   "source": [
    "### Performing some basic data visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd75d62e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject\n",
      "Government News     1570\n",
      "Middle-east          778\n",
      "News                9050\n",
      "US_News              783\n",
      "left-news           4449\n",
      "politics            6841\n",
      "politicsNews       11272\n",
      "worldnews          10135\n",
      "Name: text, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(data_merge.groupby(['subject'])['text'].count())\n",
    "data_merge.groupby(['subject'])['text'].count().plot(kind=\"bar\")\n",
    "plt.title(\"Articles per subject\",size=20)\n",
    "plt.xlabel(\"Category\",size=20)\n",
    "plt.ylabel(\"Article count\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e98eb8a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class\n",
      "0    23471\n",
      "1    21407\n",
      "Name: text, dtype: int64\n",
      "0 = Fake news\n",
      "1 = True news\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(data_merge.groupby(['class'])['text'].count())\n",
    "print(\"0 = Fake news\\n1 = True news\")\n",
    "data_merge.groupby(['class'])['text'].count().plot(kind=\"pie\")\n",
    "plt.title(\"Fake news and True News\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6179ed79",
   "metadata": {},
   "source": [
    "### Removing 3 columns from dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09cd41c5",
   "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>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The number of cases of cops brutalizing and ki...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Donald Trump spent a good portion of his day a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>In the wake of yet another court decision that...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Many people have raised the alarm regarding th...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Just when you might have thought we d get a br...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  class\n",
       "0  Donald Trump just couldn t wish all Americans ...      0\n",
       "1  House Intelligence Committee Chairman Devin Nu...      0\n",
       "2  On Friday, it was revealed that former Milwauk...      0\n",
       "3  On Christmas day, Donald Trump announced that ...      0\n",
       "4  Pope Francis used his annual Christmas Day mes...      0\n",
       "5  The number of cases of cops brutalizing and ki...      0\n",
       "6  Donald Trump spent a good portion of his day a...      0\n",
       "7  In the wake of yet another court decision that...      0\n",
       "8  Many people have raised the alarm regarding th...      0\n",
       "9  Just when you might have thought we d get a br...      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data_merge.drop([\"title\",\"subject\",\"date\"], axis=1)\n",
    "data.head(10) #displaying remaining dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2594826e",
   "metadata": {},
   "source": [
    "### Shuffling the dataset for better prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cee042f7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>17138</th>\n",
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       "      <th>15633</th>\n",
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       "      <th>3540</th>\n",
       "      <td>Right-wingers everywhere have been trying to d...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  class\n",
       "15901  General Michael Flynn released the statement b...      0\n",
       "11286        https://www.youtube.com/watch?v=-7Tn4gi_Os8      0\n",
       "1271   Trump spent his second day having a mental bre...      0\n",
       "17954  MOSCOW (Reuters) - U.S. government-sponsored R...      1\n",
       "16773  These grifters have no shame. How much more ev...      0\n",
       "17138  STOCKHOLM (Reuters) - An explosion overnight i...      1\n",
       "15633  BRUSSELS/MADRID/BARCELONA (Reuters) - Sacked C...      1\n",
       "14092  NEW DELHI (Reuters) - India has approved a thr...      1\n",
       "10642  Senator John McCain took another swipe at Pres...      0\n",
       "3540   Right-wingers everywhere have been trying to d...      0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sample(frac=1)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7280802",
   "metadata": {},
   "source": [
    "### Checking for null values in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d80c18e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text     0\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7aedf3",
   "metadata": {},
   "source": [
    "### Creating data filtering function to remove unwanted data from text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "638292ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtering(data):\n",
    "    text=data.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('\\w*\\d\\w*','',text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb890d9",
   "metadata": {},
   "source": [
    "### Filtering data of our text column using above function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59ae6171",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>moscow  reuters    u s  government sponsored r...</td>\n",
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       "      <td>these grifters have no shame  how much more ev...</td>\n",
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       "      <td>stockholm  reuters    an explosion overnight i...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15633</th>\n",
       "      <td>brussels madrid barcelona  reuters    sacked c...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14092</th>\n",
       "      <td>new delhi  reuters    india has approved a thr...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>right wingers everywhere have been trying to d...</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  class\n",
       "15901  general michael flynn released the statement b...      0\n",
       "11286                  https   www youtube com watch v        0\n",
       "1271   trump spent his second day having a mental bre...      0\n",
       "17954  moscow  reuters    u s  government sponsored r...      1\n",
       "16773  these grifters have no shame  how much more ev...      0\n",
       "17138  stockholm  reuters    an explosion overnight i...      1\n",
       "15633  brussels madrid barcelona  reuters    sacked c...      1\n",
       "14092  new delhi  reuters    india has approved a thr...      1\n",
       "10642  senator john mccain took another swipe at pres...      0\n",
       "3540   right wingers everywhere have been trying to d...      0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"text\"]= data[\"text\"].apply(filtering)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a121d591",
   "metadata": {},
   "source": [
    "### Creating Dependant and independant variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7375fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data[\"text\"]\n",
    "y=data[\"class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7564f4",
   "metadata": {},
   "source": [
    "### Spliting dataset for training and testing purpose "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "783e6db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=ttp(x,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30af7723",
   "metadata": {},
   "source": [
    "### Vectorizing the text\n",
    "#### The sklearn. feature_extraction module is used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "894f7b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e6ee204",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = TfidfVectorizer() # creating object \n",
    "#Learn vocabulary and IDF(inverse document frequency), return document-term matrix. \n",
    "#IDF returns numerical statics that how the word is important to the document\n",
    "xv_train=vector.fit_transform(x_train)\n",
    "xv_test=vector.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e168cd",
   "metadata": {},
   "source": [
    "## Classifaction using various classifers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5238efe6",
   "metadata": {},
   "source": [
    "### Logictic Regression\n",
    "#### Calculating probability of event occurence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ff9637d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5153305d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR=LogisticRegression()\n",
    "LR.fit(xv_train,y_train) #training or fitting the model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ecc615e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(xv_test,y_test).round(2) # method computes the accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d75cae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_LR= LR.predict(xv_test) # Predict using the linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6436918c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      5874\n",
      "           1       0.99      0.99      0.99      5346\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)) #creating classification report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43eb55d9",
   "metadata": {},
   "source": [
    "### Creating confusion matrix\n",
    "#### determine the performance of the classification models for a given set of test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f6d66f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5801   73]\n",
      " [  61 5285]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.metrics import confusion_matrix  \n",
    "cm= confusion_matrix(y_test,pred_LR)  \n",
    "print(cm)\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm,cmap=\"BuPu\",annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b5be9d",
   "metadata": {},
   "source": [
    "### Decision Tree Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b54684",
   "metadata": {},
   "source": [
    "#### Non-parametric supervised learning method used for both classification and regression tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cfdbc7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7872f125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT = DecisionTreeClassifier()\n",
    "DT.fit(xv_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "664f69b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT.score(xv_test,y_test).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cb8b364f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_DT = DT.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "debce86a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      5874\n",
      "           1       1.00      0.99      0.99      5346\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": "code",
   "execution_count": 32,
   "id": "f9fd3a67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5852   22]\n",
      " [  37 5309]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm= confusion_matrix(y_test,pred_DT)  \n",
    "print(cm)\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm,cmap=\"PiYG\",annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee8e2a6",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74fe062",
   "metadata": {},
   "source": [
    "#### It is a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "34028847",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "effc978f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBC = GradientBoostingClassifier(random_state=0)\n",
    "GBC.fit(xv_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d11a68c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9949197860962566"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBC.score(xv_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39ae0bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_GBC = GBC.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "52bea74c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00      5874\n",
      "           1       0.99      1.00      0.99      5346\n",
      "\n",
      "    accuracy                           0.99     11220\n",
      "   macro avg       0.99      1.00      0.99     11220\n",
      "weighted avg       0.99      0.99      0.99     11220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred_GBC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "25ed3309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5833   41]\n",
      " [  16 5330]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm= confusion_matrix(y_test,pred_GBC)  \n",
    "print(cm)\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm,cmap=\"Greens\",annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd16e524",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a01ab2",
   "metadata": {},
   "source": [
    "#### Supervised Machine learning algorithm used for classification, regression, and other tasks using decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1e71f377",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e3f8535d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(random_state=0)\n",
    "RFC.fit(xv_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "88e6c74f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9893048128342246"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.score(xv_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "27fc1ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_RFC = RFC.predict(xv_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "80ba818a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      5874\n",
      "           1       0.99      0.99      0.99      5346\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": "code",
   "execution_count": 44,
   "id": "f59fb45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5825   49]\n",
      " [  71 5275]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm= confusion_matrix(y_test,pred_RFC)  \n",
    "print(cm)\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm,cmap=\"Blues\",annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b543f037",
   "metadata": {},
   "source": [
    "## Manual Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a5ebde2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_lable(n):\n",
    "    if n == 0:\n",
    "        return \"FAKE News\"\n",
    "    elif n == 1:\n",
    "        return \"TRUE 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(filtering) \n",
    "    new_x_test = new_def_test[\"text\"]\n",
    "    new_xv_test = vector.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]))) \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adac2dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = str(input())\n",
    "manual_testing(news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "891f448f",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = str(input())\n",
    "manual_testing(news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5af128d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}