Fake_News_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a967e6fa",
   "metadata": {},
   "source": [
    "About the Dataset:\n",
    "\n",
    "id: unique id for a news article\n",
    "title: the title of a news article\n",
    "author: author of the news article\n",
    "text: the text of the article; could be incomplete\n",
    "label: a label that marks whether the news article is real or fake:\n",
    "\n",
    "    1: Fake news\n",
    "    0: real News"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb0cd65d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing modules and packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf0dcda7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Somesh\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "603750e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "# printing the stopwords in English\n",
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e781796d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading data from the dataset\n",
    "dataset = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "test1= pd.read_csv('submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adf6ea9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20800, 5)\n",
      "   id                                              title              author  \\\n",
      "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
      "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
      "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
      "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
      "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \n",
      "\n",
      "                                                text  label  \n",
      "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
      "1  Ever get the feeling your life circles the rou...      0  \n",
      "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
      "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
      "4  Print \\nAn Iranian woman has been sentenced to...      1  \n",
      "id           0\n",
      "title      558\n",
      "author    1957\n",
      "text        39\n",
      "label        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "print(dataset.head())\n",
    "print(dataset.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e497bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()\n",
    "def stemming(c):\n",
    "    #removing all non-alphabetic characters\n",
    "    stemmed_content =  re.sub('[^a-zA-Z]',' ',c)\n",
    "    #converting all characters to lower case\n",
    "    stemmed_content = stemmed_content.lower()\n",
    "    stemmed_content = stemmed_content.split()\n",
    "    stemmed_content = [ps.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
    "    stemmed_content = ' '.join(stemmed_content)\n",
    "    return stemmed_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1465bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findx(dataset):\n",
    "    #filling the empty values in the dataset with null\n",
    "    dataset=dataset.fillna(\"\")\n",
    "    dataset['content']=dataset['author']+\" \"+dataset['title']\n",
    "    dataset['content'] = dataset['content'].apply(stemming)\n",
    "    print(\"\\n The contents of the dataset:\\n\")\n",
    "    print(dataset['content'])\n",
    "    x = dataset['content'].values\n",
    "    tf = TfidfVectorizer()\n",
    "    tf.fit(x)\n",
    "    x = tf.transform(x)\n",
    "    print(\"\\n Data after vectorizing \\n\")\n",
    "    print (x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc67599a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " The contents of the dataset:\n",
      "\n",
      "0        darrel lucu hous dem aid even see comey letter...\n",
      "1        daniel j flynn flynn hillari clinton big woman...\n",
      "2                   consortiumnew com truth might get fire\n",
      "3        jessica purkiss civilian kill singl us airstri...\n",
      "4        howard portnoy iranian woman jail fiction unpu...\n",
      "                               ...                        \n",
      "20795    jerom hudson rapper trump poster child white s...\n",
      "20796    benjamin hoffman n f l playoff schedul matchup...\n",
      "20797    michael j de la merc rachel abram maci said re...\n",
      "20798    alex ansari nato russia hold parallel exercis ...\n",
      "20799                            david swanson keep f aliv\n",
      "Name: content, Length: 20800, dtype: object\n",
      "\n",
      " Data after vectorizing \n",
      "\n",
      "  (0, 15686)\t0.28485063562728646\n",
      "  (0, 13473)\t0.2565896679337957\n",
      "  (0, 8909)\t0.3635963806326075\n",
      "  (0, 8630)\t0.29212514087043684\n",
      "  (0, 7692)\t0.24785219520671603\n",
      "  (0, 7005)\t0.21874169089359144\n",
      "  (0, 4973)\t0.233316966909351\n",
      "  (0, 3792)\t0.2705332480845492\n",
      "  (0, 3600)\t0.3598939188262559\n",
      "  (0, 2959)\t0.2468450128533713\n",
      "  (0, 2483)\t0.3676519686797209\n",
      "  (0, 267)\t0.27010124977708766\n",
      "  (1, 16799)\t0.30071745655510157\n",
      "  (1, 6816)\t0.1904660198296849\n",
      "  (1, 5503)\t0.7143299355715573\n",
      "  (1, 3568)\t0.26373768806048464\n",
      "  (1, 2813)\t0.19094574062359204\n",
      "  (1, 2223)\t0.3827320386859759\n",
      "  (1, 1894)\t0.15521974226349364\n",
      "  (1, 1497)\t0.2939891562094648\n",
      "  (2, 15611)\t0.41544962664721613\n",
      "  (2, 9620)\t0.49351492943649944\n",
      "  (2, 5968)\t0.3474613386728292\n",
      "  (2, 5389)\t0.3866530551182615\n",
      "  (2, 3103)\t0.46097489583229645\n",
      "  :\t:\n",
      "  (20797, 13122)\t0.2482526352197606\n",
      "  (20797, 12344)\t0.27263457663336677\n",
      "  (20797, 12138)\t0.24778257724396507\n",
      "  (20797, 10306)\t0.08038079000566466\n",
      "  (20797, 9588)\t0.174553480255222\n",
      "  (20797, 9518)\t0.2954204003420313\n",
      "  (20797, 8988)\t0.36160868928090795\n",
      "  (20797, 8364)\t0.22322585870464118\n",
      "  (20797, 7042)\t0.21799048897828688\n",
      "  (20797, 3643)\t0.21155500613623743\n",
      "  (20797, 1287)\t0.33538056804139865\n",
      "  (20797, 699)\t0.30685846079762347\n",
      "  (20797, 43)\t0.29710241860700626\n",
      "  (20798, 13046)\t0.22363267488270608\n",
      "  (20798, 11052)\t0.4460515589182236\n",
      "  (20798, 10177)\t0.3192496370187028\n",
      "  (20798, 6889)\t0.32496285694299426\n",
      "  (20798, 5032)\t0.4083701450239529\n",
      "  (20798, 1125)\t0.4460515589182236\n",
      "  (20798, 588)\t0.3112141524638974\n",
      "  (20798, 350)\t0.28446937819072576\n",
      "  (20799, 14852)\t0.5677577267055112\n",
      "  (20799, 8036)\t0.45983893273780013\n",
      "  (20799, 3623)\t0.37927626273066584\n",
      "  (20799, 377)\t0.5677577267055112\n"
     ]
    }
   ],
   "source": [
    "x = findx(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1efcc809",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " X values \n",
      "\n",
      "  (0, 15686)\t0.28485063562728646\n",
      "  (0, 13473)\t0.2565896679337957\n",
      "  (0, 8909)\t0.3635963806326075\n",
      "  (0, 8630)\t0.29212514087043684\n",
      "  (0, 7692)\t0.24785219520671603\n",
      "  (0, 7005)\t0.21874169089359144\n",
      "  (0, 4973)\t0.233316966909351\n",
      "  (0, 3792)\t0.2705332480845492\n",
      "  (0, 3600)\t0.3598939188262559\n",
      "  (0, 2959)\t0.2468450128533713\n",
      "  (0, 2483)\t0.3676519686797209\n",
      "  (0, 267)\t0.27010124977708766\n",
      "  (1, 16799)\t0.30071745655510157\n",
      "  (1, 6816)\t0.1904660198296849\n",
      "  (1, 5503)\t0.7143299355715573\n",
      "  (1, 3568)\t0.26373768806048464\n",
      "  (1, 2813)\t0.19094574062359204\n",
      "  (1, 2223)\t0.3827320386859759\n",
      "  (1, 1894)\t0.15521974226349364\n",
      "  (1, 1497)\t0.2939891562094648\n",
      "  (2, 15611)\t0.41544962664721613\n",
      "  (2, 9620)\t0.49351492943649944\n",
      "  (2, 5968)\t0.3474613386728292\n",
      "  (2, 5389)\t0.3866530551182615\n",
      "  (2, 3103)\t0.46097489583229645\n",
      "  :\t:\n",
      "  (20797, 13122)\t0.2482526352197606\n",
      "  (20797, 12344)\t0.27263457663336677\n",
      "  (20797, 12138)\t0.24778257724396507\n",
      "  (20797, 10306)\t0.08038079000566466\n",
      "  (20797, 9588)\t0.174553480255222\n",
      "  (20797, 9518)\t0.2954204003420313\n",
      "  (20797, 8988)\t0.36160868928090795\n",
      "  (20797, 8364)\t0.22322585870464118\n",
      "  (20797, 7042)\t0.21799048897828688\n",
      "  (20797, 3643)\t0.21155500613623743\n",
      "  (20797, 1287)\t0.33538056804139865\n",
      "  (20797, 699)\t0.30685846079762347\n",
      "  (20797, 43)\t0.29710241860700626\n",
      "  (20798, 13046)\t0.22363267488270608\n",
      "  (20798, 11052)\t0.4460515589182236\n",
      "  (20798, 10177)\t0.3192496370187028\n",
      "  (20798, 6889)\t0.32496285694299426\n",
      "  (20798, 5032)\t0.4083701450239529\n",
      "  (20798, 1125)\t0.4460515589182236\n",
      "  (20798, 588)\t0.3112141524638974\n",
      "  (20798, 350)\t0.28446937819072576\n",
      "  (20799, 14852)\t0.5677577267055112\n",
      "  (20799, 8036)\t0.45983893273780013\n",
      "  (20799, 3623)\t0.37927626273066584\n",
      "  (20799, 377)\t0.5677577267055112\n",
      "\n",
      " Y values \n",
      "\n",
      "[1 0 1 ... 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "y = dataset['label'].values\n",
    "print(\"\\n X values \\n\")\n",
    "print(x)\n",
    "print(\"\\n Y values \\n\")\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08d90619",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.25, stratify=y, random_state=2)\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "19287015",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy score on the training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "038d90d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of the training data :  0.9861538461538462\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score of the training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "185add98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of the test data :  0.9776923076923076\n"
     ]
    }
   ],
   "source": [
    "# accuracy score on the test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print('Accuracy score of the test data : ', test_data_accuracy)"
   ]
  }
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